&EPA
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EPA 700 R-92-G08
June 1992
Monitoring Small-Scale
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r/EPA
United States
Environmental Protection
Agency
Prevention, Pesticides,
And Toxic Substances
(TS-788)
EPA 700 R-92-008
June 1992
Monitoring Small-Scale
Field Tests Of Microorganisms
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Acknowledgements:
This work was supported in part by USEPA Contract No. 68-02-4296, Work
Assignment No. 333, USEPA Contract No. 68-02-4294, Task No. 2-44, and USEPA
Contract No. 68-DO-0061, Task No. 1-8.
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TABLE OF CONTENTS
PAGE
INTRODUCTION 1
CHAPTER 1: Collecting Information
1.1 Gathering Information 4
1.2 The Microorganism Profile 4
1.3 The Field Site (Environmental) Profile 5
1.4 The Experimental Profile 7
CHAPTER 2: Overview of Field Plot Design and Statistical Analysis
2.1 Introduction 9
2.2 Basic Considerations for Statistical Analysis 10
2.2.1 Analysis of Variance - Introduction 10
2.2.2 Power of Test 10
2.2.3 Requirements for Analysis of Variance 11
2.2.4 Regression 12
2.3 Experimental Designs 13
2.3.1 Randomized Complete Block Design 13
2.3.2 Latin Square 13
2.3.3 Split Plot 13
2.4 Factorials 15
2.5 Design Summary . 15
2.6 Mean Separation Techniques 16
CHAPTER 3: Developing Monitoring Objectives
3.1 Formulating Monitoring Objectives 17
3.2 Determining the Appropriate Monitoring Intensity 18
3.3 Examples of Monitoring Intensities and Objectives 20
3.3.1 Low Intensity Monitoring Objectives 20
3.3.2 High Intensity Monitoring Objectives 21
CHAPTER 4: Developing a Monitoring Plan
4.1 Introduction 22
4.2 Defining Monitoring Zones 22
4.3 Defining a Sample Collection Strategy 23
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4.4 Sampling 25
4.4.1 Sample Size 25
4.4.2 Sampling Techniques 26
4.5 Implementation of the Monitoring Plan 30
CHAPTER 5: Developing Procedures to Assure Data Quality
5.1 Quality Assurance and Quality Control 31
5.2 Measurement Quality Objectives 31
5.3 QA/QC Procedures for the Field 32
5.4 QA/AC Procedures for the Laboratory 33
5.5 References Used to Prepare a QA Plan 34
CHAPTER 6: Developing Procedures to Assure Health and Occupational Safety
6.1 General Information 35
6.2 Test Site Emergency Procedures 35
6.3 Laboratory Procedures and Reference Documents 36
REFERENCES 37
APPENDIX A: Microorganism, Environmental, and Experimental Profile Variables . . 40
APPENDIX B: Common Experimental Designs 47
APPENDIX C: Common Approaches to Sampling . 48
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INTRODUCTION
This document provides individuals planning small-scale field tests of
microorganisms with guidance on the scientific principles and appropriate
methodologies for monitoring the environmental fate and impact of microorganisms
introduced into the environment. Monitoring of microorganisms can be used for
several general purposes including:
1. assessing the performance of a microorganism (including the gathering of
data about microbial survival, competitive ability, or yield of crop plants),
2. contributing to an assessment of an adverse human health risks or
environmental impact, or
3. elucidating the biology or ecology of the microorganism.
Monitoring procedures vary from qualitative to quantitative, from simple to
complex, and with the type of microorganism and environment into which it is
introduced. In most cases, monitoring for beneficial environmental impacts is
assumed. Monitoring for adverse impacts is also appropriate when uncertainty exists.
The primary focus of this document is on monitoring for potential adverse impacts on
the field site environment.
A general scheme for designing a program to monitor microorganisms in the
environment is shown in Figure 1. The first phase in monitoring program design is to
clearly define the program's objectives based on available knowledge of the
microorganism to be released (i.e., the microorganism profile), the environment (i.e.,
the field-site profile), and the field test protocol (i.e., the experimental profile). The
integration of information contained in these three profiles for a given small-scale field
introduction provides the basis for development of a specific monitoring program.
The second phase in the design of a monitoring program is to determine the
monitoring objectives and the appropriate monitoring intensity for the small-scale field
test. The monitoring objectives should establish what endpoints are to be
characterized. The endpoints might include, for example, population density of the
microorganisms in the rhizosphere, gene transfer frequencies, or effects on the plants
infected by a microorganism. The appropriate monitoring intensity is determined by
the degree of uncertainty and the potential severity of effects associated with the
microorganism.
The third phase in the design of a monitoring program is to develop the specific
monitoring plan. Development of the monitoring plan involves defining the physical
layout of the field test, monitoring zones, and sample collection and analysis
strategies.
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Throughout the course of the field test, data will be collected and analyzed. The
development of a monitoring plan should be a dynamic, iterative process in which
modifications to monitoring practices can be made during the field test in response to
changing conditions observed in the field or problems associated with sample
collection which were unforeseen at the start of the field test.
Before any samples are collected, a quality assurance plan should be in place to
ensure that the resulting data will be scientifically sound and unbiased. Quality
assurance and quality control procedures assist the researcher in balancing time
constraints and procedural costs with the data quality necessary to achieve the
monitoring objectives. Finally, appropriate health and safety procedures should be
integrated into the monitoring program in order to ensure the overall safety of persons
conducting the field test.
These guidelines serve as a starting point for the development and
implementation of an effective monitoring plan for the small-scale field testing of a
microorganism. More specific guidance on techniques used in monitoring
microorganisms in the environment can be found in Levin et al., 1992, Ginzburg 1991,
and through the work of international groups (OECD, 1991; Commission of the
European Communities et al., 1992).
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Microbe
Collect Profile Information
Environment
Exper Iment
Monitoring Intensity
Factors
I
Determine Appropriate
Monitoring Intensity
I
Develop a Monitoring Plan
Monitoring
Plan Works?
Continue Field Test
Figure 1. Schematic development of a monitoring program.
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4
CHAPTER 1: COLLECTING INFORMATION
1.1 Gathering Information
A well-designed field test considers the interaction of the microorganism to be
tested, the field test site, and the experimental design. Identification and achievement
of field test objectives are dependent upon the interaction of all three elements. The
kinds of measurements and the precision of those measurements should be based on
the understanding of the microorganism and the test site. The possibility of
unexpected effects, known adverse impacts, or incorrect conclusions should be
considered in this planning step to ensure that appropriate information will be obtained
from the field test.
In defining these decisions the investigator would consider the information to be
gathered from the field test, the time and resources available for gathering the
information, and the consequences of inadequate or erroneous information being
gathered.
The objectives of the field test should be classified as major or minor and ranked
in importance. Because monitoring designs often result in more precise information
gathering for some treatment comparisons than for others, the design should first
address those objectives judged to be of greatest importance. At this point in the
planning process, consultation with a statistician can contribute significantly to
achieving the aims of the investigator.
Appendix A contains lists of various environmental, microbial, and experimental
characteristics that might be considered in developing experimental and monitoring
designs. Not every characteristic listed in Appendix A will be pertinent or necessary
for a given field test.
1.2 The Microorganism Profile
The microorganism profile should contain information on those characteristics
that will allow the investigator to predict behavior of the microorganism in the field in
terms of fate and survival, and, if desired, to identify the microorganism in the field.
This information should also allow for identification of the possible health and
environmental effects of the release. The investigator should select methods to
effectively identify the microorganism and should determine the sensitivity of the
method in terms of minimum detection limits.
The microorganism profile will vary with the microorganism under study and
should include considerations such as: the history of environmental use of the
microorganism, the microorganism's life cycle and ecology, the phenotype expected in
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the environment of use, relevant genetic modifications that may have been made, and
the phenotypic consequences of these modifications. Information leading to
taxonomic identification is important here. Not only does the taxonomic identity help
to formulate hypotheses concerning the direct potential for adverse impacts, but where
such data are lacking, taxonomic information may be used to formulate specific
monitoring endpoints. In some cases, taxonomic information may direct the
investigator to literature on more thoroughly studied organisms that are closely related
phylogenetically to the test microorganism.
The degree of uncertainty associated with possible adverse impacts of testing
microorganisms in the field is an important consideration. Traits that should-be
addressed to reduce this uncertainty include pathogenicity, infectivity, toxicity, survival
and competitiveness, and the potential for genetic transfer. Other characteristics
related to microbial fate that may be considered include microhabitat, nutrient
requirements, oxygen requirements, motility, survivability and dormancy (see Appendix
A).
Some of the information discussed above may be available in the literature for
microorganism being field tested and for the parental organism. If it is unavailable, key
data can be obtained through testing in the laboratory or at the field test site. If
necessary, this information could then be used to determine specific experimental
procedures that should be followed during field testing to limit microorganism
dissemination between treatments or outside of the experimental plots during and
between treatments. A discussion of such determinations is found in Strauss et al.
(1985).
1.3 The Field Site (Environmental) Profile
Selecting an appropriate field test site is also an important factor in the design,
implementation, and success of any field test. The investigator should select the field
test site taking into account the characteristics of the microorganism to be tested
(e. g., ability to survive and disseminate) and the objectives of the experiment. The
site should also be evaluated in terms of these same considerations for potential
adverse impacts and/or uncertainty. Several considerations to take into account when
selecting a test site are described in this chapter. A more thorough listing is contained
in Appendix A.
If possible adverse environmental impacts are identified in the microorganism
profile, a site that is isolated spatially from inhabited areas or nontarget susceptible
species can be selected, or appropriate containment procedures, such as border
rows, can be applied. For effective monitoring within a given site, the field site profile
should recognize the heterogeneity inherent to the site. It may therefore be useful to
characterize the site as to climate, topography, hydrology, soil type and any biotic
factors which could affect the planned test (e.g., the presence of populations of
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indigenous microorganisms with the same antibiotic resistance being used to identify
the test microorganism). The investigator is advised to pay particular attention to
environmental characteristics and experimental procedures that might serve as a route
for dissemination or might affect survival of the microorganisms within and outside the
test site.
In characterizing the site the investigator may use U.S. Geological Survey
topographic maps to indicate the location of the site; U.S. Department of Agriculture
(USDA) soil survey maps to determine soil type, topography, and surface drainage
patterns; maps with information on wind direction, frequency, and strength; aquifer
maps for ground-water information; and, if available, aerial imagery for identifying
proximity of the site to inhabited areas and any other features that might not otherwise
be captured on standard maps. Many of these maps are available from local Federal,
State, or county authorities. If they are not readily available, the investigator can
contact the National Cartographic Information Center in Reston, Virginia ([703] 959-
6045), which serves as a clearinghouse for a variety of maps.
