multiple-criteria decision analysis for integrated catchment
TRANSCRIPT
Faculty of Business and Law
SCHOOL OF ACCOUNTING, ECONOMICS AND FINANCE
School Working Paper - Economic Series 2006
SWP 2006/30
Multiple-Criteria Decision Analysis for Integrated Catchment Management
Tony Prato University of Missouri-Columbia, USA
and
Gamini Herath
Deakin University
The working papers are a series of manuscripts in their draft form. Please do not quote without obtaining the author’s consent as these works are in their draft form. The views expressed in this paper are those of the author and not necessarily endorsed by the School.
Multiple-Criteria Decision Analysis for Integrated Catchment Management
Tony Prato and Gamini Herath
University of Missouri-Columbia, USA, and Deakin University, Victoria, Australia
ABSTRACT Implementation of Integrated Catchment Management (ICM) is hampered by the lack of a
conceptual framework for explaining how landowners select farming systems for their
properties. Benefit-cost analysis (a procedure that estimates the costs and benefits of alternative
actions or policies) has limitations in this regard, which might be overcome by using multiple-
criteria decision analysis (MCDA). MCDA evaluates and ranks alternatives based on a
landowner’s preferences (weights) for multiple criteria and the values of those criteria. A MCDA
approach to ICM is superior to benefit-cost analysis which focuses only on the monetary benefits
and costs, because it: 1) recognizes that human activities within a catchment are motivated by
multiple and often competing criteria and/or constraints; 2) does not require monetary valuation
of criteria; 3) allows trade-offs between criteria to be measured and evaluated; 4) explicitly
considers how the spatial configuration of farming systems in a catchment influences the values
of criteria; 5) is comprehensive, knowledge-based, and stakeholder oriented which greatly
increases the likelihood of resolving catchment problems; and 6) allows consideration of the
fairness and sustainability of land and water resource management decisions. A MCDA based on
an additive, multiple-criteria utility function containing five economic and environmental criteria
was used to score and rank five farming systems. The rankings were based on the average
criteria weights for a sample of 20 farmers in a US catchment. The most profitable farming
system was the lowest-ranked farming system. Three possible reasons for this result are
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evaluated. First, the MCDA method might cause respondents to express socially acceptable
attitudes towards environmental criteria even when they are not important from a personal
viewpoint. Second, the MCDA method could inflate the ranks of less profitable farming systems
for the simple reason that it allows the respondent to assign non-zero weights to non-economic
criteria. Third, the MCDA might provide a better framework for evaluating a landowner’s
selection of farming systems than the profit maximization model.
Keywords:
Integrated catchment management
Multiple-criteria analysis
Benefit-cost analysis
Ranking farming systems
1. Introduction
Managing land and water resources in an economically and ecologically sustainable manner is
complex because natural resources are limited, there is competition for the use of natural
resources, and there are multiple and interacting sources of land and water degradation. Since the
catchment is recognized as an appropriate scale for natural resource planning and management
(MacKenzie, 1997), integrated catchment management (ICM) has emerged as the major
paradigm for managing land and water resources. Implementation of ICM is hampered by the
lack of a conceptual framework for explaining how landowners select land and water resource
management systems (LWRMS) for their properties. A LWRMS is a spatial pattern of land uses,
soil/water conservation methods, and nutrient/chemical management practices for a property or
catchment.
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Multiple criteria decision analysis (MCDA) provides a suitable conceptual framework for
evaluating landowner selection of LWRMS. In MCDA, a decision-maker, such as a landowner
or catchment manager, evaluates alternative management systems based on their preferences for
and values of multiple criteria. The best system is the one providing the most preferred
combination of criteria. MCDA has been used or proposed for water systems analysis (Haimes
and Hall, 1974), environmental management (Bakus et al., 1982; Janssen, 1992), food security
(Haettenschwiler, 1994), forest management (Kangas and Kuusipalo, 1993; Kangas, 1994;
Penttinen, 1994), agricultural production (Xu et al., 1995; Strassert and Prato, 2002), natural
areas (Anselin et al., 1989; Gehlbach, 1975; Sargent and Brande, 1976; Smith and Theberge,
1986; Smith and Theberge, 1987), regional water quality analysis (Makowski et al., 1995),
management of agroecosystems (Prato et al., 1996a), wildlife management (Prato et al., 1996b)
and soil and water resource management (Prato, 1998).
