A global dataset for the projected impacts of climate change on four major crops

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作者
Toshihiro Hasegawa
Hitomi Wakatsuki
Hui Ju
Shalika Vyas
Gerald C. Nelson
Aidan Farrell
Delphine Deryng
Francisco Meza
David Makowski
机构
[1] National Agricultural and Food Research Organization,Institute for Agro
[2] Tsukuba,Environmental Sciences
[3] Chinese Academy of Agricultural Sciences (IEDA,Institute of Environment and sustainable Development in Agriculture
[4] CAAS),Applied mathematics and computer science (MIA 518), INRAE AgroParisTech
[5] Alliance of Bioversity International and International Center for Tropical Agriculture (CIAT),undefined
[6] University of Illinois,undefined
[7] The University of the West Indies,undefined
[8] IRI THESys,undefined
[9] Humboldt-Universität zu Berlin,undefined
[10] Pontificia Universidad Católica de Chile,undefined
[11] Université Paris-Saclay,undefined
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摘要
Reliable estimates of the impacts of climate change on crop production are critical for assessing the sustainability of food systems. Global, regional, and site-specific crop simulation studies have been conducted for nearly four decades, representing valuable sources of information for climate change impact assessments. However, the wealth of data produced by these studies has not been made publicly available. Here, we develop a global dataset by consolidating previously published meta-analyses and data collected through a new literature search covering recent crop simulations. The new global dataset builds on 8703 simulations from 202 studies published between 1984 and 2020. It contains projected yields of four major crops (maize, rice, soybean, and wheat) in 91 countries under major emission scenarios for the 21st century, with and without adaptation measures, along with geographical coordinates, current temperature and precipitation levels, projected temperature and precipitation changes. This dataset provides a solid basis for a quantitative assessment of the impacts of climate change on crop production and will facilitate the rapidly developing data-driven machine learning applications.
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