Sequential Path Analysis for Determining the Interrelationships between Yield and Its Components in Peanut

THE CURRENT work was carried out at the Agriculture Research Station of East Al-Eweinat, New Valley Governorate to evaluate the yield potential of 16 peanut genotypes during 2016 and 2017 growing seasons. The used experimental design was a randomized complete block design with three replicates. Correlation coefficients were computed between pod yields and its related attributes as well as normal and sequential path analysis models were automated to obtain information on the direct and indirect effects of important traits affecting pod yields for using them as selection criteria in future peanut breeding programs. Results showed that genotypes 7, 11 and 16 produced the heaviest pod yields while genotypes 13 and 15 recorded the lowest pod yields. Concerning the normal path analysis model, several undesirable symptoms were obtained indicating the presence of multicollinearity problem. Subsequently, the poor estimators of normal path analysis model, as a result of multicollinearity, enough to reject the normal form of path analysis. Statistically, more precise results were obtained using the sequential path analysis model. Results revealed that the pod yields depended primarily upon pod weight per plant and number of pods per plant as first-order variables accounted for nearly 98% of the variation in pod yields. The maximum positive direct effects were obtained by pods weight per plant (0.91) followed by number of pods per plant (0.14) indicting that the indirect selection for pod yields through these traits would be effective for peanut improvement. The second-order path analysis showed that seeds weight per plant had the considerable positive direct and indirect effects toward each of number of pods per plant and pods weight per plant. In fact, the sequential path analysis gave a somewhat different picture from what the normal model path analysis did.