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FULafia Journal of Science & Technology Vol. 2 No.2 December 20161


FULafia Journal ofSCIENCE & TECHNOLOGY

Vol. 2 No.2 December, 2016

FEDERAL UNIVERSITY LAFIA

Supported By:


FULafia Journal of Science and Technology (FJST) Vol.2 No.2 December, 2016A Journal of Federal University Lafia, Nigeria

EDITORIAL BOARDProf. Emmanuel Hala Kwon-Ndung

Chief Editor/Chairman of the Editorial BoardFederal University Lafia

MEMBERSDr. Ibrahim Bassi

Managing Editor Department of Mathematics,

Federal University Lafia

Prof. Baba Alfa,Associate Editor

Department of PhysicsIbrahim Badamasi Babangida

University Minna

Dr. AleruchiChuku Associate Editor

Department of MicrobiologyFederal University Lafia

Dr. T. AboyairAssociate Editor

Department of MathematicsUniversity of Agriculture, Makurdi

Dr. A.F. Donfack-KanaAssociate Editor

Department of Computer ScienceAhmadu Bello University Zaria

Dr (Mrs). Victoria Pam, Associate Editor

Department of ZoologyFederal University Lafia

Prof. Folorunso AjayiAssociate Editor

Department of Agronomy,Faculty of Agriculture,Nasarawa State

University, Shabu Campus, Lafia.

Dr. Absalom E. EzugwuAssociate Editor

Department of Computer ScienceFederal University Lafia

Dr. Jude OnwukaAssociate Editor

Department of ChemistryFederal University Lafia

Prof. Muhammad S. Liman Department of Physics / Vice-Chancellor, FULafia, Nigeria.

Prof. Martin G. Ogbe Department of Zoology/Deputy Vice Chancellor, FULafia, Nigeria.

Prof. Stephen E. Onah Director General, NationalMathematicalCentereAbuja, Nigeria.

Prof. O.R. Afolabi Department of Microbiology, Federal University of Agriculture Abeokuta, Nigeria.

Prof. Tomi Omori Professor and Head, Division of

Biostatistics, Department of Public Health and Preventive Medicine,

Oregon Health and Science University Oregon, USA.

Professor S.T. MbapDepartment of Animal Science, School

of Agriculture.Abubakar Tafawa Balewa University

Bauchi, Nigeria.

Prof. Zaki El-Kasem Department of Genetics,

Faculty of Agriculture,Minia University, Egypt.

Professor AntanioFelip Wouk Department of Veterinary Medicine, Federal University of Parana, Brazil.

Prof. Godfrey A. IwoDepartment of Crop Science

University of Calabar, Nigeria.

Prof. (Eng). Isaac N. ItodoDepartment of Agricultural and

Environmental EngineeringUniversity of Agriculture, Makurdi,

Nigeria.

Dr. John K. NdukaDepartment of Chemistry

Nnamdi Azikiwe University Awka Nigeria

Prof. HajaKadarmideenDepartment of Veterinary Clinical and

Animal Sciences, Faculty of Health and Medical Sciences,

University of Copenhagen, Denmark.

EDITORIAL ADVISERS

Chief EditorFJST, Federal University LafiaEmail: [email protected]: http//www.fulafiajst.com


EDITORIAL

The FULafia Journal of Science and Technology (FJST) is biannual publication of the Faculty of Science, Federal University Lafia published in March and December. We assure all our stakeholders that we will continue to work towards achieving the goals of the Journal. To this end, we are committed to sustain the timely production of the biannual issues of the Journal.

The Editorial Board has been reconstituted to strengthen the quality of the Editorial process with members now drawn from different Institutions within and outside Nigeria.

One of the recent achievements of the FJST was the successful acquisition of a website for the Journal. Contributors can now submit their articles online through the Manuscript Submission Form on the website. The Journal’s website is http//www.fulafiajst.com.

A total of twenty four (24) research contributions on contemporary issues from researchers from diverse Institutions in Nigeria working on different areas of the sciences such as Agricultural and Biological Sciences, Chemical Sciences, Earth Sciences, Engineering, Environmental Sciences, and Physical Sciences are covered in the current issue.

We appreciate the support and encouragement of the Management of our great University under the able leadership of Prof. Muhammed Sanusi Liman to the Editorial Board. We also want to thank the Tertiary Education Trust Fund (TETFUND) for supporting the Publication.

We encourage contributors to visit our website and further enquiries can be directed to the Chief Editor through our website or through the Journal’s email.

Prof. E.H. Kwon-NdungChief Editor


ContentsSECTION A: AGRICULTURAL AND BIOLOGICAL SCIENCESA mycological assessment of the air quality in flood-prone homes within Lafia Local Government Area of Nasarawa State.....................................................................................................................................................4

Analysis of women participation in livestock production in Mangu Local Government Area of Plateau State, Nigeria ...................................................................................................................................11

An assessment of earthworm population and soil factors in Amurum forest reserve of Jos, Plateau State, Nigeria.........................................................................................................................................16

An epidemic of coccidiosis in chickens sold in Keffi central market, Nasarawa State, Nigeria.........................23

Effects of access to pasture and integration with rabbits on performance and carcass characteristics of broiler chicken...........................................................................................................27

Microspora infiltration of gastrointestinal epithelium among HIV/AIDS patients in Keffi, Nigeria.................31

Morphometric indices and parasites of frozen Clarias gariepinus and Oreochromis niloticus sold in Jos metropolis, Plateau State..................................................................................................................35

Nematicidal potential of extracts of neem (Azadirachta indica) and lemon grass (Cymbopogon citratus) on root-knot nematodes (Meloidogyne spp) infecting sweet potato............................41

Outcome of calcium and phosphorous mineral sources on performance, carcass and bones characteristics of broilers.........................................................................................................................46

Partial characterization of protease extracted from “Yatsin biri” ginger (Zingiber officinale) cultivar of northwestern Nigeria.........................................................................................................................51

Phenotypic variability of false sesame (Ceratotheca sesamoides End L.) treated with sodium azide...................57

Protein contents of maize varieties as influenced by nitrogen and micronutrients.............................................63

Rot of seed potato (Solanum tuberosum L.) tubers as affected by storage conditions and storage duration in Jos, Plateau State, Nigeria......................................................................................... .........71

Seasonal responses of two faunal taxa to fire treaments in Yankari Game Reserve, Nigeria............................80

Some ethnobotanical uses of plant resources in Nasarawa State, Nigeria.........................................................86

Utilization of tuber and sprout characteristics in delimiting accessions of “rizga”(Plectranthus esculentus N.E. Br.) in Jos, Nigeria.............................................................................................92

SECTION B: CHEMICAL SCIENCESPreliminary assessment of heavy metals and water quality of selected wells in Talata Mafara, Zamfara State, Nigeria.....................................................................................................................................100

SECTION C: EARTH SCIENCESDelineation of mineral potential zones over Keffi – Abuja Area in North-central, Nigeria using Aeromagnetic data................................ ..................................................................................................106

The application 3D seismic data interpretation to hydrocarbon prospect mapping in “Dede”field, Niger Delta....................................................................................................................................111

SECTION D: ENVIRONMENTAL SCIENCESGeospatial modeling of land use management for sustainable urban development in Karu, Nasarawa State, Nigeria....................................................................................................................................116

SECTION E: PHYSICAL SCIENCESComparative study of the molecular dynamics of anthracene and one of its derivative (1-hydroxyanthracene)in gas phase and ethanol: rhf and dft study..................................................................124

Derivation of fitzhugh-nagumo system............................................................................................................138

On performance of some methods of detecting nonlinearity in stationary and non-stationary time series data.........................................................................................................................144

Operating system security and penetration testing...........................................................................................151

Contents


A MYCOLOGICAL ASSESSMENT OF THE AIR QUALITY IN FLOOD-PRONE HOMES WITHIN LAFIA LOCAL GOVERNMENT AREA OF NASARAWA STATE

Chuku, A.1, Arikpo, G.2, Obande, G. A.1, Akherenegbe, P.1, Uteh, P.U. and Namang, M.1

1Department of Microbiology, Federal University Lafia, Nasarawa State, Nigeria.2Department of Biological Sciences, Cross River University of Science and Technology, Cross River State,

Nigeria.Corresponding Email: [email protected]

Date Manuscript Received:06/04/2016 Accepted:20/12/2016 Published:December, 2016

ABSTRACT Air quality of three hundred (300) flood prone homes was assessed for the presence of fungal spores during the rainy season between September and November, 2015. Sixty (60) randomly selected households in five council wards namely Wakwa, Makama, Gayam, Ciroma and Akurba wards were assessed using the Koch sedimentation method by gravitational settlement on Sabouraud dextrose agar (SDA). Identification of isolates followed colonial and microscopic methods. Seventy-one fungal genera and species were identified, with Aspergillus niger (179; 59.7%) being the most predominant. Aspergillus niger was most isolated in Wakwa (40; 22.3%), Makama (31; 17.3%) and Gayam (51; 28.5%) wards while Bipolaris sp (40; 35.4%) and Aspergillus fumigatus (33; 28.2%) dominated isolates in Ciroma and Akurba wards respectively. The number of isolated genera and species in the wards was in the order Akurba (51) > Makama (42) > Wakwa (38) > Ciroma (36) > Gayam (32), while total frequency were 292, 290, 283, 274 and 249 in Akurba, Wakwa, Gayam, Ciroma and Makama respectively. The highest and least mean relative humidity obtained in the study were (76.3%) and (52.5%) respectively. Statistical analysis revealed no significant differences in the mean relative humidity, number and total frequency of isolates in the wards. Homes that reared animals were more contaminated with fungal species than those that did not. The study has revealed unhealthy presence of fungal pathogens in the homes, a conducive environment for the proliferation of fungi and therefore advocates necessary actions to reduce flooding in Lafia local government area of Nasarawa State.

Keywords: Flood, Homes, Fungi, Air quality, Relative humidity

AGRICULTURAL AND BIOLOGICAL SCIENCES


INTRODUCTIONMicroorganisms are ubiquitous in nature and thus tend to survive in a very wide range of environmental conditions. There are however certain conditions which are considered more conducive and thus lead to increased growth and consequently their large numbers in the environment. Fungal growth is supported mainly by oxygen and moisture (Deacon, 2005). However, water availability is considered more important as controlling it has been shown to reduce or even prevent the growth of fungi (Karch, 2008). Furthermore, there is a higher risk of mould infestation in areas with high amounts of water in the environment such as flood prone areas (Emerson et al., 2015; Barbeau et al., 2010: Olaf and Robert, 2011), places with humid weather conditions and or seasons characterised by high relative humidity (Talley, Coley and Kursar, 2002). Indoor air in areas with high relative humidity has been shown (Henk and Olaf, 2011) to have a higher concentration of fungi when compared with air within low relative humidity areas. In accordance with this, a relative humidity of at least 70% is generally considered ideal for the proliferation of fungi in indoor air. Some studies further suggest a variation in the species of fungi isolated at different levels of relative humidity. While xerophilic fungi such as Aspergillus penicilloides, Penicillium sp, Eurotium herbariorum (Petterson and Leong, 2011) grow best at relative humidity below 85%, Mesophilic fungi (Alternaria sp,) and Hydrophilic fungi (Yeasts, Chaetomium spp, Stachybotrys sp, Phoma, Herbarum spp, Mucor sp, Rhizopus sp ) (Park et al., 2008), and (Abe, 2011) grow best at relative humidity of above 85% and 95% respectively (Snow, 1949). The nature of the relationship between increased fungal or mould growth and high relative humidity has been described by Karch (2008), and water is considered key in the process of absorptive nutrition which is primarily employed by fungi. Here the enzymes which are necessary for the breakdown of complex substances into simpler forms that can be absorbed by the fungi, are only functional in the presence of water. Furthermore spore germination, mycellium growth and mycotoxin production is said to only occur in the presence of free water molecules within the fungal cells (Peart, 2001; Magan and Lacey, 1984). Fungal growth in damp buildings as obtainable in flood prone areas is considered an increasing problem globally mainly due to the health and financial implications which arise from it (Andersen et al., 2011). A wide range of Fungi have been isolated from indoor air of houses in flood prone areas and

the sources of these organisms are suggested to vary from soil, human or animal, or plant waste and accumulated dust which find their way into indoor air as aerosols and air which moves from the external surroundings (Yasin and Almouqatea, 2010; King and Auger, 2002). While some of these fungi do not pose a threat to health, a larger number of them are pathogenic. Previous studies (Gravesen et al., 1999; Andersen et al., 2011) which sampled indoor air of flood prone homes show the most commonly isolated fungi to include Penicillium sp, Aspergillus sp, and Cladosporium sp. Others are Trichoderma sp, Paecilomyces sp, Stachybotrys sp, Alternaria sp (Khan and Karrupayil, 2012). The documented health implications of fungi in relation to flooded environments which have included water bone disease outbreaks, wide spread fungal skin infection just to mention a few have therefore necessitated this study in the flood prone areas of Lafia, Nasarawa state.

MATERIALS AND METHODThe study was conducted in Lafia local government area, which also doubles as the capital of Nasarawa state. Lafia lies between latitude 8025’40” to 8034’15”North and longitude 8024’25” to 8038’19”East in the Guinea savannah region of Northern Nigeria (Nuhu and Ahmed, 2013). Wards within the local government area that experience flooding were identified and selected for the study, namely Wakwa, Makama, Gayam, Ciroma and Akurba wards. These wards experience extensive yearly flooding majorly during the rainy season which last all through the rainy period. Sixty (60) houses were randomly selected in each of the five (5) council wards, making a total of three hundred houses. Air sampling was done using the Koch sedimentation method by gravitational settlement. Three petri dishes containing Sabouraud Dextrose Agar (Micro Masters) supplemented with chloramphenicol at 16ug/ml were placed on flat surfaces and exposed to air in the sitting room, bedroom and bathroom of each house for at least 10 minutes, covered and incubated at room temperature for 3 – 5 days. The bedrooms and bathrooms sampled had no vents or windows except the entrance doors while some of the sitting rooms had only a window and the entrance door with no vents. The relative humidity of the room was also read using a wet and dry temperature hygrometer, and the mean relative humidity calculated. Isolates were identified using macroscopic characteristics on the growth medium as well as

MYCOLOGICAL ASSESSMENT OF AIR QUALITY IN FLOOD-PRONE HOMES


micromorphological characteristics such as shape, size and arrangement pattern of spores and other vegetative structures of the isolates, aided by identification keys (G.S. de Hoog et al., 2014, Atlas of Clinical Fungi, version 4.0). Following incubation, isolates were mounted in lactophenol cotton blue stain and viewed using a light microscope. Data collated were analyzed using SPSS version 20 (IBM Corp., Armonk, New York). Simple means, percentages and frequencies were computed. Means were compared using Chi square (χ2) test.

RESULTS AND DISCUSSION Seventy-one (71) fungal genera and species were identified as contaminants of air in the sampled homes (Table 1). Aspergillus niger (59.7%) was the most predominant isolate, followed by Aspergillus fumigatus (39.0%) and Bipolaris sp (37.7%). Species of Aspergillus were more frequently isolated in the sampled areas than other genera of fungi. Similarly, five (5) genera had a frequency of 3 (1.0%), six (6) had a frequency of 2 (0.7%) while twenty two (22) genera and species had a frequency of 1 (0.3%), the lowest occurring isolates of the study.The frequency and distribution of each isolate in the respective wards was as presented in Table 2. While some of the genera and species had high frequencies of occuarrence in some of the wards, the frequency of occurrence and distribution of others were low. Aspergillus niger the most dominant isolate, was most isolated in Gayam, Wakwa and Makama wards with a frequency of 51 (28.5%), 40 (22.3%) and 31 (17.3%) respectively. Bipolaris sp was the most dominant isolate in Ciroma ward with a frequency of 40 (35.4%). P. maneffei, A. nidulans and P. decumbens were isolated only in Wakwa ward, whereas Cladosporium sp, A. minisclerotigenes, A. tamarii, A. wentii and R. stolonifera were all isolated only in Makama ward. The mean relative humidity was in the order Makama (90.5%) > Akurba (87.7%) > Wakwa (87.4%) > Ciroma (83.8%) > Gayam (76.3%). Akurba ward had the highest number of isolated genera and species, followed by Makama (42; 59.2%), Wakwa (38; 53.5%), Ciroma (36; 50.7) and Gayam (32; 45.1) wards respectively. The total frequency of isolated genera and species was highest in Akurba ward (292) and least in Makama ward (249) respectively. Chi square test however did not reveal any significant difference between the mean relative humidity (p = 0.05), the number of isolates and total frequency of isolates in the respective wards. However, relative humidity was found to affect the number or frequency of the isolates in each of the wards. Farming activity was found to affect the total frequency of isolates (Table 4). Animal farming or

rearing of animals (815; 58.6) had the highest total frequency of isolates, while houses engaged in crop farming (43; 3.1%) had the least total frequency. Houses not engaged in any form of farming activity had more isolates (401; 28.9%) than those engaged in crop farming alone (43; 3.1%) and a mixture of both crop and animal farming (128; 9.2%)

Table 1: Frequency of fungal isolates in the five wardsIsolates Frequency

(n = 300)Percentage (%)

A. niger 179 59.7A fumigatus 117 39.0 Bipolaris sp 113 37.7 Mucor sp 68 22.7 P. chrysogenum 67 22.3 A. flavus 67 22.3 T. rubrum 57 19.0 A. terrus 56 18.7 T. mentagrophytes 51 17.0 Rhizopus sp 49 16.3 S. schenckii 41 13.7 A. corymbifera 34 11.3 T. violaceum 33 11.0 T. harzianum 31 10.3 C. lunata 30 10.0 P. griseofulvum 28 9.3 F. solani 27 9.0 C. krusei 25 8.3 A. altanata 24 8.0 T. verrucosum 23 7.7 P. digitatum 23 7.7 A. aborescens 23 7.7 A. restrictus 22 7.3 F. oxysporum 18 6.0 A. oryzae 17 5.7 R. minuta 16 5.3 A. sydowii 15 5.0 T. tonsurans 14 4.7 S. cerevisae 12 4.0 Scopulariopsis sp 11 3.7 A. versicolor 10 3.3 A. clavatus 7 2.3 P. maneffei 6 2.0 Botrytis sp 5 1.7 A. acidus 5 1.7 A. parasiticus 4 1.3 M. nanum 4 1.3 Nigrospora sp 3 1.0 A. nidulans 3 1.0 P. decumbens 3 1.0 P. purpurogenum 3 1.0 Stemphylium sp 3 1.0 A. carbonarius 2 0.7



Table 1: Frequency of fungal isolates in the five wards -continuedIsolates Frequency

(n = 300)Percentage (%)

Phialophora sp 2 0.7 R. oryzae 2 0.7 Chrysonilia sitophila 2 0.7 Phoma sp 2 0.7 Aureobasidium pullulans 2 0.7 M. audouinii 1 0.3 Ulocadium alternaria 1 0.3 Cladosporium sp 1 0.3 A. minisclerotigenes 1 0.3 A. tamarii 1 0.3 A. wentii 1 0.3 R. stolonifera 1 0.3 Cryptococcus laurentii 1 0.3 Talaromyces sp 1 0.3 Trichophyton sp 1 0.3 E. floccosum 1 0.3 Scedosporium sp 1 0.3 Rhizomucor sp 1 0.3 Paecilomyces sp 1 0.3 Eurotium sp 1 0.3 Syncephalostrum sp 1 0.3 Exserohilum sp 1 0.3 Talaromyces trachyspermus 1 0.3 Geotricum sp 1 0.3 A. glaucus 1 0.3 M. ferrugineum 1 0.3P. glabrum 1 0.3

Table 2: Frequency and distribution of isolates in each wardIsolates Frequency in each ward

Wakwa (%)

Makama (%)

Gayam (%)

Ciroma (%)

Akurba (%)

A. niger 40 (22.3) 31 (17.3) 51(28.5) 32(17.9) 25(14.0)

A. fumigatus 32 (27.4) 16 (13.7) 23(19.7) 13(11.1) 33(28.2)

Bipolaris sp 4 (3.5) 12 (10.6) 26 (23) 40(35.4) 31(27.4)

Mucor sp 15 (22.1) 14 (20.6) 18(26.5) 12(17.6) 9 (13.2)

P. chrysogenum 18 (26.9) 15 (22.4) 14(20.9) 9 (13.4) 11(16.4)

A. flavus 27 (40.3) 17 (25.4) 6 (9.0) 5 (7.5) 12(17.9)

T. rubrum 21 (36.8) 7 (12.3) 9 (14.0) 15(26.3) 6 (10.5)

A. terrus 8 (14.3) 12 (21.4) 10(17.9) 9 (16.1) 17(30.4)

T. mentagrophytes 9 (17.6) 8 (15.7) 14(27.5) 10(19.6) 10(19.6)

Rhizopus sp 13 (26.5) 7 (14.3) 6 (12.2) 13(26.5) 10(20.4)

S. schenckii 15 (36.6) 4 (9.8) 5 (12.2) 7 (17.1) 10(24.5)

A. corymbifera 7 (20.6) 6 (17.6) 10(29.4) 8 (23.5) 3 (8.8)

T. violaceum 14 (42.4) 5 (15.2) 6 (18.2) 3 (9.1) 5 (15.2)

T. harzianum 2 (6.5) 8 (25.8) 9 (29.0) 7 (22.6) 5 (16.1)

C. lunata 11 (36.7) 2 (6.7) 1 (3.3) 5 (16.7) 11(36.7)

P. griseofulvum 1 (3.6) 7 (25) 8 (28.6) 3 (10.7) 9 (32.1)

F. solana 1 (3.7) 3 (11.1) 3 (11.1) 13(48.1) 7 (25.9)

A. altanata 1 (4.2) 4 (16.7) 3 (12.5) 6 (25.0) 10(41.7)

T. verrucosum 8 (34.8) 0 (0) 7 (30.4) 0 (0) 8 (34.8)

P. digitatum 4 (17.4) 2 (8.7) 3 (13.0) 7 (30.4) 7 (30.4)

Table 2: Frequency and distribution of isolates in each ward -continued

Isolates Frequency in each ward

Wakwa (%)

Makama (%)

Gayam (%)

Ciroma (%)

Akurba (%)

A. aborescens 0 (0) 8 (34.8) 3 (13.0) 5 (21.7) 7 (30.4)

A. restrictus 1 (4.5) 6 (27.3) 10(45.5) 3 (13.6) 2 (9.1)

C. krusei 1 (4.0) 9 (36.0) 10(40.0) 4 (16.0) 1 (4.0)

F. oxysporum 0 (0) 3 (16.7) 5 (27.8) 6 (33.3) 4 (22.2)

A. oryzae 5 (29.4) 0 (0) 0 (0) 12(70.6) 0 (0)

R. minuta 1 (6.2) 5 (31.2) 7 (43.8) 1 (6.2) 2 (12.5)

A. sydowii 1 (6.7) 4 (26.7) 4 (26.7) 5 (33.3) 1 (6.7)

T. tonsurans 7 (50.0) 1 (7.1) 0 (0) 4 (28.6) 2 (14.3)

S. cerevisae 1 (8.3) 4 (33.3) 4 (33.3) 2 (16.7) 1 (8.3)

Scopulariopsis sp 2(18.2) 1 (9.1) 2 (18.2) 5 (45.5) 1 (9.1)

A. versicolor 0 (0) 3 (30.0) 0 (0) 2 (20.0) 5 (50.0)

A. clavatus 0 (0) 4 (57.1) 3 (42.9) 0 (0) 0 (0)

P. maneffei 6 (100) 0 (0) 0 (0) 0 (0) 0 (0)

T. schoenleninii 2 (33.3) 0 (0) 0 (0) 1 (16.7) 3 (50.0)

Botrytis sp 0 (0) 3 (60.0) 0 (0) 2 (40.0) 0 (0)

A. acidus 0 (0) 5 (100) 0 (0) 0 (0) 0 (0)

A. parasiticus 0 (0) 3 (75.0) 1 (25.0) 0 (0) 0 (0)

M. nanum 0 (0) 2 (50.0) 1 (25.0) 1 (25.0) 0 (0)

Nigrospora sp 1 (33.3) 1 (33.3) 0 (0) 0 (0) 1 (33.3)

A. nidulans 3 (100) 0 (0) 0 (0) 0 (0) 0 (0)

P. decumbens 3 (100) 0 (0) 0 (0) 0 (0) 0 (0)

P. purpurogenum 0 (0) 0 (0) 0 (0) 0 (0) 3 (100)

Stemphylium sp 0 (0) 0 (0) 0 (0) 2 (66.7) 1 (33.3)

A. carbonarius 1 (50) 0 (0) 0 (0) 0 (0) 1 (50)

Phialophora sp 1 (50) 0 (0) 0 (0) 0 (0) 1 (50)

R. oryzae 1 (50) 1 (50) 0 (0) 0 (0) 0 (0)

Chrysonilia sitophila

0 (0) 1 (50.0) 0 (0) 0 (0) 1 (50.0)

Phoma sp 0 (0) 0 (0) 0 (0) 0 (0) 2 (100)

Aureobasidium pullulans

0 (0) 0 (0) 0 (0) 0 (0) 2 (100)

M. audouinii 1 (100) 0 (0) 0 (0) 0 (0) 0 (0)

Cladosporium sp 0 (0) 1(100) 0 (0) 0 (0) 0 (0)

A. minisclerotigenes 0 (0) 1 (100) 0 (0) 0 (0) 0 (0)

A. wentii 0 (0) 1 (100) 0 (0) 0 (0) 0 (0)

R. stolonifer 0 (0) 1 (100) 0 (0) 0 (0) 0 (0)

Cryptococcus laurentii

0 (0) 0 (0) 1 (100) 0 (0) 0 (0)

Trichophyton sp 0 (0) 0 (0) 0 (0) 0 (0) 1 (100)

E. floccosum 0 (0) 0 (0) 0 (0) 0 (0) 1 (100)

Scedosporium sp 0 (0) 0 (0) 0 (0) 0 (0) 1 (100)

Rhizomucor sp 0 (0) 0 (0) 0 (0) 0 (0) 1 (100)

Paecilomyces sp 0 (0) 0 (0) 0 (0) 0 (0) 1 (100)

Eurotium sp 0 (0) 0 (0) 0 (0) 0 (0) 1 (100)

Syncephalostrum sp 0 (0) 0 (0) 0 (0) 0 (0) 1(100)

Exserohilum sp 0 (0) 0 (0) 0 (0) 0 (0) 1(100)

Talaromyces trach-yspermus

0 (0) 0 (0) 0 (0) 0 (0) 1(100)

Geotricum sp 0 (0) 0 (0) 0 (0) 1 (100) 0 (0)

A. glaucus 0 (0) 0 (0) 0 (0) 0 (0) 1(100)

M. ferrugineum 0 (0) 0 (0) 0 (0) 0 (0) 1(100)

P. glabrum 0 (0) 0 (0) 0 (0) 0 (0) 1(100)



Table 3: Relationship between relative humidity and frequency of isolates

Ward Mean relative humidity (%)

Total number of isolated genera and species (%)

Total frequency of isolates

Wakwa 87.4 38 (53.5) 290 (20.9)

Makama 90.5 42 (59.2) 249 (17.9)

Gayam 76.3 32 (45.1) 283 (20.4)

Ciroma 83.8 36 (50.7) 274 (19.7)

Akurba 87.7 51 (71.8) 292 (21.0) χ2 = 20.000, p = 0 .220 (p>0.05)

Table 4: Relationship between frequency of isolates and type of farming activity

Isolates Farming activity

None (%) Animal (%)

Crop (%)

Animal and crop (%)

Total(%)

A. niger 50 (27.9) 109(60.9) 4 (2.2) 16 (8.9) 179 (100)

A. fumigatus 29 (24.8) 70 (59.8) 4 (3.4) 14 (12.0) 117 (100)

Bipolaris sp 41 (36.3) 57 (50.4) 2 (1.8) 13 (11.5) 113 (100)

Mucor sp 20 (29.4) 39 (57.4) 3 (4.4) 6 (8.8) 68 (100)

P. chrysogenum 16 (23.9) 44 (65.7) 2 (3.0) 5 (7.5) 67 (100)

A. flavus 15 (22.4) 41 (61.2) 2 (3.0) 9 (13.4) 67 (100)

T. rubrum 16 (28.1) 34 (59.6) 2 (3.5) 5 (8.8) 57 (100)

A. terrus 20 (35.7) 27 (48.2) 1 (1.8) 8 (14.3) 56 (100) T. mentagrophytes 12 (23.5) 32 (62.7) 3 (5.9) 4 (7.8) 51 (100)

Rhizopus sp 14 (28.6) 27 (55.1) 1 (2.0) 7 (14.3) 49 (100)

S. schenckii 12 (29.3) 25 (61.0) 2 (4.9) 2 (4.9) 41 (100)

A. corymbifera 9 (26.5) 22 (64.7) 2 (5.9) 1 (2.9) 34 (100)

T. violaceum 8 (24.3) 22 (66.7) 2 (6.1) 1 (3.0) 33 (100)

T. harzianum 12 (38.7) 16 (51.6) 2 (6.5) 1 (3.2) 31 (100)

C. lunata 11 (36.7) 15 (50.0) 2 (6.7) 2 (6.7) 30 (100)

P. griseofulvum 5 (17.9) 21 (75.0) 0 (0.0) 2 (7.1) 28 (100)

F. solana 7 (25.9) 18 (66.7) 1 (3.7) 1 (3.7) 27 (100)

A. altanata 8 (33.3) 11 (45.8) 0 (0.0) 5 (20.8) 24 (100)

T. verrucosum 4 (17.4) 16 (69.6) 1 (4.3) 2 (8.7) 23 (100)

P. digitatum 9 (39.1) 13 (56.5) 0 (0.0) 1 (4.3) 23 (100)

A. aborescens 10 (43.5) 13 (56.5) 0 (0.0) 0 (0.0) 23 (100)

A. restrictus 7 (31.8) 13 (59.1) 1 (4.5) 1 (4.5) 22 (100)

C. krusei 11 (44.0) 14 (56.0) 0 (0.0) 0 (0.0) 25 (100)

F. oxysporum 8 (44.4) 8 (44.4) 0 (0.0) 2 (11.1) 18 (100)

A. oryzae 6 (35.3) 8 (47.1) 2(11.8) 1 (5.9) 17 (100)

R. minuta 2 (12.5) 13 (81.2) 0 (0.0) 1 (6.2) 16 (100)

T. tonsurans 2 (14.3) 9 (64.3) 1 (7.1) 2 (14.3) 14 (100)

S. cerevisae 3 (25.0) 9 (75.0) 0 (0.0) 0 (0.0) 12 (100)

Scopulariopsis sp 2 (18.2) 8 (72.7) 0 (0.0) 1 (9.1) 11 (100)

A. versicolor 1 (10.0) 6 (60.0) 0 (0.0) 3 (30.0) 10 (100)

A. clavatus 2 (28.6) 4 (57.1) 0 (0.0) 1 (14.3) 7 (100)

P. maneffei 0 (0.0) 5 (83.3) 0 (0.0) 1 (16.7) 6 (100)

T. schoenleninii 1 (16.7) 3 (50.0) 0 (0.0) 2 (33.3) 6 (100)

Botrytis sp 1 (20.0) 3 (60.0) 0 (0.0) 1 (20.0) 5 (100)

A. acidus 2 (40.0) 3 (60.0) 0 (0.0) 0 (0.0) 5 (100)

A. parasiticus 1 (25.0) 3 (75.0) 0 (0.0) 0 (0.0) 4 (100) M. nanum 2 (50.0) 1 (25.0) 1(25.0) 0 (0.0) 4 (100) Nigrospora sp 1 (33.3) 1 (33.3) 0 (0.0) 1 (3.33) 3 (100) A. nidulans 1 (33.3) 2 (66.7) 0 (0.0) 0 (0.0) 3 (100) P. decumbens 0 (0.0) 3 (100) 0 (0.0) 0 (0.0) 3 (100) P. purpurogenum 0 (0.0) 1 (33.3) 0 (0.0) 2 (66.7) 3 (100)

Table 4: Relationship between frequency of isolates and type of farming activity -cont.

Isolates Farming activity

None (%)

Animal (%)

Crop (%)

Animal and crop (%)

Total(%)

Stemphylium sp 1 (33.3) 1 (33.3) 0 (0.0) 1 (33.3) 3 (100) A. carbonarius 1 (50.0) 1 (50.0) 0 (0.0) 0 (0.0) 2 (100) Phialophora sp 1 (50.0) 1 (50.0) 0 (0.0) 0 (0.0) 2 (100) R. oryzae 1 (50.0) 1 (50.0) 0 (0.0) 0 (0.0) 2 (100) Chrysonilia sitophila

2 (100) 0 (0.0) 0 (0.0) 0 (0.0) 2 (100)

Phoma sp 0 (0.0) 1 (50.0) 0 (0.0) 1 (50.0) 2 (100) Aureobasidium-pullulans

0 (0.0) 1 (50.0) 0 (0.0) 1 (50) 2 (100)

M. audouinii 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) Ulocadium alter-naria

1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100)

Cladosporium sp 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) A. miniscleroti-genes

0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100)

A. tamarii 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) A. wentii 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) R. stolonifer 0 (0.0) 0 (0.0) 1 (100) 0 (0.0) 1 (100) Cryptococcus laurentii

1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100)

Talaromyces sp 1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100) Trichophyton sp 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) E. floccosum 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) Scedosporium sp 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) Rhizomucor sp 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) Paecilomyces sp 1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100) Eurotium sp 1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100) Syncephalostrum sp

1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100)

Exserohilum sp 1 (100) 0 (0.0) 0 (0.0) 0 (0.0) Talaromyces trachyspermus

0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100)

Geotricum sp 1 (100) 0 (0.0) 0 (0.0) 0 (0.0) 1 (100) A. glaucus 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100) M. ferrugineum 0 (0.0) 1 (100) 0 (0.0) 0 (0.0) 1 (100)P. glabrum 0 (0.0) 0 (0.0) 0 (0.0) 1 (100) 1 (100)

TOTAL 401 (28.9) 815(58.6) 43 (3.1) 128 (9.2) 1387 (100)

This study was designed to assess the mycological quality of air in flood prone homes and in achieving this, the fungal contaminants in the homes were identified, the relative humidity and its effect on fungal density was determined, and the relationship between farming activities and the occurrence of the fungal isolates was also determined. A total of 71 fungal species were isolated from the 300 homes sampled in the study. The large number of fungi isolated is clearly an indication that the prevailing environmental condition in these homes largely favours and encourages fungal proliferation. The high number of fungal contaminants observed in this study could be attributed to elevated moisture levels and the presence of suitable substrates such as damp paints, walls and furniture. Similar studies by Emerson et al. (2015) and Barbeau et al. (2010) also



reported a high number of fungi in air of houses in areas where flooding occurs. Among the fungal species isolated, Aspergillus niger (59%) and Aspergillus fumigatus (39.0%) were found to be the most frequently occurring. This could be as a result of the Aspergillus sp being the most common type of fungi in the environment. A. niger which was the most isolated is regarded as the most abundant species of Aspergillus in nature and their ability to grow in environments with very little nutrients available could also be responsible for their high occurrence in the study (Chehri, 2013). The equally high occurrence of A. fumigatus could be as a result of the organism being the most temperature tolerant among the species. It has the ability to tolerate temperatures between 20oC and 55oC. They cause infections in humans more often than any other Aspergillus species. Other isolates with relatively high frequencies include Bipolaris sp (37.7%), Mucor sp (22.7%) and Penicillium chrysogenum (22.3%). The high incidence of Aspergillus sp and Penicillium sp observed in this study, agrees with a previous report by Khan and Karrupayil (2012). Other fungal species isolated in this study have also been reported in similar studies (Barbeau et al., 2010; Khan and Karrupayil, 2012; Emerson et al., 2015). The high occurrence of Bipolaris sp in this study is considered an important finding because of its pathogenic nature. Hence, its occurrence in large quantities in indoor air poses a major threat to the health of individuals. Other important pathogens isolated in this study are the dermatophytes Epidermophyton floccosum, Tricophyton sp and Microsporum sp which cause superficial or subcutaneous infections of the hair, skin and nails including Tinea (ringworm) and onychomycosis (Gupta et al., 2005; Seddon, 1997). The presence of these pathogens in the study area clearly calls for the need for measures to control

flooding so as to safeguard the health of inhabitants. Farming activities have been reported to affect the number and types of fungi occurring in a particular environment (Swer, Dkhar and Kayang, 2011). This study has observed a relationship between farming activity, and the frequency and type of isolates in the various wards. The high frequency of isolates associated with animal farming in this study may not be unrelated to the fact that resultant materials such as animal dung and feed serve as substrates for the growth of fungi. A relatively high frequency of fungal isolates was observed in houses engaged in neither crop nor animal farming. The reason for this may not be farfetched as it was observed during sampling that harvested farm products such as grains were stored in most of the homes. This practice could have served as reservoirs and substrates for fungi such as Aspergillus sp and Penicillium sp, commonly found on stored grains. It is worthy of note that Aspergillus flavus is a common contaminant of maize grains which produces the highly potent aflatoxin known to affect the health of man and animals.

CONCLUSIONResults of this study have revealed a high incidence of fungal contamination in houses prone to flooding, due to prevailing environmental conditions which favour the growth of various fungal populations. A number of the fungal contaminants isolated in the study have been reported to be pathogenic to humans. These findings therefore stress the need for adequate measures to control flooding in the affected areas so as to safeguard the health and wellbeing of the populace. ACKNOWLEDGEMENT The authors are grateful to the Tertiary Education Trust Fund (TETFUND) for providing the research grant used for this study, and the management of Federal University Lafia, for the support and encouragement given while the study lasted.



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Abe, K. (2012). Assessment of home environments with a fungal index using hydrophilic and xerophilic fungi as biologic sensors. Indoor Air, 22 (3): 173-185.Andersen, B., Frisvad, J.C., Sondergaard, L., Rasmussen, L.S. and Larsen, L.S (2011).Association between fungal species and water-damaged building materials. Environmental Microbiology. 77 (12): 4180- 4188.Andrews, S. and Pitt, J.I. (1987). Further Studies on the Water Relations of Xerophilic Fungi,Including Some Halophiles. Journal of General Microbiology, 133: 233-238.Barbeau, D.N., Grimsley, L.F., El-Dahr, J.M. and Lichtveld, M. (2010). Mold exposure and health effects following Hurricanes Katrina and Rita. Annual Review of PublicHealth, 31(1): 165-178.Buzina, W., Braun, H., Schimpl, K., and Stammberger, H (2003). Bipolaris spicefera causes Fungus balls of the sinuses and triggers polypoid chronic rhinosinusitis in an immunocompotent patient. Journal of Clinical Microbiology, 41 (10): 4885-4887.Chao, H.J., Schwartz, J., Milton, D.K. and Burge, H.A (2002) Populations and determinants of airborne fungi in large office buildings. Environmental Health Perspectives, 110(8): 777 782.Chehri, J. (2013). Factors affecting the growth of biomass of Aspergillus niger. Medical Sciences and Public Health, 1(1): 1-5.Deacon, J. (2005). Fungal biology (4th Ed.). Wiley-Blackwell Publishing. Emerson, B. J., Keady, P.B., Brewer, T.E., Clements, N., Morgan, E.E., Awerbuch, J., Miller, L.M and Fierer N.(2013). Impacts of flood damage on airborne bacteria and fungi in homes after the 2013 Colorado front range flood . Environmental Science and Technology, 49(5): 2675-84.Fothergill, A.W. (1996). Identification of dematiaceous Fungi and their role in human disease. Clinical and infectious diseases, 22: 2.Gravesen, S., Nielsen, P.A., Iversen, R. and Nielsen, K, F. (1999). Microfungal contamination of damp buildings – examples of risk constructions and risk materials. Environmental Health Perspectives Supplements, 107 (S3).Gupta, A.K., Ryder, J.E., Chow, M and Cooper, E.A. (2005). Dermatophytosis:the management of fungal infections. SKINmed, 4(5): 305-10.Hsu, N.Y., Chen, P.Y., Chang, H.W., and Su, H.J (2011). Changes in profiles of airborne fungiin flooded homes in southern Taiwan after Typhoon Morakot. Science of Total Environment, 409 (9): 1677- 1682.Karch, C.M.S. (2008). Water and Fungi. The environmental Reporter 6 (2).Khan, A.A.H and Karuppayil, S.M. (2012). Fungal pollution of indoor environments and its management. Saudi Journal of Biological Sciences, 19: 405-426.King, N. and Auger, P. (2002). Indoor air quality, fungi, and health: How do we stand? Canadian Family Physician. 48: 298-302.Magan, N. and Lacey, J. (1984). Effect of temperature and pH on water relations on field and storage fungi. Transactions of the British Mycological Society, 82 (1): 71-81.Napoli, C., Marcotrigiano, V. and Montagna, M.T. (2012). Air sampling procedures to evaluate microbial contamination: a comparison between active and passive methods in operating theatres. BioMed Central Public Health. 12 (594).Nuhu, Z. and Ahmed, M. (2013). Agricultural Landuse in Suburban Lafia of Nasarawa State,Nigeria. Social Sciences and Humanities, 4(4): 607 – 617.Park, J., Cox-Ganser, J.M., Kreiss, K., White, S.K and Rao, C.Y. (2008). Hydrophilic fungi and ergosterol associated with respiratory illness in a water-damaged building. Environmental Health Perspectives, 116 (1): 45-50.Petterson, V.O. and Leong, S.L. (2011). Fungal Xerohiles (Osmophiles). Encyclopedia of Life Sciences.Seddon, M.E. and Thomas, M.G. (1997). Invasive disease due to Epidermophyton floccosum in an immunocompromised patient with Behcet’s syndrome. Clinical Infectious Diseases, 25:153- 154. Snow, D. (1949). The germination of mould spores at controlled humidities. Annals of Applied Biology, 36 (1): 1-13.World Health Organisation (WHO) (2010). WHO guidelines for indoor air quality: dampness and mould. WHO Regional Office. Europe.www.euro.who.int/__data/assets/pdf_file/0017/43325/E92645.pdf World Health Organisation (WHO) (2011). Methods for monitorin indoor air quality in schools. World Health Organization Regional Office for Europe.http://www.euro.who.int/__data/assets/pdf file/0011/147998/e95417.pdf [Accessed 10 January, 2016].Yassi, M.F. and Almouqatea, S. (2010). Assessment of airborne bacteria and fungi in an indoor and outdoor environment. International Journal of Environmental Science and Technology, 7(3): 535-544.



ANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION IN MANGU LOCAL GOVERNMENT AREA OF PLATEAU STATE, NIGERIA

1Onuk E.G; 2Ohen, S.B. and 3Shehu, N.D.1Faculty of Agriculture, Department of Agricultural Economics and Extension,

Nasarawa State University, Keffi, Nigeria2Department of Agricultural Economics, University of Calabar, Nigeria

3College of Agriculture, Lafia, Nasarawa State, Nigeria

Corresponding Email: [email protected]

Manuscript received 25/08/2016 Accepted: 23/12/2016 Published: December, 2016

ABSTRACTThis study examined women participation in livestock Production in Mangu local government area of Plateau State, Nigeria. Simple random sampling was used to select 90 women livestock farmers. Descriptive statistics and Participation Index were used to analyze the data. The grand participation index (2.0) implies that women rarely participated in livestock production. Women always participated in the watering (2.97) feeding of animals (2.88) and cleaning of pen (2.72). Poultry, Swine and Goat were the major types of Livestock kept by Women. The result also revealed that there was positive and significant relationship between women involvement in livestock production and extension contact and age. Extension contact was significant (p<0.01) and positive which means that the more the women have access to extension contact the more tendency for them to participate in livestock production. The major constraints faced by women in livestock production were high cost of feed and medication, inadequate capital, pest and diseases. The study recommends that the women should be linked with micro finance banks so as to have access to capital which can be used to boost their level of participation in livestock production. Also various capacity building activities in-terms of training need of women in livestock production should be identified and periodically provided. Also subsidy should place on vaccine and drugs so as to reduce the high cost of medication this will encourage women participate in livestock production.

Keywords:Analysis, Women, Participation, Livestock, Production



INTRODUCTIONLivestock has remained a significant component of the agricultural economy of Nigeria which in addition to food production has provided incentives for economically sustainable agriculture. The privacy of agriculture in the Nigerian economy has never been in doubt as it employs 70% of the populace. The popular adage remains true “that no nation that is rich in livestock is poor and no nation that is poor in livestock is rich”. Ironically, Nigeria with 19 million cattle, 39.3 million sheep, 69.4 million pigs and 153 million poultry is indeed rich in livestock and expectedly a rich nation but unfortunately Nigeria has been poor and food insecure. The Global livestock sector has been undergoing what can be termed a revolution as a result of the demand for food of animal origin with attendant increases in livestock production, technological innovations and structural sectoral changes. It is generally believed that Nigeria’s population is growing at a geometric proportion (1%), while food production is growing at arithmetic proportion (2.97%). The gap between supply and demand continues to widen (Adu, 2015). In many African countries including Nigeria, rural women account for 60 percent of the agricultural labour force and up to 80 percent of total food production, including livestock production. Achieving profitability and efficiency is one of the major aims of livestock business in most rural and urban areas in Mangu Local Government Area. Livestock farming in the Local Government predominantly done in a semi-intensive management system and intensive in the rural areas mainly at subsistence level. Onwusiribe, et al (2016) reported that livestock serves as an asset as well as a source of income for many Nigerians, creating jobs, the source of food and meat to meet the protein requirements, provides animal manure for crop production and provides power and transport options. Women generally contribute more labour inputs in areas of feeding, manage vulnerable animals (calves, small ruminant and sick, injured and pregnant animals) cleaning barns, dairy related activities, (milking, butter and cheese making) transportation of farm manure and sale of milk and its products than men and children. Men own most of the livestock species and put up for sale animals and meat (Yisehak, 2008). Damisa et al. (2007) pointed out that various researches conducted on the contribution of women to agricultural development in the country suggest that women contribution to farm work is as high as between 60 and 90% of the total farm task performed. The contribution of the women ranges from such tasks as land clearing, land tilling, and planting, weeding, fertilizer/manure application to harvesting, food processing, threshing, winnowing,

milling, transportation and marketing as well as the management of livestock. Sharon (2008) viewed that both women and men play critical roles in agriculture throughout the world, producing, processing and providingthe food we eat. Women make up half the rural population and they constitute more than half of the agricultural labor force. Rural women in particular are responsible for half of the world’s food production and produce between 60 and 80 percent of the food in most developing countries. Yet, despite their contribution to global food security, women farmers are frequently underestimated and over looked in development strategies. Okwori Esther (2013), further reported that women contribute to agricultural output but unfortunately they hardly benefit from agricultural incentives and innovation because of economic suppression, social and traditional practices which undermine the constitutional provisions on the equality of men and women.Women constitute more or less half of any Country’s population .In most countries however, women contribute much less than men towards the value of recorded production both quantitatively in labour force and participation and qualitatively in educational achievement and skilled manpower (Lawason,2008). She pointed out that, the underutilization of female in agriculture has obvious implication for economic welfare and growth. Several factors both economic and non-economic are responsible for this. Traditionally, women are regarded as home makers who oversee and coordinate the affairs and activities at home. Women typically have complete responsibility for animals that are kept close to the homestead such as poultry, calves and other small livestock and for sick animals and they rarely have major holding and management responsibilities for large stock (IFAD, 1994). There is no doubt livestock production requires full participation of women, but this will nothappen until women are perceived as the subjects of development (Rahman, 2004). The study was carried out to describe the socioeconomic characteristics of women livestock farmers, determine the level of women participation in livestock production and to identify the constraints faced in livestock production. MATERIALS AND METHODS The study was conducted in Mangu Local Government Area of Plateau State, Nigeria. The study area along withother four (4) LGAs constitute the Plateau Central Senatorial Zone, namely, Bokkos, Kanam, Kanke and Pankshin. It is located at 9°31′ 00″N 9°06′00″E and has a population of 294,931 people and a total area of 1,653km2 (638 sq miles) (NPC, 2006). This placed it third in the lists of Plateau state LGAs by population size, coming after Jos North which is home to 420,300 people, and Jos South with 306, 716 people.Mangu

ANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION INMANGU LOCAL GOVERNMENT AREA OF PLATEAU STATE, NIGERIAANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION


which lies about 77 km south of the state capital (Jos) is a semi-urban settlement with a huge farming population. It has nine (9) districts namely; Mangu, Panyam, Gindiri, Langai, Mangun, Kerang, Ampang, Kombum, and Pushit. The climate and soil conditions of the area are suitable for growing cereal crops such as maize, guinea corn, millet, wheat, acha, rice and tuber crops such as Irish Potatoes, yam, cassava, sweet potatoes, etc. The major languages spoken in the area include, Mwaghavul and Pyem (Plateau State Information and Communication Development Agency, PSICDA, 2015).The Local Government Area is located within the Northern Guinea Savannah and the climate is near temperate and could be compared to the weather found in Jos, Barakin Ladi, Bokkos and Pankshin with an average temperature of between 18 and 22 °C. Harmattan winds cause the coldest weather between December and February. The warmest temperatures usually occur in the dry season months of March and April. The mean annual rainfall varies from 131.75 cm (52 in) in the southern part to 146 cm (57 in) on the Plateau. The highest rainfall is recorded during the wet season months of July and August. (NPC, 2006) The population for the study was all women Livestock farmers in Mangu Local Government Area. Data was collected with the aid of a structured questionnaire that was administered to the respondents. Double stage sampling technique was adopted. First stage was the random selection of one village from each of the nine administrative districts. Thus, nine (9) villages were selected for the study. The second stage involves purposive selection of 10 livestock farmers from each of the selected villages to give a total sample size of ninety (90). Data was analysed using simple descriptive statistics such as frequency counts and percentages to achieveall the three objectives.Participation index was used to achieve objective 2.However, the index was constructed using a 3 point likert scale which was weighted in order of importance from; Never involved =1, rarely involve = 2, always involved =3.The respondents were asked to indicate their level of involvement in the activities of livestock production. The mean score for each of the activities was calculated and the grand mean score of the activities was divided by the number of activities to determine the level of women participation in livestock production in the study area.

RESULTS AND DISCUSSIONThe socio – economic characteristics of the respondents are represented in Table 1.The result shows that majority (56.7%) of the respondents were within middle ages of 21 - 40 years. However, the proportion of the younger women involved in livestock production was relatively small (10%). This

finding is similar to that of Bayola and Intong. (2006) who explained that though women loved animals, they totally disagreed with been used in raising livestock. The result also shows that majority (67.8%) of the women livestock farmers were married, 16.7% and 15.6% of the sample respondents were widowed and singles respectively. Also majority of the women representing 57.8% in livestock production associated themselves with one form of cooperative participation, this is contrary to the work of Ayoade et al. (2009) which reported that majority (71.1%) of the women livestock farmers did not associate themselves with any form of cooperative participation. The educational status of the women in livestock production shows that majority (94.5%) had formal education. This findings disagrees with the work of Aqeela et al. (2005) that two third of the one billion of illiterate person in the world are women and girls. The result of the analysis further shows that majority of the women in livestock production accounted for 63.3% had less than 5years of farming experience. This may possibly be due to their high level of educational attainment and so they possibly considered farming as less prestigious. The implication is that women in the study area are not too familiar with livestock production. The major motives for keeping livestock were essentially for commercial purpose and home consumption. However, 30% of the respondents indicated that they kept livestock for commercial purposes only.

Table 1: Socioeconomic factors affecting women participation in livestock Production (n=90)Variables Frequency PercentageAge1-20 9 1021-40 51 56.741-60 30 33.3Total 90 100Marital statusMarried 14 15.6Single 61 67.8Widowed 15 16.7

Total 90 100Cooperative ParticipationYes 38 42No 52 57.8Total 90 100Source of CapitalPersonal Savings 71 78.9Friends and Relatives 11 12.2Bank 5 5.6Money Lenders 3 3.3Total 90 100

ANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION INMANGU LOCAL GOVERNMENT AREA OF PLATEAU STATE, NIGERIAANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION


Table 1: Socioeconomic factors affecting women participation in livestock Production (n=90) -continuedVariables Frequency PercentageYears of Experience1-5 57 63.36-10 29 32.211-15 4 4.4Total 90 100Purpose of AnimalHome Consumption 2 2.2Commercial 27 30Both 61 67.8Total 90 100Level of EducationPrimary 10 11.1Secondary 27 30.8Tertiary 48 53.3Adult 5 5.5Total 90 100

Source: Field Survey, 2013

Level of Women Participation in Livestock ProductionThe results from Table 2 expressed that cleaning of pen, (mean 2.72), watering (mean 2.96), feeding of animals (mean 2.87) and marketing are the livestock management practices that women always participated in. This finding agrees with that of Aqeela et al. (2008) that women participate in various activities of livestock management such as fodder cuttings, watering, feeding of animals, animal shade cleaning, milking and dung cake making.The result further indicated that women rarely participated in activities such as vaccination( mean 2.30) and records keeping ( mean 2.04), castration (mean1.73), culling (mean 1.73) and diagnosing (mean 1.64) these findings agrees with that of Bayola and Intong (2006) and Ayoade (2009) that women are moderately involved in maintaining sanitation and in tethering animals inside shed at night. However, women in the study area never participated in activities such as branding (mean 1.27) and fencing (mean 1.40) .The grand mean for the participation index (mean= 2.0) indicated that women in the study area rarely participated in livestock production.

Table 2: Participation index result showing the level of women Participation in Livestock Production Management Practices Mean scoreFeeding 2.88Cleaning of Pen 2.72Watering 2.97Castration 1.73Vaccination 2.30Records Keeping 2.04Marketing 2.51Branding 1.27Culling 1.73Fencing 1.40Diagnosing 1.64Bringing sick animal to vet 1.64Grand Mean 2.0

Source: Field Survey 2013.Note: 1= never involved, 2 rarely involved, 3 = always involved

Factors influencing women participation in livestock productionThe result in Table 3 revealed that there was positive and significant relationship between women involvement in livestock production and extension contact and age. Extension contact was significant (p<0.01) and positive which means that the more the women have access to extension contact the more tendency for them to participate in livestock production. This finding disagrees with that of Ayoade et al (2009) who explained that access to extension contact will not increase the participation of women in livestock production. Age is another factor influencing women Participation (p<0.01) that is the more the women advance in age the more they participation in livestock production. Years of experience and education were also significant although negative. Education was significant (p<0.01) and negative, this indicates that the more the women are educated the less participation in livestock production this might be as a result of educated women are more interested in white-collar jobs.

Table 3: Factors influencing women participation in livestock production

Constant Regression coefficient

Standard error

T –value

Variables 2.251 0.404 5.572

Age X1 0.10 1.828 1.572*

Years of experience X2 -.063 0.30 1.828**

Education X3 -.246 .132 -2.862*

Extension contact X4 .150 .049 3.093***

R2 = 0.554. *** Significant 01%. ** Significant 05%. * Significant 010%

Source: Field Survey, 2013.

ANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION


Constraints faced by women in Livestock productionThe results in (Table 4) shows that the major constraints to women participation in livestock Production was high cost of feed (21.70%) which ranked first followed by inadequate capital (16.72%). Yisehak (2008) reported that women in agricultural sector are involved in home production activities which involve child care, food preparation and fetching of water and fuel for domestic purposes such as cooking, washing, etc. Pests and diseases (15.42%) and high cost of medication (15.24%) ranked 3rd, poor market situation (12.61%) and rustling and poaching (12.90%) ranked 4th.

Table 4: Constraint faced by women in Livestock productionConstraint Frequency PercentageInadequate extension staff 18 5.82Pests and diseases 53 15.42High cost of feeds 74 21.70High cost of medication 52 15.24Poor market situation 43 12.61Thieves 44 12.90Inadequate capital 57 16.72Total 341* 99.9

Source: Field Survey, 2013*Multiple responses.Hence the total frequency exceeded the sample size

ANALYSIS OF WOMEN PARTICIPATION IN LIVESTOCK PRODUCTION

CONCLUSION The majority of the women rarely participated in Livestock production. The major factors affecting their participation were high cost of feeds, inadequate capital, pests and diseases and high cost of medication. Furthermore, factors that influence the women participation in livestock production in the study area include age, education and year of experience in livestock production. There is need to identify the training needs of women participation in livestock production. Women should be linked with micro finance banks in other to have access to capital which can be used to boost their level of participation. The men should be encouraged to assist their wives so that they could have ample time to participate in livestock production activities.

REFERENCESAdu, I.F. (2015). Investigating in the Livestock Sub-sector and its Value Chains: Impact on Nigeria’s Economy Beyond oil.Edited Address of the Proceedings of the 49th Annual Conference of the Agricultural Society of Nigeria, Delta 2015, pp 1 – 6.Aqeela, S., Tanyir, N., Munir, A., and Muhammad, Z. (2005). Gender Participation in Livestock Production activities and their Consumption Trends in Proteineous Diet in TEHSIL.FATEH JUNG.Pakistan Journal of Agricultural Science. 42:3 - 4Bayola, D. L. and Intong, J. D. (2006).Participation by Women and Children in Livestock Production in Bukidnon, Southern PhilliphinesAyoade, J. A., Ibrahim, H. I. and Ibrahim, H. Y. (2009): Analysis of Women involvement in livestock production in Lafia of Nasarawa State, Nigeria. Livestock Research for Rural Development 21 (12). Guide for preparation of papers.Damisa, M.A, Samndi, J.R, and Yohanna, M. (2007). ‘Women Participation in Agricultural Production: A Probit Analysis. J. of Applied Science sci. 7(3):412-416.IFAD (1994). Women Livestock Manager the third World: Focus on Technological Issues Related to Rolein Livestock Production, staff workingpaper18,Rome.htt:www.ifad.org/gender/thematic/livestock/live2.htmLawason, O. I. (2008): Female Labour Force Participation in Nigeria. Determinant and Trend; Oxford Business and Economic Conference Program: Oxford United Kingdom. June 22-24.NPC (2006): National Population Census, 2006Okwori Esther (2013). Women’s Inclusion as a Panacea to the challenges of Agricultural Sustainability in Nigeria: Proceedings, 18th Annual National Conference AESON (5th – 9th, May, 2013) pg. 169.Onwusiribe C.N.; Okpokiri C.I and Nzeako F.C. (2016). Determinants of Efficiency among Livestock Farmers in Abia State, Nigeria. Proceedings of 50th Annual Conference of Agricultural Society of Nigeria (ASN) Abia 2016 p145.PSICDA, (2015). Plateau State Information and Communication Development Agency, (PSICDA).The Governor’s Office, Jos, Plateau State, Nigeria.Rahman, S. A. (2004). Gender Differential in Labour Contribution and Productivity in Farm Production Empirical Evidence from Kaduna State of Nigeria. Paper Presented at the National Conference on Family held at New Theatre Complex. Benue State University, Makurdi, Nigeria. 1st-5th March.Sharon,B.H. (2008). Rural women and food Security FAO Participation in panel Discussion on the occasion of the International day of Rural Women held in NewYork;15th October,2008.Yisehak, K (2008). Gender Responsibility in Small Holder Mixed Crop- Livestock Production Systems of Jimma Zone, South West. Ethiopia. Livestock Research for Rural Development..Vol. 20.Article 11.



AN ASSESSMENT OF EARTHWORM POPULATIONAND SOIL FACTORS IN AMURUM FOREST RESERVE OF JOS, PLATEAU STATE, NIGERIA

1Abiem, I., 2Shiiwua, M. and 3Saha, S.1Department of Plant Science and Technology, University of Jos, P. M. B. 2084, Jos, Nigeria

2A. P. Leventis Ornithological Research Institute, P.O. Box 13404, Jos-East, Nigeria4Touro College South, 1703 Washington Ave, Miami Beach FL 33139-7541, USA

Corresponding email: [email protected]

Date Manuscript Received: 31/03/2016 Accepted: 13/09/2016 Published: December 2016

ABSTRACTThis study examined and compared the properties of soil inside and outside the Amurum Forest Reserve in Jos, Nigeria. Earthworms and soil samples were collected from 300 randomly laid 1x1m quadrats. Soils were analyzed for total nitrogen, available phosphorous and soil organic matter. Soil moisture retention capacity and pH were also measured. Other variables measured included percentage litter cover, and percentage grass cover. Earthworm abundance did not significantly differ between the reserve and outside the reserve. Available phosphorous and organic matter contents were significantly higher outside the reserve than inside the reserve. Percentage litter cover and percentage grass cover related positively with earthworm occurrence and abundance. Earth worm occurrence significantly related to litter cover. The earthworms sampled in this study were epigeic species which live in the litter and top soil. The significantly higher available phosphorous and organic matter contents in the surrounding areas of the reserve as compared to the reserve could be attributed to the grazing activities in the surrounding areas of the reserve. Protected areas as well as unprotected areas are important for the conservation of biodiversity.

Keywords: Amurum Forest Reserve, biodiversity, earthworm, epigeic, grass cover, litter cover


INTRODUCTIONSince 1990, the world’s protected areas have increased in number by 58% and in their extent by 48%. In terms of terrestrial area, protected areas are now one of the most important land-use allocations on the planet (UN 2012 as reported by Bertzky et al., 2012). Today, well managed protected areas support not only healthy ecosystems and threatened species, but they also provide multiple benefits to people, which include a wide range of ecosystem services such as clean water provision, food security, disaster risk reduction and climate regulation (Sritharan and Burgess, 2012). Many studies (Mosallam, 2007; Dhaou et al., 2010) in some parts of the world have shown that protected areas can be a successful way of maintaining natural and semi-natural habitats and preventing their degradation (Bruner et al., 2001). Ecologists are interested in investigating species composition and interactions in natural and anthropogenically influenced communities (Ilorkar and Khatri, 2003; Shameem and Kangroo, 2011). Soil is an essential component that influences species composition and ecosystem function in a landscape (Chapin III et al., 2002). Soil nutrient availability influences species distribution and community composition (Chapin III et al., 2002; Toledo et al., 2012).Many species of invertebrates play important roles in altering the structure and fertility of soil (Fabricius, et al., 2003). Of these, earthworms are a key taxon for soil functioning (Fonte, et al., 2009). They participate in litter decomposition, mix organic and mineral matter, create and maintain soil structure by digging burrows and modifying aggregation, regulate microbial diversity and activity, and protect plants against pests and diseases (Lavelle et al.,2006). They develop very complex interactions with other soil biota (Lavelle, 1997). They have been referred to as ‘soil engineers’ (Brossard et al., 2007, Brown et al., 2000) and have been shown to affect availability of nitrogen (N) and phosphorous (P), the main growth-limiting nutrients. Numerous studies have shown increases in plant growth in the presence of earthworms (Atiyeh et al., 2000, Buckerfied and Webster, 1998, Orozco et al., 1996). Earthworm density is influenced by the intensity of and number of soil disturbance events like tillage and traffic, the abundance and quality of food sources, the chemical environment of the soil, and soil microclimate (Donahue, 2001). Soil and vegetation have a complex interrelationship. Soil properties influence the vegetation and vice versa. A central question in ecology is how species and communities respond to variation in environmental conditions. There have been attempts to describe and explain the

relationships between soils and vegetation (Chen et al., 1997, Ilorkar and Khatri, 2003, Shameem and kangroo, 2011) although no generalities are possible. Soil is the medium of plant productivity. Kubota et al., (1998) suggest that both vertical and horizontal variations of soil characteristics are imperative in vegetation distribution, composition and biomass. The Amurum Forest Reserve in Jos, Nigeria was established in 2001. Prior to this time, the local community in the area who own the forest used part of it as farmlands and it was also a continuous source of fuel wood (Mwansat et al., 2011). Outside the reserve, habitat degradation is evident from along the boundaries of the reserve with the savanna giving way to more open and degraded grazing and arable land (Stevens, 2010). Amurum Forest Reserve is a vulnerable site of conservation concern because of its small size and proximity to the urban community of Jos. Though the reserve is a protected area, there are still a few sporadic cases of wood cutting and, grazing and setting of fire (Agaldo, 2010). The aim of this study was to assess theearthworm population and soil factorsof Amurum Forest Reserve of Jos, Plateau state,Nigeria.The study aimed at measuring and comparing the abundance of earthworms in the reserve and its surrounding, the nitrogen and phosphorous concentration and moisture retention capacity, organic matter concentration, pH of the soil and its surrounding areasand the determination of the relationship between earthworm abundance and other vegetation variables.

MATERIALS AND METHODSThe study was carried out at the Amurum Forest Reserve (9°53’N, 8°59’E) located 15 km northeast of Jos in north-central Nigeria. It is a 2km2 fragment which holds the last remnants of natural Guinean savannah vegetation on the Jos Plateau, Nigeria. It is made up of three habitat types - rocky outcrops, gallery forest and savannah scrub (Yessoufou, et al., 2012). The area has an average rainfall of about 1400mm per annum and daily temperatures range between 20 – 25oC. The soil is, for the most part brick red laterite around gullies and a mixture of sand and clay in the savannah. The reserve is protected against anthropogenic disturbance. The reserve is one of the Important Bird Areas (IBA) of Nigeria (Ezealor 2002), because it houses many bird species including the endemic LagonostictasanguinodorsalisPayne (Rock Firefinch) and Viduamaryae Payne (Jos Plateau Indigo Bird).In one hundred 100m2 study plots already established inside and outside the reserve, three 1x1m quadrats were established at random in each plot by

ASSESSMENT OF EARTHWORM POPULATION IN SOILS


throwing an object (a stick) whilst standing at the edge of the plot and facing away, giving a total of 300 quadrats. Earthworms and soil samples were collected from these quadrats and other variables were also measured.Earthworms were searched for and hand sorted from soil dug to a depth of 10cm. The earthworms were taken to the laboratory and stored in 0.5% formaldehyde. Soil samples were collected to a depth of 15cm using a cylindrical core. Composite samples were generated from the 1x1m quadrats in each 10x10m plot making a total of one hundred soil samples. The samples were taken to the laboratory, dried and sieved. The samples were analyzed for total nitrogen and phosphorous concentrations and moisture and organic matter contents. The pH of each soil sample was also measured using a pH meter.Total nitrogen was determined by using the Kjeldahl method (Rutherford et al., 2007). TheAvailable phosphorous was determined by Bray II method (Bray and Kurtz, 1945). Thirty grams (30g) of each of the aggregate fractions were weighed into rubber bands (rings). These were used to determine the water content of aggregate fractions at 1.5Mpa (15 bar) and 0.01 MPa (0.1 bar), using the pressure-plate apparatus. Organic carbon was determined using the Walkley and Black method (Walkley, 1947) as modified by Allison (1965). Organic matter was then determined by multiplying the percentage organic carbon by the conventional “Van Bemmelen factor” of 1.724. Soil pH was determined in water using water to soil ratio of 1:2.5. After stirring for 30 minutes, the pH values were read off using a Beckman zeromatic pH meter.Other variables measured in the 1x1m quadrats were percentage grass cover and percentage litter cover.All data collected was compiled using Microsoft Excel 2007® and analyzed using R version 2.15 (R Development Core Team, 2012).Mann-Whitney U test was used to evaluate the difference in the abundance of earthworms between the reserve and its surrounding areas.Independent sample t-test was carried out to compare nitrogen, phosphorous, moisture, organic matter content and pH between the soils of the reserve and that outside the reserve. The relationship between earthworm occurrence and earthworm abundance with other soil variables were also assessed using generalized linear models with a binomial error distribution and poisson error distribution.

