Predicting Yield and Stability Analysis of Wheat under Different Crop Management Systems across Agro-Ecosystems in India

Abstract

The objectives of the study were as follows: 1) to evaluate the GxExM for wheat genotypes; 2) to predict yield performance and identify high stable wheat genotypes in different management practices; and 3) to make genotype-specific management and high performing genotype recommendations within and across agro-ecological regions. A diverse set of twenty-one genotypes was evaluated over three years (2012, 2013 and 2014) under two levels of crop management practices (CT and ZT) across three agro-ecological regions (BR, MP and PB) of India in replicated trials. Data were analyzed with SASGxE and RGxE programs using SAS and R programming languages, respectively. Across and within a location(s), the pattern of GxExM and GxMxY interactions (respectively) among univariate and multivariate stability statistics, grouping of genotypes in divisive clusters and estimates (with a prediction interval) of genotype varied in management practice CT and ZT. Across locations, the genotypes "Munal" and "HD-2967" were the best performers and high stable in CT and ZT, respectively. Genotypes "HD-2824" and "DPW-621-50", and "Munal" may serve as diverse parents for developing high quality, climate smart, locally adapted genotypes for BR in CT and ZT, respectively. Genotypes "HD-2932", "BAZ" and "JW-3288", and "GW-322" and "HD-2967" are suitable for developing locally adapted stress tolerant genotypes for MP in management practices CT and ZT, respectively. Relatively small GxM and GxExM interactions in PB preclude in making definitive conclusions.

AMMI and GGE biplot analysis of yield of different elite wheat line under terminal heat stress and irrigated environments

“…Thus, there is a particular need to gain heat tolerance and develop heat-tolerant new germplasm through wheat breeding (Bhatta et al, 2008). The genotype selection depends on the understanding of the interaction among the genotypes, environment, and crop management practices which can be characterized using statistical methods (Jat et al, 2017). The variety with the highest average yield in all the test environment alone cannot be used for recommendation to the farmers; analysis of the stability of variety in the environment that is G Â E interaction and physiological basis are also to be studied (Vergas et al, 2001;Thamson and Philips, 2006).…”

GGE Bi-Plot Analysis of Genotype by Environment Interaction in Medium Maturing Upland Rice Genotypes in Ethiopia

“…GGE bi-plot analysis showed that genotype (G7) performed best in almost all environments except in E3 and E11 (Figure 1). If no environment is present in a sector, then vertex genotype of that sector is considered to be a poor performer in all test environments [15].…”

“…According to [15]report, responsive genotypes were those having either best or the poorest performance in one or all environments. Accordingly, among the vertex genotypes G5 and G7 were identified as the high yielding genotypes while G12 and G16 were the low yielding genotypes across the testing environments ( Fig.…”

“…2). This can be evaluated by average environment coordination (AEC) method [13,15]. In Fig.2 the line with single arrow head is the AEC abscissa.…”

AMMI and GGE biplot analysis of yield of different elite Wheat line under terminal heat stress and irrigated environment

“…The genotype selection is dependent on the understanding of interaction among the genotypes, environment and crop management practices which can be characterized using statistical method [29]. The variety with highest average yield in all the test environment alone cannot be used for recommendation to the farmers; analysis of stability of variety in the environment that is G × E interaction and physiological basis are also to be studied [30].…”