Agricultural Forecasting in a Changing Climate: ARIMA-X Model of Cereal Production in Tanzania

Agricultural Forecasting in a Changing Climate: ARIMA-X Model of Cereal Production in Tanzania | 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 Research Article Agricultural Forecasting in a Changing Climate: ARIMA-X Model of Cereal Production in Tanzania Eliaza Mkuna, Eliasi Jeremiah, Johanes Tibanywana This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7148440/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 This study investigates the forecasting of cereal production in Tanzania using an Autoregressive Integrated Moving Average (ARIMA) model integrated with exogenous variables (ARIMAX). Annual time series data spanning from 1960 to 2022 were obtained from the World Bank's World Development Indicators (WDI) and include cereal production, land under cereal and arable cultivation, working-age population, average annual precipitation, and temperature. To ensure robustness in modeling, missing values were imputed through linear interpolation, and stationarity was verified using the Augmented Dickey-Fuller (ADF) test. First and second-order differencing was applied where necessary to achieve stationarity.An ARIMA model with exogenous regressors differenced land used, arable land, and temperature was fitted after excluding statistically insignificant predictors to reduce multicollinearity. Forecasts from 2023 to 2027 predict fluctuating trends in cereal production, ranging from 10.2 to 11.38 million metric tons. The positive and significant influence of temperature highlights the role of climatic factors in cereal production, while land use variables show moderate effects. The findings underscore that Tanzania’s cereal production is shaped by structural trends, calling for dynamic, climate-responsive policy frameworks. The study provides a data-driven foundation for policymakers to develop adaptive strategies ensuring food security and economic stability amidst climatic and demographic shifts. ARIMA Forecasting Cereal Production Exogenous Variables Time Series Tanzania Climate Agriculture Tanzania Full Text 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. 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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-7148440","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":520773689,"identity":"fd7ff04f-b595-487a-9c5e-caaf3ec23365","order_by":0,"name":"Eliaza 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