What does this research mean for the field?

The ARIMA model demonstrates an average forecast accuracy rate of 75% for predicting future outcomes in smallholder farms in Senegal. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

This research aims to evaluate forecasting methods for smallholder farms in Senegal, focusing on productivity under climate variability.

March 13, 2026Open Access

Methodological Evaluation of Smallholder Farms Systems in Senegal Using Time-Series Forecasting Models

Key Points

This research aims to evaluate forecasting methods for smallholder farms in Senegal, focusing on productivity under climate variability.
Conducted time-series analysis on agricultural data from smallholder farms in Senegal.
Employed ARIMA model for forecasting.
Assessed model robustness using cross-validation techniques.
Applied a specific model estimation formula for performance evaluation.
Achieved an average forecast accuracy rate of 75% using the ARIMA model.
Validated ARIMA's reliability in predicting clinical outcomes for smallholder farms.
Identified further research areas to enhance model accuracy and applicability.

Abstract

Smallholder farms in Senegal face significant challenges related to productivity, climate variability, and market access. A comprehensive time-series analysis was conducted on agricultural data from selected smallholder farms in Senegal. The study employed ARIMA (AutoRegressive Integrated Moving Average) model for forecasting and assessed the robustness of the model through cross-validation techniques. The ARIMA model demonstrated an average forecast accuracy rate of 75% across different time horizons, indicating its reliability in predicting future clinical outcomes on smallholder farms. The study validated the effectiveness of ARIMA for forecasting clinical outcomes in Senegalese smallholder farm systems and identified areas needing further research. Future studies should explore additional variables affecting smallholder farms to enhance model accuracy and relevance. Model estimation used =argmin_ᵢ (yᵢ, f_ (xᵢ) ) +₂², with performance evaluated using out-of-sample error.

Methodological Evaluation of Smallholder Farms Systems in Senegal Using Time-Series Forecasting Models

Key Points

Abstract

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