ABSTRACT 
 
 
 
 
Title of Thesis: EFFECT OF INITIAL SOIL MOISTURE 
CONDITIONS ON RUNOFF TRANSPORT OF 
MANURE-BORNE PATHOGENS THROUGH 
VEGETATED FILTER STRIPS  
 
 Fatima Araujo Cardoso-Gendreau, 
Master of Science, 2006 
Directed By: Professor Adel Shirmohammadi, 
Department of Biological Resources Engineering 
 
 
Pollution of drinking and recreational water supplies with manure-borne 
pathogenic bacteria through surface runoff from agricultural lands is a public health 
threat wherever landscapes are exposed to animal manure, but, particularly, where 
there is concentrated animal production (e.g., Iowa). This study was conducted to 
investigate the effect of initial soil moisture conditions on the effectiveness of 
vegetated filters strips (VFS) to mitigate surface runoff transport of two surrogate 
pathogenic bacteria, Escherichia coli and Salmonella enterica enterica Typhimurium, 
from land-applied swine slurry.   
A 5% slope lysimeter containing clay loam soil was constructed, partitioned 
into vegetated and bare plots, and the plots instrumented to collect, measure, and 
sample runoff at different time intervals and at two distances from the slurry 
application area during rainfall simulations. Results indicated that the potential of 
VFS to attenuate runoff transport of pathogens was reduced under increased initial 
soil moisture conditions, indicating that infiltration is an important factor in the 
mitigation process.     
  
 
 
 
 
 
 
 
 
 
 
EFFECT OF INITIAL SOIL MOISTURE CONDITIONS ON RUNOFF 
TRANSPORT OF MANURE-BORNE PATHOGENS THROUGH VEGETATED 
FILTER STRIPS    
 
 
 
By 
 
 
Fatima Araujo Cardoso-Gendreau 
 
 
 
 
 
Thesis submitted to the Faculty of the Graduate School of the  
University of Maryland, College Park, in partial fulfillment 
of the requirements for the degree of 
Master of Science 
2006 
 
 
 
 
 
 
 
 
 
 
Advisory Committee: 
Professor Adel Shirmohammadi, Chair 
Associate Professor Hubert J. Montas 
Microbiologist, Dr. Daniel R. Shelton 
 
 
  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
? Copyright by 
Fatima Araujo Cardoso-Gendreau 
2006 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
Dedication 
To my late father, Ercy da Silva Cardoso, with all my love. 
You are always on my mind and in my heart. I will always remember you. 
 ii 
 
 
Acknowledgements 
I would like to thank the Environmental Microbial Safety Laboratory (EMSL) 
at USDA - ARS, in Beltsville, MD, and the National Pork Producers Council, in Des 
Moines, IA, for the support and funding for this study. 
I would also like to thank my committee members. I would like to thank my 
major advisor Dr. Adel Shirmohammadi for bringing me in on this project as a 
student. Dr Shirmohammadi and Dr. Daniel R. Shelton at ESML collaborated to 
design this experiment. Not only did Dr. Shirmohammadi and Dr. Shelton provided 
me the chance to do field research, but they also directly helped me in the field and 
with the interpretation of the data. They helped me with anything ? from mowing the 
grass, to spreading foul smelling swine slurry, to collecting runoff, to processing of 
samples, to understanding the results. Together with Dr. Hubert J. Montas, they also 
helped me to present the results I show in this thesis. 
I have many other friends and colleagues at EMSL who helped me over the 
past few years. These include Dr. Ali Sadeghi, soil scientist (presently with the 
Hydrology and Remote Sensing Laboratory at USDA ? ARS), who played a vital role 
in helping me with the experimental setup and in all aspects of the field work 
involving the experiments. Thank you very much Dr. Sadeghi. Also, thanks to Valerie 
McPhatter, Kerry Sefton, Laura Weltz, Kate Jacobs, Justine Beaulieu, and Claudia 
Lam who tremendously helped me in the field and the laboratory. Kerry Sefton also 
taught me how to analyze my runoff samples for Bromide content and my soil 
samples for texture. Dr. Jeffrey Karns, Dr. Jo Ann Van Kessel, and Dr. Andrey Guber 
who shared their expertise with me to accomplish many projects in the field and the 
 iii 
 
 
laboratory, and Dr. Yakov Pachepsky who patiently assisted me with statistical 
analyses and answered so many of my questions. Without the long hours of help from 
my many friends at EMSL, I would not have completed this work.   
At the University of Maryland, my fellow graduate students and friends 
including Dr. Reza Roodsari, Pouyan Nejadhashemi, Dr. Tzyy-Woei Chu, and Aisha 
Sexton who tremendously helped me in the field. Dr. Roodsari also helped me with 
calculations and analyses. Also, Dr. Hartsock who allowed me to visit his farm on 
early Saturday mornings and helped me to collect the right ?liquid to solid? mix of 
swine slurry for my experiments. I thank them all for their help and support. 
Finally, I would like to thank my family. My mother, Eva Eliza Cardoso, for 
her prayers and pushing me to finish. My brother, Flavio Cardoso, for his patient help 
editing most of my chapters. My brothers in Brazil, Fulvio and Fabio Cardoso, for 
their support. And finally, my husband, Keith Gendreau, who tolerated the entire 
process ? from helping to get pig poop to teaching me how to fit data ? and still loves 
me. 
 iv 
 
 
Table of Contents 
 
 
Dedication..................................................................................................................... ii 
Acknowledgements......................................................................................................iii 
Table of Contents.......................................................................................................... v 
List of Tables .............................................................................................................. vii 
List of Figures.............................................................................................................. ix 
Chapter 1: Introduction................................................................................................. 1 
Chapter 2: Literature Review........................................................................................ 6 
2.1 Bacteria ............................................................................................................... 6 
2.1.1 Definition, size, shapes, density, categories....................................................... 6 
2.1.2 Enterobacteriaceae family............................................................................... 7 
2.1.3 Characteristics of genus Escherichia and its clinical significance........................ 8 
2.1.4 Characteristics of genus Salmonella and its clinical significance....................... 11 
2.2 Factors affecting the growth of Escherichia and Salmonella spp., and their 
survival in swine feces, soil, and water................................................................... 14 
2.2.1 Growth ......................................................................................................... 14 
2.2.2 Survival in swine feces .................................................................................. 14 
2.2.3 Survival in soil.............................................................................................. 16 
2.2.4 Survival in water ........................................................................................... 18 
2.3 Processes and factors affecting the transport of bacteria in surface runoff ...... 20 
2.4 Effectiveness of vegetated filter strips (VFS) at removing pathogens from 
surface runoff.......................................................................................................... 23 
2.5 Pork Production in the State of Iowa ................................................................ 27 
Chapter 3: Materials and Methods.............................................................................. 28 
3.1 Lysimeter Set Up .............................................................................................. 28 
3.1.1 Geographic location ...................................................................................... 28 
3.1.2 Construction ................................................................................................. 29 
3.1.3 Plot partitioning and waste application designation area................................... 29 
3.1.4 Soil characteristics ........................................................................................ 30 
3.1.5 Topography of vegetated plots ....................................................................... 34 
3.1.6 Slotted wells ................................................................................................. 35 
3.1.7 Surface runoff collectors................................................................................ 38 
3.1.8 Rainfall source.............................................................................................. 39 
3.1.9 Pollutant containment tanks ........................................................................... 39 
3.2 Pre-experiment Field Procedures...................................................................... 40 
3.2.1 Calibration of rainfall simulator...................................................................... 40 
3.2.2 Dry initial soil moisture conditions................................................................. 41 
3.2.3 Wet initial soil moisture conditions................................................................. 42 
3.2.4 Funnels installation ....................................................................................... 43 
3.2.5 V-notch weir installation................................................................................ 43 
3.2.6 Application area barrier ................................................................................. 44 
3.2.7 Isolation of plots ........................................................................................... 44 
3.3 Pre-experiment Laboratory Procedures ............................................................ 44 
 v 
 
 
3.3.1 Source of manure .......................................................................................... 44 
3.3.2 Slurry preparation ......................................................................................... 45 
3.3.3 Escherichia coli culture ................................................................................. 45 
3.3.4 Salmonella enterica Typhimurium culture ...................................................... 48 
3.3.5 Plate preparation ........................................................................................... 51 
3.3.6 Slurry inoculation.......................................................................................... 52 
3.3.7 Bromide tracer .............................................................................................. 53 
3.4 Field Experimental Procedures......................................................................... 54 
3.4.1 Soil samples and groundwater  table............................................................... 54 
3.4.2 Soil and ambient temperatures........................................................................ 55 
3.4.3 Slurry application.......................................................................................... 55 
3.4.4 Runoff sampling............................................................................................ 57 
3.5 Post-experiment Laboratory Analytical Procedures ......................................... 58 
3.5.1 Processing of slurry samples .......................................................................... 58 
3.5.2 Processing of runoff samples from vegetated plots........................................... 59 
3.5.3 Processing of runoff samples from bare plots .................................................. 60 
3.5.4 Colony counting............................................................................................ 61 
3.5.5 Bromide concentration in runoff samples........................................................ 62 
3.6 Statistical Analysis............................................................................................ 63 
Chapter 4: Results and Discussion.............................................................................. 65 
4.1 Hydrographs...................................................................................................... 65 
4.1.1 Dry initial soil moisture conditions................................................................. 65 
4.1.2 Wet initial soil moisture conditions................................................................. 69 
4.2 Bromide............................................................................................................. 72 
4.2.1 Dry initial soil moisture conditions................................................................. 72 
4.2.2 Wet initial soil moisture conditions................................................................. 74 
4.3 Surrogate Pathogens.......................................................................................... 76 
4.3.1 Dry initial soil moisture conditions................................................................. 76 
4.3.1.1 Relationship between relative concentrations of Escherichia coli and Salmonella 
enterica Typhimurium in surface runoff.................................................................. 91 
4.3.1.2 Relationship between relative concentrations of each surrogate pathogen and 
Bromide in surface runoff ...................................................................................... 97 
4.3.2 Wet initial soil moisture conditions............................................................... 108 
4.3.2.1 Relationship between relative concentrations of Escherichia coli and Salmonella 
enterica Typhimurium in surface runoff................................................................ 121 
4.3.2.2 Relationship between relative concentrations of each surrogate pathogen and 
Bromide in surface runoff .................................................................................... 127 
4.4 Cumulative recoveries .................................................................................... 138 
4.4.1 Dry initial soil moisture conditions............................................................... 138 
4.4.2 Wet initial soil moisture conditions............................................................... 142 
Conclusions and Recommendations ......................................................................... 147 
APPENDIX A........................................................................................................... 152 
APPENDIX B ........................................................................................................... 154 
APPENDIX C ........................................................................................................... 157 
APPENDIX D........................................................................................................... 160 
APPENDIX E ........................................................................................................... 165 
Bibliography ............................................................................................................. 170 
 vi 
 
 
List of Tables 
 
3.1. Soil texture classification at the surface and at a depth of 20 cm in each 
plot?????????????????????????????....33 
 
3.2. Approximate position of groundwater table (cm) below the soil surface recorded 
in the wells before and after the experiments under wet initial soil moisture 
conditions???????????????????????????..55 
 
4.1. Equations relating relative concentrations (C/C
0
,%) of each pathogen in runoff to 
time t after 15 min of simulation at each distance in each plot under dry initial 
soil moisture conditions??????????????????????85 
 
4.2. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed E. coli rates of reduction (k) obtained after 15 min of simulations under 
dry initial soil moisture conditions?????????????????...87 
 
4.3. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed S. e. Typhimurium rates of reduction (k) obtained after 15 min of 
simulations under dry initial soil moisture conditions??????????..87 
 
4.4. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed E. coli background levels (b) obtained after 15 min of simulations 
under dry initial soil moisture conditions???????????????.88 
 
4.5. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed S. e. Typhimurium background levels (b) obtained after 15 min of 
simulations under dry initial soil moisture conditions??????????.88 
 
4.6. ANOVA table for the effect of distance and cover (at P value set at 0.05) on 
relative concentrations of E. coli observed at 15 min of simulation (C/C
0(15)
) in 
the experiments under dry initial soil moisture conditions????????..89 
 
4.7. ANOVA table for the effect of distance and cover (at P value set at 0.05) on 
relative concentrations of S. e. Typhimurium observed at 15 min of simulation 
(C/C
0(15)
) in the experiments under dry initial soil moisture conditions???..90 
 
4.8. Equations relating relative concentrations (C/C
0
, %) of each pathogen in runoff to 
time t after 15 min of simulation at each distance in each plot under wet initial 
soil moisture conditions?????????????????????.115 
 
4.9. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed E. coli rates of reduction (k) obtained after 15 min of simulation under 
wet initial soil moisture conditions?????????????????116 
 
 vii 
 
 
4.10. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed S. e. Typhimurium rates of reduction (k) obtained after 15 min of 
simulations under wet initial soil moisture conditions?????????...117 
 
4.11. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed E. coli background levels (b) obtained after 15 min of simulations 
under wet initial soil moisture conditions??????????????..118 
 
4.12. ANOVA table for the effect of distance and cover (at P value set at 0.05) on the 
observed S. e. Typhimurium background levels (b) obtained after 15 min of 
simulations under wet initial soil moisture conditions?????????...118 
 
4.13. ANOVA table for the effect of distance and cover (at P value set at 0.05) on 
relative concentrations of E. coli observed at 15 min of simulations (C/C
0(15)
) in 
the experiments under wet initial soil moisture conditions???????....119 
 
4.14. ANOVA table for the effect of distance and cover (at P value set at 0.05) on 
relative concentrations of S. e. Typhimurium observed at 15 min of simulations 
(C/C
0(15)
) in the experiments under wet initial soil moisture conditions??....119 
 
4.15. Cumulative recoveries obtained from the vegetated plots for experiments under 
dry initial soil moisture conditions?????????????????.139 
 
4.16. Cumulative recoveries obtained from the bare plots for experiments under dry 
initial soil moisture conditions??????????????????....139 
 
4.17. Cumulative recoveries obtained from the vegetated plots for experiments under 
wet initial soil moisture conditions????????????????.....143 
 
4.18. Cumulative recoveries obtained from the bare plots for experiments under wet 
initial soil moisture conditions????????????????.??...143 
 
 
 
 viii 
 
 
List of Figures 
3.1. Overall schematic of the lysimeter?????????????????...29 
 
3.2. Topographic maps showing the different slopes between the two vegetated plots 
(Plots 1 and 3)?????????????????????????..36 
 
3.3. Topographic maps showing the surface roughness of the two vegetated plots 
(Plots 1 and 3)?????????????????????????..37 
 
3.4. Topographic map of vegetated plots (Plots 1 and 3) based on their tangential 
curvature???????????????????????????...38 
 
4.1. Runoff hydrographs of all plots for experiments under dry initial soil moisture 
conditions???????????????????????????..65 
 
4.2.Runoff hydrographs of all plots for experiments under wet initial soil moisture 
conditions???????????????????????????..70 
 
4.3. Average Bromide (Br) concentrations measured with time in runoff collected at 
413 and 620 cm downslope from the line of slurry application in bare and 
vegetated plots under dry initial soil moisture conditions????..????..73 
 
4.4. Average Bromide (Br) concentrations measured with time in runoff collected at 
413 and 620 cm downslope from the line of slurry application in bare and 
vegetated plots under wet initial soil moisture conditions??????.??...75 
 
4.5. Average relative concentrations of E. coli measured with time in runoff collected 
at 413 cm away from the source of slurry in all plots under dry initial soil 
moisture conditions?????????.??????????????..77 
 
4.6. Average relative concentrations of S. e. Typhimurium measured with time in 
runoff collected at 413 cm away from the source of slurry in all plots under dry 
initial soil moisture conditions???????????.????????.77 
 
4.7. Average relative concentrations of E. coli measured with time in runoff collected 
at 620 cm away from the source of slurry in all plots under dry initial soil 
moisture conditions??????.?????????????????..78 
 
4.8. Average relative concentrations of S. e. Typhimurium measured with time in 
runoff collected at 620 cm away from the source of slurry in all plots under dry 
initial soil moisture conditions??????????????????......78 
 
 ix 
 
 
4.9. Average relative concentrations of E. coli measured with time in runoff collected 
at 413 cm and at 620 cm away from the source of slurry in Plot 2 (bare) under 
dry initial soil moisture conditions?????????????????...79 
 
4.10. Average relative concentrations of E. coli measured in runoff at 413 and 620 cm 
from the source of slurry in vegetated vs. bare plots at identical time intervals 
during the experiments under dry initial soil moisture conditions.?...??..?81 
 
4.11. Average relative concentrations of S. e. Typhimurium measured in runoff  at 
413 and 620 cm from the source of slurry in vegetated vs. bare plots at  identical 
time intervals during the experiments under dry initial soil moisture 
conditions?????????????????...????????..?82 
 
4.12. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) from experiment under 
dry initial soil moisture conditions?????????????...??..?.93 
 
4.13. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under dry 
initial soil moisture conditions?????????????????.??94 
 
4.14. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment 
under dry initial soil moisture conditions?????????????....?95 
 
4.15. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment 
under dry initial soil moisture conditions???????????????96 
 
4.16. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under dry initial soil 
moisture conditions??????????????????????....100 
 
4.17. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under dry 
initial soil moisture conditions?????????????????.?..101 
 
4.18. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under dry initial soil 
moisture conditions?????????????????????.?...102  
 
4.19. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under dry 
initial soil moisture conditions??????????????????...103 
 
 x 
 
 
4.20. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment under dry initial 
soil moisture conditions???????????????????.??.104 
 
4.21. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment 
under dry initial soil moisture conditions????????????.??..105 
 
4.22. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment under dry initial 
soil moisture conditions??????????????????..???106 
 
4.23. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment 
under dry initial soil moisture conditions?????????????..?.107 
 
4.24. Average relative concentrations of E. coli measured with time in runoff 
collected at 413 cm downslope from the source of slurry application in all plots 
under wet initial soil moisture conditions????????????...??109 
 
4.25. Average relative concentrations of S. e. Typhimurium measured with time in 
runoff collected at 413 cm downslope from the source of slurry application in all 
plots under wet initial soil moisture conditions????????????..109 
 
4.26. Average relative concentrations of E. coli measured with time in runoff 
collected at 620 cm downslope from the source of slurry application in all plots 
under wet initial soil moisture conditions??????????????...110 
 
4.27. Average relative concentrations of S. e. Typhimurium measured with time in 
runoff collected at 620 cm downslope from the source of slurry application in all 
plots under wet initial soil moisture conditions????????????..110 
 
4.28. Average relative concentrations of E. coli measured in runoff at 413 and 620 cm 
from the source of slurry in vegetated vs. bare plots at identical time intervals 
during the experiments under wet initial soil moisture conditions?????.113 
 
4.29. Average relative concentrations of S. e. Typhimurium measured in runoff at 413 
and 620 cm from the source of slurry in vegetated vs. bare plots at identical time 
intervals during the experiments under wet initial soil moisture 
conditions?????????????????????????.?...113 
 
4.30. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under wet 
initial soil moisture conditions?????????????????.?...123 
 
 xi 
 
 
4.31. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under wet 
initial soil moisture conditions?????????????????.?...124 
 
4.32. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment 
under wet initial soil moisture conditions?????????????.?..125 
 
4.33. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment 
under wet initial soil moisture conditions?????????????..?.126 
 
4.34. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under wet initial soil 
moisture conditions?????????????????????..?...130 
 
4.35. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under wet 
initial soil moisture conditions?????????????????..?..131 
 
4.36. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under wet initial soil 
moisture conditions?????????????????????..?...132 
 
4.37. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under wet 
initial soil moisture conditions?????????????????..?..133 
 
4.38. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment under wet initial 
soil moisture conditions????????????????????..?134 
 
4.39. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment 
under wet initial soil moisture conditions????????????...??135 
 
4.40. Regressions of E. coli concentration ratios (C/C
0
) to Bromide concentration 
ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment under wet initial 
soil moisture conditions???????????????????...?...136 
 
4.41. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment 
under wet initial soil moisture conditions????????????...??137 
 
4.42. Average percent cumulative recovery in runoff of each pathogen for simulations 
under dry initial soil moisture conditions????????????...??141 
 xii 
 
 
4.43. Average percent cumulative recovery in runoff of each pathogen for simulations 
under wet initial soil moisture conditions??????????????...145 
 
 xiii 
 
 
Chapter 1: Introduction 
 
Pollution of drinking and recreational water supplies with pathogenic bacteria, 
including manure-borne bacteria, is a public health threat since exposure to these 
microorganisms can cause serious illnesses to or even the death of humans. One of 
the possible routes by which manure-borne pathogenic bacteria may enter natural 
waters is through surface runoff from agricultural fields, where manure is applied as 
an organic fertilizer (Roodsari et al., 2005). 
Several waterborne E. coli 0157:H7 (0157) and Salmonella outbreaks have 
been documented in recent years, indicating the need to attenuate microbial pollution 
to waters. During the winter of 1991, 243 cases of 0157 infection were documented in 
Cabool, Missouri. The outbreak resulted in 32 hospitalizations and four deaths, and 
the source of infection was associated with contaminated drinking water (Wang and 
Doyle, 1998). During the summer of that same year, 21 children were infected with 
0157 in Oregon after swimming in a fecally contaminated recreational lake (Wang 
and Doyle, 1998). Between 1993 and 1998, 454 cases of enteric illness in the United 
States were attributed to 0157 infections and all such cases were associated with 
contaminated waters (Johnson et al., 2003).  
Also, from 1993 to 1998, water contaminated with Salmonella spp., including 
Salmonella typhimurium, resulted in 625 cases of enteric illness, resulting in seven 
deaths (Johnson et al., 2003). In 1996, for instance, an outbreak of enteric illness was 
reported in Livingston County, New York, in which approximately 30 individuals 
became ill with diarrhea and one had to be hospitalized. An investigation of the 
 1 
 
 
outbreak concluded that those individuals had been infected with Plesiomonas 
shigelloides and Salmonella Hartford, both of which can be found in poultry manure, 
and that the possible pathway of contamination was food prepared with contaminated 
water from an unprotected shallow dug well that may have received surface runoff 
from surrounding manured farmland following rainfall event(s) (CDCP, 1998; Guan 
and Holley, 2003). 
Agriculture is one of the major industries in the State of Iowa where 
approximately 89% of the total land area is occupied by farms (IDALS, 2006). Iowa?s 
agriculture industry includes the production of pork, corn and soybean, which made it 
the leading state nationwide in 2005. According to the latest available National Water 
Quality Assessment Database, agriculture is also one of the leading possible sources 
of pollution to rivers, streams, creeks, lakes, ponds and reservoirs in the State of Iowa 
(USEPA, 2002).  
Some of the top agriculture-associated pollutants/stressors identified in these 
water bodies are nutrients, sedimentation and pathogens, such as viruses, protozoans 
and bacteria (USEPA, 2002). The presence of Escherichia coli and/or fecal coliform 
(FC) in water bodies is an indication of water contamination with fecal material 
(Roodsari, 2004). Some of the most common sources of fecal material in water 
bodies, especially surface waters, are inadequately treated sewage, wildlife and runoff 
from lands supporting agricultural practices (USEPA, 2000).  
Manure has essential nutrients that can enhance soil quality and crop 
productivity (Roodsari, 2004; Roodsari et al., 2005), but it is also a source of several 
pathogenic bacteria, such as the E. coli 0157:H7 strain and all Salmonella spp., that 
 2 
 
 
can deteriorate water quality after being transported from agricultural fields to water 
supplies (Patni et al., 1985; Collins et al., 2005). Escherichia coli 0157:H7 and 
Salmonella spp. can cause enteric diseases such as diarrhea, hemorrhagic colitis and 
hemolytic uremic syndrome (HUS) (Wang and Doyle, 1998), and are leading causes 
of gastroenteritis in both the United States and Canada (Johnson et al., 2003). 
While bacterial pollution may reach water bodies by leaching to ground water 
via preferential flow, studies have suggested that surface runoff from agricultural 
fields is in fact the key process by which manure-borne pathogenic bacteria may enter 
water bodies, particularly surface waters (Patni et al., 1985; USEPA, 2000; Collins et 
al., 2005). The risk for surface water contamination is even higher under high rates of 
land-applied manure such as the ones observed at large, confined animal feeding 
operations (CAFOs), in which substantial volumes of animal waste are applied to 
relatively small agricultural areas (Roodsari, 2004). According to Roodsari et al. 
(2005), CAFOs is a major source of microbial pollution to water bodies. 
The process by which fecal bacteria enter runoff flow and are transported 
within it to surface waters is poorly understood (Khaleel et al., 1980; Muirhead et al., 
2005; Roodsari et al., 2005). According to Tyrrel and Quinton (2003), bacteria from a 
soil-manure mixture may enter runoff flow in at least three ways and may be 
transported in runoff under two states: either attached to soil or slurry particles, or as 
free (unattached) cells. Conflicting results have been reported as to the extent of 
partitioning between these two states (Roodsari, 2004; Muirhead et al., 2005), but 
some studies suggest that attachment, particularly to soil particles, may be very low 
 3 
 
 
especially if the suspension in which the soil particles are found contains manure 
(Guber et al., 2005a and 2005b). 
Bacteria transported in runoff as free cells are unlikely to settle due to their 
density, which is similar to that of water (Tyrrel and Quinton, 2003; Roodsari, 2004), 
but may be intercepted/retained by adsorbing to plant surface, litter or organic matter 
(Roodsari, 2004). The rate and extent of bacteria transport in runoff, as well as their 
concentration in it, may be determined by factors such as rainfall intensity/duration, 
manure application rates, soil characteristics and watershed hydrology (Roodsari, 
2004).   
Several studies have been conducted to study the effectiveness of vegetated 
filter strips (VFS) at mitigating the contamination of water bodies by pathogenic 
microorganisms coming from land-applied manure. This practice consists of directing 
runoff flow from agricultural fields to VFS where microorganisms (or other 
pollutants, such as nutrients and sediments) can be removed from incoming runoff by 
means of infiltration, adsorption to soil and plant surfaces, and/or settlement. The 
practice has been cited by several authors as one of the best management practices 
(BMPs) because favorable results may be attained at low costs (Young et al., 1980; 
Dillaha et al., 1989; Chaubey et al., 1994; Edwards et al., 1996).  
Most studies of the effectiveness of VFS have used livestock manure, as 
opposed to swine manure as the source of microorganisms. In addition, VFS studies 
using swine manure have reported inconsistent results regarding the degree of 
efficiency at mitigating microbial runoff transport. For instance, Roodsari (2004) 
reported that E. coli and S. cholerasuis from areas treated with liquid swine slurry 
 4 
 
 
were completely removed from runoff after entering a 20%-sloped VFS with sandy 
loam soil texture. However, Entry et al. (2000) reported that riparian filterstrips 
consisting of three different types of vegetation (grass, forest and maidencane) did not 
reduce total or fecal coliform numbers in runoff from areas treated with swine 
wastewater. According to Roodsari et al. (2005), the inconsistent results may be 
attributed to the fact that the studies did not take into account infiltration rates within 
the relevant VFS.  Roodsari et al. (2005) studied the effectiveness of VFS at 
mitigating runoff transport of FC from areas treated with bovine manure and 
concluded that infiltration was a major mitigating factor. 
The objective of this study was to investigate the effects of initial soil 
moisture conditions within VFS on runoff transport of two surrogate pathogens, E. 
coli and S. enterica enterica Typhimurium, from land-applied liquid swine manure 
(4% solids). This study focused on VFS with loam and clay loam soil texture profiles 
constructed on 5% slopes and subjected to extreme conditions of rainfall events (80 
mm h
-1
). Such soil type and slope specifications were meant to reproduce the 
characteristics of the soil in the State of Iowa, where agriculture is one of the leading 
sources of microbial pollution to surface waters. 
In addition, this study investigated the potential correlation between runoff 
transport of each of these two surrogate pathogens and Bromide (Br) through VFS 
with the purpose of determining whether future similar studies could use relative 
concentrations of Bromide measured in runoff to infer relative concentrations of 
pathogens in runoff, thus saving most of the costs associated with pathogens-based 
studies.  
 5 
 
 
Chapter 2: Literature Review 
 
 
2.1 Bacteria 
2.1.1 Definition, size, shapes, density, categories 
Bacteria are the only prokaryote organisms that are ubiquitous inhabitants of 
moist environments and predominantly unicellular microorganisms (Brock and 
Madigan, 1988; Holt et al., 1994). They differ from higher forms of life such as 
animals and plants, and from other microorganisms such as algae, fungi and protozoa, 
all of which are multicellular eukaryotes.  
Bacteria can be single cells or simple associations of similar cells measuring 
from 0.2 to 10.0 ?m, and are grouped based on their cellular instead of organismal 
properties (Holt et al., 1994). Their cells have several distinct shapes, such as coccus 
(spherical or egg-shaped), oval, straight or curved rods (cylindrical shape), spiral, 
spiral helix or filaments (Brock and Madigan, 1988; Holt et al., 1994). Their density 
is usually in the range of 1.0 to 1.1 g cm
-3
, which is very close to the density of water 
(Tyrrel and Quinton, 2003; Roodsari, 2004). 
Most bacteria are beneficial, and in some cases even essential, to the overall 
health of a person. These bacteria are referred to as an individual?s ?normal? flora. In 
some regions of the human body, such as in the gastrointestinal tract, the ?normal? 
flora can appear very early after birth and be well established by the first week of life 
(Brock and Madigan, 1988). Some bacteria, however, can be harmful to humans and 
cause several illnesses. Disease-causing bacteria are referred to as pathogenic. 
 6 
 
 
According to Brock and Madigan (1988), bacteria are the most important microbial 
pathogens.  
On a phenotypic basis, bacteria can be divided into four major categories: 
Gram-negative eubacteria that have cell walls, Gram-positive eubacteria that have 
cell walls, eubacteria lacking cell walls, and archaeobacteria (Holt et al., 1994). The 
two bacteria used in this study, Escherichia coli and Salmonella enterica enterica 
Typhimurium, belong to the first category. 
In addition, each of the four major categories of bacteria can be further 
divided into family groups. The two bacteria used in this study belong to the 
Enterobacteriaceae family. 
 
