Method / resourcemodel-written summary

Subregional rice yield forecasting models in Vietnam improved on the one-fits-all approach by 20% up to 60%.

A claim from Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices · 2022

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Subregional rice yield forecasting models in Vietnam improved on the one-fits-all approach by 20% up to 60%.

Outcome
subregional rice yield forecasting modelse0
Direction
improvede0
Value
20% up to 60%e0
Comparator
one-fits-all approache0
Setting
Vietname0

Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentQualifier

Organisms pictured · Representative pictures of the matched species, not figures from the paper.

Oryza sativa
Oryza sativa · Taxon · picture from its Wikimedia Commons categoryDesmanthus4food · CC BY-SA 3.0 us · Wikimedia Commons ↗

Verbatim

e0

“Compared to the one-fits-all approach, subregional rice yield forecasting models over Vietnam improved by an average level of 20% up to 60%”

✓ located verbatim in the source · abstract, characters 1021–1160

Title

Enhancing Crop Yield Prediction Utilizing Machine Learning on Satellite-Based Vegetation Health Indices

Hoa Thi Pham; Joseph Awange; Michael Kuhn; et al. Sensors 2022. doi:10.3390/s22030719

Other claims from this paper (2)

Record rev-dde765fcb3448222544975bfdfb62b45a6c74e51dc9a6acc00668b54ffb883be · schema claims-v0.9 · position 1 in the model’s answer