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Projecting Spatiotemporal Shifts in Phenology of Southeast Asian Commodity Crops Through A Simple Growing Degree Day Model For Near and Midterm Climate Change Scenarios
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Abstract
Understanding the impacts of climate change on crop phenology is vital for regional agricultural planning. This is especially pertinent to agricultural regions like Southeast Asia (SEA) dominated by small-holder modes of production. This study projected the Days to Maturity (DTM) trends for key commodity crops across the region over different future emission scenarios. A simple Growing Degree Day (GDD) model was used with data from ERA5 and CMIP6 projections (2026 - 2045, 2046 - 2065) in order to estimate trends in crop phenology until the mid-century. The results demonstrate that crop maturity across the region has already begun advancing due to rising temperatures, a trend projected to accelerate into the future. The magnitude of these shifts exhibits significant spatial heterogeneity and varies considerably based on crop species as well as seasonal timing. The majority of SEA will experience varying degrees of phenological acceleration, while some special sub-regions will remain unaffected. These findings underscore the necessity of continued emission mitigation and adaptation strategies. The gradual onset of temperature-driven shifts provides a critical temporal window to implement measures such as climate control technologies, agroforestry, and resilient crop varieties. Ultimately, urgent action is imperative to safeguard the smallholder-dominated agricultural systems that form the agricultural backbone of SEA.
DOI
https://doi.org/10.31223/X5X528
Subjects
Agriculture, Earth Sciences
Keywords
Phenology, Agriculture, Climate Change, Southeast Asia, CMIP6
Dates
Published: 2026-11-11 20:33
Last Updated: 2026-11-11 20:33
License
CC BY Attribution 4.0 International
Additional Metadata
Data Availability:
The datasets used in this study, including the ERA5 atmospheric reanalysis and CMIP6 climate projections, are publicly available and accessible through the Google Earth Engine (GEE) platform (https://developers.google.com/earth-engine/datasets)
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