Claim · model-written summary
Models accounting for enviromic information decreased error variance.
- Taxon
- alfalfa (Medicago sativa L.)
Medicago sativae1 - Treatment
- models accounting to the enviromics informatione0
- Outcome
- error variancee0
- Direction
- decreasee0
Not reportedComparator
Verbatim
e0“models accounting to the enviromics information led to an increase of genetic variance and a decrease in the error variance”
e1“Breeding for dry matter yield and persistence in alfalfa (Medicago sativa L.)”
Title
И. А. Орловский Радіоелектроніка, інформатика, управління 2011. doi:10.1002/tpg2.20306
Other claims from this paper (11)
- Method / resource Models accounting for enviromic information increased genetic variance.
- Method / resource Incorporating enviromic information indicated better biological explanation.
- Method / resource Models using enviromic data had higher predictive ability when fewer harvests were used for training.
- Method / resource Enviromic model M2 outperformed GBLUP model-M0 in predicting adaptability and persistence across all cross-validation scenarios.
- Method / resource Enviromic model M3 outperformed GBLUP model-M0 in predicting adaptability and persistence across all cross-validation scenarios.
- Method / resource Environmental covariates increased persistence predictive ability from 0 to 0.16 in CV00 compared with the base model.
- Method / resource Environmental covariates increased persistence predictive ability from 0 to 0.20 in CV1 compared with the base model.
- Method / resource Environmental covariates increased persistence predictive ability from 0 to 0.56 in CV0 compared with the base model.
- Method / resource Environmental covariates increased persistence predictive ability from 0 to 0.46 in CV2 compared with the base model.
- Method / resource GBLUP without enviromics had low power to predict persistence.
- Recommendation The authors recommend adoption of enviromics as a cheap and efficient way to increase prediction accuracy and biological meaning.