Mechanical Engineering, Electrical and Computer Engineering, Plant Sciences Institute, Agronomy, Industrial and Manufacturing Systems Engineering, Bioeconomy Institute (BEI)
Journal or Book Title
This perspective lays out a framework to enable the breeding of crops that can meet worldwide demand under the challenges of global climate change. Past work in various fields has produced multiple prediction methods to contribute to different plant breeding objectives. Our proposed framework focuses on the integration of these methods into decision-support tools that quantify the effects on multiple objectives of decisions made throughout the plant breeding pipeline. We discuss the complementarities among these methods with an emphasis on integration into tools that utilize operations research and systems approaches to help plant breeders rapidly and optimally design new cultivars under extant time, cost, and environmental constraints. In illustrating this potential, we demonstrate the interconnectedness and probabilistic nature of plant breeding objectives and highlight research opportunities to refine and combine knowledge across multiple disciplines. Such a framework can help plant breeders more efficiently breed for future environments, including so-called minor crops, leading to an overall increase in the resiliency of global food production systems.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.
Kusmec, Aaron; Zheng, Zihao; Archontoulis, Sotirios; Ganapathysubramanian, Baskar; Hu, Guiping; Wang, Lizhi; Yu, Jianming; and Schnable, Patrick S., "Interdisciplinary strategies to enable data-driven plant breeding in a changing climate" (2021). Mechanical Engineering Publications. 474.