ABSTRACT: Water quality issues in agriculture are growing inimportance. A common theme is the provision of better informationto decision makers. This study reports the trial of a prototype deci-sion support system by the U.S. Department of Agriculture NaturalResources Conservation Service and the Agricultural Research Ser-vice in the NRCS Harrison County Field Office in 1998. Observeddata collected at the Deep Loess Research Station (DLRS) nearTreynor, Iowa, were extrapolated using a modified GLEAMS fieldscale simulation model that included a nitrogen leaching compo-nent and a crop growth component. An accounting tool was used toconvert crop yield estimates into crop budgets. A model interfacewas built to specify the climate, soil, and topography of the field, aswell as the management scenarios for the alternative managementsystems. For the Deep Loess Hills area of Harrison County, a totalof six soil and slope groups, with 66 total combinations of manage-ment practices forming management systems, were defined andsimulated based on previously calibrated data from DLRS. A multi-objective decision support system, the Water Quality Decision Sup-port System, or WQDSS, was used to examine the tradeoffs in acomprehensive set of variables affected by alternative managementsystems with farmers in Harrison County. The study concludedthat a multiobjective decision support system should be developedto support conservation planning by the NRCS. Currently, a largerscale effort to improve water quality decision making is underway. (KEY TERMS: water quality; decision support systems; farm man-agement model; agricultural hydrology.)

Heilman, Philip, Jerry L. Hatfield, Martin Adkins, Jeffrey Porter, and RussellKurth, 2004. Field Scale Multiobjective Decision Making: A Case Study FromWestern Iowa. Journal of the American Water Resources Association (JAWRA)40(2):333-345.

INTRODUCTION

A fundamental conflict exists in any effort toimprove the science used to manage agricultural landfor water quality issues. Our best understanding ofthe physical processes controlling water quality comeswhen using a reductionist approach focusing on a sin-gle component of the overall system at a particularlocation. On the other hand, agriculture is anextremely complex system covering very large areaswith many variations and interactions in the process-es that control nonpoint source pollution.

Nevertheless, maintaining and improving waterquality is such an important goal that society cannotbe paralyzed by scientific uncertainty: some actionmust be taken. The obvious solution is to focus on themost important locations and issues, use the bestinformation available, implement the best plan possi-ble given current information, then modify the planbased on results while improving the science baseover time. In essence, society has implemented thatapproach.

The problem is that over time, the list of issues tooimportant to ignore has grown. Urban populations areplacing increasing demands on waterways for bothwater supply and recreation. There is a growingrecognition that ecosystems are dependent on ade-quate water quality and quantity. Many issues unre-lated to water, such as animal habitat andagriculture’s effect on the carbon cycle, need to be

1Paper No. 02031 of the Journal of the American Water Resources Association (JAWRA) (Copyright © 2004). Discussions are open untilOctober 1, 2004.

2Respectively, Research Biologist, USDA-ARS Southwest Watershed Research Center, Tucson, Arizona 85719; Laboratory Director, USDA-ARS National Soil Tilth Laboratory, 2150 Pammel Drive, Ames, Iowa 50011-4420; Assistant State Conservationist and Assistant State Con-servation Engineer, USDA-NRCS, 210 Walnut Street 693 Federal Building, Des Moines, Iowa 50309-2180; and District Conservationist,Harrison County SWCD, 715 North 2nd Avenue, Logan, Iowa, 51546-1038 (E-Mail/Heilman: pheilman@tucson.ars.ag.gov).

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JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATIONAPRIL AMERICAN WATER RESOURCES ASSOCIATION 2004

FIELD SCALE MULTIOBJECTIVE DECISION MAKING:A CASE STUDY FROM WESTERN IOWA1

Philip Heilman, Jerry L. Hatfield, Martin Adkins,Jeffrey Porter, and Russell Kurth2

addressed simultaneously. Consequently, instead offocusing on a few problem locations and issues,impacts of management on water quality have to beaddressed across large areas of agricultural land, fora large and growing list of resource issues. Ultimately,water quality issues have to be addressed at the fieldscale since that is the primary scale of agriculturalmanagement. The intent of this paper is to describe astructured approach to field scale decision makingabout multiple resource concerns, especially waterquality on Midwestern croplands, and to illustrate theapproach with a case study. With appropriate modifi-cations, a similar approach could be used on range-lands and irrigated areas.

BACKGROUND

For decades, the foundation of technical assistancein U.S. agriculture has been the control sheet and rillerosion on cultivated fields. The approach is conceptu-ally straightforward. The Universal Soil Loss Equa-tion (USLE), or the Revised Universal Soil LossEquation (RUSLE), is used to estimate erosion on ahillslope. The USLE and RUSLE are empirical mod-els of the form:

A = RKLSCP

where A is soil loss in tons per acre, R is a rainfall/runoff erosivity factor, K is a soil erodibility factor, Lis a slope length factor, S is a slope steepness factor, Cis a cover management factor, and P is a support prac-tice factor (Wischmeier and Smith, 1978; Renard etal., 1997). With the USLE and an estimate for themaximum acceptable soil loss, known as a soil losstolerance (or T factor), soil conservationists had apowerful tool for identifying fields that needed conser-vation practices, and even identifying a range of prac-tices that would lead to acceptable levels of erosion.Erosion at a rate less than T, by definition, shouldlead to sustainable levels of crop production, at leastas far as soil quantity is concerned.

To illustrate, on a field with a suspected soil ero-sion problem, annual soil loss would be calculated forthe current management system. If T was exceeded,the RKLS factors would be held constant and smallerC and P factors corresponding to alternative manage-ment practices could be tried until annual erosion wasless than T. Some practices, such as installing ter-races, would affect the LS factors as well. As conser-vationists gained experience under local conditionswith the USLE, it became the standard tool, in partbecause of the ease with which it could be explained

to farmers to support voluntary efforts to reduce ero-sion.

Although simple and powerful, the USLE andRUSLE do not address all resource problems. In par-ticular, the models estimate soil detachment but notdeposition. Soil eroded from an agricultural fieldcould be deposited within or along the field bound-aries, and so the USLE is not in itself designed toaddress sediment delivery issues, though sedimentdelivery ratios can be used to get a rough idea of howmuch sediment enters watercourses. Further, theUSLE does not address the movement of nutrientsand pesticides from farm fields to water bodies. Suchissues require a field scale simulation model.

