These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
219 related articles for article (PubMed ID: 31827479)
1. Combining Crop Growth Modeling and Statistical Genetic Modeling to Evaluate Phenotyping Strategies. Bustos-Korts D; Boer MP; Malosetti M; Chapman S; Chenu K; Zheng B; van Eeuwijk FA Front Plant Sci; 2019; 10():1491. PubMed ID: 31827479 [TBL] [Abstract][Full Text] [Related]
2. From QTLs to Adaptation Landscapes: Using Genotype-To-Phenotype Models to Characterize G×E Over Time. Bustos-Korts D; Malosetti M; Chenu K; Chapman S; Boer MP; Zheng B; van Eeuwijk FA Front Plant Sci; 2019; 10():1540. PubMed ID: 31867027 [TBL] [Abstract][Full Text] [Related]
3. Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping. Baba T; Momen M; Campbell MT; Walia H; Morota G PLoS One; 2020; 15(2):e0228118. PubMed ID: 32012182 [TBL] [Abstract][Full Text] [Related]
4. Exploring Niches for Short-Season Grain Legumes in Semi-Arid Eastern Kenya - Coping with the Impacts of Climate Variability. Sennhenn A; Njarui DMG; Maass BL; Whitbread AM Front Plant Sci; 2017; 8():699. PubMed ID: 28536585 [TBL] [Abstract][Full Text] [Related]
5. Bioenergy Sorghum Crop Model Predicts VPD-Limited Transpiration Traits Enhance Biomass Yield in Water-Limited Environments. Truong SK; McCormick RF; Mullet JE Front Plant Sci; 2017; 8():335. PubMed ID: 28377779 [TBL] [Abstract][Full Text] [Related]
6. Combining Crop Growth Modeling With Trait-Assisted Prediction Improved the Prediction of Genotype by Environment Interactions. Robert P; Le Gouis J; ; Rincent R Front Plant Sci; 2020; 11():827. PubMed ID: 32636859 [TBL] [Abstract][Full Text] [Related]
7. Multi-trait genomic prediction using in-season physiological parameters increases prediction accuracy of complex traits in US wheat. Shahi D; Guo J; Pradhan S; Khan J; Avci M; Khan N; McBreen J; Bai G; Reynolds M; Foulkes J; Babar MA BMC Genomics; 2022 Apr; 23(1):298. PubMed ID: 35413795 [TBL] [Abstract][Full Text] [Related]
8. Canopy Temperature and Vegetation Indices from High-Throughput Phenotyping Improve Accuracy of Pedigree and Genomic Selection for Grain Yield in Wheat. Rutkoski J; Poland J; Mondal S; Autrique E; Pérez LG; Crossa J; Reynolds M; Singh R G3 (Bethesda); 2016 Sep; 6(9):2799-808. PubMed ID: 27402362 [TBL] [Abstract][Full Text] [Related]
9. Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments. Rahman MM; Crain J; Haghighattalab A; Singh RP; Poland J Front Plant Sci; 2021; 12():633651. PubMed ID: 34646280 [TBL] [Abstract][Full Text] [Related]
10. Enhancement of Plant Productivity in the Post-Genomics Era. Thao NP; Tran LS Curr Genomics; 2016 Aug; 17(4):295-6. PubMed ID: 27499678 [TBL] [Abstract][Full Text] [Related]
11. A conceptual framework for the dynamic modeling of time-resolved phenotypes for sets of genotype-environment-management combinations: a model library. van Voorn GAK; Boer MP; Truong SH; Friedenberg NA; Gugushvili S; McCormick R; Bustos Korts D; Messina CD; van Eeuwijk FA Front Plant Sci; 2023; 14():1172359. PubMed ID: 37389290 [TBL] [Abstract][Full Text] [Related]
12. Increased Prediction Accuracy Using Combined Genomic Information and Physiological Traits in A Soft Wheat Panel Evaluated in Multi-Environments. Guo J; Pradhan S; Shahi D; Khan J; Mcbreen J; Bai G; Murphy JP; Babar MA Sci Rep; 2020 Apr; 10(1):7023. PubMed ID: 32341406 [TBL] [Abstract][Full Text] [Related]
13. Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes. Guo J; Khan J; Pradhan S; Shahi D; Khan N; Avci M; Mcbreen J; Harrison S; Brown-Guedira G; Murphy JP; Johnson J; Mergoum M; Esten Mason R; Ibrahim AMH; Sutton R; Griffey C; Babar MA Genes (Basel); 2020 Oct; 11(11):. PubMed ID: 33126620 [TBL] [Abstract][Full Text] [Related]
14. Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping. Campbell M; Walia H; Morota G Plant Direct; 2018 Sep; 2(9):e00080. PubMed ID: 31245746 [TBL] [Abstract][Full Text] [Related]
15. Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize. Anche MT; Kaczmar NS; Morales N; Clohessy JW; Ilut DC; Gore MA; Robbins KR Theor Appl Genet; 2020 Oct; 133(10):2853-2868. PubMed ID: 32613265 [TBL] [Abstract][Full Text] [Related]
16. Field and in-silico analysis of harvest index variability in maize silage. Ojeda JJ; Islam MR; Correa-Luna M; Gargiulo JI; Clark CEF; Rotili DH; Garcia SC Front Plant Sci; 2023; 14():1206535. PubMed ID: 37404539 [TBL] [Abstract][Full Text] [Related]
17. Stay-green traits to improve wheat adaptation in well-watered and water-limited environments. Christopher JT; Christopher MJ; Borrell AK; Fletcher S; Chenu K J Exp Bot; 2016 Sep; 67(17):5159-72. PubMed ID: 27443279 [TBL] [Abstract][Full Text] [Related]
18. Resource allocation optimization with multi-trait genomic prediction for bread wheat (Triticum aestivum L.) baking quality. Lado B; Vázquez D; Quincke M; Silva P; Aguilar I; Gutiérrez L Theor Appl Genet; 2018 Dec; 131(12):2719-2731. PubMed ID: 30232499 [TBL] [Abstract][Full Text] [Related]
19. Canopy spectral reflectance indices correlate with yield traits variability in bread wheat genotypes under drought stress. Mohi-Ud-Din M; Hossain MA; Rohman MM; Uddin MN; Haque MS; Ahmed JU; Abdullah HM; Hossain MA; Pessarakli M PeerJ; 2022; 10():e14421. PubMed ID: 36452074 [TBL] [Abstract][Full Text] [Related]
20. Assessment of the Potential Impacts of Wheat Plant Traits across Environments by Combining Crop Modeling and Global Sensitivity Analysis. Casadebaig P; Zheng B; Chapman S; Huth N; Faivre R; Chenu K PLoS One; 2016; 11(1):e0146385. PubMed ID: 26799483 [TBL] [Abstract][Full Text] [Related] [Next] [New Search]