The Evidential Basis of Decision Making in Plant Disease Management

The Evidential Basis of Decision Making in Plant Disease Management The evidential basis for disease management decision making is provided by data relating to risk factors. The decision process involves an assessment of the evidence leading to taking (or refraining from) action on the basis of a prediction. The primary objective of the decision process is to identifyat the time the decision is madethe control action that provides the best predicted end-of-season outcome, calculated in terms of revenue or another appropriate metric. Data relating to disease risk factors may take a variety of forms (e.g., continuous, discrete, categorical) on measurement scales in a variety of units. Log10-likelihood ratios provide a principled basis for the accumulation of evidence based on such data and allow predictions to be made via Bayesian updating of prior probabilities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annual Review of Phytopathology Annual Reviews

The Evidential Basis of Decision Making in Plant Disease Management

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Publisher
Annual Reviews
Copyright
Copyright 2017 by Annual Reviews. All rights reserved
ISSN
0066-4286
eISSN
1545-2107
D.O.I.
10.1146/annurev-phyto-080516-035342
Publisher site
See Article on Publisher Site

Abstract

The evidential basis for disease management decision making is provided by data relating to risk factors. The decision process involves an assessment of the evidence leading to taking (or refraining from) action on the basis of a prediction. The primary objective of the decision process is to identifyat the time the decision is madethe control action that provides the best predicted end-of-season outcome, calculated in terms of revenue or another appropriate metric. Data relating to disease risk factors may take a variety of forms (e.g., continuous, discrete, categorical) on measurement scales in a variety of units. Log10-likelihood ratios provide a principled basis for the accumulation of evidence based on such data and allow predictions to be made via Bayesian updating of prior probabilities.

Journal

Annual Review of PhytopathologyAnnual Reviews

Published: Aug 4, 2017

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