Estimation of association among growth and yield related traits in Bread Wheat (Triticum aestivum....

Estimation of association among growth and yield related traits in Bread Wheat (Triticum aestivum. L) Genotypes at Gurage Zone, Ethiopia

IJPBCS


Kifle Zerga1*

Firew Mekbib2 and Tadesse Dessalegn

3

1Wolkite University, College of Agriculture and Natural Resource Management, Department of Horticulture, P.O. Box,

O7, Gurage Zone, Ethiopia. 2Haramaya University, College of Agriculture and Environmental Science, School of Plant Science, PO Box, 138, Dire

Dawa, Ethiopia. 3Deputy Team Leader, IP-consult Project “Innovation for Agricultural Productivity–Arsi”, on behalf to the Green

Innovation Center – Ethiopia, A programme assisted by the German government via The German Society for International Cooperation (GIZ) GmbH, Addis Ababa, Ethiopia.

A total of twenty five bread wheat (Triticum aestivum L.) genotypes were evaluated for trait

association and path coefficient analysis among yield and yield contributing traits at Gurage zone of two different environments. The genotypes were grown in triplicate randomized

complete block design. Data were collected on 13 agronomic characters. It was found results that grain yield showed positive and significant correlations with above ground biomass, tillers per plant, kernel per spike, spikelet per spike and plant height at Fereziye and negatively

correlated with tiller per plant (rg= -0.535) and plant height (rg= -0.284) at Kotergedra. Selection on the basis of positive association of grain yield with its contributing traits may be helpful to

improve grain yield of wheat. Path coefficient analysis revealed that above ground biomass and tillers per plant exerted high and favorable direct effects on grain yield at Fereziye. Both genotypic and phenotypic correlation and path coefficient analysis revealed that grain filling

period exerted high and favorable direct effect on grain yield at Kotergedra which indicated that selection on such traits may be useful to improve the grain yield. It was moreover suggested

that the evaluation of wheat genotypes for grain yield under multi-zonal locations should be carried out to exploit more yield potential.

Keywords: Bread wheat, Triticum aestivum, correlation, grain yield, path analysis, trait association.

Introduction

The genetic origin of wheat is of interest; since it is a

classic example of how closely related species may be combined in nature into a polyploid series. The species of Triticum (T.) and their close relatives can be divided into

diploid, tetraploid and hexaploid groups, with chromosome numbers of 2n = 14, 28 and 42,

respectively, in which the basic chromosome number of wheat is x = 7. Triticum durum originated thousands of years ago from a hybridization between the wild diploid T.

monococcum L. (A genome donor) and the donor of the

B genome which, according to morphological,

geographical and cytological evidence, has been recognized as T. speltoides (Tausch) Gren or a closely related species (Abu, 2012).

*Corresponding author: Kifle Zerga, Wolkite University,

College of Agriculture and Natural Resource

Management, Department of Horticulture, P.O. Box, O7, Gurage Zone, Ethiopia. Email: [email protected]

International Journal of Plant Breeding and Crop Science Vol. 3(2), pp. 123-134, August, 2016. © www.premierpublishers.org. ISSN: 2167-0449

Research Article


Zerga et al. 123

Wheat is grown at an altitude ranging from 1500 to 3000 m.a.s.l, between 6-16

0 N latitude and 35-42

0 E longitude

in our country. The most suitable agro- ecological zones, however, fall between 1900 and 2700 m.a.s.l (Abu,

2012). Wheat in Ethiopia is an important cereal crop and it ranks fourth in total area coverage next to teff, maize and sorghum; also fourth in total production next to

maize, teff and sorghum. 4.23 million tons of wheat is produced on an area of 1.7 million ha and about 4.6

million farmers were involved. Oromia, Amhara, SNNP and Tigray are the major wheat producing regions in the

country with area coverage of 875641.45, 529609.63, 137294.72 and 108865.39 ha respectively. Furthermore, 47259 farmers were involved with un-estimated area

coverage in Gurage Zone in 2015 main production season (CSA, 2015).

The integrated approach of evaluating the germplasms uses both visual and statistical approaches. The relative influence of various traits and their degree of associations

can be estimated statistically by correlation (Dewey and Lu, 1959). Determination of relationships of characters

can help to identify traits of economic importance. The contribution of different traits for yield can also be

quantified by path coefficient analysis. Path analysis also splits the correlation coefficient of a set of independent variables on dependent variables into direct and indirect

effects. In view of this the present study was undertaken with the following objectives to:

1. Estimate the magnitude of correlation among grain yield and yield contributing characters.

2. Partition the correlation coefficients of yield with its related traits into direct and indirect effects through path analysis.

