Page:OMB Climate Change Fiscal Risk Report 2016.pdf/17

 The dot plot above shows the percent change in mean yield and percent change in the standard deviation of U.S. soybean yield in the unmitigated climate change scenario compared to the reference scenario. Each dot represents a modeling region, and the size of the dot corresponds to the number of acres in production in that region. Dots above the 45 degree line have an increasing coefficient of variation (CV), a measure of variability per unit of crop production insured (standard deviation divided by mean). CV is highly correlated with premium rates. The plot clearly shows that far more of the regions are above the 45 degree line than below, indicating that yield variability (as indicated by CV) increases in most cases in the unmitigated climate change scenario.

For this study, the simulated changes in means and standard deviations are calibrated to historical yields to preserve risk that is unrelated to weather and climate. Note that this calibration procedure involves a number of important and untested assumptions about future crop yields and, in particular, the nature of idiosyncratic (non-weather-related) yield risk. The calibration procedure is discussed in the Technical Supplement accompanying this report.

Since the crop insurance program insures against expected yield (or revenue), shifts in mean yields can be as important in changing yield risk as shifts in yield variability. Most regions see both a reduction in mean soybean yield and an increase in the variability of yield, which leads to increases in production risk (shown in the illustration above). In some regions, yield variability actually declines, but proportionately less so than mean yield, resulting in a net increase in risk. Some regions also see a reduction in risk (below the 45 degree line), including some regions where the standard deviation of yield increases but average yields increase proportionately more, which leads to a decline in risk.

This assessment builds on prior ERS modeling of climate change impacts on crop yield, cost, and production nationwide (Marshall et al., 2015) by estimating not only mean yields and prices but also yield risk and price risk when producers optimize planting decisions based on expectations but are exposed to weather variability—both as observed historically and as affected by various climate change scenarios. ERS then estimates total premiums and premium subsidies for revenue protection policies—the most popular insurance product for producers of major field crops.

For more information about the biophysical and economic crop production and acreage allocation models used for this assessment, see Marshall et al. (2015). For more information about the modifications made to these models for this assessment and the premium estimation methods, see the Technical Supplement accompanying this report.