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The Future of Farming: The (Non)-Sense of Big Data Predictive Tools for Sustainable EU Agriculture

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  • Margot Luyckx

    (Public Law, Planning & Environment, Bar of Antwerp, 2600 Antwerp, Belgium)

  • Leonie Reins

    (Tilburg Institute for Law, Technology and Society, Tilburg University, 5037 AB Tilburg, The Netherlands)

Abstract

The agricultural sector is one of the key sectors that need to be transformed in order to mitigate climate change. The use of predictive models supported by big data (“big data predictive tools”) has already been named in the literature as one key possibility to facilitate this change. This contribution maps out the possibilities and potential harms of big data predictive tools for sustainable agricultural use and analyses the role that regulation can play to address these challenges, answering the following question: how can the EU Common Agricultural Policy (CAP) and the European Green Deal address potential harms of big data predictive tools for sustainable agriculture while safeguarding its possibilities. Based on a combination of doctrinal legal research and a review of secondary sources, this contribution concludes that in theory, both instruments recognize the possibilities of big data predictive tools for agriculture and emphasize the necessity of environmental sustainability in this regard. However, some of the most promising and essential elements of achieving sustainable digitalisation in agriculture, risk not being substantiated because of a watered-down CAP, significant focus on larger farms and strong member state margin of appreciation. Although at first sight the CAP and Green Deal seem aligned, it can be concluded that the depth has yet to be proven. Whether this depth can be substantiated will also determine the extent to which digital technologies, such as big data predictive tools, will help in enforcing a sustainable agriculture or risk intensifying unsustainable practices in the EU.

Suggested Citation

  • Margot Luyckx & Leonie Reins, 2022. "The Future of Farming: The (Non)-Sense of Big Data Predictive Tools for Sustainable EU Agriculture," Sustainability, MDPI, vol. 14(20), pages 1-17, October.
  • Handle: RePEc:gam:jsusta:v:14:y:2022:i:20:p:12968-:d:938641
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    References listed on IDEAS

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    1. Dupraz, Pierre, 2021. "Policies for the ecological transition of agriculture: the livestock issue," Review of Agricultural, Food and Environmental Studies, Institut National de la Recherche Agronomique (INRA), vol. 101(4), January.
    2. John M Antle, 2019. "Data, Economics and Computational Agricultural Science," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 101(2), pages 365-382.
    3. Schimmelpfennig, David, 2016. "Farm Profits and Adoption of Precision Agriculture," Economic Research Report 249773, United States Department of Agriculture, Economic Research Service.
    4. Rim Lassoued & Diego M. Macall & Stuart J. Smyth & Peter W. B. Phillips & Hayley Hesseln, 2021. "Expert Insights on the Impacts of, and Potential for, Agricultural Big Data," Sustainability, MDPI, vol. 13(5), pages 1-18, February.
    5. Beatrice Garske & Antonia Bau & Felix Ekardt, 2021. "Digitalization and AI in European Agriculture: A Strategy for Achieving Climate and Biodiversity Targets?," Sustainability, MDPI, vol. 13(9), pages 1-21, April.
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