MATERIALS AND METHODS Experimental Materials

Experimental materials comprised of twenty five bread

wheat genotypes released from different agricultural research centers. Experimental Design and Field Management

The genotypes were planted in early July 2015 at Wolkite University stations (Kotergedra and Fereziye). The

genotypes were grown in randomized complete block design (RCBD) with three replications. Each plot

consisted of six rows spaced 20cm × 2.5m long. The plot area was 3m

2 (2.5m × 1.2m). A 1.5 meter distance was

maintained between replication and 50cm between plots

used for both sites. Fertilizers (both N and P2O5) was applied at the rate of 150 kg/ha urea and 100 kg/ha DAP

at the time of planting and tillering. Seeding was done at the rate of 125Kg/ha. Seed and fertilizer was drilled uniformly by hand. Weeding and other agronomic

practice was carried out as per recommendations of the respective sites. Data collection

The data on the following attributes was collected on the basis of the central four rows in each plot. 1. Days to heading (DTH): The number of days from

date of sowing to the stage where 75% of the spikes

have fully emerged. 2. Days to maturity (DTM): The number of days from

sowing to the stage when 90% of the plants in a plot have reached physiological maturity. 3. Grain filling period (GFP): The number of days from

heading to maturity, i.e. the number of days to maturity minus the number of days to heading. 4. Grain yield (GY): Grain yield in grams obtained from

the central four rows of each plot and converted to kilograms per hectare at 12.5% moisture content. 5. 1000-kernel weight (TKW): Weight of 1000 seeds in

gram. 6. Above ground biomass (AGB): The plants with in the

four central rows were harvested and weighed in grams. 7. Harvest index (HI): On a plot basis, the ratio of dried

grain weight to the dried above ground biomass weight multiplied by 100. Ten plants were randomly selected

from the four central rows for recording the following observations: 1. Tillers/plant (TPP): The average number of tillers 2. Plant height (PHT): The average height in cm from

ground level to the tip of the spike. 3. Kernels per spike (KPS): The average number of

kernels per spike 4. Spikelet per spike (SkPS): The average number of

spikelet's per spike. 5. Spike length (SL): The average spike length in cm

from its base to the tip. 6. Spikes per plant (SPP): The average number fertile

spikes per plant including tillers.

Data Analysis

The data recorded were subjected to analysis by using

General Linear Model and the statistical package SAS version 9.1 was used for the following statistical

procedures.

Association of characters

Phenotypic and genotypic correlations between yield and

yield related traits were estimated using the method described by Miller et al. (1958).

Phenotypic correlation coefficient (rpxy) between character x and y

yx

xy

xyVpVp

Covprp


Int. J. Plant Breeding Crop Sci. 124

Where: Covpxy = Phenotypic covariance between character x and y

Vpx = Phenotypic variance for character x Vpy = Phenotypic variance for character y Genotypic correlation coefficient (rgxy) between character

x and y

yx

xy

xyVgVg

Covgrg

Where: Covgxy = Genotypic covariance between

character x and y Vgx = Genotypic variance for character x Vgy = Genotypic variance for character y

The coefficient of correlations at phenotypic level was tested for their significance by comparing the value of

correlation coefficient with tabulated r-value at g-2 degree of freedom. However, the coefficient of correlations at

genotypic level was tested for their significance using the formula described by Robertson (1959) indicated below: Genotypic correlation coefficient was tested with the

following formula suggested by Robertson (1959).

xy

xy

SEg

rgt

)(

The calculated „t‟ value was compared with the tabulated

„t‟ value at g-2 degree of freedom at 5% level of significance, where, g = number of genotypes

yx

xy

xyhh

grSEg

.2

)1( 2

Where: SEgxy= Standard error of genotypic correlation

coefficient between character x and y hx = Heritability value of character x hy = Heritability value of character y

The calculated absolute t value was tested against the tabulated t-value at g-2 degree of freedom for both

phenotypic and genotypic correlations. Environmental correlation coefficients was tested at [(g-1) (r-1)-1)] degree of freedom, where g is the number of genotypes.

Path coefficient analysis was conducted as suggested by Wright(1921) and worked out by Dewey and Lu(1959)

using the phenotypic as well as genotypic correlation coefficients to determine the direct and indirect effect of

yield components on seed yield based on the following relationship. 𝑟𝑖𝑗 = 𝑝𝑖𝑗 + ∑ 𝑟𝑖𝑘𝑝𝑘𝑗

Where: 𝑟𝒊𝒋 = Mutual association between the independent

character (i) and dependent character, grain yield (j) measured by the correlation coefficients. 𝑃𝑖𝑗 = Component of direct effects of the

independent character (i) as measured by the path

coefficients and ∑ 𝑟𝑖𝑘𝑝𝑘𝑗 = Summation of components of indirect

effect of a given independent character, (i) on a given

dependent character (j) via all other independent characters (k).

The residual effect, which determines how best the causal factors account for the variability

of the dependent factor, was calculated using the following formula. 1 = 𝑃2𝑟 + ∑ 𝑃𝑖𝑦. 𝑟𝑖𝑦

Where: P2r is the residual factor, Piy is the direct effect of

yield by ith trait, and riy is the

Correlation of yield with the ith trait.

