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African Crop Science Journal, Vol. 7. No. 4, pp. 327-331, 1999
A PATH COEFFICIENT ANALYSIS OF SOME YIELD COMPONENT INTERACTIONS IN COWPEA C.K. Nakawuka and E. Adipala Code Number: CS99022
ABSTRACT
Correlation and path-coefficient analyses were done to find associations among cowpea characters and assess the indirect and direct contribution of each character to grain yield. Branch number, pod number and seeds/pod were the major direct contributors to grain yield. These characters should therefore receive the highest priority in developing high yielding cowpea varieties.
Key Words: Correlation, indirect and direct effects, yield determinants, Vigna unguiculata RÉSUMÉ
L’analyse du coefficient de chemin et de correlation a été faite pour étudier des associations entre caractères et évaluer la contribution directe et indirecte pour chaque caractère au rendement en grains. Le nombre de branches, le nombre de gousses et les graines par gousse ont été les majeurs caractères contribuant au rendement en grains. Ces caractères devraient alors recevoir une priorité dans le developpment des variétés du niébé.
Mots Clés: Correlation, effets direct et indirect, déterminants du rendement, Vigna unguiculata Introduction
Cowpea (Vigna unguiculata L. Walp) is considered the second most important grain legume in Africa (Purseglove, 1974). In Uganda, the crop is grown predominantly in the northern and north-eastern parts of the country, i.e., in Tororo, Pallisa, Kumi, Soroti, Lira, Nebbi and Arua (Sabiti et al., 1994). However, the few cowpea varieties grown in the country are landraces with low yield potential (Adipala et al., 1997). Before yield improvement can be realised, selection criteria must be established. One way to do this is to carry out correlation analysis which quantifies the relationship between any given pair of traits (Obisesan, 1985). However, yield is a complex trait, and it is difficult from correlations alone to determine which traits contribute more to grain yield. Therefore, it is important to carry out other analyses so as to establish the direct and indirect contribution of each trait on grain yield. Path coefficient analysis, developed by Wright (1921, 1923), determines the significance of correlations between yield components and assigns relative importance to yield relations (McGiffens et al., 1994). The coefficients generated by path analysis measure the direct and the indirect influence of a variable upon another (Dewery and Lu,1959). This study determined the phenotypic and genotypic correlations and evaluated the direct and indirect effects of yield components of cowpea.
Materials and Methods The study was conducted at Makerere University Agricultural Research Institute, Kabanyolo, and involved 25 cowpea lines. Plantings were done on 14 April and 16 October 1994 and 21 March 1995. All experiments were laid out in randomised complete blocks replicated three times. Rows, 4.5m long were spaced 75cm apart with 30cm between plants. There was no fertiliser application but Dimethoate (200 g a.i ha-1) and Chlorpyrifos (Dursban), at 600 g a.i ha-1 were applied to control insect pests. Data were collected from five randomly selected plants in each row on the following parameters; 1) branches per plant, 2) pods per plant, 3) pod length, 4) seeds per pod, 5) 100 seed weight, 6) plant height, and 7) grain yield per plant. For characters 3 and 4, 20 pods were taken from each sample. Analysis of variance (ANOVA) found no significant differences between the three seasons’ data, and as such data were pooled for other statistical analyses. Variance and covariance analyses for each variable and each pair of variables, respectively, were carried out. Path-analysis was also carried out to determine the relationships among the yield components. Phenotypic and genotypic correlation were calculated from the variance and covariance components using the formula:
where r(x,y) is either genotypic or phenotypic correlation between variables x and y;
Cov(x,y) is the genotypic or phenotypic covariance between the two variables;
sx2 is the genotypic or the phenotypic variance of the variable x; and
sy2 is the genotypic or the phenotypic variance of the variable y.
Correlations and path-analyses were done to determine the interrelationships among yield components and their direct and indirect contribution to the grain yield. Basing on the genotypic correlations, path coefficients were calculated by solving a series of simultaneous equations as suggested by Dewery and Lu (1959). The residual effect, which determines how best the causal factors account for the variability of the dependent factor, yield in this case, was obtained using the formula;
1 = P2R7 + å Piyriy
where P2R7 is the residual effect Piyriy = the product of direct effect of any variable and its correlation coefficient with yield.
