Evaluation of a predictive model for Mycosphaerella graminicola for economic and environmental benefits

A - Papers appearing in refereed journals

Te Beest, D. E., Shaw, M. W., Paveley, N. D. and Van Den Bosch, F. 2009. Evaluation of a predictive model for Mycosphaerella graminicola for economic and environmental benefits. Plant Pathology. 58 (6), pp. 1001-1009. https://doi.org/10.1111/j.1365-3059.2009.02142.x

AuthorsTe Beest, D. E., Shaw, M. W., Paveley, N. D. and Van Den Bosch, F.
Abstract

A method was developed to evaluate crop disease predictive models for their economic and environmental benefits. Benefits were quantified as the value of a prediction measured by costs saved and fungicide dose saved. The value of prediction was defined as the net gain made by using predictions, measured as the difference between a scenario where predictions are available and used and a scenario without prediction. Comparable 'with' and 'without' scenarios were created with the use of risk levels. These risk levels were derived from a probability distribution fitted to observed disease severities. These distributions were used to calculate the probability that a certain disease induced economic loss was incurred. The method was exemplified by using it to evaluate a model developed for Mycosphaerella graminicola risk prediction. Based on the value of prediction, the tested model may have economic and environmental benefits to growers if used to guide treatment decisions on resistant cultivars. It is shown that the value of prediction measured by fungicide dose saved and costs saved is constant with the risk level. The model could also be used to evaluate similar crop disease predictive models.

KeywordsAgronomy; Plant Sciences
Year of Publication2009
JournalPlant Pathology
Journal citation58 (6), pp. 1001-1009
Digital Object Identifier (DOI)https://doi.org/10.1111/j.1365-3059.2009.02142.x
Open accessPublished as non-open access
FunderDEFRA - Department for Environment, Food and Rural Affairs UK
Biotechnology and Biological Sciences Research Council
Funder project or codeCentre for Mathematical and Computational Biology (MCB)
Project: 4913
ISSN00320862
PublisherWiley

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