Integrating Climate Variables into Coconut Production Forecasting: A Comparative Analysis of ARIMA and ARIMAX Models for Climate-Informed Decision Support

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Published Aug 21, 2026
https://doi.org/10.55043/jaast.v10i3.558
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Vol. 10 No. 3 (2026): Journal of Applied Agricultural Science and Technology

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Hermiza Mardesci
Universitas Lancang Kuning
Mulono Apriyanto
Universitas Lancang Kuning
Dita Fitriani
Universitas Lancang Kuning
Herman Farmi
Universitas Lancang Kuning
Abdullah Abdullah
Universitas Lancang Kuning

Abstract

Coconut is a strategic plantation commodity in Padang Pariaman Regency, Indonesia, whose productivity is increasingly influenced by climate variability. Accurate, climate-responsive forecasting is therefore essential to support production planning and early warning systems. This study aims to develop and evaluate a climate-informed ARIMAX model for forecasting coconut production and assessing its potential application in an early warning framework. Annual coconut production data for 2014–2024 were combined with climate variables, including rainfall, temperature, humidity, wind speed, and the number of rainy days. A baseline ARIMA model was first identified, followed by ARIMAX modeling using Maximum Likelihood Estimation. Model selection was based on Akaike Information Criterion (AIC), while forecasting performance was evaluated using Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results showed that ARIMA(1,1,1) was identified as the optimal baseline model and achieved the lowest forecasting errors based on RMSE and MAPE, indicating its strong capability in capturing the temporal pattern of coconut production. The incorporation of climate variables through ARIMAX demonstrated that rainfall and the number of rainy days significantly influenced coconut production, while temperature, humidity, and wind speed exhibited weaker effects. To improve model stability and avoid multicollinearity, a simplified log-ARIMAX(1,1,1) model was developed by retaining rainfall as the primary exogenous variable. This model achieved the lowest AIC value among the evaluated climate-based models, indicating improved parsimony and explanatory capability. Forecasting results for 2025–2029 indicate a moderate and continuous increase in coconut production. Although ARIMA provides superior predictive accuracy, the rainfall-based ARIMAX model offers additional insights into climate–production relationships, making it valuable for supporting climate-informed forecasting and the future development of early warning frameworks for coconut plantation productivity.

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Author Biographies

Hermiza Mardesci, Universitas Lancang Kuning

Master of Agricultural Science, Graduate Program, Universitas Lancang Kuning, Pekanbaru, Indonesia

Mulono Apriyanto, Universitas Lancang Kuning

Center for Environmental & Sustainable Tropical Agricultural Research, Universitas Lancang Kuning, Pekanbaru, Indonesia

Dita Fitriani, Universitas Lancang Kuning

Department of Agricultural Product Technology, Faculty of Agriculture, Universitas Lancang Kuning, Pekanbaru, Indonesia

Herman Farmi, Universitas Lancang Kuning

Graduate Student, Master of Agricultural Science, Graduate Program, Universitas Lancang Kuning, Pekanbaru, Indonesia

Abdullah Abdullah, Universitas Lancang Kuning

Graduate Student, Master of Agricultural Science, Graduate Program, Universitas Lancang Kuning, Pekanbaru, Indonesia

How to Cite
1.
Mardesci H, Apriyanto M, Fitriani D, Farmi H, Abdullah A. Integrating Climate Variables into Coconut Production Forecasting: A Comparative Analysis of ARIMA and ARIMAX Models for Climate-Informed Decision Support. J. appl. agricultural sci. technol. [Internet]. 2026Aug.21 [cited 2026Aug.21];10(3):525-42. Available from: https://www.jaast.org/index.php/jaast/article/view/558

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