184 related articles for article (PubMed ID: 28724067)
1. Multitrait, Random Regression, or Simple Repeatability Model in High-Throughput Phenotyping Data Improve Genomic Prediction for Wheat Grain Yield.
Sun J; Rutkoski JE; Poland JA; Crossa J; Jannink JL; Sorrells ME
Plant Genome; 2017 Jul; 10(2):. PubMed ID: 28724067
[TBL] [Abstract][Full Text] [Related]
2. 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]
3. High-throughput phenotyping platforms enhance genomic selection for wheat grain yield across populations and cycles in early stage.
Sun J; Poland JA; Mondal S; Crossa J; Juliana P; Singh RP; Rutkoski JE; Jannink JL; Crespo-Herrera L; Velu G; Huerta-Espino J; Sorrells ME
Theor Appl Genet; 2019 Jun; 132(6):1705-1720. PubMed ID: 30778634
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. Genomic Prediction and Indirect Selection for Grain Yield in US Pacific Northwest Winter Wheat Using Spectral Reflectance Indices from High-Throughput Phenotyping.
Lozada DN; Godoy JV; Ward BP; Carter AH
Int J Mol Sci; 2019 Dec; 21(1):. PubMed ID: 31881728
[TBL] [Abstract][Full Text] [Related]
6. Combining Genomic and Phenomic Information for Predicting Grain Protein Content and Grain Yield in Spring Wheat.
Sandhu KS; Mihalyov PD; Lewien MJ; Pumphrey MO; Carter AH
Front Plant Sci; 2021; 12():613300. PubMed ID: 33643347
[TBL] [Abstract][Full Text] [Related]
7. Genomic Prediction with Pedigree and Genotype × Environment Interaction in Spring Wheat Grown in South and West Asia, North Africa, and Mexico.
Sukumaran S; Crossa J; Jarquin D; Lopes M; Reynolds MP
G3 (Bethesda); 2017 Feb; 7(2):481-495. PubMed ID: 27903632
[TBL] [Abstract][Full Text] [Related]
8. Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat.
Juliana P; Montesinos-López OA; Crossa J; Mondal S; González Pérez L; Poland J; Huerta-Espino J; Crespo-Herrera L; Govindan V; Dreisigacker S; Shrestha S; Pérez-Rodríguez P; Pinto Espinosa F; Singh RP
Theor Appl Genet; 2019 Jan; 132(1):177-194. PubMed ID: 30341493
[TBL] [Abstract][Full Text] [Related]
9. Combining High-Throughput Phenotyping and Genomic Information to Increase Prediction and Selection Accuracy in Wheat Breeding.
Crain J; Mondal S; Rutkoski J; Singh RP; Poland J
Plant Genome; 2018 Mar; 11(1):. PubMed ID: 29505641
[TBL] [Abstract][Full Text] [Related]
10. Accuracy of genomic selection for grain yield and agronomic traits in soft red winter wheat.
Lozada DN; Mason RE; Sarinelli JM; Brown-Guedira G
BMC Genet; 2019 Nov; 20(1):82. PubMed ID: 31675927
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Joint Use of Genome, Pedigree, and Their Interaction with Environment for Predicting the Performance of Wheat Lines in New Environments.
Howard R; Gianola D; Montesinos-López O; Juliana P; Singh R; Poland J; Shrestha S; Pérez-Rodríguez P; Crossa J; Jarquín D
G3 (Bethesda); 2019 Sep; 9(9):2925-2934. PubMed ID: 31300481
[TBL] [Abstract][Full Text] [Related]
13. Genomic Selection in Winter Wheat Breeding Using a Recommender Approach.
Lozada DN; Carter AH
Genes (Basel); 2020 Jul; 11(7):. PubMed ID: 32664601
[TBL] [Abstract][Full Text] [Related]
14. Multi-trait Genomic Prediction Model Increased the Predictive Ability for Agronomic and Malting Quality Traits in Barley (
Bhatta M; Gutierrez L; Cammarota L; Cardozo F; Germán S; Gómez-Guerrero B; Pardo MF; Lanaro V; Sayas M; Castro AJ
G3 (Bethesda); 2020 Mar; 10(3):1113-1124. PubMed ID: 31974097
[TBL] [Abstract][Full Text] [Related]
15. Genomic prediction models for grain yield of spring bread wheat in diverse agro-ecological zones.
Saint Pierre C; Burgueño J; Crossa J; Fuentes Dávila G; Figueroa López P; Solís Moya E; Ireta Moreno J; Hernández Muela VM; Zamora Villa VM; Vikram P; Mathews K; Sansaloni C; Sehgal D; Jarquin D; Wenzl P; Singh S
Sci Rep; 2016 Jun; 6():27312. PubMed ID: 27311707
[TBL] [Abstract][Full Text] [Related]
16. Multitrait machine- and deep-learning models for genomic selection using spectral information in a wheat breeding program.
Sandhu K; Patil SS; Pumphrey M; Carter A
Plant Genome; 2021 Nov; 14(3):e20119. PubMed ID: 34482627
[TBL] [Abstract][Full Text] [Related]
17. Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat.
Krause MR; González-Pérez L; Crossa J; Pérez-Rodríguez P; Montesinos-López O; Singh RP; Dreisigacker S; Poland J; Rutkoski J; Sorrells M; Gore MA; Mondal S
G3 (Bethesda); 2019 Apr; 9(4):1231-1247. PubMed ID: 30796086
[TBL] [Abstract][Full Text] [Related]
18. Enhancing the potential of phenomic and genomic prediction in winter wheat breeding using high-throughput phenotyping and deep learning.
Kaushal S; Gill HS; Billah MM; Khan SN; Halder J; Bernardo A; Amand PS; Bai G; Glover K; Maimaitijiang M; Sehgal SK
Front Plant Sci; 2024; 15():1410249. PubMed ID: 38872880
[TBL] [Abstract][Full Text] [Related]
19. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform.
Hassan MA; Yang M; Rasheed A; Yang G; Reynolds M; Xia X; Xiao Y; He Z
Plant Sci; 2019 May; 282():95-103. PubMed ID: 31003615
[TBL] [Abstract][Full Text] [Related]
20. Phenomic selection in wheat breeding: identification and optimisation of factors influencing prediction accuracy and comparison to genomic selection.
Robert P; Auzanneau J; Goudemand E; Oury FX; Rolland B; Heumez E; Bouchet S; Le Gouis J; Rincent R
Theor Appl Genet; 2022 Mar; 135(3):895-914. PubMed ID: 34988629
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]