Comparison of discriminatory effects of corn yield test locations based on their genetic variation expression among hybrids

Abstract

It is important for yield test sites to elicit differences among genotypes, minimize redundancy of performance information, and generate a genotype × environment interaction representative of the targeted region. The objective of this study was to use corn (Zea mays L.) variety trial data from three relative maturity group (MG) tests (early, medium, and late) at five Tennessee locations spanning 14 yr to compare test location uniqueness and redundancy via genetic variance estimates and cluster analysis. Principle component analyses allowed for location comparisons of genetic variance expression among cultivars in different MG tests. Individual location–test–year genetic variance estimates ranged from <1 to 584 (Mg ha−1)2. Years and locations affected the magnitude of genetic variance, with years largely driving genetic expression within MGs in these environments. However, the late MG consistently had the greatest variance estimates. Across 14 yr, there was minimum duplicative information provided across the five yield test sites. Consequently, all locations were needed and none could be eliminated without compromising cultivar yield trial information for the three MG tests. Correlations between mean yields and genetic variance showed a weak relationship across locations (environments), years, and MG tests (r = .021); thus, high‐yielding environments did not correspond with increased genetic variance expression. These types of analyses are useful for evaluating the discriminating ability and duplicative or correlated expression of genetic information across locations for robust variety trials within and across maturity groups and targeted regions.

Using Partial Least Squares and Regression to Interpret Temperature and Precipitation Effects on Maize and Soybean Genetic Variance Expression

“…Following Ashworth et al (2020b) [10], estimates of Vg for individual maize yield trials for three MGs (early, medium, and late) at the same five locations for the same 14 years were calculated. Briefly, three different variety trials were included in this study for maize and were separated by relative MG tests (early < 114 d after planting (DAP); medium = 114-116 DAP; late > 116 DAP).…”

“…Each year, the planting dates for soybean tests generally occurred mid to late May, and mid to late April for maize. The five MG tests for soybeans and the three MG tests for maize at the five locations over 14 years (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013)(2014)(2015) were each analyzed with mixed models (SAS 9.4, SAS Institute, Inc., Cary, NC, USA) with random replications, incomplete blocks, and random cultivars to estimate restricted maximum likelihood Vg [9,10]. The genetic variance estimates obtained in the two above studies were utilized in the study reported herein.…”

“…However, specific G × E responses were not quantified. These evaluations also identified that two of the locations were duplicative for soybean and none were duplicative for maize, and that such analyses are worthwhile for comparing yield trial locations for their discriminating ability and representativeness [9,10].…”

“…The main considerations for yield trial environment selection are the ability to discriminate among genotypes (e.g., enhancing genetic variance (Vg) among cultivars, minimize redundancy, and generating a G × E interaction representative of the targeted market region and optimize production). In previous studies by Ashworth et al (2020aAshworth et al ( , 2020b soybean (Glycine max L.) and maize (Zea mays L.) variety trial results of five and three relative maturity groups (MG), respectively, were used to compare test location uniqueness and/or redundancy via Vg estimates and cluster analysis [9,10]. In both crop evaluations, years and locations affected the magnitude of Vg, with years largely driving genetic expression within MGs in these environments.…”

A regression approach to identify discriminating locations

“…Another relevant approach is that of Ashworth et al. (2020) that uses principal component analysis to identify locations with nonredundant genetic expression. Considerable work has also been done in using GGE biplots to enable the identification and evaluation of favorable locations (Fan et al., 2008; Meng et al., 2016).…”