RESULTS AND DISCUSSION

Character Associations

Grain yield is the most complex trait and it is influenced by many factors (known and unknown) that determine

productivity. Therefore, understanding of the inheritance and interrelationships of grain yield and of characters influencing these traits are highly important for

formulating selection criteria. Thus, estimation of the magnitudes of genotypic and phenotypic correlations of

grain yield and its component among yield related traits are highly crucial to utilize the existing variability through selection. Phenotypic, genotypic and environmental

correlations among the characters are presented in Tables 3-6. Grain yield was highly and positively

correlated at genotypic level with days to heading, above ground biomass, tillers per plant, kernel per spike, days

to maturity and plant height at Fereziye (Table 3). It had positive genotypic correlation with kernel per spike and spikelet per spike, spike length and spikes per plant but

negatively significant correlated with tiller per plant and days to heading at Kotergedra. Similarly Abderrahmane

et al. (2013), reported that total biomass, number of spikes per plant, number of grains per spike are positively

correlated with grain yield and Adhiena (2015), similarly biomass yield that had positive and highly significant (P≤0.01) genotypic correlation coefficient with grain yield

(r = 0.91) had the highest direct effect on grain yield. Phenotypically, days to heading showed significant and

positive correlation with days to maturity, grain filling period, grain yield, above ground biomass, tillers per plant, plant height, kernel per spike, spikelet per spike,

spike length and spike per plant at Fereziye. Adhiena (2015), reported that grain yield and days to heading

showed negative and significant correlation in controversial with the present study. Phenotypically days

to heading highly and positively correlated with days to maturity and tillers per plant but highly negatively correlated with grain yield and 1000 kernel weight at

Kotergedra. Similar results were also reported (Aalyia et al., 2016; Ali et al., 2007; Ali et al., 20013; Ali et al., 2016;

Awale et al., 2013; Kumar et al., 2013) who showed the presence of highly significant association of days to

heading with days to maturity on bread wheat. Moreover,


Zerga et al. 125

Table 1. Location and descriptions of weather conditions for the two testing sites

Sites Seasonal Temperature ( oC) Soil type Soil

PH Seasonal Rainfall

(mm)

Location

Max Min Latitude Longtiude Altitude

Fereziye 24.37 10.2 Eutric Nitisols 5.4 1336.8 8.20N 37.9

0E 1980 masl

Kotergedra 23 8 Eutric Nitisols 5.7 1450 8.050N 37.5

0E 2600 masl

Figure 1: Geographical Map of the study area

Table 2. List of Genotypes

Entry Variety Name Source Center Year of Release

1 ETBW 5879 Kulumsa 2014 2 ETBW 6095 Kulumsa 2014 3 WORRAKATTA/PASTOR Sinana 2014 4 UTQUE96/3/PYN/BAU//MILLAN Sinana 2014

5 Hidasse Kulumsa 2012 6 Ogolcho Kulumsa 2012

Int. J. Plant Breeding Crop Sci. 126 Table 2. Cont.

7 Hoggana Kulumsa 2011

8 Hulluka Kulumsa 2012 9 Mekelle-3 Mekelle 2012 10 Mekelle-4 Mekelle 2013 11 Shorima Kulumsa 2011 12 Mekelle-1 Mekelle 2012

13 Mekelle-2 Mekelle 2011 14 Ga'ambo Werer 2011 15 Kakaba Kulumsa 2010 16 Danda'a Kulumsa 2010 17 Gassay Adet 2007 18 Alidoro Holleta 2007

19 Digelu Kulumsa 2005 20 Tay Adet 2005 21 Sofumar Sinana 1999 22 Mada-Wolabu Sinana 1999 23 Pavon-76 Kulumsa 1982 24 Jefferson Kulumsa 2012

25 King Bird Kulumsa 2014

Experimental Design and Field Management

Table 3. Genotypic (above diagonal) and phenotypic correlation coefficient for the 13 characters studied at Fereziye

