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A Risk Analysis of Precision Agriculture Technology to Manage Tomato Late Blight

Yangxuan Liu, Michael Langemeier, Ian M. Small, Laura Joseph, William E. Fry, Jean B. Ristaino, Amanda Saville, Benjamin Gramig and Paul Preckel ()
Additional contact information
Ian M. Small: North Florida Research and Education Center, University of Florida, Quincy, FL 32351, USA
Laura Joseph: Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Ithaca, NY 14853, USA
William E. Fry: Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Ithaca, NY 14853, USA
Jean B. Ristaino: Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
Amanda Saville: Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA

Sustainability, 2018, vol. 10, issue 9, 1-19

Abstract: Precision agriculture technology can transform farming related data into useful information, which may lead to more efficient usage of agricultural resources and increase sustainability. This paper compares precision agriculture technology with traditional practices in scheduling fungicide application so as to manage late blight disease in tomato production. The following three fungicide scheduling strategies were evaluated: a calendar-based strategy, the BlightPro Decision Support System based strategy (DSS-based strategy), and a strategy that does not involve fungicide application. The data from field trials and computer simulation experiments were used to construct distributions of the net return per acre for the calendar-based and the DSS-based strategies. These distributions were then compared using three standard approaches to ranking risky alternatives, namely: stochastic dominance, stochastic dominance with respect to a function, and stochastic efficiency with respect to a function. Assuming no yield differences between the calendar-based and the DSS-based strategies, the calendar-based strategy was preferred for highly late blight susceptible cultivars, and the DSS-based strategy was preferred for moderately susceptible and moderately resistant cultivars. Assuming no yield differences, the value of the BlightPro Decision Support System ranged from −$28 to $48 per acre. With the yield improvement for the DSS-based strategy included, the DSS-based strategy was preferred for the cultivars in all of the disease-resistance categories with the value ranging from $496 to $1714 per acre.

Keywords: risk analysis; tomato; precision agriculture; stochastic dominance; stochastic efficiency with respect to a function; disease management; late blight; decision support system (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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