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Evaluation of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) and the Collected Data

机译：对粮食和农业部门危急度评估工具（FASCAT）的评估和所收集的数据

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To protect and secure food resources for the United States, it is crucial to have a method to compare food systems' criticality. In 2007, the U.S. government funded development of the Food and Agriculture Sector Criticality Assessment Tool (FASCAT) to determine which food and agriculture systems were most critical to the nation. FASCAT was developed in a collaborative process involving government officials and food industry subject matter experts (SMEs). After development, data were collected using FASCAT to quantify threats, vulnerabilities, consequences, and the impacts on the United States from failure of evaluated food and agriculture systems. To examine FASCAT's utility, linear regression models were used to determine: (1) which groups of questions posed in FASCAT were better predictors of cumulative criticality scores; (2) whether the items included in FASCAT's criticality method or the smaller subset of FASCAT items included in DHS's risk analysis method predicted similar criticality scores. Akaike's information criterion was used to determine which regression models best described criticality, and a mixed linear model was used to shrink estimates of criticality for individual food and agriculture systems. The results indicated that: (1) some of the questions used in FASCAT strongly predicted food or agriculture system criticality; (2) the FASCAT criticality formula was a stronger predictor of criticality compared to the DHS risk formula; (3) the cumulative criticality formula predicted criticality more strongly than weighted criticality formula; and (4) the mixed linear regression model did not change the rank-order of food and agriculture system criticality to a large degree.
机译：为了保护和确保美国的粮食资源，至关重要的是要有一种方法来比较粮食系统的重要性。 2007年，美国政府资助了粮食和农业部门关键性评估工具（FASCAT）的开发，以确定哪些粮食和农业系统对美国最关键。 FASCAT是在政府官员和食品行业主题专家（SME）的共同协作下开发的。开发后，使用FASCAT收集数据，以量化评估的粮食和农业系统的失败所带来的威胁，脆弱性，后果以及对美国的影响。为了检验FASCAT的效用，使用线性回归模型来确定：（1）FASCAT中提出的哪些问题组是累积临界评分的较好预测指标； （2）FASCAT关键性方法中包含的项目还是DHS风险分析方法中包含的FASCAT项目的较小子集是否预测了相似的关键性评分。 Akaike的信息标准用于确定哪种回归模型最能描述临界度，而混合线性模型用于缩小单个食品和农业系统的临界度估计。结果表明：（1）FASCAT中使用的一些问题强烈预测了粮食或农业系统的重要性； （2）与国土安全部风险公式相比，FASCAT临界公式更能预测危险。 （3）累积关键度公式比加权关键度公式更能预测关键度； （4）混合线性回归模型并未在很大程度上改变粮食和农业系统关键性的等级顺序。

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