Search claims · PlantClaims

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Bayesian models had slightly higher imputation accuracy than non-Bayesian models.

Outcome: accuracy of estimation performancee0
Direction: slightly highere0
Comparator: non-Bayesian analysise0

Not reportedTaxon

Verbatim

e0
“the models using Bayesian analysis had slightly higher accuracy of estimation performance than those using non-Bayesian analysis”
✓ located verbatim in the source · abstract, characters 957–1085

Title

Application of Multiple Imputation for Missing Values in Three-Way Three-Mode Multi-Environment Trial Data

Ting Tian; Geoffrey J. McLachlan; Mark J. Dieters; et al. PLoS ONE 2015. doi:10.1371/journal.pone.0144370

Other claims from this paper (2)

Method / resource Bayesian models took more time than non-Bayesian models.
Method / resource Multiple agglomerative hierarchical clustering performed best overall.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

MTME models gave higher mean accuracy of progeny than single-trait versions.

Outcome: mean accuracy of progenye0
Direction: highere0
Comparator: single-trait versionse0

Not reportedTaxon

Verbatim

e0
“The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; [Formula: see text]) and mean accuracy of progeny than their respective single-trait versions.”
✓ located verbatim in the source · abstract, characters 1240–1484

Title

Multi-trait multi-environment models in the genetic selection of segregating soybean progeny

Leonardo Volpato; Rodrigo Silva Alves; Paulo Eduardo Teodoro; et al. PLoS ONE 2019. doi:10.1371/journal.pone.0215315

Other claims from this paper (4)

Method / resource Bayesian and REML/BLUP estimates and predicted breeding values and gains were similar.
Method / resource MTME models estimated higher broad-sense heritability than single-trait versions.
Method / resource Bayesian analysis provided credibility intervals for heritability estimates.
Method / resource MTME produced greater predicted gains from selection.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Multi-environment genomic prediction models gave 2-3 times higher prediction accuracies than the single-environment model for four wheat traits.

Taxon: Triticum aestivumTriticum aestivumt0
Outcome: prediction accuraciese1
Direction: highere1
Value: 2-3 timese1
Comparator: baseline single-environment (SE) GS modele4

Verbatim

t0
“Spring Wheat (Triticum aestivum L.)”
✓ located verbatim in the source · title, characters 108–143

e1
“Both ME models provided 2-3 times higher prediction accuracies for all four traits across the four tested environments”
✓ located verbatim in the source · abstract, characters 952–1070

e4
“The ME predictions were compared with the baseline single-environment (SE) GS model”
✓ located verbatim in the source · abstract, characters 629–712

Title

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.)

Vipin Tomar; Daljit Singh; Guriqbal Singh Dhillon; et al. Frontiers in Plant Science 2021. doi:10.3389/fpls.2021.720123

Other claims from this paper (2)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The heteroscedastic MTME model had better fit and provided greater accuracy than the homoscedastic alternative among the two models fitted to eucalyptus clone data.

Taxon: eucalyptus clonest0
Outcome: better fit and provided greater accuracye1
Comparator: homoscedastice2

Verbatim

t0
“eucalyptus clones”
✓ located verbatim in the source · title, characters 79–96

e1
“Of the two models, the heteroscedastic MTME model had better fit and provided greater accuracy”
✓ located verbatim in the source · abstract, characters 718–812

e2
“Two MTME models were fitted, for which residuals were considered homoscedastic and heteroscedastic”
✓ located verbatim in the source · abstract, characters 417–515

Title

Multi-trait multi-environment models for selecting high-performance and stable eucalyptus clones

Filipe Manoel Ferreira; Saulo Fabrício da Silva Chaves; Marco Antônio Peixoto; et al. Acta Scientiarum Agronomy 2023. doi:10.4025/actasciagron.v45i1.61626

Other claims from this paper (6)

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Combining model predictions most often improved maize yield prediction accuracy over individual models.

