Path Analysis in Cowpea Reveals Protein Accumulation Dependency of Mineral Nutrients Accumulation

Abstract

This work aimed at evaluating the effects of the nutrients on the protein content of cowpea grains. The trial was carried out in completely randomized design, in the Instituto Federal do Ceará (Federal Institute of Ceará), Limoeiro do Norte, Ceará State, Brazil, between October and November 2018. Grains of 10 cowpea cultivars were evaluated about its mineral nutrient phosphorus, potassium, calcium, magnesium, sulfur, chlorine, iron, zinc, copper, manganese, boron, sodium and protein contents. The seeds of the cultivars were obtained from the farmers market in three municipalities of the Ceará State, Brazil, in the crop year 2017. The components of variances within and between families were computed by analysis of variance, and the genetic variance and correlation were therefrom estimated. The statistical analyses of variance, Pearson’s correlation and Path analysis were carried out. The protein content broad sense heritability was 60.47%, and the other cowpea traits evaluated had high broad heritability values, ranging from 49.91% (sulfur content) to 99.69% (zinc content). No mineral nutrient content presented any genotype correlation with protein content, that is, no gene function is related to mineral nutrients and protein accumulation. Potassium (0.44), chlorine (0.38) and calcium (0.35) presented the higher path coefficients in protein of cowpea accumulation but are still weak indices (<0.50) to be indicated for screening. In screening cowpea cultivars for protein content, potassium and related traits are not the most important but present some degree of dependency with protein accumulation in the grains, resulting from path effects.

metan: An R package for multi‐environment trial analysis

“…Multi‐environment trials often generate data on several traits, and these data should be exploited. In breeding trials (as well as in many other areas), indirect selection helps geneticists and breeders to select superior genotypes (Ferrari et al, 2018; Fonseca, Lima, Dardengo, Silva, & Xavier, 2019; Gediya et al, 2019; Lopes Costa, Melo, & Oliveira Mano, 2019; Meira et al, 2017; Olivoto, de Souza, et al, 2017; Olivoto, Nardino, et al, 2017; Santos et al, 2018); thus, any tool that facilitates this work is welcome. metan provides useful functions for implementing biometrical models easily.…”

metan: an R package for multi-environment trial analysis

“…Olivoto, Nardino, et al, 2017; T. Olivoto, Souza, et al, 2017; Ferrari et al, 2018; Santos et al, 2018; Fonseca, Lima, Dardengo, Silva, & Xavier, 2019; Gediya et al, 2019; Lopes Costa, Melo, & Oliveira Mano, 2019); thus, any tool that facilitates this work is welcome. provides useful functions for implementing biometrical models easily.…”