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This is to certify that the
thesis entitled

EFFECTS OF GROWING AND DESICCATED ROOTS
ON E. COLI MOVEMENT THROUGH SOIL COLUMNS

presented by

Paula Noel Steiner

has been accepted towards fulfillment
of the requirements for the

 

 

MS. degree in Biosystems Engineering
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Majo‘r Professor’f Signature

that. \\ 1 2006—1

 

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5/08 K:IProj/Acc&Pres/ClRC/DateDue.indd

EFFECTS OF GROWING AND DESICCATED ROOTS
ON E. C 0L1 MOVEMENT THROUGH SOIL COLUMNS

By

Paula Noel Steiner

A THESIS

Submitted to
Michigan State University
in partial fulfillment of the requirements
for the degree of

MASTERS OF SCIENCE
Biosystems Engineering

2009

ABSTRACT

EFFECTS OF GROWING AND DESICCATED ROOTS
ON E. COLI MOVEMENT THROUGH SOIL COLUMNS

By

Paula Noel Steiner

Pathogen transport to surface and groundwater is a serious environmental concern when
untreated livestock manure is applied to farmland. Artificially drained farmland is
particularly at risk because preferential flow pathways can transport contaminants directly to
subsurface drains with no opportunity for filtration. A column study was performed to
evaluate the effects of the grth and decay of roots on the saturated hydraulic conductivity
(Ksat), miscible displacement of a bromide tracer, transport of E. coli, and breakthrough of
the P22 bacteriophage in loamy sand soil. Compared to bare soil, the initial com grth
decreased the saturated hydraulic conductivity (Ksat) at a rate of -O.5 to -O.75 cm/h-week, but
Ksat values increased when the com plants were killed and the roots decayed. Root regrowth
caused a slight decrease in the Ksat (0.06 cm/h-week). There was no detectable difference in
the displacement of a bromide tracer through the columns due to root growth/decay. When
swine manure was applied there was no significant difference between the initial
concentration of E. coli in the effluent from bare columns with and without manure.
Growing or decaying roots increased the rate of bacterial transport through the soil columns.
The recovery of the P22 bacteriophage applied in manure was greater than when applied in
deionized water due to a lack of competition for adsorption sites. When applied in manure to
a cereal rye cover crop, the bacteriophage broke through and peaked at the same time or
slightly before the E. coli. The P22 bacteriophage appeared to be a suitable microbial marker

for field testing for linking a manure application with water contamination at the field scale.

ACKNOWLEDGEMENTS
I would like to thank:

~ The department stafl their assistance makes the work that happens in the
department possible.

~ My fellow graduate students, for the fun times.

~ The individuals that assisted in this project, specifically: Tracy Kerchkof Tracy
Svanda, Chip Cogan, Abby Richmond, Tim Boring, Dana Kirk, and Steve Marquie.

~ The members of Dr. Rose’s lab (Shikha Singh, Rebecca Ives, Y oshifumi Masago,
and Theng T heng F ong) for their assistance answering my questions and assisting

with lab analysis.

~ My committee (Dr. William Northcott, Dr. Alvin Smucker, and Dr. Joan Rose) for
their help and direction.

~ My advisor, Dr. Harrigan for his support, direction, and time.

~ My family, for always believing in me and encouraging me.

~ Paula

iii

TABLE OF CONTENTS

LIST OF TABLES ................................. vi
LIST OF FIGURES ................................ ix
1.0 CHAPTER 1 — INTRODUCTION ....................... 1
2.0 CHAPTER 2 — LITERATURE REVIEW .................... 5
2.1 Microbial water quality .......................... 5
2.1.1 Pathogens ........................... 6

2.1.2 Indictor organisms ....................... 7

2.2 Factors affecting pathogen survival ..................... 10
2.2.1 Soil moisture and temperature ................. 10

2.2.2 Nutrients, carbon, soil texture and organic matter ........ 11

2.2.3 pH, sunlight, and oxygen concentration ............. 12

2.2.4 Manure effects on soil and pathogen survival .......... 13

2.2.5 Soil fauna ........................... 14

2.3 Adsorption, filtering, and transport ..................... 15
2.3.1 Filtering ............................ 16

2.3.2 Adsorption ........................... 16

2.3.3 Transport ........................... 17

2.4 Rhizosphere effects ........................... 20

2.5 Tracers for hydrologic studies ..................... .22

2.6 Column studies ............................. 24
2.6.1 Intact or undisturbed vs. repacked or disturbed .......... 24

2.6.2 Column dimensions ...................... 25

3.0 CHAPTER 3 - METHODS AND MATERIALS ............... 26
3.1 Soil columns ‘ ............................ ' . .26
3.1.1 Soil column construction and filling ............... 26

3.2 Soil column flow regime ........................ 27
3.2.1 Saturated hydraulic conductivity ................ 27

3.2.2 Anionic tracers ......................... 29

3.2.2.1 Chloride tracer ................... 29

3.2.2.2 Bromide tracer ................... 30

3.3 Experimental treatments ........................ 30

3 .4 Instrumentation ............................. 3 1

3.5 Growing conditions ........................... 33

3.6 Manure application procedure ...................... 33

3.7 Rain events and sample collection .................... 34

3.8 E. coli quantification .......................... 34

3.9 Breakthrough of P22 bacteriophage ................... 35
3.9.1 Column preparation ...................... 35

3.9.2 P22 tracer preparation ..................... 36

3.9.3 Sample collection ....................... 36

iv

3.9.4 Sample analysis ........................ 37

3.10 Statistical analysis ........................... 37
3.10.1 Saturated hydraulic conductivity ................ 37

3.10.2 Anionic tracers ......................... 38

3.10.2.1 Chloride ...................... 38

3.10.2.1 Bromide ...................... 38

3.10.3 E. coli ............................. 39

4.0 CHAPTER 4 — RESULTS AND DISCUSSION ................ 40
4.1 Measurement of the column flow regime ................. 40
4.1.1 Saturated hydraulic conductivity ................ 40

4.1.2 Breakthrough of anionic tracers ................. 43

4.1.2.1 Chloride ...................... 43

4.1.2.2 Bromide ...................... 44

4.2 Simulated storm event: E. coli ...................... 46
4.2.1 Cumulative E. coli recovery .................. 46

4.3 P22 bacteriophage ........................... 50
4.3.1 Breakthrough curves ...................... 50

4.3.2 Phage/manure vs. phage/water ................. 54

4.3.3 Cumulative recovery ...................... 55

4.4 Discussion ............................... 56
4.4.1 Soil moisture and temperature influences ............ 56

4.4.2 Difficulties in examining the results ............... 57

4.4.2.1 Soil problems .................... 57

4.4.2.2 E. coli variability .................. 57

4.4.3 E. coli reduction ........................ 58

4.4.4 Broader application of bacteriophage tracer ........... 58

5.0 CHAPTER 5 — CONCLUSIONS ....................... 59
6.0 CHAPTER 6 — RECOMMENDATIONS ................... 61
APPENDIX A ................................... 63
APPENDIX B ................................... 78
APPENDIX C ................................... 83
APPENDIX D ................................... 92
APPENDIX E .................................. 116
REFERENCES .................................. 129

LIST OF TABLES

Table 2.1: Source-specific daily fecal indicator output (USEPA, 2001) ........ 8
Table 2.2: EPA recommended criteria for indicators (USEPA, 2001) ......... 9
Table 3.1: Eight treatments, full factorial design with two factors ........... 31
Table 4.1: Chloride and bromide breakthrough by crop treatment ........... 44
Table 4.2: Elution and retention of the E. coli .................... 48
Table 4.3: Cumulative P22 bacteriophage recovery .................. 56
Table 4.4: Amount of water applied and recovered during each treatment. ...... 57
Table A1: Column soil properties .......................... 64
Table A2: Soil chemical properties ......................... 66
Table A3: Manure characteristics for E. coli experiment ............... 66
Table A4: Formula for the NSF corn nutrient solution ................ 67
Table A5: Saturated hydraulic conductivity values at various stages in the

experiment ............................... 67
Table A6: Chloride breakthrough curve statistics .................. 68
Table A7: Bromide breakthrough curve statistics .................. 69
Table A8: Manure characteristics for bacteriophage experiment ........... 70
Table A9: Cumulative E. coli recovered from each column .............. 72
Table A10: E. coli concentrations for treatment 1, bare with no manure. ....... 74
Table A11: E. coli concentrations for treatment 2, bare with manure ......... 74
Table A12: E. coli concentrations for treatment 3, rye with no manure ........ 75
Table A13: E. coli concentrations for treatment 4, rye with manure. ......... 75

Table A14: E. coli concentrations for treatment 5, desiccated corn with no manure . . 76

vi

Table A15: E. coli concentrations for treatment 6, desiccated corn with manure. . . . 76

Table A16: E. coli concentrations for treatment 7, desiccated corn/rye with no
manure ................................ 77

Table A17: E. coli concentrations for treatment 8, desiccated corn/rye with manure. . 77
Table Bl: Two-Factor ANOVA: Ksat values by treatment and replication. ...... 79

Table B2: Two-Factor ANOVA: Peak chloride concentration by treatment and
replication. ............................... 79

Table B3: Two-Factor ANOVA: Pore volume applied at chloride peak by treatment
and replication .............................. 79

Table B4: Two-Factor ANOVA: Pore volume at 10 % recovery of applied chloride by
treatment and replication ......................... 79

Table B5: Two-Factor ANOVA: Pore volume at 50 % recovery of applied chloride
by treatment and replication ....................... 79

Table B6: Two-Factor ANOVA: Peak bromide concentration by treatment and
replication ................................ 80

Table B7: Two-F actor ANOVA: Pore volume applied at bromide peak by treatment
and replication .............................. 80

Table B8: Two-Factor ANOVA: Pore volume at 10 % recovery of applied bromide
by treatment and replication ....................... 80

Table B9: Two-Factor ANOVA: Pore volume at 50 % recovery of applied bromide
by treatment and replication ....................... 80

Table B10: Two-Factor ANOVA: Pore volume at 90 % recovery of applied bromide
by treatment and replication ....................... 80

Table B11: General Linear Model (GLM): Ln (E. coli) vs. Crop, Manure, and
Replication ............................... 8 1

Table BIZ: P-values for pairwise comparisons of the main effects of crop using
Tukey. Groupings are on the diagonal. Ln (E. coli) is the response
variable ................................ 8 1

Table B13: P-values for pairwise comparisons of the main effects of manure using

Tukey. Groupings are on the diagonal. Ln (E. coli) is the response
variable ................................ 81

vii

Table B14:

Table B15:

Table B16:

Table B17:

Table B18:

P-values for pairwise comparisons of the main effects of crop using
Tukey. Groupings are on the diagonal. Ln (E. coli) is the response
variable ................................ 81

General Linear Model (GLM): Ln (Cumulative E. coli) by treatment and
replication ............................... 82

Table BIZ: P-values for pairwise comparisons of the main effects of crop
using Tukey. Groupings are on the diagonal. Ln (Cumulative E. coli) is
the response variable .......................... 82

General Linear Model (GLM): Cumulative E. coli percent retention by
treatment and replication ........................ 82

P-values for pairwise comparisons of the main effects of crop using
Tukey. Groupings are on the diagonal. Cumulative E. coli percent
retention is the response variable. (a = 0.10) ............... 82

Table D1: Average daily soil and air temperature highs and lows ........... 93

viii

Figure 2.1:

Figure 3.1:

Figure 3.2:
Figure 3.3:

Figure 4.1:

Figure 4.2:

Figure 4.3:

Figure 4.4:

Figure 4.5:

Figure 4.6:

Figure 4.7:

Figure 4.8:

Figure 4.9:

LIST OF FIGURES

Effect of the influence of root growth, die-off and regrowth on saturated

hydraulic conductivity (Smucker et al., 1995) .............. 22
Diagram of constant head permearneter for measuring saturated hydraulic

conductivity .............................. 28
Diagram of instrument location within the columns ............ 32

Initial saturated hydraulic conductivity (Ksat) for each treatment group. 38

Average measured saturated hydraulic conductivity of the four crop
treatments ................................ 4]

Normalized Ksat values. *Rye line reflects only the growth period
growth. ................................ 42

Representative chloride and bromide breakthrough curves ......... 43

Fitted cumulative distribution function of the chloride breakthrough
measured in the pre-treatment columns ................. 44

Fitted cumulative distribution function of the bromide breakthrough
measured in the different crop treatment columns ............. 45

Fitted cumulative distribution fimction of the chloride and bromide
breakthrough measured in the bare columns ............... 46

Cumulative E. coli eluted by treatment. Standard error bars indicate the
dispersion in sample data ........................ 49

Breakthrough concentration of P22 bacteriophage and E. coli in the
bare/manure treatments ......................... 51

Breakthrough concentration of P22 bacteriophage and E. coli in the
rye/manure treatments ......................... 52

Figure 4.10: Breakthrough curves of bromide and P22 for the bare/manure treatment. 53

Figure 4.11: Breakthrough curves of bromide and P22 for the rye/manure treatment. 53

Figure 4.12: Concentration of the P22 bacteriophage in each of the three replicates for

the bare/manure and bare/water treatments. .............. 54

ix

Figure 4.13

Figure A1:

Figure A2:

Figure A3:

Figure A4:
Figure C1:

Figure C2:

Figure C3:

Figure C4:

Figure C5:
Figure C6:
Figure C7:
Figure C8:

Figure C9:

Figure C10:
Figure C11:
Figure C12:
Figure C13:
Figure C14:
Figure C15:

Figure C16:

: Concentration of the P22 bacteriophage in each of the three replicates for

the rye/manure and rye/water treatments ................ 55
Wiring diagram for Campbell Scientific AM32 Multiplexer ........ 65
Empirical cumulative distribution function for the 50% chloride
breakthrough for each of the treatment groups .............. 71
Empirical cumulative distribution function for the 50% bromide
breakthrough for each of the treatment groups .............. 71
Box plot of E. coli concentration by treatment and sample number. . . . .73
Construction of the bottom of the column ................. 84
Campbell Scientific 107 temperature sensor ............... 84
Decagon Devices ECHZO probe moisture sensor, side view ........ 85
Decagon Devices ECHgO probe moisture sensor, top view ........ 85
Campbell AM32 multiplexer with sensors attached ............ 86
Instrumented columns .......................... 86
Overview of columns in greenhouse ................... 87
Another overview of columns in greenhouse ............... 87
Saturation tank for saturated hydraulic conductivity ............ 88

Saturated hydraulic conductivity set up ................. 88

Set up of a rain event .......................... 89

Sample collection during a rain event. ................. 89

Rain event sample collection, d. corn-rye/manure treatment ....... 90

View into column after rain event .................... 90

Membrane filtration lab set up ..................... 91

An example of blue fluorescing E. coli colonies under UV light ..... 91

Figure D1:
Figure D2:
Figure D3:
Figure D4:
Figure D5:
Figure D6:
Figure D7:
Figure D8:
Figure D9:
Figure D10:
Figure D11:
Figure D12:

Figure D13:

Figure D14:

Figure D15:

Figure D16:

Figure D17:

Figure D18:

Figure D19:

February temperature data ....................... 100

March temperature data ........................ 100
April temperature data ................. ’ ........ 101
May temperature data ......................... 101
June temperature data ......................... 102
July temperature data ......................... 102
August temperature data ........................ 103
September temperature data ...................... 103
October temperature data ....................... 104

November temperature data ...................... 104

December temperature data ...................... 105

January temperature data ....................... 105

Soil temperature during the week before the bare, no manure simulated
rain event .............................. 106

Soil moisture during the week before the bare, no manure simulated
rain event .............................. 106

Soil temperature during the week before the bare, manure simulated
rain event. ............................. 107

Soil moisture during the week before the bare, manure simulated rain
event ................................ 107

Soil temperature during the week before the rye, no manure simulated rain
event ................................ 108

Soil moisture during the week before the rye, no manure simulated rain
event ................................ 108

Soil temperature in column 35 during the week before the rye/manure
simulated rain event ......................... 109

xi

Figure D20:

Figure D21:

Figure D22:

Figure D23:

Figure D24:

Figure D25:

Figure D26:

Figure D27:

Figure D28:

Figure D29:

Figure D30:

Figure D31:

Figure D32:

Soil moisture in column 35 during the week before the rye, manure
simulated rain event ......................... 109

Soil temperature in column 36 during the week before the rye, manure
simulated rain event ......................... 1 10

Soil moisture in column 36 during the week before the rye, manure
simulated rain event ......................... 110

Soil temperature in column 42 during the week before the rye, manure
simulated rain event ......................... 111

Soil moisture in column 42 during the week before the rye, manure
simulated rain event ......................... 1 11

Soil temperature during the week before the desiccated corn, no manure
simulated rain event ......................... 112

Soil moisture during the week before the desiccated corn, no manure
simulated rain event ......................... 112

Soil temperature during the week before the desiccated corn, manure
simulated rain event ......................... 1 13

Soil moisture during the week before the desiccated corn, manure
simulated rain event ......................... 1 13

Soil temperature during the week before the desiccated corn/rye, no
manure simulated rain event ..................... 114

Soil moisture during the week before the desiccated corn/rye, no
manure simulated rain event ..................... 114

Soil temperature during the week before the desiccated corn/rye,
manure simulated rain event ..................... 115

Soil moisture during the week before the desiccated corn/rye, manure
simulated rain event ......................... 115

xii

CHAPTER 1
1. INTRODUCTION

In Michigan and throughout the Midwest, drainage improvement is a common
practice (Fausey et al., 1995; Brown and Ward, 1997). The Great Lakes states include
three of the top four states (Illinois, Indiana, Iowa, and Ohio) in the US. in the area of
land with drainage improvement. Drainage improvement in these states accounts for 37
percent of the total cropland in the region, over 20.6 million hectares of drained land
(U SDA-ERS, 1987). Much of this land is in animal production areas where animal
manure is returned to the land as part of an important nutrient cycling, soil improvement
and manure treatment program. Some livestock producers have reported high bacteria
levels in drain effluent following manure application even though they followed specific
guidelines outlined in comprehensive nutrient management plans (CNMP’s). The first
rainfall produced the greatest bacterial concentrations irrespective of the application
technique or length of time the manure was on the ground (Saini et al., 2001; McMurry et
aL,1998)

Contamination of surface and groundwater from the land application of livestock
manure has been well documented (Crane et al., 1983; Mawdsley et al., 1994; Pell,
1997). Preferential flow through macropores and soil cracks has been shown to
contribute to the movement of manure contaminants to subsurface drains (Fleming and
Bradshaw, 1992; Shipitalo and Gibbs, 2000; Jamieson et al., 2002). Macropores are

large, continuous openings in the soil formed by plant roots, soil fauna, cracks, fissures

and other natural phenomena. Such pollutants contribute to the eutrophication of surface
waters, and water-borne pathogens are an immediate health threat. Shelton et al. (2003)
reported that the average velocity of bacteria transport was seven time faster than the
average pore velocity of the water, indicating that most of the flow bypassed the soil
matrix.

Over the last several years, many farmers have adopted low-disturbance tillage and
soil conservation practices that improve profitability and protect the environment.
Cropping systems that reduce tillage intensity and increase the use of cover crops
improve soil quality and protect the environment in many ways. Tillage can influence
the flow of water and transported pollutants in surface and subsurface water by altering
the orientation of soil aggregates, disconnecting preferential flow channels and soil
cracks, anchoring plant residues within the surface layer, and inducing physical changes
in the soil macro- and micro-pore structures. No-till soils often have more continuous
flow channels (macropores) than tilled soils (Shipitalo and Protz, 1987; Drees et al.,
1994; Pagliai et al., 1995), and this may contribute to the rapid movement of liquid
manure to subsurface drains in no-till cropping systems.

When manure was applied to a bare soil surface, near-surface filtration and
accumulation of bacteria increased the chance of contaminant transport in the runoff
water (Crane et al., 1980). When manure slurry is applied to a vegetative surface, the
near-surface zone of high biomass and organic matter can enhance adsorption, straining

and filtering of enteric bacteria. Lim et al. (1998) showed complete coliform removal of

up to 2x107 colony forming units (ch1) per 100 ml, in passing through a 6.1 m wide, tall

fescue filter strip. Coyne et a1. (1995) reported 43 -74% removal of coliforms, up to 108

cfu per 100 ml, in passing through a 9 m wide mixed Kentucky bluegrass and tall fescue
filter strip.

While cover crops have been shown to be effective in mitigating overland flow of
sediment and bacterial contaminants, little is known regarding the effects of a cover crop
on bacterial movement through the soil. In Michigan, liquid manure is often applied after
corn grain or corn silage harvest when the corn roots are in decay. Roots create channels
through which bacteria may travel and which may reduce the filtering effects of soil
particles (Dazzo, 1972), but roots also may preferentially inhabit macropores created by
the decayed roots of the previous crop and thus restrict water movement through the soil
(Smucker et al., 1995). And, there is evidence that some plants create a rhizosphere
environment favorable to the survival of enteric pathogens while others may create a
more unfavorable environment. Gagliardi and Kams (2002) reported greater persistence
of E. coli 01572H7 on rye roots (47-96 d) and alfalfa roots (92 (1) than in bare soil (25-41
d), but the persistence on crimson clover and hairy vetch roots were similar to bare soil.

In livestock cropping systems, the persistence and retention of microbial
contaminants in the root zone may be important considerations when selecting cover
crops for water quality protection. Where manure land application and sub-surface
drainage coexist, a vegetative cover and active root system may provide an important
opportunity to mitigate contaminant loss to the environment through sub-surface drains.
There is a need to evaluate root systems as mechanisms for remediation of microbial

contaminants and enteric pathogens.

The overall goal of this work was to improve the management of soil for water

quality protection in livestock-based agro-ecosystems. The specific objectives were to:

1.

Evaluate the effects of a growing or decaying root system on the saturated
hydraulic conductivity and breakthrough for a bromide tracer in loamy-sand soil
columns.

Evaluate the effects of a growing or decaying root system on the bacteriological
water quality of effluent from soil columns following the application of liquid
swine manure and a simulated 25-year storm event.

Evaluate the breakthrough of the P22 bacteriophage and E. coli from swine
manure applied to soil columns as a potential in-tank tracer for linking specific

on-farm manure applications with water quality impacts.

CHAPTER 2

2. LITERATURE REVIEW
2.1 Microbial water quality

Water contamination has caused many disease outbreaks in the last several decades,
and can cause widespread disease in communities as large populations are served by
distribution systems (Guan and Holley, 2003). The increase in outbreaks has been
attributed to certain pathogens due to a greater ability to identify specific organisms, and
increased surveillance and reporting requirements. E. coli 0157:H7 was not identified as
a human pathogen until 1982 (Kudva et al., 1998). E. coli 0157:H7 and
Cryptosporidium are among the most well known causes of waterborne disease
outbreaks. Since 1985, there have been 12 documented outbreaks of Cryptosporidium in
North America (Rose, 1997). One of the largest and deadliest Cryptosporidium
outbreaks (403,000 cases, 54 deaths) occurred in Milwaukee, WI in 1993 (Auld et al.,
2004). Contamination of the Walkerton, ON, Canada municipal water supply by E. coli
0157:H7 in May 2000 sickened 2,300 people and resulted in seven deaths (Unc and G053,
2003; Stratton et al., 2004).

Linking water pollution with specific sources or activities can be difficult. In many
cases, agricultural sources have been suspected as the source of the fecal pathogens, but
this has rarely been confirmed (Guan and Holley, 2003). Besides the source, there is also
a statistically significant relationship between heavy rainfall and the occurrence of
disease outbreaks (Auld et al., 2004). In the Milwaukee case, cattle were the suspected

source of the Cryptosporidium oocysts, which contaminated the water supply (Rose,

1997). The contamination of the Walkerton, ON water supply by E. coli 0157:H7 and
Campylobacter occurred when runoff from farmland contaminated a municipal well after
several days of heavy rainfall. A rain event of this magnitude was only expected every
60 to 100 years (Auld et al., 2004).

