Assessing the Impact of Climate Change on Agricultural Economic Prediction Models: An Example of the Rice Yield Prediction Model

Agricultural product prices have risen due to climate change in recent years. Therefore, more agriculture policies must be adopted to stabilize prices. A suitable agricultural policy development requires past experience and appropriate agricultural prediction models. However, an excellent agricultural prediction model is not easy to develop because it is affected by many climate factors. In addition, climate change varies from region to region, making it challenging to develop accurate prediction models. Thus, this study proposes an agricultural prediction model by selecting climate change variables. The experiment results show that the proposed model brings higher prediction accuracy (measured by the mean absolute percentage error (MAPE), coefficient of variation (CV), and directional symmetry (DS)), especially in prediction direction trends. Accordingly, the proposed model could be used to assist in agricultural policymaking and stabilize the agricultural economy. For example, the results can be used to predict yields early on to improve risk management and crop insurance programs. Furthermore, it offers advantages in assessing the effect of climate change on the prediction model, exploring the different effects of climate change in various regions, and providing a cross-regional agricultural economic prediction model.