Characters DTH DTM GFP GY TKW AGB HI TPP PHT KPS SKPS SL SPP

DTH 0.873** 0.448* 0.599** 0.150 0.612** -0.014 0.735** 0.704** 0.556** 0.677** 0.345 0.446*

DTM 0.807** 0.826** 0.491* 0.143 0.520** 0.014 0.679** 0.637** 0.704** 0.717** 0.250 0.328

GFP 0.313** 0.813** 0.208 0.088 0.247 0.042 0.395 0.356 0.651** 0.535** 0.060 0.087

GY 0.575** 0.424** 0.116 -0.038 0.878** 0.180 0.832** 0.444* 0.475* 0.368 0.376 0.418*

TKW 0.090 0.132 0.123 0.01 -0.153 0.242 -0.239 0.006 0.038 0.099 0.380 0.329

AGB 0.558** 0.433** 0.145 0.809** -0.078 -0.296 0.816** 0.551** 0.495* 0.494* 0.281 0.349

HI 0.006 0.005 0.003 0.182 0.094 -0.397** -0.005 -0.261 0.015 -0.059 0.114 0.040

TPP 0.664** 0.603** 0.315** 0.769** -0.147 0.764** -0.076 0.591** 0.394 0.528** 0.167 0.262

PHT 0.613** 0.522** 0.236* 0.398** 0.032 0.544** -0.291* 0.541** 0.499* 0.802** 0.491* 0.544**

KPS 0.428** 0.490** 0.367** 0.365** 0.038 0.418** -0.061 0.365** 0.401** 0.676** 0.336 0.333

SKPS 0.587** 0.596** 0.381** 0.409** 0.094 0.423** -0.041 0.489** 0.714** 0.608** 0.61** 0.612**

SL 0.321** 0.193 -0.005 0.348** 0.281* 0.298** -0.028 0.191 0.485** 0.348** 0.579** 0.944**

SPP 0.362** 0.224 0.0047 0.344** 0.206 0.333** -0.058 0.230* 0.526** 0.346** 0.557** 0.86**

Where: DTH= Days to heading, DTM= Days to maturity, GFP= Grain filling period, GY= Grain yield, TKW= Thousand kernel weight, AGB= Above ground biomass, HI= Harvest index, TPP= Tillers per plant, PHT= Plant height, KPS= kernel per spike, SKPS= Spikelet per spike, SL= Spike length, SPP= Spike per plant. *, ** Indicate significant and highly significant at the 0.05 and 0.01 probability levels, respectively


Zerga et al. 127

phenotypically days to maturity shown highly significant and positive association with grain filling period, grain

yield, above ground biomass, tillers per plant, plant height, kernel per spike and spikelet per spike at

Fereziye. Grain filling period shown highly significant and positive association with grain yield at Kotergedra both genotypic and phenotypic levels.

As shown in Table 4, grain yield was negatively

associated with days to heading, days to maturity and tillers per plant at Kotergedra both at genotypic and

phenotypic levels. Similar finding with Ali et al., (2014), Gelalcha and Hanchinal (2013), Gautam and Sethi (2002), Masood et al. (2014ab), Mohammad et al. (2009),

Mohammadi et al. (2012), Tsegaye et al. (2012) and Zafarnaderi et al. (2013), reported negative relationship

between days to flowering and grain yield per plant in their studies in advanced wheat lines. Above ground biomass was shown highly significant and positive

association with days to heading, days to maturity, grain yield, tillers per plant, plant height, kernel per spike,

spikelet per spike, spike length and spikes per plant but negatively association with harvest index phenotypic level

at Fereziye. Similar finding with Adhiena (2015) and Raza et al. (2015), reported that biomass yield correlation coefficients were positive and highly significant with

number of productive tillers per plant (r=0.47) and spike length (r=0.31) at phenotypic level. Above ground

biomass also shown significant and positively association with days to heading, days to maturity, grain yield, tillers

per plant, plant height, kernel per spike, spikelet per spike, spike length and spike per plant genotypic level at Fereziye.

Tillers per plant shown highly significant and positively

association with plant height and spikelet per spike both phenotypic and genotypic level at Fereziye. Similar finding with Khan and Dar (2009), reported that the

number of spikelets plant-1

showed positive and significant correlation with effective tillers plant

-1 and grain

yield plant-1

at both the phenotypic and the genotypic levels. Kernel per spike shown significant and positively

association with spikelet per spike, spike length and spike per plant both genotypic and phenotypic level at both locations. Hussain et al. (2014) and Farooq et al.

(2011ab), reported positive and significant association of kernels per spike with days to maturity and number of

spiklets per spike. Spikelet per spike was highly significant and positively correlated with spike length and

spike per plant and also Spike length highly significant and positively association with spike per plant both genotypic and phenotypic level at both locations. Soylu

and Akgün (2003) and Yücel et al. (2009), also reported positive and significant association between spikelet

number per spike and grain number per spike and grain yield per spike. The weak associations of grain yield with spike length and above ground biomass at Kotergedra

may imply that reduced number of spike length and above ground biomass in less favorable environments

could increase yield. This may be due to the high-energy loss for the production of biomass than starch to produce

grain yield (Donald, 1981). However grain yield was strong positive correlated with above ground biomass (rg=0.878 and rph=0.809) and tiller per plant (rg=0.832 and

rph=0.769) at Fereziye that indicated increased tiller number and above ground biomass leads to increase

grain yield. Ahmad et al. (2010) and Naseem et al. (2015), reported that both genotypic and phenotypic

correlations among biological and grain yields were positive and significant for direct cross under normal planting (rg = 0.93, P ≤ 0.01; rp = 0.74, P ≤ 0.01) and its

reciprocal cross (Saleem-2000 × Fakhre Sarhad) under late planting (rG = 0.92, P ≤ 0.01; rp = 0.64, P ≤ 0.01).

Grain yield weak and negatively correlated with 1000 kernel weight (rg= -0.007). It had weak and positively

correlated with above ground biomass (rph= 0.009) at Kotergedra. Moreover, grain yield negatively correlated

with tiller per plant (rg= -0.535) and plant height (rg= -0.284) but positively correlated with grain filling period

(rph=0.784), spikelet per spike (rph=0.214) and spikes per plant (rph=0.101). It had weak correlation with 1000 kernel weight (rph=0.057), above ground biomass (rph=0.009)

and spike length (rph=0.067) and negatively correlated with days to maturity (rph=-0.417) at Kotergedra. Grain

yield was weak correlated with 1000 kernel weight (rg= -0.0379 and rph= 0.01) at Fereziye. Grain filling period was

strong and positive correlation with kernel per spike (rg= 0.651 and rph= 0.367) and spikelet per spike (rg= 0.535 and rph= 0.381) but weak correlation with spike length (rg=

