Thayer School of Engineering
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 U.S.in 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.
Ye H, Nicholas RE, Roth S, Keller K (2021) Considering uncertainties expands the lower tail of maize yield projections. PLOS ONE 16(11): e0259180. https://doi.org/10.1371/journal.pone.0259180
Dartmouth Digital Commons Citation
Ye, Haochen; Nicholas, Robert E.; Roth, Samantha; and Keller, Klaus, "Considering uncertainties expands the lower tail of maize yield projections" (2021). Dartmouth Scholarship. 4198.