Su, Han; Bruckner , Martin; Taherzadeh, Oliver; Sun, Zhongxiao; Hogeboom, Rick J. et. al. (2024): Data underlying the publication: Large-scale agriculture dominates green and blue virtual water flows. Version 2. 4TU.ResearchData. dataset. https://doi.org/10.4121/4d32300f-28cf-45f8-8e9d-77759bf6e9ce.v2

Dataset

x choose version: version 3 - 2024-03-28 (latest) version 1 - 2024-01-08

• Land and Water Management
• Environmental Science and Management
• Agriculture, Land and Farm Management
• Agricultural and Veterinary Sciences
• Environmental Sciences
• Environment

consumption perspective, international trade, large-scale agriculture, small-scale agriculture, smallholders, virtual water flows, water footprint

around 2010

CC BY 4.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Han Su , Martin Bruckner , Oliver Taherzadeh , Zhongxiao Sun , Rick J. Hogeboom , Maarten Krol

This dataset aims to quantify the contribution of small-scale and large-scale agriculture to food-related virtual water flows. Small-scale and large-scale agriculture were explicitly disaggregated in Environmentally-Extended Multi-Regional Input-Output analysis (EE-MRIO), which was later used to calculate virtual water flows. The EE-MRIO consists of three tables to increase the resolution of food-related sectors, considering the importance of these sectors in water consumption (FABIO, GLORIA, and the linking table). Gridded crop production and water consumption data are used, and their production allocation to trade and non-food uses was estimated based on the farming system. Different crop water footprints were used for virtual water flows, non-food uses and for domestic purposes. 

This dataset contains country-level virtual water flow divided by green or blue water, type of final use, year, from water-scarce or water-abundant regions; and gridded data describing where the virtual water flow comes from at the 30-arcmin grid cell level per crop

A detailed method description and analysis are under preparation and review. We will update it here as soon as possible. All the code and other data will be available upon request. 

• 2024-01-08 first online

• 2024-01-11 published, posted

4TU.ResearchData

*.csv

https://doi.org/10.5281/zenodo.5747615

University of Twente, Faculty of Engineering Technology (ET), Multidisciplinary Water Management (MWM); ETH Zurich, Institute of Environmental Engineering; Vienna University of Economics and Business (WU); Water Footprint Network, Enschede, NL; Leiden University, Institute of Environmental Sciences; China Agricultural University, College of Land Science and Technology