The investigator can check with appropriate federal, state, and local agencies for
any other information that may affect the use of the site for a field test. This
information might include the presence of threatened or endangered species in the
area, migration routes for protected fauna, restrictions on sampling or the
transportation of environmental samples, and specific permitting or licensing
requirements.
The site profile should be evaluated in light of the microorganism to be tested.
This evaluation should consider the characteristics of the microorganism under study,
the possibility of adverse impacts, and the degree of uncertainty concerning predicted
behavior. If there is a possibility of adverse impacts, then the investigator should
carefully examine the field test site characteristics to determine possible consequences
of those effects at that site and to identify how such effects could be measured. Much
knowledge has been gained from experience with tests of nonindigenous
microorganisms, plant or animal pests, and microbial pesticides that have been safely
conducted.
Careful attention may be necessary to determine the presence or absence of
indigenous biota that (1) have a capacity for readily accepting or transferring genetic
information of concern (e. g., antibiotic resistance) from or to the test microorganisms;
(2) have been shown to transport microorganisms (either in their intestinal tracts,
hemolymph, or salivary glands, or on their body surfaces); (3) are reported to be
sensitive to the introduced microorganism and/or its products; (4) are natural hosts for
the microorganism to be released; or (5) are listed as endangered, threatened, or of
special concern to Federal or State governments. For example, if there is the potential
for transfer of genetic material from the test microorganisms, the investigator may
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need to include in the field site profile information concerning the indigenous microbial
community of the test site environment.
While much of the information needed for the site profile may be readily available
from existing sources, some of the information may have to be obtained directly from
the intended field site in order to fully characterize it. The intensity of the field site
characterization will be inversely proportional to familiarity with the behavior of the
microorganism in the test environment. The greater the uncertainty or potential for
adverse impacts, the greater the intensity of the field site characterization.
Site characterization can also include surveying nearby areas that could serve as
sites for microorganism establishment outside of the designated field test area in order
to evaluate possible avenues of dissemination. In many cases microorganism survival
and transport considerations indicate distinct zones surrounding a field site in which
monitoring may be desirable. These zones may be delineated by topographical
considerations (e. g., slope), prevailing wind direction, or may include distinct sampling
zones at or below the soil surface. For example, if the microorganism is mobile in
ground water and the release site is composed of porous soils, then several vertical
sampling horizons down to and including the ground-water aquifers may need to be
considered. The definition of monitoring zones includes: the medium to be sampled,
the lateral and vertical extent of the zones, and a clear description of the rationale for
selecting those zones.
The results of the site characterization can be used to: 1) develop testable
hypotheses concerning microbial fate within the site and beyond and potential effects
in the environment; 2) provide a measure of site variability as a basis for field test
protocol design and selection of analytical techniques (considering precision and
accuracy); and 3) establish a "before" picture of the site that may be used for
comparison when monitoring for "after" effects.
1.4 The Experimental Profile
The experimental profile is shaped by both of the preceding profiles. The
information gathered in the preceding profiles is used to justify the choices made in
the actual layout of the test site, how the microorganisms are applied to the test site,
and the endpoints measured during the field test. As outlined in Appendix A, the
experimental profile is composed of three major parameters contributing to the field
test design: the experimental design, the treatment design, and the monitoring design.
The experimental design includes: field plot design, physical layout of the treatments,
sampling strategy, statistical model for analyzing the data collected, and the choice of
environmental factors to be monitored during the experiment. Apart from field tests
purely for basic research, experimental design is intended to monitor beneficial
impacts or efficacy of a microbial treatment. The treatment design would include: site
preparation prior to the experiment and site maintenance during the experiment
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(including the test area, border areas, and the monitoring zones), and treatment
characteristics such as methods of applying the microorganism (e.g., seed coating or
aerosol spray) or different concentrations of the microorganism being applied. The
monitoring design includes: identification of monitoring zones (taking into account
features of the specific field site), determining the endpoints to be measured within
each monitoring zone, and determining the sample collection strategy for each
monitoring zone. This document addresses monitoring design for adverse impacts.
Experimental design includes the chosen statistical model which will be used to
compare treatment effects. Statistical procedures are used to examine and reduce the
effects of experimental error in the measured data. Appendix B briefly outlines
common experimental designs for field tests and the statistical models that can be
applied to evaluate the treatment effects. Chapter 2 provides an extensive overview of
field plot design and statistical analysis. Experimental design should be considered a
flexible feature in planning a field test because selection of the statistical model for the
experiment is considered in conjunction with the physical layout of treatments and
monitoring design. The experimental design should be chosen prior to the initiation of
the field test. However, the choice of the experimental design does not limit the
options for monitoring and data analysis.
Treatment design, including site preparation and maintenance, may affect the
behavior of the microorganism upon release and the necessary monitoring of the
microorganism in the field. The method of microorganism application would include:
the number of microorganisms to be released per application; the frequency and
duration of release; and the manner of the release (e.g., droplet size when using spray
application). The investigator can draw upon the literature or the documented results
of current research activities that address phenomena associated with specific
microorganism application procedures.
Monitoring design is determined by the field test site, the characteristics of the
microorganism, and the certainty with which the interaction between microorganism
and environment can be characterized. Within the field test site, monitoring zones are
chosen with consideration of important field heterogeneities at the site such as soil
type, fertility, surface topography, climatology, hydrology and microbial populations.
To focus the results on the treatments being tested, test plots are selected to minimize
the effects of field heterogeneities. Monitoring design is discussed in the following
chapters which describe considerations that would determine monitoring intensity and
the sampling strategy.
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CHAPTER 2: OVERVIEW OF FIELD PLOT DESIGN AND STATISTICAL ANALYSIS
2.1 Introduction
This section is intended to help scientists work with statisticians to design
experiments for the field by highlighting relevant statistical methodology that can be
found in detail in agricultural and biological statistical references (Cochran and Cox,
1957; Gomez and Gomez, 1984; Little and Hills, 1978; Steel and Torrie, 1980). Other
chapters on statistics relevant to monitoring the environmental release of
microorganisms (Mclntosh, 1991), or on evaluating chemical controls of plant diseases
(Nelson, 1986) may also be useful. This section is not intended to be a cookbook of
how to plan and analyze field research on microorganisms, nor is it intended to be a
substitute for statistical references.
Field tests involving microorganisms, like all field experiments, are dependent on:
1) the development of a hypothesis that can be tested, 2) a well-designed carefully
controlled test, and 3) proper interpretation of the results in the context of existing
environmental conditions at the time and location of the trial. Statistical procedures
are simply the tools by which experiments can be properly designed and interpreted
so that the true effect can be inferred with a degree of certainty. Biological
experimentation in particular is greatly aided by the proper use of statistics, because
rarely, if ever, will a simple description of a limited number of observations of a
biological phenomena yield the same result. Variability is the rule in biology and not
the exception. Ideally if a biological experiment could be repeated indefinitely under
the same conditions the responses would eventually produce a convincing pattern and
the true effect would be known. Statistics provides the means by which this true effect
can be inferred from a more economically feasible number of observations.
Proper experimental design and interpretation, however, does not guarantee
successful final results from field trials because many unforeseen circumstances may
occur in the field that can have profound effects (drought, wind, hail, lightning,
allelopathic effects of previous plants, weed competition, soil heterogeneity, etc.).
Planning is the most important phase of experimentation that can ensure that the
treatments selected will provide relevant estimates for testing hypotheses, and can
reduce the impact of unforeseen problems. Careful planning and consultation with a
statistician at the initial stages are the most important steps in developing field plot
experiments that will yield a maximum return. Biologists who understand some of the
principles underlying experimental designs will have more fruitful planning sessions
with statisticians.
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2.2 Basic Considerations for Statistical Analyses
2.2.1 Analysis of Variance - Introduction
The basic procedure for determining whether or not some treatment has a
significant effect on a population (testing a hypothesis) is the analysis of variance
(ANOVA). The ANOVA structures the information about the variability of the
measurements by grouping them according to the source of variability. For example,
one expected source of variability is the treatment, another may depend on the way
the treatments are arranged (experimental design), and the remainder is called the
error. The error is also known as the random variation or unexplained variation and is
critical to the ANOVA. The comparison of the amount of variation that can be
assigned a source, e. g., treatment, to the error determines whether the differences
between the treatment means are sufficiently distinct from the differences that random
variability will cause. The variations among the different sources in an ANOVA are
estimated by quantities called the mean squares. The ratio of mean squares produces
a statistic called the F-statistic. F can be tested for significance against "critical values"
that have been determined for given probabilities that true differences can be assumed
to exist. Most researchers typically use a 5% probability level as a scientifically
comfortable cutoff for significance. Because field experimentation is inherently more
variable than laboratory experimentation, levels of probability between 5 and 10% are
commonly used to indicate that something is happening that may require further
experimentation to verify. Significance levels greater than 10% should not in general
be used.
2.2.2 Power of test
The power of a test is the ability of the test to find two means significantly
different from each other. The power of a test can be increased particularly by
increasing the number of replications of samples taken. The calculation of the power
of the test depends on an understanding of the different types of errors, which is
beyond the scope of this overview. This may be an important consideration for a high
risk introduction for which a single field trial with a limited number of replications may
be attempted. For those cases consultation with a statistician is essential. As a
general rule, when no background information on variability is available, repeated
trials, or more than one location for a trial that has approximately 10 degrees of
freedom for the error mean square increase the likelihood of determining whether or
not real differences exist. Degrees of freedom (df) are dependent on the treatment
design and numbers of replications used. An examination of an F table, which
provides critical levels of F for determining significance, at a 5% probability level where
the error has 10 df and a treatment has 1 df (e. g., genetically-engineered
microorganism (GEM) vs. no GEM) indicates that the treatment would need to account
for approximately 5 times the variation attributable to random error to be significant.
Whereas, if the error term had only 3 df, for the same example, the treatment variance
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would have to be more than 10 times greater than random variation. The exact
meaning for degrees of freedom and formulas for their calculation are available in
statistical texts.
2.2.3 Requirements for Analysis of Variance
There are three requirements for F-tests in ANOVA to be valid. The actual
statistical requirements for practical purposes are not listed here, only the working
requirements are discussed. Two of the requirements are generally satisfied if the
treatments are randomized and replicated. The last requirement, that the variances
associated with each treatment be similar, depends in part on the type of data being
collected and may require that the data be transformed to ensure that the F-test is
legitimate.
There can be no estimate of error without replication. Treatments should be
applied to more than one experimental unit (plot) to be considered replicated.
