NAPPFAST: An Internet System for the Weather-Based Mapping of Plant Pathogens

Magarey, Roger D.; Fowler, Glenn; Borchert, Daniel M.; Sutton, T. B.; Colunga-Garcia, Manuel; Simpson, Jane

doi:10.1094/pdis-91-4-0336

Cited by 113 publications

(102 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For the third factor, the Panel undertook some exploratory analysis using leaf wetness models and the generic infection model (Magarey et al, 2005) that is incorporated within the USA's plant pest forecasting system NAPPFAST (Magarey et al, 2007 Paul et al (2005) had been made and there were errors in the input data.…”

Section: Summary Of the Climex Analysis Undertaken By South Africamentioning

confidence: 99%

“…It is especially helpful for modeling pathogens for which extensive epidemiological data are unavailable (Magarey et al, 2005). The generic infection model has been recently implemented in the NAPPFAST system and is being used by USDA-APHIS to create pest risk maps (Magarey et al, 2007). The model predicts climate suitability based on the environmental requirements for infection by the target pathogen.…”

Section: Modelling Climate Suitability For G Citricarpa By the Simplmentioning

confidence: 99%

See 1 more Smart Citation

Pest risk assessment and additional evidence provided by South Africa on Guignardia citricarpa Kiely, citrus black spot fungus - CBS - Scientific Opinion of the Panel on Plant Health

2009

EFSA Journal

View full text Add to dashboard Cite

Section: Summary Of the Climex Analysis Undertaken By South Africamentioning

confidence: 99%

Section: Modelling Climate Suitability For G Citricarpa By the Simplmentioning

confidence: 99%

Pest risk assessment and additional evidence provided by South Africa on Guignardia citricarpa Kiely, citrus black spot fungus - CBS - Scientific Opinion of the Panel on Plant Health

2009

EFSA Journal

View full text Add to dashboard Cite

“…Recent advances in information technology have provided various information delivery systems through which crop growers can have easy access to plant disease forecast information. For example, common use of personal computers with powerful computing resources, web-based Internet systems and wireless communication infrastructures allowed us to do instant data processes for collecting weather data from AWS's installed at remote places, analyzing huge data sets to generate disease forecast information and distributing the information to crop growers at near real-time basis (Kang et al, unpublished;Kim, 1995;Magarey et al, 1997;Magarey et al, 2007;Rajotte et al, 1992).…”

Section: Abstract : Disease Forecasting Infection Risk Map Weather mentioning

confidence: 99%

A Web-based Information System for Plant Disease Forecast Based on Weather Data at High Spatial Resolution

Kang¹,

Hong²,

Han³

et al. 2010

The Plant Pathology Journal

View full text Add to dashboard Cite

This paper describes a web-based information system for plant disease forecast that was developed for crop growers in Gyeonggi-do, Korea. The system generates hourly or daily warnings at the spatial resolution of 240 m×240 m based on weather data. The system consists of four components including weather data acquisition system, job process system, data storage system, and web service system. The spatial resolution of disease forecast is high enough to estimate daily or hourly infection risks of individual farms, so that farmers can use the forecast information practically in determining if and when fungicides are to be sprayed to control diseases. Currently, forecasting models for blast, sheath blight, and grain rot of rice, and scab and rust of pear are available for the system. As for the spatial interpolation of weather data, the interpolated temperature and relative humidity showed high accuracy as compared with the observed data at the same locations. However, the spatial interpolation of rainfall and leaf wetness events needs to be improved. For rice blast forecasting, 44.5% of infection warnings based on the observed weather data were correctly estimated when the disease forecast was made based on the interpolated weather data. The low accuracy in disease forecast based on the interpolated weather data was mainly due to the failure in estimating leaf wetness events.

