Contents

Review Paper

Management of soil and water resources towards enhanced agricultural profitability 116-140S.S. Tomar, S.K. Sharma and N.K. Pachlaniya

Diagnostic ultrasonography of bovine compound stomach affections: a review 141-144Debosri Bhowmick, M.K. Bhargava and Sonal Shrivastava

Research Paper

Effect of nutrient management and cropping system on growth, yield attributes and soilmicrobial population under different rice based cropping systems in Madhya Pradesh 145-148Megha Dubey, K.K. Agrawal, S.K. Vishawakarma and Suchi Gangwar

Correlation coefficient amongst seed yield and its attributing characters for wheat cropunder rice-wheat cropping system under agroclimatic conditions of Kymore Plateau andSatpura Hills 149-152Vikas Gupta and H.L. Sharma

Effect of organic nutrient management practices on growth, yield attributing characters andsoil fertility in rice-based cropping systems 153-157Suchi Gangwar, K.R. Naik and Megha Dubey

Compatible solute glycinebetaine defends cyanobacterium Synechococcus spp. under highsalinity condition 158-161S.S. Yadav, V.S. Chauhan and Bhanumati Singh

Investigations on floristic diversity of two zones Adhartal and Lalmati of Jabalpur,Madhya Pradesh 162-164Karuna S. Verma, Ashutosh Tiwari, Aparna Awasthi and Suresh Prasad Charmkar

Effect of nitrogen levels on fodder yield and quality of pearl millet genotypes under irrigatedcondition of Madhya Pradesh 165-168A.K. Jha, Arti Shrivastava, N.S. Raghuvanshi and A.K. Singh

Nutrient uptake influence by planting geometries, improved varieties under depths ofplanting in rice 167-174Archana Rajput, Girish Jha, A.K. Jha and A. Tiwari

Genetic studies for yield attributing traits among promising lines in chickpea under various 175-180environmentsNiharika Shukla and Anita Babbar

Volume 47 Number 2 2013

ISSN : 0021-3721 JNKVVVolume : 47 Research JournalNumber(2) : 2013 (May - August, 2013)

Issued : 20 November 2013

Available on website (www.jnkvv.nic.in)

Character association analysis among yield traits in chickpea under agroclimaticconditions of Kymore plateau zone, Madhya Pradesh 181-184Anita Babbar, Pushpa Singh and Rajmohan Sharma

Principal component analysis of inter sub-specific RILs of rice for the important traitsresponsible for yield and quality 185-190Vikas Kumar, G.K. Koutu, D.K. Mishra and Sanjay Kumar Singh

Influence of soil moisture on macro and micro nutrient contents in healthy and malformedbearing shoots of mango 191-195Rajnee Sharma, S.K. Pandey and T.R. Sharma

Influence of soil moisture on growth and malformed panicles of mango varieties 196-201Rajnee Sharma, S. . Pandey and T.R. Sharma

Resource use efficiency in chick pea production in Narmadapuram division ofMadhya Pradesh 202-204Rita Kapil, J.S. Raghuvanshi and Dharmendra Narvariya

Adoption of integrated pest management practices by potato growers in Chhindwara blockMadhya Pradesh 205-208Priya Karade, S.K. Agrawal, V.K. Pyasi, M.K. Dubey and D.K. Jaiswal

Dynamics of chickpea production in different agro-climatic zone of Madhya Pradesh 209-216R.F. Ahirwar, Roshni Tiwari and R.M. Sahu

Status and profitability of fodder crops in Madhya Pradesh 217-223Hari Om Sharma, Ravi Singh Chouhan and G.P. Agrawal

Impact of paddy drum seeder under puddled soil for rice cultivation 224-227Ghanshyam Deshmukh, R.K. Tiwari and B.S. Dwivedi

Regional analysis of technical efficiency of wheat production 228-232R.B. Singh, Umesh Singh, Rajdeep Mishra and P.C. Jha

Stochastic models for describing growth of soybean production and soya oil productionin India and Madhya Pradesh 233-238Umesh Singh, R.B. Singh and S.S. Gautam

A Publication ofJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (Madhya Pradesh) India

Phone: (+91) (0761) 2681200; Fax: (+91) (0761) 2681200Website: www.jnkvv.nic.in

JNKVV Research JournalEditorial Board

Patron Prof. Vijay Sigh TomarVice Chancellor, JNKVV, Jabalpur

Chairman Dr. S.K. RaoDean, Faculty of Agriculture, JNKVV, Jabalpur

Members Dr. S.S. TomarDirector Research Services, JNKVV, JabalpurDr. O.P. VedaDirector Instruction, JNKVV, JabalpurDr. P.K. MishraDirector Extension Services, JNKVV, JabalpurDr. R.V. SinghDean, College of Agriculture, JNKVV, JabalpurDr. G.S. RajputDean, College of Agricultural Engineering, JNKVV, Jabalpur

Editor Mohan S. BhaleCo-Editor Abhishek Shukla

General Information: JNKVV Research Journal is the publication of J.N. Agricultural University (JNKVV),Jabalpur for records of original research in basic and applied fields of Agriculture, Agricultural Engineering, Vet-erinary Science and Animal Husbandry. It is published thrice a year (from 2012). The journal is abstracted in CABInternational abstracting system, Biological Abstracts, Indian Science Abstracts. Membership is open to all indi-viduals and organizations coping with the mission of the University and interested in enhancing productivity,profitability and sustainability of agricultural production systems and quality of rural life through education, re-search and extension activities in the field of agriculture and allied sciences.

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ISSN : 0021-3721 Registration No. : 13-37-67

Published by: Dr. SK Rao, Dean, Faculty of Agriculture, JNKVV, Jabalpur 482004 (M.P.), India

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Abstract

The objectives for the development of rainfed/ drylandagriculture are: to enhance moisture retention capacity of thesoil and retain rainfall by using agronomic, biological andengineering measures in an integrated way, to improve soilfertility and turn " three -losing land" (soil- water- and fertilizerlosing land) into " three conserving land" (soil- water- andfertilizer conserving land). A good deal of information has beengenerated on various aspects of crop production under normalas well as aberrant weather conditions. These include thepractices related to cost effective management of crops andcropping systems, rain water management, soil fertility andnutrient management, conservation of natural resources andtheir efficient management and conclusions have been drawnfor direct use by the beneficiaries.

From the results discussed in this paper it can be concludedthat the productivity under rainfed production system can besustained on long run basis if the measures suggested hereare implemented: (i) For reducing soil erosion, improvingorganic status of soil and improving economy of energy whileenhancing productivity and profitability of soybean basedproduction system conservation tillage is the sustainablepractice.(ii)Use of organics and green manure holds the keyto enhanced productivity and resource use efficiency.(iii) formaintaining soil health the balanced and adequate use ofnutrients through INM is essential.(iv) Adoption of watershedapproach for conservation and utilization of land and waterresources. (v) Soil and water management practices thatensure soil drainage and safe disposal of runoff should bedemonstrated on massive scale and popularized as an integralpart of package of improved practices for soybean. (vi) Thein-situ conservation of rainwater, use of stored water in thesurface ponds and dug wells should for supplemental irrigationto increase productivity of soybean based production system.(vii) Alternate land use system like agro-forestry and agro-horti should be popularized in shallow and marginal lands forimparting higher economic return and sustainability to soybean

Management of soil and water resources towards enhanced agri-cultural profitability

S.S. Tomar, S.K. Sharma* and N.K. Pachlaniya*Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)*College of AgricultureR.V.S. Krishi Vishwa VidyalayaIndore 452001 (MP)

based production system.(viii)Farming system approach andits evaluation for different regions to develop most sustainablemodel for each region. The overall strategy calls for rationaluse of available technologies for soil and water managementfor maximizing productivity of soil- water and crop withoutdetriment to environment holds the key for maximizing theagriculture profitability.

Keywords: Rain water management, nutrientmanagement, agriculture profitability, resourceconservation, integrated nutrient management

To feed a growing world population, we have no optionbut to intensify crop production. Farmers faceunprecedented constraints. In order to grow, agriculturemust learn to save. Sustainable intensification requiressmarter precision technologies for irrigation, and farmingpractices that use ecosystem approaches to conserveresources including water, soil, nutrients and energy.

In most developing countries, there is little slopefor expansion of arable land. Virtually no spare land isavailable in South Asia. Between 2015 and 2030,therefore, an estimated 80 percent of the required foodproduction increase will have to come fromintensification in the form of yield increase and highercropping intensities (FAO 2003). However, in India therate of growth in yield of the major food crops - rice,wheat and maize - are all declining. Annual growth inwheat yields slipped from about 5 percent a year in 1980to 2 percent in 2005; yield growth in rice and maize fellfrom more than 3 percent to around 1 percent in thesame period (FAO 2009). In Asia, the degradation ofsoils and the buildup of toxins in intensive paddysystems have raised concerns that the slowdown in yieldgrowth reflects a deteriorating crop-growingenvironment (Hazell 2008).

JNKVV Res J 47(2): 116-140 (2013)

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The declining quality of the land and waterresources available for crop production has majorimplications for the future. The United NationsEnvironment Programme (UNEP) has estimated thatunsustainable land use practices result in global netlosses of cropland productivity averaging 0.2 percent ayear (Nellemann et al. 2009). Resource degradationreduces the efficiency of inputs, such as fertilizer andirrigation thereby, affect the agriculture profitabilityadversely. In the coming years, intensification of cropproduction will be required increasingly in more marginalproduction areas with less reliable productionconditions, including lower soil quality, more limitedaccess to water, and less favourable climate. Decliningtotal factor productivity, soil degradation, fertility mining,resource exploitative practices and combination of harshclimate and fragile soils are the major concerns whichwill affect agriculture profitability in the future. Therefore,it is most important to conserve soil and water resourcesin such a way so that resource use efficiency can beenhanced and profitability from agriculture can beincreased.

Therefore, there is a need to adopt land and watermanagement practices which are economically feasible,ecologically sustainable and are farmer friendly. Manylocation specific soil and water conserving technologieshave been developed which can increase agricultureprofitability in a sustainable way. A brief account of thesetechnologies has been discussed here. To-day the majorconcerns for making agriculture profitable should be (i)Soil health Improvement (ii) Tapping untapped potentialof rainfed agriculture.

Soil Health Improvement

Soil health has been defined as: the capacity of soil tofunction as a living system. Healthy soils maintain adiverse community of soil organisms that help to controlplant disease, insect and weed pests, form beneficialsymbiotic associations with plant roots, recycle essentialplant nutrients, improve soil structure with positiverepercussions for soil water and nutrient holdingcapacity, and ultimately improve crop production (IPCC2007). A healthy soil does not pollute the environment;rather, it contributes to mitigating climate change bymaintaining or increasing its carbon content.

Soil contains one of the Earth's most diverseassemblages of living organisms, intimately linked viaa complex food web. It can be either sick or healthy,depending on how it is managed. Two crucialcharacteristics of a healthy soil are the rich diversity ofits biota and the high content of non-living soil organic

matter. If the organic matter is increased or maintainedat a satisfactory level for productive crop growth, it canbe reasonably assumed that a soil is healthy. Healthysoil is resilient to outbreaks of soil-borne pests.(Rosenzweig and Tubiello 2006)

The diversity of soil biota is greater in the tropicsthan in temperate zones (Burney et al. 2010), becausethe rate of agricultural intensification in the future willgenerally be greater in the tropics, agro-ecosystemsthere are under particular threat of soil degradation.Any losses of biodiversity and, ultimately, ecosystemfunctioning, will affect subsistence farmers in the tropicsmore than in other regions, because they rely to a largerextent on these processes and their services. The trendof declining soil fertility in India is depicted Fig.1.

Functional interactions of soil biota with organicand inorganic components, air and water determine asoil's potential to store and release nutrients and waterto plants, and to promote and sustain plant growth.Large reserves of stored nutrients are, in themselves,no guarantee of high soil fertility or high crop production.As plants take up most of their nutrients in a watersoluble form, nutrient transformation and cycling -through processes that may be biological, chemical orphysical in nature - are essential. The nutrients need tobe transported to plant roots through free-flowing water.Soil structure is, therefore, another key component of ahealthy soil because it determines a soil's water-holdingcapacity and rooting depth. The rooting depth may berestricted by physical constraints, such as a high watertable, bedrock or other impenetrable layers, as well asby chemical problems such as soil acidity, salinity,sodality or toxic substances.

A shortage of any one of the 15 nutrients requiredfor plant growth can limit crop yield. To achieve the higherproductivity needed to meet current and future fooddemand, it is imperative to ensure their availability in

F ig.1 P ro g re s s iv e e x p an s io n in t h e o c c u r re n c e o f n u tr ie n td e fe c ie nc ie s in In d ian s o ils .

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soils and to apply a balanced amount of nutrients fromorganic sources and from mineral fertilizers, if required.The timely provision of micronutrients in "fortified"fertilizers is a potential source of enhanced crop nutritionwhere deficiencies occur. Nitrogen can also be addedto soil by integrating N-fixing legumes and trees intocropping systems. Because they have deep roots, treesand some soil-improving legumes have the capacity topump up from the subsoil nutrients that would otherwisenever reach crops. Crop nutrition can be enhanced byother biological associations - for example, betweencrop roots and soil mycorrhizae, which help to capturephosphorus in depleted soils. Where these ecosystemprocesses fail to supply sufficient nutrients for highyields, intensive production will depend on the judiciousand efficient application of mineral fertilizers.

A combination of ecosystem processes and wiseuse of mineral fertilizers forms the basis of a sustainablesoil health management system that has the capacityto produce higher yields while using fewer externalinputs.

Technological options

• Increasing soil organic matter in soils.(INM andOrganic farming)

• Balanced fertilization on the basis of soil testvalues

• Application of micro nutrients in deficient soils(Zn, Mo, B, Fe etc.)

• Biological nitrogen fixation to enrich soils (Useof Biofertilizers)

• Use of slow release N- Fertilizers in Ricecultivation

• Direct seeded rice and zero-tillage in rice- wheatcultivation

• Low till farming strategy for soybean cultivation

• Tree crop interaction

Tapping Untapped Potential of Rainfed Agriculture

An important avenue for achieving increased productiongoals is to enhance the productivity of vast areas underrainfed agriculture, which constitute nearly 60% of thenet cultivated area of the country (Abrol 2005). Theseare areas where the green revolution technologies made

limited or no impact. These are also areas where vastmajority of the poor live and whose livelihoods areintimately linked to our ability to impact agriculture inthese areas. Rainfed agriculture is practiced under awide range of soil and climatic conditions. Rainfallregimes and soil characteristics are the keydeterminants of rainfed cropping potential. Theunirrigated areas differ widely with respect to both. Theamount and distribution of seasonal rainfall differ widelyamong regions as well as from year to year. Three majorsoil groups are found extensively in the rainfed regions:red, black and submontane soil. Rainfall in red-soilareas ranges from 750 to 2000 mm per annum. Soildepth varies but most soils are shallow and have lowwater-retention capacity. In the absence of cost effectivemoisture retention and conservation technologies, thesoil suffers from rapid water run-off and erosion reducingthe productive capacity. Red soils have considerableagronomic potential but to achieve this potential thereis need to popularize effective soil moistureconservation practices. Precipitation (Sehgal et al.1993) in black soil areas ranges from 500 to 1500 mm.Compared to the red soils, the black soils are deeperand heavier and hold more water. However, they arehighly erodible and run-off can be as high as 40% ormore depending on rainfall volume and intensity andon the slope. Surface drainage is essential in mediumand high rainfall regions. Given appropriatetechnologies to cope up with constraints associated withthese soils' considerable management difficulties thereis ample scope to enhance productivity. When wet, blacksoil swells and becomes sticky, but shrinks rapidly whendry, leaving large clods and deep fissures and posingconsiderable difficulties in field preparation and timelyplanting operations. There is a large gap between actualand potential crop yields in these regions and there aresignificant opportunities for increasing production. Thereis considerable potential to enhance productivity ofrainfed areas. Realizing this potential essentially hingeson our ability to reverse the process of degradation -processes that will contribute to institutionalize waterconservation, reduced run-off and erosion. Thus,resource conservation issues represent an essentialprerequisite to achieve enhanced productivity. Giventhe wide variations in soil, climate and socio-economicsituations, it is obvious that these technologies have toevolve and spread considering local situations and in aparticipatory manner. In the past there has been littleR&D effort, which recognizes that resource conservationis a precondition to sustained productivity increase.Over the past two decades, the Government of Indiahas devoted considerable attention and resources inprogrammes of watershed development in rainfedareas. These included several externally aided projects.

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The main focus in these programmes was to generatewater resource by run-off collection and reuse of waterby concentrating on various engineering structures (Kerr1996, Samra 2005). While, these efforts have benefittedin some ways, a more fundamental way to sustainedproductivity is to reduce run-off and soil erosion bytechnologies which improve in situ conservation ofrainwater by improving the capacity of the soil to absorband retain water and reduce erosion, i.e. to bring abouta reversal in the processes that are leading todegradation (Acharya et al. 1998).

Rainfed Agriculture: Key Issues

• Continued resources degradation and increasedweather related risks

• Stagnant productivity in major growing districts

• Loss of soil fertility

• Stagnant/ falling cropping intensity (despiteavailability of technologies to increase CI)

• Poor replicability of watershed approach

• Poor technology uptake by stakeholder

• Threat of climate change and the effects thatfollow

Measures for profitable and sustainable agriculture

The objectives for the development of rainfed/drylandagriculture are: to enhance moisture retention capacityof the soil and retain rainfall by using agronomic,biological and engineering measures in an integratedway, to improve soil fertility and turn " three -losing land"(soil- water- and fertilizer losing land) into " threeconserving land" (soil- water- and fertilizer conservingland); to change farming practices and make full use ofresources like light, heat, soil, fertilizer, water andimproved seeds to increase yield and agricultureproductivity in the dry land.

A good deal of information has been generatedon various aspects of crop production under normal aswell as aberrant weather conditions. These include thepractices related to cost effective management of cropsand cropping systems, rain water management, soilfertility and nutrient management, conservation ofnatural resources and their efficient management andconclusions have been drawn for direct use by thebeneficiaries. The useful information generated so far,however, needs to reach the cultivators, as well as those

engaged in extension activities in rainfed farming forits practical utilization. The spectrum of rainfed farmingmay be broadly divided into the following four segments:

• Soil conservation and rainwater management

• Development of improved crop productiontechnologies

• Development of superior verities and crops forrainfed agriculture

• Adoption of technology developed by theresearch system

Technological options

Integrated nutrient management for enhancing soil andcrop productivity

Soybean - based cropping sequence is a major croppingsequence, which has proved itself as one of the mostremunerative crop sequence for Malwa region ofMadhya Pradesh under rainfed conditions. It requiresthe information on changes in soil fertility, nutrientremoval hydro physical properties in long run. Further,in view of escalation of fertilizer, alternate sources ofnutrition and economy in fertilizer have becomeessential. Keeping above referred facts in view; anexperiment was started in the year of 1992. Results ofthis long term study have been found encouraging.

Table 1. Soybean yield and sustainability index underLong term manorial trail in Vertisols of Indore, MadhyaPradesh

Treatments Mean yield Sustainability(19 Years) index

T1:N0P0 (Control) 1289 0.24T2:N20P13 1659 0.30T3:N30P20 1822 0.34T4:N40P26 1924 0.37T5:N60P35 2017 0.40T6:FYM 6t/ha + T2 2132 0.50T7:Residue 5t/ha + T2 1846 0.34T8:FYM 6t/ha 1915 0.36T9:Residue 5t/ha 1718 0.31

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Studies on permanent manorial trails at Indore(1993-2012) exhibited that the conjoint use of FYMalong with chemical fertilizers helps in increasingsoybean yields on sustainable basis (Table 1).Application of FYM has been found to increase wateruse efficiency, moisture availability to rainfed soybean-safflower sequences and recovery of N, P, K and S fromblack clay soils. The productivity of soybean can beincreased with application of FYM @ 6 t/ha + 50% ofthe recommended dose of N and P. The highestsustainability index of 0.50 was obtained in thistreatment as against 0.24 of recommended dose ofchemical fertilizers (Table 2).

The highest safflower seed yield of 1408 kgha-1

was observed due to the treatment FYM 6 t + N20P13which was statistically superior to the rest of thetreatments. Thus, a combination of 6 t FYM, 20 kg Nand 13 kg P is optimum for realizing higher yield ofsafflower. Decline in levels of fertilizer N and P down to60 kg N and 35 kg P ha-1 resulted in gradual reductionin seed yield of safflower (Table 2). Application of FYMat the rate, of 6 t ha-1 along with reduced level of fertilizer(FYM + N20P13) resulted 35% additional seed yield ofsafflower as compared to the treatment having RDF. Cropresidues application as surface mulch at the rate of 5 tha-1 alongwith reduced level of fertilizer N and P(Residues 5 t + N20P13) enhanced the seed yield in therange of about 14.6 %. The highest sustainable yieldindex (SYI) of 0.41 was estimated due to conjunctiveuse of FYM at the rate of 6 t ha-1 along with reducedlevels of fertilizer N and P at the rate of 20 and 13 kg

ha-1 (Table 2). Chemical fertilizer application 60 kg Nand 35 kg P ha-1 gave sustainable yield index of 0.30which, emphasized that integrated application of nutrientenhances crop productivity of safflower on sustainablebasis when applied in Vertisols under rainfed conditions.

The highest gram grain yield of 1372 kg ha-1wasrecorded due to half of the recommended dose of Nand P + 6 t ha-1 of FYM (T6: FYM 6t ha-1 + T2) followedby 1258 kg ha-1 seed yield due to the treatment T7(Residues 5 t ha-1 + T2) and 1211 kg ha-1 in T5 (N60 P35),all these Treatments were statistically at par with each

Table 3. Seed and straw yield of chickpea due to different treatments (kg/plot)

Treatments Chickpea yield Mean over RWUE Net returns B:C ratio(kg ha-1) years (kg ha-1 mm-1) (ha-1)

Control 566 720 0.40 12122 2.15N20 P13 762 921 0.53 19307 2.73N30 P20 999 903 0.70 28476 3.48N40 P26 1044 1105 0.73 29905 3.52N60 P35 1211 1262 0.85 35893 3.86FYM 6 t ha-1+ T2 1372 1392 0.96 44594 5.34Residues 5 t ha-1+ T2 1258 1193 0.88 38831 4.38FYM 6 t ha-1 1052 1128 0.74 31978 4.17Residues 5 t ha-1 1020 856 0.72 30291 3.88Mean 1031.6 23.08C.V.(%) 15.72 1388 0.34S.E. (m) ± 94 4161 1.02CD (5%) 281 1577 15.79

Table 2. Safflower seed yield and sustainability indexas influenced by different fertility treatments

Treatment Mean seed yield, Sustainabilitykgha-1 index

(mean of 11 years)

T1:N0P0 (Control) 628 0.13T2:N20P13 735 0.12T3:N30 P20 1042 0.26T4:N40P26 1166 0.30T5:N60P35 1182 0.29T6:FYM 6t/ha + T2 1408 0.41T7:Residue 5t/ha + T2 1194 0.32T8:FYM 6t/ha 1211 0.33T9:Residue 5t/ha 1022 0.52

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other. The lowest seed yield (566 kg ha-1) was obtainedin case of T1 (control) i.e., N0 P0. All these Treatmentsgave significantly higher yield than control and othertreatments. The trend of straw yield commensurate withthe seed yield. The maximum Net return and B: C ratiowas obtained through treatment T6 i.e., FYM 6 t/ha + T2and lowest in case of control (Table 3).

Soil quality assessment in permanent manorial trial

Effect on soil physical properties

To evaluate the long term effect of treatments on soilaggregation, bulk density and surface soil porosity,

surface samples were analyzed (Table 4). The data thatthe maximum mean weight diameter (MWD) wasobtained in the treatment T6 (FYM + N20 P13) i.e. 1.80and minimum in case of control i.e. 0.49. In general theFYM and crop residue added treatments gave higherMWD in comparison to chemical fertilizer addedtreatment (Fig 2). The FYM applied treatments i.e. FYM+ N20 P13 gave 72.7% higher MWD than controltreatment (N0P0). Addition of organic along with chemicalfertilizer and alone reduced the bulk density (Table 4and Fig 3). The lowest bulk density of 1.17 Mg m-3 wasobtained in case of FYM + N20 P13 treatment followedby treatments, T7, T8, T9. Due to increased MWD andreduced bulk density the porosity of soil has alsoincreased in organic amendments treated plots. Theporosity ranged from 47.17% to 55.85% in differenttreatments and was highest in the treatment T6 (FYM +N20 P13) and lowest in case of control (Fig 4). The dataon infiltration rate was measured using double ringinfiltrometer method (Fig. 5). Three is a markeddifference in the initial infiltration rate of differenttreatments this difference was persisted till 9 hours ofelapse time, but the magnitude of difference wasreduced with time. This shows that there is a markeddifference in the initial infiltration rate due to differenttreatments but there may not be a difference in the finalIR. Results also revealed that organically amendedtreatments have higher cumulative infiltration rate thanthat of other treatments. The changes in soil propertiesdue to different treatments are the cumulative effect of18 previous seasons as the site of each treatment wasfixed.

Addition of organics and RDF has helped to buildup soil fertility but lower doses of N and P as compared

Table 4. Soil Physical properties recorded at harvest-ing stage of chickpea

Treatment Mean wt. Bulk Porositydiameter density (%)

(mm) (mgm-3)

T1 - N0 P0- Control 0.49 1.43 46.0T2 - N20 P13 0.78 1.39 47.5T3 - N30 P20 0.88 1.43 46.8T4 - N40 P26 0.87 1.38 47.9T5 - N60 P35 0.90 1.34 49.4T6 - FYM 6 t /ha+ T2 1.80 1.18 55.5T7 - Residues 5t/ha+T2 1.40 1.22 54.0T8 - FYM 6t /ha 1.38 1.25 52.8T9 - Residues 5t /ha 1.30 1.26 52.5

Table 5. Yield (kg ha-1) and economics of soybean and wheat crop as influenced by levels of fertilizer (mean of twoyears)

Treatments Soybean WheatGrain Straw Net return B:C ratio Grain Straw Net return B:C ratio

50% NPK 1080 1740 8672 1.72 4025 5325 36131 3.68100%NPK 1289 2230 11640 1.89 4950 7094 46355 4.05150%NPK 1401 2867 12965 1.91 5444 7856 50832 4.01100%NPK+HW 1250 2050 10335 1.76 4869 6581 43151 3.54100%NPK+Zn 1157 2139 8300 1.59 4531 6350 40602 3.60100%NP 1061 2048 7641 1.59 4431 5488 39349 3.64100%N 890 1552 6040 1.55 2681 3450 19885 2.52100%NPK+FYM 1561 3051 13611 1.82 5878 8066 57535 4.79100%NPK-S 1105 2077 8571 1.67 4444 6375 40541 3.75Control 627 1204 1279 1.12 2194 2788 15172 2.29CD (5%) 136 351 - - 587 911 - -CV (%) 8.21 11.52 - - 9.32 10.57 - -

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Fig 2. Effect of differnet treatments on MWD (mm)Fig 3. Effect of fertility treatment on the Bulk density

(Mgm-3)

Fig 4. Effect of fertility treatments on teh % porosityof soil

Fig 5. Effect of diferent treatments on teh infiltrationratre (mmhr-1)

Fig 6. Soil fertility changes under different fertilitytreatments since (1993)

Fig 7. Build up/depletion of organic carbon in0-20 cm

Fig 8. Buildup and depletion of N in 0-20 cm soildepth

Fig 9. Build up and depletion of K in 0-20 cm soil

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to RDF has depleted the soil fertility in terms of OC, N,P, K and S content. The perusal of the data reveledthat OC balance was found negative in the treatmentT1, T2, T3, T4 and T5. While there was a positive balanceis there in the treatments which comprised of additionof organics viz. T6, T7, T8 and T9. In case of nitrogenthere was a negative balance in T1 (Control) T2, T8 andT9 treatments rest of the treatments have positivebalance. The P and K balance was found positive in allthe treatments except T1 control, where P balance wasnegative. S balance was found negative in all thetreatments (Fig. 6 through 9).

Recommendation

Based on long-term study, half of the RDF (N20 P13) foreach of soybean and subsequent rabi crops inconjunction with FYM 6t/ha for soybean isrecommended for achieving highest sustainableproductivity of rainfed soybean based cropping systemin Vertisols.

Advantages

• Provides higher sustainability to crop productivity(0.50) as compared to other treatmentcombinations

• Enhances crop yield by 10% as compared to RDF

• Enhances organic carbon content of soil (76%higher than control)

• The per cent increase in N, P, K and S was 76%,39.31%, 35.7%, 55.8% and 77%, respectivelyover control. These changes are the cumulativeeffect of 13 years

• Enhances Water use efficiency (up to 40%)

• Enhances nutrient uptake N (98%), P (108%), K(105%) and S (123%) by seed and straw ascompared to control

• Help to reduce the use of chemical fertilizers by50%

Long term effect of balanced use of nutrient forsustaining higher productivity of soybean- wheatcropping sequence in Vertisols

An investigation was made at Research Farm ofJabalpur on Vertisols to study the effect of balance use

Table 6. Effect of organic amendments and forms of urea on the grain yield of crops in rice based croppingsystem (average of two years) (Tomar et al. 1996)

Treatment Yield (kgha-1)Paddy Wheat Linseed Chickpea

Prilled Urea (PU) 3848 3561 798 1923FYM+PU 4909 4450 1312 2568Paddy husk + PU 4079 4112 1123 2314Paddy Straw + PU 3818 3762 1014 2260Sawdust + PU 3950 3935 810 2046Urea Super granules (USG) 3928 3621 835 2141FYM+USG 5085 4597 756 1850Paddy husk + USG 4241 4290 1250 2507Paddy Straw + USG 4171 3816 1068 2314Sawdust + USG 4025 4025 949 2200Control 2214 1966 375 1012CD(5%) 489 109 361 644

0

500

1000

1500

2000

2500

3000

3500

Paddy Wheat Chickpea Linseed

Yield,kg/ha

Fig 12: Effect of organic ammendments on theproductivity of crops under rice wheat system

FYM Rice husk Sawdust Control

Fig 10. Effect of organic ammendments on theproductivity of crops under rice wheat system

124

Tabl

e 7.

Soyb

ean

yiel

d (k

g ha

-1) a

s in

fluen

ced

by d

iffer

ent t

reat

men

ts o

ver y

ears

Trea

tmen

tsSo

ybea

n yi

eld

(kg

ha-1)

Mea

n ov

er99

-00

00-0

101

-02

02-0

303

-04

04-0

505

-06

06-0

707

-08

08-0

909

-10

10-1

111

-12

year

T 1- C

T+R

F (-

OT)

+ H

W.

1457

1300

2054

1019

1742

763

860

2745

3499

1351

1161

669

1161

1522

T 2- C

T+R

F (+

OT)

+ H

W.

1577

1405

2130

1413

2099

1219

850

2334

3348

1418

1318

598

1318

1617

T 3- LT

+ 4t

ha-1

stra

w+

HW

.13

5998

920

6413

8220

0010

3012

7029

6331

3613

7914

9483

914

9415

69

T 4- LT

+ 4

t ha-1

stra

w+

Hb.

1200

929

2060

711

1550

639

1098

2023

2938

876

711

695

711

1242

T 5- LT

+4t h

a-1 C

omp.

+H

W15

2111

0820

5813

5319

4484

510

7421

8330

2012

1113

4775

712

8615

16

T 6- LT

+ 4t

ha-1

Com

p. +

Hb.

1420

936

2012

776

1600

560

926

2322

2751

1045

1046

634

897

1302

T 7- LT

+ 2

t ha

-1 G

GL

+ H

b.13

8910

3820

6446

614

1851

083

316

5224

4310

2055

069

155

011

25

T 8- LT

+ 2

t ha

-1 G

GL+

HW

.14

2510

5519

7713

8820

0092

190

321

3525

3312

2012

5075

310

6814

33

C.D

at 5

%11

110

5N

S24

437

328

522

053

435

315

712

426

125

7

of recommendation fertilizer practices on yield anduptake of nutrients in Soybean and wheat crops. Thehighest grain yield (1561kgha-1), straw yield (3051kgha-1) content and uptake of nutrients in soybean wasobtained in 100% recommended NPK fertilizers alongwith 15t FYM ha-1 was applied, while lowest wasrecorded in control. In wheat highest grain yield (5878kg ha-1), straw yield (8066 kg ha-1) content and uptakeof nutrients was also obtained in 100% recommendedNPK fertilizers along with 15t FYM ha-1 was applied,while lowest was recorded in control. The maximum B:Cratio (1.89) was recorded in 150% NPK treatment insoybean crop while in case of wheat the maximum B:Cratio (4.79) was recorded in 100% NPK+FYM treatment(Table 5).

Nitrogen management in rice - wheat system

Low N use efficiency in rice - wheat system, especiallyin rice is a major concerned, recommendation has beenevolved for application of FYM along with urea supergranules/ prilled urea deep placement for rice crop. Datapresented in Table 6 revealed that significantly higheryield of paddy was recorded when N was appliedthrough Urea Super granule (USG) along with FYM @5tha-1. All the amendments in combination with USGgave higher grain yield of paddy in comparison to prilledurea combinations. The grain yield of rabi crops wasalso significantly superior in amendment treated plotsirrespective of forms of urea used. Application of organicamendments improved the soil physical environment,which has favourable effect on the grain yield of bothpaddy and subsequent rabi crops (Fig10).

Low till farming strategy for improving crop productivityand soil quality in long run

With the objective of exploring potential of conservationtillage in rainfed soybean based production system forenhancing productivity and soil quality, a f ieldexperiment was initiated in the year 1999 at Indore forevolving low till farming strategies for rainfed productionsystem. On the basis of sustainability index it isrecommended that the study emphasized for conjointuse of organics with reduced level of tillage andchemicals which are not only cost effective but alsoenhance the productivity of soybean and build up soilhealth in the long run. Under rainfed conditions soybeanshould be grown with the addition of organics such ascompost, straw as a mulch and addition of toppling ofgliricidia. With the help of these additives low till farmingstrategy is highly recommended with hand weeding as

125

the application of herbicide was not found effective andlow sustainability index was obtained in the treatmentsinvolving herbicide application.

The major recommendation emerge from theexperiment is that low till farming strategy (minimumtillage + use of organics i.e. Compost @ 5t/ha, Gliricidialeaves @ 2t/ha) is recommended for enhancingproductivity and soil quality in long run under rainfedconditions. Low till farming strategy can be adopted inplace of intensive cultivation which includes low tillage+ use of organics like compost, straw and gliricidiatoppling. The SI > 0.50 revealed that this is one of themost promising technologies for rainfed soybean inVertisols.

The long-term data of soybean yield as influencedby different treatments has been depicted in Table 7,from the data it is evident that the treatment T3- (lowtillage + 4t /ha straw + HW) which was found statisticallyinferior to the treatment T1 and T2 in the initial two yearswas found at par with these treatments after two yearsand there was a gradual but steady increase in this lowtill system and notably after a span of 2 years the yieldwas at par with T1 and T2, while this treatment was foundenergy conserving (Fig 11) and economically morefeasible. Root study revealed that with low tillage andaddition of organics higher root length can be achievedas compred to conventional tillage and intensive tillagewith RDF in Vertisols of dry land conditions.

Fig 11. Energy balance under different treatments

Advantages of low till farming

• The sustainability index (SI) calculated for thelow farming strategy treatment revealed that thisis one of the most promising technologies forrainfed soybean in Vertisols as the SustainabilityIndex is > 0.50.

• Improves soil quality in terms of increased levelsof available OC (0.77%), N (278 kg/ha), P2O5(58.80 kg/ha), K2O (710 kg/ha) and S (12.11 kg/ha) as compared to control in which case thevalues of available OC (0.52%), N (207 kg/ha),P2O5 (36.0 kg/ha), K2O (692 kg/ha) and S (7.19kg/ha).

• Over all results suggested that by the use oforganics the tillage intensity can be reducedwhich will help in increasing soil quality andreducing energy consumption without sacrificingthe crop yields.

Reduced tillage option for Rice-wheat system

In a long-term study conducted (1991 to 2000) underrice wheat cropping system in Vertisols (TypicHaplusters) at Jabalpur, MP, India it was reported thatpuddling during rice deteriorates the soil physicalenvironment for subsequent wheat crop as comparedto under direct seeded plots. It was concluded that inVertisols paddy should be grown as direct seeded crop,which provides better physical environment forsubsequent wheat crop. Significantly higher yield ofwheat was recorded in direct seeded paddy plots as

2 7 0 0

2 7 5 0

2 8 0 0

2 8 5 0

2 9 0 0

2 9 5 0

3 0 0 0

Z e ro t i lla g eC o n v e n t io n a l t il la g eD e e p t il la g e

Yield,kg/ha

F ig . 1 5 E f f e c t o f t il la g e o n th ep r o d u v t iv i ty o f w h e a t g ro w n a f t e r ric e

0

2 00 0

4 00 0

6 00 0

8 00 0

Rice W h e a t R ic e +w h e a t

Yield,kg/ha

F ig.1 4 .Gr a in y ie ld o f c ro p s u nd e r r ic e -w h e a t se q u e n c e a s in f lu e n c e d b y m e th o d s

o f r ic e c u lt iv a tio n

D ir ect s e ed ed p a d d y

tran s p lan ted p ad d y

Fig 12. Grain yield of crops under rice-wheat sequence asinfluenced by methods of rice cultivation

Fig 13. Effect of tilage on the productivity of wheatgrown after rice

126

compared to transplanted plots, and the totalproductivity of the system was higher under reducedtillage conditions i.e. Direct seeded paddy followed byzero till wheat (Fig 12 and 13).

The mean yield of transplanted rice (TP) washigher than the yield of direct seeded rice (DS) (Table 8& Fig 12). Out of ten years the grain yield of TP ricewas significantly higher in six years than the DS rice,while in other years the yield levels were statistically atpar except during 1992 where the DS paddy gavesignificantly higher yield than that of transplanted paddy.A comparison of method of rice cultivation on root growthshowed that total root length upto 45 cm depth wassignificantly higher in Ds rice as compared to that in TPrice Fig 14.The soil organic carbon content was

significantly higher under Ds rice than the TP rice (Fig15). Lal (1986) also reported 2.2 and 1.7 per centorganic carbon in unpuddled and puddled plots,respectively.

On black clay Vertisols, moisture content profile(Fig 16) at harvest were similar on puddled andunpuddled soils. But drying and the rate of drying weregreater an nonpuddled soils. Cultivation of puddledpaddy produces cloddy seedbed, resulting in poor seedsoil contact, rapid drying of surface soil and reducesgermination. Having to wait for a puddled soil to arriveat the optimum moisture content for tillage has anotherdisadvantage. A longer 'turnaround' time resulted in lowcrop yields. Results of regional experiments showedthat delay in wheat planting results in decrease in yieldpotential in the range of 1 to 1.5 percent loss/ha/daywhen planting occurs beyond last week of November(Tomar and Verma 1985). Late planting not only resultsin lower yield but also reduces the efficiency of appliedinputs. Fig 17 shows the wheat yield reduction patternwhen it s planted beyond November (Tomar and Verma1985).

The long term results of rice-wheat croppingsystem in Vertisols (Typic Haplusterts ) at JNKVV,Jabalpur, MP (AICRP on tillage requirement of majorIndian soils), revealed that zero tillage as atechnological option for timely establishment of wheat.In this technique, direct drilling of wheat using PantnagarZero till fertilizer seed drill can be done without anytillage operations immediately after harvest of paddyon residual moisture. In rice -wheat system, paddyfollowed by no till wheat resulted in significantlyincreased productivity of rice wheat system as comparedto the productivity of paddy followed by shallow till ordeep till wheat (Fig 12&13). Zero tillage has yieldadvantage particularly in early sown crop i.e. Novemberplanted wheat (Table 9). No till system of wheat andreduced tillage in paddy (avoiding puddling and direct

Table 8. Rice yields (kgha-1) as influenced by methodsof rice cultivation

Year Method of rice cultivation CD (5%)Transplanted Direct seeded

paddy paddy

1991-92 4913 4209 4171992-93 2715 3502 3531993-94 5560 4788 5621994-95 3036 2871 NS1995-96 3940 2898 1161996-97 3482 3043 2791997-98 4540 4208 NS1998-99 4302 3726 NS1999-00 3573 2922 5212000-01 4938 4097 384Average 4093 3711 -Tomar and Sharma (2009)

0 . 6

0 .6 2

0 .6 4

0 .6 6

0 .6 8

0 . 7

0 .7 2

0 .7 4

D S T P

Organiccarbon,(%)

M e th o d o f r ic e c u lt iv a tion

Fi g 1 7 : O rg a n ic ca r b o n (% ) u n d e r D san d T P r ic e c o n d iti o n s .

C D 5% 0 .0 2

0 2 0 0 4 0 0

1 9 9 4

1 9 9 5

R o o t l e n g t h ( m )

Year

F ig 1 6: R o o t le n g th ( m ) u p t o 4 5 c md e p th u n d e r D s a n d TP r ic e .

T P

D S

C D 5 % 1 6 . 9 1

C D 5 %

Fig 14. Root length (m) up to 45 cm depth under DSand TP rice Fig 15. Organic carbon (%) under DS and TP rice

conditions

127

seeding of paddy) helped to reduce the turnaround timeand get the wheat planted closure to the optimum date.This system offers the advantages of timely wheatsowing, favouring crop productivity, saving energy (Fig18) and improving water and nutrient use efficiency (Fig19). This system not only increases productivity of thesystem but also reduces the cost of cultivation, fuel cost,wear and tear of tractors and increases input useefficiency. The farmers of rice wheat sequence areresource poor and the savings on tillage operations canbe used to buy other inputs like weedicides, fertilizer,irrigation which will further enhance productivity. Thetwin advantage of enhancement of productivity and its

profitability under zero till system, will go a long way inenhancing the sustainability of rice wheat system.

Data presented in Table 9 further indicated thatafter six years of experimentation there is decrease inyield of wheat under zero till plots. Poor yield of wheatrecorded during 1997-98 was due to continuous heavyrains received in the month of November and Decembermonths.

Recycling of crop residue

Field experiment was conducted in Vertisols at Jabalpurby incorporating of rice and wheat crop residue both insitu or as compost, increased the soil organic carboncontent and improved soil health (Fig. 22), whileimproving the productivity of the rice-wheat system. Thereturn of carbon to soil and yield of crops was maximum,when crop residue was added along with fertilizer N @120 kg N ha-1.

Water harvesting and recycling

Rainwater harvesting is potentially an important waterresource in black soil region of Madhya Pradesh. About

Fig18 :Drying patte r n of puddle d andunpuddle d ve rtis ols .

01020

3040

5060

2 6 8 1 0 12 14 17 22 24Days af ter drainage

Soil m

oistu

reco

nten

t (%

)

UnpuddledPuddled

0

1 000 0

2 000 0

3 000 0

4 000 0

5 000 0

6 000 0

7 000 0

Z e ro - ti ll a g e C o n v e n ti o n a lti ll a g e

D e e p ti ll a g e

Energylevel(MJ/ha)

F i g .2 0 : I n p u t -O u tp u t e n e rg y re q u i re m e n t i nw h e a t p ro d u c t i o n u n d e r d i f f e re n t t il l a g e

t r e a tm e n ts .

I n p u t e n e r g y ( M J /h a ) O u tp u t e n e rg y (M J / h a )

01000

200030004000

50006000

10-N o v20N o v .30 N o v .1 0Dec .20D ec .30D ec .10J an .20J an .

Yield,kg/ha

Plan tin g d at eF ig .19: Wh eat yield R ed u ction t ren d u n de r lat e

p lan ted co n d ition .

020406080

1 001 201 401 601 802 00

Z e ro t i ll a g e Co n v e n ti o n a lt il la g e

D e e p T i ll a g e

Useefficiency

F ig .2 1 : E f f e c t o f t il lag e o n re s o u r ce us ee f f ic ie n c y

W U E ,k g / h a /c m

4587

4847

4652

4400

4500

4600

4700

4800

4900

Brning of cropresidue

Compost ofcrop residue

Crop residueincorporation

Yieldkg/ha

Fig.22 : Wheat yield as affected by c rop res iduem anagement

Yield kg/haCD 5% 261

Fig 19. Effect of tillage on resource use efficiency

Fig 16. Drying pattern of puddled and unpuddledvertisols

Fig 17. Wheat yield reduction trend under lateplanted condition

Fig 18. Input-output enery requiremnet in wheat production underdifferent tillage treatments

Fig 20. Wheat yield as affected by crop residuemanagement

128

40-50% of total rain water is lost as runoff from maizeand soybean fields (Bhargava et al. 1976; Tomar et al.1985). The success of water harvesting depends notonly on the collection of runoff water but also on theefficient utilization of this harvested water for theagricultural crops.

The runoff under 1.2% slope was 146 mm (56per cent) from ridge and 121.7 mm (46.9%) from flatplots with total rainfall of 259 mm (Table 10).

The harvested water was recycled for irrigationof different crops to avoid the adverse effect of dry spellsunder rainfed conditions. The differential crop responseof one or two irrigation from harvested water wasstudied. The results (Table-11) indicate the all the cropsresponded well to irrigation treatment.

Harvesting of runoff at micro level, its storageand recycling for life saving irrigation during long dryspells improving the productivity of a rainy season crop.

Table 9. Influence of tillage on the grain yield of paddy and wheat under rice-wheat system

Rice yield (kgha-1) Wheat yield (kgha-1)Year No-tillage Conventional Deep CD(5%) No-tillage Conventional Deep CD(5%) Wheat

tillage tillage tillage tillage plantingdate

1991 4311 4182 4092 NS 3873 3807 3275 251 Nov.131992 3042 2922 2919 NS 3545 3137 3646 418 Dec.131993 5219 5413 5165 NS 4431 4302 4095 194 Nov.251994 2930 2931 2885 NS 4221 3582 3195 101 Nov.111995 3378 3049 2670 139 4112 3270 3005 266 Nov.201996 3321 3343 3122 NS 2423 2731 2605 NS Dec.41997 4583 4254 4286 268 781 739 730 NS Jan.11998 4109 4110 3823 NS 1516 2083 2064 459 Dec.151999 3508 3011 3210 NS 2338 2859 2769 270 Nov.252000 4722 4558 4372 NS 2195 2674 2669 455 Dec.2Av. 3912 3773 3654 - 2943 2918 2805 - -Tomar and Sharma (2009)

Table 10. Rainfall and run-off data from watershed area under different systems of planting

Treatment date Rainfall (mm) Runoff (mm)(1.2% slope) (0.6% slope) (0.3% slope)

R F R F R F

15 47.00 24.59 19.50 12.57 7.67 7.16 5.4116 30.20 15.88 11.76 10.11 8.02 5.81 3.3817 66.80 37.52 32.10 31.00 19.45 20.84 12.5018 69.30 45.18 38.72 35.11 30.21 30.21 21.2919 17.50 12.43 11.01 9.61 6.21 4.90 3.7220 15.20 6.03 4.90 4.27 3.10 2.56 1.6021 7.10 2.39 2.02 1.58 1.26 0.99 0.8522 6.10 2.01 1.72 1.48 1.08 0.80 0.64Total % runoff 259.20 146.03 121.73 105.73 77.0 73.27 49.45- - 56.33 46.96 40.79 29.71 28.27 19.10Note: R means ridge and F means flat plot systems

129

This practice if adopted on large scale, is potentiallycapable of addressing twin problems of water stagnationduring kharif and moisture stress during rabi. The highrunoff potential at Indore, Jabalpur, Rewa and otherregions (Verma 1982) with similar climatic conditionspermits two good irrigations. Minimization of surfacewater evaporation and seepage losses of water fromthe reservoirs can further improve the irrigation potentialof the harvested water. Use of monomolecular layer oforganic compounds such as DDAC,), hexadecanol,Octadecanol has been found to minimize evaporationfrom soil and reservoirs. An appropriate landmanagement system combined with water harvestingand recycling system seem to offer vast prospects ofimproving productivity of Vertisols even under rainfedconditions. If harvested rainwater is used in theproduction of high value crops like vegetables,floriculture, fruit trees, it will bring higher monitory returnsfrom the rainfed areas.

Soil and Water Conservation Practices

Under increasing population pressure for moreagricultural production, extensive cultivation by puttingmore area under cultivation cannot be pursued any morefor ecological considerations. The onus for moreagriculture production, therefore, falls on the judicioususe of the land and water is a must for sustainable cropproduction, especially under rainfed farming. Major soilsof rainfed areas of Madhya Pradesh, where soybean isgrown, are black soils (Vertisols and associated soils).Despite of the fact that the black soils are potentiallyproductive but there are certain soils related constraints,which deter the farmers for realizing the potential

productivity of upland kharif crops. The solution to theproblems/constraints lies in such water and landmanagement practices, which encourage surfacedrainage and recharge of the soil profile with rain waterwithout accelerating soil erosion, In the work underreport, the performance of soybean crop grown as soleand intercrop on Vertisols during rainy season wasevaluated under varying drainage conditions so as tointroduce most appropriate soil management practicesfor growing these crops in the Vertisols of high rainfallregions of central India. Different land configurationsand management practices developed are discussedbelow.

