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Stagewise crop yield prediction with multisource functional indices

Jing Zou & Ostap Okhrin

Canadian Journal of Statistics2026https://doi.org/10.1002/cjs.70043article
ABDC A
Weight
0.37

What the paper says

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.

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https://doi.org/https://doi.org/10.1002/cjs.70043

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@article{jing2026,
  title        = {{Stagewise crop yield prediction with multisource functional indices}},
  author       = {Jing Zou & Ostap Okhrin},
  journal      = {Canadian Journal of Statistics},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1002/cjs.70043},
}

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Stagewise crop yield prediction with multisource functional indices

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Evidence weight

0.37

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.16 × 0.4 = 0.06
M · momentum0.53 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.