RESULTS AND DISCUSSIONThe abundance of earthworms in the reserve and outside the reserve was not significantly different

(Mann-Whitney’s U test, W = 1238.5, p-value = 0.9393) but the mean number of earthworms recorded per plot was higher outside the reserve (5.08±0.63, N=50) than inside the reserve (4.66±0.47, N=50) (Figure 2). Earthworms were present in 86% of the sample plots and mean abundance of earthworms was 5/plot which was very low. This could be because the soil was dry at the time of data collection. Earthworms live in moist environments and so are more abundant during the rains. Earthworm abundance is best known to correlate with soil texture and soil organic matter (Krück et al., 2006), soil moisture (Eggleton et al., 2009), soil organic carbon content (Mainoo et al., 2008), plant residue management (Fonte et al., 2009) and amount of litter for breakdown (Owa et al., 2003). Soil organic matter content was higher outside the reserve as compared to inside the reserve (table 1). This agrees with studies done by Whalen (2004), Nair et al. (2005) and Rossi et al. (2006). In this present study, earthworm abundance was shown to correlate with percentage litter cover where plots with higher percentage of litter cover had higher number of earthworms. This could be because the earthworms sampled in this study are epigeic which are “litter dwellers” so more litter would increase the chances of finding them. A study by Iordache and Borza (2010) in Romania relating chemical indices of soil and earthworm abundance under chemical fertilization recorded humus (mainly from litter) and total nitrogen as the greatest positive influence on earthworm abundance and biomass. Independent sample t-tests showed significant difference between phosphorous concentration (t=-2.02, df= 98 and p=0.023) and organic matter content (t=-2.54, df=98, p=0.006) of soil within the reserve and soil outside the reserve (Table 1). There was no significant difference between the nitrogen concentration (t=-0.23, df=98 and p=0.408), pH (t=2.02, df= 98 and p= 0.977) and moisture retention capacity (t=-1.11, df= 98 and p= 0.135) of the soil within the reserve and soil outside the reserve (Table 1). Bulk nutrient concentrations have been recorded to differ between protected and unprotected sites (Mossalam, 2007; Rawat et al., 2009). In this study, the soil outside the reserve had significantly higher amounts of organic matter and available phosphorous (Table 1) than the soil in the reserve. This agrees with a study carried out by Mosallam (2007) which showed that organic matter was relatively higher in the soils of the grazed flats when compared with that in the soils of the protected flats in Sudera, Taif, Saudi Arabia. This may be as a result of trampling and lying of standing dead materials by grazing animals. Also, there are studies where native landscapes have



much lower nutrients than the outside and concluded that whenever nutrients are available in protected areas, they are immediately taken up. Total nitrogen, moisture retention capacity and soil pH of the reserve and its surrounding areas did not differ significantly between the reserve and its surrounding areas (Table 1). Moreira (2000) in a study in Brazil recorded that physical and chemical soil properties did not differ significantly between protected and unprotected sites and attributed fire protection as the major factor differentiating the two sites. In contrast, Rawat et al., (2009) recorded significant differences between soil nutrient concentrations in protected and unprotected areas with the protected areas having higher nutrient concentrations. The probability of finding earthworms increased significantly (glm, F=11.15, df=1, p=0.001) with increasing percentage litter cover (Figure 3). There was also an increase in the probability of finding earthworms in plots with higher percentage grass cover (Figure 4) but this was marginally significant (glm, F=3.07, df=1, p=0.083). Earthworm abundance also increased significantly (glm, F=14.54, df=1, p<0.001 and glm, F=7.12, df=1, p=0.009 respectively) with increasing percentage litter cover and percentage grass cover (Figures 5 and 6). Earthworm occurrence significantly related to percentage litter cover (Figure 3) with plots containing higher percentage litter cover having higher probability of earthworm occurrence. Earthworm abundance was significantly related to percentage litter cover and percentage grass cover (Figures 5 and 6) with

plots containing higher percentages of litter cover and grass cover having higher earthworm abundance. Litter cover was a better predictor of earthworm abundance. The earthworms sampled in this study were epigeic species which are favoured by the accumulation of litter or by grass vegetation. Changes in the distribution and the quality of litter, soil climate and water availability are known to affect the composition of earthworm communities (Gerard, 1967; McLean et al., 1996).’

CONCLUSIONThe study shows that earthworm abundance, and soil nutrient characteristics except organic matter and available phosphorous concentrations are the same for Amurum Forest Reserve and its surrounding areas. The study also infers that earthworm occurrence and abundance is influenced by litter cover and grass cover but as earlier stated, the earthworms sampled in this study are epigeic species. The study suggests that areas lying outside fully protected zones may be of great importance for conservation of broad spectrum biodiversity. Further investigation of soil properties in different landscapes is recommended

ACKNOWLEDGEMENTWe wish to thank Dr. A.P. Leventis for funding this research through the Leventis Foundation; Biplang for his assistance in producing the map of the study area and Ezekiel, Emmanuel, Othniel, Matthew and Matilda for their assistance in data collection.

REFERENCESAgaldo, J. A. (2010). Factors determining the abundance of Lantana camara L. (Verbanaceae) in Amurum Forest Reserve, Plateau State. M.Sc. Thesis in Conservation Biology, University of Jos, Nigeria. Pp.69Allison, L.E. (1965). Organic carbon. In: Methods of Soil Analysis, Part 2, C.A. Black et al., Ed. Agronomy. 9:1367- 1378. American Society of Agronomy., Inc., Madison, WI. Atiyeh, R. M., Subler, S., Edwards, C. A., Bachman, G., Metzger, J. D. and Shuster, W. (2000): Effects of vermicomposts and composts on plant growth in horticultural container media and soil. Pedobiologia, 44: 579-590.Bertzky, B., Corrigan, C., Kemsey, J., Kenney, S., Ravilious, C., Besancon, C. and Burgess, N. (2012) Protected Planet Report 2012: Tracking progress towards global targets for protected areas. IUCN, Gland, Switzerland and UNEP-WCMC, Cambridge, UK. Pp1-20.Bray, R. H. and Kurtz, L. T. (1945). Determination of total, organic and available forms of phosphorous in soils. Soil Science, 59:39-45.Brossard, M., Lopez-Hernandez, D., Lepage, M. and Leprun, J. C. (2007). Nutrient Storage in soils and nests of mound-building Trinervitermes Termites in central Burkina Faso: consequences for soil fertility. Biology and Fertility of soils, 43: 437-447.Brown, G. G., Barois, I. and Lavelle, P. (2000) Regulation of soil organic matter dynamics and microbial activity in the driloshphere and the role of interactions with other edaphic functional domains. European Journal of Soil Biology, 26:177-198.Bruner, A. G., Gullison, R. E., Rice, R. E. and Fonseca, G. A. B. (2001). Effectiveness of parks in protecting tropical biodiversity. Science, 291:125-128.Buckerfield, J. C. and Webster, K. A. (1998) Worm-worked waste boosts grape yields: prospects for vermicompost use in vineyards. Australian and New Zealand Wine Industry Journal, 13:73–76.Chapin III, F. S., Matson, P. A. and Mooney, H. A. (2002): Geology and Soils. In: Principles of Terrestrial Ecosystem Ecology. Springer-Verlag New York, Inc.Pp 46-67.Chen, Z. S., Hsieh, C. F., Jiang, F. Y., Hsieh, T. H. and Sun, I. F. (1997). Relations of soil properties to topography and vegetation in a subtropical rain forest in southern Taiwan. Plant Ecology, 132:229-241.



Dhaou, S. O., Abdallah, F., Belgacem, A. O. And Chaieb, M. (2010). The protection effects on floristic diversity in a North African pseudo-savanna. Pak. J. Bot., 42(3):1501-1510.Donahue, S. (2001) Agricultural management effects on earthworm populations. Soil quality-Agronomy Technical Note No. 11.Eggleton, P., Inward, K., Smith, J., Jones, D.T. and Sherlock, E. (2009) A six year study of earthworm(Lumbricidae) populations in pasture woodland in southern England shows their responses to soil temperature and soil moisture. Soil Biology and Biochemistry,41:1857–1865.Ezealor A. U. (2002). Amurum Woodlands (Taboru). In: Critical sites for biodiversity conservation in Nigeria. Nigerian Conservation Foundation, Lagos, Pp 65.Fabricius, C., Burger, M. and Hockey, P. A. R. (2003): Comparing biodiversity between protected areas and adjacent rangeland in xeric succulent thicket, South Africa: arthropods and reptiles. Journal of Applied Ecology,40: 392-403.Fonte, S. J, Winsome, T. and Six, J. (2009): Earthworm populations in relation to soil organic matter dynamics and management in California tomato cropping systems. Applied Soil Ecology, 41:206-214.Gerard, B. M. (1967). Factors affecting earthworms in pastures, Journal of Animal Ecology, 36:235-252.Ilorkar, V. M. and Khatri P. K. (2003). Phytosociological study of Navegaon National Park, Maharashtra. Indian Forester, 129(3): 377-387.Iordache, M. and Borza, I. (2010). Relation between chemical indices of soil and earthworm abundance under chemical fertilization. Plant Soil Environ., 56(9):401-407.Krück, S., Joschko, M., Schultz-Sternberg, R., Kroschewski, B. and Tessmann, J. (2006) A classification scheme for earthworm populations (Lumbricidae) in cultivated agricultural soils in Brandenburg, Germany. Journal of Plant Nutrition and Soil Science,169:651–660.Kubota, D., Masunaga, T., Hermansah, Rasyidin, A., Hotta, M. Shinmura, Y. and Wakatsuki, T. (1998). Soil environment and tree species diversity in tropical rain forest, West Sumatra, Indonesia In: Soils of Tropical Forest Ecosystems, Springer Berlin Heidelberg pp159-167. Lavelle, P. (1997). Faunal activities and soil processes: Adaptive strategies that determine ecosystem function. Advanced Ecological Resources, 27:93-132.Lavelle, P., Decaens, T., Aubert, M., Barot, S., Blouin, M., Bureau, F., Margerie, P., Mora, P., and Rossi, J.P. (2006) Soil invertebrates and ecosystem services. European Journal of Soil Biology,42: S13-S15.Mainoo, N-O. K., Whalen, J. K. and Barrington, S. (2008). Earthworm abundance related to soil physical, chemical and microbial properties in Accra, Ghana. African Journal of Agricultural Research, 3(3): 186-194.McLean, M. A., Kolodka, D. U. and Parkinson, D. (1996). Survival and growth of Dendrobaena octaedra (Savigny) in pine forest floor materials. Pedobiologia,40:281-288.Moreira, A. G. (2000). Effects of fire protection on savanna structure in Central Brazil. Journal of Biogeography, , 27:1021-1029.Mosallam, H. A. M. (2007). Comparative study on the vegetation of protected and non-protected area, Sudera, Taif, Saudi Arabia. International Journal of Agriculture and Biology, 9(2):202-214.Mwansat, G. S., Lohdip, Y. N. and Dami, F. D. (2011): Activities of the A. P. Leventis, the West African foremost ornithological research center. Science World Journal,6 (1):9-12.Nair, G. A., Youssef, A. K., El-Mariami, M. A., Filogh, A. M. and Briones, M. J. I. (2005). Occurrence and density of earthworms in relation to soil factors in Benghazi, Libya. African Journal of Ecology, 43:150-154.Orozco, F. H., Cegarra, J., Trujillo, L. M. and Roig, A. (1996): Vermicomposting of coffee pulp using the earthworm Eiseniafetida: effects on C and N contents and the availability of nutrients. Biology and Fertility of Soils, 22:162–166.Owa, S. O., Dedeke, G. A., Marafa, S. O. A. and Yeye, J. A. (2003). Abundance of earthworms in Nigerian ecological zones: implications for sustaining fertilizer-free soil fertility. African Zoology, 38(2):235-244.R Development Core Team (2012). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. Rawat, N., Nautiyal, B. P. and Nautiyal, M. C. (2009): Litter production pattern and nutrients discharge from litter in a Himalayan alpine ecosystem. New York Science Journal 2(6):54-67. Rossi, J-P., Huerta, E., Fragoso, C. and Lavelle, P. (2006). Soil properties inside earthworm patches and gaps in a tropical grassland (la Mancha, Veracruz, Mexico). European Journal of Soil Biology, 42:S284-S288.Rutherford, P. M., McGill, W. B., Arocena, J. M. and Figueiredo, C. T. (2007). Total Nitrogen. Soil Sampling and Methods of Analysis. CRC Press, Taylor & Francis Group. Pp126Shameem, S. A. and Kangroo, I. N. (2011) Comparative assessment of edaphic features and phytodiversity in lower Dachigam National Park, Kashmir Himalaya, India. African Journal of Environmental Science and Technology, 5(11):972-984.Sritharan, S. and Burgess, N. D. (2012) Protected area gap analysis of important bird areas in Tanzania. African Journal of Ecology,50:66–76.Stevens, M. C. (2010): Life history trade-offs between survival, moult and breeding in a tropical season environment. PhD Thesis submitted to the University of St. Andrews, Scotland, UK. Pp 20-21.Toledo, M., Porter, L., Pena-Claros, M., Alarcon, A., Balcazar, J., Leano, C., Licona, J. C. and Bongers, F. (2012). Distribution pattern of tropical woody species in response to climatic and edaphic gradients. Journal of Ecology 100:253-263.

AN ASSESSMENT OF EARTHWORM POPULATIONAND SOIL FACTORS IN AMURUM FOREST RESERVE OF JOS, PLATEAU STATE, NIGERIAASSESSMENT OF EARTHWORM POPULATION IN SOILS


Walkley, A. (1947). A Critical Examination of a Rapid Method for Determination of Organic Carbon in Soils - Effect of Variations in Digestion Conditions and of Inorganic Soil Constituents. Soil Science, 63:251-257.Whalen, J. K. (2004). Spatial and temporal distribution of earthworm patches in cornfield, hayfield and forest systems of south western Quebec, Canada. Applied Soil Ecology, 27:143-151.Yessoufou, K., Michelle Van Der, B., Abalaka J, And Daru B. H. (2012) Evolution of fig-Frugivore interactions in West Africa. Israel Journal of ecology and evolution, V.58, Pp.39-51Table 1: Mean and Standard Error (SE) of soil properties of soil within the reserve and soil outside the reserve

AN ASSESSMENT OF EARTHWORM POPULATIONAND SOIL FACTORS IN AMURUM FOREST RESERVE OF JOS, PLATEAU STATE, NIGERIA

Soil inside reserve

Soil outside reserve

t df p

Soil properties N Mean ±SE N Mean ±SE

N (%) 50 0.13 0.01 50 0.13 0.01 -0.23 98 0.408

P(mg/kg) 50 4.99 0.08 50 5.26 0.10 -2.02 98 0.023

OM (%) 50 1.60 0.06 50 1.87 0.09 -2.54 98 0.006

MC (%) 50 0.53 0.04 50 0.60 0.05 -1.11 98 0.135

Ph 50 6.41 0.10 50 6.15 0.09 2.02 98 0.977

OM stands for Organic matter and MC stands for Moisture retention capacity. Significant P values in bold.

Figure 1: Map of study area showing points where plots were made inside and outside Amurum Forest Reserve

Figure 2: Mean number of earthworms within and outside Amurum Forest Reserve (within=protected, outside=unprotected)

Figure 3: Earthworm occurrence (95%CL) in relation to percentage litter cover

(The fitted line represents the predicted probability of the presence of earthworms in plots with higher percentage litter cover and the dashed lines represent the upper and lower confidence limits. Lines on sunflower plots represent overlapping of plots where multiple points of plots are stacked)

Figure 4: Earthworm occurrence (95%CL) in relation to percentage grass cover (The fitted line represents the predicted probability of the presence of earthworms in plots with higher percentage grass cover (not significant) and the dashed lines represent the upper and lower confidence limits. Lines on sunflower plots represent overlapping of plots where multiple points of plots are stacked)



Figure 5: Relationship between earthworm abundance and percentage litter cover (95% CL)

(The fitted line represents predicted earthworm abundance in plots with higher percentage litter cover and the dashed lines represent the upper and lower confidence limits)

Figure 6: Relationship between earthworm abundance and percentage grass cover (95% CL)

(The fitted line represents predicted earthworm abundance in plots with higher percentage grass cover and the dashed lines represent the upper and lower confidence limits)




AN EPIDEMIC OF COCCIDIOSIS IN CHICKENS SOLD IN KEFFI CENTRAL MARKET, NASARAWA STATE, NIGERIA.

1Yako, A. B. 2Nweze, C.C. 1Ogunnu, F.J. and 3Chessed G.1Department of Biological Sciences,Nasarawa State University, Keffi, Nasarawa State, Nigeria.

2Department of Biochemistry and Molecular Biology, Nasarawa State University, Keffi, Nasarawa State, Nigeria.

3Department of Zoology, Federal University of Technology, PMB 2076, Yola, Adamawa State.

Correspondening Email: [email protected]


ABSTRACTThe epidemics of coccidiosis in chickens were confined to Keffi central market of Nasarawa State. Species of Eimeria occurring in 250 stool sampled were examined using floatation method and 90(36%) were found infected with eight different species of Eimeria. The highest infection was recorded in Eimeria mitis 28(11%) with the least in E. tenella 1(0.0%) among the hybrid breeds of chicken. The different species of Eimeria were present in and among various breeds. In species predominance, shows the level of immunity of various breeds of chicken against the different species of Eimeria, though, there was no significant relevance in the spread of Eimeriasis among the hybrid and local breeds of chicken sold at Keffi central market (χ2=102.40>5.99) at 5% level of significance. However, farmers should ensure adequate safety standards and maintained the relative spread of Eimeria infection of chickens which are detrimental to man.

Keywords: Eimeria, Epidemic, Coccidiosis, Chickens.


INTRODUCTIONPoultry industry in Nigeria has accorded considerable increase in recent times (FAO, 2000), which in times has resulted in increased research into alternative and cheap feed resources urgently needed to sustain such growth. Thus, there is the need to continually focus attention on the health of the birds in order to realize the full potentials of the industry. Poultry coccidiosis remains one of the major threats to boosting poultry production in Nigeria (Halle et al., 1998) and parasitic diseases are most important due to their high incidence in poultry caused by the tropical environment conditions under which farmers operate. Coccidiosis in poultry and domestic farm animals is a parasitic protozoan disease caused by the development and multiplication of Eimeria species in the epithelial cells of the intestines of the stocks. Coccidial infections are ubiquitous in that they are found wherever susceptible livestock animals are reared because the sporulated oocysts of the parasites may survive for several months or even years in the environment which are readily available to animals. Coccidial infections are usually self limiting, but serious disease can develop when many pathogenic species of the causative agents are consumed by animals. Such observed several disease conditions result in poor feed efficiency under intensive rearing conditions (Merck, 1998). In Keffi, large chicken farms are not common although individuals rear few chickens in close contact with humans, where they roam freely and scavenge for food. Their movement is uncontrolled and they hardly receive any prophylactic treatment or vaccination (Cocciforum, 2000). It is believed that these wandering birds are exposed to variety of parasitic infections including coccidiosis and as such act as potentials reservoirs and carriers of infection to other members and to more susceptible exotic breeds in commercials enterprises (Yakubu and Ajayi, 1995)Epidemiological studies have shown the importance of coccidiosis caused by several species of eimeria which are intracellular protozoans parasites. As a major parasitic disease of poultry in Nigeria (Majaro, 1980), it is among the most common diseases of poultry established in Keffi with higher incidences during the rains. Clinical coccidiosis is common under intensive rearing condition, poor nutrition, overcrowding and poor sanitation. However, transmission of coccidiosis is chiefly by oral fecal route and without intermediate host (Tyzzer, 1932). It is determined that, the prevalence of eimeriasis in chickens sold in Keffi central market, will enlarge the best ways to prevent infection to man and maximizing output.

MATERIALS AND METHODSKeffi, in Nasarawa state Nigeria, is located on latitude 8.5oN of the equator and longitude 8.25oE of green-wich meridian. The average temperature is between 20oc and 25oc. there are two seasons; wet and dry. The annual rainfall starts from late April to middle of September; Nassarawa state is bounded by Kaduna state to the north, Plateau state to the south and kogi state to the west (Akwa et al., 2007). Chicken are brought to the market from neighbouring villages around Keffi. Cages in which they were placed, were made of wire meshes and sanitary level under which they were kept was very low, 250 stool samples from birds were collected using a spatula and floatation method was implored for the identification of the oocysts. Flotation method was adapted from Soulsby (1986) as a qualitative test for the identification of oocysts in faeces. It is based on the principle that the oocysts have a lower specific gravity than the floatation medium and such will float. After about one gram (1grm) of faeces was placed in a beaker and the faeces introduced into the medium using a glass rod, the mixture was poured into a test tube through a funnel. More of the floatation medium was added until a convex meniscus was formed. Here, a glass slide was gently placed on the preparation and left for 30 minutes. Then glass slide was carefully lifted up and the specimen covered with a glass cover.

RESULTS AND DISCUSSIONBoth breeds of the domestic fowls were examined for coccidial infections. The breeds in their category 8(3.0%) were infected with Eimeria brunette and 11 (4.0%) was high with Eimeria mivati, with the least in 1(0.0%) of E. tenella which is most virulent than E. maxima with 5(2.0%) respectively. Local breed were most infected 28(11.0%) with E. mitis follow by 18(7.0%) of E. acervulina with a difference in the prevalence rate of E. praecox 14(6.0%) (Table 1). In Table 2, eight species of Eimeria were observed in the 250 chickens sampled. The most prevalent rate occurred between the ages of 20-24 yrs old, 14(33.3%) in cocks and 20yrs old hens 58(27.9%) with the least between the ages of 25yrs old 8(19.0%) and between 10-14yrs 8(19.0%) respectively. This represents the overall rate of infection in cocks 18(42.9%) lower than hens 72(34.6%). However, the young chicken were more exposed to Eimeria infection than the adults with less risk of infection, there was no statistical significant in the distribution of Eimeriasis among chickens (χ2=102.40>5.99 at 5% level of significance). Carefully, the slide so prepared was placed under a microscope and examined for oocysts of Eimeria differentiating them into specific species as described by Soulsby (1986).

AN EPIDEMIC OF COCCIDIOSIS IN CHICKENS


Table 1: Prevalence of Eimeria species in chickens brought to keffi central market, Nasarawa State

Types of Eimeria Eimeria Eimeria Eimeria Eimeria Eimeria Eimeria Eimeria TotalBreeds acervulina brunette maxima mitis mivati necatrix praecox tenella infection (%)

HybridBreed (102) - 8(3.0%) 5(2.0%) - 11(4.0%) 5(2.0%) - 1(0.0%) 30(29.4%)

LocalBreed (148) 18(7.0%) - - 28(11.0%) - - 14(6.0%) - 60(40.5%)

Table 2: Age distribution of coccidial infection in both sexes of chickens Age (weeks) Total No.examined Total No. infected (%) Sexes 10-14 15-19 20-24 ≥25 Cocks 8(19%) 12(28.6%) 14(33.3%) 8(19.0%) 42 18(42.9%)

Hens 50(24.0%) 52(25.0%) 48(23.1%) 58(27.9%) 208 72(34.6%)

Total 58(23.2%) 64(25.6%) 62(24.8%) 66(26.4%) 250 90(36.0%) %= percentage

The prevalence and severity of avian coccidiosis was determined by a number of factors which include breed, age and sex of birds, environmental factors such as temperature, humidity and presence of oxygen and also management factors such as level of sanitary conditions, overcrowding and housing. Hence, the susceptibility of Eimeria infections and indeed the prevalence of coccidiosis in any given avian population was determined by the factors mentioned above, consequently, the prevalence of Eimeria infection is not consistent either. In this case, prevalence of Eimeria pathogenesis was 36% in contrast to the findings of kayode (1999) and Adamani (1997) who conducted research on the Jos Plateau and reported 18.7% prevalence respectively. The intensive breeding houses undergone chemical fumigation against coccidiosis and undoubtedly this explains the low infection rates among birds. Though, Radkowski et al. (1996) reported a variance of 0.04% prevalence rate among poultry birds slaughtered in the district of Olsztyn against 89.90% and 84.44% reported (McDougald et al., 1990) in Poland. It is evident that, the prevalence of coccidiosis is a product of determined factors present in the population study and location. Thus, the high infection rate of 36% obtained in this study may not be unconnected to the poor sanitary conditions, overcrowding and inappropriate housing management system which characterized the market environment. Therefore, it is not unlikely that many of the birds got infected on arrival at the market while awaiting prospective buyers. Results obtained in the present study revealed that greater infection 42.9% was more in cocks than in hens 34.6%. Similarly, this coincides with the findings of McDougald et al. (1990) who reported greater percentage of infection in the female 89.90% than in the male 84.40%.Susceptibility of various breeds studied, Eimeria was assessed and findings showed that local breeds had higher infection rate (40.5%) against hybrid breeds (26.5%). The possible factors responsible for the different levels of susceptibility among the chicken studied could not be immediately determined but it may not be unconnected with the immune status of the fowls. It is possible that the anti-coccidial therapy given to the hybrid breeds conferred on them higher immunity against Eimeria infection than the local breed. This may, however, not be conclusive as further specific study is required to establish this. Therefore, this study makes a case for prompt and routine diagnosis of Eimeria infection to prevent outbreaks of coccidiosis or at least bring the disease to the barest minimum. Though, the administration of anti-coccidial chemotherapy (drugs/vaccine) can go a long way in minimizing mortality of poultry birds and indeed reduce losses on the part of the poultry farmer.

ACKNOWLEDGEMENTWe are most grateful to the Department of Biological Science, Zoology laboratory unit for allowing us access to the facilities used during this research finding.



REFERENCESAdamani, E.W. (1997). Prevalence of coccidian (Apicomplexa: Eimeridae) and their effects inexperimentally infected chicken in Jos, Plateau state M.Sc. Thesis; University of Jos,Nigeria.Akwa,V.L.,Binbol, N.L., Samalia, K.L., and mercus, N.D.(2007). Geological perspective on Nassarawa state 1st Edition Onnivi printing and publishing company Ltd, keffi, Nigeria.Pp. 35-42Cocciforum, H.(2006). University of Maine cooperative extension bulletin 22559,http;//www.umext,maine. edu./onlinepabs/htmpubs/poultry/2259htm.Food Agricultural Organization (2000). Food and agricultural organization of the United Nation,Rome. Quarterly Bulletin of Statistics. Vol. 1234-242.Halle, P.D.,Umoh, J U. and Abdu, P.A.(1998).Diseases of poultry in Zaria, Nigeria: a ten year analysis of clinical records. Nigeria Journal of Animal Production. 25:88-92.Kayoed –Abe, B.I. (1999).Prevalence of chicken coccidiosis in some parts of Plateau State, Nigeria. B. Sc. Project Reserch. University of Jos, Nigeria.Majaro, O. M. (1980). The epidemiology and economic importance of poultry coccidiosis in oyo state, Nigeria reverse eleven. Medical Veterinarian Pays Tropic. (33): 377-379.McDougald, L.R., Fuller, A.L. and Mcmurry, B.L. (1990). An outbreak of Eimeria, necatrix coccidiosis in breeder pullers:analysis if immediate and possible long-long effects on performance. Avian Disease. 34(2):485-487.Merck, (1998).Veterinary manual, 8th edition incoperated white house station. United State of America. Pp 1888-1893.Radkowski, M, Uradzinski, J. and Szteyn, J. (1996). The occurrence of infectious and parasites disease in poultry slaughtered in district of osztyn, pol ND, 1986-1996. Avian Disease. 40(2): 285-289.Soulsby, E.J.L. (1982). Helminthes arthropods and protozoa of domesticated animals. Baillere tindall, London, 94: 80.Tyzzer, E.E. (1932). Criteria and method in the investigation of coccidiosis avian. Coccidiosis science. 75:324 -538.Yakubu, D.P., and Ajayi, J.A. (1995). Comparative efficacy of soulfural and occistop against mixed Eimeria infection in chicken. The Nigeria Journal of Parasitology,16:113-123.



EFFECTS OF ACCESS TO PASTURE AND INTEGRATION WITH RABBITS ON PERFORMANCE AND CARCASS CHARACTERISTICS OF BROILER CHICKEN

1Gambo D., 2Carew, S. N. and 2Winifred P. M. 1Department of Animal Science, Faculty of Agriculture, Nasaraw State University, Keffi

2Department of Animal Production, College of Animal Science, University of Agriculture, Makurdi Corresponding Email: [email protected]

Date Manuscript Received:31/07/2016 Accepted:20/11/2016 Published:December, 2016

ABSTRACTThe study was conducted during rainy season (June-August) to determine the performance and carcass characteristics of broiler chicken raised on partial free range and integration with rabbits using 180 unsexed Anak broiler chicks. The birds were all brooded together for 21 days. The birds were made into group of ten per replicate. Six replicates were randomly assigned to each of the three treatments using Completely Randomised Design (CRD). The treatments were intensive (control), integration with rabbits and partial free ranging. Feed intake (FI), body weight gain (BWG) and feed conversion ratio (FCR) was measured on weekly basis for starter and finisher phases respectively. At the end of the experiment, 6 birds (3 males and 3 females) were selected from each treatment for carcass assessment. The data obtained were subjected to analysis of variance procedure of SPSS Statistical software.The result indicated that, intensive system and integration with rabbits has the highest weight gain at starter and finisher phases respectively. FI and FCR were not significant (P> 0.05) at both starter and finisher phases.Carcass traits were mostly significant (P > 0.05) except for shoulder, breast, thigh and drumstick. Intensive (control) and partial free range were best for head, wing and shank while integration with rabbits were best for neck and back. It was concluded that, broiler partial free range and integration with rabbit was feasible and could thus be used as alternative system of broiler production. The integration with rabbits could be used to reduce cost of production in terms of space, cost of feeding and facilities, thereby producing rabbits and broilers concurrently. Also, the result of partial free ranging has proved that local and village farmers who could not afford intensive management associated with broilers can raise broilers on free range with minimal care.

Keywords: broiler, brooding, carcass, free range, integration with rabbits, pasture.



INTRODUCTION The increasing costs of conventional feed ingredients and its unavailability due to competition between man and livestock and the health hazards associated with some feeds, feed ingredients and medication has called for organic pasture poultry raising (O. R. C. 2007). This present competition between man and poultry for feed ingredients is due to insufficient production of local feed items. The time is approaching when government could put a ban on the use of maize (corn), barley, wheat and soya bean among others for animal production, forcing the livestock industries to look for alternative feed ingredients (World Poultry, 1997). In recent years, consumer interest in specialty poultry products derived from free-range or organic production systems has been steadily increasing in the United States and Europe (Fanaticoet al. 2006). Under free-range or organic systems, birds have access to an outside area promoting foraging, feed selection, and activity and thus theoretically improving the welfare of the birds. Although outdoor access is intrinsic to the free-range system, there are large variations concerning the amount and type of outdoor access provided in most of the free-range and organic systems that are presently in practice in Europe and the United States. Therefore, although outside access is associated with pasture and invertebrate consumption, the nutritional value derived from the intake of such products is unknown and will vary dramatically with the system in use (Walker and Gordon, 2003). Over the years, much attention has been given to oil seeds, cereals and tubers as sources of feed for poultry, with little attention given to pasture as means of poultry production and integration of poultry with rabbits. The objective of this study is to determine the effects of pasture and broiler integration with rabbits on performance and carcass characteristics of broiler chicken. This has become imperative due to the limited amount of work that has been carried out with broiler production on pasture (O. R. C. 2007) and broiler integration with rabbits.

MATERIALS AND METHODS The experiment was carried out at the Livestock Teaching and Research Farm of the University of Agriculture Makurdi, Benue State, Nigeria. Benue State falls within the Southern Guinea Savannah zone of Nigeria. The state lies between latitude 70 and 90 North and Longitude 70 and 100 East. It has a climate typical of the tropical zone because of its location. It has a temperature ranging from 250 C in October to 360 C in March while monthly rainfall varies from 13.73 cm in some places to 14cm in others (Benue State Ministry of Information 2008). Anak broiler chicks (250) were brooded for 21 days. A total of 180 broiler chicks were selected at the end of brooding using average weight of the

flock (0.4kg) to select birds for each replicates. The birds were made into group of ten per replicate and six replicates were assigned to each treatment using Completely Randomized Design (CRD). There were three treatments: intensive (T1), free range (T2) and integration with rabbits ((T3). The experiment lasted for 6 weeks, the first 2 weeks was starter phase while the last 4 weeks were finisher phase. The birds were all fed commercial feed. For the intensive, 60 broiler chicks were reared on deep litter using standard broiler feed and production practices. The birds for free range system were moved out around 8:00am and returned back to their pen by 6:00pm on daily basis with carrying creates. Their feeders and drinkers were placed in shaded area and fenced with chain link fencing materials. This was to protect them from direct sunlight and to prevent access to their feed by other animals. The fencing materials were raised about six inches above the ground to allow the birds freedom of movement in and out of the fenced area. Six different coloured ropes were used as leg bands to identify the birds belonging to the six replicates. The broilers integrated with rabbits were leg banded with 6 different coloured ropes for the groups (replicates) identification. The birds were allowed to range within the rabbitery under the hutches. Feeds and water were provided within the rabbiteryad libitum. The birds in the rabbitery had access to rabbit’s droppings as well as their wasted feed in addition to conventional feed. The birds in the three treatments and their feed were weighed weekly. Weight gain and conversion efficiency were estimated from these data on weekly basis. Feed intakes (FI), weight gain (WG) and feed conversion ratio (FCR) were recorded on weekly basis. These data were recorded for starter (2 weeks) and finisher (4 weeks) phases. The total FI were divided by WG over the period and expressed as FCR for both starter and finisher phases respectively. The phenotypic appearances of the broiler chicken (shank and beak) during growth period were noted. Assessment of colour (shank and beak) was based on visual observation that was not scored and therefore was not analyzed statistically. At the end of the experiment, 3 males and 3 females were selected per treatment and used for carcass assessment. The birds were fasted for 12 hours before slaughtering. The weight of the birds were taken before slaughter and recorded as live weight. The birds were immersed in hot water for 5 minutes before de-feathering. After de-feathering, the internal organs were removed (evisceration) and the carcass cut into various parts such as head, neck, shoulder, wing, back, breast, thigh, drumstick and shank. These parts were weighed using electronic sensitive scale. The design of the experiment was Completely Randomized Design (CRD). Data collected were subjected to analysis of variance using the procedure of SPSS Statistical Software (2011).

ACCESS TO PASTURE AND INTEGRATION WITH RABBITS ON PERFORMANCE AND CARCASS CHARACTERISTICS OF BROILERS


RESULTS AND DISCUSSIONThe body weight gains (Table 1) were significant at both starter and finisher phase. At starter phase, intensive system (T1)and integration with rabbits were the best. However, at finisher phase, partial free range and integration with rabbits were the best for body weight gain. The feed intake and feed conversion ratio were non significant statistically across the three treatments. However, intensive system had the best (least) feed conversion ratio for starter phase across the treatments while integration with rabbits had the best (least) feed conversion ratio for finisher phase. The results of carcass yield (Table 2) were generally significant. Parameters such as Head, Neck, Wing, Back and Shank were significantly different (P > 0.05). Intensive (control) and partial free range (T2) were best for Head, Wing and Shank while integration with rabbits (T3) were best for Neck and Back. Other parameters like Shoulder, Breast, Thigh and Drumstick were not significant (P > 0.05).

Table 1: The effects of partial free range (T2) and integration with rabbits (T3) on weight gain, feed intake and feed conversion ratio of Broiler Chicken

Period Parameters T1 T2 T3 SEMStarter phase BWG (g) 490a 388b 475a 11.3

FI (g) 1081 1061 1066FCR 2.21 2.73 2.24

Finisher phase BWG (g) 972b 1148a 1066a 56.3FI (g) 3,302 3,740 3,664FCR 3.40 3.26 2.80

BWG= body weight gain, FI=feed intake, FCR= feed conversion ratio. abMean values within a row with similar or without superscripts are not significantly different (p>0.05). SEM = Standard Error of the Mean.

Table 2: The effects of partial free range (T2) and integration with rabbits (T3) on weight (g) of carcass of broilers. Parameters (g) T1 T2 T3 SEMHead 2.60a 2.58a 2.01b 0.17Neck 7.37b 7.25b 7.96a 0.14Shoulder 14.93 14.26 15.76 0.51Wing 4.62a 4.26ab 3.92b 0.13Back 8.60b 8.50b 9.26a 0.22Breast 11.39 10.86 12.36 0.51Thigh 12.46 12.35 12.53 0.36Drumstick 10.54 10.53 9.83 0.33Shank 4.35a 4.17a 3.45b 0.21

Mean values within a row with similar or without superscripts are not significantly different (p>0.05). SEM

= Standard Error of the Mean.

The experiment was designed to assess the effects of partial free range and integration of broiler with rabbits on broiler chickens. The phenotypic appearances of the broiler chicken (shank and beak) during growth period were noted. Assessment of colour (shank and beak) was based on visual observation that was not scored and therefore was not analyzed statistically. It was observed then that, shank and beak colouration was noticed in partial free ranged birds as similarly reported by (Ponte et al., 2007). Weight gain (Table 1) produced significant (P > 0.05) variation at both starter and finisher phase; intensive system (control) and integration with rabbits produced the highest weight gain at starter phase while at finisher phase, partial free range and integration with rabbits showed the highest weight gain. Feed conversion ratio was best for intensive (2.21) at starter phase while at finisher phase integration with rabbits produced the best feed conversion ratio (2.80). Although there were variation in weight gain, all of the gains were better than those projected as fair weight performance by Dafwang and Ogundipe (1987). The total lifespan of the birds was 63 days (9 weeks). During these periods, birds on the three treatments almost reached an average weight of 2.00kg and above. This is also far better than those value advanced by Anthony (1990), who observed that broiler are fast growing chicken reaching average weight of 1.8-2.0kg in 8-12 weeks. The final body weight gains of bird on partial free range (1148g) werehigher than that of intensive (972g). This findings agree with the report by Ponte et al., (2007) who observed that, the final BW of birds consuming pasture were significantly greater than that of the control birds kept under the same environmental conditions but not allowed to forage. This result suggests that, in general, pasture intake promoted an increase in the consumption of the cereal-based feed. Therefore, it is possible that carcass yield may have been affected by the fact that birds with access to pasture had a more developed gastrointestinal tract (due to greater fiber intake and total feed intake). The non significant difference among the three treatments for feed conversion ratio in this study also agree with Ponte et al. (2007) who reported that there were no differences between the feed conversion ratios of birds subjected to the 3 different grazing regimens. This suggests that bird performance primarily depends on the intake of the cereal-based feed rather than from an improvement in the efficiency of nutrient utilization per se. For carcass traits measured, parameters such as Head, Neck, Wing, Back and Shank were significantly different (P > 0.05). Intensive (control) and partial free range (T2) were best for Head, Wing and Shank while integration with rabbits (T3) were best for Neck and Back. Other parameters like Shoulder, Breast, Thigh and Drumstick were not significant (P > 0.05). The report on intensive (control) and partial



free range (T2) being best for Head, Wing and Shank fairly agrees with Fanaticoet al. (2005) who showed that pasture intake had a positive effect on carcass yield. This isexpected because the greater activity of grazing birds is believed to improve the proportion of wings, thighs, and drum sticks, whereas foraging could increase the proportion of gastrointestinal tract tissues on the overall BW. However, the non significant differences observed in Shoulder, Breast, Thigh and Drumstick strongly agree with Fanatico et al., (2005) who found no differences in the carcass yield of indoor and outdoor birds.

CONCLUSION This study has demonstrated the feasibility of broilers being raised on partial free range and integration with rabbit. The integration with rabbits could thus

be usedto reduce cost of production in term of space, cost of feeding and facilities, thereby producing rabbits and broilers concurrently using the same facilities. Also, the result of partial free ranging has proved that local and village farmers who could not afford intensive management associated with broilers can raise broilers on free range with minimal cost and care. The results of this experiment suggest further investigation along the line to further clarify the situation.

ACKNOWLEDGEMENTThe authors would like to thank all those who contributed in the project and to the College of Animal Production who provided the birds, feeds and the facilities used for this experiment.

REFERENCES

Anthony, J. S. (1990). Breeds and Strain of Poultry and their Improvement in.The Tropical Agriculturist Poultry pp.12, Macmillan Pub. Centre for Tropical Vetrinary Medicine. University of Edinburgh U.K.Benue State Ministry of Information Bulletin. (2008)Dafwang, I. I. and Ogundipe, S. O. (1982). Brooding and Rearing of Chicks on Deep Litter. Extension Bulletin No. 23, Poultry Series No. 3.pp. 26. Agricultural Extension/Research Liaision Services.Ahmadu Bello University, Zaria.Fanatico, A. C., Pillai, P. B., Cavitt, L. C., Owens, C. M. and Emmert, J. L. (2005). Evaluation of slower- growing broiler genotypes grown with and without outdoor access: Growth performance and carcass yields. Poultry Science Journal, 84:1321–1327.Fanatico, A. C., Pillai, P. B., Cavitt, L. C., Emmert, J. L., Meullenet, J. F. and Owens, C. M. (2006). Evaluation of slower-growing broiler genotypes grown with and without outdoor access: Sensory attributes. Poultry Science,85:337–343.O. R. C. (2007) Organic Research Centre-Elm Farm’s Producers’ Conference.Pub. By Hamstead Marshall New bury, RG 20 OHR.Ponte,P. I. P., Alves, S. P., Gama, L. T., Ferreira, L. M. A., Bessa, R. J. B., Fontes, C. M. G. A. and Prates, J. A. M. (2007).Influence of pasture intake on the fatty acid composition, cholesterol, tocopherols and tocotrienols in meat from free-range broilers. Poultry Science, 87:80–88.SPSS (2011) Statistical Package for Social Sciences.Released 14.0 for windows. IL60611. Chicago. W. P. P. P. M. (1997). World Poultry Production, Processing and Marketing. Misset 4 13 : 5-13.Walker, A. and Gordon, S. (2003). Intake of nutrients from pasture by poultry. Proceedings Nutrition Society 62:253– 256.



MICROSPORA INFILTRATION OF GASTROINTESTINAL EPITHELIUM AMONG HIV/AIDS PATIENTS IN KEFFI, NIGERIA.

1Yako A,B. 2Nweze, C.C., 1Adebayo E.A., 3Chessed, G.1Department of Biological Sciences, Nasarawa State University, Keffi, Nasarawa State.

2Department of Biochemistry and Molecular Biology, Nasarawa State University, Keffi, Nasarawa State.3Department of Zoology, Federal University of Technology, Yola, Adamawa State.


Manuscript received: 13/05/2016 Accepted: 23/12/2016 Published: December, 2016

ABSTRACTThe infiltration of Microsporidium species in HIV/AIDS patients was subjected to parasitological examination of stool specimens at the Federal Medical Centre, Keffi. Of the total number 200/93 (46.50%) were positive for Microsporidium species. Giemsa method was used and High infection rate was observed in both sexes (50.00%) and Civil servants (50.00%) were most vulnerable to microsporidium infection. The species, Enterocyto zoonbieneusi and Encephalito zoonintestinalis (21.50%) got infiltrated in the gastrointestinal epithelium of of HIV/AIDS patients with a significant association of microsporidium and HIV virus (χ2 = 3.288 < 7.815, df = 3). However, diarrhoea was frequent in the ages of 21 and 40 years (3 – 10 times bowel/ day). This condition was considered a significant cause of death and accelerates the patient’s illness with dehydration and emaciation seen among patients. This calls for strict hygienic conditions to avoid infection with microsporidia spores contaminated in water or food.

Keywords:Microspora. Spores.infiltration. Epithelium.HIV/AIDS.



INTRODUCTIONMicrospodia is a microscopic organism and Eukaryotic parasites found in a wide range of host and that must live within other host cells in which they can produce infective spores. These spores causes microsporidiosis which are primarily found in patients with compromised immune systems such as those infected with HIV or who have undergone organ transplant, microsporidia causes gastrointestinal diseases, renal diseases, sinusitis, keratoconjuctivitis and keratitis in AIDS patients. Microsporidia, have not been studied extensively as agent of diseases because they are small, stains poorly, evokes little inflammation and is difficult to diagnose in the absence of electron microscope. Since the advent of AIDS, investigation had markedly increased leading to the identification of new species and reclassification of old ones. Microsporidiosis is been considered in AIDS patients with chronic diarrhoea, sinusitis and keratitis or renal failure (Franzen et al., 1997; Cali et al., 1993). Microsporidia is increasingly being recognized as pathogens in human. They are ubiquitous in the environment and can infect a whole range of vertebrates and invertebrates hosts, including insects, birds, fish and mammals. The spores vary in size, but those that infect human are typically oval and up to 2 micrometer (mm) in diameter (Didier et al., 2006). Reports of human infected with microsporidia were truly rare before the AIDS epidemic. Microsporidia are significant pathogens in the immunocompromised host and associated with human disease which includes anncaliia, enterocytozoon, encephalitozoon (septata), Pleistophora infections commonly have occurred in the immunocompromised host (Wolk et al., 2002., Weber et al., 1994). Humans can be infected through organ transplants, water and also bone marrow transplant. Opportunistic infections like microsporidiosis, African trypanosomiasis, leishmaniasis, and malaria can cause central nervous system (CNS) infection (Wolk et al., 2002). Since the beginning of AIDS epidemic, opportunistic infections have been recognized as common complications in HIV patients. The spectrum of opportunistic infections in HIV patients varies from one region to another (Vajpayee et al., 2003). Discrepancy in findings may be attributed to geographical variations and if not for AIDS far less will be known of microsporidia. The aim is to establish the infiltration and infiltration of spores (microsporidia) in the gastrointestinal epithelium of HIV patients at the Federal Medical Centre, Keffi.

MATERIALS AND METHODThe research study was carried out at the Federal Medical Centre (FMC), Keffi, Nasarawa State which lies between latitude 7o.45’ and 9o.25’ N of the equator and between 7o and 9o.37’ E. A total of 200 stool specimens were collected from HIV/AIDS patients who are in hospital admission and care was taken as it was transported to the laboratory for an immediate preparation of samples. Parasitological examinations, using 0.5g of faecal matter was homogenized in 10ml of 10% formalin and stirred using applicator spoon. This was filtered through mesh gauze of 90, 60, 30 and centrifuge 200g at 100rpm for 10 minutes. The top layer of the sample was dropped onto a slide and the smear was allowed to dry and which was later fixed with methanol for 1 minute and allowed to air dry.Giemsa stain of one or two drops was added on the smear for 30 to 1 hour, and then it was washed with distilled water or running tap and allowed to dry. Oil immersion was used to view the slides using a light microscope x100 with less standard error minimized (Weber et al., 1992). The nuclei of the spores were visible given a dark brown colour varying in size.Chi square (χ2) was used to established statistical significance of spores infiltration in relation to sex, age and occupational status among patients.

RESULTS AND DISCUSSIONThe proliferations and infiltration of spores (microsporidium) in the entire gastrointestinal tract identified in faecal matter of individuals with HIV/AIDS were mainly species of enterocytozoon and encephalitozoon (septata) with chronic diarrhea. Opportunistic infection like microsporidiosis is reported in Table 1. Out of 200 total stool examined, 93 (46.50%) were clinically captured with microsporidium against 107 (53.50%) without microsporidia spore and there was a significant relationship among patients without microsporidia infection (χ2 = 2.87 <7.815, df = 3), and significant in HIV patients with spore infiltration (χ2 = 3.29 < 7.815, df = 3). Through increased and improved diagnosis, microsporidiosis is now been identified in a broader range of human populations (Table 2), the infiltration of microspiridia spores in gastrointestinal tract of HIV/AIDS patients, indicative in males 97/38(39.18%) were recorded and considered less severe among the males from the first and the third examination of specimen of HIV patients. Spores rate in females 103/55(53.40) was high and considered most vulnerable with severity of illness giving that, the overall rate of spore proliferation was determined in both sexes of HIV patients suffering from

MICROSPORAINFILTRATION OF GASTROINTESTINAL EPITHELIUM AMONG HIV/AIDS PATIENTS


microsporidiosis with significance in the infection rate among sexes (χ2 = 3.29 <7.815, df = 3). Age and spore infiltration varies and was seriously considered a factor in the immunocompromised patients. In table 3, the rate of spore proliferations (48.48%) was among age 21 and 40 year olds with the least passing out mild diarrhoeaand emaciation (33.33%). Among this age group, the vulnerability was significant in relation to the rate of infection (χ2 = 1.36 < 11.07, df = 4). Though, children <10 year olds were all without spores, asymptomatic and without diarrhoea. Occupation type (Table 4), showed a greater relationship in spore proliferations of microsporidium and the occupational status of patients.From the report of occupation of HIV patients, spore infiltration was high (50.00%) among civil servants who are the working force, occupying the economic position of power. This showed also a high level of sexual tendencies among businessmen/women 28 (70.00%) who by virtue of their occupation, they interact with greater accessibility to a wider spectrum of members of the society. However, there was a close relationship in the lead of infection among patients with varied occupational status (χ2 = 8.45 < 9.48, df= 4).

Table 1: Spores infiltration detected in gastrointestinal epithelium of HIV- positive patients.Specie of organism Spores indicatorTotal % P- value

Microsporidium Spp. (+) 7 18 35 33 93 46.50 P<7.815

Microsporidium Spp. (-) 18 12 55 42 107 53.50 P<7.815

Total 25 30 70 75 200 100.00 Legend:+= Positive - = Negative Spp = Specie < = Less than %= Percentage

Table 2: Sex distribution of Microsporidia spores among HIV/AIDSpatients at Federal Medical CentreSex No. examined No. positive Percentage(%)

Male 97 38 3 9 . 1 8

Female 103 55 53.40

Total 200 93 46.50

Legend: No.=Number %=Percentage

Table3: Age distribution of Microsporidia spores in HIV/AIDS patients at Federal Medical Centre

Age Group 1-10 11-20 21-30 31-40 41-50 ≥ 60 Total

No. examined - 30 66 60 33 11 200

No. positive - 10 32 30 16 5 93

Percentage (%) - 33.33 48.48 50.00 48.48 45.45 46.50

Legend:%=Percentage ≥=Greater or equal

Table 4: Occupation and Microsporidia spores proliferation in HIV/AIDS patients at Federal Medical Centre.

Occupation Student Civil Famers Businessmen Others Total (%) servants /women

No. examined 3 60 34 40 30 200

No. Positive 10 30 14 28 11 93

(%) 27.78 50.00 41.18 70.00 36.67 46.50

Legend;%=Percentage Others=With no defined occupational status.

No=Number

Species collected from HIV patients were Eaterocyto zoonbieneusi and Encephalito zoonintestinalis. Spore infiltrations of individual with HIV/AIDS were sparsely distributed in 46.50% of patients suffering from the illness. This was with clinical relevance of pathogenesis and agreed with the previous report in man who first was diagnosed with mirosporidia spore (Canning and Hollister, 1997). The overall proliferation of spores of microsporidium was relative hence, the complexity of HIV seen with persistent diarrhoea of 3-10 bowels/day loose to watery stool. Similarly, 21.50% were cases diagnosed of Encephalito zoonintestinalis with enteritis. Coyles et al. (1996) observed cases of enteritis among innnunosupressed individuals with colicky abdominal pain as a pointer to E. intestinalis. Among sexes, spores of Enterocyto zoonbieneusi and Encephalito zoonintestinalis were sporadically detected in stool specimens of HIV-patients. Spores of E.bieneusi was observed to be more prevalent (53.40%) in females than in males. With relevant clinical significance, the spore of E. intestinalis were sparsely distributed but expressed more in females who were slow in weight loss and emaciation, this condition was considered a threat to life. However, Canning and Hollister, (1997) observed that, the diarrhoea caused by this species is debilitating and life threatening with as many as ten episode each day and does not respond satisfactorily to therapy, must therefore be considered a significant cause of death in HIV patients. Similarly, in a 36 year old homosexual HIV-1 patient, it was observed that, six months later (from the time he was screened for HIV), developed chronic diarrhoea from one to three liquid stools/day (Burgereet al., 2000; Cali et al., 1993; Field et al., 1993). Importantly, this infection among individuals affects the labour force that gradually were seen deteriorating in their condition and debility to do work with a decline in work places and in some from attending schools and absence in commercial business centers irrespective of sex. The prevalent rate (46.50%), age was depended on each other significantly, among age 21 and 70 years old were more or less susceptible to the infection with disease significance in 48.48% with less or more pathogenic strain in children probably



born of HIV parents. Both sexes were associated with diarrhoea. Reynaud et al., (1998) described infections in adult and travellers with chronic diarrhoea. This pathogen E. intestinalis (Formerly called, Septata intestinalis) was found in patient with chronic diarrhoea (Orenstein et al., 1992). In other case of disseminated microsporidiosis and from human infecting enterocytes, macrophages were associated with diarrhoea in the small intestine and incidence of diarrhoea was 0.13 episodes obsessed in sero-negative children born to HIV infected, or high risk women at the age 7.3+/- 5.7 months (Shadduck et al., 1998; Orenstein, 1992). It cannot however, be rule out that, by an oral fecal route infection, the pathogens can be transferred from close association of animals to human irrespective of age. Of the entire occupational group examined, civil servants were most affected with the microsporidia spore. This was observed under prolific measures with 50% of the infected population due to negligence to effective hygiene. Moreso, business men/woman were 70% a reflection of HIV individual who engage in some semi-manual jobs, eats food in an open place and drinking of water contaminated

with the spore of microsporidia.Observe also were farmers who relatively have access to the soil that harbors spores of microsporidia and can survive a long period of time in the soil as high as 4oc of temperature. After the days farming work, swimming in a running stream was contributable to microsporidia infection in HIV patients. These patterns of intestinal infection correspond with the report that, infection not only infects the enterocytes of man but also cells in the lamina propria including fibroblasts, microphages, and endothelial cells (Weber et al., 1994). However, data to support effective preventive measures are quite limited. The attention to meticulous hand washing and other personal hygienic measure be adhered to strictly, patients should be advice that, sexual transmission of microsporidiosis cannot be excluded and patient should be offered screening for microsporidiosis regardless of their HIV status.

ACKNOWLEDGEMENTWe thank all the laboratory scientists who supported us in the collection of faecal samples and having access to the laboratory facilities for test analysis.

REFERENCESBrugere, J.F., Comillot, E., Metenier, G., Bensionon A. and Vivares C.P. (2000). Encephalito zooncuniculi [Microspora] genome; physical map and evidence for telomere-associated DNA until on all chromosomes. Nucleic Acids Reversed. 28:2026-2030.Cali, A., Kotler, D.P. and Orenstein, J.M. (1993). Septata intestinalis NGN.SP; and Intestinal miccrosporidian Associated with chronic diarrhea and dissemination in AIDS patient. Journal of Eukaryote Microbiology, 40:101-12.Canning, E.U. and Hollister, W.S.(1997). Microsporidia mammals widespread pathogen or opportunistic curiosities. Parasitology today, 9: 267-73,Coyle’s, C.M., Witktper, M. and Kotler D.P. (1996). Prevalence of microsporidiosis due to Enterocyto zoonbieneusi and Encephalitozoon(septata) intestinalis among patients with AIDS related diarrhea: Determination by polymerase chain reaction to the microsporidian small- subunit RNA gene. Clinical Infectious Diseases, 23:1002.Didier, E.S. and Weiss, L .M. (2006). Microsporidiosis: Current status.Clinical Infectious Diseases, 19:485.Franzen, C, Muller A. (1997). Molecular techniques for detection, species differentiation,and phylogenetic analysis of microsporidia.Clinic Microbiology Reversed 12:234.Field, A.S., Hing, M.C. Milliken, S.T. and Marriott, D.J. (1993). Microsporidia in the small intestine of HIV infected patients: a new diagnostic technique and a new species. Medical Journal of Australia, 158:390.Orenstien J.M., Gaetz H.P. and Yachnis A.T.(1992). Disseminated microsporidiosis in AIDS: are any organ spared? (letter), AIDS. 11:385Raynaud L., Delbac, F. and Broussolle, V.(1998). Identification of Encephalitozoon intestinalis in travellers with chronic diairrhoea by specific PCR amplification, Journal of Clinical Microbiology,36:37.Shadduck, A. and Greely, E, (1998). Microsporidia and human infections, Pp 158-165.Vaspayee, N, and Kanswalseth, P. (2003). Spectrum of opportunistic infection and profile of CD4 counts among AIDS patients in North India (Public Health Medicine).Weber, R. and Bryan, R.T. (1994). Microsporidia infections in immunodeficient and immunocompetent patients. Clinical Infectious Diseases,19:517Weber R., kuster H. and Keller R. (1992). Pulmonary and intestinal microsporidiosis in a patient with the Acquired Immunodeficiency Syndrome. America Reversed Respiratory Diseases,146:1603.Wolk D.M., Schneider S.K. and Wengengenack, N.I. (2002). Real-time PCR method for detection of Encephalitozoon intestinalis from stool specimens, Journal of Clinical Microbiology,40:39-22.



MORPHOMETRIC INDICES AND PARASITES OF FROZEN CLARIAS GARIEPINUS AND OREOCHROMIS NILOTICUS SOLD IN JOS METROPOLIS,

PLATEAU STATE1 Doe’ogot, N. L., 1 Dakul, D. A., 1 Pam, D. D.,.2Ombugadu, A, 2 Ayuba, S. O., 3Njila, H. L.1

Department of Zoology, Faculty of Natural Sciences,PMB 2084,University of Jos, Plateau State, Nigeria.2Department of Zoology, Faculty of Science, PMB 146, Federal University Lafia, Nasarawa State, Nigeria.

3Department of Science Laboratory Technology, Faculty of Natural Sciences, PMB 2084, University of Jos,Plateau State, Nigeria.



ABSTRACTThe study on morphometric indices and parasites of frozen Clarias gariepinus and Oreochromis niloticus sold in Jos metropolis was carried out from January to April 2016. The indices measured were eye diameter, total length, standard length and weight. Twenty-two individuals of each species were measured. The mean of morphometric indices showed a very high significant difference (total length: t = 49.085, df = 42, P < 0.0001; standard length: t = 34.466, df = 42, P < 0.0001; eye diameter: t = 18.139, df = 27.906, P < 0.0001; weight: t = 2.1402, df = 28.785, P < 0.04094) between the two fish species. A total of eight parasites were recorded in this study, of which sporocyst of diplostomatid, Pallisentis tetradontis, Acanthella from Ostracod, Piscicolid leech were found in both fish species. However, Allocreaduim ghanensis, Coracidium and Rhabdochonacon golensis were found in only Clarias gariepinus, while Spinitectus allaeri was only found in Oreochromis niloticus. The prevalence of parasites in relation to internal organs was high in Clarias gariepinus and low in Oreochromis niloticus. However, there was no significant difference (P > 0.05) in prevalence rate of parasites in relation to internal organs of the two fish species. The parasites recorded are of medical importance. This study underscores the need for bio-surveillance of fish borne parasites being sold to the general public. The internal organs of fishes should be discarded before cooking the remaining parts.



INTRODUCTIONThe definition of different stock of species is obtained from morphometric measurement of specific characteristics of an individual, or group of individuals which shows the degree of speciation induced by biotic and abiotic conditions (Bailey, 1997). Morphometric variation in relation to stocksgives a base for stock structure, which can be applied for short-term studies of environmentally induced variation geared towards successful fisheries management (Murta, 2002; Pinheiro et al., 2005). The difference between fish populations is identifiable from morphometric measurements (Tzeng, 2004; Cheng et al., 2005; Buj et al., 2008; Torres et al., 2010). Fish has a remarkable impact on the lives of many individuals and communities in almost all constituents of the world being a major source of relatively cheap and affordable essential protein (Ashada et al., 2013). The oil from fish is a source of minerals like omega-3 an essential fatty acid that is important for heart,brain and immune system functioning (Horn,1999). Clarias gariepinus is widely distributed in Africa and it occur mainly in quiet waters, lakes, pools and also in fast flowing rivers (Teugels,1986). It is highly priced in Nigeria whether smoked, dried, or fresh. Tilapia species are of major economic importance in tropical and sub-tropical countries throughout the world particularly in Africa (Fagbenro, 2002). Oreochromis niloticus has been described to be the best cultured species among the Tilapia family (Arignons,1998). Many fish consumers prefer the delicate flavor and texture of uncooked fish and this can be a route of parasite transfer. Studies have shown that parasites whether ectoparasite or endoparasite affect the health, growth and survival of fish (Grabda, 1991; Auta et al., 1995; Oniye et al., 2004). There is an appreciable documentation of parasite fauna of C. gariepinus in Nigeria (Awachie,1966; Ukoli,1969; Yakubu et al., 2002;Oniye et al., 2004; Ibiwoye et al., 2004; Akinsanya and Otunbajo, 2006). All fishes are potential host to different species of parasites that are responsible for captive and wild fish stock mortality. Parasites and diseases are denying main adequate supply of fish resource. The issue of fish parasite and disease has posed serious challenge to fish biologists and agriculturists. It is an issue of both health and economic concern. The zoonotic disease that result from the consumption of raw material and uncooked fish include clonorchiasis, opisthorchiasis, diphyllobothriasis, gnathosomiasis and anasakiasis (WHO, 1995). To this end, the study onmorphometric indices and parasites of frozen Clarias gariepinus and Oreochromis niloticus sold in Jos metropolis, Plateau State was carried out.

MATERIALS AND METHODSThe research was carried out within Jos metropolis. The city is located on the Jos Plateau at an elevation of about 1,238 metres or 4,062 feet above sea level. Jos city is divided into 3 Local Government Areas, Jos North, Jos East and Jos South. The frozen fishes sold within the Jos metropolis are of different species and usually undergo proper inspection before being certified for human consumption. Samples of the two frozen fish species sold at the Jos metropolis of Plateau State were obtained for this study. The fish specimens were transported to the laboratory and preserved under refrigeration prior to identification and analysis. The procedure for examining fish for parasites by Marcogliese (2002) was used. The Total Length (TL), Standard Length (SL) and Eye Diameter were measured to the nearest 0.1cm using a meter rule on a measuring board. The weights of the fish were measured to the nearest 0.10g using a top loading meter PC 2000 electronic weighing balance. The internal organs i.e. oesophagus, large intestine, small intestine, stomach, liver, spleen and heart were dissected and searched for endo-parasites. The organs were then placed in normal saline in different petri-dishes including parasites isolated from those regions. The fixation and preservation of parasites followed the procedure employed by Ash and Orihel (1991). The worms isolated, were placed in normal saline to clear mucus and other debris. The parasites were mounted on a glass slide using cover slip. The parasites were identified under the light microscope (x10 and x40 objectives). Data obtained was analyzed using R Console software version 3.2.2. Two sample t-test was used to compare between the two fish species mean of morphometric measurements (total length, standard length, eye diameter and body weight respectively). Chi-square was used to compare the proportion of the prevalence rate of parasites in some internal organs between the two fish species. The P-value <0.05 were considered statistically significant.

RESULTS AND DISCUSSION Comparison of the mean of morphometric measurements between Clarias gariepinus and Oreochromis niloticusTotal lengthThe mean total length between Clarias gariepinusand Oreochromis niloticus showed a very high significant difference (t = 49.085, df = 42, P < 0.0001, Figure 1).

MORPHOMETRIC INDICES AND PARASITES OF FROZEN CLARIAS GARIEPINUS AND OREOCHROMIS NILOTICUS


Standard lengthThe mean SL between Clariasgariepinusand Oreochromis niloticus showed a very high significant difference (t = 34.466, df = 42, P < 0.0001, Figure 2).Eye diameterThe mean Eye D between Clariasgariepinus and Oreochromis niloticus showed a very high significant difference (t = -18.139, df = 27.906, P < 0.0001, Figure 3).WeightThe mean weight between Clarias gariepinus and Oreochromis niloticus showed a significant difference (t = 2.1402, df = 28.785, P = 0.04094, Figure 4).Checklist of Parasites Found in the Two Fish SpeciesA total of eight parasites were recorded in this study (Table 1). Sporocyst of diplostomatid, Pallisentistetradontis Acanthella from Ostracod, Piscicolid leech were found in both fish species. On the other hand, Allocreadiumghanensis, Coracidum and Rhabdochonaalleari were found in only Clarias gariepinus while Spinitectusallaeri was found in only Oreochromis niloticus.

Comparison of the Prevalence Rate of Parasites in Relation to Internal Organs of the Two Species of Fisha). Oesophagus: no parasite was found in the oesophagus organ in both fishes (Table 2). b). Stomach: there was no significant difference in the prevalence rate of parasites between the stomach of the two fishes (χ2 = 0, df = 1, P = 1,Table 2).c). Large intestine: there was no significant difference in the prevalence rate of parasites between the large intestine of the two fishes (χ2 = 1.7368, df = 1, P = 0.1875,Table 2).d). Small intestine: there was no significant difference in the prevalence rate of parasites between the small intestine of the two fishes (χ2= 0, df = 1, P = 1,Table 2).e). Liver: there was no significant difference in the prevalence rate of parasites between the liver of the two fishes (χ2 = 0.15278, df = 1, P = 0.6959,Table 2).f). Spleen: there was no significant difference in the prevalence rate of parasites between the spleen of the two fishes (χ2 = 0.15278, df = 1, P = 0.6959,Table 2).g). Heart: there was no significant difference in the prevalence rate of parasites between the heart of the two fishes (χ2 = 0.52381, df = 1, P = 0.4692,Table 2).

Figure 1: The Mean of Total Length of the two Fish Species

Figure 2: The Mean of Standard Length of the Two Fish Species

Figure 3: The Mean of eye Diameter of the Two Fish Species

Figure 4: Mean Weight of the Two Fish Species



Table 1: Checklist of Parasites Recorded in the Two Fish Species

Fish species

Parasites Clarias gariepinus Oreochromis niloticus

Allocreadiunghanensis + _

Sporocyst of Diplostomatid + +

Coracidium + _

Pallisentistetradontis + +

Acanthellafromostracod + +

Piscicolid leech + +

Rhabdochonacongolensis + _

Spinitectusallaeri _ +

+ Present- Absent

Table 2: Prevalence Rate of Parasites in Relation to Internal Organs of the two Fish Species

Organ Clarias gariepinus Oreochromis niloticusNo. infected (%) No. uninfected (%) No. infected (%) No. uninfected (%)

Oesophagus 0 (0.0) 22 (100) 0 (0.0) 22 (100)

Stomach 4 (18.2) 18 (81.8) 4 (18.2) 18 (81.8)

Large intestine 5 (22.7) 17 (77.3) 1 (4.5) 21 (95.5)

Small intestine 3 (13.6) 19 (86.4) 3 (13.6) 19 (86.4)

Liver 5 (22.7) 17 (77.3) 3 (13.6) 19 (86.4)

Spleen 5 (22.7) 17 (77.3) 3 (13.6) 19 (86.4)

Heart 2 (9.1) 20 (90.9) 0 (0.0) 22 (100)

Morphormetric measurements in relation to Clarias gariepinus and Oreochromis niloticus

Total lengthThe observed variation in the total length between the two fish species is possibly due to the position of straight bone of Clarias gariepinus while Oreochromis niloticus have numerous bones all over the body, thus accounting for the longer total length of the former. Also the variation in relation to zones of the two fish species within fresh water habitat possibly accounts for difference in their total length. Oreochromis niloticus are found in the pelagic zone of fresh waters while Clarias gariepinus are found in the bentic zone. This is in consonance with the study by Mattson and Belk (2013) who found that morphomentric characteristics of even two common intra specific marine fish species from South Africa varied with differences in benthic-pelagic zones within habitat.

