Genotype × Environment Interactions for Grain Yield in Rainfed Winter Wheat Multi‐Environment Trials in Iran

Abstract

Genotype × environment (G × E) interaction analysis was investigated on grain yield of 20 winter wheat genotypes grown in 24 environments in cold winter areas of Iran during 3 yr. Cluster and biplot analyses were applied to separate testing environments into groups with the same top‐yielding genotypes. Environment followed by G × E interaction effects accounted for the greatest proportion of the variability of grain yield of winter wheat genotypes. Cluster analysis divided both genotypes and environments into three groups, which accounted for 64.4% of the G × E sum of squares (SS). Biplot analysis confirmed the classification analysis and showed that different environment groups tend to discriminate genotype groups in dissimilar fashions. The genotype group G‐I with the highest yielding performance had the best adaptation to environment groups with the highest (E‐I) and the lowest (E‐III) yield potential. Genotype group G‐II was adapted to environment group E‐II with average yield potential, while the genotype group G‐III appeared to have the poorest adaptation to environment groups. The environmental PC1 leads to noncrossover G × E interactions, while PC2 represents a disproportional genotype response across locations, explaining crossover G × E interactions. The testing environments involved in E‐I and E‐III with large PC1 scores and low PC2 scores were the best for genotype discrimination. Results show that the highest yielding genotypes G4, G10, and G17 with the lowest changes in grain yields across environments were the most stable and are consequently good candidates for commercial release in cold winter areas of Iran.

A comparison of winter wheat cultivar rankings in groups of Polish locations

“…This result was similar to studies in which environmental effects were shown to be responsible for approximately 80% or more of the total experimental variation in wheat grain yield (Atlin and McRae 1994;Mohammadi et al 2010;Tapley et al 2013). Gauch and Zobel (1997) reported that locations accounted for approximately 80% of total variation, whereas genotype and G×E interactions each accounted for approximately 10%.…”

The Analysis of Wheat Yield Variability Based on Experimental Data from 2008–2018 to Understand the Yield Gap

“…The wheat growing environment has a significant impact on the yield. The production of wheat requires high-quality soil; thus, the best available soil should be selected when growing wheat [36][37][38][39][40]. Farmers who do not have access to valuable soils may consider growing other agricultural crops to increase production efficiency.…”

Ecophysiological modeling of yield and yield components in winter wheat using hierarchical Bayesian analysis

“…In this study, we utilize data from the Oklahoma Small Grains Variety Performance Tests, a multienvironment trial, conducted yearly by Oklahoma State University (OSU). Most multienvironment trials focus mainly on yield (Kaya et al., 2006; Mohammadi et al., 2010; Roozeboom et al., 2008; Sukumaran et al., 2017) as this is one of the more important outcomes of a cultivar, for which producers base their choice. Yet, yield data alone provide limited insight into the mechanisms for differential responses of genetic cultivars to changing environments.…”

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