Genotype by Environment Interaction in Cowpea Lines Using Gge Biplot Method

Abstract

The GGE Biplot method is efficien to identify favorable genotypes and ideal environments for evaluation. Therefore, the objective of this work was to evaluate the genotype by environment interaction (G×E) and select elite lines of cowpea from genotypes, which are part of the cultivation and use value tests of the Embrapa Meio-Norte Breeding Program, for regions of the Brazilian Cerrado, by the GGE-Biplot method. The grain yield of 40 cowpea genotypes, 30 lines and 10 cultivars, was evaluated during three years (2010, 2011 and 2012) in three locations: Balsas (BAL), São Raimundo das Mangabeiras (SRM) and Primavera do Leste (PRL). The data were subjected to analysis of variance, and adjusted means were obtained to perform the GGE-Biplot analysis. The graphic results showed variation in the performance of the genotypes in the locations evaluated over the years. The performance of the lines MNC02-675F-4-9 and MNC02-675F-4-10 were considered ideal, with maximum yield and good stability in the locations evaluated. There mega-environments were formed, encompassing environments correlated positively. The lines MNC02-675F-4-9, MNC02-675F-9-3 and MNC02-701F-2 had the best performance within each mega-environment. The environment PRL10 and lines near this environment, such as MNC02-677F-2, MNC02-677F-5 and the control cultivar (BRS-Marataoã) could be classified as those of greater reliability, determined basically by the genotypic effects, with reduced G×E. Most of the environments evaluated were ideal for evaluation of G×E, since the genotypes were well discriminated on them. Therefore, the selection of genotypes with adaptability and superior performance for specific environments through the GGE-Biplot analysis was possible.

Yield Stability and Farmer Preference of Cowpea (Vigna Uncuilata) Lines in semi-arid Eastern Kenya

“…The presence of significant genotype (G), years (Y, location (L) main effects and genotype by environment (GE) in ANOVA suggests differential responses of the genotypes and the need to identify high yielding and stable genotypes across test environments. Sousa et al, (2018) found similar results for these three sources of variation in cowpea genotypes. The location (L) variation (24%) indicated that the tested locations in this current study were diverse with the largest differences among locations effects causing the most variation in genotype performance.…”

Field Pea (Pisum sativum L.) Variety Development for Moisture Deficit Areas of Eastern Amhara, Ethiopia

“…In line to this Ermiyas [14] has reported the highest contribution (51.6%) of environmental effect for total variance of soybean grain yield. Similarly, Massaine et al [16] in cowpea and Tadesse et al [17] in common bean noticed the highest variation explained by the environmental effect.…”

Stability analysis of soybean [Glycine max (L.) Merrill] genotypes under multi-environments rainfed condition of North Western Himalayan hills

“…It is, therefore, imperative that genotypes should be identified based on detailed understanding of their genotype × environment interaction and multienvironment trials data can serve as guide for the selection of the best genotypes for target environments (Mustapha et al 2014). The GGE-Biplot method is efficient in detecting the genotype by environment interaction and identifying the most stable genotypes and best environment (Heidari et al 2016;Sousa et al 2018). Keeping this in view, the present investigation was carried out with the objective to examine the stability of upcoming soybean varieties developed for NW Himalayan hills of India.…”