Genotype x environment interaction in cowpea by mixed models

Abstract

Several methods have been proposed to measure effects of genotype × environment interaction (G×E) on various traits of interest of plant species, such as grain yield. Among these methods, mixed models using the Restricted Maximum Likelihood (REML)-Best Linear Unbiased Prediction (BLUP) procedure with random genotype effects have been reported as advantageous, since they allow the obtaining of actual genotypic values for cultivation and use. The objective of this work was to evaluate the response of grain yield to different locations and years, and the effects of G×E on the performance of cowpea genotypes by the methodology of mixed models. Twenty genotypes were evaluated under rainfed conditions in 47 locations in 2010, 2011 and 2012 using randomized block design. After joint analysis, the genotypes adaptability and stability patterns within and between years were tested by the Harmonic Mean of Relative Performance of Genetic Values (HMRPGV) statistics. The analysis within the years showed highly significant effects of the genotype × location interaction in all the years evaluated. The results of the joint analysis presented highly significant effects (p≤0.01) of the genotype, and triple interaction (genotype × location × year) (p≤0.001),  denoting a strong effect of the G×E on the genotype performances. The HMRPGV analysis was adequate to identify superior genotypes, highlighting the MNC02-676F-3, MNC03-737F-5-1, MNC03-737F-5-9, BRS-Tumucumaque, and BRS-Guariba as the genotypes with best stability and highest grain yield. The selection of these genotypes resulted in a new average yield (1,402 kg ha-1) which is higher than that obtained by selection based only on the phenotype (1,230 kg ha-1).