Predicting soybean aboveground biomass in the short‐season region of Canada: Integrating vegetative cover and canopy height

Abstract Aboveground biomass (ABM) is a key determinant of soybean ( Glycine max [L.] Merr.) yield and can be used to select for stress‐resilient cultivars. The objective of our study was to develop a predictive model describing ABM in short‐season soybean from vegetative cover (VC) and canopy height (CH). Over five growing seasons in Ottawa, Canada, actual ABM was measured weekly along with red, green, and blue and stereo‐depth images. VC and CH, derived from these images, were used to develop a linear additive model for ABM with an R 2 of 0.90 and a root mean square error of 63 g m − 2 . Model‐predicted ABM at the beginning of seed development was significantly correlated with grain yield in a 5‐year moisture‐stress trial. The model was successfully applied to unmanned aerial vehicle‐derived VC and CH data to predict ABM in the moisture‐stress trial and a plant breeding trial selecting high‐yielding natto soybean lines. The model provides a scalable approach for predicting ABM and may enhance soybean breeding by supporting the selection of cultivars with improved climatic resilience and yield potential.