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

Abstract

T HE 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.

Graphical Presentation of some Peanut Genotypes by Comparing Two Patterns of Biplot Analysis العرض البیانی لبعض التراکیب الوراثیة للفول السودانی بمقارنة نموذجین من تحلیل المحاور الثنائیة

“…A strong positive association between Pod yield, plant height (PH), Number of pods (NP) ,Number of branches (NB) and Shelling percentage (Sh) as indicated by the acute angles (< 90°) that mean dependency of these characters on each other. The observed positive associations of previous characters with pod yield were in conformity with the results of Amarsinghe et al, (2016), Afify et al, (2019) and Mahmoud et al, (2020).…”

“…From the current results, it is concluded that the used material genotypes could be good sources of for genetic studies and breeding programmes. These results agreed with those obtained by Makweti (2018), Samaha et al, (2019) and Mahmoud et al, (2020). GT Biplot graph 1.…”

Commercial Classification of Peanuts Based on Pod Physical Characteristics

“…Thus, the variables NG, GM, and SM are the most suitable for direct selection since they have a cause-and-effect relationship with peanut pod TM. Tirkey et al (2018), Rao &Venkanna (2019), andMahmoud et al (2020) also found a strong direct effect of GM on TM of peanut pods through a path analysis. When studying different peanut varieties, Bassanezi et al (2021) observed that larger numbers of peanut grains per pod increase peanut yield, corroborating our study.…”

“…Correlations among such variables have been widely used to find how to increase yield in most crops. For peanut breeding programs, path analysis has been used (Tirkey et al, 2018;Rao & Venkanna, 2019;Mahmoud et al, 2020).…”

Low temperature exposure decreases Cry1Ac insecticidal endotoxin content in cotton seeds

“…Path analysis can decompose the direct correlation between the independent variables and the dependent variables, study their direct and indirect importance, and provide a theoretical basis for accurate statistical decisions [50,51]. In this paper, SPSS software, Pearson method, physiological indicators related to nitrogen metabolism (soluble protein, SP content, AA content, GOT activity, GPT activity, protease activity, and peptidase activity) as the independent variable (X), Bt protein content as the dependent variable (Y), and the direct and indirect effect of physiology related to nitrogen metabolism on Bt protein content.…”