+443308186230
Correlation and Path Coefficient Analysis for Grain Yield and Related Traits in Kabuli Chickpea (Cicer arietinum L.) at Central Highland of Ethiopia
DOI: 10.4172/2329-8863.1000469
Abstract
Chickpea (Cicer arietinum L.) is one of the most important pulse crop in Ethiopia. However, the national average productivity of chickpea is lower than the potential. The present study was carried out to assess the relationship between yield and yield related traits and measure the direct and indirect contribution of yield components on grain yield in kabuli chickpea using statistical procedures. Forty nine kabuli chickpea genotypes were studied for thirteen traits at Debre Zeit and Akaki using 7 × 7 simple lattice design in 2019 cropping season. The study results showed that grain yield had significant and positive correlations with harvest index, biological yield, number of primary branches, days to flowering and number of pod per plant both at genotypic and phenotypic levels indicating the possibility of improving these traits through selection. On the contrary, hundred seed weight had a significant and negative relationship with grain yield at the genotypic level. Path coefficient analysis revealed that harvest index (0.668) followed by biological yield (0.578) had the highest positive direct effect on grain yield, indicating that both traits could be considered important in kabuli chickpea yield improvement.
Keywords: Association; Chickpea; Correlation coefficient; Grain yield; Path coefficient; Relationship; Yield components
Introduction
Chickpea (Cicer arietinum L.) is a cool season food grain legume cultivated almost all over the world, including temperate and subtropical regions. It is cultivated on 17.8 million ha with a production of 17.2 million tons and productivity of 0.97 ton ha-1. Asia accounts 1 for 76.9% of the global chickpea production and Africa accounts for 4.3%. Ethiopia is the major chickpea producer in Africa, with a region occupying about 47% of the total area [1].
Chickpea is a good source of energy, protein, minerals, vitamins, fiber and also contains potentially health beneficial phytochemicals [2,3]. The protein concentration of chickpea seed ranges from 12.6%- 30.6% and is commonly 2-3 times higher than that of cereal grains [4]. Chickpea also exhibits total lipid concentration ranges from 2.9%- 8.8%. Since chickpea is high in fiber, low in sodium and fat, and also cholesterol free, it is a healthy food that is beneficial to the prevention of coronary and cardiovascular diseases. It may also lower blood cholesterol levels due to the high content of soluble fiber and vegetable protein [5]. It is consumed in various ways; seed, young shoots and pods are used for human consumption.
Chickpea is a widely grown pulse crop in Ethiopia, mainly in the central, northern and eastern highland areas of the country at an altitude of 1400-2300 m.a.s.l. where annual rainfall ranges between 700 and 2000 mm [6,7]. It is maintaining soil fertility through biological nitrogen fixation and saves fertilizer costs in subsequent crops. The crop is grown mainly on vertisol from late August to the end of September on residual moisture where seasonal water logging is severe. Its straw is used as animal feed and stalk and roots as fuel. It is also a cash source, which provides income for growers [8].
Grain yield is the most important quantitative trait, which is governed by both various genetic and environmental factors. Due to quantitative interaction, direct selection considering only grain yield could not be much more effective since both factors determine plant characteristics [9]. Therefore, effective improvement in yield may be through selection based on yield contributing characters. In this regard, information on correlation and path coefficients analyses is very important to plant breeders for selection and increased grain yield. The correlation coefficient is a measure of a linear association between traits, whereas path coefficient analysis measures the direct and indirect contribution of various yield components and other morphological characters on grain yield [10-12]. Therefore, the present study aimed to determine the association between different traits of kabuli chickpea and their contribution to grain yield improvement.