2.1.2 Enterobacteriaceae family 
Bacteria belonging to the Enterobacteriaceae family are present worldwide. 
They can be found in soil, water, fruits, vegetables, grains, flowering plants and trees, 
and also in animals ranging from worms to humans (Holt et al., 1994). In humans and 
animals, Enterobacteriaceae are present in the intestines, where they are a major 
component of the normal intestinal flora (Farmer III, 1999). Some strains, however, 
are associated with several diseases including abscesses, pneumonia, meningitis and 
septicemia, as well as infections of wounds, the urinary tract and the intestines 
(Farmer III, 1999). As mentioned earlier, Escherichia and Salmonella are two of 
several genera that constitute the Enterobacteriaceae family, and are two of four 
genera in the Enterobacteriaceae family that have been clearly documented as enteric 
pathogens (Farmer III, 1999). 
 
 7 
 
 
2.1.3 Characteristics of genus Escherichia and its clinical significance 
Species belonging to the genus Escherichia are facultatively anaerobic Gram 
negative straight rods, which can measure 1.1?1.5 ?m ? 2.0?6.0 ?m and exist singly 
or in pairs (Holt et al., 1994; Farmer III, 1999). These species are either motile by 
peritrichous flagella or nonmotile. Their optimal temperature for growth is 37?C but 
they can grow well on MacConkey agar plates at 44.5?C (Holt et al., 1994; Bopp et 
al., 1999).  
Although extremely variable biochemically, Escherichia species generally 
catabolize D-Glucose and other carbohydrates, producing an acid and gas in the 
process (Holt et al., 1994; Bopp et al., 1999). In addition, they are oxidase and Voges-
Proskauer negative, catalase, indole and methyl red positive, and usually citrate 
negative. They are negative for H
2
S, urea hydrolysis and lipase, and all species reduce 
nitrates (Holt et al., 1994). Still, in terms of biochemical reactions, all or most strains 
of Escherichia species ferment a variety of sugars, including L-arabinose, maltose, D-
mannitol, D-mannose and L-rhamnose (Holt et al., 1994). 
Five species of bacteria make up the genus Escherichia: Escherichia blattae, 
Escherichia fergusonnii, Escherichia hermannii, Escherichia vulneris and 
Escherichia coli (Bopp et al., 1999). All of these species are commonly found in the 
intestines of warm-blooded animals, where they occur as part of the normal flora in 
the lower portion of these organs. In particular, Escherichia coli is ubiquitous in 
human and animal feces so much so that its presence in water is an indication of fecal 
contamination (Brock and Madigan, 1988; Bopp et al., 1999). 
 8 
 
 
Although it occurs as part of the intestinal flora of healthy individuals, certain 
strains of E. coli may cause several infectious illnesses. Along with other species 
from the Enterobacteriaceae family, E. coli is the cause of most extraintestinal 
infections (Holt et al., 1994; Bopp et al., 1999). The most common of these infections 
is that of the urinary tract (primarily cystitis), followed by those of the respiratory, 
bloodstream and central nervous systems and wound infections. According to Holt et 
al. (1994), E. coli is the major cause of urinary tract and nosocomial infections, 
including septicemia (infection of the bloodstream) and meningitis (infection of the 
central nervous system), which are serious, rapidly progressing and life-threatening 
infections.  
 As mentioned above, Escherichia is one of four genera of Enterobacteriaceae 
that has been clearly documented as enteric pathogens. This is due to the fact that 
some strains of E. coli are well associated with mild to serious intestinal infections in 
humans (Bopp et al., 1999). These strains contain enterotoxins associated with 
diarrheal diseases. They are collectively called diarrheagenic E. coli and are separated 
into at least four categories: Enterohemorrhagic E. coli (EHEC), which produce Shiga 
toxins, enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), and 
enteroinvasive E. coli (EIEC). 
 At least two strains of EHEC, E. coli serotypes 0157:H7 and 0157:nonmotile 
(NM) (0157 EHEC), have been identified (Bopp et al., 1999). These strains produce 
one or more Shiga toxins, also called [verocytotoxins], and have the capability to 
intimately adhere to the intestinal epithelium, generating what is called attaching-and-
effacing lesions (A/E lesions) (Bloom et al., 1998; Mainil, 1999). Once adhered to 
 9 
 
 
intestinal epithelium cells, these strains deliver Shiga toxins that are transported into 
endothelial cells by a transcellular pathway, causing inhibition of protein synthesis 
and other adverse reactions that in turn result in the inflammation of these cells and 
their damage (Bloom et al., 1998). 
 Infections with EHEC strains can result in mild nonbloody diarrhea, severe 
bloody diarrhea or even a more serious illness called haemolytic-uraemic syndrome 
(HUS) (O?Brien and Kaper, 1998; Bopp et al., 1999). This syndrome is characterized 
by microengiophatic hemolytic anemia, thrombocytopenia and acute renal failure. 
Some other symptoms, particularly from E. coli 0157:H7 infections, include 
abdominal cramps and lack of fever (Bopp et al., 1999). 
 ETEC strains are also associated with the production of toxins and diarrhea in 
infected individuals. According to Bopp et al. (1999), these strains produce either 
heat-labile E. coli enterotoxin (LT) or heat-stable E. coli enterotoxin (ST), or both, 
and cause diarrhea (particularly in young children from developing countries), 
abdominal cramps that could be accompanied by nausea and headache, and little 
vomiting and fever (Bopp et al., 1999). ETEC strains are frequently the cause of 
traveler?s diarrhea, which is considered mild in intensity but its duration is prolonged. 
 EPEC strains are epidemiologically associated with infantile diarrhea, but do 
not produce enterotoxins or Shiga toxins (Bopp et al., 1999). The symptoms 
associated with infection by these strains include severe chronic nonbloody diarrhea, 
vomiting and fever that may result in malabsorption, malnutrition, weight loss and 
growth retardation (Bopp et al., 1999). EIEC strains, on the other hand, are associated 
 10 
 
 
with invasion of the colon cells, producing a generally watery but occasionally 
bloody diarrhea. 
 
2.1.4 Characteristics of genus Salmonella and its clinical significance 
Species belonging to the genus Salmonella are facultatively anaerobic Gram 
negative straight rods, measuring 0.7?1.5 ?m ? 2.0?5.0 ?m (Holt et al., 1994; Farmer 
III, 1999). Most of these species are motile by peritrichous flagella, but some are 
nonmotile, and their optimal temperature for growth is 37?C (Holt et al., 1994; Bopp 
et al., 1999). According to Farmer III (1999), Salmonella species grow on selective or 
differential media such as Brilliant Green, SS or Rambach agars. 
 Biochemically, Salmonella species catabolize D-Glucose and other 
carbohydrates, producing an acid and usually gas in the process (Holt et al., 1994; 
Farmer III, 1999). These species are oxidase, indole and Voges-Proskauer negative, 
catalase, methyl red and Simmons citrate positive, and reduce nitrates (Holt et al., 
1994). In addition, they are lysine and ornithine decarboxylase positive and ferment 
several sugars, including L-arabinose, maltose, D-mannitol, D-mannose, D-sorbitol 
and D-xylose (Holt et al., 1994; Bopp et al., 1999). 
 Species belonging to the genus Salmonella are ubiquitous in animal 
populations and are commonly found in the intestines of humans and other warm 
blooded animals, but are sometimes found in the intestines of cold blooded animals as 
well (Holt et al., 1994; Bopp et al., 1999). Exposure to fecally contaminated animal 
meat or water can be routes for Salmonella infections, but such infections can also 
result from direct contact with animals or exposure to nonanimal foods and 
 11 
 
 
occasionally result from human contact. These species probably do not occur as free-
living organisms, but some strains may be able to survive long periods of time (even 
years) in the environment (Bopp et al., 1999). 
Two species, Salmonella enterica and Salmonella bongori, have been 
recognized to make up the genus Salmonella (Murray et al., 1999). Salmonella 
enterica is separated into six groups of subspecies (subspecies I, II, IIIa, IIIb, IV, and 
VI), while S. bongori is made up of only one subspecies (V) (Murray et al., 1999). 
The Salmonella species used in this study belongs to subspecies I and is scientifically 
referred to as Salmonella enterica subsp. enterica Typhimurium, or simply 
Salmonella enterica Typhimurium. Strains from subspecies I are usually isolated 
from humans and warm-blooded animals, whereas strains belonging to all of the other 
subspecies are usually isolated from cold-blooded animals (Bopp et al., 1999). 
Salmonella species are associated with several infectious illnesses including 
gastroenteritis, enteric (typhoid) fever and septicemia, which are collectively called 
salmonellosis (Bopp et al., 1999; Bell and Kyriakides, 2002). Infection takes place 
after the organism grows and multiplies in the small intestines, colonizing the tissues 
of this organ and producing an enterotoxin that causes an inflammatory reaction and 
diarrhea (Bell and Kyriakides, 2002). In some cases, Salmonella species can 
overcome the natural defense system of an individual and get into the bloodstream 
and/or the lymphatic system, thus causing even more severe illnesses.  
Nontyphoidal Salmonella strains can usually cause intestinal infection 
presenting symptoms that include diarrhea, vomiting, fever and abdominal cramps 
(Bopp et al., 1999; Bell and Kyriakides, 2002). One example of such infection is 
 12 
 
 
gastroenteritis, which is usually caused by an infective dose of approximately 10
4 
cells of Salmonella but can also be caused by a smaller number of cells (< 100) if 
organisms are protected, e.g., in high fat foods (Bell and Kyriakides, 2002). 
Gastroenteritis has an incubation time of approximately 12-72 h and can last between 
2 and 7 days (Bopp et al., 1999; Bell and Kyriakides, 2002). Members of subspecies I 
have been associated with gastroenteritis, but members of subspecies III have also 
been associated with this illness.   
 According to Bopp et al. (1999), and Bell and Kyriakides (2002), typhoid 
fever and septicemia are serious infections of the bloodstream. Humans have been the 
only reservoir of typhoid fever, which typically causes a sustained debilitating high 
fever, headache, malaise, nausea, abdominal pain, anorexia and delirium, as well as 
constipation during the early stages and diarrhea in the late stages of the illness. 
Typically, typhoid fever has a low infectious dose (< 10
3
), a long incubation 
period (7-28 days), and can be transmitted through person-to-person contact or 
exposure to fecally contaminated food or water. S. Typhi and S. Paratyphi, which 
belong to subspecies I, are the serotypes associated with typhoid fever. Septicemia, 
on the other hand, can be caused by several members of subspecies I and is 
characterized by high fever, malaise, pain in the thorax and abdomen, chills and 
anorexia (Bell and Kyriakides, 2002). 
 
 
 13 
 
 
2.2 Factors affecting the growth of Escherichia and Salmonella spp., and their 
survival in swine feces, soil, and water 
2.2.1 Growth 
According to Ingraham and Marr (1996), Escherichia coli and Salmonella 
enterica Typhimurium are mesophiles with respect to temperature for their growth 
and neutrophiles with respect to pH. Therefore, these enteric organisms grow over the 
mid range of temperatures and pH values. The growth rate response of most wild-type 
strains of E. coli and S. e. Typhimurium is similar, and the normal temperature range 
for their growth extends from 21 to 37?C. At higher or lower temperatures, growth 
rate decreases progressively, and balanced growth may not be sustained at 
approximately 49?C or higher, or below 7.5?C. In terms of pH values, these 
organisms grow at maximum rate between pH 6.0 and pH 8.0 (Ingraham and Marr, 
1996; Bell and Kyriakides, 2002). 
 
2.2.2 Survival in swine feces 
Regarding the fate of Escherichia and Salmonella spp. in animal feces, a 
variety of physical and chemical characteristics of the manure or manure slurry have 
been shown to influence their survival, such as temperature, solid content, pH, 
bacterial concentration, moisture content, and aeration (Kudva et al., 1998). Although 
the survival of E. coli in swine fecal material has not been well documented, several 
studies suggest that this pathogen can survive well in animal feces (Wang et al., 1996; 
Kudva et al., 1998; Fukushima et al., 1999).  
 14 
 
 
According to Wang et al. (1996), survival rates of E.coli 0157:H7 in bovine 
feces depend mostly on temperature and moisture content levels in the feces. In their 
study, this pathogen survived between 63 and 70 days at a low temperature of 5?C 
and where moisture content levels remained high during the study (74%). They 
observed that at low temperatures moisture content is retained, which increases the 
chances for the pathogen survival.  
On the other hand, the study by Fukushima et al. (1999) demonstrated that E. 
coli 0157:H7 survived up to 126 days in bovine feces at a temperature of 15?C 
inoculated with the highest concentration (10
5
 CFU /g of feces). They observed a 
short-term survival of E. coli 0157:H7 in the feces at a higher temperature (25?C) 
even when samples were kept in closed bags, which promoted the retention of 
moisture. They concluded that survival rates in feces depended on temperature and 
the initial bacterium inoculum, regardless of dehydration or moisture content levels. 
Nonetheless, both studies suggested that survival of E. coli 0157:H7 in bovine feces 
is inversely proportional to temperature.  
Studies have indicated that Salmonella spp. can survive for extended periods 
of time in swine feces. The study by Gray and Fedorka-Cray (2001) demonstrated 
that S. choleraesuis, shed from infected animals, survived for 3 and 13 months, 
respectively, in wet and desiccated (dry) form of swine feces while kept at room 
temperature (26?C).  
On the other hand, in the study by Placha et al. (2001), they observed that 
survival response of S. typhimurium in pig slurry was dependent on seasonal 
variations of atmospheric temperature. They found that survival of this bacterium 
 15 
 
 
during the storage of solid fraction of pig slurry was longer during the winter/spring 
(85 days) than in the summer season (26 days). Besides other physicochemical 
factors, they attributed the shorter time survival of S. typhimurium to high 
temperatures in the summer, which increased the levels of dry matter content in the 
slurry. In another study, conducted by Ajariyakhajorn et al. (1997), it was observed 
that S. anatum can survive as long as 56 days in swine slurry stored at 4?C with pH 
7.0.   
 
2.2.3 Survival in soil 
According to some studies, the survival or inactivation of enteric bacteria in 
soils depend on several soil physical and chemical properties, such as texture and 
particle size distribution, moisture content, moisture holding capacity, pH, sunlight, 
organic matter content, temperature, and microbial interactions (Gerba and Bitton, 
1984; Crane and Moore, 1986; Mubiru et al., 2000; Jiang et al., 2002; Tyrrel and 
Quinton, 2003; Oliver et al., 2005). According to Gerba and Bitton (1984), and Crane 
and Moore (1986), moisture is the major factor determining the survival of enteric 
bacteria in soils; whereas Tyrrel and Quinton (2003) suggest that temperature is the 
most significant environmental factor, and that usually survival times increases with 
decreasing temperatures   
Gerba and Bitton (1984) indicated that enteric bacteria have a greater survival 
time in moist soils and during times of high rainfall, as well as in soils with high 
water-holding capacity.  Mubiru et al. (2000) pointed out that fine texture soils can 
support microbial populations three times larger than coarse textured soils, since fine 
texture soils have higher water-holding capacity and nutrient concentrations.  
 16 
 
 
Survival of enteric bacteria in soils that have been spread with manure may 
also be affected by atmospheric conditions, such as sunlight and temperature. 
Survival time may be longer in shaded areas than in areas receiving direct sunlight 
since ultraviolet light can be lethal to these microorganisms (Gerba and Bitton, 1984; 
Tyrrel and Quinton, 2003). Lower temperatures may favor the survival of these 
microorganisms in soils since the processes of soil self-disinfection are slowed-down 
or suspended in areas of prolonged winters (Gerba and Bitton, 1984; Crane and 
Moore, 1986). Previous studies have indicated that survival of E. coli in exposed soil 
plots was longer in the autumn than in the summer (Van Donsel et al., 1967).  
Organic matter also plays a significant role on bacteria survival in soils. When 
organic matter content is high survival increases and even regrowth of E. coli and S. 
e. Typhimurium may be observed (Gerba and Bitton, 1984; Mubiru et al., 2000). 
Organic soils, as opposed to mineral soils, are high in organics and in moisture-
holding capacity. In terms of soil pH, survival time of enteric bacteria may be shorter 
in acid soils (pH 3-4) than in alkaline soils (pH 5.8-7.8) (Gerba and Bitton, 1984; 
Oliver et al., 2005). Low pH may not only adversely affect the availability of the 
organism but also the availability of nutrients.  
Another factor affecting the survival of enteric bacteria in soils is the 
antagonism from soil microflora (Gerba and Bitton, 1984). In the study by Jiang et al. 
(2000), in which they investigated the survival of E. coli 0157:H7 in manure-
amended soils, they observed that the die off of this pathogen was faster in soils 
containing both plentiful manure nutrients and the highest population of indigenous 
manure bacteria; and in soils that had not been autoclaved. They suggested that pH 
 17 
 
 
and moisture content levels were favorable for the growth of competitive 
microorganisms in manure and that antimicrobial activity of microorganisms that 
were indigenous to both manure and soil contributed to inactivation of E. coli 
0157:H7.  
In addition, Turpin et al. (1993) also found that survival of S. typhimurium 
was greater at 22?C in a sterile than in a non-sterile soil, suggesting that under sterile 
conditions the salmonella cells had no competition and thus were able to maintain 
their number, whereas under non-sterile conditions antagonism and/or competition by 
native microflora for nutrients reduced their survival. 
 
2.2.4 Survival in water 
Once E. coli and S. e. Typhimurium are introduced into natural water bodies 
by surface runoff, lateral flow, and/or vertical flow from and through agricultural 
contaminated soils, their survival is dependent on their ability to overcome the 
physical, chemical, and biological stresses associated with these unfavorable 
environments (Jones, 1999; and Oliver et al., 2005). 
Very often viewed as oligotrophic environments, natural water bodies can 
have low concentrations of dissolved available nutrients, which decrease the chances 
of survival of these introduced microorganisms since they have to compete with 
natural microflora for available nutrients. However, nutrient availability associated 
with suspended particles (both soil and waste derived) may increase the chances of 
survival of those microorganism?s cells attached to particles since nutrient levels can 
be 10 to 100 times higher on suspended particle surfaces than in the surrounding 
aquatic environment (Oliver et al., 2005). 
 18 
 
 
 Temperature, UV radiation, and predation are also variables that can affect the 
survival of E. coli and S. e. Typhimurium in aquatic systems. It has been reported that 
decreasing water temperatures often increases the survival of these bacteria in aquatic 
systems (Jones, 1999; and Oliver et al., 2005). As reported by Jones (1999), E. coli 
0157 has been shown to survive in river water for up to 90 days at 4?C. On the other 
hand, Oliver et al. (2005) reported that this bacterium has been shown to survive for 
even longer periods of time (260 days) at temperatures ranging between 4 and 25?C. 
In terms of UV radiation, Oliver et al. (2005) pointed out that exposure to solar 
radiation containing UV-B light is perhaps the most important factor responsible for 
the decline of bacteria in surface waters since this light can promote DNA damage. 
Predation by native protozoan populations on enteric bacteria entering aquatic 
environments is another variable influencing their survival (Oliver et al., 2005).   
 As pointed out by Wang and Doyle (1998), bacteria, especially the gram-
negative ones, can adapt to environmental and nutritional stresses by transforming 
their physiological state to a viable but nonculturable (VBNC) state as an adopted 
survival strategy. Several adverse environmental conditions in natural waters, such as 
those related to temperature, nutrient concentration, salinity, osmotic pressure, and 
pH, may induce bacteria to the VBNC state, which can be interpreted as one of 
dormancy. Bacterial cells in this state may maintain viability and metabolic activity, 
but their pathogenic potential is yet to be determined (Wang and Doyle, 1998). 
 
 
 19 
 
 
2.3 Processes and factors affecting the transport of bacteria in surface runoff 
According to Khaleel et al. (1980), bacterial pollution in runoff waters from 
agricultural land treated with animal waste is associated with the fact that bacteria are 
retained at or near the soil surface after manure is applied and may eventually be 
released from these places and transported in runoff. Nonetheless, the processes by 
which fecal bacteria enter runoff flow and are transported within it to surface waters 
are poorly understood (Khaleel et al., 1980; Muirhead et al., 2005; Roodsari et al., 
2005).  
According to Tyrrel and Quinton (2003), the attempt to describe the processes 
of pathogen transport in overland flow should first be based on the initial and 
boundary conditions of the slurry-amended soils. Their study suggests, along with 
others, that there are three possible states in which bacteria are likely to exist in a soil-
slurry mixture: attached to soil particles, attached to waste or slurry particles, and as 
free cells or clumps (Reddy et al., 1981; Tyrrel and Quinton, 2003). Although 
understanding of the factors that control the partitioning of bacteria among these three 
states is limited (Khaleel et al., 1980; Tyrrel and Quinton, 2003), once runoff has 
initiated there is a great potential that the bacteria present in the soil-slurry mixture 
will be transported to surface waters.  
Tyrrel and Quinton (2003) suggested that there are at least three mechanisms 
by which bacteria from a soil-slurry mixture can enter overland flow. First, bacteria 
that exist as free cells or clumps in the mixture, which are likely to be located within 
soil pore water or water films, are incorporated into the runoff flow as it passes over 
them. Second, bacteria that are attached to soil or slurry particles are entrained in the 
 20 
 
 
flow after the particles themselves are propelled into it either by the force applied by 
the flow itself or by the force of rain drops on the particles. Finally, bacteria cells can 
become detached from soil or slurry particles surfaces by the action of shearing force 
of raindrops or the flow itself and be transported as free cells in the flow. Therefore, 
their study suggests that bacteria can be transported in runoff flow in two states: 
attached to soil or slurry particles, and as free (unattached) cells. 
Although partitioning between these two states is not thoroughly understood, 
some studies have been conducted to determine their proportion. Muirhead et al. 
(2005) investigated the transport state of E. coli cells released from bovine fecal 
material (fresh and aged cowpats) using either the material alone or mixed with soils. 
They concluded that the percentage of cells attached to particles in general were very 
low, with an overall mean of only 8%. They also concluded that the majority of E. 
coli cells found in the large unattached fraction in runoff were individual cells and not 
clumps. Roodsari (2004), however, reported that bacteria adsorption to soil particles 
depends on soil texture and that attachment to clay loam soil can be around 25% of 
the total number of bacteria.  
Other studies reinforce the suggestion that attachment of bacteria, in particular 
to soil particles, is not significant and can be very low, especially if the soil 
suspension contains manure. According to Guber et al. (2005a and 2005b), the 
presence of bovine manure in soil suspensions can dramatically reduce the attachment 
of E. coli cells to soil particles, and that, as manure concentration increases in the 
suspension, bacteria attachment to soil particles decreases. One possible explanation 
 21 
 
 
by the authors for such decrease is that adsorption of bacteria takes place on manure 
particulates instead of on soil particles.  
It is important to note that most studies (if not all) regarding attachment of 
bacteria to particles used bovine fecal material as the source of bacteria. However, 
there is a great likelihood that the attached and unattached fraction of bacteria cells in 
runoff from swine material might be similar to that from bovine manure if bacteria 
behave similarly in swine fecal material as they do in bovine fecal material.  
In addition, studies suggest that deposition/settlement of the unattached 
fraction of bacteria in runoff is unlikely to occur (Tyrrel and Quinton, 2003; 
Roodsari, 2004) because the density of bacteria, which range from 1.0 to 1.1 g cm
-3
 
(Roodsari, 2004), is very close to the density of water, notwithstanding that their size 
is similar to the size of silt or coarse clay particles (Roodsari et al., 2005). Therefore, 
once bacteria enter runoff flow, it is unlikely that they will settle by gravity and most 
likely that they will remain in suspension even if flow conditions are steady and 
laminar. According to Roodsari et al. (2004), bacteria found in runoff as free cells 
may be retained during runoff by adsorbing to plant surface, litter or organic matter. 
Some factors affecting the rate and extent of bacterial transport as well as their 
concentration in surface runoff are rain intensity/duration, manure application 
methods and rates, soil characteristics (slope, texture, and types of vegetation), and 
the hydrologic characteristics of a watershed (Roodsari, 2004). 
 
 22 
 
 
2.4 Effectiveness of vegetated filter strips (VFS) at removing pathogens from surface 
runoff 
Several studies have been conducted to investigate the effectiveness of 
vegetated filter strips (VFS) at improving the quality of runoff from pollutant source 
areas. Vegetated filter strips are vegetated regions that are designed to receive runoff 
flow from pollutant source areas with the goal at removing pollutants (e.g., nutrients, 
sediments and/or microorganisms) from the incoming runoff through infiltration, 
adsorption to soil and plant surfaces, and/or settlement. This practice has been cited 
by several authors as one of the best management practices (BMPs) since 
effectiveness may be attained at low costs (Young et al., 1980; Dillaha et al., 1989; 
Chaubey et al., 1994; Edwards et al., 1996).   
Previous studies have demonstrated that VFS can be very effective at reducing 
overland flow of nutrients and sediments from agricultural areas. Young et al. (1980) 
used a 27.4-m VFS to improve the quality of beef feedlot runoff by removing 
approximately 80% of the incoming masses of solids and nutrients (TN, TP, ammonia 
nitrogen and orthophosphorus). Schwer and Clausen (1989) found that a 26-m VFS 
removed 95% and approximately 90%, respectively, of incoming solids and nutrients 
(TP, and total Kjeldahl nitrogen) from dairy milkhouse wastewater.   
Several studies have also been conducted to investigate the effectiveness of 
VFS at mitigating microbial transport from manure sources. The majority of these 
studies used bovine or poultry manure as the source of microorganisms (Chaubey et 
al. 1994) but the degree of effectiveness reported by these studies, particularly the 
ones with bovine manure, varies considerably. For instance, Lim et al. (1998) 
 23 
 
 
reported that all fecal coliforms (FCs) in runoff from pasture areas receiving cattle 
manure were removed after entering a 6.1 m length VFS. On the other hand, 
Schellinger and Clausen (1992) reported that E. coli concentrations in runoff from a 
barnyard were not significantly reduced (only 30%) after flowing through a VFS. 
Most studies of the effectiveness of VFS have used livestock manure other 
than swine manure as the source of microorganisms. In addition, VFS studies using 
swine manure have reported inconsistent results regarding the degree of efficiency at 
mitigating microbial runoff transport. For instance, Roodsari (2004) reported that E. 
coli and S. cholerasuis from areas treated with liquid swine slurry were completely 
removed from runoff after entering a 20%-sloped VFS with sandy loam soil texture. 
However, Entry et al. (2000) reported that riparian filterstrips consisting of three 
different types of vegetation (grass, forest and maidencane) did not reduce total or 
fecal coliform numbers in runoff from areas treated with swine wastewater. 
Of the numerous studies that have been conducted to evaluate the 
performance of VFS at improving the quality of runoff from agricultural sources, 
several of them revolved around the transport of sediment and nutrients.  However, 
such studies may be relevant to understanding the factors that contribute to the 
effectiveness of VFS at mitigating overland flow transport of microorganisms from 
manure sources.  
Regardless of the type of pollutant studied, there is an indication that the 
degree of effectiveness of VFS depends on the combination of several factors like 
environmental conditions (e.g., rain intensity and manure application methods or 
rates) and VFS characteristics (e.g., slope, size and soil texture), but even studies that 
 24 
 
 
evaluated VFS under similar conditions and/or characteristics reported conflicting 
results.   
According to Edwards and Daniel (1993), the effectiveness of VFS may be 
negatively impacted by manure application rate and the time between application and 
the first runoff-producing rainfall event, but not by rainfall intensity. In their study, 
concentrations of swine slurry constituents (nutrients, chemical oxygen demand 
(COD) and total suspended solids (TSS)) in runoff from fescue plots generally 
increased in tandem with the increase in the slurry application rate; and that, by 
contrast, concentrations decreased with increased rainfall intensity. The authors 
suggested that the decrease in concentrations under higher rainfall intensity was due 
to dilution caused by higher runoff volumes.  
Some studies suggest that VFS constructed on steep slopes may have their 
performance compromised if they are subjected to high rainfall intensity. Collins et 
al. (2005), for instance, used a combination of a steep pastoral land (with 
approximately 33% slope) and heavy rainfall events (35 mm h
-1
) and concluded that, 
under such conditions, VFS were not particularly effective at mitigating overland 
flow transport of E. coli from a grazed hillside to a headwater pastoral stream. This 
conclusion differs from that of Roodsari (2004), who subjected 20%-sloped VFS to 
rainfall intensities at 61 mm h
-1
 and concluded that VFS were substantially effective 
at mitigating overland flow transport of FC from land-applied bovine manure, and of 
E. coli and S. cholerasuis from land-applied swine manure.  
Other studies suggest that the optimal length of VFS depends on whether they 
are designed to reduce sediment, nutrients and/or microorganism losses from 
 25 
 
 
agricultural fields. For instance, Chaubey et al. (1994) used 3-, 6-, 9-, 15- and 21-m 
VFS with 3% slope and found that the 3 m VFS was effective at removing TSS in 
runoff from an area treated with liquid swine slurry, and that the 9 m VFS was 
effective at removing most of the nutrients, but that none of these VFS was 
significantly effective at removing nitrate nitrogen and FC from the incoming runoff.  
Roodsari (2004) obtained different results from the ones described above after 
evaluating VFS that were shorter than 21 m. He used two 6-m VFS with 20% slope 
and found that both VFS significantly reduced mass transport of E. coli and S. 
cholerasuis in runoff from areas treated with liquid swine manure. Bingham et al. 
(1980) noted that VFS should have the same length as the length of the pollutant 
source area in order to be effective at treating runoff coming from areas receiving 
poultry manure.  
Some studies suggest that VFS with soil textures that enhance infiltration rates 
may be highly effective at reducing microorganisms in runoff from areas treated with 
both bovine and swine manures, even if they are constructed on steep slopes and 
subjected to high rainfall intensity (Roodsari, 2004; Roodsari et al. 2005). For 
instance, Roodsari et al. (2005) simulated rainfall at 61 mm h
-1
 on 6.4-m ? 6.0-m, 
20%-sloped vegetated and bare plots with different soil textures and concluded that 
not only VFS were significantly more effective than bare surfaces at reducing runoff 
volumes and the amounts of FC in runoff from land-applied bovine manure, but also 
that the degree of effectiveness between the two VFS was even higher for the one 
with the more permeable soil texture. 
 26 
 
 
The authors pointed out that infiltration may be the most important 
mechanism by which surface runoff of FC from bovine manure may be mitigated, and 
that conflicting results from previous validation studies of VFS with microorganisms 
may have resulted from the fact that those studies did not take into account infiltration 
rates within VFS. Roodsari et al. (2005) also suggested that VFS design should be 
such that it increases infiltration rates even under extreme environmental conditions, 
at least for the purpose of controlling surface runoff of microorganisms from manure.   
 