Standard Length The variation observed in standard length between Clarias gariepinus and Oreochromis niloticus may also be linked with the skeletal bone structure of two species. Clarias gariepinus has few bones and possesses a straight bone giving it a slender shape with a long body length while Oreochromis niloticus has many bones all over the body making it to be

more robust and shorter. The superiority exhibited by Clarias gariepinus over Oreochromis niloticus may be as a result of genetic attributes. In a similar study Turan (2004) reported that phenotypic and genetic differentiation may occur among fish populations, which may be recognizable as a basis for separation and management of distinct population.

Eye DiameterVariation in the eye diameter in the two fish species was evident and this may be due to the anatomical characteristics of the head of the fishes.Clarias gariepinus which has a flat head was observed to have a smaller eye diameter while Oreochromis niloticus has a higher eye diameter on the pointed head. In addition, Oreochromis niloticusis predominantly found in the pelagic zone of fresh water making it vulnerable to predatory attack from above water surface. De Busserolles et al., (2013) obtained a great variability in relative eye size within the Myctophidae at all taxonomic levels (from subfamily to genus), suggesting that this character may have evolved several times. The bigger eye could be anadaptive way to vigilance in detecting predators unlike Clarias gariepinus that is predominantly in the benthic zone. The visual capabilities of an eye are influenced by its size (Walls, 1942). Malcolm et al., (2012) explained that a larger eye would provide an advantage for fishes in the pelagic zone as it will increase the chance of photon capture, since the larger the eye, the more energetically costly it will be. Smaller eyes are less energetic and can act as a distance filter by reducing the visibility of a bioluminescent signal against a completely dark background especially in the bentic zone (Warrant et al., 2003). This could explain why Oreochromis niloticus has a larger eye because a smaller eye would be a disadvantage as the higher column of water habitat has high levels of background illumination needing an increased sensitivity. The smaller eye diameter of Clarias gariepinus on the other hand is well adapted for its zone.

WeightThe observed differences in the weight of the two species of fish could be explained by the higher fluid content Clarias gariepinus than Oreochromis niloticus hence making the former heavier than the latter. Genetic variation in weights and body yields of fishes has also been reported by Diadatti et al., (2008). Usman et al. (2004) explained that Clarias gariepinus has a heavy weight of ovaries containing eggs than Oreochromis species thus significantly making them heavier. However, this observation is limited to the female species of frozen fish.



Parasite in the Two Fish SpeciesThe overall prevalence of Parasite observed in Orechromis niloticus was 36.36%. This is in agreement with the result obtained by Bichi and Ibrahim (2009) and Olotintoye (2006) who recorded 43.3% and 60.23% respectively. All the five parasites recorded in this study from Oreochromis niticus were of medical importance with the Acanthocephala species having dominant (3 parasites) than nematode and trematode species having 1 species each. The dominance of acanthocephalan species over other species may mean that; physio-chemical factors present in the habitat of the fish support the survival of these parasites. This may also explain that Oreochromis niloticus is a good host for parasites survival. Akinsanya and Otubanjo (2006) also had similar observation that the most encountered parasites were that of Acanthocephalans. The overall prevalence of parasites observed in Clarias gariepinus was 40.91%. This is relatively similar to 63.0% obtained by Owuliri and Mgbenena (1987) and 34.7% recorded by Anosike et al. (1992). This slight variation in the proportions recorded might be explained by the different management practices, environmental condition of the fish habitat, handling method by fish famers and sellers. Syndenhem (1974) stated that parasitism of fishes varies among farms, river, streams and lakes depending on several factors prevailing and the aquatic ecosystem. Acanthocephala, Cestoda, Nematoda and Trematoda species which are medically important parasites all encountered in the frozen Clarias gariepinus with Acanthocephala having a higher frequency of occurrence. On the contrary, Ayanda (2009) found nematodes to be the predominant parasites in Clarias gariepinus. This disagreement may be attributed to the type of intermediate host presents in the habitat of the fish as well as other environmental factors (Paperna, 1980). It was discovered that all the frozen fishes used in this study harboured at least one species of parasites. This finding is in consonance with what was observed by Sieszko (1975) and Daniel (1978), that under natural condition this could indicate that parasitism is much more common diversified in farms, ponds and the wild.

Prevalence Rate of Parasites in the Internal Organs of the Two Fish SpeciesThe lack of variation in the prevalence rates of the parasites in relation to internal organs of the two fish species may be possible due to the fact that they were harvested from the same habitat. The habitat may possess physiochemical parameters that are suitable for the survival of parasites in water bodies thereby infecting their fish host. Majority of the parasites were found in the intestinal region which is possibly due to the fact that Acanthocephala and Cestodes lack digestive tract making them to depend on end product of digested food in their host (Hickman et al., 2006).Ayanda (2009) also agrees with this finding but however stated that the stomach of fishes harbours few parasites as a result of high concentration of acid being secreted in the stomach which is capable of killing them. In this study the oesophagus did not harbour parasites. This could be as a result of no food reserve concentration and chemotactic responses in these sites.The heart of Clarias harboured parasites while that of Oreochromis did not.All parasites obtained from the two fishes are of medical importance.

CONCLUSIONThe morphometric measurements between Clarias gariepinus and Oreochromis niloticus vary. Clarias gariepinus was observed to have a higher total length, standard length and weight than Oreochromis niloticus. However, Orechromis niloticus had a higher eye diameter than Clarias gariepinus. Eight species of the parasites which spread across Acanthocephala, Cestoda, Trematoda and Nematoda phyla were recorded. Most parasites were more abundant in the liver, spleen and intestine of the frozen fish while the oesophagus and the heart were relatively parasites free.The gastrointestinal tract of the fishes should be discarded before cooking. In order to prevent parasites infestation,the fish farmers should be educated on the need to maintain proper sanitary condition of the environment and regular introduction of fish antibiotic into the water body, pond and dam.

REFERENCESAkinsanya, B. &Otubanjo, O.A. (2006).Helminth parasites of Clariasgariepinus (clariidae) in Lekki Lagoon, Lagos, Nigeria.Revista. De Biologia. Tropical, 54(1):93-99.Anosike, J.C., Omoregre, E., Ofojekwu, PC and Nweke, I.E. (1992).A survey of helminth parasites of Clariasgariepinus in Plateau State, Nigeria.Journal of Aquatic Sciences, 7:39-43.Arignons, J.C. V. (1998).Tilapia. The tropical Agricultural CTA Macmilla Education. Limited, Pp 78.Ash, L.R.&Orihel, T.C. (1991). Parasites: A guide to laboratory procedure and identification. ASCP Press, Chicago.Ashada, O.O., Osineye, O.M. &Kumoye, E.A. (2013). Isolation, Identification and preva lence of parasites on Oreochromisniloticus from three selected River system. Journal Fisheries Aqua. Science, 8:115-121.Auta, J., Oniye, S.J. &Adakola, J.A. (1995).The helminthes parasites of gastro intestinal tract of synodontis species in Zaria, Nigeria, Journal of Pure and Applied Sciences, 2(2), 47-53. Awachie, J.B.E. (1966). Preliminary notes on parasites of fish in the little area of the Kainji reservoir. In the first Scientific report of the Kainji Biological Research Team, U.K .I. 65-69.



Ayanda, O.I. (2009). Comparative parasitic helminth infection between cultured and wild species of Clariasgarepinus in Ilorin, North-central Nigeria.Scientific Research and Essay, 4(1), 018-021.Bailey, K. M. (1997). Structural dynamics and ecology of flatfish populations. Journal of Sea Resources, 37(3-4):269-280.Bichi, A.H, and Ibrahim, A.A (2009). A survey of ecto and intestinal parasite of Tilapia zilli (Gervias) in Tigalake, Kano, Northern Nigeria. Bayero Journal of pure and Applied Sciences, 2(1), 79-82.Buj, I.; Podnar, M.; Mrakovcic, M.; Caleta, M.; Mustafic P.; Zanella, D. &Marcic, Z. (2008).Morphological and genetic diversity of Sabanejewiabalcanicain Croatia. Folia Zool., 57(1-2):100-10.Cheng, Q.; Lu, D. & Ma, L. (2005). Morphological differences between close populations discernible by multivariate analysis: A case study of genus Coilia (Teleostei: Clupei forms). Aquat. Living Resour.,18(2):187-92.Daniel, W.C. (1978). Sublethal effects of three ectoparasites on fishes.Journal of Fish Biology, 7, 283-294.De Busserolles, F., Fitzpatrick, J. L., Paxton, J. R., Marshall, N. J., & Collin, S. P. (2013). Eye-Size Variability in Deep-Sea Lanternfishes (Myctophidae): An Ecological and Phylogenetic Study. PLoS ONE, 8(3):e58519. http://doi.org/10.1371/journal.pone.0058519Diadatti, F.C, Fonseca, R.T., Freato, T.A., Perez, P.A and Solis, L.D. (2008).Morphometric measurement in the yield of body component in Nile of Tilapia (Oreochromisniloticus).Animal Vetinary, 24, 45-55.Fabgenro, O.A. (2002). Tilapia: Fish for thought inaugural lecture series 32. Delivered at Federal University of Technology, Akure, 77.Grabda, J. (1991). Marine Fish Parasitology: An outline. Polish Scientific PublishersWarszawa, 306p.Hickman, P. C., Roberts, S. L., Larson, A., Lanson, H. &Eisenhour, D. J. (2006).Integrated principles of Zoology. Thirteenth Edition. McGraw-Hill companies Inc. New York, USA.Pp 319.Horn, S. (1999). Essential fatty acid http://www.nutritionsupplement.com/s/fattyacid.htm. Accessed 20th June, 2003.Ibiwoye, T.I.I., Balogun, A.M., Ogusisi, R.A. &Agbontale, J.J. (2004). Determination of the infection densities of mudfish Eustongylides in clariasgariepinus and clariasanguillaris from Bida flood plain of Nigeria.Journal Apply Science. and Environmental Mg, 8(2), 39-44.Malcolm, J., Huntingford, F. &Kadri, S. (2012). Fish in Aquaculture Environments.In: Aquaculture and Behaviour, Blackwell Publishing Ltd. Pp 36 – 65.Marcogliese, D. J. (2002).Parasites of fishes in freshwater.http://www.emanerese.html Accessed June, 2008. Mattson, E. & Belk, M. C. (2013). Intraspecific morphological variation in two common marine fish species from South Africa. The Open Fish Science Journal, 6:87-91. Murta, A. G. (2002). Morphological variation of horse mackerel (Trachurustrachurus) in the Iberian and North African Atlantic: Implications for stock identification. J. Mar. Sci., 57(4):1240-8.Oniye, S.J., Adebote, D.A. &Ayanda, O.L. (2004).Helminth parasites of Clariasgariepinusin Zaria, Nigeria.Journal of Aquatic Sciences, 19(2), 71-76.Onwuliri, C.O.E. &Mgbemena, M. O. (1987). The parasitic fauna of some fresh water fish from Jos Plateau, Nigeria. Nigeria Journal of Applied Fisheries and Hydrobiology, 2:33-37.Paperna, D. (1980). Parasites, infections and diseases of fish in Africa–Anupdate CIFA Technology.Paper, 31, FAO, Rome, Italy, 200.Pinheiro, A.; Teixeira, C. M.; Rego, A. L.; Marques, J. F. & Cabral, H. N. (2005).Genetic and morphological variation of Solealascaris(Risso, 1810) along the Portuguese coast.Fish. Res., 73(1-2):67-78.Sieszko, S. F. (1975). Effects of environmental stress on outbreak of infection diseases of fishes.Journal of fish Biology, 6, 157-208.Syndenhem DHJ (1974). Observation on the fish population of a Nigerian forest stream. Revista Zoology of Africa, 84(2):257-269. Teugels, C.G. (1986). A systematic revision of the African species of the genus Clarias (Pisces: Clariidae) Annual Museum Research of African Centre of Science Zoology, 247: 199.Torres, R. G. A.; Gonzalez, P. S. & Pena, S. E. (2010).Anatomical, histological and ultraestructural description of the gills and liver of the Tilapia (Oreochromisniloticus).Int. J. Morphol., 28(3):703-12.Turan, C. (2004). Stock identification of Mediterranean horse macherel (Trachurnsmediterraneus) rising morphometre and meristic character, Journal Mar. Sciences, 61, 774-781.Tzeng, T. D. (2004). Morphological variation between populations of spotted mackerel (Scomberaustralasicus) off Taiwan.Fish. Res., 68(1-3):45-55.Ukoli, F. M. A. (1969). Preliminary report on the helminth infection of fish in the River Niger at Shagamu.In man- made lakes (Ed. Obang L.E) Ghana University Press, Ghana, 269-288.Usman, A., Solomon, S. G. &Okayi, R. G. (2014). Aspect of the biology of some selected fish species from lakeAlau, zone Nigeria. Nigerian Journal of Fishers and Aqnaculture, 2(2), 18 – 23. Wall, G. L. (1942). The vertebrate eye and its adaptive radiation Bloomfield Hills,. Mich.: Cranbrook Institute of Science. Pp. 785.Warrant, E. J., Collin, S. P., Locket, N. A. (2003).Eye design and vision in deep-sea fishes.In: Collin SP, Marshall NJ, editors. Sensory processing in aquatic environments. New York: Springer-Verlag. Pp. 303–322.WHO (1995).Control of Food borne trematodes in cod and marine Mammals in British home waters.Journal of Apply Ecology, 9, 459-485.Yakubu, D.P., Omoreozle, E. &Wande, J.N. (2002).A comparative study of gut helminth of Tilapia zilli and Clarias gariepinus from River Uke, Plateau State, Nigeria.Journal Aquatic Sciences, 17(2), 137-139.




NEMATICIDAL POTENTIAL OF EXTRACTS OF NEEM (Azadirachta indica) AND LEMON GRASS (Cymbopogon citratus) ON ROOT-KNOT NEMATODES

(Meloidogyne spp) INFECTING SWEET POTATO.

1Okechalu, O. B,1Dalong, S., 2Okechalu, A. A., and 3Oke, F.M1Department of Plant Science and Technology, University Of Jos

2 Federal College of Forestry Jos, Plateau State, Nigeria.3Augustine University, Ilara-Epe, Lagos State


ABSTRACTThe nematicidal potential of extracts of Azadirachta indica (neem) leaves and Cymbopogon citratus (lemon grass) on root-knot nematodes infecting sweet potato was evaluated between the months of June and November, 2012 at the Botanical Nursery of the University of Jos. The root-knot nematodes were obtained from galled roots of tomato and potato in Jos farms. A total of forty (40) sweet potato cultivar TIS 87/0087 were raised in steam-sterilized soil in clay pots and used for the study.Thirty (30) of the test plants were inoculated with 2250 juveniles of root-knot nematodes each while 10 were not inoculated serving as positive control. Ten of the inoculated plants were treated with extracts of neem leaves, 10 were treated with extracts of lemon grass while 10 were not treated, they served as negative control. Growth and yield parameters were then measured from 42 days after planting to 120 DAP using agronomic traits as a measure of growth and yield. Growth and yield parameters such as number of leaves, length of vines, number of tubers, weight of tubers etc were highest in the positive control, followed by sweet potato infected with nematodes and treated with lemon grass extract, then those treated with Neem leaves extract while those not treated with extract had the least growth and yield. Statistical analysis showed that the positive control produced significantly higher (P˂ 0.05) growth and yield parameters than all the other treatments. Infected plants treated with the extracts were also found to have significantly higher growth and yield parameters than the plants that were infected but not treated with extracts (negative control) at 0.05 level of probability. Number of galls was highest among the plants from the negative control, followed by plants treated with lemon grass extracts. The findings indicated that extracts of Lemon grass and Neem leaves improved growth and yield data of nematodes infected sweet potato and thus could be utilized in the control of root-knot nematodes in gardens.

Key Words; Root-Knot Nematodes, Sweet Potato, Neem, Lemon Grass, Inoculate.


INTRODUCTION The sweet potato (Ipomoea batatas) is a dicotyledonous plant that belongs to the family Convolvulaceae (Gills, 1988). It’s large, starchy, sweet tasting, tuberous roots are an important root vegetable (Woolfe, 1992). The young leaves and shoots are sometimes eaten as greens. There are approximately 50 general and more than 1000 species of Convolvulaceae. Ipomoea batatas is the only genera of major importance, some others are used locally, but many are actually poisonous. The genus, Ipomoea that contains the sweet potato also include several garden flowers called morning glories, though that term is not usually extended to Ipomoea batatas. Some cultivars of Ipomoea batatas are grown as ornamental plants; the name ‘tuberous morning glory’ may be used in a horticultural context (Purseglove, 1984). The plant does not tolerate frost (Onwueme, 1978). It grows best at an average temperature of 240C (750F), abundant sunshine and warm nights. Annual rainfall of 750-1,000mm (30-39 in) is considered most suitable, with a minimum of 500mm (20 in) in the growing season. The crop is sensitive to drought at the tuber initiation stage (50-60 days after planting), and it is not tolerant to water logging, as it may cause tuber rots and reduce growth of storage roots if aeration is poor (Ahn, 1993). They grow well in many farming conditions and have few natural enemies, pesticides are rarely needed. Sweet potatoes are grown on a variety of soils, but well drained light to medium texture soils with pH range of 4.5 -7.0 are more favourable for the plant (Woolfe, 1992, and Ahn 1993). Sweet potato is traditionally consumed in boiled form. The roots (tubers) are frequently boiled, fried or baked. They can also be processed to make starch and partial flour substitute (Purseglove 1984). Even the leaves of sweet potatoes plant have been shown to provide important antioxidant benefits and are included in soup in many cuisines (Chang, et al., 2010). Nematodes that attack plants are worms mostly microscopic in size, cylindrical in shape, tapering towards the head and tail. Root-knot disease is caused by the root-knot nematodes Meloidogyne spp. Nematodes are microscopic warms that live in soil (Rasaki, 1981). Nematode feeds on plants roots, damaging and stunting them. The first evidence of a nematodes problem is poor growth of plants and poor colour of foliage (Jatala and Bridge, 1990). The damaged roots cannot supply sufficient water and nutrients to the above ground plant parts, and the plant is stunted or slowly dies. Symptoms are more pronounced during dry weather. Infested tubers are unsightly, but edible if peeled. The root-knot nematode cause small inconspicuous root swellings or galls to develop

(Okechalu and Wonang, 2004). The above ground symptoms exhibited by sweet potato plants infected by root-knot nematodes include stunting of plants, yellowing, wilting of plants, reduced yield and premature death of plants (IFAS, 2005). Small bumps or blisters on other varieties are important below-ground symptoms in sweet potato. There may be brown to black spots in the outer layers of flesh which are not evident unless the storage root is peeled. Root-knot nematodes disease has been reported by some workers to be one of the major limitations of agriculture in the tropics (Adesiyan et al., 1990). Recent approach to plant disease control de-emphasises the use of chemicals to control plant disease. Oyedunmade (2011) reported that extracts of plants such as lemon grass can be used as alternative to nematicides.Neem has also been reported to have nematicidal abilities (Kausik 2002) these, therefore, forms the background to this work which seeks to assess the nematicidal potential of extracts of Neem leaves and lemon grass on root knot nematodes infecting sweet potato.

MATERIALS AND METHODS The research work was carried out at the Botanical Garden of the University of Jos located on lat 08° 53’E and long 09° 57’N at an altitude of 1159m above sea level. Sweet potato cultivar TIS 87/0087 was used for the experiment. Extracts of Neem leaves (Azadirachta indica) and lemon grass (Cymbopogon citratus) collected from Jos and environs were tested on root-knot nematodes infecting sweet potato planted in clay pots. Vines of sweet potato cultivar TIS 87/0087 were collected from National Root Crop Research Institute Sub-Station Maraba, Nasarawa State Nigeria on the 5th June, 2012. Lemon grass samples were collected from the University of Jos Staff Primary School. Neem leaves samples were collected from different locations within Jos, Plateau State Samples of sweet potato plants together with the soil around their roots were collected from gardens behind the student village hostel of the University of Jos. A hand trowel was used to up- root the plants and a sharp knife to cut the root from top and carefully put inside black polythene bags. All the samples collected were taken to Plant Pathology Laboratory of the University of Jos for further processing. Soil was collected from the garden in the Department of Plant Science and Technology, University of Jos, the soil was sterilised by heating it to a temperature of 800C, then put in 40 clay pots and arranged into four rows, labelled as A, B, C and D, of 10 pots each, and were watered for 4 days before the

NEMATICIDAL POTENTIAL OF EXTRACTS OF NEEM AND LEMON GRASS ON ROOT-KNOT NEMATODES


plants were introduced into itSweet potato vines collected were cut into 10cm as suggested by (Purseglove, 1984) and planted in the 40 pots. Lemon grass and Neem leaves were air dried and pulverised into powdered form and kept in separate containers. Afterwards, 50g of each extract was mixed in 1ml of water to obtain 50g/ml concentration of the extract. Plant parasitic nematodes were extracted from the roots of sweet potato sourced from gardens around the Student’s Village Hostel. The soil particles or sample collected from the adhering roots of sweet potato were carefully mixed to break up larger particles from the polythene bags then weighed out into 40g. The soil was then mixed with water for further processing. The modified Baermann funnel technique as described by Southey (1970) was employed in extracting nematodes from the soil and root samples. This method is one of the fastest methods of recovering nematodes from roots and soil. The nematode population was estimated thus; a drop (1 drop) of nematode extract viewed under microscope had 15 nematodes, therefore 10 drops contained 150 nematodes, 10 drops marked 1ml i.e. 1 drop = 15 nematodes 10 drops = 150 nematodes 10 drops = 1ml (which contained 150 nematodes) 15mls = 2250 nematodesAt three (3) Weeks after planting, sweet potato in plots A, B, and C were inoculated with 2250 Juveniles of root-knot nematodes each. This was done by pipetting 15mls of nematode suspension into a 5cm deep hole made near the root of each plant. After three weeks of inoculation of the plants with nematodes, 50g/ml extracts of lemon grass and Neem leave were applied to plots A and B respectively, and were covered with sand. Plot C had no extracts, they served as negative control while plot D served as positive control (i.e. was not inoculated with nematodes). Data on growth and yield parameters such as number of leaves per plant, length of vines per plant and number of galls per plant were all recorded at harvest. All data generated were subjected to statistical analysis using one-way analysis of variance.

RESULTS AND DISCUSSIONThe result of this investigation revealed that root knot nematode parasitize and reduce growth and yield of sweet potato plant with chlorosis on leaves of infected plants. The result also showed that the plant extracts used had nematicidal properties but to varying degree. Chlorosis of leaves was greatly noticed in inoculated but not treated plants. This result tallies with the observation of several workers (Sardenelli, 2010).

He reported that root-knot nematode infection leads to physiological instability in plants that may be accompanied by symptoms such as chlorosis among others. Mean number of leaves was highest (64.4) in plants grown in the control plot D (un-inoculated plants). This was followed by plants inoculated with nematodes and treated with lemon grass extract (47.6) then plants inoculated with nematodes and treated with neem leaves extract (47.4) while plants inoculated with nematodes but not treated with any extracts (the negative control) had the least mean number of leaves (30.2) (Table 1). The result also showed that the positive control plants had significantly higher mean number of leaves than all the other plants (P<0.05). Infected plants treated with neem and lemon grass did not differ from each other in mean number of leaves at 0.05 level of probability while infected plants not treated with any extracts had significantly lower mean number of leaves than all other treatments at 0.05 level of probability (Table 1). Mean length of vines was highest (101.36cm) in plants grown in the positive control plots (i.e. un-inoculated) this was followed by plants inoculated with nematodes and treated with neem leaves extract (70.7) then plants inoculated with nematodes and treated with lemon grass extract (64.12) while plants inoculated with nematodes but not treated with any extract (negative control) had the least mean length of vine (33.04cm) (Table 1). The result also showed that the un-inoculated plants (positive control) had significantly higher mean length of vine than all the other plants (P<0.05). Infected plants treated with neem extracts and infected plants treated with lemon grass did not differed from each other in mean length of vines at 0.05 level of probability while plants from the negative control had significantly lower mean length of vines as compared to all other plants at 0.05 level of probality (Table 1). Mean number of tubers was highest in plants inoculated with nematodes and treated with lemon grass extracts (3.2) this was followed by plants grown in the positive control plots (un-inoculated) (3.1) while plants inoculated with nematodes but not treated with any extract (negative control) had the least mean number of tubers (1.5) (Table 1). The result also showed that the plants inoculated with nematodes but not treated with any extract (the negative control) had significantly lower mean number of tubers than all the other treatments (P<0.05). Infected plants treated with neem did not differ from infected plants treated with lemon grass each at 0.05 level of probability (Table 1). Mean weight of vines was highest (58g) in plants grown in the positive control plot (i.e. un-



inoculated) followed by plants inoculated with nematodes and treated with neem leave extract (45g) then plants inoculated with nematodes and treated with lemon grass extract (44.81g) while plants inoculated with nematodes but not treated with any extract (the negative control) had the least mean weight of vine (17.78g) (Table 2). The result further showed that the positive control plants had significantly higher mean weight of vine than all the other plants (P<0.05). Also, the negative control had significantly lower mean weight of vine than all treatments at 0.05 level of probability while plants treated with lemon grass and neem leaves did not differ from each other in mean weight of vines (Table 2). Mean weight of tubers was highest (81.4g) in plants grown in the positive control plots (un-inoculated) this was followed by plants inoculated with nematodes and treated with lemon grass extract (66.85g) then plants inoculated with nematodes and treated with neem leave extract (66.4g) while plants inoculated with nematodes but not treated (the negative control) had the least mean weight of tubers (17.1g) (Table 2). The observed better growth and yield of the positive control plants was not unexpected since they were not challenged with the nematodes.The two plants extracts i.e lemon grass and neem leaves improved growth and yield of nematode infected plants suggesting they had nematicidal properties on root knot nematodes.. This results tallies with the observation of Oyedunmade (2011) who conducted phytochemical analysis of lemon grass to determine the chemical compounds that had nematicidal activities. They reported that lemon grass controlled nematodes and consequently improved growth and yield of treated okra plants. Chopra and Hanson (1985) reported that lemon grass can be used as a nematicide. Also earlier reports by Sangwan et al., (1985) showed that lemon grass has nematicidal properties. This may have accounted for the increased growth and yield in infected plants treated with lemon grass as observed in this study. Infected plants treated with neem leaves performed better than their untreated but infected controls. This observation is in agreement with the report by Kausik et al., (2002) who stated that neem leaves have nematicidal properties. Plants inoculated with nematodes and not treated with any extract showed greatest reduction in growth and yield parameters. This agrees with the report by Dropkin (1977) that nematodes cause loss of yield in crops. The observations in this study also tallies with the report of Adesiyan et al., (1990) that a wide variety of crop plants are damaged by nematodes. These include roots and tuber crops such as cassava, yam, cocoyam, ginger and potato

Mean number of galls was highest (2.2) in plants inoculated with nematodes but not treated with any extract (the negative control) this was followed by plants inoculated with nematodes and treated with lemon grass extract (0.5) then plants inoculated with nematodes and treated with neem leaves extract (0.4) while plants grown in the control plots (uninoculated) had zero (0) mean number of galls (Table 2). The result also showed that the plants inoculated with nematodes but not treated with any extract (the negative control) had significantly higher mean number of galls than all the other plants (P<0.05). All other treatments did not differ from each other in mean number of galls at 0.05 level of probability (Table 2). Galls on infected roots are characteristics symptoms of root-knot nematodes (Adesayan, et al.,1990) The plants showed greatest reduction in growth and yield with highest number of galls. The stunting of plants, chlorosis of leaves and poor yield are indicative of lack of physiological stability and it is often associated with reduced translocation, inadequate nutrients absorption and abnormal production of growth regulators as suggested by Wallace (1963). Reduction of yield can also be caused by pollen sterility induced by the nematodes as noted by Sasser and Carter (1975). These plants (negative control) showed highest root galling. Formation of galls on root is a distinctive feature of infection by root-knot nematodes (Bergeson, 1968).Uninoculated potato plants (positive control) were free from root galls. This may be because they were free from the root-knot nematodes.

Table 1: Mean Number of Leaves, Tubers and Length of Vine Per Sweet Potato Subjected to the varying treatments. Treatment Mean No. of

Leaves Mean No. of Tubers

Mean Length Of Vine

A 47.4 2.9 70.7B 47.6 3.2 64.12C ( - Control) 30.2 1.5 33.04 D (+ Control) 64.4 3.1 101.36LSD = 16.44 0.83 10.87

Table 2: Mean Weight of Vine, Tubers and number of Galls per Sweet potato subjected to varying treatments. Treatment Mean No. of

Leaves Mean No. of Tubers

Mean Length Of Vine

A 45 66.4 0.4B 44.81 66.85 0.5C ( - Control) 17.78 17.1 2.2D(+Control) 58 81.4 0 LSD = 11.68 11.06 0.65



Plate 1: Infected Sweet Potato Tubers

Plate 1: Sweet Potato Tubers showing signs of root-knot nematodes infection

CONCLUSION The result of this investigation has shown that neem leaves extract and lemon grass extract have nematicidal properties on root knot nematode infecting sweet potato. It is therefore suggested that leaves of these plants be utilized in the control of root-knot nematodes by farmers. This will reduce the dependence on pesticides which will go a long way in reducing cost of production as well as environmental effect of pesticides.

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OUTCOME OF CALCIUM AND PHOSPHOROUS MINERAL SOURCES ON PERFORMANCE, CARCASS AND BONES CHARACTERISTICS OF BROILERS

Ari, M. M., Gambo, D., Alu, S.E., Edwin, E.E., Aminu, R.A., Isa, J .M. and Yaduma, M.Department of Animal Science, Faculty of Agriculture, Nasarawa State University, Nigeria.

Correspondence Email: [email protected], [email protected]

Date Manuscript Received: 21/12/2016 Accepted: 25/12/2016 Published: December, 2016

ABSTRACTThis study was conducted to evaluate the effects of utilization of Bone meal, Limestone, Oyster shell and Egg shell as Calcium and Phosphorous mineral sources on performance, carcass and bones characteristics of broilers. A total of sixty (60) four (4) weeks old broiler chicks were raised on commercial chick mash (1- 30 d) prior to the commencement of the experiment and experimental diets were formulated to provide approximately 3050-3100 kcal/ME, 19-20% crude protein (CP) using the least cost feed formulation software feed win during the 21 d feeding trial period. The experimental birds were assigned to four (4) dietary treatments groups of two (2) replicates in a completely randomized design with 2.5 % inclusion of limestone), oyster shell, bone meal) and egg shell constituting the treatments. Significant (P<0.05) differences were observed in some performance parameters like feed intake (FI), weight gain (WG) and feed conversion ratio (FCR). Similarly some carcass parameters were also significantly affected by the experimental treatments. The effects on bone characteristics were significant (P<0.05). There was no consistent trend in the differences observed for all test Ca and P sources. It is therefore recommended that all the experimental calcium sources can be included to constitute up to 2.5% in the diet of broiler chicken. This will afford small scale poultry producers the control of using eggshell, bone meal, limestone and oyster shell as sources of calcium, phosphorus and other minerals for sustainable broiler chicken production.


INTRODUCTIONThe major limitation to the growth of the poultry industry in Sub Saharan Africa (SSA) countries has consistently been attributed to the high cost of feed ingredients which constitutes about 70% of the total cost hence, the un ending evaluation of the alternative feed materials with potentials to serve as cost and nutritionally effective replacement of competitive and costly feed resource (Ari and Ayanwale, 2012). Yasothai and Kavithaa (2014) however observed that studies on the evaluation of alternative sources of feed have been more concerned with energy and protein feedstuffs without corresponding attention given to the evaluation of local alternative sources for the major mineral nutrients like calcium and phosphorus. This position was earlier presented by Tumova et al., (2004) and Shastak (2012) who listed oyster shells, periwinkle shell, egg shell, bone meal, di-calcium phosphate and limestone as the important minerals sources that are always considered in livestock rations in SSA countries. The utilization of cockerels as major sire in community poultry upgrade programmes and as good source of protein meat commanding high demand due to its meat flavour and toughness (Yakubu et al., 2014 and Ari et al., 2016) is hinged on good quality feed with right mineral mix. Almeida and Bruno (2006) reported that bone tissue rigidity associated with Layers and cockerels’ emanate from the deposition of calcium and phosphorus as hydroxiapatite during bone mineralization process as both minerals make up about 70% of the bone composition. However, emphasis is placed on the supply of large particle size calcium material resources (Nakano, 2003; Hunton, 2005) sources especially to layers and cockerels during growing and laying stages. These vital minerals are of important welfare and economic consideration for the poultry industry (Adebiyi et al., 2009) in view of their influences on the physiology, metabolism and performances of all classes of poultry and most importantly on bone structures and composition. There are however, observed effects in the use of some of these mineral sources in the diets of meat-type poultry which includes production and health concerns associated with minerals originating as by- products from animal origin like Bone meal, egg shell (Rath et al., 1999;Tion et al., 2012) in spite of relative cost advantage.Similarly, industrial application of calcium carbonate obtained from mussel and oyster shells as filler in polypropylene (Hamester et al., 2012) again provides another important focus for the need for diversification of calcium and phosphorous mineral sources to the poultry feed industry. The basic

assumption of this study was that the utilization of calcium mineral sources by cockerels will differ with sources of calcium minerals in the diets. The study aimed at evaluating the effect of different calcium minerals sources on the performance, carcass and bone characteristics of broilers.

MATERIALS AND METHODSThis research was conducted at the Research and Teaching Farm of the Faculty of Agriculture, Nasarawa State University Keffi, Shabu Lafia Campus. The trial site is located in Guinea Savannah Zone, North Central of Nigeria and found on latitude 080 35N and longitude 080 33E (NIMET, 2008). The test ingredients which comprised of bone meal, limestone, egg shell and oyster shell were sourced and processed. Bone meal was sourced locally within the Lafia abattoir and other locations within the Lafia metropolis. The collected Bone meal were cleaned up, sundried, burnt and crushed to small particle sizes, bagged and stored. Limestone was procured already in coarse form from Jos, Plateau State and transported to Lafia.Oyster shell was procured from Sabon Gari market in Kano State, The raw oyster shell were cleaned to remove debris prior to boiling, drying, and crushing into coarse form. Egg shell was sourced locally within Lafia metropolis from processed egg sellers. The collected egg shell was sundried and processed into powder form. A total of sixty (60) four (4) weeks old broiler chicks raised on commercial chick mash (1- 30 d) prior to the commencement of the experiment were used. The experimental birds obtained from Munbolas Lynx Farms, Ibadan Nigeria were brooded together and subjected to the same routine management practices during the 21 d feeding trial period. The experimental birds were randomly selected and assigned to four (4) dietary treatment groups of two (2) replicates in a completely randomized design. The treatments included 2.5 % inclusion of limestone (T1), oyster shell (T2), bone meal (T3) and egg shell (T4). The compounded experimental diets were formulated to provide approximately 3050-3100 kcal/ME, 19-20% crude protein (CP) using least cost feed formulation software feed win as presented in table 2.

Data was collected for the following: Performance evaluation. At the end of the feeding trial, feed intake, weight gain, feed conversion ratio, and survival percentages were assessed as measures of Performance according to methods adopted by (Ari et al., 2012).

OUTCOME OF CALCIUM AND PHOSPHOROUS MINERAL SOURCES ON PERFORMANCE, CARCASS AND BONES CHARACTERISTICS OF BROILERSOUTCOME OF CALCIUM AND PHOSPHOROUS MINERAL SOURCES ON PERFORMANCE, CARCASS AND BONES CHARACTERISTICS


Carcass evaluation. At the end of feeding trial, two birds per treatment were randomly selected and fasted for about 8 h before slaughter. This was to reduce gut content and thus reducing the risk of contamination of the carcass during dressing without affecting meat quality. Measurements were taken on cut parts: breast, back, thigh, and drum stick. Bone evaluation: The bones were obtained from thigh and the drum stick which comprises of the femur, tibia and tubular. The flesh was properly removed with the use of razor blade and sharp knife, weighed and labelled accordingly. The width and length of the bones were measured with the use of Vernier calliper and microscrew gauge for the length and diameter measurements respectively according to the methods described by Nkukwana (2012). The left and right tibia bones from each of the experimental groups were further sun dried for three (3) days before oven drying preparatory to ashing. The dried samples were pounded and placed in labelled crucibles before ashing using muffle furnace at 550C. The values of ash were obtained by difference between initial weight of crucible and weight of bones before ashing and the weight of crucible after ashing.Data collected were subjected to one-way analysis of variance (ANOVA) using SPSS 20; Means were separated using the Duncan’s Multiple Range Test (Duncan, 1955).

RESULTS AND DISCUSSIONThe effects of different calcium source in performance of broilers (Table 3) shows that there were significant (P<0.05) differences in feed intake, weight gain and feed conversion ratio. The initial weight and survival percentage were however not significant. T2 was best for feed intake while T4 was best for weight gain. However, T1, T2 and T3 were all significantly better than T4. Carcass characteristics (Table 4) of broilers such as live weight, body weight gain, dressed weight, head, neck, wings, breast, back, thigh, drum stick and shank were all not significant. Effects of different mineral sources on bone characteristics (Table 5) were significant (P<0.05) for femur weight, tibia/fibular weight, left and right weight of bone ash. Femur length, femur/tibia length, femur diameter and femur/tibia diameter were however not significant. Values of weight of femur bones were the highest in T1 at 5.55g. Also, the weight of tibia and fibula is highest in T1 (8.40g) and lowest in T2 (5.45g). The same occurrence in the length of femur, tibia and fibula bone was observed which indicate that T1 has the highest value (7.18g) and lowest in T4 (6.21g). However, for the diameter of the femur bone, T4 has the highest value (7.53g)

while T2 has the lowest value (6.22g). Diameter of the tibia and fibula had the highest value of 6.56g in T4 and the lowest value of 5.24g in T2. The weight of the ash of right indicate the highest value at T2 (4.00g) and lowest in T4 (2.50g). However, the left ash shows that the treatment with the highest value was T1 (4.00g) and the one with the lowest was T4 (2.00g) The utilization of egg shell by broilers as obtained in this studydoes not have detrimental effects on the birds. This result is supported by the previous reports of other researchers (Froning and Bergquist, 1990; Scheideler, 1998) in which feeding chicken eggshells as a Ca source to laying hens did not have detrimental effects on BW, feed intake, and egg production. There were significant (P<0.05) differences in feed intake, weight gain and feed conversion ratio in this findings across treatments as similarly reported by Skinner et al. (1992) and Kingori (2011). The all non significant (P<0.05) differences of carcass characteristics of broilers chicken (table 4) from the present study also confirm the findings of Scheideler (1998) who reported that dried chicken eggshells could be used as the sole Ca source in poultry (broilers or layers) diets without detrimental effects. This same worker also noted that dried chicken eggshells had no effects on egg weight, albumen weight, yolk weight, and egg specific gravity. There were significant (P<0.05) differences in some bone parameters (femur weight, tibia/fibular weight, left and right weight of bone ash). From the result, it shows that limestone (T1) have the highest value for all bone parameters (both the significant and non significant) followed by bone meal as similarly reported by Omole et al.,(2005) and Skinner et al., (1992). Skinner et al. (1992) worked on the effects of calcium and nonphytate phosphorus level fed during 42 to 56 days of age on performance and bone characteristics of male broilers.

CONCLUSIONBased on the results obtained in this study, it was concluded that though limestone and bone meal has the potentials of producing best percentages of bone characteristics of broiler chicken, the inclusion of bone meal, limestone, oyster shell, and egg shell up to 2.5% level in the diet has no adverse effect on performance, carcass and bones characteristic of broilers.It is therefore recommended that all the calcium sources used in this experiment can be used as alternate source in the diet of broiler chicken up to 2.5% and thus giving small scale poultry producers the option of using eggshell, bone meal, limestone and oyster shell as sourcesof calcium, phosphorus and other minerals for broiler chicken.

OUTCOME OF CALCIUM AND PHOSPHOROUS MINERAL SOURCES ON PERFORMANCE, CARCASS AND BONES CHARACTERISTICS


ACKNOWLEDGEMENTThe Livestock Farm and Laboratory staff of Department of Animal Science, Nasarawa State University, Lafia Campus are gratefully acknowledged.

REFERENCESAdebiyi, O.A. Sokunbi, O.A. and Ewuola, E.O. (2009) Performance Evaluation and Bone Characteristics of Growing Cockerel Fed Diets Containing Different Levels of Diatomaceous Earth Middle-East Journal of Scientific Research, 4 (1): 36-39Almeida Paz, I.C.L. and Bruno, L.D.G. (2006) Bone mineral density: review Brazilian Journal of Poultry Science, 8.2: 69 – 73Ari, M. M and Ayanwale, B.A (2012) Nutrient Retention and Serum Profile of Broilers Fed Fermented African Locust Beans (Parkiafilicoide). Asian Journal of Agricultural Research, 6(3):129-136 Ari, M. M., Ayanwale, B.A., Adama, T .Z and Olatunji, E.A (2012) Effects of diets containing Alkali-treated Soyabeans on performance traits, nutrient digestibility and cost benefits of Broiler chickens Nig J.Anim Prod 39(2)125-134Ari, M.M., Alu, S.E., Daniel, M.A., Omede, A.A., Umoren, E. and Iji, P.A. (2016) Gross Responses, Haematological Indices and Serum Biochemistry of Growing Cockerels Fed Diets Containing Graded Levels of Rumen Liquor Fermented Sugarcane Scrappings as Replacement for Maize. J Agri Res, 1(1): 000103.Duncan, D.B. (1955) Multiple range and multiple tests. Biometrics 11: 1-42.

Table 1: Proximate Nutrient Composition of the Mineral SourcesMINERALS ME CP LYS METH CF Ca P (K/cal) (%) (%) (%) (%) (%) (%)Limestone 3049.88 19.02 0.72 7.84 5.19 1.18 0..44Bone meal 3087.38 20.04 0.77 0.67 7.98 0.63 5.19Oyster shell 2700/2800 18.9 0.05 0.15 4.16 4.05 0.57Eggshell 3100 16 0.2 0.15 4.4 3.4 0.75

Table 2: Composition of Experimental DietsFeed ingredient (kg) T1 T2 T3 T4Maize 52.04 52.04 52.04 52.04GNC 32.01 32.01 32.01 32.01Maize bran 5 5 5 5Rice bran 2.5 2.5 2.5 2.5Methionine 0.1 0.1 0.1 0.1Lysine 0.1 0.1 0.1 0.1Premix 0.25 0.25 0.25 0.25Salt 1 1 1 1Fish meal 1.5 1.5 1.5 1.5Palm oil 3 3 3 3Limestone 2.5 0 0 0Oyster shell 0 2.5 0 0Bone meal 0 0 2.5 0Egg shell 0 0 0 2.5Total 100 100 100 100Calculated analysisME 3049.88 3100 3087.38 3100CP % 19.02 19 20.04 19Lysine % 0.72 0.95 0.77 0.95Methionine % 0.39 0.44 0.40 0.44EE 7.84 10 7.98 10CF 5.19 5 5.19 5Ca 1.18 0.9 0.63 0.9

*premix, the vitamin- mineral premix supplied the following per 100kg of diet. Vitamin A 15,000 I.U, Vitamin D3 3000,000 I.U, Vitamin E 3000 I.U, Vitamin K 2.50mg, Thiamine, (B1) 200mg Riboflavin (B2) 600mg Pyridoxine (B6) 600mg, Niacine 40.0mg, Vitamin B1 2mg, Panthothenic acid 10.0mg, Folic acid 100mg, Biotic 8mg, Choline 50g, Anitioxidant 12.5g, Manganese 96g, Zinc 6g, Iron 24g, Copper 0.6g, Iodine 0.14g, Selenium 24g, Cobalt 214mg.

Table 3: Effect of the calcium sources on performance of broilers Parameters T1 T2 T3 T4 SEMFeed intake 387.68c 464.64a 440.71b 432.86b 4.25*Initial weight 775.00 950.00 1062.50 962.50 39.72NSWeight gain 273.79c 200.00bc 362.50ab 395.64a 21.07*FCR 2.33a 3.02a 2.91a 1.18b 0.86*Survival 95.61 95.32 93.29 95.00 0.86NSmeansfollowed by the same letter(s) are not significantly different (p>0.05) ; SEM: Standard Error of Mean

Table 4: Effect of the different minerals sources on the carcass characteristics of broilers Parameters T1 T2 T3 T4 SEMLive weight (Kg) 1.8 1.3 1.9 1.5 0.21NSWeight gain (Kg) 1.35 0.85 1.45 0.9 0.32 NSDressed weight (Kg) 1.6 1.2 1.7 1.3 0.43 NSHead (g) 43.7 33.3 37.1 37.6 0.44 NSNeck (g) 86.8 61.5 60.9 77.0 0.22 NSWings (g) 123.1 92.0 101.9 118.5 0.33 NSBreast (g) 324.6 202.0 221.9 243.2 0.65 NSBack (g) 237.1 142.2 175.9 210.5 0.47 NSThigh (g) 169.3 122.8 142.0 143.0 0.14 NSDrum stick (g) 159.6 105.8 108.2 133.5 0.55 NSShank (g) 80.6 57.8 65.2 82.0 0.65 NS

Table 5: The effects of different mineral sources on bone characteristics of broilersParameters T1 T2 T3 T4 SEMFemur weight (g) 5.55 a 3.65 b 4.55 a 4.50 a 0.37*Tibia/Fibula weight (g) 8.40 a 5.45 b 6.60 ab 7.20 0.66*Femur length (cm) 7.19 6.64 6.48 6.21 020NSTibia/Fibula length (cm) 9.96 9.28 9.51 9.97 0.26NSFemur diameter (mm) 6.71 6.22 7.22 7.54 0.46 NSTibia/Fibula diameter (mm)6.28 5.24 5.91 6.56 0.30 NSLeft weight of ash (g) 4.00a 3.00ab 3.00ab 2.00b 0.31*Right weight of ash 3.00ab 4.00a 3.00ab 2.50b 0.23*

meansfollowed by the same letter(s) are not significantly different (p>0.05) ; SEM: Standard

Error of Mean



Froning, G. W. and Bergquist, D. (1990). Utilization of inedible eggshells and technical albumen using extrusion technology. Poult. Sci. 69:2051–2053.Hamester,M.R.R, Balze, P.S. and Becker,D.(2012)Characterization of Calcium Carbonate Obtained from Oyster and Mussel Shells and Incorporation in Polypropylene Materials Research. 2012; 15(2): 204- 208Hunton, P (2005) Research on eggshell structure and quality: An historical overview Brazilian Journal of Poultry Science7. 2: 67 – 71Julian, R. J. (2005) Production and growth related disorders and other metabolic diseases of poultry. A review. Veterinary Journal, 169, 350–369.Kingori, A. M. (2011).A Review of the Uses of Poultry Eggshells and Shell Membranes.International Journal of Poultry Science, 10 (11): 908-912,Nakano, T., Ikama, N. I. and Ozimek, L. (2003). Chemical composition of eggshell and shell membranes. Poultry Sci., 82: 510-514.NIMET (2008).Nigeria Metrological Agency, Lafia Nasarawa State.Nkukwana, T.T. (2012) The effect of Moringa Oleifera Leaf Meal on growth performance, gut integrity, bone strength, quality and oxidative stability of meat from broiler chickens PhD Thesis Department of Livestock and Pasture Sciences ,Faculty of Science and Agriculture ,University of Fort Hare ,Alice, South AfricaOmole, A. J., Ogbosuka, G. E., Salako, R. A. and Ajayi, O. O. (2005). Effect of Replacing Oyster Shell with Gypsum in Broiler Finisher Diet Journal of Applied Sciences Research 1(2): 245-248.Rath, N. C., Balog, J. M., Huff, W. E., Huff, G. R., Kulkami,G. B. and Tierce, J. (1999).comparative differences in the composition and biochemical properties of tibiae of sevenand seventy-two-week old male and female broiler breeder chickens. Poult. Sci. 78: 1232-1239Scheideler, S. E. (1998).Egg shell calcium effects on egg quality and Ca digestibility in first- or third-cycle laying hens.Journal of Appl. Poult. Res. 7:69–74.Shastak, Y. (2012). Evaluation of the Availability of Different Mineral Phosphorus Source In Broilers , Online Research Work V2 .2Skinner, J. T., Adams, M. H., Watkins, S. E. and Waldroup, P. W. (1992). Effects of calcium and nonphytate phosphorus levels fed during 42 to 56 days of age on performance and bone strength of male broilers. Journal of Appl. Poult. Res. 1:167–171.SPSS (2011) Statistical Package for Social Sciences.Released 14.0 for windows. IL60611. Chicago. Tion, M. A., Offiong, S. A., and Njoku, P. C. (2012).The Effect of Limestone Deposits as Calcium Source on the Performance of Broiler Chickens.Nigerian Journal of Animal Production, 39(1):122-125.Tumova, E., Skrivanova, V., Zita L., Skrivan, M. and Fucikova, A. (2004).The Effect of Restriction on Digestibility of Nutrient, Organ Growth and Blood Picture in Broiler and Rabbit.Pp 1008-1014 in proc. 8th World Rabbit. Cong. Puebla. Mexico, WRSA.Yakubu,A., Ari, M. M., Ogbe, A. O., Ogah, D. M., Adua, M. M., Idahor, K. O., Alu, S. E., Ishaq,A. S and Salau, E. S (2014) Preliminary investigation on community-based intervention through cockerel exchange programme for sustainable improved rural chicken production in Nasarawa State, Nigeria Livestock Research for Rural Development 26 (10) 2014Yasothai, R. and Kavithaa, N.V. (2014) Chemical characterization of egg shell meal International Journal of Science, Environment and Technology 3(4): 1436 – 1439



PARTIAL CHARACTERIZATION OF PROTEASE EXTRACTED FROM “YATSIN BIRI” GINGER (Zingiber officinale) CULTIVAR OF NORTHWESTERN NIGERIA

1Murtala, Y., 1Babandi, A., 1Babagana, K. 1Alhassan, A. J. and 2Shehu, D.1Department of Biochemistry, Bayero University, Kano-Nigeria

2Institute of Biological Sciences, University of Malaya, Malaysia



ABSTRACTThe recurrent increase in prices of calf rennet and ethical considerations linked to the production of such enzymes for cheese making and related processes have ignited a flame of scientific enquiries on the possibility and suitability of their substitution by other enzymes of plant sources. In this study, ammonium sulphate fractionation, characterization and milk clotting activity (MCA) of protease extracted from YatsinBiri ginger rhizome cultivar of the family Zingiberaceae from northwestern Nigeria were analysed. The protease extracted showed optimum activity at 50 °C and pH value of 5.5. Relative activity of the enzyme was also observed within a broad pH range of 4.5 to 7.0 accordingly. The enzyme was completely denatured at 100 °C and alkaline pH of 11.5. The milk clotting property of the protease indicated 2.83 and 1.81 folds of MCA and MSCA respectively in relation to commercial calf rennet with MCA/PA ratio of 2.18. These properties of YatsinBiri ginger protease, especially its milk clotting activity, broad pH ranges and moderately elevated temperature of 50 °C, may favour its suitability as substitute calf rennet in the food industries, especially in cheese making and related products.

Key words: Ginger Protease, Milk Clotting Activity, Calf rennet, Characterization,YatsinBiri



INTRODUCTIONEnzymes are organic catalyst capable of handling biochemical reactions in biological systems. The first use of enzymes occurred more than 5,000 years ago, when people stored milk in animal stomachs, which contains enzymes called “rennet,” that turn milk into cheese (Hou-Pin et al., 2009; Hashim et al., 2011). There is a wide and considerable use of protease enzymes in food industry nowadays, particularly in meat tenderization, milk curdling and wine and beer turbidity clearance. Ginger protease, gingepain (EC 3.4.22.67) is a plantprotease (Cysteine endopeptidase) from ginger rhizome with a valuable application in food industry. It has 10-folds proteolytic activity in meat tenderization compared to papain from pawpaw and bromelain from pineapple, increasing both the flavour and nutritional value of meat products(Naveena and Mendiratta,2001;Kim et al., 2007). It acts as a milk solidifying factor, acting on a milk protein, casein (Zhang et al., 1999; Su et al., 2009). The use of ginger proteases as milk coagulants is very interesting since they are natural enzymes that can be used for producing cheeses (Gómez et al., 2001; Galán et al.,2008).Ginger rhizome (Zingiber officinale roscoe), the main source of ginger proteases is grown in Nigeria, particularly in the northwestern region (Job and Philemon, 2013).The plant is widely grown in this region with a massive annual production (FAO, 2010). Southern Kaduna State remains the largest producer of fresh ginger in northwestern Nigeria, majorly around Kachia and Kagarko areas (KADP, 2004;Bernard, 2008). The commonly grown variety produced in those axes is ‘Yatsin Biri’ gingers cultivar (Kaduna State Ministry of Agriculture, 2007). Calf rennet has been extensively exploited as a milk coagulating agent, especially in cheese production. However, increasingly higher prices of calf rennet and ethical ground associated with the production of such enzyme for cheese making, especially to the local entrepreneurs have led to scientific enquiries on the suitability of its replacementby other enzymes of plant sources (Sousa and Malcata 1997; Malik et al., 2011). However, there is still a paucity of relevant data regarding the milk coagulating ability of ginger proteases and their application in cheese making (Malik et al., 2011), particularly proteases from certain ginger cultivars. Research on the potentiality and suitability of proteases from some ginger cultivars is thus required. This research therefore focused on extraction, partial purification and characterization of ginger protease extracted from Yatsin Biri ginger cultivar commonly grown in Kagarko axis in northwestern Nigeria.

MATERIALS AND METHODSThe fresh ginger rhizome was collected from a harvesting site in Kagarko ginger farming area of Kaduna state, north western Nigeria. The sample was identified and authenticated as Yatsin Biri ginger cultivar at the Herbarium Unit of the Department of Plant Biology, Bayero University, Kano. The sample was issued with an accession number (BUKHAN 0299). The fresh ginger rhizome was washed and minced. The minced sample (90g) was weighed and homogenized with 180 cm3 of distilled water. The homogenate was filtered through a piece of cheese cloth and the filtrate was centrifuged at 4000rpm for 30 minutes. The supernatant was collected and filtered through vacuum pump and 80 cm3 of the filtrate was used for precipitation while the remaining 100cm3 was used as crude extract and tested for the protease characteristics. The protein was precipitated using a modified method of Qiao et al., (2009). The supernatant (80cm3crude extract) was mixed with acetone which was pre-cooled in refrigerator (1:1) and then, the sediment was collected after centrifuged at 3000rpm for 20 minutes. The sediment was dissolved in 0.05 M phosphate buffer (pH: 6.0) and centrifuged again at 3000 rpm for 20 minutes. At this point the supernatant collected was precipitated using 6.0 g ammonium sulphate to 40 cm3 of the enzyme extract by gently adding and stirring pinch by pinch for 45 minutes to saturation of 20%.The sample solution was incubated at 4ºC for 16 hours. The precipitated protein was then removed by centrifugation at 3000rpm for 20 minutes and 30cm3 supernatant was collected to which 9.0 g ammonium sulphate was added pinch by pinch for 45 minutes to yield 40% saturation and again incubated at 4ºC for 16 hours. The fraction of precipitated proteins between 20 and 40% saturation is recovered by centrifugation, the sediment collected at this point was subjected to residual ammonium sulphate removal using 0.05M phosphate buffer (pH 6.0) and then centrifuged. The supernatant collected after centrifugation was tested for the protease characteristics. Total Protein Concentration Determination Total protein was determined using BioAssay Systems’ QuantiChromTMprotein assay kit based on an improved Coomassie Blue G method (Bradford, 1976) using Bovine Serum Albumin (BSA) as standard. The proteases activity (ginger protease and calf rennet) was assayed using casein as substrate. The assay was carried out using a modified method of Tsuchida et al., (1986). The substrate, 100µl of

PARTIAL CHARACTERIZATION OF PROTEASE EXTRACTION


casein (2mg/cm3) in 1M glycine-NaOH buffer (pH 10.5) was added to 100µl of the enzyme solution which were incubated at 500C for 30minutes and the reaction was terminated with the addition 100µl of chilled TCA (10%), which were allowed to stand in ice for 15minutes to precipitate the insoluble proteins. The white soluble precipitate was filtered through Whatman filter paper, 5cm3of Na2CO3 (0.5M) solution was added to the soluble product. Colorations were observed on both the crude and the precipitated extract upon the addition of 0.lcm3 Folin-Ciocalteau reagent and the absorbance was measure at 660nm.One Unit of protease activity is the amount in micromoles of tyrosine equivalents released from casein per minute using 0.2 mg/cm3 L-tyrosine as Standard. The precipitated protein (2cm3) was added to 4cm3 of tris-HCL buffer. The activity of the enzyme was determined by incubating the reaction mixture with 100µl of casein substrate at different temperatures ranging from 0-1000C at 100C intervals for 5minutes and the optimum temperature of the enzyme was determined by plotting a graph of enzyme activity against temperature. The protease activity of the precipitated enzyme was measured at different pH values. The enzyme solution (2cm3) was mixed with each of the following buffers, 0.1M acetate buffer, 0.1M phosphate buffer, 0.1M Tris-HCl buffer, and 0.1M glycine-NaOH in each labeled test-tube of pH 1.5, 3.5, 5.5, 7.5, 9.5 and 12.0 respectively. The reaction mixtures were incubated with 100µl of casein substrate at 500C for 30minutes and the optimum pH of the enzyme was determined by plotting a graph of enzyme activity against pH. Milk-clotting activity (MCA) was measured by a modification of Sousa and Malcata (1998) procedure as modified by Hang et al.(2016). The milk substrate was prepared by dissolving 12 g of skim milk powder in 100cm3 CaCl2solutions (0.01mol L−1). The pH value of the milk was adjusted to 5.5 with 1 M HCl before use. The milk substrate (2 cm3) was heated at 500C, and then thoroughly mixed with 0.2 cm3 of the enzyme solution. The time for the formation of fragments was measured with a stopwatch.One unit of milk clotting activity (MCA) is equal to the amount (mg) of enzymes required to coagulate 1cm3 of reconstituted skim milk in 1 min at 500C and pH 5.5 calculated as follows:

MCA = 2400/t × F

Where t is the time for the formation of fragments (s), and F is the dilution coefficient.

RESULTS AND DISCUSSION

Table 1: Partial Purification of Ginger Protease Extracted from Yatsin Biri Ginger Cultivar from Northwestern NigeriaPurification Steps Total Proteolytic Proteolytic Specific Purification Fold % Yield Protein Activity Activity (mg) (PA) (Units) a (PSA) (Units/mg)

Crude 740.81 326.8 ± 10 0.44 - -Enzyme Extract

Ammonium Sulphate 103.02 77.7 ± 12 0.75 1.70 170% Precipitate b

a –One Unit of enzyme activity is the amount in micromoles of tyrosine equivalents released from casein per minuteb – Precipitation under 15% to 30% Saturation Table 2: Milk Clotting Activities of Protease Extracted Yatsin Biri Ginger Cultivar and Calf Rennet Milk Clotting Milk Clotting Specific MCA/PA Ratioc

Activity Activity (MCSA) (MCA) (Units/cm3) a (Units/mg of Protein) b

Calf Rennet 60 0.91 -

Ginger Protease 170 1.65 2.18

a – A unit (U) equals the amount (mg) of enzymes required to coagulate 1cm3 of reconstituted skim milk in 1 min at 500C and pH 5.5.b – 103.02mg total protein content for ginger protease and 65.7 mg of protein for calf rennetc -- PA for ginger protease: 77.7 Units

The activity of ammonium sulphate precipitated ginger protease was progressively increased as the temperature rise from the range of 10 to 500C where the optimum activity of the enzyme was observed near 500C (figure 1). However, as the temperature progressively elevated above 500C a decrease in the activity of the protease was observed. An elevated temperature of 1000C denatures the enzyme protein with complete loss of its proteolysis. Considering the optimum temperature (500C) of YatsinBiri protease, the enzyme may have some applications in food industry, especially dairy and cheese making processes where temperatures near 500C often relevant (Hashim et al., 2011). The optimum temperature of ginger protease extracted from Yatsin Biri ginger cultivar of northwestern Nigeria showed similar temperature trend as previously reported by Hashim et al., 2011; Nafi et al., 2013, 2014 in proteases extracted from Chinese and Malaysian ginger cultivars respectively. Protease extracted from YatsinBiri ginger cultivar of northwestern Nigeria showed proteolytic activity with a broad pH range of 4.5 to 7.5 (figure 2). However, the optimum activity of the protease was observed at pH near 5.5 and at higher pH value of 11 the enzyme completely lost its activity due to denaturation of the enzyme protein. This broad range of effective pH (slightly acidic and mildly alkaline) observed, may suggest a possibility of the presence of multiple proteases in the ammonium sulphate precipitate. Nafi et al.,(2014) reported similar findings that protease from Malaysian ginger crude extract has a broad range of effective pH which could be advantageous in food processing. In some studies on the properties of the pure ginger protease by Thompson et al., (1973) and Hashim et al., (2011) showed a wide range of pH values of 4.5 to 6.0 which was slightly lower relative to the pH



values revealed by this study. Sometime the difference in optimum pH could be attributable to factors such as plant variety, types and nature of proteolytic enzymes present in the source, extraction protocols and degree of purity of the enzyme. The effective pH range (4.5-7.5) observed inYatsin Biri ginger protease may be an advantage to the enzyme on its proteolytic action on skim milk that has a pH that is often slightly acidic that favoursthe dissociation of casein from milk micelles (Skelte and Henning, 1997). The partial purification of Yatsin Biri ginger protease using one step ammonium sulphate ((NH4)2SO4) fractionation, an extensively used technique in enzyme purification (Qiao et al., 2009; Malik et al., 2011) was performed on the crude extract. The enzyme obtained from ginger rhizome crude extract using 20–40% (NH4)2SO4 saturation showed 1.70-fold purification and 170% recovery with specific activity of 0.75 Units/mg (Table 1). The one step extraction protocol employed in this study indicates a considerable value of the % yield (170%) with a low value of specific activity of the protease compared with related findings by Nafi et al,. (2014). This suggests further purification steps are required to optimize the purity of the enzyme protein as indicated in the previous findings by Qiao et al,. (2009); Nafi et al., (2014).It is quite interesting using plant proteases as milk coagulants since they are natural enzymes from readily available sources that can be used optimally for producing cheeseand related products (Gómez et al., 2001;Galán et al., 2008). In the kinetics study of (NH4)2SO4 fraction of Yatsin Biri ginger protease carried out in this study, the observed optimal enzyme activity conditions (pH 5.5 at 50 ᵒC) were used in evaluating both the proteolytic and milk clotting activities of the enzyme. The PA of YatsinBiri ginger protease was 77.7Unit/mg (Table 1). This value suggests a possibility of good yield in cheese processing. Milk-clotting protease with strong PA would excessively hydrolyze casein substrate, and thus led to reduction in cheese yield and organoleptic attributes. The MCA/PA ratio of Yatsin Biri ginger protease was

2.18 (Table 2), which is relatively a favourable value for cheese processing. Protease MCA/PA ratio is a very important criterion for evaluating protease potential as rennet substitutes (Abel-Fattah and El-Hawwary, 1974;He et al., 2012). However, PA and MCA/PA ratio of milk-clotting protease varies significantly with regard to determination and definition methods (Vishwanatha et al., 2010; De Castro et al., 2014), which often bring about complications in comparisons with related studies. Thus, MCA/PA of different milk-clotting proteases should be evaluated under the similar protocols, and similar to those employed in the cheese-making. Nevertheless, the MCA and MCSA of the Yatsin Biri ginger protease are 2.8 and 1.8 folds respectively compared to that of commercial calf rennet. This finding was in conformity with findings of Su et al., (2009), Hashim et al. (2011) and Nafi et al., (2013) that ginger protease could be a suitable choice for cheese making compared to some commercial milk curdling agentsand other milk-clotting enzymes of natural origins in improving the bitterness of milk products caused by papain and bromelain. Thus, ginger protease extracted from Yatsin Biri ginger cultivar from northwestern Nigeria possess higher milk clotting activity relative to that of the compared commercial calf rennet.

CONCLUSIONThe one-step ammonium sulphate extracted protease from Yatsin Biri ginger rhizome cultivar of northwestern Nigeria showed optimum activity at temperature near 50

°C and a broad range of pH values of 4.5 to 7.5 with an optimum pH at 5.5. The enzyme protein was completely denatured at elevated temperature and alkaline pH. Additional characteristics of the protease obtained in this study, especially its milk clotting activity, broad pH range and moderate temperature make it a suitable candidate for application in the food industries, particularly in cheese making processes. However, further purification of enzyme is required to optimize its application in food industries.