Materials and Methods
Forty nine genotypes of kabuli chickpea were grown in a simple lattice design with two replications at Debre Zeit Agricultural Research Center and Akaki research station. Of these 49 experimental materials, the two released varieties and all others were pipeline and obtained from Debre Zeit Agricultural Research Center (Table 1). The experiment was conducted under field conditions during 2018 main cropping season. The Debre Zeit Agricultural Research Center is characterized by annual minimum and maximum temperatures of 11.6 and 26.5° C, respectively and receives 821 mm annual rainfall. The soil type of the center is classified as black soil (vertisol). The Akaki research station has minimum and maximum annual temperatures of 7°C and 22.5° C, respectively and receives 1055 mm annual rainfall.
| No | Genotypes | Status | No | Genotype | Status |
|---|---|---|---|---|---|
| 1 | DZ-2012-CK-0260 | Pipeline | 26 | DZ-2012-CK-0259 | Pipeline |
| 2 | DZ-2012-CK-0261 | Pipeline | 27 | DZ-2012-CK-0264 | Pipeline |
| 3 | DZ-2012-CK-0265 | Pipeline | 28 | DZ-2012-CK-0263 | Pipeline |
| 4 | DZ-2012-CK-0268 | Pipeline | 29 | DZ-2012-CK-0271 | Pipeline |
| 5 | DZ-2012-CK-0273 | Pipeline | 30 | DZ-2012-CK-0287 | Pipeline |
| 6 | DZ-2012-CK-0275 | Pipeline | 31 | DZ-2012-CK-0282 | Pipeline |
| 7 | DZ-2012-CK-0277 | Pipeline | 32 | DZ-2012-CK-0276 | Pipeline |
| 8 | DZ-2012-CK-0279 | Pipeline | 33 | DZ-2012-CK-0266 | Pipeline |
| 9 | DZ-2012-CK-0281 | Pipeline | 34 | DZ-2012-CK-0291 | Pipeline |
| 10 | DZ-2012-CK-0283 | Pipeline | 35 | DZ-2012-CK-0243 | Pipeline |
| 11 | DZ-2012-CK-0284 | Pipeline | 36 | DZ-2012-CK-0309 | Pipeline |
| 12 | DZ-2012-CK-0285 | Pipeline | 37 | DZ-2012-CK-0274 | Pipeline |
| 13 | DZ-2012-CK-0286 | Pipeline | 38 | DZ-2012-CK-0278 | Pipeline |
| 14 | DZ-2012-CK-0288 | Pipeline | 39 | DZ-2012-CK-0300 | Pipeline |
| 15 | DZ-2012-CK-0242 | Pipeline | 40 | DZ-2012-CK-0290 | Pipeline |
| 16 | DZ-2012-CK-0244 | Pipeline | 41 | DZ-2012-CK-0280 | Pipeline |
| 17 | DZ-2012-CK-0061 | Pipeline | 42 | DZ-2012-CK-0310 | Pipeline |
| 18 | DZ-2012-CK-0305 | Pipeline | 43 | DZ-2012-CK-0272 | Pipeline |
| 19 | DZ-2012-CK-0246 | Pipeline | 44 | DZ-2012-CK-0303 | Pipeline |
| 20 | DZ-2012-CK-0065 | Pipeline | 45 | DZ-2012-CK-0294 | Pipeline |
| 21 | DZ-2012-CK-0249 | Pipeline | 46 | DZ-2012-CK-0306 | Pipeline |
| 22 | DZ-2012-CK-0064 | Pipeline | 47 | DZ-2012-CK-0220 | Pipeline |
| 23 | DZ-2012-CK-0178 | Pipeline | 48 | Ejere | Released variety |
| 24 | DZ-2012-CK-0248 | Pipeline | 49 | Hora | Released variety |
Table 1: List of Experimental material used for the study.
The soil type of the experimental station is classified as Vertisol. Each plot consisted of four rows of four meters in length with a spacing of row to row and plant to plant distance was maintained at 30 and 10 cm, respectively. All recommended management practices were followed during the crop season. Observations for days to 50% flowering, grain filling period, days to maturity, plant height, number of primary branches, number of secondary branches, number of pods per plant, number of seeds per pod and number of seeds per plant were recorded on plot and plant basis. The data collected were subjected to statistical analysis. Phenotypic and genotypic correlation coefficients among different characters were estimated [13] and Path Coefficient Analysis were calculated (Table 1) [11].
