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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Scientia Agricola - Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study

A review of disease-warning systems used as decision support tools designed to help growers determine when to apply control measures to suppress crop diseases.


The authors stress that weather data are nearly ubiquitous inputs to warning systems. Grower-operated weather monitoring is contrasted with obtaining data from networks of weather stations, and the advantages and disadvantages of measuring vs. estimating weather data are discussed. Special emphasis is given to leaf wetness duration (LWD), not only because LWD data are inputs to many disease-warning systems but also because accurate data are uniquely challenging to obtain. 


Gleason et al. (2008) Obtaining weather data for input to crop disease-warning systems: leaf wetness duration as a case study.  Scientia Agricola (Piracicaba, Braz.) vol.65 Dec. 2008

http://dx.doi.org/10.1590/S0103-90162008000700013

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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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WEATHER AND CLIMATE FORECASTS FOR AGRICULTURE

Weather plays an important role in agricultural production. It has a profound influence on crop growth, development and yields; on the incidence of pests and diseases; on water needs; and on fertilizer requirements.


For optimal productivity at a given location, crops and cropping practices must be such that while their cardinal phased weather requirements match the temporal march of the relevant weather element(s), endemic periods of pests, diseases and hazardous weather are avoided. In such strategic planning of crops and cropping practices, short-period climatic data, both routine and processed (such as initial and
conditional probabilities), have a vital role to play.

Read more in GUIDE TO AGRICULTURAL METEOROLOGICAL PRACTICES.

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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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