Virtual water trade and water footprint of agricultural goods: the 1961–2016 CWASI database

Abstract

Abstract. To support national and global assessments of water use in agriculture, we build a comprehensive database of country-specific water footprint and virtual water trade (VWT) data for 370 agricultural goods. The water footprint, indicating the water needed for the production of a good including rainwater and water from surface water and groundwater bodies, is expressed as a volume per unit weight of the good (or unit water footprint, uWF) and is here estimated at the country scale for every year in the period 1961–2016. The uWF is also differentiated, where possible, between production and supply, referring to local production and to a weighted mean of local production and import, respectively. The VWT data, representing the amount of water needed for the production of a good and virtually exchanged with the international trade, are provided for each commodity as bilateral trade matrices, between origin and destination countries, for every year in the period 1986–2016. The database, developed within the CWASI project, improves upon earlier datasets because it takes into account the annual variability of the uWF of crops, it accounts for both produced and imported goods in the definition of the supply-side uWF, and it traces goods across the international trade up to the origin of goods' production. The CWASI database is available on the Zenodo repository at https://doi.org/10.5281/zenodo.4606794 (Tamea et al., 2020), and it welcomes contributions and improvements from the research community to enable analyses specifically accounting for the temporal evolution of the uWF.

Water Footprints, Intra‐National Virtual Water Flows, and Associated Sustainability Related to Pork Production and Consumption: A Case for China

“…(2020) found that the method has an error of less than 20% at the provincial level in China. The method has the characteristics of low calculation cost and high reliability of results and has been widely used (Gao et al., 2020; Soligno, Malik & Lenzen, 2019, Soligno, Rodolfi & Laio, 2019; Tamea et al., 2020; Xie et al., 2020).…”

Role of trade agreements in the global cereal market and implications for virtual water flows

“…In our analysis, we use detailed trade matrices provided by FAOSTAT 32 , which report the bilateral trade flows of each cereal between countries in two units of measurement: weight (tonnes) and economic value (US$). To these units, we add the related flows (m ) in terms of virtual water obtained from the CWASI database 29 , which provides detailed matrices of water trade for each crop according to FAOSTAT classification. The added value of this database is to translate trade flows into virtual water flows by applying country-specific coefficients (unit water footprint of supply), which account for the country originating the flow, by proportionally weighting the contributions from local production and import.…”

“…The time interval covers 22 years, from 1993 to 2015, and includes the most important recent reforms in the agricultural sector. Our analysis is based on a dataset that combines the structure of trade agreements provided by the World Bank 28 , grain trade flow data from FAOSTAT, and virtual water flows data from the CWASI 29 . Among the agricultural products, we focus on cereals since they are the most traded crops 30 , representing more than half of the world’s daily caloric intake 31 .…”

The uneven water stress implications of global refugee migrations

“…The water footprint of primary and processed crops were obtained from the CWASI dataset 21 (freely available at https: //www.watertofood.org/download/). The dataset combines FAO trade data with a model estimating the crop-specific water requirement on production sites in the countries where the food was produced 47,48 .…”

“…We leverage recent data on the water footprint of the production and international trade of 370 food products 21 to provide a first estimate of the refugee-related increase in water demand that drives these effects. By keeping track of the ultimate origin of the water embedded in traded goods, we elucidate the direct (migration) and indirect (trade) effect of refugee displacement on country-level water stress.…”