Nonlinear Temperature Effects Indicate Severe Damages to US Crop Yields Under Climate Change

The United States produces 41% of the world’s corn and 38% of the world’s soybeans. These crops are two of the four largest sources of caloric energy produced and are therefore critical for world food supply. With the evidence that greenhouse gases are warming the world’s climate it is pertinent that we understand the effect of temperature on crop yields. This study links the relationship of weather and crop yield of soybeans, corn, and another warm weather crop, cotton, the crops with the largest production values in the United States. The data that are used in the study are composed of a new fine-scale model of weather outcomes merged with a large panel of crop yields from most U.S. counties in the time span of 1950–2005. The new weather data include length of time each crop is exposed to each one-degree Celsius temperature interval in one day. Then it is summed across all the days of the growing season within each county. The result of this study is that high temperatures are much more damaging than previously thought and that yields of all three crops are likely to decline greatly if warming predictions are accurate. Hannah Carr
Schlenker, W., Roberts, M., 2009. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. PNAS 106, 15594–15598.

The study shows that yield growth increases gradually with temperature up to 29–32 °C, depending of the crop, and then decreases sharply for all three crops. For the corn, the critical threshold temperature is 29 °C, for the soybeans it is 30 °C and for cotton it is 32 °C. With the current growing regions fixed, average yields are predicted to decrease by 30–46% before the end of the century, with the slowest warming scenario and decrease 63–82% with the most rapid warming scenario. The driving force behind these large and significant predicted impacts is the projected increase in frequency of extremely warm temperatures.

This study of crop yields uses a system of models in which each model is estimated 1,000 times, randomly choosing 48 years of the 56 year history. The models are compared with three specifications of temperature effects (step function, polynomial, and piecewise linear) along with three alternative specifications: 1) a model with average temperatures for each of four months 2) an approximation of growing degree days based on monthly average temperatures and 3) a measure of growing-degree days, calculated by using daily mean temperatures. To demonstrate the study throughout the country, it divided the United States into three regions: the northern cooler states, the southern warmer states and the middle states. I these regions the study used corn as it is a main crop across the entire country. It was found that there is a nonlinear relationship between yield and temperature throughout the country, and that greater precipitation will partially mitigate the damages from extreme temperature increases. The study also proves that estimated climate change impacts are not sensitive to the specific growing season and consistent with time separability. The findings of the study are notable for the consistency of the estimated nonlinear temperature effects across time, location, crops, and the many sources of variation in temperature and precipitation considered. 

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