Figure1: Effect of Temperature on Protease Figure 2: Effect of pH on Protease Extracted from Extracted from YatsinBiri Ginger YatsinBiri Ginger Cultivar



REFERENCESAbel-Fattah, A.F.and El-Hawwary, N.M. (1974).Studies on the production of milkclotting enzymes, proteolytic enzymes and mucilage by fungi. J. Gen. Microbiol,84: 327–331.Bernard,A.(2008). Diseases, pest and other factors limiting ginger(Zingiber officinale Rose) production in River State. Being the text of a paper delivered during the Agricultural Product Development Strategy Workshop organized by Uptonville Foundation under the aegis of Rivers State Sustainable Development Agency(RSSDA).Retrieved from htt://uptonvilleoginstu.org/ginger.litm.Bradford, M. M. (1976). Rapid and Sensitive Method for the Quantitation of Microgram Quantities of Protein Utilizing the Principle of Protein-Dye Binding. Analytical Biochemistry, 72: 248-254.De Castro, R. J. S., Nishide, T. G. and Sato, H. H. (2014).Production and biochemical properties of proteases secreted by Aspergillus niger under solid state fermentation in response to different agro industrial substrates.Biocatal. Agric. Biotechnol, 3: 236–245.FAO, (2010). Production Quantity of Ginger in the World Total 1961-2009. Retrieved from www.fao. Mongabay.com/g/5000-World +++/T. Galán, E., Prados, F., Pino, A., Tejada, L.and Fernández-Salguero, J. (2008). Influence of different amounts of vegetable coagulant from cardoon Cynaracardunculus and calf rennet on the proteolysis and sensory characteristics of cheeses made with sheep milk. Int. Dairy J. 18:93–98 Gómez, R., Sanchez, E., Vioque, M., Ferreira, J., Tejada, L. and Fernández-Salguero, J. (2001). Microbiological characteristics of ewe’s milk cheese manufactured using aqueous extracts of flowers from various species of cardoon Cynara L. Milchwissenschaft, 56:16–19 Hang,F., Peiyi, L., Qinbo, W., Jin, H., Zhengjun, W., CaixiaGao, Z. L., Hao, Z. and Wei, C. (2016). High Milk- Clotting Activity Expressed by the Newly Isolated Paenibacillus spp. Strain BD3526. Molecules, 21(73):1-14Hashim, M. M., Mingsheng, D., Iqbal, M. F. and Xiaohong, C. (2011). Ginger rhizome as a potential source of milk coagulating cysteine protease. Phytochemistry,72:458–464 He, X., Zhang, W., Ren, F., Gan, B. and Guo, H. (2012).Screening fermentation parameters of the milk- clotting enzymeproduced by newly isolated Bacillus amyloliquefaciens D4 from the Tibetan Plateau in China.Ann. Microb.,62: 357–365.Hou-Pin, S., Mei-Ju, H. and Han-Tsung, W. (2009). Characterization of ginger proteases and their potential as a rennin replacement J. Sci. Food Agric., 89: 1178–1185Job, N. and Philemon, L. M. (2013).Efficiency of Ginger Production in Selected Local Government Areas of Kaduna State, Nigeria, International Journal of Food and Agricultural Economics, 1(2): 39-52 KADP (Kaduna State Agricultural Development Project).(2004). Annual Report. Kaduna State Agricultural Development Project, Kaduna. Kaduna State Ministry of Agriculture (2007).A Survey Report on Ginger Production.Kaduna State Agricultural Development Project, Zone IV, MOA, Kaduna 1-10.Kim, M., Hamilton, S. E., Guddat, L. W. and Overall, C. M. (2007). Plant collagenase: unique collagenolytic activity of cysteine proteases from ginger. Biochimicaet Biophysica Acta. 1770: 1627-1635.Malik, M. H., Mingsheng, D., Muhammad, F. I., Wang, L. and Xiaohong, C. (2011).Ginger protease used as coagulant enhances the proteolysis and sensory quality of Peshawari cheese compared to calf rennet. Dairy Sci. and Technol, 91:431–440Nafi, A., Foo, H. L., JamilahB.and Ghazali, H. M. (2014).Partial Characterization of an Enzymatic Extract from Bentong Ginger (Zingiber officinalevar.Bentong).Molecules,19:12336-12348Nafi, A., Foo, H. L., Jamilah, B. and Ghazali. H. M. (2013). Properties of proteolytic enzyme from ginger (Zingiber officinale Roscoe). International Food Research Journal,20(1): 363-368Naveena,B.M.andMendiratta,S.K.(2001). Tenderization of spent hen meat using ginger extract.British Poultry Science.42:344-350.Qiao, Y., Tong, J., Wei, S., Du, X. and Tang, X. (2009). Computer and computing technologies in agriculture II; InIFIP International Federation for Information Processing; Li, D., Chunjiang, Z., Eds.; Springer: Boston, MA, USA, Pp. 1619–1628.Skelte, G. A. and Henning, K. (1997).Heat-Induced, pH-Dependent Dissociation of Casein Micelles on Heating Reconstituted Skim Milk at Temperatures below 100°C. J. Agric. Food Chem., 45 (4):1108- 1115Sousa, M. J. and Malcata, F. X. (1997).Comparison of plant and animal rennets in terms of microbiological, chemical, and proteolysis characteristics of ovine cheese. J. Agric. Food Chem. 45:74–81



Sousa, M. J. and Malcata, F. X. (1998).Proteolysis of Ovine and Caprinecasiens in solution by enzymatic extract from flowers of Cynaracardunculus. Enzyme and Microbial Technology, 22(5):305-314Su, H. P., Huang, M. J. and Wang, H. T. (2009).Characterization of ginger proteases and their potential as a Rennin replacement. Journal of the Science of Food and Agriculture, 89: 1178-1185.Thompson, E. H., Wolf, I. D. and Allen, C. E. (1973). Ginger rhizome: a new source of proteolytic enzyme. Journal of Food Science, 38: 652-655.Tsuchida, O., Yamagota, Y. and Ishizuka, J. (1986). An Alkaline Proteinase of an Alkalophilic Bacillus spp. Curr Microbiol.,14: 7-12Vishwanatha, K., Rao, A. A. and Singh, S. A. (2010).Acid protease production by solid-state fermentation using Aspergillusoryzae MTCC 5341: Optimization of process parameters. J. Ind. Microbiol. Biotechnol,37: 129–138. Zhang, P., Huang, X. and Liu, X. (1999).Studies on the Milk Clotting of Ginger Juice.China Dairy Industry,27 (5):17-19



PHENOTYPIC VARIABILITY OF FALSE SESAME (CERATOTHECA SESAMOIDES ENDL.) TREATED WITH SODIUM AZIDE

Aliyu, R.E, Aliyu, A and Adamu, A.K.Department of Botany, Faculty of Life Science, Ahmadu Bello University, Zaria, Nigeria.

Corresponding Email: [email protected], [email protected]


ABSTRACTThe mutagenic efficiency and effectiveness of sodium azide to induce genetic variabilityin false sesame (Ceratotheca sesamoides) was evaluated with the aim of obtaining beneficial mutants. The seeds of false sesame were subjected to four concentrations (0.5mM, 1.0mM, 1.5mM and 2.0mM) of sodium azide. Treated and untreated seeds were sown on the field. Harvested M1 false sesame seeds were sown to raise the M2seedlings.The sodium azide concentration of 1.0mM significantly (p<0.05) induced benefitial variabilities on the agronomic traitsevaluated at M1 and M2 generation of false sesame. The mutagenic effectiveness, efficiency and mutation frequency of sodium azide were not obtained due to the absence of chlorophyll deficient mutants. Lethality due to mutagen was observed not to be dose dependent. Broad sense heritability estimates for the agronomic traits evaluated ranged from 2.14% to 92.01%. High heritability values recorded for and days to flowering (92.10%), thousand seed weight (75.00%), height at maturity (63.84%) and leaf area (60.35%) broadens the scope for improving false sesame via selection. Results are further discussed to validate the potential of this mutagenic treatment on false sesame breeding and selection.

Keyword: False sesame, Heritability, Mutation, Mutagens, Sodium azide



INTRODUCTION Across the world, most indigenous plant species that are cultivated for food are neglected and underutilized. These plants play a crucial role in the food security, nutrition, and income generation of the rural farmers (Magbagbeola et al., 2010). One of such underutilized crop is false sesame (Ceratotheca sesamoides). It has a chromome number of 2n=32 (Bedigen, 2004), belong to the family Pedaliaceae which has 16 genera and 60 species predominantly in Africa, Indo-Malayan region, and tropical Australia (Falusi et al., 2002). It is known by various names such as Eku (Yoruba-Western Nigeria), Tchaba-laba (Guinea Bissau), Lalu-caminho (Senegal) and False Sesame (English) (Adegoke et al., 1968) and is colloquially referred to as false sesame owing to its marked similarities with common sesame (Sesamum indicum). In northern Nigeria it is commonly called karkashi.Itis one of the Traditional Leafy Vegetables (TLVs) whose leaves, young shoots and flowers are acceptable for use as vegetable (FAO, 2006). It is a source of protein, vitamins and minerals (Dansi et al., 2009) with the dry seed yielding 35 percent oil with characteristics practically identical to those of sesame oil (LePelly, 1959). False sesame shows strong antibacterial activity (Maikere-Faniyo et al., 1989). Its leaf maceration also facilitates delivery in both humans and animals (Bedigian and Adetula, 2004), the leaves when ground with the rhizome of ‘Anchomanes difformis’ can be applied in cases of leprosy (Bedigian and Adetula, 2004). It has also been reported to be used as an aphrodisiac, against jaundice, snakebites and skin diseases (Bedigian, 2003).Despite the medical, pharmaceutical, cultural and commercial values of false sesame, they have been poorly researched because the architectures of false sesame are poorly adapted to modern farming system due to their indeterminate growth habit, sensitivity to wilting under intensive management and seed shattering at maturity (Uzun and Cagirgan, 2006). They are also faced with low seed yield which has been attributed to lack of agricultural inputs such as improved varieties, poor management and lack of appropriate breeding programmes (Pham et al., 2010).This has led to the deterioration in the vast reservoir of wealth of this plant (Attere,1999) with the danger of continued genetic erosion and disappearance. Hence, this research was aimed at determining the phenotypic and genetic variabilities induces by sodium azide towards producing beneficial mutation in false sesame and utilization of these mutants in breeding programmes.

MATERIALS AND METHODS This study was conducted at the Botanical garden of the Department of Botany, Ahmadu Bello University, Zaria, (lat. 11o12’N, long 7o 33’E and on altitude 660m above sea level). Twenty five grams (25g) of false sesame seeds wereobtained and identified from Jigawa State Agricultural and Rural Development Authority (JARDA), Ringim, Jigawa. Sodium azide (made in Kem light laboaratory PVT, LTD Mumbai, India) wasobtained from the store of Department of Biological Sciences, Ahmadu Bello University, Zaria.False sesame seeds were treated (5g for each mutagenic treatment) with four concentrations (0.5, 1.0, 1.5 and 2.0mM) of Sodium azide for 4 hours. The control (0.0Mm) was not exposed to the mutagen. Exposed seeds were thoroughly washed with distilled water and were left to dry for 24 hours. The experiments were laid in a randomized complete block design (RCBD) with 4 replications. Each replication was laid out on a field size of 1.5m by 0.75m with a row to row and plant to plant distance of 30 and 15 cm respectively (Mensah and Tope, 2007).Harvested seeds from M1 generation were sown to raise the M2 generation.All cultural practices were done as described by Bedigian and Adetula (2004).Data were collected at both M1 and M2 generations for the following growth parameters according to the method described by Nura et al. (2014) Germination percent was determined on the seventh and fourteen days after planting (7 and 14 DAS) when the plumule completely emerged out of the soil by counting the number of plants that germinated per treatment divided by the six seeds planted and multiply by hundred. Seedling height was taken 30 days after sowing using meter rule in centimeters per treatment. The height was determined by holding the highest leaves erect and recording the highest point of the highest leaf from the soil level or base of the shoot and averaged over three (3) plants. Number of Days to 50% flowering was taken per treatment when 50% of the plants in each treatment produced flowers.Height at Maturity (cm) was recorded in centimeters using meter rule by recording the height from soil level or base of the shoot to the tip of the highest leaf and averaged over three (3) plants. Plants were considered matured after the emergence of the first flower. Survival Rates (%) was recorded by counting the number of plants that survived per treatment and recordedafter the plants have attained 50% flowering.Number of Leaves per Plant was determined by the number of leaves per plant in each treatment and recorded after the plants have attained 50% flowering and averaged over three



(3) plants.Internodes Length (cm) was measured as the lengths between two successive leaves per plant in each treatment by the use of meter-rule after the plants produced podsand averaged over three (3) plants. Leaf Area (cm2)was determined by measuring the length and width of randomly selected leaves and applying the formula outlined by Pearce et al. (1979): A= [(L) (W) (0.75)]× 2 Where: A=Leaf Area per plant, L=Length of Leaves

and W=Width of leaves.

The number of pods produced per plant per treatment was counted and and averaged over three (3) plants. The Number of Seeds per Pod was done by breaking four (4) pods from four (4) plants per treatment and the number of seeds produced per pod counted and averaged over the three (4) plants.Thousand seeds weight (g) was measured by counting 1000 seeds per treatment and measuring the weight using a Sartorius electronic weighing balance (model: cp8201).Dry Weights (g) was determined for all the treatments after the plants are uprooted and dried in the oven for three days at 70 0C. Their dry weights were taken using a Sartorius electronic weighing balance (model: cp8201) per treatment.The number of seedlings that showed chlorophyll deficiency was identifiedat M2based on the foliar coloration and recorded (Giri and Apparao, 2011). The mutagenic efficiency and effectiveness were calculated by adopting the formulae recommended by Konzak et al. (1965), where:Mutation frequency (%) = Chlorophyll mutants at M2x 100 Total number of plants studied

Mutagenic effectiveness(%) =Mutation frequencyx 100 Dosage or time x concentration

Mutagenic efficiency(%) =Mutation frequency Percentage lethality

Morphological data on growth biometrics were analyzed statistically by analysis of variance (ANOVA) using SAS statistical software version 9.1 and treatment effects were compared using Fisher’s protected least signigicant difference (LSD) test at p<0.05.Broad sense heritability (HB) was computed at M2as specified by the method of Singh and Chaudhary (1985) and Moll et al., (1960): HB= (δ²g )/δ²pWhere:HB= Broad sense heritability, δ2g=Genotypicvariance, δ2p=Phenotypicvariance

RESULTS AND DISCUSSIONThe mutagenic efficiency, effectiveness, lethality percentage of sodium azide on false sesame is presentedin Table 1. Mutation frequency, mutagenic efficiency and effectivnes were not evaluated due to the absence of chlorophyll deficient mutants. Lethality in the mutants was not dose dependent. However, lethality induced by sodium azide in false sesame was highest (14.60%) at a concentration of 0.5mMand lowest (8.25%) at a concentration of 1.5mM. The treatment of false sesame with sodium azide at M1 improved its agronomic traits (Table 2) except for the number of seeds per pod which was not significantly different across treatments. Seeds treated with 1.0mM sodium azide showed better improvement in morphological traits compared to other treatments. Most M2 mutants showed better improvements in agronomic traits compared to the control (Table 3). However, there was no significant improvement in seedling heights, internode length and number of seeds per pod between mutants and control. The germination percentage at 7 DAS (77.10%) was highest in the control treatment. At 14 DAS, germination percentage was highest in seeds treated with 0.5mM sodium azide. At this concentration traits like survival rate, number of leaf per plant, number of pod per plant, thousand seed weight and dry weight were highest compared to other treatments. Height at maturity and leaf area were significantly higher (p<0.05) in mutants treated with 1.0mM of sodium azide, while days to flowering was lowest (70.25) at this concentration. The agronomic traits of mutants treated with 1.5mM and 2.0mM concentration of sodium azide were in most cases comparable to the control treatment. The comparative responses of Ceratotheca sesamoides treated with sodium azide at M1 and M2are presented in Table 4. There was no comparative advantage of the M1 false sesame mutants over the M2 mutants.however, the germination percentages at 7 and 14 days after seeding were significantly higher in M1 mutants (Table 4).

Table 1: Mutagenic Frequency, Efficiency and Effectiveness of Sodium Azide in False sesame

Conc/ dose MF (%) LT(% ) ME(%) Me(%)

0.5mM 0.00 14.60 0.00 0.001.0mM 0.00 10.45 0.00 0.001.5mM 0.00 8.25 0.00 0.002.0Mm 0.00 10.65 0.00 0.00



TABLE 2: Mean Performance of Ceratotheca sesamoidesat M1 Generation induced by Sodium AzidePlants CON GPSD(%) GPFD(%) SH(cm) DF HM(cm) SR(%) NLPP LA(cm2) IL(cm) NPOD NSPP THSWT(g) DW(g)

0.0mM 70.83 62.48 10.75 80.75 59.75 37.48 44.00 23.33 5.23 52.00 47.50 3.01 38.78 0.5mM 70.83 66.28 14.38 70.00 76.00 37.50 53.25 44.75 5.70 54.75 54.00 3.20 64.60 1.0mM 81.28 75.03 11.38 76.50 66.86 41.68 50.50 44.75 5.85 103.00 57.75 3.30 54.30 1.5mM 70.83 62.50 12.13 75.00 64.50 39.60 45.50 29.10 4.98 64.25 56.00 3.04 59.15 2.0mM 66.67 68.73 11.13 73.75 72.25 39.60 46.75 24.03 5.25 58.00 55.00 3.05 48.60 LSD (P<0.05) 7.24 7.55 2.21 7..50 5.70 4.12 3.85 8.00 0.77 26.34 11.01 0.11 9.62

Key: GPSD- Germination % 7 days after sowing, GPFD- Germination % 14 days after sowing SH- Seedling height,DF- Days to 50% flowering , HM- Height at maturity, SR- Survival rate, NLPP- Number of leaves per plant, LA- Leaf Area, IL- Internode length , NPOD- Number of pod per plant, NSPP- Number of seeds per pod, THSWT- Thousand seeds weight , DW- Dry weight of the plants, CON- Concentration of sodium azide TABLE 3: Mean Performance of False sesame at M2 Generation induced by Sodium AzidePLANTS CON GPSD(%) GPFD(%) SH(cm) DF HM(cm) SR(%) NLPP LA(cm2) IL(cm) NPOD NSPP THSWT(g) DW(g) 0.0Mm 77.10 50.00 13.43 72.25 83.75 37.50 42.75 43.15 4.90 35.25 56.75 3.00 38.83 0.5Mm 64.58 62.50 13.78 73.25 86.25 47.90 52.25 62.80 5.38 48.25 64.00 3.35 58.08 1.0mM 58.35 54.20 14.68 70.25 100.75 43.75 50.00 77.60 5.63 42.25 59.50 3.30 51.13 1.5Mm 58.35 52.00 13.63 73.75 85.75 43.75 44.75 43.98 5.38 44.75 62.75 3.24 43.90 2.0Mm 58.35 52.00 13.55 75.00 85.00 41.65 45.00 58.15 5.25 40.25 57.00 3.20 47.80 LSD p<0.05) 11.24 10.80 3.60 12.50 13.50 6.20 9.33 15.56 2.81 7.32 14.44 0.11 13.26

Key: GPSD- Germination % 7 days after sowing, GPFD- Germination % 14 days after sowing SH- Seedling height, DF- Days to 50% flowering , HM- Height at maturity, SR- Survival rate, NLPP- Number of leaves per plant, LA- Leaf Area, IL- Internode length, NPOD- Number of pod per plant, NSPP- Number of seeds per pod, THSWT- Thousand seeds weight of plants, DW- Dry weight of the plants, S.E- Standard error, Con: concentration of siodium azide.

Table 4: Combined Performance of False sesame treated with Sodium Azide at M1 and M2 GenerationPLANTS GENS GPSD(%) GPFD(%) SH(cm) DF HM(cm) SR(%) NLPP LA(cm2) IL(cm) NPOD NSPP THSWT(g) DW(g)

M1 67.08 67.08 11.95 74.40 67.88 39.17 48.00 28.43 5.40 66.40 55.25 3.14 53.09

M2 63.35 53.64 13.81 72.9 88.30 42.91 46.95 57.14 5.31 42.15 60.00 3.18 47.95

LSD (p<0.05) 3.94 13.10 2.60 10.67 28.65 9.48 11.05 30.00 2.12 15.34 8.87 0.73 7.42

Keys: GPSD- Germination % 7 days after sowing, GPFD- Germination % 14 days after sowing SH- Seedling height,DF- Days to 50% flowering ,HM- Height at maturity, SR- Survival rate, NLPP- Number of leaves per plant, LA- Leaf Area, IL- Internode length , NPOD- Number of pod per plant, NSPP- Number of seeds per pod, THSWT- Thousand seeds weight , DW- Dry weight of the plants, S.EM-Standard Errorof Mean, Gens: Generation Genetic Variation and Heritability of M2False sesame induced by Sodium azide

The estimation of genotypic, environmental, phenotypic variance and broad sense heritability (H2) for traits evaluated at M2 generation in Ceratotheca sesamoides treated with sodium azide are presented in table 5.The results indicated that the estimates of most of the environmental variance were greater in magnitude compared to the corresponding genotypic variance.Broad sense heritability was height for days to flowering (92.10%). High heritability values werealso recorded for and thousand seed weight (75.00%), height at maturity (63.84%) and leaf area(60.35%).

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Table 5: Variance Component Estimates for Sodium Azide at M2 Generation of Ceratotheca sesamoidesTraits δ2g δ2e δ2ph h2 (%)GPSD(%) 32.35 394.96 427.31 7.57GPFD(%) 51.61 313.43 365.04 14.14SH(cm) 0.01 1.0 1.00 1.00DF 3.11 0.27 3.38 92.01HM(cm) 43.21 24.47 67.68 63.84SR(%) 32.39 186.71 219.10 14.78NLPP 10.58 21.38 31.96 33.10LA (CM2) 176.23 115.77 292.00 60.35IL(cm) 0.01 0.30 0.30 33.3NPOD 17.24 26.23 43.47 39.66NSPP 1.04 47.54 48.58 2.14THSWT(g) 0.03 0.01 0.04 75.00DW(g) 16.96 144.44 161.40 10.51

δ2g- Genetic Variance, δ2e- Environmental Variance, δ2ph- Phenotypic Variance, h2- Heritability, GPSD- Germination % 7 days after sowing, GPFD- Germination % 14 days after sowing SH- Seedling height,DF- Days to 50% flowering , HM- Height at maturity, SR- Survival rate, NLPP- Number of leaves per plant, LA- Leaf Area, IL- Internode length , NPOD- Number of pod per plant, NSPP- Number of seeds per pod, THSWT- Thousand seeds weight , DW- Dry weight, S.EM-Standard Error Mean. Mutagenic effectiveness is an index of the response of a genotype to the increasing doses of the mutagen, whereas mutagenic efficiency indicates the extent of genetic damage recorded (Wani, 2009). The absence of chlorophyll deficient mutants in false sesame may be due to the fact that oil seed crops are resistant to induced chlorophyll mutations as also



reported by Bharathi et al, (2014). It may be further attributed to the probable reasons of the elimination of gametes carrying mutations or zygote inviability.This also explains the lethality variations observed with increasing concentration of the mutagen. High lethality at low concentration might be due to greater damage caused by the mutagens, thus prohibiting the plants to express the induced mutation successfully. This result is not in conformity with the work of Konzak et al,(1965)who reported that injuries are low when concentration of mutagenic treatment is low. The improvement in morphological traits of M1

false sesame at low concentration ofsodium azide could be due to the alteration of their genome integrated by environmental signals as reported by Uno et al, (2001) probably by increasing the rates of cellular division and expansion at their meristematic regions. This was in agreement with the findings of Hoballah (1999) who reported increase in morphological traits such as plant height due to mutagenesis. The improvement in number of leaves per plant, leaf area, number of pods per plants and thousand seed weight compared to the control at M2

at 1.0mMconcentration of sodium azide could be as a result of environmental factors (Udensi, 2012).The reduced germination percentage at second mutant generation could be as a result of toxic nature of the mutagen that cause damage on the embryo of the seeds (Devi and Mullainathan, 2012). This finding agrees with the work of Samiullah et al. (2004). who reported that all the mutagenic treatments brought reduction in seed germination when two mungbean varieties, K-851 and Pusa Baisakhi were treated with sodium azide. Meanwhile the improvement in number of leaf per plants, number of pod per plant, thousand seed

weight and dry weight at lowconcentration of sodium azide could be due to promotion of physiological and biological processes necessary for growth of the plant which includes enzyme activity. This is in conformity with the work of Bind et al.(2016) who reported that low concentration of mutagen improve biological parameters.The higher germination percentage of the M1 mutants over the M2 mutant generation might be because mutations are recessive therefore variations can only be expressed in the M2 generation of the false sesame after segregation might have occurred during meiosis in the M1 generation. This is in agreement with the work of Samiullah et al. (2004) who reported that the two varieties of mung bean showed significant shift in mean values for quantitative characters in M2 and M3 generations Phenotypic heritability induced by environmental variance for most traits indicates the influence of environmental effect on the mutants. Grace et al. (2014) in their work on Fadherbia albida reported that phenotypic variance was greater than genotypic variance for seed length and width. The high genetic variance for thousand seed weight and days to flowering in sesame indicates a highly significant effect of the genotype on phenotypicexpression for these traits with very little effect of environment. However, high heritability for these traitsshows that variation for these characters is due to high additive gene effects and consequently increases the scope for the improvement of false sesame through selection.In conclusion, sodium azide best improved the agronomic traits of false sesame at a sodium azide concentration of 1.0mM. High genetic heritabilities recorded for days to 50% flowering and thousand seeds weight, height at maturity and leaf area broadens the scope for improving false sesame via selection.

REFFRENCESAdegoke, E., Akinnsanya, A. and Nagu, A. (1968). Studies of Nigerian medicinal plants. Journal of West African Science Association., 13:13-39.Attere, A.F. (1999). Preface In: Chweya J.A. and Eyzaguirre P.B. (Eds.). The Biodiversity of Traditional Leafy Vegetables: IPGRI Rome Pp 6-7.Bedigian, D. (2004). Sesamum radiatum Thonn.ex Hornem. In: G.J.H. Grubben and O.A. Denton (eds.), PROTA2:Vegetables. PROTA, Foundation, Wageningen, the Netherlands/Backhuys Publishers, Leiden, the Netherlands/CTA, Wageningen, the Netherlands, Pp. 465-467.Bedigian, D. (2003). Evolution of sesame revisited: domestication, Diversity and prospects, Genetic Resources and Crop Evolution, 50:779-787.Bedigian, D. and Adetula, O.A. (2004). CeratothecasesamoidesEndl. In: Grubben, G.J.H. and Denton, O.A (Editors). PROTA 2: Vegetables/Légumes. PROTA, Wageningen, Netherlands.Bharathi, R.,Ganesamurthy, K., Angappan, K. and Gunasekaran, M. (2014).Mutagenic Effectiveness and Efficiency of Gamma Rays in Sesame (Sesamum indicum L.) Global Journal of Molecular Sciences 9 (1): 01-06,Bind, D., Dwivedi, V. K. and Singh, S. K. (2016). Induction of Chlorophyll Mutations through Physical and Chemical Mutagenesis in Cowpea (Vigna unguiculata (L.) Walp.). International Journal of Advanced Research, 4 (2): 49-53.



Dansi, A., Adjatin A., Adoukonou-Sagbadja, H., Adomou, A., Faladé, V.,Yedomonhan, H. Akpagana, K.and de Foucault, B. (2009). Traditional leafyvegetables in Benin: Folk nomenclature, species under threat and domestication. Acta Botanica Gallica, 156 (2):183-1999.Devi, S.A. and Selvakumar, G. (2012). Chemical mutagens induced alterations in chlorophyll mutants and flower development of chilli (Capsicum annuum L.). International Journal of Modern Agriculture,2: 39-42.Falusi, O.A, Salako, E.A.and Funmi, F.M. (2002). Inheritance of hairiness of stem and petiole in a selection from local (Nigeria) germplasm of sesame. Tropicultura, 20:156-158FAO (Food and Agricultural Organization), 2006: FAO statistical data base for food crops. Giri, S. P. and Apparao, B. J. (2011). Studies on effectiveness and efficiency of ethyl methannesulphate in pigeon pea. Bioscience Discovery, 2 (1): 55-98.Grace, k., Daniel, O., Anne, W.T.M., Martha, M., Mariuki, J., Mowo, G.J and Jammdars, R. (2014). Genetic Variability and divergence of seed traits and seed germination of five provenances of Fadherbia albida. African journal of Plant Sciences, 8 (11) 482-491.Hoballah, A.A. (1999). Selection and Agronomic evaluation of induced mutant lines of sesame. In:Induced Mutations for Sesame Improvement IAEA-TECDOC, IAEA, Vienna,Pp 71-84.Konzak, C.F., Nilan, R.A. Wagner, J. and Faster, R. J. (1965). Efficient chemical mutagenesis. In:use of induced Mutations in Plant Breeding. Radiation botany,5: 49-70LePelly, R.H.(1959). Agricultural Insects of East Africa.East African Common Services Organization. Nairobi, Kenya. Magbagbeola, J. A. O., Adetoso, J. A. and Owolabi, O. A. (2010). “Neglected and underutilized species (NUS): a panacea for community focused development to poverty alleviation/poverty reduction in Nigeria,” Journal of Economics and International Finance, 2(10) 208–211.Maikere-Faniyo, R.L., Van Puyvelde, A., Mutwew, I. and Habiyaremo, F.X. (1989). Study of the Rwandese medicinal plants used in the treatment of diarrhea. I. Journal of Ethnopharmocology, 26:101-109.Mensah, J. K., Obadoni, B. O., Akomeah, P. A, Lkhajiagbe, B. and Ajibolu, J. (2007). The effect of sodium azide and colchicine treatments on morphological and yield traits of sesame seed (Sesame indicum L.). African Journal of Biotechnology, 6(5): 534-538. Moll, R.H., Robinson, F.H. and Cockerham, C.C. (1960) Genetic variability in advanced generation of across of two open-pollinated varieties of Corn. Agronomy Journal, 52:171-173.Nura, S., Adamu, A.K., MuAzu, S., Dangora, D.B and Shehu, k. (2014). Assessment of the growth responses of Sesame (Sesamun indicum L.) and false sesame (Ceratotheca sesamoidesEndl.) to colchicine treatment. American Journal of Experimental Agriculture,4: 902-912.Pham, D.T, Nguyen, T.T.D., Carlsson, A.S. and Bui, M.T. (2010). Morphological evaluation of sesame (Sesamumindicum L.) varieties from different origins. Australian Journal of Crop Science, 4(7), 498- 504.Samiullah, K., Wani, M.R. and Parveen, K. (2004). Induced genetic variability for quantitative traits inVigna radiate (L) wilczek. Pakistan Journal of Botany,36:845-850.Statistical Analysis Software/SAS (2004): SAS/STAT 9.1 User’s guide. SAS Institute Inc.,Cary, NC.Udensi, O., Edu, E.A., Ikpeme, E.V., Ebigwai, J.K. and Ekpe, D.E.(2012a). Biometrical evaluation and yield performance analysis of cowpea ( Vigna unguoiculata L) walp) landraces grown under lowland tropical condition. InternationaJournal Plant Breeding and Genetics, 6:47-53Uno G, Storey, R. and Moore, R. (2001).Principles of Botany.Mc Graw Hill New York Pp1-550.Uzun, B., and Cagirgan, M.I. (2009): Identification of molecular markers linked to determinate growth habit in sesame. Euphytica,166(3): 379-384.Wani, A.A. (2009). Mutagenic eff ectiveness and efficiency of gamma rays, ethyl methane sulphonate and their combination treatments in cowpea (Cicer arietinum L.). Asian Journal Plant Science, 8(4):318 - 32.



PROTEIN CONTENTS OF MAIZE VARIETIES AS INFLUENCED BY NITROGEN AND MICRONUTRIENTS

1 Olowookere, B.T., 2 Uyovbisere, E.O., 2 Malgwi, W.B. and 3 Oyerinde, A.A.1 Department of Soil Science, University of Abuja, Abuja

2 Department of Soil Science, Ahmadu Bello University, Zaria3Department of Crop Science, University of Abuja, Abuja

Corresponding Email: [email protected],


ABSTRACTField experiments were conducted in 2008 and 2009 in the Guinea Savanna ecology of Nigeria to investigate the protein content of maize varieties (Quality Protein Maize QPM and normal varieties) as influenced by nitrogen fertilizer and micronutrients. The treatments were four rates of inorganic fertilizer N (0, 50, 100, 150kgNha-1) and two rates of cocktail micronutrient (Fe, Zn, B, Mo, and Cu). These were tested in a Randomized Complete Block Design with three replications and the treatments were laid out in factorial design.The results from the study revealed that micronutrients rate of 22.85g/ha applied increased the lysine and tryptophan content of the QPM varieties.The result also showed that addition of nitrogen fertilizer and micronutrients increased the crude protein content of the maize varieties and so also with micronutrients addition the QPM varieties differed significantly from each other with respect to lysine and tryptophan contents (P< 0.05). It can be inferred from this that though normal maize and QPM varieties could be exposed to the same environmental conditions and take up same amounts of micronutrients, the QPM varieties have genetic capacity to synthesize high levels of amino acids and so would have nutritionally higher quality grains. Plant breeders therefore may find this attribute useful in genetic manipulation and cultivar development to enhance protein biochemical components.

Keywords: Protein Content, Quality Protein Maize, Normal Maize, Guinea Savannah



INTRODUCTIONImproving nutritional quality of agricultural crops is a noble goal, which is particularly important in cereal crops where meeting protein nutrition needs is one of the greatest challenges because plants tend to be low in protein and the protein is of poor nutritional quality (Vassal, 2006). The nutritional quality of maize is determined by the amino acid make up of its protein. Proteins are linear polymers built of monomer units called amino acids, which contain a wide range of functional groups which include alcohols, carboxamides, carboxylic acid, thioethers and a variety of basic groups (Berg et al., 2001). The predominant protein in maize is the alcohol soluble prolamins protein called Zein. Zein stores N, C, S and other nutrients butis characterized by low levels of lysine and tryptophan (Biston et al.,1996). Nitrogen (N) is an important plant nutrient and is the most frequently deficient of all nutrients in tropical soil systems. This is because of the relatively large amount required by plants and its high mobility in the soil. Nitrogen is of particular interest because it is usually the most limiting nutrient for crop production while fertilizer N represents a major variable input cost (Gil and Fick, 2001). On the other hand, the micronutrients are absolutely essential and also play an active role in gene expression, biosynthesis of protein, nucleic acids, plant metabolism processes starting from cell wall development to respiration, photosynthesis, chlorophyll formation, enzyme activity, nitrogen fixation and reduction (Bishnu et al., 2010). Micronutrients are becoming increasingly important to world agriculture as crop removal of these essential element increases (Adhikary et al., 2010). Maize is assuming the position as the major crop of the sub-humid and semi-arid savanna with respect to economic prospects for the farmers and being a staple food crop in the ecological zone. However it is devoid of some major amino acids, such as lysine and tryptophan (Obi, 1982; Okai et al., 2005; Vassal, 2006). The development of QPM varieties improved the nutritional properties of maize and has given hope to many as a source of affordable protein for good health. However,the QPM varieties introduced to the Nigerian Savanna ecologies still have problems of adaptation when the levels of amino acid content that characterize the varieties are considered. Several studies have been conducted on maize especially the QPM varieties but this work wants to establish the effect of nitrogen and micronutrient levels on lysine and tryptophan content of maize varieties (convectional and quality protein maize).

MATERIALS AND METHODSThis study was carried out at Samaru, Zaria for two years (2008/2009 and 2009/2010) at the Institute for Agricultural Research (IAR) Experimental Farm in Samaru, Zaria, Nigeria (Longitude 11o 11’N and Latitude 7o 38’ E, at an elevation of 686m above Sea Level).The soil is classified as Alfisol in the USDA Soil Classification System and it is developed in deeply weathered pre-Cambrian, basement complex rock overlain by Aeolian drift materials of varying thickness (Moberg and Esu, 1989; Ogunwole, 2000).The main soil subgroup is typicHalplustalf (USDA Soil Survey Staff, 2006). The site was divided into three blocks each consisting of 32 plots, giving a total of 96 plots and each plot measuring 12m2.There were 4 ridges in a plot, 5m long at 0.75m x 0.25m spacing on a row.The experiment was laid out in a Randomized Complete Block Design with three replications and treatments were factorially combined. Two maize seeds were sown per stand and thinned to one per stand at two weeks after germination.Four rates of nitrogen fertilizer (0, 50, 100 and 150 kg/ha) was applied as Ureain 2 split doses at 2WAP and 4WAP. The micronutrients treatments (Fe, Zn, B, Mo, and Cu) were applied as cocktail of the mixture to half the number of plots at the rate of 22.85g/ha. Basal application of phosphorus and potassium were done at 60kg P2O5ha-1 as Single Super Phosphate (SSP), and 60kg K2Oha-1 Potash (MOP), (60%) respectively. All the fertilizers were applied at planting. In addition to the initial herbicide application to control weed, plots were manually weeded with hand hoe.Before 50% silking stage, leaf sampling was done. The index plant samples were oven-dried at 65oC for 48 hours and then ground in a mill and stored for tissue analysis. N, P, K and micronutrients (Cu, Fe, Zn, B and Mo) were analyzed. Analysis of the maize grain was carried out on the endosperm of the maize seed (open pollinated and the quality protein maize varieties) as follows: Random sample of 30 seeds were soaked in distilled water for 30 minutes. The pericarp was then peeled off and the germs were removed with scalpel and tweezers.The remaining endosperm was thereafter air-dried overnight and ground in a mill to fine powder and this was used for the determination of grain N, crude protein and the amino acids. The protein content of the leaves and grain sample was determined from total nitrogen and multiplied by a factor of 6.25.Data collected were subjected to statistical analysis using SAS statistical computer software (SAS, 2007). Analysis of Variance (ANOVA) was employed to determine significant differences between means while Duncan Multiple Range Test (DMRT) was used to compare treatments means.

PROTEIN CONTENTS OF MAIZE VARIETIES (QPM AND NORMAL MAIZE) AS INFLUENCED BY NITROGEN AND MICRONUTRIENTS


RESULTS AND DISCUSSIONTable 1 shows properties of the soil used for the experiment. The soil is sandy–loam in texture and very low in N and available P. Micronutrient contents indicated low to moderate. It is expected that the maize varieties would benefit from the added fertilizers. The effect of nitrogen fertilization on nitrogen concentration of the maize grain, crude protein, lysine and tryptophan content shows increase in nitrogen rates increased the concentration of N in the grain from the control (0kg Nha-1) to the highest level of nitrogen applied (150kg Nha-1). The combined analysis also showed that grain N increased as the nitrogen rates increased from the control to 150kg Nha-1 in the QPM varieties SAMMAZ 14 (2.78%) and SUSUMA (2.80%) while for the normal maize varieties (SAMMAZ 12 and SAMMAZ 11) though N increased to the highest rate of N applied (150kgNha-1) the N content was lower than it was for QPM varieties. SAMMAZ 12 and SAMMAZ 11 produced (2.52%) and (2.64%) nitrogen in the grains respectively (Table 2). The nitrogen concentration in the grain increased as amount of nitrogen increased from zero level to the highest level of nitrogen supplied (150kg Nha-1). This was supported by Thomison et al. (2004) who reported that grain protein concentration showed more consistence response to increasing nitrogen rates than did yield.The application of N at the rate of150kgha-1 gave maximum crude protein contents of 17.45% for SAMMAZ 14, (17.36%) for SUSUMA, (15.72%) for SAMMAZ 12 and (14.94%) for SAMMAZ 11 (Table 2) while the lysine and tryptophan contents of the maize varieties were combined for the two years, SUSUMA recorded lysine of 3.14%and tryptophan contents 0.72% at (100kgN) while SAMMAZ 14 had maximum lysine content of 3.06% and tryptophan content of 0.55% at (150kgN). SAMMAZ 11 had lysine content of 2.96% and 0.48% tryptophan and SAMMAZ 12 had maximum lysine content of 2.87% and tryptophan content was 0.43% both at the highest nitrogen applied (150kgN). The grain crude protein significantly increased with increase in nitrogen rates. Since N is a major constituent of protein, applying N fertilizer would enhance protein synthesis or build up in cereal grains like maize. The highest crude protein recorded in this work was 17.45% for SUSUMA (QPM) and 15.72% for SAMMAZ 11 (normal maize) these were high compared to 9.11% recorded by Osei et al. (1999) and Prasanna et al. (2001) for QPM. Also, Aduku (2005) reported a value of 8.0% crude protein for normal maize and QPM. The variation in the quantity and quality of the crude protein in the grain maize

could be attributed to the level of nitrogen in the soil since the level of nitrogen fertilizer influences the quantity and quality of protein in maize (Deosthale et al.,1972). The effect of micronutrients on nitrogen, crude protein, lysine and tryptophan contents of the maize grain revealed that SUSUMA (QPM) had N content of 2.74% with micronutrients application while SAMMAZ 14 (QPM) produced combined N content of 2.67% in the grain. The normal maize, SAMMAZ 12 (normal maize) recorded N content of 2.40% without micronutrients application while SAMMAZ 11 (normal maize) had 2.55% N in the grain with micronutrients application. The application of micronutrients increased the crude protein of the QPM varieties in such a way that SUSUMA had the highest crude protein of 17.09% followed by SAMMAZ 14 with 16.67% and SAMMAZ 11 had 15.93% all with micronutrients application while SAMMAZ 12 produced crude protein of 14.97% without micronutrients application. Also, QPM varieties SUSUMA had the lysine and tryptophan contents of 3.20% and 0.53%, SAMMAZ 14 had lysine and tryptophan contents of 3.01% and 0.49% and SAMMAZ 11 produced lysine and tryptophan contents of 2.93% and 0.47% all with micronutrients application. SAMMAZ 12 had lysine content of 2.84% and tryptophan content of 0.44% without micronutrients application (Table 3). It is obvious from these results that micronutrients application increased the lysine and tryptophan content of the QPM . This clearly suggests that QPM varieties had higher capacity to utilize applied micronutrients for the synthesis of the relevant amino acids. It can be inferred from this that though normal maize and QPM varieties could be exposed to the same environmental conditions and take up same amounts of micronutrients, the QPM varieties have genetic capacity to synthesize high levels of amino acids and so would have nutritionally higher quality grains. In addition, SAMMAZ 11 though, a normal maize responded to micronutrients application although at the highest application of N fertilizer which subsequently increased the protein content. This infers that the micronutrients content of the QPM varieties are similar to the normal maize. The effect of treatments on the grain parameters (Table 4) showed grain N generally seemed to increase with N rates in the QPM varieties and decrease in the normal maize varieties, with or without the application of micronutrients. SAMMAZ 14 had 2.54kgNha-1 and 2.53kgNha-1 nitrogen content with or without micronutrients application. SUSUMA had a nitrogen content of 2.90kgNha-1 and 2.77kgNha-1



with added micronutrients or without micronutrients application. The normal maize SAMMAZ 12 and 11 recorded 2.85 kgNha-1, 2.74 kgNha-1, 2.83 kgNha-1 and 2.68kgNha-1 with or without the application of micronutrients respectively. The results also showed that the crude protein content varied from 16.78% to 19.60% among the varieties. SAMMAZ 14 had crude protein content of 16.78% at 150kgNha-1 with micronutrient addition and had 14.27% at 150kg Nha-1without micronutrients. Also SUSUMA had the highest crude protein of 17.91% at 50kgNha-1 with no micronutrient addition, but had 19.60% at 50kgNha-1 with addition of micronutrients. SAMMAZ 12 had crude protein content of 18.32%with micronutrients and 17.23% without micronutrient at 150kgNha-1. For SAMMAZ 11, (normal maize) recorded the crude protein of 17.24% and 16.41%withand without micronutrients at150kgha-1N respectively. The mean crude proteins content among the varieties were between 14.45% - 18.90% for both years respectively (Table 4). The lysine and tryptophan contents of the different varieties were presented in Table 5. The lysine content was3.19% and 2.82% for SAMMAZ 14 with or without micronutrients. SUSUMA had highest lysine content of 3.30% and 3.24% with or without micronutrients with 50kgNha-1. SAMMAZ 12 recorded a lysine content of 3.02% and 2.89% with or without micronutrients while SAMMAZ 11 had lysine content of 3.20% and 2.93% with or without micronutrients respectively. The mean values for tryptophan in the two years were highly significant, P<0.05 with the highest value of 0.59% (SUSUMA) and the lowest value of 0.39% (SAMMAZ 14).The tryptophan content of 0.55% (100kgNha-1) and 0.43% (0kgNha-1) were recorded with or without micronutrients application by SAMMAZ 14 while SUSUMA variety had tryptophan content of 0.59% and 0.52% at 50kgNha-1with or without micronutrient application. SAMMAZ12 had tryptophan content of 0.46% (0kgNha-1) without micronutrients and 0.45% (150kgNha-1) with micronutrient applications. SAMMAZ11 recorded 0.50% and 0.50% (50kgNha-1) with or without micronutrients. In the study, SUSUMA (QPM) had highest crude protein, lysine and tryptophan contents with nitrogen fertilizer and micronutrients while SAMMAZ 14 performed better at no micronutrients but with optimal level of nitrogen. The conventional maize had protein, lysine and tryptophan contents at the highest application of nitrogen. SUSUMA, Quality Protein Maize had just lysine content of 1.23% and 11.21% with or without micronutrient better and tryptophan content of 3.85% and 15.25% better than SAMMAZ 11 a normal maize variety. This implies that giving the normal maize variety same environment by exposing them to same

management practices and soil factors can help the normal maize pick up more essential amino acids. The QPM and the normal maize differed significantly from each other with respect to lysine and percentage tryptophan (P<0.05). This probably suggests a high variability that exists in maize genotypes with respect to these biochemical components. Plant breeders may therefore find this attribute useful in genetic manipulation and cultivar development for enhanced protein biochemical components. Forages and some cereals other than maize support the view that nitrogen fertilization up to and beyond the point of maximum yield increases the concentration of nitrogen in the tissue. Lysine and tryptophan values in this study were comparable with Vassal (1993) who reported range of lysine content of 1.8- 2.0% and tryptophan content of 0.9-1.06%. Santayehu (2008) reported higher lysine content of 4.08 g/100g protein and tryptophan content of 0.75 g/100g protein in QPM. They reported lysine contents of 3.04 g/100g protein and tryptophan contents of 0.59 g/100g protein in the normal maize. SUSUMA had higher marginal protein, lysine and tryptophan contents with micronutrients application than the normal maize which infers that QPM cultivars had greater lysine and tryptophan contents than the normal cultivars and that lysine and tryptophan contents increased in both the QPM and normal varieties as the N level in the soil increased. This shows that given the set of conditions that influenced quality in QPM, the quality of the normal maize may be improved. This is consistent with the results of Pixley and Bjamason (1993) and Bhatnajar et al., (2003) who reported the superiority of QPM cultivars over non- QPM cultivars for protein quality. The contrast analysis showed no significant difference in all the parameters however this may indicate there is genotypic variation in grain protein content in both the QPM and the normal cultivars. Santayehu (2008) reported that the protein content of the kernels of corn increased with increasing nitrogen supply in the soil while Whitehouse (1971) also reported that the cause of high protein content in maize is a restriction on growth which is due to a shortage of water or some adverse condition during the later stages of grain-filling.Anonymous (2004) showed that there was a direct relationship between the soil and the nitrogen applied to the soil and the contents of crude protein, zein and leucine in maize grain. He concluded that variation in content of the amino acids suggest that nitrogen-fertilization in relation to plant population as well as variety has an important effect on protein composition. The contrast analysis showed no significant difference in all the parameters however this may indicate there is genotypic variation in grain protein content in both the QPM and the normal cultivars.



CONCLUSIONThe addition of nitrogen fertilizer and micronutrients increased the nitrogen and protein contents of the QPM. The crude protein recorded was higher in this study thanvalues earlier reported.SUSUMA had higher marginal protein, lysine and tryptophan contents with micronutrients application than the normal maize which shows that QPM cultivars had greater lysine and tryptophan contents than the normal cultivars and that lysine and tryptophan contents increased in both the QPM and normal varieties as the N level in the soil increased.SUSUMA, the better of the QPM had just lysine content of 3.19% and 5.08% tryptophan better than SAMMAZ 11 a conventional maize variety which implies that giving the normal maize variety the same environment and same management factors can help the normal maizeto pick up more essential amino acids. This shows the superiority of QPM cultivars over non- QPM cultivars for protein quality.

REFERENCESAdhikary, B. H., Shrestha, J. and Baral, B. R. (2010).Effects of micronutrients on growth and productivity of maize in acidic soil. International Research Journal of Applied and Basic Sciences. 1(1):8-15.Aduku, A. O. (2005). Tropical Feedstuffs Analysis Table Department of Animal Science A.B.U. Zaria, Nigeria. Anonymous (2004).Fertilizer News, 49 (9): 122. Berg, J. M., Tymoczko, M. and Clarke, N.D. (2001).Biochemistry.W.H. Freeman and company, 41 Madison Avenue, New York.(5th Edition).Bhatnagar, S., Betran, F.J. and Transue, D.K. (2003). Agronomic performance, aflatoxin accumulation and protein quality of subtropical and tropical QPM hybrids in southern U.S. Maydica 48: 113-124.Bishnu, H. A. Jiban, S. and Bandhu R. B. (2010). Effects of micronutrients on growth and productivity of maize in acidic soil.International Research Journal of Applied and Basic Sciences, 1 (1) 8-15.Biston, R.S., Kodrzyeki, R. and Laikufis, B.A. (1996).olecuar Biology of Seed Storage Proteins and lectins (eds). Shannon, L.M. and Crispeels, M.J.) An. Soc. Plant Physiologists, Rocksville Maryland, 117 – 126.Deosthale, Y.G., Nagarajan, V. and Visweswar Rao, K. (1972). Some factorsinfluencing the nutrient composition of sorghum grain. Indian J. Agric. Sci. 42:100-108.Gil, J.L. and Fick, W.F. (2001).Soil N mineralization of mixtures of eastern Gama grass with alfalfa and red clover. Agron J. 93: 902 – 910. Moberg, J.P. and Esu, I. E. (1989).Characteristic and composition of soils of Northern Nigeria. Report submitted to the Institute for Agricultural Research IAR, ABU, Zaria.Obi, J.U. (1982).Application of the 2, 4, 6 – Trinitro benzene 1-Silfonic acid (TNBS) method for determination of available lysine in maize seed. Agricultural and Biological Chem. 46: 1520.Ogunwole, J.O. (2000). The macro- environment of cereal based intercrop at Samaru, Northern Nigeria. Ph.D Thesis Ahmadu Bello University, Samaru- Zaria.Okai, D.B., Osei, S.A., Haag, W.L and Dzah, B.D. (2005).The role of Quality Protein Maize (QPM) in pig nutrition and production. Paper presented at the Sasakawa Global 2000 training workshop on QPM, Development and seed delivery system, Kumasi, Ghana 4th – 5th August 2005.Osei, S.A., Dei, K.K., and Tuah, A.K. (1999).Evaluation of quality protein maize as a feed ingredient for layer pullet. Journal of Animal Feed Science 8:181-189.Prasanna, R.M.; Vassal, S.K., Kassahum, B. and Sin.N.(2001). Review Article on Quality Protein Maize. Current Science 81:0:1308-1319.Pixley, K.V. and Bjarnason, M.S. (1993).Combining ability for yield and protein quality among modified endosperm opaque-2 tropical maize inbreds. Crop Science,33: 1229-1234.SAS Institute (2007): SAS system for Windows version 9.2. SAS Institute, Cary, NC, USA.Sentayehu, A. (2008): Protein, tryptophan and lysine contents in quality protein maize, North India. Ethiop. J. Health Sci., 18(2):9-15. Thomison, P.R., Geyer, A.B., Bishop, B.L., Young, J.R. and Lentz, E. (2004). Nitrogen fertility effects on grain yield, protein, and oil of corn hybrids with enhanced grain quality traits. Plant Management Network.USDA Soil Survey Staff (2006).Key to soil taxonomy.United State Department of Agriculture, Natural resources Conservation Services, 10th Edition, Pp.331.Vassal, S.K. (1993).Per cent lysine and tryptophan contents in grain protein.Crop Sci.,33: 51-57.Vassal, S.K. (2006): The quality protein maize story. Food and nutritional Bulletin, 21 (4): 45-50.Whitehouse, R.N.H. (1971). Variation in Protein and amino acid levels in barley in Jones, J.G.W. (1973). The biological efficiency of protein production.The Cambridge University Press, New York.



Table 1: Physico –chemical properties of the soil used for the study

Parameters Field Study 0-15 (cm) 15-30 (cm)Sand (gkg-1) 540 525Silt (gkg-1) 330 350Clay (gkg-1) 130 125Textural class Sandy-loam pHH20 5.70 5.60pHCaCl2 5.40 5.20Organic carbon (g kg-1) 5.20 5.00Total nitrogen (%) 0.06 0.07Available P (mgkg-1) 7.58 6.80Exchangeable acidity (cmolkg-1) 0.60 0.62Exchangeable bases (cmolkg-1) Calcium 3.98 4.50Magnesium 1.36 1.59Sodium 0.40 0.30Potassium 0.70 0.58Effective CEC (cmolkg-1) 2.60 3.45Micronutrients (mgkg1) Extractable Zinc 16.75 18.40Extractable Iron 52.00 45.50Extractable Copper 0.58 0.55Extractable Molybdenum 11.00 11.08Extractable Boron 0.10 0.11

Table 2: The effect of nitrogen on nitrogen, crude protein, lysineand tryptophan contents of different maize varieties grains

Variety Nitrogen Nitrogen in grains (%) Crude protein in grains (%) Lysine (%) Tryptophan (%) (Kgha-1) 2008 2009 Combined 2008 2009 Combined 2008 2009 Combined 2008 2009 Combined

SAMMAZ 14 0 2.14 2.79 2.46 13.39 17.41 15.40 2.85 2.66 2.76 0.47 0.38 0.42 50 2.28 2.95 2.62 14.22 18.41 16.32 3.14 2.78 2.96 0.45 0.42 0.44 100 2.42 2.95 2.69 15.13 18.59 16.86 2.73 2.99 3.00 0.49 0.47 0.44 150 2.45 3.09 2.78 15.31 19.33 17.36 3.19 2.93 3.06 0.53 0.47 0.55 Mean 2.32 2.97 2.64 14.51 18.50 16.51 3.04 2.76 2.87 0.50 0.41 0.45

SUSUMA 0 2.14 2.93 2.54 13.39 18.29 15.84 3.11 3.12 3.12 0.48 0.53 0.51 50 2.11 2.93 2.52 13.22 18.32 15.77 3.00 3.28 3.00 0.52 0.58 0.44 100 2.38 3.08 2.73 14.85 18.23 16.54 3.11 3.41 3.14 0.48 0.61 0.72 150 2.46 3.14 2.80 15.40 19.59 17.45 2.97 3.42 3.13 0.52 0.58 0.55 Mean 2.27 3.01 2.64 14.22 18.54 16.38 2.95 3.25 3.06 0.47 0.57 0.52

SAMMAZ 12 0 1.69 2.67 2.18 12.21 14.14 13.18 2.75 3.08 2.79 0.31 0.42 0.37 50 1.95 2.69 2.32 14.39 17.14 14.54 2.68 2.89 2.84 0.41 0.35 0.43 100 1.69 2.48 2.09 10.57 18.61 14.59 2.47 2.78 2.68 0.38 0.48 0.38 150 2.06 2.98 2.52 12.21 16.68 14.94 2.65 3.08 2.87 0.39 0.47 0.43 Mean 1.97 2.71 2.34 12.35 16.98 14.63 2.64 2.96 2.79 0.37 0.47 0.42

SAMMAZ 11 0 1.94 2.78 2.36 12.12 16.04 14.08 2.69 2.98 2.79 0.39 0.48 0.43 50 2.03 2.81 2.42 12.12 15.04 14.36 2.84 2.81 2.82 0.45 0.49 0.47 100 2.04 2.83 2.44 12.76 17.98 15.37 2.88 2.82 2.85 0.45 0.42 0.44 150 2.30 2.98 2.64 12.85 18.59 15.72 3.14 2.78 2.96 0.45 0.42 0.48 Mean 2.00 2.79 2.41 12.62 17.47 15.04 2.87 2.94 2.90 0.45 0.47 0.49

Mean 2.15 2.87 2.51 13.42 17.88 15.64 2.87 2.97 2.91 0.44 0.48 0.47SE+ 0.28 0.31 0.11 1.77 1.89 0.67 0.27 0.26 0.10 0.03 0.02 0.03CV(%) 22.81 8.74 21.19 22.82 18.37 20.89 16.51 15.35 16.83 11.63 8.50 32.03V*N NS NS NS NS NS NS NS NS NS ** * * **

ContrastQPM vs Normal NS NS NS NS NS NS NS NS NS NS NS NSQPMAvs QPMB NS NS NS NS NS NS NS NS NS NS NS NS



Table 3: The effect of micronutrients on nitrogen in grains, crude proteins, lysine and tryptophan content of the maize varieties

Micronutrients (gha-1) Nitrogen in grains Crude protein in Grains Tryptophan (%) Lysine (%)2008 2009 2008 2009 2008 2009 2008 2009

SAMMAZ 14 M+M

2.032.31

2.883.02

12.6714.45

18.0318.89

0.470.53

0.360.45

2.943.13

2.622.89

SUSUMA -M+M

1.912.39

2.663.08

11.9414.95

16.7719.23

0.430.51

0.590.54

2.833.07

3.163.33

SAMMAZ 12 -M+M

2.042.00

2.752.71

14.0812.53

17.7716.96

0.390.35

0.480.46

2.692.59

2.992.92

SAMMAZ 11 -M+M

2.112.25

1.992.85

13.0012.76

16.1817.18

0.420.47

0.470.46

2.792.94

2.962.92

MeanSE+CV(%)

2.150.2822.81

2.870.31

18.74

13.42 1.7722.82

17.881.8918.37

0.440.03

11.63

0.480.028.50

2.870.27

16.51

2.97 0.2615.35

V*N NS NS NS NS ** ** ** **

Table 4: The effect of treatments on nitrogen and crude proteins contents of different maize grainsVariety Ni Nitrogen (kgha-1) Micronutrients (gha-1) Variety Nitrogen (kgha-1) Micronutrients (gha-1)

Nitrogen in grains Crude protein in grains

2008 2009 Combined 2008 2009 Combined

-M +M -M +M -M +M -M +M -M +M -M +MSAMMAZ 14 0 2.68 2.97 2.01 2.10 2.35 2.07 16.77 18.59 12.57 13.48 13.03 16.04

50 3.06 2.71 2.04 1.84 2.48 2.28 18.23 16.93 12.76 11.48 14.67 14.21100 3.12 2.82 1.84 2.25 2.48 2.54 19.51 17.68 14.04 13.03 15.50 15.36150 2.80 2.33 2.25 1.84 2.53 2.54 17.50 14.58 11.04 11.48 14.27 16.78Mean 2.88 2.71 2.03 2.01 2.26 2.36 18.00 16.95 12.67 12.53 15.34 14.74

SUSUMA 0 2.92 3.04 2.13 2.08 2.53 2.56 15.68 18.59 12.58 11.85 14.13 15.2250 2.51 3.12 2.74 2.19 2.77 2.90 19.32 19.50 13.67 16.31 17.91 19.60100 2.51 2.83 1.43 1.95 1.97 2.39 15.68 17.68 8.93 12.21 12.31 14.95150 3.06 1.89 2.48 1.84 2.66 1.87 19.14 11.85 13.31 11.49 16.23 11.62Mean 2.75 2.66 1.91 2.02 2.33 2.34 17.18 16.78 11.94 12.62 14.56 14.23

SAMMAZ 12 0 3.06 2.51 1.87 2.42 2.47 2.47 19.14 15.68 11.66 15.13 15.40 15.4150 2.98 2.98 2.30 2.54 2.64 2.46 18.59 18.59 14.40 15.87 16.50 17.23100 2.77 3.12 2.48 2.07 2.63 2.60 17.32 19.50 15.49 12.94 16.41 16.20150 3.12 3.15 2.36 2.54 2.74 2.85 19.51 14.76 19.68 15.86 17.23 18.32Mean 3.08 2.84 2.25 2.39 2.67 2.62 19.23 17.77 14.08 14.95 16.64 16.36

SAMMAZ 11 0 2.77 3.08 1.72 2.57 2.25 2.39 17.32 19.27 10.75 16.04 14.04 17.6650 3.09 2.77 2.19 2.04 2.64 2.41 19.14 17.32 11.05 12.76 14.64 15.08100 3.05 3.12 2.30 2.45 2.26 2.83 16.95 19.51 14.39 13.67 15.67 17.01150 2.92 2.89 2.01 2.08 2.68 2.79 18.98 18.98 17.14 15.49 16.41 17.24

Mean 2.99 3.02 2.24 2.31 2.62 2.67 18.18 18.90 13.99 14.45 16.09 16.98Mean 2.92 2.81 2.11 2.18 2.52 2.50 18.15 17.50 13.17 13.64 15.66 15.62SE+ 0.11 0.12 0.09 0.11 0.10 0.12 0.55 0.77 0.55 0.69 0.55 0.74CV (%) 15.13 21.43 20.32 24.96 19.00 23.01 14.87 21.53 20.32 24.96 18.19 23.06ContrastQPM vs Normal NS NS * NS NS * NS NS ** NS * NSQPMAvs OPMB NS NS NS NS NS NS NS NS NS NS NS NS



Table 5: The interactive treatments on grain lysine and tryptophan contents of the maize varietiesVariety Nitrogen(kgha-1) Variety Nitrogen (kgha-1) Micronutrients (gha-1)

Lysine Tryptophan

2008 2009 Combined 2008 2009 Combined-M +M -M +M -M +M -M +M -M +M -M +M

SAMMAZ 14 0 3.00 3.38 2.63 2.66 2.70 2.85 0.49 0.61 0.37 0.38 0.43 0.5050 50 2.90 2.79 2.66 2.90 2.78 2.95 0.45 0.42 0.38 0.45 0.41 0.44100 100 2.90 3.01 2.50 3.36 2.82 3.19 0.45 0.49 0.32 0.61 0.39 0.55150 150 2.95 3.33 2.68 2.63 2.82 3.50 0.47 0.59 0.38 0.37 0.43 0.48

Mean 2.94 3.13 2.62 3.13 2.78 3.13 0.47 0.53 0.36 0.45 0.42 0.49SUSUMA 0 3.01 2.93 3.25 2.98 3.13 2.96 0.49 0.46 0.57 0.48 0.53 0.4750 50 3.19 3.03 3.41 3.44 3.24 3.50 0.55 0.49 0.62 0.53 0.52 0.59100 100 3.17 2.77 3.40 3.36 2.89 3.07 0.54 0.41 0.61 0.61 0.58 0.51150 150 2.90 2.60 3.20 3.17 3.05 3.29 0.45 0.36 0.55 0.54 0.50 0.45

Mean 3.07 2.83 3.32 3.24 3.20 3.04 0.51 0.43 0.54 0.59 0.55 0.49SAMMAZ 12 0 2.66 2.68 3.04 3.01 2.85 2.85 0.38 0.38 0.53 0.49 0.46 0.4450 50 2.77 2.58 3.01 2.87 2.75 2.73 0.41 0.35 0.49 0.53 0.45 0.44100 100 2.74 2.20 2.90 2.66 2.82 2.43 0.40 0.22 0.45 0.38 0.43 0.30150 150 2.60 2.90 2.90 3.14 2.89 2.02 0.36 0.45 0.45 0.44 0.41 0.45

Mean 2.69 2.59 2.96 2.92 2.83 2.76 0.38 0.35 0.48 0.46 0.43 0.41SAMMAZ11 0 2.63 2.74 3.01 2.95 2.82 2.85 0.37 0.40 0.49 0.47 0.43 0.4450 50 2.68 3.33 3.01 3.07 2.85 2.25 0.38 0.59 0.49 0.44 0.41 0.50100 100 2.87 2.88 2.77 2.87 2.82 2.88 0.44 0.45 0.39 0.44 0.42 0.45150 150 2.98 2.79 2.87 2.98 2.93 2.89 0.48 0.42 0.51 0.48 0.50 0.45Mean 2.79 2.94 2.92 2.97 2.86 2.96 0.42 0.47 0.47 0.46 0.45 0.47

Mean 2.99 2.17 2.87 2.87 2.89 2.92 0.44 0.52 0.44 0.44 0.46 0.48SE+ 0.11 0.09 0.10 0.10 0.11 0.09 0.01 0.06 0.01 0.02 0.01 0.04CV (%) 18.13 14.72 17.19 16.34 17.95 15.45 10.22 10.22 12.86 10.22 11.32 12.96 CONTRASTQPM vs Normal NS NS NS * NS * NS NS NS ** NS **QPMAvs QPMB NS NS NS NS NS NS NS NS NS NS NS NS




ROT OF SEED POTATO (SOLANUM TUBEROSUM L.) TUBERS AFFECTED BY STORAGE CONDITIONS AND STORAGE DURATION IN JOS, PLATEAU STATE,

NIGERIA

Deshi, K. E., Nanbol, K.K., Shutt, V.M., Okechalu, B.O. and Ifenkwe, O.P.Department of Plant Science and Technology, Faculty of Natural Sciences,

University of Jos P.M.B. 2084, Jos, Plateau State, Nigeria



ABSTRACTA study was carried out at the National Root Crops Research Institute (NRCRI), Potato Programme Kuru, Jos Plateau State, Nigeria (Longitude 08oE 47l, Latitude 09oN 441and 1,239 meters above sea level (msl)) during three seasons to investigate ‘the rot of potato (Solanum tuberosum L.) seed tubersas affected by storage conditions and storage duration.’ Five potato varieties (Nicola, Bertita, Diamant, BR63-18 and Roslin Ruaka) were stored for three durations (12, 24 and 32 weeks) in three kinds of stores (room temperature store (RTS), diffused light store (DLS) and air conditioned store (ACS). The experimental design used was completely randomized design in factorial combination of 5 potato varieties forming the main plots, three storage conditions and three storage durations constituting the split plots. There were 45 treatment combinations replicated 3 times. Weekly temperatures and relative humidity were recorded in each type of store.For percentage tuber rot, the result showed that all main effects were significant (P<0.05) except the main effect of variety and store type during season 2. All the varieties had similar percentage tuber rot except the variety Nicola which showed significantly lower tuber rot. RTS and DLS resulted in significantly higher tuber rot than the ACS. Tuber rot (%) was lowest at 24 weeks of storage in all the 3 seasons while 32 weeks of storage resulted in significantly higher rot than the other periods. All interactions were significant in the first cropping season, only the interaction of variety X storage duration was significant in the second cropping season, while inthe third cropping season, all interactions were significant except the interaction of store type X storage duration. For number of whole tubers left after storage, all the main effects and interactions were significant. Variety Nicola resulted in the highest number of whole tubers leftwhile Roslin Ruaka had the lowest tubers left in cropping seasons 1 and 2 and variety Diamant had lowest tubers left in cropping season 3. The number of tubers left decreased with storage period with the lowest number of tubers left after 32 weeks of storage. Tuber storage in ACS resulted in highest number of tubers left while RTS was lowest.The RTS and DLS characterized by higher temperature enhanced rot of tubers while the ACS with lower temperature reduced rot of tubers, it is therefore suggested that for prolong storage (8-9 months), the ACS should be used.

