Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.
about
Quantifying Variety-specific Heat Resistance and the Potential for Adaptation to Climate Change.Next generation crop models: A modular approach to model early vegetative and reproductive development of the common bean (Phaseolus vulgaris L).Genomic prediction contributing to a promising global strategy to turbocharge gene banks.Enhancing genetic gain in the era of molecular breeding.Optimization of multi-environment trials for genomic selection based on crop models.Combining Genome-Wide Information with a Functional Structural Plant Model to Simulate 1-Year-Old Apple Tree Architecture.Using numerical plant models and phenotypic correlation space to design achievable ideotypes.Genetic relationships between spring emergence, canopy phenology, and biomass yield increase the accuracy of genomic prediction in Miscanthus.Bayesian optimization for genomic selection: a method for discovering the best genotype among a large number of candidates.Projected impact of future climate on water-stress patterns across the Australian wheatbelt.The Quest for Understanding Phenotypic Variation via Integrated Approaches in the Field Environment.Mapping and Predicting Non-linear Brassica rapa Growth Phenotypes Based on Bayesian and Frequentist Complex Trait Estimation.Toward integration of genomic selection with crop modelling: the development of an integrated approach to predicting rice heading dates.Loci That Control Nonlinear, Interdependent Responses to Combinations of Drought and Nitrogen Limitation.Genome-Based Prediction of Time to Curd Induction in Cauliflower.Genomic and environmental determinants and their interplay underlying phenotypic plasticityMultivariate GBLUP Improves Accuracy of Genomic Selection for Yield and Fruit Weight in Biparental Populations of AitStatistical and Computational Challenges in Whole Genome Prediction and Genome-Wide Association Analyses for Plant and Animal Breeding
P2860
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P2860
Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation.
description
2015 nî lūn-bûn
@nan
2015年の論文
@ja
2015年論文
@yue
2015年論文
@zh-hant
2015年論文
@zh-hk
2015年論文
@zh-mo
2015年論文
@zh-tw
2015年论文
@wuu
2015年论文
@zh
2015年论文
@zh-cn
name
Integrating Crop Growth Models ...... roximate Bayesian Computation.
@ast
Integrating Crop Growth Models ...... roximate Bayesian Computation.
@en
type
label
Integrating Crop Growth Models ...... roximate Bayesian Computation.
@ast
Integrating Crop Growth Models ...... roximate Bayesian Computation.
@en
prefLabel
Integrating Crop Growth Models ...... roximate Bayesian Computation.
@ast
Integrating Crop Growth Models ...... roximate Bayesian Computation.
@en
P2093
P2860
P1433
P1476
Integrating Crop Growth Models ...... proximate Bayesian Computation
@en
P2093
Carlos D Messina
Frank Technow
L Radu Totir
P2860
P304
P356
10.1371/JOURNAL.PONE.0130855
P407
P50
P577
2015-06-29T00:00:00Z