Claim · model-written summary
The results support integrating field-based physiological data into grain yield prediction to improve genetic gain in soft wheat.
Not reportedMaterialGeneCompoundOrgan / stageInteractorInteractor roleSecond interactorSecond interactor roleTreatmentComparatorSettingDirectionValueQualifier
Verbatim
t0“Soft Wheat Panel”
e4“The results of this study support the integration of field-based physiological data into GY prediction to improve genetic gain from selection in soft wheat under a multi-environment context”
Title
Jia Guo; Sumit Pradhan; Dipendra Shahi; et al. Scientific Reports 2020. doi:10.1038/s41598-020-63919-3
Other claims from this paper (3)
- Method / resource The multi-kernel model combining physiological and genomic data increased grain yield prediction accuracy by 35 to 169% over genomic-only models.
- Method / resource The model combining physiological and genotype × environment interaction effects captured a higher response to selection than other models.
- Method / resource Including physiological traits and/or interaction effects improved grain yield prediction accuracies in four environments compared with genomic-only models.