Abstract
Genomic selection is an extension of marker-assisted selection by leveraging thousands of molecular markers distributed across the genome to capture the maximum possible proportion of the genetic variance underlying complex traits. In this study, genomic prediction models were developed by integrating phenological, physiological, and high-throughput phenotyping traits to predict grain yield in bread wheat (Triticum aestivum L.) under three environmental conditions: irrigation, drought stress, and terminal heat stress. Model performance was evaluated using both five-fold cross-validation and leave-one-environment-out (LOEO) schemes. Under five-fold cross-validation, the model incorporating vegetation indices derived from spectral datasets from the grain-filling phase achieved the highest accuracy. In LOEO validation, the model that included days to heading performed best under irrigation, whereas under drought stress, the model utilizing vegetation indices from the vegetative stage showed the highest accuracy. Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain-filling phases. Although incorporating multiple covariates can improve prediction accuracy or reduce the normalized root mean square error, using an extended model with all available covariates is not recommended due to the marginal predictive accuracy gains, increases in phenotyping, costs and complexity of data collection analysis. Overall, our findings show the importance of tailored phenomic inputs to specific environmental contexts to optimize genomic prediction of wheat yield.
Abstract from the paper’s OpenAlex record; open copy licensed cc-by. Read at the source ↗
The text the model read, with the source record’s inline markup (italics, sub- and superscripts) rendered and its character entities decoded; quote offsets refer to the raw text.
Claims 8
Claim · model-written summary
Grain-filling spectral vegetation indices gave the most accurate wheat yield prediction in five-fold validation.
- Taxon
- wheat
Triticum aestivumt0 - Organ / stage
- grain-filling phasee0
- Outcome
- accuracye0
- Direction
- higheste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under five-fold cross-validation, the model incorporating vegetation indices derived from spectral datasets from the grain-filling phase achieved the highest accuracy.”
t0“wheat”
Claim · model-written summary
Days to heading gave the best model performance under irrigation in leave-one-environment-out validation.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- irrigatione0
- Outcome
- performed beste0
- Direction
- beste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“In LOEO validation, the model that included days to heading performed best under irrigation”
t0“wheat”
Claim · model-written summary
Vegetative-stage vegetation indices gave the most accurate model under drought stress.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- drought stresse0
- Outcome
- accuracye0
- Direction
- higheste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“under drought stress, the model utilizing vegetation indices from the vegetative stage showed the highest accuracy.”
t0“wheat”
Claim · model-written summary
A genotype by environment interaction model was among the best under terminal heat stress.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- terminal heat stresse0
- Outcome
- performed beste0
- Direction
- beste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain-filling phases.”
t0“wheat”
Claim · model-written summary
A vegetative-stage vegetation index model was among the best under terminal heat stress.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- terminal heat stresse0
- Outcome
- performed beste0
- Direction
- beste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain-filling phases.”
t0“wheat”
Claim · model-written summary
A model combining vegetative and grain-filling spectral reflectance was among the best under terminal heat stress.
- Taxon
- wheatTriticum aestivumt0
- Treatment
- terminal heat stresse0
- Outcome
- performed beste0
- Direction
- beste0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Under terminal heat stress, three models performed best: one incorporating genotype by environment interaction, one using vegetation indices during the vegetative stage, and one integrating spectral reflectance data from both the vegetative and grain-filling phases.”
t0“wheat”
Claim · model-written summary
Multiple covariates can improve prediction accuracy or reduce normalized root mean square error.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- prediction accuracy or reduce the normalized root mean square errore0
- Direction
- improvee0
Not reportedComparator
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“incorporating multiple covariates can improve prediction accuracy or reduce the normalized root mean square error”
t0“wheat”
Claim · model-written summary
The authors advise against using all available covariates because gains are marginal and phenotyping costs and analytical complexity rise.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- using an extended model with all available covariatese0
- Qualifier
- marginal predictive accuracy gainse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“using an extended model with all available covariates is not recommended due to the marginal predictive accuracy gains, increases in phenotyping, costs and complexity of data collection analysis.”
t0“wheat”