Repeated measurements (samples) from a single plot do not constitute replication of a
treatment. For example, most experiments will be designed to test several hypotheses
related to both plant effects as well as soil microbial effects, and therefore are likely to
have plot dimensions of 2 -10 m2. Yet only 1-10 grams of soil may be necessary for
plating out on media to quantify the microbial entity. Soil sampling for a
microorganism is necessarily done by taking several samples from a given area
because of the natural variability in microbial dispersion. Therefore, each plot would
require multiple samples just to estimate the microbial population. The estimate of the
population in the one plot, although it required multiple samples, is just one replicate
measure of that population for the treatment. In general, increasing the number of
replications is the simplest means of increasing the sensitivity of any experiment. Of
course, there is a point of diminishing returns as the cost of increasing the size of the
experiment with increased replications will eventually outweigh the return in increased
precision. There are procedures for determining the minimum number of replications
(formulas and tables) which require previous knowledge similar to the methods used
to determine sample sizes (Cochran and Cox, 1957). Where no prior knowledge is
available, a general guideline as discussed in section 2.3.2 is to adjust the number of
replications so that the error mean square has approximately 10 degrees of freedom.
Randomization is particularly important in field studies so that measurements are
not inadvertently biased. The field environment often has gradients, e. g., slope of the
field, shading from neighboring trees, variability due to differences in soil type, in
texture or previous cropping, and especially in moisture and fertility. In order to
ensure that these sources of variability do not overlap with treatment patterns and
confound the effects of the treatments, assignment of treatments to the plots is done
randomly. True randomization is best achieved with random number tables found in
statistical texts, or with computer randomization schemes.
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Variances associated with each treatment should be similar in order for the F-test
to be legitimate. Tests for homogeneity of variance can be done using Bartlett's chi-
square or the Fmax test. Transformations of the data are used in cases where
variances are heterogeneous. Microbial populations characterized by colony counts
often have variances that will increase by orders of magnitude as the mean increases,
thus violating the assumption of homogeneous variance. Transformation of the raw
data by taking the log of the values (or log of values +1 when there are zero counts)
will usually correct for this violation (Steel and Torrie, 1980). Percentage data that
cover a range of 30-70% or are calculated as percentages of a control usually does
not require transformation. However, percentage data that cover a wide range, 0-
100%, 0-50%, or 50-100%, usually need an arcsine transformation. Bimodal
percentage data covering 0-20% and 80-100%, is best transformed by taking the
square root of the values. When many of the raw data values are less than 10 and
especially when there are a large number of zeros, taking the square root of the
values and adding 0.5 is helpful. Transformed data should be tested in the same
manner as the raw data to determine appropriateness of the transformation.
2.2.4 Regression
Regression is another method of statistical analysis that has two important
functions relative to hazard assessment of microorganisms. Regression analysis is the
appropriate procedure whenever the treatments that are being tested are a quantitative
series, e. g., dosage response experiments. In its simplest form, linear regression
analysis can be thought of as a statistical procedure for fitting a line. Regression
establishes whether or not the response to the quantitative series of treatments can be
adequately described by the line and also provides estimates of the rate (slope) and
background level of response (intercept). This analysis can be extended to curvilinear
responses and to describing the response to more than one set of treatments
(multidimensional response surfaces). Use of regression analysis in this form requires
making an assumption about the functional relationship (linear, curvilinear, etc.)
between treatment and response, and that the treatment variables are quantitative.
Regression analysis can also be used to provide an ANOVA, and through the
proper choice of statistical models produce an equivalent analysis (Neter and
Wasserman, 1974). The advantage of the regression procedure (also known as a
linear model approach to ANOVA) in obtaining an ANOVA is that the problem of
missing data can be effectively handled. Experimental units in field experimentation
often are lost during the course of the trial due to unforeseen circumstances. The
regression procedure allows for an unbiased estimation of error even when there are a
few missing data points. Missing value estimation procedures are also available for
use in ANOVA, but are more limited in scope and effectiveness.
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2.3 Experimental Designs
An experimental design is the physical way the experimental units are arranged.
This provides a means of control over error by reducing the effect of natural variation
on the differences between treatments. The simplest of designs is the Completely
Randomized Design (CRD) in which all replications of all treatments are randomly
assigned to the plots in the field (Figure 2). The CRD is simple, easy to analyze and
flexible. However, it is rarely used in field experimentation except when the experiment
is very small and the site is known to be very uniform because it provides no control
over natural sources of variability (soil heterogeneity, etc.)
2.3.1 Randomized Complete Block Design
The most commonly used basic design is the Randomized Complete Block
(RGB). In the RGB, instead of randomly assigning all replicates of all treatments to a
plot anywhere in the field, each complete set (replicate) of treatments is assigned to a
block of adjacent plots (Figure 2). This arrangement reduces the effect of random
error because adjacent plots are usually more alike than nonadjacent plots. Each
treatment is still randomly assigned to a plot within each block. Blocks should actually
be selected by visiting the field, or based on previous experience with the site, in such
a way as to minimize variation within a block. Usually blocks are selected on the basis
of environmental sources of variation, particularly slope, shading, moisture, fertility, or
soil type. Even if there is no visible evidence of potential problems or differences the
field area is probably not uniform. When there are no visible sources of variation it is
recommended that blocks be as small as practical and be square. Randomized
complete blocks are simple to design and analyze and missing plot values can be
readily estimated.
2.3.2 Latin Square
When there is a small number of treatments in the experiment, usually between two
and ten, a design that may further increase control over natural sources of variability is
the Latin Square (LS). The LS design uses blocking in two directions (Figure 2). In
order to use the LS design, the number of replications should equal the number of
treatments, and several squares may be needed when only 2-3 treatments are being
tested to obtain a good estimate of error. The Latin square is more difficult to
arrange, analyze, and estimate missing values. It is usually used where a small
uniform experimental site is difficult to obtain, e. g., orchards in hilly production areas.
2.3.3 Split Plot
As the number of treatments in an experiment increases, and thus larger areas are
required for establishing a complete set of treatments, experimental error naturally
increases. Designs are available to subdivide the complete set of treatments
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Completely Randomized
D
D
D
A
Latin
A
B
C
D
Qi
B
B
C
B
jare
B
A
D
C
A
A
A
C
C
D
B
A
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C
D
C
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Randomized Complete Block
B
D
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D
A
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D
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alope
Split Plot
B2
D2
C2
A2
C1
A1
B1
01
C1
B1
A1
D1
D2
A2
C2
B2
A1
D1
C1
B1
02
B2
A2
C2
Figure 2 Illustration of four experimental designs: 1) Completely
Randomized with four replications of four treatments (A-D); 2)
Randomized Complete Block design with five replications (blocks) of
four treatments (A-D), note blocks are columns and each block has a
complete set in random order of treatments (A-D); 3) Latin square
design with four replications and four treatments, blocks in two directions
(rows and columns) have a randomized complete set of treatments (A-
D); and 4) Split plot design with two sets of treatments A-D and 1-2 that
are replicated three times, blocks are two sets of columns in this case.
Note treatments 1-2 in the split plot have experimental units that are 4
times larger than treatments A-D.
according to certain rules so that each incomplete block is more uniform and a good
estimate of error is still possible. There are many different incomplete block designs
each with different restrictions and increased difficulty in setup, analysis and estimation
of missing values (Cochran and Cox, 1957). The most commonly utilized variation of
an incomplete block design is the split plot. The split-plot design is used when either
the experimental material or mechanical difficulties arise in randomizing all the
treatment combinations to the same size plot. For example, experiments involving
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15
tillage or previous cropping history are easier to manage as larger contiguous sections
of a block rather than as individually randomized plots scattered throughout a block.
The split-plot design, in this case, would use two different sizes of experimental units
with two different randomizations, one for the larger units or whole plots and one for
the smaller units, the subplots (Figure 2). The effects of the treatments on the whole
plots are estimated with less precision than those at the subplot level. Split-plots and
variations of these reduce some of the logistical problems of managing the experiment
but are more difficult to analyze, and to estimate missing values.
2.4 Factorials
The need to obtain as much information as possible from a given field experiment
often dictates the need to investigate several different types of treatments
simultaneously. This was alluded to in the split plot example, where one set of
treatments (tillage) even required a different size experimental unit. Experiments where
all of the treatments can be grouped together in a way that they can be considered
qualitative or quantitative levels of the specific groups, e. g., presence or absence of a
strain of a microorganism, or 103 vs 10* cfu's of a microorganism (both examples are
2 levels of one factor), are considered factorial experiments. Factorial experiments can
be arranged in any of the standard experimental designs discussed previously, and
are thus sometimes referred to as a treatment design. When the objective of the
experiment is exploratory work to determine the effects of several different factors over
a specified range, and to determine if there is a relationship between the factors, then
factorial experiments are very useful. Factorial experiments, particularly those that are
designed with two levels of each factor, are a very powerful way to gain a wide range
of information. Factorials with only two levels of each factor are analyzed in an
ANOVA as separate sources of variability. Associated with each factor is an
independent F-test for significance. In comparison to an experiment that had multiple
treatments or levels, the F-test for that larger group of treatments would be a test of
significance averaged over all real and nonreal differences. In that case we might not
be able to detect the real differences that occurred. The major disadvantage of
factorials is in their most basic form in which all possible treatment combinations
constitute one replication of the experiment, they rapidly become very large and
difficult to manage.
2.5 Design Summary
The best design is the simplest design available that provides the desired precision.
Several other points to consider that will affect experimental error are size and shape
of the plots, collecting quantitative observations on unforeseen sources of variation
that become apparent during the course of the experiment or that could not be
sufficiently controlled by design, and guard or border rows within a plot. In the field
the experimental units upon which the treatments are applied are the plots of land.
The size and shape of the plots will also affect the precision and accuracy of the
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16
experiment. In most cases involving row crops, plots should be long and narrow with
the length of the plots running in the direction of the gradient that is used as the
blocking factor. This will in effect average out some of the variation within a plot, and
allow for management with typical row-crop equipment. When tests involve aerosols,
or spores that readily move from plot to plot, square plots are preferable with
particular attention to using border rows and collecting data from the center portion
only. The use of border rows or guard rows in each plot is required when a treatment
applied to one plot may affect the neighboring plot. At least one border row on each
side of the section of the plot from which data is collected is advisable for row crops
even when interplot interference problems are not expected. This ensures more
typical growth of plants within a treatment, and allows the researcher to infer what
could happen on a larger scale. Data collected quantitatively on factors that develop
during the course of an experiment can be used in a mathematical process called
covariance analysis to account for the errors not removed or accounted for by
blocking.
2.6 Mean Separation Techniques
After the data have been analyzed in an ANOVA and a significant F-test has been
obtained, a procedure to determine which treatment differences are real needs to be
chosen. An F-test, because it is based on a ratio, has degrees of freedom associated
with the numerator (treatment or factor) and with the denominator (error). A significant
F-test that has more than one degree of freedom associated with the numerator only
indicates that at least one of the pairs of means is different. There are numerous
techniques available that help to separate which means are significantly different, but
many multiple comparison procedures are also easily abused (Nelson and Rawlings,
1983). The multiple comparison procedures should be reserved for those situations
where no obvious treatment structure exists. The preferred method is to plan the
treatment comparisons prior to analysis and set up plots with this in mind. The use of
orthogonal contrasts to set up single degree of freedom F-tests for those planned
comparisons then provides for a more powerful comparison (Steel and Torrie, 1980;
Gomez and Gomez, 1984; Little and Hills, 1978).
