Prediction of Maize Phenotypic Traits With Genomic and Environmental Predictors Using Gradient Boosting Frameworks - Westhues, Cathy C.; Mahone, Gregory S.; da Silva, Sofia; Thorwarth, Patrick; Schmidt, Malthe; Richter, Jan-Christoph; Simianer, Henner; Beissinger, Timothy M. (Frontiers in Plant Science, 2021)
The development of crop varieties with stable performance in future environmental conditions represents a critical challenge in the context of climate change. Prediction of genotype performance for complex crop agronomic traits can include environmental information such as soil and climate information, in addition to genomic data. Modern predictive modeling approaches can efficiently handle various data types and are able to capture complex nonlinear relationships in large datasets. In particular, machine learning techniques have gained substantial interest in recent years. Here we examined the predictive ability of machine learning-based models for two phenotypic traits in maize using data collected by the the Maize Genomes to Fields (G2F) Initiative. The data we analyzed consisted of multi-environment trials (METs) dispersed across the United States and Canada from 2014 to 2017. An assortment of soil- and weather-based variables was derived and used in prediction models alongside genotypic data. Several linear random effects models were compared to a linear regularized regression method (elastic net) and to two nonlinear gradient boosting methods based on decision tree algorithms (XGBoost, LightGBM). These models were tested with and without integrating environmental data using a leave-one-year-out cross-validation scheme. Accuracy in forecasting grain yield performance in new environments improved by up to 17.7 % by using available environmental predictors with XGBoost, compared to our baseline model. For the same trait, gradient boosting approaches resulted in an increase of the average predictive ability of new environments, even without incorporating environmental-based covariates. Environmental variables related to temperature at flowering stage, precipitation during the vegetative and grain filling stage, and soil organic matter content appeared as important predictors for grain yield in our panel of environments.