2.1.1 Pathogens

More than 150 zoonotic pathogens have been identified in animal manure (Gerba and
Smith, 2005). Zoonotic pathogens are microorganisms that are capable of causing
disease in multiple species and are of particular interest in regard to human health when
dealing with animal waste. These pathogens include bacteria, protozoa, and viruses.
Some of the most well known pathogens are Escherichia coli 0157:H7, Salmonella,
Yersinia, Campylobacter, Giardia spp., and Cryptosporidium parvum (Pell, 1997;
Stratton et al., 2002; Gerba and Smith, 2005).

Pathogens commonly enter the environment through land application of manure from
livestock operations. The most commonly applied are dairy, swine, and poultry manures.
The concentration of pathogens in swine and poultry manure is generally higher than in
cattle manure (Unc and G055, 2004). Livestock operations have become increasingly
large leading to an increased amount of manure to distribute over a smaller land base and
farmers generally apply manure close to the source because it is expensive to transport.
Environmentally, this practice increases the likelihood of contamination of surface water
and groundwater from both nutrients and pathogens. A large part of the cropland in
Michigan is tile-drained and managed with no-till practices, thus increasing the number

of macropores and preferential flow pathways available. This combination of factors

allows for the rapid transport of water and pollutants directly into the tile lines and to the
surface water.
2.1.2 Indicator organisms

Pathogenic organisms present in the environment are often difficult to identify and
isolate. In order to monitor these organisms, indicator organisms are used. Fecal
indicator organisms are organisms that when present, indicate the possible presence of
fecal and enteric pathogenic organisms (USEPA, 2001). There are several characteristics
that an organism should have, to be useful as an indicator of water pollution. The
organism should: (1) not normally be present in the environment, (2) be easy to detect
using simple laboratory methods, (3) have concentrations that are correlated to the
pathogenic organism, but in greater concentration, (4) have a longer survival than the
pathogenic organism, and (5) not be able to replicate in the environment (USEPA, 2001;

Scott et al., 2002).

The most commonly used indicators are total coliforms, fecal coliforms, and E. coli.
Total coliforms are a group of bacteria found in animal and human feces (but also in
soil). This indicator is often used by regulators to assess the safety of drinking water and
groundwater that is going out for distribution. They are not as useful for environmental
samples as they can be naturally present in the environment. Fecal coliforms are often
used as an indicator organism for fecal contamination from warm-blooded animals and
the potential presence of human pathogens (Stratton et al., 2004). The fecal coliforms
include E. coli, which is often used as an indicator because it is naturally found in high

numbers in fecal matter (Stratton et al, 2002; Table 2.1).

Table 2.1: fecal indicator

       
         
     
  
    
    
  
  

2.0xl
1.0x10
1.0x10

   

2.3x10
7.04x10
2.2x10

   

 
 

2.4xl
1.4x108
8.9x10
1.1x 10l
l.8xlO
1,2x1olo
* Metcalf and Eddy, 1991
2 ASAE, 199s

 
 

0

 
 

 
 
   

Enterococci, a sub-group of the fecal streptococci group, is of increasing interest
because it seems to better predict the risk of gastrointestinal illness from recreational
exposure (USEPA, 2001; Scott et al., 2002). For ambient waters, the EPA recommends
the use of E. coli and enterococci rather than fecal coliforms, because they show a
stronger correlation to gastroenteritis associated with swimming (USEPA, 2001; Table

2.2).

Table 2.2 EPA recommended criteria for indicators (USEPA, 2001).

 

 

Designated Indicator Criteria
Use
Recreation E. coli Geometric mean of 126 cfu per 100 ml over a 30-day

period with no sample exceeding the:
75% Confidence limit (CL) for designated bathing beach
82% CL for moderate use for bathing
90% CL for light use for bathing
95% CL for infrequent use for bathing

 

Enterococci Geometric mean of 33 cfu per 100 ml over a 30-day period
with no sample exceeding the 75 to 95% confidence limit
based on the designated use as shown in E. coli criteria.

 

 

 

 

 

 

Fecal Geometric mean of 200 cfu per 100 ml over a 30 day
coliform period and no more than 10 percent of the samples
exceeding 400 cfu per 100 ml.
Public E. coli 90 % of the daily water samples must be under 100 cfu per
drinking 100 ml to remain unfiltered.
water Enterococci 90 % of the daily water samples must be under 20 cfir per
sources 100 ml to remain unfiltered.
Fecal 10 cfu per 100 ml as an annual average for lakes and
coliform reservoirs
50 cfu per 100 ml as an annual average for flowing rivers
and streams

 

Important criteria for indicator organisms are an inability to survive for long periods
outside of the host, and an inability to multiply in the natural environment (Stratton et al.,
2002). There is evidence that E. coli are not reliable indicators under certain conditions
in the soil environment. Regrowth of E. coli has been documented after a summer
manure application (Stratton et al., 2004). Two to three month survival for enteric
microorganisms are typical, but some have been documented to survive for as long as
five years (Mubiru et al., 2000). E. coli and Enterococcus spp. from swine has been

shown to survive in soil for 40 to 68 days (Unc and G033, 2004).

2.2 Factors affecting pathogen survival

Pathogens have been shown to have long survival periods in the soil environment and
several researchers have reported on the factors associated with the survival of enteric
organisms. Long survivals for E. coli 0157:H7 have been recorded depending on the soil
type (Guan and Holley, 2003). Survival of greater than 8 weeks was observed for E. coli
0157:H7 in moist soil at 25 °C (Mubiru et al., 2000). Survival for up to 90 days was
recorded under fluctuating environmental temperatures between -6.5 and 19.6 °C (Guan
and Holley, 2003). Some of the most important environmental factors affecting survival
include: moisture content, temperature, pH, exposure to sunlight, amount of nutrients and
carbon available, organic matter content, soil type, oxygen concentration, age of the
manure source, concentration of the source, and antagonistic effects and competition with
other soil micro-fauna (Holden and F ierer, 2005; Wang and Mankin, 2001; Dazzo et al.,
1973; Reddy et al., 1981; Warnemuende and Kanwar 2002).
2.2.1 Soil moisture and temperature

Soil moisture and temperature, such as cool, moist conditions are known to prolong
survival (Jamieson et al., 2002; Sjogren, 1994; Dazzo et al., 1973; Gerba et al., 1975).
Under saturated conditions, survival was 2 to 3 times longer at 5 ° and 10 °C than above
20 °C (Sjogren, 1994). Survival at field capacity (15% of saturation) was reduced for all
temperatures evaluated (Sjogren, 1994).

Soil texture influences soil moisture. In sand, because of rapid drying, the survival of
Salmonella typhosa was 4 to 7 days; in moist loamy sand, the organisms survived longer
than 42 days (Gerba et al., 197 5). Greater soil moisture was correlated with greater

numbers of fecal coliforms in the soil (Entry et al., 2000a).

10

Temperature has also been shown to be key to survival. Reddy et al. (1981) reviewed
several studies and reported that die-off of several enteric bacteria doubled with every 10
°C rise in temperature from 5 ° to 30 °C. E. coli survived longest at low temperatures -20,
4, and 23 °C (Kudva et al., 1998). The survival was greater at lower temperatures
because microbial metabolic rates were slower which enabled stressed organisms to
survive longer and adapt to the different environment (Guan and Holley, 2003).
However, J arnieson et a1. (2002) noted several studies indicating that freezing and
thawing cycles were detrimental to bacterial survival. Kibbey et a1. (1978) reported that
treatments with the most freeze-thaw cycles had the fewest survivors.

2.2.2 Nutrients, carbon, soil texture and organic matter

The proportion of fine soil particles and organic matter have a large influence on
moisture retention (J amieson et al., 2002). Three times greater survival of E. coli was
observed in soils with fine texture and high organic matter than in coarse textured soil
(Wang and Mankin, 2001). Microbial activity is enhanced on silt and clay particles
partly due to its ability to concentrate nutrients through adsorption (Dazzo, 1972). Fine
textured soils also have the ability to provide refuge from predators (Wang and Mankin,
2001). Soil texture seems to play an important role in the vertical transport of fecal
coliforms; they are transported deeper in a sandy loam than a clay loam (Roodsari et al.,
2002)

In agricultural production, soil tillage affects the level of nutrients in the soil; fields in
no-till cropping systems have greater levels of carbon, nitrogen, and organic matter than
conventionally tilled land (Doran, 1980). Holden and Fierer (2005) reported that

microbial biomass was greatest near the surface and declined rapidly with depth due to

11

the decreasing availability of nutrients. The die-off of E. coli in soil is negatively related
to nutrient availability and the ability to sustain metabolic activities (Sjogren, 1994).
Entry et al. (2000b) reported that fecal coliform concentrations were greatest in the top 5
cm of the soil. Gagliardi and Kams (2000) reported that levels of coliform bacteria and
E. coli 0157:H7 were positively correlated with nitrogen (NH3 and N03) in the leachate;
perhaps the organisms survived longer in the presence of available N, or were moved by
chemotaxis following N sources through the soil. Coliform and E. coli levels were not
correlated with phosphate content or turbidity, indicating that these organisms did not act
as particulates.

2.2.3 pH, sunlight, and oxygen concentration

Most enteric organisms survive best in neutral to alkaline pH environments (Sjogren,
1994; Warnemuende and Kanwar, 2002).' Reddy et al. (1981) reported that the survival
of enteric bacteria outside the pH range of 5.8 to 8.4 was adversely affected. The
survival rate in acidic peat soil (pH 2.9-4.5) was very low (Unc and G035, 2004). The pH
of the soil also affected the adsorptive capacity of the soil; lower soil pH was better for
sorption (Marshall, 1971).

Sunlight has a detrimental affect on survival (Gerba et al., 1975; Bell, 1976). It is
unclear how much of an effect ultra-violet (UV) light has on the survival of bacteria in
surface applied manure. It is impossible to isolate the effects of UV light, high
temperatures and low humidity in field data because they occur simultaneously (Bell,
1976). The increased die-off from sunlight appeared to be primarily from an increase in
temperature and drying effects rather than the exposure to UV radiation (Dazzo et al.,

1973). Pell (1997) noted that bacterial survival was limited by exposure to oxygen.

12

Enteric bacteria are adapted to the anaerobic conditions of the digestive tract. Anaerobic
conditions prolong fecal coliform survival (Wang and Mankin, 2001; Pell 1997).
Anaerobic conditions are also most likely to occur when the soil is saturated, increasing
the amount of moisture available.

2.2.4 Manure effects on soil and survival of enteric bacteria

The application of manure to soil increased nutrient availability and was shown to
extend the survival of enteric bacteria (Dazzo et al., 1973). Stratton et a1. (2004) reported
that manure significantly increased the number of E. coli in the soil. Manure increased
both the nutrient level and organic matter content of the soil. Organic matter is important
in bacterial survival and retention in several ways. Because of high levels of nitrogen in
manure, carbon is the limiting factor to bacterial growth (Unc and G055, 2004). The soil
environment is carbon-limiting, so increasing organic matter increases the amount of
carbon available for microbial growth (Buckley and Schmidt, 2002). Organic matter also
increases the available water capacity (Lal and Shukla, 2004) thereby increasing survival.
Adsorption and retention of enteric bacteria in the soil also increased with increases in
organic matter (Marshall, 1971).

The concentration and type of microbial organisms in manure depends on the source
animal, animal’s state of health and age, and how the manure was stored and treated
before use (Gagliardi and Kams, 2000; Crane et al., 1983). Young animals were shown
to be more likely to shed E. coli 0157:H7 (Pell, 1997). E. coli 0157:H7 remained
present in manure much longer after the animals stopped excreting it and this is thought

to be a source for reinfection of livestock (Guan and Holley, 2003).

13

Long-term storage (6 to 30 weeks) of manure decreased the concentration of fecal
coliform, total coliform, and fecal streptococci by more than 99%; however, reductions
were inconsistent when fresh manure was added to the storage structure (Patni et al.,
1985). E. coli 0157:H7 survived best at temperatures below 23 °C without aeration
(Kudva et al., 1998). Survival of more than a year was reported for E. coli 0157:H7 in
non-aerated ovine manure (Guan and Holley, 2003).

The addition of manure to soils can change the physical and chemical properties of
the soil. In no-till soils, the presence of manure enhances the survival of E. coli probably
due to the addition of nitrogen and enhanced microsite habitat (Gagliardi and Kams,
2000). Frequent addition of manure extended the survival of Salmonella enteritidis by
modifying the soil environment (Dazzo et al., 1973). Finally, at least one study found the
soil’s ability to strain and filter the microbes can be enhanced by the fibrous organic
matter from manure (Unc and Goss, 2004).

2.2.5 Soil fauna

Soil represents a complex web of physical, chemical, and biological interactions.
Many microenvironments provide niches supporting great species richness (Buckley and
Schmidt, 2002). In agricultural fields, there was greater biological activity near the
surface in no-till than with conventional tillage systems, and this provided favorable
conditions for the survival of indigenous soil microbes (Unc and Goss, 2004). Protozoa,
nematodes, bacteriophage, and Bdellovibro, a soil bacterium, prey on enteric bacteria
introduced in manure (Jamieson et al., 2002; Unc and Goss, 2004). Competition with
other soil microbes for nutrients was reported to be a major factor in the die-off of enteric

bacteria in the soil. Enteric organisms do not readily adapt to low nutrient availability.

14

Some native soil organisms produced antibiotics or toxic substances that inhibited the
growth of enteric organisms (Reddy et al., 1981; Warnemuende and Kanwar, 2002).
When enteric organisms were added to sterilized soil they exhibited greater persistence
and enhanced survival from an increase in available nutrients and a reduction in
inhibitory compounds (Guan and Holley, 2003; Jamison et al., 2002). The increase in
nutrients from manure application may also increase the predatory population.

2.3 Adsorption, filtering, and transport

The fate and transport of bacteria in the soil matrix are unpredictable because they are
subject to adsorption, physical filtration, growth, and death. The retention of bacteria
depends on soil chemistry, properties of the microbial cells, the physical configuration of
the soil, and the flow characteristics (Unc and Goss, 2004). Soil chemistry and the
properties of the microbial cells are the primary influences on bacterial sorption to the
soil particles. Flow characteristics and the configuration of the soil particle influence the
number of bacteria that are removed through filtration, and transported through water
movement.

Stamm et al. (2001) reported that transport processes affected nutrients and bacteria
in different ways. Bacteria had more interaction than many nutrients with the soil matrix,
because a lot of nutrients were water-soluble and bacteria were large particles subject to
filtering and adsorption (Stratton et al., 2004). Physical filtration depends on soil particle
size; fine textured soils with smaller pore space were more efficient filters. The removal
of bacteria was inversely proportional to the particle size of the soil (Gerba et al., 1975).
Significant removal of bacteria was achieved when the bacteria cell size was at least 5%

of the size of the soil particles (Warnemuende and Kanwar, 2002). Adsorption appeared

15

to be the predominate cause of retention within soil, but it was often difficult to separate
the adsorption and filtration processes (Reddy et al., 1981). Both living and dead cells
appeared to act the same with respect to adsorption and transport (Marshall, 1971).
2.3.1 Filtering

Soil is generally assumed to be an efficient biological and mineral filter for many
microbial organisms (Damault et al., 2004). There was a positive correlation between the
size of the bacteria and the fraction retained by the soil (Rockhold et al., 2004). Particle
size was important in the transport of E. coli under saturated flow in sand columns. The
most likely mechanism was physical entrapment based on particle size (Scholl et al.,
1990). Wang et al. (2003) reported that 97% of E. coli moved through coarse sand, but
only 67% moved through fine sand. Smaller pores were responsible for the majority of
retention, but particle adsorption capacity was larger for smaller soil particles (Wang et
al., 2003). Retention by filtration is likely less permanent and allows remobilization.
2.3.2 Adsorption

The factors influencing adsorption and desorption from surfaces are complex and
include van der Waals’ forces, electrostatic interactions, hydrophobic effects, and specific
adhesion (Rockhold et al., 2004). Two of the most important factors that affect
adsorption are the clay and organic matter content (Guber et al., 2005; Mawdsley et al.,
1994; Warnemuende and Kanwar, 2002). Increases in either clay or organic matter
increase the adsorptive capacity, but clay is the most important because of its large
surface area (Ling et al., 2002).

The air-water interface is an important site for bacterial adsorption. Powelson and

Mills (2001) reported that bacteria preferentially attach to the triple phase contact point

16

where air, water, and solid particles meet. Small amounts of air can drastically reduce
transport of hydrophobic bacteria (Powelson and Mills, 2001). Bacteria preferentially
colonize interfaces within the soil environment (Lehman et al., 2004). Compared to
hydrophobic effects, surface charge plays only a minor role in adsorption of bacteria to
mineral surfaces (Unc and Goss, 2004). Guber et al. (2005) reported that maximum
attachment occurred in the absence of manure. The addition of manure modifies the
properties of the soil and microbial surfaces because the presence of ions in the manure
alters the adsorption potential (Unc and Goss, 2004). Some bacteria produce
extracellular polymeric substances that buffer cells from desiccation and promote
adhesion (Rockhold et al., 2004).

2.3.3 Transport

Bacterial transport to subsurface drains is affected by the timing, method and rate of
manure application, soil structure, and rainfall intensity (Wang et al., 2001; Saini et al.,
2001). Bacteria do not readily move through the soil matrix under unsaturated
conditions, mainly due to the filtering and adsorptive properties of the soil and air-water
interface (Wang and Mankin, 2001). Gravity and capillary forces dominate unsaturated
transport processes in soil near saturation (Mohanty et al., 2001 ). Guber et al. (2005)
found that more E. coli were retarded in slower flow columns.

Transport of bacteria is mostly passive. The path followed by water determines the
direction of transport of the bacteria and the majority of bacterial movement happens
under saturated conditions (Jamieson et al., 2002; Powelson and Mills, 2001).
Researchers have reported that bacteria moved through preferential flow pathways

(Guber et al., 2005; Mawdsley et al., 1994). Preferential flow occurs through two

17

mechanisms: 1) redistribution on soil surfaces prior to leaching based on surface
elevation differences (some areas will infiltrate more liquid), and 2) movement through
macropores (McMurry et al., 1998). Preferential flow pathways mainly consist of
macropores; large continuous openings in the soil. Macropores are formed by plant
roots, soil fauna such as earthworms, and cracks. Factors that affect the initiation and
intensity of macropore flow are pore geometry, distribution, and continuity (Monhanty et
aL,2001)

Large, interconnected pores are primarily responsible for bacterial transport (Guber et
al., 2005; Scholl et al., 1990). Transport over significant distances requires that the pores
are several times larger than the dimensions of the bacterium (Unc and Goss, 2004).
During saturated conditions, water flowing through the macropores will bypass the
majority of the soil profile, thereby reducing the amount of filtration and adsorption that
can occur (McMurry et al., 1998). There can be significant transport through the soil
profile even when cracks are not visible and the application rate does not exceed water-
holding capacity of the soil (Shipitalo and Gibbs, 2000).

There is substantial proof of bacterial transport by preferential flow. Shelton et al.
(2003) reported that the average velocity of bacteria transport was seven times faster than
the average pore velocity of the water, indicating that most of the flow bypassed the soil
matrix. Even though most macropores have tortuous paths, the velocity of the water
flowing through them is much faster than the water that is moving through the bulk soil.
McMurry et al. (1998) reported that more than 50 percent of the drainage was collected in
less that 20 percent of the soil’s cross-sectional area. This indicates that bypass flow is

the rule rather than the exception in well-structured soils. There is also a significant

18

correlation between the percentage of drainage water exiting in an area, and the
percentage of the fecal coliforms that were in that water (McMurry et al., 1998). The
areas that had the most drainage also had the greatest concentration of coliforms. This
reinforces the understanding that bacteria are transported with water movement.

No-till soils often have more continuous macropores than conventionally tilled soils
due to the lack of tillage (Shipitalo and Gibbs, 2000; Stratton et al, 2005). Wang et al.,
(2001) found that disturbing the top 5-10 cm resulted in lower bacteria concentrations in
the leachate. Tillage disrupts the structure of the soil and the surface connection of the
macropores and retards the movement of bacteria (McMurry et al., 1998).

Timing and intensity of rainfall also influence the movement of bacteria through the
soil profile. It has been widely reported that the first rain event is crucial in leaching of
bacteria through the soil. The first rainfall produced the greatest bacterial concentrations
irrespective of the application technique or length of time the manure was on the ground
(Saini et al., 2001; McMurry et al., 1998). In one study, the percent recoveries of E. coli
ranged from 69.3% to 72.2% for the first rain event (Saini et al., 2001). Increasing the
intensity, frequency, or duration of rainfall, lead to greater movement of bacteria in the
soil (Saini et al., 2001). Stratton et al. (2005) found that the non-growing season was
responsible for more than 60% of the subsurface flow mainly due to the large amount of
precipitation and snowmelt during this period. There was also a correlation between flow
in the tile lines prior to manure application and the detection of bacteria in the tile lines
(Joy et al., 1998). Saturated and near saturated conditions prior to manure application

increases the probability of transport.

19

2.4 Rhizosphere effects

Anderson et al. (1993) described the rhizosphere as a zone of increased microbial
activity and biomass at the root-soil interface that is under the influenced of the plant
root. The rhizosphere is thought to influence the survival and growth of naturally
occurring soil microorganisms (Ibekwe and Grieve, 2004). The presence of plant roots
enhances microbial populations. The rhizosphere has a greater number of
microorganisms than the surrounding bulk soil because it provides surface area for
colonization, and root exudates or excretions are a source of nutrients (Anderson et al.,
1993). Roots lose between 40 and 60% of the total photoassimilates, during each
growing season (Smucker, 1984). They excrete several forms of soluble organic
chemicals and can shed as many as 10,000 cells per day per plant providing many of the
nutrients and carbon needed for bacterial growth (Ibekwe and Grieve, 2004; Anderson et
al., 1993). An additional benefit of the rhizosphere is that it may provide a refuge from
predators (Dazzo, 1972).

Little has been reported on the interactions between the rhizosphere and the fate and
transport of enteric organisms. Bacterial communities differ in different root zones.
There is continual change at the root-soil interface. These differences are dependent on
several factors including soil type, plant species, nutritional status, age, stress, disease,
and environmental influences (Yang and Crowley, 2000; Ibekwe and Grieve, 2004).
Some plant root systems may create a favorable environment for enteric organisms while
others may not (Dazzo, 1972). Gagliardi and Kams (2002) evaluated the persistence of

E. coli on the roots of various plants in the presence and absence of manure and reported

20

that rye roots with manure greatly increased the persistence and activity of E. coli
0157:H7.

There are considerable differences between the root structures of different species.
Root systems that exhibit primarily downward growth create root channels through which
the bacteria are transported with water movement, reducing the filtering capacity of the
soil (Mawdsley et al., 1994). In a lysimeter study, the removal of fecal coliforms was
most efficient in bare soil (99.8% removal). Amongst different root systems, millet (99.5
% removal) and sorghum (99.4% removal) were more efficient than oats (76.6%
removal) (Dazzo, 1972).