The U.S. Department of Agriculture Soil Conserva-tion Service had long recognized that a producershould have a conservation plan that addresses man-agement impacts on all resources. To emphasize thefact that the agency considered all natural resources,the agency’s name was changed to the NaturalResources Conservation Service, or NRCS. At roughlythe same time, the NRCS introduced a method knownas the Conservation Practices Physical Effects, orCPPE, matrix (NRCS, 2003a). The goal was to ensurethat conservationists and producers looked broadlyacross all potential resource problems when formulat-ing alternative management systems.

The CPPE is used as part of the conservation plan-ning process. While performing a resource inventoryon a farm, a conservationist would look for any of 66potential resource problems. These problems aregrouped under the headings of Soil, Water, Air,Plants, Animals, and Humans, and are known collec-tively as SWAPA+H. Each potential resource problemhas a quality criterion to indicate when it should beconsidered a problem. Table 1 shows three potentialresource problems related to soil erosion, togetherwith their quality criteria and management practicesthat are likely to improve the resource problem.

A set of management systems that address allidentified resource problems can be formulated bycreating a table consisting of identified resource prob-lems across the top. A management practice is addedalong the left side and a qualitative estimate (slight,moderate, or significant) of the effect of the manage-ment practice under the conditions in question ismade for the first identified resource problem. Oncethe first resource problem has been adequatelyaddressed, practices are added until the second, third,and all remaining problems have been adequatelyaddressed.

Table 2 presents a management system designedfor the Ida soils in the Loess Hills of western Iowa,taken from the Guidance Documents found in Section III of the Iowa NRCS State Field Office Technical Guide (NRCS 2003b). The first practices address soil

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(1)

erosion issues, and the last address additional prac-tices necessary for water quality. Typically, severalalternative management systems are defined to givethe producer a range of choices, for example, toresolve all problems soon, but requiring an immediatelarge investment, or to resolve the problems over alonger time frame with a smaller immediate invest-ment. The NRCS Guidance Documents are the bestdescription of how agricultural management canaffect a range of resources for specific conditions thatis available across the entire Midwest. Nevertheless,from a scientific point of view, there is much room forimprovement, particularly in defining variables thatcould be measured or simulated to indicate the severi-ty of the SWAPA+H resource problems, as well asdefining thresholds indicating that there is a problem.

In the early 1990s, Leonard Lane, a hydrologistwith the U.S. Department of Agriculture, AgriculturalResearch Service (USDA-ARS), saw the CPPEmethod as a significant advance toward a structuredframework to integrate the effects of managementacross natural resources, and as a “smorgasbord” ofresearch opportunities. He also recognized that therewas a critical need to improve methods used to evalu-ate management alternatives and to help producersdecide which management system to implement. Thesimple, effective approach of using the USLE and T todecide which management system to implementwould not work if water quality, plant, and animalresource problems had to be considered at the same

time. Thus, he led an initiative to address theseissues by developing a Water Quality Decision Sup-port System (WQDSS) consisting of an interface, acomprehensive field scale simulation model, and amultiobjective decision making component. In thispaper the accomplishments that resulted from theWQDSS initiative are described, including a field testin the Harrison County NRCS Field Office in westernIowa, other applications, and current efforts to buildon the same approach to improve decision making inagriculture for water quality.

MULTIOBJECTIVE DECISION MAKINGAPPROACH

Decision support systems (DSS) are an active areaof research with seminal contributions by Keen andMorton (1978) and Bonczek et al. (1981). DSS applica-tions for water resources are described in Loucks(1995), El-Swaify and Yakowitz (1998), Hays andMcKee (2001), and Rizzoli and Jakeman (2002). Typi-cally, a DSS will consist of an interface, database andlinks to models, knowledge bases, or geographic infor-mation systems (GIS) applications, with the intentionof helping a decision maker reach a decision on anunstructured problem. Many land and water issuesinvolve problems with multiple objectives. Multiobjec-tive decision making theory is described in Keeney

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TABLE 1. Several Common Soil Resource Problems, Quality Criteria, and the ManagementPractices That Address Those Problems. Source: NRCS (2003c).

Resource Concern: Definition/ Quality Criteria/Management PracticesAssessment Method Used to Address the Resource Concern

Sheet and Rill: Removal of uniform layer of soil from the land The average annual soil loss is at or below tolerance (“T”) for the soilsurface caused by rainfall and surface water runoff. map unit. Conservation Cover; Conservation Crop Rotation; Contour

Buffer Strips; Contour Farming; Contour Strip Cropping; Cover andAssessment Method: RUSLE 2. Green Manure Crop; Diversion; Field Border; Filter Strip; Residue

Management – No-Till and Strip Till, Mulch Till, Ridge Till, andSeasonal; Terrace.

Ephemeral Gully: Reoccurring gullies on cropland caused by Affected areas are stabilized. Conservation Cover; Conservation Cropconcentrated flow of runoff water. They are obliterated by Rotation; Contour Buffer Strips; Contour Farming; Contour Stripnormal tillage operations. Cropping; Critical Area Planting; Diversion; Grade Stabilization

Structure; Grassed Waterway; Field Border; Lined Waterway orAssessment Method: Visual observation and Erosion Outlet; Mulching; Residue Management - No-Till and Strip Till,and Sediment Delivery Equation. Mulch Till, Ridge Till, and Seasonal; Structure for Water Control;

Subsurface Drain; Terrace; Water and Sediment Control Basin.

Classic Gully: Eroded channels that are too deep to be Sheet and rill, ephemeral gully, and classic gully erosion arecrossed with farm equipment. They may enlarge by head controlled and sediment leaving the site does not cause damage.cutting and lateral widening. Conservation Cover; Contour Buffer Strips; Contour Farming;

Contour Strip Cropping; Critical Area Planting; Diversion; GradeAssessment Method: Visual observation and Sediment Stabilization Structure; Grassed Waterway; Pond; Terrace; Waterand Gully Prediction Model. and Sediment Control Basin.

and Raiffa (1993) and applications to land manage-ment are discussed in Beinat and Nijkamp (1998).General multiobjective decision support approachesinclude the Analytic Hierarchy Process of Saaty(1990) and DEFINITE by Janssen and van Herwijnen(1994).

