Computational design for more engaged, impactful, and dynamic agricultural research.

Authors: KANTAR, MICHAEL - University Of Hawaii; Ewing, Patrick; BANCIC, JON - University Of Edinburgh; BLAIR, HAVA - University Of Wisconsin; GARBA, ISMAIL - Commonwealth Scientific And Industrial Research Organisation (CSIRO); JAMSHIDI, SAJAD - Purdue University; Jannink, Jean Luc; JHA, PRAKASH - Mississippi State University; JUNGERS, JACOB - University Of Minnesota; PATHAK, HARSH - Purdue University; PAUL, SIDDARTHO - Purdue University; RAGHAVAN, BARATH - University Of Southern California; RUNCK, BRYAN - University Of Minnesota; SINGH, JASDEEP - Cornell University; SUBEDI, SAMIKSHYA - University Of Minnesota; JOSHI, VIJAYA - University Of Florida; WANG, DIANE - Purdue University

Submitted to: Crop Science
Publication Type: Review Article
Publication Acceptance Date: 2/12/2025
Publication Date: 3/9/2025
Citation: Kantar, M., Ewing, P., Bancic, J., Fumia, N., Garba, I., Jamshidi, S., Jannink, J.- L., Jha, P., Jungers, J., Pathak, H., Paul, S. S., Raghavan, B., Runck, B., Singh, J., Subedi, S., Joshi, V., & Wang, D. (2025). Computational design for more engaged, impactful, and dynamic agricultural research. Crop Science, 65, e70034. https://doi.org/10.1002/csc2.70034
DOI: https://doi.org/10.1002/csc2.70034

Interpretive Summary: Computational design in agriculture is the use of computational tools to propose and evaluate new agricultural systems to meet the dynamic challenges facing agriculture today and tomorrow. It is unique from digital agriculture – the use of remote sensing, variable rate technology, and optimization/control procedures – in that it integrates computational and crop science approaches to formulate problems rather than simply mitigating them. In this special issue, we highlight the state of the art of computational design to adapt agricultural systems to better meet societal goals more rapidly and at lower cost. All disciplines within crop sciences are represented, from breeding strategy through cropping system design. Using a symposium at a major scientific conference as a case study, we also demonstrate how computational design as a framework can facilitate transdisciplinary research. Critically, all participants highlighted the potential of computational design to facilitate stakeholder engagement through eliciting, formalizing, and evaluating their values and experiences. This is especially important within the grand challenge contexts of changing climates and market demands, where intuition developed in the past may break down. By leveraging the power of computational design, we can make informed decisions to create sustainable agricultural systems that maximize productivity while minimizing environmental impact.

Technical Abstract: Computational design in agriculture is the use of computational tools to propose and evaluate new agricultural systems to meet the dynamic challenges facing agriculture today and tomorrow. It is unique from digital agriculture – the use of remote sensing, variable rate technology, and optimization/control procedures – in that it integrates computational and crop science approaches to formulate problems rather than simply mitigating them. In this special issue, we highlight the state of the art of computational design to adapt agricultural systems to better meet societal goals more rapidly and at lower cost. Using a symposium at a major scientific conference as a case study, we also demonstrate how computational design as a framework can facilitate transdisciplinary research. Critically, all participants highlighted the potential of computational design to facilitate stakeholder engagement through eliciting, formalizing, and evaluating their values and experiences. This is especially important within the grand challenge contexts of changing climates and market demands, where intuition developed in the past may break down. By leveraging the power of computational design, we can make informed decisions to create sustainable agricultural systems that maximize productivity while minimizing environmental impact.