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Data, Economics and Computational Agricultural Science

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  • John M Antle

Abstract

In this address I discuss the potential for the revolution in data infrastructure, data science and computation to support and accelerate the transformation towards a more productive, healthy and sustainable agricultural systems. A theme that emerges from both the agricultural systems science and economic-behavioral sciences is that improved acquisition and use of data is a critical constraint on agricultural research and its successful application, both for on-farm production system management and for technology and policy decision making. This in turn suggests potentially high returns to public investment in the data needed to enable computational agricultural science. I conclude with a prototype private-public scheme for investment in the data needed to support advanced computational methods and models, and discuss the economic, technical, legal and institutional challenges to its implementation.

Suggested Citation

  • 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.
    • Handle: RePEc:oup:ajagec:v:101:y:2019:i:2:p:365-382.
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    File URL: http://hdl.handle.net/10.1093/ajae/aay103
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    Citations

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    Cited by:

    1. Christian Troost & Julia Parussis-Krech & Matías Mejaíl & Thomas Berger, 2023. "Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output," Computational Economics, Springer;Society for Computational Economics, vol. 62(3), pages 721-759, October.
    2. Carauta, Marcelo & Troost, Christian & Guzman-Bustamante, Ivan & Hampf, Anna & Libera, Affonso & Meurer, Katharina & Bönecke, Eric & Franko, Uwe & Ribeiro Rodrigues, Renato de Aragão & Berger, Thomas, 2021. "Climate-related land use policies in Brazil: How much has been achieved with economic incentives in agriculture?," Land Use Policy, Elsevier, vol. 109(C).
    3. Palatnik, Ruslana Rachel & Freer, Mikhail & Levin, Mark & Golberg, Alexander & Zilberman, David, 2023. "Algae-Based Two-Stage Supply Chain with Co-Products," Ecological Economics, Elsevier, vol. 207(C).
    4. Neal Hughes & Michael Lu & Wei Ying Soh & Kenton Lawson, 2022. "Modelling the effects of climate change on the profitability of Australian farms," Climatic Change, Springer, vol. 172(1), pages 1-22, May.
    5. Kosior, Catherine, 2021. "Towards a common data space for agriculture in the European Union. A perspective of sustainable development," International Journal of Agricultural Sciences and Technology (IJAGST), SvedbergOpen, vol. 191(2), February.
    6. Hughes, Neal & Soh, Wei Ying & Lawson, Kenton & Lu, Michael, 2022. "Improving the performance of micro-simulation models with machine learning: The case of Australian farms," Economic Modelling, Elsevier, vol. 115(C).
    7. 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.

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