Data underlying the publication: Large-scale agriculture contributes to over 70% of green and blue virtual water flows

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. <br>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<br>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.

本数据集旨在量化小规模与大规模农业对食品相关虚拟水流动的贡献。研究人员在环境拓展多区域投入产出分析（Environmentally-Extended Multi-Regional Input-Output Analysis，EE-MRIO）中对小规模、大规模农业进行了明确拆分，并依托该分析框架计算虚拟水流动。EE-MRIO包含三张数据表以提升食品相关部门的分辨率——考虑到此类部门在水资源消耗中的重要性，三张表分别为FABIO、GLORIA与连接表。
本数据集采用网格化作物生产与水资源消耗数据，并基于农业系统估算了生产分配至贸易与非食品用途的占比。针对虚拟水流动、非食品用途及国内用途，本研究采用了不同的作物水足迹计算方案。
本数据集包含按国家层面划分的虚拟水流动数据，其分类维度包括绿水或蓝水类型、最终用途类型、年份，以及水资源稀缺或丰裕地区；同时包含网格化数据，可描述每类作物的虚拟水流动来源地的30弧分网格单元尺度分布。
本数据集的详细方法描述与分析正处于筹备与审稿阶段，我们将尽快在此处更新相关内容。所有代码与其他数据可应要求提供。