Advanced Forecasting of Maize Production using SARIMAX Models: An Analytical Approach

 (*)Gregorius Airlangga Mail (Atma Jaya Catholic University of Indonesia, Jakarta, Indonesia)

(*) Corresponding Author

Submitted: January 3, 2024; Published: January 24, 2024

Abstract

Agricultural production forecasting is crucial for food security and economic planning. This study conducts a detailed analysis of maize production forecasting using the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, emphasizing the applicability of time-series models in capturing complex agricultural dynamics. Following a comprehensive literature review, the SARIMA model was justified for its ability to integrate seasonal fluctuations inherent in agricultural time series. Optimal model parameters were meticulously determined through an iterative process, optimizing the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best-performing SARIMA(1, 1, 2)x(2, 2, 2, 12) model achieved an AIC of 339914.85450182937 and a BIC of 339950.64499813004, indicating its strong fit to the historical data. This model was applied to a historical dataset of maize production, providing forecasts that align closely with actual production trends on a short-term basis. Notably, the model's short-term predictions for the subsequent year showed less than a 2% deviation from the actual figures, affirming its precision. However, long-term forecasts revealed greater variability, underscoring the challenge of accounting for unforeseen environmental and economic factors in agricultural production systems. This research substantiates the efficacy of SARIMA models in agricultural forecasting, delivering strategic insights for resource management. It also points towards the integration of SARIMA with other variables and advanced modeling techniques as a future avenue to enhance forecasting robustness, particularly for long-term projections. The findings serve as a valuable resource for policymakers and stakeholders in optimizing decision-making processes for agricultural production.

Keywords


Forecasting; Maize Production; Sarimax; Statistics; Time Series

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