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Publication Date
18 November 2021

Considering Uncertainties Expands the Lower Tail of Maize Yield Projections

Considering uncertainties surrounding climate forcings as well as parameters in crop-yield models can lead to more extreme projections of low yields.
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Crop yields depend on a variety of factors including weather, soils, and changes in technology and management practices. Accounting for key uncertainties can have a substantial impact on modeled projections of future yields. Photo by USDA from flickr.

Crop yields are sensitive to temperature and precipitation changes. Building a statistical model between crop yields and weather conditions is a common method to project future yields. One key concern is the possibility of extremely low yields. We sample model parameter uncertainty using a pre-calibration method and sample climate forcing uncertainty using an ensemble of downscaled climate projections. We then quantify their relative importance for maize yield projections using a cumulative uncertainty approach.


We find that the distribution of maize yield projections can change drastically as a function of the consideration of these uncertainties. Sampling both uncertainty sources leads to a longer tail of extreme low yield projections. We also find the model parameter uncertainty explains more of the yield projection variance. This approach demonstrates how ignoring uncertainties surrounding model parameters and climate forcings can underestimate extreme low yield projections.


Crop yields are sensitive to extreme weather events. Improving the understanding of the mechanisms and the drivers of the projection uncertainties can help to improve decisions. Previous studies have provided important insights, but often sample only a small subset of potentially important uncertainties. Here we expand on a previous statistical modeling approach by refining the analyses of two uncertainty sources. Specifically, we assess the effects of uncertainties surrounding crop-yield model parameters and climate forcings on projected crop yield. We focus on maize yield projections in the eastern this century. We quantify how considering more uncertainties expands the lower tail of yield projections. We characterized the relative importance of each uncertainty source and show that the uncertainty surrounding yield model parameters is the main driver of yield projection uncertainty.

Point of Contact
John Weyant
Stanford University
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