Global Journal of Engineering and Technology Research (GJETR) · Research article · Open access
Forecasting Rice Production in Northern Samar: A Multi-Model Analysis Using Cyclone Frequency and Harvested Area as Predictors
Kyle Arthur P. Sabilao, PhD Elvin L. Jarito
13 March 2026 · Vol. 2, Issue 3, pp. 74-82
DOI: 10.65150/EP-gjetr/V2E3/2026-01
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What is this article about?
This study forecasted rice production in Northern Samar using three predictive models: Multiple Linear Regression (MLR), Random Forest Regression (RF), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX). The analysis utilized cleaned datasets from 2016 to 2024, covering 23 municipalities after excluding San Vicente due to data insufficiency.
Article at a glance
- Published
- 13 March 2026
- Volume / issue / pages
- Vol. 2, Issue 3, pp. 74-82
- ISSN
- 3051-3782 (online) · 3051-3774 (print)
- Licence
- CC BY 4.0 — free to read and reuse with attribution
- Peer review
- Double-blind, at least two independent reviewers
- Publisher
- EP Journals Group
Abstract
This study forecasted rice production in Northern Samar using three predictive models: Multiple Linear Regression (MLR), Random Forest Regression (RF), and Seasonal Autoregressive Integrated Moving Average with Exogenous Variables (SARIMAX). The analysis utilized cleaned datasets from 2016 to 2024, covering 23 municipalities after excluding San Vicente due to data insufficiency. Two explanatory variables—area harvested and tropical cyclone frequency—were used, with the former projected through Random Forest (SMAPE = 12.75%) and the latter using a constant mean approach. For rice production forecasts, MLR demonstrated steady upward trends and yielded the lowest MAPE in 4 municipalities, notably Catarman (5.48%) and Las Navas (3.91%). RF achieved the lowest MAPE in 17 municipalities, with exceptional accuracy in Lapinig (1.63%), Laoang (3.58%), and Lavezares (3.40%); however, it failed to capture long-term growth as it produced identical forecasts for 2030, 2035, and 2040—indicating limitations in trend sensitivity due to capped inputs. SARIMAX, while incorporating time-series structure and external factors, displayed inconsistent performance with the lowest MAPE in only 2 municipalities (Allen: 6.56%, Biri: 25.43%). Its trend outputs were often unstable in areas with irregular patterns, making it less suited for long-range forecasting. Among the three models, MLR proved most practical for long-term planning due to its interpretability and ability to reflect realistic growth. RF was best suited for short-term accuracy and baseline projections, while SARIMAX offered mixed results depending on data structure. These forecasts serve as valuable tools for local agricultural planning, guiding food security initiatives and resource allocation in Northern Samar.
Keywords: Rice production, forecasting models, Random Forest, Multiple Linear Regression, SARIMAX, Northern Samar
Cite this article
Kyle Arthur P. Sabilao, & PhD Elvin L. Jarito (2026). Forecasting Rice Production in Northern Samar: A Multi-Model Analysis Using Cyclone Frequency and Harvested Area as Predictors. Global Journal of Engineering and Technology Research, 2(3), 74-82. https://doi.org/10.65150/EP-gjetr/V2E3/2026-01
@article{Kyle2026,
title = {Forecasting Rice Production in Northern Samar: A Multi-Model Analysis Using Cyclone Frequency and Harvested Area as Predictors},
author = {Kyle Arthur P. Sabilao and PhD Elvin L. Jarito},
journal = {Global Journal of Engineering and Technology Research},
year = {2026},
volume = {2},
number = {3},
pages = {74-82},
doi = {10.65150/EP-gjetr/V2E3/2026-01},
url = {https://doi.org/10.65150/EP-gjetr/V2E3/2026-01}
}Questions about this article
Who wrote this article?
Kyle Arthur P. Sabilao, PhD Elvin L. Jarito.
Where and when was it published?
In Global Journal of Engineering and Technology Research, Volume 2, Issue 3, pages 74-82, published by EP Journals Group on 13 March 2026.
Is this article free to read?
Yes. It is open access under a Creative Commons Attribution 4.0 (CC BY 4.0) licence, so anyone can read, share and reuse it with attribution.
What is the DOI of this article?
10.65150/EP-gjetr/V2E3/2026-01 (https://doi.org/10.65150/EP-gjetr/V2E3/2026-01).
How do I cite this article?
APA: Kyle Arthur P. Sabilao, & PhD Elvin L. Jarito (2026). Forecasting Rice Production in Northern Samar: A Multi-Model Analysis Using Cyclone Frequency and Harvested Area as Predictors. Global Journal of Engineering and Technology Research, 2(3), 74-82. https://doi.org/10.65150/EP-gjetr/V2E3/2026-01
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