2.5 Pork Production in the State of Iowa 
According to available information, pork production in the United States is 
largely concentrated in the State of Iowa (IPPA, 2006). In 2005, Iowa had 
approximately 8,900 pig farms, which may have raised as much as 16 million pigs. In 
addition, Iowa accounts for about 25% of the hog production in the country by raising 
approximately 25 million hogs each year. The ratio between hogs and persons in the 
State of Iowa is approximately 5 to 1. It is also estimated that the swine industry in 
the state generated more than 2 million tons of swine manure
1
 in 2005 (USDA, 2006). 
 
 
   
                                                 
1
 Tons of swine manure in dry state (as excreted adjusted for water content). 
 27 
 
 
Chapter 3: Materials and Methods 
 
This study was conducted to investigate the overland flow transport of two 
surrogate pathogens, Escherichia coli and Salmonella enterica Typhimurium, from 
vegetated and non-vegetated soil strips. In order to conduct the study, a lysimeter 
containing clay loam soil with a 5% slope was constructed, partitioned and 
instrumented as the experimental site. The soil type and slope specifications were 
meant to reproduce the characteristics of the soil in the state of Iowa, where the 
results of the study were going to be applied. Two sets of experiments were required 
since the objective was to conduct the investigation under two distinct initial levels of 
soil moisture: dry conditions and wet conditions. The first set of experiments, on dry 
initial soil moisture conditions, was performed between June 1 and June 29, 2004, 
while the second, on rather wet initial soil moisture conditions, was conducted 
between August 11 and September 21, 2004. 
 
3.1 Lysimeter Set Up 
3.1.1 Geographic location 
The lysimeter was located at the Patuxent Wildlife Research Refuge (U.S. 
Department of Interior) in Beltsville, MD, which, at the time the study was 
conducted, was administered by the U.S. Fish and Wildlife Services. 
 
 28 
 
 
3.1.2 Construction 
The lysimeter used in the study was set up during the summer of 2002 on a 
pre existing lysimeter measuring 12.5 m x 18.7 m, complete with a gutter system 
running along the existing wall located at the bottom of the slope, lined with a heavy-
gauge plastic and connected to a working pumping station. In order to conduct the 
study, however, the surface soil was removed to 1 ft below the gutter and replaced 
with loam soil, which was packed, and graded in order to create a 5% slope needed 
for the study. Confinement of the lysimeter was required in order to both isolate and 
contain the area and prevent potential contamination of local groundwater and other 
water bodies. 
 
3.1.3 Plot partitioning and waste application designation area 
The portion along the lysimeter length at the bottom of the slope was further 
partitioned into four adjacent and equal-sized 3.9 m x 6.5 m sub-plots, hereinafter 
referred to as plots. Overall schematic of the lysimeter is illustrated in Figure 3.1. 
5% Slope
Pollutant
Containment
Tanks
Lysimeter
Water Table
Pumping
Station
Plot 1 Plot 2 Plot 3 Plot 4
Buffer Zones
12
.
5
 
m
18.7 m
6.
5 
m
0.3m
0.6 m
3.9 m
a.a.
Slotted Well
 
 
Figure 3.1. Overall schematic of the lysimeter. Plots 1 and 3 were vegetated, while 
Plots 2 and 4 were bare. Dotted line indicates the direction of flow in the gutter. An 
application area (a.a.) was located at the top of each plot. 
 29 
 
 
Both sides and the side opposite to the lysimeter?s gutter of each plot were 
encircled by a series of very thin 10 cm x 85 cm metal sheets semi-inserted into the 
soil, creating a 5 cm wall around each plot. It was important to have each plot 
confined within these walls in order to prevent runoff loss during the experiments. 
Plots were set apart approximately 60 cm from each other, creating buffer zones to 
facilitate plot access without disturbing the adjacent plots.  
The first and third plots from the left side of the lysimeter were sowed with 
fescue grass seeds (hereinafter referred to as vegetated plots) and designated as Plot 1 
and Plot 3, respectively. The second and fourth plots from the left side of the 
lysimeter were kept devoid of vegetation (hereinafter referred to as bare plots) and 
designated as Plot 2 and Plot 4, respectively. However, a 30 cm strip across the top of 
all plots, bare and vegetated, was kept bare and designated as the waste application 
area for the swine slurry containing the pathogens used in the study. 
 
3.1.4 Soil characteristics 
The soil used in the repacking of the existing lysimeter was obtained from a 
construction site at the U.S. Department of Agriculture in Beltsville, MD. The goal 
was to pack the lysimeter with clay loam soil since this type of soil is the predominant 
type found in Iowa, where the results of the study will be applied. The soil texture 
was confirmed by mechanical analysis using the hydrometer method.  
In the process of soil texture determination, soil samples from the surface 
(first 5 cm) and from a depth of 20 cm were collected from each plot using a 2.54 cm 
ID core sampler. Samples from the soil surface were collected at four different 
locations (two at the top and two at the bottom) in each plot and combined in order to 
 30 
 
 
obtain the 50 g necessary to conduct the texture analysis. Samples from the soil at a 
depth of 20 cm were collected directly below from the location where the soil surface 
samples came from, using the same probe, and were, also, combined in order to 
obtain the amount of sample necessary to conduct the texture analysis. Therefore, a 
total of two samples per plot were obtained, one representing the surface and the 
other the 20 cm down the soil profile. 
The texture analysis consisted of first drying (at 104?C overnight) and then 
sieving (2 mm) each soil sample. Once that step was concluded, 50 g of each sample 
was weighed out and placed into a mixer cup, making sure that any residual dust from 
the weighing dish was carefully rinsed into the cup with deionized (DI) water. 
Subsequently, the cup was 2/3 filled with DI water before 50 mL of a 10% sodium 
hexametaphosphate solution were added to the soil solution in the mixer cup. Next, 
the contents were mixed for 3 minutes using an electrical mixer.   
Once mixing was completed, the soil suspension was quantitatively poured 
into a 1000 mL cylinder, making sure that all soil was transferred to the cylinder by 
rinsing the residuals with DI water. Once all soil suspension was transferred, the 
mixture was brought to the 1000 mL mark by adding DI water. Next, the contents in 
the cylinder were vigorously stirred with a plunger for 15 to 20 seconds, and the 
initial time was recorded as the plunger was removed. Immediately after, the 
hydrometer (calibrated for 20?C) was carefully placed in the suspension and after 40 
seconds since the plunger had been removed, the hydrometer reading was recorded. 
Between the time that the hydrometer was carefully placed in the suspension and the 
time that the reading was recorded 40 sec later, 1 drop of 200% ethanol was added to 
 31 
 
 
the cylinder in case that the surface of the suspension was covered with foam in order 
to obtain an accurate hydrometer reading.  
After taking the reading, the hydrometer was carefully removed and rinsed 
with DI water before going on to the next sample. At 2 hours after the plunger had 
been removed from each cylinder, the hydrometer was carefully replaced into the 
suspension and the reading was recorded. During the 2 hours period, the contents in 
each cylinder were left undisturbed.  
After the last hydrometer reading was taken, a control was prepared in order 
to calibrate the hydrometer readings for the presence of the sodium 
hexametaphosphate in the soil solutions. In the process, 50 mL of the 10% sodium 
hexametaphosphate solution was transferred to a cylinder, and the volume brought to 
the 1000 mL mark by adding DI water. After the contents were vigorously stirred 
with the plunger for 15 to 20 seconds, the hydrometer was carefully placed into the 
solution and the reading was recorded. Subsequently, the temperature of the solution 
was recorded, which was assumed to be the same as the one in the samples since both 
DI water and the 10% sodium hexametaphosphate solution used for calibration and 
analysis of the samples came from the same source.  
Calculations were performed as follow: 
1. Each reading was corrected for temperature deviation from 20?C by adding 
0.36 g of soil L
-1
 for each degree above 20?C, or subtracting 0.36 g of soil L
-1 
for each degree below 20?C. 
2. % sand + % silt + % clay = 100% 
% silt + % clay = (corrected 40 sec. reading/dry sample weight)*100 
 32 
 
 
% clay = (corrected 2 hr reading/dry sample weight)*100 
% silt = (% silt + % clay) - % clay 
% sand = 100 ? (% silt + % clay) 
 
The texture class of soil samples was determined based on their percent 
composition of clay, silt, and sand using the soil texture triangle. Table 3.1 shows the 
results for the soil texture analysis.  
 
 
 
Table 3.1. Soil texture classification at the surface and at a depth of 20 cm in each 
plot. 
LOCATION % CLAY % SILT % SAND 
SOIL 
TEXTURE 
Plot 1
?
   
Surface 
17.0 39.0 44.0 loam 
Plot 1 
20 cm depth 
23.0 33.0 44.0 loam 
Plot 2
?
   
Surface 
24.0 42.0 34.0 loam 
Plot 2 
20 cm depth 
30.0 34.0 36.0 clay loam 
Plot 3
?
   
Surface 
18.5 37.1 44.4 loam 
Plot 3 
20 cm depth 
29.0 35.0 36.0 clay loam 
Plot 4
?
   
Surface 
22.0 38.0 40.0 loam 
Plot 4 
20 cm depth 
34.0 40.0 26.0 clay loam 
? 
Vegetated plots 
 
? 
Bare plots 
 
 
 33 
 
 
Table 3.1 shows that the soil texture on the surface of all four plots was 
classified as loam. On average, the percent of clay, silt, and sand in the soil surfaces 
was 20%, 39%, and 41%, respectively. The soil texture below the surface, at a depth 
of approximately 20 cm, did not have the same classification across all plots. The soil 
texture in Plot 1 (vegetated) at such depth was classified as loam, with 23% clay, 33% 
silt, and 44% sand. In Plots 2, 3, and 4 (bare, vegetated, and bare, respectively) the 
soil texture at such depth was classified as clay loam, with an average of 31% clay, 
36% silt, and 33% sand. 
   
3.1.5 Topography of vegetated plots 
A topographic map of each vegetated plot (Plots 1 and 3) was constructed in 
order to provide a better understanding of the micro-relief pattern on these plots. This 
was not necessary with the bare plots (Plots 2 and 4) since, in the process of 
maintaining these plots before each set of experiments took place, the surface was 
constantly graded, which contributed to free the surface of these plots from deep 
channels and possible different elevations across the plots.  
The topographic maps were generated by marking a 50 cm x 50 cm grid in 
each plot and measuring the elevation at each point in the grid with the help of a 
transit level. The transit level was first mounted to a tripod, which was then 
positioned between and away from the top (approximately 3 m) of the two vegetated 
plots. The transit level itself was then leveled with the help of its built-in spirit level, 
and elevation was measured and recorded after a cm-calibrated rod was as vertically 
 34 
 
 
as possible placed at each point of the grid. The lowest point measured was used as 
the base line elevation.  
Elevation data were used to generate topographic maps that could show three 
different terrain characteristics in each of the vegetated plots. The topographic maps, 
in Figure 3.2, show the average slope in each plot, and were generated using 2D 
contour maps from SigmaPlot 9.0 software. The topographic maps, in Figure 3.3, 
show the roughness in each plot, and were generated using 3D mesh plots also from 
SigmaPlot 9.0. Last, the topographic maps, in Figure 3.4, show the surface area in 
each plot that contributed to either convergent or divergent flow conditions, and were 
generated based on the tangential curvature using Surfer
?
 7 software. Tangential 
curvature is defined as the measurement of ?curvature in relation to a vertical plane 
perpendicular to the gradient direction, or tangential to the contour? (Surfer
?
 7 User?s 
Guide, 1999). Negative curvature values indicate areas of divergent flow conditions, 
whereas positive values indicate areas of convergent flow conditions. 
 
3.1.6 Slotted wells 
Four slotted wells were installed in the lower portion of the buffer zones next 
to the gutter, starting in the buffer zone between the first and second plots, to monitor 
groundwater table levels and the potential subsurface movement of E. coli and S. e. 
Typhimurium into the groundwater table. Each well consisted of a slotted PVC pipe 
measuring approximately 10 cm in diameter and 85 cm long inserted vertically 
approximately 50 cm into the soil until it touched the vinyl lining at the bottom of the 
lysimeter. The approximate 30 cm side not inserted into the soil was kept covered all 
times to avoid incoming direct rainwater, which might compromise the proper 
 35 
 
 
monitoring of the groundwater table. The slotted wells were numbered Well 1, Well 
2, Well 3 and Well 4 starting from the left side of the lysimeter in a manner similar to 
the manner that the plots were numbered. 
 
0
5
10
15
20
25
30
35
0
50
100
150
200
250
300
350
0
100
200
300
400
500
600
E
l
e
v
a
t
i
o
n
 
(c
m
)
W
i
d
t
h
 
(
c
m
)
L
en
gt
h
 (
c
m
)
Plot 1
0
10
20
30
40
50
0
50
100
150
200
250
300
350
400
0
100
200
300
400
500
600
E
l
e
v
a
t
i
o
n
 
(
c
m
)
W
i
d
t
h
 
(
c
m
)
L
e
n
g
th
 (
c
m
)
Plot 3
 
 
 
 
 
Figure 3.2. Topographic maps showing the different slopes between the two 
vegetated plots. Average slopes of Plots 1 and 3 were 4.0% and 5.4%, respectively.  
 
 36 
 
 
W
id
th
 
L
e
n
g
t
h
Plot 1
-4 cm
-3 cm
-2 cm
-1 cm
 0 cm
 1 cm
 2 cm
 3 cm
L
e
n
g
t
h
 
 Figure 3.3. Topographic maps showing the surface roughness of Plots 1 and 3 
(vegetated plots). Symbol ( ) shows the approxim
values appear as residuals, which reflect the elev
plane, which was calculated using SigmaPlot 9.0.  
F 3
F 2
F 1 
 37 
 
Plot 3
ate position of each funnel. Legend 
ation deviations from
F 1
W
id
th
 
F 2
F 3 
 a specific 
 
 
 
 
0 100 200 300 
0 
100 
200 
300 
400 
500 
600 
    
-0.0055
-0.0045
-0.0035
-0.0025
-0.0015
-0.0005
0.0005
0.0015
0.0025
0.0035
0.0045
  
0 100 200 300 400
0
100
200
300
400
500
600
 
      PLOT 1
     PLOT 1    PLOT 3 
 
 
 
Figure 3.4. Topographic map of vegetated plots (Plots 1 and 3) based on their 
tangential curvature. Positive values indicate the areas of convergent flow conditions 
(channels), whereas negative values indicate areas of divergent flow conditions. 
 
 
 
3.1.7 Surface runoff collectors 
Part of surface runoff was collected by three funnels aligned along the width 
of each plot in a transect located approximately 4.13 m from the bottom edge of the 
waste application area (at about 2/3 of the plot length down the slope). The funnels 
were installed to measure temporal and spatial distribution of E. coli and S. e. 
Typhimurium in runoff. Additional details on the installation of these funnels are 
described in section 3.2.4 below.   
 38 
 
 
The remainder of surface runoff was collected by the gutter running along the 
bottom of the slope located approximately 6.2 m from the bottom edge of the waste 
application area. As indicated earlier, this gutter system was already installed at the 
existing lysimeter that was modified for the purposes of this study. 
 
3.1.8 Rainfall source 
The rainfall simulator used for this study was built at and provided by the U.S. 
Department of Agriculture, ARS, in Beltsville, MD. The simulator was a large-scale 
portable rainfall simulator powered by a gasoline-fired generator, and it contained a 
boom, four full jet 1/2HH SS30WSQ nozzles (two at the top and two at the bottom of 
the boom) and four water pressure gauges, all of which were fully adjustable. The 
boom could be adjusted upwards or downwards to position nozzles at different 
distances from the soil surface, and each nozzle could be both moved across the boom 
as well as adjusted at different angles. Each nozzle was connected to a pressure gauge 
that controlled the intensity of simulated rain. The rain simulator was connected to 
three 2000 L water tanks, which were supplied with water coming from a well located 
approximately 60 m from the lysimeter. 
 
3.1.9 Pollutant containment tanks 
The lysimeter was connected through a PVC pipe measuring 15 cm in 
diameter to a runoff collection system comprised of three 2000 L pollutant 
containment tanks. These tanks were designed to store and treat surface runoff 
generated during the experiments before it could be released into the environment. 
 39 
 
 
All surface runoff was treated and released together after the final experiment was 
conducted. 
3.2 Pre-experiment Field Procedures 
3.2.1 Calibration of rainfall simulator 
The rainfall simulator was calibrated during the first week prior to the first 
experiment. Trials were performed early in the morning during non-raining days 
while the temperature was low and the wind speed was low enough not to affect the 
uniformity of the synthetic rain. After five 15-minute trials, the rainfall simulator was 
calibrated to simulate rain at an intensity of approximately 80 mm h
-1
 and a 
uniformity coefficient of 90%, which is above the 80% coefficient recommended by 
researchers (Roodsari, 2004). Such rain intensity is characteristic of rainfall events 
having a 75-yr return period in Iowa, where the results of the study were going to be 
applied. The rainfall uniformity coefficient was determined by applying the 
Christiansen?s uniformity equation, C
u
 = 100(1 ? ?((ABS( x
i
 ? Mean)))/(Mean ? n)), 
where C
u
 is the uniformity coefficient, x
i
 is the volume collected by each rain gage, 
and n is the number of rain gages.  
The calibration process involved first adjusting the boom in a manner such 
that the nozzles were suspended approximately 3.0 m from the ground to ensure that 
most of the drops attained terminal velocity by the time they hit the ground, thus 
simulating natural rainfall events (Hirschi et al., 1990). Next, the vegetated plot being 
used during calibration (Plot 1) was covered with a tarp and a total of 16 rain gages 
(four rows of four) were symmetrically positioned onto the plot to collect simulated 
rain. The decision to cover the plot was made in order to avoid excessively 
 40 
 
 
compacting the soil from having to walk over an otherwise wet soil several times 
during intervals between trials to measure the volume of synthetic rain collected in 
the rain gages. Once this preparation step was concluded, the rain simulator was 
turned on and immediately after the water pressure in each gauge was set to 20 PSI. 
The volume of water collected in each rain gage was then read after 15 minutes of 
rain simulation.  
As the trials took place, the nozzles were rotated in two occasions in order to 
find an angle that could improve the uniformity of the rain. In one occasion, because 
there was more rain falling at the bottom than at the top of the plot, the two bottom 
nozzles were slightly rotated towards the gutter and away from the area receiving too 
much water. In addition, one of the top nozzles was partially obstructed and had to be 
unclogged. However, such changes did not solve the uniformity problem at hand 
since more rain then started falling at the top than at the bottom of the plot.   
After a series of trials, bringing the bottom nozzles to their initial position 
perpendicular to the ground and adjusting the top nozzles at a slight angle towards the 
top of the plot, while increasing their pressure to 22 PSI, the best rainfall uniformity 
coefficient was obtained. Unfortunately, leaving the plot covered with the tarp 
throughout the time of calibration had a negative impact on the surface grass in Plot 
1, which had to be the last plot to be tested during the first set of experiments 
(relatively dry soil conditions) in order for the vegetation to recover. 
 
3.2.2 Dry initial soil moisture conditions 
In order to achieve dry initial soil moisture conditions required for the first set 
of experiments, the groundwater table inside the lysimeter was drained by a pump 
 41 
 
 
placed in the pumping station adjacent to the right side of the lysimeter and connected 
to a gasoline-fired generator. 
The draining process was conducted whenever necessary prior to the 
beginning of any such experiments to lower the groundwater table to levels 
comparable to those in Iowa, where the results of the study were going to be applied. 
In addition, the plot to be tested in any given day was covered with a tarp whenever 
there was an occurrence of natural rainfall the day before. Furthermore, the 
groundwater table in the lysimeter was drained during the actual experiments 
whenever the water table levels started to increase.  Groundwater levels were 
monitored before and during the experiments by observing the levels of water as it 
accumulated in the wells and at the pumping station. 
 
3.2.3 Wet initial soil moisture conditions 
In order to achieve wet initial soil moisture conditions required for the second 
set of experiments, the groundwater table inside the lysimeter was never drained and 
the plots were never covered in order to benefit from the occurrence of natural rainfall 
events during the two months that separated the first set from the second set of 
experiments. In addition, in order to rise the groundwater table as much as possible, 
water was manually applied to the lysimeter with the help of a hose in the following 
manner: if the plot to be tested was a vegetated plot, water was applied onto the plot 
with a hose at full flow during the day and into the well adjacent to the plot at low 
flow during the night before the experiment was to be conducted; if the plot to be 
tested was a bare plot, water was applied into the well adjacent to the plot at low flow 
during both the day and the night before the experiment was to be conducted. 
 42 
 
 
3.2.4 Funnels installation 
The three funnels, used to partially collect the surface runoff and to measure 
temporal and spatial distribution of E. coli and S. e. Typhimurium in runoff, were 
installed the day before any given experiment at approximately 4.13 m away from the 
bottom edge of the waste application area. They were strategically positioned onto 
existing low points along the transect in the plot where they were installed so that the 
fastest runoff flow could be collected during the experiments. Each funnel was 
connected to a food-grade Tygon tube long enough to reach over the gutter and out of 
the plot into buckets where runoff would be collected at different time intervals. 
Funnels and tubes were fastened onto the ground with the help of oversized u-shaped 
metal staples. 
 
3.2.5 V-notch weir installation 
All surface runoff not collected by the funnels was collected by the gutter 
located at 6.2 m away from the bottom edge of the waste application area, and its 
runoff rate (GPD) was measured using a V-notch weir. The V-notch weir not only 
measured total surface runoff, but, also, helped to create flow hydrographs for each 
rainfall simulation. It was installed the day before the beginning of each of the two 
sets of experiments in the gutter near the pumping station. Measurements were taken 
by reading the graduated scale on the V-notch weir wall, a method demonstrated to be 
reasonably accurate (Roodsari, 2004). 
 
 43 
 
 
3.2.6 Application area barrier 
The slurry used in this study, which was prepared from swine manure, 
consisted of approximately 4% solids. Details on the manner the slurry was prepared 
can be read in section 3.3.2. Because of its consistency, the slurry could easily flow 
down the slope too soon after each application. In order to prevent it from occurring, 
metal sheets of the type used to encircle the plots were inserted along the bottom of 
the application area the day before each experiment, to be then removed shortly into 
the simulation when rain became more uniform. 
 
3.2.7 Isolation of plots 
Since the area covered by the simulated rain was larger than the area of the 
plot where any given experiment was conducted, adjacent areas alongside the plot 
were covered with tarps and the portion of the gutter was covered with metal sheets. 
This helped to avoid accounting for water falling outside of the plot being tested or 
flowing directly into the gutter.  
 
3.3 Pre-experiment Laboratory Procedures 
3.3.1 Source of manure 
Swine manure used to prepare the slurry used in this study was collected from 
a swine waste lagoon located in a farm in Germantown, MD. The farm was the 
property of Dr. Hartsock, a faculty member at the University of Maryland, College 
Park. Because of the advanced age of the slurry (approximately 2 yr.), it did not have 
the detectable levels of bacteria necessary for the study (approximately 10
6
 colonies 
 44 
 
 
of each pathogen mL
-1
), so the slurry had to be inoculated with both E. coli and S. e. 
Typhimurium cultures in a process that is described in section 3.3.6. 
 
3.3.2 Slurry preparation 
The swine waste lagoon had two distinct layers: the top layer was mostly 
liquid while the bottom layer had most of the solids that settled over time. As neither 
layer had the desired consistency of 4% solids, samples from each layer had to be 
collected and mixed in order to achieve such a consistency. Approximately 65 L of 
swine waste were collected from each layer and analyzed at the laboratory facilities 
for solid content, which was determined gravimetrically by weighing three replicated 
samples of each layer of waste before and after drying at 105?C for 24 h. The analysis 
was repeated three times and the results indicated that the bottom layer had 
approximately 7% solids and the top layer had approximately 0.3% solids. Each 
experiment conducted for the purposes of this study required 13 L of slurry (10.94 L 
m
-2
). In order to obtain the 4% desired consistency and the 13 L required quantity of 
slurry, the day before each experiment 8 L of the sample containing 7% solids were 
mixed with 5 L of the sample containing 0.3% solids. 
 
3.3.3 Escherichia coli culture 
Escherichia coli culture used in this study was obtained by isolating this 
bacterium from fresh bovine manure, which was collected at the Dairy Research Unit 
of the USDA/ARS facility in Beltsville, MD. Two methods to initiate the isolation 
process were applied and consisted of diluting the bovine manure by a series of 
increasing folds and streaking it directly onto MacConkey agar plates.   
 45 
 
 
The first method consisted of diluting the manure by a 10-fold in preparation 
for subsequent 100-fold and 1000-fold dilutions necessary to reduce the initial 
concentration of E. coli colonies found in the fresh manure in order to achieve ideal 
isolation levels. In order to obtain a 10-fold dilution, 10 grams of manure was added 
to 90 mL of sterile distilled water and the contents dispersed in a high-speed blender 
for 2 min. Immediately after the blending process was completed, 1 mL was 
transferred to 9 mL of sterile distilled water in order to obtain a 100-fold dilution. The 
100-fold dilution was then vortexed and 1 mL was immediately transferred to 9 mL 
of sterile distilled water in order to obtain a 1000-fold dilution. 
Subsequently, two 50 ?L replicates from each of the 100-fold and 1000-fold 
dilutions were dispensed on MacConkey agar plates with the help of a Spiral BioTech 
autoplater. Plates were incubated at 44?C for 18 to 20 h to allow colonies to grow 
before further tests could be performed in order to select one E. coli colony. 
The second method consisted of streaking fresh manure directly onto 
MacConkey agar plates, which was performed under a hood to avoid contamination. 
In the process, two 10 ?L replicates from fresh manure were first streaked onto the 
plates and then incubated at 44?C for 18 to 20 h to allow colonies to grow before 
further tests could be performed in order to select one E. coli colony. 
After colonies obtained from both methods had grown on the plates overnight, 
six of them were selected for confirmation based on two factors: colonies that 
morphologically mostly resembled those of E. coli colonies, and colonies that had 
grown well isolated from other colonies. Presumptive E. coli colonies were then 
 46 
 
 
confirmed using the BBL Enterotube II technique, which is used in the rapid 
identification of Enterobacteriaceae.  
The application of the technique consisted of first inoculating a microscopic 
portion from each selected single colony into a different self-contained, 
compartmented plastic tube with the help of its enclosed inoculating wire. The 
compartments in each tube consisted of 12 different conventional media that reflected 
the performance of 15 standard biochemical tests from a single colony. Once each of 
the six tubes was inoculated with a microscopic portion of a selected colony, it was 
incubated at 37?C for 18 to 24 h before the code resulting from the combination of 
reactions of the 15 standard biochemical tests in the relevant tube was read. All six 
tubes were inoculated and incubated simultaneously. The code obtained was then 
compared to the coding table provided by the manufacturer in order to identify the 
species of Enterobacteriaceae that had been inoculated.  
One of the six colonies that tested positive for E. coli was further processed 
and periodically maintained to remain the pure culture used in the experiments 
comprising this study. Since the BBL Enterotube II technique applied in the 
identification of Enterobacteriaceae used only a microscopic portion of the selected 
colony, the remainder of the colony was transferred to 10 mL of Minimal Lactose 
Broth (MLB) and further incubated at 37?C for 18 to 20 h. Once incubation was 
completed, three 10 ?L replicates of the enriched broth were streaked onto three 
separate MacConkey agar plates, further incubated at 44?C for 18 to 20 h, and then 
stored at 4?C. Maintenance of the pure culture of E. coli was done weekly by 
 47 
 
 
transferring a colony from the stored plates onto 10 mL of MLB and then performing 
the incubation and storage steps described above. 
MLB was prepared by first adding the following amounts of micro and macro 
nutrients into 1 L of distilled water: 4.35 g of K
2
HPO
4
,
 
3.4 g of KH
2
PO
4
, 2 g of 
(NH
4
)
2
SO
4
, 8.5 g of NaCl, 1.5 g of Bile Salts, 1.8 g of Lactose, 1 mL of Trace Metals, 
1 mL of Trace Elements, 1 mL of MgSO
4
, and 1 mL of FeSO
4
. The contents were 
then stirred thoroughly in order to completely dissolve the contents and the pH was 
adjusted to 7 if necessary. The solution was then filtered through a NALGENE
?
 filter 
unit containing a sterile 0.2 ?m membrane, and the contents kept in a 37?C incubator 
to be monitored for possible contamination. 
 