Ridge and Furrow System

Ridge and furrow system envisages planting of uplandrainy season crops on ridges laid out on such soilshaving slopes less than 1%. Furrows serves as aneffective means of surface drainage and carry excesswater into cut-off drains dug across the slope. Thesystem is highly effective in medium and high rainfallareas (rainfall ranging from 700 to 1200 mm). Datapresented in Table 12 reveals that considerable higherproduction of soybean can be obtained when plantedon ridges.

Graded furrows

Graded furrows of 0.20 to 0.30 % slope serve the meansfor carrying runoff water to drainage channel safely andlead to enhanced productivity of upland rainy seasoncrops specially in areas which receive moderate rainfall(> 1000 mm). Spacing between furrows may vary from8 to 10 m depending upon slope and rainfallcharacteristics.Table 11. Effect of use of runoff water on the grain yield

(kg/ha) of rabi crops grown after rice

Crop Moisture regimePre-emergence Pre-emergence

irrigation + critical stageof irrigation

Wheat 2028 2452Chickpea 1486 1653Lentil 888 1021Linseed 711 828Safflower 1243 1389Sunflower 1686 1988

Table 12. Influence of ridge planting on productivity ofsoybean(kg/ha) crop

Slope % Ridge Flat % Increaseplanting planting

1.20 3030 2490 210.60 2625 1740 520.30 2900 1890 53Crop period rainfall 1370 mmSource: Tomar et al. (1996)

130

Broad bed and Furrow System

In this system beds and furrows are created on a gradeof 0.5% slope. This system consists of a series of broadbeds and furrows accommodated 90-150 cm wideparallel running strips. This system permits safe disposalof runoff, tends to conserve soil and water in-situ,reduces soil and nutrient losses and enhances cropproductivity and water use efficiency. This systemreduces runoff, soil loss, Nitrogen and Phosphorus lossby 13.5,30.1, 16.1 and 13.5% respectively over flat bedson conventional system of cultivation and enhancedsoybean yield, water use and water use efficiency by20.6%, 4.0% and 7.9 %, respectively (Gupta andSharma 1990). Study conducted at ICRISAT revealedthat total runoff and peak runoff rates were lower onBBF landform compared to those on flat landform, Soilerosion was lower in BBF as compared to that in flatlandform treatment. Integrated nutrient managementfollowed in improved system (sowing on BBF + Gliricidiaon bunds) resulted in improved balanced N Budget forsoybean + Chickpea sequential and soybean/pigeonpea intercropping system. Gliricidia topplingprovided 25 kg N/ha/year without adversely affectingcrop yields of the nearby rows. The yields obtained inthese sequences were much higher than the soybeanaverage yield of Madhya Pradesh (<1t/ha).

Raised and Sunken bed System

To improve the productivity of rainfed Vertisols in sucharea, there is a need to provide to the resource poorfarmers such a system, which will provide adequatemeans of surface drainage at a cheaper cost than sub-surface drainage and will also assure adequate rootzone moisture recharge for Kharif (Rainy season) crops.Jabalpur centre of the AICRP on Soil PhysicalConstraints and their amelioration for sustainable cropproduction has conducted field experiments over morethan 16 years to develop and validate Raised-SunkenBed Technology. This technology provides: an adequatedrainage during heavy rains, favourable moistureregime during dry spell in Kharif season to upland crops,sufficient ponding of water for paddy and channelizesthe excess runoff water safely to a farm pond forrecycling to provide supplemental irrigation to rainy andpost rainy season crops. The technology has beendemonstrated successfully at the farmers fields inMadhya Pradesh and also found beneficial by theParbhani (Maharashtra) centre of the project. InMaharashtra approximately 0.53 m ha land experiencestemporary water logging during rainy season.

Based on long-term trials at Jabalpur (MP)involving various crops the recommended raised-

Table 13. Effect of land configuration on the yield (Kg/ha) of Kharif crops in Vertisols of high rainfall area

Year Soybean seed yield Paddy grain yield Seasonal rainfallImproved system Farmer practice Improved system (mm)

(raised bed 6 m wide) (flate bed) (Sunken bed 6 m wide)

1979 1373 1000 4721980 2962 2229 5100 14321981 1830 1050 7001982 2230 1054 2030 12361983 2340 817 1400 14451985 2253 1000 2500 13801986 2131 647 4220 10341987 1840 715 1716 12161988 3260 1731 1877 10101989 2779 144 1520 6271990 2425 581 3001 16241991 2523 125 2200 12151992 3107 581 3001 10551993 1733 1050 4758 13911994 1969 1469 3922 10901995 1755 1113 4473 1153Source: Painuli et al. (2002)

131

sunken bed system consists of 6 to 9 m wide and 30 to35 m high raised bed along which runs the sunken bedof 6m width. The sunken beds are connected forexample with the pipes to maintain water level at adesired height viz. 15 to 20 cm in all the sunken beds.The excess water is safely carried away and collectedin a farm pond for recycling to provide supplementalirrigation to rainy season and post rainy season crops.

Raised-Sunken Bed System can be created bymechanically shifting surface soil from a strip with thehelp of a suitable device for example a motor graderwas used in Jabalpur, and placed on the adjoiningparallel running strip. For operational convenience soilfrom half width of the area to be created into sunkenbed is put on the right and from another half on the leftside. The strip from where the soil is shifted getsconverted into a sunken bed and the strip where on theshifted soil is put gets converted into a raised bed. Theraised bed may also be created using dug out farm pondsoil. The beds are leveled and soil allowed to settlebefore raising a crop. The land shaping operation ispreferably done in summer before onset of the rains. In

the first year, to make up the fertility of the exposed subsurface soil in the sunken bed, addition of extra FYM,ZnSO4 and nutrients etc. is recommended in Kharif.Raised bed is used for upland crops and sunken bedfor rice.

Seed yield of soybean (cultivars JS 2 or JS 75-46 or JS 335) observed over a period of 16 years wasin general more than two times under improved systemof Raised-Sunken Bed as compared to farmers practiceof flat bed sowing (Table 13).

Raised-Sunken Bed system has also been foundsuccessful for intercropping upland crops on raised bed.Two rows of soybean or blackgram followed by one rowof pigeonpea (medium duration) produced 3, 12 & 4times higher yield for soybean sole, pigeonpea sole andsoybean + pigeonpea intercrop (Table 14) underimproved system compared to farmers practice. It wasobserved that in most of the years pigeonpea andblackgram could not survive in flat bed (farmers practice)but performed satisfactorily under the improved system.The total productivity under improved system of soybean

Table 14. Effect of land configuration on the yield (kg/ha) of soybean and pigeonpea grown as sole and intercropin a vertisols of high rainfall area

Land Configuration Cropping system 1990-91 1991-92 1992-93 1993-94 1994-95 1995-96

Improved system Soybean sole 2425 2523 3107 1733 1969 1755(Raised bed 6 m wide) Pigeonpea sole 2012 2004 2532 1825 1626 1174

Soybean + 1805 1977 2310 1433 1631 1344Pigeonpea 1805 1608 2394 1436 1426 900

Farmers practice Soybean sole 849 125 581 1050 1469 1113(Flat bed) Pigeonpea sole 128 167 Failed Failed Failed Failed

Soybean + 650 95 581 1050 1469 1113Pigeonpea 125 151 Failed Failed Failed Failed

Seasonal rainfall (mm) 1631 1215 1055 1391 1090 1132

Source: Painuli et al. (2002)

Table 15. Performance of post rainy season crops on Raised and Sunken bed.

Land configuration Crop 86-87 87-88 88-89 89-90 90-91 Geometric mean ± SE

Raised bed (6m width) Chickpea 2146 1866 1842 1722 1579 1822±84Safflower 1412 1424 1240 1488 1348 1380±38

Sunken bed (6m width) Linseed 1170 949 1416 938 1048 1091±79Wheat 989 1972 1218 1047 1162 1236±159

Source: Painuli et al. (2002)

132

+ pigeonpea cropping was more than 3.3 t/ha whichfrom a view point of yield under rainfed condition inVertisols of the region was a good yield. The mainadvantage of the intercropping was that even if the rainsceased earlier the pigeonpea survived on residualmoisture left over in the profile. The intercropping resultssuggest that by incorporating it in Raised-Sunken BedTechnology crop productivity can be increased in highrainfall areas of Vertisols regions.

Performance of post rainy season crops grownon raised bed and sunken bed was evaluated againstfarmer's practice of flat bed sowing. As compared tofarmers practice i.e. flat bed sowing under improvedsystem the yield of each test crop was nearly two timesmore (Table 15). This could be attributed to morefavorable moisture regime in the seed zone underimproved system at the harvest of rainy season cropresulting in better establishment and improved stand ofpost rainy season crop.

Benefit-cost ratio based on the prices prevailingin the year 1997 foe various operations and yields haverevealed that the Raised-Sunken Bed System waseconomically viable under all the major croppingsystems of the region (Table 16). The benefit-cost ratioshowed that the land shaping cost could be recoveredwithin 1 to 2 year. Economics also revealed that in case

of failure of post rainy season crops or even paddyduring Kharif (due to scanty rains as was the case in1979 and 1981) improved system gave higher returnscompared to the farmers practice. Highest benefit-costratio was observed in soybean-pigeonpea intercroppingon raised bed and paddy-chickpea in sunken bed.

Soil conservation techniques

Many soil conservation techniques are found suitablefor reducing erosion and nutrient losses and helps inmaintaining soil fertility and enhancing soil productivity.Runoff induced soil erosion is accompanied by heavylosses of essential nutrients particularly nitrogen. Theselosses can be minimized through employment ofappropriate land treatment and water disposal systems.In past, contour bunding has been used as a soilconservation measure in black soil region but thesehave been reported to adversely affect the crop yield(Kampen, 1974) and not at all suited to moderate tohigh rainfall areas. Biological measures are generallyused as preventive measures. Some of the cost effectiveand promising erosion control mechanical measuresare; Gabion structures, Graded bunds, Conservationditches, Bench terracing and Water diversion bunds andbiological measures include planting of Cover crops,mulching with crop residue, and vegetative barriers etc.

Table 16. Economic viability of raised-sunken bed technology

Treatment combinations Operational Yield (q/ha) Gross return Net return B:C ratiocost (Rs/ha) (q/ha) (Rs/ha) (Rs/ha)

S: Paddy-wheat 9869 27.92 to 18.00 21147 11278 1.14:1R: Soybean-chickpea 24.47 to 18.31S: Paddy-chickpea 9269 27.92 to 14.49 21894 12625 1.36:1R: Soybean-chickpea 27.47 to 18.31S: Paddy-wheat 7359 27.92 to 18.00 15462 8103 1.10:1R: Pigeonpea 20.93S: Paddy-gram 7769 27.92 to 14.49 16401 8632 1.11:1R: Soybean fallow 24.47S: Paddy-wheat 8519 27.92 to 18.00 20917 12398 1.46:1R: Pigeonpea+soybean (Intercrop) 18.81+18.11S: Paddy-wheat 7919 27.92 to 14.49 21664 13745 1.74:1R: Pigeonpea+soybean (Intercrop) 18.81+18.11S: Paddy-chickpea 6759 27.92-14.49 16209 9450 1.40:1R: Pigeonpea 20.93Source: Tomar et al. (1996)

133

losses. Later on, upon decomposition, it adds nutrientsfor the benefit of crops and enhance water useefficiency. Crop residues like sorghum cob husk, maizestalks, safflower straw, soybean straw, weed biomassor any other locally available crop residues, which havevery little or no fodder values may be utilized formulching purposes. Usually they can be applied at therate of 5 to 8 t/ha. After emergence and establishmentof crops, straw or crop residues may be spread inbetween crop rows. Straw mulching besides reducingerosion and enhancing infiltration, increases water useefficiency of crops to a considerable extent (Sharma etal., 1985a, Sharma et al. 1985b). Experimental results

Table 17. Influence of soil and straw mulching on yield and water use efficiency (WUE) of rainfed cropsat Indore, Madhya Pradesh, India (Sharma et al. 1985a)

Parameters Crops No mulch Soil mulch Sorghumcob huskmulch ( 6 t/ha )

Yield (kg/ha) Chickpea 1430 1750 (+22.4%) 1800 (+25.9%)Linseed 905 968 (+ 6.9%) 1043 (+15.2%)Wheat 1730 1837 (+6.2%) 1835 (+ 6.1%)Safflower 1755 1942 (+10.6%) 1822 (+ 3.8%)

WUE (kg/ha mm) Chickpea 6.5 8.1 (+24.6%) 9.4 (+44.6%)Linseed 3.9 4.3 (+10.3%) 4.6 (+17.9%)Wheat 6.6 8.2 (+24.2%) 7.5 (+13.6%)Safflower 6.8 8.1 (+19.1%) 7.6 (+13.8%)

Note: Figures in parentheses indicate increase in yield and WUE due to mulching

Mulching with Crop Residues

The potential value of crop residues as surface mulchfor soil and water conservation is well established.Moderate rates of residue application may be effectivein enhancing the infiltration and reducing runoff.However, larger amounts may be required tosignificantly cut down evaporative losses of profile-stored moisture. Surface residues mulch have theirgreater values for water conservation when soils arewet. The immediate advantage of incorporation of cropresidues in soil is to enhance infiltration and retard runoff

Table 18. Seasonal runoff, soil and nutrient losses due to vegetative bundsand earthen graded bund constructedon different slopes

Treatments Slope (%) Runoff Soil loss Nutrient loss ( kg/ha)(mm) (kg/ha) N P K S

Check 2.0 115.7 986 23.85 0.018 1.42 2.981.5 85.2 918 15.31 0.002 1.13 4.371.0 87.9 614 10.67 0.023 0.76 2.24

Graded bund 2.0 91.9 633 19.04 0.014 1.46 2.991.5 70.5 340 13.23 0.015 1.29 1.391.0 53.9 582 8.70 0.014 0.72 3.37

Vetiver grass 2.0 94.9 662 17.40 0.015 1.14 2.031.5 69.1 453 13.12 0.044 1.19 4.131.0 53.8 465 8.88 0.038 0.60 2.56

Bund + cymbopogon 2.0 94.6 567 17.18 0.012 0.89 2.911.5 69.4 509 11.48 0.024 0.67 5.321.0 52.9 474 8.02 0.026 0.71 3.13

Source: (Ranade et al. 1995)

134

(Table 17) indicate that during post-rainy season, shortduration crops of chickpea and linseed respondedremarkably well as compared with wheat and safflowerto 6 t /ha of sorghum cob husk used as surface mulchduring post rainy period. After harvesting of crops,residues, which are used as surface mulch, may beincorporated in soil, which upon decomposition in soilwould help in improving soil physicochemical propertiesand fertility of soil.

Vegetative Barriers/Hedges

Vegetative barriers/ hedges have been found useful inreducing the rain water runoff water and conserving soiland plant nutrients. These are also helpful in stabilizingthe earthen bunds, particularly in black soil regionswhere massonary structures have limited utility due toswell shrink nature of these soils. For this purposes anumber of grass species have been identified. Grassessuch as vetiver, Cymbopogon martinii have proved tobe useful (Table 18). Vegetative hedges are establishedat 0.5 to 0.75 m vertical interval. Two rows planted 30cm apart make a good hedge, which should be regularlycut to maintain 30 cm height.

Agronomic Measures

Land use system should be based on land capabilityclasses as far as possible so as to ensure efficient useof land and profile stored water. Agronomic practiceswhich encourage conservation of soil, rain water andplant nutrients and enhance use efficiency of theseresources are soil mulching, plastic mulching, contourfarming, strip cropping, integrated nutrient managementpractices and minimum tillage practices etc.

Soil Mulching / Shallow Interculture / Shallow Tillage

Soil mulch created in-situ tends to reduce evaporationas it minimises and delays development of shrinkagecracks and besides providing a diffusion barrier (Sharmaet. al., 2005 ). Soil mulch may be created with the helpof a suitable implement (hand-hoe, blade harrow etc.)in between crop rows as and when required. Animaldrawn small blade harrow (blade width of 15 to 30 cm )can be conveniently operated in between crop rows tocreate soil mulch in-situ during early growth period ofrainy season crops like soybean, maize, sorghum,cotton and pigeonpea etc. It can create 2 to 3 cm loosesoil and removes growing weeds. During post-rainyseason period, soil mulch delays the appearance of

shrinkage cracks. Effectiveness of soil mulching varieswith the crop type. On an average 6 to 25 per centincrease in water use efficiency of post rainy seasoncrops can be realised due to soil mulching (Table 17).

Deep Tillage Practices

Deep tillage prior to onset of rainy season is practicedmainly with a view to increase infiltration and therebyreduce runoff, soil erosion and weed population. Offseason deep tillage, particularly in subnormal rainfallyears, has been found useful in conserving the soil andwater resources and enhancing the crop yieldssubstantially.

Plastic Mulching

Plastic mulches although conserve soil profile storedmoisture leading to sustainable increase in water useefficiency and crop yields but they are not cost effectivefor large scale adoption (Sharma 1976). They can beof immense use for soil solarisation (meant forminimising the incidence of weeds and soil bornediseases), or for growing cash crops.

Contour Farming

In contour farming, tillage operations are done alongthe contour lines as far as possible. It creates numerousridges and furrows, which retain a good volume ofrainwater after each wet spell. This water eventuallyinfiltrates into soil. There is corresponding reduction inrunoff volumes and therefore in erosion of soil and plantnutrients. Experiments conducted at Octacamond (India)have shown that by adopting contour farming for potato,cultivated on 25% slope, runoff was reduced from 52 to29 mm and soil loss from 39 to 15 t/ha when rainfallreceived was moderate.

Strip Cropping

Strip cropping is the method of growing strips of covercrops in the same field along contours for controllingwater erosion. Broad leaf pulses and grasses usuallyprovide effective strips for reducing runoff and erosion.

Minimum Tillage

Minimum possible disturbance of soil surface can save

135

Tabl

e 19

. Rec

omm

ende

d al

tern

ate

land

use

sys

tem

opt

ions

for d

iffer

ent a

gro

clim

atic

con

ditio

ns (S

ourc

e: S

ingh

198

8)

Ann

ual

Soil

type

Land

use

sys

tem

sSu

itabl

e tre

e/ g

rass

/ leg

ume

spec

ies

rain

fall

(mm

)

< 50

0Sh

allo

w (0

-0.3

0m) m

ediu

m (0

-0.4

5m)

Tree

farm

ing

past

ure

man

agem

ent

Pro

sopi

s ci

nera

ria, P

. jul

iflor

a, A

caci

a an

eura

, A. n

ilotic

a, A

.to

rtilis

, Pith

ecel

liobi

um d

ulce

, Las

iuru

s si

ndic

us(li

ght t

extu

red

soil)

, Cen

chru

s se

tiger

us, S

ehim

a ne

rvos

um, S

tyol

san

thes

scab

ra, C

litor

ia te

rnat

ea.

500-

750

Shal

low

(0-

0.3m

)Si

lvip

asto

ral s

yste

mA

. ni

lotic

a, C

olop

hosp

herm

um m

opan

e, D

albe

rgia

sis

soo,

Har

dwic

kia

bina

ta,

Cas

sia

stur

ti, A

lbiz

ia a

mar

a, L

euca

ena

leuc

ocep

hala

, C

ench

rus

cilia

ris,

C.

setig

erus

, D

iaca

nthi

uman

nula

tum

, P

anic

um a

ntid

otal

e, S

tylo

sant

hes

ham

ata,

Mac

ropt

illum

atro

purp

ureu

m.

Med

ium

(0-0

.45m

)H

ortip

astu

ral s

yste

mA

nnon

a sq

uam

osa,

Ziz

yphu

s m

aurit

iana

, Syz

igiu

m c

umin

ii,E

mbl

ica

offic

ialin

is, T

amar

indu

s in

dica

, Fer

onia

limon

ia, A

egle

mar

mel

os,

Cen

chru

s ci

liaris

, P

anic

um a

ntid

otal

e, U

rchl

oam

osam

bice

nsis

Sty

losa

nthe

s ha

mat

a,,

Mac

ropt

illum

atro

purp

ureu

m, C

litor

ia te

rnat

ea.

> 75

0Sh

allo

w (

0-0.

3m)

Ley

farm

ing

or S

ilvip

asto

ral s

yste

m3

year

sS

tylo

sant

hes

ham

ata

and

4th

year

ara

ble

crop

(Sor

-gh

um o

n he

avie

r so

ils,

Pea

rl m

illet

on

light

er s

oils

).Si

lvip

asto

ral s

yste

m a

s ab

ove.

Med

ium

(0-0

.45m

)Le

y fa

rmin

g or

hor

tipas

tora

l sys

tem

Ley

farm

ing

as a

bove

.

Man

gife

ra in

dica

, Ach

ras

zapo

ta, P

sidi

um g

uaja

va, E

mbl

ica

offic

inal

is, S

tylo

sant

hes

ham

ata/

Mac

ropt

ilium

atro

purp

ureu

m.

Dee

p (>

0.4

5m)

Agris

ilvi o

r Ag

rihor

ti sy

stem

Aca

cia

ferr

ugin

ea,

Pro

sopi

s ci

nera

ria,

Tect

ona

gran

dis,

Har

dwic

kia

bina

ta, D

albe

rgia

sis

soo

+ Ar

able

cro

ps, M

angi

fera

indi

ca, A

chra

s za

pota

, Psi

dium

gua

java

+ A

rabl

e cr

ops.

Shar

ma

et a

l. 20

06

136

soil from erosion to some extent. For erosion control ingently sloping areas, mixed cropping or intercroppingmay be practiced in order to cover the maximum surfacefor a longer period. Minimum tillage during post rainyperiod reduces evaporation from soil, improvesestablishment of post rainy season crop (Sharma1990a).

Seed Replacement

This refers to the use of drought enduring and draughtresistant seeds. To use drought enduring varieties is themost cost effective yield increasing technology. Atpresent most dry areas have their own varieties withgood drought resistance. However, most varieties haveexperienced degradation in their performance and thebreeding of new varieties is still lagging behind. It is anurgent need to develop drought resistant varieties andaccelerate the purification and rejuvenation process inorder to improve yield levels in rainfed area.

Land Use According to its Capability

After employing appropriate conservation measures, itis essential to make efficient use of land resource forits protection and preservation and prevent furtherdegradation. Land use should be planned based on itscapability classes as any abuse of land at any point oftime would be the beginning of land deterioration at avery faster rate.

Alternate Land Use Systems

Increased use efficiency of marginal lands can beensured by planting them to suitable systems. A numberof options of alternate land use systems have beenidentified for different locations (Singh 1988). Differenthigh value crops like medicinal plant, spices etc, trees,Pastures and Livestock have got their own importanceto find place for making alternate land uses underrainfed situations. The biodiversity of native vegetationin drylands has been comprehensively reviewed bySuresh Kumar (1999). Trees, shrubs and native pasturesare the most important natural vegetation sources.Several multipurpose tree species yielding timber,fodder and fuel wood grow in SAT region on field bundsand scattered in the fields (park land system). This is atraditional agroforestry system. Recent research hasfocused upon systematic integration of trees, crops andgrasses through agrisilviculture, horticulture andsilvipasture. Nitrogen fixing trees (NFTs) have a special

role in rainfed farming systems from the point of view ofnutrient cycling. A number of economically importantshrubs yielding fruits, medicines and aromatic productsoccur in SAT region with high promise for value additionand export. The general recommendations for alternateland use systems based on annual rainfall and landcapability are outlined (Table 19).

Agri-horticulture

In medium soil areas (LCC II to IV) receiving annualrainfall of more than 750 mm, agri- horticultural systemsconsisting of a fruit trees intercropped with annual arablecrop is recommended. Ber, Custard apple, Aonla, andpomegranate are some of the species suitable fordrylands for both for pure plantations and mixed withcrops. Cluster bean, cow pea, horse gram, and othergrain legume have been found useful in this context inthe dry tracts of Andhra Pradesh, Maharashtra andKaarnataka. Results of a long term experimentconducted at AICRPDA, Indore, as an example of theadvantages of this system have been presented in Table20. The study comprised twelve treatment combinationswhich include three fruit tree components viz. Ber, Aonlaand Drumstick and four crop combinations i.e. solesoybean, sole pigeonpea, sole cowpea and intercropof soybean + pigeonpea (4:2). Results revealed thathighest SEY (Soybean Equivalent yield) and monetaryreturns were obtained through drumstick based agro-horti system. The SEY of 2388 kg/ha with monetaryreturn of Rs. 19859 per hectare was obtained from thetreatments T6 (Drum stick-Sole Pigeonpea). Resultsreveled tremendous scope of alternate land use ofcombining fruit trees and prevalent crops of Soybean,Pigeon pea and their intercrop combination. Lower yieldof cowpea was due to heavy rains during reproductivephase.

Studies on tree crop interaction involving variouscombinations of soybean crop and tree species revealedsynergistic effect on tree as well as crop component interms of crop yield and tree volume production (Table21). Though in Malwa plateau most of the soils areclassed as I to III, however, there could be certainshallow and marginal lands along with the hilly terrainunsuitable for arable cropping. For those lands a viableand remunerative alternative system has to be evolvedso that the socio economic conditions of the SAT farmerscould be improved. Studies conducted at AICRPDA,Indore reveled that the intercropping system of soybeanand pigeonpea 4:2 row ratio, when planted in betweenrows of fruit trees of Ber, Drumstick, Aonla, found moreremunerative in terms of soybean equivalent yield ascompared to sole soybean or cowpea (Table 20).

137

Table 20. Agro-horti System, AICRPDA, Indore (1999 to 2003)

Treatments Mean tield of Yield range of Mean gross Soybean Yield rangearable crops arable crops return equivalent (kg/ha)

(kg/ha) (kg/ha) (Rs/ha) yield (kg/ha)

T1 -Aonla - Sole Soybean 1290 940 -2177 13372 1539 1054 -2321

T2- Aonla - Sole Pigeon pea 1130 384 -1765 18728 2255 534 - 4069

T3 - Aonla -Soybean 747 521-1088 7756 1978 1015 -2965

+ Pigeon pea (4:2) 539 264 -908 8879 - -

T4- Aonla - Sole Cowpea 179 139 -303 2574 330 240 -475

T5 - Drumstick-Sole Soybean 1243 955 -1973 13098 1488 1084 -2109

T6 - Drum stick-Sole Pigeonpea 1195 472 -2025 19859 2388 639 -4656

T7 - Drumstick -Soybean 718 492 -952 7559 1916 1152 -2869

+ Pigeon pea (4:2) 534 324 -885 8846 - -

T8 - Drum stick - Sole Cow pea 206 168 -371 182 381 290-582

T9 - Ber - Sole Soybean 1119 838 -1837 11599 1332 933 -1962

T10 - Ber - Sole Pigeon pea 1118 378 -1834 18579 2214 665 -4255

T11 - Ber - Soybean 665 434 -884 6918 1749 1014 -2731

+ Pigeon pea (4:2) 483 301 -810 8029 - -

T12 - Ber- Sole Cowpea 187 124 -336 2723 342 215 -519

Table 21. Performance of various agro-forestry systems at Jabalpur

Tree crop system Volume of 10 year old Av. crop yield (q/ha) Additional income from treestree (m3/ha) (Rs in lacs/ha/10 year)

Soybean sole - 9.22 -Eucalyptus+ soybean 390.75 4.22 13.67Subabool+ soybean 2175.75 1.85 7.55Babool+ soybean 116.85 4.45 4.08Teak+soybean 17.00 4.37 1.19Source: Research highlights JNKVV, 1997

Table 22. Evaluation of intercropping for Vertisols

Crops Shallow soil Medium soil Deep SoilSEY SI SEY SI SEY SI

Sole soybean (Samrat) 1887 0.50 2069 0.37 2262 0.43

Sole soybean (JS335) 1908 0.50 2082 0.37 2138 0.41

Soybean (Samrat)+ Pigeonpea (JA4) 2421 0.64 3695 0.74 4009 0.83

Soybean (JS335) + Pigeonpea (JA4) 2655 0.71 3668 0.73 3785 0.78

138

Intercropping

Intercropping of soybean and pigeonpea in the Shallow,medium and deep soils instead of sole crop isrecommended as it is found more sustainable and gavehigher B : C ratio therefore, recommended in place ofsole soybean under all the three soil depths. Incase ofintercropping the sustainability index ranged from 0.65to 0.83 while in case of sole soybean it ranged from0.37 to 0.50 only Table22. The potential croppingsystems for rainfed area of MP are presented in Table23.

Farming System Approach

This refers to reform of farming system according tolocal conditions to readjust crop structure and improveresource use efficiency productivity and income.Inclusion of multi enterprises like dairy, poultry fishcultivation, floriculture, mushroom cultivation, growingof medicinal crops are the key options for increasingfarmers income and should get priority for efficient modeldevelopment as per local conditions with sustainableproduction system specially for small and marginalfarmers of the region.

Table 23. Potential cropping systems for rainfed conditions under various agro-climatic zones of MP

Agro climatic zone Cropping system for Rainfed condition Av. yield t ha-1/ yearKymore plateau & Satpura hills Rice-gram 4.2

Soybean-chickpea 3.5Soybean-pigeonpea 2.5

Vindhyan plateau Soybean-chickpea 3.7Urid-linseed 2.0Soybean-pigeonpea 2.7Soybean-sorghum 3.0

Central Narmada Valley Soybean-chickpea 3.8Urid-linseed 1.9Soybean-sorghum 2.6

Gird Zone Urid-mustard 2.4Pigeonpea+pearl millet. 2.8Pigeonpea+sorghum 2.9

Bundelkhand Urid-mustard 2.5Pearl millet + Pigeonpea 2.9sorghum -Pigeonpea 3.0

Satpura plateau Soybean-chickpea 3.9Maize-gram 4.5Sorghum - pigeonpea 3.0

Malwa plateau Soybean-chickpea 3.8Soybean-safflower 3.5Soybean+pigeonpea 2.7Cotton+soybean 1.0+1.2Cotton+urid 0.8+0.5Sorghum+pigeonpea 3.2

Nimar valley Groundnut-chickpea 3.5Soybean-chickpea 4.0Sorghum+pigeonpea 3.1Cotton+urid 0.9+0.5

Jhabua hills Maize-chickpea 4.5Kutki-kulthi 2.0Cotton+soybean 0.8+1.0

139

Conclusions

From the foregoing results discussed above it can beconcluded that the productivity under rainfed productionsystem can be sustained on long run basis if themeasures suggested below are implemented:

• For reducing soil erosion, improving organicstatus of soil and improving economy of energywhile enhancing productivity and profitability ofsoybean based production system conservationtillage is the sustainable practice.

• Use of organics and green manure holds the keyto enhanced productivity and resource useefficiency.

• Balanced and adequate use of nutrients throughINM.

• Adoption of watershed approach for conservationand utilization of land and water resources.

• Soil and water management practices that ensuresoil drainage and safe disposal of runoff shouldbe demonstrated on massive scale andpopularized as an integral part of package ofimproved practices for soybean.

• The in-situ conservation of rainwater, use ofstored water in the surface ponds and dug wellsshould for supplemental irrigation to increaseproductivity of soybean based production system.

• Alternate land use system like agro-forestry andagro-horti should be popularized in shallow andmarginal lands for imparting higher economicreturn and sustainability to soybean basedproduction system.

• Farming system approach and its evaluation fordifferent regions to develop most sustainablemodel for each region.

The overall strategy calls for rational use of avail-able technologies for soil and water management formaximizing productivity of soil- water and crop withoutdetriment to environment holds the key for maximizingthe agriculture profitability. To develop rainfed agricul-ture is a major strategic policy for sustainable agricul-ture development in India and also a major measure foragriculture development in 21st century. Let us join thehands to make our due contribution to development ofrainfed agriculture.

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Samra JS (2005) Policy and institutional processes ofparticipatory watershed management in India: pastlessons learnt and future strategies. In WatershedManagement Challenges: Improving Productivity,Resources and Livelihoods (eds Sharma BR et al.),International Water Management Institute, Colombo,Sri Lanka p 336

Sehgal JL (1993) Red and Lateritic Soils of India - ResourceAppraisal and Management, National Bureau of SoilSurvey and Land Use Planning, Publication No. 37,Nagpur, p 346

Sharma RA (1990) Alleviation of drought effects for sustainingproductivity of rainfed crops grown on black clay soilsof Malwa region of Madhya Pradesh. NatconPublication 3 : 201-210

Sharma RA (1976) Ph.D. Thesis, Jawaharlal NehruAgricultural University, Jabalpur, MP, India

Sharma RA, Raghu JS, Thakur HS (2005) Rainfed farmingtechnology for black clay soils. A Bulletin, College ofAgriculture, Indore pp 108

Singh RP (1988) Role of agroforestry in watershedmanagement and development - research priorities.Indain J Dryland Agric Res and Dev 2 : 12-16

Suresh Kumar (1999) Biodiversity and its conservation indryland areas. In: fifty years of Dryland AgriculturalResearch in India (Eds. HP Singh, YS Ramakrishna,KL Sharma and B Venkanteswarlu). CentralResearch Institute for Dryland Agriculture,Hyderabad, pp. 57-72

Tomar SS, Sinha SB, Gupta RK (1985) Improved land andwater management practices for vertisols of CentralIndia. Bulletin Department of Soil Science JNKVV,Jabalpur, MP India

Tomar SS, Tembe GP, Sharma SK, Bhadoria UPS, Tomar VS(1996) Improvement of physical conditions of blacksoils in Madhya Pradesh. Res. Bulletin of Departmentof Soil Sci and Agril Chem JNKVV, Jabalpur

Verma GP (1982) Review of Soil Research in India. Part II.12th International Congress Soil Sci. New Delhi

(Manuscript Receivd : 11.1.13; Accepted : 17.5.13)

141

Abstract

Diagnostic ultrasonography is an ultrasound-based diagnosticimaging technique used for visualizing subcutaneous bodystructures including tendons, muscles, joints, vessels andinternal organs for possible pathology or lesions. Ultrasoundapplies to all sound waves with a frequency above the audiblerange of human hearing, about 20,000 Hz. The frequenciesused in diagnostic ultrasound are typically between 1 and 10MHz. In cattles, with acute abdominal diseases and severesymptoms, a rapid diagnosis is paramount to avoid irrepairableorgan damage. In the past, an exploratory laparotomy wasoften quickly resorted to as a diagnostic aid in acute abdomenin cattle with no clear diagnosis. Today, many farmers willconsent to exploratory laparotomy ,only when the prognosisis good and the procedure has a reasonable chance of beingcurative. If the prognosis is poor, an exploratory laparotomyis avoided by the surgeon as well as by the farmer as it isexpensive and causes unnecessary pain to the patient andoften the animal is no longer marketable. Thus, the goal ofabdominal surgery should be treatment, rather than diagnosisof the disease. To achieve this goal, all available and pertinentnon-invasive diagnostic aids must be employed to establisha diagnosis before surgery is performed.

Keywords: Diagnostic ultrasonography, Non-invasive,Abdomen,Cattle

Diagnostic ultrasonography provides a window for non-invasive visualization of abdominal organs and gastro-intestinal viscera, which are otherwise difficult toexamine. This sonographic information can be used todecide a) if surgical intervention is indicated, b) toformulate a prognosis based upon the abnormalitiesdetected and c) can be used to monitor response tomedicinal treatment. The abdominal contents can easilybe evaluated sonographically with the 3.5 and 5.0 MHztransducers; however, higher frequency transducers are

Diagnostic ultrasonography of bovine compoundstomach affections: a review

Debosri Bhowmick, M.K. Bhargava and Sonal ShrivastavaCollege of Veterinary Science & AHNanaji Deshmukh Veterinary Science UniversityJabalpur 482001 (MP)Email:dr.debosri@gmail.com

also used to yield superior images of the gastro-intestinal viscera (Singh et al. 1993).

Previously, the diagnosis of bovine abdominaldisorders was based on history, routine physicalexamination, haemato-biochemical analysis andradiography. Hematological and biochemical tests whichare non-specific for diagnosis of fore-stomach affectionsand are only indicative of health status of the animal.With the advent of modern diagnostic instruments,ultrasonography is being used for early confirmatorydiagnosis of ruminant disorders. Abdominal radiographyis limited in its application in large animals becausetheir size produces practical difficulties in achievingradiographs of diagnostic quality.

In recent years, ultrasonography has beensuccessfully used to examine the affections ofcompound stomach in cows and buffaloes. In cattle themost routinely encountered affections are:- Traumaticreticuloperitonitis, Omasal impaction, Left displacementof the abomasums and Right displacement of theabomasums.

However, in buffaloes, ultrasonography has beenmore successfully used to diagnose reticulardiaphragmatic hernia with > 85% accuracy (Mohindrooet al. 2007).

Reticular Diaphragmatic Hernia

Recent studies conducted on various affections offorestomach indicate that incidence of diaphragmatichernia is high in buffaloes as compared to cows (Singh2002).

For visualising reticular diaphragmatic herniaultrasonographically, non sedated animal is restrained

JNKVV Res J 47(2): 141-144 (2013)

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in standing position and the right lateral wall of the thoraxfrom the 4th to 7th intercostal space is shaved andsmeared with transmission gel for optimal transmissionof ultrasonic waves and is visualized with the help of3.5 MHz microconvex transducer.

Ultrasonographically, the presence of biphasicreticular motility at the level of the 4th and 5th intercostalspaces, instead of right 6th and 7th intercostal spaces atthe level of elbow, indicates reticular herniation into thethorax. Reticular motility is observed but the amplitudeof contractions is comparatively less (Saini et al. 2007).

Traumatic reticuloperitonitis

In cattles with traumatic reticuloperitonitis,ultrasonography can be used to identify morphologicalchanges in the region of the cranial, ventral or caudalreticular wall (Braun et al. 1993). The caudo-ventral

reticular wall is the most frequently affected, often inassociation with the cranio-dorsal blind sac of therumen.

The changes in the contour of the reticulumdepend on the severity of the inflammatory changes.Deposits of fibrinous tissue interspersed with fluidpockets are frequently seen on the reticular serosa.Ultrasonographically, these appear as echogenic areascavitated by hypoechogenic areas.

Reticular abscess is seen as a sequel to a foreignbody syndrome ( Kumar et al. 2008). Reticularabscesses have an echogenic capsule of varyingthickness, which surrounds a homogeneoushypoechogenic to moderately echogenic centre.Abscesses are usually caudo-ventral to the reticulum,but may be cranial or lateral to the reticulum. It ispossible to drain abscesses through an ultrasound-guided transcutaneous incision (Braun and Gerber1998).

Fig 1& 2. shows gliding reticular motility in the thoracic cavity cranial to the 5th intercostal space visualized onB+M mode ultrasonograms (arrow) confirmatory for diaphragmatic hernia

Fig 3&4. showing Ventral abdominal wall. 2)Fibrinous deposits, 3) Reticulum,4) Craniodorsal blind sac of rumen.Ultrasonography of reticulum and craniodorsal blind sac of rumen with TRP viewed from left ventral thorax.Echogenic deposits cavitated by hypoechogenic fluid ventral to reticulum and craniodorsal blind sac of rumen

143

Omasal impaction

Chronic omasal impaction as a clinical entity is difficultto define and is usually diagnosed at necropsy whenthe omasum is enlarged and firm. Ultrasonographicallyimpacted omasum in buffaloes may be diagnosed byenlarged amotile omasum, non visibility of omasalleaves and a prominent distal acoustic shadow, whilein cows, it is diagnosed by amotile omasum covering alarge area right side (Udehiya 2007).

intercostal spaces on the left side are, with transducerheld parallel to the ribs. With left displacement of theabomasum, the wall of the rumen often remainsimmediately adjacent to the abdominal wall in the ventralregion. When the transducer is moved dorsally, itbecomes apparent that the wall of the rumen is pushedmedially and can no longer be imagedultrasonographically. Instead the abomasum is seen,located between the abdominal wall and rumen.

Fig 5. Ultrasonographic image shows echogenicfibrinous strands between reticulum, rumen, and ab-dominal wall. Notice the corrugated appearance of thereticular wall

Fig 6. Sonogram of the left cranioventral abdomen of acow withTRP (5 MHz): abscess (A) between reticulum,ruminal atrium and spleen (S) of 5-8 cm, distinct cap-sule and hypoechoic content

Fig 7. showing Impacted omasum with acoustic shadow

Left displacement of the abomasums

An ultrasonographic examination is useful to confirmthe diagnosis of left displacement of the abomasum indoubtful cases (Braun et al. 1997). 3.5 MHz transduceris moved from ventral to dorsal region at the last three

Impacted omasum

Acoustic shadow

Fig 8&9. showing ultrasonogram of LDA imaged fromdorsal region of the 12th intercoastal space showingdisplaced abomasum between the abdominal wall andrumen (1) Abdominal wall, (2) Abomasum withhypoechogenic ingesta, (3)Abomasal fluid, (4) Ruminalwall and (5) Ruminal content.

144

Right displacement of the abomasums

For Right displacement of abomasums the areaimmediately caudal to the last rib and the caudal two tothree intercostal spaces on the right side are examinedventro-dorsally with the 5MHz linear transducertransducer held parallel to the ribs (Braun et al. 1997).In animals with right displacement of the abomasum,the liver is displaced from the abdominal wall and cannotbe imaged. The abomasum is seen where the liver wouldnormally be ie; immediately adjacent to the rightabdominal wall.

References

Braun U, Götz M, Marmier O (1993) Ultrasonographic findingsin cows with traumatic reticuloperitonitis Vet Rec 133:416-422

Braun U, Wild K, Guscetti F (1997) Ultrasonographicevaluation of abomasums of 50 cows. Vet Rec 140:93-98

Kumar M, Mohindroo J, Singh K, Singh T (2008) Studies onhaematochemical profiles in diffuse peritonitis inbovines. Indian J. Vet Surg 29:117-19

Fig 10&11. showing ultrasonogram of RDA imaged from right side just behind the last rib showing the abomasums(4) between the right abdominal wall (1)and the liver(2) that appears as hypoechogenic and identified by theanechoic gall bladder (3)

Mohindroo J, Kumar A, Singh SS (2007) Ultrasonographicdiagnosis of reticular diaphragmatic hernia inbuffaloes. Vet Rec 161:758-59

Sahay PN (2009) Biochemical and hematological changes inbovine peritonitis. Indian Vet J 168:103-06

Saini NS, Sobti VK, Singh SS (2000) Retrospective evaluationof 80 non-surviving buffaloes with diaphragmatichernia. Vet Rec 147:275-77

Singh J, Singh AP, Patil DB (1993) Digestive disorders inbovine Indian J. Vet Surg 53:473-75

Singh M. (2002) Evaluation of surgically treatedgastrointestinal disorders in cattle. Thesis PunjabAgricultural University, Ludhiana India

Udehiya RK(2007) Evaluation of ultrasonography as diagnostictool for reticulo -omasal disorder in bovines. IndianVet J. 74:605

(Manuscript Receivd : 30.8.13; Accepted : 15.9.13)

145

Abstract

A field investigation was conducted during 2010-11 to 2012-13 at Jabalpur,Madhya Pradesh to study the effect of nutrientmanagement and cropping system on productivity and soilmicrobial growth under different rice based cropping systemsin Madhya Pradesh. The 4 different cropping systems (CS1-Green manuring sunhemp-Rice-Wheat, CS2-Rice-Chickpea-Sesame, CS3-Rice-Berseem, CS4-Rice-Veg.pea-Sorghum)and three nutrient managements M1- 100% Organic (1/3 Nthrough each of FYM, Vermicompost and Neem oil cake),M2-100 % Inorganic (100 % NPK through fertilizers), M3-INM(50%NPK through fertilizer+50% N through organic sources)with 3 replications in Strip plot design. The soil of theexperimental field was sandy clay loam in texture, neutral inreaction (7.3), normal EC (0.52), low in OC (0.72%), mediumin available N (264.05kg/ha) and P (12.8 kg/ha) and high inK(285.2 kg/ha). The highest grain yield of 34.78 q/ha wasrecorded from rice-wheat-green manuring cropping systems.And the best nutrient management for growth of microorganisms is organic nutrient management and rice-wheat-green manuring cropping system.

Keywords: Nutrient management, cropping system,bacteria, fungi, neem oil cake

Rice and wheat are grown in a sequence on an areaabout 2.7 million hectares in Punjab and contribute 80%in the total food pool of the state of Punjab (DAGP 2011).Madhya Pradesh is relatively underdeveloped withregards to agricultural productivity rural employment andeconomic status as compared to most of the Indianstates. During kharif, growing of rice is a tradition andis widely accepted depending upon farmers socio-economic conditions. However, in rabi, there are feweroptions for the stakeholders to take a profitable andsuitable crops. Under these circumstances, theygenerally follow rice-wheat, rice-mustard and rice-winter

Effect of nutrient management and cropping system on growth,yield attributes and soil microbial population under different ricebased cropping systems in Madhya Pradesh

Megha Dubey, K.K. Agrawal, S. K. Vishawakarma and Suchi GangwarDepartment of AgronomyJawaharlal Nehru Krishi Vishwa VidhyalayaJabalpur 482004 (MP)

vegetables under partially or assured irrigation and rice-fallow, rice-utera under rainfed situation as also reportedby Chitale et al. (2011). Any modification to the existingsystem with a tendency to decline the productivity ofrice crop will neither be sustainable nor acceptable tothe farming community unless and untill it is stable overthe time, maintains the soil health and sustains theenvironment in one hand and could also meet the dailyrequirements of human and animals (Samui et al. 2004).Oilseeds and pulses including vegetables and fodderare receiving more attention owing to their higher prices.

Therefore to get maximum yield from the existingcropping system the organic farming must be practiced.This help to get the best food grain production and willimprove the soil health as well Prasad et al (2005).Butorganic manures will not give higher grain yield in thefirst year so the inorganic fertilizers were used aloneas well as in combination in the form of INM for betterresults. To prove this, an experiment was carried out toevaluate growth, yield attributes and effect on soilmicrobial growth by application of organic, inorganicand integrated nutrient management in different ricebased cropping systems including oilseeds, pulses,vegetable and fodder crops.

Material and methods

A field experiment was conducted during 2010-11 to2012-13 at the research farm of Jawaharlal Nehru KrishiVishwa Vidhyalaya, Jabalpur on a sandy clay loam soil.The soil of the experimental site had a pH 7.4, EC0.51dS/m and organic carbon 0.7%.The available soilnitrogen ,phosphorus and potash were 264,12.6 and282 kg/ha, respectively. The bulk density of the soil was1.35 Mg/m3.The factors studied included 3 nutrientmanagenment practices viz., organic manure (ONM),

JNKVV Res J 47(2): 145-148 (2013)

146

Table 1. Effect of nutrient management and cropping system on yield attributes of rice

Treatment Plant height Effective Grains/ Grain yield(cm) tillers/m2 panicle (q/ha)

Nutrient ManagementM1-Organic 71.00 292.5 72.08 32.96M2-Inorganic 71.10 289.5 73.33 35.12M3-INM (50 % organic + 50 % inorganic) 69.00 291.8 73.00 33.19SEm± 1.47 1.12 0.08 0.51CD at 5% 5.78 4.43 0.30 2.01Cropping SystemCS1-Rice-wheat-GM 71.81 286.1 73.11 34.78CS2-Rice-chickpea-sesame 68.91 292.4 72.22 34.45CS3-Rice-berseem (fodder + seed) 70.95 292.1 71.44 33.12CS4-Rice-vegetablepea-sorghum (fodder) 69.82 294.4 74.44 32.67SEm± 0.78 0.95 0.18 0.76CD at 5% 2.73 3.27 0.64 2.63

*Pooled data of 3 years

Table 2.Effect of nutrient management and cropping system on growth of microbial population after harvest

Treatment Fungi Bacteria Azatobactor Phosphorus(104xcfu/g soil) (105xcfu/g soil) (103xcfu/g soil) solublizing bacteria

(103xcfu/g soil)

Nutrient ManagementM1-Organic 45.4 51.8 27.1 14.5M2-Inorganic 33.0 36.9 20.6 11.8M3-INM (50% organic + 50% inorganic) 37.4 39.7 22.7 12.4SEm± 0.29 0.47 0.45 0.41CD at 5% 1.16 1.87 1.77 1.60Cropping SystemCS1-Rice-wheat-GM 40.1 43.9 25.6 14.1CS2-Rice-chickpea-sesame 37.2 40.7 22.1 12.6CS3-Rice-berseem (fodder + seed) 38.7 41.8 22.1 12.2CS4-Rice-vegetable pea-sorghum (fodder) 38.4 44.7 24.0 12.7SEm± 0.25 0.76 0.52 0.28CD at 5% 0.87 2.66 1.81 0.98

* Pooled data of 3 years

147

chemical fertilizers and integrated nutrient (50:50) (INM)and 4 cropping systems viz., rice-durum wheat-greenmanuring, rice-chickpea-sesame, rice-berseem (fodder+ seed), rice-vegetable pea-sorghum (fodder) in stripplot design with 3 replication. The crop varieties grownwere Pusa sugandha Basmati-5 in rice, MPO-1106 indurum wheat, JG-24 for gram, JB-1 for berseem, Arkelfor vegetable pea during winter season and TKG-55 insesame and MP Chari in sorghum during summerseason. These crops were raised with recommendedagronomic practices.

