![]() ![]() However, many public-sector plant breeding programs in both developed and developing countries have struggled to keep pace with technological change since the Green Revolution (Jain 2010 Pingali 2012 Baranski 2015). 2018), plant breeding will play an essential role in feeding 9 billion people sustainably by 2050 (Godfray et al. In the face of climate change, annual reductions in arable land, and localized malnutrition (Ritchie et al. ![]() 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. 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. 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. To enable this, research managers will need to consider and proactively manage the, accountability, strategy, and resource allocations of breeding teams. These are complex processes and will require breeding organizations to adopt a culture of continuous optimization and improvement. 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. 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 breeder’s equation is the foundational application of quantitative genetics to crop improvement. 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. ![]()
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