Page:OMB Climate Change Fiscal Risk Report 2016.pdf/18



Key Limitations and Uncertainties
The cost to the Federal Government of the crop insurance program over the course of this century will depend upon many factors, including climate change. Market conditions and technology will determine the total value of production. For example, a combination of strong demand growth and strong crop yield growth that continues historical trends would result in higher gross revenues, which in turn imply higher liabilities and therefore higher premiums and associated subsidies. The design of the insurance program and farmer participation decisions will also determine program costs. This assessment isolates the impact of climate change by assuming baseline levels of demand and supply growth, and holding program design and farmer participation decisions constant.

Estimates of the increase in crop insurance premiums due to climate change vary considerably across GCMs, reflecting sensitivity to variable climate change projections (e.g., changes in regional temperatures and precipitation patterns). In addition, the impacts of climate change on crop yield risk vary significantly by region; yield risk even decreases in some regions in the climate change scenarios. However, there is strong agreement across the GCMs that climate change will increase both price risk and yield risk in aggregate at the national level. The GCMs also demonstrate a high degree of consistency with respect to the direction of change in yield risk within regions. In particular, yield risk is increasing in much of the Corn Belt across GCMs, and decreasing in a portion of the Northern Plains. ERS also found reasonable consistency between the biophysical crop model and two alternative econometric crop yield models estimated on the same baseline weather data. Finally, while there is a fairly wide spread in fiscal impact estimates across GCMs simulations, four of the five models produce climate outcomes under which total premiums increase on the order of billions of dollars each year.

In addition to uncertainty stemming from the GCMs, the biophysical and economic crop production and acreage allocation models have several limitations that could cause estimates to be too high or too low. First, the models may underrepresent the full impact of climate change. The models capture the direct effects of changing temperature and precipitation patterns and CO2 fertilization, but the crop production results are calibrated to hold constant the effects of other climate-related impacts on crops such as those due to pests, disease, exacerbated ozone concentrations, and the frequency of certain kinds of storms such as tornadoes, hurricanes, and flooding. The timeframe used to simulate weather conditions (40 years) was selected to capture the 30-year return frequency of major droughts, but may not provide a good measure of extreme risk—such as changes in the probability of a 1-in-100 year or 1-in-1,000 year mega-drought. The models also do not place constraints on irrigation water supply, even though ERS has found that irrigation water supply will decline significantly in some regions due to climate change (Marshall et al., 2015); irrigated acres currently represent roughly 15 percent of total insured acres for principal field crops.

Second, the models do not capture changes in global crop prices due to climate-related events outside of the United States. For example, a decline in wheat production abroad due to rising temperatures could put upward pressure on global wheat prices, increasing the value of the insured wheat crop and associated crop insurance premiums in the United States.

Third, the models likely underrepresent the potential for adaptation by producers and the agricultural sector in general. For example, although crop productivity is assumed to increase year over year in both the reference and climate change scenarios due to general technological advancement, the possibility for technological improvements that may affect resilience to climate change is not represented. Some adaptive responses could reduce yield risk. For example, a considerable body of current research is focused on improving crop drought tolerance. However, as seen in the modeling results, other adaptive