Plant Pest Modeling
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Plant Pest Modeling
About computer systems using forecasting models, databases and decision support schemes in managing plant pest interactions with crop/canopy and weather/climatic data
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Biosecurity and Invasive Species: F1000 July review

Biosecurity and Invasive Species: F1000 July review | Plant Pest Modeling | Scoop.it

Invasion ecologists have invoked phenology as one explanation of the apparent success of non-indigenous forest species compared to native species. This paper shows that the invasion of forest species with canopies that persist later into autumn is resulting in a seasonal change in primary productivity that may rival climate change in size and extent.

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Development of Weather-Based Predictive Models for Fusarium Head Blight and Deoxynivalenol Accumulation for Spring Malting Barley

Development of Weather-Based Predictive Models for Fusarium Head Blight and Deoxynivalenol Accumulation for Spring Malting Barley | Plant Pest Modeling | Scoop.it

 The associations between Fusarium head blight (FHB), caused by Gibberella zeae, and deoxynivalenol (DON) accumulation in spring malting barley (Hordeum vulgare) and hourly weather conditions predictive of DON accumulation were examined using data from six growing seasons in the U.S. Northern Great Plains. Nine predictor variables were calculated using hourly temperature and relative humidity during the 10 days preceding full head spike emergence. Simple logistic regression models were developed.

 

Bondalapati KD, Stein JM, Neate SM, Halley SH, Osborne LE, Hollingsworth CR (2012) Development of Weather-Based Predictive Models for Fusarium Head Blight and Deoxynivalenol Accumulation for Spring Malting Barley.- Plant Disease May 2012, Volume 96, Number 5, Pages 673-680 http://dx.doi.org/10.1094/PDIS-05-11-0389.

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California PestCast: Disease Model Database-UC IPM

Information about Disease Model Database, California PestCast, on the University of California IPM Web site...


Their database is a clearinghouse of information about models developed for economically important crop and turf diseases in California. A model is included in the database if it uses weather, host, and/or pathogen data to predict risk of disease outbreak. The database is a part of a project called "PestCast," a regional weather network to support the development, validation, and implementation of crop disease models.

Statewide Integrated Pest Management Program staff members work closely with area IPM advisors, farm advisors, specialists, and researchers to develop educational programs that reflect the newest advances in pest management. The online courses are offered free of charge. Additional training materials, including videos, DVDs, books, and leaflets, are available in the Publications section. 

Insects, diseases, and invasive weeds threaten California's natural environments as well as homes, gardens, and agriculture. Warnings about exotic and invasive pests are included. Useful links to articles, fact sheets, and other information prepared by UC scientists on topics related to pests could be found on this page.

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Interactive Effects of Temperature and Leaf Wetness Duration - Cucurbit Downy Mildew

Interactive Effects of Temperature and Leaf Wetness Duration - Cucurbit Downy Mildew | Plant Pest Modeling | Scoop.it

Outbreaks of cucurbit downy mildew caused by Pseudoperonospora cubensis are dependent on the weather but effects of temperature and leaf wetness duration on infection have not been studied for different cucurbits. To determine the effects of these two weather variables on sporangia germination and infection of cucurbit host types by P. cubensis, three host types; cucumber, cantaloupe, and acorn squash, were inoculated and exposed to leaf wetness durations of 2 to 24 h at six constant temperatures ranging from 5 to 30°C in growth-chamber experiments


Interactive effects of temperature and leaf wetness duration on sporangia germination and infection of cucurbit hosts by P. cubensis were used on risk charts based on prevailing or forecasted temperature and leaf wetness duration. These results will improve the timing and application of the initial fungicide spray for the control of cucurbit downy mildew.



Neufeld KN & Ojiambo PS (2012) Interactive Effects of Temperature and Leaf Wetness Duration on Sporangia Germination and Infection of Cucurbit Hosts by Pseudoperonospora cubensis. Plant Disease - 96(3):345-353. 

http://dx.doi.org/10.1094/PDIS-07-11-0560 

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Modeling of Yield Losses Caused by Potato Late Blight on Eight Cultivars with Different Levels of Resistance to Phytophthora infestans

Modeling of Yield Losses Caused by Potato Late Blight on Eight Cultivars with Different Levels of Resistance to Phytophthora infestans | Plant Pest Modeling | Scoop.it

The Shtienberg model for predicting yield loss caused by Phytophthora infestans in potato was developed and parameterized in the 1990s in North America. The predictive quality of this model was evaluated in France for a wide range of epidemics under different soil and weather conditions and on cultivars different than those used to estimate its parameters.


Plant Disease, Volume 96, Issue 7, Page 935-942, July 2012.

Toky Rakotonindraina, INRA; Jean-Éric Chauvin and Roland Pellé, INRA; Robert Faivre, INRA; Catherine Chatot; Serge Savary and Jean-Noël Aubertot, INRA, France http://dx.doi.org/10.1094/PDIS-09-11-0752

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Weather center predicts reseeding 50% of winter crops in Ukrain

Weather center predicts reseeding 50% of winter crops in Ukrain | Plant Pest Modeling | Scoop.it

NRCU - Ukrainian Radio: Weather center predicts reseeding 50% of winter crops. This is what Tetiana Adamenko, head of the agrometeorology department, reported.

More accurate assessment of the state of winter crops after hibernation will be made by the end of March. The expert reminded that this year record low temperatures were registered in some regions at a depth of the tillering node. About 20% of wheat have not risen and the cold will add at least 20-30%.

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Hierarchical Bayesian models for estimating the extent of plant pest invasions | QUT ePrints

Hierarchical Bayesian models for estimating the extent of plant pest invasions | QUT ePrints | Plant Pest Modeling | Scoop.it
Stanaway, Mark Andrew (2011) Hierarchical Bayesian models for estimating the extent of plant pest invasions. PhD thesis, Queensland University of Technology.


The thesis contributes novel statistical approaches to estimating the extent of pests and develops applications to assist decision-making across a range of plant biosecurity surveillance activities.

Hierarchical Bayesian models provide a cohesive statistical framework that can formally integrate the available information on both pest ecology and data. The overarching method involves constructing an observation model for the surveillance data, conditional on the hidden extent of the pest and uncertain detection sensitivity. The extent of the pest is then modelled as a dynamic invasion process that includes uncertainty in ecological parameters. Modelling approaches to assimilate this information are explored through case studies on spiralling whitefly, Aleurodicus dispersus and red banded mango caterpillar, Deanolis sublimbalis.

Statistical methods, based on time-to-event models, are developed to apply hierarchical Bayesian models to early detection programs and to demonstrate area freedom from pests. The value of early detection surveillance programs is demonstrated through an application to interpret surveillance data for exotic plant pests with uncertain spread rates. 

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