Rice Yield Forecasting in Northeast China with a Dual-Factor ARIMA Model Incorporating SPEI1-Sep. and Sown Area

Amid escalating global climate change and geopolitical tensions threatening food supply chains, the three provinces of Northeast China, which serve as a major grain production base, play a crucial role in ensuring national food security. However, the region is experiencing more frequent extreme climatic events and increasing limitations on arable land. This necessitates an evaluation of the combined effects of climate conditions and sown area on rice (Oryza sativa L.) yields. Utilizing provincial panel data from 1990 to 2022, this study conducts baseline panel regression analyses at both the national and Northeast China levels. The results consistently identify the value of the standardized precipitation evapotranspiration index (SPEI) on September as a key climatic factor exerting a significant negative effect on rice total yield, whereas the rice sown area is a robust positive determinant. Based on these findings, we develop a dual-factor analytical framework that incorporates both climatic conditions and rice sown area, utilizing SPEI1-Sep. to identify critical growth stages of rice, with the aim of providing a more comprehensive understanding of their combined effects on yield. To further support predictive accuracy, the comparative performance assessments of the Extreme Gradient Boosting (XGBoost), random forest (RF), and Autoregressive Integrated Moving Average (ARIMA) models are conducted. The results show that the ARIMA model outperforms others in forecasting. Forecasts for 2023–2027 indicate slow yield growth in Jilin Province, with a 1.5% annual increase. Heilongjiang shows minor fluctuations, stabilizing between 24.97 and 25.56 million tons. Liaoning’s yield remains stable, projected between 5.13 and 5.20 million tons. These trends suggest limited overall yield expansion, highlighting the need for region-specific policies and resource management to ensure China’s grain security. This study clarifies the interplay between climate and sown area, demonstrates the relative forecasting advantage of ARIMA in this setting, and provides evidence to support managing yield variability and optimizing agricultural policy in Northeast China, with implications for long-term national food security.