Results and Discussion
Analysis of variance
The pooled analysis of variance showed genotype effects was statistically significant for all traits. These highly significant differences indicate the existence of variability among genotypes for all traits studied (Table 2).
| Mean squares | |||||||
|---|---|---|---|---|---|---|---|
| Traits | Replication | Block(rep) | Genotypes | Location | Genotype x location | Error | CV |
| DF | 0.25 | 5.14 | 130.40** | 326.58** | 15.14** | 4.51 | 3.77 |
| DM | 28.7 | 5.05 | 48.32** | 4614.29** | 8.55** | 4.33 | 1.7 |
| GFP | 8.58 | 2.26 | 43.64** | 1160.86** | 10.48** | 2.39 | 2.39 |
| NPB | 0.02 | 0.07 | 0.49** | 15.32** | 0.20** | 0.07 | 8.42 |
| NSB | 1.26 | 0.43 | 6.59** | 150.06** | 1.45** | 0.58 | 9.2 |
| BY | 19281717 | 1147181 | 2834392.7** | 95865546** | 1909765.6** | 634066.4 | 13.61 |
| PLHT | 12.05 | 3.63 | 46.92** | 2057.27** | 18.42** | 4.34 | 4.22 |
| NPP | 48.2 | 16.46 | 136.23** | 3854.64** | 54.21** | 12.91 | 11.05 |
| NSPP | 116.02 | 25.78 | 236.22** | 6482.55** | 94.41** | 20.7 | 12.07 |
| NSP | 0.02 | 0.004 | 0.03** | 0.805** | 0.02** | 0.004 | 5.77 |
| HSW | 6.69 | 2.41 | 71.90** | 1259.24** | 6.19** | 2.32 | 4.39 |
| GY | 1191743 | 169206.6 | 860734.7** | 140232187.5** | 430985.8** | 136058 | 13.1 |
| HI | 363.49 | 32.53 | 233.51** | 14776.43** | 73.03ns | 61.06 | 16.26 |
| *, ** Significant at p ≤ 0.05, and p ≤ 0.01 probability level, respectively. DF=days to flowering, DM=days to maturity, GFP=grain filling period, PLHT=Plant height, NPB=number of primary branches, NSB=number of secondary branches, NPP=number of pods per plant, NSPP=number of seeds per plant, NSP=number of seeds per pod, BY=biological yield, HSW=hundred-seed weight, GY=grain yield, HI=harvest index, MS=mean square. | |||||||
Table 2: Mean square values and coefficient of variation from analysis of variance.
Correlation coefficient of agronomic traits
In the present study, estimates of phenotypic and genotypic correlation coefficients among 13 traits combined over two locations are presented in the below table. The magnitudes of genotypic correlation coefficients for most of the traits were higher than phenotypic correlation coefficients, which indicate the presence of inherent or genetic association among traits. The result of correlation analysis revealed that grain yield had significant and positive genotypic and phenotypic correlations with harvest index, biological yield, and number of primary branches, days to flowering, number of secondary branch and number of pods per plant.
These results indicate that genotypes with high harvest index, biological yield, number of primary branches, days to flowering and number of pods per plant produce high seed yield and vice versa. A significant and positive correlation of grain yield with the number of pod per plant, number of primary branches, number of seeds per plant and biological yield at both phenotypic and genotypic levels was reported [14,15]. On the contrary, hundred seed weight had a significant and negative relationship with grain yield at the genotypic level. This may indicate the fact that when a hundred seed weight (seed size) increases the number of seeds per pod and number of pods per plant reduced in most cases, which in turn leads to a reduction in yield. This significant and negative correlation of grain yield with hundred seed weight is in accordance with the findings of [15,16]. Days to maturity, number of seed per plant and number of seed per pod showed positive and significant association with grain yield at the genotypic level. These indicate that the genotype with late maturity, the high number of seed per plant and the high number of seed per pod produce high seed yield and a similar finding was previously reported [17].
