Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States

We compared climatic relationships to insurance loss across the inland
Pacific Northwest region of the United States, using a design matrix
methodology, to identify optimum temporal windows for climate variables by
county in relationship to wheat insurance loss due to drought. The results
of our temporal window construction for water availability variables
(precipitation, temperature, evapotranspiration, and the Palmer drought
severity index [PDSI]) identified spatial patterns across the study area
that aligned with regional climate patterns, particularly with regards to
drought-prone counties of eastern Washington. Using these optimum
time-lagged correlational relationships between insurance loss and
individual climate variables, along with commodity pricing, we constructed
a regression-based random forest model for insurance loss prediction and
evaluation of climatic feature importance. Our cross-validated model
results indicated that PDSI was the most important factor in predicting
total seasonal wheat/drought insurance loss, with wheat pricing and
potential evapotranspiration having noted contributions. Our overall
regional model had a R2 of 0.49 and a RMSE of $30.8 million. Model
performance typically underestimated annual losses, with moderate spatial
variability in terms of performance between counties.