United States
Environmental Protection
Agency
EPA/600/R-09/134
A Methodology for the
Preliminary Scoping of Future
Changes in Ecosystem
Services
With an Illustration from the Future
Midwestern Landscapes Study
RESEARCH AND DEVELOPMENT
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EPA/600/R-09/134
September 2009
vwwv.epa.gov
A Methodology for the
Preliminary Scoping of Future
Changes in Ecosystem
Services
With an Illustration from the Future
Midwestern Landscapes Study
Randall J.F. Bruins1
Susan E. Franson1
Walter E. Foster2
F. Bernard Daniel1
Peter B. Woodbury3
1U.S. Environmental Protection Agency, National Exposure Research Laboratory, Cincinnati, OH
2U.S. Environmental Protection Agency, Region 7, Kansas City, KS
department of Crop and Soil Science, Cornell University, Ithaca, NY
Notice: Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official
Agency policy. Mention of trade names and commercial products does not constitute endorsement or
recommendation for use.
U.S. Environmental Protection Agency
Office of Research and Development
Washington, DC 20460
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ACKNOWLEDGMENTS
This report has been developed as part of the Future Midwestern Landscapes (FML) Study,
which is co-led by Drs. Betsy Smith and Randy Bruins. Ms. Brenda Groskinsky and several
members of U.S. EPA Region 7 staff organized a stakeholder workshop that was instrumental in
identifying the change drivers of concern that form the basis of the FML Study's future
scenarios. The basic study design for the FML Study has been developed with significant
intellectual contributions from Drs. Betsy Smith, Alex Macpherson, Megan Mehaffey, Lisa
Wainger and Liem Tran. Those individuals and Dr. Mark Ridgley contributed to the
development of the values hierarchy described herein. We acknowledge these underlying
contributions, without which this scoping analysis could not have been conducted. We also
acknowledge constructive review comments on a draft of this report provided by Drs. Alex
Macpherson, Mark Bagley, Heather Sander and Ms. Brenda Groskinsky. Any shortcomings in
this study, however, remain the responsibility of the authors.
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EXECUTIVE SUMMARY
When designing studies of future environmental change, it is useful to have working hypotheses
about drivers of change, stressors of concern, and potential ecological outcomes to guide the
development of scenarios and the choice of models. Studies of ecosystem services must be
concerned with multiple, simultaneous outcomes, because decision-makers are faced with the
reality of making trade-offs among services. The extreme complexity presented by multiple
ecosystem service endpoints can overwhelm typical approaches for hypothesis formation, such
as the use of graphical conceptual models.
Therefore, we developed a new methodology for constructing hypotheses about the potential
effects of future change scenarios on ecosystem services, which we call scoping. The scoping
method is to first develop a hierarchy of relevant societal values, identify the ecosystem services
that support those values, and then cross-link these services to a list of critical environmental
elements that are sensitive to the drivers of change. Researchers then use best professional
judgment (based on experience and supported by scientific literature) to rate these expected
effects one by one in a large matrix. Ratings are then combined and graphically arrayed to create
snapshots of the kinds of changes the researchers hypothesize to be most likely. These findings
are then used to answer a set of scoping questions that can help ensure that studies focus on
important changes, using appropriate models. This new methodology offers a well-defined
procedure for managing ecological complexity and improving study design. Without this
scoping methodology, ecosystem service assessments may suffer from lack of rigor in the design
process, and therefore default to approaches of convenience.
We applied the scoping methodology in a proof of concept demonstration using the Future
Midwestern Landscapes (FML) Study as an example of the extreme complexity presented when
dealing with multiple ecosystem service endpoints. The FML Study will examine the effects of
future scenarios of landscape change upon ecosystem services throughout the Midwestern United
States. This scoping demonstration was conducted by a small group of researchers and was not
intended to provide robust conclusions. Therefore, the following preliminary findings for FML
Study design should be considered to be illustrative of the outcomes of the scoping method and
not definitive recommendations:
(a) Studies of future changes in ecosystem services in response to current biofuel policies should
give special attention to the potential impacts of corn stover removal on soil productivity and
soil carbon sequestration.
(b) Agricultural conservation practices fall into two broad groups that differ in the patterns of
changes in service production that are expected to result from their implementation. A
distinction is found between practices that involve conversion of at least some cultivated land
to non-crop cover, and those which only change agricultural management. This distinction
should be addressed when developing future scenarios that focus on increased incentives for
adoption of conservation practices. Studies of the differences between these two groups
should include evaluation of pesticide impacts and evaluation of the potential for changes in
human disease vectors.
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TABLE OF CONTENTS
Acknowledgments iii
Executive Summary iv
Table of Contents v
List of Tables vii
List of Figures vii
I. Introduction 1
II. Scoping Approach 4
A. Overview of Scoping Methodology 4
B. Future Scenarios 7
C. Values Hierarchy and Ecosystem Services 10
D. Concept Maps 14
E. Creating an Influence Matrix 16
F. Scoring the Influence Matrix 18
G. Scenario-Related Changes and Weighting Factors 19
H. Calculation and Plotting of HxC Values, Ranges and Uncertainties 21
III. Results and Conclusions 25
A. Biofuel Targets (BT) Scenario 25
B. Multiple Services (MS) Scenario 27
C. Summary and Hypotheses 28
References 29
Appendix A. Hierarchy of values, ecosystem services and technical contributors
used for the FML scoping analysis A-l
Appendix B. Information used to develop area and cost weighting factors for
conservation practices B-l
Appendix C. Plots by Ecosystem Service for each BT Scenario-Related Change C-l
Appendix C. 1. Unweighted HxC Values by Service for the BT Scenario C-2
Appendix C.2. Unweighted HxC Values and Ranges by Service for
the BT Scenario C-6
Appendix C.3. Unweighted HxC Values and Uncertainties by Service for
theBT Scenario C-10
Appendix C.4. Area-weighted HxC Values by Service for the BT Scenario C-14
Appendix C.5. Sum of Area- weighted HxC Values by Service for
theBT Scenario C-15
Appendix D. Plots by Service for each MS Conservation Practice D-l
Appendix D. 1. Unweighted HxC Values by Service for the MS Scenario D-2
Appendix D.2. Unweighted HxC Values and Ranges by Service for
the MS Scenario D-8
Appendix D.3. Unweighted HxC Values and Uncertainty by Service for
the MS Scenario D-l4
Appendix D.4. Area-weighted HxC Values by Service for the MS Scenario D-20
Appendix D.5. Sum of Area-weighted HxC Values by Service for
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the MS Scenario D-26
Appendix D.6. Cost-weighted HxC Values by Service for the MS Scenario D-27
Appendix D.7. Sum of Cost-weighted HxC Values by Service for
the MS Scenario D-33
Appendix E. Plots by Scenario-related Change for each First Level Hierarchy Value..E-l
Appendix E. 1. Unweighted HxC Values by Scenario-related Change E-l
Appendix E.2. Area-weighted HxC Values by Scenario-related Change E-6
Appendix E.3. Cost-weighted HxC Values by Scenario-related Change,
MS only E-ll
Appendix E.4. Cost-weighted HxC Values by Scenario-related Change,
Omitting Nutrient Management E-l6
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LIST OF TABLES
Table 1. Agricultural land use/land cover (plantings) for the 12-state FML region
for the Base Year and Biofuel Targets (2022) scenarios 3
Table 2. Overall phases of the Future Midwestern Landscapes Study 3
Table 3. Questions which the scoping analysis is intended to answer and implications
for study design 5
Table 4. Outline of scoping methodology 6
Table 5. Summary of projected changes in land use from the Base Year to the
Biofuel Targets scenario 8
Table 6. Candidate conservation practices considered for the Multiple Services
scenario 9
Table 7. List of ecosystem services to be considered in the FML Study, showing
full name and corresponding short label 13
Table 8. Weighting factors for conservation practices by area and cost, respectively 20
LIST OF FIGURES
Figure 1. States included within the FML study area, shown in relation to the
location of existing bioethanol refineries 2
Figure 2. Overview of the scoping process and its relevance for the design of the
FML Study and use of FML Study findings 7
Figure 3. A fragment of the FML hierarchy of values, ecosystem services and
technical contributors 12
Figure 4. Illustration of the complexity of the base conceptual model diagram for
the FML Study 15
Figure 5. Importance of technical contributors to ecosystem services in the
FML Study 17
Figure 6. Example plot showing HxC values for one scenario-related change
(other row crops to corn), grouped on the x-axis by ecosystem service 22
Figure 7. Example plot showing interscorer range of HxC values for one
scenario-related change (other row crops to corn), grouped on the X axis
by ecosystem service 23
Figure 8. Example plot showing HxC values for one first-level hierarchy value
(Maximize outdoor recreation), grouped on the x-axis by scenario-
related change 24
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I. INTRODUCTION
Human society depends on the services of nature (Daily, 1997, Millennium Ecosystem
Assessment 2005). However, these services are rarely valued by current economic and social
systems, and thus, degradation of resources threatens the provision of critical ecosystem services
in many parts of the world. It is crucial that we develop methods to account for ecosystem
services in societal decision-making processes (Daily et al. 2009). There are many scientific
assessment methods and models developed to examine effects of one or more stressors on a
single or a small set of closely related services. However, well-informed decisions require
scientific assessment practices capable of evaluating many services at once. For example, a
single action that preserves an intact ecosystem can protect many different kinds of services. In
such a case, the assessment problem is to identify which services are at stake, estimate their
magnitude, and determine their (direct or indirect) value to society. Such analyses can clarify
benefits, damage, and trade-offs, and guide decisions that will provide greater benefits. More
complex decisions may involve choosing among alternative land tracts to be preserved,
improving the management of ecosystems (such as by changing agricultural, forestry, range,
wildlife, fisheries or coastal management practices), or designing strategies for the rehabilitation
of ecosystems. In addition to the assessment of many ecosystem services, these decisions
require evaluation of trade-offs among services (Chan et al. 2006, Nelson et al. 2008). In any of
these cases, the quality of the decisions may be compromised if assessment is limited to one or
two well-recognized services (Kareiva et al. 2007, Nelson et al. 2009).
The evaluation of multiple services can quickly become extremely complex. A single policy
change may induce many societal actions that vary over space and time and affect ecosystems in
multiple ways. Conceptual models of ecosystems, or of linked socioeconomic and ecological
systems, are useful tools for managing complexity when designing ecological research or
assessment (USEPA 1998, Gentile et al. 2001). Conceptual models typically are a combination
of visual and written depictions of causal relationships that are hypothesized to exist among
system components. Conceptual models that guide large programs of research often depict only
broad relationships between systems and services (e.g., Groffman et al. 2004). More focused
models can offer detail on hypothesized interactions between system components and particular
services (e.g., Kremen et al. 2007). Models developed as interactive tools can provide links to
evidence supporting each hypothesized interaction (e.g., see, a conceptual model of stream
impacts of phosphorus developed as part of EPA's Causal Analysis/Diagnosis Decision
Information System, http://cfpub.epa.gov/caddis/icm/lCM.htm). In limited cases, Bayesian
approaches have been used, in conjunction with expert opinion, to estimate functional values for
these relationships (Borsuk et al. 2004, Marshall et al. 2007)
The extreme complexity presented by some assessment problems can overwhelm the capability
of a graphical conceptual model to provide a useful depiction of hypothesized causal pathways of
influence between systems and services. Therefore, we developed a new methodology for
developing detailed, highly structured hypotheses of the expected effects of multiple influences
on multiple ecosystem services, and using best professional judgment to rate the sign (direction),
magnitude and certainty of those effects. We are applying this methodology as one phase of the
Future Midwestern Landscapes (FML) Study, an ongoing study of the effects of future scenarios
of landscape change on ecosystem services throughout the Midwestern United States.
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The FML Study is a component of USEPA's Ecosystem Services Research Program, which
seeks effective ways to bring information on ecosystem services into decision-making spheres
(http://epa.gov/ecology). The FML Study is one of several place-based studies being carried out
in locations where society faces critical choices. In the Midwest, a large-scale shift is now
occurring from a historical focus on a single ecosystem service, food production, to addition of a
new focus, energy production. For a 12-state area of the Midwest (Figure 1; Table 1), the FML
Study is developing alternative future scenarios that will contrast a current trajectory of land-use
change, emphasizing biofuels production, with an alternative path emphasizing increases in the
uses of agricultural conservation practices. These future scenarios are termed Biofuel Targets
(BT) and Multiple Services (MS), respectively. These scenarios will be examined in comparison
to one another and each will also be compared to a Base Year (BY) scenario representing current
conditions. The FML Study will develop detailed landscapes corresponding to each scenario and
then use models of air quality, water quality and wildlife habitat suitability to estimate a myriad
of environmental changes that are relevant to the provision of ecosystem services. We plan to
use these results to estimate sen-ice changes, and to make this information available to a variety
of decision-makers through an online, interactive 'Environmental Decision Toolkit' (via a
process similar to that described in Mehaffey et al. 2008). The planned phases of the FML study
(Table 2) were adapted from those of Liu et al. (2008) by adding a 'scoping analysis' as a
distinct project phase.
Figure 1. States included within the FML study area, shown in relation to the location of
existing bioethanol refineries.
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Table 1. Agricultural land use/land cover (plantings) for the 12-state FML region for the Base
2002
(BY)
2022
(BT)
Land Use/Land Cover
Total Area
Percent
Total Area
Percent
(106 acres)
(%)
(106 acres)
(%)
Corn
66.5
28.2
90.4
38.6
Soybean
61.8
26.2
51.9
22.2
Wheat
37.3
15.8
33.4
14.2
All cultivated crops
181.8
77.0
186.5
79.6
Hay
27.2
11.5
23.2
9.9
Conservation Reserve
22.0
9.3
19.4
8.3
Program (CRP)
a
All Agriculture Uses
235.9
100.0
234.0
100.0
Source: Center for Agricultural and Rural Development, unpublished study
includes the 12 cultivated crops with highest production acreages
Table 2. Phases of the Future Midwestern Landscapes Study.
1. Scenario definition
a. Define problem and change drivers of concern
b. Define study area
c. Identify stakeholder values and future concerns
d. Identify base year for analysis
e. Define key policy aspects associated with future scenarios
i. Biofuel Targets (BT) Scenario (business-as-usual scenario based on future biofuel
production targets contained in existing policy)
ii. Multiple Services (MS) Scenario (hypothetical scenario having only generally defined
goals at this stage in the process)
2. Scoping analysis
See Table 4.
3. Landscape construction
a. Develop spatially explicit baseline landscape
b. Project economic conditions corresponding to each future scenario
c. Create detailed landscape corresponding to each future scenario
4. Landscape evaluation for ecosystem services
a. Biophysical modeling
i. Select biophysical models (water quality, air quality, etc.)
ii. Parameterize and run models for baseline and each scenario
b. Ecosystem services evaluation
i. Define ecosystem service indicators and production functions
ii. Calculate ecosystem service changes in relation to interscenario comparisons
5. Decision support
a. Develop online spatially-explicit decision support tool
b. Load landscape and ecosystem service metrics
c. Work with users to refine tool and conduct case studies
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The scoping analysis phase was designed to develop detailed hypotheses about the sign and
magnitude of expected changes in a number of different ecosystem services. There were three
main goals for this phase of analysis. First, we wanted to assist the process of scenario design by
identifying those environmental practices that appeared most likely to increase a wide range of
ecosystem services and therefore were worthy of inclusion in the MS future scenario. Second,
we wanted to ensure that project analytical resources are devoted toward analysis of the types of
effects expected to be important. Third, we wanted to develop a comprehensive picture of
effects of multiple causal pathways on multiple services, independently from the subsequent
modeling phases of the study, to serve as a point of reference for evaluation of the modeling
results.
This paper describes the methodology we have developed to address the scoping task. It also
provides an illustrative demonstration, based on a small number of scorers, of how the process
can produce ecosystem service hypotheses, which then can be used to adjust the design of
subsequent phases of the FML study. In our future research, we plan to increase the number of
scientists providing best professional judgment scores for a more robust demonstration, and
apply this method in other ecosystem services studies.
II. SCOPING APPROACH
A. Overview of Scoping Methodology
The purpose of scoping is to develop a set of expected outcomes from each scenario. We use the
term ecosystem service change hypotheses to describe these expectations. This is a borrowing
from the language of ecological risk assessment, in which beliefs about the key relationships
between ecological stressors (or their sources) and adverse effects on ecological receptors are
termed risk hypotheses (USEPA 1998; Bruins et al. 2005). Risk hypotheses usually are too
general to be statistically testable, but they can be used to develop testable hypotheses. Once the
assessment participants and stakeholders agree that these hypotheses are correctly formulated,
the computational phases of assessment are then aimed at substantiating or rejecting these
hypotheses. In a similar vein, the hypotheses to be developed in scoping are general in form but
can be used to develop testable statements. We avoid the term risk because we are concerned
about scenario outcomes that include both increases and decreases in services.
Specific questions to be addressed by the FML scoping analysis are presented in Table 3. This
analysis required development of a new methodology (Table 4). The first step was to develop a
values hierarchy, which helped us identify the services provided by ecosystems that are valued
by stakeholders within the region. Next, we created both general and scenario-specific concept
maps to help clarify the key drivers and factors potentially affecting ecosystem services. Using
the values hierarchy and the concept maps, we created a matrix in which the elements in the
values hierarchy, augmented by key environmental factors (technical contributors) identified
from the concept maps, are arrayed against the primary changes defined by each scenario
{scenario-related changes). We then made quantitative ratings of the expected effect of each
change on each contributor, and of each contributor on each item in the hierarchy. We combined
these ratings to develop ecosystem service change hypotheses (Figure 2).
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Table 3. Questions which the scoping analysis is intended to answer and implications for study
Scenario comparison for
which scoping question
applies
Scoping question
Implications of result for
FML study design
MS-BY comparison only
Which conservation
practices appear to have the
potential to strongly
increase multiple services?