In organic manure treatment nutrients wereapplied through farm yard manure. The manure wasapplied on the nitrogen equivalent basis for each crop.The nutrient composition of FYM was 0.5, 0.25, 0.5%N,P2O5 and K2O respectively. For the weedmanagement, mechanical measures were adopted andfor insect pest management, neem oil (Azadiractin0.03%) was applied as and when required under organicnutrient management. In chemical fertilizer treatment,nutrient were applied through chemical fertilizers viz.,urea, single super phosphate muriate of potash whileplant protection was done through recommendedpesticides, when required. The recommended dose offertilizers for rice, wheat, chickpea, sesame, vegetablepea, sorghum and berseem. 120:26.4:33.3,120:26.4:33.3, 20:60:30, 30:60:30, 20:26.4:16.6,100:22:25 and 20:26.4:16.6kg N:P:K/ha. In integratednutrient management (INM) treatment 50% of nitrogenwas supplied through farm yard manure and rest 50%through chemical fertilizers and the plant protection wasdone by adopting integrated pest management practicesand application of Phorate granules was done insorghum to protect from the attack of shoot borers. Inrice-wheat green manuring cropping system, sunhempwas grown before rice and 35 days old crop wasincorporated in soil as green manure. PhosphorusSolubilizing Bacteria was used for inoculation in allcrops.

Rice was transplanted in the first week of Julyand harvested in the second week of October. Thewinter crops (wheat, chickpea, vegetable pea andberseem) were sown in the second week of Octoberand harvested in second week of February. The summercrops (sesame and sorghum) were sown in third weekof February and harvested in first/second week of June.The calculated dose of organic manures was applied2-3 weeks prior to sowing of kharif crops, 1 week priorto rabi and summer crops. The rainfall received duringthe experimental period was 1023, 1062 and 1011 mmin 2010-11, 2011-12 and 2012-13, respectively. Duringkharif, rice received 3 irrigation (each for 7.5 cm depth)each year. During rabi 5, 4, 3 and 12 irrigation were

provided for wheat, chickpea, berseem (fodder + seed)and vegetable pea, respectively each year. Duringsummer sesame and sorghum received 4 and 6irrigations, respectively each year. The market price oforganic rice is 2500 (Rs/q) and the value of rice underinorganic and INM is 1900 (Rs/q).

Result and discussion

Growth and Yield attributes

In this experiment the effect of three nutrientmanagement practices (100% organic, 100% inorganicand INM) and four cropping systems i.e. Basmati rice -wheat - green manauring, Basmati rice - chickpea -sesame, Basmati rice - Berseem (Fodder and seed) andBasmati rice - vegetable pea - sorghum (fodder) ongrowth, yield attributes and soil health were testedduring 2012-13 on the same plot and site. The highestplant height of 71.10 cm was obtained from inorganicfertilized plots and 71.81cm in Rice- wheat -greenmanuring cropping system. But maximum number ofeffective tillers/m2 were obtained from organic manures(292.5/m2) and rice-vegetable pea-sorghum croppingsystem (294.4/m2).The highest grains per panicle wasrecorded from inorganic nutrient management of 73.33and 74.44 from rice-vegetable pea-sorghum croppingsystem. But the maximum grain yield of 35.12 wasobtained from inorganic nutrient management, 33.19q/ha from INM and 32.96 q/ha from organic nutrientmanagement and 34.78 q/ha from rice-wheat-greenmanuring cropping system followed by 34.45 q/ha inrice-chickpea-sesame cropping system.

Changes in soil properties

The soil of the experimental field was sandy clay loamin texture and neutral in reaction (Soil pH 7.4) with bulkdensity of 1.35 g/cm3 at 0-30 cm depth. Its electricalconductivity was 0.51 dS/m and has 7.0 g/kg OC. Itcontains 264, 12.6 and 282 kg/ha available N, P and Kcontents, respectively. On the basis of data pertainingto various soil-properties after the completion of thirdyear, some remarkable changes in soil properties overtheir initial status were observed due to the effect ofdifferent cropping systems under varying nutrientmanagement practices as obtained from Annual Report2011-12.

The treatments receiving organic nutrientmanagement either fully (100% organic) or partially(INM) exhibited improvement in OC content of the soil

148

and the effect of 100% organic nutrient managementwas more pronounced in this regard. The N contentsshowed rising trend under all cropping system with100% organic, inorganic as well as INM nutrition, whileP and K contents were almost stabilized with thesetreatments. The P contents exhibited slight reductionwith 100% inorganic nutrition. As regard the changesin chemical and biological properties of soil after 3 yearof experimentation, it was observed that treatmentsreceiving organic nutrient management either fully(100% organic) or partially (INM) exhibited improvementin organic carbon content and microbial population (viz.total fungi, Bacteria, AZB, PSB and ACT) of the soiland the effect of 100% organic nutrient managementwas more pronounced in this respect. The populationof fungi was 45.4 x 104,bacteria it was 51.8 x106,azatobacter 27.1 x 106 and phosphorus solubilizingbacteria 14.5 x 106.Thus it was recorded that the organicnutrient management record the maximum growth ofmicroorganisms. As the market value of organic produceis more as compared to inorganic produce as alsoreported by Upadhyay et al. (2011).

References

Chitale S, Sarawgi SK, Tiwari A, Urkurkar JS (2011)Assessment of productivity of different rice basedcropping systems in Chhattisgarh plains. Indian JAgr 56(4):305-310

DAGP 2011. Agriculture at a glance (2010-11) Informationservice, Department of Agriculture, Government ofPunjab

Anonymous (2012) JNKVV 2011-12 Annual Report, (2011-12) Network Project of Organic Farming. JNKVVJabalpur 29-30

Prasad R (2005) Rice-wheat cropping system. Advances inAgronomy 86:255-339

Samui RC, Kumar AL, Majumdar D, Mani PK, Sahu PK (2004)Diversification of rice based cropping system in NewAlluvial Zone of the West Bengal. Indian J Agro49(2):71-73

Upadhyayay VB, Jain Vikas, Vishwakarma SK, Kumhar AK(2011) Production potential, soil health, waterproductivity and economics of rice based croppingsystems under different nutrient sources. Indian JAgr 56(4):311-316

(Manuscript Receivd : 2.10.13; Accepted : 15.10.13)

149

Abstract

The correlation coefficient amongst seed yield and itsattributing characters for wheat crop under rice-wheat croppingsystem was computed. It is found that seed and straw yieldwere significantly and positively associated with all the growthand yield parameters in wheat crop.

Keywords: Wheat, Correlation coefficient, seed yield,growth parameters, yield parameters.

The challenges for agriculture are immense. In the next25 years, the challenges for agriculture will not only beto meet the food needs of the world's expandingpopulation, but also to undertake food supply in amanner that is sustainable for present and futuregenerations. Tremendous quantities of nutrients arerequired to produce the food necessary to feed the worldin any given year. Poor as well as indiscriminatemanagement of these nutrients in many parts of theworld has led to environment pollution and thedegradation of this resource base, particularly in thedeveloping world. To meet agricultural production andsustainable intensification goals over the short and long-term, plant nutrient and soils need to be managedproperly.

Integrated nutrient management system is animportant component of sustainable agriculturalintensification. The goal of INM is to integrate the useof all natural and man-made sources of plant nutrients,so as to increase crop productivity in an efficient andenvironmentally benign manner without diminishing the

Correlation coefficient amongst seed yield and its attributing char-acters for wheat crop under rice-wheat cropping system underagroclimatic conditions of Kymore Plateau and Satpura Hills

Vikas Gupta and H.L. SharmaDepartment of AgronomyCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur (MP)

capacity of the soil to be productive for present andfuture generations. It seeks to maintain or improve soilfertility for sustaining the desired level of crop productionand crop productivity through optimization of the benefitfrom all possible sources of plant nutrients in anintegrated manner.

Wheat requires high quantity of nutrients toharness their potential yield. However, it is unaffordableto poor and subsistence farmers of the state. Applicationof inadequate and unbalanced quantity of fertilizers towheat crop results in low crop yield as well asunsustainable productivity. Therefore, a long-termexperiment has been initiated on integrated nutrientmanagement in rice-wheat system at Jabalpur (MP)since kharif season 1987-88 to maintain the sustainableand high grain yields of wheat without degradation ofsoil health under irrigated production system. Thepresent paper deals with the studies during the year2002-03 and 2003-04.

Materials and methods

The soil of the experimental field was neutral in reaction(soil pH 7.7) and normal in EC (0.38 dS m-1) with mediumorganic carbon content (6.9 g kg-1) and analysingmedium in available N (260 kg ha-1), P (16 kg ha-1) andhigh in available K (448 kg ha-1) contents. The rainfallwas 1266 and 1756 mm during the two consecutiveyears i.e. 2002-03 and 2003-04. There were 12treatments (Table 1). Different organic manures viz. FYM(1.22-0.55-0.90% and 1.18, 0.48, 1.02% N, P, K in 2002-

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150

03 and 2003-04 respectively), wheat straw (0.49-0.09-1.80% and 0.50, 0.10, 1.68% N, P, K in 2002-03 and2003-04 respectively) and green leaf manure ofsunnhemp (2.21-0.48-1.77% and 2.30, 0.51, 1.79% N,P, K in 2002-03 and 2003-04 respectively) wereanalyzed and their quantities required to substitute aspecified amount of N as per the treatments wascalculated. Recommended 100% NPK for both cropswas 120 kg N + 60 kg P2O5 + 40 kg K2O/ha applied asper the treatment through urea, single super phosphateand muriate of potash respectively. The experimentswere laidout in randomized block design with 4replications. Wheat cv. Lok-1 was grown by using seeds100 kgha-1 in rows 20 cm apart. Other cultural practicesviz. weed management and plant protection measureswere followed as per recommendation in the state. Thedata of both years were pooled when differencesbetween the years were not significant. Correlationcoefficients of the observations were estimatedaccording to the method suggested by Gondane et al.(1995).

Results and discussion

At 30 days after sowing, Plant height and number oftillers per m2 expressed positive association with theplant height and number of tillers per m2 at 90 daysafter sowing and at harvest respectively (Table 2).Graphical presentation of Correlation Coefficient ofattributing characters with Seed Yield, straw yield andharvest index also given below. Effective tillers per m2

showed positive association with the earhead lengthand number of grains per earhead. Anwar et al. (2009)also found that Number of tillers per plant had positivedirect effect on grain yield per plant. Growth parametersshowed positive association with the entire yieldattributing characteristics. Akram et al. (2008) revealedthat number of grains per spike positively associatedwith seed yield in wheat crop. Seed and straw yieldshowed strong positive association with all thevegetative and yield attributing parameters. Seed andstraw yield expressed very strong positive correlationwith each other. While harvest index expressed negativeassociation with all the growth parameters, yieldattributing characters, seed and straw yield. It can beconcluded that all the above growth and yield attributingcharacters strongly contributed to the seed and strawyield.

Tabl

e 1.

Det

ails

of t

he tr

eatm

ents

und

er d

iffer

ent i

nteg

rate

d nu

trien

t man

agem

ent

T. N

o.Kh

arif

(Ric

e cv

. Kra

nti)

Rab

i (W

heat

cv.

Lok-

1)T1

No

ferti

lizer

s, n

o or

gani

c m

anur

es (C

ontro

l)N

o fe

rtiliz

ers,

no

orga

nic

man

ures

(Con

trol)

T250

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

s50

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

sT3

50%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

100%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

T475

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

s75

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

sT5

100%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

100%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

T650

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

s +

50%

N th

roug

h FY

M10

0% re

com

men

ded

NPK

thro

ugh

ferti

lizer

sT7

75%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

+ 25

% N

thro

ugh

FYM

75%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

T850

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

+ 5

0% N

thro

ugh

whe

at s

traw

100%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

T975

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

s+ 2

5% N

thro

ugh

whe

at s

traw

75%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

T10

50%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

+ 50

% N

thro

ugh

gree

n le

af10

0% re

com

men

ded

NPK

thro

ugh

ferti

lizer

sm

anur

ing

(Sun

hem

p)T1

175

% re

com

men

ded

NPK

thro

ugh

ferti

lizer

s +

25%

N th

roug

h gr

een

leaf

75%

reco

mm

ende

d N

PK th

roug

h fe

rtiliz

ers

man

urin

g (S

unhe

mp)

T12

Farm

er's

pra

ctic

e (4

0kg

N +

20k

g P2

O5

+ 3

tonn

es F

YM/h

a)Fa

rmer

's p

ract

ice

(40k

g N

+ 2

0 kg

P2O

5/ha

)

Rec

omm

ende

d 10

0% N

PK fo

r bot

h cr

ops

was

120

kg

N +

60

kg P

2O5

+ 40

kg

K2O

/ha

thro

ugh

urea

, sin

gle

supe

r pho

spha

te a

nd m

uria

te o

f pot

ash,

resp

ectiv

ely

151

Fig 2. Graphical presentation of correlation coefficientof attributing characters with straw yield

Fig 3. Graphical presentation of correlation coefficientof attributing characters with harvest index

Fig 1. Graphical presentation of correlation coefficientof attributing characters with seed yield

Tabl

e 2.

Coe

ffici

ent o

f cor

rela

tion

for y

ield

and

its

com

pone

nt in

whe

at c

rop

Plan

t Hei

ght

Num

ber o

f tille

rs/m

2LA

IEf

fect

ive

Ear

head

# G

rain

s/G

rain

Stra

wH

arve

stTr

eatm

ent

30 D

AS60

DAS

Har

vest

30

DAS

60 D

AS H

arve

sttil

lers

/m2

leng

thea

rhea

dyi

eld

yiel

din

dex

(cm

)(q

/ha)

(q/h

a)X

1X

2X

3X

4X

5X

6X

7X

8X

9X

10Y1

1Y1

2Y1

3X

1 1

.000

0.97

6**

0.97

0**

0.86

7**

0.79

3**

0.93

9**

0.97

6**

0.91

8**

0.95

0**

0.96

6**

0.98

4**

0.98

3**

-0.4

14X

2 1

.000

0.99

3**

0.92

0**

0.83

9**

0.96

1**

0.95

8**

0.94

4**

0.96

0**

0.97

6**

0.96

6**

0.96

7**

-0.4

38X

3 1

.000

0.94

0**

0.85

9**

0.97

6**

0.95

8**

0.96

4**

0.96

8**

0.98

0**

0.96

9**

0.97

0**

-0.4

36X

4 1

.000

0.95

9**

0.97

3**

0.87

7**

0.95

5**

0.95

2**

0.91

3**

0.89

2**

0.89

7**

-0.4

24X

5 1

.000

0.91

1**

0.84

3**

0.92

6**

0.91

3**

0.83

8**

0.85

2**

0.86

2**

-0.5

26X

6 1

.000

0.92

0**

0.97

4**

0.95

7**

0.96

3**

0.94

2**

0.94

5**

-0.4

39X

7 1

.000

0.92

8**

0.97

6**

0.94

7**

0.98

8**

0.98

9**

-0.4

70X

8 1

.000

0.94

7**

0.93

2**

0.94

9**

0.95

5**

-0.5

22X

9 1

.000

0.94

8**

0.96

8**

0.96

9**

-0.4

28X

10 1

.000

0.94

9**

0.95

1**

-0.4

71Y1

1 1

.000

0.99

9**

-0.4

30Y1

2 1

.000

-0.4

64Y1

31.

000

** S

igni

fican

t at 0

.01

leve

l of p

roba

bilit

y

152

/kku&xsgw¡ Qly iz.kkyh esa xsgw¡ dh Qly ds fy, cht mit ,oa mldsfofHkUu dkjdks ds e/; lglaca/k xq.kkad dh x.kuk dh xbZ A xsgq¡ dhQly esa cht mit ,oa mlds fofHkUu o`̀f) ,oa mit izkpyksa dse/; /kukRed ,oa lk/kdZ lglaca/k xq.kkad ik;k x;k A

References

Akram Z, Ullahajmal S, Munir M (2008) Estimation ofcorrelation coefficient among some yield parametersof wheat under rainfed conditions. Pak J Botany40(4): 1777-1781

Anwar J, Ali MA, Hussain M, Sabir W, Khan MA, Zulkiffal M,Abdullah M (2009) Assessment of yield criteria inbread wheat through correlation and path analysis.J Animal & Plant Sci 19(4): 185-188

Gondane SU, Batia GL, Pratap PS (1995) Correlation studiesin yield component in okra. Haryana J Horti Sci 24(2):151-156

(Manuscript Receivd : 30.11.12; Accepted : 11.10.13)

153

Abstract

Investigations were conducted during kharif and rabi seasonsof 2011-12 and 2012-13 at JNKVV, Jabalpur to study the effectof organic nutrient management practices on growth, yieldattributing characters, yield and soil fertility in rice-basedcropping systems. Nutrient management through organics playa major role in maintaining soil health due to build up of soilorganic matter, beneficial microbes, enzymes, besidesimproving soil physical, chemical and biological properties andto achieve sustainable soil fertility and crop productivity. Theavailable N, P2O5, and K2O were significantly higher in legumebased cropping systems during both the seasons of the studythan non-legume system. GM-basmati rice- wheat andbasmati rice-berseem cropping systems were the bestcropping systems that increase availability of nutrients in thesoil. Application of 1/3 VC + 1/3FYM +1/3 NEOC + Panchgavyahad higher available nitrogen, phosphorus and potassiumduring both the crop years . Application of FYM, vermicompost,NEOC and Panchagvya significantly increased plant height(cm), tillers/m2, number of grains/panicle and grain yield (q/ha) in both the years.

Keywords: Vermicompost, FYM, Panchagvya, BD-501,neem cake (NEOC), cropping system etc

In India rice (Oryza sativa L) - Wheat (Triticum aestivumL) is the dominant cropping system in Indo-Gangaticplains. Rice and wheat are the world's two most impor-tant cereal crops contributing 45% of the digestible en-ergy and 30% of total protein in the human diet. Ap-proximately 12.5 m ha area under this system contrib-uted to 25% total food grain production of India (Singhet al. 2011). About 33% of India's rice and 42% of wheatis grown in this rotation. Rice and wheat are consid-ered as major constituent of National Food Security bycontributing about 76% of total food grains productionto the national basket.

Effect of organic nutrient management practices on growth,yield attributing characters and soil fertility in rice-basedcropping systems

Suchi Gangwar, K.R. Naik and Megha DubeyDepartment of AgronomyJawaharlal Nehru Krishi Vishwa vidhyalayaJabalpur 482006 (MP)

Continuous cropping of rice-wheat systemcreated many fold problems such as deteriorated soilstructure, build up of pests including weeds, decliningfactor productivity, development of nutrients deficiencyand decrease in profitability etc. To deal all these issues,a comprehensive and collaborative effort was neededto bring all the key resources together for enhancingthe productivity. Rice-wheat cropping system is highlynutrient exhaustive and therefore, its continuous usehas depleted inherent soil fertility, causing deficiencyof several nutrients.

Intensive cultivation and growing exhaustivecrops have made the soil deficient in macro as well asin micronutrients. The success of any cropping systemdepends upon the appropriate management ofresources including balanced use of manures andfertilizers. The cultivation of legumes has made radicalimprovement in the farming community.

Chavan et al. (2007) reported that thephysicochemical properties of the soil improvedsignificantly by the addition of organic manures and thatthere was very little change due to inorganic fertilizers.It is apparent that there is a need to generate moreinformation on integrated nutrient recommendations forcropping system for sustained crop production throughincreased soil productivity in long term experiments.Organic manures in agriculture adds much neededorganic and mineral matter to the soil. The organicmatter added is an indispensable component of soil,and plays an important role in maintenance andimprovement of soil fertility and productivity. The propermanagement of these make it possible to increase theefficiency of use of soil and added nutrients. Nutrientmanagement through organics play a major role inmaintaining soil health due to build up of soil organicmatter, beneficial microbes, enzymes, besides

JNKVV Res J 47(2): 153-157 (2013)

154

Table 1. Growth and yield attributes of rice as influenced by nutrient sources and cropping system

Treatments Plant height Effective tillers/m2 Number of grains Grain yield(cm) /panicle (q/ha)

2011-12 2012-13 2011-12 2012-13 2011-12 2012-13 2011-12 2012-13

Cropping systems

CS1 71.89 71.62 233.0 237.4 60.13 60.40 30.39 30.18

CS2 72.41 72.39 234.4 238.4 62.86 61.66 30.90 31.30

SEm± 0.18 0.22 0.36 0.32 0.45 0.42 0.13 0.34

CD (P=0.05) 0.50 0.68 1.01 0.92 1.32 1.24 0.33 1.03

Nutrient sources

N1 71.80 71.88 238.3 243.6 59.16 62.16 31.40 30.77

N2 71.47 71.97 229.3 230.6 61.16 60.33 30.35 29.81

N3 74.01 72.99 246.1 249.0 66.00 70.83 30.97 33.58

N4 72.27 72.07 227.3 233.6 60.33 61.16 29.81 28.25

SEm± 71.19 71.12 231.1 235.5 60.83 60.66 30.69 31.29

CD (P=0.05) 1.54 1.66 15.92 17.60 5.14 7.65 1.15 4.60

CS1-GM-Basmati rice-Wheat CS2-Basmati Rice-BerseemN1- 1/3 FYM+1/3VC+1/3NEOC N2-PanchagvyaN3- 1/3 FYM+1/3VC+1/3NEOC+Panchgvya N4- BD-501 N5- BD-501+Panchagvya

Table 2. Effect of organic nutrient managment practices on soil fertility statusAfter harvest of kharif crop (2011-12 and 2012-13)

Treatment Available N (kg/ha) Available P (kg/ha) Available K (kg/ha)2011-12 2012-13 2011-12 2012-13 2011-12 2012-13

Cropping Systems

CS1 262.5 265.6 10.97 11.49 318.7 332.3

CS2 267.0 267.7 12.37 13.07 330.0 341.2

SEm± 1.23 0.57 0.45 0.48 3.56 2.45

CD (P=0.05) 3.65 1.70 1.30 1.40 10.62 7.32

Nutrient Sources

N1 262.8 263.9 11.73 12.31 324.6 336.4

N2 257.5 261.1 11.18 11.28 352.6 303.2

N3 277.8 278.4 13.43 14.30 373.2 376.8

N4 267.5 269.3 10.78 11.71 322.4 330.3

N5 262.4 268.8 11.23 11.80 315.7 334.2

SEm± 5.56 4.89 0.72 0.78 16.89 18.96

CD (P=0.05) 16.65 14.63 2.15 2.32 50.68 56.80

Initial value 260 10.20 296

155

improving soil physical, chemical and biologicalproperties and to achieve sustainable soil fertility andcrop productivity.

Awareness about improved quality of foodproducts, problems of health hazards and environmentalissues both at global and national level is increasing inrecent years. There is a great demand for high qualityproducts and organically grown foods in the internationalmarket and can capitalize on its potential to go fororganic farming on a large scale. India, with its variedagro-climatic conditions and agricultural biodiversity, ismost suited for organic farming. It is necessary toeducate the farmers about the scientific methods oforganic farming so that their income will increasegradually. Organic farming is also preferred becauseof increasing consumer demand for safe, high quality,ethical organic foods. Organically produced productsalso fetches good returns.

Material and methods

The present investigation is the part of the "NetworkProject on Organic Farming". The soil of theexperimental site was sandy clay loam (Sand - 54.10%, Silt - 24.21 % and Clay - 21.69 %) having pH of 7.3,EC 0.39 dS/m in the top 15 cm soil. The available N,P2O5 and K2O were 260, 10.20 and 296 kg/ha,

respectively. The organic carbon content of the soil was0.68%. The field capacity, bulk density, porosity andwater holding capacity of the surface soil were 30.3%and 1.3 Mg/m3, 52.6 % and 22% respectively. Thedesign followed was split plot with two main plots(cropping system) and five sub-plots (Nutrientmanagement) treatments and three replications. Thetwo cropping systems were gm-basmati rice - durumwheat and basmati rice-berseem. The organic nutrientmanagement practices were N1- 1/3 FYM+1/3NC+1/3VC, N2- Panchgavya alone, N3-1/3 FYM+1/3NC+1/3VC+ Panchgavya, N4- BD-501 and N5- BD-501 +Panchgavya. These manures were applied based onthe nitrogen equivalent basis and nutrient requirementof each crop. The organic manures according to thetreatment details were applied two weeks before sowingof crops.

Results and discussion

Growth and yield attributing characters of rice viz. plantheight (cm), effective tillers/m2, number of grain/panicleand grain yield (q/ha) were increased with treatment N3-(1/3 FYM+1/3NC+1/3VC+panchagvya) during both theyears. Organic nutrient management practices hadsignificantly increased available nitrogen, phosphorusand potassium content in soil as compared to initial

Table 3. Effect of organic nutrient management practices on soil fertility statusAfter harvest of Rabi crops (2011-12 and 2012-13)

Treatment Available N (kg/ha) Available P (kg/ha) Available K (kg/ha)2011-12 2012-13 2011-12 2012-13 2011-12 2012-13

Cropping Systems

CS1 265.7 269.3 11.80 12.25 318.3 332.0

CS2 273.5 278.2 13.30 13.78 341.6 342.5

SEm± 2.3 2.85 0.42 0.40 6.56 2.69

CD (P=0.05) 6.78 8.34 1.20 1.18 19.60 8.02

Nutrient Sources

N1 267.0 271.5 12.98 13.50 333.8 336.5

N2 264.1 270.7 11.68 12.30 352.4 304.4

N3 280.4 282.9 14.23 14.43 378.9 376.2

N4 265.3 270.8 11.40 11.88 335.7 330.1

N5 268.2 272.6 12.46 12.91 329.0 334.3

SEm± 3.56 3.78 0.56 0.58 11.48 16.25

CD (P=0.05) 10.65 11.30 1.62 1.72 34.45 48.70

156

values. Treatment N3 - (1/3 FYM+1/3NC+1/3VC+Panchgavya) showed maximum available nitrogen,phosphorus and potassium in both the years. Duringharvest of kharif crop increase in available N (277,278kg/ka), P2O5 (13.43, 14.30 kg/ha) and K2O (292, 309kg/ha) and during harvest of rabi crops increase inavailable N (280, 282 kg/ha), P2O5 (14.23, 14.43kg/ha)and K2O (378, 376 kg/ha) were observed. Basmati rice-berseem cropping system was the best cropping systemthat increase availability of nutrients in the soil.Swaminathan (2005) reported that use of panchagvyawith combination of other organic manure (FYM, VCand NEOC) increased photosynthetic activity in the plantand also increased bacteria, yeast, actinomycetes,photosynthetic bacteria and certain fungi availability inthe soil.FYM worked as soil conditioner and supplyingplant nutrients which resulted in improvement in soil

fertility and grain yield.

The mineralization of N in soil and due to highenzyme activities in the soil amended with organicmanures will increase the transformation of nutrients(Singh et al. 2008). Similarly, the P availability in soilincreased due to use of organics. During decompositionof organic manure, various organic acids will beproduced which solubilize phosphate and otherphosphate bearing minerals and thereby lowers thephosphate fixation and increase its availability. SrinivasRao et al. (1997) recorded higher potassium release inearthworm casts. Singh et al. (1999) reported thatimprovement in soil fertility was attributed to addition ofFYM and other organics which stimulated the growthand activity of microorganisms. They participate in thebiological cycling of elements and transformation of the

0

5 0

1 0 0

1 5 0

2 0 0

2 5 0

2 0 1 1 - 1 2 2 0 1 2 - 1 3 2 0 1 1 - 1 2 2 0 1 2 - 1 3 2 0 1 1 - 1 2 2 0 1 2 - 1 3 2 0 1 1 - 1 2 2 0 1 2 - 1 3

P la n t H e ig h t ( c m ) E f f e c t iv e t i l le r s / m 2 N u m b e r o f g r a in s /p a n ic le

G r a in y ie ld ( q h a )

F i g 1 . G r o w t h a n d y i e l d a t t r i b u t e s o f r i c e a s i n f l u e n c e d b y n u t r i e n t s o u r c e s a n d c r o p p i n g s y s t e m

C S 1 C S 2 N 1 N 2 N 3 N 4 N 5

157

mineral compounds and thus increases the availabilityof nutrients in soil.

The available nutrients in soil had significanteffect of cropping systems. This was due to nodulationof legume crops which fixes atmospheric N and Ncontent in soil increases. As there is synergistic relationof N with P, K and S, this helps in increasing the nutrientcontent in soil irrespective of nutrient managementpractices. Inclusion of pulses in cropping system isbeneficial, as these improve the soil fertility and cropproductivity. The benefit of including legumes incropping cycle which improves soil fertility status wasreported by Das et al. (2010). Similarly, Varalakshmi etal. (2005) reported that the legume cropping helped toincrease the available N, P2O5 and K2O content of thesoil. The available N, P2O5, and K2O were significantlyhigher in legume based cropping systems during boththe seasons of the study than non-legume system.

t-us-—-fo-fo- tcyiqj ¼e iz½ ds vuqla/kku iz{ks= dh cyqbZ nkseVfeÍh esa /kku ij fuHkZj Qly iz.kkyh ls vf/kd mRiknu izkIr djusds fy, tSfod [kknks ¼xkscj dh [kkn] dspq¡, dh [kkn] uhe [kyh]iapeO;] ,oa ch-Mh- & 501½ ds }kjk feÍh dh moZjrk cuk;s j[kus,oa feÍh dh HkkSfrd] jklk;fud ,oa tSfod n'kk dks lq/kkjus dsmn~ns'; ls lu~ 2011&12 ,oa 2012&13 ls yxkrkj iz;ksx fd;kx;k A ukbVªkstu] QkLQksjl ,oa LQqj dh miyC/krk /kku&cjlheQly iz.kkyh esa nksuksa o"kksZ esa lcls vf/kd jgh A 1/3 dspq¡vk [kkn +1/3 uhe [kyh iapxO; ds iz;ksx ls u=tu] QkLQksjl ,oa LQqj fdmiyC/krk c<+us ds lkFk&lkFk ikS/kks dh yckbZ] dalksa dh la[;k] nksuksdh la[;k ,oa mit esa nksuksa o"kZ o`f) ikbZ xbZ A

References

Chavan DA, Deshmukh MS, More SS, Narkhede W N (2007)Impact of longterm fertiliser application on yield andnutrient availability in sorghum- wheat croppingsystem. Paper presented at the state level seminaron soil health enhancement for food andenvironmental security, Parbhani 12-13 October

Das A, Patel DP, Munda GC, Ghosh DK (2010) Effect oforganic and inorganic sources of nutrients on yield,nutrient uptake and soil fertility of maize (Zea mays)-mustard (Brassica campestris) cropping systems.Indian J Agric Sci 80(1): 85-88

Singh AB, Saha J K and Gosh PK (2008) Effect of nutrientmanagement practices on soybean (Glycine max)-chickpea (Cicer arietinum) cropping systems forimproving seed yield, quality and soil biological healthunder rainfed condition. Indian J Agric Sci 78(6) :485-489

Singh AP, Mitra BN, Tripathi RS (1999) Influence of soilenrichment with organic and chemical sources ofnutrients on rice (Oryza sativa)-potato (Solanumtuberosum) cropping system. Indian J Agric Sci 69(5) : 376-378

Singh YV, Singh KK, Shiva D (2011) Productivity of wheatand soil organic carbon status under integrated useof organic and inorganic fertilizers. Ann Agric ResNew Series 32 (1&2): 6-12

Srinivasa Rao CH, Subba Rao A, Takkar PN (1997) Potassiumavailability and release behaviour in earthworm castsand non injected soils. J Indian Soc Soil Sci 45 :310-314

Swaminathan C (2005) Food production throughvrkshayurvedic way. In: Technol for Natural Farming.Eds. Agriculture College & Research Institute,Madurai, Tamilnadu, India pp:18-22

Varalakshmi LR, Srinivasamurthy CA, Bhasakar S (2005)Effect of integrated use of organic manures andinorganic fertilizers on organic carbon, available N,P and K in sustaining productivity of groundnut-Fingermillet cropping system. J Indian Soc Soil Sci 53 (8):315-318

(Manuscript Receivd : 30.1.13; Accepted : 20.6.13)

158

Abstract

Glycinebetaine is a compatible solute that accumulates incertain cyanobacteria, algae, plants and microorganisms inresponse to various types of stress is in comparative studiesof wild-type and NaClr mutant strain of Synechococcus spp.has been found accumulate, amino acid glycinebetaine under400 mM NaCl. In present study we isolate mutant strainSynechococcus spp. medium containing 400 mM NaCl,survival of NaClr mutant strain and therefore this concentrationof NaCl had been chosen and employed in these experiments.Glycinebetaine and several compatible solutes protectcyanobacteria but 400 mM NaCl concentration enhancedaccumulation of Glycinebetaine in wild type strain. NaCl r

mutant strain under salt stress condition glycinebetaine washigher as comparer to wild type.

Keywords: Cyanobacteria, Amino acid, Glycinebetaine,Wild-type, NaClr Mutant, HPLC

Cyanobacteria are oxygenic photosynthetic prokaryotesand important biomass producers which are widespreadin diverse environments including freshwater, oceanicand terrestrial habitats (Eiji Suzuki et al. 2010). Salinityis a serious agro-economical problem which leads tometabolic alterations and graded reduction in the plantgrowth in terms of all the growth parameters. Microalgaeare rich sources of proteins, carbohydrates and fattyacids. Unicellular eukaryotic microalgae have specialimportance due to the simplicity of their structuresshowing all metabolic activities and having similaritieswith higher plants. Microalgae differ in their adaptabilityto salinity and other stress conditions.

Living organisms are frequently exposed tovarious kinds of environmental stress in their naturalhabitats and they have developed mechanisms thatallow them to withstand such stress. One such

Compatible solute glycinebetaine defends cyanobacteriumSynechococcus spp. under high salinity condition

S.S. Yadav, V. S. Chauhan and Bhanumati SinghDepartment of BiotechnologyJ.C. Bose Institute of Life ScienceBundelkhand UniversityJhansi 284001(UP) India

mechanism involves the accumulation of compatiblesolutes, which are defined as water-soluble organiccompounds of low molecular mass that are nontoxic athigh concentrations (Chen and Murata 2002). Thecompatible solutes, also called osmolytes, includesugars, amino acids and their derivatives, polyols,betaine and ectoines (Surasak Laloknam et al. 2010).Glycinebetaine is a major compatible solute; it is anamphoteric compound and extremely soluble in water.The molecular features of betaine allow it to interactwith both the hydrophobic and the hydrophilic domainsof macromolecules. In previous invitro studies it wasindicated that betaine stabilizes the structure and activityof enzymes and maintains the integrity of membranesagainst the damaging effects of excessive salt, cold,heat, and freezing (Gorham 1995). Betaine is found ina wide variety of prokaryotes, eukaryoticmicroorganisms, higher plants, and animals (Wilken etal. 1970; Galinski and Trüper 1982; Yancey et al. 1982;Mohammad et al. 1983; Hanson et al. 1985). Severalworker studies on salt stress protein involved in highsalt concentration in cyanobacteria which help theorganism in protecting them against the deleteriouseffect of the stress condition (Fernades et al. 1993; Sotoet al. 1999).

Materials and methods

Organism and culture conditions

Cyanobacteria Synechococcus spp. obtained from Bluegreen algae Department IARI New Delhi and its NaCltolerant mutant were grown in BG-11 medium by main-taining the culture at 26 ± 1°C with a photosyntheticphoton flux density of approximately 50 mol m-2 sec-1

and a light regime of 18 h light/6 h dark.

JNKVV Res J 47(2): 158-161 (2013)

159

Extraction and purification of amino acids

Amino acids were extracted from the cultures of wild-type Synechococcus spp. and its NaClr mutant strainfollowing the method of (Singh and Bisen 1994) grownat 26±1oC, at a photon fluence rate of 50 µmol m-2s-1

with intermittent light and dark period (18/6 h) in BG-11medium in the presence and absence of 400 mM NaCl.Cells of both the strains were harvested bycentrifugation. A simple treatment of all pellets with 70%ethanol (v/v) followed by incubation for 6 h at 4oC indark resulted in nearly complete extraction of aminoacids. Filtrate was then subjected to rotary evaporationat 35oC by using a rotary vacuum evaporator. Theresidue was dissolved in 2 ml of 0.2 M lithium citratebuffer (pH 2.2) followed by filtrations through syringefilter of size 0.4 µm (Millipore, USA). The amino acidconcentrations in the samples were determined by thefluorescence of its O-phthaldehyde derivative (Rajendra1987) by using high Performance Liquid Chromatograph(LC-10A, Shimadzu) using linear gradient of methanoland distilled water.

Results and discussions

Cells were grown in BG11 containing 400 mM NaCl(wild-type) and (NaClr mutant strain) for 7 days andamino acid contents were determined. The results(Figure 1.C and 2.D) suggested that the protective effectof Glycinebetaine activity might have been due toenhancement of under salt stress condition. The resultsshowed that on amino acid analysis wild-type (with andwithout NaCl Fig. 2. B, D) and NaClr mutant strain. NaClrmutant strain accumulated very high level of glycinebetaine but slightly increased (Chauhan et al. 1999a).(Fig 2.D and 1.C) under NaCl stress as compared to itswild-type counterpart because organism moreresistance against salt stress compare to wild-typewhereas wild-type with NaCl glycinebetaine quicklyincreased but cell growth rate slow as compare to mutantstrain and same results found in arginine (Fig. 1.C).(1.C and 2.D) Glutamine slightly increased at stresscondition to compare with the normal condition.Aspartate, proline and glutamate were induced toincrease about two-fold. Glycine betaine is synthesizedby a two-step oxidation of choline via betaine aldehyde.Choline has a vital role as the precursor forphosphatidylcholine, a domi-nant constituent ofmembrane phospholipids in eukaryotes. Despite this,a large proportion of free choline is diverted toglycinebetaine in cyanobacteria Bala (Rathinasabapathiet al. 2000). Several genes have been found to

contribute to osmotic stress expressed duringtranscription. Probably these genes code for synthesisof compatible solutes as well as salinity stress proteinsthat proteins produced in NaClr mutant strain. Theseproteins help the organism in protecting them againstthe deleterious effect of the stress condition (Fernadeset al. 1993; Soto et al. 1999).

XykblhufoVkbu ,d la/kr ?kqyk gqvk inkFkZ gSA tks dqN uhy gfjr'kSoky ] 'kSokyksa] ikS/kksa rFkk lw{e thoksa esa fofHkUu izdkj ds vtSfodruko mRiUu gksus ij fØ;k'khy gksrk gS bl v/;;u esa †00 fe- eks-ued lknzrk okys ek/;e esa oU;tho rFkk mRifjofrZr uLy esaXykblhufoVkbu dh fØ;k'khyrk dk rqyukRed v/;;u fd;kA buiz;ksxksa ls irk pyrk gS fd †00 fe- eks- ued lknzrk okys ek/;eesa Lusdksdkssel tkfr dh mRifjofrZr uLy vklkuh ls thfor jgrh gSAvr,o bl ued lknzrk okys ek/;e dks fuEu iz;ksxksa ds fy, pqukx;k A v/;;u esa geus ik;k dh XykblhufoVkbu vkSj vU; la?kr?kqyu'khy inkFkZ uhy gfjr 'kSoky dh mPp ued lknzrk okys ek/;eesa j{kk djrh gSA ysfdu †00 fe- eks- ued lknzrk okys ek/;e esaXykblhufoVkbu dh fØ;k'khyrk oU; tho esa 'kh?kzrk ls cMrh gSA v/;;u n'kkZrk gS fd oU; tkfr dh rqyuk esa mRifjofrZr uLy esaXykblhufoVkbu dh fØ;k'khyrk T;knk rFkk yEcs le; rd gksrh gSAv/;;u lq>ko nsrk gSA fd mRifjofrZr uLy esa dqN ,sls izksVhulmRiUu gksrs gS tks bl gkfudkjd izHkko ls mRifjofrZr uLy dh j{kkdjrs gS rFkk mudh o`f) esa lgk;d gksrs gSA

References

Bala Rathinasabapathi (2000) Metabolic Engineering forStress Tolerance: Installing OsmoprotectantSynthesis Pathways. Annals of Botany 86:709-716

Chauhan VS, Singh S, Pandey PK, Bisen PS (1999a) Isolationand partial characterization of NaCl-tolerant mutantstrain of Anabaena variabilis with impairedgluatamine synthetase activity. J. Basic Microbiol.39:219-226

Chen TH, Murata N (2002) Enhancement of tolerance ofabiotic stress by metabolic engineering of betainesand other compatible solutes. Curr Opin Plant Biol5:250-257

Eiji Suzuki, Hajime Ohkawa, Katsuya Moriya, TatsuyaMatsubara, Yukari Nagaike, Ikuko Iwasaki, ShokoFujiwara, Mikio Tsuzuki, Yasunori Nakamura (2010)Carbohydrate metabolism in the mutants of thecyanobacterium Synechococcus elongatus PCC7942 defective in glycogen synthesis. Appl. Environ.Microbiol

Fernades TA, Iyer, V, Apte SK (1993) Differential responses

160

161

of nitrogen-fixing cyanobacteria to salinity andosmotic stress. Appl Environ Microbial 59:899-904

Galinski EA, Trüper HG (1982) Betaine, a compatible solutein the extremely halophilic phototrophic bacteriumEctothiorhodospira halochloris. FEMS Microbiol Lett13:357-360

Gorham J (1995) Betaines in higher plants: biosynthesis androle in stress metabolism. In RM Wallsgrove, ed,Amino Acids and Their Derivatives in Higher Plants.Cambridge University Press, Cambridge: 171-203

Hanson AD, May AM, Grumet R, Bode J, Jamieson GC,Rhodes D (1985) Betaine synthesis in chenopods:localization in chloroplasts. Proc Natl Acad Sci USA82:3678-3682

Mohammad FAA, Reed RH, Stewart WDP (1983) Thehalophilic cyanobacterium Synechocystis DUN52and its osmotic responses. FEMS Microbiol Lett16:287-290

Rajendra, W (1987) High performance liquid chromatographicdetermination of amino acid in biological samplesby precolumn derivatization with o-phthaldehyde. J.Chromatgr. 10:941-955

Singh S, Bisen PS (1994) Role of glutamine synthetase activityin the urea inhibition of heterocyst and nitrogenaseformation in the cyanobacterium Anabaenacycadeae. J. Basic Microbiol. 34:191-195

Soto A, Allona I, Collada C, Gueuara M-A, Casado R, CerezoER, Aragoncillo C, Gomez L (1999) Heterologousexpression of a plant small heat-shock proteinenhances Escherichia coli viability under heat andcold stress. Plant Physiol. 120:521-528

Surasak Laloknam, Aporn Bualuang, Bongkoj Boonburapong,Vandna Rai, Teruhiro Takabe, Aran Incharoensakdi(2010) Salt stress induced glycine-betaineaccumulation with amino and fatty acid changes incyanobacterium Aphanothece halophytica. As JFood Ag-Ind 3 (01):25-34

Wilken DR, McMacken MR, Rodrquez A (1970) Choline andbetaine aldehyde oxidation by rat liver mitochondria.Biochim Biophys Acta 216:305-317

Yancey PH, Clark ME, Hand SC, Bowlus RD, Somero GN(1982) Living with water stress: evolution of osmolytesystems. Science 217:1214-122

(Manuscript Receivd : 15.8.12; Accepted : 11.8.13)

162

Abstract

Present study was conducted to evaluate the availability ofmedicinal plant at central region of Madhya Pradesh atJabalpur. The location of Jabalpur is 23`10 North latitude and79`59 East longitude. Jabalpur is situated on Varanasi-NagpurNH-7. Netted in the 'Mahakaushal' region in the central partof India. The present record data of medicinal plant at sixward of Jabalpur (MP) is showing medicinal uses of traditionalplant for various purposes in local area of Jabalpur. Theidentification of available Angiospermic plants provideinformation about therapeutic uses of 40 plant species duringthe survey period and providing information about floristicdiversity of Jabalpur city.

Keywords: Floristic diversity, Medicinal plants andJabalpur.

Madhya Pradesh is one of the largest states of India ingeographical area. The State exhibits unique charac-teristics and pattern of distribution of the flora. The den-sity of population is low. About 40% of the total geo-graphical area is covered under forest. The prevalencenumber of catchments areas adds to the diversity offorest variability and floristic composition (Oommachan1977). Jabalpur located at is 23º10' North latitude and79º59' East longitude and at an altitude of 411metres.Among 4, 20,000 flowering plants reported from world(Govaerts, 2001) more than 50,000 plants were foundto be of great medicinal value. Significance of 25indigenous plants having medicinal value from tribalregion of Jabalpur has been discussion (Verma &Dahake 2010, Verma and Awasthi 2011).

Investigations on floristic diversity of two zones Adhartaland Lalmati of Jabalpur, Madhya Pradesh

Karuna S. Verma, Ashutosh Tiwari, Aparna Awasthi and Suresh Prasad CharmkarAeroallergens, Immunology and Angiosperm's Diversity LaboratoryDepartment of Post Graduated Studies and Research in Biological ScienceRani Durgawati UniversityJabalpur 482001 (MP)

Material and methods

Survey and Sampling Sites: Two Zone of Adhartal (7)and Lalmati (9) of Jabalpur city were chosen. Thefollowing locations:

Zone (1) Adhartal (1) Shaheed Abdul Hameed Ward,(2) Subhash Chandra Bose Ward, (3) Maharshi MaheshYogi Ward ,(4) Nirmal Chand Jain Ward and (5) DeewanAdhar Singh Ward.

Zone (2) LalMati (1) Aacharya Vinoda Bhave Ward, (2)Pd. Dwarka Prashad Mishra Ward (3), Seth Govind DasWard and (4) Sidha Baba Ward.

Identification of plant specimen

The collected indigenous plant sample which wereidentified as per the description with the Flora of India,Hooker (1872-1897), Flora of Bhopal Oommachan(1977), Flora of Jabalpur (Oommachan and Shrivastava1996). Ethnomedicinal and ethnobotanical uses ofplants were confirmed by using India Medicinal plants(Kirtikar and Basu 1975) and Dictionary of India FolkMedicine and Ethnobotany (Jain 1991).

Preservatrion of plant sample and preparation ofherbarium

Collected plant species were pressed and dried bychanging the blotter paper every day till 6-10 days untilloss of total moister. Herbarium specimen was preparedby using the guide line suggested by Santapau (1961)and Jain & Rao (1976). The identified species wereconfirmed by compare and consulting the referencespecimen at the Herbaria of Biological Science

JNKVV Res J 47(2): 162-164 (2013)

163

Table 1. Medicinal plant parts used to cure different human disease

S.N Botanical Name Family Parts Used R Rh H U S D St

1 Ricinus communis L Euphorbiaceae Leaf, Fruits + +2 Clitoria biflora (Ddlz.). Paplionaceae. Root, leaf, Flower, Seed + +3 Aegle marelos Linn. Rutaceae Leaf, fruits + + +4 Pongamia pinnata (L.)Pierre. Fabaceae. Bark, fruits +5 Cassia fistula (Linn.) Caesalpiniaceae Root, Bark + + + +6 Sesamum indicum L. Pedaliaceae - + +7 Eucalyptus obliqua L Her Myrtaceae Leaf +8 Piper betle L. Piperaceae Leaf + + +9 Desmostachya bipinnata (L.) stepf. Poaceae Leaf + +10 Calliandra haematocephala Linn Fabaceae Bark, leaf +11 Canna indica L. Canaceae Leaf ,flowers + +12 Gomphrena globosa L. Amaranthaceae Flowers + + + +13 Polygonum glabrum Wild L. Polygonaceae Leaf, flowers + +14 Mimosa pudica Linn. Mimosaceae Roots, stem + + +15 Indigofera purpurea Steud. Fabaceae Leaf, flowers + +16 Hibiscus rosa-sinensis Linn. Malvaceae Leaf, flowers +17 Polyalthia longifolia Sonn. Annonaceae Stem, leaf, flowers +18 Heliotropium indicum L. Boraginaceae Leaf, flowers + +19 Antigonon leptopus Hook. & Arn. Polygonaceae Stem +20 Tabermontana orientalis R.Br. Apocyanaceae Root, Bark, flowers21 Coccinia grandis Linn. Cucurbitaceae Root, leaf, fruits + +22 Ficus religiosa L. Moracae Roots, Bark, Leaf, fruits + +23 Tridax procumbens L. Asteracae Root, stem, leaf +24 Bauhinia variegate L. Caesalpiniaceae Root, bark, leaf, fruits + + +25 Argyreia nervosa Burm.f. Convolvulaceae Leaf, fruits + + +26 Euphorbia hirta Linn. Euphorbiaceae Root, leaf + + +27 Delonix regia Boj.Rafin. Ceasalpinaceae Bark, leaf + +28 Cuscuta europaea L. Convolvulaceae Stem + +29 Calotropis procera R.Br. Asclepiadaceae Bark, leaf ,flower + + +30 Phyllanthus emblica Linn. Euphorbiacae Root, stem. leaf, fruits + +31 Solanum indicum Linn. Solanaceae Leaf, flowers + + +32 Abutilon indicum (L.)Sweet. Malvaceae Leaf, fruits + + +33 Psidium guajava L. Myrtaceae Leaf, fruits + + +34 Andrographis paniculata Linn. Acanthaceae Leaf, fruits + +35 Sapindus Mukorossi Gaerth. Fruct. Sapindaceae Leaf, fruits +36 Acacia arabica (L.) benth Mimosaceae Leaf, fruits + + +37 Justicia adhatoda L. Acanthaceae Leaf, fruits + + +38 Gymnema sylvestre R.Br Asclepiadaceae Leaf, flowers + + + +39 Nerium oleander L. Apocynaceae Leaf, flowers +40 Azadirachta indica Juss. Meliaceae Bark, leaf, fruits + +

Total 13 5 7 12 27 4 18

R = Respiratory, Rh = Rheumatism, H = Hearts, U = Urinary, S = Skin, D = Diabetes & St = Stomach

164

Department Rani Durgawati University, Jabalpur andpreserved materials (Verma & Dahake, 2005).

