Causal inference for agricultural yields and crop rotations

Music City Center 

Causal inference methods are essential for analyzing observational data in ecology and environmental science, yet their application to large-scale, spatiotemporal datasets remains challenging. This paper compares four causal inference approaches-structural causal models, matching, inverse probability weighting, and causal forest-to estimate the impact of crop rotation on corn yield in the Midwestern United States. Using remotely sensed and modeled data, we evaluate these methods across datasets by increasing complexity, incorporating spatial, temporal, and spatiotemporal dimensions. Our findings highlight the strengths, limitations, and robustness of each method, providing practical guidance for addressing key challenges such as autocorrelation, heterogeneity, and continuous versus discrete variables. This study advances understanding of crop rotation effects while offering a framework for applying causal inference to environmental research.

spatial modeling

causal inference

crop rotation

diversied farm systems 

Section on Statistics and the Environment