Person: Mondal, S.
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Mondal
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S.
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Mondal, S.
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0000-0002-8582-8899 7 results
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- Multi-modal deep learning improves grain yield prediction in wheat breeding by fusing genomics and phenomics(Oxford University Press, 2023) Togninalli, M.; Xu Wang; Kucera, T.; Shrestha, S.; Juliana, P.; Mondal, S.; Pinto Espinosa, F.; Velu, G.; Crespo Herrera, L.A.; Huerta-Espino, J.; Singh, R.P.; Borgwardt, K.; Poland, J.
Publication - Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data(John Wiley & Sons Inc., 2022) Lopez-Cruz, M.; Dreisigacker, S.; Crespo Herrera, L.A.; Bentley, A.R.; Singh, R.P.; Poland, J.; Shrestha, S.; Huerta-Espino, J.; Velu, G.; Juliana, P.; Mondal, S.; Pérez-Rodríguez, P.; Crossa, J.
Publication - Breeding increases grain yield, zinc, and iron, supporting enhanced wheat biofortification(John Wiley and Sons Inc, 2022) Velu, G.; Atanda, A.S.; Singh, R.P.; Huerta-Espino, J.; Crespo Herrera, L.A.; Juliana, P.; Mondal, S.; Joshi, A.K.; Bentley, A.R.
Publication - Effects of glutenins (Glu-1 and Glu-3) allelic variation on dough properties and bread-making quality of CIMMYT bread wheat breeding lines(Elsevier, 2022) Guzman, C.; Crossa, J.; Mondal, S.; Velu, G.; Huerta-Espino, J.; Crespo Herrera, L.A.; Vargas, M.; Singh, R.P.; Ibba, M.I.
Publication - Integrating genomic-enabled prediction and high-throughput phenotyping in breeding for climate-resilient bread wheat(Springer, 2019) Juliana, P.; Montesinos-Lopez, O.A.; Crossa, J.; Mondal, S.; González Pérez, L.; Poland, J.; Huerta-Espino, J.; Crespo Herrera, L.A.; Velu, G.; Dreisigacker, S.; Shrestha, S.; Pérez-Rodríguez, P.; Pinto Espinosa, F.; Singh, R.P.Genomic selection and high-throughput phenotyping (HTP) are promising tools to accelerate breeding gains for high-yielding and climate-resilient wheat varieties. Hence, our objective was to evaluate them for predicting grain yield (GY) in drought-stressed (DS) and late-sown heat-stressed (HS) environments of the International maize and wheat improvement center?s elite yield trial nurseries. We observed that the average genomic prediction accuracies using fivefold cross-validations were 0.50 and 0.51 in the DS and HS environments, respectively. However, when a different nursery/year was used to predict another nursery/year, the average genomic prediction accuracies in the DS and HS environments decreased to 0.18 and 0.23, respectively. While genomic predictions clearly outperformed pedigree-based predictions across nurseries, they were similar to pedigree-based predictions within nurseries due to small family sizes. In populations with some full-sibs in the training population, the genomic and pedigree-based prediction accuracies were on average 0.27 and 0.35 higher than the accuracies in populations with only one progeny per cross, indicating the importance of genetic relatedness between the training and validation populations for good predictions. We also evaluated the item-based collaborative filtering approach for multivariate prediction of GY using the green normalized difference vegetation index from HTP. This approach proved to be the best strategy for across-nursery predictions, with average accuracies of 0.56 and 0.62 in the DS and HS environments, respectively. We conclude that GY is a challenging trait for across-year predictions, but GS and HTP can be integrated in increasing the size of populations screened and evaluating unphenotyped large nurseries for stress?resilience within years.
Publication - Genetic gains for grain yield in CIMMYT’s semi-arid wheat yield trials grown in suboptimal environments(Crop Science Society of America (CSSA), 2018) Crespo Herrera, L.A.; Crossa, J.; Huerta-Espino, J.; Vargas Hernández, M.; Mondal, S.; Velu, G.; Payne, T.S.; Braun, H.J.; Singh, R.P.Wheat (Triticum aestivum L.) is a major staple food crop grown worldwide on >220 million ha. Climate change is regarded to have severe effect on wheat yields, and unpredictable drought stress is one of the most important factors. Breeding can significantly contribute to the mitigation of climate change effects on production by developing drought-tolerant wheat germplasm. The objective of our study was to determine the annual genetic gain for grain yield (GY) of the internationally distributed Semi-Arid Wheat Yield Trials, grown during 2002–2003 to 2013–2014 and developed by the Bread Wheat Breeding program at the CIMMYT. We analyzed data from 740 locations across 66 countries, which were classified in low-yielding (LYE) and medium-yielding (MYE) environments according to a cluster analysis. The rate of GY increase (GYC) was estimated relative to four drought-tolerant wheat lines used as constant checks. Our results estimate that the rate of GYC in LYE was 1.8% (38.13 kg ha−1 yr−1), whereas in MYE, it was 1.41% (57.71 kg ha−1 yr−1). The increase in GYC across environments was 1.6% (48.06 kg ha−1 yr−1). The pedigrees of the highest yielding lines through the coefficient of parentage analysis indicated the utilization of three primary sources—‘Pastor’, ‘Baviacora 92’, and synthetic hexaploid derivatives—to develop drought-tolerant, high and stably performing wheat lines. We conclude that CIMMYT’s wheat breeding program continues to deliver adapted germplasm for suboptimal conditions of diverse wheat growing regions worldwide.
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.
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