Interdisciplinary strategies to enable data-driven plant breeding in a changing climate

Kusmec, Aaron
Zheng, Zihao
Archontoulis, Sotirios
Ganapathysubramanian, Baskar
Hu, Guiping
Wang, Lizhi
Yu, Jianming
Schnable, Patrick
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Mechanical Engineering
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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.


This article is published as Kusmec, Aaron, Zihao Zheng, Sotirios Archontoulis, Baskar Ganapathysubramanian, Guiping Hu, Lizhi Wang, Jianming Yu, and Patrick S. Schnable. "Interdisciplinary strategies to enable data-driven plant breeding in a changing climate." One Earth 4, no. 3 (2021): 372-383. DOI: 10.1016/j.oneear.2021.02.005. Posted with permission.