Articles | Volume 15, issue 3
https://doi.org/10.5194/essd-15-1357-2023
© Author(s) 2023. 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-15-1357-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
24 Mar 2023
Data description paper |
| 24 Mar 2023
| 24 Mar 2023
Estimating local agricultural gross domestic product (AgGDP) across the world
Yating Ru
Department of City and Regional Planning, Cornell University, Ithaca, NY, USA
Brian Blankespoor
CORRESPONDING AUTHOR
Development Data Group, World Bank, Washington, DC, USA
Ulrike Wood-Sichra
International Food Policy Research Institute (IFPRI), Washington DC, USA
Timothy S. Thomas
International Food Policy Research Institute (IFPRI), Washington DC, USA
Liangzhi You
International Food Policy Research Institute (IFPRI), Washington DC, USA
Erwin Kalvelagen
International Food Policy Research Institute (IFPRI), Washington DC, USA
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Susmita Dasgupta, Brian Blankespoor, and David Wheeler
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2024-241, https://doi.org/10.5194/essd-2024-241, 2024
Preprint under review for ESSD
Short summary
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This study leverages recent advances in machine-based pattern recognition to estimate occurrence maps for over 600,000 species, using georeferenced data from the Global Biodiversity Information Facility (GBIF). A pilot application for priority-setting identifies 30 nations that host nearly 80 percent of threatened species with small ranges limited to a single country. The algorithms are designed for rapid map updates and estimating new maps as growth in GBIF species occurrence reports continues.
Hanqin Tian, Zihao Bian, Hao Shi, Xiaoyu Qin, Naiqing Pan, Chaoqun Lu, Shufen Pan, Francesco N. Tubiello, Jinfeng Chang, Giulia Conchedda, Junguo Liu, Nathaniel Mueller, Kazuya Nishina, Rongting Xu, Jia Yang, Liangzhi You, and Bowen Zhang
Earth Syst. Sci. Data, 14, 4551–4568, https://doi.org/10.5194/essd-14-4551-2022, https://doi.org/10.5194/essd-14-4551-2022, 2022
Short summary
Short summary
Nitrogen is one of the critical nutrients for growth. Evaluating the change in nitrogen inputs due to human activity is necessary for nutrient management and pollution control. In this study, we generated a historical dataset of nitrogen input to land at the global scale. This dataset consists of nitrogen fertilizer, manure, and atmospheric deposition inputs to cropland, pasture, and rangeland at high resolution from 1860 to 2019.
Qiangyi Yu, Liangzhi You, Ulrike Wood-Sichra, Yating Ru, Alison K. B. Joglekar, Steffen Fritz, Wei Xiong, Miao Lu, Wenbin Wu, and Peng Yang
Earth Syst. Sci. Data, 12, 3545–3572, https://doi.org/10.5194/essd-12-3545-2020, https://doi.org/10.5194/essd-12-3545-2020, 2020
Short summary
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.
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Short summary
Economic statistics are frequently produced at an administrative level that lacks detail to examine development patterns and the exposure to natural hazards. This paper disaggregates national and subnational administrative statistics of agricultural GDP into a global dataset at the local level using satellite-derived indicators. As an illustration, the paper estimates that the exposure of areas with extreme drought to agricultural GDP is USD 432 billion, where nearly 1.2 billion people live.
Economic statistics are frequently produced at an administrative level that lacks detail to...
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