<![CDATA[Oil palm production is a key driver of regional economic resilience in Indonesia, yet its predictability remains highly uncertain due to climate variability, particularly rainfall fluctuations. Although time series forecasting models such as ARIMA are widely applied, prior studies largely treatenvironmental variables as external factors rather than integrating them into a structured analytical interpretation. This study addresses this limitation by developing an environmentally-informed forecasting framework that explicitly links rainfall dynamics with oil palm production patterns using the Autoregressive Integrated Moving Average (ARIMA) approach. Secondary data from 2005 to 2024 were analyzed through a rigorous Box–Jenkins procedure, including stationarity testing, model identification, parameter estimation, and diagnostic validation. The results demonstrate that ARIMA (1,2,1) provides the most consistent representation of production dynamics, while ARIMA (0,2,2) captures rainfall variability. Forecasting results reveal a stable production trajectory over the next five years despite declining rainfall trends, indicating a decoupling pattern between environmental variability and production output. Furthermore, correlation analysis confirms a weak negative relationship, suggesting that excessive rainfall may disrupt production efficiency rather than enhance it. This study advances the existing literature by reframing time series forecasting as an integrated environmental–production system rather than a purely statistical exercise. The findings offer both methodological insight and practical relevance for improving adaptive decision-making in climate-sensitive agricultural systems]]>
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