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Appears in Collections:Biological and Environmental Sciences Journal Articles
Peer Review Status: Refereed
Title: The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management
Author(s): Garrett, Karen A
Dobson, Andrew
Kroschel, Jurgen
Natarajan, Bala
Orlandini, Simone
Tonnang, Henri E Z
Valdivia, Corinne
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Keywords: Colored noise
Decision-making under uncertainty
Early warning systems
Environmental variability
Environmental time series
Global warming
Issue Date: Mar-2013
Citation: Garrett KA, Dobson A, Kroschel J, Natarajan B, Orlandini S, Tonnang HEZ & Valdivia C (2013) The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management, Agricultural and Forest Meteorology, 170, pp. 216-227.
Abstract: If climate change scenarios include higher variance in weather variables, this can have important effects on pest and disease risk beyond changes in mean weather conditions. We developed a theoretical model of yield loss to diseases and pests as a function of weather, and used this model to evaluate the effects of variance in conduciveness to loss and the effects of the color of time series of weather conduciveness to loss. There were two qualitatively different results for changes in system variance. If median conditions are conducive to loss, increasing system variance decreases mean yield loss. On the other hand, if median conditions are intermediate or poor for disease or pest development, such that conditions are conducive to yield loss no more than half the time, increasing system variance increases mean yield loss. Time series for weather conduciveness that are darker pink (have higher levels of temporal autocorrelation) produce intermediate levels of yield loss less commonly. A linked model of decision-making based on either past or current information about yield loss also shows changes in the performance of decision rules as a function of system variance. Understanding patterns of variance can improve scenario analysis for climate change and help make adaptation strategies such as decision support systems and insurance programs more effective.
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