Quantifying Genotype × Environment Effects in Long‐Term Common Wheat Yield Trials from an Agroecologically Diverse Production Region

Abstract

Multienvironment trials (METs) are used to investigate the performance of crop genotypes. To efficiently generate reliable performance estimates, the magnitude and patterns of genotype × environment interaction (G×E) in MET data must be known. We quantified G×E in fall‐planted common wheat (Triticum aestivum L.) in California, with the goal of increasing the reliability and efficiency of statewide variety testing activities. Linear mixed models and the genotype main effects plus G×E interaction effects (GGE) biplot method were used to analyze MET data for 211 common wheat genotypes, the MET consisted of 9 locations and 14 seasons. The representativeness and discriminating power of the MET locations were tested, and estimates of the optimum number of test locations were made. The analyses did not find evidence for significant, repeatable, crossover G×E. The genotype and G×E effects were of a similar magnitude, and the G×E effects were relatively strong compared with other sources of variance but were dominated by seasonal effects, with potentially repeatable genotype‐by‐location (G×L) effects being relatively weak. The GGE analyses did not detect repeatable G×L patterns across seasons. As a result, we conclude that the cereal production regions of California consist of a single, but unstable, mega‐environment for common wheat grain yield. The test location evaluation found few significant differences between test locations in terms of how well they represent the target production environment. We estimate that the number of test locations could be reduced while maintaining trial accuracy, which would improve the resource use efficiency of statewide trial activities without sacrificing information about variety‐specific common wheat yield performance.

Using soybean historical field trial data to study genotype by environment variation and identify mega-environments with the integration of genetic and non-genetic factors

“…For example, we determined that environmental, i.e ., non-genetic sources of variability were the predominant source of variance for seed yield. Similar results were found in wheat in California (USA) (G eorge and L undy 2019), winter wheat field trials in Germany between 1983 to 2014 (L aidig et al . 2017a) and winter rye (L aidig et al .…”

Integrating univariate and multivariate statistical models to investigate genotype × environment interaction in durum wheat

“…Stability of genotypes for grain yield is a key requirement to enhance germplasm and wide adaptation for cultivation (Chairi et al, 2020;George & Lundy, 2019;Mohammadi et al, 2014;Subira et al, 2015). This investigation revealed the existence of significant GE interaction for grain yield across environments which includes locations differing in winter temperature and led to mega-environment identification.…”

Estimation of the Optimal Number of Replicates in Crop Variety Trials

“…The so-estimated number of replicates may be referred as the number of replicates for optimal replication. Equation 3 was adopted to estimate the required number of replicates for China national cotton ( Xu et al, 2016 ) and wheat ( Zhang et al, 2020 ) variety trials, the required number of replicates for wheat and cotton variety trials in the Mediterranean regions ( Baxevanos et al, 2017a , b ), the required number of replicates in soybean variety trials in Brazil ( Woyann et al, 2020 ), and the required number of replicates for winter wheat in California ( George and Lundy, 2019 ).…”