MCDA approaches to ICM avoid many of the limitations of single-criterion, efficiency-based
approaches, such as benefit-cost analysis (BCA). A MCDA approach to ICM is superior to BCA
for several reasons:
It recognizes that human activities within a catchment are motivated by multiple and
often competing objectives and/or constraints, such as maximizing economic returns,
reducing soil erosion and water pollution, reducing flood damages, and protecting fish
and wildlife habitat.
It does not require monetary valuation of criteria, as does BCA.
It allows measurement and evaluation of the trade-offs between criteria.
It explicitly considers how the spatial configuration of LWRMS in a catchment
influences the values of criteria.
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It is comprehensive, knowledge-based and stakeholder oriented, which greatly increases
the likelihood of resolving catchment problems.
It allows consideration of the fairness and sustainability of land and water resource
management decisions (Costanza and Folke, 1997).
This paper discusses the basic elements of MCDA and applies the approach to a catchment in the
United States.
2. Catchment Approach
One of the best ways to implement ICM is through a community-based approach that empowers
people to make informed management decisions. A top-down approach is unappealing to
landowners and rural communities because it generally provides results and recommendations
that lack practical significance and broad-based community support. Lee and Stankley (1992)
indicate that, “Large-scale (regional) ecological systems can be most effectively regulated by
small-scale (local) social organizations.” Naiman et al. (1997) state that, “… watershed
management demands unparalleled cooperation between citizens, industry, governmental
agencies, private institutions, and academic organizations.” Local social organizations and
cooperation require decentralized decision-making. Because BCA is a top-down evaluation
technique which does not use individual preferences, it is less compatible with community-based
decision making than MCDA (Cameron, 1997).
In many areas of the world, catchment alliances have formed to reduce the adverse cumulative
ecological effects of land use and water resource management. Alliances typically include a wide
range of stakeholders such as landowners, federal and state resource management agencies,
commodity and environmental groups, local government, private industry and others. The basic
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premise underlying the formation of catchment alliances is that assessments of sustainable
resource management and the design of policies to alleviate unsustainable resource management
should occur at the local level. A catchment alliance can utilize MCDA to evaluate the social,
economic, and ecological sustainability of resource management (Prato and Hajkowicz, 1999). If
a catchment alliance or environmental authority determines that resource management is not
sustainable, then it is appropriate for them to evaluate alternative policies (education, technical
assistance, and economic incentives) that encourage sustainable resource management.
3. Previous Applications
MCDA can be implemented using a variety of methods including multiple-criteria utility
functions, Ideal Point, Electre, goal programming, analytical hierarchy process, benefit–cost
analysis, and others (Janssen, 1992). These methods differ in terms of how the decision-maker’s
preferences for criteria are measured and the way preference information is used to rank
alternatives. Because of its simplicity and relevance to real world problems (Keeney and Raiffa,
1976), the following additive multiple-criteria utility function is frequently used to evaluate and
rank alternatives (Yakowitz et al., 1993; Foltz et al., 1995; Tecle et al., 1995; Prato, 1999):
(1)
where vk is the utility score for the kth LWRMS, srk is the standardized value of the rth criterion
(r = 1 ,…, R) with the kth LWRMS (0 ≤ srk ≤ 1), wr is the weight for the rth criterion, and ∑=
R
1r
wr
= 1. vk is a simple weighted additive sum of standardized criteria. An additive utility function
,swv1
rkk r∑=
=R
r
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implies that criteria are mutually utility independent, or that the marginal utility of one criterion
does not depend on the amounts received of all other criteria.
Criteria weights are typically estimated using three methods: fixed-point scoring, paired
comparisons, and judgment analysis. Fixed-point scoring requires the decision-maker to allocate
100 percentage points among the criteria. Criteria weights are set equal to the percentage points.
