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首页> 外文期刊>Risk analysis >A Predictive Model for Survival of Escherichia coli 0157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure
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A Predictive Model for Survival of Escherichia coli 0157:H7 and Generic E. coli in Soil Amended with Untreated Animal Manure

机译:未经治疗的动物粪肥的土壤中的预测模型:H7和土壤中的普通大肠杆菌

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摘要

This study aimed at developing a predictive model that captures the influences of a variety of agricultural and environmental variables and is able to predict the concentrations of enteric bacteria in soil amended with untreated Biological Soil Amendments of Animal Origin (BSAAO) under dynamic conditions. We developed and validated a Random Forest model using data from a longitudinal field study conducted in mid-Atlantic United States investigating the survival of Escherichia coli O157:H7 and generic E. coli in soils amended with untreated dairy manure, horse manure, or poultry litter. Amendment type, days of rain since the previous sampling day, and soil moisture content were identified as the most influential agricultural and environmental variables impacting concentrations of viable E. coli O157:H7 and generic E. coli recovered from amended soils. Our model results also indicated that E. coli O157:H7 and generic E. coli declined at similar rates in amended soils under dynamic field conditions.The Random Forest model accurately predicted changes in viable E. coli concentrations over time under different agricultural and environmental conditions. Our model also accurately characterized the variability of E. coli concentration in amended soil over time by providing upper and lower prediction bound estimates. Cross-validation results indicated that our model can be potentially generalized to other geographic regions and incorporated into a risk assessment for evaluating the risks associated with application of untreated BSAAO. Our model can be validated for other regions and predictive performance also can be enhanced when data sets from additional geographic regions become available.
机译:本研究旨在开发一种预测模型,捕获各种农业和环境变量的影响,并且能够在动态条件下预测动物来源(Bsaao)的未处理生物土壤修正案的土壤中肠道细菌的浓度。我们使用来自大西洋中大肠中的纵向实地研究的数据进行了随机森林模型,调查了大肠杆菌O157的存活:H7和土壤中的普通大肠杆菌进行了未处理的乳制品粪便,马粪或家禽垃圾。修正类型,自上述取样日以来的雨水,土壤水分含量被确定为影响可行大肠杆菌O157:H7和从修正的土壤中恢复的通用大肠杆菌的最有影响力的农业和环境变量。我们的模型结果表明,大肠杆菌O157:H7和通用大肠杆菌在动态现场条件下的修正土壤中下降。随机森林模型在不同农业和环境条件下随着时间的推移准确地预测了可行的大肠杆菌浓度的变化。我们的模型还通过提供上下预测估计,精确地表征了大肠杆菌浓度随时间在修正的土壤中的可变性。交叉验证结果表明,我们的模型可能是普遍推广到其他地理区域,并纳入风险评估,以评估与未经处理的Bsaao应用相关的风险。我们的模型可用于其他地区,并且当来自额外地理区域的数据集可用时也可以增强预测性能。

著录项

  • 来源
    《Risk analysis》 |2020年第7期|1367-1382|共16页
  • 作者单位

    US FDA Ctr Food Safety & Appl Nutr Off Analyt & Outreach College Pk MD USA|Univ Maryland Joint Inst Food Safety & Appl Nutr College Pk MD 20742 USA;

    US FDA Ctr Food Safety & Appl Nutr Off Analyt & Outreach College Pk MD USA|Booz Allen Hamilton 4747 Bethesda Ave Bethesda MD 20814 USA;

    US FDA Ctr Food Safety & Appl Nutr Off Analyt & Outreach College Pk MD USA;

    US FDA Ctr Food Safety & Appl Nutr Off Analyt & Outreach College Pk MD USA;

    US FDA Ctr Food Safety & Appl Nutr Off Food Safety College Pk MD USA;

    ARS USDA Northeast Area Beltsville Agr Res Ctr Environm Microbial & Food Beltsville MD USA;

    ARS USDA Northeast Area Beltsville Agr Res Ctr Environm Microbial & Food Beltsville MD USA;

    US FDA Ctr Food Safety & Appl Nutr Off Analyt & Outreach College Pk MD USA;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Biological soil amendments of animal origin; enteric bacteria; predictive model; random forest;

    机译:动物来源的生物土壤修正;肠道细菌;预测模型;随机森林;

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