1. Evaluating the Potential Benefits of Variable-rate Sulfur Management in Minnesota Corn Using Machine-learning Analysis

Sulfur (S) management in corn is complicated by strong within-field variability in crop response, driven by interactions among soil properties, landscape position, and prior management. As a result, uniform S applications can create unnecessary input costs in nonresponsive areas while undersupplying responsive zones. We developed and demonstrated a practical machine-learning (ML) workflow to estimate within-field agronomic optimum sulfur rate (AOSR) and economic optimum sulfur rate (EOSR) on commercial... R.P. Negrini, Y. Miao