Results
Correlations. The genotypic and phenotypic correlation coefficients for yield and its various components are presented in Table 1. Except for 100-seed weight, all other characters showed significant genotypic correlations with grain yield. Number of pods per plant had the highest positive genotypic correlation with grain yield (rg=0.894, P=0.001), and number of seeds per pod was highly correlated with pod length (rg=0.678, P=0.01). However, plant height and 100-seed weight showed negative correlations with most traits including grain yield. Seeds per pod and plant height had the highest negative genotypic correlation. The phenotypic correlations were significant and positive for grain yield per plant, branches per plant, pods per plant, and seeds per pod. Correlations between grain yield and pod length, and between plant height and 100-seed weight were not significant (P>0.05). In all significant cases, genotypic correlations were higher than the phenotypic correlations. TABLE 1. Genotypic (G) and Phenotypic (P) correlation coeffiecients for relations among yield componentsof cowpea
*,** significant at P=0.05 and 0.01, repectively Path-coefficient analysis. Because of the presence of significant associations of grain yield with other characters, the genotypic correlations of six causal variables were partitioned into direct and indirect effects (Dewery and Lu, 1959). The path-coefficient analysis based on grain yield as a dependent variate revealed that the contribution of all the six characters towards variation in grain yield was 75.3% (Table 2). All the characters exhibited direct effect on grain yield (P>0.100) but the estimates were higher and positive for branches per plant (0.592), pods per plant (0.270), seeds per pod (0.257) and pod length (0.213); plant height and 100-seed weight had the highest negative direct effect on the yield of cowpea. TABLE 2.The direct and indirect effects contributions of various characters
to grain yield in cowpea
*,** significant at P=0.05 and 0.01, repectively Discussion
Genotypic correlation coefficients provide a measure of the genotypic associations between characters and give an indication of the characters that may be useful as indicators of more important ones under consideration (Johnson et al., 1955). Generally, genotypic correlations were higher than the corresponding phenotypic ones. This implied that the traits under consideration were genetically controlled. Similar observations were obtained on soybean by Weber and Moorthy (1952) and Johnson et al. (1955). The closeness of phenotypic and genotypic correlation coefficients in some instances suggest that environment had some effect on the yield correlated characters. The significant positive genotypic correlations between grain yield and its components suggest that these characters contributed positively towards yield and should be considered when selecting for high grain yield in cowpea. Similar results were reported by Ombakho and Tyagi (1987) . The highly positive correlation between number of seeds per pod and pod length indicates that with longer pods more space is provided for seeds. However, the highly negative significant genotypic correlation between seeds per pod and 100-seed weight indicated that with many seeds per pod the seed size was reduced. The decrease in seed size could lead to unacceptability of some cultivars, especially where large seeded cultivars are preferred. For example, in parts of eastern Uganda (Kumi and Pallisa) the cultivar Ebelat is preferred because of its large seeds (Sabiti et al., 1994). Although pod number had the highest correlation coefficient with yield, its direct effect was lower than that of branch number. Therefore, its high correlation (r=0.894) could be attributed to the substantial positive indirect effect through branch number. One hundred-seed weight had a negligible correlation (-0.046) with yield, and its direct effect was negative (-0.327) but significant (P=0.01). This suggests that 100-seed weight was indirectly influenced by the positive effects of seeds per pod, plant height and branch number. However, this effect was offset by the negative direct and indirect effects through pod number, ultimately resulting in the small negative correlation. The residual effect of 0.247 explains that the variables in consideration accounted for 75.3% of the variability in cowpea yield. The highest contributers to seed yield appear to be branch number, pod number and seeds per pod (Jackai, 1995). Although earlier research showed that seed size is a primary determinant of yield in cowpea (Imrie and Bray, 1983; Obisesan, 1985), this was not the case in the present study. This discrepancy may have been due to the different genotypes used. Thus for cowpea yield improvement, branch and pod numbers, and seeds/pod should be part of the selection criteria.
Acknowledgement
The research was jointly funded by USAID and the Rockefeller Foundation Forum Grant RF 93040 #13.
References
©1999, African Crop Science Society
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