The biological yield had a positive and significant phenotypic and genotypic correlation with days to flowering, days to maturity, number of primary branches and number of secondary branches. This indicates that breeding for increased biomass yield might result in a high number of primary and secondary branches and produce late flowering and late maturing genotypes. Similar results were reported [14]. Days to flowering was positively and significantly correlated with days to maturity, number of primary and secondary branches. The result showed that late flowering genotypes are expected to mature late and produce a high number of primary and secondary branches. A similar association between days to flowering and days to maturity was reported [18].
The number of pods per plant had a positive and significant correlation with days to maturity, number of secondary branches, and number of seeds per plant and harvest index at the genotypic level. On the other hand number of pods per plant had a positive and significant correlation with the number of seeds per plant and the number of seeds per pod both at the phenotypic and genotypic levels. Improving these traits increases the development of the number of pods per plant that support increasing grain yield [19]. The number of primary branches had a significant and positive phenotypic and genotypic correlation with the number of secondary branches and days to maturity. This result is in harmony with [20], who reported a significant and positive correlation number of primary branches with the number of secondary branches and days to maturity.
Hundred seed weight had a significant and negative phenotypic and genotypic correlation with the number of pod per plant, number of seeds per pod and number of seeds per plant. This negative association indicates a compensatory relationship between them. Similar to the present result, [16,17] found the correlation of hundred seed weight with number of pods per plant and number of seeds per pod to be significant and negative. The correlation coefficient analysis exhibited that harvest index showed a significant and positive association with days to maturity, number of secondary branch and number of seeds per plant at the genotypic level. These show that genotype with high number of secondary branches, high number of seeds per plant and late maturity producing high harvest index (Table 3).
| Traits | DF | DM | GFP | PLHT | NPB | NSB | NPP | NSPP | NSP | BM | HSW | HI | GY |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| DF | 1 | 0.642** | -0.332* | 0.07 | 0.438** | 0.403** | 0.435** | 0.416** | 0.303* | 0.484** | -0.149 | 0.470** | 0.694** |
| DM | 0.546** | 1 | 0.510** | 0.199* | 0.484** | 0.429** | 0.444** | 0.450** | 0.311* | 0.572** | -0.307* | 0.429** | 0.718** |
| GFP | -0.465** | 0.488** | 1 | 0.166 | 0.104 | 0.076 | 0.057 | 0.086 | 0.043 | 0.161 | -0.210* | 0 | 0.105 |
| PLHT | 0.174 | 0.377** | 0.217* | 1 | 0.082 | 0.084 | 0.058 | 0.069 | 0.044 | 0.094 | -0.032 | 0.099 | 0.137 |
| NPB | 0.426** | 0.368** | -0.054 | -0.1 | 1 | 0.499** | 0.412** | 0.432** | 0.246* | 0.551** | -0.183 | 0.335** | 0.624** |
| NSB | 0.296* | 0.206 | -0.091 | 0.053 | 0.447** | 1 | 0.288* | 0.247* | 0.074 | 0.429** | -0.081 | 0.380* | 0.572** |
| NPP | 0.224 | 0.076 | -0.154 | -0.031 | 0.181 | -0.065 | 1 | 0.932** | 0.500** | 0.373* | -0.536** | 0.435** | 0.598** |
| NSPP | 0.227 | 0.101 | -0.13 | -0.041 | 0.192 | -0.12 | 0.933** | 1 | 0.620** | 0.391** | -0.584** | 0.447* | 0.610** |
| NSP | -0.055 | -0.128 | -0.079 | -0.119 | -0.076 | -0.352* | 0.381** | 0.547** | 1 | 0.289* | -0.545** | 0.410** | 0.507** |
| BY | 0.551** | 0.563** | 0.022 | 0.224 | 0.489** | 0.340* | 0.083 | 0.066 | -0.048 | 1 | -0.077 | 0 | 0.656** |
| HSW | 0.125 | 0.038 | -0.09 | 0.119 | 0.032 | 0.216 | -0.543** | -0.639** | -0.603** | 0.198 | 1 | -0.358** | -0.331* |
| HI | 0.138 | -0.332* | -0.494** | -0.143 | 0.043 | 0.106 | 0.21 | 0.274 | 0.28 | -0.365* | -0.162 | 1 | 0.738** |
| GY | 0.642** | 0.175 | -0.485** | 0.039 | 0.487* | 0.395* | 0.304* | 0.233 | 0.205 | 0.503** | 0.034 | 0.601** | 1 |
| *, ** Significant at p ≤ 0.05 and p ≤ 0.01 probability level, respectively. DF=days to flowering, DM=days to maturity, GFP=grain filling period, PLHT=Plant height, NPB=number of primary branches, NSB=number of secondary branches, NPP=number of pod per plant, NSPP=number of seed per plant, NSP=number of seed per pod, BY=biological yield, HSW=hundred seed weight, GY=grain yield, | |||||||||||||
Table 3: Correlation coefficient (r) of different traits at genotypic (above diagonal) and phenotypic level (below diagonal) in chickpea.
