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首页> 外文期刊>Bulletin OEPP: = EPPO Bulletin >Risk maps for targeting exotic plant pest detection programs in the United States.
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Risk maps for targeting exotic plant pest detection programs in the United States.

机译：针对美国外来植物有害生物检测计划的风险图。

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摘要

In the United States, pest risk maps are used by the Cooperative Agricultural Pest Survey for spatial and temporal targeting of exotic plant pest detection programs. Methods are described to create standardized host distribution, climate and pathway risk maps for the top nationally ranked exotic pest targets. Two examples are provided to illustrate the risk mapping process: late wilt of corn (Harpophora maydis) and the giant African land snail (Achatina fulica). Host risk maps were made from county-level crop census and USDA Forest Inventory and Analysis data, respectively. Climate risk maps were made using the North Carolina State University-USDA APHIS Plant Pest Forecasting System (NAPPFAST), which uses a web-based graphical user interface to link climatic and geographic databases with interactive templates for biological modelling. Pathway risk maps were made using freight flow allocation data sets to move commodities from 7 world regions to 3162 US urban areas. A new aggregation technique based on the Pareto dominance principle was used to integrate maps of host abundance, climate and pathway risks into a single decision support product. The maps are publicly available online (http://www.nappfast.org). Key recommendations to improve the risk maps and their delivery systems are discussed.Digital Object Identifier http://dx.doi.org/10.1111/j.1365-2338.2011.02437.x
机译：在美国，合作农业害虫调查使用了有害生物风险图，将外来植物有害生物检测计划用于时空目标。描述了为国家排名靠前的外来有害生物目标创建标准化宿主分布，气候和途径风险图的方法。提供了两个示例来说明风险绘图过程：玉米晚枯萎病（ Harpophora maydis ）和非洲大蜗牛（ Achatina fulica ）。宿主风险图分别由县级作物普查和美国农业部森林清单和分析数据制成。使用北卡罗莱纳州立大学-美国农业部APHIS植物病虫害预测系统（NAPPFAST）制作了气候风险图，该系统使用基于Web的图形用户界面将气候和地理数据库与用于生物建模的交互式模板相链接。使用货运量分配数据集制作了路径风险图，将商品从7个世界区域转移到3162个美国城市区域。基于帕累托优势原理的一种新的聚合技术被用于将宿主丰度，气候和途径风险图整合到单个决策支持产品中。这些地图可在线公开获得（http://www.nappfast.org）。讨论了改善风险图及其交付系统的关键建议。数字对象标识符http://dx.doi.org/10.1111/j.1365-2338.2011.02437.x

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