Journal of Multidisciplinary Applied Natural Science
Open Access Journal
Scopus CiteScore 2024
4.8
Calculated on 05 May, 2025
SJR 2024
0.31
Powered by scimagojr.com
Open Access Journal
4.8
Calculated on 05 May, 2025
0.31
Powered by scimagojr.com
https://doi.org/10.47352/jmans.2774-3047.320
Hernan Pantolla
Emmanuel Dotong
This study investigates the impact of weather variability on abaca production, a vital agricultural commodity in the Philippines. Balanced datasets of quarterly abaca production, aggregated rainfall, maximum temperature, minimum temperature, and relative humidity from selected top-producing provinces from the first quarter of 2010 to the third quarter of 2023 for the provinces of Catanduanes, Northern Samar, Bukidnon, and Surigao del Sur for a total of 212 datapoints were used. The final model employs a fixed effects regression model with bootstrapping using a 3-week moving average (MA) to filter short‑term shocks and better assess the impact of each regressors. Bootstrapping was done to produce statistically appreciable estimates given the that the distributions of the regressors are not all normal and to reduce the standard errors of the estimates. The results, with varying significance values, show that the lag of MA of abaca production, the third lag of the MA of maximum temperature, the MA of minimum temperature, and the third lag of MA of minimum temperature have significant effects on the MA of abaca production. The final model shows an R2 value of 97.47% as well, indicating a very high level of explainability of the variability of the abaca production in terms of the regressors. The results highlight the criticality of (a) including lags, (b) separating maximum temperature and minimum temperature as spatiotemporal regressors for crops, and (c) accounting for potential long-term effects of rising temperature at nighttime. Among other things, supply chain management people, local government units, and policymakers can use the findings of this paper for anticipatory action amidst the changing climate.
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