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Agricultural and Forest Meteorology
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License: CC BY NC ND
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Agricultural and Forest Meteorology
Article . 2013 . Peer-reviewed
License: Elsevier TDM
Data sources: Crossref
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The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management

Authors: Garrett, K. A; Dobson, A. D. M.; Kroschel, J.; Natarajan, B.; ORLANDINI, SIMONE; Tonnang, H. E. Z.; Valdivia, C.;

The effects of climate variability and the color of weather time series on agricultural diseases and pests, and on decisions for their management

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.

Countries
France, United Kingdom, United States, Italy, France, United Kingdom
Related Organizations
Keywords

Livestock, Time series, 330, Cropping systems, simulation models, adaptation, Decision support systems, Colored noise, 333, 630, decision making, Early warning systems, Insurance, Pests, models, Environmental time series, Climate change, Decision-making under uncertainty, Climate variability, climate, agriculture, Colored noise; Decision-making under uncertainty; Early warning systems; Environmental variability; Environmental time series; Global warming, Global warming, climatology, Environmental variability, climate change, plant diseases

  • BIP!
    Impact byBIP!
    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    74
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
74
Top 10%
Top 10%
Top 10%
Green
hybrid