Effect of warming temperatures on US wheat yields

Abstract

Climate change is expected to increase future temperatures, potentially resulting in reduced crop production in many key production regions. Research quantifying the complex relationship between weather variables and wheat yields is rapidly growing, and recent advances have used a variety of model specifications that differ in how temperature data are included in the statistical yield equation. A unique data set that combines Kansas wheat variety field trial outcomes for 1985-2013 with location-specific weather data is used to analyze the effect of weather on wheat yield using regression analysis. Our results indicate that the effect of temperature exposure varies across the September-May growing season. The largest drivers of yield loss are freezing temperatures in the Fall and extreme heat events in the Spring. We also find that the overall effect of warming on yields is negative, even after accounting for the benefits of reduced exposure to freezing temperatures. Our analysis indicates that there exists a tradeoff between average (mean) yield and ability to resist extreme heat across varieties. More-recently released varieties are less able to resist heat than older lines. Our results also indicate that warming effects would be partially offset by increased rainfall in the Spring. Finally, we find that the method used to construct measures of temperature exposure matters for both the predictive performance of the regression model and the forecasted warming impacts on yields.

Keywords: agriculture; climate change; global warming; wheat; yield.

Fig. 1.

Impacts of freezing and degree day temperature variables on wheat yield for the preferred model (SI Appendix, Table S4, column 3), expressed per 24 h (days). D_x,y refers to the degree day variables created from the IDD variables for the thresholds x and y. Each four-bar cluster shows impacts for the Fall, Winter, and Spring months. Bars show 95% confidence intervals using SEs clustered by year and variety.

Fig. 2.

Predicted warming impacts on wheat yields under alternative uniform temperature changes across the entire Fall−Winter−Spring growing season. Impacts are reported as the percentage change in yield relative to historical climate. Each two-bar cluster shows estimates for a given scenario across different regression model specifications. The preferred model (SI Appendix, Table S4, column 3) estimates the warming effects using all temperature variables, whereas the alternative holds all effects from the Freeze Days variables at zero. Bars show 95% confidence intervals using SEs clustered by year and variety.

Fig. 3.

Predicted warming impacts on wheat yields under alternative uniform temperature changes across subsets of the growing season. Each four-bar cluster shows estimates from the preferred model accompanied by alternatives that restrict subsets of these effects to be zero. Just Fall ignores temperature effects in the Winter and Spring. Just Winter ignores temperature effects in the Fall and Spring. Just Spring ignores temperature effects in the Fall and Winter. Bars show 95% confidence intervals using SEs clustered by year and variety.