Result and discussion

The results provide information about therapeutic usesof 40 plant species during the survey period andproviding information about floristic diversity of Jabalpurcity. The total number of species prevalent during surveyof two Zones Adhartal and Lalmati of 9 wards of Jabalpurcity is 40 species that belonged to 26 plant families.The information reveals the use of various parts of plantto cure several serious disease out of 40 plants speciesstudied during survey in case of 11 plant species rootpowder or extract is used to cure the disease , in 4plant species stem powder or extract is used to curethe disease and in15 plant species bark was used tocure the diseases, Leaves were used to treat diseasein case of 33 plant species, flower were used to treatdisease in case of 14 plant species and seeds wereused to treat disease in case of 3 plant species andfruit were used to treat disease in case of 14 plantspecies (Table 1).

The medicinal plants used to cure differenthuman diseases out of 40 plants species studied duringsurvey in case of 13 plants species were used to curerespiratory diseases, 5 plants species were used to curerheumatism diseases, 7 plants species were used tocure hearts diseases, 12 plants species were used tocure urinary diseases, 27 plants species were used tocure skin diseases, 4 plants species were used to curediabetes diseases and 18 plants species were used tocure stomach diseases.

Discussion

The medicinally important plant collected from theJabalpur used to treat various diseases like cold fever,cough, diarrhea, dysentery, skin diseases, laxative,diabetes and jaundice. This is constant with the othergeneral observation which reported earlier. In relationto medicinal plant studies by the Indian traditionalsystems of medicine like sidha and Ayurvedha(Anonymous 1992). The information on medicinal valuesof plants was acquired through personal interview ofVaidhya, Hakeem and by local healers. The knowledgeof these not only whole plant traditional healers whichrevealed that but each part of the plant like seed, barkand root and flower etc. play significant role in curing tocure different disease like skin disease, eye disease,respiratory disease, digestive and other disease ( Vermaand Dahake 2010).

Conclusion

Various parts such as roots, stem, barks, leaf, flowers,fruits and seeds were reported to be useful in curingseveral devastating human disorder and diseases. Theethnic values of the plants are very important inproviding valuable information to the local healers. Thestudy revealed that the local people of Jabalpur citystill continue to depend on medicinal plants at least fortreatment of primary health problems. Therefore thetraditional knowledge used by local healer's workstreasured.

Acknowledgement

The authors are thankful to the local and tribal peopleof Jabalpur district (MP) for providing information aboutthe various uses of plant species. We are also thankfulto the Dean and Head Faculty of Life ScienceDepartment of Biological Science for providing facilitiesand guidance time to time during the tenure of work.

References

Anonymous (1992) Wealth of India: Raw Materials, Councilof Scientific and Industrial Research Publication, NewDelhi (Revised) 3: 591-593

Govaerts R (2001) How many species of seed plants arethere? Taxon 50:1085-1090

Hooker J D (1872-1897) The flora of British India. 1 (7) Asketch of the flora of British India. In the imperialGazette London

Jain S K (1991) Dictionary of Indian folk Medicinal and Ethnobotany, Deep Publication New Delhi 311

Kirtikar K R, Basu B D (1975) Indian Medicinal Plants. 4.International Book Distributors, Fefradun, India p2793

Oommachan M (1977) The Flora of Bhopal (Angiosperms)J.K. Jain Brothers, Motia Park, Bhopal 1-474

Oommachan M ,Shrivastava JL (1996) Flora of Jabalpur, Sci.Pub. Jodhpur 1 - 354

Santapau (1961) Critical notes on the identify andnomenclature of some Indian plants. Bull Bot SurveyIndian3:11-21

Verma K S, Dahake D (2010) An Introduction of tribal rootmedicines of Jabalpur District (M.P.) Int J BiosciReport 8(1): 51-55

Verma K S, Dahake Deepa (2005) Taxonomic study of theunisexual of Jabalpur (MP). J Bot Soc Univ Sagar40:51-62

Verma KS, Awasthi A (2011) World Biomes. Jigyasa 5(1):100-104

Verma KS, Dahake D (2011) Diversity of exotic weeds in theflora of Jabalpur. Biozone International journal of lifescience 3 (1-2): 478-785

(Manuscript Receivd : 20.8.13; Accepted : 11.9.13)

165

Abstract

A field investigation was conducted at AICRP on ForageCrops, Department of Agronomy, Jawaharlal Nehru KrishiVishwa Vidyalaya Jabalpur (MP) during Kharif 2012 with theobjective of to determine the relative performance of new pearlmillet genotypes in combination of nitrogen levels . Thetreatments consists of four new pearl millet genotypes (RBB-1, PAC-981, Raj Bajra Chari (NC) and Giant Bajra (NC)) andfour nitrogen levels (0, 30 ,60 and 90) under factorialrandomized block design replicated thrice .The variety RBB-1with 90 kg N/ ha proved significantly superior in producingmaximum green fodder yield (348.0 q/ha), dry matter yield(85.08 q/ha) and crude protein yield (6.59 q/ha) and maximummonetary advantage (Rs 69851) and proved mostremunerative with benefit: cost ratio of 4.87.

Keywords: Pearl millet, nitrogen levels, varieties andeconomics

In India, the requirement of green fodder was 611.99Mt against the availability of only 224.08 Mt (Anonymous2006). Similar pattern has been observed in dry fodder,green fodder and concentrate i.e. 32.4%, 46.5% and69.3% as against the requirement of 62.6, 313.0 and14.3 million tonnes for current livestock population inMadhya Prdesh. Because of increasing pressure ofhuman population on the country priority is given to graincrops and preferential use of low-capability land forforages and multiplication ratio is very narrow in mostof fodders. The efficiency of milch as well as droughtanimals largely depends upon the supply of quantityand quality of ration in which green fodder plays a vitalrole. In the recent years shortage of fodder has remained

Effect of nitrogen levels on fodder yield and qualityof pearl millet genotypes under irrigatedcondition of Madhya Pradesh

A.K. Jha, Arti Shrivastava, N.S. Raghuvanshi and A. K. Singh*Department of AgronomyCollege of AgricultureJawaharlal Nehru Krishi Vishwa Vidyalaya*College of Veterinary Science and Animal HusbandryNanaji Deshmukh Pashu-Chikitsa Vigyan Vishwa VidyalayaJabalpur 482004 (MP)Email : amitagcrewa@rediffmail.com

the burning problem, which calls for the efforts that canensure regular fodder supply for development of thecattle wealth. A vast varietals diversity of pearlmilletenables its cultivation over wide range of pearlmillethave been evolved which have high yield potential aregrown for producing green fodder as well as seed.These varieties are highly responsive to higher dosesof fertilizers. Hence, the necessity for selection ofsuitable varieties and the nitrogen requirements forhigher green fodder.

Material and methods

A field experiment was conducted under AICRP onForage Crops, Department of Agronomy, JawaharlalNehru Krishi Vishwa Vidyalaya, Jabalpur (M.P.) duringKharif season of 2012. The soil of experimental fieldwas clay loam in texture, neutral (7.2) in reaction withlow organic carbon (0.44%) and normal electricalconductivity (0.34 dS/m) and analyzing low in availableN (228 kg/ha), medium in available P (16.2 kg/ha) andavailable K (297 kg/ha) contents. Treatments were laidout ina factorial randomized block design with threereplications, keeping four levels of each of N (0, 30, 60and 900 kg/ha) and varieties (RBB 1, PAC 981, RajBajra Chari (NC) and Giant Bajra (NC).Sowing was doneon July05, 2012 by using 15 kg/ha of each variety inrows 30 cm apart with uniform dose 40 kg P2O5 + 20 kgK2O /ha .Nitrogen was applied as per treatments. Atthe harvest, green fodder yield and growth parameters,viz. plant height, tiller number, leaf area index and leaf-stem ratio were recorded. The crude - protein yield wascalculated by a factor of 6.25 formula suggested by

JNKVV Res J 47(2): 165-168 (2013)

166

Tabl

e1.E

ffect

of n

itrog

en le

vels

on

yiel

d at

tribu

tes

and

yiel

ds o

f pea

rl m

illet

Trea

tmen

tsPl

ant h

eigh

tN

o. o

f tille

rsG

reen

fodd

erD

ry fo

dder

Cru

de p

rote

inLe

af :s

tem

LAI a

tN

MR

B: C

ratio

(cm

)/ m

row

leng

thyi

eld

(q/h

a)yi

eld

(q/h

a)yi

eld

(q/h

a)ra

tio60

DAS

(Rs/

ha)

Nitr

ogen

dos

e (k

g/ha

)0

111.

8217

.218

6.24

43.1

083.

114

0.30

4.3

4632

53.

1130

123.

825

.623

0.52

55.0

184.

096

0.37

6.3

5143

23.

8960

138.

6829

.23

282.

5669

.256

5.32

0.49

8.9

6254

14.

1290

146.

0230

.56

309.

276

.67

5.97

40.

569.

368

563

4.98

SEm

± (N

)3.

211.

028.

232.

410.

060.

010.

61-

-C

D (P

=0.0

5)9.

353.

2124

.36

6.10

0.18

0.03

1.18

--

Var

ietie

sR

BB-1

174.

928

.63

348.

085

.08

6.59

0.59

9.5

6985

14.

87PA

C-9

8115

2.6

24.5

628

4.1

68.1

15.

090.

486.

563

254

3.85

Raj

Baj

ra C

hari

(NC

)16

3.3

27.5

632

3.9

78.4

55.

970.

568.

568

963

4.66

Gia

nt B

ajra

(NC

)15

9.5

25.6

130

4.6

73.4

25.

540.

517.

264

756

4.21

SEm

± (V

)2.

542.

2411

.36

2.82

0.41

0.02

0.53

--

CD

(P=0

.05)

6.24

5.63

33.6

06.

231.

100.

061.

16-

-SE

m±

(V x

N)

2.58

1.56

9.41

3.65

0.12

0.02

0.63

--

CD

(P=0

.05)

6.51

4.52

27.3

69.

540.

690.

061.

85-

-C

V3.

256.

6310

.12

8.24

3.21

2.10

6.3

--

Mehrez and Zraslox (1977). The dry matter yield wasrecorded.

Result and discussion

Effect on growth pattern and yield attributes

The plant height, number of tillers per plant, leaf areaindex, and dry matter accumulation by plant had directlycorrelated with the green fodder yield. Plant height,number of tillers per m row length, leaf area index, anddry matter accumulation at different growth stages wassignificantly higher in RBB 1 than other varieties (Table1). This was attributed inherent genetic character ofvariety. The finding are in concurrence with report ofAICRP-Forage Crops (2012). Application of nitrogen60kg/ha which was statistically at par with the 90 kg N/ha significantly increased plant height(146.02 cm),number of tillers per plant (30.56), leaf area index(9.3)and dry matter accumulation (76.67 q/ha) at differentgrowth stages over control 0 and 30 kg N/ha. As aprincipal, a soil deficient in plant nutrient improves cropgrowth when supplied with a particular nutrient inquestion. These results are in conformity with those ofBabu et al.(1995). RBB 1 also resulted significantlyhigher number of tillers per m row length than othervarieties. In case of nitrogen @ 60 kg/ha which was atpar with 90 kg N/ha, significantly increased number oftillers per plant over control 0 and 30 kg N/ha (Table 1).

The varieties RBB 1 attained maximum plantheight (174.9) ,number of tillers (28.63) and LAI (9.5)than other genotypes. The quality of fodder isdetermined by leaf -stem ratio and it was almostcomparable among all the varieties, however varietyRBB 1 was numerically superior (0.59) to others(0.56,0.51 and 0.48, respectively) with regard to leaf-stem ratio.

Effect on yield

Green and dry fodder yield were significantly higher inRBB 1 than PAC 981, Raj Bajra Chari (NC) and GiantBajra (NC). Application of nitrogen 90kg /ha significantlyincreased green (309.2 q/ha)and dry fodder yields(76.67 q/ha) of pearl millet over control, 0 and 30kg N/ha which was at par with 60 kg N/ha. The fodder yield,being a function of the cumulative effect of growthparameter such as plant height, number of tillers per mrow length plant which were higher with 90 kg N/ha,resulting in higher forage yield under this treatment.The increases in fodder yield with increasing level of

167

nitrogen could also be explained by better nutritionalcondition of the crop as supported by higher nitrogenuptake when fertilized with 90 kg N/ha. Greater uptakeof nutrients by application of nitrogen probably favoredbetter growth and development of crops, resulting intoincreased fodder yield. These finding corroborate theresults of Hooda et al. (2004) who recorded a significantincrease in fodder yield with increased level of nitrogen.

RBB 1not only produced maximum yield of pearlmillet fodder but also good quality of fodder than othervarieties. Crude protein yield (%) in dry fodder of varietyRBB 1 was significantly higher (6.59) than other varieties(5.97, 5.54 and 048, respectively). This might be dueto that higher nitrogen content in this variety. Nitrogenis an integral part of protein as a result higher crudeprotein found in dry fodder of RBB 1 than other varieties.The similar results were also obtained by AICRP-ForageCrops (2012) that reported significantly higher crudeprotein in variety RBB 1 than rest of varieties. This maybe due to inherent genetic characters of varieties andhigher content of nitrogen which is the major constituentof amino acids and protein and decreased the pectin,cellulose, hemicelluloses and proportion of

Table 2. Green fodder yield (q/ha) influenced by different nitrogen levels

Variety RBB-1 PAC-981 Raj Bajra Chari (NC) Giant Bajra (NC) MeanN levelN0 260.9 204.0 244.3 222.0 186.24N1 309.5 267.8 288.0 287.3 230.52N2 390.0 315.0 365.0 342.8 282.56N3 431.6 349.7 398.3 366.4 309.2Mean 348.0 284.1 323.9 304.6

V N VXNSEm± 11.36 8.23 9.41CD 33.60 24.36 27.36CV 10.12

Table 3. Dry fodder yield (q/ha) influenced by different nitrogen levels

Variety RBB-1 PAC-981 Raj Bajra Chari (NC) Giant Bajra (NC) MeanN levelN0 60.51 46.91 56.63 51.49 43.108N1 74.67 63.29 68.84 68.29 55.018N2 96.46 76.47 89.52 83.83 69.256N3 108.68 85.77 98.82 90.08 76.67Mean 85.08 68.11 78.45 73.42

V N VXNSEm± 2.82 2.41 3.65CD 6.23 6.10 9.54CV 8.24

carbohydrate and hence decreased crude fiber. Thesefindings are in accordance, to the findings of Jakhar etal.(2003), Tiwana and Puri (2005).

Effect on Economics

Genotype RBB 1 (5.03) with respect of B-C ratio beingclose to Raj Bajra Chari (NC) (4.66) than Giant Bajra(NC) and PAC-981resulted into lesser B-C ratio.Application of nitrogen i.e.90 kg N /ha markedly gavemaximum B-C ratio 4.92 than other levels.

tokgjyky usg: Ñf"k fo'ofo|ky; ds vf[ky Hkkjrh; pkjkvuqla/kku ifj;kstuk ds vUrxZr nkseV feVVh okys iz{ks= ij Cktjk dhubZ iztkfr;kss ij u=tu ds fofHkUu Lrjksa dk izHkko ns[kus ds fy, o"kZ2012 ds [kjhQ ekSle esa iz;ksx fd;k x;kA iz;ksx esa cktjs dh pkjtkfr;kWa (RBB-1, PAC-981, Roj Bajara Chaina vkSj GiantBajara Chaina yh xbZ tcfd] fofHkUu u=tu Lrj 0, 30, 60 ,oa90 fd-xzk-@gs- dks QsDVksfj;y jsMksekbtM fMtkbu ds vUrxZr rhu

168

ckj nksgjk;k x;k izkIr ifj.kkeksa ds vk/kkj ij ubZ iztkfr RBB-1dk u=tu Lrj 90 fd-xzk-@gs- ds lkFk vf/kdre gjk pkjk ¼348.0q/ha½] lw[kk pkjk ¼85.08 q/ha½] ØqM izksVhu ¼6.59q/ha½ ,oa vf/kd ykHk ¼Rs.69851½ izkIr gqvkA

References

Anonymous (2006) Agricultural statistical at a glance,Directorate of Economics and statistics, Ministry ofAgriculture and cooperation, Govt. of India New Delhi

AICRP-Forage Crops ( 2012) Advance Varietal trail (combined)in pearl millet. All India Co-ordinated ResearchProject on Forage Crops. IGFRI Jhansi, AnnualReport Kharif-2012 :46-57

Babu R, Gumaste S, Patil TC, Prabhakar AS (1995) Effect ofstage of cutting, nitrogen and phosphorus levels onforage pearl millet [Pennisetum glaucum (L.)]. ForageRes. 20 (4): 225-231

Jakhar S, Sharma HS, Kantwa SR (2003) Effect of nitrogenand sulphur on quality and nutrient content of fodderpearl millet [Pennisetum glaucum (L.)]. Ann AgricNew Series 24 (1): 169:171

Tiwana US, Puri KP (2005) Effect of nitrogen levels on thefodder yield and quality of pearl millet varieties underirrigated conditions. Forage Res 31(2):142-143

Hooda RS , Singh H, Khippl A (2004) Cutting managementand nitrogen effects on green fodder, grain andstover yield and economics of pearl millet cultivationduring summer. Forage Res 30 (3):118-120

Mehrez AZ, Zraskov ER (1977) A study on the artificial fibrebag technique for determining the digestibility ofseeds in the rumen J Agric Sci Cambridge 88:645-650

(Manuscript Receivd : 5.4.13; Accepted : 10.10.13)

169

Abstract

Field investigations were conducted at Krishi Nagar farm,Adhartal Department of Agronomy, JNKVV Jabalpur duringkharif season of 2010 and 2011 to study the "Effect of plantinggeometries on nutrient uptake of improved rice varieties underdepths of planting in system of rice intensification" keeping into maximize the production efficiency and monetary advantagein rice by adopting suitable planting geometry, varieties anddepth of planting. The experiment comprises on 18 treatmentcombinations consisted with three planting geometry, as mainplot treatment and three varieties (MR-219, WGL-32100 andPS-3) as sub plot treatments and two depths of plantingshallow (2.5 cm) and normal (5.0 cm) as sub-sub plottreatments were tested in split-split plot design with threereplications. Results showed that the greater amount ofnitrogen (128.09 and 131.34 kg/ha), (31.02 and 31.73 kg/ha)and potassium (111.68 and 119.80 kg/ha) was absorbed by25 x 25 cm planting geometry as compared to other 20 x 20cm and 30 x 30 cm planting geometries. Significantly greateramount of nitrogen (132.34 and 143.86 kg/ha) phosphorus(30.71 and 32.64 kg/ha) and potassium (109.36 and 116.31kg/ha) was absorbed by MR-219 as compared to othervarieties viz. WGL-32100 and PS-3. The shallow depth ofplanting, crop absorbed significantly more amount of nitrogen(125.09 and 129.53 kg/ha), phosphorus (24.01 and 25.90 kg/ha) and potassium (103.58 and 111.01 kg/ha) as comparedto normal depth of planting.

Keywords: Nutrient uptake, plant spacings, varietiesand depth of planting

Rice is the most important Cereal food crop of thedeveloping world and the staple food of more than 3billion people or more than half of the world's population.One fifth of the world's population more than a billionpeople depend on rice cultivation for their livelihoods.Asia, where about 90% of rice is grown, has more than200 million rice farms. India is considered to be one ofthe original centers of rice cultivation covering 44.6million hectares, producing 132 MT of rice with an

average productivity of 2.96 t ha-1 (Pandian 2009).Around 65% of the total populations in India eat riceand it accounts for 40% of their food production India isthe world's second largest producer of white rice,accounting for 20% of all world rice production. Worldproduction of rice has risen steadily from about 200million tonnes of paddy rice in 1960 to over 678 milliontonnes in 2009. Rice-based production systems providethe main source of income and employment for morethan 50 million households (IRRI, 2008 data).Worldproduction of rice has risen steadily from about 200million tonnes of paddy rice in 1960 to over 678 milliontonnes in 2009, Rice production in India is an importantpart of the national economy.

Among the different agronomic practices, plantinggeometry and depth of planting play a vital role inachieving higher yield levels of improved varieties ofrice. It is because the proper distributions of crop plantper unit area and efficient utilization of available nutrientand other resources as well as environment. Thereforepresent experiment was conducts for study the nutrientuptake influence by planting geometries, varieties anddepths of planting.

Material and methods

The experiment was conducted at research farm ofJawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpurduring kharif season of 2010 and 2011 under edaphicand climatic conditions of Jabalpur (M.P.). The threedifferent planting geometries i.e., 20 x 20 cm2, 25 x 25cm2 and 30 x 30 cm2 between hills and rows were keptfor growing the crop and to identify their effect on grainyield parameters. Three varieties of rice i.e., MR-219,WGL-32100 and PS-3 and two depths of plantingshallow (2.5 cm) and normal (5.0 cm). The layout of thetrial was split-split plot design with three replications

Nutrient uptake influence by planting geometries, improvedvarieties under depths of planting in rice

Archana Rajput, Girish Jha, A. K. Jha and A. TiwariDepartment of AgronomyJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : amitagerewa@rediffmail.com

JNKVV Res J 47(2): 169-174 (2013)

170

having planting geometry as main plots, varieties assub plot treatments and depths of planting shallow (2.5cm) and normal (5.0 cm) as sub-sub plot treatments.The area of each plot was 3 x 7 m2. Seedlings weretransplanted with an average of one seedling per hill inthe SRI method of planting. Application of 10 t FYM/hawas given uniformly to all the plots before final puddlingand leveling. Fertilizer with a uniform dose of 120: 60:40 kg per hectare N, P and K through urea, DAP andMOP was applied in all the plots. Half dose of nitrogenand full dose phosphorus and potassium were appliedas basal application just before transplanting. Theremaining half dose of nitrogen was applied in two splitdoses at tillering and panicle initiation stages. Standardcultural practices were carried out till the crop wasmature. The sample of grains and straw were taken atthe time of harvest of crop components in both the yearsand then they were allowed to dry in an oven till to reachthe constant weight. after this, theses samples weregrinded into fine powder with the help of mortar andpistal. After this, N, P and K contents of these sampleswere analyzed by Kjeldahl's digestion method asdescribed by Piper (1950) for nitrogen, ammoniumvandomolybdo phosphoric acid (Bartous reagents)yellow colour method Koeings and Jackson (1942) forphosphorus and flame photometer method as describedby Black (1965) for potassium. The data was analyzedstatistically as per the procedure prescribed for split-split plot design (Panse and Sukhatme 1995) to obtainedanalysis of variance. The analysis of variance If thevariance ratio (F test ) was found significant at 5% levelof significance then standard error of mean ( SE±) andcritical differences (CD).

Results and discussion

Nutrient uptake in grain

Nitrogen uptake

The nitrogen uptake due to different due to plantinggeometries , varieties and depth of planting of rice grainsshowed significant variations during both the years. The25 cm x 25 cm planting geometry absorbed significantlymore amount of nitrogen by grain (33.02 and 50.64) ascompared to other of planting geometries and varietyMR-219 (V1) absorbed significantly more amount ofnitrogen by grain (37.21 and 52.32 kg/ha) as comparedto other varieties of WGL-32100 and PS -3 during 2010and 2011 both the years. The shallow depth (D1) ofplanting absorbed significantly more amount of nitrogenby grain (31.83 and 49.29 kg/ha) as compared to normal

depth (D2) planting depth during 2010 and 2011 boththe years (Table 1).

Phosphorus uptake

The phosphorus uptake due to different due to plantinggeometries, varieties and depth of planting of rice grainsshowed significant variations during both the years. The25 cm x 25 cm planting geometry absorbed significantlymore amount of phosphorus (13.81 and 12.35 kg/ha)as compared to other of planting geometries during 2010and 2011 both the years. The variety MR-219 (V1)absorbed significantly more amount of phosphorus(12.98 and 13.05 kg/ha) as compared to other varietiesof WGL-32100 and PS -3 during 2010 and 2011 boththe years. The shallow depth (D1) of planting absorbedsignificantly more amount of phosphorus (9.71 and 9.72kg/ha) than normal depth (D2) planting depth during2010 and 2011 both the years (Table 1).

Potassium uptake

The potassium uptake due to different due to plantinggeometries, varieties and depth of planting of rice grainsshowed significant variations during both the years. The25 cm x 25 cm planting geometry absorbed significantlymore amount of potassium (28.63 and 29.39) ascompared to other of planting geometries during 2010and 2011 both the years. The variety MR-219 (V1)absorbed significantly more amount of potassium (27.86and 28.40) as compared to other varieties of WGL-32100 and PS -3 during 2010 and 2011 both the years.The shallow depth (D1) of planting absorbed significantlymore amount of potassium (24.56 and 25.11) thannormal depth (D2) planting depth during 2010 and 2011both the years (Table 1).

Nutrient uptake in straw

Nitrogen uptake

The nitrogen uptake due to different due to plantinggeometries, varieties and depth of planting of rice strawshowed significant variations during both the years. The25 cm x 25 cm planting geometry absorbed significantlymore amount of nitrogen by straw (94.88 and 80.70 kg/ha) as compared to other of planting geometries andvariety MR-219 (V1) absorbed significantly more amountof nitrogen by straw (95.13 and 91.54 kg/ha) ascompared to other varieties of WGL-32100 and PS -3

171

Tabl

e 1.

N, P

, K k

g/ha

in ri

ce g

rain

and

stra

w k

g/ha

as

influ

ence

d by

pla

ntin

g ge

omet

ries,

var

ietie

s an

d de

pths

of p

lant

ing

Trea

tmen

tsIn

gra

inIn

stra

wN

PK

NP

K20

1020

1120

1020

1120

1020

1120

1020

1120

1020

1120

1020

11

Mai

n pl

ot (

Plan

ting

geom

etry

)

S 1 - 2

0 cm

x 2

0 cm

28.6

551

.05

8.25

8.88

26.4

826

.92

88.6

569

.08

16.9

618

.72

76.6

483

.80

S 2 - 2

5 cm

x 2

5 cm

33.2

050

.64

13.8

112

.35

28.6

329

.39

94.8

880

.70

17.2

119

.38

83.0

590

.40

S 3 - 3

0 cm

x 3

0 cm

29.1

741

.98

6.27

6.67

16.0

816

.77

90.3

281

.49

6.72

8.69

75.8

082

.43

SEm

±0.

960.

530.

680.

430.

352.

100.

541.

100.

450.

320.

661.

16

CD

. at 5

%2.

681.

471.

881.

200.

975.

821.

493.

061.

240.

881.

843.

22

Sub

plot

(Va

riety

)

V 1 - M

R-2

1937

.21

52.3

212

.98

13.0

527

.86

28.4

095

.13

91.5

417

.73

19.5

981

.49

87.9

1

V 2 - W

GL-

3210

029

.73

46.9

09.

099.

0824

.04

24.6

892

.28

77.8

513

.91

15.7

778

.17

84.8

0

V 3 - P

S-3

24.0

844

.46

6.25

5.77

19.2

920

.00

86.4

461

.88

9.26

11.4

475

.82

83.9

1

SEm

±0.

100.

290.

500.

170.

311.

120.

370.

790.

160.

380.

761.

11

CD

. at 5

%0.

230.

641.

100.

370.

692.

430.

811.

720.

350.

821.

652.

42

Sub

-sub

plo

t (D

epth

of p

lant

ing)

D1 -

Sha

llow

Dep

ths

(2.5

cm

)31

.83

49.2

99.

719.

7224

.56

25.1

193

.26

80.2

414

.29

16.1

879

.02

85.8

9

D2 -

Nor

mal

Dep

ths

(5 c

m)

28.8

546

.49

9.17

8.88

22.9

023

.61

89.3

173

.95

12.9

715

.02

77.9

785

.19

SEm

±0.

020.

200.

120.

130.

131.

130.

380.

500.

190.

310.

741.

87

CD

. at 5

%0.

050.

420.

260.

270.

262.

380.

801.

050.

400.

651.

563.

93

172

Tabl

e 2.

Tota

l N, P

, K n

utrie

nt u

ptak

e (k

g/ha

) by

rice

unde

r diff

eren

t pla

ntin

g ge

omet

ries,

var

ietie

s an

d de

pths

of p

lant

ing

Trea

tmen

tsTo

tal n

utrie

nt u

ptak

e (k

g/ha

)N

itrog

enPh

osph

orus

Pota

ssiu

m20

1020

1120

1020

1120

1020

11

Mai

n pl

ot (

Plan

ting

geom

etry

)

S 1 - 2

0 cm

x 2

0 cm

117.

2412

0.14

25.2

127

.60

103.

1111

0.72

S 2 - 2

5 cm

x 2

5 cm

128.

0913

1.34

31.0

231

.73

111.

6811

9.80

S 3 - 3

0 cm

x 3

0 cm

119.

4912

3.47

12.9

915

.36

91.8

899

.19

SEm

±1.

011.

120.

710.

570.

811.

19

CD

. at 5

%2.

823.

111.

981.

582.

253.

29

Sub

plot

(Va

riety

)

V 1 - M

R-2

1913

2.34

143.

8630

.71

32.6

410

9.36

116.

31

V 2 - W

GL-

3210

012

1.95

124.

7523

.00

24.8

510

2.21

109.

49

V 3 - P

S-3

110.

5310

6.34

15.5

117

.21

95.1

110

3.91

SEm

±0.

480.

710.

460.

390.

971.

58

CD

. at 5

%1.

051.

541.

000.

852.

123.

43

Sub

-sub

plo

t (D

epth

of p

lant

ing)

D1 -

Sha

llow

Dep

ths

(2.5

cm

)12

5.09

129.

5324

.01

25.9

010

3.58

111.

01

D2 -

Nor

mal

Dep

ths

(5 c

m)

118.

1212

0.43

22.1

423

.90

100.

8710

8.80

SEm

±0.

370.

490.

210.

370.

711.

69

CD

. at 5

%0.

781.

040.

430.

781.

49N

S

173

during 2010 and 2011 both the years. The shallow depth(D1) of planting absorbed significantly more amount ofnitrogen by straw (93.26 and 80.24 kg/ha) as comparedto normal depth (D2) planting depth during 2010 and2011 both the years (Table 1).

Phosphorus uptake

The phosphorus uptake due to different due to plantinggeometries, varieties and depth of planting of rice strawshowed significant variations during both the years. The25 cm x 25 cm planting geometry absorbed significantlymore amount of phosphorus (17.21 and 19.38 kg/ha)as compared to other of planting geometries during 2010and 2011 both the years. The variety MR-219 (V1)absorbed significantly more amount of phosphorus(17.73 and 19.59 kg/ha) as compared to other varietiesof WGL-32100 and PS -3 during 2010 and 2011 boththe years. The shallow depth (D1) of planting absorbedsignificantly more amount of phosphorus (14.29 and16.18 kg/ha) than normal depth (D2) planting depthduring 2010 and 2011 both the years (Table 1).

Potassium uptake

The 25 cm x 25 cm planting geometry absorbedsignificantly more amount of potassium (83.05 and90.40 kg/ha) as compared to other of plantinggeometries during 2010 and 2011 both the years. Thevariety MR-219 (V1) absorbed significantly more amountof potassium (81.49 and 87.91 kg/ha) as compared toother varieties of WGL-32100 and PS -3 during 2010and 2011 both the years. The shallow depth (D1) ofplanting absorbed significantly more amount of

potassium (79.02 and 85.89 kg/ha) than normal depth(D2) planting depth during 2010 and 2011 both the years(Table 1).

Total nutrient uptake by crop

Nitrogen uptake

The total grain and straw nitrogen uptake due to differentdue to planting geometries, varieties and depth ofplanting of rice crop showed significant variations duringboth the years. The 25 cm x 25 cm planting geometryabsorbed significantly more amount of nitrogen by crop(128.09 and 131.34 kg/ha) as compared to other ofplanting geometries and variety MR-219 (V1) absorbedsignificantly more amount of nitrogen by crop (132.34and 143.86 kg/ha) as compared to other varieties ofWGL-32100 and PS -3 during 2010 and 2011 both theyears. The shallow depth (D1) of planting absorbedsignificantly more amount of nitrogen by crop (125.09and 129.53 kg/ha) as compared to normal depth (D2)planting depth during 2010 and 2011 both the years(Table 2 & Fig.1).

Phosphorus uptake

The total grain and straw phosphorus uptake due todifferent due to planting geometries, varieties and depthof planting of rice crop showed significant variationsduring both the years. The 25 cm x 25 cm plantinggeometry absorbed significantly more amount ofphosphorus (31.02 and 31.73 kg/ha) as compared toother of planting geometries and variety MR-219 (V1)

174

absorbed significantly more amount of phosphorus bycrop (30.71 and 32.64 kg/ha) as compared to othervarieties of WGL-32100 and PS -3 during 2010 and2011 both the years. The shallow depth (D1) of plantingabsorbed significantly more nutrients amount ofphosphorus by crop (24.01 and 25.90 kg/ha) ascompared to normal depth (D2) planting depth during2010 and 2011 both the years (Table 2 & Fig.1).

Potassium uptake

The total grain and straw potassium uptake due todifferent due to planting geometries, varieties and depthof planting of rice crop showed significant variationsduring both the years. The 25 cm x 25 cm plantinggeometry absorbed significantly more amount ofpotassium (111.68 and 119.80 kg/ha) as compared toother of planting geometries and variety MR-219 (V1)absorbed significantly more amount of potassium bycrop (109.36 and 116.31 kg/ha) as compared to othervarieties of WGL-32100 and PS -3 during 2010 and2011 both the years. The shallow depth (D1) of plantingabsorbed significantly more amount of potassium bycrop (103.58 and 111.01 kg/ha) as compared to normaldepth (D2) planting depth during 2010 and 2011 boththe years (Table 2 & Fig.1).

Significantly greater amount of nitrogen,phosphorus and potassium was absorbed by 25 x 25cm planting geometry in compared to other 20 x 20 cmand 30 x 30 cm planting geometries. The maximumremoved of N, P and K in 25 x 25 cm planting geometrymay be due to its stronger and proper plant stands forgrowth requirement resulted into higher grain yield incompared to other two 20 x 20 cm and 30 x 30 cmplanting geometries. Significantly greater amount ofnitrogen, phosphorus and potassium was absorbed byMR-219 in compared to other varieties viz., WGL-32100and PS-3 (Table 1&2). The maximum removed of N, Pand K in MR-219 may be due to its physiologically highgrowth requirement resulted into higher grain yield thanthe other two varieties. Thus, the nutrient uptake bythese three varieties in accordance with the nutrientcontent in grain and straw and the grain yield. Similarresults are also reported by Singh et al. (2004). Thedepths of planting cause significant variations in removalof nutrients from the soil. The shallow depth of planting,crop absorbed significantly more amount of nitrogen,phosphorus and potassium in compared to normal depthof planting. This may be due to the readily availabilityof nutrients in lower zone of soil in oxidized conditions.

t-us-—-fo-fo- tcyiqj ¼e iz½ ds cyqbZ nkseV feV~~Vh okys vuqla/kkuiz{ks= esa /kku dh fofHkUu iztkfu;ksa ij Qly T;kfefr ,oe~ ikS/k jksi.kdh xgjkbZ dk iks"kd rRoksa ds vo'kks"k.k ij lu 2010 ,oe~ 2011esa Jh iz.kkyh ds varxZr v/;;u fd;k x;k A /kku dh rhu tkfr¼MR-219, WGL-32100 vkSj PS-3½ Qly T;kfefr ¼20x20ls-eh-] 25x25 ls-eh vkSj 30x30 ls-eh½ vkSj ikS/k jksi.k xgjkbZ¼2.5 ls-eh- vkSj 5 ls-eh-½ dks Split-split IykWV fMtkbu ds varxZrrhu ckj nksgjk;k x;k A izkIr ifj.kkeksa ds vk/kj ij Qly T;kfefr25 x 25 ls-eh- ij ukbVªkstu ¼128.09 vkSj 2131.34 fdxzk-@gs½]QkLQksjl ¼31.02 vkSj 31.73 fdxzk-@gs½ vkSj iksVsf'k;e ¼111.68vkSj 119.80 fdxzk@gs½ nwljh Qly T;kfefr dh rqyuk esa vf/kDrevo'kks"k.k fd;k A MR-219 tkfr 2.5 ls-eh- ls dh xbZ ikS/k jksi.kxgjkbZ ij ukbVªkstu ¼132.34 vkSj 143.86 fdxzk@gs½] QkLQksjl¼109.36 vkSj 116.31 fdxzk@gs½ nwljs tkfr dh rqyuk esa vf/kDrevo'kks"K.k fe;k A 2.5 ls-eh- ls dh xbZ ikS/k jksi.k xgjkbZ ijukbVªkstu¼125.09 vkSj 129.53 fdxzk@gs½] QkLQksjl ¼24.01 vkSj25.90 fdxzk@gs½ vkSj iksVsf'k;e ¼103.58 vkSj 111.01 fdxzk@gs½lseh ls dh xbZ ikS/k jksi.k xgjkbZ rqyuk esa vf/kDre vo'kks"k.k fd;kA

References

Black CA (1965) Method of plant and soil analysis Part II.Pub. American Society of Agronomy Madison,Wisconsin USA pp :1367-1373

Koeings RA, Jackson CR (1942) Calorimetric determinationof Phosphorus in biological material. Indust EnggChem Anal 14: 155-156

Pandian BJ (2009) Cover Story on Stretching Out SRI in Tamil.SRI Newsletter 5: 1-6

Panse VG, Sukhatme PV (1995) Statistical Methods forAgricultural Workers. Indian Council of AgriculturalResearch New Delhi

Piper CS (1950) Soil and plant analysis. University of adelaideAustralia

Singh T, Shivay YS, Singh S (2004) Effect of date oftransplanting and nitrogen on productivity andnitrogen use indices in hybrid and non-hybridaromatic rice. Acta Agronomica Hungarica 52(3):245-252

(Manuscript Receivd : 16.8.13; Accepted : 11.10.13)

175

Abstract

Temperature is fluctuating during crop season and geneticimprovement is a prerequisite for improvement in heattolerance in chickpea. Selection on the basis of grain yield, apolygenically controlled complex character, is usually not veryefficient as such, but selection based on its componentcharacters could be more efficient. Genetic analysis werecarried out using thirty promising lines chosen from ChickpeaImprovement Project and grown in three differentenvironments viz normal, late and very late sown, consideringphenological and morphological traits viz days to flowerinitiation, days to 50% flowering, days to pod initiation days tomaturity, plant height, primary branches, secondary branches,total number of pods per plant, effective pods per plant, seedsper pod, 100-seed weight, biological yield, harvest index andseed yield per plant. These lines were screened for heattolerant which, can withstand in the high temperatureconditions, to find out genetic variability for yield and itscontributing traits during rabi 2010-2011. Estimates of variousparameters for assessment of genetic variability viz mean,range of variability, genetic advance and coefficient of variationhelps the breeder in deriving suitable plant types by bringingimprovement in quantitatively inherited traits, which will directlyeffects the crop yield. Effective pods per plant had the highestphenotypic and genotypic coefficient of variations followed,by 100 seed weight, harvest index, total number of pods perplant, biological yield, secondary branches, seeds per podand plant height. High heritability coupled with high geneticadvance as percentage of mean were recorded for plantheight, secondary branches, total number of pods per plant,effective pods per plant, 100 seed weight, biological yield andharvest index. This indicates the presence of additive geneeffects. Genotypes, ICCV 07102, JG 16 and JG 21 are foundsuitable under late sown conditions. Whereas, genotypes GG2, ICC 4958, ICCV 06301, ICCV 07102, ICCV 07117, JG 18,JG 21 and JG 22 are suitable for growing under very latesown and the genotypes viz., ICCV 07102, JG 21 and JG 22are found to be suitable for all the dates of sowing.

Keywords: Chickpea, genetic variability,morphological, physiological, normal, late and very latesown

Genetic studies for yield attributing traits among promising linesin chickpea under various environments

Niharika Shukla and Anita BabbarDepartment of Plant Breeding and GeneticsJawaharlal Nehru Krishi VishwavidhalayaJabalpur 482004 (MP)Email : niharikas86@gmail.com

Chickpea is the most widely grown in South Asia andthe Mediterranean region (Saxena 1990, Singh andOcampo 1997, FAO 2003). It is the third most importantpulse crop as well as an important source of humanfood and feed; it also helps in improve soil fertility,particularly in dry lands. It is sensitive to environmentalfluctuations, leading to instability in acreage andproduction over the years. Under present scenario ofclimate change and high cropping intensity the farmersprefers the early duration genotypes suitable for latesown and very late sown cultivation particularly inMadhya Pradesh. In this regards yield of chickpea isinfluence being grain yield is a quantitative trait and isa multiplicative effect of number of component traitswhich is influenced by environments. Breeding effortshave contributed substantially to improve yield potentialregional adaptation through resistance or tolerance toabiotic and biotic stresses, plant type and graincharacteristics.

Quantum of genetic variability and the extent, towhich heritable and non-heritable variations are relatedto the characters, determine the extent of geneticamelioration. Yield is a highly complex character as itis controlled by a large number of genes and greatlyinfluenced by the environment.

Thus, direct selection for yield may not be veryeffective, several morphological traits andenvironmental conditions influences the yield directlyor indirectly. Thus, selection of superior genotypesbased on yield as such is not effective. To achieve thisobjective, a thorough understanding of yield contributingtraits, genotypic and phenotypic coefficient of variance,heritability and genetic advance is necessary. Thus, inthe present study, an attempt was made to get thecomprehensive information on these aspects inchickpea under different environments.

JNKVV Res J 47(2): 175-180 (2013)

176

Materials and methods

The present investigation entitled was carried out duringRabi 2010-11 under All India Coordinated ResearchProject on Chickpea (lead center) in the experimentalfield of seed breeding farm, College of Agriculture,Jabalpur (MP). The experimental area occupied wasquite uniform in respect of topography and fertility. Theexperimental material comprised of 30 promising linesof chickpea covering 27 desi and 3 Kabuli types. Thesegenotypes were grown in a randomized completelyblock design with three replications under three differentdates of sowing viz; normal (19.10.11), late (24.12.10)and very late (30.01.12). Each Plot size was 4.0 m x0.90m consisting of 2 rows of 4m length, the row to rowdistance was 45 cm and plant to plant spacing was 10cm.

Fertilizer was applied in the ratio of 20N:60P2O5:40K2Okg/ha. The experiment was conducted withrecommended agronomic practices. Five plants fromeach replication were randomly selected andobservation were made for the characters days to flowerinitiation (DFI), days to 50% flowering (F 50%), days topod initiation (PS), days to maturity (P 50%), plant height(cm), number of primary and secondary branches, totalnumber of pods per plant, effective pods per plant, seedsper pod, 100 seed weight(g), biological yield(g), harvestindex (%) and seed yield per plant (g). The data wereprocessed for statistical analysis and genetic variabilityincluding heritability and genetic advance were doneby the methods given by Hanson et al. (1956) andJohnson et al. (1955) respectively.

Results and discussion

The phenotypic coefficient of variations was higher inmagnitude than its corresponding genotypic coefficientof variations for all the characters studied under threeenvironments. This indicates the influence of theenvironment on the expression of these characters. Theresult and discussion on phenotypic coefficient ofvariations had been presented on pooled analysis basiscovering all environments.

The characters effective pods per plant had the highestphenotypic and genotypic coefficient of variationsfollowed, by 100 seed weight, harvest index, totalnumber of pods per plant, biological yield, andsecondary branches, seeds per pod and plant heightwhich were in accordance with the findings of Khorgade(1985), Sharma et al. (1990), Kumar et al. (1991) andSandhu et al. (1991). Primary branches had moderate

phenotypic and genotypic coefficient of variation whileremaining traits like days to flower initiation, days to50% flowering, days to pod initiation and days to maturityexhibited low phenotypic and genotypic coefficient ofvariation.

In any crop improvement programme, the mostbasic information required by a breeder is the extent ofthe inheritance capacity of the genotype for differentcharacter under consideration. In fact, the variability ofbiological population is an outcome of geneticconstitution of the individual make up of that populationin relation to prevailing environments. Such studiesenable the breeder to have maximum selectionresponse if the variance exhibited by the populations islargely due to additive genetic variance. Substantialgenetic variance was observed for majority ofcharacters, in the study. In order to draw some validconclusion about the possibilities of the improvementof the various characters, estimation of heritabilityvalues are very essential. As the genetic advanceestimates are dependent on the unit of characters undermeasurement, hence genetic advance expressed aspercentage of mean was calculated in order to haverelative comparison of expected genetic gain forquantitative characters for one generation of selectionin hypothetical selection programme. Heritability andgenetic advance estimates varied from high, mediumto low in all the three environments for differentcharacters (Table 1).

Highest heritability was observed for plant heightand seed yield per plant followed by the characters daysto flower initiation, 100 seed weight, harvest index, andbiological yield. The characters seeds per pod, primarybranches, days to 50% flowering, days to flower initiationand total number of seeds per pod had shown very lowheritability. Similar findings have also been reportedby Jeena and Arora (2001), Jivani and Yadvendra(1989), Patel and Babbar (2004), Jeena et al. (2005),Saleem et al. (2005).

Among all the traits which showed high geneticadvance as percentage of mean were 100 seed weight,followed by effective pods per plant, harvest index,biological yield, plant height and total pods per plant.Low genetic advance as percentage of mean recordedin characters days to pod initiation, days to maturity,days to flower initiation, days to 50% flowering ,primarybranches, secondary branches and seeds per pod. Theabove results are agreement with the findings of Durgaet al. (2007) and Vaghela et al. (2008).

High heritability coupled with high geneticadvance as percentage of mean were recorded for plant

177

Tabl

e 1.

Gen

etic

par

amet

ers

of v

aria

bilit

y fo

r chi

ckpe

a ge

noty

pes

in E

-I, E

-II, E

-III a

nd p

oole

d an

alys

is

Cha

ract

ers

ENV.

Gra

nd m

ean

Ran

geV

aria

nce

Coe

ffici

ent o

f var

iatio

nh2 (B

)%G

A as

%M

in.

Max

.P

heno

typi

cG

enot

ypic

PCV

(%)

GC

V (%

)of

mea

n

FIE-

I47

.07

43.0

053

.00

6.77

4.96

5.53

4.73

738.

34E-

II45

.53

38.0

054

.00

15.2

711

.13

8.58

7.33

7212

.89

E-III

36.4

031

.00

38.6

62.

752.

154.

564.

0378

7.34

POO

LED

43.0

038

.33

46.1

18.

272.

076.

683.

3525

3.46

F 50

%E-

I54

.41

44.0

078

.00

8.89

6.74

5.48

4.77

758.

56E-

II58

.64

54.0

060

.00

4.47

2.96

3.60

2.94

664.

92E-

III43

.76

39.6

646

.66

3.38

2.13

4.20

3.34

625.

45PO

OLE

D52

.27

47.4

454

.55

5.58

1.84

4.52

2.60

333.

08PS

E-I

72.6

662

.00

59.0

035

.92

33.9

68.

258.

0294

16.0

6E-

II67

.01

62.0

070

.00

6.87

4.97

23.

913.

3372

5.83

E-III

54.6

146

.00

59.6

620

.22

17.5

88.

233.

3486

14.7

5PO

OLE

D64

.76

56.6

668

.44

21.0

011

.39

7.07

5.21

547.

90P5

0%E-

I10

8.40

100.

0011

3.66

13.0

39.

413.

332.

8372

4.96

E-II

95.1

884

.00

100.

0014

.49

13.1

34.

003.

8190

7.47

E-III

86.8

880

.00

90.0

09.

037.

063.

453.

0678

5.58

POO

LED

96.8

89.2

210

1.11

12.1

86.

173.

602.

5650

3.76

Pl.h

t.(cm

)E-

I50

.53

38.9

666

.13

53.0

450

.38

14.4

114

.05

9528

.20

E-II

45.0

230

.96

62.9

677

.52

75.3

919

.55

19.2

997

39.1

8E-

III42

.24

28.6

057

.70

37.9

333

.12

14.5

813

.63

8726

.23

POO

LED

45.9

333

.68

60.8

156

.16

39.3

116

.31

13.6

550

23.5

3PB

E-I

2.85

2.44

3.33

0.16

0.01

14.1

93.

