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

Paul B. Hegedus (), Bruce Maxwell, John Sheppard, Sasha Loewen, Hannah Duff, Giorgio Morales-Luna and Amy Peerlinck
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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

Agriculture, 2023, vol. 13, issue 3, 1-20

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.

Keywords: agroecology; crop modeling; crop production; decision support system; ecological management; on-farm experimentation; optimization (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
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