Keywords: Potato, tuber rot, store type, storage period


INTRODUCTIONPotato is the third most important food crop in the world after rice and wheat and more than a billion people eat potato world-wide (CIP, 2010).Over two thirds of the global production is eaten directly by human beings with the rest being fed to animals or used to produce starch (Struik and Wiersema, 1999).Storability or keeping quality of potatoes should be regarded as equally important as yield, disease resistance and quality during potato breeding and selection (Carli et al., 2010). Storability is one of the considerations that need to be evaluated before releasing any variety so that farmers are able to store their produce for a desired period of time at their farm under traditional storage conditions or in refrigerated storage infrastructures, depending whether the end-use is for fresh consumption, processing or planting as seed. The aim of any type of storage is to minimise sprouting, respiration, evaporation and storage losses due to microorganism and to minimize all of this losses, it is necessary to create a low temperature environment e.g. cold store or a structure constructed in a way as to lower the room temperature appreciably (Okwonkwo et al., 1986; Owe Sonnewald, 2001). An increase or decrease in potato storage temperature can be used to minimize disease development (Kibar, 2012). The pathogens enter the tuber through wounds caused during harvesting and handling e.g. desprouting. Struik and Wiersema (1999) suggested that the best protection against these diseases is proper handling, wound healing in storage and removal of infected tubers. They also reported that temperature has effect on the rate of wound healing and also on the growth of the disease causing agent (bacteria or fungi).They therefore suggested that temperature should be relatively high first to allow rapid wound healing and then should be lowered to reduce developmental rate of the disease. Most disease organism logarithmically increases their population growth at temperatures ranging from 4.4oC to 26.7oC. Lower temperature lessens the possibility of disease incidence during storage. High tuber loss due to dry rot (Fusarium spp) has been reported at high than low storage temperatures (Folsum et al., 1995). At temperatures above 10oC the growth and development of disease organisms increase dramatically. Temperatures above 10oC should be avoided during long- term storage. The hatching of flies is also inhibited below 10oC, thus the presence of flies indicates that the temperature is too high somewhere in the storage and break down may become a problem. The typical storage diseases include early blight, Fusarium dry rot, late blight, pink rot, pythium

leak and silver scurf, bacterial soft rot is always a concern especially as a secondary invader to the primary diseases listed (Olsen and Brandt, 2013). Olsen and Brandt (2013) also observed that variety ‘Clear Water Russet’ and ‘Premier Russet’ potatoes had higher susceptibility to fusarium dry rot. Variety ‘Western Russet’ may have more early blight tuber lesionswhereas; silver scruf may plague ‘Russet Nurkotah’ more often. Disease development in storage will also be dependent upon storage temperature. Storing at lower temperature provides opportunity to potentially slow down the development of the diseases. Unfortunately depending upon variety and if they will be processed or not, lower storage temperatures may not be possible as a disease control tool. Shortage of seed potato is usually an outcome of poor seed storage. In Nigeria as much as 40% seed loss has been recorded in farmers’ stores in 3 months of storage due to dry rot (Fusarium spp), soft rot (Erwinia spp) and high temperature (Okonkwo et al., 1986). This study is therefore aimed at evaluating the rot of seed potato as affected by storage conditions and storage duration under Jos Plateau conditions.

MATERIALS AND METHODSThe study consisted of two stages: multiplication of seed tubers in the field aimed at generating sufficient tubers for storage and storage of seed tubers.Five potato varieties viz: Nicola, Bertita, Diamant, BR 63-18 and Roslin-Ruaka were obtained and multiplied at the National Root Crops Research Institute (NRCRI) Kuru, Jos Plateau State, Nigeria (09° 44I N, 08° 47I E; 1,239m above sea level) during the 2010-2011, 2011-2012 and 2012-2013 seasons.In storage, a completely randomized design in factorial combination of five potato varieties forming the main plots, three storage conditions and three durations constituting the split plots. There were 45 treatment combinations replicated 3 times. Each treatment was represented by 40 seed tubers. Three types of stores were used: Diffused Light Store (DLS), Air conditioned store (ACS) and room temperature store (RTS) (Control) as described by Deshi et al. (2015). The conditions in the storage includes: temperature, relative humidity, and light. Thermo-hydrometers (Humidity and temperature data loggers were installed in each store room and was used to measure the temperature and relative humidity in each store on 6 hour period. Mean minimum and maximum temperature for each day was therefore calculated for each season and weekly mean was then calculated.Each store had tubers of each variety stored for the same period. The tubers were stored for 12, 24 and

ROT OF SEED POTATO (SOLANUM TUBEROSUM L.) TUBERS AS AFFECTED BY STORAGE CONDITIONS


32 weeks in each type of store and then taken to the field for planting. Seed storage for 32 weeks was from rain fed harvest to rain fed planting (prolonged storage). Beukema and Van Der Zaag (1990) reported that in principle seed should be at least 3 months old before it is planted again and not older than 5-11 months (depending on the storage method and storage temperature). The harvested tubers from seed multiplication were properly screened by physical examination to remove any diseased or damaged tubers. This was followed by seed selection (sizes of 30-40 mm grade) of each variety.The tubers were kept in an open room for two weeks for curing before storage.Mean weight of tubers for each variety were taken before storage. Tuber storage started at 3 weeks after harvest (August 26th, 2010; September 8th, 2011 and September 7th, 2012 in seasons 1, 2 and 3 respectively). Observations were done fortnightly and the storage observations included:Number of rotted tubers/treatment and number of whole tubers left after storage.The storage data was subjected to analysis of variance (ANOVA) and the means were separated using least significant difference (LSD) using the mini tab software.

RESULTS AND DISCUSSIONThe main effects of variety, storage duration and store type on percentage tuber rot were all significant (P < 0.05)during season 1 (Table 1). Variety Diamant had the highest number of rot(21.73%) although it was similar to BR 63-18 and Roslin Ruaka which had 18.03% and 18.33% respectively. Nicola resulted in significantly lower tuber rot of 2.23% (Table 1).Storage in RTS and DLS resulted in similar tuber rot of 19.4% and 18.55% respectively but storage in the air conditioned store resulted in significantly lower tuber rot of 7.13% (Table 1). Tubers stored for 12 weeks resulted in 18.05% rotted tubers. Storage for 24 weeks had significantly lower tuber rot of 4.15%. But tuber storage for 32 weeks resulted in the highest percentage tuber rot of 22.88% (Table 1).The interactions between variety and storage duration, variety and store type and storage duration and store type were significantly different (P < 0.05) (Table 1).In season 2, the main effect of variety with regard to tuber rot was not statistically different. The main effect of store type on percentage rot of tubers was not significant. While that of storage duration was significant. Storage of tubers for 12 and 24 weeks gave similar rotscompared with significantly higher rots (10.68%) recorded at 32 weeks (Table 1). Amongst the interactions only that of variety and storage duration was significant (P<0.05) (Table 1). During season 3, the main effects of variety, storage

duration and store type were all significant (P < 0.05) with respect to tuber rot (Table 1). All the varieties resulted in similar rot of tubers except Nicola which had significantly lower rot of tubers (1.10%) (Table1). Storage in RTS and DLS resulted in similar rot of tubers (11.35% and 11.08% respectively), while storage in ACS resulted in significantly lower rot of tubers (8.48%).The rot of tubers was similar at 12 and 24 WOS (2.50% and 4.13%) but storage of tubers for 32 weeks resulted in significantly higher rot of tubers (24.23%) (Table1). The interactions of variety and storage duration and variety and store type were significantly different but the interaction of storage duration xstore type was not significantlydifferent (P < 0.05) (Table 1). In season 1, the interaction of variety and store type on percentage number of rotted tubersrevealed that in the RTS, all the varieties had similar tuber rot except Nicola which had significantly lower tuber rot of 1.95%. In the DLS, the varieties were significantly different in respect of tuber rot. Diamant was the highest with 30% although it was similar with BR 63-18 which had 22.23% tuber rot. In the ACS, Roslin Ruaka resulted in the highest tuber rot of 13.05% although, this was similar to Bertita, Diamant and BR 63-18 (Table 2). During season 3, the interaction of variety and store type on percentage number of rotted tubers showed that in the RTS, Diamant resulted in the highest rot of tubers (17.78%),in the DLS, Roslin Ruaka had the highest rot of tubers (12.78%) and in ACS, BR63-18 had the highest rot (15.28%), while Nicola had significantly lower rot (1.40%, 0.28% and 1.68% in the RTS, DLS and AC stores respectively (Table 2). In season 1, the interaction of variety and storage duration on percentage number of rotted tubersshowed that at 12 weeks of storage, Roslin Ruaka had the highest tuber rot of 33.90% although this was similar to Diamant that had 28.90%. At 24 weeks of storage, tuber rot was similar in all the varieties. At 32 weeks of Storage, all the varieties had similar tuber rot except Nicola which had significantly lower tuber rot of 1.95% (Table 3). In season 2, the interaction of variety and storage duration on tuber rot showed that at 12 and 24 WOS, all the varieties resulted in similar percentage rot of tubers. At 32 WOS, BR 63-18 resulted in highest rot of tubers (19.42%) although this was similar to Bertita, Diamant and Roslin Ruaka (Table 3).During season 3, the interaction of variety and storage duration on percentage number of rotted tubers showed that at 12 WOS, all the varieties resulted in similar rot of tubers. The same pattern was repeated at 24 WOS. At 32 WOS, BR 63-



18 had the highest rot of tubers (36.68%) although it was similar to Diamant and Roslin Ruaka while Nicola and Bertita resulted in significantly lower rot of tubers (1.10% and 14.23% respectively) (Table 3). Interaction of store type and storage duration on percentage number of rotted tubers was significant (P<0.05) only during season 1.At 12 weeks of storage, the room temperature store had the highest tuber rot of 20.15% although this was similar to DLS that had Rot of 19.58%. The ACS resulted in lowest tuber rot of 14.43%. At 24 weeks of storage, all the store types resulted in similar percentage tuber rot. At 32 weeks of storage, RTS and DLS resulted in similar tuber rot of 33.70% and 31.80% respectively while the ACS resulted in significantly lower rot of tubers with 3.08% (Table 4). The main effects of variety, storage duration and store types on mean number of whole tubers left after storage were significant during season 1 (Table 1). Nicola had the highest mean number of whole tubers (36.89) left after storage while Roslin-Ruaka had the lowest mean number of tubers (14.85) left after storage and the differences were significant (Table 1). Duration of storage significantly affected the mean number of tubers left after storage. More than thirty (30.24) tubers were left after 12 weeks of storage while 28.72 tubers were left after 24 weeks of storage. Storage for 32 weeks resulted in the lowest mean number of tubers left (18.72) (Table 1). RTS and DLS had significantly lower mean number of 22.75 and 21.52 tubers left respectively. Storage in ACS resulted in highest mean number of 33.41 tubers left (Table 1). The interaction between variety and storage duration, variety and store type and storage duration and store type were significantly different (P < 0.05) (Table 1). In season 2, the main effects of variety, store type and storage duration with respect to mean number of whole tubers left after storage was significant (P < 0.05) (Table 1).Variety Nicola had the highest mean number of whole tubers left after storage (30.00) while Bertita had the lowest mean number of tubers left after storage (20.92) although it was similar to Roslin- Ruaka which had 21.81 whole tubers left after storage (Table 1).Storage of tubers for 12 weeks resulted in the highest mean number of tubers left after storage (38.53). 19.96 whole tubers were left after 24 weeks of storage while 14.69 whole tubers were left after 32 WOS (Table 1). Storage of tubers in diffused light store resulted in the highest mean number of whole tubers left after storage (34.02). 25.98 tubers were left after storage in the room temperature store, while 13.24 tubers were left after storage in the air conditioned store. All interactions were significant (P < 0.05) (Table 1). During season 3, the main effects of variety,

storage duration and store type were significant in respect to mean number of tubers left after storage (Table 1). Nicola resulted in significantly higher mean number of tubers left after store (39.11) while Diamant resulted in lowest mean number of tubers left after storage (31.93) although it was similar with Roslin Ruaka which had 32.19 (Table 1).The mean number of tubers left decreased with increased period of storage. 38.79 tubers were left after 12 WOS, 37.13 tubers were tubers were left after 24WOS and 27.61 tubers were left after 32 WOS (Table 1). The number of tubers left was significantly affected by type of storage. RTS resulted in significantly lower mean number of tubers left after storage (30.72%), this was followed by storage in DLS with 33.61 tubers left after storage while ACS resulted in significantly higher mean number of tubers lefty (39.20) (Table 1). The interaction of variety and storage duration, variety and store type and storage duration and store type were all significantly different (P < 0.05) (Table 1). Interaction of variety and store type on mean number of tubers left after storage revealed that Roslin Ruaka had the lowest mean number of tubers (10.22) left in the RTS. In the DLS, Diamant and Roslin Ruaka resulted in similar and lowest mean numbers of tubers (10.96 and 9.00) left after storage respectively. In the ACS, Roslin Ruaka resulted in the lowest mean number of tubers (25.33) left after storage (Table 5). During season 2, the interaction of variety and store type on mean number of whole tubers left after storage showed that Roslin-Ruaka resulted in lowest mean number of whole tubers left in the room temperature store after storage (17.33), although it was similar with Bertita which had 18.67 tubers left after storage. In the diffused light store, BR 63-18 resulted in the lowest mean number of whole tubers left after storage (30.67) although this was similar to Bertita that had 31.11 tubers left after storage. In the air conditioned store, all the varieties had similar mean number of tubers left after storage (Table 5). In season 3, Nicola had the highest mean number of tubers left in the RTS (38.22) while Diamant had the lowest (22.67) although it was similar with Roslin Ruaka which had 27.89 tubers left after storage. The same pattern was repeated in the DLS. In the ACS, all the varieties had similar mean number of tubers left after storage (Table 5). During season 1, the interaction of variety and storage duration on mean number of tubers left after storage revealed that after storage for 12 weeks Variety Nicola resulted in significantly higher (P<0.05) mean number of whole tubers left (37.78) while Roslin Ruaka had the lowest mean number of tubers (18.89)



left. The same pattern was repeated after storage for 24 weeks and at 32 weeks (Table 6). In season 2, all the varieties had a similar mean number of tubers left at 12 weeks of storage. At 24 weeks of storage, Nicola had the highest mean number of tubers left after storage (26.42). This was followed by BR 63-18 which had 24.22 tubers left after storage. Diamant had 19.11 tubers left after storage. At 32 weeks of storage, Nicola had the highest mean number of whole tuber left after storage (24.89). This was followed by BR 63-18 with 14.00 tubers left although this was similar to Diamant which had 13.67 tubers left after storage while Roslin-Ruaka and Bertita had significantly lower mean number of whole tubers left after storage 10.78 and 10.13 respectively (Table 6).During season 3, the interaction of variety and storage duration on mean number of whole tubers left after storage showed that at 12 WOS, all the varieties had a similar mean number of tubers left. At 24 WOS, Nicola resulted in significantly (P<0.05) higher mean number of tubers left after storage (39.00) while Roslin Ruaka resulted in the lowest (35.67). At 32 WOS, Nicola was the highest (38.67) while Diamant had the lowest mean number of tubers left after storage (20.78) although it was similar with Roslin Ruaka which had 22.22 tubers left after storage (Table 6). During season 1, the interaction of store type and storage duration on mean number of tubers left after storage revealed thatat 12 weeks of storage, ACS had the highest mean number of tubers (34.04) left while the DLS had the lowest mean number of tubers (26.37) left. At 24 weeks of storage, ACS resulted in the highest mean number tubers (34.12) left while the DLS had the lowest mean number of tubers (25.00) left. At 32 weeks of storage, RTS resulted in the lowest mean number of 10.90 tubers left (Table 7). In season 2, theinteraction of store type and storage duration on mean number of whole tubers left after storage showed that at 12 weeks of storage, tubers stored in Air conditioned store and Diffused light store had significantly (P<0.05) higher mean number of whole tubers left after storage (39.72 and 39.03 respectively) while Room temperature store had a lower mean number of whole tubers left after storage (36.92).At 24 and 32 weeks of storage, Diffused light store resulted in highest mean number of whole tubers left after storage (36.72 and 26.23 respectively). This was followed by storage in Room temperature store while the Air conditioned Store had no tubers at 24 and 32 weeks of Storage due to fire disaster that razed down the store (Table 7). In season 3, the interaction of store type and storage duration on mean number of whole tubers left after storage showed that at 12 WOS, ACS and DLS

had similar mean number of tubers left (39.65 and 38.68 respectively) while RTS resulted in significantly lower mean number of tubers left (38.03). At 24 WOS, RTS and DLS had similar mean number of tubers left (35.48 and 36.81 respectively) while the ACS had significantly higher mean number of tubers left (39.09). At 32 WOS, ACS resulted in significantly higher mean number of tubers left (38.85) followed by DLS with 25.35 tubers left after storage while RTS had the lowest mean number of tubers left (18.64) (Table 7). The differences observed amongst the varieties in percentage number of rotted tubers during seasons 1 and 3 might be attributed to their genetic variability. Ifenkwe and Nwokocha (1986) observed that the behaviour of potato (which is a living tissue) in storage is not only influenced by storage environment but also by genetic variability, agronomic practices during growth, pest and disease attacks. One of the factors evaluated to assess storability of a variety has been reported to be disease susceptibility (Carli et al., 2010; Olsen and Brandt, 2013).Olsen and Brandt (2013) reported that while variety Clear water Russet and Premier Russet potatoes have a higher susceptibility to Fusarium dry rot, variety Western Russet may have more early blight lesions, whereas silver scurf may plague Russet Nurkotah more often. The significant effect of store typeon percentage number of rotted tubers during seasons 1 and 3may be attributed to environmental conditions in the different store types. During season 1, the temperature range in the different store types was 15.70-31.57oC, 15.14-28.04OC and 14.20-24.19oC in the RTS, DLS and ACS respectively. In season 2, the temperature range was 17.38-27.84oC and 16.51-27.19oC in the RTS and DLS respectively. The ACS was razed down by fire disaster. In season 3, the temperature range was 19.06-29.73oC, 17.69-28.26OC and 14.00-23.56oC in the RTS, DLS and ACS respectively. The temperature in the ACS was appreciably lower than the other store types. The observed interaction of variety and store type in percentage tuber rotmight be as a result of the differences in environmental conditions (e.g. temperature and relative humidity) of storage and genetic composition of the varieties. Each variety has been reported to have specific field and storage management conditions (Olsen and Brandt, 2013) and they differ in disease susceptibility (Carli et al., 2010). Disease development in storage may also be dependent upon storage temperature. The higher temperature in the room temperature and Diffused light stores suggested why tuber rot was higher in those stores than the air conditioned store. Kibar (2012) observed that an increase or decrease in potato



storage temperature can be used to minimize disease development. By reducing holding temperature many storage disease problems can be minimized. Struik and Wiersema (1999) reported that storage temperature has effect on rate of wound healing and also on the growth of disease causing agents (bacteria and fungi). Folsom et al., (1995) found high tuber loss due to dry rot (Fusarium spp) at high storage temperature than low storage temperature. At temperatures above 10oC the growth of disease causing organisms increases dramatically. High relative humidity >90% and high temperature >40oC are favourable for development of silver scurf disease of potato (Merida and Loria, 1991). Irrespective of store type used tuber rot was lowest in variety Nicola suggesting that it had high storability irrespective of storage conditions. Storage duration significantly affected tuber rot in all the 3 seasons. During season 1, tuber rot was high after 12 and 32 weeks of storage with 18.05 and 22.88% of rots respectively. During season 3 it was only high after 32 weeks of storage with 24.23% of rot. 32 weeks of storage (duration 3) was around the months of March, April and May when environmental temperature is high (See Appendix: meteorological data table). The high temperature might have favoured the growth of microorganisms that causes rot of tubers. Folsom et al., (1995) found higher tuber loss at high temperature than low temperature. 24 weeks of storage (duration 2) was during the cold harmattan winds of November, December and January (see meteorological data). The cold must have slowed down the activities of disease causing agents. Kibar (2012) observed that decrease in potato storage temperature can be used to minimize disease development.The interaction of variety and storage duration on tuber rot observed might be attributed to varietal genetic variability, and physiological age which resulted from length of storage period. Tuber rot observed in the three storage durations were (18.05, 4.15 and 22.88 % at 12, 24 and 32 weeks respectively). The high loss at 12 weeks of storage might be as a result of the high moisture content of tubers at harvest and high relative humidity. The potato at harvest is about 80% water making its storage much more difficult than the grains with less than 20% moisture at harvest (Ifenkwe and Nwokocha, 1986). Storage losses of up to 30% in only two months of storage due to rots and loss of moisture have been reported (Williams, 1962 and Okonkwo et al., 1986). Tuber rot reduced in all the varieties at 24 weeks of storage probably because 24 weeks of storage was during the cool harmattan wind (Months of December to February) when minimum temperature was between 11-15oC (see Appendix: meteorological data 2011

and 2012. The high tuber rot at 32 weeks of storage observed might be that the period coincided with the months with high temperature (months of March to May).The significant interaction of store type and storage duration observed might be attributed to the environmental conditions in the store types at different storage periods. The period of harvest (August to September) was very wet and humid. As the months of storage progresses, it gets dryer especially during the harmattan period (November to February) and shortly before the rains set in, it gets very hot and humid (March to April) (see meteorological data). For prolonged storage, the use of air condition kept storage losses very low.The number of whole tubers left after storage varied significantly (P<0.05) with variety in all the 3 seasons. Nicola resulted in highest number of whole tubers left in all the 3 seasons, while Roslin Ruaka, Bertita and Diamant had the lowest number of whole tubers in seasons 1, 2 and 3 respectively. Each variety has been reported to have specific field and storage management conditions (Olsen and Brandt, 2013). This suggests reasons why some varieties stored well and others do not. The number of whole tubers left varied significantly with store type in all the 3 seasons. Air conditioned store resulted in highest number of whole tubers left in seasons 1 and 3, while diffused light store had highest number of whole tubers left in season 2. The differences might be attributed to environmental conditions in the store types especially temperature. The highest number of whole tubers left in the air conditioned store was as a result of cooling which lowered the temperature reducing activities of microorganisms causing rot. During season 2, the air conditioned store was razed down by fire after 12 weeks of storage that may be the reason why DLS resulted in highest number of whole tubers left after storage.

CONCLUSIONStorage duration significantly affected the number of whole tubers left after storage in all three seasons.Storage for 12 weeks had the highest, followed by storage for 24 weeks, while 32 weeks of storage had the lowest number of whole tubers left, implying that the number of whole tubers left decreased with time in storage. As tubers are stored over time, losses are encored. The reduction in tuber number may be due to loss of tubers because of rots and diseases, insect and rodent pest attacks.



REFERENCESBeukema, H. P. and van der Zaag, D. E. (1990). Introduction to Potato Production. Pudoc, Wageningen Netherlands, Pp208.Carli, C.; Mihovilovich, E.; Yuldashev, F.; Khalikov, D. and Kadian, M. S. (2010). Assessment of dormancy and sprouting behavior of cip elite and advanced clones under different storage conditions in Uzbekistan. Potato Research 53: 313-323.CIP (2010). Annual Report Research for Development, International potato centre. Lima, Peru.Deshi, K.E., Odiaka, N. I., Obasi, M. O., Kalu, B. A. and Ifenkwe, O.P. (2015). Breaking of Dormancy and Sprouting of some Potato (Solanum tuberosum L.) Varieties under Different Storage Conditions and Durations in Jos, Plateau State, Nigeria.Fulafia Journal of Science and Technology, 1(1): 4-13.Folsom, B.L.Jr; Lee, C.R. and Bates, D. J. (1995). Chemical control of dry rot of potatoes caused by Fusarium pp. The Botanical Review. 61(4): 374-387.Ifenkwe, O. P. and Nwokocha, H. N. (1986). The most limiting factors for potato crop production in Nigeria. Nigeria-Netherlands workshop Paper “Towards increased potato Production in Nigeria, Vom Plateau state, Nigeria. Feb 20-21, 1986.Pp27-32. Kibar, H. (2012). Design and management of post-harvest potato (Solanum tuberosum L.) storage structures. Ordu University Journal of Science and Technology 2 (1):23-48.Merida, C.L. and Loria, R. (1991). Organic potato production guide. Idaho center for potato Research and Education. Pp 101-104.Okonkwo, J. C.; Ifenkwe, O. P.; Nwokocha, H. N. and Knipers, H. (1986). Possibilities of overcoming the limiting factors for potato production in Nigeria. III. Varieties and seed potato availability. In: “Towards increased potato production in Nigeria”, Nigerian Netherlands workshop papers. Pp. 45-49.Olsen, N. and Brandt, T. (2013). Understanding Dormancy. Selecting varieties for short to long term storage. In:Potatoes Grower. University of Idaho Outreach 49:50/55.Owe, S. (2001). Control of potato tuber sprouting. Trends in plants Science,6: 335-337.Shahbazi, F. and Rajabipur, A. (2008). Resistance of Potato to Airflow. Journal of Agricultural Science and Technology. 10(1):1-9.Struik, P. C. and Wiersema, S. G. (1999). Seed potato technology. Wageningen Pears, Wageningen. Pp383.Williams, G. G. (1962). Potato growing in Plateau Province. Samara Agricultural Newsletter, Institute of Agric. Res.Samaru 51: 1-11.



Table 1: Effect of Variety as Affected by Storage Duration and Storage Type on Percentage Number of Rotted Tubers during the 2010 -2011, 2011-2012 and 2012-2013 (Seasons 1, 2 and 3 respectively) in Jos

Treatment Percentage number of rotted tubers (%) Number of whole tubers left

Variety Season 1 Season 2 Season 3 Season 1 Season 2 Season 3Nicola 2.23c 2.03a 1.10b 36.89a 30.00a 39.11aBertita 14.83b 3.823a 7.38a 28.68b 20.92d 35.44bDiamant 21.73a 6.03a 14.53a 19.86c 23.67c 31.93dBR63-18 18.03ab 8.00a 14.08a 29.19b 25.67b 33.89cRoslin-Ruaka 18.33ab 4.90a 14.35a 14.85d 21.81d 32.19dLS ** NS * ** ** **LSD 0.05 5.83 7.38 9.30 1.09 1.05 1.62Storage Duration (Weeks)12 18.05b 3.13b 2.50b 30.24a 38.53a 38.79a24 4.15c 1.08b 4.13b 28.72b 19.96b 37.13b32 22.88a 10.68a 24.23a 18.72c 14.69c 27.61cLS ** * ** ** ** **LSD 0.05 3.28 4.55 5.85 0.99 0.96 0.90Storage TypeRoom temperature store 19.4a 4.523a 11.35a 22.75b 25.98b 30.72cDiffused Light Store 18.55a 4.723a 11.08a 21.52b 34.02a 33.61bAir conditioned store 7.13b 5.523a 8.48b 33.41a 13.24c 39.20aLS ** NS * ** ** **LSD 0.05 3.38 2.08 2.20 1.31 1.04 0.72InteractionVariety x storage duration ** ** ** ** ** **Variety x store type ** NS * ** ** **Storage duration x store type ** NS NS ** ** **

Table 2: Interaction of Variety and Store Type on Percentage Number of Rotted Tubers

Season 1 Season 3Treatment Store type Store type

RTS DLS ACS RTS DLS ACSVarietyNicola 1.95b 2.75c 1.95b 1.40b 0.28b 1.68bBertita 19.55a 19.10b 5.76ab 6.22b 6.55ab 9.33abDiamant 26.68a 30.00a 8.53ab 17.78a 11.10a 14.73aBR 63-18 25.55a 22.23ab 6.30ab 15.28ab 11.68a 15.28aRoslin Ruaka 23.33a 18.60b 13.05a 16.10a 12.78a 14.18aLSD0.05 8.03 9.75

DLS. Diffused light store RTS. Room temperature store ACS. Air condition store

Table 3: Interaction of Variety and Storage Duration on Percentage Number of Rotted Tubers

Season 1(2010-2011) Season 2(2011-2012) Season 3(2012)Treatment Storage duration (Weeks) Storage duration (Weeks) Storage duration (Weeks)

12 24 32 12 24 32 12 24 32VarietyNicola 4.18c 1.10a 1.95b 3.90a 0.52a 1.68b 1.10a 1.10a 1.10bBertita 11.10bc 5.78a 5.76ab 1.78a 0.90a 8.90ab 4.45a 3.45a 14.23bDiamant 28.90a 5.18a 8.53ab 3.60a 1.26a 13.33a 3.05a 5.55a 35.00aBR 63-18 12.23b 3.70a 6.30ab 2.50a 1.92a 19.42a 1.68a 3.90a 36.68aRoslin Ruaka 33.90a 5.00a 13.05a 3.90a 0.83a 10.00ab 2.23a 6.68a 34.67aLSD0.05 8.03 10.58 13.50

ROT OF SEED POTATO (SOLANUM TUBEROSUM L.) TUBERS AS AFFECTED BY STORAGE CONDITIONS AND STORAGE DURATION IN JOS, PLATEAU STATE, NIGERIAROT OF SEED POTATO (SOLANUM TUBEROSUM L.) TUBERS AS AFFECTED BY STORAGE CONDITIONS


Table 4: Interaction of Store Type and Storage Duration on Percentage Number of Rotted

Tubers During Season 1Treatment Storage duration (Weeks)

12 24 32Store typeRTS 20.15a 4.38a 33.70aDLS 19.58ab 4.35a 31.80aACS 14.43b 3.85a 3.08bLSD0.05 5.63


Table 5: Interaction of Variety and Store Type on Mean Number of Whole Tubers Left after Storage

Season 1 Season 2 Season 3Treatment Store Type Store Type Store Type

RTS DLS ACS RTS DLS ACS RTS DLS ACSVarietyNicola 35.33a 37.22a 38.11a 37.52a 39.22a 13.22a 38.22a 39.33a 39.78aBertita 25.78b 24.36b 35.91ab 18.67c 31.11c 12.93a 31.47b 35.73b 39.11aDiamant 16.33c 10.96c 32.30c 23.33b 34.33b 13.33a 26.67d 29.67d 39.44aBR 63-18 26.07b 26.07b 35.41b 33.00a 30.67c 13.33a 29.33c 33.00c 39.33aRoslin Ruaka 10.22d 9.00c 25.33d 17.33 34.78b 13.33a 27.89cd 30.33d 38.33aLSD0.05 2.56 2.08 1.97


Table 6: Interaction of Variety and Storage Duration on Whole Tubers Left in Storage

Season 1(2010-2011) Season 2 (2011-2012) Season 3 (2012)Treatment Storage duration (Weeks) Storage duration (Weeks) Storage duration (Weeks)

12 24 32 12 24 32 12 24 32VarietyNicola 37.78a 37.11a 35.78a 38.63a 26.42a 24.89a 39.67a 39.00a 38.67aBertita 34.67b 33.42b 17.96b 38.53a 14.04d 10.13c 38.04a 36.98ab 31.29bDiamant 25.33c 22.67c 11.59c 38.22a 19.11c 13.67b 38.44a 36.56b 20.78dBR 63-18 34.52b 33.63b 19.41b 38.73a 24.22b 14.00b 39.11a 37.44ab 25.11Roslin Ruaka 18.89d 16.78d 8.89d 38.63a 16.00d 10.78c 35.67b 35.67b 22.22dLSD0.05 2.04 1.97 2.19

Table 7: Interaction of Store Type and Storage Duration onMean Number of Whole Tubers Left after Storage

Season 1(2010-2011) Season 2 (2011-2012) Season 3 (2012)Treatment Storage duration (Weeks) Storage duration (Weeks) Storage duration (Weeks)

12 24 32 12 24 32 12 24 32Store typeRTS 30.30b 27.04c 10.90c 36.92b 23.13b 17.82b 38.03b 35.48b 18.64cDLS 26.37c 25.00b 13.20b 39.03a 36.72a 26.23a 38.68a 36.81b 25.35bACS 34.04a 34.12a 32.07a 39.72a - - 39.65a 39.09a 38.85aLSD0.05 1.88 1.67 1.44




SEASONAL RESPONSES OF TWO FAUNAL TAXA TO FIRE TREATMENTS IN YANKARI GAME RESERVE, NIGERIA.

1Mwansat, G.S, 1Da’an, S. A & 2Nwabueze, E1Department of Zoology, University of Jos, P.M.B 2084, Jos, Nigeria.

2Department of Animal and Environmental Biology, Rivers State University of Science and Technology, Port Harcourt Nigeria.



ABSTRACTThe custom of using fire as a management tool in protected areas needs to be within a frame work of an understanding of the responses of biodiversity. This is to avoid or reduce the negative impacts of ecological disturbances in an ecosystem. More responses of floral than faunal components of biodiversity to fire have been studied and reported.This study was aimed at determining the faunal responses to fire in both wet and dry seasons at the Yankari Game Reserve (YGR)located in lat10° 30’ E and long 9° 45’ N. The birds and insects were used in the study to bridge the knowledge gap of fire ecology in Nigeria. Point transects treated with late burns during the wet season (April to June) and early burns during the dry season (November) were used to record birds and insects. Data was collected from 37 points in the wet season and from 45 points in the dry season. Results show that fire had a significant effect on insect abundance during the wet season and there was also a significant difference between mean insect diversity in wet and dry seasons and a higher bird and insect abundance and diversities was observed in wet than in dry season. The early burn fire regime is therefore, recommended as a fire treatment regime in the YGR. The need for conservation managers to maintain equilibrium between management practices and population dynamics in ecosystems is further highlighted.

Key Words: Fire, abundance, diversity, insects, birds, Reserve



INTRODUCTIONFire has been used by hunters for thousands of years throughout the tropical and temperate zones to manipulate natural resources by creating grasslands for hunting, opening farmland for agriculture and driving game into traps (Meffe et al., 1994). Contemporary conservation managers now use a fire regime technique generally referred to as “prescribed burning’’ as a management tool to restore the ecological functions that were formerly provided by natural fires. However, fire does not only influence the total biomass of vegetation, it also markly influences their structure. Structural changes influence the microclimate and the distribution of resources such as nutrients and moisture (Ludwig et al., 2004). These changes in turn have cascading effects on biodiversity (Walker and Peet, 1983;Bigalke and William 1984) with some organisms responding to microclimate and resource availability. Fire is an example of abiotic ecological disturbance (Sutherland, 1998) and has key roles in the ecology of organisms. Plants and animals respond differently to fire treatments. Burnt coniferous forest has been shown to support more bird species and larger species than unburnt forest presumably because of higher productivity of food supplies (Cody 1985;O’Reilly et al., 2006). It has also been shown that mortality of dominant trees increased linearly with increasing fire intensity while grass layer vegetation was resilient with fire having no detectable effect on either the diversity or floristic composition. He was been reported that fire had greater effect on individual animal species than was indicated by group functional response(Alan et al., 2006). Apart from the long history of bushfires in YGR(Green,1989; Birdlife International, 2007),fire is still being used as a management tool. However, very few fire studies have been conducted at the YGR (Da’an et al., 2015). Also,most fire studies have focused on only vegetation excluding the faunal component (Parr and Chown, 2003). Birds and insects species are sensitive to disturbance by fire and are known to be ecological indicators (Danks, 1992;McGeoch, 1998; Gregory et al.,2003).The studytherefore, determined the responses of birds and insects to fire treatments in dry and wet seasons.

MATERIALS AND METHODSThe study was carried out between April and November 2008 in YGR, Nigeria. The study site is located in the north east central part of Nigeria (10° 30’ E, 9° 45’ N), with an area of 2244 km square and 150-750 masl. The YGR is bisected by the Gaji River. The two major habitat types that occur within the reserve are dry savanna woodlands and riparian

vegetation, which includes a large area of Fadama. Common woodland trees include Afzelia africana, Burkea africana, Isoberlinia doka, Combretum glutinosum and Anogeissusleiocapus. In the riparian forest, floral species such as Khaya senegalensis, Vitex doniana, Acacia sieberiana, Tamarindus indica among others are common (Geerling, 1973;Birdlife International, 2007).While there are very few records on insects, about 337 species of birds have been recorded in the study area with several other faunal taxa (Ezealor, 2002). Bird counts and insect collections were carried out along a total of 9 transects of 1 km each (distance was marked with a GPS 60) divided into 5 points 200 m apart cutting across burnt and unburntareas of the savanna woodland. Point count technique was used to record bird species (Bibby et al., 2000); all birds seen and heard were recorded according to Borrow and Demey(2004). Aerial insects were collected using sweep nets.Fifteen sweeps were made within a 10 m quadrat at each 200m point along the transects. Insects collected were sorted out and transferred into sample bottles containing 70% alcohol for identification in the laboratory according to Shattuck and Barnett (2001) and Castner (2000).Analysis of Variance (ANOVA) was used to test the statistical significance of the effect of fire on treatments of bird and insect abundance in wet and dry seasons. Bird and insect diversity was calculated using Shannon Weiner diversity index. A one sample T-Test was used to compare mean bird and insect diversities in wet and dry seasons. Statistical analysis was carried out using the Statistical Package for Social Sciences Version 12.0 (SPSS 2003), Microsoft® Excel (2007) and Excel Add-in module Diversity.xla (The University of Reading)

RESULTS AND DISCUSSIONIn this study, a total of 569 birds distributed in 75 species were recorded while a total of 532 insects distributed in 24 orders and 79 families were recorded. 275 birds distributed in 31 species were recorded in the wet season while a total of 294 birds distributed in 44 species were recorded in the dry season. Also, a total of 228 insects distributed in 12 orders and 40 families were recorded in the wet season while a total of 204 insects distributed in 12 orders and 39 families were recorded in the dry season (Tables 1 & 2, Figures 4 & 5). There was no significant difference between mean bird diversity in wet and dry seasons (One sample T-Test: t=18.88, df=1, P>0.05), however, there was a significant difference between mean insect diversity in wet and dry seasons (One sample T-Test: t=4.653, df=1, P<0.05)

SEASONAL RESPONSES OF TWO FAUNAL TAXA TO FIRE TREAMENTS


Table 1: Effect of fire treatment on bird species abundance in wet and dry seasons

TAXON SEASON ABUNDANCE MEAN ABUNDANCE df F P

Bird Wet 275 1.69±0.11 160 0.86 >0.05

Dry 294 2.25±0.16 118 1.553 0.215

Table 2: Effect of fire treatment on insect species abundancein wet and dry seasons

TAXON SEASON ABUNDANCE MEAN ABUNDANCE df F P

Insect Wet 228 1.41±0.06 160 4.043 <0.05

Dry 204 1.70±0.15 118 0.001 0.957

Higher bird and insect diversities in wet than dry season may be indicative of more availability of food in the former as availability of food is one of the key factors that affect population dynamics in ecosystems (Raman et al., 2002, Skowno and Bond, 2003, Ripple and Beschta, 2004, Ludwig et al., 2004). Therefore, absence of resource(s) can limit populations that are dependent on it for survival. There was a significant effect of fire on insect abundance in the wet season and there was also a significant difference in mean diversity between wet and dry seasons.This agrees with O’Reilly et al., (2006) who reported a similar trend in Kenya and Da’an et al.,(2015) who reported a significant effect of fire on the diversity and abundance of birds in YGR and recommended an early fire treatment to be used as a fire regime in YGR. Any management tool like fire should be used such as to maintain minimal ecological disturbance which is a prerequisite for getting ecological equilibrium as in the specific case of bird and insect interactions (Connell and Slatyer 1977, Sousa 1984, Begon et al., 1996, Maffe and Carrol, 1994). However, it is known that fire alone may not effectively account for the abundance of many wildlife species especially most birds and many invertebrates like insects because

they are highly mobile (Elgood et al.,1994, David et al., 2004). Therefore, more research is needed on the effects of fire on biodiversity in YGR. This is in view of the different important interactions in ecosystems asserted by Turshak (2010). While our study concentrated on the responses of bird and insects to fire treatments and suggests similarities in response by these faunal taxa, many other aspects were not studied such as the effect of fire on bird and insect interactions. We therefore, recommend that future research should aim to fill these gaps especially in terms of predator-prey feeding relationships of bird and insects respectively and other aspects of their ecology, effects of fire on vegetation and other faunal taxa like the large herbivorous mammals as well as their interactions with other forms of ecological disturbances.

CONCLUSIONThe current study has shown that fire had no statistically significant effect on bird species abundance in wet and dry seasons and there is no significant difference between bird diversity in wet and dry seasons but the direct opposite is the case for the insect species. However, the abundance and diversity indices of both species were higher in wet than dry seasons. We therefore, recommend that early fire treatment can be used as a fire regime in YGR.

ACKNOWLEDGEMENTWe are grateful to Dr. LongtongTurshak who read through the manuscript and made useful comments, we also appreciate Dr.Taiwo. O.Crossby and Dr. Onoja Joseph for their assistance in the field. Very specially, we acknowledge the logistic support given to us by the management and staff of A.P.Leventis Ornithological Research Institute (APLORI) University of Jos during this work.

Figure 1: Map showing study site.



Figure 2: Bird species diversity with fire treatments in dry and wet seasons

Figure 3: Insect species diversity with fire treatments in dry and wet seasons

Figure 4: Insect Order abundance in the dry season



Figure 5: Insect Order abundance in the wet season

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Kimmins.J.P. (1997). Forest Ecology, a Foundation for Sustainable Management (Second Edition). Prentice- Hall, Inc.Ludwig, F., de Kroon, H., Berendse, F., and Prins H.H.T. (2004). The influence of savanna trees on nutrient, water and light availability and the understorey vegetation. Plant Ecology 170:93-105.Meffe, K. Gary., Carroll. R. C and contributors (1994): Principles of Conservation Biology. Sineuer Associates, Inc. Publishers.O’Reilly L., Darcy, O., Todd M. P. and Felicia, K. (2006): Effects of fire on bird diversity and abundance in an East African savannah. African Journal of Ecology, 44,165-170(6).Parr, C.L. and Chown, S.L (2003). Burning issues for conservation: a critique of faunal fire research in Southern Africa. Austral Ecology 28,384-395.Raman, T.R.S., Rawat, G.S., and Johnsingh, A.J.J. (1998).Recovery of Tropical rain forest Avifauna in relation to vegetation succession following shifting cultivation in Mizoram, North-east India. Journal of Applied Ecology 35:214-231.Ripple, W.J., and Beschta, R.L. (2004). Wolves and the ecology of fear: Can predation risk structure ecosystems? BioScience 54:755-766.Savory, C.J. (1989).The importance of invertebrates to chicks of gallinaceous species. Proceedings of the Nutrition Society 48, 113 – 133.Shattuck, S.O. and Barnett, N.J. (2001).Australian Ants.An Atlas by, CSIRO.Skowno, A.L., and Bond, W.J. (2003).Bird community composition in actively managed savanna reserve, importance of vegetation structure and vegetation composition.Biodiversity and Conservation 12:2279- 2294.Stephen, J. M. (2004). Birds of Lowland Arable Farmland: The importance and identification of invertebrate diversity in the diet of chicks. In H.F.Van Emden and M. Rothschild (Eds).Insect and Birds interactions. Intercept Ltd. Hampshire, SP10 1YG, UK.Sousa, W.P. (1984). The role of disturbance in natural communities. Annual Review of Ecology and Systematics 15, 353-91SPSS 12.0 (2003).SPSS Inc., Chicago III.Sutherland, W. J. (Ed) (1998). Conservation science and action. Blackwell Science Ltd. The University of Reading. Excel Functions for Calculating Diversity Indices. Statistical service centre, The University of Reading, Harry Pitt Building, Reading RG6 6FN UK.Turshak, L.G. (2010). Effects of fire on tree species utilized by birds in Yankari Game Reserve. A report submitted to The Rufford Small Grants Foundation. Walker, J and R.K.Peet (1983).Composition and species diversity in pine-wiregrass savannas of the Greenswamp, North Carolina.Vegetatio 55:163-179.



SOME ETHNOBOTANICAL USES OF PLANT RESOURCES IN NASARAWA STATE, NIGERIA.

Terna, T.P.,Kwon-Ndung, E.H., Akomolafe, G.F., Goler, E.E., Okogbaa, J.I., Waya, J.I., and Markus, M.Department of Botany,

Federal University Lafia, PMB 146 Lafia, Nasarawa State, Nigeria.Corresponding Email: [email protected]

Date Manuscript Received: 11/01/2016 Accepted: 25/03/2016 Published: March, 2016

ABSTRACTA study was carried out to evaluate the ethnobotanical uses of plant resources in Nassarawa State, Nigeria. A total of 80 plants belonging to 37 families were surveyed through oral interviews of local inhabitants from different locations in the State, who use various plant materials for their livelihood. Uses ranged from Ornamental, food, forage, timber and construction, pest control, and textile. Plants belonging to the families Poaceae and Caesalpinniaceae were mostly used for food (9.76% respectively), Poaceae(42.85%) for forage, Combretaceae, Poaceae and Moraceae (18.18% respectively) for timber, building constructions and fuel wood. Only members of the family Lamiaceae and Malvaceae were used for pest control and textile purposes respectively. Majority of plant use in the study area was for food (51.25%) followed by forage (17.15%). The genus Ficus had the highest number of ethnobotanical uses with four applications, followed by Terminalia and Ceibaboth having three applications each. The rich biodiversity of ethnobotanical significance in the study area needs to be adequately conserved in order to forestall over exploitation and extinction of economically important plant families which is the main source of economy to the inhabitants.

Key Words: Ethnobotanical, Plant Families, Oral interviews, Nassarawa State



INTRODUCTIONNature totally depends on the life of plants. Plants feed us, clothe us, warm us, shelter us, define our livelihoods and underlie the myriad cultures of human populations. It is on plant life that all animal life, including that of humans, ultimately depend (Pedulosi et al., 2013). Plant resources are used in a variety of ways which may include food, medicine, shelter, forage, farming, hunting, fuel, clothing, traditional ceremonies and religious practices (Nichter, 1992). Ethnobotany establishes a relationship between human cultural practices and plant utilization (Aworinde and Erinoso, 2013). In Tropical Africa, only 6, 376 (21%) out of the estimated 30,000 plant species are used by man (PROTA, 2004). The 6,376 useful indigenous African plants are comprised of 1,975 medicinal plants, 820 timbers, 611 forages, 533 ornamentals, 477 fruits, 397 vegetables, 377 fibers, 240 essential oil and exudates, 220 auxiliary plants, 176 carbohydrate plants, 130 spices and condiments, 129 dyes and tannins, 104 fuel plants, 80 cereals and pulses, 54 vegetable oils and 53 stimulants (Adebooye and Opabode, 2004) In recent years, there has been a consistent growth in the demand for plant-based products from a variety of species. This has given rise to large-scale selective extraction of plants from their natural habitats, habitat degradation, loss of plant diversity, and the growing extinction of a number of valuable plant species (Jabeen et al., 2009). In Africa, traditional plant use systems of many ethnic groups have impacted negatively on plant biodiversity, and in most instances, loss of traditional knowledge of plants and culture is synonymous to the disappearance of biodiversity (Huai and Pei, 2009). It is therefore important to take urgent action especially in developing African countries to ensure sustainable exploitation of plant resources and their continued availability for posterity (Fonge et al., 2012). This work was carried out to evaluate the uses of plant resourcesin Nasarawa State, Nigeria in an effort to providing knowledge data base for plant conservation in the study area.

MATERIALS AND METHODSStudy AreaNasarawa State is located on 8032’N8018’E in the Southern Guinea Savannah region of North-Central Nigeria, and has 13 Local Government Areas spread out on a total land mass of 27,117km2. The state is known chiefly for the presence of high deposits of solid minerals and agricultural activity. Local Government Areas surveyed in the study include: Akwanga, Awe, Nasarawa Eggon, Keffi, Kokona, Nasarawa, Obi,Toto and Wamba. Data on local names and ethnobotanical uses of plants were obtained from local inhabitants of the sampled areas through oral interviews. Representative parts of sampled plants were collected, pressed and conveyed to the Botany Laboratory of Federal University Lafia for further identification and preservation. Plant samples collected from the field were identified on the spot and in the Botany Laboratory of Federal University Lafia in consultation with appropriate guides such as plant taxonomic manuals(NNMDA 2004; 2006; 2008a; 2008b).

SOME ETHNOBOTANICAL USES OF PLANT RESOURCES


RESULTSTable 1: Ethnobotanical Uses of plant species in Nasarawa State, Nigeria.

Uses (%). Family Ornamental Food Forage Timber, building

Construction and fuel

Pest Control Textile Frequency of use (%)

1 .Annonaceae

-Uvariachamae, Annona senegalensis

- - - - 2(2.5)

2. Mimosaceae-

Parkiabiglobossa- - - -

1(1.25)

3. Sapotaceae - Vitellaria paradoxa 1(1.25)4 . Papilionaceae - Cajanuscajan Desmodium velutinum,

Stylosanthe shamata, Alysicarpus vaginalis, Securidacalonge pedunculata, Sylosanthes micronata

Pericopsis laxiflora - - 7(8.75)

5. Caesalpinniaceae-

Detarium microcarpum, Tamar indusindica,Daniella oliveri, Dialium guineense

- - - -4(5.00)

6. Verbanaceae Gmelinaarborea Vitex doniana - Gmelinaarborea - - 3(3.75)

7. Rubiaceae Sarco cephaluslatifolius

- - - - 1(1.25)

8. Mimosaceae - Propsopsis africana Dachro stachyscinerea - - - 2(2.50)9. Anacardiaceae

-Mangifera indica, Anacardium occidentalis

- - - - 2(2.50)

10. Musaceae - Musa paradisiaca - - - - 1(1.25)

11. Arecaceae - Elaeis guineensis - Elaeis guineensis - - 2(2.50)12. Zingiberaceae - Zingiber officinales - -

- -1(1.25)

13. Malvaceae - Abelmoschu sesulentum, Hibiscus sabdariffa

- - - Gossypium barbadens

3(3.75)

14. Convolvulaceae Ipomoea spp Ipomoea batatas - - - - 2(2.50)15. Asteraceae - Vernonia spp. - - - - 1(1.25)16. Myrtaceae Eucalyptus globulus Psidium guajava - - - - 2(2.50)17. Araceae - Colocasiae sculenta - - - - 1(1.25)18. Rutaceae - Citrus sinensis - - - - 1(1.25)19. Combretaceae - Terminalia catapa Terminalia avicennoid Anogeisusleiocarpa,

Terminalia superba- - 3(3.75)

20.Euphorbiaceae Alchorneacordifolia Manihot esculentum

- - - -2(2.50)

21. Poaceae - Sorghum bicolor, Pennisetum typhoides, Zea mays, Oryza sativa

Andropogon tectorum, Brachiariajubata, Panicum maximum, Hyperrheniarufa, Paspalu morbiculare, Panicum spp.

Imperata cylindrica, Schizachyrium exile

- - 12(15)

22. Fabaceae Crotalaria spp. Vignaun giculata - - - - 2(2.50)23. Lamiaceae Ocimum

gratissimum- - Hyptissuaveolens - 2(2.50)

24. Sterculiaceae Sterculiasetigera - - - - - 1(1.25)

25. Apocynaceae - Landolphia owariensis

- - - - 1(1.25)

26. Arecaceae Borassiusaethiopium - - - - - 1(1.25)27. Liliaceae Asparagus africanus

- - - - -1(1.25)

28. Moraceae Ficusexasperata Ficus capensis Ficus capensis Ficus exasperate, Ficus polita

- - 5(6.25)

29. Amarantheceae-

Amaranthus spinosus- - - -

1(1.25)

30. Solanaceae - Lycopersicon esculentum,

- - - - 1(1.25)

31. Tiliaceae - Corchorus spp. - - - - 1(1.25)32. Meliaceae Khayasenegalensis - - Khayasenegalensis - - 2(2.50)33. Asclepiadaceae Calotropisprocera - - - - - 1(1.25)34. Balanitaceae - Balanitesae gyptiaca - - - - 1(1.25)35. Portulacaceae - Portulacao lecacea - - - - 1(1.25)36. Dioscoreaceae - Dioscorea alata - - - - 1(1.25)37. Bombacaceae Ceibapentandra Ceibapentandra - Ceibapentandra - - 3(3.75)

- = Not applicable



Data on ethnobotanical uses of plant species in Nasarawa State (Table 1) reveals that uses of plant resources in the study area included ornamentals, food, forage, timber and construction, pest control, and textile. The genus Ficus had the highest number of uses (4) followed by Terminalia and Ceibaboth having 3 applications each. Plant species belonging to the family Poacea had the highest frequency of use (15%) in the study area followed by Papilionaceae(8.75%).

Table 2:Frequency of Ethno-Botanical Usage of Different Plant Families in Nasarawa State, Nigeria.

Uses (%)S/No. Family Ornamental Food Forage Timber,

building Construction and fuel

Pest Control

Textile

1. Annonaceae 0.00 4.88 0.00 0.00 0.00 0.002. Mimosaceae 0.00 2.44 0.00 0.00 0.00 0.003. Sapotaceae 0.00 2.44 0.00 0.00 0.00 0.004. Papilionaceae 0.00 2.44 37.71 9.09 0.00 0.005. Caesalpinniaceae 0.00 9.76 0.00 0.00 0.00 0.006. Verbanaceae 8.33 2.44 0.00 9.09 0.00 0.007. Rubiaceae 0.00 2.44 0.00 0.00 0.00 0.008. Mimosaceae 0.00 2.44 7.14 0.00 0.00 0.009. Anacardiaceae 0.00 4.88 0.00 0.00 0.00 0.0010. Musaceae 0.00 2.44 0.00 0.00 0.00 0.0011. Arecaceae 0.00 2.44 0.00 9.09 0.00 0.0012. Zingiberaceae 0.00 2.44 0.00 0.00 0.00 0.0013. Malvaceae 0.00 4.88 0.00 0.00 0.00 100.0014. Convolvulaceae 8.33 2.44 0.00 0.00 0.00 0.0015. Asteraceae 0.00 2.44 0.00 0.00 0.00 0.0016. Myrtaceae 8.33 2.44 0.00 0.00 0.00 0.0017. Araceae 0.00 2.44 0.00 0.00 0.00 0.0018. Rutaceae 0.00 2.44 0.00 0.00 0.00 0.0019. Combretaceae 0.00 2.44 7.14 18.18 0.00 0.0020. Euphorbiaceae 8.33 2.44 0.00 0.00 0.00 0.0021. Poaceae 0.00 9.76 42.85 18.18 0.00 0.0022. Fabaceae 8.33 2.44 0.00 0.00 0.00 0.0023. Lamiaceae 0.00 2.44 0.00 0.00 100.00 0.0024. Sterculiaceae 8.33 0.00 0.00 0.00 0.00 0.0025. Apocynaceae 0.00 2.44 0.00 0.00 0.00 0.0026. Arecaceae 8.33 0.00 0.00 0.00 0.00 0.0027. Liliaceae 8.33 0.00 0.00 0.00 0.00 0.0028. Moraceae 8.33 2.44 7.14 18.18 0.00 0.0029. Amarantheceae 0.00 2.44 0.00 0.00 0.00 0.0030. Solanaceae 0.00 2.44 0.00 0.00 0.00 0.0031. Tiliaceae 0.00 2.44 0.00 0.00 0.00 0.0032. Meliaceae 8.33 0.00 0.00 9.09 0.00 0.0033. Asclepiadaceae 8.33 0.00 0.00 0.00 0.00 0.0034. Balanitaceae 0.00 2.44 0.00 0.00 0.00 0.0035. Portulacaceae 0.00 2.44 0.00 0.00 0.00 0.0036. Dioscoreaceae 0.00 2.44 0.00 0.00 0.00 0.0037. Bombacaceae 8.33 2.44 0.00 9.09 0.00 0.00

Results of survey of ethno-botanical uses among different plant families in Nasarawa State, Nigeria (Table 2), indicates that plants belonging to the family Poaceae and Caesalpinniaceae were the most frequently used for food (9.76% respectively), Poaceaefor forage (42.85%), Poaceae, Combretaceae and Moraceae(18.18% respectively) for timber, building construction and fuel wood. Members of the families Annonaceae, Caesal Pinniaceae, Convol vulaceae, Asteraceae, Myrtaceae, Euphorbiaceae, Fabaceae, Sterculiaceae, Arecaceae, Liliaceae, Meliaceae, Asclepiadaceae and Bombacaceae were respectively used most frequently as ornamentals (8.33%). Only members of the family Lamiaceae and Malvaceae were used for pest control and textile purposes respectively.



Table 3: Frequency of plant use in the study area.

Plant Use Frequency (%)Ornamentals 15Food 51.25Forage 17.15Building, construction and fuel wood 13.75Pest control 1.25Textile 1.25

Results of frequency of plant use in the study area (Table 3) reveal that plant resources were used mostly for food (51.25%) followed by forage (17.15%). The least applications of plant resources in the study area were in the aspects of pest control and Textile each with 1.30% frequency use respectively. DISCUSSIONIn the reported study, plant use for food was higher than every other category under consideration.Humans require food to provide the needed energy for daily activities. Food is no doubt the highest need for plant use worldwide (Krishnamurthy, 2003). Plants belonging to the family Poaceae and Caesalpinniaceae,were most frequently used for food compared to other families.Members of the family Poaceae including maize, wheat, millet and rice, are quite popular in the study area. Local foods prepared from the Poaceae include; Akanmu, Tuwonchikafa and Maasa. Purseglove (1992); Osagie and Eka (1998) also agree that the Poaceaeare among the most important food crops in the world with regard to cultivation areas and total production. The use of plant parts of members of family Caesalpinniaceae in the preparation of various foods has also been documented by Seidemann (2005). The extensive usage of members of the Poaceae for forage observed in the study can be explained by their relative abundance, nutritional content and preference by most livestock for food in the study area. In support of the enormous abundance

of grasses for forage purposes, Oregon State University (OSU) (2008) also stated that Seventy-five percent of all forages are grasses. Structurally resilient plant members of the Poaceae such as spear grass (Imperata cylindrica, L) and bamboo (Bambusa vulgaris) as well as Combretaceae such as Anogeissu sleiocarpa and Malvaceae such as Ficus spp. are also relatively abundant in the study area and account for their common usage for building, timber and construction as well as fuel wood purposes in the study area. Plants belonging to the Malvaceae family were the only ones used for textile in the study area.Cotton (Gossypium spp), a member of the Malvaceae family is a common textile plant used in the study area for making wool and cotton fabrics. The use of cotton in textile production is popular across the globe and their applications have been reported by several researchers (Brown and Ware, 1958). Plant families used for ornamental purposes in the study included Annonaceae, Caesalpinniaceae, Convolvulaceae, Asteraceae, Myrtaceae, Euphorbiaceae, Fabaceae, Sterculiaceae, Arecaceae, Liliaceae, Meliaceae, and Asclepiadaceae. This also represents the category with the highest plant diversity in the study. Several plants belonging to these families are often utilized for beatification purposes in homes, public places and places of religious worship in the study area.

CONCLUSION AND RECOMMENDATIONSData obtained from the study establishes a relationship between human practices and plant utilization in the study area. This has serious implications in the extinction of wildly harvested plant species and loss of vegetative cover from extensive logging and other human activities in the area. There is therefore need for proactive sensitization of the local dwellers on the benefits of renewable plant use and sustainable agricultural practices in order to conserve plant biodiversity in the study area.

REFERENCESAdebooye, O.C. and Opabode, J.T. (2004). Status of conservation of the indigenous leaf vegetables and fruits of Africa. African Journal of Biotechnology.3 (12): 700-705.Aworinde, D.O. and Erinoso, S.M. (2013). Relationship between species composition and homegarden size in Odedalga of Ogun State Nigeria. Bayero Journal of Pure and Applied Sciences, 6(2): 10 – 18.Brown, H. B. and Ware, J. O. (1958). Cotton (third ed.). McGraw-Hill Book Company, Inc. p. 1.Fonge, B. A., Egbe, E. A., Fongod, A. G. N., Focho, D. A., Tchetcha, D. J., Nkembi, L. and Tacham, W. N. (2012). Ethnobotany survey and uses of plants in the Lewoh- Lebang communities in the Lebialem highlands, South West Region, Cameroon.Journal of Medicinal Plants Research, 6(5): 855-865.Huai HY, Pei SJ (2002). Medicinal ethnobotany and its advances.Chin. Bull. Bot., 2(19): 129-136.Jabeen, A, Khan, M.A., Ahmad, M., Zafar, M., and Ahmad, F. (2009). Indigenous uses of economically important flora of Margallah Hills National Park, Islamabad, Pakistan. African Journal of Biotechnology, 8 (5): 763-784.Krishnamurthy, K. V. (2003). Textbook of Biodiversity. Enfield, New Hempshire, USA: Science Publishers Inc. p77.



Nichter, M. (1992).Anthropological Approaches to the Study of Ethnomedicine. Amsterdam: Gordon and Breach. Pp1071.NNMDA (2004). Medicinal Plants of Nigeria: South West Nigeria, Vol. 1. Lagos: Sup-Del Prints & Co. Ltd. Pp1-117.NNMDA (2006). Medicinal Plants of Nigeria: North-Central Zone, Vol. 1. Lagos: The Regent. Ltd. Pp1-100.NNMDA (2008a). Biodiversity of The Sukur World Heritage Site Adamawa State, North-East Nigeria. Lagos: Olucouger Prints. Pp 1-122.NNMDA (2008b). Medicinal Plants of Nigeria: North-West Nigeria, Vol. 1. Lagos. Pp 1-138.Osagie, A.U. and Eka, O.U. (1998).Nutritional Quality of Plant Foods. Post-Harvest Research Unit, University of Benin, Benin.Pp34 - 41.OSU (2008).Describe the major differences between the plant families used as forages. http://forages. oregonstate.edu/nfgc/eo/onlineforagecurriculum/instructormaterials/availabletopics/plantid/ differences. Accessed: 19/1/2016.Padulosi, S., Thompson, J., Rudebjer, P. (2013). Fighting poverty, hunger and malnutrition with neglected and underutilized species (NUS): needs, challenges and the way forward. Rome: Bioversity International. Pp1-60.PROTA (2004). Plant Resources of Tropical Africa: Vegetables. Grubben GJH, Denton OA (Editors). Leiden, Netherlands: PROTA Foundation, Netherlands/Backhuys Publishers. Pp 667.Purseglove, J.W. (1992). Tropical Crops: Monocotyledons. New York: Longman Scientific and Technical. Pp 300-305.Seidemann, J. (2005).World Spice Plants Economic Usage, Botany & Taxonomy”.Springer-Verlag: Berlin Heidelberg. p591.




UTILIZATION OF TUBER AND SPROUT CHARACTERISTICS IN DELIMITING ACCESSIONS OF RIZGA(Plectranthusesculentus N.E. Br.)IN JOS, NIGERIA.

Agyeno, O. E.Department of Plant Science and Technology, University of Jos, Nigeria.

Corresponding Email: [email protected], [email protected]

Date Manuscript Received: 18/03/2016Accepted: 11/12/2016 Published: December, 2016

ABSTRACTThe classification of tubers and sprouts into morphological categories is useful in the selection of seed tubers.A study was conducted to assess the morphological characteristics of 12 accessions of Plectranthus esculentus. Twenty traits of both tubers and developing sprouts were measured and counted. These were analyzed statistically.Qualitative characters observed in both tubers and sprouts basically revealed the traits in either binary or single state. The quantitative characters on the other hand, showed significant differences (P<0.05) which is indicative of their discreteness. Although polar ordination method revealed few interesting associative contrasts such as positions of Isci 01 and Long’at 02 in space not being a reflection of the high similarity coefficient existing between them, the results of cluster analysis suggest that 47% of the characters assessed could delimit Nigerian accessions of this taxon into two distinct land races or systematic varieties.

Keywords: Tubers, Sprouts, Morphology, Plectranthus, Nigeria, Accessions, delimit


INTRODUCTIONPlectranthus esculentus N.E.Br.Natal commonly called Rizga, Dazo, Finger potato and Livingstone potato is a species of plant in the family Lamiaceae (Aigbokhan, 2014). It occurs from the Equatorial Africa southward to Angola, the Eastern Transvaal Swaziland and Coastal Natal in dry wooded areas. Fox and Young (1962) and Pursglove (1974) reported that its growth in Africa is for its elongated edible stem tubers which have been under cultivation in some states in Nigeria such as Gombe, Kaduna, Nasarawa, Plateau, Taraba, Niger, Adamawa and parts of Bauchi and Borno. Howeverthe two centres of high diversity in Nigeria appear to be the Jos and Mambila Plateau since at least two land races may be found under cultivation in each of these areas. Irrespective of which one is being cultivated, they have been referred to by the same name in different dialects such as Korogangi in Nupe, Bugumol in Fulfude, Agbishe in Tiv , Nvu in Mwagwavul, Isci in Kuteb, Va’at in Berom and Aki’gu in Alago (Grueter et al., 2000). In otherwords, no reported attempt has been made in Nigeria so far to evaluate the taxonomic status of land races of Plectranthus esculentus. Underground parts of plants such as rhizomes and bulbs have been utilised as important taxonomic criteria in the genera Iris and Allium. Davies (1960) divided Turkish species of the subgenus Ranunculus of the genus Ranunculus based on rootstock and habit. Hather (1994) had also reiterated the relevance of roots and tubers in classification based on their utilization as vegetative propagules and importance in archaeobotanical analysis. The stem tubers of P. esculentus are the edible part and they are formed in clusters at the base in a vertical position with extensive fibrous, adventitious roots under the soil surface. They are creamy when fresh but the bark turns dark as tuber ages which help the plant to withstand the harmattan drought and store well when left underground. It has been described in several literatures as being finger-like with some branching types ( Allemann et al., 2003; Asfaw and Tadesse, 2001). Features of tubers have been utilised for classification purposes by many researchers (Hather, 1994). Also, Struik and Lommen (1999) emphasized on the importance of tuber characteristics in improving performance of crops. The aim of this study is to utilise systematic lines of evidence derived from gross and micro- morphology on tubers and sprouts of Plectranthus esculentus to ascertain whether such variations could enhance the classification of members of this specie

MATERIALS AND METHODSLiving specimens were used in this study. They were sourced from the germplasm at the National Root Crops Research Institute (NRCRI) Kuru Station, natives of Heipang and Riyomin Plateau State, as well as Takum and Serti in Taraba State.Representative types were brought under cultivation in Jos, Nigeria,

by planting tubers 5-10 cm deepon 40x40 cm ridges that were set out in a complete randomised design (CRD) in the botanical nursery of the Department of Plant Science and Technology, University of Jos, Nigeria. The harvested tubers were carefully observed for characteristic features such as pigmentation, bud-eye phyllotaxy and protuberance as well as sprout pigmentation and development. Also, number of tubers par stand, tuber and sprout length (cm), internodal distance (cm) as well as tuber girth (cm) and weight (g) were also measured, counted and recorded. Epidermal sections of 0.25 cm2 were made of each accession and viewed under calibrated dissecting microscope for trichome counts, and measurement of thickness of epidermis. At 15 weeks after harvest between the months of March and April, when climatic conditions are most suitable for breaking of dormancy, three healthy tubers were selected from each specimen and kept under room temperature so as to observe the number of bud eyes that would break dormancy on tubers, and the rate of sprout development.Values obtained were subjected to analysis of variance (ANOVA) to test the treatment effect for significance using the F-test, while means were compared by use of the Duncan’s Multiple Range Test (DMRT) (Snedecor and Cochran, 1969). Standard deviation (SD) was also worked out for each mean. The characters assessed were coded in two states (Table 1) and then simple matching coefficient (SSM) used to determine percentage similarity as introduced by Sokal and Michener (1958) and represented thus: SSM= Matches Matches + Mismatches Results of cluster schedules were compared with those of ordination which presented linear spatial relationship of operational taxonomic units (OTUs) under study.

