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


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.


Forecasting; Maize Production; Sarimax; Statistics; Time Series

Full Text:


Article Metrics

Abstract view : 87 times
PDF - 30 times


R. A. Fischer and D. J. Connor, “Issues for cropping and agricultural science in the next 20 years,” F. Crop. Res., vol. 222, pp. 121–142, 2018.

S. Babu, K. P. Mohapatra, A. Das, G. S. Yadav, M. Tahasildar, R. Singh, A. S. Panwar, V. Yadav, and P. Chandra, "Designing energy-efficient, economically sustainable and environmentally safe cropping system for the rainfed maize--fallow land of the Eastern Himalayas," Science of The Total Environment, vol. 722, p. 137874, 2020.

R. D. Norton, Agricultural development policy: Concepts and experiences. John Wiley & Sons, 2004.

U. Grote, A. Fasse, T. T. Nguyen, and O. Erenstein, “Food security and the dynamics of wheat and maize value chains in Africa and Asia,” Front. Sustain. Food Syst., vol. 4, p. 617009, 2021.

S. A. Tanumihardjo, L. McCulley, R. Roh, S. Lopez-Ridaura, N. Palacios-Rojas, and N. S. Gunaratna, "Maize agro-food systems to ensure food and nutrition security in reference to the Sustainable Development Goals," Global Food Security, vol. 25, p. 100327, 2020.

M. Kaushal, R. Sharma, D. Vaidya, A. Gupta, H. Kaur Saini, A. Anand, C. Thakur, A. Verma, M. Thakur, Priyanka, and others, "Maize: An underexploited golden cereal crop," Cereal Research Communications, vol. 51, no. 1, pp. 3-14, 2023.

H. Jiang, H. Hu, R. Zhong, J. Xu, J. Xu, J. Huang, S. Wang, Y. Ying, and T. Lin, "A deep learning approach to conflating heterogeneous geospatial data for corn yield estimation: A case study of the US Corn Belt at the county level," Global change biology, vol. 26, no. 3, pp. 1754-1766, 2020.

R. J. Henry, "Innovations in plant genetics adapting agriculture to climate change," Current Opinion in Plant Biology, vol. 56, pp. 168-173, 2020

N. Kumar, A. Balamurugan, M. M. Shafreen, A. Rahim, S. Vats, and K. Vishwakarma, "Nanomaterials: emerging trends and future prospects for economical agricultural system," Biogenic Nano-Particles and their Use in Agro-ecosystems, pp. 281-305, 2020

R. Sharma, S. S. Kamble, A. Gunasekaran, V. Kumar, and A. Kumar, “A systematic literature review on machine learning applications for sustainable agriculture supply chain performance,” Comput. & Oper. Res., vol. 119, p. 104926, 2020.

P. Greve et al., “Global assessment of water challenges under uncertainty in water scarcity projections,” Nat. Sustain., vol. 1, no. 9, pp. 486–494, 2018.

L. E. Pozza and D. J. Field, "The science of soil security and food security," Soil Security, vol. 1, p. 100002, 2020.

R. H. Hariri, E. M. Fredericks, and K. M. Bowers, “Uncertainty in big data analytics: survey, opportunities, and challenges,” J. Big Data, vol. 6, no. 1, pp. 1–16, 2019.

L. Zhao, “Event prediction in the big data era: A systematic survey,” ACM Comput. Surv., vol. 54, no. 5, pp. 1–37, 2021.

Y. Gil et al., “Artificial intelligence for modeling complex systems: taming the complexity of expert models to improve decision making,” ACM Trans. Interact. Intell. Syst., vol. 11, no. 2, pp. 1–49, 2021.

T. Van Klompenburg, A. Kassahun, and C. Catal, “Crop yield prediction using machine learning: A systematic literature review,” Comput. Electron. Agric., vol. 177, p. 105709, 2020.

S. I. Hassan, M. M. Alam, U. Illahi, M. A. Al Ghamdi, S. H. Almotiri, and M. M. Su’ud, “A systematic review on monitoring and advanced control strategies in smart agriculture,” Ieee Access, vol. 9, pp. 32517–32548, 2021.

P. C. S. Reddy and A. Sureshbabu, “An applied time series forecasting model for yield prediction of agricultural crop,” in Soft Computing and Signal Processing: Proceedings of 2nd ICSCSP 2019 2, 2020, pp. 177–187.

P. K. Sharma, S. Dwivedi, L. Ali, and R. K. Arora, “Forecasting maize production in India using ARIMA model,” Agro-Economist, vol. 5, no. 1, pp. 1–6, 2018.

H. Storm, K. Baylis, and T. Heckelei, “Machine learning in agricultural and applied economics,” Eur. Rev. Agric. Econ., vol. 47, no. 3, pp. 849–892, 2020.

A. Pole, M. West, and J. Harrison, Applied Bayesian forecasting and time series analysis. Chapman and Hall/CRC, 2018.

W. C. Labys, Commodity models for forecasting and policy analysis. Taylor & Francis, 2024.

M. Rashid, B. S. Bari, Y. Yusup, M. A. Kamaruddin, and N. Khan, “A comprehensive review of crop yield prediction using machine learning approaches with special emphasis on palm oil yield prediction,” IEEE access, vol. 9, pp. 63406–63439, 2021.

C. S. Yarrington, Review of forecasting univariate time-series data with application to water-energy nexus studies & proposal of parallel hybrid SARIMA-ANN model. West Virginia University, 2021.

E. Njuki, B. E. Bravo-Ureta, and C. J. O’Donnell, “A new look at the decomposition of agricultural productivity growth incorporating weather effects,” PLoS One, vol. 13, no. 2, p. e0192432, 2018.

P. K. Singh, A. K. Pandey, S. Ahuja, and R. Kiran, “Multiple forecasting approach: a prediction of CO2 emission from the paddy crop in India,” Environ. Sci. Pollut. Res., pp. 1–12, 2022.

Y. Ru, B. Blankespoor, U. Wood-Sichra, T. S. Thomas, L. You, and E. Kalvelagen, “Estimating local agricultural gross domestic product (AgGDP) across the world,” Earth Syst. Sci. Data, vol. 15, no. 3, pp. 1357–1387, 2023.

Bila bermanfaat silahkan share artikel ini

Berikan Komentar Anda terhadap artikel Advanced Forecasting of Maize Production using SARIMAX Models: An Analytical Approach


  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

STMIK Budi Darma
Secretariat: Sisingamangaraja No. 338 Telp 061-7875998

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.