Path coefficient analysis
The phenotypic and genotypic direct and indirect effects of different grain yield characters were presented in Tables 4 and 5, respectively. The result showed that the harvest index had the highest direct effect on grain yield, followed by biological yield at the phenotypic level. The two traits had a significant and positive correlation with yield. The highest positive and direct effect of harvest index and the biological yield on grain yield were reported [21]. Although, the number of primary branches, days to flowing and number of secondary branches had a positive direct effect on grain yield, its high indirect effect via biological yield counterbalances the very low direct effect of those traits on grain yield. The number of pod per plant had a small positive direct effect on grain yield. However, these smaller positive direct effects can be counterbalanced by the high direct effect through the harvest index. The grain filling period had a negative direct effect and the phenotypic correlation it had with grain yield was also negative and significant.
| Traits | DF | GFP | NPB | NSB | NPP | BY | HI | rp |
|---|---|---|---|---|---|---|---|---|
| DF | 0.065 | 0.025 | 0.02 | 0.004 | 0.009 | 0.405 | 0.113 | 0.642 |
| GFP | -0.03 | -0.054 | -0.003 | -0.001 | -0.006 | 0.016 | -0.406 | -0.485 |
| NPB | 0.028 | 0.003 | 0.047 | 0.006 | 0.007 | 0.36 | 0.035 | 0.487 |
| NSB | 0.019 | 0.005 | 0.021 | 0.014 | -0.003 | 0.251 | 0.087 | 0.395 |
| NPP | 0.015 | 0.008 | 0.009 | -0.001 | 0.04 | 0.061 | 0.173 | 0.304 |
| BY | 0.036 | -0.001 | 0.023 | 0.005 | 0.003 | 0.736 | -0.299 | 0.503 |
| HI | 0.009 | 0.027 | 0.002 | 0.002 | 0.008 | -0.268 | 0.821 | 0.601 |
| DF=days to flowering, DM=days to maturity, GFP=grain filling period, PLHT=Plant height, NPB=number of primary branches, NSB=number of secondary branches, NPP=number of pod per plant, NSPP=number of seed per plant, NSP=number of seed per pod, BY=biological yield, HSW=hundred seed weight, GY=grain yield, HI=harvest index, rp=phenotypic correlation with grain yield. | ||||||||
Table 4: Phenotypic direct (bold and diagonal) and indirect effects of different traits on grain yield of chickpea.
