Abstract
Although extreme weather events recur periodically everywhere, the impacts of their simultaneous occurrence on crop yields are globally unknown. In this study, we estimate the impacts of combined hot and dry extremes as well as cold and wet extremes on maize, rice, soybean, and wheat yields using gridded weather data and reported crop yield data at the global scale for 1980–2009. Our results show that co-occurring extremely hot and dry events have globally consistent negative effects on the yields of all inspected crop types. Extremely cold and wet conditions were observed to reduce crop yields globally too, although to a lesser extent and the impacts being more uncertain and inconsistent. Critically, we found that over the study period, the probability of co-occurring extreme hot and dry events during the growing season increased across all inspected crop types; wheat showing the largest, up to a six-fold, increase. Hence, our study highlights the potentially detrimental impacts that increasing climate variability can have on global food production.
| Original language | English (US) |
|---|---|
| Article number | 3583 |
| Journal | Scientific reports |
| Volume | 13 |
| Issue number | 1 |
| DOIs | |
| State | Published - Dec 2023 |
Bibliographical note
Funding Information:The study was funded by Maa- ja vesitekniikan tuki ry, Academy of Finland funded project WATVUL (Grant No. 317320), Academy of Finland funded project TREFORM (grant no. 339834), and European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 819202). M.J.P. acknowledges support from the Army Research Office/Army Research Laboratory under the Multidisciplinary University Research Initiative (Grant # W911NF1810267) and the Defense Advanced Research Project Agency (DARPA) under the World Modelers program (Grant # W911NF1910013). The views and conclusions contained in this manuscript are those of the authors and should not be interpreted as representing the official policies either expressed or implied of the Army Research Office or the US Government. D.K.R. acknowledges support from the Institute on the Environment, University of Minnesota. We thank Ilkka Mellin from Aalto University for his comments and advice regarding the methodology of this study. Finally, we acknowledge the Aalto high-performance computing cluster Triton for making this study possible.
Funding Information:
The study was funded by Maa- ja vesitekniikan tuki ry, Academy of Finland funded project WATVUL (Grant No. 317320), Academy of Finland funded project TREFORM (grant no. 339834), and European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant Agreement No. 819202). M.J.P. acknowledges support from the Army Research Office/Army Research Laboratory under the Multidisciplinary University Research Initiative (Grant # W911NF1810267) and the Defense Advanced Research Project Agency (DARPA) under the World Modelers program (Grant # W911NF1910013). The views and conclusions contained in this manuscript are those of the authors and should not be interpreted as representing the official policies either expressed or implied of the Army Research Office or the US Government. D.K.R. acknowledges support from the Institute on the Environment, University of Minnesota. We thank Ilkka Mellin from Aalto University for his comments and advice regarding the methodology of this study. Finally, we acknowledge the Aalto high-performance computing cluster Triton for making this study possible.
Publisher Copyright:
© 2023, The Author(s).
PubMed: MeSH publication types
- Journal Article
- Research Support, Non-U.S. Gov't
- Research Support, U.S. Gov't, Non-P.H.S.