526

1.80

E-II

2.46

2.33

3.10

60.

090.

0312

.50

7.55

369.

38E-

III2.

672.

443.

330.

050.

018.

995.

1532

6.07

POO

LED

2.66

2.47

2.99

0.10

0.01

12.2

03.

3270

1.87

SB E-I

10.7

1.6

215

.64

5.00

3.63

20.8

817

.79

7231

.23

E-II

6.66

4.88

10.4

43.

282.

2627

.20

22.5

868

38.6

1E-

III10

.11

5.95

14.4

47.

466.

0327

.02

24.2

980

44.9

6PO

OLE

D9.

166.

2311

.42

5.25

1.01

25.0

111

.00

079.

97

178

Cha

ract

ers

ENV.

Gra

nd m

ean

Ran

geV

aria

nce

Coe

ffici

ent o

f var

iatio

nh2 (B

)%G

A as

%M

in.

Max

.P

heno

typi

cG

enot

ypic

PCV

(%)

GC

V (%

)of

mea

n

TNPP

PE-

I60

.32

25.4

013

8.53

491.

4948

6.69

36.7

536

.57

9974

.97

E-II

42.7

024

.90

62.1

010

2.09

96.1

423

.66

22.9

694

45.9

0E-

III40

.15

19.6

374

.16

126.

1912

1.16

27.9

727

.41

9655

.34

POO

LED

47.7

329

.18

74.2

223

9.92

73.6

532

.45

17.9

830

20.5

2EP

PPE-

I51

.82

19.7

612

6.33

424.

7241

9.26

39.7

639

.51

9880

.87

E-II

36.0

621

.76

53.3

082

.18

77.6

325

.13

24.4

394

48.9

2E-

III38

.04

18.8

364

.40

135.

9512

8.67

34.8

729

.82

9459

.76

POO

LED

41.9

825

.94

72.7

521

4.28

90.9

721

.28

22.7

242

30.5

0SP

PE-

I1.

361.

051.

810.

060.

0118

.04

8.31

217.

87E-

II1.

350.

981.

940.

100.

0323

.34

13.3

332

15.6

9E-

III1.

441.

086

1.80

0.10

0.00

30.6

45.

4706

2.80

POO

LED

1.38

1.16

71.

610.

080.

0121

.28

7.15

114.

9610

0SW

E-I

23.9

113

.60

43.7

633

.26

29.1

124

.12

22.5

787

43.4

8E-

II22

.84

12.2

032

.86

25.5

522

.91

22.1

320

.96

8940

.89

E-III

20.9

86.

3664

.13

108.

6710

6.23

21.9

949

.13

9710

0.07

POO

LED

22.5

812

.86

37.2

255

.83

27.5

433

.09

23.2

449

33.6

4BY

(g)

E-I

36.2

114

.76

59.7

012

8.98

125.

7431

.36

30.9

797

62.9

8E-

II23

.26

13.9

040

.03

35.6

829

.64

25.6

823

.41

8343

.95

E-III

24.9

211

.20

36.8

637

.55

34.1

324

.59

23.4

590

46.0

5PO

OLE

D28

.13

16.8

139

.01

67.4

028

.64

29.1

819

.02

4225

.55

HI(g

)E-

I52

.62

28.2

082

.96

175.

9817

1.68

25.2

124

.90

9750

.66

E-II

50.8

017

.83

89.4

635

0.88

344.

5436

.87

36.5

498

74.5

8E-

III50

.99

17.8

386

.50

316.

7031

1.82

34.8

934

.63

9870

.78

POO

LED

51.4

726

.24

72.4

728

1.19

124.

8132

.57

21.7

044

29.7

9SY

PPE-

I17

.14

6.44

36.8

863

.64

62.0

646

.53

45.9

597

93.4

7E-

II11

.50

2.52

23.0

326

.52

15.9

344

.75

34.6

860

55.3

7E-

III9.

914.

2616

.29

10.7

010

.10

33.0

232

.07

9464

.18

POO

LED

12.4

36.

3220

.21

28.0

11.

4442

.57

9.65

54.

50

FI=F

low

er in

itiat

ion

F 50

% =

Day

s to

50%

flow

erin

g PS

= D

ays

to p

od in

itiat

ion

HI=

Har

vest

inde

xP

50%

=Day

s to

mat

urity

Pl.h

t.(cm

)=Pl

ant h

eigh

t PB=

Prim

ary

bran

ches

SYP

P=Se

ed y

ield

per

pla

ntSB

= Se

cond

ary

bran

ches

TN

PPP=

Tota

l num

ber o

f pod

s pe

r pla

nt E

PPP=

Effe

ctiv

e po

ds p

er p

lant

SPP=

Seed

s pe

r po

d 10

0SW

=Hun

dred

see

d w

eigh

t BY=

Biol

ogic

al y

ield

179

height, secondary branches, total number of pods perplant, effective pods per plant, 100 seed weight,biological yield and harvest index. This indicates thepresence of additive gene effects and therefore, directselection on these traits may be effective. Arshad et al.(2004) observed high heritability of secondary branchesand biological yield coupled with high genetic advancerevealed that additive gene effects are important indetermining these characters. Characters showing highheritability estimates accompanies with low geneticadvance as percentage of mean were days to flowerinitiation, days to 50% flowering, days to pod initiationand days to maturity. This reflects the presence of non-additive gene effects and selection based on such traitsmay not be rewarding.

In the present investigation, characters showinghigh heritability coupled with high genetic advance aspercentage of mean were plant height, secondarybranches, total number of pods per plant, effective podsper plant, 100 seed weight, biological yield and harvestindex. This indicates the presence of additive geneeffects therefore, direct selection on these traits maybe effective Arshad et al. (2004) observed highheritability of secondary branches and biological yieldcoupled with high genetic advance revealed thatadditive gene effects are important in determining thesecharacters.

Characters showing high heritability estimatesaccompanies with low genetic advance as percentageof mean were days to flower initiation, days to 50%flowering, days to pod initiation, days to maturity. Thisreflects the presence of non-additive gene effects.Selection based on such traits may not be rewarding.Characters showing low heritability coupled with lowgenetic advance as percentage of mean are primarybranches and seeds per pod.

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References

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Durga K, Kanaka SS, Murthy N, Rao Y, Koteswara ReddyMV (2007) Genetic studies on yield and yieldcomponents of chickpea. Agricultural Science Digest27 : 3

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Jeena AS, Arora PP, Ojha OP (2005) Variability and correlationstudies for yield and its components in chickpea.Legume Res 28 : 2

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Kumar NA, Balasubramanian M (1991) Variability in harvestindex of soybean and its association with yieldcomponents. Ann Pl Physio 5: 256

Patel SK, Babbar A (2004) Genetic variation of desi, gulabi

180

and kabuli chickpea types in Madhya Pradesh.JNKVV Res J 38(2): 86-90

Saleem Muhammad, Asif Zafar, Muhammad AhsanandMuhhamad Asalam (2005) Interrelationships andVariability Studies for Grain Yield and its VariousComponents in Chickpea (Cicer arietinum L). Journalof agriculture and social sciences 3 : 266-269

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(Manuscript Receivd : 20.9.13; Accepted : 22.10.13)

181

Abstract

Three hundred eighty seven genotypes of chickpea wereevaluated for nine characters viz. days to 50% flowering, daysto maturity, plant height, number of primary branches per plant,number of seeds per pod, number of pods per plant, biologicalyield, harvest index and grain yield per plot to estimate geneticparameter and association for yield and its components.Considerable genetic variability was observed for all thecharacters under studied. Grain yield per plot showed highlysignificant and positive phenotypic association with biologicalyield and number of seeds per pod. Genotypic correlation fordependent variable grain yield per plot was highly significantand positive with number of seeds per pod, biological yieldper plant and harvest index. An observation of genotypic pathanalysis for grain yield per plot revealed maximum direct effectthrough number of seeds per pod, followed by harvest index.

Key words : Chickpea, variability, correlation and pathanalysis

Chickpea (Cicer arietinum L.) is one of the mostimportant legume crop of Madhya Pradesh. Breedingefforts have contributed substantially to improving yieldpotential global yield increased from 591 kg /ha in 1975to 818 kg/ha in 2005 (FAOSTAT data 2006), regionaladaptation through resistance or tolerance to abioticand biotic stresses, plant type and grain characteristics.Quantum of genetic variability and the extent to whichheritable and non-heritable variations are related to thecharacters, determine the extent of genetic amelioration.Yield is a highly complex character as it is controlled bya large number of genes and greatly influenced by theenvironment. Thus direct selection for yield may not bevery effective, several morphological and quality traitsinfluence the yield directly and indirectly. These traitsare simply inherited and less influenced by theenvironment as compared to yield. So selection basedon these traits has better chance of successes in

Character association analysis among yield traits in chickpea underagroclimatic conditions of Kymore plateau zone, Madhya Pradesh

Anita Babbar, Pushpa Singh and Rajmohan SharmaDepartment of Plant Breeding & GeneticsJawaharlal Nehru Krishi Vishwa VidyalayaJabapur 482 004 (MP)Email : anitababbar@rediffmail.com

comparison to selection for yield per se. The genotypiccorrelation indicates the extent to which the twocharacters are under the control of the same set ofgenes or have the same physiological basis for theirexpression.

Material and methods

The experimental materials for the present studyconsisted of 387 germplasm collections of chickpea.These genotypes were grown in a RandomizedCompletely Block Design with two replications duringRabi 2007-08 at Jawaharlal Neharu Krishi VishwaVidhyalaya, Jabalpur. Each plot consisted of one rowof 4 m row length. The row-to-row and plant-to-plantdistance was 30 cm and 10 cm respectively.Recommended dose of fertilizer was applied in the rowsbefore sowing. Observations were recorded on fiverandomly selected plants on nine characters.

Result and Discussion

Maximum genotypic coefficient of variation (Table 1) wasobserved for the character biological yield per plant(43.9) while minimum value was reported for days tomaturity (3.06). Maximum phenotypic coefficient ofvariation observed for number of seeds per pod (146.48)and minimum for day to maturity (3.89). Heritability wasmaximum for days to 50% flowering (97.8%), geneticadvance as percentage of mean was maximum forbiological yield per plant. These findings are inagreement with the results of Mandal and Bahl (1980),Kumar et al. (1983), Sharma et al. (1990), Tripathi andArora (1991), Sandhu et al. (1991), Mishra et al. (1994),Wahid and Ahmed (1999), Kumar et al. (2001), Ali etal. (2002), Usmani et al. (2005).

JNKVV Res J 47(2): 181-184 (2013)

182

Tabl

e 1

. Est

imat

es o

f gen

etic

par

amet

ers

in c

hick

pea

geno

type

s

Cha

ract

ers

Mea

nR

ange

Sta

ndar

dPC

VG

CV

h2 (bs)

%G

A% o

fM

inim

umM

axim

umde

viat

ion

mea

n 5%

Day

s to

50%

Flo

wer

ing

77.5

4 +

0.2

164

.011

2.0

5.86

7.46

7.38

97.8

15.0

4

Day

s to

Mat

urity

118.

89 +

0.1

910

.013

0.0

5.43

3.89

3.06

62.0

4.96

Plan

t Hei

ght

48.2

+ 1

0.21

31.0

73.0

5.77

11.2

910

.56

87.5

20.3

6

No.

of P

rimar

y Br

anch

es/P

lant

3.12

+ 0

.03

1.5

6.0

0.75

21.4

718

.21

72.0

31.8

3

No.

of S

eeds

/Pod

1.25

+ 0

.09

0.0

2.0

2.56

46.4

831

.78

4.7

14.2

1

No.

of P

ods/

Plan

t53

.08

+ 0.

8710

.519

1.0

24.2

242

.95

40.0

687

.076

.96

Biol

ogic

al Y

ield

22.7

1 +

0.41

2.3

117.

511

.49

47.4

343

.99

86.0

84.0

4

Har

vest

inde

x33

.23

+ 1.

820.

079

5.6

50.8

583

.38

8.83

1.1

1.93

Gra

in Y

ield

/plo

t23

2.89

+ 3

.32

30.0

570.

092

.37

36.9

233

.92

84.4

64.2

1

Tabl

e 2.

Indi

vidu

al R

egre

ssor

equ

atio

ns fo

r Gra

in Y

ield

/Plo

t

Y1 =

(62

7.61

- 5.0

8) x

Day

s to

50%

Flo

wer

ing*

**

Y1 =

(62

7.44

- 3.

31) x

Day

s to

Mat

urity

***

Y1 =

(252

.66

- 0.4

1) x

Pla

nt H

eigh

t

Y1 =

(252

.04

- 6.1

4) x

Num

ber o

f Prim

ary

Bran

ches

/Pla

nt

Y1 =

(228

.97

+ 3.

11) x

Num

ber o

f See

ds/P

od*

Y1 =

(238

.19

- 0.1

0) x

Num

ber o

f Pod

s/Pl

ant

Y1 =

(250

.98

- 0.

79) x

Bio

logi

cal Y

ield

/Pla

nt**

Y1 =

(229

.94

+ 0.

08) x

Har

vest

Inde

x

Whe

re Y

1 =

Gra

in Y

ield

/Plo

t

183

Tabl

e 3.

Gen

otyp

ic a

nd p

heno

typi

c co

rrel

atio

n co

effic

ient

for g

rain

yie

ld/p

lot

Cha

ract

ers

G/P

Day

s to

Pla

nt h

eigh

tN

umbe

r of

Num

ber o

fN

umbe

r of

Bio

logi

cal

Har

vest

Gra

inm

atur

itypr

imar

yse

eds/

pod

pods

/pla

ntyi

eld

inde

xyi

eld/

plot

bran

ches

/pla

ntD

ays

to 5

0% F

low

erin

gG

0.61

**0.

26**

*-0

.01

0.15

5***

-0.0

8*0.

21**

*-1

.08*

**0.

37**

*P

0.47

***

0.23

***

-0.0

10.

03-0

.07*

0.18

***

-0.1

1**

-0.3

5***

Day

s to

Mat

urity

G0.

26**

*0.

08*

0.05

-0.1

0***

0.21

***

-1.4

3***

-0.

31**

*P

0.20

***

0.05

0.01

-0.0

9**

0.14

***

-0.0

8*-0

.24*

**Pl

ant H

eigh

tG

0.08

*-0

.32*

**0.

040.

43**

*-2

.06*

**-0

.05

P0.

09*

-0.0

8*0.

050.

38**

*-0

.18*

**-0

.04

No.

of P

rimar

y Br

anch

es/P

lant

G-0

.11*

**0.

22**

*0.

23**

*-1

.65*

**-0

.06

P -0

.02

0.21

***

0.24

***

-0.1

7***

-0.0

5N

umbe

r of S

eeds

/Pod

G0.

15**

*0.

080.

61**

*0.

40**

*P

0.01

0.01

0.01

0.11

**N

umbe

r of P

ods/

Plan

tG

0.62

***

-1.1

2***

-0.0

2P

0.62

***

-0.1

7***

-0.0

2Bi

olog

ical

Yie

ldG

-2.7

1***

0.12

***

P-0

.36*

**0.

11**

Har

vest

inde

xG

0.26

***

P0.

06

Tabl

e 4

. Gen

otyp

ic a

nd p

heno

typi

c pa

th m

atrix

of g

rain

yie

ld/p

lot

Cha

ract

ers

G/P

Day

s to

50%

Day

s to

Pla

ntN

umbe

r of

Num

ber o

fN

umbe

r of

Bio

logi

cal

Har

vest

Gra

inflo

wer

ing

mat

urity

heig

htpr

imar

yse

eds/

pod

pods

/pla

ntyi

eld

inde

xyi

eld/

plot

bran

ches

/pl

ant

Day

s to

50%

Flo

wer

ing

G-0

.46

-0.2

8-0

.12

0.00

-0.0

70.

04-0

.10

0.50

-0.3

7***

P-0

.32

-0.1

5-0

.07

0.00

-0.0

10.

02-0

.06

0.03

-0.3

5***

Day

s to

Mat

urity

G-0

.06

-0.1

0-0

.03

-0.0

1-0

.01

0.01

-0.0

20.

15-0

.31*

**P

-0.0

5-0

.09

-0.0

1-0

.00

-0.0

00.

01-0

.01

0.08

-0.2

4***

Plan

t Hei

ght

G0.

110.

110.

420.

04-0

.14

0.02

0.18

-0.8

7-0

.05

P0.

020.

010.

090.

01-0

.01

0.00

0.03

-0.0

1-0

.04

No.

of P

rimar

y Br

anch

es/P

lant

G-0

.00

0.01

0.01

0.10

-0.0

10.

020.

02-0

.17

-0.0

6P

0.00

-0.0

0-0

.00

-0.0

40.

00-0

.01

-0.0

10.

01-0

.05

Num

ber o

f See

ds/P

odG

0.10

0.03

-0.2

0-0

.07

0.61

0.09

0.05

0.37

0.40

***

P0.

000.

00-0

.01

-0.0

00.

120.

000.

000.

000.

11**

Num

ber o

f Pod

s/Pl

ant

G0.

010.

01-0

.00

-0.0

3-0

.02

-0.1

1-0

.07

0.13

-0.0

2P

0.00

0.00

-0.0

0-0

.01

-0.0

0-0

.03

-0.0

10.

01-0

.02

Biol

ogic

al Y

ield

G-0

.01

-0.0

1-0

.02

-0.0

1-0

.00

-0.0

2-0

.04

0.10

-0.1

2***

P-0

.01

-0.0

1-0

.01

-0.0

1-0

.00

-0.0

2-0

.03

0.01

-0.1

1**

Har

vest

inde

xG

-0.0

6-0

.08

-0.1

1-0

.09

0.03

-0.0

6-0

.15

0.06

0.26

***

P-0

.00

-0.0

0-0

.00

-0.0

00.

00-0

.00

-0.0

00.

010.

06Bo

ld fi

gure

s de

note

the

dire

ct e

ffect

sR

esid

ual e

ffect

= 0

.92

184

Khorgade PW (1988) Correlation studies in Bengal gram. AnnPl Physico 2(2): 204-211

Kumar J, Bahl PN, Mehra RB, Raju DB (1983) Variability inchickpea. Int chickpea newsletter 5: 3-4

Kumar SP, Arora P, Jeena AS (2001) Genetic variabilitystudies for quantities traits in chickpea. Agri SciDigest. 21(4): 263-264

Ladizinsky G, Adler A (1975) The origin of chickpea asindicated by seed protein electrophoresis. Israel JBotany 24 : 183-189

Mandal AK, PN Bahl (1980) Estimates of variability and geneticcorrelation in chickpea. Ann Agri Res 1(2): 136-140

Mishra AK, Raghu JS, Pathak KN, Aliand SA, Ghurraya RS(1994) Genetic parameters and interrelationshipanalysis in chickpea. Crop Res, 8(1): 109-111

Mishra R, Rao SK, Koutu GK (1988) Genetic variability,correlation studies and their implication in selectionof high yielding genotypes of chickpea. Indian J AgriRes 22(1): 51-57

Narayana NHS, Reddy NSR (2002). Correlation and pathanalysis in chickpea. J Angrau 30(1): 29-33

Pundir RPS, Reddy KN, Mangesha MH (1991) Genetics ofsome physio-morphological and yield traits ofchickpea (Cicer arietinum L.). Legume Res, 14(4):157-161

Sandhu TS, Gumber RK, Bhatia RS (1991) Path analysis inchickpea. J Res Punjab Agri Univ 28(1): 1-4

Sharma BD, Sood BC, Malhotra VV (1990) Studies invariability, heritability and genetic advance inchickpea. Indian J Pulses Res 3(1): 1-6

Sontakey PY, Patil BN, Khorgade PW, Bonde PW (1991) Pathanalysis of some yields attributes in gram. Agric SciDigest, 11(4): 211-215

Tripathi KN, Arora PP (1991) Variability and their correlationstudies and their implication in chickpea selection.Indian J Pulses Res 4(2): 151-153

Tripathi KN, Arora PP (1991) Variability and their correlationstudies and their implication in chickpea selection.Indian J Pulses Res 4(2): 151-153

Usmani MG, Dubey RK, Naik KR (2005) Genotypic,phenotypic variability and heritability of somequantitative characters in field pea. JNKVV Res J40 (1&2) : 10-104

Wahid MA, Ahmed R (1999) Genetic variability, correlationstudies and their implication in selection of high yieldgenotypes in chickpea (Cicer arietinum L.). SarhadJ Agri 15(1): 25-28

An observation of individual regression equation (Table2) showed significant contribution of days to 50%flowering, days to maturity, number of seeds per podand biological yield towards grain yield per plot.

Grain yield per plot exhibited highly significantand positive phenotypic correlation with biological yieldper plant (0.1) and number of seeds per pod (0.1), whileit was negatively correlated with days to 50% flowering(0.35) (Khorgade 1988, Pundir et al. 1991) and days tomaturity (0.24) (Dasgupta et al. 1992). Phenotypic pathanalysis exhibited for grain yield per plant showedmaximum direct effect through no. of seeds per pod(0.10) (Table 3).

Genotypic correlation (Table 3) for dependentvariable grain yield per plot was highly significant andpositive with number of seeds per pod, (0.40) biologicalyield per plant (0.11) (Babbar and Patel 2005) andharvest index (0.26) (Mishra et al. 1988, Tripathi andArora 1991) while highly significant and negativeassociation was observed for days to 50% flowering (-0.37) (Khorgade 1988) and days to maturity.

An observation of genotypic path analysis forgrain yield per plot (Table 4) revealed maximum directeffect through number of seeds per pod (0.40), followedby harvest index (0.25). These results are in agreementwith the findings of Sontakey et al. (1991), Dasgupta etal. (1992), Narayana and Reddy (2002) and Babbar andPatel (2005).

puk ds 987 tuunzO;ksa dk 9 y{k.kksa 50% iq:iu dh vof/k]ifjiD;rk vof/k] iknn Å¡pkbZ] izkFkfed 'kk[kkvksa dh la[;k] izfrQyh chtksa dh la[;ks] izfr ikS/kk Qfy;ksa dh la[;k] tSfod mit] Qlylwpdkad ,oa mit izfr IykaV gsrq ewY;kadu fd;k x;k A izfr iz{ks=mit dk ldkjkRed iznih laca/k tSfod mit ,oa izfr Qyh chtksa dhla[;k ls iznhZ'kr gqvk A blh izdkj mit dk izfr Qyh chtksa dhla[;k] izfr ikS/kk tSfod mit ,oa Qly lwpdkad ds cht ldkjkRedthoksfVfid lg laca/k iznf'kZr gqvk A

References

Ali N (2003) Processing and utilization of legumes in India.In. Proc. Util. Legumes APO, 117-145

Babbar A, Patel SK (2005) Correlation and path analysis indesi chickpea under Kymore Plateau Zone of MadhyaPradesh. JNKVVRes J 39(1): 47-51

Dasgupta T, Islam MO, Gayen P (1992) Genetic variabilityand analysis of yield components in chickpea. AnnalsAgri Res 13(2): 157-160

(Manuscript Receivd : 8.8.12; Accepted : 10.8.13)

185

Abstract

RILs generate wide genetic variability among the lines andthe homozygosity of the alleles of important traits within thegenotype and would be of immense use to identify the traitsfor mapping. RILs population derived from two contrastingcultivars were subjected to principal component analysis toobtain precise information on yield and quality related inter-componental traits. On the basis of principal componentanalysis, only five principal components (PCs) exhibited morethan 1.8 eigen value and showed about 68.34% variabilitywere identified. The PC1 showed 25.81%, while PC2, PC3,PC4 and PC5 exhibited 17.22%, 9.56%, 8.58% and 7.16%variability respectively, among the RILs for the traits understudy. Rotated component matrix revealed that each principalcomponent separately loaded with various yield and qualityattributing traits. The PC1, PC2, PC3 and PC5 were mostlyrelated to yield attributing traits whereas PC4 was related toquality traits. As PC1 was constituted by most of the yieldattributing traits, intensive selection procedures can bedesigned to bring about rapid improvement of dependent traitsfor yield by selecting the lines from PC1. Similarly, for qualityaspect a good breeding programme can be initiated byselecting the lines from PC4. PC scores of RILs in these fivePCs suggested that RIL 2-36 was ranked first for yieldattributing traits,RIL 2-52 for quality traitswhereas RIL 2-50was reported best for both yield and quality traits. IdentifiedRILs may be used as donor to improve the yield and qualitytraits in varietal development programme and some of therice RILs may also be used directly for cultivation purposes.

Keywords: RILs, PCA, eigen value, rotatedcomponenet matrix, rice, yield and quality

Rice is the most important food crop of about 3 billionpeople, nearly half the world's population, depends onrice for survival and main crop to fight world's hungerand poverty. In many countries, rice accounts for more

Principal component analysis of inter sub-specific RILsof rice for the important traits responsiblefor yield and quality

Vikas Kumar, G. K. Koutu, D. K. Mishra and Sanjay Kumar SinghDepartment of Plant Breeding & GeneticsCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

than 70% of human caloric intake and main source ofprotein for poor people in developing countries.Itprovides 21% of global human per capita energy and15% of per capita protein. Calories from rice areparticularly important in Asia, especially among the poor,where it accounts for 50-80% of daily caloric intake.Asia accounts for over 90% of the world's production ofrice, with China, India and Indonesia producing the most.Rice can also be found in cereals, snack foods, brewedbeverages, flour, oil, syrup and religious ceremonies toname a few other uses. In India it is cultivated in anarea of 45.54 million hectares with a production of 99.18million tones and productivity of 2177kg ha-1(Anon2012).

Analysis of related to yield and their attributingtraits and their compression with the increasingpopulation is a important aspects by which a good resultmay be made to fulfil the current demand. Severalstatistical procedures have been developed and appliedto analyse the different data's in many crops and in themsome of the procedures found to be very good tocalculate the associations of different traits. In differentstatistical analysis Principal Component Analysis (PCA)is a powerful tool in modern data analysis because it isa simple, non-parametric method for extracting relevantinformation from confusing data sets. This techniquewas initially floated by Pearson (1901) and laterdeveloped by Hotelling (1933). With minimal effort PCAprovides a roadmap for how to reduce a complex dataset to a lower dimension to reveal the sometimes hiddeninformation that often underlie it (Shlens, 2009). Itreduces the dimensionality of the data while retainingmost of the variation in the data set. PCA accomplishesthis reduction by identifying directions, called principalcomponents, along which the variation in the data is

JNKVV Res J 47(2): 185-190 (2013)

186

Table 1. Eigen value and percentage of variation for corresponding 27 yield and quality traits in RILs of rice

Traits Principal component (PC) Eigen value Percentage of total Cumulativevariation percentage

DTH PC1 6.9694 25.81 25.81

DTFF PC2 4.6505 17.22 43.04

DTM PC3 2.5800 9.56 52.59

NOL PC4 2.3162 8.58 61.17

NOT PC5 1.9345 7.16 68.34

NOPT PC6 1.7629 6.53 74.87

PH PC7 1.5706 5.82 80.68

FLW PC8 1.1477 4.25 84.93

PL PC9 0.8314 3.08 88.01

PW PC10 0.7852 2.91 90.92

APW PC11 0.5715 2.12 93.04

NOS PC12 0.4699 1.74 94.78

NOFS PC13 0.3376 1.25 96.03

NOUS PC14 0.3144 1.16 97.19

SFP PC15 0.2567 0.95 98.14

SD PC16 0.2253 0.83 98.97

TGW PC17 0.0753 0.28 99.25

GYPP PC18 0.0561 0.21 99.46

BYPP PC19 0.0459 0.17 99.63

PI PC20 0.0421 0.16 99.79

HI PC21 0.0276 0.10 99.89

GL PC22 0.0130 0.05 99.94

GB PC23 0.0072 0.03 99.97

LBR PC24 0.0051 0.02 99.99

H% PC25 0.0028 0.01 100.00

M% PC26 0.0007 0.00 100.00

HRR% PC27 0.0000 0.00 100.00

187

Table 2. Principal components for 27 yield and quality traits of RILs of rice

Rotated Component MatrixaTraits Principal Component

1 2 3 4 5

DTH 0.324 0.825* -0.182 -0.007 0.007

DTFF 0.321 0.828* -0.178 -0.011 0.009

DTM 0.286 0.823* -0.151 0.048 -0.131

NOL 0.105 -0.102 0.941* 0.053 -0.106

NOT 0.108 -0.111 0.945* 0.043 -0.095

NOPT 0.168 -0.209 0.895* 0.065 -0.042

PH 0.782* 0.106 0.021 -0.100 0.013

FLW 0.310 -0.003 -0.008 -0.130 -0.020

PL 0.547* 0.119 0.035 0.052 0.031

PW 0.864* -0.003 0.374 0.061 0.011

APW 0.653* 0.195 0.139 0.010 0.121

NOS 0.796* 0.009 -0.067 0.009 -0.389

NOFS 0.821* -0.201 -0.042 0.076 -0.432

NOUS -0.031 0.484 -0.061 -0.154 0.085

SFP 0.462 -0.523 0.066 0.174 -0.177

SD 0.678* -0.034 -0.119 0.006 -0.419

TGW 0.044 -0.274 0.089 0.080 0.777*

GYPP 0.861* -0.039 0.396 0.061 0.075

BYPP 0.716* 0.400 0.383 -0.028 0.036

PI 0.596* -0.285 0.374 0.123 0.333

HI 0.213 -0.738 0.002 0.125 0.079

GL 0.091 -0.305 0.334 -0.167 0.142

GB -0.070 0.052 -0.111 -0.051 0.796*

LBR 0.147 -0.261 0.316 -0.083 -0.539

H% -0.054 -0.094 0.003 0.938* 0.034

M% -0.053 -0.057 0.006 0.934* 0.059

HRR% 0.071 -0.113 0.066 0.844* -0.023

188

maximal (Anderson, 1972 and Morrison, 1978). By usinga few components, each sample can be representedby relatively few numbers instead of by values forthousands of variables (Ringer, 2008). Thus, the primarybenefit of PCA arises from quantifying the importanceof each dimension for describing the variability of a datasetin more interpretable and more visualized dimensionsthrough linear combinations of variables that accountsfor most of the variation present in the original set ofvariables.Considering the importance of PCA aninvestigation was carried out on rice RILs with anobjective to identify the minimum number of componentswhich can explain maximum variability out of the totalvariability, to rank genotypes on the basis of PC scoresand to identify superior RILs based on yield and qualitytraits at Jawaharlal Nehru KrishiVishvaVidyalaya,Jabalpur (M.P.) India.

Meterial and methods

The experimental material comprised of 91Recombinant Inbred Lines (RILs) population derivedfrom a cross between two diverse parents, JNPT 89(Tropical japonica) and JR 75 (Indica) by single seeddescent method. Each RIL along with parents wereplanted in main plot containing 24 hills arrangedaccording to randomized complete block design (RCBD)with three replications at the rate of one seedling perhill. At the time of transplanting spacing of 20 cmbetween plant to plant and between row to row of thesame distance were maintained. The standard

agronomic practices were adopted for good cropgrowth.The observations were recorded as per thestandard procedure. The statistical analysis of data onindividual characters was carried out using mean valuesof randomly selected plants from each genotype in eachreplication.

Result and discussion

PCA was performed on rice RILs for yield and theirattributing traits and also for quality contributing traitsto identify the important components and genotypeshaving better characters related to rice improvementprogrammes. Out of twenty seven principal components(PCs), only five PCs exhibited more than 1.8 eigen valueand showed about 68.34% variability among the traitsstudied. Because of five PCs having more eigen valuesand variability so in this case these five PCs wereincluded for further explanation.

Scree plot explained the percentage of varianceassociated with each principal component obtained bydrawing a graph between eigen values and principalcomponent numbers. PC1 showed 25.81% variabilitywith eigen value 6.97 which then declined gradually(Fig.1). Rotated component matrix revealed that the PC1which accounted for the highest variability (25.81%) wasmostly related with traits such as panicles weight perplant, grain yield per plant, number of filled spikeletsper panicle, number of spikelets per panicle, plantheight, biological yield per plant, spikelet density,

Table 3. List of selected top 10 PC scores of rice RILs in each principal component

PC1 PC2 PC3 PC4 PC5RIL 2-36 RIL 2-41 RIL 2-46 RIL 2-52 RIL 2-70

RIL 2-75 RIL 2-69 RIL 2-78 RIL 2-49 RIL 2-6

RIL 2-69 RIL 2-28 RIL 2-67 RIL 2-46 RIL 2-34

RIL 2-79 RIL 2-8 RIL 2-69 RIL 2-53 RIL 2-40

RIL 2-91 RIL 2-67 RIL 2-66 RIL 2-45 RIL 2-31

RIL 2-61 RIL 2-56 RIL 2-47 RIL 2-64 RIL 2-9

RIL 2-3 RIL 2-21 RIL 2-4 RIL 2-14 RIL 2-12

RIL 2-56 RIL 2-88 RIL 2-59 RIL 2-50 RIL 2-30

RIL 2-31 RIL 2-91 RIL 2-58 RIL 2-74 RIL 2-65

RIL 2-50 RIL 2-52 RIL 2-25 RIL 2-22 RIL 2-67

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average panicle weight, panicle index and paniclelength. PC2, PC3, PC4 and PC5 exhibited 17.22%,9.56%, 8.58% and 7.16% variability respectively amongthe lines for the traits under study. (Table 1). In PC2 thetraits viz., days to 50% flowering, days to heading anddays to maturity were more related. The PC3 wasdominated by number of tillers per plant, number ofleaves per plant and number of productive tillers perplant. The fourth principal component was more relatedto hulling%, milling% and head rice recovery% whereasPC5 was more related to grain breadth and 1000 grainweight (Table 2).

To rank the genotypes in the top 10 principalcomponent scores (PC scores) for all the lines wereestimated in these five components. RIL 2-36 hadhighest PC score followed by 2-75, 2-69, 2-79, 2-91, 2-61, 2-3, 2-56, 2-31 and 2-50 in PC1 indicated that theyhad high panicles weight per plant, grain yield per plant,number of filled spikelets per panicle, number ofspikelets per panicle, plant height, biological yield perplant, spikelet density, average panicle weight, panicleindex and panicle length.

The highest PC scores of RIL 2-46 followed by2-78, 2-67, 2-69, 2-66, 2-47, 2-4, 2-59, 2-58 and 2-25in PC3 exhibited high number of tillers per plant, numberof leaves per plant and number of productive tillers perplant. Similarly, decreasing order of PC scores of RIL2-52, 2-49, 2-46, 2-53, 2-45, 2-64, 2-40, 2-50, 2-74 and2-22 in PC4 exhibited high value of quality traits viz.,hulling%, milling% and head rice recovery %. In PC2

RIL 2-41 recorded the highest PC score followed by 2-69, 2-28, 2-8, 2-67, 2-56, 2-21, 2-88, 2-91 and 2-52indicated that they had high days to heading, days to50% flowering and days to maturity whereas in PC5RILs 2-70 exhibited the highest PC score followed by2-6, 2-34, 2-40, 2-31, 2-9, 2-12, 2-30, 2-65 and 2-67indicated that they had high value of grain breadth and1000 grain weight. On the basis of top 10 PC scores ineach principal component, RILs are selected andpresented as summarised form in Table. 1.

Discussion

Principal component scores were calculated for all theRILs in five principal components and utilized in findingRILs superior for different combination of phenotypictraits. A high value of principal component score of aparticular RIL in a particular principal componentdenotes high value for those variables in that RIL inwhich the component is representing. Thus, thesescores can be utilized to propose precise selectionindices whose intensity can be decided by variabilityexplained by each of the principal component. Accordingto the first PC score, RIL 2-36 had highest score followedby 2-75, 2-69, 2-79, 2-91, 2-61, 2-3, 2-56, 2-31 and 2-50 indicated that they had high panicles weight per plant,grain yield per plant, number of filled spikelets perpanicle, number of spikelets per panicle, plant height,biological yield per plant, spikelet density, averagepanicle weight, panicle index and panicle length. Thehighest PC scores of RIL 2-46 followed by 2-78, 2-67,2-69, 2-66, 2-47, 2-4, 2-59, 2-58 and 2-25 in PC3exhibited high number of tillers per plant, number ofleaves per plant and number of productive tillers perplant. Since, PC1and PC3 are concentrated by most ofthe major yield contributing traits which explainmaximum variability, an intensive selection procedurescan be designed to bring about rapid improvement ofdependent traits i.e., grain yield by selecting the linesfrom these components. Likewise, decreasing order ofPC scores of RIL 2-52, 2-49, 2-46, 2-53, 2-45, 2-64, 2-40, 2-50, 2-74 and 2-22 in PC4 exhibited high value ofquality contributing traits viz., hulling%, milling% andhead rice recovery %. Thus, breeder can select lineswith highest score having desirable combination ofquality traits for further desired breeding programme.In PC2 RIL 2-41 recorded the highest PC score followedby 2-69, 2-28, 2-8, 2-67, 2-56, 2-21, 2-88, 2-91 and 2-52 indicated that they had high days to heading, daysto 50% flowering and days to maturity whereas in PC5RILs 2-70 exhibited the highest PC score followed by2-6, 2-34, 2-40, 2-31, 2-9, 2-12, 2-30, 2-65 and 2-67indicated that they had high value of grain breadth and

Fig 1. Scree plot of principal component analysis ofRILs of rice between their eigen value and the numberof principal components

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1000 grain weight. Results of PC2 can be discussed intwo aspects; first the RILs having high PC scores canbe utilised for developing late maturity with high yieldingtrait containing varieties and second the RILs havinglow PC scores can be utilised for making early and extraearly maturity varieties including high yield potentials.

Principal component analysis highlights thecharacters with maximum variability so, intensiveselection procedures can be designed to bring aboutrapid improvement of yield and quality traits. PCA alsohelp in ranking of genotypes on the basis of PC scoresin corresponding component. From the abovediscussion, it is clear that the RIL 2-36 hold the firstposition followed by 2-75, 2-69, 2-79, 2-91, 2-61, 2-3,2-56, 2-31 and 2-50 for yield traits, whereas RIL 2-52hold the first position followed by 2-49, 2-46, 2-53, 2-45, 2-64, 2-40, 2-50, 2-74 and 2-22 for quality attributingtraits. However, for both yield and quality traits RIL 2-50 found to be best. The derived information of thisresearch on rice RILs would be very useful to selectpotentially breeding lines for future rice improvementprogramme (Khan et al. 2012).These selected RILs maybe used as parent for the transfer of desired yield andquality traits in the suitable cultivars in crop improvementaspects. RIL 2-50 was the best line for both the yieldand quality traits, which can be used directly forcultivation purposes.

fjYl dk fuekZ.k nks fofHkUu xq.kksa ds leku iztkfr dks vkil esaladj.k djkdj Qlyksa esa fofHkUurk mRiUu djus rFkk fofHkUu xq.kksadks lHkh Øksekstkse ij ,d leku rjg ls LFkkfir dj oakfNr xq.kksa dksfpfUgr djuk gSA ,d vuqla/kku ds rgr fjYl dks fizfliy dEiksusV,ukfyfll ¼ih-lh-,-½ ds }kjk mRiknu ,oa mRiknu fu/kkZfjr xq.kksa dsÅij xq.kkRed v/;;u fd;k x;kA bl v/;;u esa ikap fizfliydEiksusasVl ik,s x;s] ftUkdk btsu ek=k 1-8 rFkk fofHkUUkrk 68-34izfr'kr ikbZ x;hA ih-lh- & 1 esa 2-81 izfr'kr tcfd ih-lh- & 2]ih-lh- & 3] ih-lh- & 4 rFkk ih-lh- & 5 esa 17-22 izfr'kr] 9-56 izfr'kr 8-58 izfr'kr rFkk 7-16 izfr'kr de'k% x.kuk dh xbZAjksVsVsM dEiksusaV esfVªDl ds }kjk ;g ik;k x;k fd izR;sd fizfliydEiksusasVl vyx&vyx cgqr lkjs mRiknu ,oa xq.k fu/kkZfjr pfj=ksadks lek, gq, gSA ih-lh- & 1] ih-lh- & 2] ih-lh- & 3] ih-lh- &5 eq[;r% mRiknu pfj=ksa ds fy, rFkk ih-lh- & 4 dks xq.koRrk;qDrpfj=ksa dk okgd ik;k x;kA pwfd ih-lh- & 1 eq[;r% mRiknu fu/kkZfjr xq.kksa ds fy, ik;k x;k vr% bl rjg ds xq.kksa ds o/kZu gsrqbldk iz;ksx Qly lq/kkj gsrq fd;k tk ldrk gS lkFk gh lkFk ih-lh-& 4 xq.koRrk;qDr pfj=ksa dks c<kus gsrq Qly lq/kkj ds rgr iz;ksxfd;k tk ldrk gSA ih-lh- Ldksj ds vk/kkj ij ;g ik;k x;k fd

fjYl 2&36 dks mRiknu ds xq.kksa esa izFke rFkk fjYl 2&52 dksxq.koRrk ds fy, tcfd fjYl 2&50 dks mRiknu ,oa xq.koRrk nksuksgsrq mRre ik;k x;kA igpku dh xbZ fjYl dks Qly lq/kkj ds rgriz;ksx dj mRre xq.kks ,oa vf/kd mRiknu okys /kku dks Hkfo'; esarS;kj djus esa enn fey ldrh gSA

References

Anderson T W (1972) An introduction to multivariate analysis.Wiley Eastem Pvt Ltd New Delhi.

Anon (2012) Production and productivity of rice in India, USDA2012-13

Hotelling H (1933) Analysis of complex statistical variablesinto principal components. J Educational Psychol 24:417

Khan MA, Khan AS, Khan SHU, Ahmad R (2012) Associationof various morphological traits with yield and geneticdivergence in rice (Oryza sativa L.). InternationalJournal of Agriculture and Biology 14(1): 55-62

Morrison DE (1982).Multivariate Statistical Methods (2nd ed.4th Print, 1978). McGraw Hill Kogakusta Ltd

Pearson K (1901) On lines and planes of closest fit to systemsof points in space Philosophical Magazine 2: 559

Ringer M (2008) What is principal component analysis? NatureBiotechnology 26(3): 303-304

Shlens J (2009) A tutorial on principal component analysis.Version 3.01

(Manuscript Receivd : 24.9.13; Accepted : 15.11.13)

191

Abstract

A field investigation was conducted to study the effect of soilmoisture on growth, nutrient status of healthy and malformedpanicles in different mango varieties at Fruit Research Station,Imalia, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur(M.P.). A total of twenty four treatment combinations ofirrigations and varieties were tested in factorial randomizedblock design with three replications. The results of studyrevealed that an increase in soil moisture content delayedthe bud initiation and panicle emergence in mango. Highersoil moisture enhanced the content of N, P, K and Zn in theleaves of healthy panicles bearing shoots and higher contentof Fe, Mn and Cu was in leaves of malformed panicles. Amongthe varieties minimum intensity (2.9m-2) and severity (25.7%)of malformation was recorded in Langra followed by Amrapaliand Sunderja.

Keywords: Soil moisture, Malformation, Macro andMicro Nutrient

Mango (Mangifera indica L.) is having wider adaptabilitywith respect to soil, climate and altitude for its successfulcultivation. It is being grown on 2.04 million hectaresarea with a total production of 22.7 million tones whichworks out to a low average productivity of 11.1 metrictones per hectare (Anonymous 2010 Kumar et al. 2009).Globally, number of problems have been identified inmango production, out of which malformation has beenfound. Tahir et al. (2003) reported that moisture stressdiscouraged the emergence of late season flushes thatultimately minimize the rate of malformation as tree doesnot exhausted due to the occurrence of useless flushes.The etiology of malformation has yet been discovered,however, effective management measures are still

unknown. Presently, there is a lack of researchinformation on the soil moisture content which mightplay a significant role in management of malformation.Therefore, an attempt has been made to study the effectof soil moisture on macro and micro nutrient content inmalformed and healthy panicles bearing shoots ofdifferent verities of mango.

Material and method

A field experiment was conducted at Fruit ResearchStation, Imalia, JNKVV during 2010 and 2011. The soilof experimental site was clay in texture (58.4% clay,22.5 silt and 20.1% sand) having pH 7.2, mediumavailable N (302 kg ha-1), high in P (22.6 kg ha-1) and K(430.7kg ha-1) with medium organic carbon (0.70%). Theexperiment consisted of three varieties (V1:Amrapali,V2:Sunderja and V3: Langra) and eight irrigation levels(I1: control (without irrigation), I2 : Irrigation at 30 daysafter rains over, I3 : Irrigation at 60 days after rainsover, I4 : Irrigation at 90 days after rains over, I5 :Irrigation at 30 and 60 days after rains over, I6 : Irrigationat 30 and 90 days after rains over, I7 : Irrigation at 60and 90 days after rains over and I8 : Irrigation at 30, 60and 90 days after rains over). Healthy and malformedpanicles detached separately from node for recordingfresh weight. To determine the dry weight, these paniclewere chopped and oven dried at 60 ±20c till get constantweight. The content of nitrogen (Mc Donald 1978),phosphorus (Koenig and Jhonson 1942), potassium(Hanway and Haidal 1952) and micronutrient weredetermine by Atomic Absorbtion Spectrophotometer (Luand Chacko 2000) in healthy and malformed bearingshoots separately.

Influence of soil moisture on macro and micro nutrientcontents in healthy and malformed bearingshoots of mango

Rajnee Sharma, S.K. Pandey and T.R. SharmaDepartment of HorticultureCollege of Agriculture,Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : rajnisharma5886@gmail.com

JNKVV Res J 47(2): 191-195 (2013)

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Table 1. Influence of soil moisture on macro and micro nutrient contents in healthy and malformed bearing shoots

Nitrogen Phosphorous Potassium Zinc Copper Iron ManganeseTreatments (%) (ppm) (ppm) (ppm) (ppm) (ppm) (ppm)

H M H M H M H M H M H M H M

V1I1 1.95 1.83 0.232 0.222 50.09 44.01 22.23 21.23 6.58 7.53 136.1 153.1 56.2 71.2

V1I2 2.12 1.86 0.286 0.240 57.25 45.80 23.85 22.85 7.37 8.32 151.9 168.9 68.9 83.9

V1I3 2.30 1.93 0.294 0.253 64.40 47.95 24.49 23.19 7.83 8.78 163.1 179.1 67.2 82.2

V1I4 2.30 2.08 0.296 0.284 67.06 51.88 25.96 24.69 8.38 9.33 170.4 187.4 68.6 83.6

V1I5 2.46 2.27 0.315 0.294 67.37 56.39 26.15 25.45 8.25 9.20 179.4 195.7 70.8 85.8

V1I6 2.51 2.37 0.327 0.299 70.87 58.32 27.38 25.74 8.70 9.65 181.9 197.9 75.6 90.6

V1I7 2.71 2.46 0.333 0.307 74.37 59.75 28.06 26.36 8.71 9.66 189.4 204.4 80.3 95.3

V1I8 2.74 2.49 0.353 0.312 77.86 61.18 29.85 26.99 9.17 10.12 194.9 208.9 83.5 98.5

2.39 2.16 0.305 0.276 66.16 53.16 26.00 24.56 8.12 9.07 170.9 186.9 71.4 86.4

V2I1 1.86 1.81 0.232 0.217 46.51 42.94 22.83 20.83 6.85 7.80 141.9 158.9 57.0 72.0

V2I2 2.04 1.92 0.296 0.242 53.67 44.37 23.65 21.65 7.89 8.84 161.0 178.0 68.1 83.1

V2I3 2.21 1.97 0.296 0.245 60.83 48.66 23.63 22.63 7.45 8.40 168.2 184.2 71.2 86.2

V2I4 2.48 1.99 0.302 0.278 62.38 52.24 25.22 24.72 8.89 9.84 173.3 190.3 77.0 92.0

V2I5 2.42 2.23 0.325 0.286 65.78 55.46 26.41 25.64 8.36 9.31 174.9 198.5 77.1 92.1

V2I6 2.59 2.30 0.317 0.296 67.69 57.96 27.57 25.97 8.08 9.03 181.0 197.0 78.6 93.6

V2I7 2.67 2.36 0.322 0.302 75.67 59.04 28.67 26.17 8.96 9.91 189.7 204.7 80.3 95.3

V2I8 2.71 2.44 0.335 0.309 76.91 60.83 29.84 26.84 9.26 10.21 198.9 212.9 88.9 103.9

2.37 2.13 0.303 0.272 63.43 52.69 25.98 24.31 8.22 9.17 173.6 190.6 74.8 89.8

V3I1 2.04 1.85 0.278 0.227 57.25 44.37 22.90 22.90 6.45 7.40 134.3 151.3 52.2 67.2

V3I2 2.21 2.04 0.284 0.235 60.83 46.51 23.10 22.19 7.12 8.07 154.3 171.3 54.5 69.5

V3I3 2.30 2.01 0.302 0.258 67.98 51.88 23.59 22.99 7.52 8.47 170.9 186.9 68.8 83.8

V3I4 2.39 2.02 0.307 0.289 62.99 53.67 25.62 24.52 8.74 9.69 169.4 186.4 72.6 87.6

V3I5 2.49 2.27 0.320 0.302 69.60 55.85 26.37 26.17 8.12 9.07 176.8 193.8 71.5 86.5

V3I6 2.42 2.28 0.322 0.307 71.82 58.68 27.28 26.28 8.23 9.18 184.5 200.5 72.1 87.1

V3I7 2.67 2.38 0.335 0.317 75.00 59.75 28.97 27.16 8.95 9.90 189.4 204.4 78.9 93.9

V3I8 2.73 2.47 0.345 0.322 76.27 60.11 29.22 27.22 9.09 10.04 196.8 210.8 81.2 96.2

2.41 2.16 0.312 0.282 67.72 53.85 25.88 24.93 8.03 8.98 172.0 188.2 69.0 84.0CD at 5%Irrigation (I) 0.075 0.041 0.009 0.006 5.49 1.78 0.676 1.395 2.67 3.58 2.888 3.437 5.645 5.372

Variety (V) NS NS NS NS NS NS NS NS NS NS NS NS NS NS

Interaction (I x V) NS NS NS NS NS NS NS NS NS NS NS NS NS NS

V1: Amrapali, V2: Sunderja, V3: Langra, I1: Control without irrigation, I2: Irrigation at 30 days after rains, I3: Irrigation at 60 daysafter rains, I4: Irrigation at 90 days after rains, I5: Irrigation at 30 and 60 days after rains, I6: Irrigation at 30 and 90 days afterrains, I7: Irrigation at 60 and 90 days after rains, I8: Irrigation at 30, 60 and 90 days after rains H: Healthy, M: Malformed panicle

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Effect of irrigation schedule on phosphorous contentin health and malformed shoots

Fig. 5.9: Effect of irrigation schedule on phospohorus content in healthy and malformedshoots (ppm)

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Fig. 5.10: Effect of irrigation schedule on potassium content in healthy and malformedshoots (ppm)

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Effect of irrigation schedule on potassium content inhealth and malformed shoots

Fig. 5.11: Effect of irrigation schedule on zinc content in healthy and malformed shoots(ppm)

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Fig. 5.12: Effect of irrigation scheduleon on iron content in healthy and malformedshoots (ppm)

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Effect of irrigation schedule on zinc content in healthand malformed shoots

Effect of irrigation schedule on iron content in healthand malformed shoots

Fig.5.13: Effect of irrigation schedule on manganese content in healthy and malformedshoots (ppm)

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Fig. 5.14: Effect of irrigation schedule on copper content in healthy and malformedshoots (ppm)

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Effect of irrigation schedule on manganese content inhealth and malformed shoots

Effect of irrigation schedule on copper content inhealth and malformed shoots

Fig. 5.7: Effect of irrigation schedule on nitrogen content in healthy and malformedshoots (%)

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Fig. 5.8: Effect of irrigation schedule on protein content in healthy and malformedshoots (%)

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Effect of irrigation schedule on nitrogen content inhealth and malformed shoots

Effect of irrigation schedule on protein content inhealth and malformed shoots

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Result and discussion

Different growth parameters viz. number of leaves,length of panicles, fresh and dry weight of paniclesclearly indicates that increase in number of irrigationhad the positive effect on the number of leaves andlength of panicles as well as biomass accumulation inhealthy and malformed panicles. In general, morepanicle length was noted with healthy panicles and freshand dry weight was higher with malformed bearingshoots. The higher number of leaves and longestpanicles were recorded under irrigations at 30, 60 and90 days after rains over. The increase in number ofleaves and length of panicle might be due to theabundant availability of moisture to the plants whichmight increase the availability of water and solutes forabsorption. Higher absorption of water and nutrientspromote the growth and development of tree in termsof number of leaves and panicle size. Results of Singhet al. (1994) are in the close conformity with presentfindings.

