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

• Published in: Scientific data Vol. 9; no. 1; pp. 58 - 11
• Main Authors: Hasegawa, Toshihiro, Wakatsuki, Hitomi, Ju, Hui, Vyas, Shalika, Nelson, Gerald C., Farrell, Aidan, Deryng, Delphine, Meza, Francisco, Makowski, David
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 16-02-2022; Nature Publishing Group; Nature Portfolio
• Subjects: 631/449/2661/2663; 704/172/4081; Agricultural sciences; Artificial Intelligence; Carbon dioxide; Climate change; Computer Science; Crop production; Crops; Data Descriptor; Datasets; Environmental Sciences; Global Changes; Glycine max; Humanities and Social Sciences; Learning algorithms; Life Sciences; Machine learning; multidisciplinary; Oryza sativa; Precipitation; Science; Science (multidisciplinary); Triticum aestivum; Zea mays; yields
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/s41597-022-01150-7

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.
Measurement(s)
relative yield change
Technology Type(s)
crop simulation model
Factor Type(s)
geographic location • current average temperature • current annual precipitation • future mid-point • climate scenario • temperature change • annual precipitation change • CO2 ppm
Sample Characteristic - Organism
Zea mays • Oryza sativa • Glycine max • Triticum aestivum
Sample Characteristic - Environment
climate change
Sample Characteristic - Location
global
Machine-accessible metadata file describing the reported data:
https://doi.org/10.6084/m9.figshare.17427674