RESULTS AND DISCUSSIONResults of both qualitative and quantitative characters have been presented in Tables 2 and 3 respectively. The observed qualitative characters exhibit two states of the same character across accessions. Characters such as pigmentation and prominence of bud-eyes clearly showed two states, while character states such as bud-eye phyllotaxy and trichome type and orientation are uniform in all accessions studied. Careful observation made from tubers sourced from Plateau and Taraba States revealed that some accessions were dark brown and having sunken bud-eyes (Bebot 01, Bebot 02, Bebot 04, Riyom 01, Riyom 04, Riyom 0 02 and GSK 01) and others light brown with prominent bud-eyes (Long’at 01, Long’at 02, LGN 03, Riyom 01 and Isci 01).

UTILIZATION OF TUBER AND SPROUT CHARACTERISTICS IN ACCESSIONS OF RIZGA(Plectranthus esculentus )


All accessions were covered with persistent uniserateeglandulartrichomes which seemed to be heavily distributed around the bud-eyes. The protective nature of hairy trichomes has been reported in many literatures. They may be so persistent in order to protect the tender bud-eyes since the plant is best preserved when left underground. Trichome distribution and characteristics of the epidermis were utilised by Abdulrahman and Oladele (2005), and Olowokudejo and Pereira-Sheteolu (1988) as diagnostic features in the grouping of species of the related genus Ocimum.It may be asserted from this study that the abundance and ephemeron of trichomes determined the color of the tubers’ epidermis becauseaccessions such as Long’at 01 (8.667) and Isci01 (8.333) which recorded higher number of trichomes were observed to be light brown. The genetic background and characteristics of the epidermis of plant organs have also played important taxonomic roles in many species (Essiet, 2004).The ten quantitative characters assessed all showed mean significant differences (P<0.05) and overlapping range of values except in the number of trichomes per 0.25cm2, tuber weight (g) as well as number of bud-eyes on tubers and sprouts, which wereeasily distinguishable across individuals. Accession Bebot04 produced as little as 3 tubers per stand that

were up to 14.9cm in length as compared with Isci01 which produced many short tubers(6.5cm). Table 4 shows that there were no significant differences (P<0.05) in number of bud eyes that broke dormancy and recorded rates of development of the sprouts throughout the period of observation. This could be an indication of the same physiological age and activity across the accessions. However, the large size accessions evidently produced and supported more sprouts probably because of the large food reserves as compared with the smaller accessions which possesseven more bud eyes on their tubers (Kyesmu and Mantell, 2002). PlatesA-Dshows the observable characteristics of tubers and developed sprouts in four accessions including Bebot02, Riyom04, Isci01 and Riyom02. The thick, purplish lightgreen and short sprouts of Bebot02 can easily be distinguished from the slim purple and long sprouts length in Isci01.Although the internodes displayed different types across the accessions such as light green on Bebot 02, purple on Long’at 01 and purplish light-green on Bebot 04 and Long’at 02, the uniformity in sprouts’ purplish colour can be seen in the picture. This probable plasticity in pigmentation of sprouts across accessions may be attributed to differences in genome as well as the prevailing room temperature environment.

TABLE 1: DATAMATRIX SHOWING BINARY CODING OF MORPHOLOGICAL CHARACTERS OBSERVED ON TUBERS AND SPROUTS OF PLECTRANTHUS ESCULENTUS N.E.Br.

CH

AR

AC

TER

S

So

ur

ce

: Pl

atea

u st

ate

=1;T

arab

a st

ate=

0

Tube

r pi

gmen

tatio

n:D

ark

brow

n=1;

pu

rple

=0

Spro

ut

Nod

al

pigm

enta

tion

Rig

ht

gree

n=+;

Purp

le

Spro

ut

inte

rnat

iona

l pi

gmen

tatio

nPu

rple

=+;li

ght

gree

n=-

Bud

eyes

pr

otub

eran

cePr

omin

ent=

+;

sunk

en=-

Bud

eyes

A

rran

gem

ent

Supe

rpos

e=+;

D

eccu

sate

Tric

hone

O

rient

atio

nTo

war

d bu

deye

=+;

Away

=-

Tric

hone

ty

peU

nser

ate=

+;

Mul

tiser

ate=

-

Tube

r le

ngth

6-10

.5=+

10

.5-1

5cm

=-

Num

ber

of

tube

rs

per

stan

d.3-

7.7=

1;

7.8-

11=0

Num

ber

of

Bud

eyes

on

Tu

ber

21-3

3=1;

34

-45=

0N

umbe

r of

B

udey

es

on

Spro

ut:

7-13

=1;

14-3

2=0

Inte

rnod

al

dist

ance

(c

m)

0.9-

1.45

cm=1

1.

5-2c

m=0

Tube

r G

irth

(cm

):3-

6.4=

1;

6.5-

10=0

Tube

r w

eigh

t (g

)

2.5-

23.5

=1;

25-4

5.5=

0

Num

ber

of t

richo

mes

per

0.2

5cm

21-

5=1;

6-

10=0

Thic

knes

s of

Ep

ider

mis

(µ

m)

14-3

7=1;

38

-63=

0

ACCESSIONSBebot01 1 1 1 1 0 0 1 1 1 1 1 0 0 0 0 0 1Bebot02 1 1 1 0 0 0 1 1 0 1 0 1 0 1 0 0 0Bebot04 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 1 1Long’at01 1 0 1 1 1 0 1 1 0 0 0 0 1 1 1 0 1Long’at02 1 0 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1LNG03 1 0 0 0 1 0 1 1 1 0 1 0 1 1 1 0 0Riyom03 1 1 1 1 0 0 1 1 0 1 0 0 0 1 1 1 0Riyom04 1 1 1 0 0 0 1 1 0 1 0 0 0 1 1 1 1Riyom01 1 0 1 1 1 0 1 1 1 0 1 0 0 1 1 0 1Riyom02 1 1 1 1 0 0 1 1 1 1 1 1 0 1 1 0 1Isci01 0 0 1 1 1 0 1 1 1 0 1 0 1 1 1 0 1GSK01 0 1 1 1 0 0 1 1 0 1 0 0 1 0 0 1 1



Values presented in Tables 5 and 6 have been used to construct ordination graph and dendrogram respectively. The dendrogram of similarity relationships based on 7 qualitative characters and 9 quantitative characters noticed on the external morphologies of tubers and sprouts is shown in Figure 1. The dendrogram has been clearly divided into two clusters or clades of phenetically related accessions at a similarity level of about 47% as defined by the phenon line. Raj et al., (2011) however reiterated that cluster analysis may fail to delimit some OTUs in a precise manner. Six sub clades have been identified across four similarity levels thus: subclade I at 94%, subclades II and III at 88%, subclades IV and V at 84% and subclade VI at 77%. These records could imply that the accessions cannot be separated until after the main similarity level of 47% while further separation of accessions may be achieved between 77% and 94%. Accessions Isci01 and Long’at02 occupy the highest phenon line (subclade I), Bebot02 occupies the 77% phenon line alone (subclade VI). Such values have also been interpreted by many authors as the proportion of characters which have contributed to the separation at that phenon level.

Table 2: Qualitative morphological characters in tubers and sprouts of sampled accessions of P. esculentus N.E.Br.Natal.Accessions Source Tuber

PigmentationArrangement of budget

Sprout pigmentationNodal

Internodal TrichomeOrientation

TrichomeType

Budeye

Bebot01 NRCRI, Vom, Plateau State

Dark brown Deccusate Light green Puple Toward budeye Uniserateeglandular Sunken

Bebot02 Sho village, B/ladi, Plateau State

Dark brown Deccusate Light green Light green Toward budeye Uniserateeglandular Sunken

Bebot04 Bachit, Plateau State

Dark brown Deccusate Light green Purplish light green

Toward budeye Unisserateeglandular Sunken

Long’at01 NRCRI, Vom, Plateau State

Light brown Deccusate Light green Purple Toward budeye Uniserateeglandular Protruded

Long’at02 NRCRI,vom Plateau State

Light brown Deccusate Light green Purplish light green

Toward budeye Uniserateeglandular Protruded

LGN03 Heipang, Plateau State

Light brown Deccusate Purple Light green Toward budeye Uniserateeglandular Protruded

Riyom 03 Heipang,Plateau State

Dark brown Deccusate Light green purple Rowardbudeye Uniserateeglandular Sunken

Riyom 04 Sho village, Plateatu State

Dark brown Deccusate Light green Light green Toward budeye Uniserateeglandular Sunken

Riyom 01 NRCRI, vom, Plateau State

Light brown Deccusate Light green purple Toward budeye Uniserateeglandular Protruded

Riyom 02 NRCRI, Vom, Plateau State

Dark brown Deccusate Light green purple Toward budeye Uniserateeglandular Sunken

Isa 01 Bika,Takum, Taraba State

Light brown Deccusate Light green purple Toward budeye Uniserateeglandular Protruded

GSK 01 Serti, Taraba State Dark brown Deccusate Light green Purplish light green

Toward budeye Uniserateeglandular Sunken

TABLE 3: Quantitative morphological characters in tubers and sprouts of sampled accessions ofP.esculentus

A c c e s s i o n

Tube

r len

gth

(cm

)

Tube

r Num

ber (

cm)

Tube

r Bud

eyes

Spro

uted

Bud

eyes

at

23W

AH

Spro

uted

Bud

eyes

at

24W

AH

Inte

rnod

al d

ist.(

cm)

Tube

r girt

h (c

m)

Tube

r wei

ght(g

)

Tric

hom

e N

o/0.

25cm

2

Epid

erm

al th

ickn

ess

(um

)/100

Bebot01 8.9ac 4a 33ad 24.667a 32.a 1.5ad 9.8a 30.1a 8.667a 14.5a

±0.15 ±0.31 ±0.18 ±0.34 ±0.51 ±0.03 ±0.35 ±0.59 ±0.25 ±0.70

Bebot02 12.9bc 4.667bd 34ad 8.1b 8.1b 2b 6.b 25.2b 6.333b 40.333b

±0.30 ±0.28 ±0.07 ±0.58 ±0.64 ±0.12 ±0.14 ±0.88 ±0.40 ±0.48

Bebot04 14.9b 3ab 41b 18.667ab 20.667ab 1.87abd 8.2ab 45.5c 1.333c 9.667c

±0.38 ±0.35 ±0.44 ±0.22 ±0.24 ±0.11 ±0.30 ±0.88 ±0.37 ±0.71

Long’at01 13.6b 10.667ac 45b 29.333a 30ac 1.07ac 4bc 7.76d 8.667a 26.967d

±0.33 ±0.31 ±0.55 ±0.50 ±0.45 ±0.11 ±0.19 ±0.53 ±0.25 ±0.38



TABLE 3: Quantitative morphological characters in tubers and sprouts of sampled accessions of P.esculentus-continuedP.esculentus

Tube

r len

gth

(c

m)

Tube

r Num

ber

(cm

)

Tube

r Bud

eyes

Spro

uted

Bud

eyes

at

23W

AH

Spro

uted

Bud

eyes

at

24W

AH

Inte

rnod

al d

ist.

(cm

)

Tube

r girt

h (c

m)

Tube

r wei

ght(g

)

Tric

hom

e N

o/0.

25cm

2

Epid

erm

al

thic

knes

s (u

m)/1

00

Long’at02 8.ac 10.333ce 26cd 23.333a 26.667ac 1.23ac 3.9b 5.55d 10a 28.033d

±0.22 ±0.29 ±0.48 ±0.28 ±0.33 ±0.08 ±0.19 ±0.58 ±1.90 ±2.04

LGN03 7a 10.667ce 25c 18.667ab 22abc 0.93c 4.5bc 7.58d 8.333a 62.833e

±0.27 ±0.31 ±0.50 ±0.22 ±0.14 ±0.12 ±0.15 ±0.53 ±0.23 ±0.92

Riyom03 10.5c 6.333d 37a 17.33ab 17.333bc 1.77bd 4.9bc 16.28e 3.667d 50.267f

±0.15 ±0.17 ±0.28 ±0.30 ±0.39 ±0.10 ±0.11 ±0.20 ±0.28 ±0.71

Riyom04 14.7b 6.333d 41b 28.667a 29.333ac 1.73bd 5.4b 16.65e 4d 29d

±0.37 ±0.17 ±0.44 ±0.50 ±0.43 ±0.10 ±0.44 ±0.17 ±0.26 ±0.30

Riyom01 7.8a 8.667c 33a 27.333a 32a 1.6abd 4.2bc 7.44d 6.667b 35.667g

±0.23 ±0.19 ±0.18 ±0.43 ±0.51 ±0.10 ±0.18 ±0.53 ±0.88 ±0.31

Riyom02 6.1a 5.667d 21c 5.333b 7.333b 1.73bd 3.7bc 17.8e 6.667b 24h

±0.32 ±0.22 ±0.60 ±0.65 ±0.65 ±0.10 ±0.21 ±0.11 ±0.88 ±0.48

Isci01 6.5a 11.667e 31d 21.333a 22.667ac 1.1ac 2.5c 2.74f 8.333a 28.667d

±0.30 ±0.34 ±0.30 ±0.13 ±0.03 ±0.10 ±0.28 ±0.65 ±0.23 ±0.31

GSK01 13.6b 6.667d 43b 22.333a 24.333ac 1.13ac 6.8b 30.2a 4d 36.667g

±0.33 ±0.14 ±0.50 ±0.23 ±0.21 ±0.10 ±0.20 ±0.59 ±0.26 ±0.35

F-ratio ** ** ** * * * ** ** * *

LSD 2.662 2.062 5.682 14.942 14.554 0.476 2.843 3.206 1.464 2.478

NOTE: Means followed by the same alphabet(s) are not significantly different using DMRT **=Significant at 99% probability level;* = significant at 95% probability level A phenon line describes a specific level in the dendrogram at which similar organisms are recognised and used as ranks by cluster analysis. A simple interpretation of this diagram is that accessions Isci01 and Long’at02 show greater affinity while LNG03, Long’at01 and Riyom01 show as much affinity as GSK01 is to Riyom02, Bebot01 and Bebot04.Polar ordination method have been utilised to indicate the position that each accession specifically occupies in relation to other accessions (Figure 2). The simultaneous use of this method with cluster analysis in this study was to ascertain how they complement each other in the categorization of the accessions. The most distant related accessions based on the dissimilarity value of 71% are LNG03 and Bebot04. The positions of all other accessions have been plotted on the axis to indicate their poorness of fit. The distance between accessions Bebot04 and GSK01 on one hand, and Isci01 and LNG03 on the other hand, has been well depicted in Fig 2. The accessions Long’at01 and Long’at02 lie closer unlike Riyom01 which has now become located well away from other Riyom accessions, just as also revealed by the cluster analysis (Fig.1). However, the positions of Isci01 and Long’at02 in space is not a reflection of the high similarity coefficient existing between them. It is likely that Isci01 and Riyom01 are phenetic duplicates of accessions of Long’at although sourced from different localities. Thus, this study agrees with Duncan and Baum (1981) who asserted that ordination methods may support or debunk the clades generated by cluster analysis.



TABLE 4: Effect of accession on breaking of dormancy on tubers and rate of development of sprouts in 12 accessions of P. esculentus.

18WAH 19WAH 20WAH 21WAH 22WAH 23WAH

Accessions No. of Budeyes

S p r o u t l e n g t h ( c m )

No. of Budeyes


No. of Budeyes

Sprout l e n g t h ( c m )

No. of Budeyes


No. of Budeyes


No. of Budeyes

S p r o u t l e n g t h (cm)

Bebot01 4.00 0.83 4.33 0.93 4.33 2.23 4.33 3.63 4.33 5.63 4.33 8.47

Bebot02 0.00 0.00 0.33 0.07 0.33 0.07 0.33 0.13 0.33 0.37 0.33 1.13

Bebot04 2.60 0.83 3.33 2.07 3.33 3.23 3.33 5.57 3.33 9.27 3.33 13.03

Long’at01 1.00 1.03 1.33 1.40 1.33 1.57 1.33 2.63 1.33 3.90 1.33 5.80

Long’at02 2.30 0.60 2.67 0.83 3.00 0.83 3.00 1.23 3.33 1.97 3.33 3.53

LGN03 1.00 0.77 1.33 1.00 1.67 1.03 2.00 1.73 2.00 2.00 2.00 2.80

Riyom03 0.60 1.33 1.00 1.87 1.33 1.27 1.33 3.57 1.67 5.03 1.67 7.37

Riyom04 1.60 0.70 1.67 1.23 1.67 1.50 1.67 2.77 1.67 3.90 1.67 5.80

Riyom01 1.30 0.57 2.00 1.03 2.00 1.47 2.00 2.60 2.00 4.10 2.00 6.70

Riyom02 0.00 0.00 0.33 0.50 0.33 0.50 0.33 0.50 0.33 0.70 0.33 1.63

Isci01 1.60 0.57 1.67 0.90 1.67 1.23 1.67 2.23 1.67 3.87 1.67 6.83

GSK01 2.63 0.90 3.33 2.01 3.33 3.20 3.33 4.98 2.67 8.87 3.67 12.87

F-ratio * NS NS NS NS NS NS NS NS NS NS NS

LSD2.225

Plates A-D: Tubers and sprouts of A=Bebot02, B=Riyom04, C=Isci01 and D=Riyom02

10 20 30 40 50 60 70 80 90 100 Fig. 1 Cluster diagram showing similarity relationships among accessions of Plectranthusesculentus.

G

F

Bebot 02

Riyom 04

Riyom 03

Bebot 04

Bebot 01

Riyom 02

GSK 01

Long’at 01

Riyom 01

Isci 01

Long’at 02

LNG03 V

I

III

IV

II

VI

Fig. 1 Cluster diagram showing similarity relationships among accessions of Plectranthus esculentus

6.3

5.6

4.9

4.2

3.5

2.8

2.1

1.4

0.7

LNG03

Isaio1

Long’at02

Riyom01

Long’at01

GSK01

Bebot02 Riyom02

Riyom03 Riyom04

Bebat01 Bebot04

0.7

1.4

2.1

2.8

3.5

4.2

4.9

5.6

6.3

Axis 1

Axis 2

Fig.2: Two dimensional polar ordination of 12 accessions of P. esculentus based on 17 characters.

A B

C D



An indented key has been presented below for the identification of sampled accessions using the tuber and sprout characteristics recorded in the research. This would enhance seed tuber selection (Askew, 1993):1a Tubers less than thrice as long as thick with sunken bud eyes......................................................22a brown epidermis-----------------------------------33a Trichomes 0-10 per 0.25cm2 and epidermal thickness less than30x103um-------Bebot04, GSK013b Trichomes 20-30 per 0.25cm2 and epidermal thickness greater than 30x103um..................................................................Bebot01, 02, Riyom02, 03, 04.2b Light brown epidermis--------------------Riyom011b Tubers more than thrice as long as thick with protruded budeyes Trichomes 20-30 per 0.25cm2-------------------------44aEpidermal thickness greater than 30x103um-----------------LNG03

4b Epidermal thickness less than 30x103um----Long’at01, 02, Isci01

CONCLUSIONSince little has been reported on the composition of the Nigerian Plectranthusesculentus, the current study has presented more information about the features of tubers in the various accessions under cultivation.The differences in the majority of tuber characters across accessions are indicative of diversity of traits which could warrant the delimitation of this taxon as suggested by Agyeno et al., (2014). However, a comprehensive morphological and molecular studies of more tubers collected from other localities within the guinea savannah region of Nigeria could form the basis for the construction of a better classification.

REFERENCES.Abdulrahman, A. A. And F. A. Oladele (2005). Stomata, trichomes and epidermal cells as diagnostic features in six species of Ocimum (Lamiaceae). Nigerian Journal of Botany 18:214-222. Allemann, J, P. J. Robbertse and P. S. Hammes (2003).Organographic and anatomical evidence that the edible storage organs of Plectranthus esculentus N.E.Br. (Lamiaceae) are stem tubers. Field Crop Research 83(1):35-39. Agyeno, O. E., A. A. Jayeola, B. A. Ajala and Blessing J. Mamman (2014). Exo-morphology of vegetative parts support the combination of Solenostemonrotundifolius(Poir) J.K.Morton with Plectranthus esculentus N.E.Br. Natal (Lamiaceae) with insight into infra-specific variability. AAB Bioflux 6(1): 16-25.Aigbokhan, E. I. (2014). Annotated checklist of vascular plants of southern Nigeria- A Quick Reference Guide to the Vascular Plants of Southern Nigeria: a systematic approach. Uniben Press, Benin City. 346p.Asfaw, Z. And M. Tadesse (2001). Prospects for sustainable use and development of wild food plants in Ethiopia. Economic Botany 55(1):47-62.Askew, M. F. (1993).Volunteer potatoes from tubers and true potato seed.Aspects of Applied Biology 35:9-15.Davies, P. H. (1960). Materials for the flora of Turkey. IV. Ranunculaceae, II. Notes Royal Botanic Garden Edinburgh 23: 103-161. Duncan, T. And Baum, B. R. (1981).Numerical phonetics: its uses in botanical systematic. Annual Review of Ecology and Systematics 12:387-404. Essiett, U. A. (2004).Biosystematic studies of some Nigerian Dioscorea species. Unpublished Ph.D. Thesis, Department of Botany and Microbiology, University of Uyo, Uyo, Nigeria.Fox, F. W. and M. E. N. Young (1962).Edible wild plants of southern Africa botanically identified and described. Delta Books, Johannesburg. Greuter, W., J. Mcneill, F. R. Barrie, H. M. Burdet, V. Demoulin, T. S. Filgueiras, D. H. Nicolson, P. C. Silva, J. E. Skog, P. Trehane, N. J. Turland, D. L. Hawksworth(editors and compilers). (2000). International code of botanical nomenclature (St. Louis Code) adopted by the Sixteenth International Botanical Congress St. Louis, Missoufi, July-August 1999. Regnum Vegetation Vol. 138Hather, J. G. (1994). A morphological classification of roots and tubers and its bearing on the origin of agriculture in southeast Asia and Europe. Journal of Archaeological Science 21(6):719-724.Kyesmu, P. M. And Mantell, S. H. (2002). Callus initiation and regeneration in a minor tuber crop Rizga (Plectranthus esculentus N.E.Br.). Global Journal of Agricultural Sciences 1(1): 55-62.Olowokudejo, J. D. And O. I. Pereira-Sheteolu (1988). The taxonomic value of epidermal characters in the genus Ocimum (Lamiaceae). Phytomorphorlogy 38(2,3):147-158.Purseglove, J. N. (1974). Tropical crops-Dicotyledons Vol. 112. Longman Group Ltd., U.K.Raj, L. J. M., S. J. Britto, S. Prabhu and S. R. Senthilkumar (2011). Identification if agronomically valuable species of Crotalaria based on phenetics. Agriculture and Biology Journal of North



America2(5):840-847.Snedecor, G. W. and W. G. Cochran (1969).Statistical methods, 6th ed. Iowa State University Press USA. 607ppSokal, R. R. and C. D. Michener (1958).A statistical method for evaluating systematic relationships. University of Kansas Science Bulletin 44: 467-507.Struik, P. C. And W. J. M. Lommen (1999). Improving the field performance of micro and mini tubers. Potato Research 42: 559-568. TABLE 5DISIMILARITY MATRIX BASE ON SIMPLE MATCHING COEFFICIENT (SOKAL AND MICHENER, 1958)

Bebo

t01

Bebo

t02

Bebo

t04

Long

’at0

1

Long

’at0

2

LNG

03

Riyo

m03

Riyo

m04

Riyo

m01

Riyo

m02

Isci

01

GSK

01

Bebot01 0.0 Bebot02 36 0.0 Bebot04 16 29 0.0 Long’at01 47 47 41 0.0 Long’at02 36 49 53 12 0.0 LNG03 53 53 71 29 23 00 Riyom03 36 23 16 36 47 53 0.0 Riyom04 36 23 23 36 47 53 12 0.0 Riyom01 29 53 47 16 12 23 41 41 0.0 Riyom02 16 29 36 41 29 47 29 29 23 0.0 Isci01 41 65 59 16 06 16 53 53 19 36 0.0 GSK01 29 29 16 41 53 71 29 29 59 47 47 0.0

TABLE 6SIMILARITY MATRIX BASED ON SIMPLE MATCHING COEFFICIENT (Sokal and Michener, 1958)

Bebo

t01

Bebo

t02

Bebo

t04

Long

’at0

1

Long

’at0

2

LNG0

3

Riyo

m03

Riyo

m04

Riyo

m01

Riyo

m02

Isci

01

GSK0

1

Bebot01 100 Bebot02 64 100 Bebot04 84 71 100 Long’at01 53 53 59 100 Long’at02 64 41 47 88 100 LNG03 47 47 29 71 77 100 Riyom03 64 77 84 64 53 47 100 Riyom04 64 77 77 64 53 47 88 100 Riyom01 71 53 41 84 88 77 59 59 100 Riyom02 84 71 64 59 71 53 71 71 77 100 Isci01 59 35 41 84 94 84 47 47 81 64 100 GSK01 71 71 84 59 473 29 71 71 41 53 53 100



CHEMICAL SCIENCES

PRELIMINARY ASSESSMENT OF HEAVY METALS AND WATER QUALITY OFSELECTED WELLS IN TALATA MAFARA, ZAMFARA STATE, NIGERIA

1Zubairu, A.Y. , 1Mukhtar, M., Sokoto, A.M. 2Zubairu, A. and 3 Ladifa, H.M. 1Department of Pure and Applied Chemistry, Usmanu Danfodiyo University, P.M.B, 2346, Sokoto, Nigeria.

2Department of Biochemistry, Kebbi State University of Science and Technology, P.M.B. 1144,Aleru, Nigeria.

3Department of Pure and Industrial Chemistry, Bayaro University, P.M.B. 3011, Kano, Nigeria.


Manuscript received:18/11/2016 Accepted: 30/12/2016 Published: December, 2016

ABSTRACTContamination of drinking water source by pollutants is one of the major problems affecting rural areas in developing countries like Nigeria. Accessibility of high-quality water is necessary for preventing diseases and improvingquality of life. Well water is the main source of drinking water in Talata Mafara, Zamfara State, Nigeria.Well water samples collected from two major locations, Tashar Kali (TKM) and Yarkurna area (YAM)ofTalata Mafara metropolis of Zamfara State, Nigeria were assessed for heavy metal concentration using atomic absorption spectroscopy (AAS). Also, a physicochemical analysis was carried out using standard procedure of water analysis approved by Association of Analytical Chemist and American Public Health Association. The results obtained revealed that both samples have neutral pH of 6.8, Turbidity of (3.23 and 5.46 NTU), electric conductivity of (29.5 and 29.6µ/cm), Phosphate (0.12 and 0.13mg/L), Temperature (21 and 21OC), TDS (1.5 and 1.7ppt), B.O.D (0.1 and 0.3mg/L), C.O.D (2.397 and 3.196mg/L) and D.O (4.0 and 2.0mg/L). The concentrations of heavy metals obtained were Cd 219µg/ml and ND, Cr 0.954 and 0.587µg/ml, Fe 1.948µg/ml and ND, Pb 0.587 and 0.158µg/ml, Mn 0.159 and 0.052µg/ml, Ni 6.620 and 3.104µg/ml, more over Cu, Se,and Zn were not detected in the both samples respectively. the results obtained are compared with the National standard (NSDWQ 2007) and International standard (WHO 2011), The research recommended that the Government should provide some water treatment agencies in the study areas to reduce the level of heavy metals in the water for safety purpose in terms of drinking and domestic uses.

Keywords: Heavy metal; Talata Mafara; Tashar kali; Water pollution;Well water; Yarkurna.


INTRODUCTIONWater is an important factor for survival of any human being and animals. Globally quality of portable water is major concerns for environmental, public health and domestic used (Maria et al., 2016). The ecological circumstances within water bodies are frequently altered as a result of differences in nature and anthropogenically persuaded dynamics. Moreover water superiority is definitely exaggerated from the origin of the water, rate of movement, proximate composition and population of algae. Additional features like sewage and agricultural run offs, different harmful chemicals and natural contaminants like (animal faeces) contact the natural origins of water bodies and as well pollute the ground water. Heavy metals are believed to be the major source of water pollution as they are non-biodegradable andcan destroy human and animal organs(Elsayed et al., 2015). Almost all the sources of water are surrounded by heavy metals.N Heavy metals from anthropogenic behavior may possibly roam or penetrate into aquifers and mingle with groundwater (Fahad, 2015).Heavy metal pollution within human organs have turned out to be a critical hazardsince they are non-biodegradable and can cause contemporary health challenges like environmental tumors. Metalshave been organized and moved into food webs due to the leaching from waste deposits, contaminated soils and water. The metals enhance in absorption within each stage of food chain and are moved to the subsequently advanced stage, an event described as bio-magnification. The pollution of water through matters that contain serious consequences to human beings, animal and plant is called water pollution(Khairia, 2015). Water pollution is a global crisis also to organize such problem has developed addicted to progressively more vital in current years. Urban or industrialized runoff extravagances by waste water management plants include many quantities of organic matter and contaminants counting metals like Cu, Zn, Cd and Pb etc. The uptake of metals by sludge flocks is among critical consequence in pollution control (Khairia, 2015). Almost the entire forms of water are surrounded by heavy metals; numerous of this occurs as result of the natural weathering of the globe. Additionally, wastewater utilized for irrigation land, as well as runoff from city sewage and industrialized wastewater, possibly will considerably influence water purity. Heavy metals from anthropogenic behavior may possibly roam or penetrate into aquifers and mingle with groundwater (Fahad, 2015).Nickel (Ni) is generally used in recent industry. Its more

expose in human beings cause inflame considerable effects as well as the lung, cardiovascular and kidney illness. Additional consideration been paying attention on the toxicity of Ni since it can proceed to allergic reactions and its positives compounds could be carcinogenic. Ni and various heavy metals go into in water bodies by the means of natural process from dissolution of rocks and soils, biological cycles, atmospheric fallout, mainly from industrial practices and disposal of waste batteries produce from Ni–Cd and pigments, electronic products, deposit electrode and at the same time as catalyst involve in hydrogenation reactions, which may boost the correlation of this element (Naeemullah et al., 2014).Iron and copper are vital bioelements, moreover there are important to their biological function and there are essential elements for body growth and besides there areharmless when found in excess than the standard limit require in the body (Jamal 2012). Cadmium and lead are also recognizedwith highly poisonous consequence moreover is not considered as important element for human being and identified to be element injurious organs like kidneys, liver, and lungs. It is already identified that cadmium gathers in liver and kidneys and the small of this element in human body is within one and four decades,in contact to lagerquantity of cadmium can effect destroyed to the central nervous and immune systems as well as to infertility confusion and rate of unusual cancer categories (Sezgin et al., 2013).Chromium is highly toxic which was released into the environment in a hugeamountmostly from metal finishing industry, petroleum refinery, leather tanning, iron and steel industries,which led to serious risk to human health. Nearly every one of the industrial wastes contains chromium which is current in hexavalent form as chromate (CrO4

2-) and dichromate (Cr2O7

-2)2. This species is very toxic to plant and animal. Therefore drinking contaminated water with Cr (iv) causes lung cancer, respiratory problems, bronchitis and various cause on body immunity system (Vinuth et al., 2015). Lead is very toxic heavy metal, its affect organs like bones, brain, blood, kidneys, and thyroid glands. Existence of lead within release and poisonous nature proceed to additional difficult problems on receiving waters in aquatic system. Even at very little concentration of heavy metals in water is extremely toxic to aquatic life. The main origin of lead and cadmium in water are the waste matter of processing industries like electroplating, paint, pigment, basic steel work, textile industries, metal finishing and electric accumulators’ batteries (Khairia 2015).As a result of the harmful and poisonous effects of iron, copper, lead and cadmium, it is essential to determine

ASSESSMENT OF HEAVY METALS AND WATER QUALITY OF SELECTED WELLS


and observe their content in water nowadays (Jamal 2012).Heavy metals pollution corresponds to a critical difficulty as these metals leach into ground water or soil, which isharmful to human health. Ground water pollution is an outcome of numerous activities like chemical manufacturing, painting and coating and mining. Metals apply a toxic effect on fauna and flora of lakes andstreams (Hanuman et al., 2012).Due to high toxicity of heavy metal in our source of water, which led to serious havoc to human health as well as affecting the environmental sustainability in any given society,thus, aimedto preliminary investigation of heavy metal in well water samples of Talata Mafara which was one of the major source of water for drinking and domestic used in the area.

MATERIALS AND METHODSThe samples of well water were obtained from two major locations of Talata Mafara metropolisof Zamfara State, Nigeria. These were collected in four polyethylene bottles each containing two liters.All samples were collected at the same time; the bottles were earlier washed with 10% HNO3 for 48h before collecting the samples in order to avoid loss of metals. The rubber bottles were labeled instantly as sample TKM and sample YAM.Where

• TKM = water sample collect from well at Tashar Kali behind Model Primary School TalataMafara.

• YAM = water sample collect from Yarkurna area TalataMafara.

50 cm3 of each water sample was transfer into evaporating dish, 10cm3 conc. Nitric acid (HNO3) were added for each sample, the samples were place on stem bath to evaporate 25 cm3, the samples were transfer to sample bottle, follow by addition of distilled water up to 50cm3 mark the preparesamples were taking to an AAS machine for analysis or determination (Fahad 2015) Heavy metals were analysis using a Perkin Elmer model 306 Atomic Absorption spectrophotometer at Usman Danfodiyo University Central laboratory. All the physicochemical analysis were carried out according to standard analytical methodsof analysis approved byAssociation of Analytical Chemist (AOAC 2005) and Standard methods for the examination of water and waste water approved by American Public Health Association APHA (2005).The pH, of the samples were measured immediately after sample collection using pH meter, while Analysis for total dissolved solid (TDS) and turbidity were determined using model 1000 TDS scanner and

model 2100p turbidity meter HACH, USA(AOAC 2005).Temperature of water samples were also measured at the support of collection using thermometer (AOAC, 2005). The phosphate was determined using the following process, after the preparations of the samples were poured into cubed and inserting into spectrophotometer to take the absorbance at 660 wavelengths. The percentage of phosphate was calculated using the equation reported by AOAC (2005)and APHA (2005) In Lawal et al., (2015).Chemical oxygen demands (C.O.D) were determined using appropriate procedure reported by winklers and calculated using the equationin Lawal et al., (2015) (AOAC 2005).Dissolved oxygen (DO) was determinedinitially and finally after 5-days incubation at 25oC, using the model 2002 filter HACH, which ledto the computed of biochemical oxygen demand (BOD) from the relation between them, this was achieved using procedure reported by winklers, AOAC (2005) In Lawal et al., (2015).

RESULTS AND DISCUSSIONTable 1: Results of Physicochemical Analysis

Parameters Result (TKM)

Result (YAM)

WHO Guidelines for drinking 2011

B.O.D (mg/L) 0.1 0.3˂5C.O.D (mg/L) 2.397 3.196 5.0E.C (µ/cm) 29.5 29.6 1400D.O (mg/L) 4.0 2.0 ˂5Temperature (OC) 21 21 273TDS (ppt) 1.5 1.7 500Turbidity (NTU) 3.23 5.46 1000pH 6.86.8 6.5-9.5Phosphate (mg/L) 0.12 0.13 50

Table 2:Results of AAS AnalysisElement Symbol Result

(TKM) Result (YAM)

WHO Guidelines for drinking 2011

Cadmium Cd 0.219 ND 0.05Copper Cu ND ND 0.05Chromium Cr 0.954 0.587 0.05Iron Fe 1.948 ND 0.3Manganese Mn 0.159 0.052 0.1Nickel Ni 6.620 3.104 0.07Lead Pb 0.587 0.158 0.01Selenium Se ND ND 0.04Zinc Zn ND ND 5.00

The results are compared with NSDWQ 2007 and WHO 2011.Keys: NSDWQ = Nigerian Standard for Drinking Water quality 2007WHO = World Health Organization 2011 Unit of concentration = µg/ml.



The biological oxygen demand (BOD) were found to be 0.1mg/l and 0.3mg/l for sample TKM and YAM, all results where bellow the maximum allowable limit (MAL˂5mg/l) required by (WHO 2011) and (NSDWQ 2007). The result BOD where compute from the relationship between initial dissolved oxygen (DO0) and final dissolved oxygen (DO1).The biological oxygen demand (BOD) is used to measure ecological organics. Chemical oxygen demand (COD)were found to be 2.397mg/l and 3.96mg/l for sample TKM and YAM which are lower than the maximum allowable limit (MAL 5.0mg/l) required by (WHO 2011)and (NSDWQ 2007). The dissolved oxygen (DO) were obtained to be4.2 and 2.6, sample TKM and YAM, all results where bellow the maximum allowable limit (MAL˂5mg/l) required by (WHO 2011)and (NSDWQ 2007).In general dissolved oxygen, biochemical oxygen demand and chemical oxygen demand are essential parameters which assist environmental scientists and engineers to make vital decisions about how to handle the treatment of waste water generally. Electrical conductivity (EC) were obtained to be 29.5(µ/cm) and 29.6(µ/cm) for sample TKM and YAM, the results were obtained bellow the maximum allowable limit(MAL 1400µ/cm) required by (WHO 2011) and (NSDWQ 2007). Temperature was observed to be 21 and 21 for sample A and B, the results are below the maximum allowable limit (MAL273OC) required by (WHO 2011) and (NSDWQ 2007). Total dissolved solids (TDS) was observed to be.5mg/l and 1.7mg/l for sample TKM and YAMthe results were found bellow the maximum allowable limit (1000mg/l) required by (WHO 2011)and (NSDWQ 2007).Turbidity was 3.23NTU and 5.46NTUfor samples TKM and YAM andthe results were found to be below the maximum allowable limit (25NTU) required by (WHO 2011)and (NSDWQ 2007).The pH valueswas found to be 6.8 and 6.8 for sample TKM and YAM were within the maximum allowable limit (6.5-9.5) required by (WHO 2011)and (NSDWQ 2007). All the physicochemical analysis were obtained within the maximum allowable limit for water quality index approved by Word Health Organization (2011) and benchmark of Nigerian Standard for Drinking Water Quality (2007). The concentrations of Cadmium Cd in the water samples obtained are presented in Table 2.The levels of cadmium found in sample (TKM) was found to be 0.219µg/ml this was above the detection limit approved by (WHO 2011)and (NSDWQ 2007)

while in sample (YAM) was found no any amount of cadmium concentration.Cadmium is a poisonous and carcinogenic metal. The main sources of cadmium publicity are cigarette smoke, food intake (shellfish, offal and certain vegetables), and ambient air, mostly in urban areas and in the surrounding area of industrial settings as well as those found in surface water (Jamal, 2012).The high concentration of cadmium found (0.219µg/ml) in sample (TKM) found above the required limit, when drink without undergoing any special treatment will lead to injurious of organs e.g. (kidneys, liver, and lungs) and other (Sezgin et al., 2013). Copper was found not detected in all water samples,this indicate that there is no any copper infectivity in the samples. Copperare important to several physiological processes(Rita et al., 2015).Chromium (Cr) was foundto be higher in all samples,0.954µg/ml and 0.589µg/ml founds in sample TKM and (YAM) were above the maximum allowable limit(MAL 0.05µg/ml) required for drinking purpose by (WHO 2011)and (NSDWQ 2007).Even do chromium Cr species with oxidation state (+3) is an important biological metal that participate in plants and animals metabolism (Vinuth et al., 2015), but higher concentration of chromium insample can affect the human health as well as environment (Vinuth et al., 2015). Iron (Fe) was found not detected (ND) in sample YAM but, 1.948µg/ml was found in sample TKM, this was above the maximum allowable limit(MAL 0.3µg/ml) approved by (WHO 2011)and (NSDWQ 2007).Manganese (Mn) was obtained to be 0.05µg/ml for sample YAM within the range (MAL 0.1µg/ml) approved by (WHO 2011) and (NSDWQ 2007), while 0.159µg/ml found higher in sample TKM of well water which was above the maximum allowable limit (MAL 0.1µg/ml) approved by (WHO 2011)and (NSDWQ 2007). Nickel (Ni) was found to be 6.620µg/ml and 3.104µg/ml for sample TKM and YAM the results were founds abovethe maximum allowable limit (MAL 0.07µg/ml) approved by (WHO 2011)and (NSDWQ 2007).It has been reported that Nickel (Ni) is generally used in present industry. Its more publicity in human beings can inflame considerable effects as well as the lung, cardiovascular and kidney diseases (Naeemullahet al., 2014). Lead (Ph) was found to be high in all samples 0.587µg/ml and 0.158µg/ml for sample TKM and YAMthe results exceeds the maximum allowable limit (MAL 0.01µg/ml) approved by (WHO 2011)and (NSDWQ 2007), high toxicity of lead in water sample can destroyed nervous links, particularly in



young children’s and cause high blood pressure and brain disarrays (Jamal 2012). Selenium(Se) was not detected (ND) in all samples. The maximum allowable concentration of Selenium(Se) is (MAL 0.04µg/ml) approved by (WHO 2011)and (NSDWQ 2007). Zinc (Zn) was not detected (ND) in all samples.The maximum allowable limit ofZinc (Zn)(MAL 5.00µg/ml) required by (WHO 2011)and (NSDWQ 2007). Although, such heavy metals as manganese, zinc and copper in small amount are essential for the physiological functions of living tissue and control many biochemical processes (Fahad 2015).

CONCLUSIONFrom the results obtained in this research, high concentration ofCadmium Cd, Chromium (Cr), Iron (Fe), Manganese (Mn),Nickel (Ni) and Lead (Pb) were available in the samples. The heavy metals are

known to promote pollution of the water. It istherefore worth noting that the well water is not a good source of drinking water or for domestic use even thoughthe physicochemical analysis was moderate. The Government should promote the establishment of some water treatment agency in the study areas to control the level of heavy metals in the water. It is recommended that further research to be carried out in the area of study, in order to determine the presence of other heavy metals such as Mercury (Hg), Cobalt (Co), Arsenic (As) or Silver (Ag),which have been reported to be among the heavy metals that pollute the environment and negatively affect human health.

ACKNOWLEDGEMENTWe acknowledgement the efforts ofProf. Abdullahi Abdu Zuru and Prof. Sani Muhammad Dangoggo of the Department of Pure and Applied Chemistry, Usmanu Danfodiyo University, Sokoto, Nigeria for their support throughout the research.

REFERENCES AOAC,(2005). Official method of analysis.15th edition, Association of analytical Chemist,Washington DC. 11-14.APHA,(2005). Standard methods for the examination of water and waste water. 21stediton,American Public Health Association, Washington DC, USA.Elsayed, M. Y., Nasser A. A., Abdel-Wahab A. A., and Abdullah A. A.,(2015). Seasonal Variations in the Body Composition and Bioaccumulation of Heavy Metals in Nile Tilapia Collected From Drainage Canals in Al-Ahsa. Saudi Arabia. Saudi Journal of Biological Sciences. 22: 443–447. Fahad, N. A.,(2015). Assessment of the Levels of Some Heavy Metals in Water in Alahsa Oasis Farms, Saudi Arabia, with Analysis by Atomic Absorption Spectrophotometry. Arabian Journal of Chemistry. 8: 240–245.FEPA,(2003). Guideline and Standards for Environmental Pollution and Control in Nigeria. Federal Environmental Protection Agency, Nigeria.Hanuman, R. V., Prasad, P. M. N., Ramana, R. A. V. and Rami, R. Y. V.,(2012). Determination of Heavy Metals in Surface and Ground Water in and Around Tirupati, Chittoor (Di), and Hra Pradesh, India. Der Pharma Chemica. 4(6): 2442-2448.Jamal, A. M.,(2012). Determination of Iron, Copper, Lead and Cadmium Concentrations in Rain Water Tanks in Misurata Libya. Journal of Science and Technology. 2(8): 7225-7217. Khairia M. A.,(2015). Water Purification Using Different Waste Fruit Cortex for Heavy Metals Removal. Journal of Taibah University Science. 15: 1-24. Lawal, A. M., Galadima A., Mukhtar, M., Zubairu, A. Y., Saidu, I., Isa, Z. I.and Nasiru, Y.,(2015). Evaluation of Heavy Metals Concentration in Borehole Water from Talata Mafara Metropolis, Zamfara State, Nigeria. International Journal of Science for Global Sustainability. 1(1): 69-74. Maria, J. W., Chad, T. J. and Loring, F. N.,(2016). The assessment of water use and reuse through reported data: A US case study. Science of the Total Environment,539: 70-77.Naeemullah, Tasneem, G. K., Hassan I. A., Faheem, S., Sadaf, S. A., Kapil, D. B., Jamshed, A., and Mariam, S. A.,(2014). Simultaneous determination of silver and other heavy metals in aquatic environment receiving wastewater from industrial area, applying an enrichment method. Arabian Journal of Chemistry. 8: 1878-5352.NRC,(2012). National Research Council. Water Reuse: Potential for Expanding the Nation’s Water Supply through Reuse of Municipal Wastewater. The National Academies Press.NSDWQ,(2007). Nigerian Standard for Drinking Water Quality. Nigerian Industrial Standard NIS 554. Standard Organization of Nigeria. Pp: 30.



Rita, M., Sara, B. P., Marianna, M., Rui, F., Arlete, S., Caroline, A. E., Roberto, D. P., Phillip, C. W and Paula, T., (2015). Effects of heavy metals on Cyanothece sp. CCY 0110 Growth, Extracellular Polymeric Substances (EPS) production, Ultrastructure and Protein Profiles. Journal of Proteomics. 120: 75-94.Sawyer, C. N., McCarty, P. L., and Parkin, G. F.,(2006). Chemistry for Environmental Engineering and Science, 5th Edition, Tata McGraw-Hill Put. Coy. Ltd, New Delhi.Sezgin, B., Tolga, Y., Nihan, T., Mehmet, D., Kemal, A. F., Onur, M., and Abdullah, K.,(2013). Determination of As, Cd, and Pb in Tap Water and Bottled Water Samples by Using Optimized GFAAS System with Pd-Mg and Ni as Matrix Modifiers. 2013: 7.Tommy, M. P. and Alfonds, A. M.(2015). Heavy Metals in Water of Stream Near an Amalgamation Tailing Ponds in Talawaan-Tatelu Gold Mining, North Sulawesi, Indonesia. Procedia Chemistry. 14: 428-436.UNWater,(2007). Coping with water scarcity.Challenge of the twenty-first century. UNESCO,(2000). Ground Water Pollution. International Hydrological Programme. Guidelines for drinking water quality, 4th edition, WHO, 2011. USEPA, (2012). IRIS US Environmental Protection Agency. Intergrated Risk Information System, Environmental Protection Agency Region I, Washington DC 20460. Accessible on line at http://www. epa.gov/iris/USEPA,(2015). United State Environmental Protection Agency, 2012b. Discharge Monitoring Report (DMR) Pollutant Loading Tool. Accessed 17 August 2015 at http://cfpub.epa.gov/dmr/.USEPA,(1995). Method 1638: Determination of Trace Metals in Ambient Waters by Inductively Coupled Plasma-Mass Spectrometry, EPA 821-R-95031. Washington, DC. USNWIS, (2015).United State National Water Information System, 2002. NWISWeb, New Site for the Nation’s Water Data. U.S. Dept. of the Interior, U.S. Geological Survey, Reston, Accessed 17 August 2015 at http://waterdata.usgs.gov/nwisVinuth, M., Bhojy-naik, H. S., Chandra-sekhar, K., Manjanna, J., and Vinoda, B. M.,(2015). Environmental Remediation of Hexavalent Chromium in Aqueous Medium Using Fe (II) – Montmorillonite as Reductant. Procedia Earth and Planetary Science. 11: 275-283.WHO,(2008). Guideline for Drinking-water Quality. 3rd edition. Incorporating 1stand 2nd Agenda, Recommendations. Geneva, 1: 668.WWD 2015. World Water Day. Accessed 12 February 2015 athttp://www.fao.org/nr/water/docs/escarcity.pdf.Yang, C. L., Guo, R. P., Yue, Q. L., Zhou, K., and Wu, Z. F., (2013). Environmental Quality Assessment and Spatial Pattern of Potentially Toxic Elements in Soils of Guangdong Province, China. Environ. Earth Sc. 70: 1903-1910.



EARTH SCIENCES

DELINEATION OF MINERAL POTENTIAL ZONES OVER KEFFI – ABUJA AREA

IN NORTH-CENTRAL, NIGERIA USING AEROMAGNETIC DATA.1 Jiriko, A. K. 2Mohammed, M. A. Kalu, O. 1 Udensi, E. E.

1Federal University of Technology Minna, Niger State, Nigeria 2Federal University Lafia, Nasarawa State, Nigeria



ABSTRACTAeromagnetic data over Keffi-Abuja,North-central Nigeria bounded by latitudes N08o50′, N10o00′ and longitudes E07o00′ and E08o00′ were obtained with the aim of delineating the mineral potentialzones and structural lineaments in the area. The data was subjected to series of processing to enhance the interpretation. First and second vertical derivatives filters were applied to the data to enhance shallow features. The result obtained revealed a NE-SW trend as the dominant trend of the structures of the shallow anomalies with a less dominant E-W and NW-SE trends and also the central to Western part around the Kagarko, Kafin and Abuja area are particularly promising for mineral prospecting.

Key words: Aeromagnetic Data, Mineral potential Zones, Lineaments, Basement Complex


INTRODUCTIONAeromagnetic survey is a rapid and cost effective technique for locating both hidden ores and structures associated with mineral deposits. Roughly about 60% of magnetic surveys are carried out for regional geological mapping and mineral exploration purposes while the remainder being mainly for petroleum exploration (Sharma, 1987).Mineral potential zones are areas with high concentration of minerals. These minerals are found in rocks. Most rocks are made up of aggregates of various minerals; however, some rocks are made up of entirely one mineral type. When these minerals occur in anomalous concentration within a rock, they become of interest to the exploration geophysicist.Lineaments are mappable linear or curvilinear structures such as faults, joints, and folds of a surface whose parts align in straight or nearly straight relationships. These structures can be mapped on different scales, from regional (continental), local (outcrop) to microscopic (thin section), (Onyewuchi et al., 2012).The process of extracting these structures from an aeromagnetic map involves several enhancement techniques or manual interpretations with the scope of analysing the density, orientation and intersection for mineral exploration.Several articles have been published on the Nigerian Basement Complex’s structural and tectonic framework, based on analyzing aeromagnetic data, (Ajakaiye et al., 1986; Olasehinde et al., 1990).

Ajakaiye et al., (1986) studied the Benue Trough’s tectonic framework and parts of the adjoining Nigerian Basement Complex using aeromagnetic maps, delineating NE-SW and ENE-WSW directions as being the dominant aeromagnetic lineament trends. They stated that these aeromagnetic lineaments depicted a possible continental continuation of the four Atlantic fracture zones (St Paul’s, Romanche, Chain and Charcot) abutting the West African coast into the Nigerian Basement Complex. However, the present study is concerned with the delineation of mineral potential zones Over Keffi – Abuja Area in North-Central, Nigeria using aeromagnetic data. The study area is an elongated N-S trending block situated within the north central part of Nigeria. It covers an area of about110 km x 165 km. The objectives of the study include: identification of prominent structural lineaments and their trends in the study area through the application of first and second vertical derivative filters. Aeromagnetic (data) method was used for the study because it has the ability of mapping magnetic mineral deposit (such as iron ore) and in delineating structural lineaments. The study area lies entirely within the Basement Complex of North-central Nigeria. It comprises rocks of the migmatite-gneiss and schist and generally intruded by the Pan African Older Granite rocks. The rocks of the study area have undergone various episodes of deformation and have ages ranging from Precambrian to Pan African. Fig. 1 shows the geology of the study area.

Fig.1: Geological Map of the Study Area, Modified from NGSA (2006)

DELINEATION OF MINERAL POTENTIAL ZONES OVER KEFFI – ABUJA


MATERIALS AND METHODSFor the purpose of this study, aeromagnetic data comprising of six (6) maps sheet (Bishini-165, Kachia-166, Abuja-186, Gitata-187, Kuje-207 and Keffi-208) covering the study area within the north central part of Nigeria were used. The digitized data were obtained from the Nigeria Geological Survey Agency (NGSA). The data from the six (6) maps were then combined into a single data from which the composite Total Magnetic Intensity (TMI) contour map was produced.The TMI map was analysed qualitatively to interpret closures and trends of magnetic anomaly over the area.Several other filters were applied to the magnetic data including derivatives (horizontal and vertical derivatives) in order to further interpret and delineate the mineral potential and structural boundaries in the study area.

RESULTS AND DISCUSSIONThe Total Magnetic Intensity (TMI) contour map (Fig. 2) shows variations in magnetic intensity across the area in contours. The map has been contoured at 10 nT interval using Surfer version 10 software. High contour values indicate areas of high magnetic intensities while low contour values indicate low magnetic intensities. Where the contours become rounded or elliptical in shape, we have magnetic closures and these closures could be high or low and often indicate anomalies. Where a set of contours appear in parallel or near parallel orientation indicates a fracture or fault zone often referred to as discontinuity. This map has also been aided with colour aggregates. Here, the portions with brownish colourations show areas with high magnetic intensities while bluish colourations indicate low magnetic intensities. The map reveals magnetic high values around Bishini and Kafin in the north-western portion and around Kuje, Keffi and Gitata in the south-eastern portion. Magnetic lows on the other hand dominate the areas around Abuja, Kagarko and Kachia to the central and north-eastern part. The variation in magnetic intensity across the area is an evidence of the polycyclic nature of the Nigerian Basement Complex, comprising rocks of different types with different magnetic intensities.

Fig.2: Total Magnetic Intensity Contour Map of the Area Contoured at 10 nT interval, after 25,000 nT have been Removed.

Magnetic Trends and ClosuresMagnetic trend refers to the orientation of the magnetic structures such as closures and discontinuities or lineaments in the area. Observation of the TMI contour map (Fig. 2) reveals a dominant NE-SW trend, however, E-W trends are observed around Bishini and Kafin area in the NW portion and around Keffi in the SE portion. Also, NW-SW trend is observed around Kagarko in the central portion of the area. These trends of E-W NW-SE and NE-SW correspond with the lineament trends of the Basement Complex of Nigeria as reported by (Ajekaiye et al., 1986) and (Olasheyinde et al.,1990).Numerous magnetic closures dot the entire survey area. Both magnetic highs and lows closures were observed and are elliptical in shape. The magnetic high closures are marked H while the magnetic low closures are marked L (Fig. 3). The magnetic high closures dominate the area around Bishini and Kafin in the north-western part and around Kuje, Keffi and Gitata area in southern and south-eastern parts. On the other hand, the magnetic low closures dominate the central portion around Kagargo and the north-eastern portion around Kachia.The magnetic low closure actually represents high magnetic anomalies intruding into the surrounding pre existing basement rocks. This is because at lower latitudes, due to the dipolar nature of the geomagnetic field, high magnetic anomalies appear as low magnetic anomalies.



Fig. 3: Magnetic Closures and Discontinuity within the Study AreaH = Magnetic high, L = Magnetic low, dd = Discontinuity

First Order Derivatives The first order derivative enhances magnetic anomalies associated with faults and other structural discontinuities (Dobrin and Savit, 1988; Telford et al., 1990). It can be either vertical or horizontal. It was used to infer reasonably to the orientation of the lineament in the area. The lineament on first vertical derivative map produced showed a dominant NE-SW orientation and a less dominant E-W and NW-SE orientations. These lineament trends correspond with that of the geological lineament map of the area from NGSA in terms of orientation. (Fig. 5).

Fig. 4: Lineaments on First Vertical Derivative Map of the Area

Fig. 5: Geological Lineament Map of the Study Area, NGSA (2006)

Second Vertical Derivative (SVD)Second vertical derivative filter was used to enhance subtle anomalies while reducing regional trends. This filter is considered most useful for defining the boundaries of anomalies and for amplifying fault trends. The SVD map (Fig. 6) reveals the boundaries of those shallow anomalies clearly. This made it possible to delineate the various mineral potential zones (A,B,C and D). The map shows that the central to western portions around the Kagarko, Kafin and Abuja are particularly promising for mineralisation. There are also smaller mineral potential zones to the NW around Bishini, SW around Kwali and Keffi in the SE region.

Fig. 6:Mineralisation Zones on Second Vertical Derivative Map of the Area



CONCLUSIONThe interpretation of the aeromagnetic data over the Keffi-Abuja area using various filters indicates that the area has good potential for high magnetic mineral, possibly iron ore. The TMI map produced show variation in magnetic intensity across the study area which is attributed to the occurrence of various rock types with different magnetic susceptibilities. The magnetic intensity values across the study area range from 7690 nT to 7990 nT (after 25,000 nT has been removed). High magnetic intensities dominate the southern to south-eastern region around Kuje, Keffi and Gitata while low magnetic intensities dominate the central to north-eastern portion around Kagarko and Kachia. The lineament map orientation showed a dominant NE-SW trend and a less dominant E-W and NW-SE trend within the study area. The possible mineral potential zones observed have been isolated and labelled A-D on the Second Vertical Derivatives (SVD) map. These zones show good prospects for high magnetic mineralisation.

REFERENCESAjakaiye, D.E., Hall, D.H., Miller, T.W., Verhergen, P.J.T., Awad, M.B. and Ojo, S.B. (1986).Aeromagnetic anomalies and tectonic trends in and around the Benue Trough, Nigeria. Nature, 319, 582-584.Dobrin, M.B. and Savit, C. H. (1988).Introduction to geophysical prospecting (4thed.). New York: McGraw- Hill. Pp. 867.Obaje, N.G. (2009). Geology and mineral resources of Nigeria. Berlin: Springer-Verlag, Heidelberg, Pp. 221.Olasehinde, P.I., Pal, P.C. and Annor, A.E. (1990).Aeromagnetic anomalies and structural lineaments in the Nigerian Basement Complex. Journal of African Earth Science,11, (3/4):Pp.351-355.Onyewuchi, R.A., Opara A.I., Ahiarakwem C.A. and Oko F.U., (2012). Geological interpretations inferred from airborne magnetic and Lansat. data: Case study of Nkalagu area, Southeastern Nigeria. International Journal of Science and Technology, 2 (4).Nigerian Geological Survey Agency, (2006).Geology and Structural Lineament Map of Nigeria. Abuja: NGSA.Sharma, P.V. (1987). Geophysical methods in geology; Amsterdam, Oxford New York: Elservier Scientific Publishing Company.Telford, W. M., Geldart, L. P., Sherriff, R. E. and Keys, D. A. (1990).Applied geophysics (Ed.), Cambridge: Cambridge University Press, Pp. 860



EARTH SCIENCES

THE APPLICATION 3D SEISMIC DATA INTERPRETATION TO HYDROCARBON PROSPECT MAPPING IN “DEDE”FIELD,NIGER DELTA.

1Adeoye, T.O., 2 Johnson, L. M. and 3Ologe, O.1Department of Geophysics, University of Ilorin.

2Department of Geology, University of Ilorin.3Department of Applied Geophysics, Federal University, BirninKebbi.

Corresponding Email: [email protected].


ABSTRACTIn certain frontiers, the dense sampling of 3D seismic surveys can help to identify subtle subsurface structures and thin beds through detailed imaging. The 3D technology can also provide a hint on reservoir quality. This research was carried out toshow how 3D seismic data interpretation is applied to robust subsurface imaging and prediction of lateral reservoir quality.The integrated study involved 3D visualization, direct reservoir identification, structural interpretation, amplitude extraction and seismic reservoir evaluation constrained by porosity at the wells. Initial 3D visualization was done by scanning through seismic lines to locate seismic amplitude anomalies that can be related to direct hydrocarbon indicators. Amplitudes of interpreted horizons were tracked to access stratigraphic features. The tie between the significant seismic reflections and borehole section were established from the synthetic seismogram. Well log analysis was done and results compared with seismic interpretation.The seismic interpretation confirmed reservoirs which have appeared on well logs. In addition, a bright spotwasobserved on some of the lines in areas not penetrated by wells.Trapping mechanism for the bright spotmay be stratigraphic.In addition, structural maps revealedfour way closures as traps for the reservoirs interpreted from well logs at the center of the field. Implications of these results in determining reservoir characteristics of sandstones and in permitting hydrocarbon production from well log analysis is further discussed.

Keywords: 3D seismic, well ties, reservoir, amplitude, horizons.


INTRODUCTION There are many published worksof exploration and exploitation successes attributed to 3D seismic data acquisition and interpretation (Brown, 2004). Today, many exploration and production projects rely upon 3-D seismic exploration technology to assess prospects and optimally position wells. The advantages of 3D seismic acquisition and interpretation over 2D seismic are enormous. In 2D seismic data acquisition, since lines along which geophysical measurements are made are far apart, such that there are significant gaps between adjacent lines, there are limitations to the subsurface information that can be available for interpretation.3D seismic is distinguished by the acquisition of seismic lines at closely spaced intervals. This leads to a true data volume from which lines, slices or ‘probes’ can be extracted in any orientation. The key components of 3D seismic interpretation may include the following: structural interpretation/modeling, stratigraphic interpretation, seismic inversion and reservoir evaluation(Bacon et al., 2003).Traps are accurately revealed using a well packaged structural interpretation workflow. To aid structural interpretation, seismic attributes may be employed for faults identification. Attributes may also be important in defining the shape, limits and characteristics of reservoir bodies. Sometimes, on a horizon slice, for example, a pattern which is not related to structure may be interpreted as a depositional, lithologic or erosional feature (Brown, 2004). Reservoir evaluation on the other hand, deals with assessing the reservoir quality. The challenges of reservoir quality characterization include - the ability to assess and predict reservoir facies, its geometry, distribution as well as reservoir porosity estimation. Starting with the petrophysical information at well locations, this is averaged for a given reservoir, therefore a spatial distribution of porosity can be obtained on seismic, constrained at the wells and depending on the quality of seismic data available (Cosentino, 2001). It should be noted however, that, there may or may not be relationships inherent between seismic and log data. Therefore, a correlation must be established between well log data and seismic amplitude or acoustic impedance (David and Michael, 2010). If the correlation exists, the seismic data can be integrated with the well log data to predict the reservoir property of interest. With this level of activity and euphoria, the issue that needs attention is that of maximizing the potential of 3D technology and applying the technology appropriately. Horizons need to be tied correctly and attention must be given to the limitations

posed by data defects. This study was carried out to explore the impact of 3D seismic interpretation on the dataset; imaging the subsurface with a view to delineateadditionalreservoirs. This was achieved bytying structures that may correspond to potential hydrocarbon traps to wells, identifying hydrocarbon prospects in area not penetrated by wells, determining the relationships between seismic properties and well log properties (amplitude and porosity) for a given areaand generating a model illustrating lateral and vertical distribution of reservoir porosity.Data analyzed for the study include poststack 3D seismic data which has 161inlines and 195 crosslines. The study covers an approximate area of 24sqkm. Three wells are drilled in the field, labeled A, B and C respectively. Most of the wells have lithology, resistivity and porosity logs. Check shot surveys arealso available fromthe wells.

MATERIALS AND METHODSThisintegrated study involved structural interpretation, amplitude extraction of key surfacesand reservoir evaluation from log calibration.Schlumberger’s Petrel workflow tools and The IHS kingdom suite were used for interpreting the data.Using 3D visualisation, the whole volume was inspected to get a general impression of structural and stratigraphic features of interest.Reservoir horizons were picked from well logs.Well to seismic tie is based on a synthetic seismogram correlation using sonic log, density log and checkshot data from well A. The tie, when achieved, formed the first step in picking events, which represents the tops of the reservoir sands for interpretation on the seismic sections. Subsequent interpretation procedure for structural interpretation followed the process of manual picking of horizons on inlines and crosslines. This was combined with volumeautotracking and interpolation.Structural interpretation also involved seismic attribute analysis for fault identification. Co variance attributes was generated for the seismic cube and studied throughout the length of the data. Time to depth conversion of the mapped time events was carried out using interval velocity obtained from checkshot data.Chosen reference surfaces are the surfaces above the 3D grid model. Velocity information was obtained using Linvel’s method, the linear expression: V=V0 for the first layer. Where V0= Interval velocity;For the subsequent layers, where it is assumed that velocity changes vertically by a factor of K, the equation: V=V0+kZwas used. Where K= constant values between 0 and -0.2 and Z= Distance from point of observation to datum.

APPLICATION 3D SEISMIC DATA INTERPRETATION TO HYDROCARBON PROSPECT MAPPING.


Assessment of stratigraphic features was conducted for target zones based on amplitude extraction of interpreted horizons. Reflection which could be misinterpreted for bright spots on the horizon slices were identified first on the seismic sections. Before reservoir evaluation, quality check was done by plotting (from statistical extraction) seismic amplitudes against porosity logs in the wells for the reservoir zones. Porosity was modeled in the reservoir Sands only for scenarios where we have agood correlation between porosity and seismic amplitudes.

RESULTS AND DISCUSSIONBright spots are identified in regions where well penetrations are not available. The natural pairing of the amplitudes was used to validate the existence of the bright spots (Fig. 2).In addition, structural features of interest, represented by seismic amplitude anomalies are revealed from seismic visualization (Fig. 2). The seismic anomalies fall within the hydrocarbon producing intervals in the Niger Delta and wells penetrating this zones have revealed hydrocarbon presence. This was observed in the central part of the field (Fig. 3). Hydrocarbon zones were interpreted on the wells. Prospect P1 is shown in Fig. 4. Matching seismic with log data revealed a moderate tie between prospective intervals on well A and seismic (Fig. 4). Well to seismic matching in Well B did not reveal a good match and therefore is not included in the study. Overall three prospects were identified, labeled as P1, P2 and P3. Prospect P1 and Prospect P2 were mapped on well logs and tied to structures on seismic, while the P3 reservoir was outside the range of well controlin most parts and observed as a bright spot (Fig. 2). On the depth structure map in Fig.5, the hydrocarbon sands are trapped in the area, marked

“P1”. The areas covered by “P1 and P2” are large (Approximately 9km2 and 8.6km2 respectively). They are interpreted as a four way dipping closures(Fig 5& 6). The maps shows moderate vertical relief for the dip of the observed closures. This is considered good for hydrocarbon amassing.Co variance cube did not show any evident faulting along the hydrocarbon trap area for prospect 1(Fig.5). Prospect P1 is therefore interpreted as anunfaultedsimple rollover anticlinal structure (Fig. 5). The crestal blocks of simple structures are highly prospective and commonly contain considerable volumes of hydrocarbon (Ajaikaiye and Bailey, 2002). The same structural patterns are observed at deeper sections (Fig. 6). Prospect P2 was identified at deeper depth but, in terms of location, there is a shift away from the center of the field (Fig. 6).

Attribute tracking on the prospective surface P1 , using seismic attributes (r.m.s amplitude) did not reveal any stratigraphic features as amplitude conforms with structure.The generated attribute map revealed a well-defined area of high RMS amplitude values at the center of the field. This is a possible flat spot(Fig. 7). It is possible however, that, some of the high amplitudes are as a result of tuning phenomena. The amplitude anomalyin the bright spot region may represent porous channel belts since it can easily detect porous lithologies.