Use this information
(together with feasibility of
modeling each practice) in
the selection of practices to
include in the MS scenario
Both BT-BY and MS-BY
comparisons
Which services appear
likely to vary strongly in
this scenario comparison
(based on magnitude and
certainty)?
Be sure these services are
addressed in modeling; if
they aren't, and we can't
add them, be sure to make
information users aware
Which services appear
likely to vary little or none
in this scenario comparison
(based on magnitude and
certainty)?
If modeling these services
demands significant
resources, consider
dropping them from the
modeling plan
For which services is the
expected variation in this
scenario comparison most
uncertain?
In each case, if the service
will be modeled, determine
whether this is an
uncertainty that is likely to
be addressed by modeling,
or is the source of the
uncertainty outside the
scope of modeling? If the
latter, consider amending,
dropping or caveatting the
modeling result.
In the remainder of this section, we first provide necessary details about the two intended FML
future scenarios and explain why they were treated differently in the scoping process. We then
explain the scoping process in further detail.
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Table 4. Outline of scoping methodology.
1.
Create value hierarchy to identify key ecosystem services
a.
Create a structured hierarchy of the components of stakeholder well-being (a value tree)
b.
Identify ecosystem contributions to values-hierarchy components
c.
Define as ecosystem services the highest-level components which are aspects of ecosystems
2.
Create general concept map
a.
Identify and diagram linkages between major ecological and social system components
b.
Identify, incorporate key drivers of socioeconomic or environmental change and stressors of concern
c.
Incorporate identified ecosystem services
3.
Create scenario-specific concept maps
a.
Identify change drivers specific to each scenario (e.g., policy changes, extrinsic environmental changes)
b.
Determine scenario-related changes (i.e., expected primary effects) of each change driver
i. land use, land cover, or land management changes
ii. resource use changes
c.
Examine, qualitatively, how these primary changes will be causally propagated through the mapped
system to influence each ecosystem service.
d.
Refine concept map as needed to reflect influences
4.
Create influence matrix (i.e., scoring spreadsheet)
a.
For each ecosystem service identified in value hierarchy, use concept map to identify key technical
contributors (i.e., environmental components potentially influenced by one or more scenario-related
changes
b.
Add technical contributors to hierarchy
c.
Create matrix in which hierarchy elements are rows and scenario-related changes are columns
d.
Identify any appropriate weighting factors for scenario-related changes, such as:
i. areas affected
ii. costs or other feasibility considerations for management actions
5.
Score the influence matrix
a.
Identify scorers with appropriate knowledge/experience
b.
Provide background information on scenarios, concept maps, and hierarchy
c.
Score sign/magnitude (-5 to +5) and uncertainty (1 to 5) of influences for each cell in matrix,
specifically:
i. influence of each scenario-related change on each technical contributor ("C score")
ii. Influence of each technical contributor on element above it in the hierarchy ("H score")
d.
Discuss scores with differences in sign or large ranges among scores to check for differences in
interpretation of matrix elements or scoring task
e.
Revise scores as appropriate
f.
Compute interscorer means and ranges for each matrix cell
g-
Compute product scores (HxC/5) for each technical contributor for each scorer
h.
Apply weights and within-scenario summations as appropriate
i.
Perform quality assurance checks
6.
Interpret results to create ecosystem service hypotheses
a.
Plot means and ranges of product scores to visualize patterns of expected influence
b.
Identify services judged most and least likely to be affected by a given change
7.
Apply findings to subsequent phases of study
a.
Adjust scenario specification to include scenario-related changes with potentially large influence
b.
Adjust modeling plans to ensure coverage of likely influences
c.
Examine model results; investigate reasons for discrepancies between hypotheses and model findings
d.
Include in decision support tools information about expected influences that were not modeled
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Figure 2. Overview of the scoping process and its relevance for the design of the FML Study
and use of FML Study findings. (BPJ - best professional judgment.)
B. Future Scenarios
In this paper we use the term scenario to define a set of driving conditions that will cause
change. While many kinds of factors could constitute driving conditions (e.g., climate change,
oil price shocks), in the FML study our scenario drivers are existing or hypothetical policies, so
the terms scenario and policy are used synonymously. We use the term landscape to describe
the spatially explicit land cover, land use and land management practices that result from a given
policy/scenario.
The BT scenario is a 'business-as-usual' scenario with respect to biofuel policy, and therefore its
primary assumptions are already established based on existing policies. The BT landscape
therefore is intended to approximate the land uses, crop rotations and land management practices
that would be expected in the year 2022, if biofuel-related laws and policies remain in place as
they currently exist. These include the renewable fuel standards established by the Energy
Independence and Security Act of 2007 (EISA; Public Law 110-140) requiring, by 2022: 16
billion gallons (Bgal) cellulosic ethanol, 5 Bgal other advanced biofuel and 15 Bgal corn starch
ethanol. Projections for the 12-state FML area indicate that net shifts will occur from soybeans
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Table 5. Summary of projected changes in land use from the Base Year to the Biofuel Targets
scenario.
Projected land use change
Total area of change
(106 acres)
Change as percent of all
agricultural lands (%)
CRP to corn
2.6
1.4
Other row crops to corn
19.1
10.3
Hay/pasture to corn
4.0
2.2
Utilization of corn stover
90.4
48.5
and other row crops to corn, from Conservation Reserve Program (CRP)-enrolled lands to corn
and from hay/pasture to corn (Table 5). To meet the EISA mandate for cellulosic ethanol, this
scenario also assumes that up to 30% by weight of corn stalk residues, which ordinarily would
remain in the field, will be removed from all corn-growing fields for biofuel production. The BT
scenario further assumes that adoption rates of conservation practices remain at current levels.
The MS scenario, which is currently being constructed, will define strategic shifts in agricultural
practices that can enable agricultural landscapes to produce both conventional commodities and
additional ecosystem services (Jordan et al. 2007). The MS landscape therefore is intended to
approximate the land uses, crop rotations and land management practices that would be expected
in the year 2022, in the absence of US biofuel incentives and in the presence of a hypothetical
new program of incentives for agricultural conservation practices. The first step in constructing
this scenario is the selection of a manageable set of conservation practices which, if increased
through incentives, would collectively be capable of increasing the amount and variety of
ecosystem services. The second step is the construction of a target landscape that would
optimize ecosystem services and agricultural production through the placement of land uses and
conservation practices, subject to a set of societal values and constraints. The final step entails
modeling the process of land-manager adoption of these practices, given a set of incentive
payments. This step would be iterated, with adjustments to the incentive payment structure, to
achieve nearest approach to the target. A key aspect of designing this scenario, therefore, will be
judging the ability of various practices to provide a range of ecosystem services. We have
selected a set of candidate practices which correspond to, or are composites of, practices
described by the USDA Natural Resources Conservation Service (Table 6). Since we need to be
capable of modeling the uses of and outcomes from these conservation practices, individually
and collectively, modeling feasibility, as well as service provision, is important in their selection.
This scoping study did not address modeling feasibility, however.
For scenarios that reflect an existing or otherwise described policy, such as the BT scenario, the
goal of a scoping analysis should be to determine which ecosystem services appear likely (or
unlikely) to vary significantly relative to other scenarios. For scenarios to be designed, such as
the MS policy, the goal of scoping can also include shorthand evaluation of alternative policy
strategies to determine which ones are worth full development and evaluation.
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Table 6. Candidate conservation practices considered for the Multiple Services scenario
Conservation
practice (with
applicable NRCS
codes)1
Description of Practice2
Land retirement for
conservation (327)
and upland wildlife
habitat management
(645)
Establish and maintain perennial vegetative cover to protect soil and
water resources and to establish natural areas and wildlife habitat on
land retired from agricultural
production.
Wetland restoration
(644, 657)
A rehabilitation of a degraded wetland where the soils, hydrology,
vegetative community, and biological habitat are returned to the
original condition to the extent practicable for watershed protection
and improvements to habitat for waterfowl, fur-bearers, or other
wetland associated flora and fauna.
Wetland creation for
water treatment
Creation of acreages that have wetland hydrology, hydrophytic
plant communities, hydric soil conditions, and wetland functions
and/or values.
Nutrient management
Application of best management practices for the amount, source,
placement, form and timing of the application of plant nutrients and
soil amendments.
Reduced tillage
(includes no-till, 329;
mulch till, 345; ridge
till, 346)
Includes practices for managing the amount and orientation of year
around crop residues on the field surfaces to limit soil-disturbances
and/or the and utilization of alternating ridges and furrows to reduce
water and wind erosion, improve soil organic matter, slow moisture
losses, and provide food and cover for wildlife.
Winter ground cover
(340)
Utilizes plant cover including grasses, legumes and forbs for
seasonal cover to reduce wind and water erosion, increase soil
organic matter content, capture/recycle soil nutrients, suppress
weeds, manage soil moisture and promote other conservation
purposes.
Contour farming
(330), contour buffer
strips (332), terracing
(600)
Using ridges and furrows formed by tillage, planting and other
farming operations to change the direction of runoff from directly
downslope to around the hillslope. Cropped strips may be
alternated with narrow strips of permanent, herbaceous vegetative
cover; or an earth embankment, or a combination ridge and channel,
may be constructed across the field slope.
Riparian forest buffer
(391)
Use of trees, shrubs, and other vegetation adjacent and up-gradient
from water bodies for reducing sediments, nutrients, pesticides and
other pollutants in surface runoff and to create shade for lower
water temperatures and provide a source of detritus and large
woody debris for fish and other aquatic organisms, and to provide
wildlife corridors.
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Table 6 (continued).
Conservation
practice (with
applicable NRCS
codes)1
Description of Practice2
Grassed waterways
(412)
Utilization of natural or designed channels shaped and established
with suitable vegetation to permit conveyance of runoff water from
terraces, diversions, or other water concentrations without causing
erosion or flooding, reduce gully erosion and to protect/improve
water quality.
Drainage water
management (554)
Control of water surface elevations and discharge from surface and
subsurface drainage systems, to improve water quality, enable
seasonal shallow flooding and prevent discharge of nutrient laden
water carried through surface or subsurface drainage.
Flood plain grassland
or forest serving for
flood control
(actively managed or
passively)
Include conservation practices designed to restore floodplains,
including wetlands, to condition and function that is as close to
natural conditions as is practicable.
Source: http://www.nrcs.usda.gov/technical/Standards/nhcp.html
2Adapted from NRCS descriptions.
We conducted scoping as a comparison of the base year to each future scenario separately (a BT-
BY comparison and a MS-BY comparison), so that only interscenario differences had to be
considered. In developing the BT and MS landscapes we hold constant all protected natural
areas that existed in 2002 (i.e., not including temporary restrictions such as Conservation
Reserve Program (CRP)), so these did not enter the scoping analysis. In both the BT and MS
landscapes we make identical assumptions about the future locations of urban growth, based on a
set of projections developed by USEPA (2008). Therefore, urban land use is changed compared
to the BY, but this change is not large in the Midwest and was neglected for scoping purposes.
Finally, although the BT-MS comparison is of interest in the FML Study, our scoping exercise
did not undertake this comparison.
C. Values Hierarchy and Ecosystem Services
Adopting a definition put forward by Fisher et al. (2009), "ecosystem services are aspects of
ecosystems utilized (actively or passively) to produce human well-being." Any attempt to deal
with ecosystem services in a rigorous fashion encounters difficulties of definition, because their
definition is specific to the contexts of both ecological production and societal benefit (Fisher
and Turner 2008, Fisher et al. 2009). Nor is there usually a fine line between ecological and
social systems demarking a point at which the service is provided, especially when ecosystems
are intensively managed.
10
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We addressed this problem by constructing a hierarchy of values, and the ecosystem aspects
contributing to those values, to provide a context within which ecosystem services could be
identified. The hierarchy is shown in full in Appendix A; an illustrative portion is shown in
Figure 3. In accordance with the above definition of ecosystem services, the highest level of the
value hierarchy (not shown in Figure 3, but corresponding to level zero) is human or societal
well-being. We then differentiated nine first-level values as primary components of well-being,
as follows:
• Minimize health risks
• Maximize agricultural productivity/benefits
• Maximize forest productivity/benefits
• Maximize industrial productivity/benefits
• Maximize benefits from subsistence
• Maximize commercial fishery productivity/benefits
• Minimize nonindustrial property loss
• Maximize benefits from outdoor recreation
• Minimize broad-scale risks
These were chosen so as to represent, according to the judgment of the FML project team, a
broad set of goals related to well-being of Midwestern residents and also potentially sensitive to
the changes anticipated under our scenarios. Most are self explanatory but a few require further
explanation. Subsistence refers to activities that derive food or sustenance from, e.g., hunting,
fishing, collecting, rather than from agriculture. Commercial fishery benefits are differentiated
from recreational fishery benefits (which are part of 'outdoor recreation'). Although several
species (e.g., common carp, buffalo, catfish and freshwater drum) are harvested commercially in
the upper and mid Mississippi River, for our study commercial fisheries were limited to those in
the Great Lakes. Finally, broad-scale risks are effects whose primary impacts are felt outside the
Midwest yet may still be considered important to Midwesterners and, to that extent, matter to
their well-being as well. Certain effects overlap these categories. For example, Midwesterners
can benefit directly from outdoor recreation (as participants or service providers) centered
around the presence of migratory birds; they can also benefit from the knowledge that the
Midwest provides critical habitat for internationally important avian biodiversity. Similarly, they
can benefit directly from the production of agricultural commodities and also take satisfaction in
the knowledge that their region contributes to international food security (as 'breadbasket to the
world') or to national energy security.
Each first-level value was further subdivided - initially into constituent elements and later into
contributing elements. For example, outdoor recreation was initially subdivided into component
activities (hunting, fishing, hiking, boating and wildlife watching) as well as atmospheric
visibility. One such component, fishing, was determined to depend on 'abundant aquatic habitat
(recreational fishing species)' as a contributor, which depended in turn on 'water quality' and
'natural cover.' This section of the hierarchy was limited to general categories understandable to
the public, so that it could be used later in public interactions. We used up to six levels to define
these goals, their components and their contributors, and we defined as the ecosystem service the
highest-level entity that could be considered, per the Fisher et al. 2009 definition, more as
'aspects of ecosystems' than of socioeconomic systems. In this fashion we identified 45 distinct
11
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services; they are denoted in bold and italic font in Figure 3 and Appendix A, and are
summarized in Table 7.
Hierl
Hier2
Hier3
Hier4
Hier5 Hier6
Technical Contributor
Maximize
benefits from
outdoor
recreation
Water quality
Sustain/ improve
Hunting
opportunities
Abundant wildlife
habitat
(recreational
hunting species)
Natural cover
Landscape Mix
Sustain/ improve
Fishing
opportunities
Abundant aquatic
habitat
(recreational
fishing species)
Water quality
Natural cover
Sustain/ improve
Hiking
opportunities
Landscape
conducive to
hiking
Natural cover
Landscape mix
Landscape
conducive to
boating
Natural cover
Landscape mix
Sustain/ improve
Boating
opportunity
Water quality
conducive to
boating
Water quality
Wetland quantity
Perennial riparian vegetation
Water, sediment and chemical transport
Pesticide applications
Nutrient applications
Wetland quantity & habitat quality
Patch connectivity
Upland resting habitat
Foraging habitat
Nesting habitat
Landscape heterogeneity
Wetland quantity
Perennial riparian vegetation
Water, sediment and chemical transport
Pesticide applications
Nutrient applications
Diverse channel structure (ditches,
streams)
Diverse floodplain habitats (rivers)
Woodland quantity/ quality
Grassland quantity/ quality
Perennial riparian vegetation
Landscape heterogeneity
Perennial riparian vegetation
Landscape heterogeneity
Wetland quantity
Perennial riparian vegetation
Water, sediment and chemical transport
Nutrient applications
Surface water storage
Surface water withdrawals
Wetland quantity
Water availability
for boating
Flood moderation
Water, sediment and chemical transport
Diverse channel structure (ditches,
streams)
Floodplain flood storage capacity
Figure 3. A fragment of the FML hierarchy of values, ecosystem services and technical
contributors. Ecosystem services are indicated in bold and italic font.
12
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Table 7. List of ecosystem services to be considered in the FML Study, showing full name
and corresponding short label. (See Appendix A for context of each service within the values
hierarchy.) Double lines indicate groupings of similar services; this order of listing is the
Full name
Short label
Abundant agricultural land cover
Ag cover
Biofuel feedstock production
Biofuel prod
Food production
Food prod (gbl)
Abundant forest cover (forestry)
Forest cover
Land cover that minimizes vector-borne illness
Land cover (illness)
Air quality that maximizes agricultural production
AO (ag)
Air quality (pollutant export)
AQ (export)
Air quality that maximizes forest production
AQ (forest)
Air quality that minimizes respiratory health risks
AQ (health)
Air quality conducive to visibility
AQ (visibility)
Abundant aquatic habitat (Great Lakes commercial fisheries)
Aqua hab (GL)
Abundant aquatic habitat (recreational fishing species)
Aqua hab (recr)
Abundant aquatic habitat (subsistence fishing)
Aqua hab (subs)
Water quality that maximizes agricultural production
WQ (ag)
Water quality conducive to boating
WQ (boat)
Water quality (pollutant export)
WQ (export)
Water quality that maximizes forest production
WQ (for)
Water quality that minimizes water-borne illness
WQ (illness)
Water quality that maximizes industry
WQ (ind)
Flood moderation that minimizes crop loss
Fid mod (crops)
Flood moderation that minimizes forest stand loss
Fid mod (for)
Flood moderation that minimizes risks to life and limb
Fid mod (health)
Flood moderation that minimizes industrial loss
Fid mod (ind)
Flood moderation that minimizes nonindustrial loss
Fid mod (non ind)
Water availability for agriculture
Water amt (ag)
Water availability for boating
Water amt (boat)
Water availability for forestry
Water amt (for)
Water availability for industry
Water amt (ind)
Carbon storage
Carbon storage
Productivity of agricultural soils
Soil prod (ag)
Productivity of forest soils
Soil prod (for)
Resistance of agricultural soils to erosion
Soil stability (ag)
Resistance of forest soils to erosion
Soil stability (for)
Abundance of insects beneficial to agriculture
Bene insets (ag)
Abundance of insects beneficial to forestry
Bene insets (for)
Abundant native species (subsistence)
Native spp (subs)
Biodiversity of vegetation communities
Veg diversity
Abundant wildlife habitat (recreational hunting species)
Wlf hab (hunt)
Abundant wildlife habitat (viewed spp)
Wlf hab (spp view)
Abundant wildlife habitat (globally important spp, e.g. T&E)
Wlf hab (spp gbl)
Abundant wildlife habitat (subsistence species)
Wlf hab (subs)
Diverse wildlife habitat (all native spp)
Wlf hab (com gbl)
Diverse wildlife habitat (all native spp)
Wlf hab (com view)
Landscape conducive to boating
Landscape (boat)
Landscape conducive to hiking
Landscape (hiking)
13
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D. Concept Maps
We examined key drivers of change and impacts of concern for the FML Study region through
the development of a comprehensive conceptual model (Figure 4). This model detailed causal
pathways from global drivers and national policies to all possible services provided by the range
of overlapping subsystems: agricultural production systems, industrial systems, aquatic and
terrestrial ecosystems, energy systems, etc. At all stages of model development, however, we
sought to focus on activities or processes that were likely to be affected by the future scenarios
we are considering. (For example, water quality in many areas is dependent on reservoir
management; but the latter is not affected by any of our scenarios and was omitted.)
