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

Abstract

Soybean [Glycine max (L.) Merr.] provides plant-based protein for global food production and is extensively bred to create cultivars with greater productivity in distinct environments through multi-environment trials (MET). The application of MET assumes that trial locations provide representative environmental conditions that cultivars are likely to encounter when grown by farmers. A retrospective analysis of MET data spanning 63 locations between 1989 and 2019 was conducted to identify mega-environments (ME) for soybean seed yield in the primary production areas of North America. ME were identified using data from phenotypic values, geographic, soil, and meteorological records at the trial locations. Results indicate that yield variation was mostly explained by location and location by year interaction. The phenotypic variation due to genotype by location interaction effects was greater than genotype by year interaction effects. The static portion of the genotype by environment interaction variance represented 26.30% of its total variation. The observed locations sampled from the target population of environments can be divided into two or three ME, thereby suggesting that improvements in the response to selection can be achieved when selecting directly within clusters (i.e., regions and ME) versus selecting across all locations. In addition, we published the R package SoyURT that contains the datasets used in this work.