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

Abstract

Partial least squares (PLS) is a statistical technique that can evaluate the association of large numbers of external environmental variables with biological responses. PLS is a good method for analyzing the relative importance of variables and compressing the data for regression analyses. The objective of this study was to use PLS and regression analyses on soybean (Glycine max L.) and maize (Zea mays L.) variety trial results for five (soybean) or three (maize) maturity group (MG) tests, at five Tennessee locations spanning 14 years, in order to determine the environmental effects (weekly minimum and maximum air temperature and precipitation) on the expression of yield genetic variance (Vg). Overall, PLS excelled at identifying combinations of weather variables to develop models with high R2 values (41–59%) relative to the regression analysis (R2 = 34–44%), but they did not address the effects of specific variables as in regression analysis. In both maize and soybean, differences in genetic variance occurred among MG tests and locations. Overall, precipitation was the driving variable for maize Vg, indicating maize is more sensitive to rain events during the growing season than soybean, i.e., with each cm of precipitation, maize Vg increased by 11.38–23.78 (Mg ha−1)2. The results suggest that ensuring adequate water, particularly during weeks 3 and 6, is critical for maize Vg, regardless of the 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 the maximum expression of genetic differences in maize and soybean cultivars, particularly for management planning during future stochastic weather events.