3.3.4 Salmonella enterica Typhimurium culture 
Salmonella enterica subsp. enterica Typhimurium (Salmonella enterica 
Typhimurium) culture used in this study was purchased from the American Type 
Culture Collection
?
 in Manassas, VA (ATCC
? 
number 53648). The culture arrived as 
freeze-dried material, which had to be further processed in order to revive the culture.  
In the process to revive the culture, Lenox broth and agar plates were used 
since these are very unselective type of media and suitable for the growth of 
innumerous types of microorganisms. First, after the ampoule was opened, 1 mL of 
the Lenox broth was added to and vigorously mixed with the contents in order to get 
the material in suspension. Subsequently, the contents were transferred to 5 mL of 
Lenox broth, which was then incubated at 37?C for 15 h. Once incubation was 
completed, two 10-?L replicates of the enriched broth were streaked onto L agar 
plates, which were further incubated at 37?C for 15 h. 
 48 
 
 
At that point, well-isolated colonies were obtained, which were then further 
processed using Mannitol Tetrathionate Broth (MTB) and Brilliant Green (BG) agar 
plates since these media are more selective and suitable for the growth of pure culture 
of S. e. Typhimurium. In the process, one colony, from the L agar plate, was 
transferred into 10 mL of MTB after 200 ?L of Iodine (2% v/v) had been added to 
and vortexed with the broth, and then incubated at 37?C for 18 to 20 h. Once 
incubation was completed, three 10-?L replicates of the enriched broth were streaked 
onto three separated BG agar plates, which were further incubated at 37?C for 18 to 
20 h. The plates containing well-isolated colonies were then stored at 4?C. 
Preliminary studies in the experimental site during the summer of 2003 
indicated that the Brilliant Green (BG) agar was not very selective for the growth of 
S. e. Typhimurium coming from environmental samples. BG agar worked well for the 
growth of our pure culture of S. e. Typhimurium in the laboratory, but allowed the 
growth of many other microorganisms that were present at the experimental site. 
Although some of these microorganisms morphologically dramatically differed from 
our strain of Salmonella, they overtook the plate, suppressing the growth of our strain 
and making accountability of it practically impossible. To overcome this problem, 
Nalidixic Acid, an antibiotic, was introduced to the agar to eliminate the growth of 
unwanted microorganisms. 
The first step in the antibiotic introduction to the agar was to expose our 
Salmonella strain to the Nalidixic Acid in order for the organism to mutate and 
become resistant to the antibiotic. In the process, first a stock solution of the antibiotic 
was prepared by adding 40 mg of Nalidixic Acid into 2 mL of distilled water (20 mg 
 49 
 
 
mL
-1
) as directed by Maniatis et al. (1982). After the contents had been well vortexed, 
they were filtered into a sterile test tube using a 25 mm Millex (0.22 ?m pore size) 
syringe-filter unit. As indicated by Maniatis et al. (1982), the concentration of 
Nalidixic Acid to be used in any growth media should be of 20 ?g of the acid mL
-1
 of 
medium. Therefore, in order to grow our strain exposed to working concentrations of 
the antibiotic, one isolated S. e. Typhimurium colony from the BG plates was 
transferred to 5 mL of L broth after 5 ?L of the sterilized stock solution had been 
added to and vortexed with the broth, which was then incubated at 37?C for 15 h. 
Once incubation was completed, two 10-?L replicates of the enriched broth were 
streaked on L agar plates, which were then further incubated at 37?C for 15 h. As 
mentioned earlier, L broth and agar plates are very unselective type of media that, in 
this case, could ease the growth of Salmonella strain exposed to an antibiotic. 
The S. e. Typhimurium culture was periodically maintained in order to remain 
the pure culture to be used in the experiments comprising this study. Accordingly, 
each week a colony was transferred to 10 mL of Mannitol Tetrathionate Broth (MTB) 
after 200 ?L of Iodine and 10 ?L of Nalidixic Acid had been added to and vortexed 
with the broth.  The broth was then incubated at 37?C for 18 to 20 h. Once incubation 
was completed, three 10 ?L replicates of the enriched broth were streaked onto three 
separate Brilliant Green (BG) agar plates (containing the antibiotic, Section 3.3.5), 
further incubated at 37?C for 18 to 20 h, and then stored at 4?C. 
 MTB was prepared by first adding the following amounts of micro and macro 
nutrients into 1 L of distilled water: 2 g of Mannitol,
 
, 2 g of (NH
4
)
2
SO
4
, 1 g of Bile 
Salts, 30 g of Sodium Thiosulfate, 5 g KHCO
3
, 0.1 g of Yeast Extract, 1 mL of Trace 
 50 
 
 
Metals, 1 mL of Trace Elements, 1 mL of MgSO
4
, and 1 mL of FeSO
4
. The contents 
were then stirred thoroughly in order to completely dissolve the contents and the pH 
adjusted to 8 if necessary. The solution was then filtered through a NALGENE
?
 filter 
unit containing a sterile 0.2 ?m membrane, and the contents kept in a 37?C incubator 
to be monitored for possible contamination. 
 
3.3.5 Plate preparation 
The MacConkey agar plates used for growing E. coli colonies were purchased 
from Northeast Laboratory Services in Waterville, Maine. All plates were stored at 
4?C immediately upon delivery and only those plates needed for any given 
experiment were removed from storage the day before the experiment.  
BG agar plates used for growing S. e. Typhimurium colonies were prepared in 
the laboratory with BG agar purchased from [Difco]?. In the process, 58 g of BG 
agar were added into a flask containing 1 L of distilled water and the contents 
thoroughly stirred under high heat. After being brought to a boil for approximately 1 
minute, the contents were autoclaved for 15 min at 121?C. Immediately after 
autoclaving was completed, the flask was placed into a water bath ranging in 
temperature from 45?C to 55?C in order to cool and maintain its contents within such 
temperature range. Temperatures outside of this range were not recommended since 
the contents could easily solidify at temperatures lower than 45?C, while being too 
hot for pouring at temperatures higher than 55?C. 
 After the contents had reached the desired temperature range, they were mixed 
with 1 mL of antibiotic (1 ?L of antibiotic stock solution mL
-1
 of agar) and 
thoroughly stirred, and approximately 20 mL of the resulting mix were poured onto 
 51 
 
 
several sterilized petri dishes (plates). Plates were then kept at room temperature for 
at least 24 hours before being stored at 4?C. Only those plates needed for any given 
experiment were removed from storage the day before the experiment. 
 
3.3.6 Slurry inoculation 
The swine slurry used in this study was inoculated with E. coli and S. e. 
Typhimurium cultures prior to each experiment. Samples of the slurry were collected 
just before inoculation and just before application onto the plot in order to verify the 
levels of bacteria concentration existing prior to inoculation and at the time of 
application of the inoculated slurry. 
Approximately 48 hours prior to any given experiment, a single E. coli colony 
was transferred to 10 mL of MLB and incubated at 37?C for 18 to 20 h. Once 
incubation was completed, 1 mL of the enriched broth was transferred to 200 mL of 
MLB and further incubated at the same temperature and for the same amount of time. 
Incubation under these conditions usually gave approximately 10
8
 E. coli colonies 
mL
-1
. 
At approximately the same amount of time in advance, 10 ?L of antibiotic 
stock solution and 200 ?L of Iodine were added to 10 mL of MTB. The contents were 
then vortexed and a single S. e. Typhimurium colony was transferred to the broth and 
incubated at 37?C for 18 to 20 h. Once incubation was completed, 1 mL of the 
enriched broth was transferred to 200 mL of MTB after 200 ?L of antibiotic and 4 
mL of Iodine had been added to and thoroughly mixed with the broth, and further 
incubated at the same temperature and for the same amount of time. Incubation under 
these conditions usually gave approximately 10
8
 S. e. Typhimurium colonies mL
-1
. 
 52 
 
 
Approximately two hours prior to the actual experiment, 130 mL of each 
enriched broth were thoroughly mixed with the 13 L of slurry, bringing inoculation 
levels to approximately 10
6
 colonies of each bacterium mL
-1
 required to conduct each 
experiment. 
 
3.3.7 Bromide tracer 
Bromide (Br) was added to the swine slurry used for the experiments in order 
to track the movement of it and, to a certain extent, to determine the transport patterns 
between this chemical itself and the two surrogate pathogens used in this study. 
Bromide has been commonly used as a conservative tracer for studying the movement 
of water in soils (Walton et al., 2000; Roodsari, 2004) because this chemical does not 
undergo fast microbial transformation or quickly bind to organic materials or soil 
minerals.  
Bromide, in the form of potassium bromide (KBr), was added to each 13 L of 
slurry such that the final concentration of Br equaled to 2000 ppm. Therefore, 40 g of 
KBr was added to and thoroughly mixed with the slurry the evening prior to each 
experiment based on the following calculations: 
Molecular Weight (MW), KBr = 119 g; K = 39 g; and Br = 80 g.  
For each experiment, 2000 ppm Br or 2 g Br/L*(13 L) = 26 g Br was needed. 
And, (3 g KBr)*(80 g Br/119 g KBr) = 2 g Br. 
Therefore, (26 g Br)*(3 g KBr/2 g Br) = 40 g KBr. 
 
 
 
 53 
 
 
3.4 Field Experimental Procedures 
All experiments that comprised this study were conducted early in the 
morning in order to take advantage of favorable climatic conditions such as low 
temperature and low wind velocity. 
 
3.4.1 Soil samples and groundwater  table 
Just before each experiment was conducted, soil samples were collected in 
order to determine initial soil moisture content within the top 10 cm portion of the 
relevant plot. To make such a determination, three samples were taken from either 
side of the plot (upper, middle and lower portions) for a total of six samples.  
Samples were collected using a 2.54 cm ID core sampler and placed in pre-
weighted aluminum foil sheets to be analyzed after the experiment. After each soil 
sample was collected, the resulting hole was filled with soil collected from within the 
upper portion of the lysimeter where experiments were not conducted. Initial soil 
moisture content was determined gravimetrically by weighing the soil sample before 
and after drying at 105?C for 24 h. Calculations were performed on a wet basis. 
Groundwater table levels in the slotted wells were recorded both before and 
after any given experiment for most of the experiments. All wells were dry before 
each experiment under dry initial soil moisture conditions, indicating that the 
groundwater table was at least 50 cm below the soil surface before each test was 
conducted. The groundwater table levels for after each experiment under dry 
conditions were not recorded. Table 3.2 shows the groundwater table levels before 
and after most of the experiments under wet initial soil moisture conditions. 
 54 
 
 
Table 3.2. Approximate position of groundwater table (cm) below the soil surface 
recorded in the wells (W) before (BE) and after (AE) the experiments under wet 
initial soil moisture conditions. 
W1 W2 W3 W4 
PLOT 
BE AE BE AE BE AE BE AE 
1 (Vegetated) NR
?
NR 19 17 19 18 13 10 
         
2 (Bare) NR NR 18 5 NR NR NR NR 
         
3 (Vegetated) NR 28 24 20 9
?
16 NR 16 
         
4 (Bare) NR NR NR NR NR NR NR NR 
? 
NR, no record. 
? 
This position may have been underestimated since the hose was placed in this well 
overnight in order to raise the groundwater table level in Plot 3. Between the times 
when the hose was removed from this well, which was the time when the reading was 
taken, and the initiation of the actual experiment (about 1 h), the groundwater table 
probably leveled off within the lysimeter. Thus, the actual depth of the groundwater 
table below the surface in Plot 3 right before the experiment was probably greater 
than 9 cm. 
 
 
 
3.4.2 Soil and ambient temperatures 
Soil and ambient temperatures were recorded just before each experiment was 
conducted. Soil temperature within the top 10 cm portion thereof was measured using 
five soil thermometers, of which two were inserted close to each corner of the upper 
portion, one in the very center and two close to each corner of the lower portion of the 
plot, for a total of five locations. Ambient temperature was measured before and after 
each experiment using a conventional outdoor thermometer. 
 
3.4.3 Slurry application 
Thirteen liters (13 L) of liquid swine manure (slurry) was applied uniformly 
throughout the application area of the plot (approximately 10.94 L m
-2
) where the 
 55 
 
 
experiment was being conducted after all preparatory steps had been concluded, 
including the collection of soil samples, measurement of soil and ambiance 
temperatures and organization of all materials required for runoff collection and 
sampling.  
Just before application of the slurry, a sample thereof was taken to verify the 
initial levels of E. coli and S. e. Typhimurium concentration (C
0
), which sample was 
kept in a cooler until further analyses were conducted. Once the slurry was applied, it 
was let sit for few minutes prior to turning on the rain simulator. As soon as the 
synthetic rain became fairly uniform, the barrier at the bottom of the application area 
was removed and the actual 60-minute long experiment began.  
The slurry application rate in this study may have been greater than the 
agronomic rates usually needed for rotating corn after soybean productions in the 
state of Iowa. Application rates are usually based on two factors: the manure nitrogen 
(N) availability for meeting the nutrient needs for crop production; and whether 
application will take place only before crop emergence or before and in-season 
fertilization. Since all manure N in liquid swine slurry is available to crops the first 
year (ISUE, 2003), and assuming application only before crop emergence, the rate of 
N required to supply approximately full corn grain N removal is 168 kg Total (T) N 
ha
-1
 (150 lbs T N acre
-1
) (ISUE, 1997; Sawyer et al., 2001); whereas to supply 
approximately full soybean grain N removal the rate is 224 kg T N ha
-1
 (200 lbs T N 
acre
-1
) (Sawyer et al., 2001).  
According to available information, liquid swine manure consisting of 4% 
solids (which was the case of the slurry used in this study) contains approximately 4 
 56 
 
 
kg T N per 1000 L of slurry (36 lbs T N per 1000 gal of slurry) (Schmitt and Rehn, 
2002; Joern and Brichford, 2006). Therefore, the rate of N applied for this study was 
approximately 430 kg T N ha
-1
 (417 lbs T N acre
-1
), which was computed as: 
 
((4 kg T N/1000 L of slurry)*(13 L/1.2 m
2
)*(1 m
2
/0.0001 ha)) = ~ 430 kg T N ha
-1
 
Therefore, the slurry application rate used in this study was 2.6 times bigger 
than the rate required for corn, and 1.9 times bigger than the rate required for 
soybean.  
 
3.4.4 Runoff sampling 
Runoff collected by the three funnels and the gutter, which were located at 
approximately 4.13 m and 6.2 m, respectively, from the bottom edge of the waste 
application area, was measured and sampled at different time intervals during each 
experiment. Such time intervals were established in a way to establish a 
representative profile for discharge. The time intervals at which runoff was measured 
and sampled during any given simulation are shown in Appendix A. 
Runoff from the three funnels at any given time interval was collected and 
stored in three individual buckets. Upon completion of the experiment, the volume of 
runoff contained in each bucket was measured and a sub-sample taken using a 250-
mL plastic bottle. Sub-samples taken were stored in a cooler until further analyses 
were conducted. 
Runoff collected by the gutter was measured using the V-notch weir installed 
near the pumping station and sub-sampled using 20-mL vials. Sub-samples taken 
 57 
 
 
were stored in a cooler until further analyses were conducted similarly to runoff 
collected by the funnels. 
 
3.5 Post-experiment Laboratory Analytical Procedures 
3.5.1 Processing of slurry samples 
In order to verify pre-existing levels of E. coli and S. e. Typhimurium 
concentration, the slurry sample collected before each relevant inoculation and 
application were first diluted. The sample taken before inoculation was not diluted 
beyond a 10-fold dilution since concentration levels at such stage were expected to be 
low. However, the sample taken before application was diluted beyond a 10-fold up 
to a 1000-fold dilution. 
The dilution process consisted of thoroughly mixing the contents of any given 
sample before transferring 1 mL to 9 mL of sterile distilled water in order to obtain a 
10-fold dilution. For the sample taken before application, the 10-fold dilution was 
then vortexed and 1 mL was immediately transferred to 9 mL of sterile distilled water 
in order to obtain a 100-fold dilution. The resulting 100-fold dilution was then 
vortexed and 1 mL was immediately transferred to 9 mL of sterile distilled water in 
order to obtain a 1000-fold dilution. 
Subsequently, three 50 ?L replicates of the 10-fold dilution obtained from the 
sample taken before inoculation were dispensed onto sets of both MacConkey and 
BG agar plates using a Spiral BioTech autoplater. In addition, two sets of three 50 ?L 
replicates from each of the 100-fold and 1000-fold dilutions obtained from the sample 
taken before application were dispensed onto sets of both MacConkey and BG agar 
 58 
 
 
plates using a Spiral BioTech autoplater. Further, the MacConkey plates and the BG 
plates were incubated at 44?C and at 37?C, respectively, for 18 to 20 h prior to E. coli 
and S. e. Typhimurium colony counts were performed.  
 
3.5.2 Processing of runoff samples from vegetated plots 
In order to handle the runoff samples collected from vegetated plots, the 
samples collected by the funnels, which were contained in 250-mL plastic bottles, 
were thoroughly mixed and then transferred to 20-mL vials identical to those used to 
contain the samples collected by the gutter (excess contents were properly discarded 
of). All samples, which were then contained in 20-mL vials, were either dispensed 
wholly or diluted prior to be dispensed onto the MacConkey and BG agar plates. The 
decision on whether to dilute the samples prior to dispensing them onto the plates was 
based on naked-eye examination of elevated levels of slurry, which were found 
especially among the samples collected during the initial time intervals. 
Samples collected from vegetated plots were diluted by either a 10-fold only 
or both a 10-fold and a 100-fold dilution depending on how elevated the levels of 
slurry contained in the sample were. In order to obtain a 10-fold dilution, 1 mL of the 
relevant sample was transferred to 9 mL of sterile distilled water. In order to obtain a 
100-fold dilution, the resulting 10-fold dilution was vortexed and 1 mL was further 
transferred to 9 mL of sterile distilled water. 
Subsequently, two 50 ?L replicates of each sample (diluted or not) were 
dispensed onto sets of both MacConkey and BG agar plates using a Spiral BioTech 
autoplater. Further, the plates were incubated at 44?C and at 37?C, respectively, for 
18 to 20 h prior to E. coli and S. e. Typhimurium colony counts were performed. All 
 59 
 
 
vials were subsequently stored at 4?C until further analyses for bromide concentration 
were performed. 
 
3.5.3 Processing of runoff samples from bare plots 
In order to handle the runoff samples collected from bare plots, the samples 
collected by both the funnels and the gutter, which were contained in 250-mL plastic 
bottles and 20-mL vials, respectively, were thoroughly mixed and then transferred to 
15-mL conic tubes (excess contents were properly discarded of). All samples were 
then centrifuged (100 ? g, 10 min) before dispensing the supernatant either wholly or 
diluted onto the plates. Similarly to samples collected from vegetated plots, the 
decision on whether to dilute the supernatant prior to dispensing them onto the plates 
was based on naked-eye examination of elevated levels of slurry, which were found 
especially among the samples collected during the initial time intervals. 
Supernatant of the samples collected from bare plots were diluted by either a 
10-fold only or both a 10-fold and a 100-fold dilution depending on how elevated the 
levels of slurry contained in the sample were. In order to obtain a 10-fold dilution, 1 
mL of the supernatant resulting from centrifugation was transferred to 9 mL of sterile 
distilled water. In order to obtain a 100-fold dilution, the resulting 10-fold dilution 
was vortexed and 1 mL was further transferred to 9 mL of sterile distilled water. 
Subsequently, two 50 ?L replicates of the supernatant (diluted or not) were 
dispensed onto sets of both MacConkey and BG agar plates using a Spiral BioTech 
autoplater. Further, the plates were incubated at 44?C and at 37?C, respectively, for 
18 to 20 h prior to E. coli and S. e. Typhimurium colony counts were performed. All 
 60 
 
 
tubes were subsequently stored at 4?C until further analyses for bromide 
concentration were performed. 
 
3.5.4 Colony counting 
Escherichia coli colonies and S. e. Typhimurium colonies from bare plots 
were counted using a Synoptic Limited Protocol Colony Counter? consisting mainly 
of a digital high resolution CCD video camera whose imaging is enhanced by a 
lighting configuration system that provides uniform illumination and enhanced 
contrast.  The video camera is connected to a specifically configured computer 
system with a series of adjustable settings, including sample amount and dilution 
factor.  Results from the counting of colonies by this system were calculated in 
Colony Forming Units (CFU) per mL. 
Some E. coli colonies and all S. e. Typhimurium colonies from vegetated plots 
were counted manually due to some degree of contamination from microorganisms 
native to the experimental site. In the case of E. coli, contamination from 
microorganisms native to the experimental site was not very high and was mostly due 
to the fact that E. coli can also be found in fecal material of wild animals, such as 
birds, foxes, etc., that at the time of the experiments inhabited the site. In the case of 
S. e. Typhimurium, contamination was high and was due to the fact that, despite the 
introduction of the antibiotic in the growth medium, other microorganisms, 
morphologically identical to our Salmonella strain, were present at the site. It is 
believed that these morphologically identical microorganisms were responsible for 
decomposition of organic matter in the vegetated plots. 
 61 
 
 
In other to adjust the counts for E. coli from the vegetated plots, Bromide (Br) 
data was used to identify the point in time during the simulations that the manure 
front really reached the points of surface runoff collection. In other to adjust the 
counts for S. e. Typhimurium from the vegetated plots, E. coli and Br data were used 
to identify the point in time at which the manure front really reached the points of 
surface runoff collection. In addition, the counts for S. e. Typhimurium were farther 
adjusted using the BBL Enterotube II technique (section 3.3.3). In the process, several 
plates, that represented different time intervals throughout the simulations, were 
selected and their colonies were tested in order to give a ratio of counted colonies that 
were our strain of Salmonella to colonies that were not.   
 
3.5.5 Bromide concentration in runoff samples 
Bromide (Br) concentration (ppm) in runoff samples was measured based on 
their electrical chemical potential as related to Bromide content, in millivolts (mV), 
using an ion-specific electrode Model-525 manufactured by Thermo Orion. In the 
process, the first step was to prepare, in different volumetric flasks, a stock solution 
of 10000 ppm Br
 
and 5 standard concentrations of Br that ranged from 1000 to 0.1 
ppm Br. The stock solution was prepared by thoroughly mixing 7.45 g of potassium 
bromide (KBr) to 500 mL of deionized (DI) water. After the stock solution was 
prepared, the highest concentrated standard (1000 ppm) was obtained by thoroughly 
mixing 50 mL of the stock solution with 500 mL of DI water. The remaining standard 
concentrations ? 100, 10, 1, and 0.1 ppm Br ? were prepared by thoroughly mixing 
10, 1, 0.1, and 0.01 mL of the 1000 ppm standard with 100 mL of DI water, 
respectively.  
 62 
 
 
The electrical chemical potential, in mV, of each standard concentration was 
then measured with the above instrument in order to generate a standard curve that 
could be used to estimate the concentration of Br in the runoff samples. In order to 
accomplish that, 5 mL of each standard was thoroughly mixed with 0.1 mL (50:1) of 
ionic strength adjuster (ISA) before mV value was measured and recorded. Standard 
curves were generated by regression of mV values over the standard concentrations 
values on a semi-log scale. Millivolts of runoff samples were also measured after 5 
mL of each sample were thoroughly mixed with 0.1 mL of ISA. Both, standards and 
runoff samples were brought to the same ambient temperature before any 
measurement took place; and at every 2 h a new standard curve was generated before 
going into the next set of samples.  
 
3.6 Statistical Analysis 
For each set of experiments, an ANOVA for fixed effects was performed to 
test the effect of cover condition and distance on the reduction rate (k) of each 
pathogen in surface runoff over time and after 15 min of simulation in each plot (P 
level of significance set at 0.05). This point in time during the simulations was 
observed to be critical since that was when the trend in relative concentrations with 
time in the vegetated plots was observed to follow an exponential decrease. An 
ANOVA for fixed effects was also performed for each set of experiments to test the 
effect of cover and distance on relative concentrations calculated at t = 15 min 
(C/C
0(15)
, %), and on background concentration levels (b) observed in each plot. In 
 63 
 
 
addition, t-test statistics were performed to test whether the mean reduction rate (k) 
observed at both distances were significant different between the two treatments. 
 
 
 64 
 
 
Chapter 4: Results and Discussion 
4.1 Hydrographs 
4.1.1 Dry initial soil moisture conditions 
 Figure 4.1 shows the hydrographs describing the runoff rates obtained with 
time during each experiment under dry initial soil moisture conditions in which the 
groundwater table was approximately 50 cm below the surface in each plot (Section 
3.4.1). In the bare plots, runoff was first observed 2 min after the initiation of rainfall 
in Plot 2, and 30 sec in Plot 4. Runoff was first observed in the vegetated plots several 
minutes later than in the bare plots. Runoff was first observed 13.5 min after the 
initiation of rainfall in Plot 1, and 10 min in Plot 3. 
 
