Sampling for detection: Leek rust as an example

Jong, Piet de

doi:10.1080/09670879509371918

Cited by 10 publications

(7 citation statements)

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“…Moreover, it appeared that the variancemean relationship is generally consistent over the three countries. An aggregated pattern of density data is often observed for plant diseases, e.g., tobacco blue mould (Waggoner, 1981), apple powdery mildew (Barlow, 1977;Xu and Madden, 2002), leaf blight of carrot caused by Cercospora carotae (Boivin et al, 1990), and leek rust (de Jong, 1995). There are two likely explanations for the observed aggregation of FEB-infected spikelets on an ear.…”

Section: Discussionmentioning

confidence: 95%

Relationship between the incidences of ear and spikelet infection of Fusarium ear blight in wheat

Parry

Edwards

et al. 2004

European Journal of Plant Pathology

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There is a urgent need to develop a rational strategy for managing Fusarium ear blight in order to reduce current reliance on routine fungicide applications, based on an objective assessment of disease risks. One of important components for such a management strategy is a fast, easy, accurate and reliable method for disease assessment. The relationship between incidence of Fusarium ear blight ear infection and number of spikelets infected on an ear (or incidence of spikelet infection) were investigated during three seasons and in four countries in order to derive a simple relationship for predicting disease at the spikelet level using ear incidence. More than half of the data sets of the number of infected spikelets on an ear could not be fitted satisfactorily by a Poisson distribution. Three two-parameter discrete distributions (negative binominal, Neyman type A and Polya-Aeppli) provided a significantly better fit than the Poisson distribution, indicating a degree of aggregation of number of infected spikelets on an ear. Taylor's power-law satisfactorily described the observed variance-mean relationship for the number of infected spikelets on an ear; this relationship was generally consistent over years and countries. A robust relationship between incidence of ear infection and average number of infected spikelets per ear was obtained assuming a fixed variancemean relationship and a negative binomial distribution for the number of infected spikelets. A relationship between incidences of spikelet and ear infection was also obtained based on the complementary log-log or logit transformation of ear and spikelet infection incidence. These models appeared to be consistent over years and countries and thus may be used in making practical disease management decisions involving fungicide applications.

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Section: Discussionmentioning

confidence: 95%

Relationship between the incidences of ear and spikelet infection of Fusarium ear blight in wheat

Parry

Edwards

et al. 2004

European Journal of Plant Pathology

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“…Applying fungicides to a leek crop in the absence of the disease is undesirable, both from environmental and economic points of view. A sampling procedure for detection of leek rust has already been developed [de Jong, 1995]. Although the procedure of delaying chemical control until detection of the disease does not use a quantitative control threshold, it can still be regarded as a form of super-vised control (sensu Theunissen [1984]).…”

Section: Discussionmentioning

confidence: 99%

“…when the mean number of uredosori per plant is one. With an appropriate sampling method, this is practically feasible [de Jong, 1995]. The fungicide does not affect the uredosori already present, but it protects the crop against new infections.…”

Section: Modelmentioning

confidence: 99%

“…Research on the control of leek rust has concentrated on breeding for resistance [Jennings et al, 1990a;Uma and Taylor, 1991;Smith and Crowther, 1992] and the optimal use of fungicides [von Meyer and Kessler, 1990;de Jong, 1995], but little attention has been given to preventive measures. This study focuses on the opportunities for and benefits of preventive measures for individual growers, to reduce the need for chemical control against leek rust; these can be divided into strategic measures and tactical measures [Conway, 1984].…”

Section: Introductionmentioning

confidence: 99%

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The reduction of chemical control of leek rust, a simulation study

Jong¹,

Daamen²,

Rabbinge

1995

Eur J Plant Pathol

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A model is presented which simulates chemical control against leek rust under a system of supervised control. The spread of disease between fields in one farm and the influx of spores from outside is part of the model. The possible reduction in fungicide sprays against leek rust following various preventive measures is estimated with this model. Results from this preliminary model suggest that supervised control of leek rust will result only in a considerable reduction in the number of fungicide sprays if the leek fields are sufficiently isolated and the planting material is initially disease free. Where isolation between the fields is not feasible, overlap between successive crops should be avoided. Without the use of these preventive measures, there is little prospect for supervised control and the grower will have no option but the heavy use of fungicides.

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“…When this is not the case and a large epidemiological research investment is needed first, the increased efficiency of our approach is in the application of the first step, assessment of response of decision rules to weather. While our methodology was tailored to a weather-based warning system, similar approaches have been developed and applied for warning systems relying on field monitoring of diseases (de Jong, 1995;de Jong et al, 1995) or animal pests (van der Werfetal., 1996).…”

Section: Bowas Versionmentioning

confidence: 99%

Computer‐aided design of improved warning systems – a case study for Botrytis control in flower bulbs*

Kraker

Ende²,

Rossing

et al. 2000

EPPO Bulletin

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Development and improvement of warning systems are often done empirically, relying on extensive field testing. As this approach is both costly and time-consuming, there is a need for a more rational and efficient alternative. In a study to explore the options for improvement of a Botrytis warning system in flower bulbs, we applied a computer-based approach to systems design. The approach consisted of the construction and evaluation of modified versions of the warning system using epidemiological knowledge, data sets of recorded and forecast weather and a simulation model of epidemic development and fungicide spray impact. Performance of modified versions was evaluated with regard to fungicide input, efficacy of disease control and sensitivity to the prediction error in weather forecasts. This approach can be more efficient than a purely empirical one, as it enables the designer to limit the number of alternative versions to be field-tested on the basis of explicit performance criteria. It also has the advantage that it provides insight into the potentials for improvement of the warning system. IntroductionDisease warning or forecasting systems are used to improve the efficiency and efficacy of disease control, by timing fungicide application according to identified need. Often, an empirical approach is taken to the development and improvement of warning systems. Development involves the definition of predictive equations and decision rules, and choosing values for parameters and decision thresholds. These may be based on epidemiological relationships established in controlled experiments or correlations derived from field observations, but they usually also include arbitrary elements, e.g. the choice of an action threshold. Subsequently, the warning system is evaluated in field experiments, commonly over several seasons, and several cycles of modification and field testing may follow before acceptable or improved performance is achieved. In consequence, this empirical approach requires a large research investment and is very time-consuming. Design of warning systems could become more efficient if alternative versions of the warning system could be screened with regard to their performance before field evaluation. Field testing could then be restricted to the most promising alternatives. In a study to explore the options for the improvement of a Botrytis warning system in flower bulbs, we applied such a screening technique in the form of computer-based evaluation. In this paper, we will describe the current weather-based Botrytis warning system and its *Paper presented at the EPPO Conference on Warning Services for Plant Protection, Piacenza (IT), 1999-05-12/14, background, explain the methodology we used, present first results and, finally, discuss various aspects of our approach.

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Sampling for detection: Leek rust as an example

Cited by 10 publications

References 10 publications

Relationship between the incidences of ear and spikelet infection of Fusarium ear blight in wheat

Relationship between the incidences of ear and spikelet infection of Fusarium ear blight in wheat

The reduction of chemical control of leek rust, a simulation study

Computer‐aided design of improved warning systems – a case study for Botrytis control in flower bulbs*

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