Stagewise crop yield prediction with multisource functional indices

Abstract Index insurance design involves integrating weather data, soil moisture, phenology information, and satellite imagery, which presents challenges in data fusion. This article addresses the modelling of multisource functional indices of varying lengths by constructing a stagewise ensemble of sequential models. The implemented methods, including nonparametric regression and deep learning models, aim to improve crop yield prediction by systematically capturing spatiotemporal dependence across indices of different temporal spans. Results from an applied case study demonstrate both the feasibility and practical value of stagewise modelling, highlighting its potential to reduce basis risk and improve the hedging effectiveness of index insurance contracts.