Prediction of hybrid maize adaptation in China using extensive climatic-phenotypic data and machine learning

The environment has an important impact on maize ( Zea mays L.) production, making it necessary to identify plant adaptation regions that are suitable for different maize varieties. Traditional methods using field trials are costly and restricted to a limited number of areas. Identifying adaptation regions based on climate data has great potential, but a basic understanding and a prediction approach for diverse maize varieties are lacking. Here, we collected a representative dataset comprising 32,840 data points from the National Maize Variety Trial Data Management Platform. We employed three traits to characterize the adaptability of different maize varieties: PH (plant height), DTS (days to silking), and yield. First, we quantified the contributions of variety (V), environment (E), and V × E to variance in the three adaptation-related traits. The mean contributions of E to variance in PH, DTS, and yield were 54.50 %, 82.87 %, and 75.92 %, respectively, suggesting that environmental effects are crucial for phenotype construction. Second, we analyzed correlations between the three traits and three environmental indices: GDD (growing degree days), PRE (precipitation), and SSD (sunshine duration). The highest absolute correlation coefficients between phenotypes and environmental indices were 0.15–0.69 at the whole-data level. To predict variety adaptation on a national scale, we modeled the three traits using environmental indices and Best linear unbiased predictors (BLUPs) via the random forest algorithm. The predictive abilities of our models for PH, DTS, and yield were 0.90 (MAE = 9.95 cm), 0.99 (MAE = 1.09 d), and 0.95 (MAE = 0.55 t ha −1 ), respectively, indicating that our proposed framework can predict adaptation-related traits for diverse maize varieties in China.