Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies

scientific article published on 27 November 2019

Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies is …
instance of (P31):
scholarly articleQ13442814

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P356DOI10.3389/FPLS.2019.01491
P932PMC publication ID6890853
P698PubMed publication ID31827479

P50authorScott C ChapmanQ58212630
Daniela Bustos-KortsQ88423934
Marcos MalosettiQ42055477
P2093author name stringFred A van Eeuwijk
Martin P Boer
Karine Chenu
Bangyou Zheng
P2860cites workPrediction of total genetic value using genome-wide dense marker mapsQ29619613
Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in riceQ30596586
Breeding for the future: what are the potential impacts of future frost and heat events on sowing and flowering time requirements for Australian bread wheat (Triticum aestivium) varieties?Q30750266
Accuracy of multi-trait genomic selection using different methodsQ35137232
Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity AnalysisQ35901278
Improvement of Predictive Ability by Uniform Coverage of the Target Genetic Space.Q36145699
Contribution of Crop Models to Adaptation in WheatQ36340479
Genome-enabled predictions for fruit weight and quality from repeated records in European peach progenies.Q36393254
Multiple-trait genomic selection methods increase genetic value prediction accuracyQ36439850
Application of unmanned aerial systems for high throughput phenotyping of large wheat breeding nurseriesQ37037976
Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environmentsQ37100622
Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in WheatQ37242027
Genomic prediction in CIMMYT maize and wheat breeding programsQ37385794
Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approachQ37454653
Methodology for High-Throughput Field Phenotyping of Canopy Temperature Using Airborne Thermography.Q37475059
Detection and use of QTL for complex traits in multiple environments.Q37688415
High-throughput phenotyping and genomic selection: the frontiers of crop breeding convergeQ37993914
Field high-throughput phenotyping: the new crop breeding frontier.Q38153969
Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain YieldQ38671838
Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and PoplarQ58103137
A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platformQ60551931
The GP problem: quantifying gene-to-phenotype relationshipsQ74306117
Genomic selection: marker assisted selection on a genome wide scaleQ80189480
Prediction of response to marker-assisted and genomic selection using selection index theoryQ80189491
Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat BreedingQ87965215
Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breedingQ90021619
High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stageQ91694464
Improving process-based crop models to better capture genotype×environment×management interactionsQ92676219
Large-scale characterization of drought pattern: a continent-wide modelling approach applied to the Australian wheatbelt--spatial and temporal trends.Q38943259
Environment characterization as an aid to wheat improvement: interpreting genotype-environment interactions by modelling water-deficit patterns in North-Eastern AustraliaQ38943263
Stay-green traits to improve wheat adaptation in well-watered and water-limited environmentsQ38946621
Wheat floret survival as related to pre-anthesis spike growthQ39735650
Dynamic quantification of canopy structure to characterize early plant vigour in wheat genotypes.Q42380873
The Quest for Understanding Phenotypic Variation via Integrated Approaches in the Field EnvironmentQ48132261
QU-GENE: a simulation platform for quantitative analysis of genetic modelsQ48714545
QTL methodology for response curves on the basis of non-linear mixed models, with an illustration to senescence in potato.Q51725198
Translating High-Throughput Phenotyping into Genetic Gain.Q53700233
Genome-based prediction of maize hybrid performance across genetic groups, testers, locations, and yearsQ57206093
Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuceQ57246798
P275copyright licenseCreative Commons Attribution 4.0 InternationalQ20007257
P6216copyright statuscopyrightedQ50423863
P921main subjectstatisticsQ12483
P304page(s)1491
P577publication date2019-11-27
P1433published inFrontiers in Plant ScienceQ27723840
P1476titleCombining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies
P478volume10

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Q97420890Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactionscites workP2860

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