multiple-criteria decision analysis for integrated catchment

21
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 Please do not quote without obtaining the author’s consent as these works are in their draft form.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.

Upload: lethien

Post on 03-Jan-2017

223 views

Category:

Documents


0 download

TRANSCRIPT

Page 1: Multiple-Criteria Decision Analysis for Integrated Catchment

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 Please do not quote without obtaining the author’s consent as these works are in their draft form.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.

Page 2: Multiple-Criteria Decision Analysis for Integrated Catchment

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

Page 3: Multiple-Criteria Decision Analysis for Integrated Catchment

2

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.

Page 4: Multiple-Criteria Decision Analysis for Integrated Catchment

3

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.

Page 5: Multiple-Criteria Decision Analysis for Integrated Catchment

4

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

Page 6: Multiple-Criteria Decision Analysis for Integrated Catchment

5

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

Page 7: Multiple-Criteria Decision Analysis for Integrated Catchment

6

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).

Page 8: Multiple-Criteria Decision Analysis for Integrated Catchment

7

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:

Page 9: Multiple-Criteria Decision Analysis for Integrated Catchment

8

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

Page 10: Multiple-Criteria Decision Analysis for Integrated Catchment

9

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

Page 11: Multiple-Criteria Decision Analysis for Integrated Catchment

10

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

Page 12: Multiple-Criteria Decision Analysis for Integrated Catchment

11

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

Page 13: Multiple-Criteria Decision Analysis for Integrated Catchment

12

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

Page 14: Multiple-Criteria Decision Analysis for Integrated Catchment

13

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.

REFERENCES

Anselin, A., Meire, P.M., Anselin, L., 1989. Multicriteria techniques in ecological evaluation: an

example using the Analytic Hierarchy Process. Biological Conservation 49, 215-229.

Backus, G.J., Stillwell, W.G., Latter, S.M., and Wallerstein, M.C., 1982. Decision making with

applications for environmental management. Environmental Management 6, 493-504.

Cameron, J.I., 1997. Applying socio-ecological economics: a case study of contingent valuation

and integration watershed management. Ecological Economics 23, 155-165.

Cooksey, R.W., 1996. Judgment Analysis: Theory, Methods and Applications. Academic Press,

Sydney, Australia.

Costanza, R., Folke, C., 1997. Valuing ecosystem services with efficiency, fairness, and

sustainability as goals. In: Daily, G.C. (Ed.), Nature’s Services: Societal Dependence on

Natural Ecosystems. Island Press, Washington, DC. pp. 49-68.

Fischer, G., Makowski, M., Antoine, J., 1996. Multiple criteria land use analysis. Working Paper

WP-96-006, International Institute of Applied Systems Analysis, Laxenburg, Austria.

Foltz, J.C., Lee, J.G., Martin, M.A., Preckel, P.V., 1995. Multiattribute assessment of alternative

cropping systems. American Journal of Agricultural Economics 77, 408-20.

Gehlbach, F.R., 1975. Investigation, evaluation, and priority ranking of natural areas. Biological

Conservation 8,79-88.

Page 15: Multiple-Criteria Decision Analysis for Integrated Catchment

14

Haimes, Y.Y., Hall, W.A., 1974. Multiobjectives in water resource systems: the surrogate

tradeoff method. Water Resources Research 10, 615-624.

Haimes, Y.Y., Hall, W.A., 1977. Multiobjective analysis in the Muamee River Basin: a case

study on level-B planning. Report SED-WRG-77-1, Case Western University, Cleveland,

OH.

Haettenschwiler, P., 1994. Decision support systems applied to Swiss federal security policy and

food supply. (draft). DSS workshop, International Institute of Applied Systems Analysis

Workshop, Laxenburg, Austria.

Hajkowicz, S., Prato, T., 1998. Multiple objective decision analysis of farming systems in

Goodwater Creek watershed, Missouri, CARES Research Report No. 24, University of

Missouri-Columbia, Columbia, MO, June.

Heidenreich, L.K., Zhou, Y., Prato, T., 1996. Watershed scale water quality impacts of

alternative farming systems, Proceedings for Watershed '96, June 8-12, Baltimore,

Maryland.

Janssen, R., 1992. Multiobjective Decision Making for Environmental Management. Kluwer

Academic Publishers, The Netherlands.

Kangas, J., 1994. An approach to public participation in strategic forest management planning.

Forest Ecology and Management 70, 75-88.

Kangas, J., Kuusipalo, J., 1993. Integrating biodiversity into forest management planning and

decision-making. Forest Ecology and Management 61, 1-15.

Keeney, R.L., Raiffa, H., 1976. Decisions with Multiple Objectives: Preferences and Value

Trade-offs. John Wiley & Sons, New York.

Lee, R.G., Stankey, G.S., 1992. Major issues associated with managing watershed resources. In:

Adams, P.W., Atkinson, W.A. (Eds.), Balancing Environmental, Social, Political, and

Page 16: Multiple-Criteria Decision Analysis for Integrated Catchment

15

Economic Factors in Managing Watershed Resources. Oregon State University Corvallis,

OR.

