Data from: Temporal and agricultural factors influence E. coli survival in soil and transfer to cucumbers

<p><em>Escherichia coli</em> survival in soils containing either composted poultry litter (CPL), heat-treated poultry pellets (HTPP), poultry litter (PL) or unamended (chemical fertilizer). Test plots were either covered with plastic mulch (M) or not mulched (NoM). The study was conducted in 2018 and 2019 during cucumber growing seasons at the University of Delaware research farm and each study lasted 120 days. Data from the current study were collected to examine the survival of non-pathogenic <em>Escherichia coli</em> and transfer to cucumbers grown in same field in two separate years. Soil moisture, total nitrogen, nitrate, total carbon, soluble carbon, soluble solids, rainfall, soil temperature and air temperature, along with the number of days needed for <em>E. coli</em> to decline by 4 log CFU/gdw, were included in random forest models used to a) predict 4-log declines of <em>E. coli</em> inoculated to soils and b) transfer of <em>E. coli</em> to cucumbers from soils with different biological soil amendments. The data included here are specifically for other investigators to use to make different forms or versions of three different statistical models used in the submitted manuscript. Data for three models are included: 1) Dpi4log, the number of days needed for <em>E. coli</em> levels in various combinations of year, amendment and mulch, were calculated by applying sigmoidal (single, double, triple, or quadruple) model to <em>E. coli</em> data collected over time. 2) A random forest model using soil and weather data was used to determine which factors listed above best predicted dpi4log values. This model accounted for 98% of the observed variance. 3) A random forest model using soil and weather data, along with dpi4log, was used to predict transfer of <em>E. coli</em> to soils from cucumbers (log MPN/cucumber). This model accounted for approximately 63% of the variance in the study. </p> <div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Graph of E. coli levels over 120 days in soils under various conditions.</p> <p>File Name: Graphs of Fitted Sigmoidal Regression Models onto Observed gEclog vs DPI.pdf</p><p>Resource Description: Graph of *E. coli* levels in 24 different combinations of year, amendment, and mulch status over 120 days </p></li><br><li><p>Resource Title: Comparison of actual model-generated log CFU/gdw data .</p> <p>File Name: Observed and Sigmoidal Model Predicted gEcLog values - Daily Increment.csv</p><p>Resource Description: Comparison of sigmoidal model-generated log CFU/gdw vs observed data </p></li><br><li><p>Resource Title: Soil temperature, Air temperature and Cumulative Rainfall observed in 2018 and 2019.</p> <p>File Name: Soil air temp cumulative rainfall 2018 2019.xlsx</p><p>Resource Description: These are the climate data used to inform and predict E. coli survival in soils containing biological and chemical fertilizer</p></li><br><li><p>Resource Title: Data set used in Random Forest model to predict transfer of E. coli from soils to cucumber fruits .</p> <p>File Name: UD ARS Cucumber Study Consolidated Data Version 2 Single Transference Column Original Data Scale.csv</p><p>Resource Description: This data set includes the sigmoidal model-estimated values of dpi4log (the number of days needed to achieve 4 log decline in E. coli levels) in this model </p></li><br><li><p>Resource Title: Dataset used in Random Forest model to identify variables and factors which predict dpi4log values of E. coli in soils containing biological soil amendment of animal origin.</p> <p>File Name: Formatted Soil Data for Random Forest Analysis.xlsx</p><p>Resource Description: Dataset used in the Random Forest model to identify variables and factors which predict dpi4log values - the number of days needed to observe a 4 log reduction, estimated by sigmoidal modeling of collected E. coli data - of E. coli in soils containing biological soil amendment of animal origin</p></li></ul><p></p>