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CHAPTER 3: DEVELOPING MONITORING OBJECTIVES
3.1 Formulating Monitoring Objectives
The monitoring objectives should establish what is to be characterized or
determined based on the endpoints that have been identified. Generally, the more
uncertainty or greater possibility of adverse health or environmental impacts, the more
extensive the data needs associated with the monitoring objective. Where there is
greater uncertainty about the potential behavior of the test organism, the investigator
should develop additional objectives and a more detailed monitoring design to
monitor. If adverse impacts are likely, then specific objectives should be developed to
monitor intensively for specific adverse health or environmental impacts. (See
examples at the end of this chapter.)
Each scenario of a field test can be defined as a process that begins with the
release of the microorganism, continues with the fate of the introduced microorganism,
and concludes with an endpoint such as an effect. An endpoint may be a biological,
physical, or chemical characteristic measured in the experiment. In determining the
monitoring objectives, the investigator defines specific measurable endpoints at critical
points in the process.
Survival, transport, and fate considerations are endpoints that help identify areas of
likely or potential colonization, both in the field test environment as well as in other
environments that may be potentially accessible to the microorganism after its release.
The identification of potentially accessible environments is critical in establishing
monitoring zones, because then the objectives for each monitoring zone can be
established.
There are likely to be several endpoints for any given hypothesis. The endpoints
may range from: potentially beneficial (e.g., increased plant production or reduced
pest populations), to neutral, or to potentially adverse impacts (e.g., death of nontarget
plants). The investigator should select those endpoints which define the monitoring
objectives for the field test.
The importance of the planning process cannot be emphasized enough. Without
careful planning, it is quite possible that the field test will fail to achieve the desired
monitoring objectives. For example, sampling or laboratory analytical errors could
lead to data with little value to determine endpoints. Conversely, generation of more
extensive or detailed data than is required to address the monitoring objective could
result in an unnecessary expenditure of resources.
The collection of environmental data is a complex process of many iterative steps
and there is usually more than one way of performing any given step. Every option
that exists within a given step carries a different implication in terms of cost, time, data
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quality, and the risk of arriving at an incorrect conclusion. These guidelines attempt to
allow investigators the flexibility to design their monitoring programs to achieve their
specific goal(s) within the bounds of a sound quality assurance program. (Discussion
of quality assurance and quality control is found in Chapter 5.)
3.2 Determining the Appropriate Monitoring Intensity
After evaluating the microorganism and field test site profiles and before deciding
on an experimental design, the investigator should consider various hypotheses and
define specific endpoints associated with efficacy, possible adverse impacts, or the
fate of the microorganism or its genetic material.
This step may be complicated by uncertainty concerning microbial ecology of the
microorganism in question. The investigator may need to extrapolate from data
derived from laboratory experiments. There may be some doubt about whether the
assumptions made in the laboratory can be transferred to the field. Here, investigators
should use their best professional judgment to determine the likelihood of certain
endpoints. Uncertainty surrounding the interaction of the microorganism in a given
environment should be viewed not so much as a liability, but rather as a variable in the
monitoring design.
The degree of uncertainty in predicting how a microorganism will survive and
possibly affect the environment can be combined with the potential for adverse
impacts to indicate the appropriate monitoring intensity for a given experiment. The
interaction between knowledge about the microorganism to be tested and the potential
for adverse impacts in a given environment is illustrated in Figure 3. The greater the
certainty that adverse impacts are possible, the greater the monitoring intensity
required to confirm that an environmental hazard does not occur. The greater
monitoring intensity is justified by the increased risk of obtaining false negative results.
For example, field test data generated through a low intensity survey could indicate
that no adverse impacts occurred, when indeed adverse impacts did occur and would
have been detected through a high intensity sampling plan. The purpose of the
monitoring design is to ensure that the probability of such a false negative will be low.
Qualitative sampling can, in some instances, help to identify the dispersal and
distribution of microorganisms over a geographical area and provide information on
survival during and after the intended period of performance in the test environment.
Qualitative samples can also be used to note adverse impacts. For example, the
spread or development of plant disease could be characterized by using a qualitative
number system to indicate severity of disease. For some low intensity monitoring,
however, the objectives may be to quantify microorganism populations, in addition to
obtaining qualitative measure of presence or absence. Qualitative endpoints may also
be appropriate for detecting the presence of such unintended effects as
transconjugants and gene exchange with indigenous microorganisms.
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Monitoring ntensity Factors
Possible Adverse Effects
Low
High
Low
.(0
+J
Q)
U
C
D
High
Low
Monitoring Intensity
High
Figure 3. Monitoring intensity factors.
The greater uncertainty concerning characteristics of the microorganisms being
tested at a specific field site could coincide with increased monitoring intensity due to
the lack of information concerning the interaction between the microorganism and the
new environment.
Determining appropriate monitoring intensity is one of the most important steps in
constructing the monitoring design. It is also the most difficult to direct by way of
generic guidelines because the hypotheses will be site- and microorganism-specific.
One approach is for the investigator to create these hypotheses for each field test
through an interrogatory process. While not a complete list, the following questions
may serve as a useful template for such an approach:
Will the microorganism under study survive in the area of application? How long
and at what population density is the microorganism expected to survive? Are
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20
there selection factors potentially favoring the microorganism or allowing a
competitive advantage?
o How could the microorganism be transported outside the release site?
o Could this microorganism cause adverse human health or environmental impacts in
or outside of the release site?
o Does the microorganism excrete any metabolites that could alter the environment
to which it is released, for example, lowering the soil pH or contributing to
eutrophication of aquatic environments.
o How effective will the microorganism be in producing the desired effect (e.g., an
increase in nitrogen fixation)?
o What is the probability that the microorganism could transfer genetic material to
another organism within or outside the release site? What is the likelihood that
such a transfer could cause adverse health or environmental impacts?
3.3 Examples of Monitoring Intensities and Objectives
3.3.1 Low Intensity Monitoring Objectives.
Ğ
The lowest monitoring intensity would be appropriate for a well-studied
microorganism for which information on its behavior in the proposed test site is
available, and it is known that adverse human health or environmental impacts are
unlikely. This is the most basic design. In its simplest form, the monitoring objective
may be satisfied by providing regularly scheduled visual inspections for any out-of-the-
ordinary phenomena or conditions that are contrary to what were predicted, such as a
sudden die-off of plants in the test site.
Since no adverse impacts are likely, there are no measurable health or
environmental effect endpoints around which to design a monitoring effort.
Consequently, the primary monitoring objective will be associated with designing a
field test protocol to characterize fate and survival of the microorganism in the field.
When artificial or natural barriers are part of the release scenario, a monitoring
objective might focus on the performance of these barriers. The field test protocol
should contain contingency plans for additional monitoring or early termination of the
test if unexpected phenomena are observed.
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Example: A microorganism, indigenous to the field test site, is to be used for the
trial. The field test site has been well characterized: surface water runoff is possible
due to the slope of the field test site and ground-water contamination is unlikely due to
soil types. The method by which the microorganism is to be applied is by seed
coating. Extensive references and data show no adverse human health or
environmental impacts and suggest little or no potential for adverse impacts upon
release to the field. There may be a slight potential for wind dispersal and surface
water runoff. No measurable endpoints for human health or environmental effects
have been identified. The monitoring objective is to characterize the fate and survival
of the microorganism by measuring the mean population size of the microorganism in
various zones in the upper soil layers.
3.3.2 High Intensity Monitoring Objectives.
The need for increased monitoring intensity could be indicated where there is little
information about the microorganism's interaction with the proposed environment or
where there is some potential for adverse impacts (e.g., competition with indigenous
populations, gene transfer). If there is uncertainty about survival or possible adverse
impacts, then the investigator should, in addition to performing the procedures
outlined above, establish monitoring objectives to measure and test hypotheses about
endpoints associated with possible adverse health or environmental impacts, as well
as endpoints associated with fate.
The monitoring objectives directed towards possible adverse impacts will address
whether more or less monitoring is needed in subsequent field tests. While additional
time and resources may be needed to gather data to resolve uncertainties, the effort
will be balanced by reducing the risk of allowing possible adverse impacts to go
undetected and by gathering additional information concerning the behavior of the
organism.
Where data exist to show that the microorganism under study has commonly
caused adverse health or environmental impacts, the investigator should design the
field test protocol as described above and add an objective aimed at characterizing
the nature and extent of the adverse impact. Additional precautions in the field test
protocols can be included to attempt to confine the microorganism (e.g., the use of
borders, control of insect vectors, or use of dikes and channels to control runoff).
Example: In this case assume that laboratory studies have shown that under the
right conditions the microorganism could cause an adverse impact on a nontarget
species of plant. A reconnaissance survey reveals that the susceptible nontarget plant
species inhabits certain areas surrounding the test site. In addition to the fate and
survival objectives stated in the first example, another objective may be added to
quantitatively determine adverse impacts on the nontarget plant species outside of the
test site.
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CHAPTER 4: DEVELOPING A MONITORING PLAN
4.1 Introduction
The monitoring plan should consider both the experimental design and physical
layout of the test plots as well as the area surrounding the field test site and the
potential for transport of the organisms outside the test plots. The monitoring plan is
developed to address the monitoring objectives as identified in the previous chapter.
This chapter will describe further steps and considerations in determining how to
address the monitoring objectives. Throughout the discussion of the monitoring plan,
the investigator should keep in mind that this plan is not static. It should adapt to
changing conditions observed in the field or problems with sample collection and
analysis often encountered only after the field test is underway and data are
evaluated.
4.2 Defining Monitoring Zones
To address the monitoring objectives and endpoints of interest, monitoring zones
should be determined. The definition of monitoring zones should include: the medium
to be sampled, the lateral and vertical extent of the zones, and a clear description of
the rationale for selecting those zones. These zones may be limited to surface
sampling, or may include distinct sampling zones below or above the surface. For
example, if the microorganism is known to disseminate through porous soils, then
several vertical sampling horizons down to and including the ground-water aquifers
may need to be considered.
It is essential that each monitoring objective be related to a specific measured
endpoint in each monitoring zone. Generally, a variety of physical, chemical, and
biological properties can be measured on each collected sample. For example,
population counts of the released microorganism, population counts of competing
microorganisms, and the presence of genetic sequences from the test microorganism
in indigenous microorganisms might all be measured as endpoints from a single soil
sample.
