Claim · model-written summary
The no-G × E model had higher grain-zinc prediction accuracy of 0.27.
- Taxon
- upland rice
Oryza sativat0 - Material
- PCT27Be0
- Outcome
- prediction accuracye0
- Direction
- highere0
- Value
- 0.27e0
- Comparator
- models assumed no genotype-by-environment (G × E) interactione0
Not reportedGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingQualifier
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27).”
t0“Cirad-CIAT upland rice breeding program”
Title
Hugues de Verdal; Cédric Baertschi; Julien Frouin; et al. Rice 2023. doi:10.1186/s12284-023-00661-0
Other claims from this paper (10)
- Method / resource Training on PCT27B at Santa Rosa gave grain zinc predictive ability of 0.19.
- Method / resource Training on PCT27B at Santa Rosa gave grain yield predictive ability of 0.30.
- Method / resource Adding PCT27A increased plant-height predictive ability by 5%.
- Method / resource Adding PCT27A increased grain-zinc predictive ability by 61%.
- Method / resource Adding PCT27A improved predictive abilities for other traits except grain yield.
- Method / resource The no-G × E model had higher flowering prediction accuracy of 0.38.
- Method / resource A single-G × E-variance model gave higher plant-height accuracy of 0.28.
- Method / resource An environment-specific G × E deviation model gave greatest grain-yield predictive gain of 0.25.
- Method / resource Multi-location and multi-generation calibration yielded low predictive gains overall.
- Recommendation The authors suggest this approach could increase selection intensity, speed breeding, and deliver economic advantage.