The basic approach taken in the WQDSS was out-lined in Lane et al. (1991). A decision maker definesthe problem, then a conventional or baseline conditionis determined and alternatives defined. The variablesto be used in the decision are selected (decision vari-ables), and the effects of the conventional and alterna-tive systems are estimated using a simulation modeltogether with all available observed data, expert opin-ion, databases, and knowledge bases. The decisionvariables are scored for each alternative, weights areselected for each decision variable, and an overall

score determined for each alternative by summing theproducts of the weights and scores for all decisionvariables. Any alternatives scoring more than the cur-rent system are recommended for adoption.

Wymore (1988) proposed the multiobjectiveapproach in the WQDSS. This method consists of twomajor steps: first, scoring each decision variable foreach alternative; and second, assigning weights toeach decision variable to calculate an overall score forthe alternative. Decision variables are scored to elimi-nate the units and so make them comparable. By con-vention, scores range from 0 to 1.0, with 1.0 being asdesirable as possible. The current, or conventional,management system by definition scores 0.5. Thus, all other systems are scored relative to the existingsystem to highlight the fact that the decision maker iscomparing alternatives to the system currently in

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TABLE 2. An Example of Formulating an Alternative Management System Usingthe Conservation Practices Physical Effects Approach From the NRCS (2003b).

Present Resource Condition Identified ProblemsLocation: MLRA 107 Sheet and rill erosion is 16 tons/acre. EphemeralSoils: 1D3 Ida Silt Loam gullies are present. Pesticides and nutrients contaminateSlope: 12 surface and ground water. Pheasant population is wellSlope Length: 150 feet below potential and habitat diversity is limited.Land Use: Cropland (Corn-Corn-Soybeans)Applied Practices: Cross Slope 5 Percent Residue GradientTillage Operation: Fall Mulch Corn, 20 percent, Spring Mulch Beans 10 percent

RESOURCE CONCERNSSOIL WATER ANIMALS

Erosion Condition Quantity Quality HabitatDepos.

Sheet Damageand Off- Excess Cover/Rill Ephemeral Tilth Compaction Site Runoff Nutrient Pest. Sediment Shelter

Present ConditionsPractices SIG- SIG- SL- MOD- SIG- MOD- SIG- MOD- MOD- SL-

Alternative #1

600 Terraces SIG+ SIG+ 0 SL- SIG+ SIG+ SL+ SL+ MOD+ SL+

330 Contour Farming SIG+ SL+ 0 0 MOD+ MOD+ SL+ SL+ SL+ 0

328 Crop Rotation SIG+ 0 0 0 SL+ 0 SL- SL- 0 0Continuous Corn

329 Cons. Tillage SIG+ SL+ SL+ MOD+ MOD+ SIG+ SL+ SL+ MOD+ SL+No Till 50% Residue

590 Nutr. Mgmt. 0 0 0 0 0 0 SIG+ 0 0 0

595 Pest Mgmt. 0 0 0 0 0 NA 0 SIG+ 0 SL-

Supplemental Practices (streams/rivers present)

393 Filter Strip 0 0 0 0 SIG+ 0 SIG+ SIG+ SIG+ MOD+

391 Riparian Forest Buffer 0 0 0 0 SIG+ 0 SIG+ SIG+ SIG+ MOD+

place. Score functions are selected for each decisionvariable from among the following choices: more isworse, more is better, desirable range, or undesirablerange. The “more is worse” score function is used for adecision variable like the quantity of pollutants leav-ing a field. Net returns are scored using a “more isbetter” score function. The desirable and undesirableranges were available for cases like soil water, whereone would not want the soil to be too dry or too wet,although in practice few decision makers chose vari-ables having desirable or undesirable ranges.

The second step in Wymore’s approach assumes asimple additive value function of the form:

to calculate an overall value, V, as the sum of theproducts of a weight, w, associated with each decisionvariable, or criterion, i, and the score, v, for that deci-sion variable. Although conceptually simple, theapproach can be difficult to apply in practice becausedecision makers find it difficult to directly assignweights. Yakowitz et al. (1993a) developed a methodthat eliminates the need for decision makers to speci-fy a weight for each decision variable. Instead, deci-sion makers rank the decision variables in order ofimportance. The cost of using the method is that arange of values representing the overall value for thealternative is calculated, rather than a scalar valuethat quantifies the overall value of a particular alter-native.

The method developed by Yakowitz has an intuitiveappeal to decision makers. Suppose there are n crite-ria, which the decision maker has ranked in impor-tance. Let Vij be the score of alternative j evaluatedwith respect to criterion i in the importance order. Ifwi indicates the unknown weight factor associatedwith criterion i, the highest (lowest) or best (worst)additive composite score for alternative j, consistentwith the importance order, is found by solving thefollowing linear program described for the weights wi.

maximize (minimize)

In both cases (maximizing or minimizing) the firstconstraint normalizes the sum of the weights to 1,while the second requires that the solution be consis-tent with the importance order and restricts the

weights to be nonnegative. The solution to the twoprograms indicated above yields the full range of pos-sible composite scores given the importance order.That is, any weight vector that is consistent with theimportance order will produce a composite score thatfalls between the best and worst composite scores.

To enhance the intuitive appeal of this approach,Yakowitz et al. (1993a) also showed that the best andworst composite scores could be calculated in closedform, as the maximum or minimum composite scorecould be calculated by solving the following k prob-lems:

The best or worst composite score for alternative j isthen selected from the results as:

BestScore = BVj = maxk {vkj}

WorstScore = WVj = mink {vkj}

Examples of this method applied to water qualityproblems in agriculture can be found in Yakowitz etal. (1992, 1993b). For clarity, the next sectiondescribes an example using the WQDSS across acounty.

WQDSS TRIAL IN HARRISON COUNTY, IOWA

Overall Approach

The 1999 NRCS report “Field Level Evaluation of aPrototype Multiple Objective Decision Support Sys-tem for Conservation Planning,” describes a trial ofthe WQDSS in Harrison County, in western Iowa. Thepurpose of the trial was to determine if a DSS forwater quality would be a useful tool for NRCS conser-vation planning. There were two parts to the trial.First, the NRCS State Office used the interface andsimulation model inside the WQDSS to quantify theeffects of management on a number of resource con-cerns. Second, a District Conservationist used themultiobjective decision making component with farm-ers to assess its potential role in conservation plan-ning.

