article · Cogent Food & Agriculture
This research evaluates the determinants of maize production in Tanzania using long-term time-series data from 1961 to 2022. It examines the effects of fertilizer, land use, precipitation, and temperature, with a specific focus on handling multicollinearity among these factors. By comparing traditional multiple linear regression with principal component regression, the analysis shows that both approaches account for 43.2 percent of the variation in maize yields. However, principal component regression offers a clearer view of underlying drivers by identifying temperature alongside land use and fertilizer as statistically significant factors. In contrast, standard regression overlooked the role of temperature because of correlated predictors. Accounting for multicollinearity allows the model to capture climatic influences on crop yields more reliably, providing an empirical baseline of 13,051 tons under average conditions.
Accurately identifying what drives crop yields is essential for food security and resilient farming strategies. Traditional statistical models often obscure the true impact of climate because weather and farm inputs are closely correlated. Using techniques that correct for this reveals the significant influence of temperature on maize output, helping planners better understand climate risks facing staple crops.
This work provides an early-stage statistical modelling framework that could inform agricultural policy and planning tools. Potential users include agricultural policymakers, climate adaptation planners, and analysts designing climate-smart agricultural programmes. The research remains at an analytical and methodological stage, with no commercial software or direct field-ready tool indicated in the abstract.
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The study aims to examine the key determinants of maize production using fertilizer, land use, precipitation and temperature as predictors. It specifically seeks to address multicollinearity among predictors and to assess whether principal component regression (PCR) provides more robust results compared to traditional multiple linear regression (MLR). Time-series data covering the period 1961–2022 were used. PCR is applied to transform correlated predictors into orthogonal principal components and its performance is compared with that of the MLR model to evaluate explanatory power and statistical significance of determinants. The findings showed that, PCR model explains 43.2% of the variation in maize production, demonstrating moderate predictive power similar to MLR. PCR identifies land use , temperature and fertilizer as statistically significant at the 5% level, while MLR finds only land use and fertilizer significant. This indicates that PCR effectively uncovers the previously masked effect of temperature due to multicollinearity. The intercept suggests a baseline production level of 13,051 tons when predictors are at their mean values. In modeling under multicollinearity conditions, the PCR demonstrated the methodological advantage in agricultural production. The PCR provides enhanced empirical evidence to inform climate-smart and data-driven agricultural policy decisions by revealing the hidden climatic influences, especially temperature.
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DOI: 10.1080/23311932.2026.2705613
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