We used the concept mapping tool, Cmap (http://cmap.ihmc.us/; Canas et al. 2004), to perform
this conceptual modeling task. Concept maps are an effective means of representing and
communicating knowledge. Novak (1998) proposed that the primary elements of knowledge are
concepts and the relationships between concepts are propositions. A concept map is a graphical,
two-dimensional display of concepts connected by directional lines that are labeled to
characterize the relationships between pairs of concepts.
In our models, ecosystem drivers, elements, processes and services became concepts. As can be
seen in Figure 4, the general model for the FML study is an extremely complex web of
interactions and linkages. We first developed a base model showing the connections between
these concepts, and then additional models that compared two scenarios. In these comparative
models, a policy change corresponding to the scenario comparison was introduced at a given
location in the model, and then connections between other concepts were labeled as positive or
negative according to the expected propagation of the influence of the policy change through the
system: positive for influences that were increased and negative for those that were decreased. A
search of the literature provided foundational documentation for the direction of the connections
where it could be identified.
14
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General Model
2-6-2009
MtfOUao
Figure 4. Illustration of the complexity of the base conceptual model diagram for the FML
Study.
15
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E. Creating an Influence Matrix
With the six-level values hierarchy as a point of departure, we used the concept maps to identify
one additional level consisting of environmental elements, termed technical contributors, which
we expected to be causally related to each of the lowest-level items in the hierarchy and likely to
be affected by scenario-related changes (Appendix A). A total of 37 technical contributors
(listed in Figure 5) were identified as potentially affecting one or more of the ecosystem services.
For the example given above, technical contributors to water quality that could vary under our
scenarios were determined to be:
• wetland quantity
• perennial riparian vegetation
• water, sediment and chemical transport (i.e., field runoff)
• pesticide applications, and
• nutrient applications.
When repeated instances of the technical contributors were accounted for, the total number of
rows in the hierarchy was 208. The degree to which a given technical contributor was repeated
can be appreciated in Figure 5.
We examined the likely influence of a given scenario by examining separately the various
changes that the scenario would directly cause. The BT-BY comparison entailed changes in both
land use (due to demand for feedstocks) and biofuel production and use. We summarized the
projected land use changes into four categories. Three of these were increases in corn plantings
to meet projected increased demand (CRP to corn, other row crops to corn and hay/pasture to
corn). The fourth was the expected harvesting of corn stover for use as cellulosic biofuel
feedstock; we made a simple assumption of 30% stover removal from all land planted to corn
(Table 5). We also summarized the projected changes in biofuel production and use into four
categories (ethanol production; emissions from ethanol use; biodiesel production; emissions from
biodiesel use) making a total of eight BT scenario-related changes. The MS scenario entails
increases in the use of conservation practices; we are considering 11 candidate practices so each
of these was separately considered a MS scenario-related change. At this stage we also
identified various sets of weighting factors to reflect differences among the scenario-related
changes in area and cost; these will be described below.
Using a Microsoft Excel spreadsheet, we created an influence matrix in which hierarchical
elements were rows and scenario-related changes were columns.
16
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Minimize
health risks
Maximize
agricultural
productivity
Figure 5. Importance of technical contributors to ecosystem services in the FML Study. First-
level hierarchy values (on left) are connected to all technical contributors (on right) that
influence it; the thickness of the line indicates the number of times the technical contributor
appears within that first-level hierarchy value. (See Appendix A for context of each technical
contributor within the values hierarchy.)
acid rain precursors
biofuel feedstock production
diverse channel structure (ditches, streams)
diverse floodplain habitats (rivers)
floodplain flood storage capacity
food production
foraging habitat
grassland quantity
grassland quantity/quality
ground water recharge
ground water withdrawals
habitats to support large predator populations
land in crop/hay/pasture
land managed for forestry production
landscape heterogeneity
Lyme's disease habitat
mosquito habitat
native insect habitat/refugia
native perennial vegetation communities
nesting habitat
nutrient applications
ozone
particulates
patch connectivity
perennial riparian vegetation
pesticide applications
riverine, lacustrine wetland quantity
soil organic carbon
soil structure
surface water withdrawals
upland resting habitat
water, sediment and chemical transport
wetland quantity
wetland quantity & habitat quality
wind erosion
woodland quantity
woodland quantity/quality
Maximize
subsistence
activities
Maximize
commercial
fishery
productivity
Minimize
nonindustrial
property loss
Maximize
outdoor
recreation
Minimize
broad-scale
risks
17
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F. Scoring the Influence Matrix
Scoring of the expected influences of agricultural changes on such a wide range of ecosystem
services requires broad expertise spanning agricultural management practices, environmental
science and ecology. Obtaining the judgment of 'experts' (i.e., leading authorities) for each of
these topic areas would be extremely difficult. The goal of a scoping analysis is not to put
forward scores that represent the best available knowledge, although we believe this method
could be used for that purpose. Its purpose instead is to organize and concretize well-reasoned
hypotheses about change to guide the design of a computational study. Therefore, the procedure
we recommend is to rely on the judgment of scientists or practitioners with a broad knowledge of
the pertinent subject area. For the illustrative demonstration presented in this paper, we used
four environmental professionals on the FML project team, and each item was scored by at least
three of the four scorers. Each scorer has more that 25 years professional experience in
environmental science and at least three years addressing environmental issues related to
agriculture. The scorers reviewed literature on biofuel feedstock production and agricultural
conservation practices (including NRCS descriptions of these practices) in the process of
conceptual model development.
Working independently from one another, the scorers scored the sign and magnitude (and
scorer's level of certainty of the sign and magnitude) of the expected influence of each scenario-
related change on each technical contributor. This included eight scenario-related changes for
the BT scenario and 11 for the MS scenario, for a total of 19 scenario-related changes, multiplied
by 37 technical contributors for a total of 703 influence scores. These were denoted 'C' scores,
since they denoted expected influence on a 'contributor.' In a similar process, scorers also
scored the influence of each technical contributor on the item immediately above it in the
hierarchy (i.e., to its left in Appendix A); we called these hierarchy scores 'H' scores. H scores
had to be assigned individually to each of the 208 rows in the hierarchy so that the hierarchical
context could be taken into account.
Influence was scored with a positive integer if the change was expected to increase the
contributor and a negative integer if the contributor would decrease. The magnitude of the
influence value could range from zero to five; thus the overall potential range for any C or H
score was -5 to +5, with zero indicating a lack of influence. For example, in the BT-BY
comparison, one scenario-related change was conversion of Conservation Reserve Program
(CRP) land to corn, and one technical contributor was 'channel structural diversity (ditches,
streams).' For this case, the scorer considered the following question: "When a given area (size
unspecified) is changed from CRP to corn, what is the effect on channel structural diversity of
ditches or streams in or immediately adjacent to that particular area?" A score of +5 meant it
would go from uniformly channelized to completely restored (e.g., natural meanders, floodplain,
instream habitat diversity); -5 meant they would go from completely natural condition to all
channelized.
Scorer certainty was rated from 1 to 5, as follows:
1. Both sign and magnitude are based more on intuition rather than professional knowledge.
2. Moderate certainty about the sign of the effect, but the magnitude is a best guess.
18
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3. Moderate certainty about both sign and magnitude.
4. Certain about the sign and moderately certain of magnitude of the effect.
5. Certain about both the sign and the magnitude.
Cases of sign disagreement among scorers were examined through discussion to determine
whether they were true disagreements or evidence of different interpretations of some part of the
scoring task. Among the C scores there were 24 cases of sign disagreement of which 13 were
resolved through discussion and 11 remained as disagreements. Of those resolved, 5 were found
to be typographical errors, 4 involved different understandings about a conservation practice and
were resolved through discussion and 4 involved different opinions about an influence where a
change resulted from discussion. Among the H scores there were 4 instances of sign
disagreement, all of which were resolved through discussion.
The final H and C scores from each of the individual scorers were compiled into one file for
analysis. Calculations and plotting of results were carried out using SAS® software.
G. Scenario-Related Changes and Weighting Factors
In development of our hypotheses it was important to take into account potential differences in
area or cost between certain of these changes. We decided to carefully separate these
considerations as well. When scoring the influence of a land use change, we did so on an equal-
area basis by assuming that the change occurred for the entirety of a given area (size not
specified) and then we scored the effect of that change on a given technical contributor within or
immediately adjacent to that area. Even when considering linear features such as grassed
waterways or riparian buffers, we considered the area of the practice itself when scoring, not the
areas through which the linear feature passed. We made an exception to this rule, however, in
the case of wetlands constructed for treatment of drainage from higher-position crop land. In this
case we considered the whole cropped area that the wetland was designed to address, and we
assumed that the lowest 0.5 to 2% of the area was converted to wetland. This allowed us to
score the expected effectiveness of the wetland for the contributing area.
In scoring, then, we could ignore whether the area of change expected in the scenario over the
whole 12-state area was comparatively large or small. As a separate procedure we estimated the
expected fraction of the total agricultural area of the FML expected to undergo that change. We
were then able to make subsequent computations with or without the use of this fraction as a
weighting factor. For the BT scenario, these area fractions were known based on available
projections (Table 5). For the MS scenario (Table 8) these fractions were unknown because the
FML research team has not determined which conservation practices to include and has not
estimated their areas of increase. We created one set of weights by assuming a doubling of the
area over which a practice is currently used (or a halving of the total potential use area in which
it is not used, whichever was least). We created a second set of weights based on the reciprocal
of the estimated per-acre cost. Potential areas, actual areas and costs were based on a review of
the conservation practice literature; in our judgment, very rough estimates of central tendencies
19
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Table 8. Weighting factors for conservation practices by area and cost, respectively. Rationales for the selection of these values are
given in Appendix B.
Conservation Practice
Potential
Area
(ma)
Estimated
BY use
(%)
Estimated
BY use
(ma)
Assumed
MS use
(%)
Assumed
MS use
(ma)
Approx.
annualized
cost
($/acre/yr)
Weighting factors
BY-MS
change as
fraction of
total ag area
Reciprocal
of cost
Nutrient management
145
36
52
68
99
1
0.20
1.00
Reduced tillage
182
71
129
86
155
20
0.11
0.050
Winter cover
182
15
27
30
55
30
0.12
0.033
Drainage water management
40
1
0.40
2
0.80
12
0.0017
0.083
Land retirement for conservation
204
10
20
20
41
100
0.087
0.010
Wetland restoration
40
1
0.40
2
0.80
350
0.0017
0.0029
Wetland creation
145
1
1.45
2
2.91
80
0.0062
0.013
Contouring/ terracing
56
10
5.6
20
11
40
0.024
0.025
Riparian forest buffer
22
45
9.9
73
16
150
0.026
0.007
Grassed waterways
56
15
8.5
30
17
360
0.036
0.0028
Floodplain conservation
easement
27
52
14.0
76
21
300
0.028
0.0033
20
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of the available ranges were sufficient for the purposes of, and in keeping with the goals of, a
scoping exercise. (Further information on potential and actual areas and costs of conservation
practices is presented in Appendix B.) We used both weighted and unweighted values in the
scoping process.
The costs of nutrient management require special explanation. The reported per-acre cost range
for this practice in one analysis was from $-30 to $14, with a mean cost of $-1 (i.e., a mean
savings). Since we could not use a zero or a negative cost as a weighting factor, we assigned a
cost of $1 to this practice. Even this low cost caused this practice to dominate the other practices
when expressed on a cost basis. Therefore certain comparative plots were generated with and
without inclusion of nutrient management, so that the effects of other practices could be more
easily examined.
H. Calculation and Plotting of HxC Values, Ranges and Uncertainties
Our overall scoping goal was to characterize the expected influence of a given scenario (for
example, the BT scenario) upon each ecosystem service of interest. Our first computational step
was to examine the influence of each scenario-related change on each service via a given
contributor. We combined the influence of 'Contributor' or C scores and 'Hierarchy' or H
scores through geometric aggregation; that is, by taking the product, Hm x Cm, where the
subscript denotes mean across scorers. We judged geometric aggregation to be preferable to
additive aggregation. First, it appropriately aggregates signs; i.e., if a technical contributor that
negatively affects a service (Hm is negative) is reduced by a service-related change (Cm is also
negative), the service is expected to increase (Hm x Cm is positive). Second, it ensures that a
component (H or C) score of zero (no influence) yields an aggregate score of zero. We divided
Hm x Cm by 5 so that the resulting combined value, like its constituent values, was within a -5 to
+5 range; for convenience, however, we referred to these simply as HxC values. HxC values
could be area-weighted (i.e., multiplied by the fraction of total FML agricultural area affected by
the service-related change) or cost weighted (i.e., multiplied by the reciprocal of cost). Within a
given row of the hierarchy, we practiced additive aggregation of cost- or area-weighted HxC
values representing different scenario-related changes within the same scenario. Summed area-
weighted values were used to indicate overall impact on that row of changes with differing
respective areas of influence caused by that scenario. We did not, however, aggregate across
rows since the relationship among different technical contributors to a given service is unclear;
for example, the degree to which wetland quantity compensates for pesticide application in
determining water quality is not obvious.
We examined the variability associated with the HxC values in two ways. First, we transformed
the certainty scores assigned to each H and C score by the scorers themselves, to uncertainty
scores, Uh and Uc, by subtracting from 5 (i.e., 5 - certainty = U). Uncertainty thus was a value
from 0 to 4, with higher values indicating greater uncertainty. We then took the product of the
interscorer mean uncertainty for H and C scores and divided the result by 5 (i.e., Uh x UJ 5) as
we had done with the influence scores, to indicate the comparative uncertainty of each HxC
value. Second, the range across scorers for each H and C score, Rh and Rc was used as a measure
of interscorer agreement. The range value was a positive number, potentially as high as 10, with
21
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higher values indicating greater disagreement among scorers. As before, the product of the range
for H and C divided by 5 (i.e., Rh x Rc /5) was computed to indicate the comparative interscorer
agreement of each HxC value.
To characterize the expected influence of the various scenarios on ecosystem services, we first
visually examined patterns in the weighted and unweighted HxC values and the associated
variability. These patterns were explored by plotting values in a variety of combinations. Plots
were of three primary types. Each of these is explained in detail here to orient the reader prior to
the discussion of results.
In the first type, weighted or unweighted HxC score is on the y-axis, and the 208 HxC values
(corresponding to the 208 hierarchy rows) are grouped along the x-axis according to ecosystem
service (abbreviations are explained in Table 7). Figure 6 shows unweighted HxC values for one
scenario-related change; in this case, the change of other row crops to corn. As indicated by the
arrows on the figure, eleven HxC values pertain to the service Abundant wildlife habitat
(recreational hunting species), abbreviated Wlfhab (hunt). The reason for this can be found in
Hierarchy x Contributor Score (equal area) by Service for:
Other rcw crops to corn
¦3
1
O -t<
x
x
+ +
* +t+t+
T + +
5*pR+-
+#+
++
" Minimi?® health risks
Maxiiniza industrial productivity
" Minimize nonindustrtal property loss
"+H~ Maximize agricultural productivity
-+H" Maximize subsistence activities
Maximize outdoor recreation
-+H- Maximize forest productivity
"Hf Maximize commercial fisltery productivity
-Hf Minimize broad —scale risks
Figure 6. Example plot showing HxC values for one scenario-related change (other row crops to
corn) grouped on the x-axis by ecosystem service. Symbol colors denote first-level hierarchy
value to which each HxC value contributes influence.
22
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Figure 3, where it is seen that 11 rows of the hierarchy pertain to this service. As explained
above, we did not mathematically combine these values since one positive value does not
necessarily counteract a negative value, and vice versa. A limitation of our displays is that they
do not identify the specific technical contributor that corresponds to each plotted value.
The ordering of ecosystem services from left to right in Figure 6 (also as shown in Table 7)
groups services that are similar in type, even though found in different sections of the hierarchy.
For example, the six kinds of wildlife-habitat services (shown by bracket) are grouped for easy
distinction. By contrast, symbol colors indicate the first-level value of the hierarchy to which the
score pertains. All values in this plot that are under the first-level value Maximize benefits from
outdoor recreation are in light blue.
