Literature DB >> 35645604

Assessing expected utility and profitability to support decision-making for disease control strategies in ornamental heather production.

Marius Ruett1, Tobias Dalhaus2, Cory Whitney1,3, Eike Luedeling1.   

Abstract

Many farmers hesitate to adopt new management strategies with actual or perceived risks and uncertainties. Especially in ornamental plant production, farmers often stick to current production strategies to avoid the risk of economically harmful plant losses, even though they may recognize the need to optimize farm management. This work focused on the economically important and little-researched production system of ornamental heather (Calluna vulgaris) to help farmers find appropriate measures to sustainably improve resource use, plant quality, and profitability despite existing risks. Probabilistic cost-benefit analysis was applied to simulate alternative disease monitoring strategies. The outcomes for more intensive visual monitoring, as well as sensor-based monitoring using hyperspectral imaging were simulated. Based on the results of the probabilistic cost-benefit analysis, the expected utility of the alternative strategies was assessed as a function of the farmer's level of risk aversion. The analysis of expected utility indicated that heather production is generally risky. Concerning the alternative strategies, more intensive visual monitoring provides the highest utility for farmers for almost all levels of risk aversion compared to all other strategies. Results of the probabilistic cost-benefit analysis indicated that more intensive visual monitoring increases net benefits in 68% of the simulated cases. The application of sensor-based monitoring leads to negative economic outcomes in 85% of the simulated cases. This research approach is widely applicable to predict the impacts of new management strategies in precision agriculture. The methodology can be used to provide farmers in other data-scarce production systems with concrete recommendations that account for uncertainties and risks. Supplementary information: The online version contains supplementary material available at 10.1007/s11119-022-09909-z.
© The Author(s) 2022.

Entities:  

Keywords:  Decision support; Fungicide; Model simulation; Precision agriculture; Risk preference; Sensor application

Year:  2022        PMID: 35645604      PMCID: PMC9124294          DOI: 10.1007/s11119-022-09909-z

Source DB:  PubMed          Journal:  Precis Agric        ISSN: 1385-2256            Impact factor:   5.767


  11 in total

1.  Perceptions of risk, risk aversion, and barriers to adoption of decision support systems and integrated pest management: an introduction.

Authors:  David H Gent; Erick De Wolf; Sarah J Pethybridge
Journal:  Phytopathology       Date:  2011-06       Impact factor: 4.025

2.  Judgment under Uncertainty: Heuristics and Biases.

Authors:  A Tversky; D Kahneman
Journal:  Science       Date:  1974-09-27       Impact factor: 47.728

Review 3.  Will decision-support systems be widely used for the management of plant diseases?

Authors:  Dani Shtienberg
Journal:  Annu Rev Phytopathol       Date:  2013-06-13       Impact factor: 13.078

4.  Unskilled and unaware of it: how difficulties in recognizing one's own incompetence lead to inflated self-assessments.

Authors:  J Kruger; D Dunning
Journal:  J Pers Soc Psychol       Date:  1999-12

Review 5.  Quantitative and qualitative phenotyping of disease resistance of crops by hyperspectral sensors: seamless interlocking of phytopathology, sensors, and machine learning is needed!

Authors:  Anne-Katrin Mahlein; Matheus Thomas Kuska; Stefan Thomas; Mirwaes Wahabzada; Jan Behmann; Uwe Rascher; Kristian Kersting
Journal:  Curr Opin Plant Biol       Date:  2019-08-03       Impact factor: 7.834

6.  Quantifying fungal infection of plant leaves by digital image analysis using Scion Image software.

Authors:  C P Wijekoon; P H Goodwin; T Hsiang
Journal:  J Microbiol Methods       Date:  2008-04-03       Impact factor: 2.363

7.  Technical workflows for hyperspectral plant image assessment and processing on the greenhouse and laboratory scale.

Authors:  Stefan Paulus; Anne-Katrin Mahlein
Journal:  Gigascience       Date:  2020-08-01       Impact factor: 6.524

8.  Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions.

Authors:  Matheus Kuska; Mirwaes Wahabzada; Marlene Leucker; Heinz-Wilhelm Dehne; Kristian Kersting; Erich-Christian Oerke; Ulrike Steiner; Anne-Katrin Mahlein
Journal:  Plant Methods       Date:  2015-04-15       Impact factor: 4.993

9.  Low-Cost Hyperspectral Imaging System: Design and Testing for Laboratory-Based Environmental Applications.

Authors:  Mary B Stuart; Leigh R Stanger; Matthew J Hobbs; Tom D Pering; Daniel Thio; Andrew J S McGonigle; Jon R Willmott
Journal:  Sensors (Basel)       Date:  2020-06-09       Impact factor: 3.576

Review 10.  Proximal Methods for Plant Stress Detection Using Optical Sensors and Machine Learning.

Authors:  Alanna V Zubler; Jeong-Yeol Yoon
Journal:  Biosensors (Basel)       Date:  2020-11-29
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