MacKenzie, S.H., 1997. Integrated Resource Planning and Management. Island Press,

Washington, DC.

Makowski, M., 1994. Methodology and a modular tool for multiple criteria analysis of LP

models. Working Paper WP-94-102, International Institute of Applied Systems Analysis,

Laxenburg, Austria.

Makowski. M., Somlyody, L., Watkins, D., 1995. Multiple criteria analysis for regional water

quality management: the Nitra River case. Working Paper WP-95-022. International

Institute of Applied Systems Analysis, Laxenburg, Austria.

Naiman, R.J., Bisson, P.A., Lee, R.G., Turner, M.G., 1997. Approaches to management at the

watershed scale. In: Kohm, K.A., Franklin, J.F. (Eds.), Creating Forestry for the 21st

Century: The Science of Ecosystem Management. Island Press, Washington, DC, pp. 239-

253.

Nelsen T.A., Blackwelder, P., Hood, T., McKee, B., Romer, N., Zarikian, A., Metz, C., 1994.

Time-based correlation of biogenic, lithogenic and authigenic sediment components with

anthropogenic inputs in the Gulf of Mexico. Estuaries 17, 873-885.

Penttinen, M., 1994. Forest owner’s decision support systems–a management solution for

nonindustrial private forest owners. International Institute of Applied Systems Analysis

Workshop, Laxenburg, Austria.

Prato, T., Fulcher, C., Wu, S., Ma, J., 1996a. Multiple-objective decision making for

agroecosystem management. Agricultural. and Resource. Economics Review 25, 200-212.

Page 17: Multiple-Criteria Decision Analysis for Integrated Catchment

16

Prato, T., Fulcher, C., Zhou, Y., 1996b. Integrated resource management using a decision support

system. Southern African Wildlife Management Association Conference, Sustainable Use of

Wildlife, University of Cape Town, South Africa, April 9-11, p. 48.

Prato, T., 1998. Protecting soil and water resources through multi-objective decision making. In:

El-Swaify, S.A., Yakowitz, D.S. (Eds.), Multiple Objective Decision Making for Land,

Water and Environmental Management. St. Lucie Press, Delray Beach, FL, pp. 385-394.

Prato, T., Hajkowicz, S.. 1999. Selection and sustainability of land and water resource

management systems. Journal of the American Water Resources Association 35, 739-752.

Prato, T., 1999. Risk-based multiattribute decision making in property and watershed

management. Natural Resource Modeling 12, 307-334.

Saaty, R.W., 1987. The Analytic Hierarchy Process - what it is and how it is used? Mathematical

Modeling 9, 161-176.

Sagoff, M., 1988. The Economy of the Earth, Cambridge University Press Cambridge, p. 271.

Sargent, F.O., Brande, J.H., 1976. Classifying and evaluating unique natural areas for planning

purposes. Journal of Soil and Water Conservation May-June, 113-116.

Smith, P.G.R., Theberge, J.B., 1986. A review of criteria for evaluating natural areas.

Environmental Management 10, 715-734.

Smith, P.G.R., Theberge, J.B., 1987. Evaluating natural areas using multiple criteria: theory and

practice. Environmental Management 11, 447-460.

Strassert, G. and T. Prato. 2002. Selecting farming systems using a new multiple criteria decision

model: the balancing and ranking method. Ecological Economics 40, 269-277.

Tecle, A., Duckstein, L., Korhonen, P., 1994. Interactive, multiobjective programming for forest

resources management. Applications in Mathematics and Computation 63, 75-93.

Page 18: Multiple-Criteria Decision Analysis for Integrated Catchment

17

Tecle, A., Szidarovszky, F., Duckstein, L., 1995. Conflict analysis in multi-resource forest

management with multiple decision-makers. Nature and Resources 31, 8-17.

Wu, S., 1994. Economic and water quality impacts of alternative farming systems in Goodwater

Creek watershed: A stochastic programming analysis. Ph.D. dissertation, Department of

Agricultural Economics, University of Missouri-Columbia, Columbia, MO.

Xu, F., Prato, T., Ma, J.C., 1995. A farm-level case study of sustainable agricultural production.

Journal of Soil and Water Conservation 50, 39-44.

Yakowitz, D.S., Lane, L.J., Szidarovszky, F., 1993. Multi-attribute decision-making: dominance

with respect to an importance order of the attributes. Applications in Mathematics and

Computation 54, 7-81.

Page 19: Multiple-Criteria Decision Analysis for Integrated Catchment

18

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.

Page 20: Multiple-Criteria Decision Analysis for Integrated Catchment

19

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

Page 21: Multiple-Criteria Decision Analysis for Integrated Catchment

20

Figure 1. Goodwater Creek catchment, northcentral Missouri, USA