The second type of plot is identical to the first except that symbols and colors now give an
indication of the variability, either interscorer disagreement or uncertainty, associated with each
HxC value plotted. Dots represent HxC values with no variability and larger circles indicate
greater variability. For example, in Figure 7 where the symbol size represents interscorer
disagreement, it is evident that scorers disagreed about the effect of this change on the overall
amount of agricultural cover (range > 2.5).
Hierarchy x Contributor Score (equal area) by Service for:
Other row crops to corn
Size of circle (radius) indicates Range of H x C Scores
«.oo-
4 M -
2.00 -
f
• _ 0 o 0 —< 05 O 0.5 1.0 o 1.0 -T 1.5 o 15 -< 2.0 ( >20 25 O ^ 25
Figure 7. Example plot showing interscorer range of HxC values for one scenario-related change
(other row crops to corn), grouped on the x-axis by ecosystem service. Symbol sizes and colors
denote interscorer range.
23
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In the third type of plot, HxC values are shown for one first-level value in the hierarchy, grouped
according to the scenario-related changes which are now arrayed on the x-axis (Figure 8). From
the left, the four BT changes related to land use are followed by the four related to biofuel
production or combustion. Next, the four candidate conservation practices are shown that we
have described as Conservation Practices Group I (i.e., involving only changes in management
practice but not land cover) followed by Group II (those in which some land cover changes).
Symbol colors now indicate the specific ecosystem services that contribute to that value in the
hierarchy. Unweighted plots such as Figure 8 include HxC values for all of the scenario-related
changes. Area-weighted plots omit the four changes related to biofuel production or combustion,
since these cannot be area-weighted. Cost-weighted plots omit all BT scenario-related changes.
Hierarchy x Contributor Score by Scenario—related Change to
Maximize outdoor recreation
o
X
x
+
-s.m i -§-
-l» 4*
15
£ s
t *
"
+ S *
+ ?
I±
6
+
/s ///////// / / / s / / ,
'/////// ' / 6'///////
/
•'* vv/*y"
-Hf AQ (l
-H4-WI
>11 tab (met) # WQ (boot)
tab (eon vhw) -Hf Lantecajw
ttf VMw nmt (boat) * WW tab (hut)
Landscape (hieing)
Figure 8. Example plot showing HxC values for one first-level hierarchy value (Maximize
outdoor recreation), grouped on the x-axis by scenario-related change. Symbol colors denote
ecosystem services to which each HxC score contributes influence.
24
-------
III. RESULTS AND CONCLUSIONS
Prior to presenting any results and conclusions, we acknowledge several important caveats to the
application of this scoping approach in general, and to the interpretation of the results of the
scoping analysis of the FML Study reported here. Earlier we pointed out that scorers had
extensive experience and pertinent knowledge but were not experts on every subject scored.
Unless unusual efforts are made to assemble a large group of experts, this will always be a
limitation of this scoping method. The particular results reported here are further limited by the
fact that each item was scored by only three or four individuals; therefore the results obtained are
only illustrative.
In adopting a matrix approach to hypothesis generation, we implicitly assume that influences of a
given scenario-related change (column) upon a given element of the hierarchy (row) are all
independent. We know that in any complex system there are interactions among elements. This
simplification is necessary for a manageable process of hypothesis generation, but as a result we
cannot examine potentially important interactions which could affect ecosystem service
outcomes.
This scoping approach constitutes a supply-side examination of ecosystem service change
hypotheses, in that it examines potential changes in the provision of services but ignores
potentially large differences in demand for these services, which could change their relative
importance. The latter could be examined by eliciting stakeholder weights for items within the
hierarchy. Our demonstration did not involve such an elicitation process, though a scoping
analysis could benefit from including such a step.
A. Biofuel Targets (BT) Scenario
Different ecosystem services are effective at different scales. Some services matter only over
large scales. For example, although biofuel feedstock production can be measured at the farm
scale, if processing only occurs at regional scales then service provision only occurs regionally
and should be examined at that scale. Similarly, carbon storage (as related to climate regulation)
ultimately matters only at global scales. All services defined in the FML hierarchy as
contributing to the goal of 'minimize broad-scale risks' should, by definition, be examined at
regional or larger scales. By contrast, services such as flood moderation may be important to
landowners both at local scales (such as a watershed of a few thousand acres) and large-basin
scales. We can examine the extremes of this range by examining both (a) unweighted service
scores, which consider local service changes without regard for the likely regional extent of a
given land use change, and (b) service scores that are weighted by expected total area of the
practice.
If one thinks about services only in the immediate vicinity of a given land use change, without
regard for how regionally widespread that change would be, all four types of land use changes in
the BT scenario are associated with mostly negative impacts on a wide range of ecosystem
25
-------
service contributors (Appendix C. 1, Figures C. 1.1 - C. 1.4). In general, the strongest negatives
are related to change of perennials (hay/pasture or CRP) to corn. Impacts associated with
conversion of other row crops to corn were not only lesser in magnitude but also more mixed in
sign, with some positive as well as negative contributors, presumably due to the positive
influences of corn on certain wildlife populations. Impacts attributed to stover removal were
uniformly negative though generally of lesser magnitude. The only potential positive effects are
reduced illness risks (Lyme disease and mosquito-borne illnesses) and increased agricultural
cover.
Considering the relative proportions of areas projected for each land cover change, and summing
all these impacts (Appendix C.5, Figures C.5.1), only a single improvement, increased biofuel
production, appears likely to be important. Considering the four area-weighted changes
individually (Appendix C.4, Figures C.4.1 - C.4.4), we see that this increase arises primarily
from stover utilization and secondarily from changes from other row crops to corn, and includes
comparatively small contributions from conversions of hay/pasture and CRP; no other
improvements in services are expected.
The greatest expected reductions were found in productivity and carbon storage and were mainly
attributable to stover utilization. Scorer agreement was high for these productivity scores and
moderate for carbon storage (Appendix C.2, Figure C.2.4). Scorers rated their uncertainties for
these services as low (Appendix C.3, Figure C.3.4). The scoping conclusion from these
observations is that BT scenario-related changes in soil productivity and carbon storage should
be modeled if possible. If it is not possible to do so, it will be important to conduct more detailed
literature investigation of these concerns, and/or to advise users of FML Study findings that these
effects were expected but could not be characterized.
Conversely, from inspection of Figure C.5.1, seven ecosystem services can be identified as
relatively unaffected at this scale (arbitrarily, having no score > |0.15|):
• Abundant agricultural land cover
• Food production
• Land cover that minimizes vector-borne illness
• Abundant forest cover (forestry)
• Biodiversity of vegetation communities
• Landscape conducive to hiking
• Landscape conducive to boating
The scoping conclusion from this observation is that one might consider dropping these seven
ecosystem services if their evaluation was resource-intensive; however, this would apply only if
the same conclusion was reached at the local scale (i.e., using unweighted scores). We could not
say this except for food production, since we include this only as a broad-scale concern (i.e., we
have not concerned ourselves with food security within the Midwestern region).
26
-------
B. Multiple Services (MS) Scenario
We compared the candidate conservation practices to one another to evaluate relative importance
for inclusion in our Multiple Service scenario. Comparison of HxC values grouped by service
(Appendix D. 1, Figures D. 1.1 - D. 1.11) suggested the 11 practices comprise two groups.
Although the distinction between these groups is not absolute, we have taken advantage of this
difference by ordering the conservation practices accordingly in the presentation of some of the
results, and separating the groups in the figures in Appendices C.5, D.5 and D.7 and Appendix E.
Group I practices, while positive on balance, tended to include a mix of positive and negative
scores:
• Nutrient management
• Reduced tillage (includes no-till, mulch till, ridge till)
• Winter ground cover
• Drainage water management
Group II practices yielded scores that tended to be uniformly non-negative with exception of a
few services (i.e., agricultural cover, food production and fuel production). Group II included
the following practices:
• Land retirement for conservation and upland wildlife habitat management
• Wetland restoration
• Wetland creation for water treatment
• Contour farming, contour buffer strips and/or terracing
• Riparian forest buffer or grass filter strip
• Grassed waterway
• Floodplain conservation easement
The primary difference between these groups is that, with the partial exception of contour
farming and terracing, the practices in the second group replace row crops with perennial
vegetation, either in whole tracts (land retirement, wetland restoration or creation, floodplain
easement) or in linear features (buffers and waterways). By contrast, the first group changes the
management of row crops without reducing harvested area. A second difference, and a
consequence of the first, is that the Group II practices are more expensive. Therefore, when
services are examined on a cost basis (Appendix D.6, Figures D.6.1 - D.6.11; Appendix E.3,
Figures E.3.1 - E.3.9; Appendix E.4, Figures E.4.1 - E.4.9), the Group I practices appear to have
the potential to outperform Group II as service providers, although the mixing of positive and
negative scores in Group I weakens this conclusion.
A slightly modified picture emerges if one assumes that success in increasing implementation of
a given practice will be a function of its current adoption rate. When we assumed, as a simple
example, that a doubling of the current rate of adoption is the most that could be hoped for any
practice (Appendix D.4, Figures D.4.1 - D.4.11), then the first three of the Group I practices
appear as important because they are already widely practiced, but since drainage water
management is not widely practiced at present, it becomes less important in spite of its low cost.
27
-------
By contrast, land retirement in Group II becomes important as well, due to its well-established
use. Summing over these area-weighted changes (Appendix D.5, Figures D.5.1 and D.5.2),
negative values are found to be related to health (air and water), aquatic habitat, and wildlife
habitat. Inspection of the scoring data (not shown) reveals that in all cases these negatives are
HxC values related to pesticide use. Scorers tended to show strong agreement (Appendix D.2,
Figures D.2.1 - D.2.4), although they reported substantial uncertainty (Appendix D.3, Figures
D.3.1 - D.3.4), about these negative Group I scores. Except for these pesticide-related negatives,
there appears otherwise to be little difference between the Group I and Group II practices (when
each is taken as a group), with two exceptions. The first exception is the obvious economic
effects of removal of some land from crop production by the group II practices. A second
exception is the presence of a weak concern about illness related to increased habitat for
mosquitoes or the ticks that are vectors of Lyme disease.
An important scoping conclusion from these comparisons is that the FML computational study
should give attention to quantifying changes in pesticide usage and impacts associated with
Group I conservation practices. If this cannot be done, it will be important to conduct more
detailed literature investigation of these concerns, and/or to advise users of FML findings that
these effects were expected but could not be characterized. A secondary conclusion is the
potential role of Group II conservation practices in the increase of habitat for human disease
vectors. This concern also should be addressed via modeling, literature investigation and/or
advice to FML information users.
Because the equal-area, area-weighted and cost-weighted results correspond to different goals for
scenario creation, selection of practices for inclusion in a scenario will depend on whether the
goal is to maximize services without regard to adoption-readiness or cost as a way to examine
possibilities, or to focus on adoption-readiness or cost as a way to reflect feasibility. As pointed
out earlier, selection will also depend on feasibility of modeling a practice which we have not
evaluated in this exercise.
C. Summary and Hypotheses
In summary, we have developed and illustrated a highly structured method for gathering and
displaying investigators' expectations about impacts of two alternative future scenarios for the
Midwestern United States on a broad range of ecosystem services. This method, which we have
termed scoping, depends on the development of hierarchically structured conceptual models of
socioeconomic and environmental change, and the extensive use of best professional judgment
(BPJ) scoring of elements within that hierarchy. Scoring is carried out using a Microsoft Excel
spreadsheet; mathematically simple calculations of scores, interscorer ranges and scorer
uncertainties are carried out and plotted using SAS® software. This new methodology offers an
explicit procedure for managing ecological complexity and improving study design. Without
such a scoping methodology, ecosystem service assessments may suffer from lack of rigor in the
design process, and therefore default to approaches of convenience.
28
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Although based on the best professional judgment of scorers with broad knowledge about the
subject matter, these expectations or hypotheses should not be considered on par with the
findings of experimental or computational studies or 'expert' determinations. Further, because
each item in this demonstration was scored by only three or four individuals, these results are
only illustrative and need to be confirmed through the use of additional scorers. Nonetheless,
they have served to highlight several considerations for design of the Future Midwestern
Landscapes (FML) Study, and/or use of the FML study results, that may not otherwise have been
clear to our study team.
Based on this limited demonstration, we hypothesize that for the FML Biofuel Targets (BT)
future scenario, the most widespread negative impacts will be on soil productivity and carbon
storage. We also hypothesize that the FML BT scenario would have minimal impact on food
production at the broad (e.g., global scale). The potential effects of increased biofuel production
on global food security is a critically important issue, and we do not discourage the examination
of this impact, but if resources for the FML Study are limited, investigating this issue might be
given a lower priority.
Keeping in mind the limits of this demonstration, we hypothesize that for the FML Multiple
Services (MS) scenario, the conservation practices under consideration for inclusion fall into two
broad groupings: 'Group I practices' which involve agricultural management changes that do not
decrease crop land cover, and 'Group II practices' which do change at least some land from crop
to non-crop cover (and tend to be more expensive than Group I). A doubling of the current
adoption level of both groups (where doubling is one way of thinking about the effects of
incentives) would be hypothesized to result in generally similar increases of a broad range of
ecosystem services. However, some negative influences due to pesticide use would be expected
to result from the increase of Group I practices, and some concern would exist for increases in
disease vectors from Group II practices.
REFERENCES
Borsuk, M.E., C.A. Stow, and K.H. Reckhow. 2004. A Bayesian network of eutrophication
models for synthesis, prediction, and uncertainty analysis. Ecological Modelling 173(2-3)
219-239. doi: 10.1016/j.ecolmodel.2003.08.020.
Bruins, R.J.F., M.T. Heberling, and T.A. Maddock. 2005. Conceptual approach for integrated
watershed management. pl85-206 in R.J.F. Bruins and M.T. Heberling, Eds. 2005.
Economics and Ecological Risk Assessment: Applications to Water shed Management. CRC
Press, Boca Raton, FL.
Canas, A. J., G. Hill, R. Carff, N. Suri, J. Lott, G. Gomez, T.C. Eskridge, M. Arroyo, and R.
Carvajal. 2004. CmapTools: A knowledge modeling and sharing environment, p. 125-133
in: Concept Maps: Theory, Methodology, Technology, Proceedings of the First International
Conference on Concept Mapping, A.J. Canas, J.D. Novak, and F.M. Gonzalez, Eds.
Universidad Publica de Navarra, Pamplona, Spain. .
29
-------
ChanK.M.A., M.R. Shaw, D.R. Cameron, E.C. Underwood, G.C. Daily. 2006. Conservation
planning for ecosystem services. PLoSBiology 4(11):2138- 2152
doi: 10.1371/journal.pbio.0040379.
Daily, G.C. 1997. Introduction: what are ecosystem services? p. 1-10 in G.C. Daily, Ed..
1997. Nature's services: societal dependence on natural ecosystems. Island Press,
Washington, DC.,
Daily, G.C., S.Polasky, J. Goldstein, P.M. Kareiva, H. Mooney, L. Pejchar, T. Ricketts, J.E.
Salzman, and R. Shallenberger. 2009. Ecosystem Services in Decision Making: Time to
Deliver. Frontiers in Ecology and the Environment 7(l):21-28. doi: 10.1890/080025.
Fisher, B. and R.K. Turner. 2008. Ecosystem services: classification for valuation. Biological
Conservation 141(5): 1167-1169. doi:10.1016/j.biocon.2008.02.019.
Fisher, B., R.K.Turner, and P. Morling. 2009. Defining and classifying ecosystem services for
decision making. Ecological Economics 68(3):643-653. doi:10.1016/j.ecolecon.2008.09.014.
Gentile, J.H., M.A. Harwell, W. Cropper Jr., C.C. Harwell, D. DeAngelis, S. Davis, J.C. Ogden,
and D. Lirman. 2001. Ecological conceptual models: a framework and case study on
ecosystem management for South Florida sustainability. The Science of The Total
Environment 274(l-3):231 -253. doi: 10.1016/S0048-9697(01)00746-X.
Groffman, P.M., C.T. Driscoll, G.E. Likens, T.J. Fahey, R.T. Holmes, C. Eagar, and J.D. Aber.
2004. Nor gloom of night: a new conceptual model for the Hubbard Brook Ecosystem
Study. Bioscience 54(2): 139-148. doi: 10.1641/0006-
3568(2004)054[0139:NGONAN]2.0.CO;2.
Jordan, N., G. Boody, W. Broussard, J.D. Glover, D. Keeney, B.H. McCowan, G. Mclsaac, M.
Muller, H. Murray, J. Neal, C. Pansing, R.E. Turner, K. Warner, and D. Wyse. 2007.
Sustainable development of the agricultural bio-economy. Science 313(5831): 1570—71.
doi: 10.1126/science. 1141700.
Kareiva, P., S. Watts, R. McDonald, and T. Boucher. 2007. Domesticated nature: shaping
landscapes and ecosystems for human welfare. Science 316(5833): 1866-1869. DOI:
10.1126/science. 1140170.
Kremen, C., N.M. Williams, M.A. Aizen, B. Gemmill-Herren, G. Lebuhn, R. Minckley,
L. Packer, S.G. Potts, T. Roulston, I. Steffan-Dewenter, D.P. Vazquez, R. Winfree,
L. Adams, E.E. Crone, S.S. Greenleaf, T.H. Keitt, A.-M. Klein, J. Regetz, and T.H. Ricketts.
2007. Pollination and other ecosystem services produced by mobile organisms: a conceptual
framework for the effects of land-use change. Ecology Letters 10 (4), 299-314.
doi: 10.1111/j. 1461-0248.2007.01018.x
Liu, Y., M. Mahmoud, H. Hartmann et al. 2008. Formal scenario development for
environmental impact assessment studies, p 145-162 in A. Jakeman, A. Voinov, A.E. Rizzoli
and S. Chen. Eds. 2008. Environmental Modelling, Software, and Decision Support: State
of the Art and New Perspectives. Elsevier, Amsterdam, The Netherlands.