0.060 and rph= -0.0045) and spike per plant (rg= 0.087 and rph= 0.0047) at Fereziye. Days to heading was

negatively correlated with grain yield, 1000 kernel weight and harvest index; days to maturity with grain yield and harvest index; grain filling period with tiller per plant and

plant height both at genotypic and phenotypic level at Kotergedra. In opposed with the present study Anwar et

al. (2009), in which correlation between days to maturity and grain yield was positive under favorable

environmental conditions. Grain yield was strong and positively correlated with above ground biomass (rg= 0.878 and rph= 0.809) and tiller per plant (rg= 0.832 and

rph= 0.769) and above ground biomass was strong and positively correlated with tiller per plant (rg= 0.816 and

rph= 0.764) at Fereziye. Asaye et al. (2013), reported that positive and highly significant correlation of grain yield

with biological yield for bread wheat varieties. For most characters studied at both locations, the

genotypic correlation coefficients were greater than the phenotypic correlation coefficients and their ratio

(genotypic correlation to phenotypic correlation) were greater than the corresponding environmental correlation coefficients, and this suggested that the apparent



Table 4. Genotypic (above diagonal) and phenotypic correlation coefficient for the 13 characters studied at Kotergedra


DTH 0.802** 0.056 -0.468* -0.03 0.216 -0.284 0.695** 0.078 0.046 0.162 0.223 0.20

DTM 0.745** 0.037 -0.383 0.109 0.347 -0.113 0.539** 0.106 0.201 0.224 0.197 0.275

GFP 0.096 0.056 0.855** -0.026 0.028 0.061 -0.197 -0.275 0.488* 0.535** 0.356 0.253

GY -0.542** -0.417** 0.784** -0.007 -0.087 0.201 -0.535** -0.284 0.408 0.389 0.199 0.120

TKW -0.247* -0.147 -0.114 0.057 0.197 0.925** -0.265 0.652** 0.543** 0.541** 0.623** 0.316

AGB 0.010 0.146 0.019 0.009 0.199 0.107 0.167 -0.068 0.135 0.133 0.124 0.385

HI -0.408** -0.217 -0.001 0.253* 0.894** 0.174 -0.608** 0.610** 0.575** 0.530** 0.568** 0.243

TPP 0.468** 0.230* -0.221 -0.479** -0.087 0.006 -0.517** -0.163 -0.333 -0.236 -0.157 0.034

PHT 0.008 0.040 -0.186 -0.163 0.481** -0.104 0.469** -0.107 0.295 0.183 0.227 -0.001

KPS -0.082 0.034 0.293* 0.299** 0.454** 0.085 0.494** -0.234* 0.372** 0.636** 0.666** 0.509**

SKPS 0.180 0.249* 0.387** 0.214 0.311** 0.064 0.352** -0.208 0.135 0.496** 0.813** 0.507**

SL 0.213 0.204 0.238* 0.067 0.327** 0.052 0.340** -0.148 0.088 0.483** 0.685** 0.520**

SPP 0.028 0.072 0.141 0.101 0.308** 0.239* 0.243* 0.032 0.165 0.568** 0.378** 0.382**


Table 5. Environmental correlation coefficients for 13 characters studied at Fereziye


DTH 0.309* -0.187 0.303* -0.180 0.163 0.068 0.120 0.007 0.218 0.093 0.182 0.127

DTM 0.875** -0.148 0.103 -0.094 -0.010 0.138 -0.107 0.076 0.03 -0.099 -0.078

GFP -0.307* 0.198 -0.180 -0.045 0.081 -0.114 -0.032 -0.015 -0.195 -0.145

GY 0.285* 0.250 0.339* 0.243 0.063 0.19 0.056 0.181 0.141

TKW 0.199 -0.101 0.193 0.124 0.041 0.082 -0.03 -0.042

AGB -0.760** 0.44** 0.506** 0.332* 0.092 0.388** 0.332*

HI -0.265 -0.436** -0.134 -0.017 -0.305* -0.177

TPP 0.266 0.394** 0.312* 0.320* 0.158

PHT 0.258 0.336* 0.457** 0.528**

Zerga et al. 129

Table 5. Cont.

KPS 0.549** 0.436** 0.370**

SKPS 0.458** 0.445**

SL 0.702**

SPP

Where DTH= Days to heading, DTM= Days to maturity, GFP= Grain filling period, GY= Grain yield, TKW= Thousand kernel weight, AGB= Above ground biomass, HI= Harvest index, TPP= Tillers per plant, PHT= Plant height, KPS= kernel per spike, SKPS= Spikelet per spike, SL= Spike length, SPP= Spike per plant. *, ** Indicate significant and highly significant at the 0.05 and 0.01 probability levels, respectively

Table 6. Environmental correlation coefficients of 13 characters studied at Kotergedra


