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Genetic Gain Performance Metric Accelerates Agricultural Productivity

Author

Listed:
  • Joseph Byrum

    (Syngenta Seeds, Inc., Slater, Iowa 50244)

  • Bill Beavis

    (Iowa State University, Ames, Iowa 50011)

  • Craig Davis

    (Syngenta Seeds, Inc., Slater, Iowa 50244)

  • Greg Doonan

    (Syngenta Seeds, Inc., Slater, Iowa 50244)

  • Tracy Doubler

    (Syngenta Seeds, Inc., Slater, Iowa 50244)

  • Von Kaster

    (Syngenta Seeds, Inc., Slater, Iowa 50244)

  • Ron Mowers

    (Syngenta Seeds, Inc., Slater, Iowa 50244)

  • Sam Parry

    (Arizona State University, Phoenix, Arizona 85004)

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.

Suggested Citation

  • Joseph Byrum & Bill Beavis & Craig Davis & Greg Doonan & Tracy Doubler & Von Kaster & Ron Mowers & Sam Parry, 2017. "Genetic Gain Performance Metric Accelerates Agricultural Productivity," Interfaces, INFORMS, vol. 47(5), pages 442-453, October.
    • Handle: RePEc:inm:orinte:v:47:y:2017:i:5:p:442-453
    DOI: 10.1287/inte.2017.0909
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    References listed on IDEAS

    as
    1. Joseph Byrum & Craig Davis & Gregory Doonan & Tracy Doubler & David Foster & Bruce Luzzi & Ronald Mowers & Chris Zinselmeier & Jack Kloeber & Dave Culhane & Stephen Mack, 2016. "Advanced Analytics for Agricultural Product Development," Interfaces, INFORMS, vol. 46(1), pages 5-17, February.
    2. James N. Boles, 1955. "Linear Programming and Farm Management Analysis," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 37(1), pages 1-24.
    3. Xu, Pan & Wang, Lizhi & Beavis, William D., 2011. "An optimization approach to gene stacking," European Journal of Operational Research, Elsevier, vol. 214(1), pages 168-178, October.
    Full references (including those not matched with items on IDEAS)

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