In addition to measurements specific to monitoring zones, changes in the ambient
conditions (e.g., wind speed and direction, rainfall, sudden temperature changes)
during application of the microorganisms could have immediate impact on the field
study. Temperature and relative humidity are important factors affecting the survival of
airborne cells (Cox, 1987) and data on these parameters would assist in modeling
transport of viable microorganisms.
For zones associated with low intensity of monitoring, measurements may often be
of a qualitative rather than quantitative nature to inspect for the presence or absence
of the test microorganism itself or unspecified effects. For example, monitoring of field
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release trials in California in the spring of 1987 demonstrated the usefulness of a
qualitative method employing sentinel plants and deposition plates as a passive
method to detect the presence of released airborne bacteria (Lindow et al., 1988;
Seidler and Hern, 1988).
Figure 4 illustrates a release site with eight potential monitoring zones:
Zones 1 & 2 - The efficacy and strain competition study areas generally contain
experimental layouts of sampling locations; however, these areas
can also contain monitoring stations.
Zone 3 - The buffer area immediately surrounding the study areas can
provide monitoring close to the study areas. This buffer zone may
serve as the initial monitoring zone and identification of the test
organism in this zone could trigger further sampling in the
surrounding areas.
Zone 4 & 5 - In situations where aerial transport is possible or likely, a
downwind monitoring area can indicate the presence of
microorganisms away from the release site(s). For example with
spray releases, wind speed and direction greatly affect particle
transport, fate, and the placement of deposition plates and sentinel
plants.
Zones 6 & 7 - Monitoring the soil downslope can indicate movement of
microorganisms away from the release site(s) particularly if an
intensive rainfall washes away dikes or contributes to unexpected
runoff.
Zone 8 - For microorganisms that can survive in aquatic environments,
monitoring in downslope surface water bodies or downgradient
ground-water aquifers may be required.
4.3 Defining a Sample Collection Strategy
After establishing the monitoring zones at the test site, the sampling strategy for
each monitoring zone is selected. There are three important considerations: (1)
ensure that the sampling is representative of an entire zone at the selected point in
time, (2) provide numerical estimates for decision making that have quantifiable error
limits, and (3) provide estimates of sampling endpoints that are precise enough for the
lowest possible cost.
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Hypothetical Field Test Site Showing
Designated Sampling Zones
Zone 1
(S,A)
Efficacy
Strain
Competition
Legend: ,
A * Air R ~ Runoff W-Water
Figure 4. Hypothetical Field Test Site Showing Designated Sampling Zones.
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25
A variety of sampling strategies for monitoring programs have been described by
other authors (Borgman and Quimby, 1988; Barcelona, 1988; Gilbert, 1987; Size,
1987). Although most of these discussions deal with the monitoring of chemical
contaminants in the environment, the sampling strategies outlined are also applicable
to microorganism monitoring programs. Several of the most commonly used
strategies are briefly summarized in Appendix C along with their most important
advantages and disadvantages.
4.4 Sampling
For every experimental situation there is a target population from which data are
collected. The basic procedure, regardless of the experimental design, is to take
measurements on a random, representative sample of some population of interest
from which estimates of the mean and variability of the larger population are made.
Repeated measurements (samples) are needed in all cases in order to calculate any
statistic. Precision or sensitivity of an experiment depends ultimately on the amount of
information collected. A comparison of two means or treatments becomes more
sensitive, and thus can detect smaller differences, as the sample size increases.
4.4.1 Sample Size
The population from which data will be collected should be identified at the
planning stage. If possible, its properties should be determined so that appropriate
measurements, sample sizes, and sampling techniques can be determined. The
number of samples that are necessary for a good estimate of the mean and variability
of the population depends in part on the way the organism is dispersed throughout a
field (Figure 5). If the organism or population from which data are being collected is
randomly dispersed throughout the field (or evenly spaced as in a systematic
planting), a few large samples will give results similar to many small samples. Many
natural populations of microorganisms, however, are found as clumps of individuals
rather than randomly distributed individuals. It is usually better to measure more small
samples in this case. There are several different formulas that are available for
determining the number of samples to be taken. They all require prior knowledge
about the mean and variability of the population. These values can be obtained from
prior experiments or the literature, or can be estimated values based on experience.
Formulas and tables for determining sample size can be found in statistical texts. The
results of such calculations should then be weighed against what is economically
feasible.
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26
Even
a
* ,
* * "
m *
* 9
* .
Random
'.'.
i
_
'* *
'
Clustered
Figure 5. Spatial dispersion patterns.
Each dot represents the location of an
organism in a field.
Other considerations with respect to sampling include whether or not the samples
will be destroyed and whether repeated samples will be taken from the same
experimental unit. Destructive sampling may be necessary for many assay procedures
(e.g., reisolation of engineered Rhizobium strains from root tissue) but the removal of
the plant may influence the environment and subsequent growth of adjacent plants.
Care should be exercised so that subsequent measurements, either different assays or
repeated measurements on the same population, are minimally affected by the
sampling technique. Destructive sampling will also limit the number of samples that
can be taken from an experimental unit. Sampling over a period of time may be
necessary in order to determine rate or timing of certain processes. Similar to the
determination of a mean, the determination of a rate is improved with increased
frequency of sampling. Often the shape of the response curve in biological
experimentation is curvilinear and thus there is a need for a greater number of
samples are needed to produce a reliable estimate of the rate.
4.4.2 Sampling Techniques
Selecting the sampling strategy for a particular monitoring zone establishes the
sampling plan in general terms. There are three commonly used sampling techniques
in field experimentation: simple random sampling, systematic sampling, and stratified
random sampling.
Simple random sampling uses random numbers from a table or computer program
to identify which sampling units are actually examined or collected. Use of this
procedure removes the possibility of conscious or unconscious bias that may occur.
Random sampling ensures that each sampling unit has an equal probability of being
selected. Often sampling arbitrarily, where an evaluator picks a plant or a soil sample
anywhere in a plot, is done but should not be mistaken for true random sampling. A
sampling method that should be avoided is selection of a "typical" sample where the
evaluator selects what is thought to typify the population. This inherently leads to
biased samples, like the largest greenest plants Jrom a nitrogen fixation study.
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27
Systematic sampling is often employed when the individual sampling units are
difficult to identify and number. It is easier to set up and to employ and thus less
prone to errors than simple random sampling. The starting point, the first sampled
unit within a plot, is randomly determined as before, but then subsequently sampled
units are selected at a preset, regular interval e. g., every 7th plant or every 1 m. The
interval is also determined randomly within the constraints of the size of the
experimental unit and the sample size that needs to be collected. Several different
sampling patterns can be used with systematic sampling. The patterns range from a
simple diagonal transect across the experimental unit to one with multiple paths across
the experimental unit in the shape of an X or W (Figure 6). In general, increase of
sample size through increase in the number of paths is best for populations with
clumped patterns of dispersion. A simple increase in number of samples along a path
is best for randomly distributed populations (Lin et al., 1979).
The third sampling technique is known as stratified sampling. This technique first
subdivides the population into predetermined subsets, like distance from an
inoculation source, depths of soil, or strips in a field based on a soil characteristic or
an environmental gradient. Usually equal numbers of samples are obtained from each
subset using either of the two previously described techniques. The advantage of this
technique is that additional information and improvement in precision within a
subset are gained.
Figure 6. Systematic sampling patterns
used in field plots or strata (subsets of field
plots).
The different techniques employ different formulas for determination of variability
(Cochran, 1977). The techniques should be carefully considered, particularly when the
population from which samples are to be collected is large or has an inherent pattern
of dispersion in a field. In many cases, particularly for large sampling zones, grid
sampling is also easier to implement in the field; moreover, it can be more efficient in
some cases, for qualitative monitoring objectives such as a microorganism's presence
or absence. To lay out the specific sampling plan in each monitoring zone, the
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28
number and locations of samples need to be clearly defined in terms of the following
parameters, described in detail below.
Grid Configuration and Orientation. A variety of sampling grids, such as square,
rectangular, triangular, and crossed patterns, are commonly used in monitoring
programs. As long as the pattern is constructed to be representative of the
monitoring zone, any of these patterns is appropriate for most sampling objectives.
The following are some examples where a particular grid configuration and/or
orientation can provide a more efficient design:
o When the subsurface is sampled, the vertical grid spacing is often reduced
compared with the horizontal spacing because significantly more variation in most
endpoints of interest is expected in the vertical direction.
o For a fixed number of sampling locations, a triangular grid supplies more complete
coverage of a sampling zone than a square or rectangular grid.
o When looking for small zones of microorganism population or adverse
environmental impacts, the choice between a square or rectangular grid for
searching purposes may depend on the shape of the target (Gilbert, 1987).
o In situations where transport from the release site is possible, consider sampling
downwind, downgradient, and in all four compass directions. Sample distances
from the release points may be influenced by modeling of the microorganisms'
transport under field release conditions.
o Many field and monitoring endpoints exhibit spatial correlation due to the effects of
the original application process, secondary transport processes, or in situ field
heterogeneities. Spatial correlation considerations may lead to the use of
anisotropic, rectangular, or triangular grids (Flatman et al., 1988).
Number of Sampling Locations. The results from a monitoring program are always
subject to uncertainty due to sampling variability, because measurements can only be
taken at a limited number of stations and times within the release site. In monitoring
programs, the major source of sampling variability is field heterogeneity. For example,
soil and water properties such as moisture content and pH can vary greatly across a
test site, even over small distances, leading to large variations in the survival and
environmental impact of released microorganisms. It is advisable that the number of
samples to be collected and analyzed at least meets the minimum necessary to
achieve the monitoring objectives and with sufficient redundancy to ensure that the
objectives will not be jeopardized if some of the samples cannot be collected or
analyzed as planned. The largest reductions in the uncertainty of monitoring results
are often realized by taking measurements at a greater number of locations because
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too few measurements can hinder the ability to make decisions based on the
monitoring results.
Number of Replications. Replication is the statistical concept of independently
repeating the same measurement under identical conditions in order to estimate the
variance of a set of measurements. The use of replication in sampling and analysis is
critical in assessing statistical variations in the monitoring results from sources such as
treatment effects, field heterogeneity, and measurement uncertainty.
Measurement Precision. Measurement variability is a result of imprecision in the
analytical methods and instrumentation used to identify and measure microbial
populations and environmental effects. For instance, variability can arise from different
adsorptive properties of sample tubes or variation in mixing via vortex or sonication
methods. In some cases this variability can contribute significantly to uncertainty in
monitoring results. This uncertainty can be controlled by selecting reliable and
reproducible measurement techniques. From a statistical design point of view, the
choice between alternative techniques may be based on relative cost, accuracy, and
precision. The field test protocol should be designed to supply a quantitative
assessment of the measurement precision by including measurement replication as
part of the sample collection strategy.