In Harrison County there are several very distinctareas, each with specific resource problems and typi-cal management systems. Loess is windborn silt thatis prone to movement by water, particularly rills andgullies (see Figure 1). The silt particles in loess can maintain steep slopes, often forming bluffs. In

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FIELD SCALE MULTIOBJECTIVE DECISION MAKING: A CASE STUDY FROM WESTERN IOWA

V w v w vii i( , ) = ∑

V w v

s t w

w w w

j i ijin

iin

n

=

=

≥ ≥ ≥ ≥

=

=

∑

∑

1

1

1 2

1

0

. .

... .

(2)

(3)

vk

vkj ijin==∑1

1(4)

(5)

western Iowa, loess has been deposited in layers up to65 meters deep along the eastern side of the MissouriRiver Valley. Land that is not in the Loess Hills is pri-marily in the Missouri River Bottoms, which haveheavier soils with very shallow slopes that needdrainage rather than protection from erosion.

A total of 66 management systems were defined forsix common soil field topography configurations. Ofthese, the 20 management systems simulated for Idasoils are listed in Table 3. Ida soils are common in theLoess Hills, and are deep, well drained, moderatelypermeable, and with medium to very rapid surfacerunoff. All management systems include a corn/soy-bean rotation, with the major management differ-ences being timing and intensity of tillage, timing andquantity of herbicide applications, and timing ofnitrogen applications.

Modeling and Scoring the Effects of Management onResource Criteria

The simulation model in the WQDSS is a modifiedversion of the Groundwater Loading Effects of Agri-cultural Management Systems (GLEAMS) model(Leonard et al., 1987; Davis et al., 1990). Modifica-tions to GLEAMS include the addition of the nitrogenleaching component from CREAMS (Knisel, 1980),and the EPIC crop growth component (Williams etal., 1989). A modified version of the economic account-ing program based on the Cost And Returns Estima-tor by the Midwest Agricultural Research Associates(1988) was used to compute returns of the manage-ment systems based on simulated yields.

The model was calibrated to match runoff, baseflowas a proxy for percolation, sediment yield, and cornyields for several experimental watersheds on theDeep Loess Research Station near Treynor, Iowa, for

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Figure 1. A Newly Formed Rill is Evident in This Field From the Loess Hills in Western Iowa.

the period 1972 to1991. Figure 2 shows simulationmeans relative to observed means for the four vari-ables over the 20-year period. The model had difficul-ty simulating runoff, percolation, and crop yield whilemaintaining parameters within their expected ranges,as this requires modeling the entire water budget.Evapotranspiration (ET) is such a large part of thewater budget in western Iowa that a small overesti-mate or underestimate of ET can overwhelm runoff orpercolation. Because a realistic crop yield is necessaryto correctly estimate net returns, and runoff is neededto estimate sediment movement, those outputs werematched to observed amounts. As percolation was notdirectly measured and would be expected to divergesomewhat from baseflow, simulated percolationabsorbed a significant portion of the error in thewater budget, and simulated percolation remainedmuch lower than the observed baseflow. Althoughthere is a wide difference in tillage intensity betweendeep disking and ridge till with terraces, the ratios ofsimulated to observed means for the four variablesare similar.

Average corn yields increased over the simulationperiod, so yields were calibrated to the ending, ratherthan the mean yields. Based on calibrated parametersfor the observed management systems, other manage-ment systems were parameterized as described in

Heilman (1995). These parameter sets were thenextrapolated to quantify the 66 management systemsoil/slope combinations in Harrison County.

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TABLE 3. The 20 Management Systems Simulated on Ida Soils. All nitrogen is applied on corn as anhydrousammonia at the rate of 168 kg/ha, either preplanning or post-planting. The use of an additional

herbicide application in June is indicated by “+ 2nd herb.”

Corn SoybeansWeed Weed Curve Crop

No. Tillage Nitrogen Control Tillage Control Watershed No. Residue

1 Fall Plow Pre Till Fall Fall Plow Till Fall Overland Channel Hillslope 81 102 Fall Plow Post Till Fall Fall Plow Till Fall Overland Channel Hillslope 81 103 Fall Disk Pre Till Fall Fall Disk Till Fall Overland Channel Hillslope 80 354 Fall Disk Post Till Fall Fall Disk Till Fall Overland Channel Hillslope 80 355 Spring Plow Pre Till Spring Spring Plow Till Spring Overland Channel Hillslope 81 106 Spring Plow Post Till Spring Spring Plow Till Spring Overland Channel Hillslope 81 107 Spring Disk Pre Till Spring Spring Disk Till Spring Overland Channel Hillslope 80 358 Spring Disk Post Till Spring Spring Disk Till Spring Overland Channel Hillslope 80 359 No-Till Pre Burndown No-Till Burndown Overland Channel Hillslope 75 60

10 No-Till Post Burndown No-Till Burndown Overland Channel Hillslope 75 6011 No-Till Pre Burndown No-Till Burndown + 2nd Herb Overland Channel Hillslope 75 6012 No-Till Post Burndown No-Till Burndown + 2nd Herb Overland Channel Hillslope 75 6013 Spring Plow Pre Till Spring Spring Plow Till Spring Terrace Hillslope 74 1014 Spring Plow Post Till Spring Spring Plow Till Spring Terrace Hillslope 74 1015 Spring Disk Pre Till Spring Spring Disk Till Spring Terrace Hillslope 73 3516 Spring Disk Post Till Spring Spring Disk Till Spring Terrace Hillslope 73 3517 No-Till Pre Burndown No-Till No-Till Terrace Hillslope 70 6018 No-Till Post Burndown No-Till No-Till Terrace Hillslope 70 6019 No-Till Pre Burndown No-Till Burndown + 2nd Herb Terrace Hillslope 70 6020 No-Till Post Burndown No-Till Burndown + 2nd Herb Terrace Hillslope 70 60

Figure 2. The Ratios of Simulation Results to ObservedValues for Two Management Systems on Deep LoessResearch Station Fields Are Generally Close to One.