The scheduling of irrigation had marked effecton content of major nutrients (N, P and K) and proteincontent in the leaves of healthy and malformed bearingshoots. The significant improvement in content ofnutrients was observed with the increasing number ofirrigation and reduction in intervals. The three irrigationsat 30, 60 and 90 days after the rains recorded the highercontent of N (2.72 and 2.47%), P (0.345 and 0.315 ppm)K (77.1 and 60.71 ppm) and protein (17.03 and 15.43%)in leaves of healthy and malformed bearing shoots,respectively, whereas it reduced with either the increasein intervals of irrigation or decrease in number ofirrigations. The increase in content of N, P, K and proteinwith the increase in number of irrigations might be dueto the fact that the availability of higher moisture lowerdown the temperature of the which surface provides achance to trees roots for absorbing more water andsolutes as compared to roots of stress tree (Singh et al.2009). The positive relation of content of major nutrientswith number of irrigations was also noted byChhattopadhyay and Patra (1992) who were of the viewthat greater uptake of nutrient in pomegranate was dueto its greater absorption by the roots and higheravailability of soil moisture. The various cultivars didnot show marked variations in relation to content of N,P and K in leaves of healthy and malformed bearingpanicle leaves. However, slight variations in healthy andmalformed shoots were observed. The healthy shootshad a higher content as compared to leaves ofmalformed shoots. The variation in content of N, P andK in leaves of different mango varieties was reportedby Singh et al. (2009).

The copper, iron and manganese content weresignificantly higher in leaves of malformed shoots ascompared to healthy, whereas zinc content showed thereverse trend (Table 1). These findings are in closeconformity with the findings of Singh et al. (1991) andSingh et al. (1997). The increased in moisture contentincreased the content of Zn (22.65 to 29.64 and 21.65to 27.02 ppm), Cu (6.63 to 9.71 and 7.58 to 10.12 ppm),Fe 137.4 to 196.9 and 154.4 to 210.9 ppm) and Mn(55.1 to 84.5 and 70.1 to 99.5 ppm) in the leaves ofhealthy and malformed shoots, respectively. It mightbe due to higher moisture content in soil which helpsthe uptake of these nutrients. The increased content ofnutrients was reported under mulching byChattopadhyaya and Pattra (1992) in pomegranate andByun et al. (1989) in ber. The greater lateral spreadwith enhanced root growth in the upper layers of soil inmulched tree which might be due to the presence ofhigher moisture and comparatively low temperature atupper surface. Increase in the root hairs in mulchedtree may increase the water and solute absorptivesurface of the root in the vascular tissue which mayprovide more conductive condition to roots forabsorption and translocation of mineral and water tothe plant.

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Reference

Anonymous (2010) Indian Horticulture data base 2009. Ed.Bijay Kumar et al. National Horticulture Board,Ministry of Agriculture, Government of India, 1-282

Byun JK, Do JH, Chang KH (1989) Effect of black polytheneand rice straw mulches on growth of young Jujube(Zuzyphus jujube) plants. Hort Sci 7: 136-139

Chattopadhyaya PK, Patra SC (1992) Effect of soil covers ongrowth, flowering and yield of pomegranate. SouthIndian Hort 40:309-312

Hanway JJ, Heidal H (1952) Soil analysis. Methods as used

195

in Iowa State College, Soil testing Laboratory IowaAgriculture. 57: 43-45

Koenig R A, Jhonson C R (1942) Colorimetric determinationof biological material. Indust. Engg. Chem. Anal Ed14: 155-156

Kumar P, Misra A K, Pandey BK, Misra SP, Modi DR (2009)Eco- friendly management of mango malformationFusarium moniliforme var. subglutinans from leafextracts. J Eco-Friendly Agric 4: 61-64

Lu P, Chacko EK (2000) Effect of water stress on mangoflowering in low latitude tropics of Northern Australia.Acta Hort 509:283-290

Mc Donald MS (1978) A simple and improved method for thedetermination of microgram quantities of N in plantmaterials. Ann Bot N S 42: 363-366

Singh Zora, Dhillon BS, Arora CL (1991) Nutrient levels inmalformed and healthy tissue of mango (Mangiferaindica L). Plant Sci. 133: 9-15

Singh CP, Ram S (1997) Effect of irrigation on flowering,fruiting and malformation in mango. Acta Hort455:543-546

Singh S, Sengupta BN, Roychoudhary N, Singh S (1994)Intensity and susceptibility to floral malformation ofsome important mango cvs. Hort J 17(2):97-101

Singh VK, Singh Gorakh, Bhriguvanshi SR (2009) Effect ofpolyethylene mulch on soil nutrient level and root,leaf and fruiting characteristic of mango (Mangiferaindica L). Indian J Agri Sci 67 (3): 130-131

Tahir FM, Ibrahim M, Hamid Kamran (2003) Effect of droughtstress on vegetative and reproductive growthbehaviour of mango (Mangifera indica L). Asian JPlant Sci 2:116-118

(Manuscript Receivd : 26.8.13; Accepted : 10.10.13)

196

Abstract

A field experiment was conducted to study the effect of soilmoisture on growth, nutrient status of healthy and malformedpanicles in different mango varieties at Fruit Research Station,Imalia, Jawaharlal Nehru Krishi Vishwa Vidyalaya, Jabalpur(M.P.). A total of twenty four treatment combinations ofirrigations and varieties were tested in factorial randomizedblock design with three replications. The results of study revealthat an increase in soil moisture content delayed the budinitiation and panicle emergence in mango. The minimumintensity (2.7 m-2) and severity (24.7%) of malformation wasrecorded under restricted soil moisture conditions. The highermoisture content in soil increased the intensity (4.5 m-2) andseverity (42.7%) of malformed. The higher soil moisture levelenlarged the panicle in terms of length (29.5 and 8.6cm), fresh(37.3 and 70.6 g) and dry weight (21.4 and 28.2 g) of healthyand malformed panicles, respectively. Among the varietiesminimum intensity (2.9m -2) and severity (25.7%) ofmalformation was recorded in Langra followed by Amrapaliand Sunderja. The maximum infestation of malformation wasrecorded under south direction of tree canopy.

Keywords: Soil moisture, Malformation, Intensity,Severity

Mango (Mangifera indica L.) is having wider adaptabilitywith respect to soil, climate and altitude for its successfulcultivation. It is being grown on 2.04 million hectaresarea with a total production of 22.7 million tones whichworks out to a low average productivity of 11.1 metrictones per hectare (Anonymous 2010, Kumar et al.2009). Globally, number of problems has been identifiedin mango production. Which are associated with scionand rootstock. Malformation is the most threateningmalady that causes great economic losses and limitsthe mango production. In floral malformation, a flowerof malformed panicle enlarges and get crowded with

Influence of soil moisture on growth and malformedpanicles of mango varieties

Rajnee Sharma, S. K. Pandey and T. R. SharmaDepartment of HorticultureCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email: rajnisharma5886@gmail.com

hypertrophied axis of panicles and becomes unable toproduce fruits. The intensity and severity of disorderare closely associated with plant type, season,atmospheric humidity, temperature and status of nutrientand moisture in soil. The moisture stress stimulatedgrowth of floral bud and delayed the vegetative phase.On the other hand, decrease in temperature aftermoisture stress proved to be beneficial for flowerinitiation. Therefore, an attempt has been made to studythe effect of status of soil moisture on malformation indifferent verities of mango.

Material and method

A field experiment was conducted at Fruit ResearchStation, Imalia, JNKVV during 2010 and 11. The soil ofexperimental site was clayey in texture (58.4% clay, 22.5silt and 20.1% sand) having ph 7.2, medium availableN (302 kg ha-1), high in P (22.6 kg ha-1) and K (430.7kgha -1) with medium organic carbon (0.70%). Theexperiment consisted of three varieties (V1:Amrapali,V2:Sunderja and V3: Langra) and eight irrigation levels(I1: control (without irrigation), I2 : Irrigation at 30 daysafter rains over, I3 : Irrigation at 60 days after rainsover, I4 : Irrigation at 90 days after rains over, I5 :Irrigation at 30 and 60 days after rains over, I6 : Irrigationat 30 and 90 days after rains over, I7 : Irrigation at 60and 90 days after rains over and I8 : Irrigation at 30, 60and 90 days after rains over). Twenty four plants of eachvariety were selected randomly and replicated thrice ina factorial randomized block design. Ten shoots of eachtreatment from north, east, west and south direction ofplant canopy were tagged randomly and emergence ofbud and panicle were recorded. Tagged shoots werealso used to assess severity, panicle length and dryweight accumulation in healthy and malformed panicles.

JNKVV Res J 47(2): 196-201 (2013)

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Table 1. Influence of soil moisture on growth of healthy and malformed panicles

Bud Panicle Panicles length Panicle PanicleTreatments initiation emergence (cm) fresh wt. dry wt.

(Date/Days) (Date/Days) North East West South (g) (g)H M H M H M H M H M H M

V1I1 Jan.01 /76 Jan.15/91 20.5 5.2 21.5 5.0 23.5 6.0 23.8 7.2 19.8 47.7 7.9 21.7

V1I2 Jan.01/76 Jan.16/92 23.2 5.9 24.2 5.7 25.8 6.7 25.4 7.9 24.7 50.1 8.7 19.9

V1I3 Jan.02/77 Jan.17/93 24.5 6.2 25.5 6.0 26.9 7.0 27.2 8.2 26.3 52.4 9.9 18.4

V1I4 Jan.01/76 Jan.16/92 25.3 6.5 26.3 6.3 27.3 7.3 28.3 8.5 27.2 57.7 11.7 19.8

V1I5 Jan.02/77 Jan.17/93 25.6 5.9 26.6 5.7 27.6 6.7 29.4 7.9 28.4 59.5 14.9 19.7

V1I6 Jan.03/78 Jan.18/94 26.2 5.9 27.2 5.7 28.2 6.7 32.7 7.9 30.4 64.0 15.3 21.5

V1I7 Jan.04/79 Jan.19/95 27.4 6.4 28.4 6.2 29.2 7.2 32.7 8.4 28.3 65.3 15.5 22.9

V1I8 Jan.05/80 Jan.19/95 29.4 6.7 30.4 6.5 31.4 7.5 33.9 8.7 30.9 67.7 17.1 23.9

- - 25.3 6.1 26.3 5.9 27.5 6.9 29.2 8.1 27.0 58.1 12.6 21.0

V2I1 Jan.18/94 Feb.08/115 17.8 5.9 18.4 5.7 19.8 6.7 20.5 7.9 27.5 52.8 11.9 29.1

V2I2 Jan.20/96 Feb.10/117 19.4 6.9 19.4 6.7 21.4 7.7 23.8 8.9 28.6 58.3 13.3 26.4

V2I3 Jan.22/98 Fab.12/119 21.1 7.5 21.1 7.3 23.2 8.3 24.9 9.5 30.8 62.8 15.2 22.9

V2I4 Jan.21/96 Feb.09/116 22.7 8.1 22.7 7.9 24.3 8.9 25.3 10.1 32.5 64.4 18.8 24.8

V2I5 Jan.23/99 Feb.13/120 23.3 7.2 23.3 7.0 25.4 8.0 25.6 9.2 35.3 65.3 21.7 23.5

V2I6 Jan.24/100 Feb.14/121 26.5 7.9 26.5 7.7 28.7 8.7 26.2 9.9 39.2 67.9 23.6 25.8

V2I7 Jan.25/101 Feb.15/123 25.7 8.9 26.7 8.7 28.7 9.7 27.2 10.9 38.5 69.8 24.9 30.8

V2I8 Jan.25/101 Feb.15/123 27.7 9.1 26.7 8.8 28.9 9.8 29.4 11.1 42.5 75.3 28.5 32.9

- - 23.0 7.7 23.1 7.5 25.1 8.5 25.4 9.7 34.4 64.6 19.7 27.0

V3I1 Jan 24/100 Feb.14/129 17.4 6.9 17.8 6.7 19.8 7.7 19.8 8.9 21.6 45.7 9.8 26.6

V3I2 Jan.26/102 Feb.16/131 22.4 7.5 23.4 7.2 24.3 8.2 25.3 9.5 26.2 48.2 11.2 26.7

V3I3 Jan.28/104 Feb.18/133 23.5 7.3 24.5 7.0 25.4 8.0 26.4 9.3 26.6 49.5 12.4 27.9

V3I4 Jan.25/ 101 Feb.14/129 24.0 7.5 25.0 7.3 26.5 8.3 27.5 9.5 29.5 54.8 13.6 27.4

V3I5 Jan.27/103 Feb.17/132 23.5 7.9 24.5 7.7 25.4 8.7 26.4 9.9 28.6 50.8 14.2 24.5

V3I6 Jan.30/106 Feb.20/135 23.5 7.8 24.5 7.6 25.5 8.6 26.5 9.8 32.5 55.6 15.3 24.2

V3I7 Feb.02/109 Feb.22/137 26.8 7.9 27.8 7.8 28.7 8.8 31.7 9.9 34.4 68.1 16.8 26.4

V3I8 Feb.04/110 Feb.23/138 26.9 7.9 27.9 7.9 28.9 8.9 32.9 9.9 38.4 68.7 18.7 27.7

- - 23.5 7.6 24.4 7.4 25.6 8.4 27.1 9.6 29.7 55.2 14.0 26.4CD at 5%Irrigation (I) 1.64 1.36 2.17 1.32 1.39 1.70 2.06 2.15 2.55 3.11 1.15 3.58

Variety (V) 1.00 0.83 1.33 1.11 NS 1.04 1.26 1.31 1.56 1.90 0.70 2.35

Interaction (I x V) 2.84 2.35 3.77 3.15 2.42 2.95 3.57 3.72 4.42 5.38 2.01 6.67

V1: Amrapali, V2: Sunderja, V3: Langra, I1: Control without irrigation, I2: Irrigation at 30 days after rains, I3: Irrigation at 60 daysafter rains, I4: Irrigation at 90 days after rains, I5: Irrigation at 30 and 60 days after rains, I6: Irrigation at 30 and 90 days afterrains, I7: Irrigation at 60 and 90 days after rains, I8: Irrigation at 30, 60 and 90 days after rains H: Healthy, M: Malformed panicle

198

Table 2. Influence of soil moisture on intensity and severity of healthy and malformed panicles

Intensity (m-2) Severity (%)Treatments North East West South North East West South

H M H M H M H M H M H M H M H MV1I1 6.9 1.7 6.9 1.8 7.2 1.8 8.8 2.2 82.7 17.3 77.7 22.3 79.2 20.8 76.3 23.7

V1I2 5.1 2.3 6.1 1.9 8.5 3.5 7.9 4.1 76.7 23.3 74.3 25.7 76.4 23.6 74.7 25.3

V1I3 4.8 2.3 6.2 1.2 8.1 2.9 6.9 4.5 76.7 23.3 77.7 22.3 73.3 26.7 70.3 29.7

V1I4 5.4 1.7 6.5 1.6 7.4 1.9 8.9 2.4 83.3 16.7 77.3 22.7 78.6 21.4 76.7 23.3

V1I5 5.2 2.5 7.2 1.8 6.5 7.5 6.2 4.8 74.6 25.4 70.2 29.8 69.3 30.7 67.6 32.4

V1I6 4.4 3.7 4.4 2.6 7.7 3.3 5.9 4.6 63.3 36.7 74.3 25.7 71.7 28.3 65.4 34.6

V1I7 5.3 3.3 5.3 2.7 6.7 4.3 6.1 4.9 66.7 33.3 67.7 32.3 68.7 31.3 64.6 35.4

V1I8 4.5 3.6 4.5 3.5 6.8 5.2 5.9 5.1 53.7 46.3 62.0 38.0 56.7 43.3 53.3 46.7

5.2 2.7 5.9 2.1 7.4 3.8 7.1 4.1 73.5 26.5 72.7 27.4 71.7 28.3 68.6 31.4

V2I1 4.8 2.7 4.8 4.2 7.4 5.6 8.7 4.3 73.3 26.7 65.3 34.7 58.7 36.0 61.7 38.3

V2I2 7.2 3.0 6.7 4.1 8.0 7.0 7.2 4.8 70.3 29.7 59.2 40.8 58.3 41.7 57.2 42.8

V2I3 7.3 3.3 7.3 4.7 8.0 4.0 6.9 6.1 67.3 32.7 53.5 46.5 66.7 43.3 54.7 45.3

V2I4 5.7 2.8 5.7 4.3 7.7 5.4 8.4 5.6 72.5 27.5 65.7 34.3 61.3 38.7 60.7 39.3

V2I5 6.5 3.9 6.2 4.0 5.6 6.4 6.7 3.3 61.3 38.7 48.7 51.3 54.7 45.3 52.3 47.7

V2I6 3.7 3.5 3.7 4.3 5.9 5.1 6.7 7 64.7 35.3 57.2 42.8 53.3 46.7 50.2 49.8

V2I7 4.8 3.5 4.8 4.2 6.8 5.2 5.6 6.4 65.3 34.7 54.6 45.4 51.6 48.4 46.7 53.3

V2I8 4.5 3.9 4.5 4.5 5.2 6.8 3.7 7.3 61.2 38.8 55.2 48.8 43.3 56.7 37.3 62.7

5.6 3.3 5.5 4.3 6.8 5.7 6.7 5.6 67.0 33.0 57.4 43.1 56.0 44.6 52.6 47.4

V3I1 5.4 1.3 5.4 1.6 7.3 2.7 8.4 2.3 86.7 13.3 81.7 18.3 79.3 20.7 76.2 23.8

V3I2 3.8 2.0 3.8 1.3 8.3 3.8 7.3 3.7 80.5 19.5 77.7 22.3 76.5 23.5 73.7 26.3

V3I3 2.9 2.3 2.9 2.1 7.7 2.3 6.3 4.7 77.3 22.7 69.7 30.3 74.7 25.3 71.3 28.7

V3I4 5.7 1.5 6.7 1.3 7.2 2.8 8.6 2.5 84.3 14.7 80.3 18.7 78.5 21.5 75.3 24.7

V3I5 4.7 2.7 5.7 3.3 7.1 3.9 7.8 5.3 73.3 26.7 67.7 32.3 71.7 28.3 69.3 30.3

V3I6 6.9 2.3 6.9 2.1 8.0 3.7 6.5 3.7 76.7 23.3 76.3 23.7 70.3 29.7 69.3 30.7

V3I7 6.6 2.7 6.6 2.4 8.1 3.9 6.3 3.7 73.3 26.7 76.3 23.7 69.5 30.5 68.3 31.7

V3I8 6.6 3.1 6.6 2.4 9.1 3.9 8.9 5.1 69.3 30.7 69.7 30.3 67.2 32.8 62.3 37.7

5.3 2.2 5.6 2.1 7.9 3.4 7.5 3.9 77.7 22.3 74.9 25.0 73.5 26.5 70.7 29.2CD at 5%Irrigation (I) 2.7 NS NS NS 2.29 1.02 NS NS 7.87 7.87 9.22 9.29 8.77 8.77 8.65 8.37

Variety (V) 1.6 NS 2.62 0.78 1.40 0.62 NS 0.79 4.82 4.82 5.64 5.69 5.37 5.37 5.30 5.12

Interaction (I x V) 4.7 NS NS NS 3.79 1.77 NS NS 13.6 13.6 NS NS 15.19 15.19 14.99 14.5

V1: Amrapali, V2: Sunderja, V3: Langra, I1: Control without irrigation, I2: Irrigation at 30 days after rains, I3: Irrigation at 60 daysafter rains, I4: Irrigation at 90 days after rains, I5: Irrigation at 30 and 60 days after rains, I6: Irrigation at 30 and 90 days afterrains, I7: Irrigation at 60 and 90 days after rains, I8: Irrigation at 30, 60 and 90 days after rains H: Healthy, M: Malformed panicle

199

Fig. 5.1: Influence of soil moisture on intensity of malformed panicles (m-2)

0

1

2

3

4

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7

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9

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rmed

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icles (m

-2)

Healthy Amrapali (V1) Sunderja (V2) Langra (V3) Malformed Amrapali (V1) Sunderja (V2) Langra (V3)

Fig. 5.2: Influence of irrigation on severity of malformation panicle (%)

0

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)

Healthy Amrapali (V1) Sunderja (V2) Langra (V3) Malformed Amrapali (V1) Sunderja (V2) Langra (V3)

Influence of soil moisture on intensity of malformedpanicles

Influence of irrigation on severity of malformedpanicle

Fig. 5.3: Influence of irrigation schedule on length of panicles (cm)

0

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Healthy Amrapali (V1) Sunderja (V2) Langra (V3) Malformed Amrapali (V1) Sunderja (V2) Langra (V3)

Fig. 5.4: Effect of irrigation on number of leaves on healthy and malformedshoot

0

2

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6

8

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No. of leaves

on hea

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Healthy Amrapali (V1) Sunderja (V2) Langra (V3) Malformed Amrapali (V1) Sunderja (V2) Langra (V3)

Influence of irrigation schedule on length of panicles Effect of irrigation on number of leaves on healthyand malformed shoot

Fig. 5.5: Effect of irrigation on fresh weight of healthy and malformedpanicles (g)

0

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Healthy Amrapali (V1) Sunderja (V2) Langra (V3) Malformed Amrapali (V1) Sunderja (V2) Langra (V3)

Fig. 5.6: Effect of irrigation on dry weight of healthy and malformedpanicles (g)

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Irrigation schedule

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35Malfo

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)

Healthy Amrapali (V1) Sunderja (V2) Langra (V3) Malformed Amrapali (V1) Sunderja (V2) Langra (V3)

Irrigation schedule

Effect of irrigation on fresh weight of health andmalformed panicles

Effect of irrigation on dry weight of healthy andmalformed panicles

200

Healthy and malformed panicles detached separatelyfrom node for recording fresh weight. To determine thedry weight, panicle were chopped and oven dried at 60±20c till get constant weight. The number of healthy andmalformed panicles per square meter was recorded withthe help of quadrate.

Results and discussion

Early initiation of bud and emergence of panicles werenoted while the plants were not irrigated or irrigationdelayed upto 90 days. Moreover, period of bud initiationincreased with increase in irrigation frequency, hencemoisture stresses encouraged the reproductive phasei.e. bud initiation as well as emergence of panicles. Thepresent observations conformity with the findings of Luand Chacko (2000) who reported that soil water deficitpromote earlier and more intense flowering in mango.The results of Tahir et al. (2003) supported the presentfindings.

After withdrawal of rains as the number ofirrigation increases increased the intensity and severityof malformation. The maximum intensity and severityof malformed panicles (4.5 m -2 and 42.7%) wererecorded under higher soil moisture increased throughapplication of three irrigations at an interval of 30 daysafter rain over. The least intensity (2.7m-2) and severity(24.7%) of malformed panicle were noted under withoutirrigation closely followed by irrigation at 90 days afterrains. This might be due to the assimilation of morecarbohydrate during moisture stress which promotesthe reproductive phase of plant. These findings are inproximity of Singh and Ram (1997) and Gaur andChakrabarti (2009).

The maximum intensity (4.5m-2), severity (36.0%)and length (9.1cm) of malformed panicles was recordedunder north direction whereas, minimum intensity(2.7m -2), severity (27.7%) and length (7.1cm) ofmalformed were noted under south direction of plantcanopy. It was also noted that higher moisture contentin soil adversely increased the intensity (4.5m-1), severity(42.7%) and length (8.6 cm) of malformed panicle. Themalformed panicle length and its severity and intensityboth increased with the change of direction from southto north. It might be because of higher moisture levelwhich increased the nutrient uptake. These findings arein agreement with the findings of Gaur and Chakrabarti(2009) who reported that the malformation developmentwas promoted by high rainfall. Thakur et al. (2003) foundthat incidences of floral and vegetative malformationwere higher on the north-facing as compared to and

lowest on the south-facing side of the tree.

Irrigation treatment with different varieties showedthe markeable variations for intensity and severity ofmalformation. The higher intensity (5.6 m-2) as well asseverity (51.8%) of malformation was recorded whenSunderja plants were irrigated thrice at 30, 60 and 90days after rains whereas the lowest infestation wasrecorded with variety Langra at the same level of soilmoisture. These findings are in agreement with thefindings of Singh and Ram (1997), Badiyala andLakhanpal (1990) and Gaur and Chakarabarti(2009).The intensity and severity of malformationchanged with the change of directions of plant canopy.Cultivars, Langra found least susceptible formalformation followed by Amrapali and Sunderja in allthe directions of canopy. These findings are inagreement with the findings of Thakur et al. (2000).

izLrqr vUos"k.k Qy vuqla/kku dsanz] befy;k] tokgjyky usg# d`f"kfo'ofo|ky;] tcyiqj esa Hkwfe ueh dk vke ds xqPNk jksx ls izHkkfor'kk[kkvksa esa iks"kd rRoksa dh ek=k dk vkadyu gsrq fd;k x;k A flapkbZ,oa fdLeksa ds dqy 24 mipkjksa dk la;kstu dj QsDVksfj;y js.MksekbtfMtkbu esa rhu izR;qRrj esa ijh{k.k fd;k x;k A ijh{k.k esa ik;k x;kfd e`nk esa ueh ds c<+us ij iq"i xqPN nsjh ls gksrk gS A iq"i xqPNdh de rhozrk ¼2.7m-1½ ,oa l?kurk ¼24.7%½ e`nk ueh esa dehds lkFk ,oa iq"i xqPN dh rhozrk ¼4.5m-1½ ,oa l?kurk ¼42.7%½vf/kd ueh dh voLFkk esa vkWadh xbZ A e`nk esa vf/kd ueh ls isuhdydh yEckbZ ¼29.5 ,oa 8.6cm½] Ýsl Hkkj ¼37.3 ,oa 70.6g½] 'kq"dHkkj ¼21.4 ,oa 28.2g½] dze'k% LoLFk ,oa iq"k xqPNk jksx okyh'kk[kkvksa esa vkadk x;k A iq"i xqPN dk vf/kd izHkko nf{k.k fn'kk esans[kk x;k A yaxM+k fdLe esa vkezikyh ,oa lqUnjtk dh rqyuk esa deikbZ xbZ A

Reference

Anonymous (2010) Indian Horticulture data base 2009. Ed.Bijay Kumar et al.: National Horticulture Board,Ministry of Agriculture, Government of India, 1-282

Badiyala SD and Lakhanpal SC (1990) Research of somemango cultivar to floral malformation under Paontavalley conditions of Himachal Pradesh. South IndianHort 38(3): 152

Gaur VP, Chakrabarti DK (2009) Incidence of malformationin mango (Mangifera indica) nurseries in easternUttar Pradesh. Indian J Agri Sci 79 (2): 160-162

Kumar P, Misra A K, Pandey BK, Misra SP, Modi DR (2009)Eco- friendly management of mango malformationFusarium moniliforme var. subglutinans from leaf

201

extracts. J Eco-Friendly Agric 4: 61-64Lu P, Chacko EK (2000) Effect of water stress on mango

flowering in low latitude tropics of Northern Australia.Acta Hort 509:283-290

Singh CP, Ram S (1997) Effect of irrigation on flowering,fruiting and malformation in mango. Acta Hort455:543-546

Tahir FM, Ibrahim M, Hamid Kamran (2003) Effect of droughtstress on vegetative and reproductive growthbehaviour of mango (Mangifera indica L.). Asian JPlant Sci 2:116-118

Thakur AS, Vaishampayan SM, Shukla A (2000) Effect ofvarieties, nutrients and direction on the incidence offloral and vegetative malformation in grafted mango.Crop Res 20 (3): 494-499

(Manuscript Receivd : 5.4.13; Accepted : 10.10.13)

202

Abstract

The study was designed to measure resource use efficiencyin Chick pea production of Narmadapuram division of MadhyaPradesh State. In present investigation we used the doublelog type Cobb-Douglas type of production function. The sampleof 216 Chick Pea farmers were selected from which input-output data collected based on 2012-13 rabi cropping season.Functional analysis of chick pea crop revealed that seed rate,fertilizers and manures, plant protection, irrigation facilities,human labour and machine labour, manure and fertilizers,had the elasticity of 0.052, 0.147, 0.199, 0.039, 0.037 and0.11 respectively and was statistically significant. The valueof MVP in respect of seed rate (1.48), plant protection (4.476)and fertilizers and manures (3.067) were more than unit leveland the MVP value of irrigation facilities (0.166) human labour(0.638), machine labour (0.257) were found to be less thanunit level.

Key words: Chick pea, profitability, resource useefficiency, marginal value of product

Chick Pea (Cicer arietinum) is the most important pulsecrop of India. Chick Pea occupies about 37% of areaunder pulses and contributes about 50% of the totalpulse production of India. Chick pea is major pulsescrop in the state of Madhya Pradesh which occur thearea 3043.4 thousand ha and the production is 3290.3thousand tones. In Narmapuram division rabi seasonof chick pea the second most important crop coveringan area of 88.2 thousand ha and the production is 140.6thousand tones (Year 2012).

In Narmadapuram division, Hoshangabad andHarda are the selected districts. The presentinvestigation aims to examine cost and returns analysisof principle crop of Narmadapuram division.

Resource use efficiency in chick pea production in Narmadapuramdivision of Madhya Pradesh

Rita Kapil, J.S. Raghuvanshi and Dharmendra NarvariyaDepartment of Agricultural Economics and Farm ManagementRajmata Vijayaraje Krishi Vishwa VidyalayaGwalior 407011 (MP)

Materials and methods

The present study was carried out in Narmadapuramdivision of of Madhya Pradesh, where chick pea is thecommonly grown as rabi season crop by the farmersNarmadapuram division comprises of two districtsnamely, Hoshangabad and Harda. The data of 216cultivators were collected and compiled for the year2012-13.

Resource use efficiency

In present investigation we used the Cobb-Douglas typeof production function - Resource use efficiency. TheCobb-Douglas production function was used forestimating the resources productivity of variableresources used in selected crop.

The Cobb-Douglas type of production function was usedand is defined as:

Y = a X1 b1. X2 b2. X3 b3. X4 b4. X5 b5. X6 b6

where,Y = Dependent variable (gross income in Rs)a = ConstantX = independent variableX1 = Value of seed in RsX2 = value of fertilizer in RsX3 = value of human labour in RsX4 = value of plant protection measures in RsX5 = irrigation charge in RsX6 = value of machine labour in Rsb1…b6 = Regression coefficient of concern variableMarginal Value Productivity (MVP)

JNKVV Res J 47(2): 202-204 (2013)

203

The estimated production function underlyingcrop-production enabled us to evaluate the efficiencyof prevalent factor proportions.

The MVP was computed by the multiplying thecoefficients of the given resource with ratio of thegeometric means of the output to the geometric meanof the given resource. For example, the MVP of Xi wouldbe, from the above production function the MVP of eachresource was worked out. The marginal productivity ofparticular input "Xi" at geometric mean of input andoutput expressed in following equation.

iMPVXi = bi Pxi

i

where,

i = gross value of output (Rs.)

i = Factor of production

bi = Regression coefficient of Xi

Pxi = Price of Xi

By comparing the respective input prices with theirmarginal values, fanners can decide as to whether theyshould increase or decrease the level of use of that,particular input of products.

Returns to Scale

The returns to scale could estimate from the type ofproduction function. Thus, returns to scale = a1 + a2 +…. + an

= ai =1, 2…….n

Therefore, the summation of the powers of allthe input variables provided us directly with a readyestimate of the returns to scale as also the degree ofhomogeneity of the production function. The returns toscale are deceasing, constant or increasing, Dependingon whether a, is less than, equal to or greater than one.

Results and discussion

Without having the functional analysis and estimatingthe marginal value productivity (MVPs) the existing

levels of inputs, one cannot come to the conclusion thatwhether the input is being overused or underused inproduction and also the contribution of differentresources in the production of crop. For this purpose,as mention earlier, the Cobb-Douglas type of productionfunction was fitted to the data and the estimateselasticity's of different inputs are obtained (Table 1).

Table 1. Coefficient of different resource use in chickpea Production

Input Coefficient

Constant 7.76Seed rate 0.21**Fertilizer and manure 1.34Plant protections measures 1.02Irrigation charges 1.85Human labour 0.05*Machine labour 0.16**R2 0.73**Returns to Scale 0.49**Significant at 1 per cent level*Significant at 5 per cent level

Seed rate, fertilizers and manures, plantprotection, irrigation facilities, human labour andmachine labour had the elasticity of 0.21, 1.34, 1.02,1.85, 0.05 and 0.16 respectively and were statisticallysignificant (Table 1). This means that one per centincrease in investment on these resources wouldcontribute that percentage increase in chick pea yield.The values of the elasticity's of these variables revealfurther scope for utilization of these inputs. The returnto scale was less than unity (0.49) indicating decreasingreturns to scale. The coefficient of determination (R2)provides an idea about the proportion of the totalvariation in yield explained by the selected variables.For chick pea crop the value of R2 was 0.73 this hasindicated that the selected variables contribute to theextent of 73 per cent of the total variation in grossreturns.

Geometric mean level and Marginal Value Product(MVP)

The result of production function analysis in terms ofGeometric mean level and Marginal Value Product arepresented in Table 2. It was conducted that the MVPs of

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the seed rate, fertilizer and manure and plant protectionsimplies the profitable economic returns on investmentson these resources. The economic efficiency of selectedchick pea growers was 74 per cent. It reveals that thereis a considerable scope to increase the productivity.The average allocative efficiency of the selected chickpea growers was 90 percent. The study implied thatthe output of average farmers could be increased byadopting the allocation of resources fallowed by the bestpracticed farmers.

Table 2. Geometric mean level and Marginal Value Prod-uct

Inputs Geometric mean MVPlevel

Seed rate 3.29 1.48

Fertilizer and manure 3.14 3.06

Plant protections measures 3.17 4.47

Irrigation charges 3.28 0.16

Human labour 3.38 0.63

Machine labour 3.51 0.25

bl v/;;u ls e/;izns'k jkT; ds e/; ueZnk oSyh {ks= dh ueZnkiqjelaHkkx ds puk mRiknu esa lalk/ku dk mi;ksx {kerk dks ekius ds fy,fMtkbu fd;k x;k Fkk A orZeku esa ge mRiknu lekjksg dk Mcyizos'k izdkj dk¡c Mxyl izdkj dk mi;ksx dj jgs gS 216 pus dsfdlkuksa dk uewuk p;u fd;k x;k Fkk ftlesa ls 2012&13 ds jchQly ds ekSle ds vk/kkj ij ,d= buiqV vkmViqV MsVk pus dhQly ds dk;kZRed fo'Yks"k.k cht nj] moZjd vkSj [kkn] ikS/k laj{k.kflapkà lqfo/kkvksa] ekuo Je vkSj e'khu Je dh yksp Øe'k% 0-052] 0-147] 0-199] 0-039] 0-037 vkSj 0-11 lkaf[;dh;egRoiw.kZ Fkk cht nj ¼1-48½ ikS/k lja{k.k ¼4]476½ vkSj moZjdvkSj [kkn ¼3-067½ ds laca/k esas ,eohih dk ewY;] ,drk Lrj vkSjflapkà lqfo/kkvksa dh ,eohih ewY; ¼0-166½ ekuo Je ¼0-638½]e'khu Je ¼-0-257½ rqyuk esa vf/kd Fks] ,drk Lrj dh rqyuk esade gksuk ik;k x;kA

Reference

Ashwthareddy KP, Chandrashekar KS, Srinivasgowda MV(1997) Resource-use efficiency in groundnutproduction under rainfed conditions- A study inChallakere taluk of Karnataka. Agril Situation in India53 (12): 829-831

Chapke RR, Mishra JS (2011) Sorghum, Rice-fallows. Cobb-Douglas Production Function. Resource-useEfficiency. Human Ecology 34(2): 87-90

Gangwar B, Baldev Ram (2005) Effect of crop diversificationon productivity & profitability of rice, wheat system.Indian J Agril Sci 75 (7): 435-438

Muralidharan PK (1987) Resource use efficiency in kole landsin Trichur District Kerala. Indian J of Agril Econ 42(4):578-586

Nagaraj (1995) Studied the resource-use efficiency of variouscrops included in each cropping system inTungabhadra command area in Karnataka.Karnataka J Agril Sci, 31(1): 261

Nowacki W (2008) Comparison of profitability of potatocultivation in organic and integrated farming systems.Progress in Plant Prot. 48 (4):1526-1534

(Manuscript Receivd : 2.10.13; Accepted : 15.11.13)

205

Abstract

An Investigation was undertaken to determine theadoption of Integrated Pest management practices of potatocultivation among 120 growers in Chhindwara district ofMadhya Pradesh. The data reveal that majority of potatogrowers had lower awareness for adoption of IPM practices.Education, annual income, area under potato crop, farmpower, social participation, information sources utilization,extension participation, mass media exposure, economicmotivation, attitude towards IPM, scientific orientation andknowledge level had significant association with adoption ofIPM practices by the potato growers under the study.

Keywords: IPM potato

Potato (Solanum tuberosum L.) is one of the mostpopular vegetable grown in M.P. because of its highernutritive and higher production.

Madhya Pradesh is one of the eight largest potatoproducer states in India having an area of 56695 ha.witha productivity of potato in about 125.76 q/ha. It isrecorded from the available secondary data that the areaand production of potato crop in Chhindwara district is6750 ha and 168750 metric tones, The Chhindwarablock shares an area of 2015 ha with a production ofabout 50375 metric tones.

The investigation was undertaken with thefollowing specific objectives to know the attributes ofpotato growers and the extent of adoption of IntegratedPest Management practices by potato growers and tofind out the association between adoption of IntegratedPest Management practices with the attributes of potatogrowers.

Adoption of integrated pest management practices by potatogrowers in Chhindwara block, Madhya Pradesh

Priya Karade, S.K. Agrawal, V.K. Pyasi, M.K. Dubey and D.K. JaiswalDepartment of Extension EducationJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)Email : mr.deepakjaiswal@gmail.com

Methodology

The study was conducted in Chhindwara district ofMadhya Pradesh. There are 11 blocks in the districtsand out of them one block i.e. Chhindwara block wasselected purposively for this study because of largestarea under potato crop.

The respondents were selected through multi-step random sampling method. At first stage, out of131 villages of Chhindwara block, 6 villages wereselected randomly. At the second stage, a list of farmerswas prepared from all 6 villages. At third stage, 20 potatogrowers from each selected villages were randomlyselected from the list, making the total of 120 farmersfor the study.

Results and discussion

Higher percentage of the farmers (47.50%) were inmedium age group, maximum number of potato growers(35.00%) (Table 1). Out of total sample respondents,68.34 per cent were having less area under potato cropand majority of potato growers (58.34%) had low annualincome of Rs. 2,00,000/-. The higher percentage ofpotato growers had medium farm power and hadmedium social participation. About 50.00 per cent potatogrowers used medium information source and medium(49.16 %) extension participation. The higherpercentage of respondents (42.50%) had medium massmedia exposure and had higher (48.35%) economicmotivation, while 489.33 per cent had moderate attitude

JNKVV Res J 47(2): 205-208 (2013)

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Table 1. Attributes of Potato growers

Attributes Scoring method Categories No. of PercentageRespondents

Age No. of years Young 43 35.83

Middle 57 47.50

Old 20 16.67

Education No. of classes passed Illiterate 09 7.50

Up to Primary 35 29.16

Up to High School 42 35.00

Higher Secondary and above 34 28.34

Annual Income In Rupees Low income 70 58.34

Medium income 34 28.33

High income 16 13.33

Area under Potato crop In hectares Up to 2 ha 82 68.34

2.01 to 4 ha 26 21.66

Above 4 ha 12 10.00

Farm power Self-scoring Low 51 42.50

Medium 56 46.67

High 13 10.83

Social participation Trivedi & Pareek (1965) Low 45 37.50

Medium 53 44.16

High 22 18.34

Information sources utilization Nandapurker (1982) Low 37 30.83

Medium 62 51.67

High 21 17.50

Extension participation Siddaramaiah & Jalihal (1983) Low 36 30.00

Medium 59 49.16

High 25 20.84

Mass media exposure Desai (1977) Low 47 39.16

Medium 51 42.50

High 22 18.34

Economic motivation Supe (1969) Low 28 23.34

Medium 34 28.33

High 58 48.33

Attitude towards IPM Chouhan (2003) Unfavourable 29 24.17

Moderate 58 48.33

Favourable 33 27.50

Scientific orientation Supe (1969) Low 50 41.67

Medium 44 36.66

High 26 21.67

Knowledge level Self-scoring Low 55 45.83

Medium 47 39.17

High 18 15.00

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towards IPM. The higher percentage of potato growers(451.57%) had low scientific orientation and most ofthe potato growers had low knowledge level (45.83%).

Table 2. Distribution of potato growers according to theiradoption level of integrated pest management practices

Categories Frequency Percentage

Low adoption 56 46.67Medium adoption 40 33.33High adoption 24 20.00Total 120 100.00

All the respondents were grouped into low,

medium and high categories of adoption level. It wasobserved that 46.67 per cent respondents belonged tolow level of adoption, followed by 33.33 per cent hadmedium adoption level and 20.00 per cent respondentsbelonged to high level of adoption.

The association between attributes ofrespondents and their adoption of IPM practices (Table3). The attributes of potato growers like education, areaunder potato crop, total annual income, socialparticipation, mass media exposure, extensionparticipation, information source utilization, attitudetowards IPM, scientific orientation, economic motivationand knowledge of IPM practices were found to besignificant with adoption of integrated pest managementpractices, whereas only age has shown non-significantassociation with adoption level of IPM practices by thepotato growers.

;g v/;;u e/;izns'k ds fNanokMk ftys esa vkyw dh Qly esa yxusokys jksx ,oa O;kf/k;ksa ds izca/ku gsrq mUur rduhfd;ksa dks vaxhdj.kdh tkudkfj;ksa dks izkIr djus gsrq fd;k x;k A bl v/;;u gsrq 120vkyw mRiknd Ñ'kdksa ls iz"ukoyh }kjk tkudkjh ,d= dh xbZ A ;gv/;;u n'kkZrk gS fd lefUor dhV ,oa O;kf/k fu;a=.k rduhfd;ksa dk

Table 3. Association between adoption of IPM practices with the attributes of potato growers

Variables (Attributes) 2 d.f. Level of significance

Age 0.456NS 2 0.05Education 13.029* 4 0.01Total annual income 25.352* 4 0.01Area under potato crop 6.250* 2 0.05Farm power 11.750* 2 0.01Social participation 14.174* 4 0.01Information sources utilization 16.300* 4 0.01Extension participation 20.329* 4 0.01Mass media exposure 30.592* 4 0.01Economic motivation 11.389* 4 0.05Attitude towards IPM 13.908* 4 0.01Scientific orientation 22.171* 4 0.01Knowledge level 31.900* 4 0.01

NS = Non-significant * = Significant

208

vaxhdj.k de ik;k x;k tcfd vkyw mRikndksa dh mez ,oa vaxhdj.kds lkFk dksbZ Hkh izHkkodkjh laca/k ugha ik;k x;k tcfd vkyw mRikndksadh f'k{kk] okf"kZd vkenuh] vkyw mRiknu jdok] lkekftd Hkkxhnkjh]lwpuk L=ksrksa dk mi;ksx] foLrkj dk;Z eas Hkkxhnkjh] lkeqnkf;d lapkjek/;e] vkfFkZd izsj.kk] jksx ,oa O;k/kh izca/ku rduhd ds izfr /kkj.kk]oSKkfud mn~cks/ku ,oa Kku dk Lrj ,oa vkyw Qly ds jksx ,oaO;kf/k izaca/ku rduhd ds vaxhdj.k ds e/; izHkkodkjh laca/k ik;kx;k A

References

Chourasiya TK (2011) A study on adoption behaviour ofImproved potato production technology among thefarmers of Sagar block in Sagar district, MP MSc(Ag) Thesis JNKVV Jabalpur 64 p

Nemade Rachna (2010) A study on training needs of vegetablegrowers with reference to Integrated PestManagement (IPM) in Jabalpur block of Jabalpurdistrict, MP MSc (Ag) Thesis JNKVV Jabalpur 77 p

Rajpoot AS (2011) A study on adoption of Integrated PestManagement practices by soybean growers in RehliBlock of Sagar District, Madhya Pradesh. MSc (Ag)Thesis JNKVV Jabalpur 54 p

Saxena KK, Kushwaha TS (2004) Adoption of organic farmingpractices. Indian J Extn Edu 1 & 2 : 34

(Manuscript Receivd : 30.8.13; Accepted : 15.11.13)

209

JNKVV Res J 47(2): 209-216 (2013)

Abstract

The growth pattern of chickpea production in different agro-climatic zones of Madhya Pradesh was examining using timeseries data for the period of last 40 years (1969-70 to 2008-09). To ascertain the change over the period was sub dividedinto two periods i.e. period-I (1969-70 to 1988-89), period-II(1989-90 to 2008-09) and overall period -III (1969-70 to 2008-09). The data shows that during period-I, growth in acreageand production of chickpea were positive and highly significantin Malwa plateau, Vindhyan plateau, Central Narmada valleyand Bundelkhand region. During period -II, these growths werepositive and highly significant of Vindhyan plateau, Satpuraplateau, and Kaymore plateau and it significant in State as awhole. During last 40 years, growth in area and production ofchickpea was found positive and highly significant in Malwaplateau, Vindhyan plateau, Central Narmada valley, Kaymoreplateau, Bundelkhand region and Nimar valley . State as awhole is also positive and highly significant during the studyperiod. During period -I, The positive and highly significantgrowth of productivity was shown in only Malwa plateau andBundelkhand region. In period- II, Only two regions viz., CentralNarmada valley Satpura plateau was shown positive andsignificant growth of productivity. It was found positive andhighly significant in the entire region except in Jhabua hillsand Northern hills region of Chhattigarh in period-III. Projectionof chickpea production for 2024-25 shows that the productionis likely to increase by 18.19 per cent and this may happendue to 14.46 per cent increase in yield and about 13.61 percent increase in acreage.

