“accuracy of multi-environment models”

Claim · model-written summary

Multi-environment models had better prediction accuracy than the single environment model.

Outcome

prediction accuracye0

Direction

bettere0

Comparator

single environment modele0

Verbatim

e0

“the prediction accuracy of multi-environment models is better than that of the single environment model”

Claim · model-written summary

Multi-trait models increased prediction accuracy 14.4% over a single-trait single-environment approach.

Taxon

maizeZea mayst0

Outcome

prediction accuracye0

Direction

increasee0

Value

14.4%e0

Comparator

single trait in a single environment approache0

Verbatim

e0

“14.4% increase in prediction accuracy when employing multi-trait models compared to the use of a single trait in a single environment approach”

t0

“flowering traits in maize”

Claim · model-written summary

Multi-environment models outperformed single-environment models by 30% when entries were evaluated in both water systems.

Outcome

accuracy of multi-environment modelse0

Direction

outperformede0

Value

30%e0

Comparator

single environment modelse0

Not reportedTaxon

Verbatim

e0

“The accuracy of multi-environment models predicting unobserved phenotypes of entrees evaluated under both water management systems outperformed single environment models by an average of 30%”

t0

“breeding rice for adaptation to alternate wetting and drying”

Claim · model-written summary

Multi-environment models more than doubled prediction accuracy relative to a single-environment model for most maize traits.

Taxon

maizeZea mayst0

Outcome

prediction accuracye0

Direction

more than doublede0

Value

more than doublede0

Qualifier

particularly when predicting lines in a sparse testing designe0

Comparator

single-environment modele0

Verbatim

e0

“The multi-environment models more than doubled the prediction accuracy compared to the single-environment model for most traits, particularly when predicting lines in a sparse testing design”

t0

“maize”

Claim · model-written summary

Multi-environment multi-trait omics models outperformed single-environment omics models.

Taxon

oatAvena sativat0

Outcome

multi-trait models with omics data outperformede0

Direction

outperformede0

Comparator

single-environment omics modelse0

Verbatim

e0

“In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model.”

t0

“oat”

Claim · model-written summary

MTME genomic prediction models achieved the highest prediction accuracy in durum wheat, particularly under CV2 and sowing-by-season grouping.

Taxon

durum wheatTriticum turgidum subsp. durume2

Outcome

prediction accuraciese0

Direction

higheste0

Comparator

single-environment (SE), multi-trait (MT), multi-environment (ME)e1

Verbatim

e0

“MTME models achieved the highest prediction accuracies, particularly under CV2 and sowing-by-season grouping”

e1

“This study evaluates the performance of single-environment (SE), multi-trait (MT), multi-environment (ME), and multi-trait-multi-environment (MTME) genomic prediction models across seven key traits”

e2

“climate-resilient durum wheat improvement”

Claim · model-written summary

Multi-environment multi-trait omics models outperformed counterpart multi-trait GBLUP models.

Taxon

oatAvena sativat0

Outcome

multi-trait models with omics data outperformede0

Direction

outperformede0

Comparator

counterpart multi-trait GBLUP modelse0

Verbatim

e0

“In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model.”

t0

“oat”

Claim · model-written summary

Both multi-environment models had higher prediction accuracy than the single environment model when accessions were tested in at least one environment (CV2) rather than in none (CV1).

Outcome

prediction accuracye0

Direction

highere0

Qualifier

when accessions are tested in at least one environment (CV2 sampling)e0

Comparator

single environment model (G)e0

Verbatim

e0

“Both models have a higher prediction accuracy than the single environment model (G) when accessions are tested in at least one environment (CV2 sampling) compared to models where some accessions are tested in none of the environments (CV1)”

Claim · model-written summary

Multi-environment models were similar to single-environment models when predicting unobserved phenotypes of untested entries.

Outcome

performance of multi-environment modelse0

Direction

similare0

Value

- 6% to 4%e0

Comparator

single environment modelse0

Not reportedTaxon

Verbatim

e0

“performance of multi-environment models predicting unobserved phenotypes of untested entrees was similar to the performance of single environment models with differences in accuracy ranging from - 6% to 4%”

t0

“breeding rice for adaptation to alternate wetting and drying”

Claim · model-written summary

Unstructured genetic covariance modeling gave the highest prediction accuracy.

Taxon

oatAvena sativat0

Outcome

prediction accuracye0

Direction

higheste0

Comparator

unstructured covariance modele0

Verbatim

e0

“In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model.”

t0

“oat”