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14 results on '"Hong, Seung Cheon"'

6. Comparison of four modeling tools for the prediction of potential distribution for non-indigenous weeds in the United States

Author

Magarey, Roger, primary, Newton, Leslie, additional, Hong, Seung Cheon, additional, Takeuchi, Yu, additional, Christie, David, additional, Jarnevich, Catherine S., additional, Kohl, Lisa, additional, Damus, Martin, additional, Higgins, Steven I., additional, Millar, Leah, additional, Castro, Karen, additional, West, Amanda, additional, Hastings, John, additional, Cook, Gericke, additional, Kartesz, John, additional, and Koop, Anthony L., additional

Published
2017
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7. Predictive Modeling and Categorizing Likelihoods of Quarantine Pest Introduction of Imported Propagative Commodities from Different Countries.

Author

Hong, Seung Cheon, Kim, ByeongJoon, Egger, Daniel, Katsar, Catherine S., and Griffin, Robert L.

Subjects
PREDICTION models, QUARANTINE, PESTS, COMMERCIAL products, PLANT parasites
Abstract

The present study investigates U.S. Department of Agriculture inspection records in the Agricultural Quarantine Activity System database to estimate the probability of quarantine pests on propagative plant materials imported from various countries of origin and to develop a methodology ranking the risk of country–commodity combinations based on quarantine pest interceptions. Data collected from October 2014 to January 2016 were used for developing predictive models and validation study. A generalized linear model with Bayesian inference and a generalized linear mixed effects model were used to compare the interception rates of quarantine pests on different country–commodity combinations. Prediction ability of generalized linear mixed effects models was greater than that of generalized linear models. The estimated pest interception probability and confidence interval for each country–commodity combination was categorized into one of four compliance levels: "High," "Medium," "Low," and "Poor/Unacceptable," Using K‐means clustering analysis. This study presents risk‐based categorization for each country–commodity combination based on the probability of quarantine pest interceptions and the uncertainty in that assessment. [ABSTRACT FROM AUTHOR]

Published
2019
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8. Site-specific temporal and spatial validation of a generic plant pest forecast system with observations of Bactrocera dorsalis (oriental fruit fly)

Author

Hong, Seung Cheon, Magarey, Roger D., Borchert, Daniel M., Vargas, Roger I., Souder, Steven K., Hong, Seung Cheon, Magarey, Roger D., Borchert, Daniel M., Vargas, Roger I., and Souder, Steven K.

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 evelopmental 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. An index of agreement test indicated that GPFS model simulations matched with field B. dorsalis observation data with a range between 0.50 and 0.94 (1.0 as a perfect match). Of the three locations, Wuhan showed 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 estimati

Published
2015

14. Predictive Modeling and Categorizing Likelihoods of Quarantine Pest Introduction of Imported Propagative Commodities from Different Countries.

Author

Kim B, Hong SC, Egger D, Katsar CS, and Griffin RL

Abstract

The present study investigates U.S. Department of Agriculture inspection records in the Agricultural Quarantine Activity System database to estimate the probability of quarantine pests on propagative plant materials imported from various countries of origin and to develop a methodology ranking the risk of country-commodity combinations based on quarantine pest interceptions. Data collected from October 2014 to January 2016 were used for developing predictive models and validation study. A generalized linear model with Bayesian inference and a generalized linear mixed effects model were used to compare the interception rates of quarantine pests on different country-commodity combinations. Prediction ability of generalized linear mixed effects models was greater than that of generalized linear models. The estimated pest interception probability and confidence interval for each country-commodity combination was categorized into one of four compliance levels: "High," "Medium," "Low," and "Poor/Unacceptable," Using K-means clustering analysis. This study presents risk-based categorization for each country-commodity combination based on the probability of quarantine pest interceptions and the uncertainty in that assessment., (© 2018 The Authors Risk Analysis published by Wiley Periodicals, Inc. on behalf of Society for Risk Analysis.)

Published
2019
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