Other root system differences are a function of the amount of branching and surface
area. Fibrous grass roots provide a large surface area (Tufekcioglu et al., 1999). Even
among grass species, differences in root structure can be seen. Warm season grasses
usually have a greater root biomass than cool season grasses (Tufekcioglu et al., 1999).
Also, perennial crops have approximately five times more biomass than annuals (Bird et
al., 1998). Native grass species are tall, warm-season, sod forming grasses, that have
dense fibrous root systems that can extend 5 to 15 feet deep (Conservation Commission
of Missouri, 1980). Smucker et al. (1995) evaluated changes in saturated hydraulic
conductivity (Ksat) as influenced by plant roots. Corn roots increased the Ksat while
ryegrass roots caused a decrease. The die-off and decomposition of both root systems led
to large increases in the Ksat. Planting a cover crop of ryegrass after the corn roots had

decomposed reduced the Ksat to near pre-decomposition levels (Figure 2.1).

21

 

 

Root growth Root decomposition New root growth
14 ::

   
 
 
   

 

 

—o— Corn-Rye
—-— Unplanned-Rye
+ Rye-Rye

 

 

 

Ksat (Cmih r)

 

 

 

0 2 4 8 810121416182022
Time-(weeks)

 

 

 

Figure 2.1: Effect of the influence of root growth, die-off and regrowth on saturated
hydraulic conductivity (Smucker et al., 1995).

Planting of a cover crop is used to provide a living root system to absorb additional
nitrates and plug many of the macropores left by the previous crop (Smucker et al.,
1995). The root zone and rhizosphere of sod forming crops are zones of high biological
activity, able to utilize the high nutrient loading from a manure application and reduce the
likelihood of leaching. The rhizosphere may also prove to be useful in retaining the
bacteria present in manure. An increase in the survival of enteric organisms in the soil
will not significantly impact water quality if they are retained in the root zone.

2.5 Tracers for hydrologic studies

When testing for the presence of fecal contamination, E. coli are often the indicator
organism used to gauge the likelihood of pollution of surface and ground waters from
animal sources (Crane et al., 1983). E. coli has some capacity to multiply in the natural
environment and survive for long periods outside a warm-blooded host. Depending upon
the environment, it has been shown to reproduce under favorable conditions (J amieson et

al., 2002) and survive for extended periods at low temperatures. This ability to

22

reproduce, and the presence of multiple sources (e. g. from wildlife), can confound
experimental results. Artificial tracers provide an unequivocal way to quantify the
hydraulic properties of an aquifer, track the rates and pathways through a hydrologic
system, or link specific sites of contamination to discharge points.

Many methods in microbial source-tracking use DNA fingerprints from a known
reference library to identify the probable source or sources (Scott et al., 2002). Other
methods look for a specific species of virus that is exclusive to the host of interest. An
ideal tracer is chemically stable, conservative, inexpensive, and easily detectable;
interacts predictably within the system into which it is introduced; and is readily

available. Tracers need to be detectable at low concentrations and have low natural,

background concentrations. Rhodamine WT (C29H2905N2Na2Cl) and sodium

fluorescein (ConloOsNaz), referred to as fluorescein dye or simply fluorescein, are

fluorescent dyes commonly used in ground water systems. Fluorescent dyes need to be
used with caution because they are known to adsorb to subsurface media (Kasnavia et al.,
1998; Trudgill, 1987; Omoti and Wild, 1979; Smart and Laidlaw, 1977), and can break
down when exposed to sunlight.

In managing animal manure, there is a need for an effective and inexpensive tracer
for linking management actions with contamination sites. Studies of fecal contamination
and transport often use a bacteriophage along with other tracers and indicators to track
the movement of pathogens in the soil environment (McLeod et al. 2001, 2003; Vidales
et al., 2003; Nicosia et al., 2001). Bacteriophages are generally safer than bacterial
tracers, and they provide a better simulation of enteric virus movement characteristics

and lifespan (Sinton and Ching, 1987; Leclerc et al., 2000). Bacteriophage assays are

23

less expensive and easier to perform than enteric virus detection techniques (Leclerc et
al., 2000). A phage is often paired with bromide to compare the phage movement with
that of a conservative tracer. McLeod et al. (2001, 2003) found that the host specific
Salmonella phage moved more rapidly than the bromide tracer. Nicosia et al. (2001)
reported that the PRD-l bacteriophage and a bromide tracer were detected
simultaneously. Differences in breakthrough are due in part to the virus removal
capacities of the soil. Adsorption capacities increase with clay content, cation exchange
capacity, and surface area, and decreased with increased organic content (Nicosia et al.,
2001).

The bacteriophage PRD-l is a virus that infects the bacterium Salmonella
typhimurium as its host. Several aspects of this organism make it useful as a virus/colloid
transport model: 1) its size and transport properties are similar compared to human
enteric viruses, 2) detection methodology is relatively inexpensive and easy to perform,
3) it is not commonly found as a natural inhabitant of environmental waters, 4) it is
harmless to humans, animals or plants, and 5) it is rather persistent once introduced to
groundwater aquifers. PRDQI has been successfully used as a groundwater tracer in the
Florida Keys (Paul et al., 1995).

2.6 Column studies
2.6.1 Intact or undisturbed vs. repacked or disturbed columns

Several researchers have used repacked (Wang et al., 2003; Ling et al., 2002:
Gagliardi and Kams, 2000; Powelson and Mills, 2001; Brown et al., 2001) or intact
(Warnemuende and Kanwer, 2002; Gagliardi et al., 2001; Guber et al., 2005; Saini et al.,

2001) soil columns to evaluate the fate and transport of E. coli in the soil environment.

24

The soil in repacked/disturbed columns is typically air-dried and passed through a sieve,
removing stones and other debris. Intact/undisturbed soil columns are whole soil units,
extracted by inserting a tube directly into the ground and then excavating around the tube.
These columns contain all of the macropores, root channels, and wormholes that were at
the location where they were taken.

The benefits of intact or undisturbed columns are that they better represent field
conditions and provide better predictive values for natural conditions (Saini et al., 2001;
Wang et al., 2001). From an experimental perspective, repacked columns create nearly
homogenous conditions which make it possible to isolate key factors of interest that
affect solute transport through the soil profile (Saini et al., 2001). A disturbed or
repacked column lacks the macropores and aggregate structure found in the natural
environment, and is more efficient at removing bacteria than an undisturbed column.
Smith et al. (1985) reported that repacked columns removed 93% of E. coli added as a
solution, whereas intact columns removed from 21 to 78% of the E. coli.

2.6.2 Column dimensions

Soil columns of varying dimensions have been reported in experimental work.
Column widths ranging from 5 cm (Wang et al., 2003) to 22 cm (Mohanty et al., 2001)
and column lengths of 15 cm (Brown et al., 2001) to 80 cm (Mohanty et al., 2001) have
been used. Recent intact column studies evaluating bacterial transport through bare soil
reported using cores 20 cm in diameter and 30 cm in length (Warnemuende and Kanwar,
2002; Wang et al., 2001; Saini et al., 2001). Gagliardi and Kams (2002) used repacked, 5
cm by 17.5 cm columns in evaluating the effect of plant roots on the rate of die-off of E.

coli bacteria. The plant roots became constricted after 28 days of growth.

25

CHAPTER 3

3. METHODS AND MATERIALS

Corn and cereal rye crops were sown in soil columns and liquid swine manure was
applied to evaluate the effects of root growth and die-off on the transport of microbial
organisms through the soil profile. Key indicators of root-induced changes in the flow
regime were saturated hydraulic conductivity (Ksat), E. coli concentration of the column
effluent following a simulated rain event, and the breakthrough of bromide, E. coli, and a
bacteriophage marker.

3.1 Soil columns
3.1.1 Soil column construction and filling

Repacked PVC soil columns 14.5 cm in diameter and 40 cm in length were prepared
using soil from the ‘sandhill’ site (42°40’50.28” N, 84°28’00.84” W) on the University
Farms in East Lansing, Michigan. The soil was gathered from the surface-to 20-cm
depth, air-dried, passed through a 2-mm sieve and mixed to provide a homogenous soil
mass. The particle size distribution based on the hydrometer method was 87.2% sand,
8.6% silt, and 4.2% clay (Table A2). The columns were filled to within 2.5 cm of the top

using a large funnel to minimize particle size separation and layering. The soil columns

were consolidated to an air-dry bulk density of 1.453 g/cm3 (standard deviation of 0.02

g/cm3). The base of each column was secured with several layers cheesecloth and a vinyl

mesh screen held in place with a steel pipe clamp (Figure C1). Table A1 lists the soil

26

column physical properties including dry bulk density (g/cm3), volume (cm3), porosity
(%), and pore volume (cm3/cm3).

3.2 Soil column flow regime

Several methods were used to evaluate changes in the soil column flow regime due to
root growth and die-off. The saturated hydraulic conductivity was measured at the
beginning of the experiment for all columns, and then at the end of every root growth
period, after root decomposition, and upon completion of the experiment. Chloride
breakthrough was evaluated at the beginning of the trials, and bromide breakthrough was
evaluated upon completion of the experiment.
3.2.1 Saturated hydraulic conductivity

Saturated hydraulic conductivity (Ksat) measurements were made with a constant
head permearneter to evaluate the water flow rate (cm/hr) through the columns. Soil
cores were slowly wetted from the bottom, in a large tank, over three days, to ensure that
the soil was completely saturated and without entrapped air (Figure C9). Two soil cores
were tested concurrently in an apparatus similar to the one shown in Figure 3.1. A photo
of the actual set up is shown in Figure C10. The downward vertical flow method was
used, meaning that the water moved vertically through the column from the top to the
bottom. A constant head of water was maintained over the soil core to measure the
saturated hydraulic conductivity under steady state conditions. The saturated hydraulic
conductivity was calculated using a variant of Darcy’s Law as:

V*L

 

Ksat : A* I T (H+L)

27

where:
Ksat = saturated hydraulic conductivity (cm/h)
V = cumulative outflow at the base of the core (cm3)
A = surface area of the soil core (cmz)
t = time interval (h)
H = hydraulic head of the free water above soil surface (cm)
L = length of soil core (cm)

Measurements were taken of the outflow, time interval of collection, hydraulic head
over the sample, cross-sectional area of the soil core, and length of the soil core, in order
to calculate the hydraulic conductivity. Soil characteristics that affect saturated

conductivity are total porosity, the distribution of pore sizes, and pore tortuosity.

 

 

 

Constant
Head
Resemor

 

 

 

 

 

Water Supply Research

L
Soil Core

Y
Li

Figure 3.1: Diagram of constant head permeameter for measuring saturated
hydraulic conductivity.

 

 

 

 

 

 

 

28

3.2.2 Anionic tracers

When a liquid of varying concentration from that in the soil pores is introduced to a
soil column, there is a gradual mixing of the two liquids by diffusion and hydrodynamic
dispersion. Plots of the outflowing solution’s composition versus time or pore volume
applied are called breakthrough curves. Ideal breakthrough curves are sigmoidal in shape
with the inflection point representing 50% displacement at a cumulative flow of one pore
volume if the soil is saturated (Hillel, 1998). Bromide tracers are typically the first
choice for an anion tracer due to its lower concentration in the environment. Most studies
(Clay et al., 2004; Hatfield et al., 1997; McLeod et al., 2001; McLeod et al., 2003) use
bromide as a conservative tracer. In this experiment, both chloride and bromide tracers
were used to characterize the flow regime of the columns. Chloride was used first to
determine a baseline breakthrough for anion movement, at the end of the experiment
bromide was used to determine if there were changes in the breakthrough of anions due
to the applied treatments.
3.2.2.1 Chloride tracer

A chloride tracer was used at the beginning of the experiment to provide baseline
information regarding the miscible displacement of soil borne solutes. A solution
concentration of 200-ppm of potassium chloride (KCl) was used to fall within the range
of the ion selective electrode (Thermo Orion, 0 to 200 ppm, i 1.8 ppm) that was used to
quantify the samples. A peristaltic pump was used to apply 250 ml of the 200-ppm KCl
solution to the columns at a rate of 15 ml/min, followed by irrigation of tap water at the
same rate. This application rate was chosen to simulate a one-hour, 25-year storm event

for the region of southwestern Lower Michigan, approximately 5.33 cm/hr. Samples

29

were collected every 15 minutes until at least 1.25 pore volumes had been applied (3.5 to
4 liters). The applied volume was chosen to ensure that the whole breakthrough curve
was collected.
3.2.2.2 Bromide tracer

At the end of the experiment, a bromide tracer was used to evaluate treatment effects
on the movement of anions through the soil column. A tracer concentration of ZOO-ppm
sodium bromide (NaBr) was chosen to fall within the range of the ion selective electrode
(Cole Parrner, 0.4 ppm to 1000 ppm, i 2%). Following the procedures established for the
chloride tracer, 250 ml of the NaBr solution was applied to the columns, followed by
irrigation water at the same rate. This was equivalent to about 4 hours of irrigation. This
bromide breakthrough was compared with the chloride breakthrough to identify treatment
induced changes in miscible displacement.
3.3 Experimental treatments

Treatments were selected to evaluate the effects of root growth, die-off and regrowth
on saturated hydraulic conductivity and transport of E. coli bacteria through a soil
column. Eight treatments with four replications (32 soil columns) were used to represent
four conditions in a natural agro-ecosystem: 1) bare soil with no roots, 2) living root
system with no previous crop, 3) a desiccated root system, and 4) regrowth of a living
root system (cover crop) following the die-off and decay of the root system of the
previous crop. These four conditions were evaluated both with and without manure.

The eight treatments represent a full factorial experimental design with two factors;
crop (bare, rye, desiccated corn and desiccated corn replanted with rye), manure

(without and with). Table 3.1 shows the eight treatments.

30

Table 3.1: Eight treatments

 

 

 

 

 

No Manure Manure
Bare 1. Bare , 2. Bare/manure
Rye 3. Rye 4. Rye/manure
Desiccated Corn 5. D. Corn 6. D. Corn/manure
Desiccated Corn with Rye 7. D. Com-Rye 8. D. Corn-Rye/manure

 

 

 

Details pertaining to each planting treatment are below.
0 Treatments 1 & 2 — Bare soil, with and without manure — The bare soil treatments
were the first to receive the manure application and be sampled.
o Treatments 3 & 4 — Rye, with and without manure — Cereal rye was planted and
allowed to grow for 10 weeks before the manure application and sampling period.
- Treatments 5 & 6 — Desiccated corn roots, with and without manure — The corn

was planted and allowed to grow for 6 weeks, when it reached the 6th to 7th leaf

stage. The corn was killed and the roots decomposed for 12 weeks. These
treatments received a manure application before the simulated rainfall.

o Treatments 7 & 8 — Rye planted in columns that had previously contained corn,
with and without manure — The corn grew for 6 weeks before it was killed and the
roots decayed for 10 weeks. Cereal rye was planted and grew for 10 weeks before
the manure application and simulated rainfall.

3.4 Instrumentation

Ten of the 32 soil columns were instrumented with three temperature sensors (107,
Campbell Scientific, Figure C2; Figure 3.2) and three soil moisture sensors (ECHzO, 10

cm, Decagon Devices, Figure C3 and C4; Figure 3.2). See Figure C6 for a photo of the

instrumented columns. One column within each treatment was instrumented, and one

31

treatment had three columns instrumented. Additionally, ambient air temperature and

battery voltage were monitored and recorded.

   

   

 

 

tr—
Rectangles = soil morsture
Cylinders = temperature
Top sensors
40 cm
' Middle sensors
Bottom sensors
10 cm
schons

 

 

“\I

Not to scale

Figure 3.2: Diagram of instrument location within the columns.

A CR10 data logger and an AM416 multiplexer (Campbell Scientific) were used to
record the data. Readings were taken every 5 minutes and an average value was recorded
every 15 minutes. The program controlling the soil moisture and temperature collection
was written by Jason Ritter at Campbell Scientific and is included is appendix E. A
wiring diagram for the multiplexer is shown in Figure A1. A photo of the data logging
system is shown in Figure C5.

The sensors were connected as single ended inputs to attach the maximum number of

sensors to the multiplexer. A maximum of five columns could be attached to the data

32

logger at a time. This set up was feasible, as the experiment was staged over time to

allow for the differing lengths of time needed for the growth of the various treatments.
Several of the ECHzO soil moisture probes gave false readings at some point during

the experiment. They would periodically drop to zero and then return to the previous
reading. When this happened, the sensor was replaced.
3.5 Growing conditions

The soil columns were kept in the plant and soil sciences greenhouse on the MSU
campus (Figures C7 and C8). Two grow lights mounted above the soil columns were on
from 6 am to 10 pm daily. Water was added to the soil columns periodically throughout
the experiment to prevent drying and cracking. The columns that contained growing
plants were watered daily and nutrient solution was added once a week. The composition
of the nutrient solution is listed in Table A4.

The soil temperature data recorded throughout the experiment showed large
fluctuations over the course of a 24-hour period. The maximum temperature occurred
during the day and the minimum temperature occurred at night and did not fall below 15
°C. The ambient temperature was usually above 20 °C in the greenhouse. Table D1 lists
the average daily minimum and maximum soil temperatures, and minimum and
maximum daily ambient temperatures (Figures Dl-D12).

3.6 Manure application procedure

The manure was collected from the swine nursery two days prior to application. It
was sieved through a 2-mm mesh to remove any large pieces. Two days prior to the
manure application, the columns were deeply wetted and allowed to drain to bring them

to near field capacity when the manure was applied. The amount of manure applied, 93

33

ml per column was equivalent to 56,122 l/ha (6000 gpa). The same procedure was
followed for the treatments without manure except that an equal amount of water was
applied instead of manure. The manure characteristics are listed in Table A3.
3.7 Rain events and sample collection

Two days after the manure application, a one-hour, 25-year rain event was simulated
in which 5.33 cm of water was applied per column (Figure C11). The irrigation water
(source: greenhouse hose) was applied using a peristaltic pump at a rate of 15 ml/min.
Chlorine concentrations were not tested at the time of the rain events. However, the
background concentrations from the chloride breakthrough portion of the experiment
were approximately 25 ppm. Samples were collected every 10 to 15 minutes until flow
stopped (Figure C 12). The treatments that contained actively growing rye received one
and a half hours of irrigation in order to obtain samples (Figures C13 and C14). The
samples were kept on ice and were taken to the lab the same day for analysis.
3.8 E. coli quantification

The E. coli quantification was performed according to section 9222 G of the Standard
Methods for the Examination of Water and Wastewater (Clesceri et al., 1998). Water
samples were assayed for E. coli using the membrane filtration technique (Figure C15).

Several different serial dilutions were used to obtain colony counts within the countable
range, anywhere from 101 for non-manure column samples, to 10'3 for manure column

samples. The samples were diluted with sterile phosphate buffered water (PBW). Each
dilution was filtered under vacuum through a 0.45 pm pore-size membrane and then
plated on to EC Medium with MUG (Difco). Each dilution was run twice to provide a

replicate. Sterile filter housings were used for each sample and dilutions were filtered in

34

these housings from most dilute to most concentrated to reduce contamination between
dilutions. A negative control was also run after every sample. A positive control using
C-3000 E. coli (ATCC 15597) was run with each batch of media. The plates were
incubated at 44.5 °C for 24 i 2 hours before being read under a 365 nm long wave UV
light. The positive reaction for E. coli colonies fluoresced blue (Figure C16).
3.9 Breakthrough of P22 Bacteriophage

The breakthrough of the P22 bacteriophage and E. coli from an application of liquid
swine manure were evaluated to assess the potential as a microbial marker in the manure
slurry tank for linking specific manure application events with surface water
contamination.
3.9.1 Column preparation

Twelve columns were selected and divided into four treatments: 1) bare soil, no
manure, 2) bare soil, manure, 3) cereal rye cover, no manure, and 4) cereal rye cover,
manure. The bare columns from the previous experiment were reused, and the unused
columns were planted with rye. The bare treatments were run first to allow the rye to
grow. The rye treatments were run when the rye was 4 to 5 weeks old (approximately 40
to 50 cm tall). The same procedures for manure application and rainfall simulation were
used as for the earlier experiment. Two days prior to the manure application, the
columns were deeply wetted and allowed to drain to near field capacity. Swine manure
from the nursery at the MSU farm was used. The volume of manure applied, 93 ml per
column, was equivalent to 56,122 l/ha (6000 gpa). The treatments without manure
followed the same procedure except that an equal amount of water was applied instead of

manure. Both the manure and the water application contained the bacteriophage P22.

35

3.9.2 Bacteriophage P22 tracer preparation
The Salmonella phage P22 was obtained by Dr. Joan Rose from Dr. Charles Gerba,
University of Arizona, and was maintained on the host Salmonella typhimurium LT-2

(ATCC 19585). P22 stock were grown by inoculating 100-ml log-phase S. typhimurium
host with one milliliter of P22 stock (~ 10” pfu/ml) and incubated at 37 °C for

approximately 3-5 hours. After incubation, 0.01 g of lysozyme and three milliliters of 0.2
M sterile EDTA were added to the flask and mixed well. The culture was then
centrifuged at 4000 rpm for 10-15 min and the supernatant was filter sterilized through a
0.45 pm membrane. P22 stock was prepared by the staff in Dr. Rose’s lab and stored at 4
°C until used.
3.9.3 Sample collection

Two days after the manure/water application, tap water was applied using a peristaltic
pump at a rate of 15 ml/min. The simulated rain event was 3 hours in length for the bare
columns and 4 hours for the rye columns. The longer irrigation period applied one to one
and a half pore volumes and was used to ensure that the breakthrough curve for the phage
would be collected. This time period was selected based on the simulated rainfall volume
and the measured breakthrough of the bromide ion. Nicosia et al., (2001) found that the
bacteriophage PRDI was detected simultaneously or shortly after the bromide. Samples
were collected every 10 to 15 minutes until flow stopped. The samples were kept on ice

and cultured the same day.

36

3.9.4 Sample analysis

Water samples were assayed for P22 bacteriophage following the double agar layer
procedure described by Adams (1959). Samples were diluted to at least 10'3

concentration and between 1 ml and 2 ml of each sample in triplicate were assayed for
the phage presence on tryptic soy agar (TSA). The plates were incubated for 24 h at 37
°C. The detection limit of this method is less than one plaque-forming unit per ml.

For the bacteriophage portion of the experiment, the total coliform and E. coli were
enumerated by using the Colilert®/Quanti-Tray®/2000 test kit (IDEXX Laboratories,
Inc., Westbrook, ME). One ml of sample was added to 99ml of phosphate buffered
saline (PBS) in a sterile plastic cup with pre-measured IDEXX reagent added. The
mixture was mixed well and poured into an IDEXX Quanti-Tray/2000 and incubated for
24 hours at 35 °C. After incubation, a total- colifonn-positive reaction turns the medium
yellow, and an E. coli-positive reaction causes the medium to fluoresce under a long-
wave ultraviolet light (366 nm). The results (in MPN/100ml) were calculated using the
MPN table provided by IDEXX for both E. coli and total coliforms.

3.10 Statistical analysis
3.10.1 Saturated hydraulic conductivity (Ksat)

The saturated hydraulic conductivity of each soil column was measured at the
beginning of the experiment. Each column was ranked by Ksat value (cm/h) and
assigned to quartile groups. Eight treatment groups of four columns per replication were
assembled with one column randomly selected from each of the four Ksat quartile
groups. Each group of four columns was then randomly assigned to one of the eight

treatments. A two-factor ANOVA with treatment and replication as factors was used to

37

test for significant differences in Ksat values between treatment groups. There were no

significant differences between treatments (p = 0.188; Fig. 3.3).

