Generating Gridded Agricultural GDP for The World: A Spatial Disaggregation Model Based on Cross-Entropy Allocation

Authors: Yating Ru*, International Food Policy Research Institute - WASHINGTON, DC, Ulrike Wood-Sichra, International Food Policy Research Institute, Timothy Thomas, International Food Policy Research Institute, Liangzhi You, International Food Policy Research Institute (IFPRI), Brian Blankespoor, The World Bank Group, Erwin Kalvelagen, International Food Policy Research Institute (IFPRI)
Topics: Spatial Analysis & Modeling, Economic Geography, Sustainability Science
Keywords: Agricultural GDP, allocation,
Session Type: Guided Poster
Day: 4/4/2019
Start / End Time: 3:05 PM / 4:45 PM
Room: Roosevelt 3.5, Marriott, Exhibition Level
Presentation File: No File Uploaded

Agriculture GDP, as composed of gross domestic production from forestry, hunting, and fishing, as well as cultivation of crops and livestock production, is a critical indicator for economic development and rural life welfare. While high-resolution spatially explicit information of agricultural GDP could significantly benefit policy makers in targeting rural development strategies and maximizing utilization of limited resources, yet currently at the global level, only statistics at national and occasionally sub-national levels exist. To fill in the research gap, in this paper, we use a spatial allocation methodology to disaggregate national and subnational statistics into high resolution grid level data indicating agricultural production activities on the ground. It starts with various components that make up agricultural GDP, such as crop, livestock, fishery and timber productions, for which we estimate spatial distribution based on information extracted from various datasets - mostly satellite-derived - and feed into the model as priors. The priors are then reconciled with national and sub-national agricultural GDP statistics using an entropy-based data fusion method. Using the methodology, we took an innovative initiative to produce a high resolution (5x5 arc-minute or 10x10 km) global consistent dataset for agricultural GDP that can be used in various fields, such as research of international development, resilient building, and even disaster prevention.

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