Claim · model-written summary
The best-performing ML model reached R² of 0.91 for maize at the national level.
Zea maysNot reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirection
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e3“For maize, the best-performing ML model achieved 6.8% rRMSEp, while TabPFN achieved 8.8%, with R² of 0.91 and 0.86 respectively at the national level”
Title
From rows to yields: how foundation models for tabular data simplify crop yield prediction
Filip Sabo; Michele Meroni; María Piles; et al. Scientific Reports 2026. doi:10.1038/s41598-026-50338-z
Other claims from this paper (6)
- Method / resource TabPFN and ML models showed comparable accuracy, both outperforming baseline approaches in sub-national yield forecasting.
- Method / resource The best-performing ML model achieved 6.8% rRMSEp for maize at the national level.
- Method / resource TabPFN achieved 8.8% rRMSEp for maize at the national level, higher than the best-performing ML model.
- Method / resource TabPFN reached R² of 0.86 for maize at the national level.
- Method / resource TabPFN had faster tuning time than the six ML models it was benchmarked against.
- Method / resource TabPFN required reduced feature engineering compared with the six ML models it was benchmarked against.