Preprints
https://doi.org/10.5194/essd-2022-336
https://doi.org/10.5194/essd-2022-336
 
07 Nov 2022
07 Nov 2022
Status: this preprint is currently under review for the journal ESSD.

Estimating Local Agricultural GDP across the World

Yating Ru1,, Brian Blankespoor2,, Ulrike Wood-Sichra3, Timothy S. Thomas3, Liangzhi You3, and Erwin Kalvelagen3 Yating Ru et al.
  • 1Cornell University
  • 2World Bank
  • 3International Food Policy Research Institute
  • These authors contributed equally to this work.

Abstract. Economic statistics are frequently produced at an administrative level such as the sub-national division. However, these measures may lack sufficient local variation in the economic activities to analyze local economic development patterns and the exposure to natural hazards. Agriculture GDP is a critical indicator for measurement of the primary sector, on which more than 2.5 billion people depend on their livelihoods that provide a key source of income for the entire household (FAO, 2021). Through a data fusion method based on cross-entropy optimization, this paper disaggregates national and subnational administrative statistics of Agricultural GDP into a global gridded dataset at approximately 10 x 10 kilometers using satellite-derived indicators of the components that make up agricultural GDP, namely crop, livestock, fishery, hunting and timber production. The paper estimates the exposure of areas with at least one extreme drought during 2000 to 2009 to agricultural GDP is an estimated US$432 billion of agricultural GDP circa 2010, where nearly 1.2 billion people live. The data are available on the World Bank Development Data Hub (DOI: http://doi.org/10.57966/0j71-8d56; IFPRI and World Bank, 2022).

Yating Ru et al.

Status: open (until 02 Jan 2023)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on essd-2022-336', Anonymous Referee #1, 28 Nov 2022 reply

Yating Ru et al.

Data sets

Global Gridded Agricultural Gross Domestic Product (AgGDP) IFPRI and World Bank https://doi.org/10.57966/0j71-8d56

Yating Ru et al.

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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. The paper estimates the exposure of areas with at least one extreme drought to agricultural GDP is US$432 billion, where nearly 1.2 billion people live.