Using agro-hydrological machine-learning to spatially target investments in sustainable groundwater irrigation

Using agro-hydrological machine-learning to spatially target investments in sustainable groundwater irrigation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Using agro-hydrological machine-learning to spatially target investments in sustainable groundwater irrigation Anton Urfels, Andrew McDonald, Anurag Ajay, Laura Arenas-Calle, and 14 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5424317/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Groundwater irrigation supports over 40% of global crop production and stabilizes yields amidst climatic change. Yet, over-abstraction can cause water scarcity, disrupt ecosystems, and increase greenhouse gas emissions. Governments and international financial institutions have made significant investments in sustainable groundwater irrigation but require enhanced spatial targeting to increase impact. In response, this study employs an agro-hydrological machine-learning approach to analyze spatial patterns of (i) crop yield responses to increased irrigation and (ii) groundwater sustainability in South Asia – characterized by smallholder farming, increasing groundwater dependence, and post-green revolution sustainability challenges. We show that modestly increasing irrigation intensity in groundwater-rich areas with high yield responses could boost rice production by 2.22Mt annually – sufficient to feed over 33 million people with little anticipated risks of groundwater depletion. However, current investments overlook these areas. Our approach can be globally applied to catalyze sustainable irrigation through integrated use of expanding agricultural and hydrological datasets. Scientific community and society/Agriculture Scientific community and society/Developing world Scientific community and society/Water resources water scarcity sustainable irrigaNon global challenges sustainable agriculture South Asia. Full Text Additional Declarations There is NO Competing Interest. Supplementary Files au2024agrohydrosustainableirrigationSI.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5424317","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":379410571,"identity":"901ae9b0-eafa-4470-a3c2-bc4403f6348e","order_by":0,"name":"Anton Urfels","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAwElEQVRIiWNgGAWjYLCCCgYLBn5mICOBaC1nGCQYJJtJ1mJwgFjV/A08Zh8OVEjYGx/nffbhAYNNvrwDAS0SB3iMZxw4I5G47TC78YwEhjTLjQStA2ph/tgmkWB2mI0Z6JfDBoYNBHTIA7UwHPwHdFgzsVoMwFoaJBg3MEO1yBNyl+FhtmKGA8ckEmeAHWaQZmBASIvc8ebNDAdqbOz5+48xM/6osDGQJ+QwBmZUdzKQEEFwQNiWUTAKRsEoGGkAAGvINZ/03sNcAAAAAElFTkSuQmCC","orcid":"","institution":"CGIAR/IRRI, Wageningen University and Research, Cornell University","correspondingAuthor":true,"prefix":"","firstName":"Anton","middleName":"","lastName":"Urfels","suffix":""},{"id":379410572,"identity":"80507e89-bb95-43c6-8de5-500074fc040a","order_by":1,"name":"Andrew McDonald","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Andrew","middleName":"","lastName":"McDonald","suffix":""},{"id":379410573,"identity":"f1d5601e-e884-44ab-8e37-3ca1dc26086a","order_by":2,"name":"Anurag Ajay","email":"","orcid":"https://orcid.org/0000-0001-7502-6166","institution":"CGIAR/CIMMYT","correspondingAuthor":false,"prefix":"","firstName":"Anurag","middleName":"","lastName":"Ajay","suffix":""},{"id":379410574,"identity":"16fa46bd-8dfa-49c2-85f7-e35260f4ba52","order_by":3,"name":"Laura Arenas-Calle","email":"","orcid":"","institution":"Cornell University","correspondingAuthor":false,"prefix":"","firstName":"Laura","middleName":"","lastName":"Arenas-Calle","suffix":""},{"id":379410575,"identity":"dd3c3d8c-3af0-416c-97ab-dee632c1dcc6","order_by":4,"name":"Peter Craufurd","email":"","orcid":"","institution":"CGIAR/CIMMYT","correspondingAuthor":false,"prefix":"","firstName":"Peter","middleName":"","lastName":"Craufurd","suffix":""},{"id":379410576,"identity":"529c9742-1047-4e0b-bc19-fa606db19018","order_by":5,"name":"Proloy Deb","email":"","orcid":"","institution":"CGIAR/IRRI","correspondingAuthor":false,"prefix":"","firstName":"Proloy","middleName":"","lastName":"Deb","suffix":""},{"id":379410577,"identity":"342110e3-fa05-43c6-9f00-f18f172a5760","order_by":6,"name":"Inge E.M. 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