Curbing Future Crop Yield Loss: Addressing the Food-Water Nexus with Blended Policy-structural Adaptations

Abstract:

In today’s rainfed agricultural regions, such as the Midwestern United States, farmers reliably produce marketable yields without the need for irrigation technology or precise planting policies. However, climate change is expected to challenge global food security. Projections of future climate change, namely wholesale temperature rise and temporal shifts in the peak precipitation seasons, might reduce crop yields and alter spatial crop suitability. As a result, farmers might seek adaptation strategies to continue current productivity and profitability, rather than make costly changes associated with planting different crop types. This research investigates the spatiotemporal suitability of blended policy-structural adaptations to reduce yield losses without crop type and planting pattern changes in Greene County, Ohio. Using CMIP5 predictions of precipitation and temperature, and soil profile data from NRCS, a crop-water model is used to calibrate and generate field-scale yield predictions and water-deficit estimates for several climate-calibrated planting policy scenarios. Field-level yield predictions and water deficits are mapped at the county-scale through the end of the century to illustrate the efficacy of planting policy interventions. Structural adaptation, in the form of center pivot-type irrigation technology, is applied where water deficits generate persistent yield losses. An economic model is used to determine the efficient investment strategy required to purchase, operate, and maintain irrigation infrastructure at the field-scale. Preliminary results suggest planting-policy uptake alone could reduce yield loss risk by 50% through the end of the century, based on RCP8.5 projections. The resultant framework holds the potential to inform farmer and county-level decision making under future climate uncertainty, and it illustrates the tradeoffs between adaptation cost and yield security.