The higher the weight assigned to a criterion, the greater its importance. Fixed-point scoring
forces the decision-maker to consider trade-offs among criteria because it is not possible to
assign a higher weight to one criterion without reducing the weight assigned to one or more of
the other criteria. Paired comparisons are made using the analytic hierarchy process or AHP
(Saaty, 1987). AHP is a method for deriving quantitative weights for criteria based on the
decision-maker’s qualitative comparison of all pairs of criteria. In making comparisons, the
decision-maker evaluates the degree to which one criterion is more, less, or equally important
relative to another criterion on a scale of 1 to 9, where 1 designates equally important and 9
indicates extremely more important. Judgment analysis is a statistical method for estimating
criteria weights (Cooksey, 1996). It requires the decision-maker to score the feasible alternatives
on a scale of 1 to 100 based on criteria values specified by the decision analyst (person assisting
the decision-maker in doing MCDA). The relative importance of each criterion is estimated
using a multiple linear regression equation in which the scores for alternatives are regressed on
the corresponding values of the criteria values for that alternative. Criteria weights are given by
the standardized regression coefficients. Other MCDA methods used to evaluate alternatives
including the surrogate worth tradeoff method (Haimes and Hall, 1974, 1977), free iterative
search (Tecle et al., 1994), the Aspiration-Reservation Based Decision Support System (Fischer
et al., 1996; Makowski, 1994) and the balancing and ranking method (Strassert and Prato, 2002).
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4. Study area
The study area is the Goodwater Creek catchment in northcentral Missouri, USA depicted in
Figure 1. Crops grown in the watershed include wheat, sorghum, soybeans, and corn. Extensive
uses of fertilizers and/or herbicides on these crops and associated impacts on drinking water
quality and aquatic ecosystems have generated considerable local and regional concern. Of
particular concern is atrazine and nitrate-nitrogen contamination of surface water. Atrazine is a
white, crystalline solid organic compound used to control broadleaf and grassy weeds. Atrazine
concentrations in surface water in Goodwater Creek indicated have exceeded the maximum
contaminant level (MCL) for drinking water of 3 ppb established by the Environmental
Protection Agency. Although water from Goodwater Creek is not used for drinking water, the
creek flows into the Mark Twain reservoir which is a major source of drinking water for
communities in northeast Missouri (Heidenreich et al., 1996). Nitrate-nitrogen concentrations in
Goodwater Creek have not exceeded the drinking water MCL of 10 ppm. However, use of
commercial fertilizers in Goodwater Creek watershed and other midwestern watersheds have
contributed to low oxygen concentrations (hypoxia) in portions of the Gulf of Mexico (Nelsen et
al., 1994). Hypoxia is lethal to fish and other marine organisms. Nitrate-nitrogen contamination
of surface water also degrades inland aquatic ecosystems.
5. Procedures
Five farming systems were scored and ranked using the additive, multiple-criteria utility function
given in equation 1. Each farming system was characterized in terms of crop rotation, tillage
method, fertilizer application rate, and pesticide application rate (see Table 1). Use of equation
(1) requires data on the average values of the criteria for the five farming systems and average
criteria weights. Five economic and environmental criteria were included in the utility function:
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increasing net return (NR); reducing economic risk (RI); improving drinking water quality
(DW); enhancing aquatic ecosystems (AE); and reducing soil erosion (SE). Economic criteria
(NR and RI) were selected because farmers must earn a reasonable income from farming in order
to stay in business. Drinking water quality (DW), aquatic ecosystems (AE), and soil erosion (SE)
criteria were selected because many nonfarm groups and environmental agencies are concerned
about the human and environmental health impacts of farming systems. Average criteria values
were calculated using the simulated values of the criteria determined by Wu (1994). Criteria
weights were determined based on information obtained in a survey of 20 farmers in Goodwater
Creek catchment. The survey employed three criteria weighting methods: fixed-point scoring,
paired comparisons (Saaty, 1987), and judgment analysis (Cooksey, 1996). Criteria weights were
averaged over the three methods. Hajkowicz and Prato (1998) give more details on the
application of the MCDA methods to the results of the farm survey.
