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Autor: Suchismita Mondal
Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program
Ravi Singh Suchismita Mondal Leonardo Abdiel Crespo Herrera UTTAM KUMAR Muhammad Imtiaz CAIXIA LAN sridhar bhavani JULIO HUERTA_ESPINO Xinyao He Mark Lucas Jesse Poland (2016)
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
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
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
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
Xu Wang Sandesh Kumar Shrestha Philomin Juliana Suchismita Mondal Francisco Pinto Govindan Velu Leonardo Abdiel Crespo Herrera JULIO HUERTA_ESPINO Ravi Singh Jesse Poland (2023)
Artículo
New Crop Varieties Plant Breeding Programs Yield Prediction CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA LEARNING GRAIN YIELDS WHEAT BREEDING FOOD SECURITY
deepmala sehgal Suchismita Mondal Leonardo Abdiel Crespo Herrera Govindan Velu Philomin Juliana JULIO HUERTA_ESPINO Sandesh Kumar Shrestha Jesse Poland Ravi Singh Susanne Dreisigacker (2020)
Genetic architecture of grain yield (GY) has been extensively investigated in wheat using genome wide association study (GWAS) approach. However, most studies have used small panel sizes in combination with large genotypic data, typical examples of the so-called ‘large p small n’ or ‘short-fat data’ problem. Further, use of bi-allelic SNPs accentuated ‘missing heritability’ issues and therefore reported markers had limited impact in wheat breeding. We performed haplotype-based GWAS using 519 haplotype blocks on seven large cohorts of advanced CIMMYT spring bread wheat lines comprising overall 6,333 genotypes. In addition, epistatic interactions among the genome-wide haplotypes were investigated, an important aspect which has not yet been fully explored in wheat GWAS in order to address the missing heritability. Our results unveiled the intricate genetic architecture of GY controlled by both main and epistatic effects. The importance of these results from practical applications in the CIMMYT breeding program is discussed.
Dataset
Philomin Juliana Ravi Singh Jesse Poland Sandesh Kumar Shrestha JULIO HUERTA_ESPINO Govindan Velu Suchismita Mondal Leonardo Abdiel Crespo Herrera UTTAM KUMAR arun joshi Thomas Payne Pradeep Kumar Bhati Vipin Tomar (2021)
A large-scale genome-wide association study was carried out to dissect the genetic architecture of wheat grain yield potential and stress-resilience. Based on the findings, grain yield-associated marker profiles were generated for a large panel of 73,142 wheat lines and the grain-yield favorable allele frequencies were also determined. The marker profile data are presented in this dataset.
Dataset
Phenotypic data from trials conducted by the CIMMYT Bread Wheat Breeding Program
Ravi Singh Suchismita Mondal Leonardo Abdiel Crespo Herrera UTTAM KUMAR Muhammad Imtiaz CAIXIA LAN Mandeep Randhawa sridhar bhavani Pawan Singh JULIO HUERTA_ESPINO Xinyao He Francisco Pinto Lorena González Pérez Philomin Juliana Daljit Singh Mark Lucas Jesse Poland (2016)
Phenotypic data were collected in on-station field trials for advanced breeding lines from the CIMMYT Bread Wheat breeding program over several years.
Dataset