The comprehensive field scale model provided esti-mates of many potential decision criteria. Table 4shows six decision variables that a group of farmersand Soil Conservationists in the Deep Loess Hills con-sidered important for five management systems onIda soils. Some of the modeling assumptions areapparent. The net returns for the terrace systems arehigher than the same tillage systems without ter-races, as it is assumed that the cost of installing ter-races was completely subsidized, although currentprograms do not cost share 100 percent of the terraceinstallation cost. Similarly, it is assumed that terracesare placed at the bottoms of all slopes to allow no sur-face runoff or sediment movement.

Net returns are lowest for the Fall Disk systemboth because of the cost of fall tillage in addition tospring disking and field cultivation and because oflower yields during dry years. Disking greatlyincreases erosion and sediment yield compared to No-Till, particularly when done in the fall because of thepotential for heavy rains early in the season whenthere is little cover on the soil. The model does notsimulate weed populations and thus implicitlyassumes that chemical control of weeds is as effectiveas mechanical control. The different tillage systemsdo not affect nitrogen movement from the field asmuch as they do soil movement, although terraces

increase the amount of nitrogen moved below the rootzone. Additional management systems designed toconserve nitrogen could be considered if the farm-stead has elevated levels of nitrogen in the groundwater or the field is in a watershed with a recognizednitrogen problem. Phosphorus associated with sedi-ment tracks the sediment movement closely. Onepotential weakness in approaching problems on afield by field basis is that one inherently assumes thatall management systems are feasible. On a wholefarm level, there may be time to disk in the spring forseveral, but not all, fields on the farm. Also, farmersmay be averse to delays in planting during wetsprings.

To make decision variables comparable, the nextstep is to score the model results (see Table 5). As anexample, Figure 3 shows the function used to scorethe five management systems for their effect onnitrate in percolation. Although the simulation modelunderestimated the amount of percolation, if the rela-tive amounts of nitrate in percolation for each man-agement system are correct, the scored values shouldadequately express the relative gains possible byswitching to the alternative management systems.

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TABLE 4. Simulation Model Results for Five Management Systems and Six Decision Variables on Ida Soils.

Net Soil Sediment Nitrate in N in P inReturns Erosion Yield Percolation Runoff Sediment

($/ha) (t/ha) (t/ha) (kg/ha) (kg/ha) (kg/ha)

Fall Disk 055 37 38 7 4 21

Spring Disk (Terrace) 115 10 00 6 0 00

Spring Disk 104 18 18 4 4 11

No-Till (Terrace) 139 06 00 7 0 00

No-Till 135 05 06 6 2 04

TABLE 5. Scored Values for Five Management Systems and Six Decision Variables on Ida Soils.

Net Soil Sediment Nitrate in N in P inReturns Erosion Yield Percolation Runoff Sediment

Fall Disk .5 .5 .5 .5 .5 .5

Spring Disk (Terrace) .71 .98 1.0 .63 1.0 1.0

Spring Disk .67 .92 .92 .85 .53 .91

No-Till (Terrace) .78 .99 1.0 .59 1.0 1.0

No-Till .77 .99 .99 .68 .93 .99

Making a Decision

After scoring, the next step was the calculation ofbest and worst possible scores for each alternative.Individual farmers might have different importanceorders for the decision criteria, or the nature of down-stream water quality could influence the importanceorder. A typical ordering for a farmer in HarrisonCounty might be: (1) net returns, (2) soil erosion, (3)sediment yield, (4) nitrate in percolation, (5) nitrogenin runoff, and (6) phosphorus in sediment.

The best and worst possible scores for the conven-tional system, Fall Disk, are both 0.5, since the scorefor each decision variable was defined to be 0.5 for theconventional system. Calculating best and worstscores for other systems requires the importanceorder and the calculation from Equation (5). Forexample, best and worst scores for the Spring Disk(Terrace) management system could be calculated as

Maximum {.71, .85, .90, .83, .86, .89} = .90

Minimum {.71, .85, .90, .83, .86, .89} = .71

Figure 4 shows the ranges of possible scores for themanagement systems in the example. The worstscores of all of the alternatives score higher than theconventional system. Given those decision variables,alternatives, scores, and the importance order, one ofthe alternatives should be implemented, although theoverlap in ranges between the best and the worstscores makes it difficult to discriminate between four

alternative management systems. The No Till sys-tems appear to score higher than the Spring Disk sys-tems, but when the bars overlap, the decision maker’sexact weights could cause the overall highest score tovary. In this case, a farmer could select any one of thealternatives depending on his knowledge and equip-ment and do better than continue with the existingmanagement system.

Report on the WQDSS Trial

The NRCS wanted to assess a multiple objectivedecision support system because they had many toolscapable of assessing individual resource problems, butno tool capable of prioritizing goals or simulating theinteraction of conservation practices on multipleresource concerns simultaneously. There was a recog-nized need for a repeatable, defensible way of estimat-ing the interrelated effects of conservationmanagement systems for site specific conditions. Fur-ther, there was no systematic method to help produc-ers sort out the conflicting effects of alternativemanagement systems on income and the environ-ment.

The trial of the WQDSS assessed the use of a mul-tiple objective decision support system from a numberof vantage points. The WQDSS was compared againsta list of 14 desirable characteristics for decision sup-port systems drawn from similar assessments:

• use readily accessible and affordable hardwareand software,

• intuitive and adaptable user interfaces,• modularity to allow incremental development,• internet connectivity,• ability to use distributed databases and models,• interoperability to allow use of components

regardless of where they are developed,• logging and tracking to document decision pro-

cesses and produce records of decisions,• GIS capability,• visualization displays of data, relationships and

anticipated results,• mechanism to allow stakeholder involvement

and self assessment,• ability to facilitate adaptive management and

monitoring,• depiction of uncertainty in data, relationships or

results,• treatment of multiple goals, objectives and mea-

sures, and• ability to create and store scenarios.

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Figure 3. A Score Function for Nitrate in PercolationShows How Model Results Are Converted to Scores.

