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Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis

Author

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  • Paul B. Hegedus

    (Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA)

  • Bruce Maxwell

    (Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA)

  • John Sheppard

    (Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA)

  • Sasha Loewen

    (Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA)

  • Hannah Duff

    (Department of Land Resources and Environmental Sciences, Montana State University, Bozeman, MT 59717, USA)

  • Giorgio Morales-Luna

    (Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA)

  • Amy Peerlinck

    (Gianforte School of Computing, Montana State University, Bozeman, MT 59717, USA)

Abstract

Few mechanisms turn field-specific ecological data into management recommendations for crop production with appropriate uncertainty. Precision agriculture is mainly deployed for machine efficiencies and soil-based zonal management, and the traditional paradigm of small plot research fails to unite agronomic research and effective management under farmers’ unique field constraints. This work assesses the use of on-farm experiments applied with precision agriculture technologies and open-source data to gain local knowledge of the spatiotemporal variability in agroeconomic performance on the subfield scale to accelerate learning and overcome the bias inherent in traditional research approaches. The on-farm precision experimentation methodology is an approach to improve farmers’ abilities to make site-specific agronomic input decisions by simulating a distribution of economic outcomes for the producer using field-specific crop response models that account for spatiotemporal uncertainty in crop responses. The methodology is the basis of a decision support system that includes a six-step cyclical process that engages precision agriculture technology to apply experiments, gather field-specific data, incorporate modern data management and analytical approaches, and generate management recommendations as probabilities of outcomes. The quantification of variability in crop response to inputs and drawing on historic knowledge about the field and economic constraints up to the time a decision is required allows for probabilistic inference that a future management scenario will outcompete another in terms of production, economics, and sustainability. The proposed methodology represents advancement over other approaches by comparing management strategies and providing the probability that each will increase producer profits over their previous input management on the field scale.

Suggested Citation

  • Paul B. Hegedus & Bruce Maxwell & John Sheppard & Sasha Loewen & Hannah Duff & Giorgio Morales-Luna & Amy Peerlinck, 2023. "Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis," Agriculture, MDPI, vol. 13(3), pages 1-20, February.
  • Handle: RePEc:gam:jagris:v:13:y:2023:i:3:p:524-:d:1076869
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    References listed on IDEAS

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    1. Capmourteres, Virginia & Adams, Justin & Berg, Aaron & Fraser, Evan & Swanton, Clarence & Anand, Madhur, 2018. "Precision conservation meets precision agriculture: A case study from southern Ontario," Agricultural Systems, Elsevier, vol. 167(C), pages 176-185.
    2. Schimmelpfennig, David & Lowenberg-DeBoer, James, 2020. "Farm types and precision agriculture adoption: crops, regions, soil variability, and farm size," Agri-Tech Economics Papers 304070, Harper Adams University, Land, Farm & Agribusiness Management Department.
    3. Antle, John M. & Capalbo, Susan Marie, 2002. "Agriculture As A Managed Ecosystem: Policy Implications," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 27(01), pages 1-15, July.
    4. Sykuta, Michael E., 2016. "Big Data in Agriculture: Property Rights, Privacy and Competition in Ag Data Services," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, vol. 19(A), pages 1-18, June.
    5. Pham, Xuan & Stack, Martin, 2018. "How data analytics is transforming agriculture," Business Horizons, Elsevier, vol. 61(1), pages 125-133.
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