Abstract
Enhancing predictive modeling accuracy in wheat (Triticum aestivum) breeding through the integration of high-throughput phenotyping (HTP) data with genomic information is crucial for maximizing genetic gain. In this study, spanning four locations in the southeastern United States over 3 years, models to predict grain yield (GY) were investigated through different cross-validation approaches. The results demonstrate the superiority of multivariate comprehensive models that incorporate both genomic and HTP data, particularly in accurately predicting GY across diverse locations and years. These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations. The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets. Predictive accuracy improves as models incorporate data from multiple years, highlighting the importance of considering temporal dynamics in modeling approaches. The study reveals that multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations. The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic-only models, indicated by higher specificity, which is the proportion of correctly identified top-yielding lines that matched the observed top 25% performance across different sites and years. Additionally, the study addresses the prediction of untested locations based on other locations within the same year and in new years at previously tested locations. Findings show the comprehensive models effectively extrapolate to new environments, highlighting their potential for guiding breeding strategies.
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Claims 8
Claim · model-written summary
HTP and genomic models predicted yield more accurately than genomic-only models, at 0.59 to 0.68 versus 0.40-0.54.
- Taxon
- wheat
Triticum aestivumt0 - Outcome
- prediction accuraciese0
- Value
- 0.59 to 0.68e0
- Qualifier
- 0.40-0.54e0
- Comparator
- genomic-only modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
Claim · model-written summary
Comprehensive models generalized to new environments better.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- superior generalization to new environmentse0
- Direction
- superiore0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
Claim · model-written summary
Diverse training datasets gave comprehensive models their highest accuracy.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- accuracye0
- Direction
- higheste0
- Qualifier
- diverse datasetse0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“achieve the highest accuracy when trained on diverse datasets”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
Claim · model-written summary
Accuracy improved when data from multiple years were included.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- Predictive accuracye0
- Direction
- improvese0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Predictive accuracy improves as models incorporate data from multiple years”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
Claim · model-written summary
Multivariate prediction outperformed genomic prediction across years and locations.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- predicting linese0
- Direction
- outperformede0
- Comparator
- genomic prediction methodse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“multivariate prediction outperformed genomic prediction methods in predicting lines across years and locations”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
Claim · model-written summary
Multivariate prediction selected a higher proportion of observed top 25% lines than genomic-only models.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- percentage of top 25% lines selectede0
- Direction
- highere0
- Comparator
- genomic-only modelse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The percentage of top 25% lines selected based on multivariate prediction was higher compared to genomic-only models”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
Claim · model-written summary
Multivariate prediction had higher specificity for top-yielding lines.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- specificitye0
- Direction
- highere0
- Comparator
- genomic-only modelse1
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“indicated by higher specificity, which is the proportion of correctly identified top-yielding lines that matched the observed top 25% performance across different sites and years”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”
e1“These HTP-incorporating models achieve prediction accuracies ranging from 0.59 to 0.68, compared to 0.40-0.54 for genomic-only models when tested under different prediction scenarios both across years and locations”
Claim · model-written summary
Comprehensive models extrapolated effectively to new environments.
- Taxon
- wheatTriticum aestivumt0
- Outcome
- extrapolate to new environmentse0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“Findings show the comprehensive models effectively extrapolate to new environments”
t0“Enhancing prediction accuracy of grain yield in wheat lines adapted to the southeastern United States through multivariate and multi‐environment genomic prediction models incorporating spectral and thermal information”