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Journal ArticleDOI

Modelling the daily progress of light leaf spot epidemics on winter oilseed rape (Brassica napus), in relation to Pyrenopeziza brassicae inoculum concentrations and weather factors

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TLDR
Seasonal variations in temperature had a large effect on the length of the latent period, which increased when mean daily temperatures were less than 5 °C, and an assumption that the leaf birth rate changed around the time stem extension began (GS 2-0) improved the fit of the model.
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This article is published in Ecological Modelling.The article was published on 2002-02-15. It has received 17 citations till now. The article focuses on the topics: Pyrenopeziza brassicae & Leaf spot.

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Journal ArticleDOI

The intensity of a coffee rust epidemic is dependent on production situations

TL;DR: Watching the development of rust epidemics in Honduras provided an understanding of how intensifying Arabica cultivation, through its effects on yield and soil acidification, increased the risk of a serious coffee rust epidemic occurring.
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Integrating Remotely Sensed and Meteorological Observations to Forecast Wheat Powdery Mildew at a Regional Scale

TL;DR: The integrated model constructed with both remote sensing and meteorological data has produced a higher accuracy (increasing overall accuracy from 69% to 78%) of forecasting the PM occurrence.
Journal ArticleDOI

Simulation of vertical spread of plant diseases in a crop canopy by stem extension and splash dispersal

TL;DR: Results showed that stem extension was an important factor in influencing vertical light leaf spot spread in the model oilseed rape crop, and rain events contributed to the splash dispersal of conidia to the plant apex and the resulting infections were directed vertically by internode growth.
Journal ArticleDOI

Predicting light leaf spot (Pyrenopeziza brassicae) risk on winter oilseed rape (Brassica napus) in England and Wales, using survey, weather and crop information

TL;DR: Data from surveys of winter oilseed rape crops in England and Wales in growing seasons with harvests in 1987–99 were used to construct statistical models to predict, in autumn (October), the incidence of light leaf spot caused by Pyrenopeziza brassicae, at both regional and individual crop scales.
Journal ArticleDOI

The sensitivity of the epidemic growth rate to weather variables, with an application to yellow rust on wheat.

TL;DR: The method derived derives a method to quantify the sensitivity of r to a weather variable as a function of the pathogen life cycle variables of latent period, basic reproductive number, and the mean and standard deviation of the sporulation curve, which concludes that the most important weather variable for the progress of yellow rust is temperature.
References
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Book

Brassica Oilseeds: Production and Utilization

TL;DR: Part 1 The field crop: physiology agronomy diseases weeds and their control pests plant breeding biotechnology environmental aspects and processing and utilization: seed chemistry seed analysis processing oil utilization and human nutrition meal utilization and animal nutrition utilization as a condiment industrial uses.
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State-of-the-art of ecological modelling with emphasis on development of structural dynamic models

TL;DR: Developing structural dynamic models which are models using goal functions to reflect a current change of the properties of the biological components in the models to facilitate the estimation of parameters is discussed.
Journal ArticleDOI

The production and release of ascospores of Pyrenopeziza brassicae on oilseed rape.

TL;DR: Results of observation for three seasons in oilseed rape crops of the production and release of ascopores of Pyrenopeziza brassicae show that apothecia can continue to release ascospores for up to 3 weeks even when they are subjected to wet and dry cycles.
Journal ArticleDOI

Epidemiology in relation to methods for forecasting light leaf spot (Pyrenopeziza brassicae) severity on winter oilseed rape (Brassica napus) in the UK

TL;DR: An autumn forecast of the incidence of light leaf spot is provided, based on survey data which assesses the occurrence of disease the previous July, and weather factors, such as deviations from summer mean temperature and winter rainfall, which can be updated throughout the autumn and winter.
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