Taxon: maizeZea mayse0
Outcome: accuracye0
Direction: bettere0
Comparator: any one of the models on their owne0

Verbatim

e0
“within large multi-environment and multi-genotype maize trials that combining predictions from disparate models using simple ensemble approaches most often results in better accuracy than using any one of the models on their own”
✓ located verbatim in the source · abstract, characters 523–751

Title

Ensemble of best linear unbiased predictor, machine learning and deep learning models predict maize yield better than each model alone

Daniel R Kick; Jacob D Washburn in silico Plants 2023. doi:10.1093/insilicoplants/diad015

Other claims from this paper (3)

Method / resource Ensembling generally improved performance even with two models.
Method / resource Increasing ensemble size yielded diminishing accuracy improvements.
Method / resource An inverse expected error weighted combination of five model types performed best on the maize dataset.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The no-G × E model had higher flowering prediction accuracy of 0.38.

Taxon: upland riceOryza sativat0
Material: PCT27Be0
Outcome: prediction accuracye0
Direction: highere0
Value: 0.38e0
Comparator: models assumed no genotype-by-environment (G × E) interactione0

Verbatim

e0
“Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27).”
✓ located verbatim in the source · abstract, characters 1587–1805

t0
“Cirad-CIAT upland rice breeding program”
✓ located verbatim in the source · abstract, characters 281–320

Title

Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population

Hugues de Verdal; Cédric Baertschi; Julien Frouin; et al. Rice 2023. doi:10.1186/s12284-023-00661-0

Other claims from this paper (10)

Method / resource Training on PCT27B at Santa Rosa gave grain zinc predictive ability of 0.19.
Method / resource Training on PCT27B at Santa Rosa gave grain yield predictive ability of 0.30.
Method / resource Adding PCT27A increased plant-height predictive ability by 5%.
Method / resource Adding PCT27A increased grain-zinc predictive ability by 61%.
Method / resource Adding PCT27A improved predictive abilities for other traits except grain yield.
Method / resource The no-G × E model had higher grain-zinc prediction accuracy of 0.27.
Method / resource A single-G × E-variance model gave higher plant-height accuracy of 0.28.
Method / resource An environment-specific G × E deviation model gave greatest grain-yield predictive gain of 0.25.
Method / resource Multi-location and multi-generation calibration yielded low predictive gains overall.
Recommendation The authors suggest this approach could increase selection intensity, speed breeding, and deliver economic advantage.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

The no-G × E model had higher grain-zinc prediction accuracy of 0.27.

Taxon: upland riceOryza sativat0
Material: PCT27Be0
Outcome: prediction accuracye0
Direction: highere0
Value: 0.27e0
Comparator: models assumed no genotype-by-environment (G × E) interactione0

Verbatim

e0
“Models with the PCT27B phenotyped in two locations resulted in higher prediction accuracy when the models assumed no genotype-by-environment (G × E) interaction for flowering (0.38) and grain zinc concentration (0.27).”
✓ located verbatim in the source · abstract, characters 1587–1805

t0
“Cirad-CIAT upland rice breeding program”
✓ located verbatim in the source · abstract, characters 281–320

Title

Optimization of Multi-Generation Multi-location Genomic Prediction Models for Recurrent Genomic Selection in an Upland Rice Population

Hugues de Verdal; Cédric Baertschi; Julien Frouin; et al. Rice 2023. doi:10.1186/s12284-023-00661-0

Other claims from this paper (10)

Method / resource Training on PCT27B at Santa Rosa gave grain zinc predictive ability of 0.19.
Method / resource Training on PCT27B at Santa Rosa gave grain yield predictive ability of 0.30.
Method / resource Adding PCT27A increased plant-height predictive ability by 5%.
Method / resource Adding PCT27A increased grain-zinc predictive ability by 61%.
Method / resource Adding PCT27A improved predictive abilities for other traits except grain yield.
Method / resource The no-G × E model had higher flowering prediction accuracy of 0.38.
Method / resource A single-G × E-variance model gave higher plant-height accuracy of 0.28.
Method / resource An environment-specific G × E deviation model gave greatest grain-yield predictive gain of 0.25.
Method / resource Multi-location and multi-generation calibration yielded low predictive gains overall.
Recommendation The authors suggest this approach could increase selection intensity, speed breeding, and deliver economic advantage.