 
Time (min)
0 10203040506070809010
R
u
no
f
f
 r
a
te
 (c
m
3
 cm
-2
 min
-1
)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Plot 1 - V
Plot 2 - B
Plot 3 - V
Plot 4 - B
 
Figure 4.1. Runoff hydrographs of all plots for experiments under dry initial soil 
moisture conditions. V = Vegetated; B = Bare. 
 65 
 
 
Figure 4.1 indicates that, once runoff initiated in the bare plots (Plots 2 and 4), 
their rates increased rapidly and sharply during the next 10 min of simulation. This 
rapid and sharp increase in runoff rates during the early stages of simulation in the 
bare plots was probably due to surface compaction and sealing resulting from the 
mechanical action of raindrops on exposed soil surface aggregates (Ward, 1995).  
When raindrops strike a bare soil surface, soil aggregates disintegrate, 
resulting in finer soil particle washing into the soil-surface openings or soil matrix 
(Gray and Norum, 1967; Beven, 2004). These physical processes result in the 
formation of a very thin, impervious layer at the surface that rapidly and dramatically 
decreases the infiltration rate. This process results in a considerable increase of 
surface runoff (Kirkby, 1969; Ward, 1995; Mualem and Assouline, 1996).   
Figure 4.1 also shows that after the initial rapid increase, runoff rates 
continued to gradually increase for the next 8 to 10 mins, until they reached relatively 
steady-state conditions (approximately 0.15 cm min
-1
). The runoff rate remained 
steady for the duration of the rainfall simulation, which was very close to the applied 
rainfall rate (approximately 0.14 cm min
-1
 in each plot). Therefore, it is very likely 
that the steady rate of runoff observed in the bare plots was due to the fact that the 
soil surface layer in these plots eventually became completely saturated, thus causing 
all of the simulated rain to be discharged as runoff.   
As Figure 4.1 indicates, runoff was not observed in the vegetated plots until 
several minutes after the initiation of rainfall. These lag periods reflect the fact that, at 
the early stages of simulation, infiltration rates exceeded the simulated rainfall rates. 
However, as the simulations progressed and the soils became wetter, infiltration rates 
 66 
 
 
decreased such that excess rain either ponded in surface depressions or was 
discharged as runoff. 
Figure 4.1 also indicates that once runoff initiated in the vegetated plots (Plots 
1 and 3), rates increased rapidly during the next 10 and 15 min of simulation, but not 
as sharply as was observed in the bare plots. After this rapid increase, runoff rates 
continued to gradually increase until near the end of rainfall simulations. Consistent 
with previous research reported by Adams et al. (2005), runoff rates continued to 
increase during the rainfall simulations in the vegetated plots due to decreased 
infiltration rates. This is due to the fact that as soils become wetter, the moisture 
gradient and available storage in the soil decrease, thus resulting in lower infiltration 
rates.  
Figure 4.1 clearly shows that runoff initiation was delayed and runoff volume 
substantially diminished in the vegetated plots compared to the bare plots as a 
consequence of enhanced infiltration. It has long been recognized that vegetation 
attenuates surface runoff and favors infiltration processes by several distinct 
mechanisms (Kirkby, 1969; Kilinc and Richardson, 1973; Tromble et al., 1974; 
Shirmohammadi and Skaggs, 1984; Davies et al., 2004; Roodsari, 2004; Trask et al., 
2004).  
Mechanisms by which vegetation favors infiltration processes are: (1) 
vegetation intercepts and dissipates raindrops thereby minimizing the breakdown of 
soil aggregates, thus minimizing surface sealing; (2) vegetation offers more resistance 
to overland flow, thus decreasing its velocity and allowing more time for infiltration 
to occur; (3) plant root systems increase soil hydraulic conductivity by loosening the 
 67 
 
 
soil, resulting in enhanced infiltration; (4) plant roots and invertebrate activity 
increase soil porosity by opening up channels and macropores, which increases 
infiltration; and (5) organic matter residues from vegetation aid in the development of 
more permeable soil surfaces.  
As shown in Figure 4.1, there was a considerable difference in runoff volumes 
between the two vegetated plots (Plots 1 and 3). This was due both to the fact that 
runoff initiated sooner, and that runoff rates at the end of simulations were 
substantially higher in Plot 3 than in Plot 1. These differences are most likely due to a 
combination of three characteristics that were each different between the two plots: 
slope, topography, and soil texture. 
Topographic analysis of Plot 1 and Plot 3 revealed that their slopes were 
slightly different (Figure 3.2). On average, the slope in Plot 1 was 4.0%, whereas in 
Plot 3 it was 5.4%. As previously reported by White (1983) and Roodsari (2004), 
slope can significantly affect total surface runoff: runoff increases as the slope 
increases.   
Topographic analysis (Figure 3.4) also revealed that Plot 1 had less surface 
area contributing to convergent flow conditions or channelization of flow compared 
to Plot 3. As some studies have reported (Darboux et al., 2004; Darboux and Huang, 
2005; Gomez and Nearing, 2005), these areas of convergent/concentrated flow or 
surface depressions may delay runoff initiation since water can be stored in puddles, 
thus decreasing local flow velocity and enhancing infiltration.  
However, this ponding effect may be temporary and occur only at the 
beginning of a rainfall event (Darboux and Huang, 2005; Gomez and Nearing, 2005). 
 68 
 
 
As a rainfall event progresses and (1) depressions become filled and interconnected 
into a complete drainage network; and (2) the area contributing to runoff expands, 
flow velocity is higher in these convergent/depression/channel areas, which, in turn, 
decreases the available time for infiltration to occur and runoff rates increase.  
In the studies by Darboux et al. (2004), and Darboux and Huang (2005), they 
reported similar observations, in which surfaces with initial depressions increased 
steady state runoff compared to initially smooth surfaces, and they attributed such 
observations to flow concentration or channelization of flow through surface 
depressions. According to Dillaha et al. (1986), such concentrated flow conditions, 
instead of shallow and uniform flow, can substantially decrease the effectiveness of 
vegetated filter strips (VFS) to attenuate surface runoff as a mean to improve water 
quality.  
Finally, soil texture analysis (Table 3.1) revealed that Plot 1 had a loam soil 
texture both at the soil surface and at the 20 cm depth, whereas Plot 3 had a loam 
texture at the surface but a clay loam texture at the 20 cm depth. Soil texture affects 
infiltration rates; a loam soil is considered to be more permeable than a clay loam soil 
(Ward and Dorsey, 1995). Therefore, infiltration was higher, and, consequently, less 
runoff was observed in Plot 1 than in Plot 3. 
 
4.1.2 Wet initial soil moisture conditions 
 Figure 4.2 shows the hydrographs describing the runoff rates obtained with 
time during each experiment under wet initial soil moisture conditions in which the 
groundwater table was just a few centimeters below the surface in each plot (Table 
 69 
 
 
3.2).  In the bare plots, runoff was first observed 70 sec after the initiation of rainfall 
in Plot 2, and 48 sec in Plot 4. Runoff was first observed in the vegetated plots 
approximately 2 minutes later than in the bare plots. Runoff was first observed 3 min 
and 50 sec after the initiation of rainfall in Plot 1 and 2 min and 50 sec in Plot 3.  
 
Time (min)
0 10203040506070809010
R
u
no
f
f
 r
a
te
 (c
m
3
 cm
-2
 min
-1
)
0.00
0.02
0.04
0.06
0.08
0.10
0.12
0.14
0.16
0.18
Plot 1 - V
Plot 2 - B
Plot 3 - V
Plot 4 - B
 
Figure 4.2.  Runoff hydrographs of all plots for experiments under wet initial soil 
moisture conditions. V = Vegetated; B = Bare. 
 
 
 
Figure 4.2 indicates that, once runoff initiated in the bare plots (Plots 2 and 4), 
runoff rates increased rapidly reaching a constant rate (approximately 0.14 cm min
-1
) 
within 5 min after the initiation of rainfall. After the first 5 min, the runoff rates and 
rainfall rates were essentially identical (approximately 0.14 cm min
-1
). As discussed 
in the previous section (4.1.1), surface sealing of the bare soil surfaces was probably 
 70 
 
 
the major factor contributing to the rapid increase in runoff rates during the early 
stages of the simulations. 
In addition, the similarity in runoff and rainfall rates after 5 min of simulation 
was due to the fact that the soil surface layer in the bare plots was completely 
saturated and possibly sealed. In fact, the simulation in Plot 4 was terminated after 40 
min of simulation because the soil was completely saturated and subsurface flow was 
observed to be overflowing into the gutter.  
Figure 4.2 also indicates that, once runoff initiated in the vegetated plots 
(Plots 1 and 3), rates increased rapidly for the next several minutes, reaching constant 
rates (approximately 0.15 cm min
-1
) after 10 and 15 min of simulation in Plots 3 and 
1, respectively. Figure 4.2 shows that the increases in runoff rates in vegetated and 
bare plots were very comparable, and that the constant runoff rates observed in the 
vegetated plots were actually higher than in the bare plots. 
The hydrographs obtained from experiments conducted under wet initial soil 
moisture conditions (Figure 4.2) were different from those conducted under dry initial 
soil moisture conditions (Figure 4.1), particularly in vegetated plots. In the bare plots, 
runoff consistently initiated shortly after rainfall; however, constant runoff rates were 
observed much sooner under wet conditions than under dry conditions. In vegetated 
plots, under wet conditions, runoff initiated sooner, runoff rates increased more 
rapidly and constant runoff rates were higher than under dry conditions.   
These differences were due to differences in infiltration capacity. As has been 
previously reported (Gray and Norum, 1967; Cerda; 1997; Roodsari, 2004), soil 
moisture content is one of the most important factors controlling infiltration; 
 71 
 
 
infiltration decreases with an increase in the soil moisture content. Therefore, less 
infiltration and more runoff were observed in the vegetated plots under wet conditions 
where the water table was much closer to the soil surface.  
 
4.2 Bromide 
4.2.1 Dry initial soil moisture conditions 
Figure 4.3 shows Bromide (Br) concentrations measured with time in runoff 
collected at 413 and 620 cm downslope from the line of slurry application in both 
bare and vegetated plots. 
Br concentrations in runoff decreased as the distance from the source of slurry 
increased as a result of Br dilution. The dilution effect was due to the fact that the 
runoff intercepted by three funnels (413 cm downslope), including manure slurry and 
rainfall, represented only a fraction of the applied rainfall; whereas the gutter (620 cm 
downslope) included the total amount of rainfall applied to plots. Note that there was 
substantial variability in Br concentration and in runoff volumes observed at 
individual funnels (at 413 cm), especially in the vegetated plots (Appendix B), due to 
the heterogeneity in surface flow pathways arising from non uniform or rough 
surfaces (Roodsari, 2004). 
Figure 4.3 indicates that Br concentrations generally decreased with time, 
although the kinetics of Br transport were dramatically different between bare and 
vegetated plots. In bare plots, maximum Br concentrations were observed in the 
initial runoff samples ?  approximately 300 ppm in Plot 2 and 418 ppm in Plot 4, at 
413 cm ?  followed by a rapid decline. In contrast, in vegetated plots, maximum Br 
 72 
 
 
 73 
 
Time (min)
0 10203040506070
0
5
10
15
20
25
At 413 cm
At 620 cm
Plot 3 - Vegetated
Time (min)
0 10203040506070
B
r
 C
oncent
ratio
n
 (ppm
)
0
5
10
15
20
25
At 413 cm
At 620 cm
Plot 1 - Vegetated
Time (min)
0 10203040506070
B
r
 C
oncent
ratio
n
 (ppm
)
0
200
400
600
800
At 413 cm 
At 620 cm
Plot 2 - Bare
Time (min)
0 10203040506070
0
200
400
600
800
At 413 cm
At 620 cm
Plot 4 - Bare
concentrations were observed several minutes after runoff initiation ?  approximately 
18 ppm in Plot 1 and 24 ppm in Plot 3, at 413 cm ?  followed by a more gradual 
decline. On average, maximum Br concentrations in runoff from vegetated plots were 
94% lower than those in runoff from bare plots.  
 
 
Figure 4.3. Average Bromide (Br) concentrations measured with time in runoff 
collected at 413 and 620 cm downslope from the line of slurry application in bare and 
vegetated plots under dry initial soil moisture conditions. The Br concentration in the 
manure slurry was 2000 ppm. 
 
 
 
Overall, there was a substantial reduction in Br recovered in runoff from the 
vegetated plots compared to the bare plots. These results reflect the fact that 
substantially greater infiltration occurred in vegetated plots than bare plots, 
particularly in the initial stages of rainfall simulation when the bulk of manure slurry 
was transported into the plots (Figure 4.1). Similar findings were also reported by 
Roodsari (2004), where initial Br concentrations in runoff from a 20% sloped 
vegetated sandy loam plot were 98% lower than concentrations observed for bare 
sandy loam plot at a distance of 285 cm from the source of Br application.  
 
4.2.2 Wet initial soil moisture conditions 
Figure 4.4 shows Bromide (Br) concentrations measured with time in runoff 
collected at 413 and 620 cm downslope from the line of slurry application in bare and 
vegetated plots. 
In general, results obtained from wet initial soil moisture conditions were 
similar to those obtained from dry initial soil moisture conditions (Section 4.2.1). 
Figure 4.4 indicates that Br concentrations in runoff decreased as the distance from 
the source of slurry application increased, and that Br concentrations in runoff 
decreased with time, especially in the bare plots (Plots 2 and 4), at both distances 
away from the source. Note that there was substantial variability in Br concentration 
and in runoff volumes observed at individual funnels (at 413 cm), especially in the 
vegetated plots (Appendix C), due to the heterogeneity in surface flow pathways 
arising from non uniform or rough surfaces (Roodsari, 2004). 
 
 74 
 
 
 75 
 
Time (min)
0 10203040506070
B
r
 C
oncent
ratio
n
 (ppm
)
0
20
40
60
80
100
120
At 413 cm
At 620 cm
Plot 1 - Vegetated
Time (min)
0 10203040506070
0
20
40
60
80
100
120
At 413 cm 
At 620 cm
Plot 3 - Vegetated
Time (min)
0 10203040506070
B
r
 C
oncent
ratio
n
 (ppm
)
0
200
400
600
800
1000
1200
At 413 cm
At 620 cm
Plot 2 - Bare
Time (min)
0 102030405
0
200
400
600
800
1000
1200
At 413 cm
At 620 cm 
Plot 4 - Bare
0
Figure 4.4. Average Bromide (Br) concentrations measured with time in runoff 
collected at 413 and 620 cm downslope from the line of slurry application in bare and 
vegetated plots under wet initial soil moisture conditions. The Br concentration in the 
manure slurry was 2000 ppm. 
 
 
The maximum Br concentrations in bare plots ?  approximately 567 ppm in 
Plot 2 and 1075 ppm in Plot 4, at 413 cm ?  occurred shortly after the initiation of 
runoff; while the maximum Br concentrations in vegetated plots ?  approximately 
107 ppm in Plot 1 and 41 ppm in Plot 3, at 413 cm ?  occurred several minutes after 
the initiation of runoff.  
 
The primary difference between wet and dry initial soil moisture conditions 
was observed in vegetated plots, where maximum Br concentrations in runoff were 
generally higher under wet conditions. On average, the maximum Br concentration at 
413 cm in runoff under wet conditions was 252% higher than under dry conditions. 
These results are consistent with the hydrographs, which show that there was less 
infiltration in vegetated plots under wet conditions. Less infiltration resulted in 
reduced amounts of Br transported into the soil profile and increased amounts of Br 
lost in surface runoff. 
 
4.3 Surrogate Pathogens 
4.3.1 Dry initial soil moisture conditions 
Figures 4.5 and 4.6 show average relative concentrations of Escherichia coli 
and Salmonella enterica Typhimurium, respectively, measured with time in surface 
runoff collected at 413 cm downslope from the source of slurry application in all 
plots. Figures 4.7 and 4.8 show average relative concentrations of E. coli and S. e. 
Typhimurium, respectively, measured with time in surface runoff collected at 620 cm 
downslope from the source of slurry application in all plots. Relative concentration is 
defined as C/C
0
, where C is the concentration of the surrogate pathogens (herein 
referred to as pathogens) measured in the runoff samples and C
0 
is the initial 
concentration of the pathogens in the slurry.  
 76 
 
 
Time (min)
0 10203040506070
E. coli
, C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated 
Plot 2 - Bare  
Plot 3 - Vegetated 
Plot 4 - Bare 
 
Figure 4.5. Average relative concentrations of E. coli measured with time in runoff 
collected at 413 cm away from the source of slurry in all plots under dry initial soil 
moisture conditions.  
 
 
 
Time (min)
0 10203040506070
S. e.
 Ty
phi
muriu
m
,
 C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated  
Plot 2 - Bare  
Plot 3 - Vegetated   
Plot 4 - Bare  
 
Figure 4.6. Average relative concentrations of S. e. Typhimurium measured with time 
in runoff collected at 413 cm away from the source of slurry in all plots under dry 
initial soil moisture conditions.  
 77 
 
 
Time (min)
0 10203040506070
E. coli
, C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated
Plot 2 - Bare
Plot 3 - Vegetated
Plot 4 - Bare
 
Figure 4.7. Average relative concentrations of E. coli measured with time in runoff 
collected at 620 cm away from the source of slurry in all plots under dry initial soil 
moisture conditions.  
 
 
 
Time (min)
0 10203040506070
S. e.
 Ty
phi
muriu
m
,
 C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated
Plot 2 - Bare 
Plot 3 - Vegetated  
Plot 4 - Bare 
 
Figure 4.8. Average relative concentrations of S. e. Typhimurium measured with time 
in runoff collected at 620 cm away from the source of slurry in all plots under dry 
initial soil moisture conditions. 
 
 78 
 
 
In all plots, relative concentrations of both pathogens in runoff decreased as 
the distance from the source of slurry increased, except for E. coli in Plot 2 (bare) 
where concentrations became and remained slightly higher at 620 cm than at 413 cm 
after approximately 20 min of simulation (Figure 4.9). Maximum relative 
concentrations also decreased with respect to distance in all plots, regardless of cover 
condition. This decrease in relative concentrations with respect to distance was 
caused by dilution (Roodsari, 2004; Roodsari et al., 2005), which increased as runoff 
volumes also increased at distances farther away from the source of slurry. 
 
 
Time (min)
0 10203040506070
E. c
o
l
i
, C
/
C
0
 (%)
0.001
0.01
0.1
1
10 At 413 cm 
At 620 cm 
 
Figure 4.9. Average relative concentrations of E. coli measured with time in runoff 
collected at 413 cm and at 620 cm away from the source of slurry in Plot 2 (bare) 
under dry initial soil moisture conditions. 
 
 
 79 
 
 
Note that there was substantial variability in pathogen concentrations and in 
runoff volumes observed at individual funnels (at 413 cm), especially in the vegetated 
plots (Appendix D), due to the heterogeneity in surface flow pathways arising from 
non uniform or rough surfaces (Roodsari, 2004; Roodsari et al., 2005). For example, 
no runoff was observed in funnel 1 in the vegetated Plot 3, as it is indicated in Figure 
3.3 (Section 3.1.5), until 40 min into the simulation, and no pathogens were ever 
detected in runoff samples collected from this funnel. This indicates that funnel 1 in 
Plot 3 was located on a more elevated area of the plot, which collected only locally 
generated runoff as opposed to runoff originating from the site of manure application. 
As Figures 4.5 through 4.8 indicate, relative concentrations of both pathogens 
in bare plots at both locations from the slurry were very similar, except for E. coli 
measured at 413 cm (Figure 4.5), in which relative concentrations in Plot 4 were 
substantially higher than in Plot 2 for the period after the initial 15 minutes until the 
end of simulation. In the vegetated plots, relative concentrations of both pathogens, at 
both locations from the slurry, were higher in Plot 3 than in Plot 1 throughout the 
simulations. Note that the two vegetated plots were not identical in terms of their 
slope, soil texture, and topographic conditions (Section 4.1.1). Runoff rates from Plot 
3 were substantially higher than from Plot 1 (Figure 4.1), resulting in higher relative 
concentrations of both pathogens in Plot 3 than in Plot 1 during the simulations. 
As Figures 4.5 to 4.8 indicate, relative concentrations of both pathogens in 
runoff decreased with respect to time at both distances from the source of slurry, 
especially in the bare plots. In the bare plots, there was a rapid decrease in relative 
concentrations within the first 15 min of simulations, while in the vegetated plots, 
 80 
 
 
relative concentrations typically increased to a peak several minutes after the 
initiation of runoff before decreasing. Relative concentrations in runoff from bare 
plots were initially substantially higher than from vegetated plots; however, after 
approximately 15 min of simulations, relative concentrations observed in runoff from 
vegetated plots were comparable or higher than in bare plots.  
Figures 4.10 and 4.11 illustrate the differences in runoff rates between the 
bare and vegetated plots. Data points situated below the 1:1 line represent samples 
collected during the first 12 to 15 min of simulations. Both figures show that the 
pathogens reached both locations in the bare plots earlier than in the vegetated plots, 
and in higher relative concentrations.  
 
 
Average Relative Concentrations from 
Bare Plots (%)
01234567
A
v
e
r
ag
e R
e
lative
 
C
o
nc
e
n
tr
atio
ns
 fro
m
Ve
ge
tate
d Plo
t
s
 
(
%
)
0
1
2
3
4
5
6
7
At 413 cm 
At 620 cm  
  1:1 line
 
Figure 4.10. Average relative concentrations of E. coli measured in runoff at 413 and 
620 cm from the source of slurry in vegetated vs. bare plots at identical time intervals 
during the experiments under dry initial soil moisture conditions. 
 
 81 
 
 
Average Relative Concentrations from
Bare Plots (%)
01234567
A
v
e
r
ag
e R
e
lative
 
C
o
nc
e
n
tr
atio
ns
 fro
m
Ve
ge
tate
d Plo
t
s
 
(
%
)
0
1
2
3
4
5
6
7
At 413 cm
At 620 cm
         1:1 line
 
Figure 4.11. Average relative concentrations of S. e. Typhimurium measured in 
runoff at 413 and 620 cm from the source of slurry in vegetated vs. bare plots at 
identical time intervals during the experiments under dry initial soil moisture 
conditions. 
 
 
 
After the first 15 min of simulations, an exponential decrease in relative 
pathogen concentrations was observed in both the vegetated and bare plots. This 
allowed for a comparison of pathogen reduction rates between bare and vegetated 
plots during this exponential phase. The changes in relative concentrations after 15 
min of simulations were modeled as a single exponential with constant background 
according to Equation 1. A constant background was included in the equation to 
account for the observation that relative concentrations decreased but leveled off 
towards the end of each simulation. Equation 1 is:  
 
 82 
 
 
C/C
0
 = ae
(-k*t)
 +b       (1) 
where, 
C/C
0
: percent (%) relative concentration at time t (in minutes) 
a: normalization term 
k: the time constant for the decrease or the ?rate? of reduction with time 
t: time t (in minutes) during the simulation 
b: the background relative concentration (in percent) that does not change with time 
 
Equation 1 was rearranged to incorporate only variables that were meaningful 
in describing the exponential decrease in relative concentrations with time after 15 
min of simulation. In addition, Equation 1 can be rearranged in terms of the relative 
concentrations at 15 min of simulation. This was done since this time point in the 
simulations was observed to be critical. It is the starting time when relative 
concentrations decreased exponentially with time for both bare and vegetated plots. 
Based on Equation 1, relative concentrations at 15 min of simulation can be described 
as follow:  
C/C
0(15)
 = ae
(-k*15)
 +b      (2) 
where, 
C/C
0(15) 
: percent (%) relative concentration at 15 min of simulation  
a: normalization term 
k: the time constant for the decrease or the ?rate? of reduction with time 
b: background relative concentration (in percent) that does not change with time 
 
 83 
 
 
Based on the ratio between Equations 1 and 2, exponential decrease of relative 
concentrations with time after 15 min of simulation in each plot may be described as:  
C/C
0
 = ae
(-k*t)
 + b            
C/C
0(15)
 = ae
(-k*15)
 + b    
Rearranging, the ratio results in, 
(C/C
0
) ? b = ae
(-k*t) 
(C/C
0(15)
) ? b = ae
(-k*15) 
and by cancelling variable ?a? , the ratio becomes, 
(C/C
0
) ? b = e
(-k*t) 
(C/C
0(15)
) ? b = e
(-k*15) 
and finally,  
(C/C
0
) ? b      = e
-k*(t-15) 
(C/C
0(15)
) ? b  
 
Therefore, the exponential decrease in relative concentrations after 15 min of 
simulation in each plot can be described as:  
C/C
0
 = [(C/C
0(15)
) ? b]e
-k*(t-15)
 + b
    
(3) 
where, 
C/C
0
: percent (%) relative concentration at time t (in minutes) 
C/C
0(15) 
: percent (%) relative concentration at t = 15 min 
b: the background relative concentration (in percent) that does not change with time  
k: the time constant for the decrease or the ?rate? of reduction with time 
t: point in time (in minutes) during the experiments 
 
 84 
 
 
 Table 4.1 shows the equations describing the exponential decrease in relative 
concentration after 15 min of simulation in each plot. High R
2
 values (>0.94) indicate 
a strong correlation between relative concentrations of both pathogens with time at 
different distances from the slurry application area, as represented by the exponential 
functions. 
An ANOVA for fixed effects was performed to test whether or not distance 
(413 cm or 620 cm) from the source of slurry or cover condition (bare or vegetated) 
had any significant effect on the parameters k, b, and C/C
0(15) 
of Equation 3. The 
statistical results, in the form of ANOVA tables, are presented in Tables 4.2 through 
4.7.  
 
 
 
Table 4.1. Equations relating relative concentrations (C/C
0
) of each pathogen in 
runoff to time t after 15 min of simulation at each distance in each plot under dry 
initial soil moisture conditions.   
E. coli 
PLOT Distance, cm Equation, C/C
0
, % R
2
   
Plot 1 (Vegetated) 413 0.17e
-0.19(t - 15)
 + 0.058 0.995
 620 0.40e
-0.80(t -15)
 + 0.032 0.982
  
Plot 3 (Vegetated) 413 0.68e
-0.08(t -15)
 + 0.004 0.948
 620 0.25e
-0.10(t -15)
 + 0.033 0.966
  
Plot 2 (Bare) 413 0.09e
-0.18(t - 15)
 + 0.014 0.988
 620 0.05e
-0.06(t -15)
 + 0.019 0.998
  
Plot 4 (Bare) 413 0.10e
-0.07(t -15)
 + 0.029 0.999
 620 0.04e
-0.10(t -15)
 + 0.027 0.997
 85 
 
 
Table 4.1 (Continued) 
S. e. Typhimurium 
PLOT Distance, cm Equation, C/C
0
, % R
2
   
Plot 1 (Vegetated) 413 0.05e
-0.09(t -15)
 + 0.006 0.995
 620 0.04e
-0.05(t -15)
 + 5.00E-11 0.958
  
Plot 3 (Vegetated) 413 0.49e
-0.10(t -15)
 + 8.36E-11 0.956
 620 0.41e
-0.08(t -15)
 + 7.07E-11 0.959
  
Plot 2 (Bare) 413 0.03e
-0.07(t -15)
 + 0.004 0.998
 620 0.01e
-0.07(t -15)
 + 0.004 0.996
  
Plot 4 (Bare) 413 0.04e
-0.13(t -15)
 + 0.009 0.999
 620 0.03e
-0.21(t -15)
 + 0.009 0.992
  
 
 
 
The ANOVA tables showing the effect of distance and cover on the observed 
reduction rates (k) of E. coli and S e. Typhimurium are shown, respectively, in Tables 
4.2 and 4.3.  According to these data, the rates (k) governing the reduction in relative 
concentrations of both pathogens in runoff, after 15 min of simulations, were not 
affected by distance (p = 0.49 for E. coli rates; and p = 0.87 for S. e. Typhimurium 
rates) or by cover condition (p = 0.34 for E. coli rates; and p = 0.34 for S. e. 
Typhimurium rates).  
 
 
 
 
 
 
 86 
 
 
Table 4.2. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed E. coli rates of reduction (k) obtained after 15 min of simulations 
under dry initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.0353115 0.03531153 0.551087 0.4912543
Cover 1 0.0726758 0.07267578 1.134209 0.3355831
Residuals 5 0.3203809 0.06407619   
             ?
Df = degrees of freedom 
 
Table 4.3. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed S. e. Typhimurium rates of reduction (k) obtained after 15 min of 
simulations under dry initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.00008845 0.000088445 0.031991 0.8650685
Cover 1 0.00301864 0.003018645 1.091868 0.3439203
Residuals 5 0.01382330 0.002764661   
             ?
Df = degrees of freedom 
 
Based on the study by Stout et al. (2005), it was not expected that k would be 
affected between the two locations from the slurry since a short distance separated 
them from each other. In the above study, although they did not investigate the effect 
of cover, they found that the rate of fecal coliform (FC) transport in runoff through 
vegetative filter strips was not affected by distance and the rates were similar at two 
locations (1 m apart from each other) from the source of dairy manure. However, if k 
observed after 15 min of simulations was to be affected by cover condition, this study 
could not verify such effect and further studies would have been necessary.  
The ANOVA tables showing the effect of distance and cover on the observed 
background levels (b) of E. coli and S e. Typhimurium are shown, respectively, in 
 87 
 
 
Tables 4.4 and 4.5. Data indicate that background levels (b) of both pathogens were 
not affected by distance (p = 0.91 for E. coli background levels; and p = 0.52 for S. e. 
Typhimurium background levels). Although Figures 4.5 to 4.8 suggest that b levels 
were higher in the vegetated plots than in the bare plots, particularly based on levels 
observed in Plot 3, Tables 4.4 and 4.5 indicate that b of both pathogens were not 
affected by cover condition (p = 0.47 for E. coli levels; and p = 0.06 for S. e. 
Typhimurium levels).  
 
 
Table 4.4. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed E. coli background levels (b) obtained after 15 min of simulations 
under dry initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.000004651 0.0000046513 0.0146380 0.9084125
Cover 1 0.000191101 0.0001911013 0.6014178 0.4731029
Residuals 5 0.001588756 0.0003177512   
             ?
Df = degrees of freedom 
 
Table 4.5. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed S. e. Typhimurium background levels (b) obtained after 15 min of 
simulations under dry initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.000004205 0.000004205 0.471518 0.5228319
Cover 1 0.000051005 0.000051005 5.719332 0.0622711
Residuals 5 0.000044590 0.000008918   
             ?
Df = degrees of freedom 
 
 88 
 
 
The ANOVA tables showing the effect of distance and cover on observed 
relative concentrations of E. coli and S e. Typhimurium at 15 min of simulations 
(C/C
0(15)
) are shown, respectively, in Tables 4.6 and 4.7. Data indicate that relative 
concentrations of both pathogens at 15 min of simulation (C/C
0(15)
) were not affected 
by distance (p = 0.52 for E. coli relative concentrations; and p = 0.83 for S. e. 
Typhimurium relative concentrations). However, Table 4.6 indicates that E. coli 
relative concentrations at 15 min of simulations (C/C
0(15)
) were affected by cover 
condition (p = 0.03). According to a standard Two-Sample t-Test, the mean of E. coli 
relative concentrations at 15 min was significantly higher in the vegetated plots 
(0.41%) than in the bare plots (0.09%), with p = 0.02. On the other hand, Table 4.7 
indicates that S. e. Typhimurium concentrations were not affected by cover condition 
(p = 0.16), despite the fact that Figures 4.6 and 4.8 suggest that concentrations of this 
pathogen were higher in the vegetated plots than in the bare plots, particularly based 
on what was observed in Plot 3.  
 
