Phytopathology (2017) 107, 158-162

From Pestinfo-Wiki
Jump to: navigation, search
People icon1.svgSelected publication
of interest to a wider audience. We would welcome
contributions to the Discussion section (above tab) of this article.
Remember to log in or register (top right corner) before editing pages.
G. Hughes, N. McRoberts and F.J. Burnett (2017)
Resolution of probabilistic weather forecasts with application in disease management
Phytopathology 107 (2), 158-162
Abstract: Predictive systems in disease management often incorporate weather data among the disease risk factors, and sometimes this comes in the form of forecast weather data rather than observed weather data. In such cases, it is useful to have an evaluation of the operational weather forecast, in addition to the evaluation of the disease forecasts provided by the predictive system. Typically, weather forecasts and disease forecasts are evaluated using different methodologies. However, the information theoretic quantity expected mutual information provides a basis for evaluating both kinds of forecast. Expected mutual information is an appropriate metric for the average performance of a predictive system over a set of forecasts. Both relative entropy (a divergence, measuring information gain) and specific information (an entropy difference, measuring change in uncertainty) provide a basis for the assessment of individual forecasts.
(The abstract is excluded from the Creative Commons licence and has been copied with permission by the publisher.)
Link to article at publishers website


Database assignments for author(s): Gareth Hughes

Research topic(s) for pests/diseases/weeds:
thresholds/decision-support systems
environment - cropping system/rotation


Pest and/or beneficial records:

Beneficial Pest/Disease/Weed Crop/Product Country Quarant.