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dc.contributor.authorWilliamson, HF
dc.contributor.authorLeonelli, S
dc.date.accessioned2022-10-27T11:12:53Z
dc.date.issued2022-10-27
dc.date.updated2022-10-27T09:23:13Z
dc.description.abstractThis chapter argues for the importance of considering conceptual and normative commitments when addressing questions of responsible practice in data-intensive agricultural research and development. We consider genetic gain-focused plant breeding strategies that envision a data-intensive mode of breeding in which genomic, environmental and socio-economic data are mobilised for rapid crop variety development. Focusing on socio-economic data linkage, we examine methods of product profiling and how they accommodate gendered dimensions of breeding in the field. Through a comparison with participatory breeding methods, we argue that the conceptual commitments underpinning current methods of integrating socioeconomic data into calculations of genetic gain can preclude the achievement of key social development goals, and that better engagement with participatory approaches can help address this problem. We conclude by identifying three key avenues towards a data-intensive approach to plant breeding that utilises the diverse sources of relevant evidence available, including socio-economic data, and maximises the chance of developing sustainable and responsible strategies and research practices in this domain: (1) reliable, long-term management of data infrastructures; (2) ongoing critical analysis of the conceptual foundations of specific strategies; and (3) regular transdisciplinary consultations including expertise in the social studies of agricultural science as well as participatory breeding techniques.en_GB
dc.description.sponsorshipAlan Turing Instituteen_GB
dc.description.sponsorshipEuropean Research Council (ERC)en_GB
dc.identifier.citationIn: Towards Responsible Plant Data Linkage: Data Challenges for Agricultural Research and Development, edited by Hugh F. Williamson, Sabina Leonelli, pp. 301 - 317en_GB
dc.identifier.doi10.1007/978-3-031-13276-6_16
dc.identifier.grantnumberEP/N510129/1en_GB
dc.identifier.grantnumber335925en_GB
dc.identifier.grantnumber101001145en_GB
dc.identifier.urihttp://hdl.handle.net/10871/131463
dc.identifierORCID: 0000-0002-6381-7638 (Williamson, Hugh)
dc.language.isoenen_GB
dc.publisherSpringeren_GB
dc.relation.urlhttp://hdl.handle.net/10871/131460en_GB
dc.rights© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.en_GB
dc.titleCultivating Responsible Plant Breeding Strategies: Conceptual and Normative Commitments in Data-Intensive Agricultureen_GB
dc.typeBook chapteren_GB
dc.date.available2022-10-27T11:12:53Z
dc.contributor.editorWilliamson, HF
dc.contributor.editorLeonelli, S
dc.identifier.isbn978-3-031-13276-6
dc.descriptionThis is the final version. Available on open access from Springer via the DOI in this recorden_GB
dc.descriptionThe book in which this chapter appears is available in ORE at http://hdl.handle.net/10871/131460en_GB
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_GB
rioxxterms.versionVoRen_GB
rioxxterms.licenseref.startdate2022-10-27
rioxxterms.typeBook chapteren_GB
refterms.dateFCD2022-10-27T11:10:23Z
refterms.versionFCDVoR
refterms.dateFOA2022-10-27T11:13:10Z


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© 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Except where otherwise noted, this item's licence is described as © 2023 The Author(s). Open Access. This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made. The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.