GRAIN - a global registry of agricultural irrigation networks

Abstract

Despite supporting roughly 40 % of the world’s agricultural output, irrigation canal networks remain a critical data gap in global geospatial archives. Currently, there is no consistent geospatial database that documents the extent of surface-water irrigation canals. The GRAIN (Global Registry of Agricultural Irrigation Networks) dataset fills this gap by leveraging the potential of volunteered geographic information (VGI) from OpenStreetMap (OSM) and applying a machine learning based classification pipeline to distinguish canals from rivers and streams. A Random Forest classifier was trained on 20,000 samples of quality-controlled canal and river data using 5 engineered geometric and topographical features. The model achieved over 98 % training accuracy, translating to a ~93.6 % median recall on independent validation datasets for primary canals, with a mean positional offset of ~98 m. The GRAIN dataset includes land cover maps and OSM tags to assign canal use cases, identifying over 3.8 million km of agricultural irrigation canals in 95 countries. There is marked regional concentration of agricultural canals with hotspots identified in Europe, South and Southeast Asia, and North America. Agricultural canal distribution also varied widely by climatic zones with over 65 % in temperate and cold zones and approximately 22 % in arid regions. A canal density analysis normalized by cropland area highlighted smaller countries such as Finland, the Netherlands, New Zealand, and Egypt having the densest irrigation canals. While the global correlation between canal density and national cereal yields was found to be modest (r = 0.31), the GRAIN dataset suggests that the presence of well-developed surface-water infrastructure may positively influence agricultural productivity as seen in the Netherlands and New Zealand. GRAIN is now publicly available at https://doi.org/10.5281/zenodo.16786488 (Suresh and Hossain, 2025) under a CC-BY-4.0 licence. Designed as a community-driven resource, GRAIN data bridges a long-standing gap, and opens new possibilities for evaluating irrigation efficiency, supporting climate adaptation, guiding infrastructure investments, and extending the value of new satellite remote sensing missions on surface water such as the Surface Water and Ocean Topography (SWOT).