Leveraging Soil Mapping and Machine Learning to Improve Spatial Adjustments in Plant Breeding Trials

Abstract

Spatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving mean and P-Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within trial spatial variability primarily comes from soil feature gradients, such as nutrients, but study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row and preliminary yield trial data of a public soybean breeding program across three years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), moving means adjustment, P-spline adjustment, and a machine learning based method called XGBoost. XGBoost modeled soil features at (a) local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both progeny row and preliminary yield trial stages of field testing, and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. These results empower breeders to further refine selection criteria to make more accurate selections, and furthermore include soil variables to select for macro- and micro-nutrients stress tolerance.