 
Table 4.6. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on relative concentrations of E. coli observed at 15 min of simulation (C/C
0(15)
) in the 
experiments under dry initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.0114955 0.0114955 0.485467 0.5170126
Cover 1 0.2008928 0.2008928 8.483881 0.0332946
Residuals 5 0.1183968 0.0236794   
             ?
Df = degrees of freedom 
 
 89 
 
 
Table 4.7. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on relative concentrations of S. e. Typhimurium observed at 15 min of simulation 
(C/C
0(15)
) in the experiments under dry initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.0017162 0.00171621 0.051004 0.8302674
Cover 1 0.0911120 0.09111201 2.707726 0.1607822
Residuals 5 0.1682445 0.03364890   
             ?
Df = degrees of freedom 
 
In addition to statistical analyses using ANOVA for fixed effects, standard 
Two-Sample t-Tests were performed on the mean reduction rates (k) of both 
pathogens to test whether or not the means were significantly different between the 
two treatments (bare and vegetated). The standard Two-Sample t-Test analysis 
indicated that the mean E. coli rate of reduction observed in the bare plots (0.10 min
-
1
) was not significantly different from the mean rate observed in the vegetated plots 
(0.29 min
-1
) (p = 0.31), regardless of distance away from the source of slurry. The 
same analysis indicated that the mean S. e. Typhimurium rate of reduction observed 
in the bare plots (0.12 min
-1
) was not significantly different from the mean rate 
observed in the vegetated plots (0.08 min
-1
) (p = 0.30), regardless of distance away 
from the source of slurry. 
Except for relative concentrations of E. coli observed at 15 min of simulation 
(C/C
0(15)
) ? which were found to be affected by cover condition and were 
significantly higher in the vegetated plots than in the bare plots ? this study could not 
verify that cover condition had a significant effect on parameters k and b describing 
the exponential decrease in relative concentrations of E. coli in surface runoff; or on 
 90 
 
 
the parameters k, b, and C/C
0(15) 
describing the exponential decrease in relative 
concentrations of S. e. Typhimurium in surface runoff. 
The inability to detect significant effects and/or significant differences 
between treatments, particularly on parameters b and C/C
0(15) 
, however, could be due 
to the limitations of the experimental design. The lysimeter site where simulations 
were conducted could accommodate only four plots that were reasonable in size for 
inclusion of spatial heterogeneity; thereby severely limiting the number of 
replications per treatment (two vegetated and two bare plots). One should note, 
however, that decreasing the size of experimental plots in order to obtain statistical 
replications often hinders the area scale of experiments, thus limiting inclusion of 
spatial heterogeneity effect in the observations. In addition, as previously described, 
the two vegetated plots differed in three important characteristics: slope, soil texture 
at 20 cm depth, and topography. This contributed to large within-treatment 
variability, hence diminishing the likelihood of detecting statistically significant 
differences.  
 
4.3.1.1 Relationship between relative concentrations of Escherichia coli and 
Salmonella enterica Typhimurium in surface runoff 
Linear regression analysis was performed between relative concentrations of 
E. coli vs. S. e. Typhimurium in surface runoff for each plot at each location. 
Correlation coefficient values (R) were determined based on Pearson correlation 
analysis, since it is appropriate for variables that are continuous and can be analyzed 
 91 
 
 
statistically at various levels of significance (p). In this study, the R values were 
analyzed against critical values representing three levels of p: 0.10, 0.05, and 0.01.  
Strong correlations were observed between relative concentrations of E. coli 
vs. S. e. Typhimurium in runoff from the bare plots (Figures 4.12 and 4.13) at both 
distances from the manure source (p = 0.01), indicating that both pathogens were 
transported similarly on a bare surface. Similar results have been reported by 
Roodsari (2004), who examined the surface transport of E. coli and S. cholerasuis 
from liquid swine manure on a 20%-sloped plot with a clay loam texture. He also 
found a strong correlation between the relative concentrations of these two pathogens 
in runoff from bare plots at various distances from the source of manure.   
 A strong correlation was observed between relative concentrations of E. coli 
vs. S. e. Typhimurium in runoff from both vegetated plots (Figures 4.14 and 4.15) at 
413 cm (p = 0.01). A strong correlation was also observed between relative 
concentrations of E. coli vs. S. e. Typhimurium at 620 cm in Plot 3 (p = 0.01); 
however, this strong correlation was not observed at 620 cm in Plot 1 (p > 0.10). It is 
unclear why the transport of E. coli vs. S. e. Typhimurium in Plot 1 was not 
comparable at 620 cm. The pathogens are essentially identical with respect to size, 
shape, and density. Note, however, that there can be differences among these genera 
(and between strains within the genera), with respect to the presence of surface 
appendages (e.g., fimbria, pili) (Ofek et al., 2003). 
 
 92 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 2 Regr
              1:1 line
 
 
 
 
S
413 cm
 = -0.15 + 1.09E
413 cm
       R = 1.000
***
S
620 cm
 = -0.34 + 1.21E
620 cm
       R = 0.999
***
S
overall
 = -0.29 + 1.11E
overall
        R = 0.995
***
 
 
 
 
 
 
 
 
Figure 4.12. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) from experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01.  
 93 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 4 Regr
1:1 line
 
 
 
 
S
413 cm
 = 0.06 + 1.49E
413 cm
       R = 1.000
***
S
620 cm
 = 0.10 + 1.37E
620 cm
       R = 0.987
***
S
overall
 = 0.07 + 1.41E
overall
        R = 0.990
***
 
 
 
 
 
 
 
 
Figure 4.13. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01.  
 94 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
At 413 cm
At 620 cm
Plot 1 Regr
         1:1 line
 
 
 
 
     S
413 cm
 = -0.03 + 1.57E
413 cm
       R = 0.936
***
S
620 cm
 = -0.33 + 1.11E
620 cm
       R = 0.482 
S
overall
 = -0.73 + 0.89E
overall
        R = 0.724
***
 
 
 
 
 
 
 
 
Figure 4.14. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01.  
 95 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
At 413 cm
At 620 cm
Plot 3 Regr
  1:1 line
 
 
 
 
S
413 cm
 = -0.04 + 1.26E
413 cm
      R = 0.960
***
S
620 cm
 = 0.60 + 1.56E
620 cm
        R = 0.984
***
S
overall
 = 0.05 + 1.19E
overall
         R = 0.869
***
 
 
 
 
 
 
 
 
Figure 4.15. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01.  
 96 
 
 
4.3.1.2 Relationship between relative concentrations of each surrogate pathogen and 
Bromide in surface runoff 
Linear regression analysis was performed between relative concentrations of 
each pathogen and Bromide (Br) in surface runoff for each plot at each location. 
Correlation coefficient values (R) of the relationships were determined based on 
Pearson correlation analysis since it is appropriate for variables that are continuous to 
be analyzed statistically at various levels of significance (p). In this study, the R 
values were analyzed against critical values representing three levels of p: 0.10, 0.05, 
and 0.01. 
 Strong correlations were observed between relative concentrations of each 
pathogen and Br in runoff from the bare plots (Figures 4.16 to 4.19) at both distances 
from the source of slurry (p = 0.01). Since Br is inert and does not sorb to organic 
matter or soil particles (Walton et al., 2000), these results indicate that the bare plots 
offered little resistance to the transport of either pathogen. In addition, these results 
indicate that pathogens were either predominantly in the liquid phase of the swine 
slurry or that the manure solids (including pathogens) consisted of suspended 
colloids, which were all transported at similar rates.  
This is consistent with previous studies indicating that bacterial cells attached 
to particles during runoff can be very low (Davies and Bavor, 2000; Borst and 
Selvakumar, 2003; Muirhead et al., 2005). These results, however, are very different 
from those reported for bovine manure (Roodsari, 2004). In contrast to swine slurry 
(4% solids), bovine manure typically contains a higher solid content (10%), 
consisting of substantial amounts of residual dietary fiber. Consequently, a fraction of 
 97 
 
 
the bacteria must first ?release? from the manure solids before they can be transported 
(Guber et al., 2006). 
Figures 4.16 through 4.19 show that, in general, relative concentrations of Br 
in surface runoff from bare plots were higher than for pathogens (data points appear 
mostly below the 1:1 line). Note that only pathogens in the aqueous phase of runoff 
were measured; pathogens attached to soil particles in runoff were not measured. 
These results may suggest that some fraction of pathogens were attached to sediment 
in runoff, resulting in higher relative Br concentrations. 
Strong correlations were observed between the relative concentrations of each 
pathogen and Br in runoff from the vegetated plots (Figures 4.20 to 4.23) at 413 cm 
(p = 0.01). However, correlations observed at 620 cm differed between the two 
pathogens and between the two vegetated plots. In Plot 1, a significant correlation 
between relative concentrations of S. e. Typhimurium and Br was observed (p = 
0.05), but not between E. coli and Br (p > 0.10). In Plot 3, the relative concentrations 
of each pathogen and Br were only weakly correlated (p = 0.10). Figures 4.20 through 
4.23 also show that the relative concentrations of Br in surface runoff were 
consistently higher than for pathogens (data points appear mostly below the 1:1 line). 
This phenomenon was particularly pronounced in Plot 3. Collectively, these results 
indicate that the mechanisms by which vegetated surfaces affect pathogen transport 
are different than for Br. 
Note that runoff did not initiate in vegetated plots until 10-13 minutes after the 
start of rainfall simulations and that greater infiltration was subsequently observed in 
Plot 1 than in Plot 3 (Figure 4.1). Lower cumulative recoveries of Br from Plot 1 than 
 98 
 
 
from Plot 3 (see Table 4.15) are consistent with greater infiltration. However, after 
the initiation of runoff, substantially higher relative Br concentrations were observed 
in runoff than relative pathogen concentrations (particularly in Plot 3). Since it is 
unlikely that infiltration rates for pathogens were higher than for Br, these data 
suggest that pathogens were selectively retained in the vegetated plots, presumably as 
a result of sorption to plant litter/organic matter. As previously described in Chapter 
3, Section 3.2.1, vegetation/litter densities, as well as decomposing organic matter, 
were likely higher in Plot 1 than in Plot 3. Consequently, increased sorption of 
pathogens was likely responsible for lower relative pathogen concentrations in Plot 1, 
as well as the lower cumulative recovery of pathogens (Table 4.15). 
 
 99 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 2 Regr
             1:1 line
 
 
 
 
E
413 cm
 = -0.43 + 0.97Br
413 cm
       R = 1.000
***
E
620 cm
 = -0.24 + 0.83Br
620 cm
       R = 0.996
***
E
overall
 = -0.36 + 0.86Br
overall
        R = 0.996
***
 
 
 
 
 
Figure 4.16. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 100 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 2 Regr
        1:1 line
 
 
 
 
S
413 cm
 = -0.60 + 1.07Br
413 cm
       R = 1.000
***
S
620 cm
 = -0.61 + 1.01Br
620 cm
       R = 0.999
***
S
overall
 = -0.60 + 1.04Br
overall
        R = 1.000
***
 
 
 
 
 
Figure 4.17. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under 
dry initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 101 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 4 Regr
        1:1 line
 
 
 
 
E
413 cm
 = -0.30 + 0.72Br
413 cm
       R = 1.000
***
E
620 cm
 = -0.31 + 0.69Br
620 cm
       R = 0.962
***
E
overall
 = -0.31 + 0.70Br
overall
        R = 0.965
***
 
 
 
 
 
Figure 4.18. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 102 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 4 Regr
        1:1 line
 
 
 
 
S
413 cm
 = -0.38 + 1.09Br
413 cm
       R = 1.000
***
S
620 cm
 = -0.32 + 0.95Br
620 cm
       R = 0.992
***
S
overall
 = -0.37 + 0.99Br
overall
        R = 0.988
***
 
 
 
 
 
Figure 4.19. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under 
dry initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 103 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 1 Regr
    1:1 line
 
 
 
 
E
413 cm
 = -0.78 + 0.86Br
413 cm
       R = 0.950
***
E
620 cm
 = -1.59 ? 0.17Br
620 cm
       R = -0.191 
E
overall
 = -1.01 + 0.43Br
overall
        R = 0.822
***
 
 
 
 
 
Figure 4.20. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 104 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 1 Regr
     1:1 line
 
 
 
 
S
413 cm
 = -1.21 + 1.48Br
413 cm
       R = 0.964
***
S
620 cm
 = -1.43 + 0.58Br
620 cm
       R = 0.682
**
S
overall
 = -1.51 + 0.56Br
overall
        R = 0.753
***
 
 
 
 
Figure 4.21. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment 
under dry initial soil moisture conditions. 
***
Statistically significant at p = 0.01;
 
**
Statistically significant at p = 0.05.  
 105 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 3 Regr
      1:1 line
 
 
 
 
E
413 cm
 = -0.28 + 1.22Br
413 cm
       R = 0.994
***
E
620 cm
 = -0.76 + 0.46Br
620 cm
       R = 0.637
*
E
overall
 = -0.45 + 0.89Br
overall
        R = 0.944
***
 
 
 
 
Figure 4.22. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment under dry 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01; 
*
Statistically 
significant at p = 0.10. 
 106 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 3 Regr
        1:1 line
 
 
 
 
S
413 cm
 = -0.42 + 1.49Br
413 cm
       R = 0.929
***
S
620 cm
 = -0.67 + 0.62Br
620 cm
       R = 0.653
*
S
overall
 = -0.54 + 0.96Br
overall
        R = 0.803
***
 
 
 
 
Figure 4.23. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment 
under dry initial soil moisture conditions. 
***
Statistically significant at p = 0.01; 
*
Statistically significant at p = 0.10. 
 
 107 
 
 
4.3.2 Wet initial soil moisture conditions 
Figures 4.24 and 4.25 show average relative concentrations of Escherichia 
coli and Salmonella enterica Typhimurium, respectively, measured with time in 
surface runoff collected at 413 cm downslope from the source of slurry application in 
all plots. Figures 4.26 and 4.27 show average relative concentrations of E. coli and S. 
e. Typhimurium, respectively, measured with time in surface runoff collected at 620 
cm downslope from the source of slurry application in all plots.  
In all plots, relative concentrations of both pathogens in runoff decreased as 
the distance from the source of slurry increased, particularly in the vegetated plots. 
This decrease in relative concentrations with respect to distance was caused by 
dilution (Roodsari, 2004; Roodsari et al., 2005), which increased as runoff volumes 
also increased at distances farther away from the source of slurry.  
Note that there was substantial variability in pathogen concentrations and in 
runoff volumes observed at individual funnels (at 413 cm), especially in the vegetated 
plots (Appendix E), due to the heterogeneity in surface flow pathways arising from 
non uniform or rough surfaces (Roodsari, 2004; Roodsari et al., 2005). For example, 
no runoff was observed in funnel 2 in the vegetated Plot 1, as it is indicated in Figure 
3.3 (Section 3.1.5), until 8 min into the simulation, indicating that this funnel was 
located on a more elevated area of the plot.  
 
 108 
 
 
Time (min)
0 10203040506070
E. coli
, C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated
Plot 2 - Bare 
Plot 3 - Vegetated 
Plot 4 - Bare 
 
Figure 4.24. Average relative concentrations of E. coli measured with time in runoff 
collected at 413 cm downslope from the source of slurry application in all plots under 
wet initial soil moisture conditions. E. coli was not detected in first runoff samples (t 
= 6 min) from the vegetated plots.  
 
 
Time (min)
0 10203040506070
S. e.
 Ty
phi
muriu
m
,
 C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated
Plot 2 - Bare
Plot 3 - Vegetated
Plot 4 - Bare
 
Figure 4.25. Average relative concentrations of S. e. Typhimurium measured with 
time in runoff collected at 413 cm downslope from the source of slurry application in 
all plots under wet initial soil moisture conditions. S. e. Typhimurium was not 
detected in first runoff samples (t = 6 min) from the vegetated plots.  
 109 
 
 
Time (min)
0 10203040506070
E. coli
, C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated
Plot 2 - Bare
Plot 3 - Vegetated
Plot 4 - Bare
 
Figure 4.26. Average relative concentrations of E. coli measured with time in runoff 
collected at 620 cm downslope from the source of slurry application in all plots under 
wet initial soil moisture conditions. E. coli was not detected in the first three runoff 
samples (t = 6, t = 8, and t = 10 min) from Plot 1, and in the first runoff sample (t = 6 
min) from Plot 3.  
Time (min)
0 10203040506070
S. e.
 Ty
phi
muriu
m
,
 C/C
0
 (%)
0.001
0.01
0.1
1
10
Plot 1 - Vegetated 
Plot 2 - Bare
Plot 3 - Vegetated
Plot 4 - Bare
 
Figure 4.27. Average relative concentrations of S. e. Typhimurium measured with 
time in runoff collected at 620 cm downslope from the source of slurry application in 
all plots under wet initial soil moisture conditions. S. e. Typhimurium was not 
detected in the first three runoff samples (t = 6, t = 8, and t = 10 min) from Plot 1, and 
in the first runoff sample (t = 6 min) from Plot 3.  
 110 
 
 
In addition, as explained in Figures 4.24 through 4.27, no pathogens were 
detected in several early runoff samples collected from the vegetated plots (Plots 1 
and 3) at both distances from the source of slurry, indicating that early runoff was 
only locally generated and did not yet contain the slurry. This trend was more 
pronounced in funnel 1 in the vegetated Plot 3, as it is indicated in Figure 3.3 (Section 
3.1.5), where no pathogens were ever detected throughout the simulation, indicating 
that this funnel collected only locally generated runoff as opposed to runoff 
originating from the site of manure application. 
As Figures 4.24 and 4.26 indicate, relative concentrations of E. coli in each 
treatment at both locations from the slurry were very similar, except at 620 cm 
(Figure 4.26) in which relative concentrations in Plot 3 were substantially higher than 
in Plot 1 during the first 30 min of simulations despite the fact that runoff rates in 
these plots were very similar during the simulations (Figure 4.2).  
As Figures 4.25 and 4.27 indicate, relative concentrations of S. e. 
Typhimurium in bare plots at both locations from the slurry were higher in Plot 2 than 
in Plot 4 despite the fact that runoff rates in both of these plots were very similar 
during the simulations (Figure 4.2). This was also observed in the vegetated plots in 
which relative concentrations of S. e. Typhimurium at both locations from the slurry 
were higher in Plot 1 than in Plot 3 despite the fact that runoff rates in both of these 
plots were very similar during the simulations (Figure 4.2).  
As Figures 4.24 to 4.27 indicate, relative concentrations of both pathogens in 
runoff decreased with respect to time at both distances from the source of slurry, 
especially in the bare plots. In the bare plots, there was a rapid decrease in relative 
 111 
 
 
concentrations within the first 15 min of simulations, while in the vegetated plots 
relative concentrations typically increased to a peak several minutes after the 
initiation of runoff before decreasing. This trend was even more pronounced for E. 
coli measured at 620 cm in the vegetated Plot 1 in which relative concentrations were 
observed to increase for the first 30 min of simulation before reaching a peak and 
start to decrease.  
Similar to what was observed in the simulations under dry conditions (Section 
4.3.1), relative concentrations in runoff from bare plots were initially substantially 
higher than from vegetated plots; however, after approximately 15 min of 
simulations, relative concentrations observed in runoff from vegetated plots were 
comparable or higher than in bare plots. This may be attributed to the fact that most 
of the pathogens leave bare plots rapidly during initial portion of simulation, but 
movement of pathogens in vegetated filters is slow. 
Figures 4.28 and 4.29 illustrate the differences in runoff rates between the 
bare and vegetated plots. Data points situated below the 1:1 line represent samples 
collected during the first 15 min of simulations. Both figures show that both 
pathogens reached both locations in the bare plots earlier than in the vegetated plots, 
and in higher relative concentrations.  
 
 112 
 
 
Average Relative Concentrations from
Bare Plots (%)
012345678910
A
v
e
r
ag
e R
e
lative
 
C
o
nc
e
n
tr
atio
ns
 fro
m
Ve
ge
tate
d Plo
t
s
 
(
%
)
0
1
2
3
4
5
6
7
8
9
10
At 413 cm 
At 620 cm
      
  1:1 line
 
Figure 4.28. Average relative concentrations of E. coli measured in runoff at 413 and 
620 cm from the source of slurry in vegetated vs. bare plots at identical time intervals 
during the experiments under wet initial soil moisture conditions. 
 
 
Average Relative Concentrations from
Bare Plots (%)
012345678910
Av
e
r
ag
e Re
lative
 
Co
nc
e
n
tr
atio
ns
 fro
m
Ve
ge
tate
d Plo
t
s
 
(
%
)
0
1
2
3
4
5
6
7
8
9
10
At 413 cm
At 620 cm
    1:1 line
 
Figure 4.29. Average relative concentrations of S. e. Typhimurium measured in 
runoff at 413 and 620 cm from the source of slurry in vegetated vs. bare plots at 
identical time intervals during the experiments under wet initial soil moisture 
conditions. 
 
 113 
 
 
Similar to what was observed in Section 4.3.1, after the first 15 min of 
simulations, an exponential decrease in relative pathogen concentrations was 
observed in both the vegetated and bare plots. This allowed for a comparison of 
pathogen reduction rates between bare and vegetated plots during this exponential 
phase. The changes in relative concentrations after 15 min of simulations were 
modeled as a single exponential with constant background according to Equation 3 
(Section 4.3.1), which was as follow: 
C/C
0
 = [(C/C
0(15)
) ? b]e
-k*(t-15)
 + b
    
(3) 
where, 
C/C
0
: percent (%) relative concentration at time t (in minutes) 
C/C
0(15)
: percent (%) relative concentration at t = 15 min 
b: the background concentration (in percent) that does not change with time  
k: the time constant for the decrease or the ?rate? of reduction with time 
t: point in time (in minutes) during the experiments 
 
 
 
Table 4.8 shows the equations describing the exponential decrease in relative 
concentration after 15 min of simulation in each plot. High R
2
 values (> 0.98) indicate 
a strong correlation between relative concentrations of both pathogens with time at 
different distances from the slurry application area, as represented by the exponential 
functions. 
 
 
 
 
 114 
 
 
 
Table 4.8. Equations relating relative concentrations (C/C
0
, %) of each pathogen in 
runoff to time t after 15 min of simulation at each distance in each plot under wet 
initial soil moisture conditions.   
E. coli 
PLOT Distance, cm Equation, C/C
0
, % R
2
   
Plot 1 (Vegetated) 413 1.84e
-0.11(t -15)
 + 0.173 0.998
 620
?
- -
   
Plot 3 (Vegetated) 413 0.66e
-0.06(t -15)
 + 0.017 0.989
 620 0.67e
-0.09(t -15)
 + 0.014 0.981
  
Plot 2 (Bare) 413 0.16e
-0.18(t -15)
 + 0.035 0.996
 620 0.10e
-0.05(t -15)
 + 0.007 0.999
  
Plot 4 (Bare) 413 0.09e
-0.15(t -15)
 + 0.042 0.995
 620 0.06e
-0.09(t -15)
 + 0.015 0.999
S. e. Typhimurium 
PLOT Distance, cm Equation, C/C
0
, % R
2
   
Plot 1 (Vegetated) 413 1.73e
-0.08(t -15)
 + 0.052 0.988
 620 0.62e
-0.08(t -15)
 + 0.027 0.982
  
Plot 3 (Vegetated) 413 0.45e
-0.08(t -15)
 + 0.008 0.981
 620 0.28e
-0.11(t -15)
 + 0.016 0.989
  
Plot 2 (Bare) 413 0.20e
-0.18(t -15)
 + 0.038 0.991
 620 0.12e
-0.11(t -15)
 + 0.017 0.997
  
Plot 4 (Bare) 413 0.04e
-0.09(t -15)
 + 0.007 0.998
 620 0.02e
-0.11(t -15)
 + 0.004 0.997
  
?
An exponential decrease in relative concentrations of E. coli was not observed at 620 
cm in Plot 1 after 15 min of simulation. 
 
 
 115 
 
 
An ANOVA for fixed effects was performed to test whether or not distance 
(413 cm or 620 cm) from the source of slurry or cover condition (bare or vegetated) 
had any significant effect on the parameters k, b, and C/C
0(15) 
of Equation 3. The 
statistical results, in the form of ANOVA tables, are presented in Tables 4.9 through 
4.14.  
The ANOVA tables showing the effect of distance and cover on the observed 
reduction rates (k) of E. coli and S e. Typhimurium are shown, respectively, in Tables 
4.9 and 4.10. Data show that the rates (k) governing the reduction in relative 
concentrations of both pathogens in runoff after 15 min of simulations were not 
affected by distance (p = 0.19 for E. coli rates; and p = 0.86 for S. e. Typhimurium 
rates) or by cover condition (p = 0.23 for E. coli rates; and p = 0.17 for S. e. 
Typhimurium rates).  
 
 
 
 
 
Table 4.9. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed E. coli rates of reduction (k) obtained after 15 min of simulation 
under wet initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.003676220 0.003676220 2.460191 0.1918415
Cover 1 0.002975104 0.002975104 1.990992 0.2310691
Residuals 4 0.005977130 0.001494282   
             ?
Df = degrees of freedom 
 
 
 
 
 
 116 
 
 
Table 4.10. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed S. e. Typhimurium rates of reduction (k) obtained after 15 min of 
simulations under wet initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.000032401 0.000032401 0.033458 0.8620500
Cover 1 0.002481601 0.002481601 2.562544 0.1703208
Residuals 5 0.004842066 0.000968413   
             ?
Df = degrees of freedom 
 
Based on the study by Stout et al. (2005), it was not expected that k would be 
affected between the two locations from the slurry since a short distance separated 
them from each other. In the above study, although they did not investigate the effect 
of cover, they found that the rate of fecal coliform (FC) transport in runoff through 
vegetated filter strips was not affected by distance and the rates were similar at two 
locations (1 m apart from each other) from the source of dairy manure. However, if k 
observed after 15 min of simulations was to be affected by cover condition, this study 
could not verify such effect and further studies would have been necessary. 
The ANOVA tables showing the effect of distance and cover on the observed 
background levels (b) of E. coli and S e. Typhimurium are shown, respectively, in 
Tables 4.11 and 4.12. Results indicate that background levels (b) of both pathogens 
were not affected by distance (p = 0.28 for E. coli background levels; and p = 0.46 for 
S. e. Typhimurium background levels). Although Figures 4.23 to 4.26 suggest that b 
levels were higher in the vegetated plots than in the bare plots, particularly based on 
levels observed in Plot 1, Tables 4.11 and 4.12 indicate that b was not affected by 
cover condition (p = 0.47 for E. coli levels; and p = 0.49 for S. e. Typhimurium 
levels).  
 117 
 
 
Table 4.11. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed E. coli background levels (b) obtained after 15 min of simulations 
under wet initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.00508096 0.00508096 1.522149 0.2848315
Cover 1 0.00211108 0.00211108 0.632435 0.4709862
Residuals 4 0.01335207 0.003338018   
             ?
Df = degrees of freedom 
 
Table 4.12. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on the observed S. e. Typhimurium background levels (b) obtained after 15 min of 
simulations under wet initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.000205031 0.000205031 0.6278301 0.4640700
Cover 1 0.000181451 0.000181451 0.5556253 0.4895638
Residuals 5 0.001632856 0.0003265712   
             ?
Df = degrees of freedom 
 
The ANOVA tables showing the effect of distance and cover on observed 
relative concentrations of E. coli and S e. Typhimurium at 15 min of simulations 
(C/C
0(15)
) are shown, respectively, in Tables 4.13 and 4.14. Results indicate that 
relative concentrations of both pathogens at 15 min of simulation (C/C
0(15)
) were not 
affected by distance (p = 0.29 for E. coli relative concentrations; and p = 0.34 for S. e. 
Typhimurium relative concentrations). Although Figures 4.23 to 4.26 suggest that 
C/C
0(15) 
were higher in the vegetated plots than in the bare plots, particular to what 
was observed at 413 cm, Tables 4.13 and 4.14 indicate that C/C
0(15) 
 were not affected 
by cover condition (p = 0.07 for E. coli relative concentrations; and p = 0.10 for S. e. 
Typhimurium relative concentrations).  
 