Marshal, L., D. Nott, and A. Sharma. 2007. Towards dynamic catchment modelling: a
Bayesian hierarchical mixtures of experts framework. HydrologicalProcesses 21(7):847-
861. doi: 10.1002/hyp.6294.
Mehaffey, M., Wainger, L., Wade, T., Yankee, D., Smith, E., Bott, V. and Yarbrough, R. 2008.
Assessing vulnerabilities from alternative development patterns. Landscape and Urban
Planning 87(l):84-95. doi:10.1016/j.landurbplan.2008.04.010.
Millennium Ecosystem Assessment. 2005. Ecosystems and human well-being: a framework for
assessment. Island Press, Washington DC, USA. 245 p.
30
-------
Nelson, E., S. Polasky, D.J. Lewis, A.J. Plantinga, E. Lonsdorf, D. White, D. Bael, and J.J.
Lawler. 2008. Efficiency of incentives to jointly increase carbon sequestration and species
conservation on a landscape. Proc. Natl. Acad. Sci. USA 105(28):9471-9476.
doi:10.1073/pnas.0706178105.
Nelson, E., G. Mendoza, J. Regetz, S. Polasky, H. Tallis, D.R. Cameron, K.M.A. Chan, G.C.
Daily, J. Goldstein, P.M. Kareiva, E. Lonsdorf, R. Naidoo, T.H. Ricketts, and M.R. Shaw.
2009. Modeling multiple ecosystem services, biodiversity conservation, commodity
production, and tradeoffs at landscape scales. Frontiers in Ecology and the Environment
7(1):4-11. doi: 10.1890/080023.
Novak, J.D. 1998. Learning, creating, and using knowledge: Concept Maps™ as facilitative
tools in schools and corporations. Lawrence Erlbaum Associates, Inc., Mahwah, NJ.
SAS® software. SAS and all other SAS Institute Inc. product or service names are registered
trademarks or trademarks of SAS Institute Inc. in the USA and other countries. ® indicates
USA registration.
USEPA. 1998. Guidelines for ecological risk assessment. Risk Assessment Forum, U.S.
Environmental Protection Agency, Washington, DC. (Published on May 14, 1998, Federal
Register 63(93):26846-26924.)
USEPA. 2008. Preliminary steps towards integrating climate and land use (ICLUS): the
development of land-use scenarios consistent with climate change emissions storylines
(External Review Draft). U.S. Environmental Protection Agency, Washington, DC,
EPA/600/R-08/076A.
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Appendix A: Hierarchy of values, ecosystem services and 'technical contributors'
used for the FML scoping exercise.
A-l
-------
Appendix A: Hierarchy of values, ecosystem services and 'technical contributors' used for the
FML scoping exercise. Items in bold and italic font were defined as ecosystem services. Their
complete names are listed here; complete and short-version names are given in Table 6.
Hierl
Hier2
Hier3
Hier4
Hier5 Hier6
Technical
contributor
Minimize
health risks
Minimize water-
borne illness
Water quality that minimizes water-borne illness
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Minimize
vector-borne
illness
Land cover that minimizes vector-borne illness
Mosquito habitat
Lyme's disease
habitat
Minimize risks
to life and limb
Flood moderation that minimizes risks to life and limb
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Minimize
respiratory
health risks
Air quality that minimizes respiratory health risks
Particulates
Acid rain precursors
Ozone
Pesticide
applications
Maximize
agricultural
productivity/
benefits
Maximize
agricultural
land
Abundant agricultural land cover
Land in crop/ hay/
pasture
Minimize crop
loss
Minimize
flooding
Flood moderation that minimizes
crop loss
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Maximize
beneficial
insects
(predators,
pollinators)
Abundance
of insects
beneficial to
agriculture
Landscape Mix
Landscape
heterogeneity
Natural cover
Native insect habitat/
refugia
Maximize/
Ensure Air
Quality
Air quality that maximizes agricultural production
Particulates
Acid rain precursors
Ozone
Maximize/
ensure Water
Quality
Water quality that maximizes agricultural production
Wetland quantity
Perennial riparian
vegetation
A-2
-------
Water, sediment and
chemical transport
Ensure Water
Availability
Water
availability for
agriculture
Groundwater storage
Ground water
recharge
Ground water
withdrawals
Surface water storage
Surface water
withdrawals
Minimize
erosion
Resistance of
agricultural
soils to
erosion
Flood moderation
Water, sediment and
chemical transport
Natural cover
Native perennial
vegetation
communities
Maintain soil
productivity
Productivity of agricultural soils
Soil organic carbon
Soil structure
Maximize
forest
productivity/
benefits
Maximize
managed forest
cover
Abundant forest cover (forestry)
Land managed for
forestry production
Minimize crop
loss
Minimize
flooding
Flood moderation that minimizes
forest stand loss
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Maximize
beneficial
insects
(predators,
pollinators)
Abundance
of insects
beneficial to
forestry
Landscape Mix
Landscape
heterogeneity
Natural cover
Native insect habitat/
refugia
Maximize/
Ensure Air
Quality
Air quality that maximizes forest production
Particulates
Acid rain precursors
Ozone
Maximize/
ensure Water
Quality
Water quality that maximizes forest production
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Ensure Water
Availability
Water
availability for
forestry
Groundwater storage
Ground water
recharge
Ground water
withdrawals
Surface water storage
Surface water
withdrawals
Minimize
erosion
Resistance of
forest soils to
erosion
Flood moderation
Water, sediment and
chemical transport
Natural cover
Native perennial
vegetation
communities
Maintain soil
productivity
Productivity of forest soils
Soil organic carbon
Water, sediment and
chemical transport
Wind erosion
A-3
-------
Maintain
genetic stocks
for breeding
Biodiversity of
vegetation
communities
Biodiversity
Landscape
heterogeneity
Natural cover
Native perennial
vegetation
communities
Maximize
industrial
productivity/
benefits
Ensure Water
Availability
Water
availability for
industry
Groundwater storage
Ground water
recharge
Ground water
withdrawals
Surface water storage
Wetland quantity
Minimize loss
to infrastructure
& property
Flood moderation that minimizes industrial loss
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Ensure Water
Quality
Water quality that maximizes industry
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Maximize
benefits from
subsistence
activities
Sustain/
improve
hunting
opportunities
Abundant
wildlife habitat
(subsistence
species)
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Wetland quantity &
habitat quality
Patch connectivity
Upland resting
habitat
Foraging habitat
Nesting habitat
Landscape Mix
Landscape
heterogeneity
Sustain/
improve fishing
opportunities
Abundant
aquatic
habitat
(subsistence
fishing)
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
A-4
-------
Natural cover
Diverse channel
structure (ditches,
streams)
Diverse floodplain
habitats (rivers)
Sustain/
improve native
species
population
viability
Abundant
native species
(subsistence)
Abundant
native
species
habitat
(subsistence)
Water quality
Wetland quantity
Perennial riparian
veqetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Native perennial
vegetation
communities
Habitats to support
large predator
populations
Reduce
impacts from
exotic
species
Water quality
Wetland quantity
Perennial riparian
veqetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Native perennial
vegetation
communities
Flood Moderation
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Maximize
commercial
fishery
productivity/
benefits
Sustain/
improve Great
Lakes fish
production
Abundant
aquatic
habitat (Great
Lakes
commercial
fisheries)
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Riverine, lacustrine
wetland quantity
Minimize
nonindustrial
property loss
Minimize flood
hazard
Flood moderation that minimizes nonindustrial loss
Wetland quantity
Water, sediment and
chemical transport
Floodplain flood
storage capacity
A-5
-------
Maximize
benefits from
outdoor
recreation
Sustain/
improve
Hunting
opportunities
Abundant
wildlife habitat
(recreational
hunting
species)
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Wetland quantity &
habitat quality
Patch connectivity
Upland resting
habitat
Foraging habitat
Nesting habitat
Landscape Mix
Landscape
heterogeneity
Sustain/
improve
Fishing
opportunities
Abundant
aquatic
habitat
(recreational
fishing
species)
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Diverse channel
structure (ditches,
streams)
Diverse floodplain
habitats (rivers)
Sustain/
improve Hiking
opportunities
Landscape
conducive to
hiking
Natural cover
Woodland quantity/
quality
Grassland quantity/
quality
Perennial riparian
vegetation
Landscape mix
Landscape
heterogeneity
Sustain/
improve
Boating
opportunity
Landscape
conducive to
boating
Natural cover
Perennial riparian
vegetation
Landscape mix
Landscape
heterogeneity
Water quality
conducive to
boating
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Nutrient applications
Water
availability for
boating
Surface water storage
Surface water
withdrawals
Flood moderation
Wetland quantity
Water, sediment and
chemical transport
A-6
-------
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Sustain/
improve wildlife
watching
opportunities
Sustain/
improve wildlife
population
viability
Abundant
wildlife
habitat
(viewed
spp)
Water Quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Wetland quantity &
habitat quality
Patch connectivity
Upland resting
habitat
Foraging habitat
Nesting habitat
Diverse
wildlife
habitat (all
native spp)
Landscape Mix
Landscape
heterogeneity
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Native perennial
vegetation
communities
Minimize
impacts
from exotic
species
Flood
Moderation
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Natural
cover
Native perennial
vegetation
communities
Maximize
visibility
Air quality conducive to visibility
Particulates
Acid rain precursors
Ozone
Minimize
broad-scale
risks
Minimize
Climate
Change
Mitigate Net
GHG Additions
Carbon storage
Woodland quantity
Grassland quantity
Soil organic carbon
A-7
-------
Minimize
broad-scale
risks
Sustain global
biodiversity
Sustain/
improve target
species (e.g.,
T&E)
Abundant
wildlife
habitat
(globally
important
spp, e.g.
T&E)
Landscape mix
Landscape
heterogeneity
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Native perennial
vegetation
communities
Diverse channel
structure (ditches,
streams)
Diverse floodplain
habitats (rivers)
Sustain/
improve
diverse
communities
Diverse
wildlife
habitat (all
native spp)
Landscape Mix
Landscape
heterogeneity
Water quality
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
Pesticide
applications
Nutrient applications
Natural cover
Native perennial
vegetation
communities
Diverse channel
structure (ditches,
streams)
Diverse floodplain
habitats (rivers)
Habitats to support
large predator
populations
Minimize
impacts
from exotic
species
Flood
Moderation
Wetland quantity
Water, sediment and
chemical transport
Diverse channel
structure (ditches,
streams)
Floodplain flood
storage capacity
Natural
cover
Native perennial
vegetation
communities
Minimize export
of pollutants
Water quality (pollutant export)
Wetland quantity
Perennial riparian
vegetation
Water, sediment and
chemical transport
A-8
-------
Nutrient applications
Air quality (pollutant export)
Particulates
Acid rain precursors
Ozone
Maximize US
energy security
Biofuel feedstock production
Biofuel feedstock
production
Maximize
global food
security
Food production
Food production
A-9
-------
Appendix B. Information used to develop area and cost weighting factors
for conservation practices.
B-l
-------
Table B-l. Information used to develop area and cost weighting factors for conservation practices.
Conservation
Practice
Area or Cost Factor
Value
Explanation
Sources
Land retirement for
conservation
Potential Area (Ma)
204
2002 total area of cultivated (12 main) crops plus CRP
CARD (unpublished);
NRI (on-line report)
Estimated BY use (%)
10
Computed.
Estimated BY use (Ma)
20
Estimates of 2002 total CRP acreage range from 14 -
22 Ma
CARD (unpublished);
NRI (on-line report)
Assumed MS use (%)
20
Computed.
Assumed MS use (Ma)
41
Assumes doubling of 2002
Annualized cost ($/acre/yr)
100
Published cost/benefits analysis from IA.
Feng,2006
Wetland restoration
Potential Area (Ma)
40
Estimates of % FML cropland vary widely, value
selected based on latest using GIS and hydric soils
analysis.
WRI (on-line report);
USDA, 1987
Estimated BY use (%)
1
Unknown, but assumed small.
Estimated BY use (Ma)
0.4
Computed.
Assumed MS use (%)
2
Assumes doubling of 2002
Assumed MS use (Ma)
1
Computed.
Annualized cost ($/acre/yr)
350
Assumes costs spread over multiple year period
BNL (on-line report)
Wetland creation
Potential Area (Ma)
145
Based on estimate that 80% FML cropland is treated
with nutrients in a given year.
CARD (unpublished);
NRI (on-line report)
Estimated BY use (%)
1
Unknown but assumed small.
Estimated BY use (Ma)
1.5
Computed
Assumed MS use (%)
2
Assumes doubling of 2002
Assumed MS use (Ma)
4
Computed
Annualized cost ($/acre/yr)
80
Assumes costs spread over multiple year period
BNL (on-line report)
B-l
-------
Table B-l (Continued)
Conservation
Practice
Area or Cost Factor
Value
Explanation
Sources
Nutrient management
Potential Area (Ma)
145
Based on estimate that 80% FML cropland is treated
with nutrients in a given year.
NRCS unpublished
data
Estimated BY use (%)
36
Based on farm surveys, approximately 36% of nutrient
treatments fully meet BMP's
NRCS unpublished
data
Estimated BY use (Ma)
52
Computed
Assumed MS use (%)
68
Assumes nonuse rate is reduced by half
Assumed MS use (Ma)
99
Computed
Annualized cost ($/acre/yr)
1
Based on field data from IA and SD
ASCS, 1991; ISU,
1991
Reduced tillage
Potential Area (Ma)
182
Area of FML cultivated cropland in 2002 assumed
potentially treated with nutrients.
CARD (unpublished);
NRI (on-line report)
Estimated BY use (%)
71
Based on farm surveys, approximately 71 % of nutrient
treatments fully meet BMP's
NRCS unpublished
study, 2009
Estimated BY use (Ma)
129
Computed
Assumed MS use (%)
86
Assumes nonuse rate is reduced by half
Assumed MS use (Ma)
156
Computed
Annualized cost ($/acre/yr)
20
Conservation tillage defined as leaving 30% crop cover
on field.
Feng, 1991; USEPA,
2003
Winter cover
Potential Area (Ma)
182
Area of FML cultivated cropland in 2002 assumed
potentially treated with nutrients.
Estimated BY use (%)
15
Based on published USDA production statistics for
2002
NASS (on-line report)
Estimated BY use (Ma)
27
For 2002 18 Ma winter wheat planted, assumes an
additional cover with other crops.
NASS (on-line report)
Assumed MS use (%)
30
Assumes doubling of 2002
Assumed MS use (Ma)
55
Computed
Annualized cost ($/acre/yr)
30
Estimates from published studies. Costs dependent
on operator options for use of cover crop
CTT on-line report;
USEPA, 2003
B-2
-------
Table B-l (Continued)
Conservation
Area or Cost Factor
Value
Explanation
Sources
Practice
Contouring/ terracing
Potential Area (Ma)
63
GIS analysis reveals 31% of FML cropland on >3%
grade.
WF, 2009
Estimated BY use (%)
10
Ohio and Iowa farm surveys suggest this practice
utilized on 10% of applicable acres.
Sogren, 2004 (on-line
report); Toigo, 2009
Estimated BY use (Ma)
6
Computed
Assumed MS use (%)
20
Assumes doubling of 2002
Assumed MS use (Ma)
13
Computed
Annualized cost ($/acre/yr)
40
Estimated from published studies but may vary greatly
depending on initial constuction required
Feng, 1991; IDALS,
2007 (on-line report)
Riparian forest buffer
Potential Area (Ma)
22
GIS computation and estimate of area within 30 m
buffer of FML NHD reach file
WF, 2009
Estimated BY use (%)
45
GIS land cover (updated NLD) computation indicates
45% of buffer area is permanent woody vegetation.
WF, 2009
Estimated BY use (Ma)
9.9
Computed
Assumed MS use (%)
73
Assumes nonuse rate is reduced by half
Assumed MS use (Ma)
16
Computed
Annualized cost ($/acre/yr)
150
National average from NRCS database
CTT (on-line report)
Grassed waterways
Potential Area (Ma)
56
GIS analysis reveals 31% of FML cropland on >3%
grade.
WF, 2009
Estimated BY use (%)
15
Farm surveys (Ohio and Iowa) suggests grassed
waterway practice utilized on 4 - 25% of applicable
acreage.
Sogren, 2004 (on-line
report); Toigo, 2009
Estimated BY use (Ma)
9
Computed
Assumed MS use (%)
30
Assumes doubling of 2002
Assumed MS use (Ma)
19
Computed
Annualized cost ($/acre/yr)
360
Estimated from published studies but may vary greatly
depending on initial constuction required
Feng, 1991; CTT (on-
line report)
B-3
-------
Table B-l (Continued)
Conservation
Area or Cost Factor
Value
Explanation
Sources
Practice
Drainage water
Potential Area (Ma)
40
Base on published estiamates of acres fitted with sub-
WRI, 2007 (on-line
management
surface drainage systems.
report); USDA 1987,
2004 (on-line report)
Estimated BY use (%)
1
Unknown but assumed small.
Estimated BY use (Ma)
0.4
Computed
Assumed MS use (%)
2
Assumes doubling of 2002
Assumed MS use (Ma)
1
Computed
Annualized cost ($/acre/yr)
12
Assumes 10-year life of structures and negligible
maintainance costs
CTT (on-line report)
Floodplain
Potential Area (Ma)
27
Flood plain computed as the area with a 500 m buffer
WF, 2009
conservation
of FML RF1 reach file
easement
Estimated BY use (%)
52
GIS assessment of permanent vegetation (NLD) within
the flood plain
WF, 2009
Estimated BY use (Ma)
14
Computed
Assumed MS use (%)
76
Assumes nonuse rate is reduced by half
Assumed MS use (Ma)
21
Computed
Annualized cost ($/acre/yr)
300
Based cost estimates for easements and construction
used by the Extension Service staff from OH, IA and
MO.
