Genetic Gain Performance Metric Accelerates Agricultural Productivity

Abstract

The agricultural seed industry invests billions of dollars each year to improve our understanding of how best to unlock a seed’s full potential. This investment brings a significant benefit to agricultural customers—the farmers who grow commodity crops, such as corn, soybeans, and wheat. Commodity farmers expect new crop varieties to be adapted to local conditions and have greater genetic potential for yield. We refer to the amount of increase in the genetic potential for yield as “genetic gain.” The agricultural seed industry needs a universal, unbiased metric for genetic gain performance (GGP). Therefore, in 2010 we developed and implemented an algorithm that calculates an unbiased GGP metric that eliminates environmental factors (e.g., solar radiation, rainfall, and temperature) and is applicable at each stage of the product development pipeline. We subsequently used this metric during the variety development stage of our breeding projects to measure the impact of operational changes. We used weighted averages of GGP to retrospectively evaluate changes in genetic gain across 10 years of our breeding pipeline to quantify the benefit. We estimate that genetic gains are now 40 percent greater than the gains seen before implementation of the GGP in 2010. Our analyses show that the GGP metric has saved Syngenta approximately $250 million in varietal development costs, which would otherwise have been required to improve genetic gain by 40 percent. Syngenta scientists now use GGP to evaluate the genetic gain of all breeding projects. It serves as a valuable early-warning system. At the end of each growing season, we collect yield data and update the GGP database. This allows our scientists to perform an annual evaluation of genetic advances in each market segment. These assessments identify potential performance gaps likely to surface in the next growing season so that they can be avoided. Our successful development and deployment of a genetic gain metric is an important advance for both Syngenta and the entire agricultural industry.

Technological advances in maize breeding: past, present and future

“…OR was created to provide quantitative risk assessments of proposed military activities for uncertain and dynamic conditions in WWII and has since been used to design optimal manufacturing, transportation, energy and communications systems and networks. Some of the first civilian applications were in agriculture (Boles 1955;Heady 1954;Heady and Pesek 1954;Rendel and Robertson 1950;Robertson 1957), but with one exception (Johnson et al 1988) OR was ignored for designing plant breeding systems until about 10 years ago (Akdemir et al 2018;Akdemir and Sanchez 2016;Byrum et al 2016Byrum et al , 2017Cameron et al 2017;Canzar and El-Kebir 2011;De Beukelaer et al 2015;Han et al 2017;Xu et al 2011).…”

“…Next, the evaluation of possible outcomes required critical thinking about appropriate metrics to quantify impacts on meeting their breeding objectives. Based on the development of novel metrics (Byrum et al 2017) and a comprehensive exploration of the modification space for each variety development project, they implemented modified variety development pipelines, resulting in over $287 M US cost savings during the period 2010 to 2015 and awarding of the 2015 Edelman prize (https ://www.infor ms.org/About -INFOR MS/News-Room/Press -Relea ses/Synge nta-Wins-2015-INFOR MS-Edelm an-Prize ). To our knowledge this approach has not been applied to maize hybrid development pipelines, but given the pressure to reduce costs, it is likely that several commercial maize breeding companies are pursuing similar approaches to quickly become more efficient.…”

Factors Affecting Response to Recurrent Genomic Selection in Soybeans

“…Pedigrees of modern soybean varieties confirm that genetic improvement of soybeans in Maturity Zones (MZs) II, III and IV has been largely through intra-population recurrent selection (Byrum, personal communication; Hyten et al 2006; Mikel et al 2010; Langewisch et al 2017; Achard et al 2020). Prior to the Plant Variety Protection Act in 1970 (https://www.ams.usda.gov/rules-regulations/pvpa), a cycle of genetic improvement would require from 10 to 14 years, whereas in the last 40 years commercial organizations have invested in development of continuous nurseries including software that streamlines inventories and logistics of seed transfer, resulting in the capacity to complete a cycle of recurrent selection in five years (Byrum et al 2017; Anderson et al 2019). Thus, using current best practices, a soybean breeder might experience three to five cycles of genetic improvement.…”

Strategies to Assure Optimal Trade-Offs Among Competing Objectives for the Genetic Improvement of Soybean

“…Responses to the selection of commodity crops have been enabled by decreasing the number of years per cycle of recurrent selection, by increasing the number of replicable genotypes (selection intensity), and by increasing the number of replicated field trials (heritability on an entry mean basis). In other words, genotypic improvements from responses to selection in commodity crops over the last 50 years (Specht et al, 2014) required monetary investments that became part of increased seed costs during the same time (Byrum et al, 2017;USDA-ERS, 2020). Since the emergence and adoption of Genomic Selection (GS), it has been possible to increase the numbers of genotypes that are evaluated, i.e., selection intensity, without significant increases in numbers of field plots (Bernardo and Yu, 2007;Bernardo, 2008;Asoro et al, 2011;Heslot et al, 2012;Nakaya and Isobe, 2012;Combs and Bernado, 2013;Crossa et al, 2014;Beyene et al, 2015;Bassi et al, 2016;Marulanda et al, 2016;Jonas and de Koning, 2016;Hickey et al, 2017;Goiffon et al, 2017).…”