Person: Mondal, S.
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Mondal
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S.
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Mondal, S.
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0000-0002-8582-889910 results
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Now showing 1 - 10 of 10
- Chapter 7. Achieving genetic gains in practice(Springer Nature, 2022) Singh, R.P.; Juliana, P.; Huerta-Espino, J.; Velu, G.; Crespo Herrera, L.A.; Mondal, S.; Bhavani, S.; Singh, P.K.; Xinyao He; Ibba, M.I.; Randhawa, M.S.; Kumar, U.; Joshi, A.K.; Basnet, B.R.; Braun, H.J.
Publication - High-resolution spectral information enables phenotyping of leaf epicuticular wax in wheat(BioMed Central, 2021) Camarillo-Castillo, F.; Huggins, T.D.; Mondal, S.; Reynolds, M.P.; Tilley, M.; Hays, D.B.
Publication - Aerial high‐throughput phenotyping enabling indirect selection for grain yield at the early‐generation seed‐limited stages in breeding programs(CSSA, 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
Publication - Aerial high‐throughput phenotyping enables indirect selection for grain yield at the early generation, seed‐limited stages in breeding programs(Crop Science Society of America (CSSA), 2020) Krause, M.; Mondal, S.; Crossa, J.; Singh, R.P.; Pinto Espinosa, F.; Haghighattalab, A.; Shrestha, S.; Rutkoski, J.; Gore, M.A.; Sorrells, M.E.; Poland, J.
Publication - Regularized selection indices for breeding value prediction using hyper-spectral image data(Nature Publishing Group, 2020) Lopez-Cruz, M.; Olson, E.; Rovere, G.; Crossa, J.; Dreisigacker, S.; Mondal, S.; Singh, R.P.; De Los Campos, G.
Publication - Breeder friendly phenotyping(Elsevier, 2020) Reynolds, M.P.; Chapman, S.; Crespo Herrera, L.A.; Molero, G.; Mondal, S.; Pequeno, D.N.L.; Pinto Espinosa, F.; Piñera Chavez, F.J; Poland, J.; Rivera-Amado, C.; Saint Pierre, C.; Sukumaran, S.
Publication - Integrating New Age Phenomics in CIMMYT Wheat Breeding Program(CIMMYT, 2018) Mondal, S.
Publication - UAV-based imagery for phenotyping in breeding and physiological pre-breeding of wheat at CIMMYT(CIMMYT, [2016?]) Pinto Espinosa, F.; Reynolds, M.P.; Molero, G.; Mondal, S.
Publication - Utilizing genomics and phenomics in CIMMYT wheat breeding(CIMMYT, 2016) Mondal, S.; Poland, J.; Haghighattalab, A.; Singh, D.; Rahmanm, M.; Sorrells, M.E.; Jin Sun; Singh, R.P.; Crossa, J.; Dreisigacker, S.; Kumar, U.; Imtiaz, M.; Juliana, P.
Publication - Canopy temperature and vegetation indices from high-throughput phenotyping improve accuracy of pedigree and genomic selection for grain yield in wheat(Genetics Society of America, 2016) Rutkoski, J.; Poland, J.; Mondal, S.; Autrique, E.; González Pérez, L.; Crossa, J.; Reynolds, M.P.; Singh, R.G.Genomic selection can be applied prior to phenotyping, enabling shorter breeding cycles and greater rates of genetic gain relative to phenotypic selection. Traits measured using high-throughput phenotyping based on proximal or remote sensing could be useful for improving pedigree and genomic prediction model accuracies for traits not yet possible to phenotype directly. We tested if using aerial measurements of canopy temperature, and green and red normalized difference vegetation index as secondary traits in pedigree and genomic best linear unbiased prediction models could increase accuracy for grain yield in wheat, Triticum aestivum L., using 557 lines in five environments. Secondary traits on training and test sets, and grain yield on the training set were modeled as multivariate, and compared to univariate models with grain yield on the training set only. Cross validation accuracies were estimated within and across-environment, with and without replication, and with and without correcting for days to heading. We observed that, within environment, with unreplicated secondary trait data, and without correcting for days to heading, secondary traits increased accuracies for grain yield by 56% in pedigree, and 70% in genomic prediction models, on average. Secondary traits increased accuracy slightly more when replicated, and considerably less when models corrected for days to heading. In across-environment prediction, trends were similar but less consistent. These results show that secondary traits measured in high-throughput could be used in pedigree and genomic prediction to improve accuracy. This approach could improve selection in wheat during early stages if validated in early-generation breeding plots.
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