The DOI matches its Crossref record; 54 of the 54 references it cites were found in a registry or an index.
The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data
Open accessAbstract
Open Access fees are received from the Bill and Melinda Gates Foundation. We acknowledge the financial support provided by the Bill and Melinda Gates Foundation [INV-003439 BMGF/FCDO Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW)] as well as USAID projects [Amend. No. 9 MTO 069033, USAID-CIMMYT Wheat/AGGMW, AGG-Maize Supplementary Project, AGG (Stress Tolerant Maize for Africa)] that generated the CIMMYT data analyzed in this study. We are also thankful for the financial support provided by the Foundations for Research Levy on Agricultural Products (F.F.J.) and the Agricultural Agreement Research Fund (J.A.) in Norway through NFR grant 267806 as well as the CIMMYT CRP (maize and wheat). Funding. Open Access fees are received from the Bill and Melinda Gates Foundation. We acknowledge the financial support provided by the Bill and Melinda Gates Foundation [INV-003439 BMGF/FCDO Accelerating Genetic Gains in Maize and Wheat for Improved Livelihoods (AG2MW)] as well as USAID projects [Amend. No. 9 MTO 069033, USAID-CIMMYT Wheat/AGGMW, AGG-Maize Supplementary Project, AGG (Stress Tolerant Maize for Africa)] that generated the CIMMYT data analyzed in this study. We are also thankful for the financial support provided by the Foundations for Research Levy on Agricultural Products (F.F.J.) and the Agricultural Agreement Research Fund (J.A.) in Norway through NFR grant 267806 as well as the CIMMYT CRP (maize and wheat).
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Citations
Cites
Genomic Selection in Plant Breeding: Methods, Models, and Perspectives
Crossa, J., Pérez-Rodríguez, P., Cuevas, J., Montesinos-López, O., Jarquín, D., de los Campos, G., Burgueño, J., González-Camacho, J. M., Pérez-Elizalde, S., Beyene, Y., et al. (2017) · Trends in Plant Science · doi:10.1016/j.tplants.2017.08.011 · Cited by 1,839
Self-citationA reaction norm model for genomic selection using high-dimensional genomic and environmental data
Jarquín, D., Crossa, J., Lacaze, X., Du Cheyron, P., Daucourt, J., Lorgeou, J., Piraux, F., Guerreiro, L., Pérez, P., Calus, M., et al. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2243-1 · Cited by 687
Self-citationBurgueño, J., de los Campos, G., Weigel, K., & Crossa, J. (2012) · Crop Science · doi:10.2135/cropsci2011.06.0299 · Cited by 601
Self-citationGarcía-Ruiz, A., Cole, J. B., VanRaden, P. M., Wiggans, G. R., Ruiz-López, F. J., & Van Tassell, C. P. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1519061113 · Cited by 573
Environmental and genotype-environmental components of variability III. Multiple lines and crosses
Perkins, J. M., & Jinks, J. L. (1968) · Heredity · doi:10.1038/hdy.1968.48 · Cited by 510
Heslot, N., Akdemir, D., Sorrells, M. E., & Jannink, J.-L. (2014) · Theoretical and Applied Genetics · doi:10.1007/s00122-013-2231-5 · Cited by 409
De Mori, G., Falchi, R., Testolin, R., Bassi, D., Savazzini, F., Dondini, L., Tartarini, S., Palmisano, F., Minafra, A., Spadotto, A., et al. (2019) · Frontiers in Plant Science · doi:10.3389/fpls.2019.01576 · Cited by 375
Beyene, Y., Semagn, K., Mugo, S., Tarekegne, A., Babu, R., Meisel, B., Sehabiague, P., Makumbi, D., Magorokosho, C., Oikeh, S., et al. (2015) · Crop Science · doi:10.2135/cropsci2014.07.0460 · Cited by 302
Self-citationGenomic prediction of maize yield across European environmental conditions
Millet, E. J., Kruijer, W., Coupel-Ledru, A., Alvarez Prado, S., Cabrera-Bosquet, L., Lacube, S., Charcosset, A., Welcker, C., van Eeuwijk, F., & Tardieu, F. (2019) · Nature Genetics · doi:10.1038/s41588-019-0414-y · Cited by 280 · cited 4×
“The use of genomics with enviromics is the basis for the prediction of cultivars across diverse growing conditions (e.g., Jarquín et al., 2014; Messina et al., 2018; Millet et al., 2019), which is useful for the prediction of global warming.”
BackgroundMethodInfluentialMessina, C. D., Technow, F., Tang, T., Totir, R., Gho, C., & Cooper, M. (2018) · European Journal of Agronomy · doi:10.1016/j.eja.2018.01.007 · Cited by 225
Cited by
Xu, Y., Zhang, X., Li, H., Zheng, H., Zhang, J., Olsen, M. S., Varshney, R. K., Prasanna, B. M., & Qian, Q. (2022) · Molecular Plant · doi:10.1016/j.molp.2022.09.001 · Cited by 363
Cooper, M., Messina, C. D., Tang, T., Gho, C., Powell, O. M., Podlich, D. W., Technow, F., & Hammer, G. L. (2022) · Plant Breeding Reviews · doi:10.1002/9781119874157.ch8 · Cited by 56
Multimodal deep learning methods enhance genomic prediction of wheat breeding
Montesinos-López, A., Rivera, C., Pinto, F., Piñera, F., Gonzalez, D., Reynolds, M., Pérez-Rodríguez, P., Li, H., Montesinos-López, O. A., & Crossa, J. (2023) · G3: Genes, Genomes, Genetics · doi:10.1093/g3journal/jkad045 · Cited by 41
Self-citationMelsen, K. A. C., Gezan, S., Tolhurst, D. J., van Eeuwijk, F. A., & Peeters, C. F. W. (2025) · arXiv · doi:10.48550/arxiv.2501.06844 · Cited by 3
“There has been significant interest in using these types of enviromics kernels to model G × E interactions, resulting in the proposal of several approaches (Costa-Neto et al. , 2021a; Crossa et al. , 2021; Jarqu´ın et al. , 2014; Malosetti et al. , 2004).”
Metadata
- Authors
- Crossa, J., Fritsche-Neto, R., Montesinos-Lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-Lopez, A., & Bentley, A. R.
- Venue
- Frontiers in Plant Science
- Publisher
- Frontiers Media SA
- Published
- 2021-04-16
- Volume
- 12
- Pages
- 651480-651480
- ISSN
- 1664-462X (ISSN-L)
- OpenAlex
- W3154319762
- Cited by
- 266
- References
- 54
- FWCI
- 23.14
- Type
- journal article
- Language
- en
- Open access
- gold
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- Crossa, J., Fritsche-Neto, R., Montesinos-Lopez, O. A., Costa-Neto, G., Dreisigacker, S., Montesinos-Lopez, A., & Bentley, A. R. (2021). The Modern Plant Breeding Triangle: Optimizing the Use of Genomics, Phenomics, and Enviromics Data. Frontiers in Plant Science. https://doi.org/10.3389/fpls.2021.651480
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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