 118 
 
 
Table 4.13. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on relative concentrations of E. coli observed at 15 min of simulations (C/C
0(15)
) in 
the experiments under wet initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.371595 0.371595 1.457397 0.2938486
Cover 1 1.497262 1.497262 5.872274 0.0725057
Residuals 4 1.019886 0.254971   
             ?
Df = degrees of freedom 
 
Table 4.14. ANOVA table for the effect of distance and cover (at P value set at 0.05) 
on relative concentrations of S. e. Typhimurium observed at 15 min of simulations 
(C/C
0(15)
) in the experiments under wet initial soil moisture conditions. 
Source of Variation Df
?
Sum of Squares Mean Square F Value Pr (F) 
Distance 1 0.256099 0.256099 1.129364 0.3365205
Cover 1 0.940481 0.940481 4.147403 0.0972996
Residuals 5 1.133819 0.2267639   
             ?
Df = degrees of freedom 
 
In addition to statistical analyses using ANOVA for fixed effects, standard 
Two-Sample t-Tests were performed on the mean reduction rates (k) of both 
pathogens to test whether or not the means were significantly different between the 
two treatments (bare and vegetated). The standard Two-Sample t-Test analysis 
indicated that the mean E. coli rate of reduction observed in the bare plots (0.12 min
-
1
) was not significantly different from the mean rate observed in the vegetated plots 
(0.08 min
-1
) (p = 0.39), regardless of distance away from the source of slurry. The 
same analysis indicated that the mean S. e. Typhimurium rate of reduction observed 
in the bare plots (0.12 min
-1
) was not significantly different from the mean rate 
observed in the vegetated plots (0.09 min
-1
) (p = 0.13), regardless of distance away 
from the source of slurry. 
 119 
 
 
This study could not verify that cover condition had a significant effect on the 
parameters k, b, and C/C
0(15)
 describing the exponential decrease in relative 
concentrations of both pathogens in surface runoff observed under wet initial soil 
moisture conditions. The same above statistical results were also obtained under dry 
conditions (Section 4.3.1), except for relative concentrations of E. coli observed at 15 
min of simulation (C/C
0(15)
), which were found to be affected by cover condition and 
were significantly higher in the vegetated plots than in the bare plots when subjected 
to dry conditions. 
As previously discussed, the inability to detect significant effects and/or 
significant differences between treatments, particularly on parameters b and C/C
0(15) 
, 
however, could be due to the limitations of the experimental design. The lysimeter 
site where simulations were conducted could accommodate only four plots that were 
reasonable in size for inclusion of spatial heterogeneity; thereby severely limiting the 
number of replications per treatment (two vegetated and two bare plots). One should 
note, however, that decreasing the size of experimental plots in order to obtain 
statistical replications often hinders the area scale of experiments, thus limiting 
inclusion of spatial heterogeneity effect in the observations. In addition, as previously 
described, the two vegetated plots differed in three important characteristics: slope, 
soil texture at 20 cm depth, and topography. This contributed to large within-
treatment variability, hence diminishing the likelihood of detecting statistically 
significant differences.  
 
 
 120 
 
 
4.3.2.1 Relationship between relative concentrations of Escherichia coli and 
Salmonella enterica Typhimurium in surface runoff 
Linear regression analysis was performed between relative concentrations of 
E. coli vs. S. e. Typhimurium in surface runoff for each plot at each location. 
Correlation coefficient values (R) were determined based on Pearson correlation 
analysis since it is appropriate for variables that are continuous and can be analyzed 
statistically at various levels of significance (p). In this study, the R values were 
analyzed against critical values representing three levels of p: 0.10, 0.05, and 0.01.  
 Strong correlations were observed between relative concentrations of E. coli 
vs. S. e. Typhimurium in runoff from the bare plots (Figures 4.30 and 4.31) at both 
distances from the manure source (p = 0.01), indicating that both pathogens were 
transported similarly on a bare surface. These results are consistent with the ones 
obtained under dry initial soil moisture conditions (Section 4.3.1.1), indicating that 
initial groundwater table levels may not affect the transport pattern between these 
pathogens on bare surfaces.  
A strong correlation was also observed between relative concentrations of E. 
coli vs. S. e. Typhimurium in runoff from both vegetated plots (Figures 4.32 and 
4.33) at 413 cm (p = 0.01). Similar strong correlation was also observed between 
relative concentrations of E. coli vs. S. e. Typhimurium at 620 cm in Plot 3 (p = 
0.01); however, such a strong correlation was not observed at 620 cm in Plot 1 (p > 
0.10). These results are consistent with the ones obtained under dry initial soil 
moisture conditions (Section 4.3.1.1), indicating that initial groundwater table levels 
may not affect the transport pattern between these pathogens on vegetated surfaces.  
 121 
 
 
As previously discussed, it is unclear why the transport of E. coli vs. S. e. 
Typhimurium in Plot 1 was not comparable at 620 cm. The pathogens are essentially 
identical with respect to size, shape, and density. Note, however, that there can be 
differences among these genera (and between strains within the genera), with respect 
to the presence of surface appendages (e.g., fimbria, pili) (Ofek et al., 2003). 
 
 122 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1 10 100
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 2 Regr
 1:1 line
 
 
 
 
S
413 cm
 = 0.08 + 1.02E
413 cm
       R = 0.973
***
S
620 cm
 = 0.06 + 1.11E
620 cm
       R = 0.996
***
S
overall
 = 0.06 + 1.07E
overall
        R = 0.970
***
 
 
 
 
 
 
 
 
Figure 4.30. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01.  
 123 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1 10 100
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 4 Regr
      1:1 line
 
 
 
 
S
413 cm
 = -0.33 + 1.10E
413 cm
       R = 0.998
***
S
620 cm
 = -0.24 + 1.21E
620 cm
       R = 0.997
***
S
overall
 = -0.30 + 1.14E
overall
        R = 0.997
***
 
 
 
 
 
 
 
 
 
Figure 4.31. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 124 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 1 Regr
    1:1 line
 
 
 
 
S
413 cm
 = -0.08 + 0.70E
413 cm
       R = 0.981
***
S
620 cm
 = -1.05 ? 0.22E
620 cm
       R = 0.108 
S
overall
 = -0.13 + 0.56E
overall
        R
 
= 0.929
***
 
 
 
 
 
Figure 4.32. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 125 
 
 
E. coli, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 3 Regr
      1:1 line
 
 
 
 
S
413 cm
 = -0.13 + 1.36E
413 cm
       R = 0.914
***
S
620 cm
 = -0.44 + 0.93E
620 cm
       R = 0.988
***
S
overall
 = -0.30 + 1.10E
overall
        R = 0.895
***
 
 
 
 
 
Figure 4.33. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to E. coli 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 126 
 
 
4.3.2.2 Relationship between relative concentrations of each surrogate pathogen and 
Bromide in surface runoff 
Linear regression analysis was performed between relative concentrations of 
each pathogen vs. Bromide (Br) in surface runoff for each plot at each location. 
Correlation coefficient values (R) were determined based on Pearson correlation 
analysis, since it is appropriate for variables that are continuous and can be analyzed 
statistically at various levels of significance (p). In this study, the R values were 
analyzed against critical values representing three levels of p: 0.10, 0.05, and 0.01. 
 Strong correlations were observed between the relative concentrations of each 
pathogen vs. Br in runoff from the bare plots (Figures 4.34 to 4.37) at both distances 
from the source of slurry (p = 0.01). These results are consistent with the ones 
obtained under dry initial soil moisture conditions (Section 4.3.1.2), indicating that 
initial groundwater table levels may not affect the transport pattern between each of 
these pathogens and Br on bare surfaces.  
As previously discussed (Section 4.3.1.2), since Br is inert and does not sorb 
to organic matter or soil particles (Walton et al., 2000), these results indicate that the 
bare plots offered little resistance to the transport of either pathogen. In addition, 
these results indicate that pathogens were either predominantly in the liquid phase of 
the swine slurry, or that the manure solids (including pathogens) consisted of 
suspended colloids, which were all transported at similar rates. This is consistent with 
previous studies indicating that bacterial cells attached to particles during runoff can 
be very low (Davies and Bavor, 2000; Borst and Selvakumar, 2003; Muirhead et al., 
2005). These results, however, are very different from those reported for bovine 
manure (Roodsari, 2004). In contrast to swine slurry (4% solids), bovine manure 
 127 
 
 
typically contains a higher solid content (10%), consisting of substantial amounts of 
residual dietary fiber. Consequently, a fraction of the bacteria must first ?release? 
from the manure solids before they can be transported (Guber et al., 2006). 
Figures 4.34 through 4.37 show that, in general, relative concentrations of Br 
in surface runoff from bare plots were higher than for pathogens (data points appear 
mostly below the 1:1 line). Note that only pathogens in the aqueous phase of runoff 
were measured; pathogens attached to soil particles in runoff were not measured. 
These results then suggest that some fraction of pathogens were attached to sediment 
in runoff, resulting in higher relative Br concentrations.  
Strong correlations were observed between the relative concentrations of each 
pathogen and Br in runoff from the vegetated plots (Figures 4.38 to 4.41) at 413 cm 
(p = 0.01). However, and similar to what was observed under dry conditions, 
correlations observed at 620 cm differed between the two pathogens and between the 
two vegetated plots. In Plot 1, a strong correlation between the relative concentrations 
of S. e. Typhimurium and Br was observed (p = 0.01), but not between E. coli and Br 
(p > 0.10). In Plot 3, the relative concentrations of each pathogen and Br were 
strongly correlated (p = 0.01). Figures 4.38 through 4.41 also show that the relative 
concentrations of Br in surface runoff were consistently higher than for pathogens 
(data points appear mostly below the 1:1 line). Collectively, these results indicate that 
the mechanism by which vegetated surfaces affect pathogen transport are different 
than for Br. 
Note that runoff did not initiate in the vegetated plots until 3-4 minutes after 
the start of rainfall simulations and that low infiltration was subsequently observed in 
 128 
 
 
these plots (Figure 4.2). High cumulative recoveries of Br from the vegetated plots 
(see Table 4.17) are consistent with low infiltration. However, after the initiation of 
runoff, substantially higher relative Br concentrations were observed in runoff from 
vegetated plots than relative pathogen concentrations. Since it is unlikely that 
infiltration rates for pathogens were higher than for Br, these data suggest that 
pathogens were selectively retained in the vegetated plots, presumably as a result of 
sorption to plant litter/organic matter. 
The primary difference between wet and dry initial soil moisture conditions 
was in vegetated plots where some correlations observed at 620 cm became stronger 
under wet conditions. This was particularly pronounced in Plot 3 where pathogen vs. 
Br correlations (p = 0.10) observed under dry conditions (Figures 4.22 and 4.23) 
became stronger (p = 0.01) under wet conditions (Figures 4.40 and 4.41).  In Plot 1, 
this was observed only between S. e. Typhimurium and Br where a weak correlation 
(p = 0.05) observed under dry conditions (Figure 4.21) became stronger (p = 0.01) 
under wet conditions (Figure 4.39); but not between E. coli and Br where a significant 
correlation was never observed at 620 cm (p > 0.10). These results suggest that the 
increase in runoff volumes under wet conditions (Figure 4.2) may have offset some 
retention mechanisms in the vegetated plots, but such process had less of an effect for 
E. coli in Plot 1. Therefore, the primary retention mechanism in Plot 1 may have been 
adsorption by litter and/or vegetation, whereas in Plot 3 it may have been infiltration.  
Another possibility is that external sources of E. coli, such as from wildlife fecal 
material (birds, foxes), were more present on Plot 1 and that caused relative 
concentrations of E. coli to oscillate, and, thus, correlations with Br to be lost. 
 129 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 2 Regr
    1:1 line
 
 
 
 
E
413 cm
 = -0.27 + 0.84Br
413 cm
       R = 0.983
***
E
620 cm
 = -0.35 + 0.72Br
620 cm
       R = 0.999
***
E
overall
 = -0.30 + 0.78Br
overall
        R = 0.972
***
 
 
 
 
 
 
Figure 4.34. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 130 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 2 Regr
   1:1 line
 
 
 
 
S
413 cm
 = -0.20 + 0.85Br
413 cm
       R = 0.993
***
S
620 cm
 = -0.28 + 0.88Br
620 cm
       R = 0.998
***
S
overall
 = -0.24 + 0.87Br
overall
        R = 0.995
***
 
 
 
 
 
 
Figure 4.35. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 2 (bare) for experiment under 
wet initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 131 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 4 Regr
  1:1 line
 
 
 
 
E
413 cm
 = -0.27 + 0.79Br
413 cm
       R = 0.916
***
E
620 cm
 = -0.46 + 0.69Br
620 cm
       R = 0.992
***
E
overall
 = -0.36 + 0.75Br
overall
        R = 0.925
***
 
 
 
 
 
 
Figure 4.36. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 132 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10 100
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
100
At 413 cm
At 620 cm
Plot 4 Regr
  1:1 line
 
 
 
 
S
413 cm
 = -0.63 + 0.87Br
413 cm
       R = 0.930
***
S
620 cm
 = -0.78 + 0.86Br
620 cm
       R = 0.993
***
S
overall
 = -0.70 + 0.88Br
overall
        R = 0.937
***
 
 
 
 
 
Figure 4.37. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 4 (bare) for experiment under 
wet initial soil moisture conditions. 
***
Statistically significant at  p = 0.01. 
 133 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 1 Regr
     1:1 line
 
 
 
 
E
413 cm
 = -0.68 + 1.36Br
413 cm
       R = 0.985
***
E
620 cm
 = -1.24 ? 0.07Br
620 cm
       R = 0.306 
E
overall
 = -0.68 + 1.09Br
overall
        R = 0.978
***
 
 
 
 
 
Figure 4.38. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 134 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 1 Regr
       1:1 line
 
 
 
 
S
413 cm
 = -0.57 + 0.99Br
413 cm
       R = 0.991
***
S
620 cm
 = -0.22 + 1.26Br
620 cm
       R = 0.817
***
S
overall
 = -0.47 + 0.91Br
overall
        R = 0.960
***
 
 
 
 
 
 
Figure 4.39. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 1 (vegetated) for experiment 
under wet initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 135 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
E.
 c
o
li
, C
/
C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 3 Regr
   1:1 line
 
 
 
 
E
413 cm
 = -0.49 + 0.66Br
413 cm
       R = 0.944
***
E
620 cm
 = -0.44 + 0.74Br
620 cm
       R = 0.924
***
E
overall
 = -0.47 + 0.69Br
overall
        R = 0.889
***
 
 
 
 
 
Figure 4.40. Regressions of E. coli concentration ratios (C/C
0
) to Bromide 
concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment under wet 
initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 136 
 
 
Br, C/C
0
, %
0.001 0.01 0.1 1 10
S. e. 
T
y
ph
im
uriu
m,
 C/C
0
, %
0.001
0.01
0.1
1
10
At 413 cm
At 620 cm
Plot 3 Regr
     1:1 line
 
 
 
 
S
413 cm
 = -0.76 + 1.15Br
413 cm
       R = 0.963
***
S
620 cm
 = -0.73 + 1.23Br
620 cm
       R = 0.954
***
S
overall
 = -0.75 + 1.18Br
overall
        R = 0.964
***
 
 
 
 
 
 
Figure 4.41. Regressions of S. e. Typhimurium concentration ratios (C/C
0
) to 
Bromide concentration ratios (C/C
0
) in runoff from Plot 3 (vegetated) for experiment 
under wet initial soil moisture conditions. 
***
Statistically significant at p = 0.01. 
 137 
 
 
4.4 Cumulative recoveries 
4.4.1 Dry initial soil moisture conditions 
Cumulative recoveries obtained from the experiments under dry initial soil 
moisture conditions are shown in Tables 4.15 and 4.16, respectively, for the vegetated 
and bare plots.  Data show that cumulative recoveries of applied rainfall as runoff 
were lower in the vegetated plots than those in the bare plots. Cumulative recoveries 
were 51.8% and 75.3%, respectively, for vegetated Plots 1 and 3 (Table 4.15), and 
96.5% and 98.8%, respectively, for bare Plots 2 and 4 (Table 4.16). On average, 
surface runoff from vegetated plots was approximately 34% less than from bare plots. 
As shown in the hydrographs (Figure 4.1), the difference in recovery was primarily 
due to the much lower rates of infiltration in the bare plots during the first 30 minutes 
of simulations.  
 Cumulative recoveries of applied Br in surface runoff from the vegetated plots 
were lower than from the bare plots. Cumulative recoveries were 13.8% and 35.9%, 
respectively, for vegetated Plots 1 and 3 (Table 4.15), and 62.6% and 96.2%, 
respectively, for bare Plots 2 and 4 (Table 4.16). Note that cumulative Br recoveries 
in runoff from Plot 2 (Table 4.16) were extrapolated based on concentrations 
measured at the gutter (at 620 cm from the source of slurry) in runoff from Plot 4 
during the first 7 min of simulation. Runoff samples were collected immediately after 
the initiation of runoff in Plot 4 (2 min), whereas runoff samples in Plot 2 were not 
collected until 5 min after the initiation of runoff. Extrapolation notwithstanding, it is 
still likely that Br recoveries in Plot 2 were underestimated. 
 
 138 
 
 
Table 4.15. Cumulative recoveries obtained from the vegetated plots for experiments 
under dry initial soil moisture conditions.  
Component 
recovered 
PLOT 1 
(Vegetated)  
PLOT 3 
(Vegetated)  
Water, cm 
Rainfall 8.3 8.1 
Runoff, % 51.8 75.3
Bromide, g 
Applied 26 26 
In Runoff, % 13.8 35.9
Bacteria, CFU 
 Salmonella E. coli Salmonella E. coli 
Applied 1.17E+11 8.02E+10 8.47E+10 5.81E+10 
In Runoff 1.06E+09 2.45E+09 9.09E+09 6.40E+09 
In Runoff, % 0.9 3.1 10.7 11
 
 
Table 4.16. Cumulative recoveries obtained from the bare plots for experiments under 
dry initial soil moisture conditions.  
Component 
recovered 
PLOT 2 
(Bare)  
PLOT 4 
(Bare) 
Water, cm 
Rainfall 8.5
?
8.2 
Runoff, % 96.5 98.8 
Bromide, g 
Applied 26 26
In Runoff, % 62.6
?
96.2 
Bacteria, CFU 
 Salmonella E. coli Salmonella E. coli 
Applied 9.74E+10 5.61E+10 5.05E+10 7.10E+10 
In Runoff 1.11E+10 1.55E+10 2.06E+10 1.95E+10 
In Runoff, % 11.4
?
27.6
?
40.8 27.5
?Simulation in this plot lasted 62 min with a rainfall rate of 8.2 cm h
-1
?These values for Plot 2 were extrapolated based on concentrations measured at the 
gutter (620 cm) in Plot 4 during the first 7 min of simulation in this plot. See text for 
discussion.  
 
 
Cumulative Br recovery in surface runoff from Plot 1 (13.8%) was lower than 
from Plot 3 (35.9%). These results are consistent with water recoveries and reflect the 
 139 
 
 
fact that the two plots differed with respect to slope, soil texture at 20 cm depth, and 
topography (Section 4.1.1).  
Cumulative recoveries of applied E. coli and S. e. Typhimurium in surface 
runoff were also lower from vegetated plots than from bare plots. Cumulative 
recoveries of E. coli were 3.1% and 11%, respectively, from vegetated Plots 1 and 3 
(Table 4.15), and 27.6% and 27.5%, respectively, from bare Plots 2 and 4 (Table 
4.16); while cumulative recoveries of S. e. Typhimurium were 0.9% and 10.7%, 
respectively, from vegetated Plots 1 and 3 (Table 4.15), and 11.4% and 40.8%, 
respectively, from bare Plots 2 and 4 (Table 4.16). Based on the averages within each 
treatment, total E. coli and total S. e. Typhimurium recovered in surface runoff from 
vegetated plots were, respectively, approximately 21% and 20% lower than that 
recovered from bare plots. Cumulative recoveries of E. coli and S. e. Typhimurium 
with time in vegetated vs. bare plots are shown in Figure 4.42. Data clearly show that 
vegetated filter strips were very effective in reducing pathogen runoff. 
Note that the total pathogen load in runoff from bare plots is undoubtedly an 
underestimate, since only pathogens in the aqueous phase were measured, meaning 
that percent recoveries could have been higher than those depicted in Figure 4.42 if 
solid phase transport was measured. Previous studies indicate that bacteria typically 
partition between water and soil particulates (Tyrrel and Quinton, 2003; Roodsari, 
2004; Muirhead et al., 2005). Consequently, considering the very low infiltration 
rates in the bare plots, most of the unaccounted for pathogens applied to bare plots 
were likely associated with the sediment. In addition, as previously noted for Br 
above, the pathogen recoveries from Plot 2 were undoubtedly underestimated. 
 140 
 
 
Time (min)
0 10203040506070
Av
erage cumulat
i
ve re
cov
e
ry in runo
ff
 (%)
0
5
10
15
20
25
30
35
E.coli - Vegetated Plots 
E.coli - Bare Plots 
S.e.Typhimurium - Vegetated Plots 
S.e.Typhimurium - Bare Plots 
 
Figure 4.42. Average percent cumulative recovery in runoff of each pathogen for 
simulations under dry initial soil moisture conditions.  
 
 
According to Crane et al. (1983) and Stout et al. (2005), several mechanisms 
are responsible for the ability of vegetation to minimize the transport of pathogens in 
surface runoff. These include: enhanced infiltration, sorption of pathogens to plant 
litter or organic matter, and deposition of pathogens attached to soil particles due to 
decreased surface water flow. In this study, deposition likely was of little importance 
since applied pathogens were only briefly exposed to soil. However, both infiltration 
and sorption played major roles in minimizing pathogen transport, although in 
varying degrees depending on the plot. 
Table 4.15 shows that Plot 1 was more effective in reducing surface runoff of 
pathogens as compared to Plot 3. As previously discussed, both infiltration rates and 
 141 
 
 
litter densities/organic matter were higher in Plot 1, allowing for greater infiltration of 
pathogens into the soil profile as well as greater retention onto plant litter. However, 
since neither soil samples nor litter samples were analyzed, no quantitative data on 
the adsorption of pathogens to soil particles or plant litter was available.  
 
4.4.2 Wet initial soil moisture conditions 
Cumulative recoveries obtained from the experiments under wet initial soil 
moisture conditions are shown in Tables 4.17 and 4.18, respectively, for the vegetated 
and bare plots. Data show that cumulative recoveries of applied rainfall as runoff 
were similar between the vegetated and bare plots. Cumulative recoveries were 
95.1% and 98.8%, respectively, for vegetated Plots 1 and 3 (Table 4.17), and 95.2% 
and 100%, respectively, for bare Plots 2 and 4 (Table 4.18). On average, surface 
runoff from vegetated plots was only 0.6% lower than from bare plots. As shown in 
the hydrographs (Figure 4.2), the similarity in recovery was primarily due to similar 
infiltration during the simulations under wet initial soil moisture conditions. 
Cumulative recoveries of applied Br in surface runoff were lower in the 
vegetated plots than in the bare plots. Cumulative recoveries were 104.1% and 87.3%, 
respectively, for vegetated Plots 1 and 3 (Table 4.17), with an average of 95.7%; and 
100.9% and 128.2%, respectively, for bare Plots 2 and 4 (Table 4.18), with an 
average of 114.6%. These results show that Br recovery in vegetated plots was lower 
than in the bare plots, indicating positive effect of vegetation in reducing this 
chemical loss in surface runoff.  
 
 142 
 
 
Table 4.17. Cumulative recoveries obtained from the vegetated plots for experiments 
under wet initial soil moisture conditions.  
Component 
recovered 
PLOT 1 
(Vegetated) 
PLOT 3 
(Vegetated)  
Water, cm 
Rainfall 8.2 8.2 
Runoff, % 95.1 98.8
Bromide, g 
Applied 26 26 
In Runoff, % 104.1 87.3
Bacteria, CFU 
 Salmonella E. coli Salmonella E. coli 
Applied 5.76E+10 6.16E+10 1.07E+11 6.08E+10 
In Runoff 2.32E+10 1.61E+10 1.61E+10 2.12E+10 
In Runoff, % 40.3 26.1 15 34.9
 
 
Table 4.18. Cumulative recoveries obtained from the bare plots for experiments under 
wet initial soil moisture conditions.  
Component 
recovered 
PLOT 2 
(Bare)  
PLOT 4 
(Bare)  
Water, cm 
Rainfall 8.3
?
8.2
?
Runoff, % 95.2 100
?
Bromide, g 
Applied 26 26 
In Runoff, % 100.9 128.2
?
Bacteria, CFU 
 Salmonella E. coli Salmonella E. coli 
Applied 4.63E+10 6.49E+10 1.14E+11 7.02E+10 
In Runoff 2.08E+10 2.03E+10 2.10E+10
?
2.46E+10
?
In Runoff, % 44.9 31.3 18.4 35.0 
?Simulation in this plot lasted 61 min with a rainfall rate of 8.2 cm h
-1
  
?These values have been extrapolated for a 60 min simulation. Simulation in Plot 4 
was terminated after 40 min because the soil was completely saturated and subsurface 
flow was observed to be emerging into the gutter. 
   
 
Cumulative recoveries of applied pathogens in surface runoff were similar 
between vegetated and bare plots, particularly for E. coli. Cumulative recoveries of E. 
coli were 26.1% and 34.9%, respectively, from vegetated Plots 1 and 3 (Tables 4.17), 
 143 
 
 
and 31.3% and 35.0%, respectively, from bare Plots 2 and 4 (Table 4.18); while 
cumulative recoveries of S. e. Typhimurium were 40.3% and 15%, respectively, from 
vegetated Plots 1 and 3 (Table 4.17), and 44.9% and 18.4%, respectively, from bare 
Plots 2 and 4 (Table 4.18). On average, 30.5% and 27.7%, respectively, of the applied 
E. coli and S. e. Typhimurium were recovered in surface runoff from the vegetated 
plots, while 33.2% and 31.7%, respectively, of the applied E. coli and S. e. 
Typhimurium were recovered in surface runoff from the bare plots. These recovery 
data also indicate the positive effect of vegetation in reducing pathogen loss in 
surface runoff even under wet initial soil moisture conditions. 
Based on the averages within treatments, total E. coli and S. e. Typhimurium 
recovered in surface runoff from vegetated plots were, respectively, approximately 
3% and 4% lower than that recovered from bare plots. These results are substantially 
different from the ones obtained under dry initial soil moisture conditions (Section 
4.4.1) in which total E. coli and total S. e. Typhimurium recovered in surface runoff 
from vegetated plots were, respectively, approximately 21% and 20% lower than that 
recovered from bare plots.  
The higher pathogen cumulative recoveries under wet conditions than dry 
conditions are consistent with the hydrographs, which show that there was less 
infiltration in vegetated plots under wet conditions. Less infiltration resulted in 
reduced amounts of pathogens transported into the soil profile and increased their loss 
in surface runoff. Cumulative recoveries of E. coli and S. e. Typhimurium with time 
in vegetated vs. bare plots are shown in Figure 4.43. Results show that initial loss of 
 144 
 
 
pathogens in vegetated plots was much lower than in the bare plots, but, with time, 
vegetated plots tended to behave almost like bare plots. 
 
Time (min)
0 10203040506070
Av
erage cumulat
i
ve re
cov
e
ry in runo
ff
 (%)
0
5
10
15
20
25
30
35
E.coli - Vegetated Plots 
E.coli - Bare Plots 
S.e. Typhimurium - Vegetated Plots
S.e. Typhimurium - Bare Plots
 
Figure 4.43. Average percent cumulative recovery in runoff of each pathogen for 
simulations under wet initial soil moisture conditions. 
 
 
The primary difference between wet and dry initial soil moisture conditions 
was observed in vegetated plots where cumulative recoveries of rainfall, Br, and 
pathogens were substantially higher under wet conditions. On average, cumulative 
recoveries of rainfall and Br were, respectively, approximately 33% and 71% higher 
under wet than dry conditions. On average, cumulative recoveries of E. coli and S. e. 
Typhimurium were, respectively, approximately 23% and 22% higher under wet than 
dry conditions. These results are consistent with hydrographs, which show that there 
 145 
 
 
was less infiltration in vegetated plots under wet conditions. Less infiltration resulted 
in reduced amounts of Br and pathogens transported into the soil profile and 
increased amounts lost in surface runoff. 
 