| Traits | DF | DM | NPB | NSB | NPP | NSPP | NSP | BM | HSW | HI | rg |
|---|---|---|---|---|---|---|---|---|---|---|---|
| DF | 0.037 | 0.026 | 0.009 | 0.006 | 0.037 | -0.024 | 0.009 | 0.28 | -0.001 | 0.314 | 0.694 |
| DM | 0.024 | 0.04 | 0.01 | 0.007 | 0.038 | -0.026 | 0.009 | 0.331 | -0.001 | 0.286 | 0.718 |
| NPB | 0.016 | 0.019 | 0.021 | 0.008 | 0.035 | -0.025 | 0.007 | 0.318 | -0.001 | 0.224 | 0.624 |
| NSB | 0.015 | 0.017 | 0.01 | 0.015 | 0.025 | -0.014 | 0.002 | 0.248 | 0 | 0.254 | 0.572 |
| NPP | 0.016 | 0.018 | 0.008 | 0.004 | 0.085 | -0.053 | 0.015 | 0.216 | -0.002 | 0.29 | 0.598 |
| NSPP | 0.015 | 0.018 | 0.009 | 0.004 | 0.079 | -0.057 | 0.019 | 0.226 | -0.003 | 0.298 | 0.61 |
| NSP | 0.011 | 0.012 | 0.005 | 0.001 | 0.043 | -0.035 | 0.03 | 0.167 | -0.002 | 0.274 | 0.507 |
| BM | 0.018 | 0.023 | 0.011 | 0.007 | 0.032 | -0.022 | 0.009 | 0.578 | 0 | 0 | 0.656 |
| HSW | -0.006 | -0.012 | -0.004 | -0.001 | -0.046 | 0.033 | -0.017 | -0.045 | 0.005 | -0.239 | -0.331 |
| HI | 0.017 | 0.017 | 0.007 | 0.006 | 0.037 | -0.025 | 0.012 | 0 | -0.002 | 0.668 | 0.738 |
DF=days to flowering, DM=days to maturity, GFP=grain filling period, PLHT=Plant height, NPB=number of primary branches, NSB=number of secondary branches, NPP=number of pod per plant, NSPP=number of seed per plant, NSP=number of seed per pod, BY=biological yield, HSW=hundred seed weight, GY=grain yield, HI=harvest index, rg==genotypic correlation with grin yield |
|||||||||||
Table 5:Genotypic direct (bold and diagonal) and indirect effects of traits on grain yield in chickpea.
Harvest index, which had a significant and positive association with grain yield, exhibited the highest direct effect on grain yield. The next highest direct effect on grain yield was recorded from biological yield, which also had a significant and positive association with grain yield. These results indicate that both traits had a true association with grain yield and their importance in determining these complex traits. Therefore, an important consideration should be given while practicing selection aimed at the improvement of grain yield. These results were in accordance with [22], who reported the highest direct effect of harvest index and the biological yield on grain yield.
Days to maturity and number of the primary branch had a positive direct effect on grain yield but low magnitude. However, both traits had a high positive indirect effect on grain yield via biological yield. Similarly, the number of pod per plant, number of secondary branches, number of seeds per pod and days to flowering had a positive indirect effect on grain yield through harvest index. The number of seed per plant had a negative direct effect on grain yield; likewise, the significant and positive correlation between the number of seed per plant and grain yield might be due to the considerable indirect impact of the number of seed per plant via harvest index and biological yield Tables 4 and 5.
Conclusion and Recommendations
Grain yield had a significant and positive phenotypic and genotypic correlation with harvest index, biological yield, and number of primary branches, days to flowering, number of secondary branch and number of pod per plant. By selecting these traits that reveal a positive and significant correlation with grain yield, there is a possibility of increasing the grain yield of chickpea. Path coefficient analysis based on grain yield as a dependent variable showed that harvest index and biological yield had the highest direct effects on yield at phenotypic and genotypic levels. Since these two traits had a positive and significant association with grain yield in the selection process, much attention should be given to them as these traits are helpful for indirect selection to improve yield in chickpea.
References
- Food and Agricultural Organization of the United Nations, FAOSTAT (2018).
- Jukanti AK, Gaur PM, Gowda CLL, Chibbar RN (2012) Nutritional quality and health benefits of chickpea (Cicer arietinum L.): A review. Br J Nutr 108: 11-26.
- Sharma S, Yadav N, Singh A, Kumar R (2013) Nutritional and antinutritional profile of newly developed chickpea (Cicer arietinum L) varieties. Int Food Res J 20: 805-810.
