Abstract Integrating soil and topographic properties into variable‐rate seeding (VRS) provides essential information for decision‐making to enhance crop production and profitability. Only a few studies have explored geospatial and machine‐learning approaches to understand factors influencing crop yield and profitability in VRS. Therefore, field studies on corn ( Zea mays L.) and soybean Glycine max (L.) Merr. production systems were conducted in Miami, OH, with five and four seed‐rate treatments, respectively, from 2017 to 2022. The objectives of this study were to explore the effectiveness of VRS, in combination with soil and topographic properties, in delineating the optimal agronomic and economic seeding rates for corn and soybeans. To achieve this goal, data on yield, soil properties, and topographic factors were collected. Spatial regression model and geospatial analyses were performed and compared with a random forest machine‐learning model to identify key variables that best explain yield. Results from spatial regression and the random forest model indicated that elevation, cation exchange capacity, slope, and soil organic matter were the key variables influencing corn and soybean yields, along with seeding rate. Hence, these field properties were used to delineate clusters. For corn, both agronomic and economic optimum seeding rates varied across clusters, indicating the importance of a cluster‐specific VRS strategy. In contrast, the agronomic and economic optimum seeding rates for soybean were insignificant across clusters. These findings underscore VRS's potential for corn in heterogeneous fields but highlight its limited applicability in soybeans. Future research should prioritize field‐specific VRS to validate cluster‐based recommendations and ensure scalability across diverse agricultural systems.
Neupane et al. (Sun,) studied this question.