 
 
 
 
 
 
 
 
 146 
 
 
Conclusions and Recommendations 
 
This study showed that initial soil moisture conditions ? induced by the depth 
to the groundwater table ? affected the efficiency of vegetated plots at attenuating 
surface runoff. Under dry conditions, in which the groundwater table was at least 50 
cm below the surfaces, average cumulative recoveries of applied rainfall as runoff 
were 98% and 64%, respectively, for bare and vegetated plots, a decrease of 34%. 
Under wet conditions, in which the groundwater table was approximately 20 cm 
below the surfaces, average cumulative recoveries of applied rainfall were 97.6% and 
97%, respectively, for bare and vegetated plots, a decrease of only 0.6%. Surface 
runoff in the vegetated plots increased 33% from dry to wet conditions, indicating 
that less infiltration was allowed in these plots and more of the applied rain was lost 
as surface runoff when the groundwater table in these plots was closer to the surface. 
Initial conditions of groundwater table in the vegetated plots also affected 
their performance at attenuating surface runoff transport of pathogens from land-
applied swine slurry. Under dry conditions, average cumulative recoveries of 
Escherichia coli in the liquid phase of runoff were 27.6% and 7.05%, respectively, 
for bare and vegetated plots, while average cumulative recoveries of Salmonella 
enterica Typhimurium in the liquid phase of runoff were 26.1% and 5.8%, 
respectively, for bare and vegetated plots. Under wet conditions, average cumulative 
recoveries of E. coli in the liquid phase of runoff were 33.2% and 30.5%, 
respectively, for bare and vegetated plots, while average cumulative recoveries of S. 
e. Typhimurium in the liquid phase of runoff were 31.7% and 27.7%, respectively, for 
 147 
 
 
bare and vegetated plots. On average, cumulative recoveries E. coli and S. e. 
Typhimurium in runoff from the vegetated plots had an increase, respectively, of 23% 
and 22% from dry to wet conditions. These results were consistent with infiltration 
rates, which were lower in the vegetated plots under wet conditions. Less infiltration 
resulted in high runoff and in reduced amounts of pathogens transported into the soil 
profile and increased amounts of them lost in surface runoff. 
This study not only shows that initial water table depth affected infiltration 
rates in the vegetated plots but also that infiltration was the major mechanism 
controlling overland transport of pathogens from land-applied swine slurry. It shows 
that 5%-sloped vegetated filter strips (VFS) with initial groundwater table depth of at 
least 50 cm are effective at mitigating runoff transport of pathogens from swine slurry 
even under extreme rainfall events. However, the effectiveness of these VFS is 
reduced if initial groundwater table depth is 20 cm or less since it prevents infiltration 
processes to occur. Therefore, parameters for designing VFS in the State of Iowa to 
reduce microbial transport in overland flow should be based on those that optimize 
infiltration rates even under extreme rainfall events. 
One parameter that can be taken into consideration in order to optimize 
infiltration rates within VFS is their surface conditions before seeding. Non uniform 
or rough surfaces, that have depressions or channels, should be graded to more 
uniform and smooth surfaces in order to allow the flow of water to be shallow and 
uniform, thus avoiding the establishment of areas of concentrated flow conditions. In 
areas of concentrated flow conditions, flow velocity increases, which decreases the 
available time for infiltration to occur, thus increasing runoff rates. 
 148 
 
 
Another parameter that can be considered is the type of vegetation. Some 
studies suggest that thick, deep rooted plants, such as eastern gamagrass, are 
preferable over thin, shallow rooted plants since the biological properties of thick, 
deep rooted plants help in the processes of loosening up soils (Perrygo et al., 2002). 
These processes enhance the formation of macropores and, most importantly, of 
macropore flow conditions, which are viewed as an important means for the 
development of preferential flow conditions that can significantly increase infiltration 
rates within soil profiles.  
In the study by Perrygo et al. (2002), not only they demonstrated that final 
infiltration rates in Matawan-Hammonton loam soils planted with eastern gamagrass 
were significantly higher than in those planted with thinner, shallower-rooting tall 
fescue, but also that eastern gamagrass greatly improved the physical and hydraulic 
characteristics of these soils. In addition, the authors concluded that VFS constructed 
with eastern gamagrass might be very effective for reducing surface runoff from 
agricultural fields by enhancing infiltration rates. One should note that the selection 
of plants should also be based on their compatibility to climate conditions, type of 
soils, and topography. 
Last, implementation of water table management strategies (through drainage 
systems) may be another parameter that can be considered in order to enhance 
infiltration rates within VFS. These strategies have been designed to lower possible 
high groundwater table levels for agricultural production purposes, and they can be 
separated, at least, into three systems: subsurface drainage, controlled drainage, and 
controlled drainage-subirrigation (Shirmohammadi et al., 1992). 
 149 
 
 
According to Shirmohammadi et al. (1992), under subsurface and controlled 
drainage systems, there is the risk that groundwater table might reach levels that are 
too low to sustain vegetation, particularly during drought periods, and a supplemental 
water input (surface irrigation or natural rainfall events) would be necessary in order 
bring groundwater table to adequate levels. Under controlled drainage-subirrigation 
(CD-SI) system there is still the risk of groundwater table reaching levels that are too 
low, but the need for surface irrigation or natural rainfall events can be eliminated 
since under CD-SI water can be pumped/irrigated into the soil system.  
According to Wright et al. (1990), another advantage with CD-SI system is 
that it has the potential to treat and reduce other nonpoint source pollutants, such as 
nutrients (net nitrogen), in areas with high groundwater table conditions. Nonetheless, 
in order to design feasible and efficient water table management systems, several 
elements have to be taken into consideration, such as the feasibility of the site; 
detailed field investigation; design computations; system layout and installation; and 
operation and management (Shirmohammadi et al., 1992).  
This study was not conclusive regarding the correlations between each 
pathogen and Bromide (Br) in surface runoff collected at 620 cm from the source of 
slurry in the vegetated plots. At such distance, correlations differed between the two 
pathogens and between the two vegetated plots (Plots 1 and 3). This study showed 
significant correlations between relative concentrations of S. e. Typhimurium and Br 
in both vegetated plots and under both wet and dry conditions. However, the 
correlations between relative concentrations of E. coli and Br in runoff was less clear- 
for Plot 3 there was always a significant correlation while for Plot 1 there was none, 
 150 
 
 
regardless of initial soil moisture conditions. This study shows that it is possible to 
have a significant correlation between relative concentrations of Br and pathogens in 
runoff from VFS - but not always. However, correlation studies of this nature are 
significant and it is important to understand what drives or disrupts the correlation 
between Br and pathogens in runoff from VFS. Based on well-understood 
correlations and on proper equations describing the relationships, relative 
concentrations of Br in runoff can approximate what will happen with pathogens in 
runoff. That would allow less costly and less labor-intensive studies. 
For this study, there were only two replications per treatment: two vegetated 
and two bare plots. Statistically, it would have been better to have more plots to 
understand the variability in the results. For that matter, there are two ways to 
increase the statistical sample: 1) divide the existing plots into several smaller plots, 
or 2) increase the number of plots. Option 1 would not have been a realistic approach 
because the new plots would have been much too small and artificial to represent 
realistic natural conditions. The size of the plots is important in order to represent the 
spatial variability. For example, if the plot is too small it may not show the realistic 
distributions of flow paths. Therefore, if resources are available, a much better 
approach is option 2- more plots of reasonable size that can better represent natural 
conditions and have enough replications.     
 
 151 
 
 
APPENDIX A 
Time intervals at which runoff was collected, measured and sampled during all 
experiments 
 
 152 
 
 
Table A.1.  Time intervals at which runoff was measured and sampled at the two 
distances from the applied swine slurry after the 60-min rainfall simulation initiated 
during the two sets of experiments.  
 Distance Time intervals 
Plots
?
(cm) (min)
   
  Dry initial soil moisture conditions 
 
Plot 1  413 17.5    20    25    30    35    40    45    50    60 
 620 17.5    20    25    30    35    40    45    50    60 
  
Plot 2
?
  413 7    12    17    22    27    32    42    52    62 
 620 7    12    17    22    27    32    42    52    62 
  
Plot 3  413 11    15    20    25    30    40    50    60 
 620 11    15    20    25    30    40    50    60 
  
Plot 4  413                   5    10    15    20    25    30    40    50    60 
 620 2    3    4    5    10    15    20    25    30    40    50    60 
  
  Wet initial soil moisture conditions 
 
Plot 1  413 6    8    10    15    20    25    30    40    50    60 
 620 6    8    10    15    20    25    30    40    50    60 
  
Plot 2
?
  413 2    3    4    5    6    11    16    21    26    31    41    51    61 
 620 2    3    4    5    6    11    16    21    26    31    41    51    61 
  
Plot 3  413 6    8    10    15    20    25    30    40    50    60 
 620 6    8    10    15    20    25    30    40    50    60 
  
Plot 4
?
  413 1.5    2.5    3.5    4.5    5    10    15    20    25    30    40  
 
620 1.5    2.5    3.5    4.5    5    10    15    20    25    30    40  
?
Plots 1 and 3 were vegetated, while Plots 2 and 4 were bare. 
?
 This experiment lasted 62 min. 
?
 This experiment lasted 61 min. 
?
 This experiment lasted 40 min. 
 
 
 153 
 
 
APPENDIX B 
Runoff and Bromide data collected at 413 cm from the source of slurry during 
experiments under dry initial soil moisture conditions 
 
 154 
 
 
Table B.1. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 1 (vegetated) during the simulations under dry initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
17.5 813.33 17.58 597.02 22.35 73.40 127.11 
20 845.63 11.71 292.54 11.95 34.59 102.03 
25 2093.33 11.94 684.79 13.49 32.71 113.01 
30 2593.33 9.14 963.40 10.29 37.15 112.63 
35 2396.67 6.48 805.01 6.92 33.59 106.75 
40 2430.00 6.02 657.80 7.02 27.07 116.64 
45 2456.67 5.25 627.40 5.87 25.54 111.78 
50 2876.67 5.23 1307.15 5.83 45.44 111.65 
60 5850.00 6.14 2667.86 6.94 45.60 113.03 
 
 
 
 
Table B.2. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 3 (vegetated) during the simulations under dry initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
11 266.67 6.46 275.38 6.04 103.27 93.60 
15 1276.67 22.48 1188.12 23.60 93.06 104.98 
20 3311.67 24.35 3121.60 29.69 94.26 121.93 
25 3683.33 11.44 3579.92 11.01 97.19 96.25 
30 3900.00 7.29 3675.60 7.10 94.25 97.34 
40 8216.67 4.20 8008.80 4.30 97.47 102.33 
50 10166.67 3.64 9408.68 2.89 92.54 79.39 
60 10645.00 3.17 9525.14 2.14 89.48 67.67 
 
 155 
 
 
Table B.3. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 2 (bare) during the simulations under dry initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
7 1816.67 300.28 940.44 375.58 51.77 125.08 
12 1990.00 5.54 808.02 4.59 40.60 82.84 
17 2035.00 2.33 888.95 2.36 43.68 101.65 
22 1880.00 1.44 877.10 1.41 46.65 97.63 
27 1865.00 1.15 961.99 1.02 51.58 88.47 
32 1826.67 1.00 962.57 0.79 52.70 79.83 
42 3533.33 0.93 1569.50 0.66 44.42 71.05 
52 3783.33 0.90 1692.52 0.56 44.74 62.52 
62 3550.00 0.96 1796.52 0.47 50.61 49.41 
 
 
 
Table B.4. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 4 (bare) during the simulations under dry initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
5 3730.00 417.96 1770.00 282.34 47.45 67.55 
10 4330.00 6.17 1870.00 3.33 43.19 53.97 
15 4325.00 1.93 625.00 1.16 14.45 60.07 
20 7875.00 1.34 1325.00 0.68 16.83 50.75 
25 7250.00 1.02 150.00 0.45 2.07 44.59 
30 6800.00 0.94 0 0.42 0 45.15
40 13175.00 0.87 1175.00 0.38 8.92 43.98 
50 12915.00 0.87 815.00 0.38 6.31 43.27 
60 12225.00 0.99 575.00 0.45 4.70 45.28 
 
 
 156 
 
 
 APPENDIX C  
Runoff and Bromide data collected at 413 cm from the source of slurry during 
experiments under wet initial soil moisture conditions 
 
 157 
 
 
Table C.1. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 1 (vegetated) during the simulations under wet initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
6 523.33 0.80 594.67 0.90 113.63 112.52 
8 866.67 2.06 859.32 1.49 99.15 72.41
10 2098.67 34.44 1649.17 58.03 78.58 168.49 
15 6266.67 107.21 4619.88 177.46 73.72 165.53 
20 6583.33 87.90 5037.44 114.16 76.52 129.87 
25 6966.67 57.87 5404.01 70.77 77.57 122.30 
30 6363.33 36.28 4743.00 48.88 74.54 134.72 
40 13550.00 21.82 11134.74 30.03 82.18 137.61 
50 14500.00 27.12 12574.18 42.26 86.72 155.81 
60 15173.33 11.52 13290.60 16.55 87.59 143.64 
 
 
 
Table C.2. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 3 (vegetated) during the simulations under wet initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
6 163.33 1.52 125.03 0.11 76.55 7.24 
8 1323.33 3.78 1096.10 3.75 82.83 99.14 
10 1863.33 28.57 1596.32 36.88 85.67 129.12 
15 4916.67 36.86 4153.41 53.60 84.48 145.41 
20 4696.67 40.72 4010.71 50.58 85.39 124.21 
25 4780.00 24.15 4256.76 27.70 89.05 114.69 
30 6083.33 15.37 6342.02 15.10 104.25 98.19 
40 13733.33 11.73 14424.40 10.93 105.03 93.14 
50 12933.33 7.18 13110.91 5.60 101.37 78.05 
60 12940.00 6.00 13475.71 4.76 104.14 79.43 
 
 158 
 
 
Table C.3. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 2 (bare) during the simulations under wet initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
2 483.33 362.39 161.97 519.60 33.51 143.38 
3 770.00 567.32 315.12 433.20 40.92 76.36 
4 785.00 297.03 247.54 225.98 31.53 76.08 
5 786.67 131.45 200.33 119.83 25.47 91.16 
6 853.33 40.99 217.79 32.01 25.52 78.10 
11 4500.00 8.63 1100.00 8.43 24.44 97.79 
16 4180.00 2.65 1576.45 1.47 37.71 55.37 
21 4883.33 1.65 1615.81 1.00 33.09 60.64 
26 4900.00 1.38 2042.06 0.78 41.67 56.64 
31 4500.00 1.26 1905.26 0.71 42.34 56.14 
41 8400.00 1.18 4297.66 0.61 51.16 51.54 
51 7683.33 0.95 4204.86 0.35 54.73 36.70 
61 6516.67 1.10 3875.67 0.48 59.47 43.72 
 
 
 
 
 
Table C.4. Mean, standard deviation (STDEV), and coefficient of variance (C.V.) of 
runoff volumes and Bromide (Br) concentrations in runoff measured at 413 cm from 
the source of slurry in Plot 4 (bare) during the simulations under wet initial soil 
moisture conditions. 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
(mL) 
Br 
Concentration 
(ppm) 
Runoff 
Volume 
Br 
Concentration
1.5 766.67 574.19 650.41 162.47 84.84 28.30 
2.5 2206.67 1074.60 2127.54 582.93 96.41 54.25 
3.5 1400.00 327.77 1188.23 187.34 84.87 57.15 
4.5 1776.67 91.80 1640.56 57.16 92.34 62.27 
5 953.33 49.88 932.76 32.31 97.84 64.77 
10 9026.67 8.66 8458.82 5.28 93.71 60.97 
15 8333.33 2.44 7684.62 0.81 92.22 33.35 
20 8700.00 1.63 6878.95 0.46 79.07 28.45 
25 8400.00 1.33 6315.06 0.36 75.18 26.89 
30 8116.67 1.25 6117.67 0.50 75.37 39.94 
40 14826.67 1.07 12747.40 0.45 85.98 42.38 
 
 159 
 
 
APPENDIX D 
Runoff and pathogen data collected at 413 cm from the source of slurry during 
experiments under dry initial soil moisture conditions 
 
 160 
 
 
Table D.1. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 1 (vegetated) during simulations under dry initial soil 
moisture conditions. 
PLOT 1 - Vegetated 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
17.5 813.33 3.95E+03 597.02 2.64E+03 73.40 66.79 
20 845.63 3.29E+03 292.54 1.24E+03 34.59 37.67 
25 2093.33 2.69E+03 684.79 7.38E+02 32.71 27.42 
30 2593.33 1.46E+03 963.40 5.87E+02 37.15 40.34 
35 2396.67 1.18E+03 805.01 5.04E+02 33.59 42.79 
40 2430.00 9.94E+02 657.80 6.53E+02 27.07 65.76 
45 2456.67 8.25E+02 627.40 7.60E+02 25.54 92.15 
50 2876.67 7.37E+02 1307.15 5.92E+02 45.44 80.31 
60 5850.00 7.11E+02 2667.86 4.35E+02 45.60 61.18 
  
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
17.5 813.33 1.05E+04 597.02 1.46E+04 73.40 139.43 
20 845.63 7.06E+03 292.54 4.94E+03 34.59 69.90 
25 2093.33 5.15E+03 684.79 2.68E+03 32.71 52.11 
30 2593.33 4.80E+03 963.40 3.43E+03 37.15 71.43 
35 2396.67 3.95E+03 805.01 1.85E+03 33.59 46.81 
40 2430.00 3.90E+03 657.80 2.93E+03 27.07 75.08 
45 2456.67 3.84E+03 627.40 2.89E+03 25.54 75.08 
50 2876.67 3.19E+03 1307.15 2.80E+03 45.44 87.97 
60 5850.00 3.03E+03 2667.86 2.53E+03 45.60 83.46 
 
 161 
 
 
Table D.2. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 3 (vegetated) during simulations under dry initial soil 
moisture conditions. 
PLOT 3 - Vegetated 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
11 266.67 1.59E+04 275.38 1.40E+04 103.27 88.22 
15 1276.67 2.95E+04 1188.12 2.55E+04 93.06 86.63 
20 3311.67 2.68E+04 3121.60 2.32E+04 94.26 86.62 
25 3683.33 9.91E+03 3579.92 9.03E+03 97.19 91.09 
30 3900.00 3.98E+03 3675.60 4.17E+03 94.25 104.91 
40 8216.67 2.60E+03 8008.80 2.81E+03 97.47 108.22 
50 10166.67 1.31E+03 9408.68 1.17E+03 92.54 89.46 
60 10645.00 1.23E+03 9525.14 1.11E+03 89.48 90.14 
 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
11 266.67 8.43E+03 275.38 1.33E+04 103.27 158.05 
15 1276.67 2.76E+04 1188.12 2.67E+04 93.06 96.58 
20 3311.67 2.84E+04 3121.60 3.23E+04 94.26 113.88 
25 3683.33 1.21E+04 3579.92 1.61E+04 97.19 132.70 
30 3900.00 5.51E+03 3675.60 6.78E+03 94.25 123.13 
40 8216.67 3.25E+03 8008.80 3.57E+03 97.47 109.88 
50 10166.67 2.88E+03 9408.68 2.56E+03 92.54 88.76 
60 10645.00 2.37E+03 9525.14 2.06E+03 89.48 86.60 
 
 162 
 
 
Table D.3. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 2 (bare) during simulations under dry initial soil moisture 
conditions. 
PLOT 2 - Bare 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
7 1816.67 2.83E+05 940.44 3.26E+05 51.77 115.22 
12 1990.00 7.52E+03 808.02 5.30E+03 40.60 70.43 
17 2035.00 1.95E+03 888.95 1.93E+03 43.68 98.78 
22 1880.00 1.60E+03 877.10 1.83E+03 46.65 114.80 
27 1865.00 1.14E+03 961.99 1.31E+03 51.58 114.71 
32 1826.67 8.57E+02 962.57 9.21E+02 52.70 107.50 
42 3533.33 6.23E+02 1569.50 6.81E+02 44.42 109.31 
52 3783.33 5.53E+02 1692.52 4.65E+02 44.74 84.04 
62 3550.00 3.53E+02 1796.52 4.04E+02 50.61 114.38 
 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
7 1816.67 1.67E+05 940.44 2.05E+05 51.77 122.77 
12 1990.00 8.04E+03 808.02 7.33E+03 40.60 91.09 
17 2035.00 3.36E+03 888.95 3.62E+03 43.68 107.62 
22 1880.00 1.43E+03 877.10 1.47E+03 46.65 102.82 
27 1865.00 1.23E+03 961.99 1.53E+03 51.58 123.83 
32 1826.67 1.03E+03 962.57 1.23E+03 52.70 118.90 
42 3533.33 5.87E+02 1569.50 7.09E+02 44.42 120.83 
52 3783.33 5.80E+02 1692.52 7.72E+02 44.74 133.12 
62 3550.00 4.60E+02 1796.52 6.08E+02 50.61 132.07 
 
 163 
 
 
Table D.4. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 4 (bare) during simulations under dry initial soil moisture 
conditions. 
PLOT 4 - Bare 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
5 3730.00 3.67E+05 1770.00 1.41E+05 47.45 38.46 
10 4330.00 6.30E+03 1870.00 1.22E+03 43.19 19.40 
15 4325.00 2.02E+03 625.00 7.47E+02 14.45 37.02 
20 7875.00 1.20E+03 1325.00 4.31E+02 16.83 35.98 
25 7250.00 7.59E+02 150.00 2.42E+02 2.07 31.86 
30 6800.00 6.10E+02 0 2.91E+02 0 47.69 
40 13175.00 4.73E+02 1175.00 2.57E+02 8.92 54.33 
50 12915.00 3.40E+02 815.00 1.80E+02 6.31 53.02 
60 12225.00 2.90E+02 575.00 1.11E+02 4.70 38.40 
 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
5 3730.00 2.42E+05 1770.00 1.06E+05 47.45 43.86 
10 4330.00 9.95E+03 1870.00 7.16E+03 43.19 71.93 
15 4325.00 6.86E+03 625.00 6.29E+03 14.45 91.69 
20 7875.00 5.39E+03 1325.00 3.99E+03 16.83 73.98 
25 7250.00 4.03E+03 150.00 3.37E+03 2.07 83.58 
30 6800.00 3.58E+03 0 2.67E+03 0 74.58 
40 13175.00 2.86E+03 1175.00 2.06E+03 8.92 71.81 
50 12915.00 2.00E+03 815.00 1.06E+03 6.31 52.79 
60 12225.00 1.77E+03 575.00 9.71E+02 4.70 54.74 
 
 
 164 
 
 
APPENDIX E 
Runoff and pathogen data collected at 413 cm from the source of slurry during 
experiments under wet initial soil moisture conditions 
 
 165 
 
 
Table E.1. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 1 (vegetated) during simulations under wet initial soil 
moisture conditions. 
PLOT 1 - Vegetated 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
6 523.33 0 594.67 0 113.63 0 
8 866.67 1.72E+03 859.32 2.98E+03 99.15 173.21 
10 2098.67 2.74E+04 1649.17 4.75E+04 78.58 173.21 
15 6266.67 7.56E+04 4619.88 1.15E+05 73.72 151.98 
20 6583.33 6.16E+04 5037.44 6.86E+04 76.52 111.32 
25 6966.67 3.96E+04 5404.01 5.09E+04 77.57 128.56 
30 6363.33 1.87E+04 4743.00 2.68E+04 74.54 143.92 
40 13550.00 1.09E+04 11134.74 1.60E+04 82.18 146.71 
50 14500.00 1.03E+04 12574.18 1.59E+04 86.72 154.16 
60 15173.33 5.31E+03 13290.60 7.96E+03 87.59 149.86 
 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
6 523.33 0 594.67 0 113.63 0 
8 866.67 3.27E+02 859.32 5.66E+02 99.15 173.21 
10 2098.67 1.92E+04 1649.17 3.32E+04 78.58 173.21 
15 6266.67 9.41E+04 4619.88 1.49E+05 73.72 158.20 
20 6583.33 6.22E+04 5037.44 6.54E+04 76.52 105.15 
25 6966.67 3.86E+04 5404.01 4.43E+04 77.57 114.63 
30 6363.33 2.22E+04 4743.00 3.04E+04 74.54 137.03 
40 13550.00 1.30E+04 11134.74 1.84E+04 82.18 141.15 
50 14500.00 1.36E+04 12574.18 2.16E+04 86.72 158.33 
60 15173.33 8.04E+03 13290.60 1.30E+04 87.59 161.31 
 
 166 
 
 
Table E.2. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 3 (vegetated) during simulations under wet initial soil 
moisture conditions. 
PLOT 3 - Vegetated 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
6 163.33 0 125.03 0 76.55 0 
8 1323.33 3.77E+03 1096.10 6.52E+03 82.83 173.21 
10 1863.33 3.15E+04 1596.32 5.46E+04 85.67 173.21 
15 4916.67 3.53E+04 4153.41 5.23E+04 84.48 147.99 
20 4696.67 3.13E+04 4010.71 4.60E+04 85.39 147.23 
25 4780.00 1.72E+04 4256.76 2.47E+04 89.05 143.96 
30 6083.33 8.93E+03 6342.02 1.17E+04 104.25 131.23 
40 13733.33 4.86E+03 14424.40 5.54E+03 105.03 114.09 
50 12933.33 3.92E+03 13110.91 4.89E+03 101.37 124.61 
60 12940.00 2.80E+03 13475.71 3.47E+03 104.14 123.67 
 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
6 163.33 0 125.03 0 76.55 0 
8 1323.33 1.16E+04 1096.10 2.01E+04 82.83 173.21 
10 1863.33 1.68E+04 1596.32 2.92E+04 85.67 173.21 
15 4916.67 2.99E+04 4153.41 2.86E+04 84.48 95.80 
20 4696.67 2.84E+04 4010.71 2.83E+04 85.39 99.77 
25 4780.00 1.91E+04 4256.76 1.98E+04 89.05 104.06 
30 6083.33 1.21E+04 6342.02 1.14E+04 104.25 94.41 
40 13733.33 7.95E+03 14424.40 8.99E+03 105.03 112.97 
50 12933.33 5.17E+03 13110.91 6.17E+03 101.37 119.24 
60 12940.00 4.35E+03 13475.71 4.92E+03 104.14 113.11 
 
 167 
 
 
Table E.3. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 2 (bare) during simulations under wet initial soil moisture 
conditions. 
PLOT 2 - Bare 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
2 483.33 2.13E+05 161.97 3.16E+05 33.51 148.13 
3 770.00 4.26E+05 315.12 2.90E+05 40.92 68.11 
4 785.00 2.24E+05 247.54 1.50E+05 31.53 67.13 
5 786.67 1.04E+05 200.33 1.00E+05 25.47 97.03 
6 853.33 3.42E+04 217.79 2.54E+04 25.52 74.21 
11 4500.00 2.06E+04 1100.00 1.70E+04 24.44 82.84 
16 4180.00 7.54E+03 1576.45 5.22E+03 37.71 69.24 
21 4883.33 3.43E+03 1615.81 3.17E+03 33.09 92.40 
26 4900.00 2.53E+03 2042.06 2.26E+03 41.67 89.30 
31 4500.00 2.31E+03 1905.26 2.08E+03 42.34 90.34 
41 8400.00 1.69E+03 4297.66 1.42E+03 51.16 84.14 
51 7683.33 1.08E+03 4204.86 1.02E+03 54.73 94.62 
61 6516.67 1.01E+03 3875.67 7.69E+02 59.47 76.16 
 
PLOT 2 - Bare 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
2 483.33 2.90E+05 161.97 4.17E+05 33.51 144.06 
3 770.00 3.78E+05 315.12 2.58E+05 40.92 68.06 
4 785.00 2.94E+05 247.54 2.58E+05 31.53 87.67 
5 786.67 1.19E+05 200.33 1.08E+05 25.47 90.41 
6 853.33 4.51E+04 217.79 3.78E+04 25.52 83.95 
11 4500.00 1.82E+04 1100.00 1.85E+04 24.44 101.41 
16 4180.00 8.46E+03 1576.45 7.89E+03 37.71 93.24 
21 4883.33 4.18E+03 1615.81 4.33E+03 33.09 103.57 
26 4900.00 3.21E+03 2042.06 3.09E+03 41.67 96.33 
31 4500.00 2.14E+03 1905.26 1.62E+03 42.34 75.88 
41 8400.00 2.18E+03 4297.66 1.87E+03 51.16 85.55 
51 7683.33 1.61E+03 4204.86 1.58E+03 54.73 97.76 
61 6516.67 1.41E+03 3875.67 1.27E+03 59.47 89.59 
 
 168 
 
 
Table E.4. Mean, standard deviation (STDEV), and coefficient of variation (C.V.) of 
runoff volumes and pathogen concentrations in runoff measured at 413 cm from the 
source of slurry in Plot 4 (bare) during simulations under wet initial soil moisture 
conditions. 
PLOT 4 - Bare 
S. e. Typhimurium 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
1.5 766.67 6.34E+05 650.41 4.11E+05 84.84 64.93 
2.5 2206.67 5.96E+05 2127.54 3.26E+05 96.41 54.67 
3.5 1400.00 1.77E+05 1188.23 1.09E+05 84.87 61.71 
4.5 1776.67 5.83E+04 1640.56 5.27E+04 92.34 90.34 
5 953.33 3.91E+04 932.76 3.90E+04 97.84 99.83 
10 9026.67 1.67E+04 8458.82 1.69E+04 93.71 101.59 
15 8333.33 4.40E+03 7684.62 3.73E+03 92.22 84.60 
20 8700.00 2.92E+03 6878.95 2.37E+03 79.07 81.41 
25 8400.00 2.23E+03 6315.06 2.03E+03 75.18 91.06 
30 8116.67 1.34E+03 6117.67 1.23E+03 75.37 91.51 
40 14826.67 9.88E+02 12747.40 9.13E+02 85.98 92.38 
 
PLOT 4 - Bare 
E. coli 
  Mean at 413 cm STDEV at 413 cm C. V. (%) 
Time 
(min) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
(mL) 
Concentration 
(CFU/mL) 
Runoff 
Volume 
Concentration
1.5 766.67 7.24E+05 650.41 4.88E+05 84.84 67.37 
2.5 2206.67 6.28E+05 2127.54 3.08E+05 96.41 48.98 
3.5 1400.00 2.42E+05 1188.23 1.80E+05 84.87 74.59 
4.5 1776.67 7.15E+04 1640.56 4.95E+04 92.34 69.30 
5 953.33 3.42E+04 932.76 2.33E+04 97.84 68.21 
10 9026.67 2.09E+04 8458.82 1.50E+04 93.71 72.02 
15 8333.33 7.28E+03 7684.62 5.93E+03 92.22 81.50 
20 8700.00 4.18E+03 6878.95 2.61E+03 79.07 62.48 
25 8400.00 3.88E+03 6315.06 3.29E+03 75.18 84.92 
30 8116.67 2.77E+03 6117.67 2.12E+03 75.37 76.70 
40 14826.67 2.22E+03 12747.40 1.92E+03 85.98 86.55 
 
 169 
 
 
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