Stochastically modeling the projected impacts of climate change on rainfed and irrigated US crop yields

Food demands are rising due to an increasing population with changing food preferences, placing pressure on agricultural production. Additionally, climate extremes have recently highlighted the vulnerability of the agricultural system to climate variability. This study seeks to fill two important gaps in current knowledge: how irrigation impacts the large-scale response of crops to varying climate conditions and how we can explicitly account for uncertainty in yield response to climate. To address these, we developed a statistical model to quantitatively estimate historical and future impacts of climate change and irrigation on US county-level crop yields with uncertainty explicitly treated. Historical climate and crop yield data for 1970–2009 were used over different growing regions to fit the model, and five CMIP5 climate projections were applied to simulate future crop yield response to climate. Maize and spring wheat yields are projected to experience decreasing trends with all models in agreement. Winter wheat yields in the Northwest will see an increasing trend. Results for soybean and winter wheat in the South are more complicated, as irrigation can change the trend in projected yields. The comparison between projected crop yield time series for rainfed and irrigated cases indicates that irrigation can buffer against climate variability that could lead to negative yield anomalies. Through trend analysis of the predictors, the trend in crop yield is mainly driven by projected trends in temperature-related indices, and county-level trend analysis shows regional differences are negligible. This framework provides estimates of the impact of climate and irrigation on US crop yields for the 21st century that account for the full uncertainty of climate variables and the range of crop response. The results of this study can contribute to decision making about crop choice and water use in an uncertain future climate.

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