IMPACT OF 3D SEISMIC DATA INTERPRETATION ONRESERVOIR QUALITY Cross plot of amplitude and porosity from well A reveals problem areas, which show up as outliers in the scatter plots (Fig. 8). Therefore, the generated porosity model is not an accurate representation of the lateral and vertical reservoir quality.Also, the heterogeneities within the reservoir caused by lithologic variations, are not captured by the petrophysical model. This may be obtained from facies modeling which is also intended at a later stage for this study. In the diagram, porosity and amplitude correlation has a correlation coefficient 0.501.The subsequent porosity map generated revealed porosity is highest in the central part of the field(Fig 9).In the diagram, porosity within the reservoir zone for prospect P1 ranges between 0.05 and 0.20 while that of Prospect P2, observed from the logs, ranges between 0.112 and 0.205. Porosity shows decrease with depth, a factor that may be caused by compaction and lithofacie association. Reservoir thickness obtained from the logs ranges between 29.73mand 30.0m for Prospect P1 while that of Prospect P2 ranges between 15.41mand15.42m. Vertical thickness and horizontal aerial delineation of sand body are good for hydrocarbon production and may be linked to environment of deposition.

CONCLUSION.Prospective zones have been mapped with 3D seismic data interpretation. There is a match on seismic with the wells for prospect P1. An additional prospect related to high amplitudes have been observed away from well zones. Trapping mechanism is mainly structural, a four way dipping closure. However trapping mechanism for the bright spot region is probably stratigraphic as no fault or structural deformation was observed in the area. Structural maps reveal a decrease in area of the closure at the center of the field at deeper depths.Changes in seismic amplitude response at the reservoir zones in the field may berelated to lithologic and porosity changes. Reservoir characteristics from



well indication are enough to permit hydrocarbon production. Seismic litho facie modeling and inversion is recommended for further studies.

ACKNOWLEDGEMENTThe authors also acknowledge the efforts of the reviewers, for their useful comments and suggestions.

REFERENCESAjakaiye, E.D. and Bally, A. W., (2002).Course Manual and Atlas of Structural styles on reflection profiles from the Niger Delta.American Association of Petroleum Geologists continuing education course note series. 41.Bacon, M., Simm, R. and Redshaw, T., (2003). 3D Seismic Interpretation.Cambridge University Press.Brown, A. R., (2004). Interpretation of three-dimensional seismic data, 6th ed.: American Association of Petroleum Geologists, Memoir 42.Consentino, L., (2001). Integrated reservoir studies. Institut Francais Du Petrole Publications, 51.David, H. J. and Michael, R. C., (2010).Methods and Applications in Reservoir Geophysics, SEG Investigation in Geophysics series.15 (15).


Fig 1:Base Map of Dede Field showing dense sampling of acquired data (seismic lines) and wells (A, B and C).Well C is a deviated well represented by a black curvy line.

Extension of Prospect P_1

Prospect P_3. Bright Spot Exhibiting Natural Pairing

Figure 2: 3D Seismic Section over Prospect P_3. Amplitude anomalies representing bright spots are enclosed within oval.

Figure 2: 3D Seismic Section over Prospect P_3. Amplitude anomalies representing bright spots are enclosed within oval

Fig 1:Base Map of Dede Field showing dense sampling of acquired data (seismic lines) and wells (A, B and C).Well C is a deviated well represented by a black curvy line.


Seismic Anomalies: Prospect Zone

P_1 located at the center of the field. Seismic Anomalies:

Prospect Zone

P_2.

Figure 3: Seismic Vizualization:

Prospect Zone P_1 and P_2 were captured at a glance as bright zones before detailed analysis was carried

Synthetic

Seismogram

Well Seismic

Gr Log

Generated Reflectivity Log

Resistivity Log

Figure 4: Well to Seismic reveals a moderatetie. A time shift of 5mswas required to match.

Reservoir P_1Top

Reservoir P_1 Bottom

Prospect P_1

Figure 5: Depth Structure Map showing Prospect trapped in anticlinal closures.

Covariance attribute not showing any prominent fault pattern.

Covariance Attribute

Fig. 6:Depth structure map draped over depth structure map of Prospect P_1. Area of closure decrease and there is a shift from the center of the field.

Amplitude conforming moderately

with structure:

possible flat spot.

Fig. 7:AnomalyR.M.S amplitude map draped over depth structure map of Prospect P_1.

Figure 8: Crossplot showing correlation between statistically extracted porosity and Seismic Amplitude for Prospect P_1 interval.

Outlier on the Plots.

Figure 9: Porosity Model showing that areas within Prospect P_1 are within good porosity values.


Figure 3: Seismic Vizualization:Prospect Zone P_1 and P_2 were captured at a glance as bright zones before detailed analysis was carried out on well logs.

Figure 4: Well to Seismic reveals a moderatetie. A time shift of 5ms was required to match

Figure 5: Depth Structure Map showing Prospect trapped in anticlinal closures. Covariance attribute not showing any prominent fault pattern

Fig. 6:Depth structure map draped over depth structure map of Prospect P_1. Area of closure decrease and there is a shift from the center of the field

Fig. 7:AnomalyR.M.S amplitude map draped over depth structure map of Prospect P_1

Figure 8: Crossplot showing correlation between statistically extracted porosity and Seismic Amplitude for Prospect P_1 interval.

Figure 9: Porosity Model showing that areas within Prospect P_1 are within good porosity values.


GEOSPATIAL MODELING OF LAND USE MANAGEMENT FOR SUSTAINABLE URBAN DEVELOPMENT IN KARU, NASARAWA STATE, NIGERIA

1Joshua, J. K., 2Jobin P.D. and 2Kuhiyop M.E.1Department of Geography, Nasarawa State University, Keffi, Nigeria

2Department of Geography, Ahmadu Bello University Zaria, Kaduna State, Nigeria

Coresponding Email: [email protected]


ABSTRACTLand use management is a major challenge of urban development in Africa. Karu Area of Nasarawa State, a principal satellite town of the Federal Capital Territory, Abuja is the fastest growing city in Africa since the relocation of the Federal Capital from Lagos to Abuja in 1991. The proximity of the area to Abuja has attracted rapid economic and population growth with speed up urban expansion and poor living environment.This study used geospatial technology in Land use management of the area. Multi-Criteria Decision Analysis using Euclidean distance and weighted overlay from spatial analyst tool of ArcGIS, supported by Analytical Hierarchical Process were used to assess the suitability of the area for urban development. The Land Use Act Cap 202 of 1990 and the Nigeria Urban and Regional planning Act of 1990 were adoptedusing buffering operation in ArcGIS to determine encroachment of buildings/structures into approved setback areas of water bodies and road network. The study revealed land acquisition through informal delivery, resulting to haphazard constructions, and encroachment of buildings/structures into the approved setback areas. In addition, the areas are prone to flooding and erosion. It is recommended that, urgent need for the use of geospatial technology for land acquisition and management be adopted for sustainable development of the area.

Keywords: Land Suitability, Urban Expansion, Geospatial Modeling, Multi-Criteria Decision Analysis

ENVIRONMENTAL SCIENCES


INTRODUCTIONLand use management as conceived by Mabogunje (1992); Durand-Lasserve (1990) and Kombe (1995) is a process` involving different stakeholders in planning, facilitation and controlling land use for sustainable development. Land use management entails decision making and the implementation of decisions about land. It involves making fundamental policy decisions about the nature and extent of investment. The scope of land use management involves private and public sectors that develop and make use of land; law which sets out rules and procedures in the management system; agencies which make decisions on how land may be used at various levels of government and plans which inform decisions on how land may be used (Nags and Kudrat, 1998).Most African countries have a history of land use management processes dating back to their respective periods of colonial rule. However, Land use management in Nigeria could be said to begin in 1863 when the Nigerian Town and Country Ordinance was enacted by the Colonial Government (Mabogunje, 1992). Formal land use management in Nigeria began in1946 with the enactment of the Town Improvement Ordinance. The content of land use management can be described in terms of ecological, social and market values which must be brought into balance by land use planners (Sui, 1992). Among the many concerns of land use planners in guiding the spatial arrangement of activities is the optimum utilization of land for the benefit of society (Shuaib, 2005). This involves making choices between available alternatives which involvesthe assessment of the fitness of the land for urban development. Land use including cultivation, residential, commercial and industrial uses has been proved to alter the structure and functioning of the ecosystem (Sodeinde, 2002).Of recent time these land use areexperiencingspatio-temporal changes due to the rapid growth of population and migration with increases pressure on land. A number of policies has been articulated and implemented to impinge urban land use in Nigeria; these include the Land use Act of 1978, Urban Development Policy of 1992, Urban and Regional Planning Act and the Housing and Urban Development Policy of 2002. Similarly, land use planning and control measures have been introduced to improve urban land use planning and urban development (Aribigbola, 2008). Despite the existence of these laws and policies, there is a general inefficiency of land policies and inadequate land use management in the study area..This study used geospatial technology and spatial multi criteria decision analysis (MCDA) for land use management of Greater Karu Urban Area of Nasarawa State, Nigeria.

MATERIALS AND METHODSGreater Karu Urban Area has a central location in the middle belt region of Nigeria. The area stretches between Latitude 8° 461 N and 9° 07I N and Longitude 7° 33IE and 7° 50IE; and covers an approximate land area of 704 SqKm. The area incorporates the settlements of Mararaba, Ado, New Karu, New Nyanya and Masaka in Karu Local Government Area of Nasarawa state. The area is bounded with Abuja, the Federal Capital Territory to the west, Keffi Local Government Area to the South and Jaba Local Government Area of Kaduna State to the North (Figure 1). The terrain of the area is gently undulating with altitude range between 180 to 500m above sea level and dissected by a network of streams and rivers, with Uke and Ado being the major. Mean annual rainfall is between 1000 mm and 1500 mm with vegetation type of the southern Guinea Savannah which consist trees, shrubs, grasses and gallery forest along major streams, valleys and pronounced depression (Illoeje, 1985). The geology of the area is founded by Precambrian basement complex structure with a combination of different metamorphic, igneous and sedimentary rocks including alluvial deposits found mainly in the stream-beds. The soils derived from this bedrock structure are generally deep and well drained with high fertility rating and variable run-off potential (Yari et al 2002, Obaje et al., 2007). GKUAhas an estimated population of 124, 427. Specific ethnic groups in the area include Gbagyi, Koro, Yeskwa, Gwandara,Gade,Mada, Igbo, Tiv, Yoruba among others Nigerian ethnic groups who migrated to the area to take advantage of the economic potentials of the area..

Figure 1: Greater Karu Urban Area

GEOSPATIAL MODELING OF LAND USE MANAGEMENT FOR SUSTAINABLE URBAN DEVELOPMENT


The data and materials used for this study were SPOT-5 image with spatial resolution of 5 meters acquiredfrom National Centre for Remote Sensing, Jos. Also the topographic, soil and geological map of the study area at a scale of 1:50,000 were also acquired from the office of the Surveyor General of the Federation and Geologic Survey of Nigerian Agency Abuja. The data sets were used to extract information on water bodies, road network and intersections, contour, soils and Geological featuresof the study area using onscreen digitization in ArcGIS 10.2 software. Global positioning system was also used to capture locations (coordinates) of schools, health centers, Public water (Bore holes), and markets. Questionnaires were also administered to stake holders (land holding households, agents, developers and professionals) to establish ways in which people access land in the study area. Data acquired under this source include; the process of land acquisition, the physical features of the study area and the players in the informal land market in the area among others. Stratified random sampling technique was used to sample 229 respondents spread across the study area. Respondent include. For each criterion, a suitability score was applied using a 10-point scale to determine the qualitative rankings of the suitability on each criterion. The rankings range from 1 (Not suitable) to 10 (Highly suitable). This “positive direction” Voogd (1983) is chosen to keep the scores understandable since the higher the score the more suitable the site is. The suitability classes are given below:-

Not suitable (1): This is attributed to sites with characteristics imposing certain constraints which cannot be overcome or technically excluded for development e.g. steep slope areas, areas prone to flooding, etc. Moderately Suitable (5): A level for sites with characteristics imposing constraints which can be overcome but by moderate and massive investment. Highly Suitable (10): Areas with characteristics imposing no significant constraints for development. This includes sites with flat topography, good soils forconstruction and lands free from flooding. Lands economically suitability were based on the distance from a specific feature. For instance the closer a lands to a schools, existing residential areas, roads, health care, public water, and existing commercial areas the higher the suitability for residential and commercial use. Though this type of suitability ranges between 1 and 10 however, the distance between value 1 and

10 may differ between features. For a certain sub-objective the suitability value of 5 could be at 1 km from the feature while for another sub-objective the value 5 is at 5 km. This depended on the importance of a feature to be at close range.

The weighted overlay technique is a GIS-based method of modeling the suitability in any particular situation. This involves setting up of an evaluation scale. For this study, the attributes of each datasets were ranked based on a scale factor of 1-10 using Analytical Hierarchical Process.The influence value of each factor was based on their suitability for urban expansion. If an objective contained sub-objectives, the result was a weighted combination of all the sub-objective maps. However, if the objective did not contain sub-objectives, it was created the same way as a sub-objective. In both cases, though the final result was a map, the creation of the goals was more or less similar as the objectives. The resulting maps of the objectives were combined and weighted which resulted in a final map for that goal.Sub-objectives were based on one or more layers depending on what they represented. Sometimes a combination of layers was needed to cover a topic. To illustrate a sub-objective from the urban expansion aimed at finding places proximal to medical centers, one data set with hospitals and another data set with medical Centre were combined to cover the topic of health care. Then the Euclidean distance from these health centers was calculated and reclassified in values between 1 and 10, where 10 represent highly suitable areas and 1 low suitable area. All these steps were modeled within the Model Builder with a map as final result.Figure 2 presents the flowchart showing the research design of Land-use model for GKUA. The weighting was done using Analytical Hierarchical Process.



Goal: 1 Land suitable for residential use

Lands most suitable for

urban development

Lands proximal to

existing commercial

land use

Soils suitable for residential

use

Lands free of flood potential

Lands proximal

to schools

Lands proximal to roads

Lands proximal to public

water service

Lands proximal to health care

Lands proximal to existing residential

areas

Lands with suitable

topography

Lands physically suitable for

residential use

Lands economically suitable for

residential use

Lands suitable for commercial use

Lands free of flood potentials

Lands with suitable topography

Lands physically

suitable for commercial

use

Lands proximal to existing commercial areas

Lands proximal to roads

Lands proximal to major roads intersections

Lands proximal to existing residential areas

Lands economically suitable for commercial

use

Goal: 2 Lands

suitable for commercial

use

Figure 2: Land-use model of GKUA modified after Carr and Zwick(2007).

Sub-objectivesSub-objectives

ObjectiveObjective

Objective Sub-objectives

RESULTS AND DISCUSSIONFrom the results presented in Table 1, 74.0% of the respondents accessed their plots of land through the landholding families who constitute the greatest suppliers of land in the informal delivery channels in the study area. The traditional authorities delivered 1%. Land allocated by government agencies for land administration accounted for about 2.9%. This reveals weak influence by the public authorities over access to land in the area.

Table 1: Channels of Plot AcquisitionChannels Frequency PercentageBought from Landholding families

154 74.0

Allocated by Traditional Authority

2 1.0

Allocated by Government 6 2.9

Gift 11 5.3Inheritance 15 7.2Not Applicable 20 9.6Total 208 100.0

Source: Author’s field survey, 2012

Table 2: Size of Plot (m2) The results presented in table 2 shows that 38.0% of the respondents who own their plot of land within GKUA had less than 900m2 of land. 29.8 had their lands measuring above 900m2, with 21.6% respondents having plots of land measured 900m2 . This shows that 38% out of 88.9% of those who own land within GKUA have their plots measure less than 900m2, which depicts a dominance of high density leading to compact pattern of development which is largely horizontally inclined. This comes with attendant consequences such as; congestion, overdevelopment, overcrowding, overstretch of utilities and infrastructure.

Size Frequency Percent Valid

Percent

Cumulative

Percent

900 Square metre 45 21.6 21.6 21.6Above 900 Square meter 62 29.8 29.8 51.4

Less than 900 Square meter 79 38.0 38.0 89.4Not Applicable 22 10.6 10.6 100.0Total 208 100.0 100.0

Source: Author’s field survey 2012

Sub-objectives



Urban ExpansionUrbanexpansion includes everything that is important when it comes to urban development. Carr and Zwick (2007) defined this category to include all land-uses commonly found within the umbrella of urban area. These include residential, commercial, institutional and industrial land uses. Two goals were particularly stressed in the urban expansion: Lands most suitable for residential and commercial use. Each goal was subdivided into two objectives; lands most suitable from both physically and economically point of view. These were then further subdivided in themes that were of relevance for the concerning objectives. In dialogue with Nils Viking (personal communication, 2009) project manager and urban planner, it was decided to create mixed development instead of block zoning. To achieve this, all urban types except industrial land-use are included in each other’s preferences and defined as suitable. This means that residential and retail areas are also suitable for office and commercial land- uses.

Lands Suitable For Residential UseThe first goal aimed at finding the most suitable lands for residential uses consist of two objectives and nine sub-objectives based on economic and physical suitability as presented in Figure 2. First, the objective dealing with economic suitability revealed that it is important to live close to facilities like schools and health care for the vast majority of the population. In general, people prefer to live near one another and therefore lands proximal to existing residential areas were included as most suitable. Furthermore, it is convenient to live close to roads. Also, it is cost-effective to have residential areas close to existing public water services. Finally, lands proximal to existing office/commercial and retail land-uses were also identified as suitable as presented in Table 3 and Figure 3.

Table 3: Weighted overlay table of Lands economically suitable for residential use

Parameters Influence (%)

Scale value

Existing residential land use 50 Ranked between 1-10 based on proximity

Land proximal to schools 10 Ranked between 1-10 based on proximity

Land proximal to health care10 Ranked between 1-10 based

on proximityLand proximal to roads (buffered)

10 Ranked between 1-10 based on proximity

Land proximal to bore holes 10 Ranked between 1-10 based on proximity

Land proximal to existing commercial land use


Source: Authors GIS analysis, 2012

Apart from sub-objectives dealing with economical suitability, there are also a number of Sub-objectives describing the physical suitability for residential land-use. Three sub-objectives were included to model this type of suitability. First of all, the soil should be suitable to build on. Lixisols is the most suitable for construction use as such ranked the highest-7. Secondly, the land must be free of potential floods in order to be a safe place to live. Finally, the topography must be suitable for residential use; flat topography is more suitable than steep sloppy terrain as presented in Tables 3, 4, 5 and Figures 3 and 4.

Table 4: weighted overlay table of Lands physically suitable for residential use Parameters Influence (%) Scale value

Soils 25 Lixisols -7Arenosols -2Leptisols -1

Flood potential 40 5- Very low 4- Low 3- Moderately low 2- High 1- Not suitable (very high)

Slope 35 Low -5Medium -3High -2


Table5: weighted overlay table of Flood potential areaswParameters Influence (%) Scale value Slope

20 Low -5Medium -3High -2

Water Bodies 65 Ranked from 1-10 based on proximity to water bodies

Soils 15 Arenosols -7

Leptosols -2Lixisols -1


Table 6: weighted overlay table of Lands suitable for residential useParameters Influence (%) Scale value Land physically suitable 70 5 High

3 Medium2 Low1 Restricted (Not suitable)

Land economically suitable 30 5 High3 Medium2 Low1 Restricted (Not suitable)




Figure 3: Flowchart showing the flood potential areas.

Figure 4: Flowchart showing the creation of objectives and goals for Lands suitable for residential uses. Fictive

weights have been added to illustrate the method.Lands Suitable For Commercial UseThe second goal is aimed at finding lands suitable for commercial use. The sub-objectives describing the physical suitability for commercial land-use were similar to the ones for residential land-use as presented in table 5.

Table 7: weighted overlay table of Land physically suitable for commercial useParameters Influence (%) Scale valueSoils 20 Lixisols -7

Leptisols -2Arenosols -1

Flood potentials 50 5- Very low 4- Low 3- Moderately2- High1- Restricted (very high)

Topography 30 Low -5Medium -3High -2


With relation to economic suitability, there were differences in what was important compared with residential land-use. For commercials, it is important to be located along roads to be easily reachable for customers. To amplify this, a sub-objective was included that such lands should be proximal to major roads which are even more attractive for offices to be located. Also lands proximal to major roads intersections are suitable for commercial uses. Furthermore, it is preferable to develop commercial areas/offices proximal to existing residential areas to increase the chance of success. Finally, areas close to existing commercial areas such as markets were identified as preferable concerning cost effectiveness as presented in Table 6 and Figure 5.

Table 8: weighted overlay table of Lands economically suitable for commercial use

Parameters Influence (%) Scale value

Land proximity to existing commercial land use


Lands proximity to roads 30 Ranked between 1-10 based on proximity

Land proximal to road intersections


Land proximal to residential 5 Ranked between 1-10 based on proximity

Slope Soil type

Proximity to water bodies

Flood potential map of GKUA

Weighted overlay



Figure 5: Flowchart showing the creation of objectives and goals of Land suitable for commercial use. Fictive weights have been added to illustrate the method.Suitability maps for residential and commercial land-use

Figure 5: Flowchart showing the creation of objectives and goals of Land suitable for commercial use. Fictive weights have been added to illustrate the method.Suitability maps for residential and commercial land-use

Figure 6: Final Suitability map for residential land-use.

\

Figure 7: Final Suitability map for commercial land-use.

Limitations of the ModelThe GIS model presented in this paper has two limitations related to the methodology and criteria. For the methodology, the weighted summation technique is limited to compensatoryproblem. This means that a cell with a low score on one criterion may gain fromother criteria on which it scores higher. The criteria limitation considers the factors of urban development. Several factors such as proximity to industry, employment, social services, land availability and prices were not included inthe design of the suitability model, yet they are important. This was due to non-availability ofdata on these factors. Furthermore, areas that are suitable for urban development are considered suitable for agricultural uses. This factor was not considered because of two reasons; first, areas suitable for agriculture are in most cases generally suitable for residential uses. Secondly, there is urban agriculture in the area, usually done on spaces between houses, open land and undeveloped plots. It is generally assumed that areas suitable for residential use are also suitable for agriculture thus it does not hinder urban agriculture since residents of the area engage in the activity.

CONCLUSION Land acquisition through informal delivery with selling of plots of land measured less than 900m2 has immensely contributed in making land available



and affordable for low and middle income class within a short period of time. This also possesses great challenges to land use management with attendant consequences of congestion, over development, overcrowding, overstretch of utilities and infrastructure, haphazard development, encroachments and poor living environment. The rapid economic and population growth with speed

up haphazard urban expansion call for geospatial management of land use in the Area. The use of geospatial techniques and multi-criteria decision analysis has been proved to be efficient, accurate, quicker and cost effective tools for sustainable future development of the area. The model presented can also be used by planners and authorities to formulate suitable plan for sustainable development

of the area.

REFERENCESAhamed, N. R. GopalRao, K. and Murthy, J. S. R. (2000).GIS-based fuzzy membership model for crop-land suitability analysis.Agricultural Systems, ESRI Press.Aribigbola, A. (2008). Improving Urban Land Use Planning and Management. The Built Environment: Innovation Policy and Sustainable Development. Covenant University, Nigeria.Pp179-185.Binbol, N.L, (2007) Climate of Nasarawa State: “Geographic Perspectives of Nasarawa State”. A Publication of the Department of Geography, Nasarawa State University, Keffi-Nigeria.Onaivi Publisher, Keffi.Carr, M.H., and Zwick, P.D., (2007). Smart land-use analysis: the LUCIS Model. Redlands,C.A.: ESRI Press.Dale, P.F. and John M. (1988). Land Information Management: An Introduction with Special Reference to Cadastral Problems in Third World Countries . Oxford: Clarendon Press. Durand-Lasserve, A. (1990): Articulation Between Formal and Informal Land Markets in Cities in Developing Countries. Issues and Trends. In Baross, P. and Van der Lindery, V. (eds.). Thetransformation of Land Supply Systems in Third World Cities, Alder-shot, Ashgate.Iloeje, N. P (1985).Geography of West Africa. London, Longman Group in Nigeria: A Case Study of Akure. Theoeretical and Emperical Research in Urban Management Adekunle Ajasin University, Nigeria. Joshua J.K, Anyanwu, N. Cand Ahmed, A. J (2013)Land suitability analysis for agricultural planning using GIS and multi criteria decision analysis approach in Greater Karu Urban Area, Nasarawa State, Nigeria. African Journal of Agricultural Science and Technology,1, (1): 14- 23, Kombe, W. J. (1995).‘Community Based Land Regularisation-Prospects for Decentralized Planning’ TRIALOG, 55:27-31.Mabogunje, A.L. (1992).A New Paradigm for Urban Development.The World Bank,Washington D.C.Nag, P and Kudrat, M. (1998).Digital Remote Sensing. New Delhi. India Obaje, N.G, Lar, U.A, Nzegbuna, A.I, Moumouni, A,Chaanda, M.S. and. Goki, N. G,(2007).Geology and Mineral Resources of Nasarawa State. (Preliminary Investigation):“In Geographic Perspectives of Nasarawa State”. A Publication of the Department of Geography, Nasarawa State University, Keffi- Nigeria.Onaivi Publisher,Keffi.Shuaib, L. (2005) A Geo-Information Approach for Urban Land Use Planning in Kampala.From Pharaohsto Geoinformatics FIG Working Week, Cairo, Egypt.Available on line at www.fews.org/fb 970527/ fb97sr4. htm.Accessed December, 2011.Sodaide.O.R, (2002). Spatial Information and land management. International congress, Washington D.C, U.S.ASui, D.Z, (1992). A Fuzzy GIS Modeling Approach for urban Land Evaluation, Computer Environment and Urban Systems, 16:101-115. Viking, N. (2009) Personal communication about using mixed zoning instead of block zoning.International congress, Washington D.C, U.S.AYari, K., Hadziga, B. and Ma-aruf, S. (2002).Karu City Alliance Initiatives, Karu Governance and Management Institutions, land use Management and Urban Services Management.In: Technical Report for the UN-HABITAT Component of Karu Development Strategy.



PHYSICAL SCIENCES

COMPARATIVE STUDY OF THE MOLECULAR DYNAMICS OF ANTHRACENE AND ONE OF ITS DERIVATIVE (1-HYDROXYANTHRACENE)IN GAS PHASE AND

ETHANOL: RHF AND DFT STUDY

1Umar, G., Chifu E. 2Ndikilar and 1John S. M.1Physics Department, Nasarawa State University, Keffi, Nigeria

2Physics Department, Federal University Dutse, P.M.B 7156, Dutse, Jigawa State, NigeriaCorresponding email: [email protected]


ABSTRACTA comparative study of the molecular geometries of the organic semi-conductor material Anthracene and one of its derivative (1-hydroxyanthracene) in gas phase and ethanol is studied at the Restricted-HartreeFock (RHF) and Density Functional Theory (DFT) levels of theory by employing 6-31G basis set. The molecular structure, dipole moment, quadrupole moment, charge transfer, polarizability, energy and vibrational frequencies with Infrared (IR) and Raman intensities have been studied. Frequency analysis was carried out in all the cases to ensure that the optimized geometries correspond to total energy minima. At both level of theory it is predicted that the solvent (ethanol) has an effect of expanding the molecules as there is slight increase in most of the bond lengths, bond angles and dihedral angles as compare to gas phase analysis. For anthracene it is predicted that the bonds with the lowest bond lengths are R(1,17), R(6,20), R(13,22), R(14,23) with a bond length of 1.0719 Å in gas phase and ranging from 1.0751 to 1.0771 Armstrong in ethanol at the RHF level of theory while for 1 Hydroxyanthraceneit is predicted that the bond with the lowest bond length is R(7,15) with a bond length of 1.0712 Å in gas phase and 1.0733 Å in ethanol at the RHF level of theory. The predicted dipole moment for Anthracene is almost zero at RHF/6-31G level and zero at B3LYP/6-31G level indicating that the molecule is not polar and the charge distribution is fairly symmetrical. The magnitude of the dipole moment obtained at B3LYP/6-31G level is slightly higher as compared to the corresponding values of the dipole moment at RHF/6-31G level. The quadrupole moment predicts that all two molecules are slightly elongated along the ZZ axis, with 1 Hydroxyanthracene showing the highest elongation at both levels of theory.


INTRODUCTIONThe properties of Anthracene derivative 1-hydroxyanthracene in their anionic, neutral and cationic charge states have been investigated using the density functional theory (Kukhta et al., 2011). The effect of addition and removal of the electron on the bond lengths, atomic charges, and frontier orbitals, ionization potential (IP), electron affinity (EA) and reorganization energy was considered in their study. Detailed polarized absorption and emission spectra for anthracene and some of its methyl and methoxy derivatives in rigid solution at 77°K have been studied (Friedrich et al., 2004). PPP-SCF calculations show that no new states are introduced below 5 eV by the substitution. The observed effects of chemical substitution on state ordering, polarizations and oscillator strength correlate quite well with the predictions of the PPP-SCF calculations. A vibrational analysis of the anthracene polarization spectra is carried out. In the near long-axis polarized absorption and fluorescence of anthracene, two vibronic origins are identified as a 1630 cm−1b1g molecular vibration and a weak 60 cm−1bg type of lattice vibration. All long-axis polarized structure in the near absorption can be assigned to Franck-Condon progressions built upon these vibronic origins in the 1La band (which is electronically short-axis polarized). Differences in the spectral integrated transition intensity (oscillator strength) between the absorption and the emission reveal an additional long-axis component in the high energy region of the 1La absorption which could be evidence for the hidden 1Lb band in anthracene. However, there is no clear cut structural evidence for this state in the polarization spectrum. This hidden transition is allowed by methoxy substitution. In the symmetric 2,3-dimethoxyanthracene, the 1Lb band appears at 365 nm as a new transition. In 2-methoxyanthracene the 1La and the 1Lb are strongly mixed. The anomalous short-axis polarization of the 1Lb band, predicted by the theory for 2-methoxyanthracene, is confirmed by the polarization data. Methoxy substitution also reveals new weak bands, located between the 1Lb and 1Bb bands, of B1g and A1g parentage, which are successfully predicted by the PPP-SCF calculations.Naoto and Atsushi(2007), investigated the evolution of the electronic structure of molecular aggregates using anion photoelectron (PE) spectroscopy for anionic clusters of anthracene (Ac) and its alkyl derivatives: 1-methylanthracene (1MA), 2-methylanthracene (2MA), 9-methylanthracene (9MA), 9,10-dimethylanthracene (DMA), and 2-tert-butylanthracene (2TBA). For their monomer anions (n = 1), electron affinities are confined to the range

from 0.47 to 0.59 eV and are well reproduced by density functional theory calculations, showing the isoelectronic character of these molecules. For cluster anions (n = 2–100) of Ac and 2MA, two types of isomers I and II coexist over a wide size range: isomers I and II-1 (n<30) or isomers I and II-2 (n ≥ ~40 for Ac and n ≥~55 for 2MA). However, for the other alkyl-substituted Ac cluster anions (i.e., 1MA, 9MA, DMA, and 2TBA), only isomer I is exclusively formed, and neither isomer II-1 nor II-2 is observed. It highlights some of the molecular dynamic properties that are essential for the development of future opto-electronic devices. Organic semi-conductors have many advantages, such as, easy fabrication, mechanical flexibility and low cost (James, 2007).

MATERIALS AND METHODSThe Schrodinger equation for a collection of particles like a molecule is very similar to that of a particle. In this case,Ψ , the wave function would be a function of the coordinates of all the particles in the system as well as the time, t . The energy and many other properties of the particle can be obtained by solving the Schrodinger equation for , subject to the appropriate boundary conditions. Many different wave functions are solutions to it, corresponding to different stationary states of the system.For a molecular system, Ψ is a function of the positions of the electrons and the nuclei within the molecule, which we will designate as rr and R

r ,

respectively. These symbols are shorthand for the set of component vectors describing the position of each particle. Note that electrons are treated individually, while each nucleus is treated as an aggregate; the component nucleons are not treated individually.The kinetic energy is a summation of 2∇ over all the particles in the molecule:

2 2 2 2

2 2 2 21T=

8 k k k k k

hm x y zπ

∂ ∂ ∂− + + ∂ ∂ ∂

∑........................(2.1)

The potential energy component is the Coulomb repulsion between each pair of charged entities (treating each atomic nucleus as a single charged mass):

0

1V=4

j k

j k j jk

e erπε < ∆∑ ∑

..........................................(2.2)

Where is the distance between the two particles, and and are the charges on particles j and k. For an electron the charge is –e, while for a nucleus, the charge is Ze, where Z is the atomic number for that atom. Thus,

COMPARATIVE STUDY OF THE MOLECULAR DYNAMICS OF ANTHRACENE AND ETHANOL


22 2

0

1V=4

I JI

i I i j i I J IiI ij IJ

Z Z eZ e er r Rπε < <

− + + ∆ ∆ ∆ ∑ ∑ ∑ ∑ ∑ ∑

............(2.3)

The first term corresponds to electron-nuclear attraction, the second to electron-electron repulsion and the third to nuclear-nuclear repulsion (Lee et al., 1988). The Born-Oppenheimer approximation is the first of several approximations used to simplify the solution of the Schrodinger equation. It simplifies the general molecular problem by separating nuclear and electronic motions. This approximation is reasonable since the mass of a typical nucleus is thousands of times greater than that of an electron. The full Hamiltonian for the molecular system can then be written as:

( ) ( ) ( ) ( ) ( )H=T +T +V , +V +Velec nucl nucl elec elec nuclr R R r r R−r r rr r r

......(2.4)

The Born-Oppenheimer approximation allows the two parts of the problem to be solved independently, so we can construct an electron Hamiltonian which neglects the kinetic energy term for the nuclei. This Hamiltonian is then used in the Schrodinger equation describing the motion of electrons in the field of fixed nuclei.

2ψ is interpreted as the probability density for the particle(s) it describes. Therefore, we require that ψ be normalized; if we integrate over all space, the probability should be the number of particles. Accordingly, we multiply ψ by a constant such that:

2

particlesc dv nψ+∞

−∞

=∫

.......................(2.5)

We can do this because the Schrodinger equation is an eigenvalue equation, and in general, if f is a solution to an eigenvalue equation, then cf is also, for any value of c (Coates, J., 2000). The first approximation to be considered comes from the interpretation of 2ψ as a probability density for the electrons within the system. Molecular orbital theory decomposes into a combination of molecular orbitals: 1φ , 2φ , …. To fulfill some of the conditions on ψ , a normalized, orthogonal set of molecular orbitals is chosen as:

1i idxdydzφ φ∗ =∫∫∫ 0i jdxdydz i jφ φ∗ = ≠∫∫∫

.............(2.6)

The simplest possible way of making ψ as a combination of these molecular orbitals is by forming their Hartree product (Friedrich et al., 2004).

( ) ( ) ( ) ( )1 1 2 2 ... n nr r r rψ φ φ φ=r r r r

...........................(2.7)

The simplest anti-symmetric function that is a combination of molecular orbitals is a determinant. However, most calculations are closed shell calculations, using doubly occupied orbitals, holding two electrons of opposite spin. For the moment, we will limit our discussion to this case. We define two spin functions, α and β as follows:

( ) 1 ( ) 0( ) 0 ( ) 1

α α

β β

↑ = ↓ =

↑ = ↓ =

..........................(2.8)

The α function is 1 for a spin up electron and the β function is 1 when the electron is spin down. The notation ( )iα and ( )iβ will designate the values of α and β for electron i ; thus (1)α is the value of α for electron 1. We can now build a closed shell wave function by defining / 2n molecular orbitals for a system with n electrons and then assigning electrons to these orbitals in pairs of opposite spin.

( )

( ) ( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( ) ( ) ( )

( ) ( ) ( ) ( ) ( ) ( )

1 1 1 1 2 1 2 1 /2 1 /2 1

1 2 1 2 2 2 2 2 /2 2 /2 2

1 1 2 2 /2 /2

(1) (1) (1) (1)... (1) (1)

(2) (2) (2) (2)... (2) (2)1 .

! ..

( ) ( ) ( ) ( )... ( ) ( )

n n

n n

n n n n n n n n

r r r r r r

r r r r r r

rn

r n r n r n r n r n r n

φ α φ β φ α φ β φ α φ β


ψ


=

r r r r r r

r r r r r r

r

r r r r r r

...(2.9)Each row is formed by representing all possible assignments of electron i to all orbital-spin combinations. The initial factor is necessary for normalization. Swapping two electrons corresponds to interchanging two rows of the determinant, which will have the effect of changing its sign.The next approximation involves expressing the molecular orbitals as linear combinations of a pre-defined set of one-electron function known as basis functions. These functions are usually centered on the atomic nuclei and so bear some resemblance to atomic orbitals. However, the actual mathematical treatment is more general than this and any set of appropriately defined functions may be used Naoto and Atsushi (2007).An individual molecular orbital is defined as:

1

N

i icµ µµ

φ χ=

=∑ ...............................(2.10)

where the coefficients icµ are known as the molecular orbital expansion coefficients. The basis functions

1.... Nχ χ are also chosen to be normalized. Gaussian and other ab initio electronic structure programs use gaussian-type atomic functions as basis functions. Gaussian functions have the general form:

2

( , ) n m l rg r cx y z e αα −=r ...........................(2.11)

Where is composed of x, y and z. α is a constant determining the size (radial extent) of the function.

ψ



In a gaussian function, 2re α− is multiplied by powers

of x, y and z, and a constant for normalization, so that:

2 1

all space

g =∫ ...............................(2.12)

Thus, c depends on α , l, m and n.Here are three representative gaussian functions (s, py and dxy types, respectively):

........................(2.13)

2

2

2

3/4

1/45

3

1/47

3

2( , )

128( , )

2048( , )

rs

ry

rxy

g r e

g r ye

g r xye

α

α

α

ααπ

ααπ

ααπ

−

−

−

=

=

=

r

r

r

Linear combinations of primitive gaussians like these are used to form the actual basis functions; the latter are called contracted gaussians and have the form:

p pp

d gµ µχ =∑ ........................(2.14)

Where the 'pd sµ are fixed constants within a given basis set. Note that contracted functions are also normalised in common practice. All of these constructions result in the following expansion for molecular orbitals:

.....................(2.15)i i i p pp

c c d gµ µ µ µµ µ

φ χ

= =

∑ ∑ ∑The problem has now become how to solve for the set of molecular orbital expansion coefficients, icµ Hartree-Fock theory takes advantage of the variational principle, which says that for the ground state of any antisymmetric normalized function of the electronic coordinates, which we will denote Ξ , then the expectation value for the energy corresponding to Ξ will always be greater than the energy for the exact wavefunction:

( ) ( )E EΞ > Ψ Ξ ≠ Ψ .........................(2.16) RESULTS AND DISCUSSIONGeometry optimizations usually attempt to locate minima on the potential energy surface, thereby predicting equilibrium structures of molecular systems (Frisch, 2004). At the minima, the first derivative of the energy (gradient) is zero. Since the gradient is the negative of the forces, the forces are also zero at such a point (stationary point). In Gaussian, a geometry optimization begins at the molecular structure specified at the input and steps along the potential energy surface. It computes the energy and gradient

at that point, and determines which direction to make the next step. The gradient indicates the direction along the surface in which the energy decreases most rapidly from the current point as well as the steepness of that slope. The optimized parameters are the bond lengths (in Armstrong), the bond angles and the dihedral angles for the optimized molecular structure. Atoms in the molecule are numbered according to their order in the molecule specification section of the input(Umar and Chifu, 2012).

Optimized Molecular Parameters of Anthracene and 1-hydroxyanthraceneThe optimized bond lengths of Anthracene in gas phase and ethanol are listed in Table 1. The optimized molecular structure is shown in Figure 1. The predicted bond lengths in gas phase at RHF/6-31G level are slightly smaller than the corresponding values in solution (ethanol) and the same is obtained at the DFT/B3LYP/6-31G level of theory. Changes in bond lengths are noticed when one goes from the RHF/6-31G to B3LYP/6-31G level, but no significant change in the bond angles is noticed in the gas phase. It seems inclusion of electron correlation expands the molecule. It is predicted that the bonds with the least bond lengths are R(1,17), R(6,20), R(13,22) and R(14,23) with a bond length of 1.0719 Å in gas at RHF level of theory. These are all C-H bonds. Thus it is predicted that the strongest bonds in Anthracene molecule are the C-H bonds. The bonds with the longest bond lengths in gas phase at RHF level of theory are R(2,3), R(4,5), R(9,15) and R(10,11) with a bond length of 1.4341 Å in gas phase at RHF level of theory which are all C=C bonds. This indicates that the C=C bonds are the weakest. Since solvation in ethanol increases the bond length at both levels of theory, it can be predicted that the bonds are generally weaker in ethanol and can be more easily broken than in gas phase.



Table 1: Optimized Bond Lengths (Å) of Anthracene moleculeGeometricalGeometrical parameter

RHF/6-31G B3LYP/6-31G

Gas Ethanol Gas Ethanol R(1,2) 1.3466 1.3546 1.3739 1.3754 R(1,6) 1.4299 1.4313 1.4288 1.4306 R(1,17) 1.0719 1.0751 1.0854 1.0877 R(2,3) 1.4341 1.4358 1.4329 1.4344 R(2,18) 1.0729 1.0762 1.0864 1.0887 R(3,4) 1.4245 1.4289 1.4491 1.4503 R(3,7) 1.3874 1.3929 1.4036 1.4051 R(4,5) 1.4341 1.4358 1.4329 1.4344 R(4,8) 1.3874 1.3929 1.4036 1.4051 R(5,6) 1.3466 1.3546 1.3739 1.3754 R(5,19) 1.0729 1.0762 1.0864 1.0887 R(6,20) 1.0719 1.0751 1.0854 1.0877 R(7,10) 1.3874 1.3929 1.4036 1.4051 R(7,16) 1.0738 1.0771 1.0873 1.0895 R(8,9) 1.3874 1.3929 1.4036 1.4051 R(8,21) 1.0738 1.0771 1.0873 1.0895 R(9,10) 1.4245 1.4289 1.4491 1.4504 R(9,15) 1.4341 1.4358 1.4329 1.4344 R(10,11) 1.4341 1.4358 1.4329 1.4344 R(11,12) 1.0729 1.0762 1.0864 1.0887 R(11,13) 1.3466 1.3546 1.3739 1.3754 R(13,14) 1.4299 1.4312 1.4288 1.4306 R(13,22) 1.0719 1.0751 1.0854 1.0877 R(14,15) 1.3466 1.3546 1.3739 1.3754 R(14,23) 1.0719 1.0751 1.0854 1.0877 R(15,24) 1.0729 1.0762 1.0864 1.0887

Table 2: Optimized Bond Angles ( ˚ ) of Anthracene molecule Geometrical Parameter


Gas Ethanol Gas EthanolA(2,1,6) 120.5026 120.4908 120.4258 120.4711

A(2,1,17) 120.4399 120.384 120.2436 120.2384

A(6,1,17) 119.0574 119.1251 119.3307 119.2905

A(1,2,3) 120.9085 120.8532 120.9857 120.8877

A(1,2,18) 120.7818 120.7035 120.5779 120.6705

A(3,2,18) 118.3097 118.4433 118.4364 118.4419

A(2,3,4) 118.5889 118.656 118.5886 118.6413

A(2,3,7) 122.1413 122.0701 122.3186 122.1876

A(4,3,7) 119.2698 119.2739 119.0929 119.1712

A(3,4,5) 118.5889 118.6559 118.5885 118.6412

A(3,4,8) 119.2697 119.2743 119.0928 119.1712

A(5,4,8) 122.1413 122.0697 122.3187 122.1877

A(4,5,6) 120.9085 120.8532 120.9856 120.8876

A(4,5,19) 118.3097 118.4426 118.4364 118.4419

A(6,5,19) 120.7818 120.7042 120.578 120.6705

A(1,6,5) 120.5026 120.4909 120.4258 120.4712

A(1,6,20) 119.0577 119.1246 119.3313 119.2911

Table 2: Optimized Bond Angles ( ˚ ) of Anthracene molecule -continuedGeometrical Parameter


Gas Ethanol Gas EthanolA(5,6,20) 120.4397 120.3846 120.2428 120.2377

A(3,7,10) 121.4606 121.4519 121.8144 121.6577

A(3,7,16) 119.27 119.2741 119.0931 119.1715

A(10,7,16) 119.2695 119.274 119.0925 119.1708

A(4,8,9) 121.4606 121.4518 121.8144 121.6577

A(4,8,21) 119.2699 119.2744 119.0932 119.1715

A(9,8,21) 119.2695 119.2738 119.0924 119.1708

A(8,9,10) 119.2697 119.2738 119.0928 119.1711

A(8,9,15) 122.1413 122.0698 122.3188 122.1878

A(10,9,15) 118.589 118.6563 118.5884 118.641

A(7,10,9) 119.2697 119.2741 119.0928 119.1711

A(7,10,11) 122.1413 122.0701 122.3186 122.1876

A(9,10,11) 118.589 118.6558 118.5887 118.6413

A(10,11,12) 118.31 118.4435 118.4365 118.442

A(10,11,13) 120.9084 120.8531 120.9856 120.8876

A(12,11,13) 120.7816 120.7034 120.5779 120.6704

A(11,13,14) 120.5026 120.4912 120.4257 120.471

A(11,13,22) 120.4396 120.3836 120.2432 120.238

A(14,13,22) 119.0578 119.1252 119.3311 119.2909

A(13,14,15) 120.5026 120.4903 120.4259 120.4713

A(13,14,23) 119.0578 119.1254 119.331 119.2908

A(15,14,23) 120.4396 120.3842 120.2431 120.2379

A(9,15,14) 120.9084 120.8533 120.9857 120.8877

A(9,15,24) 118.31 118.4431 118.4366 118.4421

A(14,15,24) 120.7816 120.7036 120.5777 120.6702

Figure 1: Optimized Structure of Anthracene

For 1-hydroxyanthracene molecule, the optimized bond lengths in gas phase and ethanol are listed in Table 3 while the optimized molecular structure is shown in Figure 2. The changes in bond lengths and bond angles are seen to be similar to the behaviour in Anthracene. An expansion of the molecule on inclusion of electron correlation is indicated in both molecules. In contrast to the case of Anthracene, for 1-Hydroxyanthracene, the replacement of H on the first benzene ring by OH has little effect on the first C-C bond R(1,2). However, the bond length between



the C-O is slightly reduced compared to the C-H in Anthracene. This is due to the fact that O is highly electronegative. The shortest and strongest C-H bond is R(7,15) with a bond length of 1.0712A and 1.0733 Å in gas phase and ethanol at RHF level theory. It is a C-H bond located at the extreme end opposite the O-H group. Thus unlike in the Anthracene molecule where all the C-H bonds are fairly stronger, only R(7,15) is stronger than the rest; though the difference is not much as in R(1,16), R(2,17), R(6,19), R(8,20) etc. It is remarkable to note that the R(24,25) bond has the least bond length of 0.976 Å at RHF and 0.9954 Å at B3LYP level of theory. This is the O-H bond and this is expected due to the electronegativity of oxygen.A(11,24,25), the bond angle between C-O-H is the strongest (Table 4) with an angle of 111.53520 in gas phase at B3LYP level of theory. A(12,11,24), the bond angle between C-C-O is the largest bond angle with a value of 123.27540 in gas phase at RHF level of theory. Table 3: Optimized Bond Lengths (Å) of 1-hydroxyanthracene molecule

Geometrical Parameter


Gas Ethanol Gas EthanolR(1,2) 1.353 1.3546 1.3744 1.3758R(1,6) 1.429 1.4309 1.4284 1.4302R(1,16) 1.0728 1.0751 1.0853 1.0877R(2,3) 1.4333 1.4349 1.4322 1.4337R(2,17) 1.0738 1.0763 1.0863 1.0888R(3,4) 1.4257 1.4269 1.4479 1.4489R(3,7) 1.3925 1.3938 1.4047 1.4059R(4,5) 1.4338 1.4353 1.4327 1.4344R(4,8) 1.3923 1.3934 1.4041 1.4052R(5,6) 1.3531 1.3547 1.3743 1.3758R(5,18) 1.0738 1.0762 1.0864 1.0888R(6,19) 1.073 1.0753 1.0855 1.0877R(7,10) 1.3881 1.3899 1.4004 1.402R(7,15) 1.0712 1.0733 1.084 1.0861R(8,9) 1.3901 1.392 1.4033 1.4052R(8,20) 1.0744 1.0768 1.087 1.0894R(9,10) 1.4266 1.4287 1.4477 1.4496R(9,14) 1.4357 1.4364 1.4335 1.4343R(10,11) 1.4354 1.4384 1.4361 1.4397R(11,12) 1.3514 1.3531 1.3749 1.377R(11,24) 1.3756 1.373 1.3928 1.3871R(12,13) 1.4286 1.4294 1.4263 1.4269R(12,21) 1.074 1.0757 1.0867 1.0884R(13,14) 1.3509 1.3529 1.373 1.3751R(13,22) 1.0727 1.0751 1.0852 1.0876R(14,23) 1.0731 1.0755 1.0855 1.0878R(24,25) 0.9498 0.9671 0.976 0.9954

Figure 2: Optimized Structure of 1-Hydroxyanthracene

Generally, there is a slight increase in the bond lengths in moving from RHF/6-31G to B3LYP/6-31G and hence electron correlation increases the size of the molecule. Also, at both levels of theory, it is predicted that the solvent has an effect of expanding the molecule. The bond angles are shown in Table 4 for 1-Hyroxyanthracene.

Table 4: Optimized Bond Angles (˚) of 1-hydroxyanthracene molecule

Geometrical Parameter


Gas Ethanol Gas EthanolA(2,1,6) 120.3309 120.4078 120.3754 120.4222A(2,1,16) 120.4673 120.4433 120.2608 120.2451A(6,1,16) 119.2018 119.1489 119.3638 119.3327A(1,2,3) 120.9381 120.8188 120.9605 120.8631A(1,2,17) 120.6655 120.7416 120.6245 120.6694A(3,2,17) 118.3964 118.4396 118.415 118.4675A(2,3,4) 118.7278 118.7769 118.6672 118.7265A(2,3,7) 121.9342 121.8671 122.097 122.0441A(4,3,7) 119.338 119.356 119.2358 119.2294A(3,4,5) 118.5628 118.6385 118.5846 118.6323A(3,4,8) 119.2596 119.2722 119.0882 119.1086A(5,4,8) 122.1776 122.0893 122.3272 122.259A(4,5,6) 120.8873 120.7836 120.9357 120.8449A(4,5,18) 118.4916 118.4763 118.4857 118.4895A(6,5,18) 120.6211 120.7401 120.5787 120.6656A(1,6,5) 120.5531 120.5743 120.4766 120.511A(1,6,19) 119.1269 119.0811 119.329 119.2845A(5,6,19) 120.32 120.3446 120.1944 120.2045A(3,7,10) 121.064 121.1426 121.2818 121.3809A(3,7,15) 119.9256 119.4074 119.7654 119.319A(10,7,15) 119.0105 119.45 118.9529 119.3001A(4,8,9) 121.6237 121.561 121.9413 121.8817A(4,8,20) 119.2325 119.278 119.1005 119.1424A(9,8,20) 119.1438 119.161 118.9583 118.9759



A(8,9,10) 118.7871 118.9089 118.5802 118.6881A(8,9,14) 122.1637 121.947 122.2657 122.1004A(10,9,14) 119.0491 119.144 119.154 119.2115A(7,10,9) 119.9277 119.7593 119.8728 119.7113A(7,10,11) 122.0543 122.186 122.1832 122.2213A(9,10,11) 118.0181 118.0547 117.944 118.0674A(10,11,12) 121.5338 121.3448 121.458 121.1943A(10,11,24) 115.1908 115.4563 115.4156 115.6425A(12,11,24) 123.2754 123.1988 123.1264 123.1632A(11,12,13) 119.9423 120.0476 120.0018 120.0838A(11,12,21) 120.5861 120.2371 120.3778 120.007A(13,12,21) 119.4716 119.7153 119.6204 119.9092A(12,13,14) 120.9565 121.0536 120.9042 121.0812A(12,13,22) 118.5628 118.4782 118.7486 118.6448A(14,13,22) 120.4807 120.4682 120.3472 120.274A(9,14,13) 120.5001 120.3553 120.5379 120.3617A(9,14,23) 118.6428 118.621 118.6997 118.7177A(13,14,23) 120.857 121.0237 120.7624 120.9205A(11,24,25) 114.5652 114.6454 111.5352 112.1765

The bond length of Anthracene (Table 1) for C1-C2 was found to be 1.3466Å and 1.3739Å in gas phase at RHF and B3LYP levels of theory respectively. For 1-hydroxyanthracene, C1-C2 bond length in gas phase at B3LYP level of theory is 1.3744Å. These bond lengths for C1-C2 bond differ from that of 2,4,6 trinitrottolune (another derivative of anthracene) for it is 1.411Å at B3LYP/6-31G and 1.406Å at B3LYP/6-311G. Thus Anthracene and 1 Hydroxyanthracene studied in this research work have shorter bond length for C1-C2 bond compared to TNT (Clarkson et al., 2003). For C2-C3, C3-C4, and C1-C6 bonds, 2,4,6 trinitrotolune is predicted to have shorter bond lengths than for the molecules studied in this work (Clarkson et al., 2003). This could be due to the heavy

F1groups (NO2, CH3) attached to C2, C1 and C6 of 2,4,6 trinitrotolune. Also, a similar trend is observed in the bond angles at B3LYP/6-31G level of theory for the two molecules in gas phase (Clarkson et al., 2003). Clarkson et al. (2003) predicted higher values for C5-C6-C1, C3-C4-C5 and C1-C2-C3 bond angles for 2,4,6 trinitrotolune compared to those of the organic compounds studied in this work and vice-versa for C4-C5-C6 and C2-C1-C6. Also, a comparative analysis of the optimized parameters obtained in this study and that of Kukhta et al. (2008) revealed a similar trend.

Dipole Moments, Quadrupole Moments and EnergiesThe dipole moment is the first derivative of the energy with respect to an applied electric field. It is a measure of the asymmetry in the molecular charge distribution and is given as a vector in three dimensions. For Hartree-Fock calculations, this is equivalent to the expectation values of X, Y and Z, which are the quantities reported in the output. The predicted dipole moments (in Debye) at different levels of theory are shown in Table 5. The dipole moment of the molecules gives the strength of the polarity of the molecule. The predicted dipole moment is almost zero at RHF/6-31G level and zero at B3LYP/6-31G level. Thus, the molecule is non polar and the charge distribution is fairly symmetrical.

Table 5: Dipole moments (in Debye) in Gas phase and ethanol.Molecule RHF/6-31G B3LYP/6-31

Gas Ethanol Gas EthanolAnthracene 0.0003 0.0003 0.0000 0.00001-Hydroxyanthracene 1.2978 1.8605 1.3867 2.1065

Quadrupole moments provide a second order approximation of the total electron distribution, providing at least a crude idea of its shape. One of the components being significantly larger than the others would represent an elongation of the sphere along that axis. If present, the off-axis components represent trans-axial distortion (stretching or compressing of the ellipsoid). The quadrupole moment for the molecule at different levels of theory is shown in Table 6. The molecules are predicted to be slightly elongated along the ZZ axis and this elongation increases in ethanol at the RHF/6-31G and B3LYP/6-31G levels.

Table 6: Quadrupolemoments(in Debye) in Gas phase and ethanol Molecule RHF/6-31G DFT/B3LYP/6-31G

Gas Ethanol Gas EthanolXX YY ZZ XX YY ZZ XX YY ZZ XX YY ZZ

Anthracene -69.4309 -70.0816 -91.7071 -66.8135 -67.8178 -89.9211 -70.2391-70.9301-86.5803 -66.5629-67.8575-86.9452

1-Hydroxyanthracene -66.8081 -78.0330 -94.6558 -59.5805 -75.1359 -95.0966 -66.6535 -77.3321 -91.8075 -59.3213 -74.2876 -92.2196

All frequency calculations include thermochemical analysis of the molecular system. By default, this analysis is carried out at 298.15 K and 1 atmosphere of pressure, using the principal isotope of each element type in the molecular system. Predicted total energy, electronic, translational, rotational and vibrational energies in kcal/mol for the molecules both in gas phase and in ethanol are listed on Tables7 and 8.



Table 7: Predicted thermal energies (kcal/mol)Energy RHF/6-31G B3LYP/6-31

Gas Ethanol Gas EthanolTotal Energy 137.058 134.846 129.172 128.283Electronic Energy 0.000 0.000 0.000 0.000Translational Energy 0.889 0.889 0.889 0.889Rotational Energy 0.889 0.889 0.889 0.889Vibrational Energy 135.281 133.069 127.395 126.506

Table 8: Predicted thermal energies (kcal/mol) for 1-Hydroxyanthracene

Energy RHF/6-31G B3LYP/6-31GGas Ethanol Gas Ethanol

Total Energy 141.165 135.685 132.297 128.469Electronic Energy 0.000 0.000 0.000 0.000Translational Energy

0.889 0.889 0.889 0.889

Rotational Energy 0.889 0.889 0.889 0.889Vibrational Energy 139.388 133.907 130.519 126.691

The translational energy relates to the displacement of molecules in space as a function of the normal thermal motions of matter. Rotational energy is observed as the tumbling motion of a molecule as a result of the absorption of energy within the microwave region. The vibrational energy component is a higher energy term and corresponds to the absorption of energy by a molecule as the component atoms vibrate about the mean center of their chemical bonds. The electronic component is linked to the energy transitions of electrons as they are distributed throughout the molecule, either localized within specific bonds, or delocalized over structures, such as an aromatic ring. It is seen that the molecules are slightly more stable in gas phase. The difference in total energies from gaseous to ethanol is a bit larger when electron correlation is included. Anthraceneis found to be most stable by approximately 2.20 kcal/mol at RHF/6-31G level and by nearly 0.1 kcal/mol at B3LYP/6-31G in gas phase as compared to ethanol. This shows that the presence of ethanol tends to reduce its stability which may be of importance in its effectiveness in use. Charge Transfer and PolarizabilitiesWithin molecular system, atoms can be treated as a quantum mechanical system. On the basis of the topology of the electron density the atomic charges in the molecule can be explained. The electrostatic potential derived charges using the CHelpG scheme of Breneman at different atomic positions in gas phase and in ethanol of Anthracene and 1-Hydroxyanthracene at RHF/6-31G and B3LYP/6-31G levels of theories is given in Tables9 and 10. The Mulliken population analysis partitions the charges among the atoms of the molecule by dividing orbital

overlap evenly between two atoms. Whereas the electrostatic potential derived charges assign point charges to fit the computed electrostatic potential at a number of points on or near the Van der Waal surface. Hence, it is appropriate to consider the charges calculated by CHelpG scheme of Breneman instead of Mulliken population analysis.

Table 9: Electrostatic Potential Derived Charges on different atomic positions of Anthracene molecule S/N Atom RHF/6-31G B3LYP/6-31G

Gas Ethanol Gas Ethanol1 C -0.099099 -0.086073 -0.070869 -0.0870242 C -0.273560 -0.284203 -0.211148 -0.2371863 C 0.231306 0.242868 0.196931 0.2035034 C 0.231306 0.233456 0.196931 0.1948605 C -0.273561 -0.272865 -0.211150 -0.2267976 C -0.099098 -0.099706 -0.070868 -0.0992007 C -0.517887 -0.533159 -0.401578 -0.4432298 C -0.517887 -0.531535 -0.401578 -0.4418379 C 0.231304 0.239461 0.196930 0.20884510 C 0.231304 0.235328 0.196930 0.20487911 C -0.273559 -0.274940 -0.211148 -0.23544412 H 0.156788 0.153876 0.112495 0.13350913 C -0.099100 -0.094367 -0.070869 -0.06900514 C -0.099100 -0.091341 -0.070868 -0.06611415 C -0.273559 -0.279741 -0.211149 -0.24001816 H 0.240497 0.248847 0.170733 0.19929617 H 0.123261 0.118999 0.088015 0.09146818 H 0.156788 0.155712 0.112494 0.12985519 H 0.156788 0.153936 0.112495 0.12818420 H 0.123261 0.121830 0.088015 0.09395721 H 0.240497 0.248328 0.170732 0.19888122 H 0.123261 0.120323 0.088015 0.11219623 H 0.123261 0.120020 0.088015 0.11190924 H 0.156788 0.154947 0.112495 0.134511

Table 10: Electrostatic Potential Derived Charges on different atomic positions of 1- Hydroxyanthracene S/N Atom RHF/6-31G

B3LYP/6-31G

Gas Ethanol Gas Ethanol1 C -0.097690 -0.132542 -0.099595 -0.1116132 C -0.217263 -0.214012 -0.160720 -0.1860253 C 0.131462 0.112474 0.114131 0.1109304 C 0.287751 0.284380 0.219171 0.2239165 C -0.284951 -0.309088 -0.225100 -0.2529416 C -0.054656 -0.064146 -0.051729 -0.0625217 C -0.289153 -0.314916 -0.256269 -0.2794938 C -0.503317 -0.507640 -0.369869 -0.4049719 C 0.314298 0.273268 0.186055 0.19746910 C -0.032936 -0.010846 0.064237 0.03218111 C 0.488970 0.399869 0.284261 0.30403912 C -0.405742 -0.301968 -0.199257 -0.222094



Table 10: Electrostatic Potential Derived Charges on different atomic positions of 1- Hydroxyanthracene

-continuedS/N Atom RHF/6-31G

B3LYP/6-31G

Gas Ethanol Gas Ethanol13 C 0.013488 -0.044472 -0.062508 -0.07358914 C -0.375532 -0.350673 -0.239033 -0.27307515 H 0.199213 0.199433 0.137688 0.15458916 H 0.102533 0.129083 0.091041 0.10915417 H 0.129207 0.142562 0.101308 0.12297318 H 0.138009 0.161032 0.112010 0.13538619 H 0.099463 0.121222 0.085159 0.10319720 H 0.210253 0.238021 0.159853 0.18843921 H 0.161373 0.195581 0.121013 0.15711322 H 0.108687 0.117757 0.085133 0.10180523 H 0.158416 0.165987 0.113962 0.13546324 O -0.782639 -0.850615 -0.684062 -0.74768225 H 0.500757 0.560249 0.473120 0.537348

It is clear from Tables 9 and 10 that the amount of charges on C2, C3, C4, C6, C7, C8, C10, C11, C15, H16 and H21 atoms increases while on C1, C5, C9, H12, C13, C14, H17, H18, H19, H20, H22, H23 and H24 atoms decreases at RHF/6-31G level and at B3LYP/6-31G level decreases in ethanol than that of gas phase charges. It means that former atoms acquire charges from the solvent medium while later atoms loses their charges to the solvent medium due to the effect of the solvent. Polarizability refers to the way the electrons around an atom redistribute themselves in response to an electrical disturbance. The polarizability tensor components of the molecules in gas phase as well as in ethanol obtained at RHF/6-31+G and B3LYP/6-31+G levels of theory is listed on Tables11 and 12. The polarizability tensor components of Anthracene molecule show a change in goingfrom gas phase to solution at both levels of theory. All the polarizability tensor components (xx, xy, yy, xz, yz and zz) of molecules increase significantly at both levels of theory, but they do not follow any regular pattern. The change in polarizability tensors is more pronounced in ethanol than in the gas phase. This may be due to the fact that the polarity of the solvent (ethanol) and the dipole moments of the molecule are more in ethanol than in the gas phase.

Table 11: Polarizabilities of AnthraceneOrientation RHF/6-31G B3LYP/6-31G

Gas Ethanol Gas EthanolXx 230.248 346.355 265.371 382.047Xy 0.000 0.004 0.000 0.004Yy 136.995 226.380 149.437 227.444Xz 0.000 0.032 0.000 0.027Yz 0.001 -0.023 0.000 -0.008Zz 30.674 47.748 40.085 48.630

Table 12: Polarizabilities of 1-HydroxyanthraceneOrientation RHF/6-31G B3LYP/6-31G

Gas Ethanol Gas EthanolXX 238.705 353.883 267.757 394.721XY -7.090 8.757 7.608 8.648YY 148.759 236.925 159.427 242.927XZ 0.002 -4.097 -0.004 -4.096YZ 0.001 8.334 0.002 6.613ZZ 40.363 51.119 41.240 52.239

The vibrational spectrum of a molecule is considered to be a unique physical property and is characteristic of the molecule. As such, the infrared (IR) spectrum can be used as a fingerprint for identification by the comparison of the spectrum from an ‘unknown’ with previously recorded reference spectra. The fundamental requirement for infrared activity, leading to absorption of infrared radiation, is that there must be a net change in dipole moment during the vibration for the molecule or the functional group under study. The vibrational frequency is usually expressed in cm-1 (Lee et al., 1988). Another important form of vibrational spectroscopy is Raman spectroscopy, which is complementary to infrared spectroscopy. The selection rules for Raman spectroscopy are different to those for infrared spectroscopy, and in this case a net change in bond polarizability must be observed for a transition to be Raman active (Coates, 2000).In this work, Gaussian software was used to predict the vibrational spectra of Anthracene molecule in its ground state. These frequency calculations are valid only at stationary points on the potential energy surface, thus our computations were performed on the optimized structures of the molecules. As 6-31G is the smallest basis set that gives satisfactory results for frequency calculations, it was used. Raw frequency calculations computed at the Hartree-Fock level contain known systematic errors due to the neglect of electron correlation, resulting to overestimates of about 10-12%. Therefore, it is usual to scale frequencies predicted at the HF level by an empirical factor of 0.8929. Use of this factor has been demonstrated to produce very good agreement with experiment for a wide range of systems. The values in this study must be expected to deviate even a bit more from experiment because of the choice of a medium-sized basis set (6-31G)- around 15%. For B3LYP/6-31G a scale factor of 0.9613 is used. Some IR and Raman intense vibrational frequencies and their approximate descriptions for the molecules in gas phase and ethanol at RHF and B3LYP levels with 6-31G basis set are presented in tables 13-16. The frequencies reported are not scaled as is usually done in comparing the similar



calculated frequency with observed frequency (as no experimental results were found for comparison). The B3LYP results show a significant lowering of the magnitudes of the calculated frequencies. The most intense IR vibrational frequency for Anthracene at RHF level in gas phase is 844.793cm-

1 (718.636 cm-1 in ethanol) corresponding to C-H symmetric stretching of the benzene rings (Table 13), while the most intense Raman vibrational frequency is 1534.75 cm-1 (1549.21 cm-1 in solution) corresponding to benzene ring distortions (Table 15). At B3LYP level of theory, the most intense IR vibrational frequency is 3221.85 cm-1 in gas phase (3060.08 cm-1 in ethanol) corresponding to C-H anti-symmetric stretching of the ring in plane (Table 14) and the most intense Raman vibrational frequency is 3222.5 cm-1 (3060.44 cm-1 in ethanol) corresponding to C-H symmetric stretching of the benzene rings (Table 16).

