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Autor: Jose Crossa
Status of implementation new tools and technologies in the GMP- EA-PP1 Africa breeding pipeline
Yoseph Beyene Andrew Chavangi Manje Gowda Suresh L.M. Vijay Chaikam Anani Bruce Berhanu Tadesse Ertiro Walter Chivasa Jose Crossa Juan Burgueño Fidelis Owino (2023)
Objeto de congreso
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA BREEDING MAIZE PHENOTYPING NEW TECHNOLOGY
Prediction models for canopy hyperspectral reflectance in wheat breeding data
Osval Antonio Montesinos-Lopez Jose Crossa Gustavo de los Campos Gregorio Alvarado Suchismita Mondal Jessica Rutkoski Lorena González Pérez Juan Burgueño (2016)
Vegetation indices (VI) generated by using some bands from hyperspectral cameras are used as predictors of primary traits. This study proposes models that use all available bands as predictors of primary traits. The proposed models were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square (PLS). The results were compared with the OLS performed using as predictors each of the eight VIs individually and combined. The data set comes from CIMMYT’s Global Wheat Program and comprises 1170 genotypes evaluated for grain yield in five environments with the reflectance data measured in 250 discrete narrow bands ranging between 492 and 851 nm. in 9 time-points of the crop cycle. Results show that using all the bands simultaneously produced better predictions than using one VI alone or all the VI together, but when used only the bands with heritabilities > 0.5 in Drought environment, the predictions improved, while in the rest of the environments, using all the bands simultaneously produced slightly better prediction accuracies. The models with highest prediction when using all bands were functional B-spline and Fourier. Time-point 6 gives gave promising prediction accuracies for wheat lines before harvesting.
Dataset
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Carolina Rivera-Amado Francisco Pinto Francisco Javier Pinera-Chavez David González-Diéguez Matthew Paul Reynolds Paulino Pérez-Rodríguez Huihui Li Osval Antonio Montesinos-Lopez Jose Crossa (2023)
Artículo
Conventional Methods Genomic Prediction Accuracy Deep Learning Novel Methods CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA WHEAT BREEDING MACHINE LEARNING METHODS MARKER-ASSISTED SELECTION
CIMMYT Eastern Africa early- to intermediate maturity maize breeding pipelines (EA-PP1)
Yoseph Beyene Andrew Chavangi Manje Gowda Suresh L.M. Vijay Chaikam Anani Bruce Berhanu Tadesse Ertiro Walter Chivasa Aparna Das Juan Burgueño Jose Crossa Prasanna Boddupalli (2023)
Objeto de congreso
CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA MAIZE BREEDING LINES MARKET SEGMENTATION HYBRIDS GERMPLASM
Prediction models for canopy hyperspectral reflectance in wheat breeding data
Osval Antonio Montesinos-Lopez Jose Crossa Gustavo de los Campos Gregorio Alvarado Suchismita Mondal Jessica Rutkoski Lorena González Pérez Juan Burgueño (2016)
Vegetation indices (VI) generated by using some bands from hyperspectral cameras are used as predictors of primary traits. This study proposes models that use all available bands as predictors of primary traits. The proposed models were ordinal least square (OLS), Bayes B, principal components with Bayes B, functional B-spline, functional Fourier and functional partial least square (PLS). The results were compared with the OLS performed using as predictors each of the eight VIs individually and combined. The data set comes from CIMMYT’s Global Wheat Program and comprises 1170 genotypes evaluated for grain yield in five environments with the reflectance data measured in 250 discrete narrow bands ranging between 492 and 851 nm. in 9 time-points of the crop cycle. Results show that using all the bands simultaneously produced better predictions than using one VI alone or all the VI together, but when used only the bands with heritabilities > 0.5 in Drought environment, the predictions improved, while in the rest of the environments, using all the bands simultaneously produced slightly better prediction accuracies. The models with highest prediction when using all bands were functional B-spline and Fourier. Time-point 6 gives gave promising prediction accuracies for wheat lines before harvesting.
Dataset
Jesse Poland Susanne Dreisigacker Sandesh Kumar Shrestha Ravi Singh Suchismita Mondal Philomin Juliana Jose Crossa BHOJA BASNET Leonardo Abdiel Crespo Herrera Trevor Rife Govindan Velu (2016)
Genetic profiling of wheat breeding lines from the CIMMYT bread wheat breeding program was carried out over several years.
Dataset
Replication Data for: Genomic Prediction of Gene Bank Wheat Landraces
Jose Crossa DIEGO JARQUIN Jorge Franco Paulino Pérez-Rodríguez Juan Burgueño Carolina Saint Pierre Prashant Vikram Carolina Sansaloni Cesar Petroli Deniz Akdemir Clay Sneller Matthew Paul Reynolds Thomas Payne Carlos Guzman Roberto Peña Peter Wenzl Sukhwinder Singh (2023)
Genomic prediction methods may be used to enhance efforts to rapidly introgress traits of interest from exotic germplasm into elite materials. This study examined the performance of different genomic prediction models using genotypic and phenotypic data related to 8416 Mexican landrace accessions and 2403 Iranian landrace accessions stored in germplasm banks. The Mexican and Iranian collections were evaluated under optimal, drought, and heat conditions for several traits including the highly heritable traits, days to heading (DTH), and days to maturity (DTM). The results of the different analyses are reported in the accompanying journal article.
Dataset
Carolina Sansaloni Jorge Franco Bruno Santos Lawrence Percival-Alwyn Cesar Petroli Jaime Campos Kate Dreher Thomas Payne David Marshall Benjamin Kilian Iain Milne Sebastian Raubach Paul Shaw Gordon Stephen Carolina Saint Pierre Juan Burgueño Jose Crossa Huihui Li Andrzej Kilian Peter Wenzl Ahmed Amri Cristobal Uauy Marianne Bänziger Mario Caccamo Kevin Pixley (2020)
A diverse panel of domesticated hexaploid and tetraploid wheat lines and their tetraploid and diploid wild relatives were genotyped using the DArtSeq technology and characterized in a global wheat diversity analysis.
Dataset