scholarly article | Q13442814 |
P50 | author | Scott C Chapman | Q58212630 |
Daniela Bustos-Korts | Q88423934 | ||
Marcos Malosetti | Q42055477 | ||
P2093 | author name string | Fred A van Eeuwijk | |
Martin P Boer | |||
Karine Chenu | |||
Bangyou Zheng | |||
P2860 | cites work | Prediction of total genetic value using genome-wide dense marker maps | Q29619613 |
Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice | Q30596586 | ||
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 | ||
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Multiple-trait genomic selection methods increase genetic value prediction accuracy | Q36439850 | ||
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Quantification of the effects of VRN1 and Ppd-D1 to predict spring wheat (Triticum aestivum) heading time across diverse environments | Q37100622 | ||
Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat | Q37242027 | ||
Genomic prediction in CIMMYT maize and wheat breeding programs | Q37385794 | ||
Simulating the yield impacts of organ-level quantitative trait loci associated with drought response in maize: a "gene-to-phenotype" modeling approach | Q37454653 | ||
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High-throughput phenotyping and genomic selection: the frontiers of crop breeding converge | Q37993914 | ||
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 Yield | Q38671838 | ||
Phenomic Selection Is a Low-Cost and High-Throughput Method Based on Indirect Predictions: Proof of Concept on Wheat and Poplar | Q58103137 | ||
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Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding | Q87965215 | ||
Modelling strategies for assessing and increasing the effectiveness of new phenotyping techniques in plant breeding | Q90021619 | ||
High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage | Q91694464 | ||
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Wheat floret survival as related to pre-anthesis spike growth | Q39735650 | ||
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 Environment | Q48132261 | ||
QU-GENE: a simulation platform for quantitative analysis of genetic models | Q48714545 | ||
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 years | Q57206093 | ||
Genetic distance sampling: a novel sampling method for obtaining core collections using genetic distances with an application to cultivated lettuce | Q57246798 | ||
P275 | copyright license | Creative Commons Attribution 4.0 International | Q20007257 |
P6216 | copyright status | copyrighted | Q50423863 |
P921 | main subject | statistics | Q12483 |
P304 | page(s) | 1491 | |
P577 | publication date | 2019-11-27 | |
P1433 | published in | Frontiers in Plant Science | Q27723840 |
P1476 | title | Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies | |
P478 | volume | 10 |
Q97420890 | Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions | cites work | P2860 |
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