Toigo, 2009
References for Appendix B
ASCS. 1991. USDA Agricultural Stabilization and Conservation Service. 1991. Oakwood Lakes-Poinsett Project 20 Rural Clean
Water Program Ten Year Report. U.S. Department of Agriculture, Agricultural Stabilization and Conservation Service,
Brookings, SD.
BNL. no date. Brookhaven National Laboratory Technology Fact Sheet Peconic River Remedial Alternatives:
WetlandsRestoration/Wetlands Construction. http://www.bnl.gov/erd/Peconic/Factsheet/wetlands.pdf.
CARD. Center for Agricultural and Rural Development, University of Missouri. Unpublished analysis.
CTT. Christina Tributary Team, Sussex Co. Delaware, USA, Agricultural BMP Cost Calculations.
http://www.wr.udel.edu/ChristinaTribTeam/
B-4
-------
Feng, H. 2006. The Cost and Benfits of Conservation Practices in Iowa. Iowa AgReviw 12:10-13.
IDALS. 2007. Iowa Department of Agriculture and Land Stewardship, Division of Soil Conservation. 2007. Silver Creek Watershed
Project Summer Newsletter, http://www.iowadnr.gov/water/watershed/silvercreek/files/silvercreeknewsletter07.pdf.
ISU. 1991. Iowa State University. 1991. Ag Programs Bring Economic, Environmental Benefits, in Extension News. Extension
Communications, Ames, IA.
NASS. USDANational Agricultural Statistical Service. http://www.nass.usda.gov/QuickStats/PullData_US.jsp
NRCS. USDA National Resource and Conservation Service. Unpublished data.
NRCS. online, http://www.nrcs.usda.gov/technical/standards/nhcp.html
NRI. National Resource Inventory. http://www.nrcs.usda.gov/technical/NRI/2003/statereports/tablel.html
Sohngen, B. 2004. Ohio Water Quality, TMDL's, and Agriculture. Ohio Environment Report, Nov. 18,2004.
http: //aede. osu. edu/peopl e/ sohngen. 1 / OER/index. htm.
Toigo, T. 2009. Iowa Department of Agriculture and Land Stewardship. (June 2009 email to FBD)
USDA. 1987. Farm drainage in the United States: History, Status and Prospects: Misc. Pub. No. 1455, Washington, DC.
USDA. 2004. U.S.Department of Agriculture. Farm drainage in the United States: History, Status and Prospects: Mies Pub. No.
1455, Washington, DC, 1987. Revised 2004 http://ohioline.osu.edu/b871/b871_3.html.
USEPA 2003. US Enviromental Protectionan Agency. Ecomomic Analyses of nutrient and sediment reduction actions to restore
Chesapeake Bay water quality. USEPA, Region III Chesapeake Bay Program Office Annapolis, Maryland.pp. 55 - 56.
WF. 2009. Walter Foster, USEPA GIS analysis, August, 2009.
WRL. 2007. World Resources Inventory. Analysis base on GIS study overlaying cultivated cropland on poorly drained soils.
http://pdf.wri.org/assessing farm drainage.pdf
B-5
-------
Appendix C. Plots by Ecosystem Service for each BT Scenario-related Change
Appendix C.l Unweighted HxC Values by Service for the BT Scenario
Appendix C.2 Unweighted HxC Values and Ranges by Service for the BT Scenario
Appendix C.3 Unweighted HxC Values and Uncertainties by Service for the BT Scenario
Appendix C.4 Area-weighted HxC Values by Service for the BT Scenario
Appendix C.5 Sum of Area-weighted HxC Values by Service for the BT Scenario
C-l
-------
Appendix C.l Unweighted HxC Values by Service for the BT Scenario.
Hierarchy x Contributor Score (equal area) by Service for:
CRP to corn
++4: +J.+± + + "l"± + + * +
+ t*+t ++4+ V++ ++ *++ *
+++ t+Vt++ t*++ +** +
++ + * ++
++¥ + ++ +
+ +
i **{
+ *-+**
t
*+ *
+ t+ t
-t—i—i—i—i—i—i—r-
... + +
++ ** + +
++ 4=
. . i i
t—i—i—r—i—r-
*
"tt+- Minimize health risks
Maximize industrial productivity
~W+~ Minimize nonlndustrial property loss
-ttf Maximize agricultural productivity
"Hf" Maximize subsistence activities
3 outdoor recreation
-ttt- Maximize forest productivity
¦Hf- Maximize commercial fishery productivity
-Hf Minimize broad—scale risks
Figure C. 1.1. CRP to corn.
Hierarchy x Contributor Score (equal area) by Service for:
Other row crops to corn
I
I
++
++++£*t *t iff t:!
* +tlu
+ *+
+ +
+
+ +
+ +
—i—i—i—r~
—i—i—i—r~
—i—i—i—i—r~
—i—i—i—i—r~
*Hf Minimize health risks
Maximize industrial productivity
-H+- Minimize nonlndustrial property loss
r Maximize agricultural productivity
y Maximize subsistence activities
Maximize outdoor recreation
4>* //V
-H+- Maximize forest productivity
"H+" Maximize commercial fishery productivity
-ttf- Minimize broad—scale risks
Figure C. 1.2. Other row crops to corn.
C-2
-------
Hierarchy x Contributor Score (equal area) by Service for:
Hay/ pasture to corn
+
+
X +
+ +
*±*tt +t*:+t&:JM+++ * '
- +*+ +++ i
+ * + ++ + + +++
+ + + *H
+ + +
¦ **§
"i $
h+ = T + ¦
+ + 4-
TS
I
health risks
industrial productivity
nonlndustrlal property l<
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Bf Maximize forest productivity
~W~ Maximize commercial fishery productivity
Figure C.1.3. Hay/ pasture to corn.
Hierarchy x Contributor Score (equal area) by Service for:
Use of corn stover
I .
TS
I
++++++ =F+ ++ + + +++ ++ +++++
++
+
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—
es -+H- Maximize agricultural productivity
productivity "Hf- Maximize subsistence activities
property loss 4H Maximize outdoor recreation
"Hf Maximize forest productivity
-Hf- Maximize commercial fishery productivity
Figure C.1.4. Use of corn stover.
C-3
-------
Hierarchy x Contributor Score (equal area) by Service for:
Ethanol combustion emissions
# Minimize health risks "Hf Maximize agricultural productivity "Hf Maximize forest productivity
Maximize industrial productivity "4tf Maximize subsistence activities "Hf Maximize commercial fishery productivity
"Hf Minimize nonlndustrlal property toss "Hf Maximize outdoor recreation "Hf Minimize broad—scale risks
Figure C.1.5. Ethanol combustion emissions.
Hierarchy x Contributor Score (equal area) by Service for:
Ethanol production increase
2.00
I .
# Minimize health risks "Hf Maximize agricultural productivity "Hf Maximize forest productivity
"ttt Maximize industrial productivity 4tf Maximize subsistence activities "Hf Maximize commercial fishery productivity
"Hf Minimize nonlndustrlal property loss "Hf Maximize outdoor recreation "Hf Minimize broad—scale risks
Figure C.1.6. Ethanol production increase.
C-4
-------
Hierarchy x Contributor Score (equal area) by Service for:
Biodiesel combustion emissions
health risks
industrial productivity
nonlndustrlal property l<
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
~W~ Maximize commercial fishery productivity
Figure C.1.7. Biodiesel combustion emissions.
Hierarchy x Contributor Score (equal area) by Service for:
Biodiesel production increase
+
++
, I
+ U**t
' - * ' ' ' ' + +-I-+ 1 1 4"£-M-± + j-_J
TS
I
health risks
industrial productivity
nonlndustrlal property U
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Bf Maximize forest productivity
"Ht" Maximize commercial fishery productivity
Figure C. 1.8. Biodiesel production increase.
C-5
-------
Appendix C.2 Unweighted HxC Values and Ranges by Service for the BT Scenario.
Hierarchy x Contributor Score (equal area) by Service for:
CRP to corn
Size of circle (radius) indicates Range of H x C Scores
- 0 o o -< 0.5 O05 -< 1.0 O 10 —< 15 O 15 —< 2.0 C>2.0 -< 2.5
Figure C.2.1. CRP to corn.
Hierarchy x Contributor Score (equal area) by Service for:
Other row crops to corn
Size of circle (radius) indicates Range of H x C Scores
yy
• -o O 0 -< 05 O 05-<1.0 O to-< 15 O 15 -<2.0 O 2.0 -<25 O >2.5
Figure C.2.2. Other row crops to corn.
C-6
-------
Hierarchy x Contributor Score (equal area) by Service for:
Hay/ pasture to corn
Size of circle (radius) indicates Range of H x C Scores
8 '
00
O
oo
D ' "
.=o 1
bM- . or^
dfl
-id,,-
y
- 0 ( > 0 -< 05 O05 -< 1.0 O 10-< 15 Ol5-< 2.0 O 2.0 -< 2.5 O > 2.5
Figure C.2.3. Hay/ pasture to corn.
Hierarchy x Contributor Score (equal area) by Service for:
Use of corn stover
Size of circle (radius) indicates Range of H x C Scores
* * * ; * * * * : :fv 'O—O' '
u o
O
>V"
- 0 O 0 - < 05 O 05 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure C.2.4. Use of corn stover.
C-7
-------
Hierarchy x Contributor Score (equal area) by Service for:
Ethanol combustion emissions
Size of circle (radius) indicates Range of H x C Scores
- 0 O 0-< 0.5 OCl5-< 1.0 Ol0-< 1.5 O2.0 — < 2.5
Figure C.2.5. Ethanol combustion emissions.
Hierarchy x Contributor Score (equal area) by Service for:
Ethanol production increase
Size of circle (radius) indicates Range of H x C Scores
s
"3
i
0
\
m
O
X
c
; ¦ ;
. *
-------
Hierarchy x Contributor Score (equal area) by Service for:
Biodiesel combustion emissions
Size of circle (radius) indicates Range of H x C Scores
- 0 O 0l5 — < 1.0 01.5 -< 2.0 O2.0-< 2.5 O > 2.5
Figure C.2.7. Biodiesel combustion emissions.
Hierarchy x Contributor Score (equal area) by Service for:
Biodiesel production increase
Size of circle (radius) indicates Range of H x C Scores
».¦
0
o°
o
¦9' ' 3
•' J.6
OO
¦: -O
• - 0 O 0 —< 0.5 O0-5 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure C.2.8. Biodiesel production increase.
C-9
-------
Appendix C.3 Unweighted HxC Values and Uncertainties by Service for the BT Scenario.
Hierarchy x Contributor Score (equal area) by Service for:
CRP to corn
S4ze of circle (radius) indicates HxC Uncertainty Score
Figure C.3.1. CRP to corn.
Hierarchy x Contributor Score (equal area) by Service for:
Other row crops to corn
Size of ctrde (radius) indicates HxC Uncertainty Score
0 -< 05 O05 —< 1.0 O 1.0 -< 1.5
Figure C.3.2. Other row crops to corn.
C-10
-------
Hierarchy x Contributor Score (equal area) by Service for:
Hay/ pasture to corn
Size of circle (radius) indicates H x C Uncertainty Score
- 0 © 0 - < 0l5 O 03 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure C.3.3. Hay/ pasture to corn.
Hierarchy x Contributor Score (equal area) by Service for:
Use of corn stover
Size of circle (radius) indicates H x C Uncertainty Score
, - i , r pi
ilefe ' °o"&
rS y-,-8 o f cCcQp
• o ¦ .
tO
• =0 0 -< 05 (5 0.5 — < 1.0 O 1.0 -< 1.5
Figure C.3.4. Use of corn stover.
C-ll
-------
Hierarchy x Contributor Score (equal area) by Service for:
Ethanol combustion emissions
Size of circle (radius) indicates H x C Uncertainty Score
- 0 O 0 -< 0.5 Q05 -< 10
Figure C.3.5. Ethanol combustion emissions.
Hierarchy x Contributor Score (equal area) by Service for:
Ethanol production increase
Size of circle (radius) indicates H x C Uncertainty Score
- 0 C 0 -< 05 O05 -< 1.0 O 1.0 -< 1.5
Figure C.3.6. Ethanol production increase.
C-12
-------
Hierarchy x Contributor Score (equal area) by Service for:
Biodieset combustion emissions
Size of circle (radius) indicates H x C Uncertainty Score
>V"
- 0 C o -< 0.5 O05 -< 10
Figure C.3.7. Biodiesel combustion emissions.
Hierarchy x Contributor Score (equal area) by Service for:
Biodiesel production increase
Size of circle (radius) indicates H x C Uncertainty Score
- 0 C 0 -< 05 O05 -< 1.0 O 1.0 -< 1.5
Figure C.3.8. Biodiesel production increase.
C-13
-------
Appendix C.4 Area-weighted HxC Values by Service for the BT Scenario.
Area—weighted Hierarchy x Contributor Score by Service for:
CRP to corn
"8
"ttf Minimize health risks 4H- Maximize agricultural productivity "H+" Maximize forest productivity
"HI Maximize industrial productivity "+H" Maximize subsistence activities -Ht- Maximize commercial fishery
Minimize nonindustr&l property loss Maximize outdoor recreation ~Hf" Minimize broad—scale risks
Figure C.4.1. CRP to corn.
Area—weighted Hierarchy x Contributor Score by Service for:
Other row crops to corn
Figure C.4.2. Other row crops to corn.
C-14
-------
Area—weighted Hierarchy x Contributor Score by Service for:
Hay/ pasture to corn
health risks
industrial productivity
nonlndustrlal property l<
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Bf Maximize forest productivity
~W~ Maximize commercial fishery productivity
Figure C.4.3. Hay/ pasture to corn.
Area—weighted Hierarchy x Contributor Score by Service for:
Use of corn stover
+
"8
s
+ +++++++++-;- +
s
% —
+++
*
¦++
r+wtitt+S n+ +++++i
:: ++ ++T 4- +
++++±+****±
+ +
+
+ +
+ + +-H
+ +
,, + + T ++
+++^ ++ +
++
health risks
industrial productivity
nonlndustrlal property V
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Bf Maximize forest productivity
~W~ Maximize commercial fishery productivity
Figure C.4.4. Use of corn stover.
C-15
-------
Appendix C.5 Sum of Area-weighted HxC Values by Service for the BT Scenario.
Sum of the Area—weighted Hierarchy x Contributor Scores by Service
for the BT Areal Scenario—related Changes
"ttf Minimize health risks 4H- Maximize agricultural productivity "H+" Maximize forest productivity
"HI Maximize industrial productivity "+H" Maximize subsistence activities -Ht- Maximize commercial fishery
Minimize nonindustrial property loss ~f Maximize outdoor recreation ~Hf" Minimize broad—scale risks
Figure C.5.1. Areal BT scenario-related changes.
Sum of the Area—weighted Hierarchy x Contributor Scores by Service
for the BT non-Area I Scenario—related Changes
"tt+" Minimize health risks "HI- Maximize agricultural productivity "H+" Maximize forest productivity
¦+H" Maximize industrial productivity "+++¦ Maximize subsistence activities -W- Maximize commercial fishery productivity
-Hf Minimize nonindustrial property loss Maximize outdoor recreation "Hf" Minimize broad—scale risks
Figure C.5.2. non-Areal BT scenario-related changes. The sum is of the Unweighted
scores.
C-16
-------
Appendix D. Plots by Service for each MS Conservation Practice
Appendix D.l. Unweighted HxC Values by Service for the MS Scenario.
Appendix D.2. Unweighted HxC Values and Ranges by Service for the MS Scenario.
Appendix D.3. Unweighted HxC Values and Uncertainty by Service for the MS Scenario.
Appendix D.4. Area-weighted HxC Values by Service for the MS Scenario.
Appendix D.5. Sum of Area-weighted HxC Values by Service for the MS Scenario.
Appendix D.6. Cost-weighted HxC Values by Service for the MS Scenario.
Appendix D.7. Sum of Cost-weighted HxC Values by Service for the MS Scenario.
D-1
-------
Appendix D.l. Unweighted HxC Values by Service for the MS Scenario.
Hierarchy x Contributor Score (equal area) by Service for:
Nutrient management
Tt
I
+ +
***************** M*tl+
H—I—
+ + + +
+ +
"Hf Minimize health risks
"til Maximize industrial productivity
-Hf Minimize nonlndustrlal property h
-Hf Maximize agricultural productivity
-Hf Maximize subsistence activities
Maximize outdoor recreation
"Hf" Maximize forest productivity
"Hf" Maximize commercial fishery productivity
Figure D. 1.1. Nutrient management.
Hierarchy x Contributor Score (equal area) by Service for:
Reduced tillage
I .
+ +
+ ++. +
ti t
±
i
¦ + + ++'f±++ ++++± ++++++ +++++J:l:+H 5~~ x -
, *"*+** ntlt tt
+ ++ *±**** +-1+ + I ***
+ + +
++++
+ ++
~~I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—I—
"til Maximize industrial productivity
#¦ Minimize nonlndustrlal property k
"Hf Maximize agricultural productivity "Hf" Maximize forest productivity
"Hf Maximize subsistence activities "Hf Maximize commercial fishery productivity
"ti Maximize outdoor recreation
Figure D. 1.2. Reduced tillage.
D-2
-------
Hierarchy x Contributor Score (equal area) by Service for:
Winter cover
TS
i
+ -H"
, + + ++ +
+ + + ++ ++ ++± T + H
, , f, It, utfttuf;;,++++ !
4- 4-
+ -t+t+ +
++ **
+ t + ++ +* 1
health risks
industrial productivity
nonlndustrlal property l<
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
-Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D. 1.3. Winter cover.
Hierarchy x Contributor Score (equal area) by Service for:
Drainage water management
I .
TS
I
+ +
++ .+++
+ ++
+ + + it++
+ ++*i**=::r ++*+
++
++
+_i_
* tt **+
+ ++*
—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—
"til Maximize Industrial productivity
#¦ Minimize nonindustr&l property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D. 1.4. Drainage water management.
