Articles
SPATIAL AND TEMPORAL ASSOCIATIONS OF POWDERY MILDEW AND TWO-SPOTTED SPIDER-MITE IN GREENHOUSES
Article number
917_2
Pages
23 – 30
Language
English
Abstract
Chemical pest management has been intensively implemented in greenhouses in order to guarantee high yields and quality of the marketable products.
Over the last few decades, a new crop protection strategy, Integrated Pest Management (IPM), has emerged as the dominant alternative to conventional pesticides.
This strategy involves the combined use of multiple pest control methods such as monitoring of pest/ biological control agent densities and decision-aid tools through which knowledge is gathered.
Regarding the aforementioned technique, a quick scouting method was designed to monitor the main pests and diseases in greenhouse rose crops.
As a result of the implementation of climatic data, statistical pest dynamics predictive models were built.
These models are devised from non-parametric regressions such as projection pursuit regression or logistic regression.
Variables from endogenous and exogenous crop factors are easily gathered and they facilitate the selection of predictors that are the most relevant variables to forecast population dynamics.
In this context, the pathogenic fungus Sphaerotheca pannosa var. rosae appears to be a predictor of the population dynamic of the two-spotted spider-mite Tetranychus urticae.
This first result underlined unexpected direct or indirect biotic interactions between these two: the main pest and the disease.
At a later date, laboratory experi¬ments on small arenas confirmed both direct interactions between the main pest and disease and an indirect interaction with the spider-mite predator Amblyseius californicus.
We proposed to improve our knowledge of this interaction by studying the spatial and temporal associations between the powdery mildew and the spider-mite on a crop scale.
For this, we used the Hellinger distance as an estimator to gain better knowledge of the occurrence of this interaction and its likely consequences on the regulation of these two bio-agressors.
Over the last few decades, a new crop protection strategy, Integrated Pest Management (IPM), has emerged as the dominant alternative to conventional pesticides.
This strategy involves the combined use of multiple pest control methods such as monitoring of pest/ biological control agent densities and decision-aid tools through which knowledge is gathered.
Regarding the aforementioned technique, a quick scouting method was designed to monitor the main pests and diseases in greenhouse rose crops.
As a result of the implementation of climatic data, statistical pest dynamics predictive models were built.
These models are devised from non-parametric regressions such as projection pursuit regression or logistic regression.
Variables from endogenous and exogenous crop factors are easily gathered and they facilitate the selection of predictors that are the most relevant variables to forecast population dynamics.
In this context, the pathogenic fungus Sphaerotheca pannosa var. rosae appears to be a predictor of the population dynamic of the two-spotted spider-mite Tetranychus urticae.
This first result underlined unexpected direct or indirect biotic interactions between these two: the main pest and the disease.
At a later date, laboratory experi¬ments on small arenas confirmed both direct interactions between the main pest and disease and an indirect interaction with the spider-mite predator Amblyseius californicus.
We proposed to improve our knowledge of this interaction by studying the spatial and temporal associations between the powdery mildew and the spider-mite on a crop scale.
For this, we used the Hellinger distance as an estimator to gain better knowledge of the occurrence of this interaction and its likely consequences on the regulation of these two bio-agressors.
Authors
A. Bout, M.M. Muller, L. Mailleret, R. Boll, C. Poncet , R. Senoussi
Keywords
Integrated Pest Management, greenhouse, biotic interactions, Sphaerotheca pannosa, Tetranychus urticae, Density Metric Based Statistics
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