 

 

 

 

 

 

 

50
45
E
\ 40-
5.
E 35-
x
30.
¢ i . . Y ,
6‘“ y°°E $‘e aoo‘e’ °§‘° 36°‘e’ «‘69 $°o‘e
«\ «“6 “\“0 09° (‘0‘6‘
9 9‘ 9.00 «I
959

 

 

Figure 3.3: Initial saturated hydraulic conductivity (Ksat) for each treatment group.
3.10.2 Anionic tracers
3.10.2.1 Chloride

At the beginning of the experiment, a two-factor ANOVA with treatment and
replication as factors was used to evaluate the breakthrough of a chloride tracer. The
measures of anion breakthrough included the peak concentration (ppm), the pore volume
applied at the peak (cm3/cm3), and pore volume applied at 10% and 50% cumulative
chloride recovery. An empirical cumulative density function (CDF) graph for each
treatment was used to compare the distributions of the 50% chloride breakthrough.
3.10.2.2 Bromide

The breakthrough of a bromide tracer was evaluated for each treatment group at the
end of the experiment. The procedures used were the same as for the chloride tracer. A
two-factor AN OVA with treatment and replication as factors was used to evaluate peak

concentration (ppm), pore volume applied at the peak (ems/cm3), and pore volume

38

applied at the 10%, 50%, and 90% cumulative bromide recovery. A CDF graph was used
to compare the distributions of the 50% bromide breakthrough.
3.10.3 E. coli

E. coli concentrations were evaluated in the four manured columns and the bare/no
manure soil columns. The E. coli data were fit to a normal distribution using the natural
log of the measured E. coli concentration. A two-factor AN OVA was used to evaluate
the cumulative number of E. coli eluted and the percent retention of E. coli for each
treatment. The cumulative number of E. coli collected for each sample were calculated
by taking the concentration of the E. coli in each sample (chml) and timing that by the
total volume of sample collected (ml). Then the cumulative E. coli for each sample were
summed for all of the samples taken for each column.

An ANOVA was run on the manured and the bare treatments using the General
Linear Model (GLM) in Minitab (Minitab Inc., 2003) with crop (Bare, Rye, D. Corn, and
D. Corn/Rye), manure (manure and no manure), and replication (1-4) as factors. The

equation for the GLM was:

y = u + Crop + Manure + Rep + Crop *Rep

where y is the natural log of the E. coli concentration, ,u is the overall mean, Crop is the

crop main effect, Manure is the manure main effect, Rep is the replication main effect,
and Crop *Rep is the interaction term.

The null hypothesis of no difference between treatments was tested at a = 0.05.
Pairwise comparisons between crop, manure, replication, and crop x replication were

conducted using Tukey’s multiple comparison tests with a family error rate of 0.05.

39

CHAPTER 4

4. RESULTS AND DISCUSSION

Winter cover crops can temporarily immobilize nutrients, especially N, prevent NO3-
N leaching losses and reduce winter soil erosion. When manure is applied to a vegetative
surface, the near-surface zone of high biomass and organic matter could enhance
adsorption, straining and filtering of microbial organisms. However, below the soil
surface, roots create channels through which bacteria may travel and reduce the filtering
effects of soil particles, but in a managed agro-ecosystem, the growing roots of a cover
crop may fill the channels left by dessicated roots from the previous crop and inhibit
preferential flow to subsurface drains or shallow ground water. This work evaluated the
effects of root growth, die-off, and regrowth on saturated flow, the breakthrough of
anionic tracers, and the movement of microbial organisms through recompacted soil
columns.
4.1 Measurement of the column flow regime
4.1.1 Saturated hydraulic conductivity

The saturated hydraulic conductivity (Ksat) was measured at the beginning of the
experiment for all columns, and then at the end of every root growth period, after root

decomposition, and upon completion of the experiment (Figure 4.1 and Table A5).

40

 

 

0 5 10 15 20 25 30
Time (weeks) . ,7- i
Figure 4.1: Average measured saturated hydraulic conductivity of the four crop
treatments.

 

The measured Ksat of the bare columns decreased fi'om near 40 cm/h at the beginning
of the experiment to between 10 and 15 cm/h at the end, presumably from soil settling.
Because the Ksat values changed over time, the measured treatments were normalized
based on the bare column Ksat values. The N(Ksat) was a measure of the change in
saturated hydraulic conductivity of each treatment relative to the Ksat of the bare
columns (Figure 4.2).

The treatment Ksat values were normalized as:

N(Ksat) = (Ksat treatment - Ksat bare)
Where:
N(Ksat) is the normalized saturated hydraulic conductivity, cm/h.
Ksat treatment is the average measured saturated hydraulic conductivity for the
treatment of interest, cm/h.

Ksat ban, is the saturated hydraulic conductivity of the bare soil columns, cm/h.

41

The Ksat of the bare columns was measured twice, at the beginning of the experiment
and after 18 weeks. Because the greatest amount of settling likely occurred early in the

trial, an exponential decay function was used to estimate the change in bare Ksat values

 

 

 

 

 

 

 

 

 

 

 

 

 

over time.
12.00
Root growth Root decomposition New root growth
9.00 —
_. r -------------------------- _,
E 6.00 -
E
.2.
‘6
Q 3.00 -
<1
+ D. Corn
0.00 ..... Rye"
+ Bare
-o—- D. Corn/Rye
-3.00 r i . . f
0 5 10 15 20 25 30
Time (weeks)

 

Figure 4.2: Normalized Ksat values. *Rye line reflects only the growth period.

The initial corn growth in the d. corn and the d. corn/rye treatments lowered the
saturated hydraulic conductivity by compressing the soil and reducing the pore space.
The rate of change in the Ksat during this initial growth ranged from -0.5 crn/h-week (d.
corn) to approximately -0.75 cm/h-week (d. corn/rye; Figure 4.2). In contrast, the rye
growth caused an increase in the rate of change of 0.3 cm/h-week. Because the rye roots
were smaller in diameter than the corn roots there may have been less lateral expansion
and soil compression with the rye.

The Ksat values increased when the corn plants were killed and the roots began to
decay. Root decay increased the Ksat likely by creating macropores in the root channels
that enhanced the rate of water movement through the soil. The rate of increase in the

Ksat during the decay period was 0.5 to 0.8 cm/h-week, similar to the rate of decrease

42

observed during the initial root growth. Regrowth of the rye roots in the d. corn/rye
treatment caused a slight decrease in the Ksat (0.06 cm/h-week) as the growing rye roots
filled the root channels from the previous corn crop.
4.1.2 Breakthrough of anionic tracers
4.1.2.1 Chloride

The breakthrough of the chloride tracer was quite consistent across the treatment
groups at the beginning of the experiment. Chloride began to break through after the
application of approximately 0.45 pore volumes (PV) of water (Figure 4.3). The peak
concentration occurred at approximately 0.75 PV, and then returned to the background
level at approximately 1.0 PV. The total recovery of the tracer ranged from 73.6 to

130.5%. Descriptive statistics for each column are listed in Table A6.

 

60

 

+ Chloride
ah- Bromide
50 ,7 #~

(9me

4O

30

20

Concentration of anion

 

 

 

 

 

Pore volume applied

Figure 4.3: Representative chloride and bromide breakthrgukgh cirves.
There was no significant difference between treatment groups in peak chloride
concentration (p = 0.196) or pore volume applied at the time of peak concentration (p =

0.704; Tables B2 and B3). Additionally, there was no significant difference between

43

treatment groups in pore volume applied at the time of 10% (p = 0.473) and 50% (p =

0.838) cumulative recovery of the chloride (Tables 4.1, B4-B5).

Table 4.1. Chloride and bromide breakthrough by crop treatment.

 

 

 

 

 

 

 

 

Treatment Chloride Breakthrough Bromide Breakthrough
10% * 50% 10% 50% 90%
PV PV PV PV PV
Bare 0.455 a 0.757 a 0.636 a 0.853 a 1.072 a
Cereal rye 0.404 a 0.723 a 0.553 a 0.747 a 0.989 a
D. corn 0.417 a 0.750 a 0.539 a 0.749 a 1.015 a
D. com/rye 0.452 a 0.761 a 0.561 a 0.769 a 0.971 a

 

 

 

 

* abc letters within columns indicate values not significantly different by Tukey’s
procedure (a=0.05)

Figure 4.4 shows a cumulative density function fitted to the empirical chloride
breakthrough data. Fifty percent chloride breakthrough was expected in 50% of the

columns with a water application of 0.71 to 0.75 PV. There was little difference between

treatment columns at the beginning of the experiment.

 

 

 

 

 

 

 

 

 

 

90 ~
70 4
H
5
g 50 -
o
n.
30 4
—— Bare
— - - - Rye
10 J D. corn
____.._.._.-_’_:.’ ’ -- D. corn/rye
0.6 0.7 0.8 0.9 1.0
Pore volume at 50% breakthrough

 

Figure 4.4: Fitted cumulative distribution function of the chloride breakthrough
measured in the pre-treatment columns.

4.1.2.2 Bromide
At the end of the experiment, the breakthrough of the bromide tracer was quite

consistent across the treatment groups. Breakthrough began after approximately 0.58

44

pore volumes (PV) of water, the peak concentration was at about 0.78 PV, and the
concentration returned to the background level at approximately 1.1 PV (Figure 4.3).
There was little difference in the column flow regime due to treatment effects. There was
no significant difference in peak bromide concentration (p = 0.783) or pore volume
applied at the time of peak concentration (p = 0.296; Table A7 and B6-B7). Additionally,
there was no significant difference between treatment groups in pore volume applied at
the time of 10% (p = 0.598), 50% (p = 0.475), and 90% (p = 0.697) cumulative recovery

of the bromide (Tables B8-B10). Recovery of the tracer ranged from 95.1 to 123.3%.

 

 

 

 

 

 

 

 

90 ~
70 -
Jul
1:
§ 50
o
o.
30 —
10 ~
D. corn/rye
0.6 0.7 0.8 0.9 1.0
Pore volume at 50% breakthrough

 

 

 

Figure 4.5: Fitted cumulative distribution function of the bromide breakthrough
measured in the different crop treatment columns.

Fifty percent breakthrough occurred in 50% of the planted columns at about 0.75 PV,
but that level of breakthrough was delayed until about 0.85 PV in the bare columns
(Figure 4.5). Presumably, the delay was caused by soil settling during the experiment.
This delay was not statistically significant. Descriptive statistics for the bromide break

through of each column are listed in Table A7.

45

There was little change between the Cl and Br tracer, in the pore volume applied at
the 50% breakthrough for the columns that had plants. The bare columns show an
apparent difference with a later breakthrough with the bromide tracer. An empirical

CDF, Figure 4.6, shows the difference between the 50% chloride breakthrough and the

50% bromide breakthrough.

 

 

 

 

 

 

 

 

 

90 ~
70 -
a
:
§ 50 ~
0
a.
30 -
10 ‘ ,l — Bromide
- , — — — - Chloride
0.6 0.7 0.8 0.9 1.0
Pore volume at 50% breakthrough

 

 

Figure 4.6: Fitted cumulative distribution function of the chloride and bromide
breakthrough measured in the bare columns.-

4.2 Simulated storm event: E. coli

4.2.1 E. coli recovery

The manured treatments received a volumetric equivalent of 56,000 L/ha (6,000 gpa)
of liquid swine manure as a surface application. The manure was applied 48 hours after a
deep wetting and draining of gravitational water so that the soil was near field capacity at
the time of manure application. F orty-eight hours after the manure application, a
simulated rainfall event with a 25-year frequency was applied and the column effluent

was collected and analyzed for E. coli concentration.

46

Factors that may have affected E. coli elution and retention were filtration; sorption;
preferential flow through root channels, soil crack and fissures; and die-off/regrowth in
the soil environment. Generally, E. coli elution increased and retention decreased with
the number of root growth/die-off cycles (Table 4.2). The concentration of E. coli eluted
from the bare and bare/manure columns was not significantly different (p = 0.9766;
Table B16). The soil was effective in filtering and retaining the E. coli in a 25-year rain
event in the absence of plant roots, desiccated root channels and other preferential flow
paths. The E. coli concentration in the column effluent was significantly greater in the
treatments with growing or desiccated roots. Rye/manure was significantly greater than
bare/manure (p = 0.0000; Table B16), as was d. corn/manure and d. corn-rye/manure (p
= 0.0000, p = 0.0000; Table B16). There was no detectible difference between
rye/manure and d. corn/manure (p = 0.4538; Table B16) or d. corn-rye/manure (p =
0.8816; Table B16). Similarly, there was no detectible difference between d.
corn/manure and d. corn-rye/manure (p = 0.8978; Table B16).

The cumulative E. coli collected in the simulated rainfall represents the volume of
effluent in each sample (ml) times the concentration of E. coli (E. coli/ 100 ml) in the
sample. No livestock manure had been applied at the collection site for at least one year,
but all of the columns contained detectable levels of E. coli (Table A9).

An analysis of variance was performed on the transformed E. coli data using the
General Linear Model in Minitab (Table B15). There was considerable variability in the
cumulative E. coli collected in the effluent from each column. Because of the column
variability we were not able to detect statistically significant differences between

treatments (Table 4.2).

47

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48

The coefficient of variation (CV, %) ranged from 94% for d. corn-rye/manure to
196% for the bare/manure treatment. The E. coli elution from the bare treatment
(26,622 cfu) with the least E. coli in the effluent was not significantly different from the
d. corn-rye/manure treatment with the greatest amount of E. coli (738,147 cfu; p = 0.312;
Table 816); however, there was a tendency to elute a greater quantity from the columns
with plant growth. Compared to the rye/manure treatment, there was a greater amount of
E. coli in the d. corn/manure effluent. This is consistent with the hypothesis that the root
channels left by the corn roots facilitate bacterial movement compared to actively
growing, turbid rye roots. The greatest cumulative E. coli was recovered from the d.
corn-rye/manure treatment (Figure 4.7). Presumably, rather than obstructing water and
bacterial movement through the desiccated root channels, the rye roots created additional
channels for bacterial movement, or were ineffective in obstructing flow through the

desiccated root channels, or both.

 

1 200000

1000000 .

coli

800000

600000

400000

Cumulative E

200000

 

 

 

 

0 Ct:

 

Bare Rye D.com D.cornlrye ‘

Bare manure manure manure manure

Figure 4.7: Cumulative E. coli eluted byitreiatment. Standard gror bars indicate
the dispersion in sample data.

49

E. coli retention in the manured columns was calculated as a percentage of the
organisms recovered in the treatment effluent relative to the amount applied in the
manure application. A goal in evaluating the initial flush of E. coli through the soil
column was to highlight the effects of root growth and die-off on the preferential
movement of water and bacteria through the column. The percent retention for the bare,
rye, and desiccated corn treatments was greater than 99% indicating that little of the E.
coli was moving through the column by macropore flow. The E. coli retention for the d.
corn-rye/manure treatment, however, was significantly lower (68%) than either of the
other manured treatments (p = 0.036; Tables Bl7, B18).

4.3 P22 bacteriophage

The P22 bacteriophage was evaluated as a potential in-tank marker for linking
manure land application with specific land management practices. The bacteriophage
was added to water or swine manure and applied in a volumetric equivalent of 56,000
l/ha (6,000 gpa) on bare soil and rye grth columns. A simulated rainfall with intensity
of a 25-year storm was used to elute E. coli and the bacteriophage.

4.3.1 Breakthrough curves

No E. coli were detected in the non-manured treatments. When the bacteriophage
was applied with swine manure on bare soil, the bacteriophage and the E. coli broke
through differently with the bacteriophage breaking through 5 to 10 minutes earlier
(Figure 4.8-4.9). The peak concentration (1.2E+05 pfu/ml) of bacteriophage in the
column effluent was recorded at about 185 minutes from the start of the simulated rainfall
in the bare/manure treatment (Figure 4.8). The peak concentration of E. coli (27 cfu/ml)

occurred at about the same time as the peak concentration of the bacteriophage. The

50

bacteriophage concentration declined at a greater rate than the E. coli concentration, and
neither the P22 nor the E. coli were tailing off at the end of the rain event. Thus, the full

breakthrough curve was not obtained due to an inadequate duration of sampling (sampled

 

 

 

 

 

 

up to 280 min).
140 -l —_P?Z_LL i
, ~- 2.0E+05
A 120 a:
E c
E 100 ‘ ~ 1.5E+05 g
a. so at
E 60 H. - 1.0E+05 g
UJ : 0‘
40 - i
L 5.0E+04
20 ~
0 - -—,—- + 0.0E+00
0 50 100 150 200 250 300
l Time (min)

 

 

 

Figure 4.8: Breakthrough concentration of P22 bacteriophage and E. coli in the
bare/manure treatments.

When the bacteriophage was applied with swine manure on growing rye (rye/manure)
it began to break through about 80 minutes after the start of the rainfall (Figure 4.9). The
peak concentration (2.3E+05 pfu/ml) was recorded about 115 minutes after the start of
rainfall and the concentration dropped off rapidly to about 5.0E+04 pfu/ml after 180
minutes. The P22 concentration approached background levels after about 300 minutes
of rainfall.

The E. coli began to breakthrough about 80 minutes after the beginning of rainfall,
similar to the bacteriophage. Compared to P22, the peak concentration of E. coli (150
cfu/ml) was delayed about 30 minutes and was recorded about 140 minutes after the

beginning of rainfall. The E. coli concentration then declined rapidly to about 90 cfu/ml

51

at 160 minutes and remained nearly constant rate until the end of the rainfall event. The
bare/manure treatments had fewer E. coli in the effluent than the rye/manure treatments,

probably due to the filtering ability of the soil.

 

 

 

 

 

 

160 . ————— — , 2.5E+05
l"" E.colii =
140 L- “EL
~2. +
c 120 - 0E 05
g 100 4 E
E . ~ 1.5E+05 5
g sol 5t
'3 60 _ .- 1.0E+05 g
L” 40 a
5.0E+04
20 - ’
o - —— , . . . . . o.oe+oo
o 50 100 150 200 250 300
Time (min)

 

 

 

Figure 4.9: Breakthrough concentration of P22 bacteriophage and E. coli in the
rye/manure treatments.

The bare/manure P22 breakthrough curve (BTC) was compared with a bromide BTC
representative of the bare columns (Figure 4.10). The peak bromide concentration
occurred earlier than the peak P22 concentration (140 versus 180 minutes), and the
bromide returned to near the background level at 250 minutes while the P22 was still
tailing off. The bromide BTC was not a suitable model for predicting the movement of

the P22 bacteriophage through the bare soil columns.

52

 

~~ 2.5E+05

 

l A 60 ‘ iii-Bromide ‘ l
E —P22 ‘
g 50 l ——. . i 2.0E+05 ‘
c ‘

5.3 40 . cg
g 1.5E+05 5
c u.

. 3 3° i e

. S . 1.0E+05 N
3 20 l S l
.‘9 .
S 10 . 5.0E+04 ‘
CD .

l 0 . -------- 77.74;: .. 0.0E+00

l o 50 100 150 200 250 300

Time (min)

 

Figure 4.10: Breakthrough curves of bromide and P22 for the bare/manure
treatment.

 

 

A60 _______ Brormde - 7 ~ 2.5E+05
E —P22
E50 V l 2.0E+05
C 1
S40 . , 7'51
g l 1.5E+05 5 l
5 30 - LL
0 $
C
0 ~ 1.0E+05 N
° 20 . a l
(D
E
510 » 5.0E+04
CO

0 - ,, - - . 0.0E+00

o 50 100 150 200 250 300

Time (min)

Figure 4.11,: Breakthrough curves of bromide an?! P22 forthe Eye/manure
treatment.

The P22 BTC (rye/manure) was compared with a representative bromide BTC for the
rye growth columns. The bromide BTC was a suitable model for predicting the P22 BTC
(Figure 4.11). The bromide began to break through at about the same time as the P22 (60
minutes for the bromide versus 75 minutes for the P22). The peak concentration was

recorded at 115 minutes for the bromide and at 125 minutes for the P22. Both the

53

bromide and P22 concentrations decreased at a similar rate and approached the
background levels at the end of the rainfall event.
4.3.2 Phage/manure vs. phage/water

The host for the P22 bacteriophage is Salmonella typhimurium. The initial
concentration applied to the columns ranged from 2.0E+09 pfu/ml on the bare/water
treatment to 5.3E+07 pfu/ml on the bare/manure treatment (Table 4.3). When the phage
was applied in deionized water, the concentration in the column effluent was about
1.0E+03 pfu/ml (Figure 4.12-4.13). When the phage was applied in swine manure, the
concentration in the column effluent was about 1.0E+05 pfu/ml. Presumably, the two-log
reduction in phage recovery from the non-manured columns, was caused by more of the
phage being adsorbed in the soil matrix. There was not competition with the manure

particle for the adsorbtion sites that occurred in the manured treatment.

 

1.0E+07

 

I

1.0E+06 *

I

1.0E+05 ‘

1.0E+04 e

I

I

1.0E+03 ‘

P22 (PFU/ml)

1.0E+02 -

T

 

 

1.0E+01 r

I

— Manure
--—— No manure
1.0E+00 t t i l t

0 50 100 150 200 250 300
Time (min)

 

 

 

 

 

 

 

Figure 4.12: Concentration of the P22 bacteriophage in each of the three replicates
for the bare/manure and bare/water treatments.

54

 

 

1.0E+07
1.0E+06 -
1.0E+05 ‘
1.0E+04 -
‘ 1.0E+03 *
1.0E+02 ~
1.0E+01 ~
1.0E+00

I I I I

P22 (PFU/ml)

I

 

 

I

 

 

 

~— No manure
1

1
I I l I

 

—0—
i—
y—

0 50 100 .150_ 200 250 300
Time(min)

 

 

 

Figure 4.13: Concentration of the P22 bacteriophage in each of the three replicates
for the rye/manure and lye/water treatments.

4.3.3 Cumulative recovery

Factors that may have affected P22 retention were filtration; preferential flow through
root channels, soil cracks and fissures; and die-off of the bacteriophage. There was
considerable variability in the P22 recovered in the column effluent. The recovery from
the bare/water and rye/water columns was less than 2% of the initial application (Table
4.3). This low recovery indicates a higher adsorption of the bacteriophage in the absence
of manure. The manure competed for the adsorption sites in the soil and thus few phage
were retained in the column. When the P22 was applied in swine manure to the
bare/manure and rye/manure treatments, the P22 recovery in the column effluent ranged
from about 59% with the rye/manure treatment to greater than 100% with the
bare/manure treatment (Table 4.3).

It is not uncommon to have variability in microbial counts. There was variability in
this case because the host stock was prepared separately for each treatment. The

concentration of the inoculate was measured on day one, and the column samples were

55

assayed on day 3 and day 4. It was common to a see a two-fold difference in the

 

 

 

 

 

recovery (50 to 200%).
Table 4.3: Cumulative P22 bacteriophage recovery
Column Cumulative Cumulative P22 recovery Average recovery
Treatments # P22 applied, P22 recovered, (y ’ (y ’
0 0
pfu/ml pfu/ml
2 2.0E+09 1.8E+06 0.092 0.1 173
Bare/water l4 2.2E+06 0. l 1
l6 3.IE+06 0.15
10 5 .3 E+07 1.3E+08 240 175
Bare/manure l 5 8 .6E+07 163
21 6.5E+07 122
8 5.1E+08 7.0E+06 1.4 1.3
Rye/water 24 6.7E+06 l .3
30 6.3E+06 1.2
12 4.2E+08 2.6E+08 61.0 58.87
Rye/manure 27 2.5E+08 58.6
32 2.4E+08 57.0

 

 

 

 

 

 

4.4 Discussion
4.4.1 Soil moisture and temperature

Soil moisture and temperatures for the week leading up to each sample event are
shown in Figures D13 to D32. The deep wetting prior to each rain event occurred on the
aftemoon of day 1, the application of the manure or water occurred on day 4 and the
simulated rain events and collection of samples took place on day 6. There was a large
decrease in the soil moisture during the days prior to sampling the rye/manure treatment
(Figures D20, D22, and D24), likely from an increase in evapotranspiration. The soil
temperature sensors that week recorded daily highs of 35 to 40 °C whereas the week of
the previous treatment the maximum soil temperatures were less than 30 °C (Figures
D17, D19, D21, and D23). There was a difference in the amount of sample that was
collected from treatments with actively growing plants and treatments that did not have

growing plants (Table 4.4). The columns with growing plants had less than 27% of the

56

applied water exit the column verses more than 45% from the bare and d. corn columns.
This lower moisture content is probably due to uptake of water by the growing plants.

