@article{a11863152dc94fb297b86aa407f39389,
title = "Contrasting impacts of dry versus humid heat on US corn and soybean yields",
abstract = "The impact of extreme heat on crop yields is an increasingly pressing issue given anthropogenic climate warming. However, some of the physical mechanisms involved in these impacts remain unclear, impeding adaptation-relevant insight and reliable projections of future climate impacts on crops. Here, using a multiple regression model based on observational data, we show that while extreme dry heat steeply reduced U.S. corn and soy yields, humid heat extremes had insignificant impacts and even boosted yields in some areas, despite having comparably high dry-bulb temperatures as their dry heat counterparts. This result suggests that conflating dry and humid heat extremes may lead to underestimated crop yield sensitivities to extreme dry heat. Rainfall tends to precede humid but not dry heat extremes, suggesting that multivariate weather sequences play a role in these crop responses. Our results provide evidence that extreme heat in recent years primarily affected yields by inducing moisture stress, and that the conflation of humid and dry heat extremes may lead to inaccuracy in projecting crop yield responses to warming and changing humidity.",
author = "Mingfang Ting and Corey Lesk and Chunyu Liu and Cuihua Li and Horton, {Radley M.} and Coffel, {Ethan D.} and Rogers, {Cassandra D.W.} and Deepti Singh",
note = "Funding Information: This study is supported by the National Science Foundation grant AGS-1934358. Funding for CR and DS is provided by the National Science Foundation grant AGS-1934383. Funding for CoL is provided by the National Science Foundation Graduate Research Fellowship under Grant No. DGE–1644869, Dartmouth Neukom Institute for Computational Science, and the Fonds de recherche du Qu{\'e}bec–Nature et technologies award #319165. E.D.C is funded by NSF grant 2049262 . We thank the UK Met Office Hadley Centre for providing the HadISD sub-daily station dataset used here and to the US Department of Agriculture{\textquoteright}s National Agricultural Statistics Service for the county level yield data. Funding Information: This study is supported by the National Science Foundation grant AGS-1934358. Funding for CR and DS is provided by the National Science Foundation grant AGS-1934383. Funding for CoL is provided by the National Science Foundation Graduate Research Fellowship under Grant No. DGE–1644869, Dartmouth Neukom Institute for Computational Science, and the Fonds de recherche du Qu{\'e}bec–Nature et technologies award #319165. E.D.C is funded by NSF grant 2049262. We thank the UK Met Office Hadley Centre for providing the HadISD sub-daily station dataset used here and to the US Department of Agriculture{\textquoteright}s National Agricultural Statistics Service for the county level yield data. Publisher Copyright: {\textcopyright} 2023, The Author(s).",
year = "2023",
month = dec,
doi = "10.1038/s41598-023-27931-7",
language = "English (US)",
volume = "13",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "Nature Publishing Group",
number = "1",
}