Articles | Volume 12, issue 4
https://doi.org/10.5194/essd-12-3545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-12-3545-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Dec 2020
Data description paper |
| 21 Dec 2020
| 21 Dec 2020
A cultivated planet in 2010 – Part 2: The global gridded agricultural-production maps
Qiangyi Yu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
Liangzhi You
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
International Food Policy Research Institute (IFPRI), Washington DC,
USA
Ulrike Wood-Sichra
International Food Policy Research Institute (IFPRI), Washington DC,
USA
Yating Ru
International Food Policy Research Institute (IFPRI), Washington DC,
USA
Alison K. B. Joglekar
GEMS Agroinformatics Initiative, University of Minnesota, Saint Paul,
Minnesota, USA
Steffen Fritz
International Institute for Applied Systems Analysis (IIASA),
Laxenburg, Austria
Wei Xiong
International Maize and Wheat Improvement Center (CIMMYT), Texcoco, Mexico
Miao Lu
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
Wenbin Wu
CORRESPONDING AUTHOR
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
Key Laboratory of Agricultural Remote Sensing (AGRIRS), Ministry of
Agriculture and Rural Affairs/Institute of Agricultural Resources and
Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081,
China
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Short summary
SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the data from coarser units such as countries and provinces to finer units such as grid cells and creates a global gridscape at the confluence between earth and agricultural-production systems. It improves spatial understanding of crop production systems and allows policymakers to better target agricultural- and rural-development policies for increasing food security with minimal environmental impacts.
SPAM makes plausible estimates of crop distribution within disaggregated units. It moves the...
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