DTH 0.714** 0.145 -0.631** -0.469** -0.233 -0.564 0.302* -0.051 -0.26 0.254 0.232 -0.192

DTM 0.08 -0.469** -0.380** -0.048 -0.335* 0.030 -0.008 -0.152 0.360** 0.252 -0.151

GFP 0.675** -0.254 0.003 -0.111 -0.260 -0.064 -0.114 0.019 -0.021 -0.095

GY 0.1505 0.176 0.333* -0.429** -0.011 0.104 -0.173 -0.189 0.068

TKW 0.204 0.849** 0.102 0.267 0.298* -0.205 -0.253 0.300*

AGB 0.301* -0.206 -0.163 -0.028 -0.133 -0.123 -0.097

HI -0.430** 0.280* 0.334* -0.087 -0.152 0.248

TPP -0.055 -0.114 -0.202 -0.152 0.033

PHT 0.520** 0.053 -0.150 0.454**

KPS 0.067 -0.0008 0.715**

SKPS 0.268 -0.018

SL 0.016

SPP




Table 7. Phenotypic path coefficients of direct (main diagonal) and indirect effects of the 9 characters studied at Fereziye

DTH DTM AGB TPP PHT KPS SKPS SL SPP rph

DTH 0.234 -0.127 0.284 0.274 -0.171 0.002 0.026 0.065 -0.01 0.58**

DTM 0.190 -0.157 0.218 0.249 -0.146 0.002 0.027 0.041 -0.006 0.42**

AGB 0.131 -0.068 0.507 0.316 -0.151 0.002 0.019 0.061 -0.009 0.81**

TPP 0.155 -0.094 0.385 0.416 -0.151 0.002 0.022 0.041 -0.007 0.77**

PHT 0.143 -0.082 0.274 0.224 -0.280 0.002 0.032 0.100 -0.015 0.4**

KPS 0.098 -0.079 0.213 0.153 -0.112 0.005 0.027 0.071 -0.01 0.37**

SKPS 0.138 -0.094 0.213 0.203 -0.199 0.003 0.045 0.115 -0.016 0.41**

SL 0.075 -0.031 0.152 0.083 -0.137 0.002 0.025 0.205 -0.025 0.35**

SPP 0.084 -0.035 0.167 0.095 -0.148 0.002 0.025 0.176 -0.029 0.34**

Residual=0.477 Where: DTH= Days to heading, DTM= Days to maturity, AGB= Above ground biomass, TPP= Tillers per plant, PHT= Plant height, KPS= kernel per

spike, SKPS= Spikelet per spike, SL= Spike length, SPP= Spike per plant and rph= Phenotypic correlation.

Table 8. Genotypic path coefficients of direct (main diagonal) and indirect effects of the 7 characters studied at Fereziye

DTH DTM AGB TPP PHT KPS SPP rg

DTH 0.070 -0.205 0.296 0.427 -0.215 0.115 0.109 0.6**

DTM 0.061 -0.235 0.252 0.392 -0.197 0.144 0.080 0.5*

AGB 0.043 -0.122 0.486 0.473 -0.169 0.103 0.085 0.9**

TPP 0.052 -0.16 0.398 0.577 -0.185 0.082 0.063 0.83**

PHT 0.049 -0.151 0.267 0.346 -0.308 0.103 0.131 0.44*

KPS 0.039 -0.167 0.243 0.231 -0.154 0.206 0.080 0.48*

SPP 0.031 -0.078 0.170 0.150 -0.166 0.068 0.243 0.42*

Residual=0.3035 Where: DTH= Days to heading, DTM= Days to maturity, AGB= Above ground biomass, TPP= Tillers per plant, PHT= Plant height, KPS= kernel per spike, SPP= Spike per plant and rg= Genotypic correlation.

associations might be largely due to genetic causes and

the environment played least role in the associations. Environmental correlation for 13 characters at Fereziye and Kotergedra are presented in table 5 and 6

respectively. Grain yield was positively correlated with 1000 kernel weight (r=0.285), above ground biomass

(r=0.250) and tiller per plant (r=0.243) and weakly correlated with plant height (r=0.063) and spikelet per

spike (r=0.056) at Fereziye. Above ground biomass highly significant and positive correlated with tiller per plant (r=0.44), plant height (r=0.506) and spike length

(r=0.388) and significant and positive correlated with kernel per spike (r=0.332) and spike per plant (r=0.332)

at Fereziye. Tillers per plant was highly significant and positively correlated with kernel per spike (r=0.394); plant

height was highly and positively significant with spike length (r=0.457) and spike per plant (r=0.528) at Fereziye. Moreover, kernel per spike highly significant

and positively correlated with spikelet per spike (r=0.549), spike length (r=0.436) and spike per plant (r=0.370) and

spikelet per spike highly significant and positively

correlated with spike length (r=0.458) and spike per plant (r=0.445); spike length also highly significant and positively correlated with spike per plant (r=0.702) at

Fereziye.