6. Results
Table 2 lists the average values of the five criteria for the five farming systems. Estimated
average weights for the five criteria were wNR = 0.339, wSE = 0.261, wRI = 0.165, wDW = 0.157,
wAE = 0.079. Since wNR is the largest weight, the average farmer in Goodwater Creek catchment
considers net return to be the most important criterion for selecting a farming system. Increasing
net return (profit) is 1.3 times more important than reducing soil erosion, approximately twice as
important as reducing economic risk and improving drinking water quality, and more than four
times as important as enhancing aquatic ecosystems.
Based on equation (1), utility scores for the five farming systems were: v4 = 0.63, v3 = 0.49, v5 =
0.42, v2 = 0.38, and v1 = 0.35. These scores imply the following ranking of the five farming
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systems: FS4 > FS3 > FS5 > FS2 > FS1. Therefore, FS4 (corn-soybean rotation, reduced tillage,
medium fertilizer application, and banded pesticide application) is the top-ranked farming system
based on the results of the MCDA.
7. Discussion
Results for the MCDA indicate that the top-ranked farming system in Goodwater Creek
catchment is not the farming system that maximizes profit. FS1 maximizes profit, but FS4 has
the highest utility score. Furthermore, even though net return (profit) has the highest average
weight, FS1 is the lowest ranked system based on the MCDA model. There are three possible
interpretations of these results.
First, the MCDA results may not accurately reflect the private interests of farmers. This
interpretation is suggested by the fact that, of the five farming systems, FS1 is closest to the
actual farming system used in the Goodwater Creek catchment. In completing the survey,
farmers might have felt the need to express socially acceptable attitudes regarding the
importance of environmental criteria even though they might not consider them to be important
in terms of their farming operation. This form of behavior has been documented for other survey
methods such as the contingent valuation method. Survey respondents for contingent valuation
surveys tend to express their willingness-to-pay or willingness-to-accept compensation for a
good or service from the viewpoint of a concerned citizen rather than as a consumer or user of
that good or service (Sagoff, 1988). Cameron (1997) commented that some respondents in a
survey of willingness-to-pay for improved water quality in the Hawkesbury-Nepean catchment
in Sydney, Australia stated that the payments they would be willing to make were symbolic and
like a donation to a worthy cause. This interpretation does not disqualify using MCDA for
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gauging the private interests of farmers. Rather, it raises the possibility that MCDA and other
valuation methods may give results that reflect the respondents’ social attitudes toward the
alternatives being compared.
If the responses of surveyed farmers in Goodwater Creek catchment reflected socially acceptable
attitudes toward environmental quality, then there is a greater likelihood that farmers inflated the
weights assigned to environmental criteria (soil erosion, drinking water quality, and aquatic
ecosystems). Inflation of environmental weights could result in a higher ranking of farming
systems that generate higher environmental values. This phenomenon is more likely to occur
with the fixed-point scoring and paired comparisons methods because they require farmers to
directly reveal their preferences for criteria. It is less likely with Judgment Analysis because
criteria weights estimated with this method are based on the scores assigned to farming systems.
In other words, it is easier for farmers to ignore environmental criteria without appearing socially
irresponsible with Judgment Analysis than with the fixed-point scoring and paired comparisons
methods. Despite this argument, there is very little difference in the relative importance of
criteria and a negligible difference in the ranking of farming systems with the three criteria
weighting methods. If rankings obtained with the MCDA are considered to be unreliable based
on the first reason, then it is inappropriate to use MCDA to evaluate the merits of subsidies for
encouraging farmer adoption of conservation practices.
A second interpretation is that the MCDA method inflates the ranks of less profitable farming
systems for the simple reason that it allows respondents to assign non-zero weights to non-
economic criteria. This possibility exists even when respondents base their evaluation of criteria
or farming systems on purely private motivations; that is, when the first interpretation is not
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relevant. The second interpretation is more likely when there are trade-offs between the
economic and environmental criteria and when the number of economic criteria is greater than
the number of non-economic criteria. Although the first condition is satisfied for the five farming
systems evaluated here, the second condition is not.
