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This research evaluates how well process-control systems can improve agricultural yields in Tanzania using historical data.

February 25, 2026Open Access

Time-Series Forecasting Model Evaluation of Process-Control Systems in Tanzanian Agricultural Yields,

Key Points

This research evaluates how well process-control systems can improve agricultural yields in Tanzania using historical data.
Conducted time-series analysis using historical yield data
Evaluated model performance with statistical testing
Assessed correlation between predicted and actual yield improvements
The model showed a significant positive correlation (R² = 0.85) with actual yield improvements
Demonstrated robustness in predicting future agricultural yields
Suggested the need for expanded datasets and consideration of additional variables

Abstract

This study aims to evaluate the effectiveness of process-control systems in improving agricultural yields in Tanzania. A time-series analysis was conducted using historical agricultural yield data alongside process-control system parameters. The model's performance was evaluated through statistical testing. The developed model showed a significant positive correlation (R² = 0. 85) with actual yield improvements, indicating an effective predictive capability for future yields. The time-series forecasting model demonstrated robustness in predicting agricultural yield improvements using process-control system data from Tanzania. Future research should expand the dataset to include more years and consider additional variables affecting yield performance. The maintenance outcome was modelled as Y₈ₓ=₀+₁X₈ₓ+uᵢ+₈ₓ, with robustness checked using heteroskedasticity-consistent errors.

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