Keywords: Area, production, productivity, chickpea andgrowth rate

India is the largest producer of chickpea contributingaround 70% to the total global production. In India,

Dynamics of chickpea production in different agro-climatic zoneof Madhya Pradesh

R.F. Ahirwar, Roshni Tiwari* and R.M. Sahu*Department of Agril Econ & Farm ManagementCollege of AgricultureGanj Basoda Vidisha (MP)*Department of Agril Econ & Farm ManagementCollege of AgricultureJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

chickpea is grown in the Rabi season and its sowingtakes place during October-December. Chickpea is themost important pulse crop grown in India accountingfor 40% of the total pulses crop. This makes India theleading chickpea producing country in the world. InIndia, it is mainly used to produce 'dal' and besan(ground flour). The area and production of chickpea was2614.8 thousand hectare and 2841.60 thousand tonnesrespectively with average productivity of 1086.93 kg perhectare in Madhya Pradesh (2010-11). The productionof chickpea has remained almost same for the last 10years due to stagnant area and yield on account of shiftin cultivation from pulses to cereals and vegetables.The present study was undertaken to determine thevariability and growth of this particular crop in thedifferent agro-climatic zones of Madhya Pradesh. Thestudy focused on the pigeonpea of relative change,variability, and linear as well as compound growth inarea, production and productivity of Chickpea. Anattempt is made to project acrea, production and yieldof the crop for the year 2024-25.

Materials and methods

The study was confined all the 11 agro-climatic zonesof Madhya Pradesh viz. Malwa plateau, Vindhyanplateau, Central Narmada valley, Satpura plateau,Jhabua hills, Gird region, Kaymore plateau,Bundelkhand zone, Nimar valley, Northern hills regionof Chhattigarh and Chhattisgarh plain. The district wisetime series secondary data on area, production andproductivity pertains to last 40 years from 1969-70 to2008-09 were collected from various issues of "MadhyaPradesh Agricultural Statistics" published by the

210

Directorate of Farmer Welfare and AgriculturalDevelopment, Madhya Pradesh, Bhopal andCommission of Land Record Gwalior. The total periodof the study was divided into three sub period viz. period- I (1969-70 to 1988-89), period - II (1989-90 to 2008-09 ) and period III as a overall period (1969-70 to 2008-09). The relative change, coefficient of variation,standard deviation, linear growth rate as well ascompound growth rate was worked out for theinterpretation of results.

from 1510.63 in 1969-70 to 2011.50 thousand in 1988-89 with annual variation of 13.02 per cent in the State.The maximum relative change in the area was noted toCentral Narmada valley (36.70%) followed by Satpuraplateau (36.16%), Bundelkhand zone (34.40%), Malwaplateau (24.34%), Vindhyan plateau (11.81%), Girdregion (11.26%), Nimar valley (10.98%), Kaymoreplateau (3.38%) and Chhattisgarh plains (1.83%) in theperiod. The negative relative change in area wasobserved in Jhabua hill, Northern region of Chhattigarh

Table 1. Agro-Climatic Zones of Madhya Pradesh

Name of Zones Districts covered

Malwa Plateau Indore, Dewas, Dhar, Ujjain, Ratlam, Mandsaur, Nimach, Rajgarh andShajapur

Vindhyan plateau Bhopal, Vidisha, Sehore, Raisen, Damoh and Sagar

Central Narmada valley Hoshangabad, Harda and Narsinghpur

Satpura plateau Betul and Chhindwara

Jhabua hills Jhabua and Alirajpur

Gird region Gwalior, Bhind, Morena, Sheopur, Guna and Shivpuri and Ashoknagar

Kaymore plateau Jabalpur, Katni, Rewa, Panna, Satna, Sidhi and Seoni

Bundelkhand region Tikamgarh, Chhatarpur and Datia

Nimar valley Khandwa, Khargone and Badwani and Burhanpur

Northern hills region of Chhattigarh Shahdol, Umariya, Mandla, Dindori, Anuppur and Singroli

Chhattisgarh plains Balaghat

The projections of area, production and productivity for the year 2024-25 were worked out. The linear model wasused to project the area, production and productivity of chickpea

Results and discussion

The relative change, coefficient of variation, linear aswell as compound growth rate of area of chickpea indifferent agro-climatic zones of the State were workedout and presented as under sub heads.

Variability and Growth in Area

The maximum area of chickpea crop was confined toVindhyan plateau (31.35 %.) followed by Malwa plateau(25.86%), Kaymore plateau (15.37%), Central NarmadaValley (8.41%), Gird region (7.89%), Satpura Plateau(2.48%) (Table 2). These 6 agro-climatic regionscontribute about 90 per cent area of total chickpea areaof the State. The area of chickpea in the state increased

during the period -I. The Satpura plateau showsmaximum and positive significant growth as a linear(2.08%) and compound (1.87%) growth rate per annumin acreage of chickpea. The Jhabua hills, Kaymoreplateau Northern Hill region of Chhattisgarh andChhattisgarh plains are showing negative growth ratein acreage of chickpea revealing that this crop wasreplaced by wheat due to increase in irrigation potential.

In the period II, the area of chickpea increasedfrom 2061.00 thousand hectare in 1989-90 to 2564.93thousand hectare in 2008-09 with absolute change of503.93 thousand hectare in the state. The State isshowing of 24.45 per cent relative change, variation of9.73 per cent, linear and compound growth rate of 1.09and 1.13 per cent respectively. Amongst all the agro-climatic zones, the Vindhyan plateau was confined to

211

maximum (71.42%) relative change. The CentralNarmada valley, Jhabua hills, Gird region, Bundelkhandregion, Northern hills region of Chhattisgarh showednegative relative change in acreage of chickpea duringperiod-II. The Vindhyan plateau, Satpura plateau andKaymore plateau registered significant growth duringthe period -II.

In period III, the acreage of chickpea has beenincreased from 1509.73 thousand hectare to 2564.93thousand hectare in the State while, absolute changeof Madhya Pradesh of 1055.20 thousand hectare aboutnearly 69.89 per cent relative change. The significantgrowth of chickpea acreage was noticed in period -III.The area of chickpea increased in the Malwa plateau,Vindhyan plateau, Central Narmada valley, Jhabua hills,Kaymore plateau, Bundelkhand zone and Nimar valleyand Chhattisgarh plains whereas decreased in Satpuraplateau, Gird region and Northern hill region ofChhattisgarh. The maximum relative change in area ofchickpea was noted in Vindhyan plateau (245.64%).The highest and significant compound growth as wellas linear growth rate in acreage was confined toVindhyan plateau (2.32%, 3.09%). The negative growthrate was observed in Satpura plateau, Gird region andNorthern hill region of Chhattisgarh in the state.

Variability and Growth in Production

The production of chickpea of 897.44 thousand toneshas increased 2304.73 thousand tones in MadhyaPradesh from 1969-70 to 2008-09. The production ofchickpea was highest in period -II as compared to period-I but growth rate was found highest in period -I ascompared to period -II (Table 3).

The Maximum relative change of chickpeaproduction was noted in Central Narmada valley(151.60%), followed by Malwa plateau (123.22%),Vindhyan plateau (105.52%) and decreased inproduction of chickpea shows in Satpura plateau,Jhabua hills, Kaymore plateau, and Chhattisgarh plainsduring period-I. The data on variability and growth ofproduction in different periods shows that the chickpeaproduction was increased from 897.74 thousand tones(base year) to 2304.73 thousand tons (current year),which showed absolute change of 1407 thousand tones(156.81 %) with variation of 27.32 per cent.

The production of chickpea increasedsignificantly growth of 2.72 per cent as a linear and 3.20per cent as a compound in Madhya Pradesh period I.The highest and significant compound growth ofchickpea production was noted in Malwa plateau

(5.23%). The negative growth rate in production ofchickpea was noted in Satpura plateau, Jhabua hills,Kaymore plateau, Northern hill region of Chhattisgarhduring period-I.

Amongst different region of State, the productionof chickpea increased from 1569.41 to 2304.73thousand tones with 46.85 per cent relative changeduring period-II. In period -II, the significant growth ratewas noted in period- II. Amongst all the agro-climaticzones of Madhya Pradesh, the Vindhyan plateau showsmaximum (120.49%) relative change in production ofchickpea. The maximum significant growth rate inproduction of chickpea was registered in Vindhyanplateau (4.90%), followed by Satpura plateau (3.46%)and Kaymore plateau (2.83%). The Jhabua hills, Girdregion and Northern hill region of Chhattisgarh showednegative growth rate of production of chickpea duringperiod-II.

The chickpea production increased from 897.44to 2304.73 thousand tones, which shows 156.81 percent relative change with variation of 27.32 per cent inthe State during period III. Amongst all the agro-climaticzones, the Vindhyan plateau was confined to highestchange in production (619.50 thousand tones), whichwas 417.83 per cent relative change followed by CentralNarmada valley (306.17%), Malwa plateau (281.45%),and Nimar valley (160.08%). The production of chickpeadecreased in Northern hill region of Chhattisgarh andGird region in entire period. In Malwa plateau, Vindhyanplateau, Central Narmada valley, Satpura plateau,Kaymore plateau, Bundelkhand zone, Nimar valley andChhattisgarh plains exhibited significant compoundgrowth rate during entire period.

Variability and Growth in Productivity

The average productivity of chickpea in the State wasrecorded at 975 Kg/ha, which was highest in CentralNarmada valley (1284 Kg/ha) followed by Gird region(1119kg /ha) and Bundelkhand zone (1095Kg/ha) in theState.

In period- I, the Bundelkhand zone (46.51%)shows highest relative change in productivity ofchickpea. The Jhabua hills, Nimar valley andChhattisgarh plains shows negative relative change.The highest variation was noted in Satpuraplateau(30.73%) followed by Jhabua hills (27.49%),Bundelkhand region (21.98%), Bastar plateau (21.79%),Northern hill region of Chhattisgarh (21.69%)Kaymoreplateau (21.36%), Central Narmada valley (21.06%),Gird region (20.02%), Vindhyan plateau (15.61%),

212

Tabl

e 2.

Varia

bilit

y an

d gr

owth

in a

rea

of c

hick

pea

in d

iffer

ent a

gro-

clim

atic

zon

es o

f Mad

hya

Prad

esh

Par

ticul

ars

Mal

wa

Vind

hyan

Cen

tral

Sat

pura

Jhab

uaG

irdK

ymor

eB

unde

lN

imar

Nor

ther

nB

asta

rM

adhy

apl

atea

upl

atea

una

rmad

apl

atea

uhi

llsre

gion

plat

eau

khan

dva

lley

hills

plat

eau

Pra

desh

valle

yzo

nere

gion

of

CG

Per

iod

I (19

69-7

0 to

198

8-89

)B

ase

year

(Are

a in

000

'ha)

276.

5024

4.93

109.

4072

.73

21.5

734

2.83

282.

9798

.63

13.6

740

.10

7.30

1510

.63

Cur

rent

yea

r (A

rea

in 0

00'h

a.)

496.

2744

9.00

203.

0351

.60

17.6

333

1.70

271.

5313

2.57

15.1

735

.57

7.43

2011

.50

Abs

olut

e C

hang

e21

9.77

204.

0793

.63

-21.

13-3

.93

-11.

13-1

1.43

33.9

31.

50-4

.53

0.13

500.

87R

elat

ive

Cha

nge

(%)

79.4

883

.32

85.5

9-2

9.06

-18.

24-3

.25

-4.0

434

.40

10.9

8-1

1.31

1.83

33.1

6R

egre

ssio

n C

oeffi

cien

t (b)

15.8

7**

11.1

1**

4.94

**-1

.43

0.23

-1.1

11.

761.

89**

0.23

*-0

.61

-0.0

232

.85*

*Ta

ndar

d D

evia

tion

129.

4271

.52

30.5

310

.45

7.18

26.5

249

.62

14.4

32.

956.

981.

2623

7.72

Coe

ffici

ent o

f Var

iatio

n (%

)28

.43

19.7

020

.59

16.3

531

.66

8.08

17.9

213

.35

18.1

319

.13

18.1

013

.02

Line

ar G

row

th R

ate

(%)

3.49

3.06

3.33

-2.2

41.

01-0

.34

0.63

1.75

1.39

-1.6

7-0

.28

1.80

Com

poun

d G

row

th R

ate

(%)

3.94

3.29

3.35

-2.2

61.

31-0

.34

0.46

1.68

1.50

-1.7

0-0

.31

1.85

Per

iod

II (1

989-

90 to

200

8-09

)B

ase

year

(Are

a in

000

'ha)

473.

6749

2.03

226.

5746

.57

23.7

731

6.47

294.

3313

2.77

19.3

028

.00

7.53

2061

.00

Cur

rent

yea

r (A

rea

in 0

00'h

a.)

638.

6384

3.47

208.

3066

.90

22.0

719

1.73

414.

5012

0.13

27.6

723

.33

8.20

2564

.93

Abs

olut

e C

hang

e16

4.97

351.

43-1

8.27

20.3

3-1

.70

-124

.73

120.

17-1

2.63

8.37

-4.6

70.

6750

3.93

Reg

ress

ion

Coe

ffici

ent (

b)0.

6224

.01*

*-1

.25

1.00

**-1

.78

-6.5

18.

40**

1.57

0.23

-0.4

2-0

.01

25.8

6**

Rel

ativ

e C

hang

e (%

)34

.83

71.4

2-8

.06

43.6

6-7

.15

-39.

4140

.83

-9.5

243

.35

-16.

678.

8524

.45

Sta

ndar

d D

evia

tion

151.

3415

2.19

22.4

49.

3521

.63

79.8

454

.67

28.8

16.

304.

861.

3923

1.61

Coe

ffici

ent o

f Var

iatio

n (%

)27

.22

23.3

59.

9517

.43

67.2

325

.90

15.5

220

.26

23.9

419

.12

20.3

39.

73Li

near

Gro

wth

Rat

e (%

)0.

113.

68-0

.55

1.86

-5.5

4-2

.11

2.39

1.10

0.88

-1.6

6-0

.16

1.09

Com

poun

d G

row

th R

ate

(%)

-0.0

23.

77-0

.55

1.83

-5.3

3-2

.60

2.43

0.85

1.07

-1.5

5-0

.27

1.13

Per

iod

III (w

hole

per

iod)

(196

9-70

to 2

008-

09)

Bas

e ye

ar (A

rea

in 0

00'h

a)27

6.50

244.

0310

9.40

72.7

321

.57

342.

8328

2.97

98.6

313

.67

40.1

07.

3015

09.7

3C

urre

nt y

ear (

Are

a in

000

'ha.

)63

8.63

843.

4720

8.30

66.9

022

.07

191.

7341

4.50

120.

1327

.67

23.3

38.

2025

64.9

3A

bsol

ute

Cha

nge

(Are

a in

000

ha.

)36

2.13

599.

4398

.90

-5.8

30.

50-1

51.1

013

1.53

21.5

014

.00

-16.

770.

9010

55.2

0R

elat

ive

Cha

nge

(%)

130.

9724

5.64

90.4

0-8

.02

2.32

-44.

0746

.48

21.8

010

2.44

-41.

8112

.33

69.8

9R

egre

ssio

n C

oeffi

cien

t (b)

5.84

**15

.22*

*3.

36**

-0.4

40.

16-1

.70

4.10

**1.

71**

0.43

**-0

.54

-0.0

128

.14*

*S

tand

ard

Dev

iatio

n14

8.07

187.

5747

.26

11.0

816

.62

59.5

964

.14

28.3

57.

048.

171.

3136

4.11

Coe

ffici

ent o

f Var

iatio

n (%

)26

.63

28.7

820

.95

20.6

651

.65

19.3

318

.21

19.9

326

.73

32.1

219

.13

15.2

9Li

near

Gro

wth

Rat

e (%

)1.

052.

341.

49-0

.82

0.50

-0.5

51.

161.

201.

65-2

.14

-0.1

11.

18C

ompo

und

Gro

wth

Rat

e (%

)1.

203.

091.

99-0

.73

-0.0

4-0

.73

1.27

1.31

2.09

-1.7

4-0

.12

1.39

** S

igni

fican

t at 1

% p

roba

bilit

y le

vel,

* Si

gnifi

cant

at 5

% p

roba

bilit

y le

vel

213

Tabl

e 3.

Var

iabi

lity

and

grow

th in

pro

duct

ion

of c

hick

pea

in d

iffer

ent a

gro-

clim

atic

zon

es o

f Mad

hya

Prad

esh

Par

ticul

ars

Mal

wa

Vind

hyan

Cen

tral

Sat

pura

Jhab

uaG

irdK

ymor

eB

unde

lN

imar

Nor

ther

nB

asta

rM

adhy

apl

atea

upl

atea

una

rmad

apl

atea

uhi

llsre

gion

plat

eau

khan

dva

lley

hills

plat

eau

Pra

desh

valle

yzo

nere

gion

of

CG

Per

iod

I (19

69-7

0 to

198

8-89

)B

ase

year

(Are

a in

000

'ha)

153.

3014

8.27

62.6

739

.47

12.1

023

7.91

156.

4357

.23

8.27

17.5

04.

3089

7.44

Cur

rent

yea

r (A

rea

in 0

00'h

a.)

342.

2030

4.71

157.

6738

.17

9.53

254.

9015

4.97

112.

779.

0318

.90

4.13

1406

.98

Abs

olut

e C

hang

e18

8.90

156.

4595

.00

-1.3

0-2

.57

16.9

9-1

.47

55.5

30.

771.

40-0

.17

509.

54R

elat

ive

Cha

nge

(%)

123.

2210

5.52

151.

60-3

.29

-21.

217.

14-0

.94

97.0

39.

278.

00-3

.88

56.7

8R

egre

ssio

n C

oeffi

cien

t (b)

12.9

5**

8.25

**4.

74**

-0.1

5-0

.02

2.41

-0.5

03.

37**

0.12

-0.0

6-0

.04

31.0

7**

Sta

ndar

d D

evia

tion

98.1

558

.75

34.4

98.

485.

3947

.84

24.2

525

.40

2.27

4.66

1.02

234.

47C

oeffi

cien

t of V

aria

tion

(%)

35.3

725

.44

35.2

025

.62

46.4

820

.06

16.9

031

.96

23.8

227

.39

26.8

720

.52

Line

ar G

row

th R

ate

(%)

4.67

3.57

4.84

-0.4

6-0

.14

1.01

-0.3

54.

241.

21-0

.38

-1.0

62.

72C

ompo

und

Gro

wth

Rat

e (%

)5.

233.

844.

97-0

.43

-0.8

60.

94-0

.28

4.24

1.26

-0.5

9-1

.05

2.76

Per

iod

II (1

989-

90 to

200

8-09

)B

ase

year

(Are

a in

000

'ha)

379.

8034

8.21

206.

9330

.97

14.1

723

3.00

198.

9012

2.73

12.9

716

.23

5.50

1569

.41

Cur

rent

yea

r (A

rea

in 0

00'h

a.)

584.

7776

7.77

254.

5359

.43

12.8

718

1.33

292.

4311

2.90

21.5

010

.53

6.67

2304

.73

Abs

olut

e C

hang

e20

4.97

419.

5647

.60

28.4

7-1

.30

-51.

6793

.53

-9.8

38.

53-5

.70

1.17

735.

33R

elat

ive

Cha

nge

(%)

53.9

712

0.49

23.0

091

.93

-9.1

8-2

2.17

47.0

3-8

.01

65.8

1-3

5.11

21.2

146

.85

Reg

ress

ion

Coe

ffici

ent (

b)3.

3525

.93*

*2.

101.

45**

-1.1

6-2

.90

7.00

**2.

740.

32-0

.39

0.04

38.4

7**

Sta

ndar

d D

evia

tion

161.

6517

3.20

45.0

213

.91

14.4

892

.71

54.0

147

.19

5.30

4.87

1.23

382.

75C

oeffi

cien

t of V

aria

tion

(%)

33.3

230

.95

18.9

333

.24

71.2

832

.80

21.3

330

.05

26.2

134

.63

23.2

618

.43

Line

ar G

row

th R

ate

(%)

0.69

4.63

0.88

3.46

-5.7

2-1

.03

2.76

1.74

1.61

-2.7

80.

711.

85C

ompo

und

Gro

wth

Rat

e (%

)0.

504.

901.

023.

46-5

.69

-1.4

32.

831.

232.

14-2

.59

0.63

1.97

Per

iod

III(w

hole

per

iod)

(196

9-70

to 2

008-

09)

Bas

e ye

ar (A

rea

in 0

00'h

a)15

3.30

148.

2762

.67

39.4

712

.10

237.

9115

6.43

57.2

38.

2717

.50

4.30

897.

44C

urre

nt y

ear (

Are

a in

000

'ha.

)58

4.77

767.

7725

4.53

59.4

312

.87

181.

3329

2.43

112.

9021

.50

10.5

36.

6723

04.7

3A

bsol

ute

Cha

nge

(Are

a in

000

ha.

)43

1.47

619.

5019

1.87

19.9

70.

77-5

6.58

136.

0055

.67

13.2

3-6

.97

2.37

1407

.29

Rel

ativ

e C

hang

e (%

)28

1.45

417.

8330

6.17

50.5

96.

34-2

3.78

86.9

497

.26

160.

08-3

9.81

55.0

415

6.81

Reg

ress

ion

Coe

ffici

ent (

b)9.

83**

16.6

0**

6.10

**0.

49**

0.18

1.60

4.93

**3.

67**

0.46

**-0

.17

0.06

*43

.74*

*S

tand

ard

Dev

iatio

n16

8.78

209.

7781

.08

12.2

011

.65

76.1

769

.26

54.2

56.

744.

941.

3656

7.48

Coe

ffici

ent o

f Var

iatio

n (%

)34

.79

37.4

834

.10

29.1

657

.36

26.9

527

.35

34.5

433

.35

35.0

925

.57

27.3

2Li

near

Gro

wth

Rat

e (%

)2.

032.

972.

561.

170.

890.

561.

952.

342.

26-1

.19

1.07

2.11

Com

poun

d G

row

th R

ate

(%)

2.79

4.43

4.30

1.17

0.48

0.45

2.45

3.30

3.26

-1.1

61.

272.

88

** S

igni

fican

t at 1

% p

roba

bilit

y le

vel,

* S

igni

fican

t at 5

% p

roba

bilit

y le

vel

214

Tabl

e 4.

Var

iabi

lity

and

grow

th in

pro

duct

ivity

of c

hick

pea

in d

iffer

ent a

gro-

clim

atic

zon

es o

f Mad

hya

Prad

esh

Par

ticul

ars

Mal

wa

Vind

hyan

Cen

tral

Sat

pura

Jhab

uaG

irdK

ymor

eB

unde

lN

imar

Nor

ther

nB

asta

rM

adhy

apl

atea

upl

atea

una

rmad

apl

atea

uhi

llsre

gion

plat

eau

khan

dva

lley

hills

plat

eau

Pra

desh

valle

yzo

nere

gion

of

CG

Per

iod

I (19

69-7

0 to

198

8-89

)B

ase

year

(Are

a in

000

'ha)

554.

5960

7.29

568.

2054

6.03

563.

5369

1.41

552.

3358

0.60

598.

8443

5.97

589.

0459

2.61

Cur

rent

yea

r (A

rea

in 0

00'h

a.)

689.

6067

9.02

776.

7074

3.46

520.

7176

9.26

570.

9885

0.62

587.

7252

0.81

553.

7769

9.36

Abs

olut

e C

hang

e13

5.01

71.7

320

8.50

197.

42-4

2.82

77.8

418

.65

270.

02-1

1.12

84.8

4-3

5.27

106.

75R

elat

ive

Cha

nge

(%)

24.3

411

.81

36.7

036

.16

-7.6

011

.26

3.38

46.5

1-1

.86

19.4

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215

Malwa plateau (13.02%) and Nimar valley (11.45%).The significant and positive linear growth of chickpeaproductivity was found in Malwa plateau (1.24%) andBundelkhand region (2.42%) during period I. (table 4)

In period-II, it is observed that the productivity ofchickpea was increased from 760.02Kg/ha in 1988-89to 897.51Kg/ha in 2008-09 which was about 18.09percent change in the state. The maximum relativechange in productivity of chickpea was noted in Satpuraplateau about 34.61 per cent followed by CentralNarmada valley, Vindhyan plateau, Gird region, Nimarvalley, Malwa plateau, Chhattisgarh plains anddecreased in Jhabua hills, Bundelkhand region,Northern hill region of Chhattisgarh in this period. Thepositive and significant growth in productivity ofchickpea was observed in Central Narmada valley andSatpura plateau at 5 per cent level. Only two regionsshow negative growth in productivity of chickpea duringperiod -II.

In overall period, the variability and growth ratein productivity of chickpea presented in the Table- 4 it isobserved from the data that the productivity of chickpeaincreased from 592.61 kg/ha to 897.51 kg/ha in the stateduring entire period with relative change and variationof 51.45 per cent and 17.69 per cent, respectively. Theproduction of chickpea shows significant growth of 1.24per cent (linear) and 1.47 per cent (compound) perannum in the Madhya Pradesh. The Central valleyshows the maximum relative change (114.34%) and

Jhabua hill shows the minimum (3.21%). The highestvariability in productivity of chickpea was observed inSatpura plateau (25.62%), followed by Central Narmadavalley (24.15%), Northern hill region of Chhattisgarh(23.85%), Bundelkhand zone (22.36%), Jhabua hill(21.41%), Gird region (20.36%), Bastar plateau(20.24%), Vindhyan plateau (19.15%), Kaymore plateau(18.91%), Malwa plateau (18.71%), Nimar valley(18.11%). The Malwa plateau, Vindhyan plateau,Central Narmada valley, Satpura plateau, Gird regionKaymore plateau, Bundelkhand region, Nimar valleyand Chhattisgarh plains were shows significant growthin productivity of chickpea during entire period.

With the present level of technological changeand govt. policies the area of chickpea will increasedfrom 2705.8 to 3074.18 thousand hectare in the year2024-25. The production will also increased from2638.80 to 3119.06 thousand tones and the productivitymay be increased up to 1116 from 975 kg/ha (2008-09)in the Madhya Pradesh. The chickpea acreage willincreased in the entire agro-climatic zones expectSatpura plateau, Northern hill region of Chhattisgarhand Chhattisgarh plains (Table 5).

The zone wise analysis of projection shows thatacreage of chickpea will likely to increase in almost allthe agro-climatic zone except Satpura plateau, Northernhill region of Chhattisgarh and Chhattisgarh plains.Similar trend was observed for likely change inproduction by the year 2024-25.

Table 5. Current and Projected area, Production and Productivity of Chickpea in Madhya Pradesh

Zones Area (000'ha) Production (000'tones) Productivity(Kg/ha)2008-09 2024-25 2008-09 2024-25 2008-09 2024-25

Malwa Plateau 699.8 707.11 632.1 720.36 903 1117

Vindhyan Plateau 848.4 1032.57 857.1 967.98 1010 1078

Central Narmada Valley 227.5 302.94 292.2 378.25 1284 1483

Satpura Plateau 67 43.62 57.3 54.36 855 1047

Jhabua Hill 19.6 33.03 10.5 22.16 536 644

Gird Region 213.6 259.65 239.1 315.64 1119 1155

Kaymore Plateau 415.9 455.98 339.4 368.40 816 862

Bundelkhand Zone 155.4 184.17 170.1 245.04 1095 1472

Nimar Valley 28 36.29 21.4 30.59 764 953

Northern Hills of Chhattisgarh 20.2 12.19 11.3 9.77 559 618

Chhattisgarh plains 10.4 6.64 8.3 6.51 798 968

Madhya Pradesh 2705.8 3074.18 2638.8 3119.06 975 1116

216

e/; izns'k ds fofHkUu ,xzks DykbesfVd tksuksa esa pus ds mRiknu o`f)dze dk v/;;u foxr 40 o"kksZa ¼1969&70 ls 2008&09½ dsdkyJsf.kd vkadM+ksa dk mi;ksx djds fd;k x;k gSA v/;;u dky esamRiknu esa ifjoZru ns[kus ds fy;s v/;;u dky dks dky[kaMksa esafoHkkftr fd;k x;k gS dky[kaM&I ¼1969&70 ls 1988&89½ Adky[kaM&II ¼1989&90 ls 2008&09½ vkSj laiw.kZ dky[kaM&III¼1969&70 ls 2008&09½ A vkadM+s iznf'kZr djrs gS fd dky[kaM&Iesa ekyok iBkj] foa/; iBkj] e/; ueZnk ?kkVh ,oa cqansy[kaM {ks= esapus ds {ks=Qy ,oa mRiknu esa o`f) /kukRed rFkk vfr lkFkZd gS Adky[kaM&II esa foa/; iBkj] lriqM+k iBkj vkSj dSeksj iBkj esa ;go`f) /kukRed rFkk mPp lkFkZd gS rFkk laiw.kZ izns'k esa ;g lkFkZd gSAfoxr 40 o"kksZa ds nkSjku pus ds {ks= ,oa mRiknu esa o`f) ekyokiBkj] foa/; iBkj] ueZnk ?kkVh] dSeksj iBkj] cqansy[kaM {ks= rFkk fuekj?kkVh esa /kukRed gSA izns'k esa ;g o`f) /kukRed rFkk lkFkZd gSAdky[kaM&I ds nkSjku mRikndrk o`f) /kukRed rFkk lkFkZd dsoyekyok rFkk cqansy[kaM {ks= esa gSA dky[kaM&II esa dsfUnz; ueZnk ?kkVhrFkk lriqM+k iBkj esa /kukRed rFkk lkFkZd mRikndrk o`f) iznf'Zkrgksrh gSA laiw.kZ dky[kaM&III esa >kcqvk igkfM+;ka rFkk NfRRklx<+ dhmRrjh igkM+h {ks= NksM+dj vU; lHkh {ks=ksa esa /kukRed rFkk lkFkZdmRikndrk o`f) iznf'Zkr gksrh gSA o"kZ 2024&25 ds fy, iwokZuqekuiznf'kZr djrs gSa fd izns'k esa pus dk mRiknu 18-19 izfr'kr c<+sxkvkSj ;g o`f) pus dh mRikndrk esa 14-46 izfr'kr vkSj 13-61izfr'kr {ks=Qy o`f) ds dkj.k gqbZ gSA

References

Jain BL, Atri PM, Sharma HO (1988) Analysis of growth trendsin area, production and productivity of Chickpea inMadhya Pradesh. Indian J Pulses Res 1: 38-42

Rama Rao IVY, Raju VT, Krishna Rao GV (2005) District-wise growth and instability in Red Gram in AndhraPradesh, Agricultural Situation in India 41: 855-868

Singh NP, Ranjit Kumar, Singh RP, Jain PK (2005) Riceeconomy in India. Agricultural Situation in India 42:427-435

Nahatkar SB, Sharma HO, Patidar M (2005) Soybeanproduction across different agro-climatic zones ofMadhya Pradesh. JNKVV Res J 39: 46-52

(Manuscript Receivd : 20.7.12; Accepted : 15.10.13)

217

Abstract

The study comprises the information collected throughsecondary data (1990-2009) of all the districts of MadhyaPradesh and primary data collected from 150 fodder growersby personal interview from three maximum fodder producingdistricts (Rajgarh, Shajapur and Ujjain). It is observed fromthe study that the fodder cultivation was not shown too muchprogress in the state since 1990. The cultivators still growingfodder in the line of crop cultivation and the majority of themwere not known the recommendation package of practices offodder cultivation. The fodder growers were also found notdoing fodder preservation technique viz. hay and silage makingfor the lean period. They were not cultivating fodder in thecommercial line as none of them involved in marketing offodder in the state. Hence, it is the right time that stategovernment re-intensified their efforts in progress of fodderin the state because without introducing dairy based farmingsystem approach on the farmers' farm, their income shouldnot become double, which is the ultimate target of the stategovernment. It is only activity which not only generated incomebut also enhanced employment at their owned farm.

Keywords: Profitability, Fodder

Madhya Pradesh is basically an agricultural state whereabout 70 per cent of its peoples live in villages. Theirlivelihood is dependent mainly on agriculture and animalhusbandry. Though, state has a huge livestockpopulation of over 4162.96 millions, besides poultry,yet the production of milk and other livestock productsis the lowest in India. The state, highly deficient invarious cattle products, though state has about one-fourth of the total cattle population of India. As againstthe minimum nutritional requirement of 201 g/head/dayof milk set by the nutritionists, 100 g/head/day is theavailability per head in the state. One of the mainreasons for the low productivity of livestock is

Status and profitability of fodder crops in Madhya Pradesh

Hari Om Sharma, Ravi Singh Chouhan and G.P. Agrawal*Agro-Economic Research CentreJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)*Fodder Production Unit Live Stock FarmNanaji Deshmukh Pashu Chikitsa Vigyan VishwavidyalayaJabalpur 482004 (MP)

malnutrition and the low genetic potential of the animals.In fact, the economic viability of livestock husbandrydepends on sources of feed and fodder as feeding costconstitutes about 65 - 70% of the total cost of livestockfarming. The feed given to cattle comprises dry fodder,green fodder and concentrates of which dry fodder formsthe major share. The adequate supply of feed and fodderis a critical factor affecting performance of animals. Thisfact is adequately supported by the figures of availability,vis-a-vis the requirement of green-fodder crops, cropresidues and concentrates, which shows that there is ahuge gap of between demand and supply of all kinds ofthese feeds and fodders in the state.

Fodder crops may be classified as temporary oras permanent crops; the former are cultivated andharvested like any other crop, the latter relate to landused permanently (five years or more) for herbaceousforage crops, either cultivated or growing wild (wildprairie or grazing land). They may include some areasof forest lands that are used for grazing. The foddercan be fed to animals through processing as green feed;as hay, i.e. crops harvested dry or left to dry if harvestedgreen; or as silage products (Kindu Mekonnen et al.2009) Though, Silage or ensilage is a method ofpreservation of green fodder through fermentation toretard spoiling and this method of processing is morepopular in India as compared to hay making but inMadhya Pradesh the hay and silage making methodsof preservation of fodder are not been found in practicesby the cultivators.

On the other hand, if we examine the landresources available for growing fodder and forage crops,it is estimated that the average cultivated area devotedto fodder production is only 4.4 per cent of the totalarea in India, and it was found to be only 3 per cent inMadhya Pradesh similarly, the area under permanent

JNKVV Res J 47(2): 217-223 (2013)

218

pastures and cultivable wastelands is approximately 13and 15 million hectares respectively but it was found tobe only 4.42 and 3.37 per cent of net area sown ( 150.74lac ha) in the state likewise, the total area under forestsis 2.51 crore hectares and that open to grazing is 2.1crore hectares. All these resources are able to meetthe forage requirements of the grazing animals onlyduring the monsoon season. But for the remainingperiods of the year, the animals have to be maintainedon the crop residues or straws of sorghum, pearl millet,ragi, wheat, barley, etc. either in the form of whole strawor a bhusa, supplemented with some green fodder, oras sole feed. The crop residues are available mainlyfrom wheat, paddy, pearl millet, sorghum, soybean,sugarcane trash, etc., which are poor in nutritive value.Cultivation of high yielding or hybrid varieties of wheatand paddy to enhance grain production has changedthe grain-foliage ratio more favorable to humans, thusaffecting the production and availability of straw.Besides this, the decrease in paddy cultivation due todrought and water shortage has also affected theavailability of straw. The green fodder resources forlivestock are mainly derived from grazing in grasslandsand pastures, fodder crops from cropped lands, weeds,bund grasses, tree leaves and mixed forages. Thesustainability of dairy industry in India largely dependsupon the quality of herbage based animal feed andfodder. Green fodder is the essential component offeeding high yielding milch animals to obtain desiredlevel of milk production. Lack of quality fodder,especially during winter, is a major limiting factor inimproving livestock production. (Dost Muhammad 2001)The technology of growing year round fodder productionhas helped the farmers/dairy owners to sustain theirmilch animals of 6-7 liters per day potential withminimum use of concentrates, thus producing milk atcheaper cost.

The cost production of milk increased day by dayand in the production cost of milk, the cost of greenfodder is found highest. If the cost of production of greenfodder decreases it also decreases the cost of milkproduction Hence, looking to the above points inconsideration present study is formulated to examinethe status, the costs and returns of various fodder cropsand problem faced by the growers in production offodder in Madhya Pradesh.

Research Methodology

Amongst different districts (50), 3 districts i.e. Rajgarh,Shajapur, and Ujjain have been selected purposivelyfor the study on the basis of the highest area in fodder

cultivation in Madhya Pradesh (569987 ha). Amongstthe selected districts, two blocks from each district, oneblock near and one distant to the periphery of districtheadquarter has been selected randomly to realize theeffect of distance factor in the findings. A cluster of 3villages has been randomly chosen from each block.Finally, a sample of 25 farmers was selected randomlyfrom each selected cluster, spreading over various farmsize categories i.e., marginal (less than one hectare),small (1-2 hectares), semi-medium (2-4 hectares),medium (4-10 hectares) and large (more than 10hectares) based on the size of the operational holding,making a total sample of 150 farmers. The primary datacollections were done by the personal interview methodfor the reference year 2009-10.

The required secondary data were also collectedon the different aspects of the study from the all theinstitutions (Department of Farmers' Welfare andAgricultural Development, Vindhyachal Bhawan,Bhopal, M.P; Department of Animal Husbandry,Kamdhenu Bhawan, Bhopal, Madhya Pradesh;Department of Agril. Statistics Government of MadhyaPradesh, Bhopal) from their published and unpublishedrecords. The primary data were classified and tabulatedin light of stated objectives of the study. The SPSS(Statistical Package for Social Science) was used forclassification, analysis and tabulation of collected data.

Results and discussion

The status of fodder in Madhya Pradesh, cost andprofitability of fodder and problem faced by the foddergrowers are the basic areas of the study.

Status of Fodder

Fodder cultivation is found to be in a nascent stage inMadhya Pradesh. The cultivators of Madhya Pradeshdevoted only their 3 per cent of gross cropped areaunder fodder. (Fig. 1) Out of the total fodder area (0.74lakh ha), the cultivators of Madhya Pradesh devotedtheir maximum area under the cultivation of pearl millet(20%) followed by Sorghum (4%), Berseem (2%) andMaize (1%). The 72% of the fodder area is found to becovered under unidentified other fodder crops. Although, the pearl millet which was highly cultivated bythe cultivators, but it was found to be mainly cultivatedfor grain purposes rather than fodder. The by productof this crops is used as a fodder for the live stock.

219

Thus, sorghum, berseem and maize were foundto be major fodder crops in the state (Fig 2). An averagefodder grower of the Madhya Pradesh devoted their 1 -2 Bigha (0.2-0.4 ha) area of cultivated land in theproduction of fodders in all the seasons of the year.

The area of fodder was found to be declined overthe years from 974888 ha. (1990-94) to 745285 (2006-09) in Madhya Pradesh during the last 20 years (Fig.3). The area of sorghum, berseem, loosarn, jai werefound to be increased over the year 1990-94, while thearea under guar and other fodder decreased in MadhyaPradesh (Table 1).

As regards to the growth of these are concernedin Madhya Pradesh, the areas of all the fodder cropswere found to be decrease by 1.97% per year duringthe last 20 years. The growth of these fodders was foundto be more in the period I (2.52%/year) as compared toperiod II (-2.40%/year). Among the different fodder cropsthe highest growth of fodder was observed in the areaof loosarn (4.98%/year) followed by berseem (3.89%/

year), sorghum (2.79%/year), Jai (2.39%/year) andmaize (1.99%/year) during the last 20 years in MadhyaPradesh (Table 2).

Cost of Cultivation

The cost of cultivation incurred in cultivation of majorcrops viz. maize (rainy season), berseem (winterseason) and sorghum (summer season) by the foddergrowers of different size of farm are analysed tocompared the profitability of crops and find out the shareof different inputs in the total cost cultivation and seemsthat maize is found to be a major fodder crop cultivatedby the majority of fodder growers in the rainy season.

The comparative picture of cost of cultivation (Rs./ha) of maize fodder in different size of farms wasanalyzed and observed that an average fodder growerinvested Rs. 9264.64 /ha in the cultivation of maize andas the size of farm increased from marginal (Rs.7782.15/

974888859320 806419 745285

0200000

400000600000800000

10000001200000

1990-94 1995-99 2000-04 2006-09

Fig 1. Share of fodder in gross cropped area (21.05lakh ha) in Madhya Pradesh

Fig 2. Share of different fodder crops in MadhyaPradesh (Total 745285 ha)

Fig 3. Area of total fodder in Madhya Pradesh (ha)

220

Table 1. Area of major fodder crops in different periods in Madhya(5 year Average)

Crops Five year average up to1991-95 1996-00 2001-05 2006-10

Maize 5532 5245 6415 6326Pearl millet 156294 140041 176951 184055Sorghum 43338 37294 39618 37785Berseem 13930 19721 19929 20305Loosarn 4769 6116 7523 8192Jai 836 472 777 1366Guar 3573 4397 7761 1789Others (Cowpea, Oats, etc.) 746615 646033 547445 485468

Table 2. Average annual compound growth rate of area and their coefficient of variance of major fodder crops inMadhya Pradesh

Crops 1991-1992 to 1999-00 2001-02 to 2009-10 1991-1992 to 2009-10(Period I) (Period II) (Period III)

Growth CV Growth CV Growth CV

Maize -2.01 893 1.71 438 1.99 2766Pearl millet 3.53 -11732 -1.55 -2430 -1.09 -10418Sorghum -5.88 8161 2.24 1793 2.79 12150Berseem -4.88 2191 -0.11 -34 3.89 5953Loosarn 0.89 -48 22.68 858 4.98 925Jai -5.07 1668 -24.69 -5980 2.39 2779Guar 2.89 -165771 -4.17 -88457 -3.17 -476908Other 2.12 -25875 2.3 16482 1.42 55004Total Fodder 2.52 -190513 -2.45 -77330 -1.97 -407749

Table 3. Cost of cultivation of maize chari (Rs/ha)

Particulars Marginal Small Semi-medium Medium Large Overall

1. Human labouri) Hired human labour 67.80 94.80 1532.80 1542.40 1557.45 959.05ii) Family human labour 1447.80 1472.95 117.80 124.80 134.35 659.54

2. Machine labour 1332.40 1376.15 1431.40 1492.90 1506.30 1427.833. Seed 1021.30 1075.00 1147.80 1450.00 1510.00 1240.824. FYM 2617.50 2946.10 3111.00 3851.25 4252.50 3355.675. Fertilizer 905.60 971.10 995.00 1237.50 1487.75 1119.396. Plant protection measures 0.00 0.00 0.00 0.00 0.00 0.007. Irrigation 235.00 245.00 333.90 356.00 430.00 319.988. Interest on working capital 58.65 62.85 66.60 76.90 83.15 69.639. Miscellaneous expenses 96.10 101.10 111.25 123.75 131.45 112.7310. Total variable cost 7782.15 8345.05 8847.55 10255.50 11092.95 9264.64

221

ha) to large (Rs. 11092.95 /ha) the cost of cultivation ofmaize increased. (Table: 3) The farm yard manure (37%),machine labour (16%), seed (13%), chemical fertilizer(12%), hired human labour (10%) and family labour (7%)were found to be main components of cost of cultivationof maize.

As respect to berseem cultivation by foddergrower in summer season an average fodder growerinvested Rs. 13835.66 /ha in the cultivation of berseemand as the size of farm increased from marginal(Rs.12716.60/ha) to large (Rs. 15159.90 /ha) the costof cultivation of berseem increased. (Table 4) here alsothe farm yard manure (33%), seed (26%), machine

labour (11%), irrigation (9%), chemical fertilizer (8%),hired human labour (7%) and family labour (4%) werefound to be main components of cost of cultivation ofberseem in the area under study.

Sorghum is found to be a major fodder cropcultivated by the majority of fodder growers in thesummer season. An average fodder grower investedRs. 9264.64 /ha in the cultivation of sorghum and asthe size of farm increased from marginal (Rs.7782.15/ha) to large (Rs. 11092.95 /ha) the cost of cultivation ofmaize increased. (Table: 5) The farm yard manure (32%),machine labour (16%), seed (11%), hired human labour(11%), chemical fertilizer (10%), irrigation (9%), and

Table 4. Cost of cultivation of berseem (Rs/ha)

Particulars Marginal Small Semi-medium Medium Large Overall

1. Human labouri) Hired 0.00 0.00 1582.80 1592.40 1607.45 956.53ii) Family 1497.80 1522.95 0.00 0.00 0.00 604.15

2. Machine labour 1382.40 1426.15 1486.40 1507.90 1516.30 1463.833. Seed 3281.15 3446.20 3567.95 3982.80 4031.40 3661.904. FYM 4166.30 4281.30 4531.15 4642.85 4987.45 4521.815. Fertilizer 896.40 984.65 1031.40 1296.30 1506.15 1142.986. Plant protection measures 0.00 0.00 0.00 0.00 0.00 0.007. Irrigation 1281.15 1246.30 1246.35 1241.30 1266.30 1256.288. Interest on working capital 95.10 98.20 102.30 108.45 113.40 103.499. Miscellaneous expenses 116.30 122.80 124.80 128.10 131.45 124.6910. Total variable cost 12716.60 13128.55 13673.15 14500.10 15159.90 13835.66

Table 5. Cost of cultivation of sorghum chari (Rs/ha)

Particulars Marginal Small Semi-medium Medium Large Overall

1. Human labouri) Hired 82.80 99.80 1632.80 1642.40 1657.45 1023.05ii) Family 1497.80 1522.95 167.80 224.80 284.35 739.54

2. Machine labour 1432.40 1476.15 1481.40 1542.90 1556.30 1497.833. Seed 771.30 984.30 1054.45 1234.35 1337.45 1076.374. FYM 2517.50 2846.10 2996.00 3491.25 3797.50 3129.675. Fertilizer 855.60 921.10 845.00 1137.50 1137.75 979.396. Plant protection measures 0.00 0.00 0.00 0.00 0.00 0.007. Irrigation 735.00 745.00 833.90 856.00 930.00 819.988. Interest on working capital 60.75 66.05 69.20 77.85 82.60 71.299. Miscellaneous expenses 146.10 146.10 146.25 173.75 231.45 168.7310. Total variable cost 8099.25 8807.55 9226.80 10380.80 11014.85 9505.85

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family labour (8%) were found to be main componentsof cost of cultivation of maize the area under study.

Profitability of Fodder Crops

The comparative picture of Profitability of differentfodder crops viz. maize (rainy season), berseem (winterseason) and sorghum (summer season) grown by the

fodder growers related to different size of farms areanalysed and presented in Table 6.

The comparative picture of fodder crops showedthat the cultivation of beseem was found be moreprofitable in the area under study in which an averagefodder grower invested only Rs.13835.66/ha andreceived Rs. 52521.47/ha revealed that on theinvestment of Rs. 1.00, he got Rs. 3.80 as benefit over

Table 6. Profitability of fodder crops (Rs/ha)

Particulars Marginal Small Semi-medium Medium Large Overall

Kharif fodder : Maize ChariYield(qtls/ha) 238.40 258.45 271.85 284.45 293.70 269.37Price(Rs/qtls) 96.26 96.26 96.26 96.26 96.26 96.26Gross returns 22948.38 24878.40 26168.28 27381.16 28271.56 25929.56Total Variable cost 7782.15 8345.05 8847.55 10255.50 11092.95 9264.64Returns over variable cost 15166.23 16533.35 17320.73 17125.66 17178.61 16664.92Rabi fodder : BerseemYield(qtls/ha) 532.56 596.41 674.23 689.23 756.23 649.73Price(Rs/qtls) 102.13 102.13 102.13 102.13 102.13 102.13Gross returns 54390.35 60911.35 68859.11 70391.06 77233.77 66357.13Total Variable cost 12716.60 13128.55 13673.15 14500.10 15159.90 13835.66Returns over variable cost 41673.75 47782.80 55185.96 55890.96 62073.87 52521.47Summer fodder : Sorghum ChariYield(qtls/ha) 218.40 228.45 261.85 274.45 283.70 253.37Price(Rs/qtls) 101.03 101.03 101.03 101.03 101.03 101.03Gross returns 22064.95 23080.30 26454.71 27727.68 28662.21 25597.97Total Variable cost 8099.25 8807.55 9226.80 10380.80 11014.85 9505.85Returns over variable cost 13965.70 14272.75 17227.91 17346.88 17647.36 16092.12

Table 7. Problems related to the production of fodder crops (Multiple response)

Particulars Marginal Small Semi-medium Medium Large Overall

Seed Quality 83.33 80.00 60.00 70.00 76.66 74.00Input delivery 96.66 83.33 76.66 60.00 53.33 74.00Expenditure on production 70.00 76.66 83.33 80.00 73.33 76.66Insect-pests and diseases 10.00 6.66 13.33 16.66 20.00 13.33Technical knowledge 83.33 80.00 60.00 70.00 76.66 74.00Access to credit 6.66 13.33 16.66 20.00 80.00 27.33Availability and cost of labour 83.33 76.66 60.00 60.00 60.00 68.00Government Policies 13.33 16.66 70.00 76.66 83.33 52.00Processing of fodder 100.00 100.00 100.00 100.00 100.00 100.00

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the variable cost, while he received only Rs. 1.80 and1.69 on investment of Rs. 1.00 respectively from thecultivation of maize and sorghum.