Sampling Times. For certain microbial monitoring studies, it will be necessary to
monitor the survival and effects of microbial populations over extended periods of time
after release. The following are examples of important parameters to be considered
concerning sampling times: 1) initial monitoring measurements taken in conjunction
with application of the microorganisms; 2) the time intervals at which measurements
are taken subsequent to application; 3) the total time period for monitoring; and 4) the
criteria that will be used to determine that no further monitoring is required, such as
the limit of detection or a change in season.
Sample Collection Methods. When selecting monitoring methods, it is wise to take
the following steps: 1) review the sampling zones and endpoints to be monitored; 2)
determine the minimum acceptable detection limit for the microorganisms to be
monitored; 3) determine the degree of accuracy and precision needed in the results.
With this information, the investigator can then review available methods and select the
appropriate method (s) that will meet the study objectives. There are a variety of
classical sampling and analytical methods to follow the fate and genetic stability of
released microorganisms and new methods are evolving rapidly. These methods have
been reviewed extensively in the literature (e. g., see Stotzky et al., 1989, and
Fredrickson and Seidler, 1989). The method should be verified in conditions as near
to field conditions as possible to ensure that the selected method will perform its
intended function.
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4.5 Implementation of the Monitoring Plan
The investigator should be prepared to make adjustments in the monitoring plan as
implemented during the field test. Numerous factors may require adjustments in order
for the field test to provide the desired information. Minor adjustments may be
necessary as the field test is implemented due, for instance, to changes in weather
conditions subsequent to application of microorganisms at the test site or changes in
microbial populations during the year.
Major adjustments to the monitoring plan, such as moving into a new monitoring
zone, should be carefully considered so that sampling in the new zone will contribute
the desired information. An expansion of the monitoring effort may be warranted
during a field test, for instance, in the case where a microorganism has been detected
in an area outside of the field test site and concerns dictate the need for further
sampling in the surrounding zones.
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CHAPTER 5: DEVELOPING PROCEDURES TO ASSURE DATA QUALITY
5.1 Quality Assurance and Quality Control
Every monitoring project should have a quality assurance project plan (QAPjP) to
ensure that the data derived during the project will be scientifically sound and
unbiased. Quality data will have a greater likelihood of yielding valid conclusions
related to the project's principal hypothesis. This section provides guidance in
establishing procedures to accomplish these goals. Additional literature on QA
principles and practice is referenced at the end of this section.
Quality assurance criteria provide a balance between the constraints of time and
procedural costs and the quality of the data necessary to achieve the research project
objectives. A QA plan is designed to accomplish the following:
o to establish criteria to control the quality and evaluate the validity of data collected
during the project,
o to provide standardized methods for sample preparation and analysis,
o to utilize reference standards or other analysis procedures to verify and assess the
quality of the data collected,
o to aid in the maintenance and calibration of analytical instruments used during the
project and ensure the equality of the reagents, chemicals and other raw materials
employed for the project.
To assist this effort, it is necessary to identify both qualitative and quantitative
estimates of the quality of the data needed to fulfill project objectives. Quality
assurance guidelines clearly identify the decisions to be made from the research effort
and specify the calculations to be applied to the data.
5.2 Measurement Quality Objectives
Measurement quality objectives (MQOs) are specific goals describing the data
quality sought for each measurement. The project MQOs are established on the basis
of project data needs utilizing appropriate referenced methods to obtain the data.
Lower-than-desired data quality could require different data analysis or result in
modifications to the levels of confidence assigned to the data. Uncertainty is expected
in experimental measurements, however, MQOs for the analytical method used should
remain constant for that particular method throughout the project. The MQOs are
defined by the following six attributes:
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1) detection limit- the lowest concentration of an organism that a specific
procedure can reliably detect
2) precision- the level of agreement among multiple measurements of the same
characteristic
3) accuracy- the agreement of the observed value with a reference or true value
4) representativeness- the degree to which the data collected accurately and
precisely describe the population of interest
5) completeness- the quantity of data that is successfully collected with respect to
the amount of data intended in the experimental design
6) comparability- the similarity of data from different sources within individual or
multiple data sets or the similarity of data from related projects
The MQOs, as well as specific procedures for assessing them, should be
addressed in the project QA plan for sampling, sample preparation and analysis of
samples. Detection limits should be discussed for each method used in the project.
The reporting units, reporting format, expected range of values and detection limit
should be included in the sections on precision and accuracy for each method used.
It is important to remember that MQOs should be discussed in terms of the project
objectives, not simply in terms of the test method's capabilities.
5.3 QA/QC Procedures for the Field
Besides validation of the quality of analytical measurement, a QA/QC procedure
provides for tracking samples effectively from field collection through laboratory
analysis and final reporting. There is little use analyzing samples accurately and
exhaustively if they have been improperly collected, rjnislabelled or improperly stored
prior to analysis.
QA/QC procedures in the field start with knowledge of sampling equipment
operation. A training program should be implemented to ensure that each person
responsible for a particular type of sampling understands the proper operation of
sampling devices and sample collection protocols. Each person involved in the
sampling effort needs to be aware of the overall sampling design and how that
person's role fits into the overall design. Common sense dictates that standard
operating procedures and maintenance procedures for all sampling devices be
available at the field test site to facilitate repairs and other contingencies.
The following is a suggested list of points to consider when developing QA/QC
procedures for the field test site.
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o All equipment should be adequately inspected, cleaned and maintained.
Equipment used for the generation, measurement or assessment of data should be
regularly tested and calibrated. A logbook to record these procedures is
recommended.
o A sample identification code should be carefully determined to assign a unique
combination of numbers or letters to each sample. The sample identification
system should permit the sample to be tracked throughout the collection and
analysis process.
o Sample data forms should be created to facilitate data collection. Sample data
forms should document all relevant information describing the sample.
o All sampling activities should be documented with a description of each day's
activities and other relevant information in a bound logbook. Every entry should be
dated and initialed.
o Plans for shipping and storage of samples to maintain sample integrity should be
established.
5.4 QA/QC Procedures for the Laboratory
Quality assurance and quality control (QA/QC) procedures in the laboratory also
start with knowledge of equipment operation. All laboratory personnel need to be
trained in the use of their particular analytical instrument. The laboratory should have
all equipment manuals and standard operating procedures on hand to address the
procedures to be performed. Published literature may supplement standard operating
procedures. Many of the same concerns listed above for equipment maintenance and
calibration are also applicable here.
The inclusion of reference standards can also aid in assuring the quality of data
obtained. This gives an in-process means of confirming the quality of the data, the
reproducibility of the instrument and the detection of any bias of the measurements.
These samples should be looked on as an addition to, not a substitution for, normal
instrument calibration. Spiked samples can also determine if the sample matrix exerts
an effect on the measurements. In microbial studies, it is also advisable to subject
occasional samples believed to be the organism of interest to confirmatory tests by
other methods. This confirmatory backup reinforces the results of the analytical
method used and allows for detection of any unexpected microbes exhibiting traits
similar to the microbe of interest.
Preparation of reagents used routinely in the project warrants special consideration
in terms of quality control. Raw material quality, including such simple components as
water and chemicals, can drastically affect results. It is advisable to have certain
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34
criteria for acceptance of raw materials, such as levels of contaminants or performance
in physical tests. Chemical products should be traced by lot number. Certificates of
analysis for the product should be routinely obtained from the supplier to aid in
determining what contaminants are responsible for poor product performance.
Utilizing a well-organized method of QA/QC will greatly aid in the generation of
valid data. In addition, a QA/QC system allows one to track product performance
more closely, ensures reproducible product production, and facilitates troubleshooting
when a product failure occurs.
5.5 References Used to Prepare a QA Plan
The following references may be useful in the preparation of a QA plan:
1) Federal Register. 40 CFR Part 792, Toxic Substance Control Act (TSCA): Good
Laboratory Practice Standards: Final Rule. 54 FR 34034. August 17,1989.
2) Taylor, J.K., and T.W. Stanley. 1985. Quality Assurance for Environmental
Measurements. American Society for Testing Materials, Philadelphia, PA.
3) U.S. Environmental Protection Agency. OTS Guidance Document for the
Preparation of Quality Assurance Project Plans. Office of Toxic Substances,
Washington, D.C. September, 1987.
4) U.S. Environmental Protection Agency. OTS Quality Assurance Guidance Document
for the Preparation of Field Test Plans for Biotechnology Programs. Office of Toxic
Substances, Washington, D.C.
5) U.S. Environmental Protection Agency. Preparing Perfect Project Plans. Risk
Reduction Engineering Laboratory, Cincinnati, OH.
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CHAPTER 6: DEVELOPING PROCEDURES TO ASSURE HEALTH AND
OCCUPATIONAL SAFETY
6.1 General Information
The health and safety of field and laboratory personnel, the general public, and the
environment can be ensured through an integrated program of standard operating
procedures, personnel training, careful site planning, supervision, and operation. The
assistance of an Institutional Biosafety Committee (IBC), an institutional review group,
can provide feedback on health and personnel safety issues as well as the overall
safety of a study.
In performing certain field tests, there is the potential for exposure to
microbiological agents that may be hazardous. Therefore, all personnel involved in
these on-site activities should be familiar with the types of hazards that may be
present, the ways in which those hazards can be mitigated, and safe practices
applicable to the conduct of those activities. Thus, the first step in addressing health
and safety considerations for a field test is making sure all personnel on-site during the
field test have been adequately trained and fully understand their respective duties.
6.2 Test Site Emergency Procedures
Test site emergency procedures include a plan documenting the corrective actions
that will be taken in the event of an accident. Examples of events needing specific
emergency procedures are the accidental spill or release of the microorganism,
sampler breakdown, compromised samples, or the breach of the test site by
unauthorized personnel. Each field test will have unique potentials for accidental
releases and concurrent emergency response procedures. It is imperative that all
personnel involved in the field test be made aware of these plans and how to
implement them. In addition, it is advisable that emergency procedures for halting the
field test be in place before the start of the field test so that unforeseen effects can be
halted quickly. These procedures may include application of chemical control agents
or the controlled burning of the field test site. Examples of unforeseen circumstances
that could result in emergency termination are an unexpectedly rapid increase in cell
density, detection of the microorganism at greater distances than expected from the
test site and unexplained damage to plants or animals in or adjacent to the test site.
The investigator, in consultation with the EPA, is responsible for designing this
termination "trigger," taking into account the characteristics of the microorganism and
the field test site. The investigator is urged to consult with the appropriate State
agency about specific policies concerning fumigation or the application of chemical
control agents to the site. Many states, for instance, prefer that certain disinfestation
procedures be conducted by a State-certified applicator.