- Wood JA, Grusak MA (2007) Nutritional value of chickpea. In: Yadav SS, R Redden, W Chen, Sharma B (eds), Chickpea Breeding and Management. CAB, Wallingford, UK, pp: 101-142.
- Summo C, De Angelis D, Ricciardi L, Caponio F, Lotti C, et al. (2019) Nutritional, physico-chemical and functional characterization of a global chickpea collection. J Food Compos Anal 84: 103306.
- Anbessa Y, Bejiga G (2002) Evaluation of ethiopian chickpea landraces for tolerance to drought. 49: 557-564.
- Ahmed AM, Tana T, Singh P, Molla A (2016) Modeling climate change impact on chickpea production and adaptation options in the semi-arid North-Eastern Ethiopia. J Agric Environ Int Dev 110: 377-395.
- Ferede S, Fikre A, Ahmed S (2018) Assessing the competitiveness of smallholders chickpea production in the central highlands of Ethiopia. Ethiop J Crop Sci 6: 51-65.
- Desai UT, Musmade AM, Ranpise SA, Chaudhari SM (1994) Correlation studies in acid lime. J Maharashtra Agric Univ 19: 162.
- Falconer DS, Mackay FC (1996) Introduction to quantitative genetics. Longman, New York.
- Deway DR, Lu KH (1959) A correlation and path coefficient analysis of components of crested wheat grasses kernel production. Agron J51: 515-518.
- Toker C, IC Cagirgan IC (2004) The use of phenotypic correlations and factor analysis in determining characters for grain yield selection in chickpea (Cicer arietinum L.). Hereditas 140: 226-228.
- Singh RK, Chaudhry BD (1977) Biometrical methods in quantitative genetic analysis. Kalyani public New Delhi.
- Mushtaq MA, Bajwa MM, Saleem M (2013) Estimation of genetic variability and path analysis of grain yield and its components in chickpea (Cicer arietinum L.). 4: 1-4.
- Samad MA, Sarker N, Deb AC (2014) Study on relationship and selection index in chickpea. Trop Plant Res 1: 27-35.
- Banik M, Deore GN, Mandal AK, Shah P (2017) Selection of yield contributing traits in chickpea genotypes by correlation and path analysis studies. J Pharm Innov 6: 402-405.
- Shafique MS, Muhammad A, Zafar M, Muhammad A, Awais S, et al. (2016) Genetic variability and interrelationship of various agronomic traits using correlation and path analysis in chickpea (Cicer arietinum L.). Acad J Agric Res 4: 82-85.
- Johnson PL, Sharma RN, Nanda HC (2015) Genetic diversity and association analysis for yield traits chickpea (Cicer arietinum L.) under rice based cropping system. The Bioscan 10: 879-884.
- Tadesse M, Fikre A, Eshete M, Girma N, Korbu L, et al. (2016) Correlation and path coefficient analysis for various quantitative traits in desi chickpea genotypes under rainfed conditions in Ethiopia. J Agric Sci 8: 112-118.
- Sharifi P, Astereki H, Pouresmael M (2018) Evaluation of variations in chickpea (Cicer arietinum L.) yield and yield components by multivariate technique. Ann Agrar Sci 16: 136-142.
- Agrawal T, Kumar A, Kumar S, Kumar A, Kumar RR, et al. (2018) Correlation and path coefficient analysis for grain yield and yield components in chickpea (Cicer arietinum L.) under normal and late sown conditions of Bihar, India. Int J Curr Microbiol App Sci 7: 1633-1642.
- Jivani JV, Mehta DR, Vaddoria MA, Lata R (2013) Correlation and path coefficient analysis in chickpea (Cicer arietinum L.). Electron J Plant Breed 4: 1167-1170.
Share This Article
Open Access Journals
Article Tools
Article Usage
- Total views: 4077
- [From(publication date): 0-2021 - Apr 27, 2024]
- Breakdown by view type
- HTML page views: 3235
- PDF downloads: 842