D-3
-------
Hierarchy x Contributor Score (equal area) by Service for:
Land retirement for conservation
+ + +
++ +
+ 4- + + + J-->- +t+^
5J
I
+ .
~ **
+ *±++
+ +
+ T + +
+++
+ + t t
+
+
+ ++ H
+
E ++++5
E±+i
+ :
¦K+
± 1
; *+i+;
if
health risks
industrial productivity
nonlndustrlal property l<
"Hf Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
¦Hf Maximize forest productivity
-Ht" Maximize commercial fishery productivity
Figure D. 1.5. Land retirement for conservation.
Hierarchy x Contributor Score (equal area) by Service for:
Wetland restoration
I .
TS
I
t
+ +
+ ++ +++ + +++
T++
+
+
+**$+** **+* **%**+~+++
+^+tt-+++
++
++++ +* ++++
++ + ++ + +
4= . ¥¥+¥+¦
=++
¦ + + ± +± -
++ +
+
"til Maximize Industrial productivity
#¦ Minimize nonindustrial property k
"Hf Maximize agricultural productivity
"Hi- Maximize subsistence activities
"Hf Maximize forest productivity
"HI- Maximize commercial fishery productivity
Maximize outdoor recreation
Figure D. 1.6. Wetland restoration.
D-4
-------
Hierarchy x Contributor Score (equal area) by Service for:
Wetland creation
I .
Tt
i
+++
+++$ ++++ +$ $
+ +. +. I ±
a.+ +J.+ +±,
++ ++++++ + + x
+ + + +++*++
+ +*+ ++ + +++++++++++
+ +± ± +,+ +++* +*++t±^
+
*
¦m
arfi -
+++
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—
¦ttf Maximize agricultural productivity "Hf Maximize forest productivity
"Hi- Maximize subsistence activities ~H+- Maximize commercial fishery productivity
"W" Maximize outdoor recreation
"H Maximize industrial productivity
#¦ Minimize nonlndu&trtal property k
Figure D. 1.7. Wetland creation.
Hierarchy x Contributor Score (equal area) by Service for:
Contouring/ terracing
TS
I
+ +
++
+ + + +
+ + ++
+ * **
»~ iiiltu
"Hf Minimize health risks
~H I Maximize industrial productivity
-Hf Minimize nonlndu&trtal property h
-Hf Maximize agricultural productivity
-Hf Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D. 1.8. Contouring/ terracing.
D-5
-------
Hierarchy x Contributor Score (equal area) by Service for:
Riparian forest buffer
I .
Tt
i
+
+
++ +
+ t
++ +
++
++
+ + +;£ +
++++
+\ i
4 + +++++
:* I ttiiii .
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r
¦ttf Maximize agricultural productivity Maximize forest productivity
"Hi- Maximize subsistence activities ~H+- Maximize commercial fishery productivity
"W" Maximize outdoor recreation
"H Maximize industrial productivity
#¦ Minimize nonlndu&trtal property k
Figure D. 1.9. Riparian forest buffer.
Hierarchy x Contributor Score (equal area) by Service for:
Grassed waterways
TS
I
health risks
industrial productivity
nonlndustrlal property t<
"Hf Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hi" Maximize commercial fishery productivity
Figure D. 1.10. Grassed waterways.
D-6
-------
Hierarchy x Contributor Score (equal area) by Service for:
Floodpfain conservation easement
+ , ++ +++ 4
+ ± + ++ ?*+ + + +
-g + ++ + + +++++ ++T+T +
1 -»+ + + J,+I + $+4- * * + +
1 ±±+Z * +++ * + T+^++ t±+ 4= ± ++++±=1=
1 + +-ti+++*+++±± ^t+ | +*+*$ : +
++ *
~~l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—l—
-H+- Minimize health risks 4H- Maximize agricultural productivity # Maximize forest productivity
"til Maximize industrial productivity "+H" Maximize subsistence activities "HI" Maximize commercial fishery productivity
"Hf" Minimize nonlndu&trtal property kiss 4 Maximize outdoor recreation
Figure D. 1.11. Floodplain conservation easement.
D-7
-------
Appendix D.2. Unweighted HxC Values and Ranges by Service for the MS Scenario.
Hierarchy x Contributor Score (equal area) by Service for:
Nutrient management
Size of circle (radius) indicates Range of H x C Scores
o
° W fP o®°q °' -o- '•
O • 0 -Q 6 ....
**
• .0 O o -< 0.5 O 0.5 -< 1.0 O 10 -< 1J5 O2.0 -< 2.5 O > 2.5
Figure D.2.1. Nutrient management.
Hierarchy x Contributor Score (equal area) by Service for:
Reduced tillage
Size of circle (radius) indicates Range of H x C Scores
(T>- -"cP °8.8
w noo _ on o c
I -D Cfeo ,0* • . * * ¦Cku' CP° ¦ cc o-. _p
0OC ' o
o ' ,-QcP... ¦. ;•
-i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
& ^ ^^y.N
• - 0 o o - < 0.5 OCl5 -< 1.0 O 1.0 -< 15 <>15 -< 2.0
Figure D.2.2. Reduced tillage.
D-8
-------
Hierarchy x Contributor Score (equal area) by Service for:
Winter cover
Size of circle (radius) indicates Range of H x C Scores
f
• - 0 0 -< 0.5 O 0.5 -< 1.0 Ol.O -< 15 015 — < 2.0 O > 2.5
Figure D.2.3. Winter cover.
Hierarchy x Contributor Score (equal area) by Service for:
Drainage water management
Size of circle (radius) indicates Range of H x C Scores
• - 0 O o -< 0.5 O 0-5 -< 1.0 O 10 -< 15 O 15 -< 2.0 <>2.0 -< 2.5
Figure D.2.4. Drainage water management.
D-9
-------
Hierarchy x Contributor Score (equal area) by Service for:
Land retirement for conservation
Size of circle (radius) indicates Range of H x C Scores
f -
vy
• - 0 () 0 -< 0.5 O 0.5 -< 1.0 O 10 -< 1J5 Ol5 -< 2.0 O2j0 -< 2.5
Figure D.2.5. Land retirement for conservation.
Hierarchy x Contributor Score (equal area) by Service for:
Wetland restoration
Size of circle (radius) indicates Range of H x C Scores
. °8 '
¦oo • C 'oo
¦ Oo
* " . o
- 0 O o -< 0.5 O05 -< 1.0 O 10 —< 1.5 O 15 —< 2.0 C>2.0 -< 2.5
Figure D.2.6. Wetland restoration.
D-10
-------
Hierarchy x Contributor Score (equal area) by Service for:
Wetland creation
Size of circle (radius) indicates Range of H x C Scores
• - 0 O 0 —< 0.5 O0-5 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure D.2.7. Wetland creation.
Hierarchy x Contributor Score (equal area) by Service for:
Contouring/ terracing
Size of circle (radius) indicates Range of H x C Scores
• -0 O o-< 05 O 0.5 — < 1jQ O 1.0 -< 1.5
Figure D.2.8. Contouring/ terracing.
D-11
-------
Hierarchy x Contributor Score (equal area) by Service for:
Riparian forest buffer
Size of circle (radius) indicates Range of H x C Scores
• - o () 0 -< 0.5 O 0.5 -< 1.0 O 10 -< 1J5 Ol5 -< 2.0 O2j0 -< 2.5
Figure D.2.9. Riparian forest buffer.
Hierarchy x Contributor Score (equal area) by Service for:
Grassed waterways
Size of circle (radius) indicates Range of H x C Scores
loo-
0
. . o
s'
"3
i
° sm
K o
¦ tO'
CP : f ¦ O
(©p. .©:(S}.
; ..: .lb
\
m
*o
I
O .
o
• - o O 0 0.5 Oo.5 -< 1.0 Ol.o -< 15 Ol5 -< £0 O > 2.5
Figure D.2.10. Grassed waterways.
D-12
-------
Hierarchy x Contributor Score (equal area) by Service for:
Rood plain conservation easement
Size of circle (radius) indicates Range of H x C Scores
O 0 —< 05 O05-<1.0 O 1.0 -< 15 015 —< 2.0
Figure D.2.11. Fioodpiain conservation easement.
D-13
-------
Appendix D.3. Unweighted HxC Values and Uncertainty by Service for the MS Scenario.
Hierarchy x Contributor Score (equal area) by Service for:
Nutrient management
Size of circle (radius) indicates HxC Uncertainty Score
GOO O r-,
° - " "
n-o v)°' ¦ - P-A o ° -ooo "o | O- ;"^o
o- ¦ - ¦ ¦
° :('S') ^ QOOcQtO
# - 0 O o -< 0.5 O 0-5 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure D.3.1. Nutrient management.
Hierarchy x Contributor Score (equal area) by Service for:
Reduced tillage
Size of ctrde (radius) indicates HxC Uncertainty Score
i -I
Q cuff • "O
o
00 o oogQdo
• - 0 o o - < 0.5 O05 -< 1.0 O 1.0 -< 15 <>15 -< 2.0
Figure D.3.2. Reduced tillage.
D-14
-------
Hierarchy x Contributor Score (equal area) by Service for:
Winter cover
Size of circle (radius) indicates H x C Uncertainty Score
f -
I
u
O
O O CT-
o
e-
•6
•o:
O
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
& ^
0-V"
• - 0 O 0 —< 0.5 O0-5 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure D.3.4. Drainage water management.
D-15
-------
Hierarchy x Contributor Score (equal area) by Service for:
Land retirement for conservation
Size of circle (radius) indicates H x C Uncertainty Score
f -
vy
• -0 O 0 — < 05 O 0.5 — < 1j0 O IjO -< 1.5
Figure D.3.5. Land retirement for conservation.
Hierarchy x Contributor Score (equal area) by Service for:
Wetland restoration
Size of circle (radius) indicates H x C Uncertainty Score
- 0 © 0 - < 0l5 O 05 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure D.3.6. Wetland restoration.
D-16
-------
Hierarchy x Contributor Score (equal area) by Service for:
Wetland creation
Size of ctrde (radius) indicates H x C Uncertainty Score
• o oo
- Oo¦ oO 0 •
! -I _ ? oq- oo a a ooco^0
.-.QCO'-p . 0-^ : O ©O
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
& ^
• - o C o -< 05 O05 -< 1.0 O 1JQ -< 1.5
Figure D.3.7. Wetland creation.
Hierarchy x Contributor Score (equal area) by Service for:
Contouring/ terracing
Size of circle (radius) indicates H x C Uncertainty Score
• - 0 0 -< 05 O 0l5 —< 1.0 O 1j0 -< 1.5
Figure D.3.8. Contouring/ terracing.
D-17
-------
Hierarchy x Contributor Score (equal area) by Service for:
Riparian forest buffer
Size of circle (radius) indicates H x C Uncertainty Score
f
Q
:qc
ioi
• ©
o
o
QqQ :g i-Jti
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
• -0 O 0 — < 05 O 0.5 — < 1j0 O IjO -< 1.5
Figure D.3.9. Riparian forest buffer.
Hierarchy x Contributor Score (equal area) by Service for:
Grassed waterways
Size of circle (radius) indicates H x C Uncertainty Score
- 0 © 0 - < 0l5 O 05 -< 1.0 O 1.0 -< 15 015 -< 2.0
Figure D.3.10. Grassed waterways.
D-18
-------
Hierarchy x Contributor Score (equal area) by Service for:
Rood plain conservation easement
Size of circle (radius) indicates H x C Uncertainty Score
,o o*
c> o
o
o
o
^CO ^o@Oq ;
00 oQcrPOoCg?! p
oo:
©
®-
©C-2
0 -< 05 O 0l5 —< 1j0 O 1.0 -< 1.5
Figure D.3.11. Floodplain conservation easement.
D-19
-------
Appendix D.4. Area-weighted HxC Values by Service for the MS Scenario.
Area—weighted Hierarchy x Contributor Score by Service for:
Nutrient management
+ + +
"8
ft
++
+ ++ +
++++
+ +
.+ +a
+ + + +
+ . +
t xt ?++
M+t+«
+ ±*
++
++++t++t++:l:+ + +++++?+++++¥+++
+ +++
+ + +
-Hf Minimize health risks
"til Maximize industrial productivity
-Hf Minimize no Hindus trial property h
-Hf Maximize agricultural productivity
-Hf Maximize subsistence activities
Maximize outdoor recreation
"Hf" Maximize forest productivity
"Hf" Maximize commercial fishery productivity
Figure D.4.1. Nutrient management.
Area—weighted Hierarchy x Contributor Score by Service for:
Reduced tillage
3
ft
?
I
t++t
+ *
r
++
++-
++++
"+++
+
+
+ +
+
+ +
++ +t*+
++T++
+ + + +
| ++ +
+ + + +
+ +
+ ++
+ + +
+
+ +
++ +
++ + ++ +
+ +t+
^.++±±^ 1
t ::
++
i +IUI+"
+
++++
+ ++
"HI Maximize industrial productivity
-ttf Minimize nonlndustrlal property Does
Figure D.4.2. Reduced tillage.
-Hf Maximize agricultural productivity
-Hf Maximize subsistence activities
3 outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
D-20
-------
Area—weighted Hierarchy x Contributor Score by Service for:
Winter cover
3
s
+++
++
t+
+ +
++
++
+ +-
+++
+*++
++++++
+++
+ ++ +++ +++
+ + + , +
+ + + +
+ + +
+.+ . T ++
+++ J++=t+f
-F +++--++
"til Maximize industrial productivity
#¦ Minimize nonindustrial property k
"Hf Maximize
"Hf Maximize
productivity
recreation
-Hf Maximize forest productivity
-Hf Maximize commercial fishery productivity
Figure D.4.3. Winter cover.
Area—weighted Hierarchy x Contributor Score by Service for:
Drainage water management
+ + -t + + + + + + + + + + + + + + + + + + + + + + +¦ + + + 4- + +
¦Hf Minimize health risks
"til Maximize industrial productivity
-Hf Minimize nonlndustrlal property k
"Hf* Maximize agricultural productivity -Hf Maximize forest productivity
"Hf Maximize subsistence activities "Hf Maximize commercial fishery productivity
-H Maximize oufctoor recreation
Figure D.4.4. Drainage water management
D-21
-------
Area—weighted Hierarchy x Contributor Score by Service for:
Land retirement for conservation
3
s
?
I
++ +
+ +!+ "
+ ^±+i**++++++± +t+^o.±+
J**
+ +++
+ ++
*****
*:++
+++
+1 t
+
I ++++]
.
i **3
1 *
: + !
"
+
-
"til Maximize industrial productivity
#¦ Minimize nonindustr&l property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.4.5. Land retirement for conservation.
Area—weighted Hierarchy x Contributor Score by Service for:
Wetland restoration
++ >+++++++++++++++++++++++¦ +++f++
health risks
industrial productivity
nonlndustrlal property k
"Hf Maximize agricultural productivity
"Hf Maximize subsistence activities
Maximize outdoor recreation
-Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.4.6. Wetland restoration.
D-22
-------
Area—weighted Hierarchy x Contributor Score by Service for:
Wetland creation
3
%
"HI Maximize industrial productivity
"Hf Maximize agricultural productivity
"Hf Maximize subsistence activities
"Hf Minimize nonindustrtel property
Figure D.4.7. Wetland creation.
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Maximize outdoor recreation
Area—weighted Hierarchy x Contributor Score by Service for:
Contouring/ terracing
£
"Hf" Minimize health risks "Hf Maximize agricultural productivity
411 Maximize industrial productivity "Hf Maximize subsistence activities
-Hf Minimize nonlndustrlal property toss
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Maximize outdoor recreation
Figure D.4.8. Contouring/ terracing.
D-23
-------
Area—weighted Hierarchy x Contributor Score by Service lor:
Riparian forest buffer
3
%
ilth risks "+H- Maximize agricultural productivity
"HI Maximize industrial productivity "+H- Maximize subsistence activities
"Hf Minimize nonindustrial property loss
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Maximize outdoor recreation
Figure D.4.9. Riparian forest buffer.
Area—weighted Hierarchy x Contributor Score by Service for:
Grassed waterways
"8
s
health risks
industrial productivity
nonlndustrlal property l<
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Ht" Maximize commercial fishery productivity
Figure D.4.10. Grassed waterways.
D-24
-------
Area—weighted Hierarchy x Contributor Score by Service for:
Rood plain conservation easement
3
I +-
„„ +
I ++ *
+ a."*- ^
-H+- Minimize health risks 4H- Maximize agricultural productivity # Maximize forest productivity
"til Maximize industrial productivity "+H" Maximize subsistence activities "HI" Maximize commercial fishery productivity
"Hf" Minimize nonlndu&trtal property loss 4 Maximize outdoor recreation
Figure D.4.11. Floodplain conservation easement.
D-25
-------
Appendix D.5. Sum of Area-weighted HxC Values by Service for the MS Scenario.
Sum of the Area—weighted Hierarchy x Contributor Scores by Service
for the MS Group I Conservation Practices
0
x
1
I
+
+ *
+-'-+++++++
+
+ ++n
s ++t
+ T+++T
i++
++++
+ ++
- Minimize health risks
Maximize Industrial productivity
Minimize nonindustrial property toss
4H- Maximize agricultural productivity
¦+H- Maximize subsistence activities
Maximize outdoor recreation
¦Hf Maximize forest productivity
"ttf~ Maximize commercial fishery productivity
¦+H- Minimize broad—scale risks
Figure D.5.1. MS Group I conservation practices.
Sum of the Area—weighted Hierarchy x Contributor Scores by Service
for the MS Group II Conservation Practices
020
-030
-0*0
-000
-OOO
-0-70
-1.00
-I JO
-i—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—1—r~
¦H+- Minimize health risks 4M- Maximize agricultural productivity -ttt- Maximize forest productivity
+ Maximize industrial productivity "Hf Maximize subsistence activities "tt+- Maximize commercial fishery productivity
"H+* Minimize nonindustrial property toss Maximize outdoor recreation "W- Minimize broad—scale risks
Figure D 5.2. MS Group II conservation practices.
