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Open AccessArticle

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

1
Department of Agricultural and Applied Economics, University of Georgia, Tifton, GA 31793, USA
2
Department of Agricultural Economics, Purdue University, West Lafayette, IN 47907, USA
3
North Florida Research and Education Center, University of Florida, Quincy, FL 32351, USA
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Department of Plant Pathology and Plant-Microbe Biology, Cornell University, Ithaca, NY 14853, USA
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Department of Entomology and Plant Pathology, North Carolina State University, Raleigh, NC 27695, USA
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Department of Agricultural and Consumer Economics, University of Illinois at Urbana-Champaign, Urbana, IL 61801, USA
*
Author to whom correspondence should be addressed.
Sustainability 2018, 10(9), 3108; https://doi.org/10.3390/su10093108
Received: 24 July 2018 / Revised: 28 August 2018 / Accepted: 29 August 2018 / Published: 31 August 2018
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. View Full-Text
Keywords: risk analysis; tomato; precision agriculture; stochastic dominance; stochastic efficiency with respect to a function; disease management; late blight; decision support system risk analysis; tomato; precision agriculture; stochastic dominance; stochastic efficiency with respect to a function; disease management; late blight; decision support system
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MDPI and ACS Style

Liu, Y.; Langemeier, M.R.; Small, I.M.; Joseph, L.; Fry, W.E.; Ristaino, J.B.; Saville, A.; Gramig, B.M.; Preckel, P.V. A Risk Analysis of Precision Agriculture Technology to Manage Tomato Late Blight. Sustainability 2018, 10, 3108. https://doi.org/10.3390/su10093108

AMA Style

Liu Y, Langemeier MR, Small IM, Joseph L, Fry WE, Ristaino JB, Saville A, Gramig BM, Preckel PV. A Risk Analysis of Precision Agriculture Technology to Manage Tomato Late Blight. Sustainability. 2018; 10(9):3108. https://doi.org/10.3390/su10093108

Chicago/Turabian Style

Liu, Yangxuan; Langemeier, Michael R.; Small, Ian M.; Joseph, Laura; Fry, William E.; Ristaino, Jean B.; Saville, Amanda; Gramig, Benjamin M.; Preckel, Paul V. 2018. "A Risk Analysis of Precision Agriculture Technology to Manage Tomato Late Blight" Sustainability 10, no. 9: 3108. https://doi.org/10.3390/su10093108

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