What plant breeding may (and may not) look like in 2050?
- PMID: 37455348
- DOI: 10.1002/tpg2.20368
What plant breeding may (and may not) look like in 2050?
Abstract
At the turn of 2000 many authors envisioned future plant breeding. Twenty years after, which of those authors' visions became reality or not, and which ones may become so in the years to come. After two decades of debates, climate change is a "certainty," food systems shifted from maximizing farm production to reducing environmental impact, and hopes placed into GMOs are mitigated by their low appreciation by consumers. We revise herein how plant breeding may raise or reduce genetic gains based on the breeder's equation. "Accuracy of Selection" has significantly improved by many experimental-scale field and laboratory implements, but also by vulgarizing statistical models, and integrating DNA markers into selection. Pre-breeding has really promoted the increase of useful "Genetic Variance." Shortening "Recycling Time" has seen great progression, to the point that achieving a denominator equal to "1" is becoming a possibility. Maintaining high "Selection Intensity" remains the biggest challenge, since adding any technology results in a higher cost per progeny, despite the steady reduction in cost per datapoint. Furthermore, the concepts of variety and seed enterprise might change with the advent of cheaper genomic tools to monitor their use and the promotion of participatory or citizen science. The technological and societal changes influence the new generation of plant breeders, moving them further away from field work, emphasizing instead the use of genomic-based selection methods relying on big data. We envisage what skills plant breeders of tomorrow might need to address challenges, and whether their time in the field may dwindle.
© 2023 The Authors. The Plant Genome published by Wiley Periodicals LLC on behalf of Crop Science Society of America.
References
REFERENCES
-
- Abbas, M. S. T. (2018). Genetically engineered (modified) crops (Bacillus thuringiensis crops) and the world controversy on their safety. Egyptian Journal of Biological Pest Control, 28, 52. https://doi.org/10.1186/s41938‐018‐0051‐2
-
- Alahmad, S., Dinglasan, E., Leung, K. M., Riaz, A., Derbal, N., Voss‐Fels, K. P., Able, J. A., Bassi, F. M., Christopher, J., & Hickey, L. T. (2018). Speed breeding for multiple quantitative traits in durum wheat. Plant Methods, 14, 36. https://doi.org/10.1186/s13007‐018‐0302‐y
-
- Alahmad, S., Kang, Y., Dinglasan, E., Mazzucotelli, E., Voss‐Fels, K. P., Able, J. A., Christopher, J., Bassi, F. M., & Hickey, L. T. (2020). Adaptive traits to improve durum wheat yield in drought and crown rot environments. International Journal of Molecular Sciences, 21(15), 5260. https://doi.org/10.3390/ijms21155260
-
- Alary, V., Yigezu, A. Y., & Bassi, F. M. (2020). Participatory farmers‐weighted selection (PWS) indices to raise adoption of durum cultivars. Crop Breeding, Genetics and Genomics, 2(3), e200014. https://doi.org/10.20900/cbgg20200014
-
- Alvarado, G., Rodríguez, F. M., Pacheco, A., Burgueño, J., Crossa, J., Vargas, M., Pérez‐Rodríguez, P., & Lopez‐Cruz, M. A. (2020). META‐R: A software to analyze data from multi‐environment plant breeding trials. The Crop Journal, 8, 745–756. https://doi.org/10.1016/j.cj.2020.03.010
Publication types
MeSH terms
Substances
Grants and funding
LinkOut - more resources
Full Text Sources