D-26
-------
Appendix D.6. Cost-weighted HxC Values by Service for the MS Scenario.
8 ::
Hierarchy x Contributor / Cost by Service for:
Nutrient management
+ + + =F~
+ +
+ + +
+ + +
"Hf Minimize health risks
"til Maximize industrial productivity
-Hf Minimize nonlndustrlal property h
-Hf Maximize agricultural productivity
-Hf Maximize subsistence activities
Maximize outdoor recreation
"Hf" Maximize forest productivity
"Hf" Maximize commercial fishery productivity
Figure D.6.1. Nutrient management.
Hierarchy x Contributor / Cost by Service tor:
Reduced tillage
+
+
+
+ +
+ + + +
+ ++ ** +
+ +
+ + + +
^ ++ ++±+ 1++
+++ X . *+++ t -
++ +++% ++
*+
++
—+-++H— +++H—
+ +
+++ + ++
+ + +
+
M- +
+ +
++ + #+ +
+ + + + *
+++++ + ++
+ . ++ + + +
+ + + + +
I nss
+
+
++
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
"til Maximize industrial productivity
#¦ Minimize nonlndustrlal property k
"Hf Maximize agricultural productivity
-Hf Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.6.2. Reduced tillage.
D-27
-------
Hierarchy x Contributor / Cost by Service tor:
Winter cover
+ + , +
¦+ +
+
+ +
+ +
" + + ++++ ++ + +tt It 1
+ +4. ?+ ++ +++ + + J.J. +a.4-J_
+ ++ + + ^ -r -r
+ + + +++++++ + ++++ + +++++++4-+++
++,+++ * + ^+* +
+ ** + + + +
:+
++
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
"til Maximize industrial productivity
#¦ Minimize nonindustr&l property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.6.3. Winter cover.
Hierarchy x Contributor / Cost by Service for:
Drainage water management
I =
+ +
+ ++
++ ++
+
++n
* +
+++ + -
++
+
+
++
+
+ + +t+
. + ++
+ .+ ++ +
+ + + +
¦+ ::
++++ +
ijxxm
4- : +
+ +
+ + + + ^
+ .
+++++++++
+ +
+ 4 + +
¦Hf Minimize health risks -Hf Maximize agricultural productivity -Hf Maximize forest productivity
I Maximize industrial productivity "Hf Maximize subsistence activities "Hf Maximize commercial fishery productivity
-Hf Minimize nontndustr&l property toss Maximize outdoor recreation
Figure D.6.4. Drainage water management.
D-28
-------
Hierarchy x Contributor / Cost by Service for:
Land retirement for conservation
+
+ +
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
"til Maximize industrial productivity
#¦ Minimize nonindustr&l property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.6.5. Land retirement for conservation.
Hierarchy x Contributor / Cost by Service for:
Wetland restoration
¦Hf Minimize health risks "Hf Maximize agricultural productivity -Hf Maximize forest productivity
"Hf Maximize industrial productivity "Hf Maximize subsistence activities "Hf Maximize commercial fishery productivity
"Hf Minimize inonlnd us trial property loss "Hf Maximize outdoor recreation "Hf Minimize broad—scale risks
Figure D.6.6. Wetland restoration.
D-29
-------
Hierarchy x Contributor / Cost by Service for:
Wetland creation
. ++++**
+++
+ + ± ,+
i±$*+*±±±$+¥ti *+*+* ****** iiii+*
+ *+ $+± +++ t M ^ I ?-¦«
-1—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—i—r~
"til Maximize industrial productivity
#¦ Minimize nonindustrial property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.6.7. Wetland creation.
Hierarchy x Contributor / Cost by Service for:
Contouring,/ terracing
I
1 =
+ ++
+ + ++
+
twt 11+ t+tntfrT i+t:+t+++V:
vy
¦Hf Minimize health risks -Hf Maximize agricultural productivity -Hf Maximize forest productivity
I Maximize industrial productivity "Hf Maximize subsistence activities "Hf Maximize commercial fishery productivity
-Hf Minimize nonlndustrlal property toss -f Maximize outdoor recreation "Hf Minimize broad—scale risks
Figure D.6.8. Contouring/ terracing.
D-30
-------
Hierarchy x Contributor / Cost by Service for:
Riparian forest buffer
"til Maximize industrial productivity
#¦ Minimize nonindustr&l property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.6.9. Riparian forest buffer.
Hierarchy x Contributor / Cost by Service for:
Grassed waterways
+++++++++--+^++-++++++*++£ 4=+¦*•*+++«
¦Hf Minimize health risks "Hf Maximize agricultural productivity -Hf Maximize forest productivity
"Hf Maximize industrial productivity "Hf Maximize subsistence activities "Hf Maximize commercial fishery productivity
"Hf Minimize inonlnd us trial property loss "Hf Maximize outdoor recreation "Hf Minimize broad—scale risks
Figure D.6.10. Grassed waterways.
D-31
-------
Hierarchy x Contributor / Cost by Service for:
Flood plain conservation easement
"til Maximize industrial productivity
Minimize nonindustrial property k
"Hf Maximize agricultural productivity
"H4~ Maximize subsistence activities
Maximize outdoor recreation
"Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
Figure D.6.11. Floodplain conservation easement.
D-32
-------
Appendix D.7. Sum of Cost-weighted HxC Values by Service for the MS Scenario.
Sum of the Hierarchy x Contributor / Cost Scores by Service
lor the MS Group I Conservation Practices
+++ +
+++
+ +
++ + + ,+++
i+ ++ +i+ *+ r
+ + +
-++++++
++
++
+
+
+
+ ++ +
$ ++|+t*Ji§* *
++:: + ?+++
-- ++++-M:++
-Hf Minimize health risks
"H ! Maximize industrial productivity
-Hf Minimize nonindustrial property toss
-Hf Maximize agricultural productivity
+H- Maximize subsistence activities
Maximize outdoor recreation
-Hf Maximize forest productivity
"HI Maximize connmercfcal fishery productivity
i broad—scale risks
Figure D.7.1. MS Group I conservation practices.
Sum of the Hierarchy x Contributor / Cost Scores by Service
for the MS Group ii Conservation Practices
-++ t
+ + , ++,
+ ++ . . + ±±+ ++ 4. ++
"5* Ife
V ' ^ ^
-Hf Maximize agricultural productivity
"Hf Maximize subsistence activities
"Hf Maximize outdoor recreation
Figure D.7.2. MS Group II conservation practices.
¦Hf Minimize health risks
"H Maximize industrial productivity
"Hf Minimize nonindustrial property loss
¦Hf Maximize forest productivity
"Hf Maximize commercial fishery productivity
"Hf Minimize broad—scale risks
D-33
-------
Appendix E. Plots by Scenario-related Change for each First Level Hierarchy Value
Appendix E.l. Unweighted HxC Values by Scenario-related Change.
Appendix E.2. Area-weighted HxC Values by Scenario-related Change.
Appendix E.3. Cost-weighted HxC Values by Scenario-related Change, MS only.
Appendix E.4. Cost-weighted HxC Values by Scenario-related Change, Omitting Nutrient
Management.
E-1
-------
Appendix E.l. Unweighted HxC Values by Scenario-related Change.
Hierarchy x Contributor Score by Scenario—related Change to
Minimize health risks
/ / / / / / / / / /
*/////// V//V////
~' v s/ /
-Hf Land cover (Illness) -Hf- AQ (hea HJi)
-Hf WQ (Illness)
FkJ mod (health)
Figure E. 1.1. Minimize health risks.
Hierarchy x Contributor Score by Scenario—related Change to
Maximize agricultural productivity
t
t
+
*
*
*
*
;
+
+
+ *
* i t
+
i
i
+
* ±
+
+ J
± *
+ $
+
*
+
¥
+ +
* t
$ i
*
t
-------
Hierarchy x Contributor Score by Scenario—related Change to
Maximize forest productivity
+
'•* + + + *
+ 1 + + =F
+ -fc +
: * +
t
+
J + J J * | 1
+ +
+
-------
Hierarchy x Contributor Score by Scenario—related Change to
Maximize subsistence activities
"HI" Aqua hab (subs) "Hf Native spp (subs) "Hf WM hab (sufce)
Figure E.1.5. Maximize subsistence activities.
Hierarchy x Contributor Score by Scenario—related Change to
Maximize commercial fishery productivity
+
+
+
+ +
+ *
+ +
4=
+
+ +
+
*T?
+ +
*
+
+
i t *
+
+ 4=
+ X*
//// // ///////
"/////// SSV//V////
~
/ y s s
Aqua hab (GL)
Figure E.1.6. Maximize commercial fishery productivity.
E-4
-------
Hierarchy x Contributor Score by Scenario—related Change to
Minimize nonindustrial property loss
+
+
-+ + +
+
-+-
+
+ t
+
+
*
t
+
+
+
+
t
+
s s ss////////
*/////// y '//S ss/s/
~ * y / / y * y * ~
"Hf Rd mod (non Ind)
Figure E.1.7. Minimize nonindustrial property loss.
Hierarchy x Contributor Score by Scenario—related Change to
Maximize outdoor recreation
¦+H- AQ {visibility) -Hf- Aqua hab (recr) "Hf WQ (boat) -Hf- Water ami (boat) -Hf- Wtf hab (hunt)
"Hf WB hab (spp vtew) -Hf WB hab (com view) "Hf Landscape (boat) "Hf Landscape (hiking)
Figure E. 1.8. Maximize outdoor recreation.
E-5
-------
Hierarchy x Contributor Score by Scenario—related Change to
Minimize broad—scale risks
"ttf Biofuel prod # Food prod (gbl) # AQ (ox port) "W+- WQ {export}
¦Hf Carbon storage -+H" WM hab (spp gbl) "Hf WH hab (com gbl)
Figure E. 1.9. Minimize broad-scale risks.
E-6
-------
Appendix E.2. Area-weighted HxC Values by Scenario-related Change.
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Minimize health risks
* i *i
t
! 1 L ¦. + # i M
+ | + + + * T ¥ -¥- T
1
j —oo
$
£
//////////////////
^ ^ ^ ^ /
/
>'
-Hf Land cover (I II rtees) -Hf- AQ (heath)
Figure E.2.1. Minimize health risks.
-Hf WQ (Illness)
FkJ mod (health)
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Maximize agricultural productivity
±ii + " i i i ^ 11
* i * +
++
*
i+
*
+1
+
~ / s J >
"v° V®
-------
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Maximize forest productivity
~ I
* -
M j »
i
+
+
T 1 1 1 1 I 1 1 1 1 I 1 1 1 1 I 1 1 1 1 1 1-
> ./
SP «*°
'//- '//// '///''/¦>/
"Hf Forest raver "W- AQ (forest} #" WQ ((or) "Hf Fid mod (for) "Hf- Water amt (for)
"Hf Soli prod (for) "Hf Soil stability (for) "Hf Bene Insets (for) "Hf Veg diversity
Figure E.2.3. Maximize forest productivity.
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Maximize industrial productivity
1
j -ou
$
+ i m
t ii I
+ +
MM
//* V///////////s///
YSA&/S //s *s/sy
J * J jt j? tr * «"» jf
° & y / * /
" WQ flnd) 4W- Fid mod flnd) "Hf Water amt (ind)
Figure E.2.4. Maximize industrial productivity.
E-8
-------
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Maximize subsistence activities
i 4 ± j-
+
11
t:
+1
X -ox
s -a
} -
8
4
¦/ > j?
/ V c/y //
" Native spp (subs) "Hf WM hab (suibs)
Figure E.2.5. Maximize subsistence activities.
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Maximize commercial fishery productivity
+
+ + + |
i f i i i + t t * t
+ t
+
+
+ * i
1
j -ou
S -«
//S.s///////////////
//'//// /ys/'SS/s'
J * J jt j? tr ^ ^ J?
° ^ ^ /
-HI-Aqua hab (GL)
Figure E.2.6. Maximize commercial fishery productivity.
E-9
-------
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Minimize nonindustriat property loss
* + +
+ *
-± 1—
+ + t + * t
SS/S / / / / /// / / / / s / / /
# / y .X > > y J / y s s s s * * " - •
-------
Area—weighted Hierarchy x Contributor Score by Scenario—related Change to
Minimize broad—scate risks
"ttf Biofuel prod # Food prod (gbl) # AQ (ox port) "W+- WQ {export}
¦Hf Carbon storage -+H" WH hab (spp gbl) "Hf WH hab (com gbl)
Figure E.2.9. Minimize broad-scale risks.
E-11
-------
Appendix E.3. Cost-weighted HxC Values by Scenario-related Change, MS only.
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Minimize health risks
| * $ +—+—+—$—+—+—+-
///////////////////
^ ^ ^ ^ /
-+H- Land cover (Illness) "Hf" AQ (heath)
Figure E.3.1. Minimize health risks.
^ y
-Hf WQ (Illness)
Fid mod (health)
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize agricultural productivity
l ~ i ± ^—+—+—+-
X / y s y y y y y y y y y y y / y / y
y//yyyy//
s * y y / y * y y
V Ag cover -Hf AQ (ag) +H- WQ (ag) 4H- Fid mod (crops)
h Water ami (ag) ~H+- Soil prod (ag) "Hi- Soli stability (ag) 4H- Bene Insets (ag)
Figure E.3.2. Maximize agricultural productivity.
E-12
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize forest productivity
-+—+—+
~ / s J j
"v° V®
'/''¦¦'//// " V/"'/'/
"Hf Forest raver "Hi- AQ (forest} "HI" WQ ((or) "Hf Fid mod (for) "Hf- Water amt (for)
"Hf Soli prod (for) "Hf Soil stability (for) ~Hf" Bene Insets (for) "Hf Veg diversity
Figure E.3.3. Maximize forest productivity.
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize industrial productivity
t * i
-+—+—+—t—+—+—+-
///////////////////
//'//// s/s ' s//y
J * J jt j? tr * «"» jf
° & y / * /
" WQ flnd) 4W- Fid mod flnd) "Hf Water amt (irtd)
Figure E.3.4. Maximize industrial productivity.
E-13
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize subsistence activities
1000
+
sr.®
IW
+
IKO
1000
+
»«*>
+
0*00
+
0.T®
+
0*00
+
E
* * £
O.KO
t i i i't + t t 1 + +
T ¦ ¦ i T T T T T ~
-.too
i '
-MO
+
+
-w
+
-000
-»00
///77/7777
/ v //
• V>V / / /V
^ ^ • *¦
~
^ y *
^ / /
¦Hf Aqua hab (subs) "Hf" M
" Kative spp (subs) "Hf WM hab (sufce)
Figure E.3.5. Maximize subsistence activities.
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize commercial fishery productivity
; tf ;
t * l\ +
-+—+—+ i +
///////////////////
//'//// s/s ' s//y
° ^ ^ /
"Hf" Aqua hab (GL)
Figure E.3.6. Maximize commercial fishery productivity.
E-14
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Minimize nonindustriat property loss
-+—* + * ! +—+—+-
—+—+—+
SS/S / / / / /// / / / / s / / /
# / y .X > > y J / y s s s s * * " - •
* y
"Hf AQ {vfcfcility) -Hf Aqua hab (recr) -Hf WQ {boat} "Hf Water amt {boat) "Hf Wlf hab (hunt)
-Hf WK hab (spp vtew) -Hf WK hab (com view) "Hf Landscape (boat) "Hf Landscape (hiking)
Figure E.3.8. Maximize outdoor recreation.
E-15
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Minimize broad—scale risks
i * i * + * + + + +
s s,/./
SP «*°
-------
Appendix E.4. Cost-weighted HxC Values by Scenario-related Change, Omitting Nutrient
Management.
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Minimize health risks
+
+
+
~r
$
-+ +
=1= *
I
+
+
ux
+ +
* i ~ t
,.'/////////// // /////
//' //// ' y
S * /
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize forest productivity
+
+
*
$
*
+
+
+
?
+
~ / s J j
"v° V®
'/''¦¦'//// " V/"'/'/
"Hf Forest raver "Hi- AQ (forest} "HI" WQ ((or) "Hf Fid mod (for) "Hf- Water amt (for)
"Hf Soli prod (for) "Hf Soil stability (for) ~Hf" Bene Insets (for) "Hf Veg diversity
Figure E.4.3. Maximize forest productivity.
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize industrial productivity
+ 1
+ 1
+ 1
+ i +
* * +
+
///////////////////
//'//// s/s ' s//y
J * J jt j? tr * «"» jf
° & y / * /
-IH-WO flnd) "Hf Fid mod flnd) -Hf- Water amt (irtd)
Figure E.4.4. Maximize industrial productivity.
E-18
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Maximize subsistence activities
+
+
+
t |
* ;;
*
+
+
+
+
*
»*
'///777/
/ v //
^ s j*
*
.s y /
// /V
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Minimize nonindustriat property loss
+ * + $
SS/S / / / / /// / / / / s / / /
# / y .X > > y j / y s s s s * * " - •
-------
Hierarchy x Contributor Score / Cost by Scenario—related Change to
Minimize broad—scale risks
t t I
+
*
+
*
+
i |:t
f
///////////////////
'"'//// 'S '/// ss///
* * y x -6 *
~
y
# Biofuel prod # Food prod (gbl) # AQ (export) "Hf" WO {oxport}
¦Hf Carbon storage "Hf WM hab (spp gbl) 4H- WM hab (com gbl)
Figure E.4.9. Minimize broad-scale risks.
E-21
-------
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United States
Environmental Protection
Agency
Office of Research
and Development (8101R)
Washington, DC 20460
Official Business
Penalty for Private Use
$300
EPA/600/R-09/134
September 2009
www.epa.gov
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