International Rice Research Institute, Los Banos, Laguna, Philippines.
Kenya Agricultural and Livestock Research Organization, Nairobi, Kenya.
Theor Appl Genet. 2019 Mar;132(3):627-645. doi: 10.1007/s00122-019-03317-0. Epub 2019 Mar 1.
The integration of new technologies into public plant breeding programs can make a powerful step change in agricultural productivity when aligned with principles of quantitative and Mendelian genetics. The breeder's equation is the foundational application of quantitative genetics to crop improvement. Guided by the variables that describe response to selection, emerging breeding technologies can make a powerful step change in the effectiveness of public breeding programs. The most promising innovations for increasing the rate of genetic gain without greatly increasing program size appear to be related to reducing breeding cycle time, which is likely to require the implementation of parent selection on non-inbred progeny, rapid generation advance, and genomic selection. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. This must be combined with thoughtful management of elite genetic variation and a clear separation between the parental selection process and product development and advancement process. With an abundance of new technologies available, breeding teams need to evaluate carefully the impact of any new technology on selection intensity, selection accuracy, and breeding cycle length relative to its cost of deployment. Finally breeding data management systems need to be well designed to support selection decisions and novel approaches to accelerate breeding cycles need to be routinely evaluated and deployed.
当新技术与数量遗传学和孟德尔遗传学原理相结合时，可在农业生产力方面实现重大突破。育种者方程是数量遗传学在作物改良中的基础应用。通过描述对选择响应的变量，新兴的育种技术可以使公共育种计划的效率发生重大变化。最有希望提高遗传增益速度而又不大大增加计划规模的创新似乎与缩短育种周期有关，这可能需要在非自交后代上实施亲本选择、快速世代推进和基因组选择。这些是复杂的过程，需要育种组织采用持续优化和改进的文化。为此，研究经理需要考虑并积极管理育种团队的问责制、战略和资源分配。这必须与精心管理优秀遗传变异以及明确区分亲本选择过程与产品开发和推进过程相结合。有了大量的新技术，育种团队需要仔细评估任何新技术对选择强度、选择准确性和育种周期长度的影响，相对于其部署成本而言。最后，育种数据管理系统需要精心设计，以支持选择决策，并且需要定期评估和部署加速育种周期的新方法。
