Modelling plant disease epidemics

Maanen, Aline Van; Xu, Xiangming

doi:10.1007/978-94-017-1452-5_2

Cited by 63 publications

(63 citation statements)

References 80 publications

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“…An epidemic is the progress of disease in time and space. Each epidemic has a structure whose temporal dynamics and spatial patterns are jointly determined by the pathosystem characteristics and environmental conditions (Maanen and Xu, 2003). Environment is an integral component of disease development without the knowledge of which disease management is very difficult (Ghent et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Epidemiological Studies of Tomato Leaf Curl Virus Disease Based upon Environmental Variables

Zeshan¹,

Khan²,

Ali³

et al. 2016

IJAB

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Tomato leaf curl virus (TLCV) is an emerging problem in tomato growing areas of Pakistan. Relationship of TLCV disease and environmental conditions were studied on five tomato cultivars. Temperature (maximum and minimum) and relative humidity significantly contributed in the development of TLCV disease during two years (2012 and 2013). The TLCV disease incidence increased with rise in maximum and minimum temperature and decreased with the increase in relative humidity. The contribution of maximum temperature was explained by linear regression which showed 83 to 91% variability in the disease incidence and minimum temperature explained 75 to 85% variability. Relative humidity exerted 78 to 85% contribution in the disease development. The linear regression could not explain the contribution of rainfall and wind speed in disease development. The characterization of favorable environmental conditions for TLCV disease development would contribute towards accurate prediction and timely management of the disease.

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Section: Introductionmentioning

confidence: 99%

Epidemiological Studies of Tomato Leaf Curl Virus Disease Based upon Environmental Variables

Zeshan¹,

Khan²,

Ali³

et al. 2016

IJAB

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“…It involves four state variables of a crop stand as healthy (H), latent (L) (Roumen and Boef, 1993), infectious (I) (Magboul, et al, 1992), and postinfectious sites (R). An epidemic is the progress of disease in time and space (van Maanen and Xu, 2003). The EPIRICE model consists of 2 modules: the site growth senescence module and SEIR module.…”

Section: Model Of Rice Blast Diseasementioning

confidence: 99%

Numerical solution of the differential equation for simulation of the rice blast disease

Sittisak¹,

Saruda²,

Angkool³

et al. 2018

Journal of Applied Sciences and Environmental Management

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ABSTRACT:Rice blast disease is one of the diseases that causes damage to rice yield in Thailand. This research aims to simulate the severity of rice blast disease using the EPIRICE model for Khao Dawk Mali 105 that caused by Pyricularia oryzae in Prachin Buri, Thailand, and evaluate the simulation results by comparing with field collection data using root mean square error (RMSE). The epidemiological model consists of a system of ordinary differential equations (ODEs) that describes the dynamic of rice leaf disease. The Euler's method is used to approximate the solutions of the ODEs. According to the RMSE, the simulation results were good agreement with the field collection data. Therefore, the numerical methods can be the technical tool for solving the severity of rice blast disease.

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“…More complex simulation models may additional include the life cycle of the host crop, the feast of the pest agents to distance crops as well as the influence of several other biotic and abiotic factors (Andrade-Piedra et al 2005;Kaundal et al 2006;Cairns et al 2008;Khaliq et al 2014). Thus, pest forecasting is a fundamental step in creating proper IPM programs (Van Maanen and Xu 2003).…”

Section: Introductionmentioning

confidence: 99%

Modular structure of web-based decision support systems for integrated pest management. A review

Damos

2015

Agron. Sustain. Dev.

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Sustainable pest management implies less pesticide use and replacement by safe control alternatives. This requires decision support for rational pest management. However, in practice, successful decision making is dependent upon the availability of integrated, high-quality information. Computer-aided forecasting and related decision support systems make pest control more sustainable by avoiding unwanted consequences of pesticide applications. Here, I review integrated pest management for web-based decision support systems. The major points are the following: (1) Principles of integrated pest management are compatible with sustainable agriculture. (2) Pest models serve as basis of decision making because they offer means to predict the exact time of pest phenological development and initiate management actions. Most models are climate driven. (3) New hardware technology has permitted the registration of automatically recorded climatic data. This data can be combined with pest models through logical operations and forecasting algorithms to develop a software of pest management. (4) Dynamic web interfaces can serve as decision support systems providing the user with real-time pest warnings and recommendations for management actions. (5) Ontology web programing and semantic knowledge representations provide a way to classify and describe agrodata to facilitate information sharing and data exploitation over distributed systems. (6) Most available pest management data is published on static web pages and, thus, cannot be classified as decision support systems. Some web-based decision support systems provide user-interactive content and real-time pest forecasts and management support.

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Modelling plant disease epidemics

Cited by 63 publications

References 80 publications

Epidemiological Studies of Tomato Leaf Curl Virus Disease Based upon Environmental Variables

Epidemiological Studies of Tomato Leaf Curl Virus Disease Based upon Environmental Variables

Numerical solution of the differential equation for simulation of the rice blast disease

Modular structure of web-based decision support systems for integrated pest management. A review

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