Grain filling period was shown highly significant and strong positive correlated with grain yield (r=0.675) but

weak correlated with above ground biomass (r=0.003) at Kotergedra. Related finding and controversial idea with Assefa et al. (2014), reported that grain yield had strong

significant positive correlations with biomass yield (r=0.89), thousand seed weight (r=0.87), harvest index (r

= 0.65) and grain filling rate (r = 0.97), but negatively correlated with days to maturity (r = -0.40) and number of

tillers per plant (r =-0.65). Plant height was shown highly significant and positively correlated with kernel per spike (r=0.520) and spike per plant (r=0.454). Days to heading

was strong negatively correlated with grain yield (r=-0.631), harvest index (r=-0.564) and 1000 kernel weight


Zerga et al. 131

Table 9. Path coefficients analysis for direct (main diagonal) and indirect effects of the 6 characters studied at Kotergedra

DTH DTM GFP HI TPP KPS rph

DTH -0.62 -0.001 0.08 0.001 -0.004 0.0001 -0.54**

DTM -0.462 -0.001 0.047 0.001 -0.002 -0.0001 -0.42**

GFP -0.06 -0.0001 0.838 9.7E-06 0.002 -0.001 0.78**

HI 0.253 0.001 -0.002 -0.005 0.004 -0.001 0.25*

TPP -0.29 -0.001 -0.184 0.002 -0.01 0.001 -0.48**

KPS 0.051 -0.0001 0.251 -0.002 0.002 -0.001 0.30**

Residual=0.086 Where: DTH= Days to heading, DTM= Days to maturity, GFP= Grain filling period, HI= Harvest index, TPP= Tillers per plant, KPS= kernel per spike and rph= Phenotypic correlation

Table 10. Path coefficients analysis for direct (main diagonal) and indirect effects of the 3 characters studied at Kotergedra

DTH GFP TPP rg

DTH -0.52 0.049 -0.002 -0.47*

GFP -0.029 0.883 0.001 0.86**

TPP -0.358 -0.174 -0.002 -0.54**

Residual=0.0447 Where: DTH= Days to heading, GFP= Grain filling period, TPP= Tillers per plant and rg= Genotypic correlation.

(r=-0.469). Days to maturity was negatively correlated with grain yield r=-0.469 and 1000 kernel weight (r=-

0.380). Moreover, kernel per spike was shown that highly

significant and strong positive correlated with spikes per plant (r=0.715) but weak and negatively correlated with

spike length (r=-0.0008) at Kotergedra.

Path coefficient Analysis

Apparently, many of the characters are correlated either negatively or positively because of mutual associations.

As more variables are considered in the correlation table, these indirect associations become more complicated and less obvious. Therefore, path coefficient analysis

provides more effective means of separating direct and indirect factors, permitting a critical examination of the

specific forces acting to produce a given correlation and measuring the relative importance of the causal factors.

Therefore, path coefficient analysis was used to determine direct and indirect associations among different attributes. The residual factor was treated as

independent of the rest of the variables. The direct and indirect effects of different characters on

grain yield are presented in Tables 7 and 8, for Fereziye. At Fereziye, the path coefficient analysis revealed that

above ground biomass (0.507) and tillers per plant (0.416) exerted high and favorable direct effects on grain yield. Days to heading (0.234) and spike length (0.2054)

had some positive direct influence on grain yield. Kernel per spike (0.005) and spikelet per spike (0.045) exerted

weak positive influences on grain yield, whereas days to maturity (-0.157), plant height (-0.280) and spikelet per

spike (-0.02) had some negative influence. Similarly

Ashraf (2014), reported that the negative direct effect of plant height (-0.290) with grain yield. The favorable direct effects of above ground biomass and tillers per plant on

grain yield indicate that, improvement of these characters will increase grain yield. The indirect effect of above

ground biomass through days to maturity (-0.068), plant height (-0.151) and spikes per plant (-0.009) in counter balanced with the direct effect of above ground biomass

on grain yield at Fereziye. The indirect effect of tillers per plant through above ground biomass (0.385) had positive

influence on grain yield (0.416). The indirect effect of days to heading through above ground biomass (0.284)

and tillers per plant (0.274) had some positive effect on grain yield. Whereas the indirect effect of days to heading through days to maturity (-0.127), plant height (-0.171)

and spikes per plant (-0.01) had some negative effect counter balanced the direct effect of days to heading on

grain yield. Moreover the indirect effect of plant height through above ground biomass (0.274), tiller per plant

(0.224) had some positive influence and through kernel per spike (0.002), spikelet per spike (0.032) and spike length (0.1007) had weak influence on grain yield at

Fereziye. The residual factor of 0.477 in the present study indicated that 47.7% of the yield related traits were

not included, this is relatively high but more than 50%


was included in the study. High residual effect (0.7248) also was found from the finding of Abebe (2006).

The results revealed that tillers per plant (0.577) and

above ground biomass (0.486) exerted high and favorable direct effect on grain yield. Similarly Gelalcha and Hanchinal (2013), reported that tillers per plant was

strong correlated with grain yield while the magnitude of the direct effect is by far less than that of the correlation

and also reports from Gautam and Sethi (2002), Kumar et al. (2013) and Khokhar et al. (2010), reported the

existence of strong positive correlation of number of tillers per plant with grain yield. A day to heading (0.070) had direct weak influence whereas; kernel per spike (0.206)

and spikes per plant (0.243) exerted some positive favorable direct influence on grain yield. Days to maturity

had negative direct effect on grain yield (-0.235). Obsa (2014), reported that days to maturity was positive direct effect on grain yield controversial with the present study.

