Abstract
Abstract Developing rice varieties adapted to alternate wetting and drying water management is crucial for the sustainability of irrigated rice cropping systems. Here we report the first study exploring the feasibility of breeding rice for adaptation to alternate wetting and drying using genomic prediction methods that account for genotype by environment interactions. Two breeding populations (a reference panel of 284 accessions and a progeny population of 97 advanced lines) were evaluated under alternate wetting and drying and continuous flooding management systems. The accuracy of genomic prediction for response variables (index of relative performance and the slope of the joint regression) and for multi-environment genomic prediction models were compared. For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations. In cross validation, prediction accuracy for the index was on average lower (0.31) than that of the slope of the joint regression (0.64) whatever the trait considered. Similar results were found for across population validation (progeny validation). Both cross-validation and progeny validation experiments showed that the 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% depending on the trait and on the statistical model concerned. 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%. Practical implications for breeding rice for adaptation to AWD are discussed.
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Claims 4
Claim · model-written summary
Three rice traits showed significant genotype by environment interactions.
- Outcome
- genotype by environment interactionse0
- Qualifier
- days to flowering, panicle weight and nitrogen-balance indexe0
Not reportedTaxon
Verbatim
e0“For the three traits considered (days to flowering, panicle weight and nitrogen-balance index), significant genotype by environment interactions were observed in both populations”
t0“breeding rice for adaptation to alternate wetting and drying”
Claim · model-written summary
Cross-validation accuracy for the index was lower than the regression slope accuracy.
- Outcome
- prediction accuracy for the indexe0
- Direction
- lowere0
- Value
- 0.31e0
- Qualifier
- 0.64e0
- Comparator
- slope of the joint regressione0
Not reportedTaxon
Verbatim
e0“prediction accuracy for the index was on average lower (0.31) than that of the slope of the joint regression (0.64)”
t0“breeding rice for adaptation to alternate wetting and drying”
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
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”