Reaction norm for genomic prediction of plant growth: modeling drought stress response in soybean

Abstract Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G×E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G×E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments and their time-series soil moisture was measured. The models were evaluated using leave-one-environment-out cross-validation, in which a treatment-by-year combination was considered the environment. These results suggest that our model can capture G×E during the early growth, especially canopy height. Significant variations in the G×E of the canopy height during the early growth period were visualized using the estimated reaction norms. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G×E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data obtained with remote sensing.