Method / resourceauthor interpretationoriginal resultmeasured

Claim · model-written summary

Combining factor analytic models with multiple traits improved genotype selection efficiency and accuracy.

Taxon: UrochloaUrochloae0
Outcome: efficiency and accuracy of genotype selectione0
Direction: enhancese0

Not reportedComparator

Verbatim

e0
“integrating FA models with multi‐trait evaluation enhances the efficiency and accuracy of genotype selection in Urochloa”
✓ located verbatim in the source · abstract, characters 1309–1429

Title

Integrating Factor Analytic Models to Enhance Urochloa Hybrid Selection Under Genotype‐by‐Environment Interactions in Colombia

L. M. Hernandez; J. S. Aparicio; P. Espitia‐Buitrago; et al. Plant Breeding 2026. doi:10.1111/pbr.70080

Other claims from this paper (6)

Observation Quality traits showed stronger genotype by environment interaction than agronomic traits.
Observation Agronomic traits had greater stability across locations.
Method / resource FA1 captured the main interaction structure across environments.
Method / resource FA2 explained a secondary environmental contrast.
Observation Br12_3067 showed broad adaptability.
Observation Other genotypes adapted specifically to individual sites.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Comprehensive models generalized to new environments better.

Taxon: wheatTriticum aestivumt0
Outcome: superior generalization to new environmentse0
Direction: superiore0

Not reportedComparatorValue

Verbatim

e0
“The comprehensive models exhibit superior generalization to new environments and achieve the highest accuracy when trained on diverse datasets”
✓ located verbatim in the source · abstract, characters 809–951

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”
✓ located verbatim in the source · title, characters 0–217

Title

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

Jordan McBreen; Md. Ali Babar; Diego Jarquin; et al. The Plant Genome 2024. doi:10.1002/tpg2.20532

Other claims from this paper (7)

Method / resource HTP and genomic models predicted yield more accurately than genomic-only models, at 0.59 to 0.68 versus 0.40-0.54.
Method / resource Diverse training datasets gave comprehensive models their highest accuracy.
Method / resource Accuracy improved when data from multiple years were included.
Method / resource Multivariate prediction outperformed genomic prediction across years and locations.
Method / resource Multivariate prediction selected a higher proportion of observed top 25% lines than genomic-only models.
Method / resource Multivariate prediction had higher specificity for top-yielding lines.
Method / resource Comprehensive models extrapolated effectively to new environments.

Method / resourceobservedoriginal resultpositivemeasured

Claim · model-written summary

Comprehensive models extrapolated effectively to new environments.

Taxon: wheatTriticum aestivumt0
Outcome: extrapolate to new environmentse0

Not reportedComparatorValue

Verbatim

e0
“Findings show the comprehensive models effectively extrapolate to new environments”
✓ located verbatim in the source · abstract, characters 1714–1796

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”
✓ located verbatim in the source · title, characters 0–217

Title

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

Jordan McBreen; Md. Ali Babar; Diego Jarquin; et al. The Plant Genome 2024. doi:10.1002/tpg2.20532

Other claims from this paper (7)

Method / resource HTP and genomic models predicted yield more accurately than genomic-only models, at 0.59 to 0.68 versus 0.40-0.54.
Method / resource Comprehensive models generalized to new environments better.
Method / resource Diverse training datasets gave comprehensive models their highest accuracy.
Method / resource Accuracy improved when data from multiple years were included.
Method / resource Multivariate prediction outperformed genomic prediction across years and locations.
Method / resource Multivariate prediction selected a higher proportion of observed top 25% lines than genomic-only models.
Method / resource Multivariate prediction had higher specificity for top-yielding lines.

“accuracy of multi-environment models”

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