Food security in Southern Madagascar informed by satellite-based remote sensing data

Food security in Southern Madagascar informed by satellite-based remote sensing data | 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 Food security in Southern Madagascar informed by satellite-based remote sensing data Alexandria Berry, Christopher Golden, Joann Upton, Angela Rigden, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7265637/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Recurrent droughts and food shortages in southern Madagascar underscore the urgent need for real-time food security monitoring. Since 2018, monthly surveys of 602 households have documented dietary coping strategies in response to food shocks. Here, we evaluate whether remotely sensed Normalized Difference Vegetation Index (NDVI) observations over crop and pasturelands can track temporal variation in household food consumption. While standard food security indices show no discernible relationship with NDVI, a leading mode of dietary variability—termed Dietary Component 1 (DC1)—exhibits a strong correspondence. DC1 captures an anticorrelation between the consumption of maize and sorghum during food-secure periods and red cactus and lower-preference roots and tubers, such as cassava, during food-insecure periods. When aggregated to the commune level, DC1 explains an average of 43% of the variance in monthly dietary patterns. Linear models using commune-averaged NDVI explain 32–58% of DC1 variability, and models including monthly fixed effects explain 38–70%, with NDVI remaining a highly significant predictor throughout. Although not a replacement for on-the-ground data collection, these results demonstrate that satellite-derived NDVI can reliably capture key dietary shifts and serve as a valuable component of food security monitoring systems in southern Madagascar. Earth and environmental sciences/Climate sciences/Climate change/Projection and prediction Scientific community and society/Agriculture Full Text Additional Declarations There is NO Competing Interest. Cite Share Download PDF Status: Under Review 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. 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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-7265637","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":499742634,"identity":"27587ccc-c880-4631-915d-2c4d3151df47","order_by":0,"name":"Alexandria Berry","email":"data:image/png;base64,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","orcid":"","institution":"Harvard University","correspondingAuthor":true,"prefix":"","firstName":"Alexandria","middleName":"","lastName":"Berry","suffix":""},{"id":499742635,"identity":"23d0af22-fad0-4fd2-a5d7-8dceed2c6fa2","order_by":1,"name":"Christopher Golden","email":"","orcid":"https://orcid.org/0000-0002-2258-7493","institution":"Harvard T.H. 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