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6.3 Laboratory Procedures and Reference Documents
Establishing health and safety procedures for the laboratory is also important in the
planning of any field test. An excellent reference specific to the safe handling of
microorganisms in the laboratory is the Centers for Disease Control (CDC) and the
National Institutes of Health (NIH) publication, Biosafety in Microbiological and
Biomedical Laboratories (NIH, 1984). Investigators are encouraged to refer to this
document for detailed information concerning laboratory practices. If the field test
involves a recombinant DNA microorganism, an additional reference is the Guidelines
for Research Involving Recombinant DNA Molecules (1986), produced by NIH.
Additional reference documents applicable to health and safety include the Toxic
Substances Control Act. Good Laboratory Practices (GLPs) Guidance (1983), the
Basic Field Activities Safety Training Manual, the Dioxin Field Sampling Guide, the
Occupational Safely and Health Guidance Manual for Hazardous Waste Site Activities.
and the Standard Operating Safety Guides developed by EPA. These guidance
manuals specify proper techniques and procedure for work involving potentially toxic
or hazardous materials.
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REFERENCES
Barcelona, M. J. 1988. "Overview of the Sampling Process." In LH. Keith, (ed.).
Principles of Environmental Sampling. American Chemical Society, pp. 3-23.
Borgman, L E., and W. F. Quimby. 1988. "Sampling for Tests of Hypothesis When
Data are Correlated in Space and Time." In LH. Keith, (ed.). Principles of
Environmental Sampling. American Chemical Society, pp. 25-43.
Cochran, W. G. 1977. Sampling Technigues. Wiley, New York. 428 pp.
Cochran, W. G. and G. M. Cox. 1957. Experimental Designs. 2nd ed. Wiley, New
York. 611pp.
Commission of the European Communities, U. S. Department of Agriculture, U. S. E.
P. A. 1992. Methods for the Detection of Microorganisms in the Environment.
National Technical Information Service, Springfield, VA, Document PB92-
1374S4/AS.
Cox, C. S. 1987. Aerobiological Pathways of Microorganisms. John Wiley and Sons,
New York.
EPA. U. S. Environmental Protection Agency. 1983. Code of Federal Regulations.
Toxic Substances Control Act, Good Laboratory Practices Guidance, CFR
November 29, 1983. Vol 48, No. 230, pp. 53922
Flatman, G. T., E. J. Englund, and A. A. Yfantis. 1988. "Geostatistical Approaches to
the Design of Sampling Regimes." In LH. Keith (ed.). Principles of Environmental
Sampling. American Chemical Society, pp. 74-84.
Fredrickson, J. K., and R. J. Seidler. 1989. Evaluation of terrestrial microcosms for
detection, fate, and survival analysis of genetically engineered microorganisms and
their recombinant genetic material. EPA-600/3-89/043.
Gilbert, R. O. 1987. Statistical Methods for Environmental Pollution Monitoring. Van
Nostrand Reinhold, New York, 320 pp.
Ginzburg, L. R. 1991. Assessing Ecological Risks of Biotechnology. Butterworth-
Heinemann, Stoneham, MA, 379 pp.
Gomez, K. A. and A. A. Gomez. 1984. Statistical Procedures for Agricultural
Research. Wiley, New York. 680 pp.
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Levin, M. A., R. J. Seidler, and M. Rogul. 1992. Microbial Ecology: Principles.
Methods, and Applications. McGraw-Hill, Inc. New York. 403 pp.
Lin, C. S., G. Poushinsky, and M. Mauer. 1979. An examination of five sampling
methods under random and clustered disease distributions using simulation. Can.
J. Plant Sci. 59:121-130.
U'ndow, S. E., G. R. Knudsen, R. J. Seidler, M. V. Walter, V. M. Lambou, P. S. Amy, D.
Schmedding, V. Prince, and S. E. Hern. 1988. Aerial dispersal and epiphytic
survival of Pseudomonas syringae during a pretest for the release of genetically
engineered strains into the environment. Appl. Environ. Microbiol. 54:1557-1563.
Little, T. M. and F. J. Hills. 1978. Agricultural Experi-mentation: Design and Analysis.
Wiley, New York. 350 pp.
Mclntosh, M. 1991. Statistical techniques for field testing of genetically engineered
microorganisms. In: Levin, Morris and Strauss (eds.), Risk Assessment in Genetic
Engineering: Environmental Release of Organisms. McGraw-Hill, New York, pp.
219-239.
Nelson, L A. 1986. Use of statistics in planning, data analysis, and interpretation of
fungicide and nematicide tests. Pages 11-23. In: K. D. Hickey (ed.) Methods for
Evaluating Pesticides for Control of Plant Pathogens. American Phytopathological
Society, St. Paul, MN. 312 pp.
Nelson, L. A., and J. O. Rawlings. 1983. Ten common misuses of statistics in
agronomic research and reporting. J. Agron. Educ. 12:100-105.
Neter, J. and W. Wasserman. 1974. Applied Linear Statistical Models: Regression.
Analysis of Variance, and Experimental Designs. Irwin, Homewood, IL 842 pp.
NIH. National Institutes of Health. 1986. Code of Federal Regulations. Guidelines for
Research Involving Recombinant DNA Molecules, CFR May 7, 1986, Vol. 51, No.
88, pp. 16958.
NIH. National Institutes of Health. 1984. Biosafety in Microbiological and Biomedical
Laboratories, March 1984. HHS Publication No. (CDC) 84-8395, Government
Printing Office.
OECD. Organisation for Economic Cooperation and Development. 1991. Report of
OECD Workshop on Monitoring of Organisms Introduced into the Environment,
OCDE/GD(92)71. OECD, Paris.
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Seidler, R. J., and S.C. Hern. 1988. EPA Special Report: The release of ice-minus
recombinant bacteria at California test sites. ERL-Corvallis, OR.
Size, W. B. 1987. "Use of Representative Samples and Sampling Plans in Describing
Geologic Variability and Trends." In W.B. Size (ed.). Use and Abuse of Statistical
Methods in the Earth Sciences. Oxford University Press, New York, pp. 3-20.
Steel, R. G. and J. H. Torrie. 1980. Principles and Procedures of Statistics. McGraw-
Hill, New York. 633pp.
Stotzky, G., M. A. Devanas, and L R. Zeph. 1989. Methods for studying bacterial
gene transfer in soil by conjugation and transduction. EPA-600/3-89/042.
Strauss, H., D. Hattis, G. S. Page, K. Harrison, S. R. Vogel, and C. Caldart. 1985.
Direct release of genetically engineered microorganisms: A preliminary framework
for risk evaluation under TSCA. Report CTPID 85-3, Center for Technology, Policy,
and Inductrial Development. Massachusetts Institute of Technology, Cambridge.
The following references may be useful in the preparation of a QA plan:
EPA. U.S. Environmental Protection Agency. OTS Guidance Document for the
Preparation of Quality Assurance Project Plans. Office of Toxic Substances,
Washington, D.C. September, 1987.
EPA. U.S. Environmental Protection Agency. OTS Quality Assurance Guidance
Document for the Preparation of Field Test Plans for Biotechnology Programs. Office
of Toxic Substances, Washington, D.C.
EPA. U.S. Environmental Protection Agency. Preparing Perfect Project Plans. Risk
Reduction Engineering Laboratory, Cincinnati, OH.
Federal Register. 40 CFR Part 792, Toxic Substance Control Act fTSCA): Good
Laboratory Practice Standards: Final Rule. 54 FR 34034. August 17,1989.
Taylor, J.K., and T.W. Stanley. 1985. Quality Assurance for Environmental
Measurements. American Society for Testing Materials, Philadelphia, PA.
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APPENDIX A.
MICROORGANISM, ENVIRONMENTAL, and EXPERIMENTAL
PROFILE VARIABLES
A1.0 MICROORGANISM PROFILE
The following list of variables should be considered for inclusion in the
microorganism profile.
A. Detection, isolation, and enumeration procedures
1. Detection procedures: molecular, immunological, marker, selective cultivation,
bioassay
2. Isolation procedures: selective cultivation, enrichment in liquid cultures
3. Enumeration procedures: marker, immunological, most probable number
techniques
B. Pathogenicity of parent microorganism and introduced microorganism
1. Mode of pathogenicity: biotroph; necrotroph; toxin producer; opportunistic or
frank; etc.
2. Host affected: human; animal; plant or microbe
C. Physiology (useful for fate and survival of microorganism, and detection and
isolation)
1. Nutritional characteristics
a. Carbon requirements
1) Chemoheterotrophic: organic carbon for energy; organic carbon for
growth
2) Chemolithotrophic: inorganic compounds for energy; inorganic carbon
(CO2) for growth
3) Photoheterotrophic: light for energy; organic carbon for growth
4) Photolithotrophic: light for energy; inorganic carbon (COj) for growth
b. Other primary nutrient requirements: sulfur, nitrogen, phosphorus,
potassium, etc.
c. Growth factor requirements: amino acids, purine and pyrimidines, vitamins
and micronutrients
2. Oxygen status/Eh requirements
a. Obligately aerobic
b. Obligately anaerobic
1) Anaerobic respiration
electron acceptor: nitrate, sulfate, CO2, etc.
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2) Fermentative
c. Facultatively anaerobic
3. pH requirements: optimum and range of tolerance
4. Water requirements; dessication tolerance; halophilic
5. Temperature optimum: psychrophilic; mesophilic; thermophilic
6. Other laboratory growth characteristics
a. Colony morphology
b. Doubling times/kinetics/growth constants
c. Motility
d. Nutrient diversity
e. Sporulation/dormant forms
f. Disinfection profile/inactivation kinetics
7. Role of unmodified parent in nature
a. Microhabitat
1) Soils: adsorption; desorption
2) Water
3) Sediment: adsorption; desorption
4) Air
5) Plants or plant parts: rhizosphere; leaf surface
b. Environmental function
c. Persistence: spores or other resistant forms
d. Responses to environmental stress: seasonally, dormancy
e. Expected role of introduced microorganism
8. Physical characteristics
a. Shape
b. Density
c. Diameter
9. Genetics
a. Chromosomal size
b. Presence or absence of plasmid(s)
1) Size
2) Incompatibility group
3) Host range
4) Phenotype
c. Susceptibility to natural mechanisms of genetic transfer
1) Conjugation
2) Transformation
3) Transduction
d. Mutability to UV irradiation or common chemical mutagenesis
e. Construction of the modified microorganism
1) Source of insert DMA
2) Characteristics of deletion, insertion, and vector (sequence, functions
encoded)
3) Methods of vector and insert construction
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4) Method of introduction of the vector into the primary microorganism
5) Amount and nature of vector or donor DNA remaining in the modified
microorganism
6) Location of insert to construct: chromosomal or autonomously
replicating (e. g. plasmid)
7) Laboratory containment conditions (NIH guidelines) for the modified
microorganism
A2.0 ENVIRONMENTAL PROFILE
The following list of variables should be considered for inclusion in the
environmental profile.