Table 4.4: Amount of water applied and recovered during each treatment.

 

 

Amount irrigation Average amount of o/ of a lication
Treatment applied per column leachate collected 0 R pp d
(ml) (ml) ecovere
Bare 900 420 46.7
Bare/manure 900 425 47.2
Rye 930 120 12.9
Rye/manure 1350 145 10.7
D. com 900 410 45.6
D. corn/manure 900 495 55.0
D. corn-rye 1200 225 18.8
D. corn-rye/manure 1200 320 26.7

 

 

 

 

For the treatments that had rye growing at the time of sampling, the rain event was
extended in order to collect enough sample to assay. The rye/manure treatment required
a longer rain event due to the high temperatures that week and increased moisture loss
from evapotranspiration.

4.4.2 Difficulties in examining the results
4.4.2.1 Soil problems

The soil used in this experiment had a high percentage of sand (87%) and may not
have held structure well. The macropores and root channels that were created by the
plants may have collapsed, contributing to the lack of significance between the treatments
when examining the anionic tracers. The settling of the soil also contributed to
difficulties in examining the data.
4.4.2.2 E. coli variability

There was a lot of variability in the number of E. coli found in these columns. All of
the non-manured columns had some level of E. coli in them and several sample had

extremely high counts. The columns could have been contaminated by birds or rodents

57

while in the greenhouse. The same can be true of field sites; outside sources of bacteria
from wildlife can add variability to both manured and non-manured plots. Aside from
the outside sources of E. coli, there was considerable variability between the columns and
samples of different treatments (Figure A4).
4.4.3 E. coli reduction

Because the concentration of E. coli in manure is so high, the allowable limits in
waters of the state are so low, extremely high filtration is needed to achieve current water
quality standards. The limit for E. coli in recreational waters is a geometric mean of 126

cfu per 100 ml (USEPA, 2001). To achieve this level after a manure application with ~

5.0 x 107 cfu of E. coli per 100 ml, the best management practices in place must provide

a 99.9997% reduction in the E. coli concentration. This is greater than a 5-log reduction.
4.4.4 Broader application of bacteriophage markers

It is important to be able to determine if a manure application is the source of E. coli
contamination. The P22 bacteriophage shows promise as an inexpensive method for
linking specific manure applications with water contamination. The phage and E. coli
had similar breakthrough curves when applied to a growing crop. The die-off of the
phage was rapid in the absence of a host organism. It is not known how long the phage
will survive in the presence of a host in a terrestrial environment with unsaturated soil.
The phage level began to decline before the E. coli (Figure 4.9); in the field, the P22

concentration may drop below detectable levels while E. coli is still present.

58

1.

CHAPTER 5

5. CONCLUSIONS
The initial corn growth in loamy sand decreased the saturated hydraulic conductivity
(Ksat) at a rate of -0.5 cm/h-week to -0.75 cm/h-week. The Ksat values increased
when the corn plants were killed and the roots began to decay. The rate of increase
was 0.5 to 0.8 cm/h-week, similar to the rate of decrease observed during the initial
root growth. Regrowth of cereal rye roots in soil with decomposed corn roots caused
a slight decrease in the Ksat (0.06 cm/h-week) as the growing rye roots filled the root
channels from the previous corn crop.
Cereal rye root growth caused an increase in the Ksat of 0.3 cm/h-week compared to
bare soil.
There was no detectable difference in the miscible displacement of a bromide tracer
through loamy sand soil columns due to root growth and decay.
When swine manure was applied at a volumetric equivalent of 56,000L/ha (6,000
gpa) there was no significant difference between the initial concentration of E. coli in
the effluent from bare columns and bare columns with manure. Growing or decaying
roots increased the rate of bacterial transport through the soil columns. The initial
concentration of E. coli in the effluent from soil columns with growing or decaying
plant roots with manure were significantly greater than either of the bare column

treatments.

59

5. The recovery of the P22 bacteriophage applied with manure was greater than when
applied in deionized water presumably because there was a lack of competition with
the manure for the adsorption sites.

6. When applied with manure to the cereal rye, the bacteriophage broke through at the
same time or slightly before the E. coli and reached the peak concentration at about
the same time. However, the bacteriophage tailed off before the E. coli. The bromide
tracer was a suitable model for the P22 transport in the cereal rye column but was not
suitable for the bare column.

7. The P22 bacteriophage appeared to be a suitable microbial marker for linking a

manure application with water contamination at the field scale.

60

CHAPTER 6

6. RECOMMENDATIONS

Future column studies for evaluating the movement of bacteria from a manure
application can be improved in several ways. Intact or undisturbed columns will better
replicate field conditions and perhaps reduce the amount of soil settling and change in
Ksat that occurred in this experiment. If repacked columns are used again, soil with a
structurally stable sand, silt and clay content should be used to insure that root channels
from decayed roots remain intact during Ksat analysis.

Evaluation of the soil Ksat was difficult because the columns were in use for several
months and settling occurred. More frequent measurements may provide a more accurate
representation of changes that occur from root growth and die-off.

The dark gray PVC soil columns absorbed a lot of the solar radiation in the
greenhouse and contributed to wider fluctuations in soil temperature than would be
expected in a natural environment. Future column studies should use white columns to
reduce soil temperature fluctuations due to solar radiation. Perhaps additional protection

from solar radiation could also be provided.
The soil moisture sensors (ECHzO-IO probes, Decagon Devices) measured the

dielectric constant of the soil and provided a volumetric water content. They tended to be
unreliable for measuring the moisture content of dry soil. TDR probes might be more

reliable.

61

E. coli were detected in all of the non-manured treatments. This could have been
from residual E. coli in the soil at the time of collection, contamination while in the
greenhouse by birds or rodents, or both. One way to reduce the difficulty in linking the
E. coli in the column effluent with the manure application would be to use a labeled
strain of E. coli in the manure application.

Another challenge in evaluating the breakthrough of the E. coli was the small number
of samples that were collected. Because membrane filtration was time consuming and
the samples needed to be assayed within 24 hours, we were not able to evaluate the entire
breakthrough event for multiple columns; however, a longer simulated rain and/or
multiple rain events would have provided a better picture of the survival and movement
of E. coli through the soil columns. A more rapid assay method would increase the
number of samples that could be evaluated within the 24 hour time limit.

The bacteriophage P22 showed promise as a microbial marker for modeling the
movement of E. coli through vegetative soil. The technique needs to be evaluated at the
field scale for linking a manure application with manure contamination of tile effluent.
The persistence and survivability of P22 in a terrestrial agro-ecosystem needs to be

evaluated.

62

APPENDIX A

63

Table A1: Column soil properties.

 

 

 

 

 

 

 

Column number Dry Bulk Density Volume Porosity Pore Volume

(g/cmj) (cm3) (cmj)

2 1.50 6109.8 0.43 2622.5

14 1.50 5862.1 0.43 2533.8

16 1.50 6076.8 0.44 2646.6

40 1.50 6010.7 0.43 2605.1

10 1.47 6043 .8 0.44 2689.0

15 1.49 6093.3 0.44 2665.0

21 1.48 5994.2 0.44 2641.4

38 1.48 6208.9 0.44 2733.4

17 1.49 6126.3 0.44 2675.4

22 1.46 6175.9 0.45 2777.6

31 1.49 6142.8 0.44 2678.7

41 1.51 5994.2 0.43 2586.7

33 1.49 6126.3 0.44 2688.6

35 1.50 6208.9 0.43 2691.9

36 1.49 6076.8 0.44 2654.1

42 1.47 6159.3 0.44 2740.5

11 1.50 6076.8 0.43 2633.4

20 1.45 6192.4 0.45 2813.1

26 1.52 6093.3 0.43 2600.8

37 1.52 6159.3 0.43 2636.7

1 1.50 6060.3 0.43 2622.5

6 1.48 6192.4 0.44 2735.8

25 1.52 6060.3 0.43 2579.1

39 1.49 5994.2 0.44 2616.9

5 1.52 5895.1 0.43 2523.4

13 1.46 6208.9 0.45 2778.7

29 1.51 6076.8 0.43 2614.5

34 1.53 6043.8 0.42 2553.2

7 1.49 6027.2 0.44 2640.5

18 1.49 6060.3 0.44 2660.3

19 1.47 6208.9 0.45 2773.0

43 1.52 5878.6 0.43 2516.4

Minimum 1.45 5862.1 0.42 2516.4

Maximum 1.53 6208.9 0.45 2813.1
Spread 0.08 346.8 0.03 296.7

Average 1.453 5915.9 0.425 2587.3
Standard Deviation 0.020 94.8 0.007 75.7

 

64

 

 

Sensor type label Wire color Sensor type label Wire color
19 H 107 Top 4 Red 18 L 107 Black
19 L 107 Black 18 H 107 Top 3 Red
20 H 107 Top 5 Red 17 L 107 Black
20 L 107 Black 17 H 107 Top 2 Red
21 H 107 Middle 1 Red 16 L 107 Black
21 L 107 Black 16 H 107 Top 1 Red
22 H 107 Middle 2 Red 15 L ECHO White
22 L 107 Black 15 H ECHO Bottom 5 Red
23 H 107 Middle 3 Red 14 L ECHO White
23 L 107 Black 14 H ECHO Bottom 4 Red
24 H 107 Middle 4 Red 13 L ECHO White
24 L 107 Black 13 H ECHO Bottom 3 Red
25 H 107 Middle 5 Red 12 L ECHO White
25 L 107 Black 12 H ECHO Bottom 2 Red
26 H 11 L ECHO White
26 L 11 H ECHO Bottom 1 Red
5 L0 connected to the CR10 El channel 10 L ECHO White
0 H1 connected to the CR10 H1 channel 10 H ECHO Middle 5 Red
27 H 107 Bottom 1 Red 9 L ECHO White
27 L 107 Black 9 H ECHO Middle 4 Red
28 H 107 Bottom 2 Red 8 L ECHO White
28 L 107 Black 8 H ECHO Middle 3 Red
29 H 107 Bottom 3 Red 7 L ECHO White
29 L 107 Black 7 H ECHO Middle 2 Red
30 H 107 Bottom 4 Red 6 L ECHO White
30 L 107 Black 6 H ECHO Middle 1 Red
31 H 107 Bottom 5 Red 5 L ECHO White
31 L 107 Black 5 H ECHO Top 5 Red
32 H 4 L ECHO White
32 L 4 H ECHO Top 4 Red
RES connected to the CR10 C1 channel 3 L ECHO White
CLK connected to the CR10 C2 channel 3 H ECHO Top 3 Red
12V 2 L ECHO White
GND 2 H ECHO Top 2 Red
1 L ECHO White
1 H ECHO Top 1 Red

All Purple, clear and bare wires to a common ground
The common grounding strip connected to AC on CR10

Figure A1: Wiring diagram for Campbell Scientific AM32 Multiplexer.

Table A2: Soil chemical roperties.

 

 

 

 

 

 

 

Soil test 1 Soil test 2
Unit 1/18/2006 1/18/2006
Soil Nutrient Levels
Soil pH 7.3 7.3
Phosphorous (P) Ppm 96 96
Potassium (K) Ppm 59 63
Magnesium (Mg) Ppm 94 96
Calcium (Ca) Ppm 496 530
CEC Meq/lOOg 3.4 3.6
% of Exchangeable Bases
Potassium (K) % 4.4 4.5
Magnesium (Mg) % 22.9 22.2
Calcium (Ca) % 72.6 73.4
% Organic Matter % 0.6 0.6
Soil Texture
Sand % 87.7 86.7
Silt % 8.6 8.6
Cliy % 3.7 4.7
Table A3: Manure characteristics for E. coli experiment.
Bare Rye Com Com/Rye
Unit 4/14/2006 6/2/2006 8/10/2006 9/22/2006
E. coli concentration (in manure cfu/ 100m 6.83E+7 2.73E+7 l.15E+8 2.48E+6
prior to application) 1
Moisture % 95.28 96.50 99.46 98.29
Solids % 4.72 3.50 0.54 1.71
Nitrogen, Total (N) % 0.483 0.261 0.057 0.259
Nitrogen, Ammonium (NH4-N) % 0.290 0.178 0.036 0.201
Nitrogen, Organic (N) % 0.193 0.083 0.021 0.058
Phosphorous (P) % 0.196 0.117 0.012 0.037
Potassium (K) % 0.172 0.153 0.034 0.238
Sulfur (S) % 0.04 0.03 0.01 0.02
Magnesium (Mg) % 0.08 0.05 0.01 0.03
Calcium (Ca) % 0.21 0.18 0.03 0.03
Sodium (Na) % 0.04 0.03 0.01 0.06
Aluminum (Al) ppm 40 35 5 5
Boron (B) ppm 3 3 l 3
Copper (Cu) ppm 7 l4 6 9
Iron (Fe) ppm 223 198 29 45
Manganese (Mn) ppm 18 12 2 4
Zinc (Zn) ppm 152 166 2 49

 

 

 

 

 

 

66

Table A4: Formula for the NSF corn nutrient solution.

 

 

Solution ml of solution per 1 L nutrient solution
1 M KH2P04 0-6
1 M KN03 2.5
1 M Ca(NO3)2 2.5
1 M MgSO4 1-2
Micronutrients 1 .0

1.43 g H3BO3 per L

0.04 CuSO4 * 5H20 per L

 

Table A5: Saturated hydraulic conductivity values at various stages in the

 

 

 

 

 

 

 

 

 

 

experiment.
Treatment Column Initial Ksat, Growing Ksat, Desiccated Ksat, Final Ksat,
number cm/h cm/h cm/h cm/h
l — Bare 16 33.65 6.72
40 39.76 13.48
2 41.70 12.08
14 45.57 6.79
2 — Bare Manure 38 33.65 15.83
10 39.07 9.77
15 42.40 1 1.54
21 46.64 9.99
3 — Rye 31 32.92 18.71
17 37.13 18.49
22 40.60 21.45
41 46.72 12.25
4 — Rye Manure 35 29.93 18.99
36 38.07 22.45
42 41.82 21.32
33 47.04 19.79
5 — Desiccated 26 32.17 17.62 17.99
Corn 37 34.35 15.56 15.21
11 42.03 26.12 23.47
20 45.71 37.53 29.85
6 —— Desiccated 25 27.43 17.44 15.68
Corn Manure 6 38.31 24.78 23.95
1 40.43 20.60 21.16
39 45.83 30.91 21.82
7 — Corn Rye 34 27.97 16.17 10.64 11.44
29 35.30 20.79 17.85 9.12
5 41.68 16.59 22.46 9.73
13 45.49 28.11 27.52 12.00
8 — Corn Rye 43 26.65 18.86 20.72 14.53
Manure 19 37.32 29.67 12.48 10.85
18 40.91 24.82 16.35 13.50
7 46.88 25.47 26.93 15.36

 

 

 

 

67

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

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69

Table A8: Manure characteristics for bacteriophage experiment.

 

 

Bare Rye
Unit 12/1/2006 12/15/2006

Moisture % 96.69 98.01
Solids % 3 .31 1.99
Nitrogen, Total (N) % 0.259 0.220
Nitrogen, Ammonium (NH4-N) % 0-089 0-159
Nitrogen, Organic (N) % 0.170 0.061
Phosphorous (P) % 0.113 0.043
Potassium (K) % 0.084 0.251
Sulfur (S) % 0.03 0.03
Magnesium (Mg) % 0.05 0.02
Calcium (Ca) % 0.19 0.06
Sodium (Na) % 0.02 0.05
Aluminum (A1) ppm 38 15
Boron (B) ppm 2 3
Copper (Cu) ppm 26 16
Iron (Fe) ppm 151 60
Manganese (Mn) ppm 10 8
Zinc (Zn) ppm 132 59

 

 

 

 

70

 

 

 

 

  
 

 

 

 

 

 

100 “w
80 —
‘5 60 —
0
U
'5 Treatment
0. 40 ‘ — Ba'e
— — — Ba'e/marue
— D. com
20 - — — —— D. corn/matte
D. corn/rye
D. corn/I'ye/ma'u'e
0 - - Rye
‘ Rye/mane
0.5 0.6 0.7 0.8 0.9

Pore Volume

 

Figure A2: Empirical cumulative distribution function for the 50% chloride
breakthrough for each of the treatment groups.

 

 

 

   
 

 

 

 

 

 

100 - If: . ..w_. -
/ 2
80 ’/
// ,/
.‘I/ 'l/
‘E 60 — f. 1:
Q / I'
U
a Treatment
0. 40 - — Ba'e
— — — Ba'e/ma'ue
—-———— D. com
20 _ — — — D. corn/mmu'e
D. corn/rye
‘ flx/ , D. com/rye/manue
fixes:- , , Rye
0 ‘ _.__£. ———————— Rye/mme
0.4 0.5 0.6 0.7 0.8 0.9 1.0 1.1 1.2

Pore Volume

 

Figure A3: Empirical cumulative distribution function for the 50% bromide
breakthrough for each of the treatment groups.

71

 

 

Table A9: Cumulative E. coli recovered from each column.

 

 

 

 

 

 

 

 

 

Cumulative E. coli Average cumulative
Treatments Column # per column E. coli per treatment
1 — Bare 16 22,957 26,622
40 75,793
2 6,042
14 1,695
2 — Bare, manure 38 564,876 143,190
10 1,183
15 98
21 6,603
3 — Rye 31 NA 346
17 642
22 306
41 90
4 — Rye, manure 35 48,159 237,325
36 285,420
42 28,470
33 587,250
5 — Desiccated com 26 1,781 3,158
37 191
11 1,556
20 9,103
6 — Desiccated corn, manure 25 249,691 355,544
6 31,057
1 937,858
39 203,570
7 — D. corn/rye 34 60 268,434
29 NA
5 660,143
13 145,100
8 — D. corn/rye, manure 43 314,430 73 8,147
19 17,284
18 1,091,125
7 1,529,750

 

 

 

 

72

 

 

 

900000 —
800000 ~
700000 —
600000 ~
500000 —
400000 -

E. coli (cful100ml)

300000 —
200000- I

100000"
0-0:.-- tl=_.n __ _____ BU l:_-L H

 

 

 

 

 

 

 

 

lllllllllIlllllllllllrllIll‘lllllj—Tlfille
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Treatment ,
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66k“ 940‘ C00“ 0 ‘
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Figure A4: Box plot of E. coli concentration by treatment and sample number.

73

 

Table A10: E. coli concentrations for treatment 1, bare with no manure.
Treatment 1 — Bare, no manure

E. coli concentration
Column Sample # (cfu/ 100 ml)

16 7,050
5,250
5,750

32,950
1 1,500
6,300
11,550
2,238

748
563
1,543
930
384
0

 

 

 

40

 

 

14

WN—thN—n.§QJNu—owN—.

 

 

 

Table All: E. coli concentrations for treatment 2, bare with manure.
Treatment 2 — Bare, manure

 

 

 

 

 

E. coli concentration
Column Sample # (cfu/ 100 ml)

38 1 285,000

2 43,250

3 8,967

4 4,400

5 20,483
10 1 80

2 10

3 235

4 585
15 1 15

2 5

3 10

4 10

5 35
21 1 148

2 195

3 1,669

4 1,032

5 2,035

 

 

 

74

Table A12: E. coli concentrations for treatment 3, rye with no manure.
Treatment 3 —— Rye, no manure

 

 

 

 

 

E. coli concentration
Column Sample # (cfu/ 100 ml)

31 1 Error in sample

2 Error in sample
17 l 724

2 210
22 1 344

2 250
41 1 100

2 0

 

 

 

Table A13: E. coli concentrations for treatment 4, rye with manure.
Treatment 4 — Rye, manure

 

 

 

 

 

E. coli concentration
Column Sample # (cfu/ 100 ml)
35 1 38,683
2 14,827
36 1 72,500
2 329,500
42 1 23,133
2 8,500
33 1 173,500
2 479,000

 

 

 

75

Table A14: E. coli concentrations for treatment 5, desiccated corn with no manure.
Treatment 5 - Desiccated Corn, no manure

 

 

 

 

 

E. coli concentration
Column Sample # (cfu/ 100 ml)
26 1 1,123
2 150
3 0
4 78
5 75
37 1 1 13
2 33
3 0
4 10
5 0
1 1 1 930
2 215
3 133
4 0
5 250
20 1 6,325
2 580
3 392
4 348
5 618

 

 

 

Table A15: E. coli concentrations for treatment 6, desiccated corn with manure.
Treatment 6 — Desiccated Corn, manure

E. coli concentration
Column Sample # (cful1 00 ml)

25 166,667
4,075
16,750
27,000
22,500
9,033

995
2,650
7,750
15,500

552,500
172,500
181,333
88,000

- 27,500
39,250
49,000
70,000
65,000

 

 

 

 

 

39

 

 

M-bUJN-‘AL’JN—MAWN—MAWN—

 

76

Table A16: E. coli concentrations for treatment 7, desiccated corn/lye with no
manure.
Treatment 7 — D. Com/Rye, no manure
E. coli concentration
Column Sample # (cfu/100 ml)
34 15
40
45
3,000,000*
950,000*
300,000*
315,000
96,250
650,000
4,550
14,900
173,750
37,750
30,333
* Possible contamination to column, this replicate was
not used in any analysis.

 

 

 

 

29*

 

 

13

 

 

WN—MADJNHWN—WN—

 

Table A17: E. coli concentrations for treatment 8, desiccated corn/rye with manure.
Treatment 8 — Com/Rye, manure

E. coli concentration
Column Sample # (cfu/ 100 ml)

43 7,717
61,250
85,000
103,000
3,317
610
2,050
7,050
855,000
257,500
367,500
137,500
740,000
282,500
622,500
5,000

 

 

 

 

18

 

 

 

AWN~ADJN—-5LQN—IAWN~

 

77

APPENDIX B

78

Table Bl: Two-Factor AN OVA: Ksat values by treatment and replication.

 

 

 

 

 

Source of Variation SS df MS F P-value
Treatment 31.17 7 4.453 1.609 0.1877
Replication 1058.12 3 352.707 127.410 1.24E-13
Error 58.13 21 2.768
Total 1 147.42 31

 

 

 

Table B2: Two-Factor AN OVA: Peak chloride concentration by treatment and
replication.

 

 

Source of Variation SS df MS F P-value
Treatment 3209.5 7 458.496 1.58 0.196
Replication 1472.8 3 490.948 1.69 0.199
Error 6093.4 21 290.162

Total 10775.7 31

 

 

 

 

 

 

Table B3: Two-Factor ANOVA: Pore volume applied at chloride peak by treatment
and replication.

 

 

 

 

 

Source of Variation SS df MS F P-value
Treatment 0.0136 7 0.001943 0.66 0.704
Replication 0.0043 3 0.001425 0.48 0.697
Error 0.0619 21 0.002949
, Total 0.0798 3 1

 

 

 

Table B4: Two-Factor AN OVA: Pore volume at 10 % recovery of applied chloride
by treatment and replication.

 

 

Source of Variation SS df MS F P-value
Treatment 0.1492 7 0.02131 0.98 0.473
Replication 0.0083 3 0.00277 0. 13 0.943
Error 0.4582 21 0.02182

Total 0.6156 31

 

 

 

 

 

 

Table BS: Two-Factor AN OVA: Pore volume at 50 % recovery of applied chloride
by treatment and replication.

