The water footprint of a crop (WF) is a common metric for assessing agricultural water consumption and productivity. To provide an update and methodological enhancement of existing WF datasets, we apply a global process-based crop model to quantify consumptive WFs of 175 individual crops at a 5 arcminute resolution over the 1990–2019 period. This model simulates the daily crop growth and vertical water balance considering local environmental conditions, crop characteristics, and farm management. We partition WFs into green (water from precipitation) and blue (from irrigation or capillary rise), and differentiate between rainfed and irrigated production systems. The outputs include gridded datasets and national averages for unit water footprints (expressed in m³ t⁻¹ yr⁻¹), water footprints of production (m³ yr⁻¹), and crop water use (mm yr⁻¹). We compare our estimates to other global studies covering different historical periods and methodological approaches. Provided outputs can offer insights into spatial and temporal patterns of agricultural water consumption and serve as inputs for further virtual water trade studies, life cycle and water footprint assessments.

The Sustainable Development Goals (SDGs) of the United Nation’s Agenda 2030 are formulated to promote the development of integrated, multisectoral policies that explicitly consider linkages across SDGs. Although multiple recent studies have tried to identify linkages across SDGs, the role of contextual factors in identifying SDG linkages is neither well described nor understood. For the case of SDG 2 and SDG 6, this study aims to (i) identify linkages—at country and SDG target level—through the application of various quantitative and qualitative identification methods, and (ii) explore contextual factors to explain the differences across identified linkages. Hereto, we first conducted a text analysis of 195 Voluntary National Reviews (VNRs) reported by 159 countries from 2016 to 2020. Next, we synthesized linkages reported by previous qualitative studies and conducted a quantitative (correlation) analysis on the UN’s SDG database. Last, we compared identified linkages across methods, paying special attention to the role of context. From the text analysis, we identified 221 country-specific linkages between 25 SDG target pairs and observed that countries increasingly report SDG linkages in their VNRs over time. Comparing text analysis, existing qualitative studies, and our quantitative correlation analysis, we found substantial differences between the number and nature of linkages identified. These differences can be explained in part by methodological considerations, but to a significant extent also by contextual factors, such as project design, technology application, phase of interventions, and project scale. We conclude by discussing the strengths and limitations of the methods involved, and suggestions for future studies.

Farms are not homogeneous. Smaller farms generally have different planted crops, yields, agricultural inputs, and irrigation applications compared to larger farms. However, gridded farm-size-specific data that are moreover crop specific, are currently lacking. This obscures our understanding of differences between small-scale and large-scale farms, e.g., with respect to climate change adaptation and mitigation strategies, contribution to (local) food security, and water consumption patterns. This study fills a significant part of the current data gap, by developing high-resolution gridded, simultaneously farm-size-specific and crop-specific datasets of harvested areas for 56 countries (i.e., covering about half the global cropland). Hereto, we downscaled the most complete global direct measurements of farm size and crop type by compiling state of the art datasets, including crop maps, cropland extent maps, and dominant field size distribution, representative for the year 2010. Using two different crop map sources, we were able to produce two new 5 arcmin gridded datasets on simultaneously derived farm-size-specific and crop-specific harvested areas: one for 11 farm sizes, 27 crops, and 2 farming systems, and one for 11 farm sizes, 42 crops, and 4 farming systems. In line with previous findings, our resulting datasets show major differences in planted crops and irrigated area (%) between farm sizes. Consistency between our resulting datasets and (i) observations from satellite images on farm-size-specific oil palm, (ii) household surveys on the farm-size-specific irrigated area (%), and (iii) previous studies that mapped noncrop-specific farm sizes and support the validity of our datasets. Although at grid level some uncertainties remain to be overcome, particularly those stemming from uncertainties in crop maps, results at country level seem robust. Source data, code, and resulting datasets are open access and freely available at https://doi.org/10.5281/zenodo.6976249 (Su et al., 2022).

Non-technical Summary: Water is a shared global resource, yet its movement is often invisible. When a country imports products, it also imports ‘virtual water’, the water used to produce those products. While agriculture accounts for 85% of global water transfers, we have lacked a clear understanding of who is actually moving this water: large industrial farms or small-scale farmers? Our study shows that small-scale agriculture plays a disproportionately large role in global virtual water flows, compared to its volume of crop production involved in trade. Technical Summary: Most global virtual water flows come from agri-food sectors, describing how commodity demand in one country drives water use in others. Despite their significance, virtual water flow assessments have largely overlooked the distinct roles of small-scale versus large-scale agriculture, hindering the development of targeted water and food security policies in both producing and consuming countries. To address this gap, we estimate the contributions of small-scale and large-scale agriculture to crop production and virtual water embedded in trade (export) using detailed hybrid multiregional input–output tables spanning 55 countries. We reveal that, although small-scale agriculture accounts for only 17% of the total crop production involved in trade (in tonnes), its role in virtual water flows is disproportionately large, accounting for 22% of blue and 30% of green virtual water flows. In 13 of the 55 countries, small-scale agriculture accounts for more than 50% of green virtual water exports. Furthermore, virtual water exports from small-scale agriculture exhibit distinct spatial patterns and are driven primarily by demand for labor-intensive crops rather than animal-sourced human food and nonfood products. Our results suggest that explicitly accounting for farmers’ roles in virtual water flows is critical for the design of context-specific, effective, and equitable policy interventions. Social Media Summary: Check out the unique role of small-scale agriculture in global crop trade and virtual water flows.