Early-season estimation of winter wheat yield: A hybrid machine learning-enabled approach

Di Qiao, Tianteng Wang, David Jingjun Xu, Ruize Ma, Xiaochun Feng and Junhu Ruan

Technological Forecasting and Social Change, 2024, vol. 201, issue C

Abstract: Accurate crop yield forecasting can help stakeholders take effective measures in advance to avoid potential grain supply risks. However, currently, yield forecasts are mostly made close to harvest (e.g. 1–3 months before harvest for Chinese winter wheat), which gives stakeholders a relatively short time to react, decide, and intervene. To satisfy stakeholders' requirements for timely and precise yield forecasting, we propose a hybrid machine learning-enabled early-season yield forecasting method integrated with an intermediate climate forecast process. The results show that: (1) Compared with the baseline model, our proposed method advances winter wheat yield prediction up to 8 months before harvest with satisfactory accuracy. (2) The climate forecast process incorporated is effective and consistently optimized in various model combinations and controlled experiments. (3) The proposed method performs robustly over different spatial scales (e.g., in the first month of Chinese winter wheat, the yield predictive accuracy is improved in 183 out of 233 counties). In summary, our work provides an effective and robust approach for early-season yield forecasting that gives stakeholders more time to take appropriate actions to cope with crop yield volatility risks.

Keywords: Crop yield forecast; Early season; Machine learning; Food security; Climate forecast (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162524000635
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:201:y:2024:i:c:s0040162524000635

DOI: 10.1016/j.techfore.2024.123267

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().