A Non-Parametric Approach to Estimating Nonlinear Impacts of Early Season Soil Moisture on Dryland Corn Yields

Abstract

Early season soil moisture is one of the few predictive factors available to agricultural decision-makers before planting begins. Farmers, merchandisers, insurers, and futures market participants often incorporate early season moisture conditions into their production forecasts and risk assessments, yet it remains unclear whether these assessments are supported by measurable impacts on dryland corn yield outcomes. While previous studies have explored the relationship between growing season moisture and yield, the role of early season soil moisture in isolation has been largely overlooked.

This study makes three key contributions. First, it provides a model for early season yield forecasting. Results indicate that shifting just one of the 30 early-season days from average to the driest soil moisture conditions is associated with a 3% national yield decline—roughly 446 million fewer bushels relative to April 2025 WASDE projections. Second, it demonstrates that the effects of early season soil moisture are highly nonlinear, underscoring the importance of using a flexible, exposure-based approach rather than assuming a parametric functional form. Third, it highlights significant regional differences in soil moisture-yield relationships, emphasizing the need for spatially specific modeling approaches. We found that exposure to low soil moisture is associated with increased yield in the central and northern corn belt, whereas it is associated with decreases in yield in other regions and the national model. This study offers a framework for improving the assessment of early-season production expectations, particularly for farmers, merchandisers, insurers, and policymakers whose decisions rely on production assumptions informed by limited information available at that time.