scholarly journals Decision support systems halve fungicide use compared to calendar-based strategies without increasing disease risk

2021 ◽  
Vol 2 (1) ◽  
Author(s):  
Elena Lázaro ◽  
David Makowski ◽  
Antonio Vicent
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Disease Risk ◽  
Disease Incidence ◽  
Fixed Number ◽  
Resistance Development ◽  
Traditional Calendar ◽  
Application Frequency ◽  
Chemical Pesticides

AbstractThe European Green Deal aims to reduce the use of chemical pesticides by half by 2030. Decision support systems are tools to help farmers schedule fungicide spraying based on disease risk and can reduce fungicide application frequency and overall use. However, the potential benefit of decision support systems compared to traditional calendar-based strategies has not yet been rigorously quantified. Here we synthesise 80 experiments and show that globally decision support systems can reduce fungicide treatments by at least 50% without compromising disease control. For a given fixed number of fungicide sprays, decision support systems were as effective as calendar-based programs in reducing disease incidence. When the number of sprays was halved, the increase in disease incidence was lower for decision support system-based strategies than calendar-based strategies. Our findings suggest that decision support systems can reduce fungicide use while limiting the risk to plant health and resistance development.

scholarly journals Perceptions of Risk, Risk Aversion, and Barriers to Adoption of Decision Support Systems and Integrated Pest Management: An Introduction

2011 ◽  
Vol 101 (6) ◽  
pp. 640-643 ◽  
Author(s):  
David H. Gent ◽  
Erick De Wolf ◽  
Sarah J. Pethybridge
Keyword(s):  
Decision Support ◽  
Integrated Pest Management ◽  
Decision Support Systems ◽  
Pest Management ◽  
Support Systems ◽  
Disease Risk ◽  
Plant Diseases ◽  
Control Measures ◽  
Management Decisions ◽  
Perceptions Of Risk

Rational management of plant diseases, both economically and environmentally, involves assessing risks and the costs associated with both correct and incorrect tactical management decisions to determine when control measures are warranted. Decision support systems can help to inform users of plant disease risk and thus assist in accurately targeting events critical for management. However, in many instances adoption of these systems for use in routine disease management has been perceived as slow. The under-utilization of some decision support systems is likely due to both technical and perception constraints that have not been addressed adequately during development and implementation phases. Growers' perceptions of risk and their aversion to these perceived risks can be reasons for the “slow” uptake of decision support systems and, more broadly, integrated pest management (IPM). Decision theory provides some tools that may assist in quantifying and incorporating subjective and/or measured probabilities of disease occurrence or crop loss into decision support systems. Incorporation of subjective probabilities into IPM recommendations may be one means to reduce grower uncertainty and improve trust of these systems because management recommendations could be explicitly informed by growers' perceptions of risk and economic utility. Ultimately though, we suggest that an appropriate measure of the value and impact of decision support systems is grower education that enables more skillful and informed management decisions independent of consultation of the support tool outputs.

scholarly journals Comparison of Frequentist and Bayesian Meta-Analysis Models for Assessing the Efficacy of Decision Support Systems in Reducing Fungal Disease Incidence

Agronomy ◽  
2020 ◽  
Vol 10 (4) ◽  
pp. 560
Author(s):  
Elena Lázaro ◽  
David Makowski ◽  
Joaquín Martínez-Minaya ◽  
Antonio Vicent
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Random Effects ◽  
Fixed Effects ◽  
Goodness Of Fit ◽  
Linear Models ◽  
Support Systems ◽  
Disease Incidence ◽  
Meta Analysis ◽  
Field Trials

Diseases of fruit and foliage caused by fungi and oomycetes are generally controlled by the application of fungicides. The use of decision support systems (DSSs) may assist to optimize fungicide programs to enhance application on the basis of risk associated with disease outbreak. Case-by-case evaluations demonstrated the performance of DSSs for disease control, but an overall assessment of the efficacy of DSSs is lacking. A literature review was conducted to synthesize the results of 67 experiments assessing DSSs. Disease incidence data were obtained from published peer-reviewed field trials comparing untreated controls, calendar-based and DSS-based fungicide programs. Two meta-analysis generic models, a “fixed-effects” vs. a “random-effects” model within the framework of generalized linear models were evaluated to assess the efficacy of DSSs in reducing incidence. All models were fit using both frequentist and Bayesian estimation procedures and the results compared. Model including random effects showed better performance in terms of AIC or DIC and goodness of fit. In general, the frequentist and Bayesian approaches produced similar results. Odds ratio and incidence ratio values showed that calendar-based and DSS-based fungicide programs considerably reduced disease incidence compared to the untreated control. Moreover, calendar-based and DSS-based programs provided similar reductions in disease incidence, further supporting the efficacy of DSSs.

Assigning Value to Clinical Information – A Major Limiting Factor in the Implementation of Decision-Support Systems

1996 ◽  
Vol 35 (01) ◽  
pp. 1-4 ◽  
Author(s):  
F. T. de Dombal
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Support Systems ◽  
Clinical Information ◽  
Limiting Factor ◽  
Major Difficulty ◽  
Computer Aided ◽  
Computer Aided Decision

AbstractThis paper deals with a major difficulty and potential limiting factor in present-day decision support - that of assigning precise value to an item (or group of items) of clinical information. Historical determinist descriptive thinking has been challenged by current concepts of uncertainty and probability, but neither view is adequate. Four equations are proposed outlining factors which affect the value of clinical information, which explain some previously puzzling observations concerning decision support. It is suggested that without accommodation of these concepts, computer-aided decision support cannot progress further, but if they can be accommodated in future programs, the implications may be profound.

Architectures for Architects

1993 ◽  
Vol 32 (01) ◽  
pp. 12-13 ◽  
Author(s):  
M. A. Musen
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Design And Development

Abstract:Response to Heathfield HA, Wyatt J. Philosophies for the design and development of clinical decision-support systems. Meth Inform Med 1993; 32: 1-8.

Characterizing Decision Support Telemedicine Systems

2006 ◽  
Vol 45 (05) ◽  
pp. 523-527 ◽  
Author(s):  
A. Abu-Hanna ◽  
B. Nannings
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Clinical Decision Support ◽  
Literature Search ◽  
Support Systems ◽  
Clinical Decision Support Systems ◽  
Clinical Decision ◽  
Problem Dimension ◽  
Process Dimension

Summary Objectives: Decision Support Telemedicine Systems (DSTS) are at the intersection of two disciplines: telemedicine and clinical decision support systems (CDSS). The objective of this paper is to provide a set of characterizing properties for DSTSs. This characterizing property set (CPS) can be used for typing, classifying and clustering DSTSs. Methods: We performed a systematic keyword-based literature search to identify candidate-characterizing properties. We selected a subset of candidates and refined them by assessing their potential in order to obtain the CPS. Results: The CPS consists of 14 properties, which can be used for the uniform description and typing of applications of DSTSs. The properties are grouped in three categories that we refer to as the problem dimension, process dimension, and system dimension. We provide CPS instantiations for three prototypical applications. Conclusions: The CPS includes important properties for typing DSTSs, focusing on aspects of communication for the telemedicine part and on aspects of decisionmaking for the CDSS part. The CPS provides users with tools for uniformly describing DSTSs.

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