 

 

Source 3f Variation SS df MS F P-value
Treatment 0.0116 7 0.00166 0.48 0.838
Replication 0.0073 3 0.00242 0.70 0.562
Error 0.0725 21 0.00345

Total 0.0914 31

 

 

 

 

 

 

79

Table B6: Two-F actor AN OVA: Peak bromide concentration by treatment and
replication.

 

 

Source of Variation SS df MS F P-value
Treatment 430.94 7 61.563 0.56 0.783
Replication 763.09 3 254.365 2.29 0.107
Error 2329.07 21 110.908

Total 3523.11 31

 

 

 

 

 

 

Table B7 : Two-Factor AN OVA: Pore volume applied at bromide peak by treatment
and replication.

 

 

Source of Variation SS df MS F P-value
Treatment 0.1502 7 0.02156 1.31 0.296
Replication 0.0369 3 0.01230 0.75 0.536
Error 0.3453 21 0.01644

Total 0.5325 31

 

 

 

 

 

 

Table B8: Two-Factor ANOVA: Pore volume at 10 % recovery of applied bromide
by treatment and replication.

 

 

Source of Variation SS df MS F P-value
Treatment 0.0549 7 0.00784 0.80 0.598
Replication 0.0267 3 0.00891 0.91 0.455
Error 0.2066 21 0.00984

Total 0.2882 3 1

 

 

 

 

 

 

Table B9: Two-Factor ANOVA: Pore volume at 50 % recovery of applied bromide
by treatment and replication.

 

 

Source of Variation SS df MS F P-value
Treatment 0.0629 7 0.00899 0.97 0.475
Replication 0.0057 3 0.00191 0.21 0.890
Error 0.1936 21 0.00922

Total 0.2622 31

 

 

 

 

 

 

Table B10: Two-F actor ANOVA: Pore volume at 90 % recovery of applied
bromide by treatment and replication.

 

 

Source of Variation SS df MS F P—value
Treatment 0.0527 7 0.00752 0.67 0.697
Replication 0.0120 3 0.00401 0.36 0.785
Error 0.2365 21 0.01126

Total 0.3012 31

 

 

 

 

 

 

80

Table B11: General Linear Model (GLM): Ln (E. coli) vs. Crop, Manure, and

 

 

Replication.
Sequence Adjusted Adjusted

Source of Variation SS SS df MS F P-value
Crop 311.11 295.06 3 98.35 31.62 0.000
Manure 17.81 20.84 1 20.84 6.70 0.012
Replication 37.42 12.78 3 4.26 1.37 0.261
Crop*Replication 193 .91 193.91 9 21.55 6.93 0.000
Error 183.50 183.50 59 3.11
Total 743.75 75

 

 

 

 

 

 

 

Table B12: P-values for pairwise comparisons of the main effects of crop using

 

 

 

Tukey. Groupings are on the diagonal. Ln (E. coli) is the response variable.
C Bare Rye D. Corn D. Com/Rye
rop Bare

manure manure manure manure
Bare a 0.9766 0.0000 0.0000 0.0000
Bare manure 0.9766 a 0.0000 0.0000 0.0000
Rye manure 0.0000 0.0000 B 0.4538 0.8816
D. Corn Manure 0.0000 0.0000 0.4538 b 0.8978

D. Com/Rye Manure 0.0000 0.0000 0.8816 0.8978 b

 

 

 

 

 

Table 813: P-values for pairwise comparisons of the main effects of manure using
Tukey. Groupings are on the diagonal. Ln (E. coli) is the response variable.

 

 

 

 

Crg) No Manure Manure
No Manure a 0.0121
Manure 0.0121 b

 

Table 814: P-values for pairwise comparisons of the main effects of crop using
Tukey. Groupings are on the diagonal. Ln (E. coli) Is the response variable.

 

 

Replication 1 2 3
1 a 0.3622 0.9235 0.9974
2 0.3622 a 0.7465 0.2663
3 0.9235 0.7465 a 0.8461
4 0.9974 0.2663 0.8461 a

 

 

 

 

 

81

Table B15: General Linear Model (GLM): Ln (Cumulative E. coli) by treatment

and replication.

 

 

Sequence Adjusted Adjusted
Source of Variation SS SS df MS F P-value
Treatment 55.45 55.45 4 13.863 2.63 0.087
Replication 9.41 9.41 3 3.136 0.60 0.630
Error 63.18 63.18 12 5.265
Total 128.04 1 9

 

 

 

 

 

 

 

Table B16: P-values for pairwise comparisons of the main effects of crop using
Tukey. Groupings are on the diagonal. Ln (Cumulative E. coli) is the response

 

 

 

 

 

 

 

variable.
Bare Rye D. Corn D. Com/Rye
Crop Bare

manure manure manure manure
Bare a 0.9766 0.6053 0.4448 0.3116
Bare manure 0.9766 A 0.3088 0.2046 0.1327
Rye manure 0.6053 0.3088 A 0.9983 0.9776
D. Corn Manure 0.4448 0.2046 0.9983 a 0.9985
D. Corn/Rye Manure 0.3116 0.1327 0.9776 0.9985 a

 

Table B17 : General Linear Model (GLM): Cumulative E. coli percent retention by
treatment and replication.

 

 

Sequence Adjusted Adjusted
Source of Variation SS SS df MS F P-value
Treatment 2979.2 2979.2 3 993.07 4.41 0.036
Replication 702.6 702.6 3 234.20 1.04 0.421
Error 2028.2 2028.2 9 225.35
Total 5710.0 15

 

 

 

 

 

 

 

Table B18: P-values for pairwise comparisons of the main effects of crop using

Tukey. Groupings are on the diagonal. Cumulative E. coli percent retention is the
response variable. (a = 0.10

 

 

 

Crop Bare Rye D. Corn D. Corn/RE
Bare a 0.9999 1 .0000 0.0606
Rye 0.9999 a 0.9999 0.0670
D. Corn 1.0000 0.9999 a 0.0616
D. Corn/Rye 0.0606 0.0670 0.0616 b

 

 

 

 

82

APPENDIX C

83

 

      

I [Figure C2: Campbell Scientific 107 temperature sensor.

84

 

ture sensor, side view.

IS

ECHZO probe mo

Ices

: Decagon Dev

Figure C3

 

IC\V.

ture sensor, top v'

[S

ECHZO probe mo

Ices

Decagon Dev

Igure C4:

F

85

 

v.1

'25..

Figure C5: Campbell AM32 multiplexer ith‘ sensors attached.

 
   
   

' 3' i 32?,"
Figure C6: Instrumen

86

 
 

 

 

 

 

 

 

 

Overview of columns in greenhouse.

 

     

we .Jv-r / - 4 ‘ ,1

Figure C8: Anther overview of columns in greenhouse.

87

 

 

 

 

 

 

"4 '1' A:

:Figure C9: Saturationtan'k fo satadurte ydaulrlc conductiv1ty.

 

‘&1.1-¢-c.»~.1 :-

11."
:5

i

F .

Figure C10: Saturated hydraulic conductivity set up.

88

 

Figure C 12: Sample collection during a rain event.

89

Mv .

to col

 

90

L..- a .

 

'1

F igurewC15:“1thmraiie filtration lab set up.

 

Figure C16: An example of blue fluorescing E. coli colonies under UV light.

91

APPENDIX D

92

Table D1: Average daily soil and air temperature highs and lows.

 

 

 

Average daily soil Average daily soil Daily air Daily air
Date temperature temperature temperature temperature
minimum maximum minimum maximum

9-Feb 23.79 28.00
10-Feb 22.83 26.34
ll-Feb 22.91 26.30
12-Feb 21.73 29.46
l3-Feb 21.82 25.68
14-Feb 23 .44 28.15
15-Feb 23.80 26.72
16-Feb 21.32 24.11
17-Feb 19.46 28.57
l8-Feb 14.30 26.16
19—Feb 16.06 27.14
20-Feb 19.04 , 27.09
21-Feb 20.64 29.45
22-Feb 23.16 31.19
23-Feb 22.84 29.56
24-Feb 21.38 29.14
25-Feb 22.40 29.54
26-Feb 19.36 28.61
27-Feb 22.22 26.86
28-Feb 20.58 29.87
l-Mar 22.94 28.20
2-Mar 23.10 25.68
3-Mar 21.75 27.31
4-Mar 20.41 31.96
2144:: 3338 :33; Air temperature sensotr was not booked up
7-Mar 22.03 32.26 ye ‘
8-Mar 22.56 29.91
9-Mar 23.49 26.19
10-Mar 23.53 27.37
1 l-Mar 23.44 30.95
12-Mar 24.10 28.44
13-Mar 23.32 31.23
14-Mar 21.75 25.86
15-Mar 20.67 31.33
16-Mar 23.32 28.33
17-Mar 21.58 31.55
18-Mar 20.19 33.13
l9-Mar 20.89 33.83
20-Mar 21.73 31.40
21-Mar 21.32 29.43
22-Mar 22.38 26.25
23-Mar 22.94 28.34
24-Mar 23.13 28.51
25-Mar 22.79 _ 27.63
26-Mar 22.88 29.16
27-Mar 22.96 33.14
28-Mar 23.28 26.71
29-Mar 22.92 27.68
30-Mar 23.19 34.38
31-Mar 23.68 30.64

 

 

 

 

93

Table Dl (cont’d)

 

 

Average daily soil Average daily soil
Date temperature temperature
minimum maximum
l-Apr 23.81 27.88
2-Apr 23.10 29.51
3-Apr 23.62 26.32
4-Apr 22.03 29.52
S-Apr 22.83 34.01
6-Apr 23.55 30.29
7-Apr 23.63 25.93
8-Apr 20.61 33.74
9-Apr 22.65 34.19
10-Apr 23.50 34.28
11-Apr 23.14 34.00
12-Apr 24.40 28.57
l3-Apr 24.11 35.28
14-Apr 24.35 36.45
15-Apr 24.19 37.76
16-Apr 23.72 28.82
l7-Apr 23.50 35.69
18-Apr 24.02 37.21
l9-Apr 24.08 37.14
20-Apr 24.19 35.56
21-Apr 24.29 37.13
22-Apr 24.26 32.52
23-Apr 23.89 27.76
24-Apr 24.13 36.29
25-Apr 23.94 32.45
26-Apr 23.11 36.97
27-Apr 23.90 35.87
28-Apr 23.91 36.29
29-Apr 24.29 31.1 1
30~Apr 24.02 27.91
l-May 24.17 31.24
2-May 23.97 27.40
3-May 24.15 35.30
4-May 24.56 33.76
5-May 24.41 30.73
6-May 23.87 31.90
7-May 24.03 33.94
8-May 24.38 34.41
9-May 24.48 34.67
lO-May 24.80 30.98
I 1-May 23.87 27.24
l2-May 22.72 26.42
13-May 23.27 26.70
14-May 23.25 27.45
15-May 23.39 26.96
l6-May 23.47 30.22
17-May 23.74 32.06
18-May 23.25 26.96
19-May 22.89 28.84
20-May 23.19 32.77
21-May 23.55 29.65

 

 

 

Daily air
temperature
minimum

25.06
21.66
23.97
25.03
24.10
25.66
25.58
24.92
24.63
24.23
24.14
24.45
24.60
24.64
24.98
24.81
24.29
24.59
24. 17
23.33
24.34
23.95
24.85
25.06
24.91
24.81
25.00
24.79
24.91
24.26
24.99
25.02
25.09
25.46
23.62
23.24
23.71
23.87
23.9
23.62
23.32
23.24
23.61
23.59
23.02

 

 

Daily air
temperature
maximum

26.14
29.66
30.12
30.99
32.19
29.53
38.75
38.54
36.66
28.49
29.15
30.85
32.13
32.17
33.81
31.73
28.40
35.06
30.03
34.53
32.05
33.36
30.67
27.96
30.17
27.18
33.06
32.93
30.28
29.76
31.8
33.00
32.85
30.58
27.48
26.58
26.95
27.51
26.94
29.90
3 1.13
27.34
28.59
30.66
27.15

 

94

Table D1 (cont’d)

 

 

Average daily soil Average daily soil Daily air Daily air
Date temperature temperature temperature temperature
minimum maximum minimum maximum
22-May 22.82 31.10 23.01 28.34
23-May 23.72 34.69 23.8 32.74
24-May 24.20 35.33 24.48 33.8
25-May 24.38 33.95 24.49 34.94
26-May 24.45 32.62 24.3 32.66
27-May 24.47 38.73 24.1 37.98
28-May 24.91 41.29 24.62 40.48
29-May 25.68 42.80 25.11 42.78
30-May 25.38 38.17 24.83 40.11
31-May 24.36 36.57 24.06 37.59
1-Jun 24.68 34.37 24.46 34.73
2-Jun 23.54 34.21 23.81 33.54
3-Jun 23.72 33.81 23.59 34.11
4-Jun 23.98 36.48 24.36 34.79
5-Jun 24.24 38.91 23.47 37.09
6-Jun 24.52 35.45 24.53 35.14
7-Jun 24.26 30.86 23.82 31.20
8-Jun 24.37 33.77 23.90 33.89
9-Jun 24.1 1 32.05 24.48 32.16
10-Jun 23.95 32.47 24.21 31.37
11-Jun 23.66 32.66 23.2 31.57
12-Jun 23.87 32. 19 22.91 31.66
13-Jun 24.12 31.13 24.19 31.06
14-Jun 24.22 34.11 24.23 32.59
15-Jun 24.32 34.49 23.65 32.69
16-Jun 24.46 37.02 24.53 35.36
17-Jun 24.90 38.68 24.51 37.54
l8-Jun 25.25 30.41 24.79 31.36
19-Jun 24.55 35.99 24.67 34.86
20-Jun 24.40 34.32 23.16 32.64
21-Jun 24.37 30.25 24.45 31.45
22-Jun 24.46 32.58 24.51 32.27
23-Jun 23.96 30.74
24-Jun 23.62 31.33
25-Jun 24.24 32.78
26-Jun 23.95 32.34
27-Jun 23.98 32.31
28-Jun 24.04 32.2
29-Jun 23.67 32.02
30-Jun 23.30 32.12
l-Jul 24.17 35.59
2-Ju1 Period between treatments, no columns 25.04 32.88
3-Jul were booked up. 24.42 32.00
4-Jul 24.17 33.26
5-Jul 23.26 29.86
6-Jul 23.14 30.22
7-Ju1 23.64 32.20
8-Ju1 23.99 32.19
9-Jul 24.56 33.70
10-Jul 24.55 31.59
ll-Jul 24.82 30.58

 

 

 

 

 

Table Dl (cont’d)

 

 

 

Average daily soil Average daily soil Daily air Daily air
Date temperature temperature temperature temperature
minimum maximum minimum maximum
12-Jul 24.87 30.59
13-Jul 24.50 35.14
14-Jul 24.45 34.46
15-Jul 24.62 39.61
16-Jul 24.64 41.34
17-Jul 25.19 41.29
18-Ju1 25.30 37.43
19-Jul 24.46 37.47
20-Jul 24.91 35.38
21-Ju1 25.82 31.51 24.88 32.90
22-Jul 24.24 35.34 24.60 33.82
23-Ju1 22.12 33.07 24.27 35.06
24-Ju1 24.30 39.56 24.32 38.94
25-Jul 25.36 36.46 25.59 36.24
26-Jul 25.34 31.26 24.72 33.00
27-Jul 24.56 38.07 24.60 38.27
28-Jul 25.03 37.66 24.80 39.16
29-Jul 25.47 39.89 25.67 40.09
30-Jul 25.18 33.90 24.68 36.04
31-Jul 24.93 41.26 24.63 42.09
1-Aug 27.07 43.05 26.29 42.97
2-Aug 27.22 42.99 25.41 42.05
3-Aug 25.31 30.56 24.87 30.86
4-Aug 24.01 37.92 24.46 38.76
5—Aug 24.43 36.84 24.54 35.79
6-Aug 24.29 35.89 24.76 34.97
7-Aug 24.57 39.14 24.03 38.06
8-Aug 24. 12 35.36 24.14 35.85
9-Aug 24.10 36.31 24.51 36.21
10-Aug 25.04 33.42 23.98 31.70
1 l-Aug 22.20 32.77 21.54 30.89
12-Aug 20.88 34.62 19.91 32.43
13-Aug 21.79 35.16 21.06 32.77
14-Aug 23.31 30.91 23.34 30.56
15-Aug 22.64 34.67 22.67 33.19
l6-Aug 21.68 35.62 21.21 33.24
l7-Aug 23.47 35.37 23.13 34.82
18-Aug 24.05 31.34 24.22 31.80
19-Aug 23.69 30.03 23.61 29.98
20-Aug 22.32 34.14 21.62 31.72
21-Aug 21.95 34.67 21.15 33.75
22-Aug 22.91 35.73 22.69 34.26
23-Aug 22.36 34.24 21.74 33.74
24-Aug 23.50 29.76 23.33 30.38
25—Aug 23.88 33.78 24. 17 32.45
26-Aug 23.91 34.98 23.84 34.64
27-Aug 24.05 28.62 24.05 28.75
28-Aug 23.51 28.78 23.52 28.04
29-Aug 23.10 30.36 23.19 30.06
30-Aug 23.27 32.17 21.59 31.55
3 l-Au 20.46 33 .26 20.05 3 1.29

 

 

 

 

 

96

Table D1 (cont’d)

 

 

Average daily soil Average daily soil Daily air Daily air
Date temperature temperature temperature temperature
minimum maximum minimum maximum
l-Sep 21 .82 30.00 20.89 31.91
2-Sep 21.82 29.69 21.26 28.37
3-Sep 20.87 32.64 20.50 30.20
4-Sep 21.91 33.81 21.29 32.77
5-Sep 22.27 32.24 21.71 32.15
6-Sep 22.72 34.64 22.52 34.86
7-Sep 22.56 33.55 22.50 33.17
8—Sep 22.65 35.52 22.56 34.79
9-Sep 22.04 25.80 21.31 25.18
10-Sep 21 .29 25.44 20.57 24.91
11-Sep 21.79 26.04 21.60 25.50
12-Sep 22.38 26.10 22.53 25.97
13-Sep 23.20 26.73 22.96 26.23
14-Sep 22.25 27.91 22.28 27.22
15-Sep 22.27 33.53 21.85 34.02
16-Sep 21.12 32.59 20.30 33.79
17-Sep 22.09 34.29 21.87 33.89
l8-Sep 23.34 26.82 22.03 26.00
19-Sep 20.55 26.23 17.98 25.08
20-Sep 21.60 27.61 21 .62 25.90
21-Sep 21.48 30.81 21.43 28.16
22-Sep 21.94 25.50 21.71 24.74
23-Sep 22.65 27.17 22.39 26.58
24-Sep 22.20 26.76 21.05 25.51
25-Sep 21.27 30.21 21.35 28.64
26—Sep 21.68 31.63 21.23 27.33
27-Sep 21.96 27.50 22.27 27.75
28-Sep 21.46 27.87 21.51 24.81
29-Sep 21.25 28.17 20.73 24.74
30-Sep 21.01 25.21 21.72 23.70
l-Oct 20.69 27.92 21.12 28.22
2-Oct 21.01 25.73 21.89 26.91
3-Oct 21.49 32.75 21.25 29.64
4-Oct 22.80 25.89 21.62 25.80
5-Oct 21.20 29.40 20.96 26.73
6-Oct 20.92 31.85 20.73 27. 15
7-Oct 21.34 33.01 21.16 28.96
8-Oct 21.36 35.67 21.57 32.56
9-Oct 21.45 31.65 21.8 29.46
10-Oct 21.12 28.30 21.11 26.69
ll-Oct 23.22 26.19 23.51 26.32
12-Oct 22.27 26.69 22.05 25.63
l3-Oct 21.10 26.14 21.52 25.15
14-Oct 22.71 29.58 22.41 27.92
15-Oct 22.91 31.77 23.15 29.15
16-Oct 23.36 25.84 24.08 25.63
17-Oct 23.53 26.18 24.24 25.70
18-Oct 23.83 27.38 24.03 27.34
19-Oct 23.78 25.55 23.53 25.09
20-Oct 23.57 27.27 22.92 25.76
21-Oct 23.22 25.21 23.70 25.08

 

 

 

 

 

97

Table D1 (cont’d)

 

 

 

Average daily soil Average daily soil Daily air Daily air
Date temperature temperature temperature temperature
minimum maximum minimum maximum
22-Oct 22.88 24.31 23.21 24.83
23-Oct 22.71 24.60 22.58 24.79
24-Oct 22.24 26.29 22.98 25.05
25-Oct 23.08 29.38 22.70 29.40
26-Oct 22.90 29.51 22.83 26.93
27-Oct 23.19 24.99 23.69 25.15
28-Oct 23.16 27.37 23.53 25.59
29-Oct 22.75 31.30 22.88 28.56
30-Oct 23.27 32.49 23.47 34.10
31-Oct 23 .82 30.09 23.48 29.99
l-Nov 23.12 29.53 23.06 26.6
2-Nov 22.46 25.84 22.35 25.78
3-Nov 22.12 25.63 23.11 25.12
4-Nov 22.53 27.48 23.55 25.62
5-Nov 23.29 31.22 23.99 31.06
6-Nov 20.18 26.18 24.18 30.81
7-Nov
8-Nov
9-Nov
lO-Nov
1 1-Nov
12-Nov
13-Nov 23.37 24.99 23.39 24.61
14-Nov 23.00 25.27 23.38 24.76
15-Nov 23. 13 26.48 23.21 24.57
l6-Nov 21.93 23.73 21.37 23.80
17-Nov 22.41 25.91 23.10 24.52
18-Nov 23.05 26.40 23 .33 25.06
19-Nov 22.88 26.03 22.85 24.70
20-Nov 22.69 28.11 23.15 25.21
21-Nov 22.64 29.23 22.23 25.93
22-Nov 22.86 30.29 22.33 29.76
23-Nov 23.02 30.25 21.73 29.91
24-Nov 23.18 30.79 22.22 30.10,
25-Nov 23.56 27.62 23.07 26.42
26-Nov 23.86 28.68 23.55 28.17
27-Nov 23.80 27.92 23.6 27.33
28-Nov 23.93 30.41 23.03 31.29
29-Nov 24.00 26.03 23.84 25.62
30-Nov 23.01 24.86 22.91 25.05
l-Dec 18.93 23. 16 16.76 24.02
2-Dec 20.90 26.79 21.74 24.69
3-Dec 20.64 26.12 20.24 24.31
4-Dec 18.89 24.63 19.39 23 .95
S-Dec 19.85 26.51 20.23 25.02
6-Dec 20.14 24.55 20.12 24.37
7-Dec 17.21 26.02 17.91 26.23
8-Dec 17.67 26.56 18.67 24.91
9-Dec 17.46 27.00 18.49 26.13
10-Dec 20.80 27.75 21.37 28.32
ll-Dec 23.33 27.11 23.24 25.91

 

 

 

 

Table D1 (cont’d)

 

 

Average daily soil Average daily soil Daily air Daily air
Date temperature temperature temperature temperature
minimum maximum minimum maximum
12-Dec 23.36 24.87 23.58 24.83
l3-Dec 23.06 25. 15 23.18 24.78
l4-Dec 22.92 26.13 23.00 24.66
15-Dec 22.81 25.05 23.58 24.71
16-Dec 23 .00 26.98 23 .41 25.63
l7-Dec 23.55 27.45 23.03 25.25
18-Dec 23.00 27.37 22.89 26.14
19-Dec 22.54 27.89 21.54 27.29
20-Dec 21.86 26.92 22.61 25.66
21-Dec 22.88 25.29 23.58 24.73
22-Dec 23.06 24.79 23.47 24.85
23-Dec 22.53 23.90 23.26 24.55
24-Dec 22.41 26.91 22.59 27.00
25-Dec 22.78 25.40 23.49 24.77
26-Dec 22.18 24.22 22.92 24.65
27-Dec 22.17 25.89 22.83 24.43
28-Dec 22.82 26.45 23.05 25.04
29-Dec 22.98 26.25 22.67 24.94
30-Dec 22.47 26.62 22.31 26.67
31-Dec 22.78 24.35 23.99 24.97
l-Jan 22.55 24.59 23.03 25. 12
2-Jan 22.17 27.03 22.44 27.72
3-Jan 21.06 26.27 22.29 26.77
4-Jan 22.45 25.74 22.99 25.85
S-Jan 22.91 25.01 24.08 25.09
6-Jan 22.73 24.79 23.67 25.02
7-Jan 22.34 24.71 23.69 25. 12
8-Jan 21.13 25.63 21.72 26.44
9-Jan 20.60 24.62 20.51 24.75
10-Jan 17.92 25.93 18.72 24.13
1 Han 19.77 25.49 20.48 24.59
12-Jan 22.42 23.52 23.07 24.59

 

 

 

 

 

99

 

 

.. ...____ .we .---__1
'---9---Soilm’nave 1.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

February 1 —I——Soilrmx ave. 1
45.00 —-—-—~~~~~~--~«m ~ - u — 1
1
40.00 1
1 1
A 35.00 1
9 .
2 1
a 30.00 i
E 1
3. 25.00 - ‘ 1
’~- . . ‘9 ' . e . .
E o t'o-w' .b‘ 1..” 0.1”.“ so.“ 1
20.00 x. ,x ~." 1
,o" 1
15.00 , y 1
10.00 . . . 1
1-Feb 6-Feb 1 1-Feb 16-Feb 21—Feb 26-Feb
Date .
Figure D1: February temperature data.
_ a fi .1 r_' ___—_—_"11
:----o~ Soilm'nave

March . 1
1—l——-SOII max avq1
45.00 “mm 1
40.00 1
8 35.00 1
a: }
5 30.00 « ' 1
a - ‘
3 1
25.00 .
g" 04.. ,o 0 ..o"""""~.. .-’-.. , ..—¢-°--ow-o-"‘v'°'1’ 1

w s ’ '~ . ‘ . . " -..
,_ 20.00 - v 0 o 1
15.00 1
1
10.00 1 , 1
1-Mar 6-Mar 11-Mar 16—Mar 21-Mar 26-Mar 31-Mar 1
Date '

 

 

Figure D2: March temperature data.