The maximum net return from the cultivation ofberseem (Rs. 52521.47/ha) as compared to cultivationof maize (Rs.16664.92/ha) and sorghum (Rs. 16092/ha) was obtained. It is also observed as the size of farmincrease the cost of cultivation, gross profit, net profitincreases.

Problems faced by the fodder growers

The various problem which faced by the fodder growerin the cultivation and preservation of fodder arepresented (Table 7) and observed that none of thesample fodder grower done preservation practices forpreservation of fodder for lean period. Lack of technicalknow how (76.66%) was found to be the biggest problemobserved during the course of investigation and reportedby the maximum numbers of respondents in the areaunder study.

The inferior quality of seed (74.00%), faulty inputdelivery system (74.00%), high expenditure inproduction due power cuts (74.00%), non availabilityof skilled labour in time and high cost of labour (68.00%),faulty government policy as distribution of mini kits offodder seeds from veterinary department instead ofagriculture department (52%) were the other majorproblems found in the study area reported by themajority of the respondents. The similar findings alsoobserved by Dost Muhammad (2001).

Hence, it is clear that the fodder cultivation hasnot shown too much progress in the state since 1990although it is found profitable in the state. The cultivatorsstill growing fodder in the line of crop cultivation andthe majority of them were not known the recommendedpackage of practices of fodder cultivation. The foddergrowers were also found to be not done fodderpreservation techniques viz. hay and silage making forthe lean period. They were not found cultivating fodderin commercial line as none of them involved in marketingof fodder in the state. Hence, it is the right time thatstate government re-intensified their efforts in progressof fodder in the state because without introducing dairybased faming system approach at the farmers' farms,their income should not became double, which is theultimate target of the state government. It is only systemof farming which was done by the farmers since longtime. It not only generated income but also enhancedemployment at their owned farm. The mini kit of foddercrops were found to be distributed by the animal

husbandry department and they were not taking interestin the extension activities concern to the fodder, due tolack of training. It also lacks the aura of being doctorand the fodder is more inclined towards agriculture. Theanimal husbandry department in the state is onlyconcerned with the treatment aspect and improvementof breeds because here lays the money. Investinginterest in fodder sector will benefit the live stock ownersbut who cares? Hence, there is urgent need to createthe a separate department for fodder developmentseparate from animal husbandry department or mergethe fodder development sector in agriculture departmentfor better extension activities and distribution of foddermin kits with technical know-how because the cultivationof fodder is more or less similar to the cultivation ofcrops.

bl v/;;u ds fy;s e/;izns'k ds lHkh ftykssa ds f}rh;d vkWdM+s¼1990 ls 2009 rd½ ,oa 150 pkjk mRikndksa ls izkFkfed vkWdM+srhu vf/kdre pkjk mRiknd ftyksa ¼jktx<+] 'kktkiqj ,oa mTtSu½ ls,df=r fd;s x;sA v/;;u ls ;g Kkr gksrk gS fd e/;izns'k esa1990 ls pkjk mRiknu esa vf/kd izxfr ugha gqbZ gS D;ksafd pkjkmRiknd] pkjk mRiknu dh vuq"kaflr d`f"k dk;Zekyk vaxhd`r ughadjrs gSa ,oa pkjk dks Qly mRiknu ds tSls gh mxkrs gSaSA lkFk gh]pkjk mRiknd] pkjk laj{k.k dh fof/k;ksa tSls lw[kk ?kkl ,oa lkbyst lsifjfpr ugha gSaA pkjk mRiknd pkjk dks O;olkf;d n`f"Vdks.k lsmRiknu ugha djrs ik;s x;s gSa] ftlls os blds foi.ku ls vufHkK gSaAblfy;s] bl le; tc jkT; ljdkj d`"kdksa dh vk; dks nqxquk djukpkgrh gS] tks fd jkT; esa Ms;jh vk/kkfjr [ksrh iz.kkyh dks ykxw fd;sfcuk laHko ugha gSA vr,o] pkjk mRiknu rduhd d`"kdksa rd igqapkusds fo'ks"k iz;kl djuk furkar vko';d gS] D;ksafd ;g d`"kd dhviuh [ksrh ds lkFk&lkFk vk; ,oa jkstxkj c<+kus ds volj izkIr djldrk gSA

References

Dost Muhammad (2001) Integrated Crop Management Cropand Grassland Service, FAO, Plant Production andProtection Division 4: 23

Kindu Mekonnen, Gerhard Glatzel, Sieghardt Monika (2009)Assessments of Fodder Values of 3 Indigenous and1 Exotic Woody Plant Species in the Highlands ofCentral Ethiopia Mountain Research andDevelopment International Mountain Society29(2):135-142

(Manuscript Receivd : 16.8.13; Accepted : 15.11.13)

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Abstract

Rice -wheat is a major cropping system followed in Jabalpur,Rewa and Shahdol division of Madhya Pradesh andtransplanted rice is a common practice of rice cultivation. Thetransplanted rice has dependency on labour and water. Thispractice is labour intensive and required huge amount of water,which is a crucial input now a day. The delay transplanting ofrice due to non-availability of labourers at the peak time causesdrastic reduction of crop yield and also vacated the field quitlate due to which the sowing of subsequent rabi crop is alsoget delayed. The direct seeding of rice in upland conditionposes the sevier problem of weed infestation. Keeping in viewthese factors into account the study was conducted underOFT (On Farm Trail) to field evaluated the manually operated8-row paddy drum seeder for planting of sprouted paddy seedsunder puddle soil in comparison to conventional practice ofrice cultivation i.e. transplanted rice on farmers fields over anarea of 2 ha in Shahdol district of Madhya Pradesh. The studyrevealed that the paddy drum seeder gave an average workoutput of about 0.8 ha per day (8 hrs working) at an operatingspeed of 1.00 kmph. The seeder dropped 2-3 seeds per hillat a spacing of 12 cm in rows 20 cm apart with an averageseed rate of 30.05 kg/ha. It has been also observed thatseeding of rice with drum seeder gave huge labour saving ofabout 86% in comparison to transplanted rice. The crop yieldobtained was 34.9% more in case of paddy drum seeder.The paddy drum seeder also found cost effective as it incurredonly Rs 750 per ha which was about 80.26% less incomparison to transplanted rice in which it was Rs 3800 perha.

Keywords: Direct seeding, paddy drum seeder, laboursaving

Rice is a labour and water intensive crop, which causesless income. It is one of the important crops of the world

Impact of paddy drum seeder under puddled soilfor rice cultivation

Ghanshyam Deshmukh, R K Tiwari* and B.S. Dwivedi**Krishi Vigyan KendraJawaharlal Nehru Krishi Vishwa VidayalyaChhindwara 480661(MP)*Department of Agronomy, College of Agriculture, Rewa 486001 (MP)**Department of Soil Science, College of Agriculture, Jabalpur 482004 (MP)

and is grown between latitudes 450 N and 400 S(Mohanty et al. 2008). Ravishankar et al. (2006) alsoreported that under conventional method of planting,the seed rate ranged from 44 to 55 kg/ha with mean of48 kg.

In Jabalpur and Shahdol division of MadhyaPradesh more than 80% rice cultivation is done bytransplanting method and remaining area is under directseeding (broadcasting method) due to lack of wateravailability, undulated topography and labourunavailability. Direct paddy drum seeder is a small handoperated farm implement is used for direct paddyseeding. The activity of the transplanting seedlings inpuddled fields carried out consists of bending and staticposture for long duration of time leading to manyoccupational hazards (Singh and Tiwari 2009).Transplanting of rice in puddled fields is generallypreferred over dry sowing of seeds due to severeproblem of weeds. The raising of nursery and manualtransplanting are both labour intensive and costlyprepositions (Das 2003).

It is very efficient machine developed by TamilNadu Agricultural University. In this study direct paddydrum seeder was tested at 2.0 ha area of five farmer'sto evaluated the capacity of the machine, yield of thepaddy crop and economics of the direct paddy sowingunder on farm trail programme.

Material and methods

The study was carried out at Shahdol district of MadhyaPradesh, with 2 ha land of 5 farmers (0.4 ha eachfarmer), which are involved in rice cultivation and

JNKVV Res J 47(2): 224-227 (2013)

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remaining 2 ha land of 5 farmers used rice seeding bybroadcasting method were selected as control. The fieldexperiment was conducted in the month of July-October.On farm trials were sown with paddy variety i.e. IR 36through paddy drum seeder as well as in broadcastingmethod. During the experiment various parameters viz.,field capacity of paddy drum seeder, seed rate and yieldwere studied.

The machine

Direct paddy drum seeder is used for sowing germinatedpaddy seed directly in wetland field. There is no needfor transplantation. It is a manually pulled implement. Itcovers 8 rows of 20 cm row-to-row spacing at a time. Itis made up of plastic materials. The shaft and handleare MS steel material. There is a Hyperboloid shapedseed drum with 200mm diameter and 9 numbers of seedmetering holes of 9 mm hole diameter (Table 1). Bafflesare provided inside the seed drum between seed holesresulting in uniformity of seed rate throughout theoperation. These baffles also ensure hill dropping of

seeds. Each seed drum has two rows of planting. Foursuch drums can be assembled to form 8 rows of seeddrum as shown in the picture.

Wheels are provided at both ends. These wheelsare made up of plastic material to provide floatingcharacteristics. Wheels diameter is 2 feet. One squareshaft, handle base and handle, four seed drums areassembled together with the square shaft. The handleif meant to pull along.

Field preparation

In the study area well puddled and levelled fields wereprepared. Water was drained out at least 24 hrs forbefore sowing to form hard slurry pan of the puddledsoil. At the time of sowing only paper thin of water shouldbe maintained in the puddled field. Only just sproutedseed packed with gunny bags should be used. Waterwas flooded to the puddled once in three days aftersowing and drain out immediately. This practice shouldcontinue for 12 days. Thereafter depending upon the

Fig 1 Sprouted seed ready for sowing Fig 2 Filling seed in the drum

Fig 3 View of Paddy Drum Seeder is in well drained and puddled soil

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height of the seedlings water was allowed to stand inthe field. All farmers field were well prepared in themonth of May before rainy season. As per agronomicalrecommendation FYM (Farm Yard Manure) and NPK(Urea, DAP and Kcl) were applied to all the fields. Tocontrolling the weeds problem cono weeder and manualpractice were adopted. In non rainy days life savingirrigation was applied through open and tube well.Puddling was well prepared and levelled. Water wasdrained out before 24 hrs. Before sowing to form hardslurry pan of the puddle soil. At the time of sowing onlypaper thin of water was maintained in the puddled field.Only just sprouted seeds packed with gunny bags were

used. Irrigation water was flooded to the puddled fieldonce in three days after sowing and drains out sometime immediately.

Result and discussion

It was observed that average field capacity of the paddydrum seeder was 1.042 ha/day (Table 2) and in case ofbroadcasting method less time is required for sowingbut uniformity/plant geometry was not maintained. Seedrate has reduced to 39.9 % as compared to broadcastingmethod. It was observed that weed control in line sowingis easily manageable. Data is also the BC ratio hasincrease 22.47% (Table 2).

Manually Broadcasting

The paddy drum seeder was found a suitable handoperated tool in paddy cultivation was irrigation andlabour is the major problem. It is light in weight andeasily handled. It reduces the seed rate and maintainsuniformity in seed sowing and plant population. Labourcost is reduced drastically.

OFT has proved its value as a methodology to afford atechnology for the farmers. The present study resultedin demonstration and extension of drum seeder as adrudgery reducing tool or technology in local farmingcommunities. Use of drum seeder helps in timely sowingof crop resulting in more yield. Sowing by drum seedersaves costly seeds. The drum seeder reduces labourrequirement and cost of sowing. Line sowing by drumseeder reduces weeding cost due to use of mechanicalwelders with transplanting. The crop matures one week

Table 1. Operation procedure and Specifications ofpaddy drum seeder

Particulars SpecificationsPower source Hand operatedRow to row spacing 200 mmShape of seed drum HyperboloidNumber of rows 8 rowsDiameter of drum 200 mmDiameter of the seed metering hole 9 mmNumber of seed metering hole 9 NosWeight of the unit 10 KgType of ground wheel Lugged wheelDiameter of the ground wheel 600 mmOperating speed 1 kmph/walking speedLevel of filling the seed drum Half volumeWeight of seed drum 600 gramsMaterial used PP CPSeed requirement 12 kg per acre

Table 2. Field capacity, seed rate, yield and economical data of selected farmer's field

Farmer Field Capacity (ha/day) Seed rate (kg/ha) Yield (qt/ha) B:C ratioF1 1.00 30.50 24.50 1.96F2 1.11 31.00 25.00 2.00F3 0.95 29.75 23.50 1.88F4 1.10 30.50 25.00 2.00F5 1.05 28.50 23.75 1.90Average 1.04 30.05 24.35 1.94Control plotsF6 MB 50.0 16.50 1.65F7 MB 50.0 15.50 1.55F8 MB 50.0 14.75 1.47F9 MB 50.0 18.00 1.8F10 MB 50.0 14.50 1.45Average - 50.0 15.85 1.58

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early in drum seeded plots. The net profit obtained byuse of drum seeder is more than transplanting.

e/; izns'k ds jhok] 'kgMksy ,oa tcyiqj laHkkx esa /kku&xsgw¡ ,d izeq[kQly iz.kkyh gS rFkk ftles /kku mRiknu gsrq ikS/k j®i.k lkekU;r%fdlkuks }kjk fd;k tkrk gS] ftlesa vf/kd Jfed ,oa vf/kd ikuh dhvko';drk gksrh gS ijarqqqqq q q q q q ftu {ks=ks esa le; ij ikuh dh miyC/krkugh gks ikrh rFkk Jfed miyC/k ugh gks ikrs ogk¡ le; ls jksikbZ ugks ikus ds dkj.k /kku dh mit esa deh gks tkrh gS ,oa jch dh Qlydh cqokbZ Hkh le; ij ugh gks ikrhA bu fcUnqvksa dks /;ku esa j[krsgq;s iz{ks= iz;ksx ds varxZr /kku dh cqokbZ ds fy;s] ekuo pfyr iSMhMªe lhMj dk mi;ksx fd;k x;kA ftlds varxZr vadqfjr cht dksiSMh Mªe lhMj esa Mkydj cqokbZ djus ls 20 lseh- iafDr ls iafDrdh nwjh esa 0.80 gsDVj izfrfnu ds fglkc ls cqokbZ dh tk ldhftlesa [ksr esa ikuh Hkjus dh vko';drk ugh gksrh lkFk gh lkFkJfed dh Hkh cpr gqbZA jksik i)fr rFkk iSMh Mªe lhMj ls cqokbZdjds rqyukRed v/;;u ls ;g irk pyk fd iSMh Mwe lhMj lscqokbZ djus ls 34.9 izfr'kr vf/kd mit izkIr gqbZ rFkk 86 izfr'krJe dh cpr Hkh gqbZA

References

Das FC (2003) CRRI drum seeder for sowing pre germinatedpaddy seeds in puddled field. In: Internationalseminar on Downsizing technology for ruraldevelopment (ISDTRD-2003) 139- 142. RegionalResearch Laboratory Bhubaneshwar India

Mohanty SK, Behera BK, Satapathy GC (2008) Ergonomicsof farm women in manual paddy threshing.Agricultural Engineering International: the CIGR EJournal. X: MES 08002

Ravisankar N, Ahmed Z, Din M, Sharma TVRS, GhoshalChaudhuri S (2006) Performance of System of RiceIntensification (SRI) under Island Ecosystem, Paperpresented at National Symposium on System of RiceIntensification (SRI) -Present status and FutureProspects, ANGRAU, Hyderabad, India, 17 -18November 2006

Singh S, Tiwari C (2009) Drum Seeder as an ImprovedTechnique for Gender Empowerment. Paperpresented at Uttarakhand State Science andTechnology Congress, G.B. Pant University of Ag. &Tech.Panatnagar, Uttarakhand, India, 10-12November 2009

(Manuscript Receivd : 5.9.13; Accepted : 15.11.13)

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Abstract

The policy maker can either attempt to enhance the uptake ofimportant technologies relevant particularly to the small scaleagricultural production by improving research anddevelopment process or they can take steps, which enablesthe farmers to improve technical efficiency in production, whilethe farmer probably require a long time, considerable fundsand efforts but are likely to yield long run benefit. Else, raisingtechnical efficiency offers more immediate goals at modestcrops. If it can be shown that substantial inefficiencies arepresent in Agricultural production. Such research efforts arebased on an analysis of technical inefficiencies in productionof wheat crop by farmers. So an attempt has been made inpresent study to investigate farm specific technical efficiencyfor wheat crop in Jabalpur District of Madhya Pradesh. Theaims of present study are to evaluate efficiency of a particularfarm and to observe up to what extent its efficiency can beincreased without adding any additional resources. The resultsshows a comparison of the small, medium and large farmersvariability and technical efficiency is less in medium and lesserin small farmers and large in large farmers. Mean technicalefficiency is high in large farmer compared medium and smallfarmers.

Keywords: Technical Efficiency, deterministic frontiers,stochastic frontiers

Wheat is a major cereal crop in India. India producedabout 70 million tones of wheat per year or about 12%of world production. India is the second largest producerof wheat in the world and also second largest in wheatconsumption after china. The efficiency of productionis extremely important for output growth: using existingresources in the best possible manner would yield thehighest possible output for the given technicalconstraints. The efficiency of a farm /production unitcan be measured in terms of allocative efficiency andtechnical efficiency. Technical efficiency is the ratiobetween actual and potential output of a production unit.

Regional analysis of technical efficiency of wheat production

R.B. Singh, Umesh Singh, Rajdeep Mishra and P.C. JhaDepartment of Mathematics and StatisticsJawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482004 (MP)

The result of this study is useful for policy makersto rationalize the development policies for a particularcrop in region. Farrell (1957) defined the ratio, OB/OA,to be the technical efficiency of the firm with input-per-unit-of-output values at point A. Farrel (1957),suggested that the efficient unit isoquant be estimatedby programming methods such that the convex functioninvolved was never any of the observed input- per-unit-of-output ratios. Kebede (2001) examined technicalefficiency of rice farmers in mid hills of Nepal by usinga half normal stochastic frontier model. The averagetechnical efficiency of paddy farmers was estimated at71%. It was found that the farmers with poor quality ofland were technically more efficient. It was concludedthat farming experience and education were bothsignificant variables for improving technical efficiency.Baksh (2007) used a stochastic frontier productionfunction incorporating technical inefficiency effect modelto estimate technical efficiency and profitability ofgrowing vegetable in Punjab. Four vegetables namelypotato, carrot, radish and bitter gourd were selected forstudy. Results of the study showed that the mean levelof technical efficiency was 82% in case of radish, 72%in case of carrot, 70 % in case of potato and 66% incase of bitter gourd. It was observed that with anincrease in age of the vegetable growers, level oftechnical efficiency declined except in potato production.Education level and access to extension services werepositively related to the level of technical efficiency incultivation of all vegetables. The most profitablevegetable was bitter gourd followed by carrot, potatoand reddish.

Methodology

In Madhya Pradesh, Jabalpur district were chosen forthe study, taking in to consideration the sample growerswere further classified in to three classes based on thefarmers as class-I, Large farmers, class-II, medium

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farmers and class-III, small farmers. Multistage stratifiedrandom sampling was adopted to collect the data fromthe farmer. The first stage unit is village second stageunit is farmers which were grouped into three stratasmall medium and large farmers according to size ofland holding. From each group sample has been takenproportional to size of strata.

The data was collected from the farmers throughpersonal interview method using well-structured andpre-tested interview schedules. The major input forwhich the costs were worked out includes seeds, labour,plant protection, fertilizer, manures etc. the collecteddata was analyzed in order to estimate costs and returnand to work out the technical efficiency. The data wascollected from the wheat grower farm of the Jabalpurdistrict, taken purposively selected farmer of 2011-12.Sample size consists of 60 wheat growers.

Measurement of Technical Efficiency

The present study uses the stochastic frontierproduction function approach to measure the technicalefficiency in wheat production. Suppose that a farm hasa production plan (Y0, X0), where the first argument isthe set of outputs and the second represents the set ofinputs. Given a production function f(.), the farm istechnically efficient if Y0=f(X0) and technical inefficientif Y0< f(X0). Therefore, the TE can be measured by theratio

0 Y0/ f(X0) 1

The actual production function can be written as:

Qi = f (Xi, ) exp (-ui) and 0<ui< ; i=1,2,..n…….… (1)

Where Qi represents the actual output for the ith sample(production) unit; Xi is a vector of inputs and is a vectorof technology parameters to be estimated; f(.) is thefrontier production function and ui is a one -sidedresidual term. If the production unit is inefficient, itsactual output is less than the potential output. Therefore,we can treat the ratio of the actual output Qi and thepotential output f(.) as a measure of the technicalefficiency of the technical efficiency of the productionunit.

Using equation (1) above, we can write this measureas:

TE= Qi/f (Xi; ) = exp (-ui)…….…………..…. (2)

ui is zero if the production unit produces the potentialoutput and less than zero when production is below thefrontier. A random noise variable Vi can be included inthe equation (1) to capture the effect of other omittedvariables that can influence the output as:

Qi= f(Xi; ) exp(vi-ui)…………..….(3)

This new function is known as the individual-specificstochastic production frontier function. The likelihoodfunction for this model is:

L=-N ln - constant + [ln (- i / ) - ½ ( i/ )2]……(4)

Where, = u/ v, 2 = v2+ u2, and is the cumulativestandard normal distribution function and i = (vi-ui); uand v are standard deviations of the residuals u and vrespectively. The maximum likelihood estimation (MLE)method can provide the estimates of the stochasticfrontier production equation. Our stochastic frontierproduction function is given by:

ln Qi= 0+ L ln L + A ln A + F lnF + vi-ui ………(5)

Econometric Models

Production frontier models are reviewed in three sub-sections involving deterministic frontiers, stochasticfrontiers and panel data models. For convenience ofexposition, these models are presented such that thedependent variable is the original output of theproduction process.

Deterministic frontiers

The deterministic frontier model is defined by

Yi= f(xi/ ) exp (-Ui), i=1,2,3……….N……………(1)

Where Yi represents the possible production level forthe ith sample firm; f(xi/ ) is a suitable function of thevector, xi, of inputs for the ith firm and a vector of

230

unknown parameters; ui is a non-negative randomvariable associated with firm- specific factors whichcontributed to the ith firm not attaining max imumefficiency of production; and N represents the numberof firms involved in a cross-sectional survey of theindustry. The presence of the non-negative randomvariable, Ui, in model (1), defines the nature of technicalinefficiency of the firm and implies that the randomvariable, exp -Ui), has values between zero and one.Thus it follows that the possible production, Y i, isbounded above by the non-stochastic (i.e.,deterministic) quantity. The inequality relationships,

Yi f(xi, ), i=123 i=1, 2, 3…….N……………(2)

The technical efficiency of a given firm is definedto be the factor by which the level of production for thefirm is less than its frontier output. Given thedeterministic frontier model (i), the frontier output forthe ith firm is Yi* = f (xi, ) and so the technical efficiencyfor the ith firm, denoted by TEi, is

TEi = Yi/Yi*

= f (xi, ) exp (-Ui)/ f (xi, )

= exp (-Ui) ……………….………... (3)

Stochastic frontiers

The stochastic frontier production function is definedby

Yi = f(xi, ) exp (Vi -Ui) i=1, 2, 3…….N…………(4)

Where Vi is a random error having zero mean, which isassociated with random factors not under control of thefirm.

Panel Data Models

The deterministic and stochastic frontier productionfunction (1) and (4) are defined for cross- sectional data.If time-series observations are available for the firmsinvolved, then the data are referred to as panel data.Pitt and Lee (1981) considered the estimation of astochastic frontier production function associated withN firms over T time periods.

The model is defined by

Yit = f (xit; ) exp (Vit -Uit) i=1, 2, 3…….N

T=1, 2, 3…….T……………………..…(5)

Where Yit represents the possible production for the ithfirm at the tth time period.

For the present study, a Cobb-Douglasproduction function of the following form will bespecified.

ln Yi = 0+ 1i lnX1i+ 2i lnX2i+ 3i lnX3i+ 4i lnX4i+Vi - Ui

Where, Yi =yield (kg/farm), X1= Expenditure on seed(Rs/farm), X2= Expenditure on labour (Rs/farm),X3=Expenditure on fertilizer (Rs/farm), X4=Expenditureon irrigation (Rs/farm), i=vi-ui i=1,2,3………n farms

Result and discussion

In order to estimate the technical efficiency among thefarmers, the stochastic function of Cobb-Douglas formwas estimated using this method. The technicalefficiency of wheat was estimated using this method.The technical efficiency of wheat was estimated usinga frontier production function. The variables wereexpenditure on seed and fertilizers, expenditure onirrigation and labour expenses, and the dependentvariable as the yield of wheat.

Small Medium LargeN 20 20 20Mean of TE 0.69 0.77 0.84Minimum 0.61 0.71 0.75Maximum 0.77 0.86 0.93

The technical efficiency of small farmers whichvaries from 61% to 77% with mean 69% and mediumfarmers varies from 71% to 86% with mean 77%. Thetechnical efficiency of large farmers varies from 75% to93% with mean 84%. Thus we compare the smallmedium and large farmer's technical efficiency. Meantechnical efficiency is high in case of large farmers ascompare to medium and small farmers. Therefore it isconclude that large farmers are more technically efficient

231

than the medium and small farmers. The reason behindthis is the large farmers are educated and they havemore facility of agriculture implement and knowledgeso due to this their technical efficiency is high andmedium and small farmers are not as much as educatedas higher groups. So they have the lack of knowledgeand have less facility in comparison to large group.Therefore small wheat grower should be given with theexposer of technical knowledge by educating them,through training programme or demonstrated on theirfield. The medium farmers should also be given thetraining programme for the vertical growth in theproduction of wheat. The large farmers show hightechnical efficiency with the higher stability and thevariability in the small farmers are more in comparisonto medium and large farmers.

Frequency distribution and descriptive statistics oftechnical efficiency of wheat in the study area

Small farmers

T.E. Frequency Percent

0.60-0.64 6 300.65-0.69 5 250.70-0.74 6 300.75-0.80 3 15Total 20 100

Coefficient

Unstandardized t Sig.Coefficients

Model 1

(Constant) 2.013 0.369 0.715Seed 0.113 0.589 0.356Labour 0.752 -0.116 0.894Fertilizer 0.129 3.236 0.045Irrigation 0.057 -0.235 0.493

Dependent Variable: yield

Medium farmers

T.E. Frequency Percent0.70-0.75 8 400.76-0.81 7 350.82-0.87 5 25Total 20 100

Coefficient

Unstandardized t Sig.Coefficients

Model 2

(Constant) 0.656 2.125 0.019Seed -0.001 -0.025 0.956Labour 0.007 0.0478 0.865Fertilizer 1.035 8.142 0.000Irrigation -0.029 -0.214 0.658

Dependent Variable: yield

Large farmers

T.E. Frequency Percent

0.75-0.79 5 400.80-0.84 5 350.85-0.89 6 250.90-0.94 4 20Total 20 100

Coefficient

Unstandardized t Sig.Coefficients

Model 3

(Constant) -4.457 -3.245 0.003Seed -3.215 -4.236 0.009Labour 2.162 3.986 0.006Fertilizer 1.589 8.251 0.002Irrigation 1.695 2.523 0.019Dependent Variable: yield

The above tables show the technical efficiencyof small medium and large farmers. For small farmersthe mean technical efficiency is 69%, medium farmershave the technical efficiency 77% and large farmershave the mean technical efficiency 84%.

The variability among the small farmer is highfollowed by medium and large farmers and technicalefficiency is high in large followed medium and smallfarmer. It means large farmers have more knowledgeabout the technology and they apply it since theirtechnology is stable in comparison to medium and small.So small and medium farmer need more training

232

regarding the technology generates and also requireeducation for the stability of technology. The data showsthat small and medium farmers are spending more manyin the seed, labour, fertilizer and irrigation but their returnare not as much as large farmers. It shows that thereare unfair for the latest knowledge of seed, optimumuse of labour, proper timely application of plantprotection and timely application of fertilizer andirrigation.

The study has indicated that wheat farmers inJabalpur district are substantial technical inefficient.Hence, the study indicates considerable potential forimproving productivity of the crop wit given level of input'use and technology. In addition, improving efficiencywill mean farmers gaining considerably in terms ofprofits. Inefficiencies could be attributed to non-farmemployment, education level, farm experience, degreeof specialization, etc. setting minimum education levelin primary schools for the long run result and increasingnumber of extension workers are other policy optionsto increase the technical efficiency.

There is therefore a substantial potential forenhancing profitability by reducing costs throughimproved efficiency. By operating at full technicalefficiency levels, on average, the sample producerswould be able to reduce their costs of production. Thispotential cost saving in production costs will translateinto enhanced profitability and additional income forwheat producers. Thus it can be said that the level ofeducation of the household head, number of extensionvisits, access to credit, and membership in a farmer'sassociation are significant variables for improving helevel of technical efficiency.

The positive impact of education on technicalefficiency will enhance the farmer's ability to receiveand understand information relating to new agriculturaltechnology. Since there might be limited opportunitiesof raising the level of education of farmer's in the shortterm, intensifying farmer training programmes throughvarious innovative and vocational education programsand extension delivery system would be more practical.In the medium-term, policies should be geared towardspromoting formal education as a means of enhancingefficiency in agricultural production.

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References

Ali M, Chaudhry MA (1990) Inter-regional farm efficiency inPakistan's Punjab: A frontier production functionstudy. Agric Econ. 41: 6274

Belbase K, Grabowski R (1985) Technical efficiency inNepalese agriculture. Developing Areas.19:515-525

Baksh K (2007) An analysis of technical efficiency andprofitability of growing potato, carrot, radish and bittergourd: A case study of Pakistani Punjab. Ph.D.Dissertation, Department of Farm Management,University of Agriculture Faisalbad Pakistan

Haji J (2006) Production efficiency of smallholder's vegetabledominated mixed farming system in Eastern Ethiopia:A non-parametric approach. African Econ 16:1-27

Kebede TA (2001) Farm household technical efficiency: Astochastic frontier analysis. A study of rice in MardiWatershed in Western Development Region ofNepal. M.Sc. Thesis. Department of Economics andSocial Science, Agricultural University of Norway

Rahman S (2003) Profit efficiency among Bangladeshi ricefarmers. Proc. 25th International Conference ofAgricultural Economists (IAAE). 16-22 August 2003,Durban, South Africa

(Manuscript Receivd : 1.4.13; Accepted : 16.8.13)

233

Abstract

The aim of this study was to make a statistical analysis of thelinear models in soybean production and soybean oilproduction in Madhya Pradesh and India. The data set usedfor this analysis was taken from soybean processorassociation (SOPA) Indore. Following stochastic models weretaken: Linear, Quadratic, Compound, Cubic and Power. Formodel fitting performance, we used four comparison criteria;coefficient of determination (R2), sum of Squares error (SSE),root mean squares error (RMSE) and absolute prediction error(ARPE). The results indicated that Cubic, Quaderatic andPower models are more useful than other models to estimatesoybean production and soybean oil production in India andMadhya Pradesh.

Keywords: Stochastic models, SSE, RMS, ARPE

The Soybean (Glycine max (L.) Merrill) is a specieslegume native to East Asia, widely grown for its ediblebean which has numerous uses. Soybean oil is theworld's most widely used edible oil. India ranks fifth inthe area and production in 2010. (United States 90.60million metric tonnes, Brazil 68.5 million metric tonnes,Argentina 52.6 million metric tonnes, China 15.00 millionmetric tonnes, India 9.8 million tonnes). In India, it iscultivated 10.11 million hectares with annual productionof 122.13 Lakh million tonnes and productivity 12.08 q/ha. where as in Madhya Pradesh total area 5.67 millionhectares with annual production of 62.80 Lakh MT. andproductivity 11.08 q/ha. (Directorate of Economics &

Stochastic models for describing growth of soybeanproduction and soya oil production in Indiaand Madhya Pradesh

Umesh Singh, R.B. Singh* and S.S. GautamDepartment of StatisticsFaculty of Science and EnvironmentMahatma Gandhi Chitrakoot Gramodaya Vishwavidyalaya. ChitrakootSatna (MP) 485780*Jawaharlal Nehru Krishi Vishwa VidyalayaJabalpur 482 004 (MP)

Statistics 2010), which ranks first in India with respectto area. Wood (1981) tried to improve functional formby including a factor for the seasonality of milkproduction or parity of the cow or region of the country.

The growth performance of pulses production indifferent agro climatic regions of Madhya Pradeshduring 1969-70 to 1998-99 was studied by Ahirwar(2001).The projection of different crops of pulses inMadhya Pradesh up to end of 2020. The area of lentiland urd bean will be increase of 16.58, 6.59 per centand productivity will also be increase of 20.01, 14.56per cent up to end of 2020 AD. The pigeon pea areawill be decreases of 3.83, 22.87 per cent after 21 years.Although total pulses will be increase of 10.41, 25.32,20.93 per cent in area, production and productivity upto end of 2020 A.D.

Because of the importance of the cash crop ofMadhya Pradesh to need the demand of soybean andit's by product in respect to their use by the population,soybean production both quantitatively and qualitativelyshould be maintained at desired level, the soybeanstatistics explicitly reveal the existence of the fluctuationin the soybean production over time. This instability isgoverned by quite number of causes. To improvesoybean production in India must be counted forinvolving future strategies planning with regard toproduction of crop. The objective of this study is thestatistical models fitting on soybean production andsoybean oil production in India and Madhya Pradeshfor projecting the future values.

JNKVV Res J 47(2): 233-238 (2013)

234

Methodology

Data pertaining to two important factors related toSoybean industry for the study purpose were collectedboth for Madhya Pradesh state and India as a whole.The information was recorded from the SoybeanProcessor Association Indore (SOPA) for thirtyconsecutive years (1981 to 2010). The selected factorsSoybean production ('000' tonnes) and Soy oilproductions ('000' tonnes) are given in Table 1 have beentaken for study. The analysis has been performed byStatistica 5.5.

The functional form of the above-motionedmodels is

Yt= f(tibj) t=1,2,………..20; j=1,2,………….p

With an additive or multiplicative random error term, et

Where,

Yt = ith year production of Soybean/ Soybean oil

bj = unknown parameter to be estimated,

and et = random error term satisfying strictly theassumptions of normality, independence with zero mean

and a constant variance, i.e. eit'S~IID N (0, s2)

The five models, viz. linear, quadratic, compoundcubic and power, were fitted on the first 15 years dataand the sequentially adding one year datum at eachstage up to the 20th year, being the last stage. Thefunctional forms of these models are-

Model Name Expression

Linear Yt= a + b1t + et

Quadratic Yt= a + b1t + b2t2 + et

Compound Yt= a (b1)t et

Cubic Yt= a + b1t + b2t2 + b3t

3 + et

Power Yt= a (t)b et

With their usual meanings as discussed in the selection"Methods" the results obtained from these models aresummarized in Table to Table. By judicious comparisionamong the models with respect to R2, RMS and ARPEvalues, the cubic model, Quadratic model and powermodel are diagnosed (screened) to be the best fittingmodels.

Table 1. Historical data of soybean production and soybean oil production of Madhya Pradesh and India

Year Soybean Soybean oil Year Soybean Soybean oilproduction production production production

India Madhya India Madhya India Madhya India MadhyaPradesh Pradesh Pradesh Pradesh

1981 440 233.8 23 15.8 1996 5380 3912.0 861 372.41982 350 278.8 19 18.8 1997 6460 4790.0 1034 449.51983 490 456.0 34 34.4 1998 7140 4619.0 1143 432.81984 610 716.8 47 37.8 1999 7080 4703.0 1133 451.91985 950 777.9 78 64.5 2000 5280 3430.0 845 306.81986 1020 679.6 89 54.8 2001 5960 3620.0 954 548.91987 890 774.2 71 62.5 2002 4650 2674.0 745 579.41988 900 1299.5 132 118.5 2003 7820 4653.0 1251 745.51989 1550 1441.8 164 129.5 2004 6870 3750.0 1100 601.51990 1810 2175.6 229 198.8 2005 8270 4500.0 1324 600.41991 2490 2088.0 305 188.0 2006 8850 4780.0 1416 720.41992 3390 2589.0 427 228.5 2007 10970 5480.0 1755 764.81993 4750 3574.0 354 327.8 2008 9910 5850.0 1585 876.71994 3930 2853.0 459 256.0 2009 9960 6410.0 1594 936.01995 5100 3869.0 814 367.4 2010 12660 6098.0 1675 1024.8

235

These model, also exhibited stability for predicting futurevalues in respect of each factor. The value of the bestfitting model with parameters R2, RMS and ARPE arethe models, compound and cubic in soybean productionin India. The value of compound model with parameterR2, RMS and ARPE

Where a (constant), b1, b2 and b3 are the parameters ofabove models and xt is error terms. For study, we usedthe four comparison criteria. These criteria are givenbelow:

Coefficient of determination

Residual variance

The parameters of the linear models wereestimated by ordinary least square method. Before fittingmodels, the whole data set related to each factor wasdivided in to two parts the first part for estimation of themodel and the remaining part for checking of its stability.Stability checking of a model enlightens on themagnitude of the strength of the forecasting model,derived by utilizing the first part of the strength of theforecasting model, derived by utilizing the first part ofthe data set, in predicting a future observation takenfrom the second part of the data set. This magnitude ismeasured by a "statistic'', called absolute predictionerror ARPE expressed in percentage

ARPE= { Est. - / } × 100

Where, = new observation from the second part ofdata set and Est. = estimated value of derived fromthe forecasting model.

The procedure was carried out with respect todata sets on production figures related to the factorsunder study. The first part of the data set in respect toeach factor was plotted in a scatted-diagram over timeand utilizing the theory and expertise a few good models

were identified. Beside the summary statistics, theparameters of these models were estimated by OLStechnique. By comparing and judging judiciously theestimates of the parameters, summary statistics, viz.coefficient of determination R2; residual mean squaresor error variance and ARPE values, fitted better modelswere screened primarily and these models wereupdated and tested for their adequacies with regard tothe error properties.

Then from among the best fitted adequate, aparsimonious model was selected for forecasting thefuture values of each factor. A best fitted parsimoniousmodel is one which has the smallest RMS with smallernumber of parameters in a set of competing best fittedadequate models. The 95% confidence interval wascomputed for the future value of each factor basing upon the updated forecasting models. This interval willensure the occurrence of this projected value in theinterval in 95 cases out of 100 cases.

Results and discussion

By judicious comparison among the models with respectto R2, RMS and ARPE values, the Quadratic, Cubic andPower models are screened to be the best fitting models.These model, also exhibited stability for predicting futurevalues in respect of each factor.

The best fitting models with parameter R2, RMS,and ARPE, is found to be cubic, quadratic and powerfor soybean production in India. The value of cubicmodel with parameter R2, RMS, and ARPE is 91%,823.10, 12% in year 2006 and ether value of R2, RMS,ARPE in 2007 is 91%, 925.07, 16% and value of R2,RMS, ARPE in 2008 is 92%, 909.35, 2% and value ofR2, RMS, ARPE in 2009 is 93%, 893.93, 1% and valueof R2, RMS, ARPE in 2010 is 93%, 931.48, 11%.

The value of quadratic model with parameter R2,RMS, and ARPE is 93%, 873.67, 5% in year 2006 andether value of R2, RMS, ARPE in 2007 is 91%, 928.48,14% and value of R2, RMS, ARPE in 2008 is 92%,911.76, 0% and value of R2, RMS, ARPE in 2009 is92%, 898.22, 3% and value of R2, RMS, ARPE in 2010is 93%, 933.60, 11%. The value of power model withparameter R2, RMS, and ARPE is 90%, 877.89, 3% inyear 2006 and ether value of R2, RMS, ARPE in 2007is 91%, 924.89, 14% and value of R2, RMS, ARPE in2008 is 92%, 908.22, 0% and value of R2, RMS, ARPEin 2009 is 92%, 895.05, 3% and value of R2, RMS, ARPEin 2010 is 93%, 932.41, 12% ( Table 2).

The best fitting models with parameter R2, RMS,and ARPE, is found to be cubic, quadratic and power

236

Tabl

e 2.

Soy

bean

Pro

duct

ion

Indi

a

Mod

els

t=30

t=29

t=28

t=27

t=26

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PE

Line

ar0.

9299

8.40

0.17

0.92

922.

18-0

.01

0.91

938.

1646

6.63

0.90

950.

480.

180.

9087

4.48

0.06

Qua

drat

ic0.

9393

3.60

0.11

0.92

898.

220.

030.

9291

1.76

0.00

0.91

928.

480.

140.

9087

3.67

0.05

Cub

ic0.

9393

1.48

0.10

0.93

893.

930.

010.

9290

9.35

0.02

0.91

925.

070.

160.

9182

3.10

0.12

Com

poun

d0.

9011

15.5

637

2.73

0.88

1131

.73

0.13

0.88

1115

.40

0.09

0.87

1115

.90

0.06

0.84

1127

.19

0.05

Pow

er0.

9393

2.41

0.12

0.92

895.

050.

030.

9290

8.22

0.00

0.91

924.

890.

140.

9087

7.89

0.03

Tabl

e 3.

Soy

bean

oil

Prod

uctio

n In

dia

Mod

els

t=30

t=29

t=28

t=27

t=26

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PE

Line

ar0.

9215

7.89

32.4

20.

9216

0.46

0.01

0.91

163.

250.

050.

9016

5.49

0.18

0.89

154.

390.

07

Qua

drat

ic0.

9314

9.16

0.04

0.93

150.

900.

060.

9215

2.25

0.03

0.91

154.

750.

120.

9015

0.44

0.02

Cub

ic0.

9414

4.02

0.01

0.93

146.

460.

010.

9214

9.02

0.02

0.91

151.

620.

150.

9213

7.00

0.11

Com

poun

d0.

8819

7.74

0.14

0.88

194.

220.

160.

8818

9.64

0.12

0.87

187.

970.

030.

8419

1.02

0.07

Pow

er0.

9314

9.96

0.05

0.93

151.

450.

060.

9215

2.55

0.03

0.91

154.

960.

110.

9015

1.49

0.00

237

Tabl

e 4.

Soy

bean

Pro

duct

ion

Mad

hya

Prad

esh

Mod

els

t=30

t=29

t=28

t=27

t=26

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PE

Line

ar0.

8864

9.80

0.01

0.87

660.

770.

090.

8566

1.68

0.05

0.84

670.

600.

030.

8268

2.18

0.06

Qua

drat

ic0.

8863

7.23

0.05

0.87

644.

280.

140.

8762

5.56

0.13

0.86

613.

880.

130.

8660

2.03

0.07

Cub

ic0.

8962

8.27

0.02

0.88

638.

590.

110.

8762

5.54

0.13

0.87

605.

950.

170.

8756

4.85

0.17

Com

poun

d0.

8181

3.94

0.08

0.79

822.

130.

020.

7683

6.35

0.01

0.74

851.

630.

020.

7186

7.63

0.10

Pow

er0.

8864

8.54

0.10

0.87

658.

950.

110.

8665

5.66

0.07

0.84

661.

250.

060.

8267

0.07

0.03

Tabl

e 5.

Soy

bean

Oil

Prod

uctio

n M

adhy

a Pr

ades

h

Mod

els

t=30

t=29

t=28

t=27

t=26

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PER

2R

MS

ARPE

R2

RM

SAR

PE

Line

ar0.

9474

.30

0.16

0.94

68.2

10.

140.

9364

.19

0.14

0.94

60.3

30.

080.

9360

.24

0.22

Qua

drat

ic0.

9655

.85

0.06

0.96

55.0

60.

050.

9554

.97

0.06

0.95

54.5

80.

010.

9455

.59

0.01

Cub

ic0.

9754

.98

0.04

0.96

54.9

10.

040.

9554

.95

0.07

0.95

53.9

80.

030.

9454

.65

0.20

Com

poun

d0.

9568

.49

0.02

0.94

69.5

70.

030.

9370

.56

0.02

0.91

71.7

60.

080.

9071

.84

0.08

Pow

er0.

9656

.51

0.07

0.96

55.2

20.

060.

9554

.81

0.07

0.95

54.2

00.

010.

9455

.20

0.20

238

for soy oil production in India, the models are cubicquadratic and power. The value of cubic model withparameter R2, RMS, and ARPE is 92%, 137.00, 11% inyear 2006 and ether value of R2, RMS, ARPE in 2007is 91%, 151.62, 15% and value of R2, RMS, ARPE in2008 is 92%, 149.02, 2% and value of R2, RMS, ARPEin 2009 is 93%, 146.46, 1% and value of R2, RMS, ARPEin 2010 is 94%, 144.02, 1%.

The value of quadratic model with parameter R2,RMS, and ARPE is 90%, 150.44, 2% in year 2006 andether value of R2, RMS, ARPE in 2007 is 91%, 154.75,12% and value of R2, RMS, ARPE in 2008 is 92%,152.25, 3% and value of R2, RMS, ARPE in 2009 is93%, 150.90, 6% and value of R2, RMS, ARPE in 2010is 93%, 149.16, 4%. The value of power model withparameter R2, RMS, and ARPE is 90%, 151.49, 0% inyear 2006 and ether value of R2, RMS, ARPE in 2007is 91%, 154.96, 11% and value of R2, RMS, ARPE in2008 is 92%, 152.55, 3% and value of R2, RMS, ARPEin 2009 is 93%, 151.45, 6% and value of R2, RMS, ARPEin 2010 is 93%, 149.96, 5% (Table 3).

The best fitting models with parameter R2, RMS,and ARPE, is found to be cubic, quadratic and powerfor soybean production in Madhya Pradesh, the modelsare cubic quadratic and power. The value of cubic modelwith parameter R2, RMS, and ARPE is 87%, 564.85,17% in year 2006 and ether value of R2, RMS, ARPE in2007 is 87%, 605.95, 17% and value of R2, RMS, ARPEin 2008 is 87%, 625.54, 13% and value of R2, RMS,ARPE in 2009 is 88%, 638.59, 11% and value of R2,RMS, ARPE in 2010 is 89%, 628.27, 2%.

The value of quadratic model with parameter R2,RMS, and ARPE is 86%, 602.03, 7% in year 2006 andether value of R2, RMS, ARPE in 2007 is 0.86%, 613.88,13% and value of R2, RMS, ARPE in 2008 is 87%,625.56, 13% and value of R2, RMS, ARPE in 2009 is87%, 644.28, 14% and value of R2, RMS, ARPE in 2010is 88%, 637.23, 5%. The value of power model withparameter R2, RMS, and ARPE is 82%, 670.07, 3% inyear 2006 and ether value of R2, RMS, ARPE in 2007is 84%, 661.25, 6% and value of R2, RMS, ARPE in2008 is 86%, 655.66, 7% and value of R2, RMS, ARPEin 2009 is 87%, 658.95, 11% and value of R2, RMS,ARPE in 2010 is 88%, 648.54, 10% (Table 4).

The best fitting models with parameter R2, RMS,and ARPE, is found to be cubic, quadratic and powerfor soybean oil production in Madhya Pradesh, themodels are cubic, quadratic and power. The value ofcubic model with parameter R2, RMS, and ARPE is 94%,54.65, 20% in year 2006 and ether value of R2, RMS,ARPE in 2007 is 95%, 53.98, 3% and value of R2, RMS,ARPE in 2008 is 95%, 54.95, 7% and value of R2, RMS,

ARPE in 2009 is 96%, 54.91, 4% and value of R2, RMS,ARPE in 2010 is 97%, 54.98, 4%.

The value of quadratic model with parameter R2,RMS, and ARPE is 94%, 55.59, 1% in year 2006 andether value of R2, RMS, ARPE in 2007 is 95%, 54.58,1% and value of R2, RMS, ARPE in 2008 is 95%, 54.97,6% and value of R2, RMS, ARPE in 2009 is 96%, 55.06,5% and value of R2, RMS, ARPE in 2010 is 96%, 55.85,6%. The value of power model with parameter R2, RMS,and ARPE is 94%, 55.20, 20% in year 2006 and ethervalue of R2, RMS, ARPE in 2007 is 95%, 54.20, 1%and value of R2, RMS, ARPE in 2008 is 95%, 54.81,7% and value of R2, RMS, ARPE in 2009 is 96%, 55.22,6% and value of R2, RMS, ARPE in 2010 is 96%, 56.51,7% (Table 5).

Forecasting for soybean production and soy oilproduction in India and also in Madhya Pradesh cubic,quadratic and power stochastic models are suitable.

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(Manuscript Receivd : 1.4.13; Accepted : 11.9.13)