Table 13: Some IR Intense Vibrational Frequencies and their Approximate Description for Anthracene Molecule at RHF/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 536.672 454.48 C-H symmetric stretching of the rings

in plane2 680.343 582.824 C-C anti-symmetric stretching of the

rings3 844.793 718.636 C-H symmetric stretching of the rings

and C-C-H angle bending4 1058.48 952.081 C-H stretching of the middle ring5 1819.65 1817.41 C-C anti-symmetric stretching of the

rings6 3367.25 3090.64 C-H anti-symmetric stretching of the


Table 14: Some IR Intense Vibrational Frequencies and their Approximate Description for Anthracene Molecule at B3LYP/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 760.28 820.3 C-H symmetric stretching of the rings

in plane2 920.096 965.578 C-H symmetric stretching of the rings

out of plane3 3206.92 3048.47 C-H anti-symmetric stretching of the

rings in plane4 3221.85 3060.08 C-H anti-symmetric stretching of the

rings in plane

Table 15: Some Raman Intense Vibrational Frequencies and their Approximate Description for Anthracene Molecule at RHF/6-31G Level of Theory

S/N Gas Ethanol Approximate Description

1 430.483 425.675 C-C symmetric stretching of the rings in plane

2 596.25 594.42 C-C anti-symmetric stretching of the rings in plane

3 823.928 824.174 rings breathing

4 894.233 910.791 C-H symmetric stretching of the rings

5 1073.84 1109.59 C-C symmetric stretching of the rings and C-C-H bending

6 1340 1337.08 C-H anti-symmetric stretching of the rings

7 1364.2 1356.32 C-C anti- symmetric stretching of the rings

8 1534.75 1549.21 Ring distortions9 1730.87 1664.54 C-H anti-symmetric stretching of rings10 3352.08 3080.47 C-H symmetric stretching of rings11 3366.85 3090.09 C-H anti-symmetric stretching of rings12 3382.38 3103.29 C-H symmetric stretching of rings

Table 16: Some Raman Intense Vibrational Frequencies and their Approximate Description for Anthracene Molecule at B3LYP/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 773 406.261 C-C symmetric stretching of the rings2 1047.39 1059.73 C-C anti-symmetric stretching of the

rings3 1451.19 1262.3 Ring distortions4 1543.41 1332.25 C-C anti-symmetric stretching of the

rings5 1610.48 1499.21 Ring distortions6 3192.13 3037.5 C-H symmetric stretching of the rings7 3206.62 3048.18 C-H anti- symmetric stretching of the


The various IR and Raman spectra for the molecules at different levels of theory and in different media are shown in figures 3-6 for anthracene and while tables 17-20 and figures 7-10 for 1-hydroxyanthracene respectively.



(a) RHF (b) B3LYPFigure 3: IR Spectrum for Anthracene in Gas Phase

(a) RHF (b) B3LYPFigure 4: IR Spectrum for Anthracene in Ethanol

(a) RHF (b) B3LYP Figure 5: Raman Spectrum for Anthracene in Gas Phase

(a) RHF (b) B3LYPFigure 6: Raman Spectrum for Anthracene in Ethanol



Table 17: Some IR Intense Vibrational Frequencies and their Approximate Description for 1-Hydroxyanthracene Molecule at RHF/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 373.799 214.42 O-H stretching in plane2 841.572 516.284 C-H symmetric stretching of the rings3 1066.39 960.246 C-H symmetric stretching of the rings4 1265.13 966.833 C-C anti-symmetric stretching of the

rings5 1416.56 1092.04 C-C anti-symmetric stretching of the

rings6 3385.71 2811.289 C-H symmetric stretching of the rings7 4046.72 3706.09 O-H stretching in plane

Table 18: Some IR Intense Vibrational Frequencies and their Approximate Description for 1-Hydroxyanthracene Molecule at B3LYP/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 391.515 294.388 O-H stretching out of plane2 756.53 1192.92 C-H symmetric stretching of the rings

out of plane3 1048.06 1269.14 C-C anti-symmetric stretching of the

rings 4 1174.49 1326.78 C-C anti-symmetric stretching of the

rings5 1315.13 1345.31 C-C anti-symmetric stretching of the

rings6 1320.41 3709.2 O-H stretching

(a) RHF/6-31G

(b) B3LYP/6-31GFigure 7: IR Spectrum for 1-Hydroxyanthracene in Gas Phase

(a) RHF/6-31G

(b) B3LYP/6-31GFigure 8: IR Spectrum for 1-Hydroxyanthracene in Ethanol

Table 19: Some Raman Intense Vibrational Frequencies and their Approximate Description for 1-Hydroxyanthracene Molecule at RHF/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 1352.78 425.044 C-C symmetric stretching of rings2 1401.31 1095.84 C-C anti-symmetric stretching of rings3 1563.37 1109.74 C-C anti-symmetric stretching of rings4 1567.97 1175.08 C-C anti-symmetric stretching of rings5 1584.39 1217.31 C-C anti-symmetric stretching of rings6 1655.94 1335.33 C-C anti-symmetric stretching of rings7 1857.72 1402.4 Ring distortions8 3350.05 1435.41 C-H symmetric stretching of rings9 3356.2 1444.36 C-H symmetric stretching of rings10 3361.95 1577.73 C-H anti-symmetric stretching of rings11 3372.23 3081.12 C-H anti-symmetric stretching of rings12 3385.71 3090.54 C-H anti-symmetric stretching of rings13 3387.49 3098.55 C-H symmetric stretching of rings14 4046.72 3706.09 O-H stretching in plane



Table 20: Some Raman Intense Vibrational Frequencies and their Approximate Description for 1-Hydroxyanthracene Molecule at B3LYP/6-31G Level of Theory

S/N Gas Ethanol Approximate Description1 1447.65 403.693 C-C anti-symmetric stretching of rings2 1454.29 773.704 C-C anti-symmetric stretching of rings3 1460.4 1058.59 C-C anti-symmetric stretching of rings4 545.01 1152.48 C-Canti-symmetric stretching of rings5 1612.99 1192.92 C-C symmetric stretching of rings6 3184.35 1244.7 C-H symmetric stretching of rings7 3185.06 1269.14 C-H anti-symmetric stretching of rings8 3197.74 1326.78 C-H anti-symmetric stretching of rings9 3206.49 1345.31 C-H anti-symmetric stretching of rings10 3219.58 1462.41 C-H symmetric stretching of rings11 3221.65 1508.71 C-H symmetric stretching of rings12 3229.72 3028.64 C-H symmetric stretching of rings13 3674.04 3709.2 O-H stretching in plane

(a) RHF/6-31G

(b) B3LYP/6-31GFigure 9: Raman Spectrum for 1-Hydroxyanthracene in Gas Phase

(a) RHF/6-31G

(b) B3LYP/6-31GFigure 10: Raman Spectrum for 1-Hydroxyanthracene in Ethanol

CONCLUSIONTo compliment this research work, the experimental part of this study can be undertaken to ascertain the accuracy of this computational technique. Also, this work can be done using other computational physics software’s and results compared with the results in this work. Anthracenecan be studied in other environments to see the effect of these environments on their physical properties. Other solvents that can be considered include hexane, benzene, hydronaphthalenes, Carbon disulfide, Chloroform and other organic solvents. The practicability of the findings in this work is an encouraging factor. This work provides the fundamental basis for all computational and experimental studies on these molecules vis-à-vis their semi-conducting and opto-electric properties. Our study has exposed the molecular and electronic properties of these molecules for use in the fabrication of organic semiconductor devices.

ACKNOWLEDGEMENTWe are thankful to the Council of Scientific and Industrial Research (CSIR), India for financial support through Emeritus Professor Scheme (Grant No. 21(0582)/03/EMR-II) to Late Prof. A.N. Singh of the Physics Department, Bahamas Hindu University, India which enabled him to purchase the Gaussian Software. We are most grateful to Emeritus Prof. A.N. Singh for donating this software to Physics Department, Gombe State University, Nigeria.



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“Investigation of chemical behavior of anthracene derivatives as monomers and reagents in synthesis of macromolecules containing anthracene groups” Journal of Polymer Science, 15, 287-295Liu, J., Jiang, L., & Hu, W. (2009). “The Application of Anthracene and Its Derivatives in Organic Field-Effect Transistors”, Progress in Chemistry, 2568-2577Umar, G., and Chifu E.N. (2012) “Electronic Structure and Properties of the Organic Semiconductor Material Anthracene in Gas phase and ethanol An Initio and DFT Study The African Review of Physics. The African Review of Physics, Pp 253-263.



PHYSICAL SCIENCES

DERIVATION OF FITZHUGH-NAGUMO SYSTEM

Aku, D. H, Alhamdu, A. M and Useni, P. FDepartment of Mathematics/Statistics, Nasarawa State Polytechnic, Lafia, Nasarawa State, Nigeria.


Manuscript received: 22/11/2016 Accepted: 30/12/2016 Published: December, 2016

ABSTRACTThis paper is concerned with a nonlinear system called the FitzHugh-Nagumo system. We concentrated on the derivation of the system from the aspect of the nature of excitable cell model and gating model of Hodgkin-Huxley system. Furthermore, reduction of a Hodgkin-Huxley system to a FitzHugh-Nagumo system is also investigated and we concluded by depicting the two different variations of FitzHugh-Nagumo system that are widely used by researchers in the fields of neurophysiology and cardiac muscle model. This paper evaluates further ways in which this FitzHugh-Nagumo system can be applied.

KEYWORDS :FitzHugh-Nagumo System, Gating Model, Excitable Cell Model.


INTRODUCTIONAlan Lloyd Hodgkin and Andrew Huxley, between 1948 and 1952 conducted an experiment on the movement of action potential in the giant squid axon, which was suitable for a large portion of the nerve tissue at that time. In an effort to give mathematical meaning for the excitable nature, they constructed a model for the patch clamp experiment. Applying the Kirchhoff’s conservation of current law and using the configuration of an equivalent circuit for space clamp axonal membrane, Hodgkin and Huxley formulated a differential equation called the Hodgkin-Huxley model Edelstein-Keshet, (2005). The main analysis of Hodgkin-Huxley model was performed independently by Richard FitzHugh and Jin-IchiNagumo who noticed that they can, under some assumption, reduce the differential system to a differential system. The outcome of their experiment is what is now known as FitzHugh-Nagumo system. The FitzHugh-Nagumo system is the simplified form of the Hodgkin-Huxley system that explains the inner working process of the Hodgkin-Huxley system and a major model in the study of neuron physiology since mid 19th century. The FitzHugh-Nagumo system has been used in many different types of biological modelling (e.g. neurophysiology model, cardiac muscle model etc).The dynamical behaviour of the FitzHugh-Nagumo system is very vital in the analysis and understanding of more difficult systems, so in this paper we focus on the system by investigating the reduction of the system from a differential system to a differential system.

NATURE OF EXCITABLE CELL MODELS- HODGKIN-HUXLEY MODELConventionally, it was known that the cell membrane separates the internal working parts of the cell from its external parts, and it allows the passage of some materials and restricting the passage of others, thus controlling the movement of materials to and from the cell. A basic model for describing the aforementioned process is that of parallel capacitor and resistance, which has the form

.appleq

m IRVV

dtdVC +

−−==

................(1)

Here Cm is the cell membrane capacitance, V the reversal potential ( is the membrane potential at which there is no net flow of that particular ion from one side to the other of the membrane ),Veq is the resting membrane potential that balance the reversal potentials for the other ionic currents, R is

the resistance, and I is the applied electric current Friedman and Kao (2014). In early 20th century, it was established in a major achievement in patch clamp experiments that many cell membranes are excitable, meaning that if sufficient current is being applied they exhibit large changes in potential. Nerve cells and some muscle cells are examples of such cells, see for example Keener and Sneyd (2009). Hodgkin-Huxley, between 1948 and 1952 conducted an experiment on the giant squid axon, which was suitable for a large part of nerve tissue at that time. In an attempt to give mathematical clarification for the excitable nature, they constructed a model for the patch clamp experiment. They assumed that the electrical activity of the squid giant axon is dominated by the movement of sodium and potassium ions across the membrane. Thus, Na+ and K+ use two different channels to go through. Furthermore a leakage channel through which chloride Cl- and other ions can pass, are also included in the neuronal membrane of the model. The equivalent circuit diagram for space-clamped axonal membrane of the Hodgkin-Huxley model is shown in the Figure 1. Here I is the current, V is the voltage, C is the capacitance and g is the electrical conductivity.The membrane act as a capacitor while the presence of channels can be modelled as resistors whose conductivities (inverse resistances) are gNa+ ,gK and

Lg for the sodium, potassium and Leakage potential channels respectively. On the other hand VNa+ , Vk and Vk represent the potentials for each each individual ion, which account for the ionic currents due to the concentration difference of the ions across the membrane.

Figure 1: The equivalent circuit for space-clamped axonal membrane of the Hodgkin-Huxley model.

The conductivities of the Na+ and K+ channels are functions of time and the membrane potential, while



the conductivity of the leakage channel is a constant and the change in the membrane potential do not affect it Kistlerl et al., (2002).The channel consists of four independent activation gates (i.e. four identical subunits) that opens when the membrane potential is depolarised, allowing the flow of current through it. Thus, the current through these channels will then be given by

))(()()( 44kkkkk VtVtngVVngI −=−=

where kg is the maximum conductivity, a constant proportionality and )(tnn = is the fraction of open activation gate at time .t In the same way, the channel contain three activation gates which are independent of each other and opens when the neuron is depolarised, and also contain an activation gate that closes the channel when the membrane potential has been depolarised for some time t . Thus, the current through this channel can be given by

where is the maximum conductivity of the channel proportional to an additional fraction of open inactivation gates variable , and is the fraction of open activation gates at time . The gating variables and constitute the fraction of all the gating variables of the Na+ channels in the open state at time .

The Gating Model of the System It was observed that the movement of sodium and potassium ions across the cell membrane of a neuron shows that sodium has a transient conductivity while potassium has a persistent conductance: see Figure 2 Mondeel, (2005).

Figure 2: (A) is an example of a persistent conductance gate. The gate opens and closes by a sensor which responds to the membrane potential. (B) is an example of a transient conductance gate, the activation gate connect with a voltage sensor that functions like the gate in A Mondeel, (2005).

For the persistent conductivity gate to open a number of changes have to take place. The potassium channel, for example consists of four identical subunits, and for the channel to open, all four must experience a systemic change. This systemic change has to do with independent events. If b is identical, then independent events are needed to open a channel, and one of these events occurs with the probability n. Thus the

conductance can be written as bng = where n is a gating variable. If the present channels are many and they function independently of each other, then the fraction of channels open at any given moment is approximately equal to the probability that any of the channels is open. This is the implementation of the law of large numbers Mondeel, (2005). If we assume that the n subunit gate controls the opening and closing state of the channel at a given time, then the probability that one of the subunit gate will be open is n and the probability that it will be close is 1-n. Therefore the transition of each subunit gate can be expressed as a first-order scheme in which the gating movement from closed to open occurs at a voltage-dependent rate )(Vnα , and the reverse movement open to close occurs at a voltage-dependent rate )(Vnβ . The probability that a subunit gate opens over a small period of time is proportional to the probability of finding the gate closed, 1-n, times the opening rate )(Vnα . Conversely, the probability that a subunit gate closes over a small amount of time corresponds to the probability of finding the gate open n times the closing rate )(Vnβ .Thus the open probability for a subunit gate changes at a rate given by the difference of these two terms and so we derive the differential equation ............ (2)

Where ,)()(

1)(vv

vnn

n βατ

+=

and .

)()()(

)(vv

vvn

nn

n

βαα+

=∞ This equation actually implies that for a fixed voltage V, n tends to the value of )(Vn∞ exponentially with time constant ).(vnτ Here )(Vnα and )(V∞β are the opening and closing rate functions of voltage. All these are achieved by suitable experimental data based on a technique called voltage clamping which Hodgkin and Huxley used in their experiments Mondeel, (2005).Applying the Kirchhoff’s conservation of current law and using the configuration of Figure 1, the Hodgkin-Huxley model can be written as

.............(3)



where is the applied current. Then equation (3) can be rewrite as ...............(4)

Hodgkin and Huxley proposed that n, m and h are the potential dependent gating variables that obey the voltage dependence described by the differential equations:

...................(5a)

......................(5b)

.........................(5c)where the quantities hnnmm αβαβα ,,,, , and hβ are assumed to be voltage dependent as follows:

....................(6)

Equations (4), (5a), (5b) and (5c) represent a differential system called the Hodgkin-Huxley model Edelstein-Keshet, (2005). The model does lay a base for qualitative behaviour for the formation of action potential and basis for nearly all models of excitable cell membrane. We can rewrite each of the equation (5a)-(5c) in the form

)]([

)(1

0 vzzv

zz

−−

=τ

for greater insight. Here z represent m, n or h. For any fixed voltage v, z tends to )(0 vz with a time constant

)(vzτ , where the asymptotic value )(0

vzτ and the time constant are given by

and

The parameters used are those specified by Hodgkin and Huxley, see Figure 3 and Figure 4 where the

functions of )(0

vzτ and )(vzτ are shown Schwemmer, (2005).

THE FITZHUGH-NAGUMO MODELReduction from a 44× to 22× a System Fitzhugh and Nagumo noticed that they can under some assumption reduce the four by four differential system (4), (5a), (5b) and (5c) to a two by two differential system. The basic concept of the reduction can also be applied to that of neuron model with various ion channels. To perform this task, we have to eliminate two out of the four variables. We start with two qualitative observations in Figure 3 and Figure 4:

Figure 3: The equilibrium function for variables m, n, and h in the Hodgkin-Huxley Model. The resting potential is at v=0.

Figure 4: The time scale for variables m, n, and h in the Hodgkin-Huxley Model. The resting potential is at v=0.

In Figure 4 we notice that the kinetics of the gating variable m changes rapidly while that of the variables n, h and v changes relatively slowly, which is a

consequence of mτ being smaller than nτ and hτ ( nm ττ , , and hτ are the time scales for m, n and h respectively). It also shows that m can be considered as an instantaneous variable that can be replaced in equation (4) by its steady-state value, )),(()( 0 tvmtm → which is called the quasi steady state approximation Schwemmer, (2005). Furthermore, from Figure



4, we observe that the time scale )(vnτ and )(vhτ are close no matter the value of v, and even more there is similarity with the graphs of )(0 vn and

)(1 0 vh− in Figure 3. This shows that the variables n and 1-h can be approximated as a single functional variable w. To make it more universal, we take the linear approximation of d-h = bn, where d, and b are constants and we let w = d-h = bn. Now h = d-w,

bwn = , )(0 vmm = . Then the system (4), (5a), (5b) and

(5c) takes the form: ............(7)

or by introducing a new variable v, it can be written as

........................(8)

Where Lg

R 1= , and is actually the time scale

of time of v while F denotes a function. Since m is regarded as constant, we are left with n and h which are lump together as a single functional equation

...........................(9)

where wτ is the time scale of w. The equations (8) and (9) define a neuron model Schwemmer, (2005).Replacing the four equations of Hodgkin and Huxley by the two equations (8) and (9), FitzHugh and Nagumo obtained sharp pulse like oscillations that are similar to that of action potentials by describing the functions F(v, w) and Q(v, w) as

wvvwvF −−=3

),(3

wvbbwvQ −+= 10),(

where v is the membrane voltage, and w is the recovery variable. F and Q are linear in w and the cubic term in v is non-linear. So we finally obtain the system

.................................(10)

where 0b and 1b are positive constants. The system (10) is an example of FitzHugh-Nagumo System Schwemmer, (2005).

Two Different Variations of FitzHugh-Nagumo SystemThe FitzHugh-Nagumo system has been derived and written in different variations by researchers to suit their specific research work. In this section we look at two different formulations and then focus on the one we will use in the current work. Firstly, we consider two variables x and y and model properly the FitzHugh and Nagumo system. We define the variable x as the measure of excitation (such as voltage in a neuronal setting), hence x is the fast variable (replacing variables V and m in the Hodgkin-Huxley system) and use y as the slow recovery variable (replacing variables n and h in the Hodgkin-Huxley system), which damps out the excitation of x when increased. Making the equation simple enough for the rate of change for x and y, we assume that x and y satisfies the linear kinetics,Segel and Edelstein-Keshet (2013):

.............................(11a)

....................................(11b)

System (11) is the FitzHugh-Nagumo system we will focus on in this work. Here the parameters c and

c1

are introduced to create a symmetry that makes x faster and y slower whenever c is increased. The parameters a, b, and c are all positive, and satisfy the

conditions 1321 <<− ab and 0 < 1. The parameter d

denotes the stimulus and can have any sign, Segel and Edelstein-Keshet (2013).In the second formulation of the FitzHugh-Nagumo system, we consider the phase portrait below

Figure 5: The profile of as a function of v Olufsen, (2015). As it is shown in Figure 5, v denotes the voltage of the action potential that has three critical values: v

= 0, as the resting potential, α=v , as the threshold



,1),10( =<< vα as the voltage level when channels are closed.We want to create a differential equation for v = v(t). To achieve that we have to express as a function of v. Since v =0 then , but when the Na+ start to open the voltage increases, so and the more v increases, the neuron fires at α=v , hence . Finally the voltage decreases such that the channels closes at x = 1, so that . The easiest description for as a function f(v) of v is expressed in Figure 5 Olufsen, (2015).

An expression that is compatible with the form of Figure 5 is given by

..........................(12)

Introducing the variable w that acts to diminish v into (12), we now have

................(13)

Introducing the applied electric current I to the right hand side of (13), and suppose that increases linearly in v and that w decreases linearly, then we get

.........................(14)

which is the FitzHugh-Nagumo model in dimensionless form, where v represents the fast variable (potential) and w denotes the slow variable (sodium gating variable). Besides ,,γα and ε are constants satisfying the conditions 10 << α and

10 ≤< α Olufsen, (2015).

CONCLUSIONHodgkin-Huxley system was used to study an excitability phenomenon for nonlinear system which resulted to FitzHugh-Nagumo system. The paper focused on the derivation of the system and its reduction from differential equation to a differential equation. The paper also investigated all the mathematics in the reduction and depict the two most widely used variations of the system by researchers in the fields of neurophysiology, cardiac muscle model etc.

REFERENCES

Edelstein-Keshet, L. (2005). Mathematical models in biology. Society for Industrial and Applied Mathematics.Friedman, A., & Kao, C. Y. (2014). Mathematical modeling of biological processes Springer.Keener, J. P., & Sneyd, J. (2009).Mathematical physiology (Vol. 1). New York: Springer.Gerstner, W., &Kistler, W. M. (2002). Spiking neuron models: Single neurons, populations, plasticity. Cambridge University Press.Mondeel, T.(2012).Modelling Neuronal Excitation: The Hodgkin-Huxley Model.(Thesis work, University ofAmsterdam) 13.Schwemmer, M. A. (2010). The Influence of Dendritic Properties on the Dynamics of Oscillatory Neurons (Doctoral dissertation, UNIVERSITY OF CALIFORNIA DAVIS).Segel, L. A., & Edelstein-Keshet, L. (2013). A primer on mathematical models in biology. Society for Industrial and Applied Mathematics.Olufsen, M.S. (2015). ``Lectures notes for BMA 771 - Biomathematics I’’, 2015.



ON PERFORMANCE OF SOME METHODS OF DETECTING NONLINEARITY IN STATIONARY AND NON-STATIONARY TIME SERIES DATA

1Akeyede, I., and 2Oyeyemi, G.M1Department of Mathematics, Federal University Lafia, PMB 146, Lafia, Nigeria

2Department of Statistics, University of Ilorin, PMB. 1515, Ilorin, Nigeria

Corresponding Email : [email protected]


ABSTRACTThere has been growing interest in exploiting potential forecast gains from the nonlinear structure of autoregressive time series. Several models are available to fit nonlinear time series data. However, before investigating specific nonlinear models for time series data,it is desirable to have a test of nonlinearity in the data. And since most of real life data collected are non-stationary, there is need to investigate which of these test is suitable for stationary and non-stationary data. Statistical tests have been proposed in the literature to help analysts to check for the presence of nonlinearities in observed time series, these tests include Keenan and Tsay tests, and they have been used under the assumption that data is stationary. However, in this paper, we investigated the performance of these two tests for the stationary and non-stationary data. The effect of the stationarity and non-stationarity were studied on simulated data based on general class of linear and nonlinear autoregressive structures using R-software. The powers of tests were compared at different sample sizes for the two cases. It was observed that the Tsay F-test performed better than Keenan’s tests with little order of autoregressive and increase in sample size when data is non-stationary and vice-versa when data is stationary. Finally, we provided illustrative examples by applying the statistical tests to real life datasets and results obtained were desirable.

Key words: Nonlinearity, Tsay’s F Test, Keenan’s Test, Stationarity and Non-Stationarity

PHYSICAL SCIENCES


INTRODUCTIONSeveral techniques used in time series modeling assume linear relationships among variables. However, in some cases, variations in data do not show simple linearity and therefore, are difficult to analyse and predict accurately. Hence, for such data, it would not be practicable to expect a single, linear model to capture these distinct behaviours. Linear relationships and their combinations for describing the behaviour of such data are often found to be grossly inadequate. In general time series analysis, it is known that there are large numbers of nonlinear features such as cycles, thresholds, bursts, chaos, heteroscedasticity, asymmetries and combinations of one or more of these. Tong (1990), Franse, and van Dijk (2000) and Tsay (2010) have presented the various types of models that can be cast into these forms. Nowadays, there are various applications of nonlinear time series models to different fields, such as meteorology, finance, engineering and econometrics. The nonlinear time series models have been used extensively in recent years for modeling time series data that cannot be adequately represented using linear models. Hipel and McLeod (1994) hypothesized that, although a linear model may be adequate to describe average annual river flows, the relationship between daily river flow and precipitation may be nonlinear. For examples, Tong (1990) provides an introduction to different types of nonlinear time series modeling primarily in the univariate setting. Chen and Tsay (1993, 1996) and Lewis and Ray (1997) investigated techniques for obtaining bivariate nonlinear models. Terasvirta (1993) mentioned vector nonlinear autoregressive processes, vector nonlinear average processes and multiple bilinear time series models in passing but concentrated on statistical inference for nonlinear models using parametric procedure. Before fitting a nonlinear time series model to a given set of data, it is good if the nonlinearity characteristics of the data can be detected. There are various tests that have been suggested over the past years to distinguish linear from the nonlinear data sets. For example, Hunnich (1982) used the bispectrum test. They used the fact that the square modulus of normalized bispectrum is constant when the time series is linear. The hypothesis is based on the non-centrality of parameters of the marginal distribution of the square moduli, where n is the sample size. Yuan (2000) modified the Hunnich’s test in such a way that the parameter being tested under the null hypothesis is the location parameters, such as the mean or variance. The problem of nonlinear time series identification and modelling has attracted considerable attention for years in diverse fields such as biometrics,

socioeconomics, transportation, electric power systems, and finance which exhibit nonlinear process. A good nonlinear model should be able to capture some of the nonlinear phenomena in the data. Once a model is selected, sufficiently strong evidence need to be found in the data to abandon the linear model. Therefore, good statistical and diagnostic tests are needed to determine the nonlinearity in time series data.This work examined the performance of two nonlinearity tests in time series analysis; these are Kennan’s test and F-test of nonlinearity. The power efficiency of each test wasstudied on different sample size, models and under the violation of assumption of stationarity based on simulated data and real data collected. StationarityIn Statistics, a stationary processis a stochastic process whose joint probability distribution does not change when shifted in time. Consequently, parameters such as the mean and variance, if they are present, also do not change over time.The most important assumption made about time series data is that of stationarity. The basic idea of stationarity is that the probability laws that govern the behavior of the process do not change over time. In indeed, the process is statistically equilibrium. Specifically, a process {Yt} is said to be strictly stationary if the joint distribution of Ytis the same as that of Yt− kfor all t and k; t = 1, 2, …, k. In other words, the Y’s are (marginally) identically distributed (see Jonathan and Kung-Sik, 2008). It then follows that E(Yt) = E(Yt− k) for all t and k so that the mean function is constant for all time. Additionally, Var(Yt) = Var(Yt− k) for all t and k so that the variance is also constant over time.

Linear Time Series ModelA relationship of direct proportionality that, when plotted on a graph, traces a straight line. In linear relationships, any given change in an independent variable will always produce a corresponding change in the dependent variable. For example, a linear relationship between production hours and output in a factory determines percentage of increase or decrease of the output. The concept of linear relationship suggests that two quantities are proportional to each other: doubling one causes the other to double as well. Linear relationships are often the first approximation used to describe any relationship, even though there is no unique way to explain what a linear relationship is in terms of the underlying nature of the quantities. For example, a linear relationship between the height and weight of an object is different from a linear relationship between the volume and weight of an object. The second relationship makes more

ON PERFORMANCE OF SOME METHODS OF DETECTING NONLINEARITY IN STATIONARY AND NON-STATIONARYTIME SERIES DATA


sense, but both are linear relationships, and they are, of course, incompatible with each other. Medications, especially for children, are often prescribed in proportion to weight. This is an example of a linear relationship.The linear time series modeling depends on the type of system that generates the data. Time series analysis may be Autoregressive Models, Moving Average Model or Autoregressive Moving Average Model (ARMA). However For the purpose of this research work we considered only general classes of second order auto regressive models.

Nonlinear Time series ModelPractitioners in many fields are increasingly faced with real data possessing nonlinear attributes. It is known that stationary Gaussian autoregressive models are structurally determined by their first two moments. Consequently, linear autoregressive models must be time reversible. Many real datasets are time irreversible, suggesting that the underlying process is nonlinear. Indeed, in Tong’s seminal paper on threshold models, he would argue that no linear Gaussian model could explain the cyclical dynamics observed in sections of the lynx data (Tong and Lim, 1980). Furthermore, he argued that characteristics of nonlinear models, such as time irreversibility and limit cycles, mandated the development of practical nonlinear models to help resolve ongoing difficulties in real data. Tong’s explanation and application of locally linear threshold models introduced striking opportunities for model building strategies. The pioneering work in time series modeling is due to Wiener who had produced a very general class of nonlinear model, called Volterra series expansion and is generally given as follows;

Xt= + 1

Xt= + 2

Where µ is the mean level of ( ) is a strictly stationary process of independent and identically distributed random variables. Obviously, Xt is nonlinear if one of the higher order coefficients

or is non zero(Ibrahim et al, 2005). For instance, the model 1 and 2 above can be illustrated with simple structures (i, j = 1, 2, k = 0) as follows;

..............3

...........4

Most linear models can be expressed into Volterra expansion form which includes the autoregressive model of order p, [AR (p)], the moving average model of order q [MA (q)] and the autoregressive moving average model of order p and q [ARMA (p,q)].

MATERIALS AND METHODSSeveral authors such as Chan and Tong(1986) and Tsay (1986) raised the issue that one nonlinearity test is not enough to detect nonlinearity in a data set. Nonetheless, it is expected that the nonlinearity test will suggest whether a data set is linear or otherwise. Thus, if any test does suggest that the data is nonlinear, we expect that a nonlinear model will improve the modeling of the data set. Indeed, in this work, a set of data were generated from model 1-4, under the assumption of stationarity stated earlier and the two tests, Keenan and F-tests of nonlinearity were applied to see the behavior of their acceptance of nonlinearity. Thereafter, another set of data were generated under the violation of the stationarity and white noise assumptions.Each test is subjected to 500 replication simulation at different sample sizes for stationarity and Non-Stationarity data structures.Both tests are based on time domain approach and suitably



applied on data generated from selected linear and nonlinear auto regressive models. Power efficiency of the tests was compared on the simulated data.

Keenan’s TestKeenan adopted the idea of Turkey one degree of freedom test for non-additivity to derive a time domain statistic. The test is motivated by similarity of Volterra expansions to polynomials, and is extremely simple, both conceptually and computationally. Assume that a time series Yt, t = 1, 2, .., n, can be adequately approximated by order of Volterra expansion in 1 and 2The approximation will be linear if the second and other higher terms on the right hand side are zero. The Keenan’s test procedure is as follows;(i)Regress Yt on ( 1, Yt-1, . . . Yt-m) and calculate the fitted values(Yt), and the residuals, , for t = m+1, . . ., n, and the residual sum of squares

(ii)Regress Yt2 on ( 1, Yt-1, . . . Yt-m) and calculate the residual (et) for t = m, . . ., n,

(iii) Regress on

and obtain from

Where is the regression coefficient, and

Follows approximately F1,n-2M-2, , where the

degrees of freedom of associated with is (n-M)-M-1.

Keenan’s test is based on the argument that if any of cij and other higher coefficients in 1 and 2 are non-zero, e.g c12, then this nonlinearity will be distributionally reflected in the diagnostics of the fitted linear model, if the residuals of the linear model are correlated with Yt-IYt-2. As in Turkey non additive test, Keenan’s test used the aggregated quantity Y2t, the square of the fitted value of Yt based on the fitted linear model, to obtain the quadratic terms upon which the residual can be correlated. The idea is extremely valuable when the sample size is small because it only requires one degree of freedom.

F-Test Tsay (1986) modifies Keenan’s test by replacing the aggregated quantity Y2t by the disaggregated variable Yt-iYt-j, i,j = 1, …., M, where M is defined in Keenan’s test. The F-test procedure is as follows:(i) Regress Yt on ( 1, Yt-1, . . . Yt-m) and calculate the fitted values(Yt), and the residuals, ( ) ̂, for t = M+1, . . ., n. the regression model is denoted by Yt = Wt + et, where, Wt = ( 1, Yt-1, . . . Yt-m) and = ( )T.

(ii)Regress vector Zt on ( 1, Yt-1, . . . Yt-m) and calculate the residuals (Xt), for t = M+1, ..., n. In this step, the multivariate regression model is Zt = WtH + Xt, where Zt is an m = dimensional vector defined by ZTt = Vech (UTtUt) with Ut = (Yt-1, . . . Yt-m), and Vech denoting the half stacking vector

(iii) Fit And define

Where the summation is over t from M+1 to n. Here, is asymptotically distributed as Fm,n-m-M-1



MODELS SELECTED FOR SIMULATIONData is generated from several linear and nonlinear second orders of general classes of autoregressive models given below:Model 1. AR(2):

Model 2.TR(2): Yti = 0.3sin( ) - 0.6cos( )+ et

Model 3:EX(2): Yti = 0.3 + exp(-0.6 ) + et

Model 4: PL(2): Yt =


The model 1, 2, 3 and 4 are linear, trigonometry, exponential and polynomial autoregressive models respectively with coefficients of Yt-1 being 0.3 and Yt-2 being -0.6. Simulation studies were conducted to investigate the performance of Keenan’s and F-test. The hypothesis test were null hypothesis of nonlinearity against the alternative hypothesis of linearity of data. Thus, if the data is linear with α = 0.05, more than 95% of the replicates are expected to have the test statistic less than the critical values. Power of the two tests is compared for the different models, sample size and distributions to know which of the two tests is acceptably good for detecting nonlinearity for time series data generated from the given model. Note that in autoregressive modeling, the innovation (error), et process is often specified as independent and identically normally distributed. The normal error assumption implies that the stationary time series is also a normal process; that is, any finite set of time series observations are jointly normal. For example, the pair (Y1,Y2) has a bivariate normal distribution and so does any pair of Y’s; the triple (Y1,Y2,Y3) has a trivariate normal distribution and so does any triple of Y’s, and so forth. Indeed, this is one of the basic assumptions of stationary data. However, in this study, the data will be generated under white noise assumption of stationarity and when the stationarity assumption is violated for order of past responses and random error terms to see behavior of the models in each case. 3000 replications were used to stabilize models estimations at different combinations of n and models.

Selection RuleTheaverage acceptance of linearity by each test was recorded as in table 1-4, at n=50, 150 and 300 representing small, mild and large samples respectively for each case (stationarity and Non-Stationarity) and model. The test with highest proportion of acceptance in a category is the best for that category. Note that only second order autoregressive models were considered in each case and situation

RESULTS AND DISCUSSIONSRelative Performance of Keenan- and F-Tests on General Class of Stationary Autoregressive Cases at Different Sample SizesThe performance of the following Keenan- and F-tests in detecting general classes of linear and nonlinear autoregressive cases were examined at sample size of 50, 150, and 300 which represent small, mild and large sample sizes respectively. The data were simulated using R statistical software following the assumption of stationarity earlier stated to fix the parameters. The parameters were fixed for each model as shown in model 1-4 to observe how the tests would accept the null hypothesis of linearity of stationary data. The white noise assumption of the error term was also observed to make the data simulated be stationary. Each created data were replicated 1000 times using TSA Package in R software.

Table 1: Empirical frequencies of rejection of the null hypothesis of linearity; n =50 and 1000 replications. Nominal significance level, 0.05 (Stationary Data)Model Keenan’s Test F-TestModel 1 0.8505 0.6198Model 2 0.6449 0.5046Model 3 0.6756 0.0283Model 4 0.0033 0.0005

Table 2: Empirical frequencies of rejection of the null hypothesis of linearity; n =150 and 1000 replications. Nominal significance level, 0.05(Stationary Data)Model Keenan’s Test F-TestModel 1 0.4731 0.9280Model 2 0.0514 0.0414Model 3 0.0088 0.0062Model 4 0.0012 0.0000


Table 3: Empirical frequencies of rejection of the null hypothesis of linearity; n =300 and 1000 replications. Nominal significance level, 0.05(Stationary Data)Model Keenan’s Test F-TestModel 1 0.5179 0.8706Model 2 0.0735 0.0179Model 3 0.0169 0.0061Model4 0.0000 0.0000

Table 4: Effect of Sample size on the Power of the Tests for model 1-4 Nominal significance level, 0.05 (Stationary Data)

Model Sample Size Model 1 Model 2 Model 3 Model 4

Keenan 50 0.6198 0.6449 0.6756 0.0033

150 0.4731 0.0514 0.0088 0.0012

300 0.5179 0.0735 0.0169 0.0000

F-test 50 0.8505 0.5046 0.0283 0.0005

150 0.9280 0.0414 0.0062 0.0000

300 0.8706 0.0179 0.0061 0.0000

Relative Performance of Keenan- And F-Tests on General Class of Non-Stationary Autoregressive Cases at Different Sample SizesOne of the objectives of this study is to find out the performance of Keenan and F-test of nonlinearity on general classes of linear and nonlinear autoregressive, simulated with violation of assumption of non-stationarity. Since most of real life data collected are non-stationary, there is need to investigate which of these tests is suitable for non-stationary data. One major assumption of stationarity is validity of white noise assumption of error term; the error term is independently distributed with zero mean and positive variance. Indeed, in this work non-stationarity was injected in our simulated data from different models used for the simulation by violating the independence and normality assumption of error term in the following way to know the effect of non-stationarity on each model at different sample sizes: the results are displayed in table 5-8

The value of mean and variance were specified based on the history of nature of real life data considered, Data on Nigeria Gross Domestic Products (GDP)

Table 5: Empirical frequencies of rejection of the null hypothesis of linearity; n =50 and 1000 replications. Nominal significance level, 0.05 (Non-Stationary Data)Model Keenan’s Test F-TestModel 1 0.3697 0.4607Model 2 0.000 0.3072Model 3 0.0000 0.3123Model 4 0.0000 0.0034

Table 6: Empirical frequencies of rejection of the null hypothesis of linearity; n =150 and 1000 replications. Nominal significance level, 0.05 (Non-Stationary Data)Model Keenan’s Test F-TestModel 1 0.3840 0.2088Model 2 0.0000 0.7596Model 3 0.0000 0.3953Model 4 0.0000 0.0017

Table 7: Empirical frequencies of rejection of the null hypothesis oflinearity; n =300 and 1000 replications. Nominal significance level, 0.05 (Non-Stationary Data)Model Keenan’s Test F-TestModel 1 0.9811 0.0819Model 2 0.0000 0.2931Model 3 0.0169 0.0061Model 4 0.0000 0.0000

Table 8: Effect of Sample size on the Power of the Tests for model 1-4 Nominal significance level, 0.05 (Non-Stationary Data)Model Sample Size Model 1 Model 2 Model 3 Model

4Keenan 50 0.3697 0.0000 0.0000 0.0000

150 0.3840 0.0000 0.0000 0.0000

300 0.9811 0.0000 0.0000 0.0000F-test 50 0.4607 0.3072 0.3123 0.0034

150 0.2088 0.7596 0.3953 0.0017300 0.0819 0.2931 0.7670 0.0000

Table 9: Results of Nonlinearity Tests on Nigeria GDPTest Test Statistic Critical value Conclusion

Keenan’s Test 10.982 0.0052 Nonlinear

F-Test 8.0797 0.0293 Nonlinear



REFERENCESChan, W. S., and Tong, H. (1986), On Tests for Non-linearity in Time Series Analysis. Journal of Forecasting,5: 217-228.Chen R. and Tsay, R.S. (1993),Nonlinear additive ARX models. J. Amer. Statist.Assoc. 88, 995-967.Chen R. and Tsay, R.S. (1996). Nonlinear transfer functions. Journal of Nonparametric Statistics, 6:193- 204.Franse , P.H. and van Dijk, D. (2000). Nonlinear error-correction models for interest ates in the Netherlands, inW.A. Hunnich, M. J. (1982), Testing for Gaussianity and Linearity of a Stationary Time Series. Journal of Time Series Analysis. 3(3): 169-176.Hipel, K. W. and McLeod, A.I. (1994). Time Series Modeling of Water Resources and Environmental Systems. Amsterdam: Elsevier.Ibrahim M., Azami Z. and Mohd., S.Y.(2005), JurnalTeknologi, 42(C), 1–10 Universiti Teknologi MalaysiaJonathan D. C. and Kung-Sik C. (2008), Time Series Analysis with Applications in R Second Edition, Springer Science+Business Media, LLCKeenan, D.M. (1985). A Tukey non-additivity-type test for time series nonlinearity. Biometrika 72, 39-44.Lewis, P., and B. Ray (1997): “Modeling nonlinearity, long-range dependence, and periodic phenomena in sea surface temperatures using TSMARS, “Journal of the American Statistical Association, 92: 881-893.Terasvirta, T. (1993): Testing linearity against smooth transition autoregressive models.Biometrika 74, 491-499.Tong, H. (1990): Nonlinear TimeSeries: A Dynamical System Approach, Oxford: Clarendon.Tong H. and Lim, K. S. (1980). Threshold Autoregression, Limit Cycles and Cyclical Data. Journal of Royal Statist. Soc., B 42:245-292.Tsay, R.S. (1986). Nonlinearity tests for time series. Biometrika 73, 461-466.Tsay, R.S. (1998). Testing and modeling multivariate threshold models. J. Amer. Statis. Assoc.,93, 1188- 1202.Tsay, R. S. (2010): Analysis of Financial Time Series; 3rd Edition, Published by John Wiley & Sons, Inc., Hoboken, New Jersey. Published simultaneously in CanadaYuan, J. (2000): Testing Linearity for Stationary Time series using the Sample Inter-quartile Range. Journal of Time series Analysis 21(6) 714-722Yule, G. U. (1926). “Why do we sometimes get nonsense-correlations betweentime-series? —Astudy in sampling and the nature of time-series.” Journal of theRoyal Statistical Society, 89:1, 1–63.

Table 1-8 show the results of analyses of performance of Keenan’s test and F-test with respect to the model 1-4 at small, mild and large sample sizes taken to be 50, 150 and 300 respectively under the assumption of stationarity and violation of the assumption of stationarity. The two tests were compared at the 5% level of significance for two tailed test in each case. The average powers of the tests for both tests were computed for easy comparison.

CONCLUSIONWe noticed that both test do not reject the linearity of the Model 1, linear autoregressive at different sample sizes. However, F-test has higher power of acceptance than Keenan test when data is stationary while Keenan’s test performs better for non-stationary data especially at large sample size. In model 2-4, trigonometric, exponential and polynomial auto regressive models respectively, most of the average p-value are less than the 5% level of significance and as the sample size increases the p-value decreases indicating the significant of linearity of the models by

the two tests. Meanwhile, the F-test perform better as its average p-values are less than Keenan’s test at the three sample sizes for stationary data and vice versa for non-stationary data as shown in the summary Table 4 and 8. More so, both tests wrongly accept the null hypothesis of linearity for model 2, 3, and 4 with their average p-values greater than 5% level of significance at sample size 50 for stationary data. When the Non-Stationarity was introduced in data generated F-test’s p-value were greater than 5% and therefore wrongly accept the null hypothesis of linearity of nonlinear autoregressive model except that of polynomial model which its linearity was rightly rejected and more powerful when the sample size increases. While Keenan’s test has the p-value close to zero which show the significant of linearity of the three nonlinear models at the three sample sizes and therefore considered as the most powerful test for non-stationary data Finally, we provided illustrative examples by applying the statistical tests to real life datasets and results obtained are desirable.



OPERATING SYSTEM SECURITY AND PENETRATION TESTING

Adoga, H. U., Ezugwu, E. A. and Umar, M. B.Department of Computer Science, Faculty of Science, Federal University Lafia, Nasarawa State, Nigeria



ABSTRACTPenetration testing is an integral part of any organization or individual that employs the use of Information Technology services. This ensures that their computing infrastructures are checked for vulnerabilities in a routine manner. This paper explains the processes and phases involved in ethical hacking. In addition, major penetration testing types available are also discussed. A Local Area Network (LAN) was designed in a virtual environment running Linux and Windows Operating Systems. We carried out a white box penetration test on the systems. Kali Linux; an open source Debian-based Linux distribution designed for penetration testing and digital forensics was used for the experiment. Several built-in tools in Kali Linux were used, while going through the main phases of penetration testing. Starting from the reconnaissance phase of the process, information about computers was gathered, the network was scanned for vulnerabilities in the scanning phase, and identified vulnerabilities were exploited in the third phase of the penetration test, which is gaining access. Backdoors to exploited system(s) were created to maintain access and event logs were deleted to prevent detection in the final phase of the process.

Keywords: Malware, operating system security, work station virtualization, penetration testing, network security.

PHYSICAL SCIENCES


INTRODUCTION With the rapid growth of Information Technology (IT) and increased accessibility in recent years, we produce more data now than ever before. Therefore, this calls for an urgent need to protect information and information systems from all sorts of manipulation and unauthorized access. Ensuring the protection of network infrastructure and computer systems, and the services they provide is paramount to the success of any business. Sensitive data, such as financial records, medical records and e-commerce services amongst other sensitive information, need to be protected by ensuring the Confidentiality, Integrity and availability (CIA) of such information.Host and server based operating systems faces an increasingly high level of security threats by the day.Penetration testing provides a way for organizations to check their computer infrastructure for vulnerabilities that could be exploited by an adversary. There are some very important objectives that we set aim to achieve by carrying out this research; these objectives are listed below. They include, using Kali Linux, which is an open source Linux distribution designed for digital forensics and penetration testing, in determining the possible vulnerabilities that exist in a typical Local Area Network environment, design of a typical Local Area Network, in a virtual environment, consisting of both open source and proprietary Operating Systems as obtainable in most network environments, the implementation of each phase of ethical hacking and penetration testing using the digital forensics tool (Kali Linux) and exploiting any vulnerability found with the aim of providing useful recommendations, which can help organizations harden their network infrastructure even better. Kali Linux is the most advanced Debian-based Linux distribution for penetration testing available at the time of this research. This distribution provides applications and tools that can be used in each of the five main phases of penetration testing. An empirical approach was adopted in reviewing ethical hacking and penetration-testing procedures with focus on commonly used operating systems in today’s network infrastructures. Ethical hacking can be described as a process of testing the computer infrastructure of an organization or individual by professional certified hacker(s) with the aim of assessing the infrastructure for possible vulnerabilities and making them known to the management of the organization for appropriate security measures. Unlike black hat hackers who gain unauthorized access to computer systems (networks) without the authorization of the owner, ethical hacking is usually done with the consent of the organization or owner of the computer system or network. A certified ethical hacker will normally use the same

tools that a black hat hacker will use to achieve their aim. According to Whitaker and Newman (2006), a penetration tester is an ethical hacker who is hired to attempt to compromise the network of a company for the purpose of assessing its data security.

There are basically five phases of ethical hacking, as reported by Engebretson (2013).Reconnaissance is basically the process of gathering information about the target infrastructure and systems. This is the first phase of the ethical hacking process as it allows the ethical hacker to have a good understanding of the organization’s infrastructure and the underlying technology in use. At the end of this phase, the pen tester should have a good idea of information such as the facility information, phone numbers, employee information, Internet Protocol (IP) address ranges, and namespaces.Nidhra and Dondeti (2012). Vulnerability Scanning is a phase that makes use of data gathered to find out the valuable resources the target has, presence of known vulnerabilities in infrastructure the target uses.According to Nidhra and Dondeti (2012), the pen tester gets information on hosts, ports on the hosts and services that may be running on the hosts. Scanning is predicated on the assumption that reconnaissance has been successfully completed. Gaining access (Exploitation) is a phase of the penetration testing lifecycle where the hacker attempts to gain access to the target system(s) using the information gathered from previous phases of the process. Broad and Bindner (2014) reported that information such as usernames, permissions and passwords gotten from early phases are utilized to exploit any known vulnerabilities on the target systems. Maintaining access to the target system after securing it is an important phase of a penetration test, as this will ensure that the pen tester stays connected long enough to carry out all the tests (attacks). The use of backdoors and key loggers are employed in this phase, backdoors will allow pen testers to connect to the same target at a later time even when a patch (update) has been implemented to fix the vulnerability. It is great to exploit a computer, networking device, or a firewall device, however the goal of penetration testers is to maintain access to the exploited system(s) Broad and Bindner (2014). This is the final phase in the ethical hacking process, as known existing vulnerabilities have been exploited. Activities in this phaseinvolves deleting event logs that might reveal that the hacker was there in the first place. Engebretson (2013) reported that anattacker who wishes to remain untraceable after successfully carrying out an attack, needs to erase all the tracks that may lead back to him/her.



MATERIALS AND METHODS A Local Area Network (LAN) was designed with both Linux and Windows Operating Systems in a virtualized environment. According Cisco Systems (2016), the benefits of a centralized design include IP address management, simplified configuration and troubleshooting, and roaming at scale. Using Kali Linux, we went through the phases of penetration testing on the LAN. Information about the Operating Systems was gathered and the computers were scanned for vulnerabilities. Finally, some of the vulnerabilities were exploited and recommendations made. Figure 1 below depicts the phases involved in performing penetration testing.

RECONNAISSANCE

START

FINISH

SCANNING

EXPLOITATION

MAINTAINING ACCESS

COVERING TRACKS

USEFUL INFO GATHERED?

VULNERABILITIES FOUND ?

SYSTEM EXPLOITED?

BACKDOOR CREATED?

Figure 1. Penetration testing flowchartBased on the information depicted in Figure 1 above, the flowchart starts with the reconnaissance phase of penetration testing, where information about live hosts were collected, upon completion, the network was scanned in order to reveal available ports and services. Vulnerabilities found in the scanning

phase were exploited in the exploitation phase of the penetration test. Backdoors were also created in order to maintain access to the exploited systems. The process completes by covering the tracks, to avoid detection by system administrators.The experiment from the reconnaissance phase of ethical hacking and walked through subsequent phases of the process in the following sections. The different phases of penetration testing were performed using Kali Linux. The network infrastructure was simulated in a virtual environment using VMware workstation 11. A comprehensive network topology structure was designed based on the information collected from the reconnaissance phase of the penetration testing carried out (See Table 1 and Figure 2).

Reconnaissance Identification of live hosts Running the ifconfig command on the Kali Linux terminal reveals the local IP address on Kali (a static IP address was assigned), having this information can help you get an idea of the subnet mask of the network you are connected to. A ping sweep was done on the network as part of the information gathering reconnaissance phase. This reveals some important information about host IP addresses on the network and the output shows that 5 hosts are up with their respective IP and mac addresses displayed.

IP addressing scheme from ping sweepUsing ping sweep with nmap tool on Kali Linux, i.e running the nmap -sP 192.168.1.0/24 command with sP option to specify ping sweep. The IP addresses on the network are as shown in Table 2 below.

Table 1. Ip addressing and MAC address discovered using nmap toolHost MAC address IP address Subnet mask00:0c:29:84:e3:39 192.168.1.1 255.255.255.000:0C:29:56:DA:E2 192.168.1.2 255.255.255.000:0C:29:A0:FF:18 192.168.1.3 255.255.255.000:0C:29:08:35:C1 192.168.1.4 255.255.255.000:0C:29:D0:EC:2F 192.168.1.5 255.255.255.0

After performing a ping sweep on the network, the IP addresses of hosts on the network were revealed.Based on the information gathered from the reconnaissance activity above, it is now known to the pen tester that 4 more devices are connected on the same network and a network topology can be drawn in order to make the subsequent phases of the ethical hacking process more efficient and precise. The network topology used to model the experimental setup is illustrated in Figure 2.



Figure 2. Network topology designed from reconnaissance.

Vulnerability scanning was performed on the e-commerce server, using the Nmap tool on Kali Linux. A list of open ports and services available on the server are shown in the table below.

Table 2: Msf console scan on the e-commerce server (192.168.1.4/24)s/n Port type Port number Status Service1 Tcp 135 open Msrpc2 Tcp 139 open Netbios-ssn3 Tcp 445 open Microsoft-ds

Nmap scan on the web server (192.168.1.2) shows a

potential vulnerability, telnet port 23 is open. We will try to exploit this vulnerability in the exploitation phase. Nmap scan on the web server (192.168.1.5/24) shows potential vulnerabilities i.e. open ports and services that can be exploited using penetration testing. Figure 3 below depicts the data collected on the web server.

Figure 3. Open ports and services on web server 192.168.1.5/24

Gaining access (Exploitation)On 192.168.1.4/24, we exploited the ms08_067_netapi vulnerability identified during the scan. Figure 4 shows the screenshot of the ms08_067 attack carried out on the e-commerce server on the network infrastructure, which gives access to the command prompt of the server, data can be modified and system settings can be tampered with.

Figure 4: Ms08_067 vulnerability exploit on e-commerce server 192.168.1.4/24.

The figure below depicts the vulnerability levels, their indications, which followed by a brief summary of each vulnerability level.

Figure 5:Vulnerability level color codes.

From the image shown above, L depicts low and this means a particular vulnerability would not course serious harm to the system even though it exists, it can be fixed by updating the Operating to get latest patches and fixes.M depicts medium level of vulnerability, which implies that, the threat level has a mild effect on the Operating System. A more specific patch, which is peculiar to the vulnerability need to be used.H depicts high level of vulnerability, it basically means that, it can cause damage to the Operating System when exploited.C depicts critical level of vulnerability, if exploited, this can bring the system to a complete halt, and an attacker can take over the system remotely.Multi/handler can be used with a payload of ‘windows/metsvc_bind_tcp’ to connect to the remote system, as shown in Figure 6 below.

Figure 6. Multi-handler to exploit metsvc backdoor on e-commerce server.



RESULTS AND DISCUSSIONThhe outcome of the penetration test carried out on the network created in the experiment phase shows that using the nmap tool on Kali Linux, we were able to discover the number of computers on the network, which comprises of both client and server operating systems. We were able to discover both the Media Access Control addresses of the devices and their respective ip addresses. Using this information, a network topology was drawn, which

assigns valid IP addresses to both client and server operating systems found on the network. The network topology presented depicts all the devices that are on the network, including the Kali Linux PC used for carrying out the pen test on the network.Although the structure of the penetration testing process requires that results are presented after each phase, we have also presented the important results in table 3 below, followed by a detailed analysis of some results in subsequent subsections.


Table 3. Results and descriptionDevice name Device Internet

Protocol address

Vulnerabilities and open ports found

Vulnerabilities exploited

Description Proposed solution

E-commerce server.

192.168.1.4/24 TCP port 135, TCP port 139, TCP port 445 (Msrpc, N e t b i o s - s s n , Microsoft-ds)

M s 0 8 _ 0 6 7 _ n e t a p i vulnerability.

This attack carried out on the E-commerce server gives full access to the command prompt, where changes can be made to the exploited Operating System.

The Ms08_067_netapi vulnerability patch should be implemented on the server, including other supported patches. Open ports on the server should be closed, allowing only ports that are in use by the server.

Web server.192.168.1.5/24

Telnet port 23, 3 known services, 10 open ports, 7 unknown services.

Using the telnet protocol on port 23 sends data in clear text form.

This gives access to remote data using a packet sniffing tool.

The telnet protocol which uses port 23 for remote access should be replaced with SSH, which is more secured and operating on port 22. Telnet port 23 should always be closed and the protocol deactivated.

E-commerce server.

192.168.1.4/24 metsvc_bind_tcp The multi-handler tool was used for exploiting the metsvc_bind_tcp vulnerability found on the server.

This vulnerability was exploited by maintaining access to the server.

The local port number 31337 on the server should be closed at all times.

E-commerce server.

192.168.1.4/24 metsvc payload Backdoor exploit using meterpreter

Using the metsvc payload gave us the ability to create a backdoor on the e-commerce server.

A patch should be selected and installed from updates that are made available by the Operating System vendor.

E-commerce server.

192.168.1.4/24 Covering tracks 425 system data were wiped, and 236 app data were also wiped on the server to cover the tracks upon completion, as shown in figure 7.

This phase of the ethical hacking process is important since it helps in clearing all the activities of the ethical hacker.

Suggested solutions from subsequent phases of the pen test process should be implemented to prevent the hacker from getting to this final stage of covering tracks.

The Ms08_067 Vulnerability AnalysisThe vulnerability could allow remote code execution if an affected system received a specially crafted RPC request. On Microsoft Windows Server systems, an attacker could exploit this vulnerability without authentication to run arbitrary code. The MS08_067 vulnerability can also be used to shut down the server remotely, which can have a catastrophic effect on the company’s business. Threat level: CriticalVulnerability level: CriticalDenial of Service attack on webserver 192.168.1.5/24The Table belowdepicts the screenshot of the Denial of Service (DoS) attack carried out on the web server.

Table 4. Denial of service attack on webserver 192.168.1.5/24 using port 80 (SYN flood)

S/N Parameter Value1 LHOST (local host) 192.168.1.12 RHOST (remote host) 192.168.1.53 RPORT (remote port) 804 SNAPLEN (LENGTH) 655355 TIMEOUT 500

Denial of Service analysisA Denial of service (DoS) attack on port 80 of the webserver results when the web server is continuously flooded with packets until it makes it unavailable or unable to reply legitimate requests from clients. Denial of service overloads the target’s resources and other system resources affected are: CPU usage, network bandwidth, Hard disk space, database pool, and server memory.


Threat level: CriticalVulnerability level: Critical

Backdoor exploit analysismetsvc payload will give access back to the exploited server at later time using meterpreter shell. Having such backdoor access has a catastrophic effect on the server. Using the clearev command on the meterpreter prompt will clear the event log on the victim’s computer and thus make it difficult for the victim to notice any changes or even know their system has been hacked. Figure 7 below shows the data that was wiped from the e-commerce server to prevent detection by the organization after completing the exploit.Threat level: CriticalVulnerability level: Critical

Figure 7: covering tracks using clearev on e-commerce server.

CONCLUSIONEthical hacking unlike black hat hacking or hacking with malicious intent is an integral part of any organization or business that employs the use of Information Technology (IT). This ensures that computer systems and infrastructure are tested in a routine manner to prevent against hackers; this paper has shed light on the procedures by going through the five phases of penetration testing. Organizations need to employ pen testers to routinely test their server and host operating systems for vulnerabilities and make recommendations that will strengthen security of such systems. Kali Linux, an open source toolkit can be used to perform these tests as it provides all the tools

needed to go through all the phases of penetration testing. The future direction of our work is to have this same technique of penetration testing, using Kali Linux, implemented in a datacenter environment, where more servers with different kinds of Operating Systems are available, and with connections to Wide Area Networks. We conclude the experiment section by presenting the following recommendations:1. The MS08_067 vulnerability found on the e-commerce server is a huge security risk as it allows a hacker access to sensitive information. Latest patches should be applied to the server to remedy this vulnerability.2. The use of telnet service for remote access should be discouraged and replaced with SSH on port 22, as it is more secure sending encrypted traffic.3. Hosts and network devices should be configured to block ICMP echo requests from unknown source addresses (broadcasts) by default. This is a suspicious behavior which hackers use for information gathering about the IP and mac addresses on the network using ping sweeps.4. Organizations making use of various types of Operating Systems as emulated in the virtual network designed in this work should always have the Operating Systems updated to versions that include latest patches with built-inIntrusion Detection Systems (IDS) and Intrusion Prevention Systems (IPS).5. Patches to Operating Systems should be managed using patch management systems, as this is a more effective and efficient way of applying patches. This will ensure a centralized control of updates to all systems, which reduces the burden of manual updates by system administrators thus enhancing overall system security.6. Penetration testing should be conducted at least twice a year and vulnerability assessment be conducted at least three times a year, this will help fix security challenges when major changes are carried out in the organization’s computer infrastructure.

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Nidhra, S and Dondeti, J. (2012). Black Box and White Box Testing Techniques- A literature Review. International Journal of Embedded Systems and Applications (IJESA). 2 (2):29-50.Oriyano, S (2014). Certified Ethical Hacker Study Guide. 8th ed. Indiana: John Wiley & Sons, Inc. Pp444-473.Simpson, T., Backman, K. and Corley J. (2011). Ethical Hacking Overview. In: Helba, S. and Bellegarde,Marah HANDS-ON ETHICAL HACKING AND NETWORK DEFENSE. Boston:Cengage Learning. Pp4-5.Stallings, W (2012). Operating Systems: Internals and Design Principles. 7th ed. NewJersey, Prentice Hall. Pp620-700.Sulagna. (2009). Introduction to API Testing. Available:http://www.scribd.com/doc/9808382/Introduction-to- API-Testing#scribd. Last accessed 20th April 2015.Tang, A. (2014) A guide to penetration testing. Network Security, Pp8-11.Whitaker, A & Newman, D. (2006). Understanding Penetration Testing. In: John Kane and BrettBartow Penetration Testing and Network Defense. Indianapolis: Cisco Press. Pp5-6Yeo, J. (2013) Using penetration testing to enhance your company’s security. Computer Fraud & Security 2013.4 (2013), Pp17-20.



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FJST

SECTION A: AGRICULTURAL AND BIOLOGICAL SCIENCESA mycological assessment of the air quality in flood-prone homes within Lafia Local Government Area ofNasarawa State.Chuku, A., Arikpo, G., Obande, G. A., Akherenegbe, P., Uteh, P.U and Namang, M.Analysis of women participation in livestock production in Mangu Local Government Area of Plateau State, Nigeria OnukE.G;Ohen, S.B. and Shehu, N.DAn assessment of earthworm population and soil factors in Amurum forest reserve of Jos,Plateau State, Nigeria.Abiem, I., Shiiwua, M. and Saha, S.An epidemic of coccidiosis in chickens sold in Keffi central market, Nasarawa State, Nigeria.Yako, A. B.Nweze, C.C. Ogunnu, F.J. and Chessed G.Effects of access to pasture and integration with rabbits on performance and carcass characteristics of broiler chicken.Gambo D., Carew, S. N. and Winifred P. M..Microspora infiltration of gastrointestinal epithelium among HIV/AIDS patients in Keffi, Nigeria.Yako A,B. Nweze, C.C.,1Adebayo E.A., Chessed, G.Morphometric indices and parasites of frozen Clariasgariepinusand Oreochromisniloticus sold in Jos metropolis, Plateau State.Doe’ogot, N. L.,Dakul, D. A., Pam, D. D.,Ombugadu, A, Ayuba, S. O., Njila, H. L.Nematicidal potential of extracts of neem (Azadirachta indica) and lemon grass(Cymbopogon citratus) on root-knot nematodes (Meloidogyne spp) infecting sweet potato.Okechalu, O. B,Dalong, S., Okechalu, A. A., and Oke, F.M.Outcome of calcium and phosphorous mineral sources on performance, carcass and bones characteristics of broilers.Ari, M. M., Gambo, D., Alu, S.E., Edwin, E.E., Aminu, R.A., Isa, J .M. and Yaduma, M.Partial characterization of protease extracted from “Yatsin biri” ginger (Zingiberofficinale) cultivar of northwestern Nigeria.Murtala, Y., Babandi, A.,Babagana, K. Alhassan, A. J. and Shehu, D.Phenotypic variability of false sesame (Ceratothecasesamoides End L.) treated with sodium azide.Aliyu, R.E, Aliyu, A and Adamu, A.K.Protein contents of maize varieties as influenced by nitrogen and micronutrients.Olowookere, B.T., Uyovbisere, E.O., Malgwi, W.B. and Oyerinde, A.A.Rot of seed potato (Solanum tuberosumL.) tubers as affected by storage conditions and storage duration in Jos, Plateau State, Nigeria.Deshi, K. E., Nanbol, K.K., Shutt, V.M., Okechalu, B.O. and Ifenkwe, O.P.Seasonal responses of two faunal taxa to fire treaments in Yankari Game Reserve, Nigeria..Mwansat, G.S, Da’an, S. A &Nwabueze, E.Some ethnobotanical uses of plant resources in Nasarawa State, Nigeria.Terna, T.P.,Kwon-Ndung, E.H., Akomolafe, G.F., Goler, E.E., Okogbaa, J.I., Waya, J.I., and Markus, M.Utilization of tuber and sprout characteristics in delimiting accessions of “rizga”(Plectranthus esculentusn.E. Br.) in Jos, Nigeria.Agyeno, O. E.SECTION B: CHEMICAL SCIENCESPreliminary assessment of heavy metals and water quality of selected wells in Talata Mafara, Zamfara State, Nigeria.Zubairu, A.Y. , Mukhtar, M., Sokoto, A.M. 2Zubairu, A. and 3 Ladifa, H.M.SECTION C: EARTH SCIENCESDelineation of mineral potential zones over Keffi – Abuja Area in North-central, Nigeria using Aeromagnetic data.Jiriko, A. K. Mohammed, M. A. Kalu, O. Udensi, E. E.The application 3D seismic data interpretation to hydrocarbon prospect mapping in“Dede”field, Niger Adeoye, T.O., Johnson, L. M. and Ologe, O.Delta..SECTION D: ENVIRONMENTAL SCIENCESGeospatial modeling of land use management for sustainable urban development in Karu,Nasarawa State, Nigeria.Joshua, J. K., Jobin P.D. and Kuhiyop M.E.SECTION E: PHYSICAL SCIENCESComparative study of the molecular dynamics of anthracene and one of its derivative (1-hydroxyanthracene)in gas phase and ethanol: rhf and dft study.Umar, G., Chifu E. Ndikilar andJohn S. M.Derivation of fitzhugh-nagumosystem.Aku D. H, Alhamdu A. M and Useni P. F.On performance of some methods of detecting nonlinearity in stationary and non-stationary time series data.Akeyede, I., and Oyeyemi, G.M.Operating system security and penetration testing.Adoga, H. U., Ezugwu, E. A. and Umar, M. B.

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