National Rice Yield Forecasting
Description
Accurate national-scale rice yield forecasting is essential for food security, import planning, strategic reserve management, and climate adaptation in rice-dependent countries such as Bangladesh. Despite recent advances in deep learning and remote sensing, many existing studies rely on locally constrained datasets or overlook the challenge of limited historical records. This study proposes a hybrid framework based on transfer learning and an Attention-LSTM architecture for national rice yield forecasting in Bangladesh by integrating climatic variables, remote sensing indicators, and agricultural economic data. The proposed model was trained and validated using a walk-forward evaluation strategy over the 2006–2022 period to simulate realistic operational forecasting conditions while preventing temporal data leakage.