766. Portfolio Optimization in Crop Breeding
Invited abstract in session TA-16: Food and Nutrition Security - Crops and menus, stream Sustainable Food & Agroforestry.
Tuesday, 8:30-10:00Room: Esther Simpson 2.07
Authors (first author is the speaker)
| 1. | Paul Deuker
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| EOR/Zero Hunger Lab, Tilburg University | |
| 2. | Marleen Balvert
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| Tilburg University | |
| 3. | Juan Vera
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| Etrie and OR, Tilburg University | |
| 4. | Jacob van Etten
|
Abstract
In many non-Western regions, agricultural yields can be as low as 10% of those in Europe due to extreme climate variability and heightened biotic and abiotic stresses. Traditional breeding methods, which typically optimize for single traits like yield, fall short in addressing the multifaceted challenges imposed by such uncertain environments, leaving farmers exposed to significant economic risks.
We introduce a two-stage stochastic programming framework that integrates breeding and planting decisions to manage environmental uncertainty. In the first stage, our model determines optimal breeding strategies that balance the trade-off between broad adaptation—ensuring resilience across diverse conditions—and specific adaptation—maximizing yield under particular environmental scenarios. Once uncertainties are resolved in the second stage, the model guides planting decisions, selecting crop varieties from a diversified portfolio tailored to the realized conditions.
By capturing the interplay between breeding and planting, our approach aims to minimize yield volatility and economic losses while enhancing the overall efficiency of breeding programs. This framework aims to provide a robust decision-support tool that helps farmers and breeders navigate variable agricultural environments, with the goal to contribute to more stable and sustainable food production systems.
Keywords
- OR in Agriculture
- Stochastic Optimization
- Programming, Stochastic
Status: accepted
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