• The P-SHEC tool predicts county-level economic impacts of soil health practices. • Machine learning links changes in soil organic matter to yield and drought resilience. • Partial budgeting estimates recurring net income effects from management practices. • The tool supports 10 crops across nearly 2,000 U.S. counties for localized analysis. • Findings highlight the potential profitability effects of soil health adoption. Soil health underpins agricultural productivity and resilience in adverse weather conditions, yet adoption of soil health practices often lags because producers have limited understanding of their long-term economic outcomes. Although conservation practices such as cover crops, reduced tillage, and nutrient management are well known for their environmental benefits, producers’ management choices are often shaped by profitability and risks associated with adoption. This underscores the need for decision-support tools that provide clear economic guidance to inform long-term soil health practice planning. The Predictive Soil Health Economic Calculator (P-SHEC) addresses this gap by estimating how changes in soil organic matter (SOM) resulting from soil health practice implementation influence long-term yield and drought resilience through machine learning models. By connecting long-term changes in yield and stability to enterprise budgets, the tool projects county-level, long-term economic outcomes of soil health practices for 10 crops across 1,962 U.S. counties. As a web-based, freely accessible tool, P-SHEC offers an economic dimension to soil health planning by delivering transparent, replicable, and locally relevant economic estimates, supporting more informed decision-making by farmers, advisors, conservation planners, and policymakers.
Maples et al. (Tue,) studied this question.