Crossmark

Machine learning-driven strategies for wheat yield prediction and climate adaptation in Arid regions: a comparative analysis of random forest and XGBoost

Crossref DOI link: https://doi.org/10.1007/s00704-026-06248-1

Published Online: 2026-04-21

Published Print: 2026-05

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Jahan, Mohsen https://orcid.org/0000-0003-2259-5124
Nassiri-Mahallati, Mehdi https://orcid.org/0000-0003-0357-1733
Soltani, Mehdi

Yaghoubi, Fateme
License Information

Text and Data Mining valid from 2026-04-21

Version of Record valid from 2026-04-21
More Information

Article History

Received: 27 August 2025

Accepted: 10 April 2026

First Online: 21 April 2026

Declarations

:

: This research complies with all ethical guidelines, including adherence to the legal requirements of the authors’ country. The authors voluntarily consented to participate in this study.

: The authors declare no competing interests.

Document is current

Any future updates will be listed below