Changes in the leaf area-seed yield relationship in soybean driven by genetic, management and environments: Implications for High-Throughput Phenotyping

High-throughput crop phenotyping (HTP) in soybean [Glycine max L. (Merr.)] has been used to estimate seed yield with varying degrees of accuracy. Research in this area typically makes use of different machine learning approaches to predict seed yield based on crop images with a strong focus on analytics. On the other hand, a significant part of the soybean breeding community still utilizes linear approaches to relate canopy traits and seed yield relying on parsimony. Our research attempted to address the limitations related to interpretability, scope and system comprehension inherent in previous modelling approaches. We utilized a combination of empirical and simulated data to augment the experimental footprint as well as to explore the combined effects of genetics (G), environments (E) and management (M). We use flexible functions without assuming a pre-determined response between canopy traits and seed yield. Factors such as soybean maturity date, duration of vegetative and reproductive periods, harvest index (HI), potential leaf size, planting date and plant population affected the shape of the canopy-seed yield relationship as well as the canopy optimum values at which selection of high yielding genotypes should be conducted. This work demonstrates that there are avenues for improved application of HTP in soybean breeding programs if similar modelling approaches are considered.