Using partial least squares and regression to interpret temperature and precipitation effects on maize and soybean genetic variance expression

Authors: Ashworth, Amanda; ALLEN, FRED - University Of Tennessee; SAXTON, ARNOLD - University Of Tennessee

Submitted to: Agronomy Journal
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/28/2023
Publication Date: 10/31/2023
Citation: Ashworth, A.J., Allen, F., Saxton, A. 2023. Using partial least squares and regression to interpret temperature and precipitation effects on maize and soybean genetic variance expression. Agronomy Journal. 13(11). Article 13112752. https://doi.org/10.3390/agronomy13112752.
DOI: https://doi.org/10.3390/agronomy13112752

Interpretive Summary: Variety trials and breeding line tests for yield require multi-environment trials (i.e., location–year combinations) to obtain reliable estimates and rankings of yield performance of different genotypes across a targeted production region. Varieties grown in different environments may respond differently to environmental fluctuations, known as genotype × environment interactions. Researchers set out to use novel statistical approaches to determine environmental effects (minimum and maximum air temperature and precipitation) on expression of genetic variance, or the ability to discriminate among genotypes, based on corn and soybean variety trial data spanning 14 years, 5 locations, and 5 maturity groups. Overall, precipitation was the driving variable for corn Vg, indicating corn is more sensitive to rain events during the growing-season than soybean. In both corn and soybean, differences in genetic variance occurred among MG tests and locations. However, for soybean there were interactions between locations and weather variables, whereas for corn, interactions were found between MG tests and weather variables. These results have important implications for breeding programs which need maximum expression of genetic differences in soybean and corn cultivars under a changing climate.

Technical Abstract: Partial least squares (PLS) is a statistical technique that can evaluate association of external environmental and/or cultivar variables with biological responses such as genotype × environment interactions (GxE). The objective of this study was to use PLS and regression analyses on soybean (Glycine max L.) and corn (Zea mays L.) variety trial results for five (soybean) or three (corn) maturity group (MG) tests, at five Tennessee locations spanning 14 yrs to determine environmental effects (minimum and maximum air temperature and precipitation) on expression of genetic variance (Vg). Overall, PLS excelled at identifying combinations of weather variables to develop models with high R2 values (41-59%), relative to regression analysis (R2 =34-44%). In both corn and soybean, differences in genetic variance occurred among MG tests and locations. Overall, precipitation was the driving variable for corn Vg, indicating corn is more sensitive to rain events during the growing-season than soybean i.e., with each cm of precipitation, corn Vg of yield increased 11.38-23.78 (Mg ha-1)2 . Results suggested that ensuring adequate water, particularly during weeks 3 and 6, is critical for corn Vg of yield, regardless of MG test and location. Genetically modified soybean cultivars responded similarly to conventional cultivars, suggesting no Vg response differences due to the glyphosate tolerance trait. These results have important implications for irrigation timing for maximum expression of genetic differences in corn and soybean cultivars, particularly for management planning during future stochastic weather events.