The report (NRCS, 1999) lists the findings and rec-ommendations for each of the desirable characteris-tics. To summarize the findings, the report concludedthat a multiple objective DSS could be used to addressboth soil erosion and water quality issues. Demon-strations of the WQDSS consistently encouragedfarmers to discuss and consider multiple naturalresource problems, including sediment, and losses ofnutrients and pesticides. Younger farmers tended tobe much more comfortable with a DSS approach thanolder farmers. All farmers wanted to ensure that theconditions of their particular farm were taken intoaccount and that the economic estimates were realis-tic. A key issue was that the system could not take toomuch time for the conservationists, nor require a longand complicated interaction with the producers. Theimplication is that the simulation modeling should bedone somewhere other than the Field Office.

The WQDSS was not recommended for widespreadadoption because the necessary databases had not been developed and the WQDSS required the Unix operating system. The NRCS report did recom-mend a long term, multiagency effort to develop and

implement an operational tool to support conservationplanning. Such a tool would facilitate the quantifica-tion of management effects on natural resources, helpaddress water quality issues, manage change andincrease the effectiveness of Conservationists in thefield. According to the report, such a tool should havethe following key characteristics:

• a user friendly interface,• the ability to pull information from both GIS

data and the interface,• the use of models to provide for more precise

information than can be gleaned from the conserva-tion practices physical effects information,

• a modular design to allow for use of differentmodels in different parts of the country,

• the incorporation of a DSS to provide for simul-taneous evaluation of conservation system options rel-ative to multiple resource and business objectives,and

• the ability to easily export information intofarmer friendly conservation plan maps and docu-ments.

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Figure 4. A Graphical Display From the Facilitator Shows the Desirability ofAlternatives Given a Typical Farmer’s Ranking of Decision Variables.

Building an operational DSS for conservation plan-ning that meets those requirements is no small undertaking. Identifying the major resource prob-lems, management systems that address the prob-lems, models that simulate key processes, and thensimulating those management system effects acrosslarge areas will require significant effort and coordi-nation across a number of institutions.

OTHER APPLICATIONS OF THE WQDSSMULTIOBJECTIVE APPROACH

The WQDSS has been used for a number of otherapplications including shallow land burial systems forlow level nuclear waste (Paige et al., 1996), targetingfarms for planning (Heilman et al., 1997), and range-land planning (Lawrence et al., 1997). Imam (1994)addressed modeling and DSS uncertainty issues.

The institution with the most experience inresource decision making with the WQDSS multiob-jective approach is the Queensland Department ofNatural Resources and Mines in Australia. TheQueensland DNRM contracted to have the decision-making component of the WQDSS implemented in theJava computer language as a generic multiobjectivedecision making tool called the Facilitator. Therewere some changes to the approach in the Facilitator,namely the score functions do not assume that thereis a current management practice that scores 0.5 bydefinition. Also, there is support for a hierarchicalimportance ordering, so objectives can be groupedunder categories such as “water pollutants” or “sus-tainability” as described in Yakowitz and Weltz(1998). The Facilitator has some added features todocument the process of reaching a land use decisioninvolving watersheds or other large areas, in which anumber of stakeholders are involved. The Facilitatoris being used for watershed planning in the UnitedStates, Mexico, India, and Zimbabwe.

Because there is no embedded simulation model,expert opinion has been the primary means of esti-mating the effects of management. Watershed plan-ning has been the primary application of theFacilitator, but it has also been used for water devel-opment, floodplain management, forestry, animal pro-duction, project evaluation, and regional strategyprioritization. The Facilitator is available for freedownloading, as is its source code (SOURCEFORGE.net, 2000). To promote the application of multiobjec-tive decision support for environmental management,a series of conferences under the name MODSS wasinitiated by Leonard Lane and others, as documentedin El-Swaify and Yakowitz (1998), Lawrence and

Robinson (2002), and as part of Rizzoli and Jakeman(2002).

FUTURE PLANS AND IMPROVEMENTS

The fundamental problem in natural resource man-agement in agriculture is the difficulty in quantifyingthe effects of management given a wide range ofresource concerns and the inherent variability of climate, soils, topography, and management thataffect farm productivity, sustainability, and offsiteeffects. Simplifying assumptions that worked in thepast, such as using estimates of annual erosion as afirst-cut indicator for many other resource concerns,will not work, as society puts more emphasis on theimportance of clean water and other resources.

On the other hand, there are opportunities to workmore efficiently. Great progress has been made indeveloping databases from field experiments, improv-ing simulation models, and furthering our under-standing of the processes that affect water qualityfrom agriculture. The challenge is to develop tools,call them decision support systems, which harnessthe information available to improve decision making.Newman et al. (2000) and Matthews and Stephens(2002) describe a number of the difficulties encoun-tered in getting DSS technology adopted in agricul-ture and suggest greater user involvement with lessresearch emphasis and iterative prototyping areapproaches more likely to lead to adoption. Informa-tion technology will allow a restructuring of ourapproach to delivering the science used for waterquality decision making. Currently, there is too largea burden on those working directly with the farmer tointegrate the available science. Specialists are neededto create the databases and run simulation models forparticular problems over large areas.

One simplification that can help make the shifttowards increased specialization (shown in the Harri-son County example) is the use of representativefields. By assuming away some of the complexity inindividual fields, it is possible to develop a finite num-ber of representative field management system combi-nations. A modeling specialist can then collectavailable data and simulate effects of managementsystems on those fields. Expert review of results couldconfirm the realism of simulation efforts. Once theinformation is in a database, the NRCS conservation-ists can learn the effects of management on the repre-sentative fields found locally, and “tell the story” tointerested producers (Heilman et al., 2002). Of course,decisions will only be as good as the link between therepresentative field and the actual field.

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If, on the other hand, each individual field alwayshad to be treated as a special case, there would neverbe enough manpower to simulate effects of alternativemanagement systems, review results, and communi-cate results to producers across wide areas of the Midwest given current technology. In fact, there is aneffort underway to link observed data from theMSEA, or Management System Evaluation Areas(Hatfield et al., 1993), the Root Zone Water QualityModel (Ahuja et al., 2000), and the Facilitator to buildand apply an expert reviewed database/DSS for conservation planning by the ARS and the NRCS inIowa.

CONCLUSION

Natural resource management in agriculture willbe much more complex in the 21st Century than inthe past. Society wants agriculture to contribute tothe reduction of a range of water quality problems inaddition to reducing erosion. Producers over verylarge areas will need a way to assess how alternativemanagement systems can address water quality prob-lems while maintaining or enhancing their lifestyleand income.

