Filtros
Filtrar por:
Tipo de publicación
- Artículo (54)
- Objeto de congreso (19)
- Capítulo de libro (2)
Autores
- Jose Crossa (10)
- Alison Bentley (6)
- Matthew Paul Reynolds (6)
- Yoseph Beyene (6)
- Berhanu Tadesse Ertiro (5)
Años de Publicación
Editores
Repositorios Orígen
- Repositorio Institucional de Publicaciones Multimedia del CIMMYT (73)
- Repositorio Institucional CICESE (1)
- Repositorio institucional de la Universidad de Colima (1)
Tipos de Acceso
- oa:openAccess (75)
Idiomas
- eng (75)
Materias
- CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA (74)
- BREEDING (31)
- MAIZE (20)
- WHEAT (17)
- BREEDING PROGRAMMES (14)
Selecciona los temas de tu interés y recibe en tu correo las publicaciones más actuales
Osval Antonio Montesinos-Lopez ABELARDO MONTESINOS LOPEZ RICARDO ACOSTA DIAZ Rajeev Varshney Jose Crossa ALISON BENTLEY (2022, [Artículo])
Genomic selection (GS) is a predictive methodology that trains statistical machine-learning models with a reference population that is used to perform genome-enabled predictions of new lines. In plant breeding, it has the potential to increase the speed and reduce the cost of selection. However, to optimize resources, sparse testing methods have been proposed. A common approach is to guarantee a proportion of nonoverlapping and overlapping lines allocated randomly in locations, that is, lines appearing in some locations but not in all. In this study we propose using incomplete block designs (IBD), principally, for the allocation of lines to locations in such a way that not all lines are observed in all locations. We compare this allocation with a random allocation of lines to locations guaranteeing that the lines are allocated to
the same number of locations as under the IBD design. We implemented this benchmarking on several crop data sets under the Bayesian genomic best linear unbiased predictor (GBLUP) model, finding that allocation under the principle of IBD outperformed random allocation by between 1.4% and 26.5% across locations, traits, and data sets in terms of mean square error. Although a wide range of performance improvements were observed, our results provide evidence that using IBD for the allocation of lines to locations can help improve predictive performance compared with random allocation. This has the potential to be applied to large-scale plant breeding programs.
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA Bayes Theorem Genome Inflammatory Bowel Diseases Models, Genetic Plant Breeding
Associations between endogenous spike cytokinins and grain-number traits in spring wheat genotypes
Gemma Molero Carolina Rivera-Amado Matthew Paul Reynolds John Foulkes (2024, [Artículo])
Spike Cytokinins Grain Number Fruiting Efficiency Wheat Breeding CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA SPIKES CYTOKININS GRAIN FRUITING HARVEST INDEX WHEAT PLANT BREEDING
Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits
Osval Antonio Montesinos-Lopez Jose Crossa Francisco Javier Martin Vallejo (2018, [Artículo])
Deep Learning Genomic Prediction Bayesian Modeling Shared Data Resources CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BAYESIAN THEORY RESOURCES DATA BREEDING PROGRAMMES
Zine El Abidine Fellahi Abderrahmane Hannachi Susanne Dreisigacker deepmala sehgal Hamenna Bouzerzour (2023, [Artículo])
Pleiotropic Effects Reduced Height Genes CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA PLANT HEIGHT TRITICUM AESTIVUM YIELD COMPONENTS ALLELES BREEDING LINES
Molecular pre-breeding in wheat physiology
David González-Diéguez (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PRE-BREEDING MOLECULAR GENETICS MARKER-ASSISTED SELECTION INTROGRESSION
Product profile development and prioritization: Important considerations
Yoseph Beyene (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE PRODUCTS BREEDING PROGRAMMES MARKET SEGMENTATION TECHNOLOGY GERMPLASM
Use of DH lines in maize breeding programs: CIMMYT experience
Yoseph Beyene (2023, [Objeto de congreso])
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING PROGRAMMES HYBRIDS MARKER-ASSISTED SELECTION GRAIN YIELDS
Vanika Garg Rutwik Barmukh Manish Roorkiwal Chris Ojiewo Abhishek Bohra MAHENDAR THUDI Vikas Kumar Singh Himabindu Kudapa Reyaz Mir Chellapilla Bharadwaj Xin Liu Manish Pandey (2024, [Artículo])
Agricultural Biotechnology Crop Genomics Genome Sequencing CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BIOTECHNOLOGY CROPS GENOMICS PLANT BREEDING AGRICULTURE GENETIC IMPROVEMENT
Breeding schemes for the implementation of genomic selection in wheat (Triticum spp.)
Filippo Maria Bassi Alison Bentley Rodomiro Ortiz Jose Crossa (2016, [Artículo])
Marker-Aided Breeding CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING VALUE GENETIC GAIN GENOTYPE ENVIRONMENT INTERACTION QUANTITATIVE TRAIT LOCI
Achla Sharma Juan Burgueño Prashant Vikram Nitika Sandhu Satinder Kaur Parveen Chhuneja (2023, [Artículo])
Plant Nitrogen Use Efficiency Pre-Breeding Lines Genome-Wide Association Study Marker Trait Association CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT PRE-BREEDING BREEDING LINES NITROGEN LANDRACES GENETIC MARKERS