The indirect effect of tillers per plant through above ground biomass (0.398) had positive influence on grain

yield (0.577) at Fereziye. The indirect effect of above ground biomass through tillers per plant (0.473)

considerable direct effect of above ground biomass on grain yield (0.486). The indirect effect of days to heading through days to maturity (-0.205) and plant height (-

0.215) had negative indirect effect whereas, through above ground biomass (0.2966), tillers per plant (0.427),

kernel per spike (0.115) and spikelet per spike (0.109) had positive indirect effect on grain. Moreover the indirect

effect kernels per spike through above ground biomass (0.243) and tillers per plant (0.231) had some positive influence. It through Days to heading (0.039) and spikes

per plant (0.0803) had weak positive indirect influence on grain yield but through days to maturity (-0.167) and plant

height (-0.154) had negative indirect influence counter balanced with direct effect of kernel per spike on grain yield.

Generally the favorable direct effect of above ground biomass and tillers per plant on grain yield at Fereziye

indicated that improvement of these characters will increase the yield for further bread wheat breeding

program. Related finding with Ali et al. (2008), reported that number of productive tillers per plant exhibited a high positive direct effect on grain yield. Adhiena (2015),

reported that the highest direct effect of biomass yield on grain yield. The residual factor 0.3035 in this study

indicated that 30.35% of the yield related traits were not included. Related residual effect (0.362) was found from

the finding of Mitsiwa (2013). The direct and indirect effects of different characters on

grain yield are presented in Tables 9 and 10 for Kotergedra. At Kotergedra, the results revealed grain

filling period (0.838) was exerted high and favorable direct effects on grain yield. Obsa (2014), reported that the highest negative direct effect on grain yield was

displayed by grain filling period (-0.411) followed by days to heading (-0.310) in opposed with the present study.

Days to heading (-0.62), days to maturity (-0.001), Harvest index (-0.005), tiller per plant (-0.01) and kernel

per spike (-0.001) were negative direct influence on grain yield indicating that less considering on such characters during genotype selection.

Indirect effect of days to heading through grain filling

period (0.08) and harvest index (0.002) had weak positive influence on grain yield reduced the correlation coefficient

to (-0.54). Moreover the indirect effect of grain filling period through days to heading (-0.06), days to maturity (-0.001), and kernel per spike (-0.001) had weak negative

influence counter balanced with the direct effect of grain filling period on grain yield. The indirect effect of tillers per

plant through days to heading (-0.29), days to maturity (-0.001) and grain filling period (-0.184) had negative influence counter balanced with the direct effect of tillers

per plant on grain yield reduced the correlation coefficient to (-0.48). Moreover the indirect effect of kernels per

spike through grain filling period (0.251) had some positive influence on grain yield. Generally the favorable

direct effect of grain filling period on grain yield indicated that selection on this character will increase the yield for further bread wheat breeding program. The residual

0.086 in this study indicated that about 8.6% of yield related traits were not included or the traits included in

the study explained high percentage of variation in grain yield (91.4%). Related residual effect (0.056) was found

from the finding of Adhiena (2015). At Kotergedra, the results revealed that grain filling period

(0.883) also exerted high and favorable direct effects on grain yield. Therefore, improvement of this character will

have an advantageous to increase the grain yield. Adhiena (2015), reported that grain filling period and thousand seed weight had negative direct effect on grain

yield in controversial with the present study. Tillers per plant (-0.383) and days to heading (-0.52) had negative

direct effect on grain yield. The indirect effect of grain filling period through tillers per plant (0.001) had weak

positive influence on grain yield. Moreover the indirect effect of tillers per plant through days to heading (-0.358) and grain filling period (-0.157) had negative influence

counter balanced with the direct effect of tillers per plant on grain yield reduced the correlation coefficient to (-

0.54). The favorable direct effect of grain filling period on grain yield indicated that selection on this character will

increase yield. The residual of 0.0447 in this study indicated that 4.47% of yield related traits were not included or the traits included in the study explained high

percentage of variation in grain yield (95.53%) it is suggested that maximum emphasis should be given on

the above characters in selecting bread wheat with higher yield. Similar residual effect (0.0446) was found from the finding of Andualem (2008).

Zerga et al. 133

Conflict of Interest

There is no conflict of interest between the authors or anybody else. Abreviation: SNNPR: South Nation Nationality People

Region. Acknowledgement

First I would like to thank Ethiopian Ministry of Education

for financial support and Haramaya University for hosting the study. I am grateful to Wolkite University for giving me the opportunity to use research field, allocating the

required labor, materials for field work and vehicle for the research field supervision. Reference

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Citation: Zerga K, Mekbib F, Dessalegn T (2016).

Estimation of association among growth and yield related traits in Bread Wheat (Triticum aestivum. L) Genotypes at Gurage Zone, Ethiopia. International Journal of Plant

Breeding and Crop Science, 3(2): 123-134.

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