Application of Normalized Difference Vegetation Index in Predicting Maize Yields: A Spaceborne Photography Solution to Agricultural Food Insecurity in Kenya

Application of Normalized Difference Vegetation Index in Predicting Maize Yields: A Spaceborne Photography Solution to Agricultural Food Insecurity in Kenya | 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 Application of Normalized Difference Vegetation Index in Predicting Maize Yields: A Spaceborne Photography Solution to Agricultural Food Insecurity in Kenya Cyrilus Wandera Otulo, Faith Khisa, Catherine Musundi, Emily Miano, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7473765/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 High levels of acute food insecurity affects approximately 2.8 million people in Kenya as of early march 2025 and as per the Global Hunger Index, kenya ranked 100th out of 127 countries, with agriculture highly vulnerable to climate change. Accurate crop yield prediction is critical to mitigating food insecurity. This study aimed to assess the effectiveness of the Normalized Difference Vegetation Index (NDVI) in predicting maize yield using a simple linear regression model. A retrospective observational study was conducted using historical maize yield data from the Kenyan Ministry of Agriculture and NDVI measurements from the Kenya Space Agency. NDVI was used as an independent variable in a linear regression model to estimate maize yield. Statistical analysis was performed using R software to evaluate the relationship between NDVI and yield. The study included multiple regions and seasons; no specific control group was applicable due to the observational design. A strong positive correlation (r > 0.86) was found between NDVI and maize yield. The regression model results were statistically significant (p < 0.05), indicating that NDVI reliably predicts maize yield. The model demonstrated consistent patterns across different datasets, confirming its robustness in yield estimation. NDVI is a strong predictor of maize yield and can support early yield forecasting and agricultural monitoring systems in Kenya. This approach may contribute to more effective strategies for managing food insecurity in the face of climate variability Earth and environmental sciences/Climate sciences Biological sciences/Ecology Earth and environmental sciences/Ecology Earth and environmental sciences/Environmental sciences Biological sciences/Plant sciences Spaceborne Photography Normalized Difference Vegetation Index Green Vegetation and Maize Yield Full Text Additional Declarations No competing interests reported. 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-7473765","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":518779167,"identity":"7aef2603-f91f-4e22-83ae-4a57feb065af","order_by":0,"name":"Cyrilus Wandera 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