A. Soils/sediments
1. Abiotic
a. Physical
1) Seasonal parameters
a) Temperature
b) Moisture
c) Oxygen levels
d) Organic matter
e) Depth to ground water
2) Parameters not affected by seasons
a) Types
b) Texture/mineralogy/percent sand, silt, clay through the profile
c) Permeability/porosity through the profile
d) Bulk density through the profile
e) Slope
f) Depth of horizons and to bedrock
g) Distance from adjacent water bodies
b. Chemical
1) pH/Eh through the profile
2) Nutrients through the profile
3) Conductivity through the profile
4) Pollutants such as agricultural chemicals through the profile
5) Cation exchange capacity
2. Biotic
a. Host microorganism/similar species/possible DNA vectors
b. Predators/parasites
c. Vectors of microbial movement
d. Competitiveness of introduced microorganism with indigenous species
e. Growth characteristics in the field
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B. Surface water
1. Abiotic
a. Physical
1) Seasonal parameters
a) Temperature
b) Flow velocity
c) Turbulence
d) Depth
e) Density/viscosity
f) Average suspended sediment concentration
g) Medium sediment diameters
h) Maximum tidal velocity
2) Parameters not affected by seasons
a) Type (lentic, lotic)
b) Surface areas
1. Length
2. Width
c) Distance from release source if release susceptible to intermediate
transport
b. Chemical: many are seasonally variable
1) pH/Eh
2) Nutrients
3) Dissolved oxygen
4) Conductivity
5) Salinity
6) Hardness
7) Pollutants
2. Biotic
a. Host organisms/similar species
b. Predators/parasites
c. Vectors of microbial movement
d. Growth characteristics in water
e. Adsorption to suspended particles/sediments
f. Competitiveness of introduced microorganism
C. Ground Water
1. Abiotic
a. Physical
1) Seasonal parameters
a) Temperature
b) Flow velocity
c) Dissolved oxygen
d) Density/viscosity
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e) Colloidal materials: dissolved organic matter; suspended
sediments
f) Depth to groundwater
2) Parameters not affected by seasons
a) Aquifer type
b) Volume
1. Depth
2. Width
3. Distance to wells from source
b. Chemical
1) pH/Eh
2) Nutrients
3) Conductivity
4) Salinity
5) Hardness
6) Pollutants
2. Biotic
a. Host microorganisms/similar species/DNA transfer vectors
b. Predators/parasites
c. Vectors of microbial movement
d. Competitiveness of introduced microorganism
e. Growth characteristics in the field
f. Interaction with the colloidal substances
D. Air/Ambient
1. Abiotic
a. Physical (seasonal parameters)
1) Wind speed
2) Turbulence
3) Wind direction
4) Humidity
5) Temperature
6) Rainfall
7) Solar intensity
b. Chemical
1) Pollutants
2) Nutrients
2. Biotic
a. Host microorganism/similar species
b. Predators/parasites
c. Vectors
d. Growth characteristics in the field
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E. Environmental Use - Presence of:
a. Well heads
b. Residences/playgrounds/etc.
c. Potable water supplies
d. Endangered or threatened species or other special concerns
A3.0 EXPERIMENTAL PROFILE
The experimental profile is shaped by both of the preceding profiles.
Therefore, the experimental profile will frequently repeat information already listed in
these. The following list of variables should be considered for inclusion in the
experimental profile. Special effort should be made to justify experimental choices
considering the features found in the microorganism and environmental profiles.
A. Experimental design: explain the rationale of the treatments and site selection in
relation to the objectives of the field test.
1. Field plot design
a. Randomized complete block; split plot; etc.
b. Number of replications
c. Expected level of variation within treatments
d. Method of randomization for treatment location assignment
2. Physical layout of treatments: a schematic diagram is essential
a. Size of individual treatment plots
b. Size of overall experimental plot
c. Aspect of site; slope; prevailing wind; drainage
d. Presence of border rows and buffer areas
e. Blocking factors if present or needed
1) Slope
2) Soil heterogeneities
3) Prior treatments or rows
3. Monitoring meteorology during experiment
a. Parameters measured: rainfall; temperature; humidity;
ensolation; etc.
1) On-site equipment: type; location in plot
2) Weather station: directional location; proximity
b. Measurement frequency and quality: Maximum/minimum;
continual
4. Sampling strategy
a. Frequency
b. Duration
c. Sampling design: grid; stratified random; judgmental
d. Sample type: plant tissue; soil sample; etc.
e. Destructive or nondestructive
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f. Effect of sampling on remaining plot population
5. Statistical model used to analyze data
a. Homogeneity of variance prior to ANOVA
b. Transformation of data prior to analysis
c. One way or two way ANOVA
d. Regression models; multivariate analysis
e. Expected level of confidence to separate means
B. Treatment Design: Consideration of site preparation prior to treatment application,
the treatment application (s) and the plot management during the experiment.
1. Site preparation: experimental plot; border areas; monitoring zones
a. Cultivation; preplant herbicides, pesticides, fertilizers; etc.
b. Planting method; seeding rate; row spacing; cultivars; planting date
c. Border rows: between treatments/around experimental
plot; size; plant species/cultivar; row spacing; planting date
2. Treatment Characteristics: application types, nature of control and other
variables
a. Aerosol, surface or subsurface applications
1) Concentration; rate
2) Frequency
3) Duration
4) Discharge characteristics: rate, velocity, surface tension, spray angle
5) Surface and subsurface application: liquid,
granular, powder; delivery pressure if liquid
b. Nature of control treatments: what is being controlled?
1) Product formulations without active microbe
2) Fertility levels to determine treatment effect
c. Other variables: strains; product formulations; etc.
3. Plot management during experiment
a. Cultivation after planting; fertilizer applications
b. Expected weed, insect or disease controls needed
c. Expected sampling/harvest schedules; sampling methods
d. Irrigation
C. Monitoring design: for environmental fate/effect
1. Endpoint determinations for monitoring zones based on release scenario
2. Establishment of monitoring zones: size; location
3. Sampling strategy and frequency within each zone
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APPENDIX B.
Experimental Designs
Experimental
Design
Application
Description
Statistical
Analysis
Completely Randomized.
One treatment factor. No
blocking factors.
Treatments are assigned com-
pletely at random so that
each experimental unit has
an equal chance of receiving
any treatment.
One-way ANOVA.
Randomized Complete Block.
One treatment factor.
blocking factor.
One All experimental units
grouped according to block
ing factor. Each block
constitutes a complete
replication of the
experiment. All treatments
are applied in every block.
Two-way ANOVA.
Randomized Incomplete Block.
a. Balanced
b. Partially balanced
Latin Square.
Complete Randomized
with
Factorial.
Fractional Factorial.
Split Plot.
One treatment factor at sev-
eral levels. One blocking
factor.
One treatment factor. Two
blocking factors. Number of
levels is the same for
treatment factor and both
blocking factors.
Two or more treatment
factors. All combinations
of all factor levels are
tested, each combination is
a separate treatment.
Two or more treatment
factors, each having several
factor levels. Too expen-
sive to run all possible
treatments.
One treatment factor.
blocking factor
One
All experimental units a. Special ANOVA.
grouped according to block- b. General
ing factor. Each block con- regression.
stitutes only a partial
replication of the experi-
ment. Not all treatments
are applied in every block.
a. Every pair of treat-
ments occurs the same
number of times
throughout the
experiment.
b. Not every pair of
treatments occurs the
same number of times
throughout the
experiment.
All experimental units Three-way
grouped according to combi- ANOVA.
nations of two blocking
factors. Each treatment
appears only once for each
level of the first blocking
factor, and only once for
each level of the second
blocking factor.
Treatments are assigned com- Factorial ANOVA
pletely at random so that
each experimental unit has
an equal chance of receiving
any treatment.
Ability to estimate higher-
order interaction effects is
sacrificed by including only
specific treatments that
allow for estimation of only
main factor and lower-order
interaction effects.
Blocking factor expected to
have larger effect is
assigned to main plots.
Each main plot is divided
into subplots to which
treatment factor is
assigned.
interactions.
Factorial ANOVA.
Special ANOVA.
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APPENDIX C. COMMON APPROACHES TO SAMPLING
Sampling Strategy Description
Advantages
Disadvantages
Adaptive As the samples are collected and
Sequential analyzed the perceptions of the
sample zone are changed by the
analysis of the results. There may
be a need for additional stages of
sampling to increase coverage of the
zone or to focus sampling within
subzones. Only a portion of the
sampling resources are allocated to
the initial stage of sampling;
remaining resources are held in
reserve for more detailed follow-up
sampling.
Grid A grid of lines is used to divide the
sampling zone into a regular
configuration of subzones called grid
cells. Dimensions and orientation of
the grid are fixed, but location of
the origin of the grid within the
zone is randomly selected. Sampling
locations are established at
intersections of grid lines.
Judgmental Sampling locations are selected by
visual inspection of the zone and
judgmental decision about those
locations that are most appropriate.
1) Even where second stage sampling is not
anticipated, studies often uncover
information that makes follow-up sampling
necessary.
2) More efficient allocation of sampling
and analysis resources.
3) Information from the initial stage of
sampling used to provide precise sampling
design for the second stage of sampling.
1) Provides representative sampling of
entire zone.
2) Provides unbiased characterization of
sampling zone.
3) Usually more efficient than simple
random and stratified random sampling for
some objectives such as qualitative
presence or absence sampling.
1) Easy to conduct in the field.
2) Focuses sampling on locations judged
significant.
1) May be more expensive because of
intermediate data analysis.
1) May be less robust against specific
departures form assumptions.
1) Does not lead to representative
sampling of the entire zone.
2) Provides biased characterization of
entire zone.
3) Cannot statistically quantify
reliability of resulting estimates.
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Sampling Strategy
Description
Advantages
Disadvantages
Stratified Random
Simple Random
A grid of lines is used to divide the
sampling zone into a regular
configuration of subzones called grid
cells. Sampling location within each
grid cell is selected by simple
random sampling.
All possible samples are numbered
from 1 to N, where N is the total
number of samples. A series of n
random numbers, each between 1 and N,
is drawn, and the samples that bear
these n numbers constitute the set of
selected samples.
1) Provides representative sampling of the
entire zone.
2) Usually more efficient than simple
random sampling.
3) Provides unbiased characterization of
sampling zone.
1) Provides unbiased characterization of
sampling zone.
1) Random sampling within call more
difficult to conduct in field than grid
sampling.
1) Does not guarantee a representative
sample from the zone; chance selection
may result in closely clustered
sampling locations.
2) Often less efficient,-may not provide
the same precision of estimate for a
given number of samples as other
sampling strategies.
3) Can be more difficult to conduct in
the field than more structured
strategies.
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