A trial of the WQDSS multiobjective decision sup-port system for water quality in the Harrison CountyField Office in western Iowa led to the specification ofrequirements for an operational system and the rec-ommendation that such a DSS be built to support con-servation planning. A DSS could meet the NRCS needfor a systematic approach to conservation planningwith quantified effects of management on a range ofresource concerns, help address water quality issuesand manage change. It would be critically importantthat such a DSS be easy to use and not require sub-stantial additional time of either the conservationistor producer. An example from the Deep Loess Hills ofwestern Iowa illustrated the DSS used in the trial forfields with Ida soils. The example indicated that sev-eral management systems using conservation mea-sures such as terraces or No-Till would have waterquality as well as economic benefits over Fall Tillage,although some of the systems could increase nitrogenmovement towards ground water. A graphical multi-objective component identified several options thatwould be more desirable than Fall Tillage, but wasunable to identify a clearly superior alternative.

Implementing an operational DSS for conservationplanning across the Midwest will require significanteffort. The Conservation Practice Physical Effectsmatrix should be reviewed to identify variables andquality criteria representative of each of the resourceconcerns. Also needed are methods to quantify the

effect of current and alternative management systemsover large areas. These methods include simulationmodels, but would also require built-in support fordefining many management systems on a number ofrepresentative fields, compare simulated data to beobserved, facilitate expert review, and display resultsin a meaningful way to producers. Implementation ofthese tools will require modeling specialists to beresponsible for quantifying the effects of managementto reduce the burden on Field Office personnel. There should also be a link between the information provid-ed and society’s incentives to encourage adoption ofmanagement systems with environmental benefits ifthose systems produce lower economic returns.

The WQDSS and follow-on DSS efforts presentedhere are just one example of the impact that LeonardLane has had on water resource management. In thiscase, Leonard’s contribution was to recognize a longterm strategic need, develop a strategy to address theneed, assemble resources and a team to implementthe strategy, and then help the team deliver a prod-uct. Any improvement in the management of our nat-ural resources to come out of these efforts will be theresult of his initiative and leadership.

ACKNOWLEDGMENTS

Diana Yakowitz, Jeffry Stone, Raj Reddy, John Masterson,James Abolt, Bisher Imam, Eric Anson, and Mariano Hernandezworked on the WQDSS. Larry Kramer, Marco Buske, Roger Web-ster, William Vorthmann, and Robert Deitchler provided usefulinformation on the Deep Loess Research Station, and the manage-ment of loess soils near Treynor, Iowa. Lyle Peterson, Karl Lan-glois, Tom Hurford, and Dave Langemier provided help through theNRCS. Paul Lawrence, Greg Davis, and Rowan Eisner worked onthe Facilitator in Queensland, Australia.

LITERATURE CITED

Ahuja, L.R., K.W. Rojas, J.D. Hanson, M.J. Shaffer, and L. Ma (Edi-tors), 2000. Root Zone Water Quality Model. Water ResourcesPublications LLC. Highlands Ranch, Colorado, 358 pp.

Beinat, E. and P. Nijkamp (Editors), 1998. Multicriteria Analysisfor Land-Use Management. Kluwer Academic Publishers,Boston, Massachusetts, 372 pp.

Bonczek, R.H., C.W. Holsapple, and A. Whinston, 1981. Founda-tions of Decision Support Systems. Academic Press, New York,New York, 388 pp.

Davis, F.M., R.A. Leonard, and W.G. Knisel, 1990. GLEAMS UserManual. Lab Note SEWRL-030190FMD USDA-ARS and Uni-versity of Georgia, Tifton, Georgia.

El-Swaify, S.A. and D.S. Yakowitz (Editors), 1998. Multiple Objec-tive Decision Making for Land, Water, and Environmental Man-agement. Proceedings of the First International Conference onMultiple Objective Decision Support Systems (MODSS), Honolu-lu, Hawaii. Lewis Publishers, New York, New York, 743 pp.

Hatfield, J.L., J.L. Anderson, E.E. Alberts, T. Prato, D.G. Watts, A. Ward, G. Delin, and R.Swank, 1993. Management SystemsEvaluation Areas – An Overview. Proc. Agricultural Research to

JAWRA 344 JOURNAL OF THE AMERICAN WATER RESOURCES ASSOCIATION

HEILMAN, HATFIELD, ADKINS, PORTER, AND KURTH

Protect Water Quality. Soil Water Conservation Society, Ankeny,Iowa, pp. 1-15.

Hays, Donald F. and Mac McKee (Editors), 2001. Decision SupportSystems for Water Resources Management. Conference Proceed-ings, Snowbird, Utah, American Water Resources Association,Middleburg, Virginia, TPS-01-2, 430 pp.

Heilman, P., 1995. A Decision Support System for Selecting Eco-nomic Incentives to Control Nonpoint Source Pollution FromAgricultural Lands. Ph.D. Dissertation, University of Arizona,Tucson, Arizona.

Heilman, P., J.L. Hatfield, K. Rojas, L. Ma, J. Huddleston, L. Ahuja, and M. Adkins, 2002. How Good is Good Enough?What Information is Needed for Agricultural Water QualityPlanning and How Can it be Provided Affordably? Journal ofSoil and Water Conservation. 57(4):98-105.

Heilman, P., D.S. Yakowitz, and L.J. Lane, 1997. Targeting Farmsto Improve Water Quality. Applied Mathematics and Computa-tion 83:173-194.

Imam, B., 1994. Nonlinear Uncertainty Analysis of Multiple Crite-ria Natural Resource Decision Support Systems. Ph.D. Disserta-tion, University of Arizona, Tucson, Arizona.

Janssen, R. and M. van Herwijnen, 1994. Definite: A System toSupport Decisions on a Finite Set of Alternatives. Kluwer Aca-demic Publishers, Dordrecht, The Netherlands, 232 pp.

Keen, P.G.W. and M.S. Morton, 1978. Decision Support Systems: AnOrganizational Perspective. Addison-Wesley, Reading, Mas-sachusetts, 264 pp.

Keeney, R.L. and H. Raiffa, 1993. Decisions With Multiple Objec-tives: Preferences and Value Tradeoffs, Cambridge UniversityPress, New York, New York, 569 pp.