100

 

 

 

- -O- - - Soil m'n ave

April 1 —I—Soil max ave
1 Ar terrp mn

1 -. Air ten'p max

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure D4: May temperature data.

101

 

 

‘3 1m
0 ' ‘—
h \
a \I
33 1
w 1
G ‘H ,v ~1\ - v- 1}
E .
a:
'—
15.00
10.00 . . r . .
1-Apr 6-Apr 11-Apr 16-Apr 21-Apr 26-Apr
Date A
Figure D3: April temperature data.
14+" Soil min ave |
May 1 —--—Soil max ave
' .. Air temp min
1 . -. Air temp max _
45.00
40.00 1
8 35.00 1 m\ 1
a: .
5 30.00 " 1
E 1‘1 4." _ 1
32500 0 ' .._ '- 1'... ‘0'. .'--v"v'"."°.‘1\. ‘- -'.. '.’v..'..~.\:h-A-A-1 ~.4\ .
E. " " '  1
,_ 20.00 1 1
1
15.00 4 1
10.00 . - , . . 1 1
1-May 6-May 11-May 16-May 21-May 26-May 31-May 1
1
Date '

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

-----9 Soil min ave
June ' +8011 max ave
Air temp min
Air temp max
45.00
40.00
g 35 00 qr—L,_A\ \ / .
e "\ /\_ \/ ' -
a 30 00 . " .
H
l!
a 25.00 40 ._- A . v' _.. 4M... 9 'v‘ ..'. ‘0_ ..,._ .A. ... ~13 0 '0‘ ',_\- ... .n.’
g .
,_ 20.00
15.00
10.00 . . . .
1-Jun 6-Jun 11-Jun 16-Jun 21-Jun 26-Jun
Date
Figure D5: June temperature data.
1------o Soil min axe
July ' —-—Soil max ave
1 . . Air temp min
.- . ..Air temp max
45.00 7
40.00
Q 35.00 -
o
5 30.00 --
1H
‘5 .
a 25.00 - _ .. . 4»
E ‘0
.2 20.00
15.00
10.00 . 1 - . ‘
1-Jul 6-Jul 11-Jul 16-Jul 21 —Jul 26-Jul 31-Jul

 

Date
Figure D6: July temperature data.

102

 

 

 

 

A

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

1-----o- Soil min a\e
AUQUSt . —-—Soil max ave
' Air temp min
1 1 ., Air temp max
1 45.00 1
—.
40.00 \ \
1 3 35.00 . V ‘-’ - _ "
.. . - \ ‘ xx“ \ .4
B ' ' 1,4/
E 0' 0‘ '
9’ 25.00 W .. .01
Q ,v_ A v. . v. ‘ A , fl _. . (\- I\. ,A‘ .
, g 3"." "u", “m.“‘m'fi ' .‘
,_ 20.00 “
15.00
1 10.00 . , A
1 1-Aug 6-Aug 11-Aug 16—Aug 21-Aug 26-Aug 31-Aug
'1 Date
Figure D7 : August temperature data.
1 1i». -- Soil min ave
- September +3911 max aye
. , A1r temp mm
1 1 _. evAirtemp max
; 45.00
40.00

Temperature (C)

 

 

 

 

 

T

11-Sep 16-Sep

Date

6—Sep

21-Sep 26-Sep

 

Figure D8: September temperature data.

103

 

 

 

men-Soil min ave

 

 

1 October 1 +80" max ave
1 1 , .-.. Air temp min
. . ., wAir temp max
1 1

45.00

‘ 40.00

 

 

Temperature (C)
;
\
<
2'

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

25.00 - 5“
Q '“ A V~’~..Vvh. *0an v
2000.1'“ “"“""" “"
1 15.00 ~
I 10.00 . w . w .
1 1-Oct 6-Oct 1 1-Oct 16-Oct 21 -Oct 26-Oct 31 -Oct
Date
Figure D9: October temperature data.
1 r
1 1---o---Soi|minave
1 November 1 +3011 max an
I Air temp min
1 1 :1 - Air temp max
1 45.00
40.00
3 35.00
2 30 00 _
3
g 25.00 " 4. 49-
E I\.“.."o.“ ,...—-Q' ‘6‘. '~)\
,2 20.00 “0
15.00
10.00 . . , .
1-Nov 6-Nov 11-Nov 16-Nov 21-Nov 26-Nov
. Date
L—_L_L v A

 

 

Figure D10: November temperature data.

104

 

 

 

 

1.....--Soilminave

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

December . +5011 max ave
~ ~Air temp min
1 -- “Airtemp max
45.00
1 40.00
1
1
1 g 35.00
m
5 30.00
E
Q 25.00
E
.2 20.00
15.00
1 10.00 . . . .
1 1-Dec 6-Dec 1 1 -Dec 16-Dec 21 -Dec 26-Dec 31 -Dec
Date
Figure Dll: December temperature data.
1----o- Soil min ave I
January +5011 max ave
, Air temp min
1 - Air temp max1
45.00
40.00
8 35.00
a:
g 30.00
in
L-
3 25 00 A“ —._../'\ M
E V v.‘ V'.-‘.'. ’
.9 20.00 ’ ' ". ,.
.O,
1 15.00
1 10.00 . w , .
' 1-Jan 6-Jan 11-Jan 16-Jan 21-Jan 26-Jan 31 -Jan
Date

 

 

Figure D12: January temperature data.

105

 

 

 

A
U'I

 

 

 

 

 

 

 

- .Top 38
, dedle 38
40 1_— Bottom 38 1 1
g 1. 1
2 35 1
3 11.1 311‘ it, 1" .1'
1! '11 1 1 . ' ~' 1
a 30 g 1 1 1 K 1
E ,
d) 1
l- .
25 .. ‘
1 20 . .. 1
0 1 2 3 4 5 6 7
Days

 

 

Figure D13: Soil temperature during the week before the bore, no manure simulated
rain event.

 

 

 

 

 

 

 

 

 

1...... Middle 38
0-4 " 1 —-Bottom 38
1 2
3
g 0.3 ~
E
.2 0.2 w
E h" ~~~~~~~ M‘HJM‘MWKfi
>0 0.1 4
i 0 .
1 0 1 2 3 4 5 6 7
1 Days

 

Figure D14: Soil moisture during the week before the bore, no manure simulated
rain event.

106

 

 

 

.5
U1

 

 

 

 

 

 

 

7 ’po 4’07j
1 ——~—~ Middle 40
40 ‘ — Bottom 40
. Q
1 2 35
1 §
8
D- 30
E
a:
l-
25
1 20
0 1 2 3 4 5 6 7
Days
Figure D15: Soil temperature during the week before the bare, manure simulated
rain event.
1 —Too 40* W
0.4 —— Middle 40

— Bottom 40

0.3

 

‘~m.‘\ / h\\.

0.1

Volumtric soil moisture

 

 

 

 

Figure D16: Soil moisture during the week before the bare, manure simulated rain
event.

107

 

A
01

 

 

 

 

 

 

 

 

Top 41 l 1 1
1---Middle 41 1 1
1 4o 5 1 ——Bottom 41? 1
Q
1 2 35
13
1 e
| q; 1
' a. 30 1 1 _
E J ‘ 1
0
l- a 1
1 l R 1 ”f" I
25 t r 1 " 1
1 20 V 1 , 1
1 o 1 2 3 4 5 6 7 1
Days 1

 

Figure D17: Soil temperature during the week before the rye, no manure simulated
rain event.

 

 

 

 

 

 

 

 

 

 

1
0.4

2

3

.9

o 0.3

E

'13. 1 1

.2 ”-2 “---¥*‘“~1~—1 Mk

H

a:

E

7:, 0.1

. Top 41
: > 1 ..— Middle 41 ,
i 1 —— Bottom 41 ;
. O . f 1‘ _. 1
0 1 2 3 4 5 6 7
1 Days
Figure D18: Soil moisture during the week before the rye, no manure simulated rain
event.

108

 

Temperature (C)

A
01

A
O

0)
U1

(.0
O

N
01

20

 

1 Top 35 1
‘ -— Middle 35 1
1 —Bottom 351 1

 

 

0

1

2 3

 

4 5 6

1 22+ 2. 71

Figure D19: Soil temperature in column 35 during the week before the rye, manure
simulated rain event.

Days

1i___

 

  

 

 

 

 

* 10533
0-4 ___ Middle 35
1 9 —Bottom 35
l a 2 fl *1
.2
O 0.3 11
E ,x' f—
1 = 1»
l g .,._
1 o 0.2
‘ '5
‘ d!
E
2 0.1
O
l >
1 o x "we
1 0 1 2 3 4 5 6 7

 

Figure D20: Soil moisture in column 35 during the week before the rye, manure
simulated rain event.

Days

 

109

 

 

A
01

 

 

 

1 ' Top 36 ’1 1

1 “Middle 36 ‘

‘ 1 —Bottom 36 I 1

1 401' ._g *4 1

1 3 1
g 35 ’1 'H 1
:l \ I 1 ' 1

1 E 1 11111 1| 1 J .

1 o l ‘ l 1
g 30 7 1 I I 1 K 1 1. 1
0 1 l 'l 1 1
I— l .

25 ,
1

20 1 l l
o 1 2 3 4 5 6 7 1
Days 1

 

 

Figure D21: Soil temperature in column 36 during the week before the rye, manure
simulated rain event.

1 7 7’ i—i ’777777777'77iigii'Fi7iiW7 iifi’i if iF—l

 

1 0.5 , ,_ v,__,1
Top 36

l —-—-—Midd|e 36 1

0.4 l —Bc&mj§l

1

Volumetric soil moisture

 

 

 

 

 

 

Figure D22: Soil moisture in column 36 during the week before the rye, manure
simulated rain event.

110

 

 

 

 

 

 

 

 

1
1 1_ 1 - -Top 42 1 1
1 1 --—--Middle 42 1 1
1 1 —Bottom 421 1
1 4O 1 1 1
1,. 1" 1
1 e 35* 1 1 1
3 1 .11. ’ 1 1
E 1‘1 1 1. 1
3 30 ‘ 1 ' '1 . 1
E 1 1
0
I- ‘ 1
25 1 1 ' 1
1 1
1 1
20 w . 1 , 1
o 1 2 3 4 5 6 7
1

Days

Figure D23: Soil temperature in column 42 during the week before the rye, manure
simulated rain event.

 

 

 

 

 

 

0.5 .2
1 1 Top 42
~- Middle 42
0'4 1 — Bottom 42

    

1—

 

 

 

 

 

 

e
a
.2
O
E 0.3
3 1:
.g 0.2
1 '2'
1 g 0.1 1
1 § \
1 0 1 1
1 o 1 2 3 4 5 6 7

1 Days

 

 

Figure D24: Soil moisture in column 42 during the week before the rye, manure
simulated rain event.

111

 

 

 

 

 

 

45 , 7,,
Top 37
———~ Middle 37
40 1’11 — 1301101137
Q 1
2 35
3
E
3 30
E
O
I—
25
20
1 o 1 2 3 4 5 6 7
1 Days

 

Figure D25: Soil temperature during the week before the desiccated corn, no manure
simulated rain event.

 

7 7 Top 37 1
0.4 1——— Middle 37 1

0.3

0.2 \._/—\

0.1 1

Volumetric soul mOIsture
1
E

 

 

 

 

Days

 

Figure D26: Soil moisture during the week before the desiccated corn, no manure
simulated rain event.

112

 

 

 

45

 

 

 

 

1‘
40 4.1. *1! 1‘4
‘3 ‘1 ’1 11 1 1
2 35
:3 .
3 ‘ »- ‘
a 30 , ’ '
E
O .
i- ' ,_,, ,, ,
25 1 1 Top 39 1
1~——Middle 39
‘ — Bottom 39:
20 Mi V F"
0 2 3 4 5 5 7
Days

 

 

 

Figure D27: Soil temperature during the week before the desiccated corn, manure

0.4

0.3

Volumetric soil moisture

simulated rain event.

 

 

 

4 7 4 v7 1
Top 39

~—- Middle 39

1— Bottom 39

 

 

Days

 

Figure D28: Soil moisture during the week before the desiccated corn, manure

simulated rain event.

113

 

45

 

 

1 1 —— Middle 34 1
40 7 1— Bottom 341

 

Temperature (C)

 

 

 

 

 

Days

 

 

Figure D29: Soil temperature during the week before the desiccated corn/rye, no
manure simulated rain event.

 

 

 

1 1 -~Top 34 1
0.4 — 1 --—--—Middle 34 1
1 —Bottom 341

 

0.3 ,

Hfl'
0.2 ~«

 

 

 

Volumetric soil moisture

0.1 -

 

 

 

 

0 1 2 3 4 5 6 7

Time (min)

 

1
1
1

 

Figure D30: Soil moisture during the week before the desiccated corn/rye, no manure
simulated rain event.

114

 

 

1 1 fiféb’lfs’"
1 ——Middle 43
1 40 —Bottom 43
<3;
1 2 35
1 3
‘ 2
1 a
.5.
.—

 

 

 

 

0 1 2 3 4 5 6 7 1

 

Figure D31: Soil temperature during the week before the desiccated corn/rye,
manure simulated rain event.

 

 

Top 437
0.4 ...... _ Middle 43
_ Bottom 43

    

0.3

0.2

Volumetric soil moisture

0.1

 

 

 

 

1
1
1

 

Figure D32: Soil moisture during the week before the desiccated corn/rye, manure
simulated rain event.

115

APPENDIX E

116

CSC File for CR-lO datalogger with AM32 multiplexer

;{CR10X}
;-Wiring for CR10-

; AM32 Multiplexer
; 1H: COM HI

; El: COM LO

; C2: CLK

; C1: RES

; G: GND

; 12V: 12V

;-Wiring for AM32 Multiplexer-

;ECHO Top (1)
; 1L: Red
; 1H: Black

;ECHO Top (2)
; 2L: Red
; 2H: Black

;ECHO Top (3)
; 3L: Red
; 3H: Black

;ECHO Top (4)
; 4L: Red
; 4H: Black

:ECHO Top (5)
; 5L: Red
; 5H: Black

;ECHO Middle (1)
; 6L: Red
; 6H: Black

;ECHO Middle (2)
; 7L: Red
; 7H: Black

;ECHO Middle (3)
; 8L: Red

117

 

; 8H: Black

;ECHO Middle (4)
; 9L: Red
; 9H: Black

;ECHO Middle (5)
; 10L: Red
; 10H: Black

;ECHO Bottom (1)
; 11L: Red
; 11H: Black

;ECHO Bottom (2)
; 12L: Red
; 12H: Black

;ECHO Bottom (3)
; 13L: Red
; 13H: Black

;ECHO Bottom (4)
; 14L: Red
; 14H: Black

;ECHO Bottom (5)
; 15L: Red
; 15H: Black

;107 Top (1)
; 16L: Red
; 16H: Black

;107 Top (2)
; 17L: Red
; 17H: Black

;107 Top (3)
; 18L: Red
; 18H: Black

;107 Top (4)

; 19H: Black
; 19L: Red

118

o
9

9

9

9

9

9

o
9

9

o
9

9

9

9

;107 Top (5)
;20H: Black
;20L: Red

;107 Middle (1)
;21H: Black
;21L: Red

;107 Middle (2)
;22H: Black
;22L: Red

;107 Middle (3)
;23H: Black
;23L: Red

;107 Middle (4)
; 24H: Black
; 24L: Red

;107 Middle (5)
;25H: Black
;25L: Red

;107 Bottom (1)
26H: Black
26L: Red

;107 Bottom (2)
27H: Black
27L: Red

;107 Bottom (3)
28H: Black
28L: Red

;107 Bottom (4)
29H: Black
29L: Red

;107 Bottom (5)
30H: Black
30L: Red

' 107 Air Temperature

31H: Black

119

; 31L: Red
; 32H:
; 32L:
*Table 1 Program
01 : 60.0000 Execution Interval (seconds)

1: Batt Voltage (P10)
1: 1 Loc [ Batt_Volt]

2: If time is (P92)

1: 0 Minutes (Seconds --) into a
2: 5 Interval (same units as above)
3: 30 Then Do

3: Do (P86)

1:41 Set Port 1 High

4: Beginning of Loop (P87)
1: 0 Delay
2: 15 Loop Count

5: Do (P86)
1: 72 Pulse Port 2

6: Excitation with Delay (P22)
1: 1 Ex Channel
2: 0 Delay W/Ex (0.01 sec units)
3: 1 Delay After Ex (0.01 sec units)
4: 0 mV Excitation
;Measure EC10 probes
7: Excite-Delay (SE) (P4)

1: 1 Reps

2: 25 5000 mV, 60 Hz Reject, Fast Range (Delay must be 0)
3: 1 SE Channel

4: 1 Excite all reps w/Exchan 1

5: 1 Delay (0.01 sec units)

6: 2500 mV Excitation

7: 2 -- Loc [ EC_Top_1 ]

8: .0936 Multiplier

9: -37 Offset

;Check for negative reading and convert to m3/m3 water content

120

8: If (X<=>F) (P89)

1: 2 -- X Loc [ EC_Top_1 ]
2: 4 <
3: 0 F
4: 30 Then Do
9: Z: F x 10An (P30)
1:0 F
2:0 11, Exponent of 10
3: 2 -- Z Loc [ EC_Top_l ]

10: End (P95)

11: Z=X*F (P37)

1: 2 -- X Loc [ EC_Top__1 ]
2: .01 F

3: 2 -- Z Loc [ EC_Top__l ]

12: End (P95)

13: Beginning of Loop (P87)
1: 0 Delay
2: 15 Loop Count

14: Do (P86)
1: 72 Pulse Port 2

15: Excitation with Delay (P22)

1: 1 Ex Channel

2: 0 Delay W/Ex (0.01 sec units)

3: 1 Delay After Ex (0.01 sec units)
4' 0 mV Excitation

16: Temp (107) (P11)

1: 1 Reps

2:1 SE Channel

3:1 Excite all reps w/Exchan 1
4:17 -- Loc [ ST_Top_1 ]
5:1.0 Mult
6: 0. 0 Offset
17: End (P95)

18: Do (P86)
1: 72 Pulse Port 2

121

19; Temp(107) (P11)

1: 1 Reps

2: 1 SE Channel

3: 1 Excite all reps w/Exchan 1
4: 32 Loc [ Tair_C ]

5: 1.0 Mult

6: 0.0 Offset
20: Do (P86)

1:51 Set Port 1 Low
21: End (P95)

22: If time is (P92)

1: 0 Minutes (Seconds --) into a

2: 15 Interval (same units as above)
3: 10 Set Output Flag High

23: Set Active Storage Area (P80)"24532
1: 1 Final Storage Area 1
2: 15 Array ID

24: Real Time (P77)"13 165
1: 1220 Year,Day,Hour/Minute (midnight = 2400)

25: Minimum (P74)"26642
1: 1 Reps

2: 0 Value Only

3: 1 Loc [ Batt_Volt]
26: Average (P71)"16767

1: 31 Reps
2: 2 Loc [ EC_Top_1 ]

*Table 2 Program
01: 10 Execution Interval (seconds)

1: Serial Out (P96)
1: 71 Storage Module

*Table 3 Subroutines

End Program

122

-Input Locations-

1 Batt_Volt 1 1 1

2 EC_Top_l 7 3 3

3 EC_Top_2 3 1 0
4 EC_Top_3 3 1 0

5 EC_Top_4 ll 1 0
6 EC_Top_S 19 1 0
7 EC_Mid_l 7 1 0

8 EC_Mid_2 11 1 0
9 EC_Mid_3 11 1 0
10 EC_Mid_4 11 1 0
11 EC_Mid_S 19 l 0
12 EC_Bot_l 7 1 0
13 EC_Bot_2 11 1 0
14 EC_Bot_3 11 1 0
15 EC_Bot_4 11 1 0
16 EC_Bot_S 19 1 0
17 ST_Top_l 7 1 1
18 ST_Top_2 11 1 0
19 ST_Top_3 11 1 0
20 ST_Top_4 11 l 0
21 ST_Top_S 19 1 0
22 ST_Mid_l 7 1 0
23 ST_Mid_2 11 1 0
24 ST_Mid_3 11 1 0
25 ST_Mid_4 ll 1 0
26 ST_Mid_S 19 1 0
27 ST_Bot_l 7 1 0
28 ST_Bot_2 ll 1 0
29 ST_Bot_3 11 1 0
30 ST_Bot_4 11 1 0
31 ST_Bot_S 19 l 0
32 Tair_C l l 1
-Program Security-
0000

0000

0000

-Mode 4-

-Final Storage Area 2-
0

-CR10X ID-

0

-CR10X Power Up-
3

-CR10X Compile Setting-

123

3

-CR10X RS-232 Setting-

-1

-DLD File Labels-

0

-Final Storage Labels-
0,15,24532
l,Year_RTM,13165
1,Day_RTM

1 ,Hour_Minute_RTM

2,Batt_Volt_MIN~1,26642
3,EC_Top_1_AVG~2,16767

3,EC_Top_2_AVG~3
3,EC_Top_3_AVG~4
3,EC_Top_4_AVG~5
3,EC_Top_5_AVG~6
3,EC_Mid_1_AVG~7
3,EC_Mid_2_AVG~8
3,EC_Mid_3_AVG~9
3 ,EC_Mid_4_AVG~1 0
3,EC_Mid_5_AVG~1 1
3 ,EC_Bot_1_AVG~1 2
3 ,EC_Bot_2_AVG~1 3
3,EC_Bot_3_AVG~1 4
3 ,EC_Bot_4_AVG~1 5
3 ,EC_Bot_5_AVG~1 6
3 ,ST_Top_1_AVG~17
3,ST_T0p_2_AVG~1 8
3 ,ST_Top_3_AVG~1 9
3,ST_Top_4_AVG~20
3 ,ST_Top_5__AVG~2 1
3,ST_Mid_1__AVG~22
3,ST_Mid_2_AVG~23
3,ST_Mid_3_AVG~24
3,ST_Mid_4_AVG~25
3,ST_Mid_5_AVG~26
3,ST_Bot_1_AVG~27
3,ST_Bot_2_AVG~28
3,ST__Bot_3_AVG~29
3,ST_Bot_4_AVG~30
3 ,ST_Bot_5_AVG~3 1
3,Tair_C_AVG~32

124

DLD File for CR-10 datalogger with AM32 multiplexer

};CR10X
;5COL_10X.DLD
;$
;:Batt_Volt:EC_Top_l :EC_Top_2 :EC_Top_3 :EC_Top_4
;:EC_Top_5 :EC_Mid_l :EC_Mid_2 :EC_ id_3 :EC_Mid_4
;:EC_Mid_5 :EC_Bot_l :EC_Bot_2 :EC_Bot_3 :EC_Bot_4
;:EC_Bot_S :ST_Top_l :ST_Top_2 :ST_Top_3 :ST_Top_4
;:ST_Top_5 :ST_Mid_l :ST_Mid_2 :ST_Mid_3 :ST_Mid_4
;:ST_Mid_5 :ST_Bot_l :ST_Bot_2 :ST_Bot_3 :ST_Bot_4
;:ST_Bot_5 :Tair_C

:3;

MODE 1
SCAN RATE 60.0000

1:P10
1:1

2:P92
1:0
2:5
3:30

3:P86
1:41

4:P87
1:0
2:15

5:P86
1:72

6:P22
1:1
2:0
3:1
4:0

125

5:1
6:2500
722--
8:.0936
92-37

8:P89
1:2--
2:4
3:0
4:30

9:P30
1:0
2:0
322--

10:P95

11:P37
1:2--
2:.01
3:2--

12:P95

13:P87
1:0
2:15

14:P86
1:72

15:P22
1:1
2:0
3:1
4:0

16:P11
1:1
2:1
3:1
4:17--
521.0
620.0

126

17:P95

18:P86
1:72

19:P11
1:1
2:1
3:1
4:32
5:1.0
6:0.0

20:P86
1:51

21 :P95

22:P92
1:0
2:15
3:10

23:P80
1:1
2:15

24:P77
1:1220

25:P74
1:1
2:0
3:1

26:P7l
1:31
2:2

MODE 2
SCAN RATE 10

1:P96
1:71

127

MODE 3

MODE 10
1:32

2:64

3:0

MODE 12
l :0000
2:0000
3 :0000

128

REFERENCES

129

Adams, M. H. 1959. Bacteriophages. Interscience, New York.

