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Advanced Analytics for Agricultural Product Development

Author

Listed:
  • Joseph Byrum

    (Syngenta, Slater, Iowa 50244)

  • Craig Davis

    (Syngenta, Slater, Iowa 50244)

  • Gregory Doonan

    (Syngenta, Slater, Iowa 50244)

  • Tracy Doubler

    (Syngenta, Slater, Iowa 50244)

  • David Foster

    (Syngenta, Slater, Iowa 50244)

  • Bruce Luzzi

    (Syngenta, Slater, Iowa 50244)

  • Ronald Mowers

    (Syngenta, Slater, Iowa 50244)

  • Chris Zinselmeier

    (Syngenta, Slater, Iowa 50244)

  • Jack Kloeber

    (Kromite LLC, Lambertville, New Jersey 08530)

  • Dave Culhane

    (Kromite LLC, Lambertville, New Jersey 08530)

  • Stephen Mack

    (Kromite LLC, Lambertville, New Jersey 08530)

Abstract

Syngenta, a leading developer of crop varieties (seeds) that provide food for human and livestock consumption, is committed to bringing greater food security to an increasingly populous world by creating a transformational shift in farm productivity. Syngenta Soybean Research and Development (R&D) is leading Syngenta’s corporate plant-breeding strategy by developing and implementing a new product development model that is enabling the creation of an efficient and effective soybean breeding strategy. Key to the new strategy is the combination of advanced analytics and plant-breeding knowledge to find opportunities to increase crop productivity and optimize plant-breeding processes. Syngenta uses discrete-event and Monte Carlo simulation models to codify Syngenta Soybean R&D best practices, and uses stochastic optimization to create the best soybean breeding plans and strategically align its research efforts. As a result of using these new analytical tools, Syngenta estimates that it will save more than $287 million between 2012 and 2016.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:orinte:v:46:y:2016:i:1:p:5-17
    DOI: 10.1287/inte.2015.0823
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    Citations

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    Cited by:

    1. Michael F. Gorman, 2021. "Contextual Complications in Analytical Modeling: When the Problem is Not the Problem," Interfaces, INFORMS, vol. 51(4), pages 245-261, July.
    2. 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.
    3. Urbinati, Andrea & Bogers, Marcel & Chiesa, Vittorio & Frattini, Federico, 2019. "Creating and capturing value from Big Data: A multiple-case study analysis of provider companies," Technovation, Elsevier, vol. 84, pages 21-36.

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