show abstract

“…In addition to the need for potential distribution maps, other phytosanitary applications of weather or climate-based models include predictions of: i) the frequency of years favorable to crop losses or epidemics (Pinkard et al 2010b); ii) the timing of life stages to deploy surveys or treatments; iii) the duration of mitigation treatments designed to achieve control or eradication based on historical or forecast weather; and iv) the extent of crop damage or injury to specific hosts (Magarey et al 2014;Magarey et al 2007;Pardey et al 2013;Pinkard et al 2010b). These kinds of applications are also relevant to the management of indigenous pests.…”

Section: Introductionmentioning

confidence: 99%

“…Although these are the basic model components, the GPFS model also includes components for: iv) Infection and sporulation modules for plant pathogens; v) Pest and host growth stages based on degree days; and vi) Potential damage based on predicted pest population and host and pest growth stages (Magarey unpublished data), however these last three components will not be presented in this study. The GPFS model is designed to run within a pest information platform such as NAPPFAST (Magarey et al 2014;Magarey et al 2007) which would supply the required hourly weather inputs. The NAPPFAST system (used by the U.S. Department of Agriculture's Animal Plant Health Inspection Service between 2002 and 2014) included an interactive template to allow users to create simple degree day, disease infection, and flexible models from U.S. and global weather databases for phytosanitary applications.…”

Section: Introductionmentioning

confidence: 99%

Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly)

Hong¹,

Magarey²,

Borchert³

et al. 2015

View full text Add to dashboard Cite

Citation: Hong SC, Magarey RD, Borchert DM, Vargas RI, Souder SK (2015) Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly). NeoBiota 27: 37-67. doi: 10.3897/neobiota.27.5177 Abstract This study introduces a simple generic model, the Generic Pest Forecast System (GPFS), for simulating the relative populations of non-indigenous arthropod pests in space and time. The model was designed to calculate the population index or relative population using hourly weather data as influenced by developmental rate, high and low temperature mortalities and wet soil moisture mortality. Each module contains biological parameters derived from controlled experiments. The hourly weather data used for the model inputs were obtained from the National Center of Environmental Prediction Climate Forecast System Reanalysis (NCEP-CFSR) at a 38 km spatial resolution. A combination of spatial and site-specific temporal data was used to validate the GPFS models. The oriental fruit fly, Bactrocera dorsalis (Hendel), was selected as a case study for this research because it is climatically driven and a major pest of fruit production. Results from the GPFS model were compared with field B. dorsalis survey data in three locations: 1) Bangalore, India; 2) Hawaii, USA; and 3) Wuhan, China. The GPFS captured the initial outbreaks and major population peaks of B. dorsalis reasonably well, although agreement varied between sites. NeoBiota 27: 37-67 (2015) doi: 10.3897/neobiota.27.5177 http://neobiota.pensoft.net Seung Cheon Hong et al. / NeoBiota 27: 37-67 (2015) 38 the highest match between the observed and simulated B. dorsalis populations, with indices of agreement of 0.85. The site-specific temporal comparisons implied that the GPFS model is informative for prediction of relative abundance. Spatial results from the GPFS model were also compared with 161 published observations of B. dorsalis distribution, mostly from East Asia. Since parameters for pupal overwintering and survival were unknown from the literature, these were inferred from the distribution data. The study showed that GPFS has promise for estimating suitable areas for B. dorsalis establishment and potentially other non-indigenous pests. It is concluded that calibrating prediction models with both spatial and sitespecific temporal data may provide more robust and reliable results than validations with either data set alone. RESEARCH ARTICLE Advancing research on alien species and biological invasions A peer-reviewed open-access journal NeoBiota

show abstract

NAPPFAST: An Internet System for the Weather-Based Mapping of Plant Pathogens

Cited by 113 publications

References 27 publications

Pest risk assessment and additional evidence provided by South Africa on Guignardia citricarpa Kiely, citrus black spot fungus - CBS - Scientific Opinion of the Panel on Plant Health

Pest risk assessment and additional evidence provided by South Africa on Guignardia citricarpa Kiely, citrus black spot fungus - CBS - Scientific Opinion of the Panel on Plant Health

A Web-based Information System for Plant Disease Forecast Based on Weather Data at High Spatial Resolution

Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly)

Contact Info

Product

Resources

About