American Society of Agricultural Engineers (ASAE). 1998. ASAE Standards: Standards,
Engineering Practices, Data, 45‘h Edition. St. Joseph, MI.

Anderson, T.A., E.A. Guthrie and ET Walton. 1993. Bioremediation in the
rhizosphere. Environmental Science Technology 27(13): 2630-2636.

Auld, H., D. Maclver, and J. Klaassen. 2004. Heavy Rainfall and Waterbome Disease
Outbreaks: The Walkerton Example. Journal of Toxicology and Environmental
Health, Part A. 67:1879-1887

Bell, R. G. 1976. Persistence of Fecal Coliform Indicator Bacteria on Alfalfa Irrigated

with Municipal Sewage Lagoon Effluent. Journal of the Environmental Quality
5(1):39-42.

Bird, G.W., M.F. Bimey and MA. Cavigelli. 1998. Soil Ecology. Michigan Field Crop
Ecology: Managing biological processes for productivity and environmental
quality. Michigan State University Extension Bulletin E-2646, 92 pp.

Brown, LC. and AD. Ward. 1997. Understanding agricultural drainage. AEX
320-97. Ohio State University Extension, The Ohio State University. 4 pp.

Brown, G. O., H. T. Hsieh, and J. Kimzey. 2001. Experimental Measurement of
Macropore flow in a Silt Loam Soil, 3-5 January 2001 Preferential Flow, Water
Movement and Chemical Transport in the Environment, Proc. 2nd Int. Symp.,
Honolulu, Hawaii, USA. Pub ID 701P0006z237-240pp

Buckley, D. H. and T. M. Schmidt. 2002. Exploring the Biodiversity of Soil — A
Microbial Rainforest. Biodiversity of Microbial Life. Wiley-Liss, Inc. 183-208.

Clay, D. E., Z. Zheng, Z. Liu, S. A. Clay, and T. P. Trooien. 2004. Bromide and Nitrate
Movement through Undisturbed Soil Columns, Journal of Environmental Quality
33:338-342.

Clesceri, L. S., A. E. Greenberg, and A. D. Eaton. (Eds). 1998. Standards Methods for the
Examination of Water and Wastewater, 20th ed. American Public Health

Association (APHA), American Water Works Association, Water Environmental
Federation; APHA, Washington, DC.

Conservation Commission of Missouri. 1980. Native Grasses. Missouri Department of
Conservation, Jefferson City, MO. Accessed Oct. 2006. URL:
http://www.de.mo.gov/documents/landown/ L040A.PDF

130

Coyne, M. S., R. A. Gilfillen, R. W. Rhodes, and R. L. Belvins. 1995. Soil and fecal
coliform trapping by grass filter strips during simulated rain. Journal of Soil and
Water Conservation 50(4):405.

Crane, S. R., P. W. Westerman, and M. R. Overcash. 1980. Die-off of Fecal Indicator
Organisms Following Land Application of Poultry Manure. Journal of
Environmental Quality 9(3):531-537.

Crane, S. R., J. A. Moore, M. E. Grismer, and J. R. Miner. 1983. Bacterial Pollution from
Agricultural Sources: A Review. Transactions of the ASAE 26:858-866.

Darnault, C. J. G., T. S. Steenhuis, P. Garnier, Y. Kim, M. B. Jenkins, W. C. Ghiorse, P.
C. Baveye, and J. Parlange. 2004. Preferential Flow and Transport of

Cryptosporidium parvum Oocysts through the Vadose Zone: Experiments and
Modeling. Vadose Zone Journal 3:262-270.

Dazzo, F., P. Smith, and D. Hubbell. 1973. The influence of manure slurry irrigation on
the survival of fecal organisms in Scranton fine sand. Journal of Environmental
Quality 2(4):470-473.

Dazzo, EB. 1972. The microbial ecology of cultivated soil receiving cow manure waste.
Unpublished Masters Thesis, Department of Microbiology, University of Florida,
Gainesville FL.

Doran, J. W. 1980. Soil Microbial and Biochemical Changes Associated with Reduced
Tillage. Soil Science Society of America Journal 44:765-771.

Drees, L.R., A.D. Karathanasis, L. P. Wilding, and R. L. Blevins.1994.
Micromorphological characteristics of long-term no-till and
conventionally tilled soils. Soil Sci. Soc. Am. J. 58:508-517.

Entry, J. A., R. K. Hubbard, J. E. Thies, and J. J. Fuhrmann. 2000a. The Influence of
Vegetation in Riparian Filterstrips on Coliform Bacteria: 1. Movement and

Survival in Water. Journal of Environmental Quality 29: 1206-1214.

Entry, J. A., R. K. Hubbard, J. E. Thies, and J. J. Fuhrmann. 2000b. The Influence of
Vegetation in Riparian Filterstrips on Coliform Bacteria: 11. Survival in Soils.
Journal of Environmental Quality 29: 1215-1224.

Fausey, N.R., L.C. Brown, H.W. Belcher and RS. Kanwar. 1995. Drainage and
water quality in Great Lakes and Combelt States. J. Irrig and Drain. Engr.
121(4):283-288.

Fleming, R. J. and S. H. Bradshaw. 1992. Contamination of Subsurface Drainage
Systems During Manure Spreading. 1992 International Winter Meeting,
Nashville, Tennessee, USA. Paper Number 92-2618213pp.

131

Gagliardi, J. V., J. S. Angle, J. J. Germida, R. C. Wyndham, C. P. Chanway, R. J.
Watson, C. W. Greer, T. McIntyre, H. H. Yu, M. A. Levin, E. Russek-Cohen, S.
Rosolen, J. Nairn, A. Seib, T. Martin-Heller, and G. Wisse. 2001. Intact soil-core
microcosms compared with multi-site field releases for pre-release testing of

microbes in diverse soils and climates. Canadian Journal of Microbiology 47:237-
252.

Gagliardi, J. V. and J. S. Karns. 2000. Leaching of Escherichia coli 0157:H7 in Diverse
Soils under Various Agricultural Management Practices, Applied and
Environmental Microbiology 66(3):877-883.

Gagliardi, J. V. and J. S. Kams. 2002. Persistence of Escherichia coli 0157:H7 in soil
and on plant roots, Environmental Microbiology 4(2), 89-96.

Gerba, C. P., C. Wallis, and J. L. Melnick. 1975. Fate of Wastewater Bacteria and
Viruses in Soil. Jouma] of Irrigation and Drainage Division 101:157-174.

Gerba, C. P. and J. E. Smith, Jr. 2005. Sources of Pathogenic Microorganisms and Their
Fate during Land Application of Wastes. Journal of Environmental Quality 34:42-
48.

Guan, T. Y., and R. A. Holley. 2003. Pathogen Survival in Swine Manure Enviroments
and Transmission of Human Enteric Illness — A Review. Journal of
Environmental Quality 32:383-392.

Guber, A. K., D. R. Shelton, and Ya. A. Pachepsky. 2005. Transport and retention of
manure-borne coliforms in soil, Vadose Zone Journal 4:828-837.

Hatfield, K. K., G. S. Warner, K. Guillard. 1997. Bromide and F D&C Blue No. 1 Dye
Movement Through Intact and Packed Soil Columns. Transactions of ASAE
40(2):309-315.

Hillel, Daniel. 1998. Environmental Soil Physics. Academic Press, a division of
Harcourt Brace & Company. San Diego, CA 92101-4495, USA.

Holden, P. A., N. Fierer. 2005. Microbial processes in the vadose zone. Vadose Zone
Journal 4:1-21.

Ibekwe, A. M., and C. M. Grieve. 2004. Changes in developing plant microbial
community structure as affected by contaminated water. FEMS Microbiology
Ecology 48:239-248.

J amieson, R.C., R]. Gordon, K.E. Sharples, G.W. Stratton and A. Madani. 2002.

Movement and persistence of fecal bacteria in agricultural soils and subsurface
drainage systems: a review. Canadian Biosystems Engineering 44: 1.1-1 .9.

132

Joy, D. M., H. Lee, C. M. Reaume, H. R. Whiteley, and S. Zelin. 1998. Microbial
Contamination of Subsurface Tile Drainage Water from Field Applications of
Liquid Manure. Canadian Agricultural Engineering 40(3): 1 53-160.

Kasnavia, T., De Vu, D.A. Sabatini, 1998. Fluorescent Dye and Media Properties
Affecting Sorption and Tracer Selection. Ground Water 37(3): 376-381.

Kibbey, H. J ., C. Hagedom, and E. L. McCoy. 1978. Use of Fecal Streptococci as
Indicators of Pollution in Soil. Applied and Environmental Microbiology
35(4):711-717.

Kudva, I. T., K. Blanch, and C. J. Hovde. 1998. Analysis of Escherichia coli 0157:H7
Survival in Ovine or Bovine Manure and Manure Slurry. Applied and
Environmental Microbiology 64(9):3 166-3 1 74.

La], R., and M. K. Shukla. 2004. Soil’s Moisture Content. Principles of Soil Physics.
Marcel Dekker Inc., New York: 287-320.

Leclerc, H., S. Edberg, V. Piezro, and J. M. Delattre. 2000. Bacteriophages as indicators

of entric viruses and public health risk in groundwaters. Journal of Applied
Microbiology 88:5-21.

Lehman, R. M., K. E. Baker, and E. D. Mattson. 2004. Distribution of Microorganisms
and their Activities in Capillary Barriers: Implications for Modeling of

Hydrologic Transport through Capillary Barriers. Vadose Zone Journal 3:134-
142.

Lim, T.T., D.R. Edwards, S.R. Workman, B.T. Larson, L. Dunn. 1998. Vegetated Filter

Strip Removal of Cattle Manure Constituents in Runoff. Transactions of the
ASAE.41(5):1375-1381.

Ling, T. Y., E. C. Achberger, C. M. Drapcho, and R. L. Bengston. 2002. Quantifying
Adsorption of an Indicator Bacteria in a Soil-Water System. Transactions of the
ASAE 45(3):669-674.

Marshall, KC. 1971. Sorptive interactions between soil particles and microorganisms.
In: Soil Biochemistry, 409-445. AD. McLaren and J. Skujins, eds. New York,
NY: Marcel Dekker.

Mawdsley, J. L., R. D. Bardgett, R. J. Merry, B. F. Pain, and M. K. Theodorou. 1994.

Pathogens in livestock waste, their potential for movement through soil and
environmental pollution. Applied Soil Ecology 2:1-15.

133

McLeod, M., J. Aislabie, J. Smith, R. Fraser, A. Roberts, and M. Taylor. 2001. Viral and
Chemical Tracer Movement through Contrasting Soils. Journal of Environmental
Quality 30:2134-2140.

McLeod, M., J. Aislabie, J. Rybum, A. McGill, and M. Taylor. 2003. Microbial and
chemical tracer movement through two Southland soils, New Zealand. Australian
Journal of Soil Research 41:1163-1169.

McMurry, S. W., M. S. Coyne, and E. Perfect. 1998. Fecal Coliform Transport through
Soil Blocks Amended with Poultry Manure. Journal of Environmental Quality
27:86-92.

Metcalf and Eddy. 1991. Wastewater Engineering: Treatment, Disposal, Reuse. 3rd ed.
McGraw-Hill, Inc., New York.

Minitab, Inc., 2003. Version 14.1. State College, Pa.: Minitab Inc.

Mohanty, B. P., P. Castiglione, P. J. Shouse, and M. Th. van Genuchten. 2001.
Measurement and Modeling of Preferential Flow Under Controlled Conditions, 3-
5 January 2001 Preferential Flow, Water Movement and Chemical Transport in
the Environment, Proc. 2nd Int. Symp., Honolulu, Hawaii, USA. Pub ID
701P0006z77-80 pp

Mubiru, D. N., M. S. Coyne, and J. H. Grove. 2000. Mortality of Escherichia coli
0157:H7 in Two Soils with Different Physical and Chemical Properties. Journal
of Environmental Quality 29:1821-1825.

Nicosia, L. A., J. B. Rose, L. Stark, and M. T. Stewart. 2001. A Field Study of Virus
Removal in Septic Tank Drainfields. Journal of Environmental Quality 30: 1933-
1939.

Omoti, U. and A. Wild, 1979. Use of fluorescent dyes to mark the pathways of solute
movement through soils under leaching conditions. 1. Laboratory experiment.
Soil Science. 128(1):28-33.

Pagliai, M., M. Raglione, T. Panini, M. Maletta, and M. La Marca. 1995. The
structure of two alluvial soils in Italy after 10 years of conventional and
minimum tillage. Soil Tillage Res. 34:209-223.

Patni, N. K., H. R. Toxopeus, and P. Y. Jui. 1985. Bacterial Quality of Runoff from
Manured and Non-Manured Cropland. Transactions of the ASAE 28(6):1871-
1877.

Paul, J .H., J .B. Rose, C. Kellogg, J. Brown, E.A. Shinn, and S. Miller. 1995. Viral tracer

studies indicate contamination of marine waters by sewage disposal practices in
Key Largo, Florida. Applied Environmental Microbiology. 61:2230-2234.

134

Pell, A. N. 1997. Manure and Microbes: Public and Animal Health Problem?. Journal of
Dairy Science 80:2673-2681.

Powelson, D. K., and A. L. Mills. 2001. Transport of Escherichia coli in Sand Columns

with Constant and Changing Water Contents. Journal of Environmental Quality
30:23 8-245.

Reddy, K. R., R. Khaleel, and M. R. Overcash. 1981. Behavior and Transport of
Microbial Pathogens and Indicator Organisms in Soils Treated with Organic
Wastes. Journal of Environmental Quality 10(3):255-266.

Rockhold, M. L., R. R. Yarwood, and J. S. Selker. 2004. Coupled Microbial and
Transport Processes in Soils. Vadose Zone Journal 3:368-383.

Roodsari, R., D. Shelton, A. Shirrnohammadi, Y. Pachepsky, Sadeghi, and J. Starr. 2002.
Pathogen Transport as Affected by Surface Condition. 2002 ASAE Annual
International Meeting, Chicago, Illinois, USA. Paper Number 022264:9pp.

Rose, J ., B. 1997. Environmental Ecology of Cryptosporidium and Public Health
Implications. Annual Review of Public Health. 18:135-161.

Saini, R., J. C. Lorimor, and L. Halverson. 2001. Effect of Manure Application and
Rainfall Timing on the Leaching of Labeled Bacteria through Soil Columns. 2001
ASAE Annual International Meeting, Sacramento, California, USA. Paper
Number: 01—2195:15pp.

Scholl, M. A., A. L. Mills, J .S. Herman, and G. M. Homberger. 1990. The Influence of
Mineralogy and Solution Chemistry on the Attachment of Bacteria to
Representative Aquifer Materials. Journal of Contaminant Hydrology 62321-336.

Scott, T. M ., J. B. Rose, T. M. Jenkins, S. R. Farrah, and J. Lukasik. 2002. Microbial
Source Tracking: Current Methodology and Future Directions. Applied and
Environmental Microbiology 68( l 2):5796-5 803 .

Shelton, D. R., Y. A. Pachepsky, A. M. Sadeghi, W. L. Stout, J. S. Kams, and W. J.
Gburek. 2003. Release Rates of Manure-Bome Coliform Bacteria from Data on
Leaching through Stony Soil. Vadose Zone Journal 2:34-39.

Shipitalo, M. J. and F. Gibbs. 2000. Potential of Earthworm Burrows to Transmit Inject
Animal Wastes to Tile Drains. Soil Science Society of America Journal

64(6):2103-2109.

Shipitalo, M.J., and R. Protz. 1987. Comparison of morphology and porosity
of a soil under conventional and zero tillage. Can. J. Soil Sci. 67:445-456.

135

Sinton, L. W. and S. B. Ching. 1987. An Evaluation of two Bacteriophages as Sewage
Tracers. Water, Air, and Soil Pollution 35:347-356.

Sjogren, R. E. 1994. Prolonged Survival of an Environmental Escherichia coli in
Laboratory Soil Microcosms. Water, Air and Soil Pollution 75:389-403.

Smart, PL. and I.M.S. Laidlaw, 1977. An evaluation of some fluorescent dyes for water
tracing. Water Resource Research. 13:15-33.

Smith, M. S., G. W. Thomas, R. E. White, and D. Ritonga. 1985. Transport of
Escherichia coli Through Intact and Distributed Soil Columns. Journal of
Environmental Quality 4(1):87-91 .

Smucker, A.J.M. 1984. Carbon utilization and losses by plant root systems. In: Roots,
Nutrient and Water Influx, and Plant Growth. Barber and Boulden, eds. Amer.
Soc. Agron., Madison, WI, pp. 27-46.

Smucker, A.J.M., W. Richner, and V.O. Snow. 1995. Bypass flow via root-induced
macropores (RIMS) in subirrigated agriculture. Clean water, clean environment,
2lst century: team agriculture, working to protect water resources: ASAE
conference proceedings, March 5-8, 1995, Kansas City, Missouri

Stamm, K. D., L. J. Halverson, J. L. Baker, and J. C. Lorimor. 2001. Effect of manure
application rate and soil moisture content on the movement of an antibiotic
resistant strain of E. coli in subsurface drainage under simulated rainfall. 2001
ASAE Annual International Meeting, Sacramento, California, USA. Paper
Number 01-2106:18pp.

Stratton, G. W., S. A. Madani, and R. J. Gordon. 2002. Pathogen Management in
Agriculture: Water Quality Monitoring in Atlantic Canada. 2002 ASAE Annual
International Meeting, Chicago, Illinois, USA. Paper Number 022058: 10pp

Stratton, G. W., A. Madani, R. J. Gordon, K. Sharples, T. Coulter, and A. Thiagarajan.
2004. Bacterial Quality of Surface and Subsurface Drainage Water From
Manured Fields. Drainage VIII Proceedings of the Eighth International
Symposium, Sacramento, California, USA. Paper Number 701P0304z9pp

Stratton, G. W., R. J. Gordon, A. Madani, A. Thiagarajan, and C. Shaw. 2005. Drainage
Water Quality Impacted by Manure Application. 2005 ASAE Annual
International Meeting, Tampa, Florida, USA. Paper Number 05-2067:12pp

Trudgill, ST, 1987. Soil water dye tracing with special reference to the use of rhodarnine
WT, lissamine FF and amino G acid. Hydrological Processes. 1: 149-170.

Tufekcioglu, A., J .W. Raisch, T.M. Isenhart and RC. Schultz. 1999. Fine root
dynamics, coarse root biomass, root distribution, and soil respiration in a

136

multispecies riparian buffer in central Iowa, USA. Agroforestry Systems 44: 163-
174.

Unc, A., and M. J. Goss. 2003. Movement of Faecal Bacteria through the Vadose Zone.
Water, Air, and Soil Pollution 149:327-337.

Unc, A. and M. J. Goss. 2004. Transport of Bacteria from Manure and Protection of
Water Resources. Applied Soil Ecology 25:1-18.

United States Environmental Protection Agency (USEPA). 2001. Protocol for
developing pathogen TMDL's. First Edition, EPA 841-R-00-002. URL:
http://www.epa.gov/0wow/tmdl/pathogen_all.pdf

United States Department of Agriculture — Economic Research Service (U SDA-ERS).
1987. Farm Drainage in the United States. History, Status, and Prospects. USDA-
Economic Research Service. Misc. Pub. No. 1455.

Vidales, J. A., C. P. Gerba, and M. M. Karpiscak. 2003. Virus Removal from Wastewater
in a Multispecies Subsurface-Flow Constructed Wetland. Water Environment
Research 75(3):238-245.

Wang, F., R. S. Kanwar, and J. C. Lorimor. 2001. Impact of Swine Manure on Bacterial
Transport though Undisturbed Soil Columns. 2001 ASAE Annual International
Meeting, Sacramento, California, USA. Paper Number 012196z54-66 pp

Wang, L. and K. R. Mankin. 2001. Dieoff and release of fecal pathogens from animal
manure. 2001 ASAE Annual International Meeting, Sacramento, California.
Paper Number 01-2237:13pp.

Wang, L., K. R. Mankin, and G. L. Marchin. 2003. Kinetic Model for E. coli Transport
through Sand Column, 2003 ASAE Annual International Meeting, Las Vegas,
Nevada, USA. Paper Number 032151 :9 pp

Warnemuende, EA. and RS. Kanwar. 2002. Effects of swine manure application on

bacterial quality of leachate from intact soil columns. Transactions of the ASAE
45(6):1849-1857.

Yang, C. H. and D. E. Crowley. 2000. Rhizosphere Microbial Community Structure in
Relation to Root Location and Plant Iron Nutritional Status. Applied and
Environmental Microbiology 661 :345-351.

137