Knisel, W.G. (Editor), 1980. CREAMS: A Field-Scale Model forChemicals, Runoff, and Erosion From Agricultural ManagementSystems. Conserv. Res. Report No. 26, U.S. Department of Agri-culture, Science and Education Administration.

Lane, L.J., J.C. Ascough, and T. Hakonson, 1991. MultiobjectiveDecision Theory – Decision Support Systems With EmbeddedSimulation Models. Irrigation and Drainage Proceedings 1991,Honolulu, Hawaii.

Lawrence, P.A. and J. Robinson (Editors), 2002. Multiple ObjectiveDecision Making for Land, Water, and Environmental Manage-ment. Proceedings of the Second International Conference onMultiple Objective Decision Support Systems (MODSS). Depart-ment of Natural Resources and Mines, Report QNRM02143,Brisbane, Queensland, Australia.

Lawrence, P.A., J.J. Stone, P. Heilman, and L.J. Lane, 1997. UsingMeasured Data and Expert Opinion in a Multiple ObjectiveDecision Support System for Semiarid Rangelands. Transac-tions of the ASAE 40(6):1589-1597.

Leonard, R.A., W.G. Knisel, and D.A. Still, 1987. GLEAMS:Groundwater Loading Effects of Agricultural Management Sys-tems. Transactions of the ASAE 30(5):1403-1418.

Loucks, D.P., 1995. Developing and Implementing Decision SupportSystems: A Critique and a Challenge. Water Resources Bulletin31(4):571-582.

Matthews, R. and W. Stephens (Editors), 2002. Crop-Soil Simula-tion Models: Applications in Developing Countries. CABI Pub-lishing, New York, New York, 277 pp.

Midwest Agricultural Research Associates, 1988. USER Manual –Cost and Return Estimator. USDA-Soil Conservation Service,Contract No. 54-6526-7-268.

NRCS (Natural Resources Conservation Service), 1999. Evaluationof the Agricultural Research Service, Multi-Objective DecisionSupport System for Conservation Planning. File Code 190.

NRCS (Natural Resources Conservation Service), 2003a. Iowa’sField Office Section V, Iowa eFOTG, National CPPEs. Availableat http://www.nrcs.usda.gov/technical/efotg. Accessed on Decem-ber 24, 2003.

NRCS (Natural Resources Conservation Service), 2003b. Iowa’sField Office Section III, Resource Quality Criteria and GuidanceDocuments for Cropland, MLRA 107-1D3. Available athttp://www.nrcs.usda.gov/technical/efotg. Accessed on December24, 2003.

NRCS (Natural Resources Conservation Service), 2003c. IowaNRCS Harrison County Section III Field Office Technical Guide,Resource Quality Criteria, Soil Section. Available at http://efotg.nrcs.usda.gov/references/public/IA/Soil_8_2003.pdf.Accessed in March, 2004.

Newman, S., T. Lynch, and A.A. Plummer, 2000. Success and Fail-ure of Decision Support Systems: Learning as We Go. Proceed-ings of the American Society of Animal Science, 12 pp.Available at http://www.asas.org/jas/symposia/proceedings/0936.pdf. Accessed on December 30, 2003.

Paige, G.B., J.J. Stone, L.J. Lane, D.S. Yakowitz, and T.E. Hakon-son, 1996. Evaluation of a Prototype Decision Support Systemfor Selecting Trench Cap Designs. Journal of EnvironmentalQuality 25:127-135.

Renard, K.G., G.R. Foster, G.A. Weesies, D.K. McCool, and D.C.Yoder (Coordinators), 1997. Predicting Soil Erosion by Water: AGuide to Conservation Planning With the Revised UniversalSoil Loss Equation (RUSLE). U.S. Department of Agriculture,Agriculture Handbook No. 703, 404 pp.

Rizzoli, A.E. and A. J. Jakeman (Editors), 2002. Integrated Assess-ment and Decision Support. Proceeding of the First BiennialMeeting of the International Environmental Modelling and Soft-ware Society, University of Lugano, Switzerland, Vols. I-III,1,650 pp.

Saaty, T. L., 1990. How to Make a Decision: The Analytic HierarchyProcess. European Journal of Operational Research 48(1):9-26.

SOURCEFORGE.net, 2002. Facilitator Multiobjective DecisionSupport System Available at http://facilitator.sourceforge.net/.Accessed in March 2004.

Williams, J.R., C.A. Jones, J.R. Kiniry, and D.A. Spanel, 1989. TheEPIC Crop Growth Model. Transactions of the American Societyof Agricultural Engineers 32:497-577.

Wischmeier, W. and D. Smith, 1978. Predicting Rainfall-ErosionLosses. Agriculture Handbook 537, USDA Science and Educa-tion Administration: Washington, D.C.

Wymore, A.W., 1988. Structuring System Design Decisions. In: Sys-tems Science and Engineering, C. Weimin (Editor). Proc. Intl.Conf. on Systems Science and Engineering (ICSSE ‘88), Beijing,China, pp. 704-709.

Yakowitz, D.S., L.J. Lane, J.J. Stone, P. Heilman, and R.K. Reddy,1992. A Decision Support System for Water Quality Modeling.Water Resources Planning and Management Proceedings of theWater Resources Sessions/Water Forum ‘92, EE, HY, IR, WRDiv/ASCE, Baltimore, Maryland, pp. 188-193.

Yakowitz, D.S., L.J. Lane, and F. Szidarovsky, 1993a. Multi-Attribute Decision Making: Dominance With Respect to anImportance Order of Attributes. Applied Mathematics and Com-putation 54:167-181.

Yakowitz, D.S., J.J. Stone, L.J. Lane, P. Heilman, J. Masterson, andB. Abolt, 1993b. A Decision Support System for Evaluating theEffects of Alternative Farm Management Practices on WaterQuality and Economics. Water Science and Technology 28(3-5):47-54.

Yakowitz, D.S. and M. Weltz, 1998. An Algorithm for ComputingMultiple Attribute Additive Value Measurement Ranges Undera Hierarchy of the Criteria: Application to Farm or RangelandManagement Decisions. In: Multicriteria Analysis of Land-UseManagement, E. Beinat and P. Nijkamp (Editors). Kluwer Aca-demic Publishers, Boston, Massachusetts, pp. 163-177.

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