One way to reduce the likelihood of conditions that favor the second interpretation is to use an
iterative procedure. Such a procedure allows decision-makers to examine how their responses to
survey questions influence the ranking of farming systems. Specifically, the ranking of farming
systems implied by a particular set of weights for criteria is shown to the respondent. If the
respondent does not agree with the ranking, then s/he is allowed to revise the criteria weights
until an acceptable ranking of farming systems is obtained. For example, the Aspiration-
Reservation Based Decision Support System (ARBDSS) is an MCDA procedure that utilizes an
iterative approach (Fischer et al., 1996; Makowski, 1994).
This study did not use an iterative approach because it was not feasible to determine the ranking
of farming systems until after survey responses were enumerated, and it was not feasible to re-
convene the respondents after the first survey. An iterative approach can be implemented using a
computerized decision support system that allows the decision-analyst to provide immediate
feedback to the decision-maker on how revealed preferences for criteria affect criteria weights
and the ranking of farming systems.
A third interpretation of differences in the ranking of farming systems is that the MCDA
provides a better framework for evaluating the selection of farming systems than the profit
maximization model. This interpretation is suggested by the fact that farmers assigned significant
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weight to the noneconomic criteria (0.40 on average). Furthermore, if the MCDA provides a
better framework than the profit maximization model, then using the latter could distort
assessments of the effects of conservation subsidies on the selection of farming systems. In this
case, it is prudent to base the design and evaluation of conservation subsidies for agricultural
management practices on MCDA.
8. Conclusions
Conventional economic approaches to evaluating land and water resource management systems
either assign values to environmental impacts (contingent valuation) or evaluate the efficiency of
preserving and restoring environmental quality (benefit-cost analysis). A MCDA approach to
integrated catchment management is superior to BCA because it: 1) recognizes that human
activities within a catchment are motivated by multiple and often competing objectives and/or
constraints; 2) does not require monetary valuation of criteria; 3) allows trade-offs between
criteria to be measured and evaluated; 4) explicitly considers how the spatial configuration of
LWRMS for a catchment influences the values of criteria; and 5) is comprehensive, knowledge-
based and stakeholder oriented which greatly increases the likelihood of resolving catchment
problems.
The MCDA conducted in this paper models how a landowner selects the most preferred farming
system for a farm based on multiple criteria. Scores are used to rank alternative farming systems.
The MCDA was used to rank five farming systems based on five economic and environmental
criteria in Goodwater Creek catchment located in Missouri, USA. Ranking was based on an
additive utility function, which is the sum of the product of criteria weights and standardized
criteria values. Results indicate that the highest ranked farming system for Goodwater Creek
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catchment is different from the farming system that maximizes profit. The most profitable
farming system ranked last. There are several explanations for these results, including the
possibility that the MCDA provides a better framework for evaluating the selection of farming
systems than the profit maximization model.
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Table 1 - Description of five farming systems
_________________________________________________________ Farming Crop Tillage Fertilizer Pesticide system rotation method application application rate rate _________________________________________________________ FS1 CB MT High High FS2 SB MT Low Medium FS3 CBW MT Medium Banded FS4 CB R Medium Banded FS5 CB NT Medium High _________________________________________________________ C = corn, B = soybeans, S = sorghum, W = wheat, MT = minimum tillage, R = reduced tillage, and NT = no tillage.
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Table 2 - Average criteria values for five farming systems
Farming system
Net return ($/ha)
Economic risk
($/ha)
Drinking water (atrazine applic.
rate, L/ha)
Aquatic ecosystems (soluble nitrogen concentration in
surface runoff, ppm)
Soil erosion rate
(tonnes/ha/yr)
FS1 328.53 27.92 4.68 12.69 4.48 FS2 241.39 20.44 3.74 4.66 6.94 FS3 218.95 19.68 1.75 7.81 5.15 FS4 296.38 24.25 1.75 8.33 4.93 FS5 201.82 23.18 4.91 5.70 1.90
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Figure 1. Goodwater Creek catchment, northcentral Missouri, USA