Claim · model-written summary
MV-CV1 did not improve grain yield prediction accuracy when modeled with correlated traits.
- Outcome
- PA for grain yielde0
- Direction
- did not improvee0
- Comparator
- five most genetically correlated traitse0
Not reportedTaxonMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentSettingValueQualifier
Verbatim
e0“The MV-CV1 scheme did not improve PA for grain yield when it was modelled with the five most genetically correlated traits”
Title
Paolo Vitale; Giovanni Laidò; Gabriella Dono; et al. PLoS ONE 2024. doi:10.1371/journal.pone.0310886
Other claims from this paper (6)
- Method / resource Prediction accuracy was higher under HNW than LNR for all traits except yellow index.
- Method / resource The lowest reported prediction accuracy was 0.34 for NDVI under LNR.
- Method / resource The highest reported prediction accuracy was 0.74 for test weight under HNW.
- Method / resource MV-CV1 improved NDVI prediction accuracy under LNR by up to 12.45% versus univariate prediction.
- Method / resource MV-CV2 improved thousand kernel weight prediction accuracy under LNR by up to 56.72%.
- Method / resource MV-CV2 significantly improved grain yield prediction accuracy by up to about 18%.