Optimizing the environmental performance of agricultural activities: A case study in La Boulouze...

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Ecological Indicators 22 (2012) 27–37

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Ecological Indicators

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ptimizing the environmental performance of agricultural activities: A casetudy in La Boulouze watershed

ounès Darradia,∗, Etienne Saurb, Ramon Laplanaa, Jean-Marie Lescota, Vanessa Kuentza,urghard C. Meyerc

Cemagref, Unité Aménités et Dynamiques des Espaces Ruraux (ADER), 50 Avenue de Verdun F-Gazinet, 33612 Cestas Cedex, FranceENITA Bordeaux, 1 Cours du Général de Gaulle, CS40201, 33175 Gradignan Cedex, FranceTU Dortmund, August-Schmidt-Straße 10, 44221 Dortmund, Germany

r t i c l e i n f o

eywords:nvironmental performancearming systemsWAToal programmingpatial optimizationatershed

a b s t r a c t

This paper introduces a method to optimize the environmental performance (EP) of agricultural activities,defined as the distance between the ‘environmental’ state of a system at a specific time or on a specificperiod and a ‘high environmental performance’ state to reach. Focus is on water management at thewatershed level with three criteria: nitrogen, sediments (water quality) and water yields (water quantity).The purpose is to provide a new land-use plan. The originality of the method lies in the coupling betweenan agro-hydrological model, the soil and water assessment tool (SWAT), and the goal programming (GP)

The method is implemented on the La Boulouze watershed (South West of France). The results are animprovement of the EP, but the ‘high environmental state’ is not reached. The main land uses changesare the decrease of the sunflower–wheat farming system in favor of the meadows.

Nowadays, the impact of agricultural activities on naturalesources is widely recognized (Lynam and Herdt, 1989; Pacinit al., 2004b) and most policies in Europe take it into account. Theommon agricultural policy (CAP) (EC, 2003) and several directives

e.g. the Water Framework Directive 2000/60/EC and Directive1/676/EC on nitrates – aim to promote environmentally friendlygriculture. Subsidies are now decoupled from production and con-traints are established. These constraints are based on standardseveloped for natural resource management (de Graaff et al., 2010;osthumus and Morris, 2010). Moreover, new policies are no longerased on means but rather on results, suggesting a global approachay be needed.As the logic behind the conception of policies has changed, the

bject of their evaluation has become the measurement of therogress achieved compared to standards (defined here as thresh-

∗ Corresponding author. Tel.: +33 5 57 89 26 95; fax: +33 5 57 89 08 01.E-mail addresses: y darradi@yahoo.fr, younes.darradi@gmail.com (Y. Darradi),

-saur@enitab.fr (E. Saur), ramon.laplana@cemagref.fr (R. Laplana),ean-marie.lescot@cemagref.fr (J.-M. Lescot), vanessa.kuentz@cemagref.frV. Kuentz), burghard.meyer@tu-dortmund.de (B.C. Meyer).

Lehtonen et al., 2007). EP has also been integrated into the environ-mental management system frameworks through two standards:ISO 14001 and ISO 14031 (Tyteca et al., 2002). It is defined asthe “measurable results of an organization’s management of itsenvironmental aspects1” (AFNOR, 2004). However, this definitionremains general and does not stress the importance of the sitewhere the activities whose EP is studied are located. The environ-mental characteristics of the area and the way they influence EP arenot assessed.

Many studies have been carried out by researchers on the assess-ment of this performance to create tools to evaluate policies. Thesestudies were first developed in industry (Tyteca, 1996) and morerecently in agriculture (Hanegraaf, 1998; van der Werf and Petit,2002; Pacini et al., 2003, 2004b; Vlahos and Beopoulos, 2003;Seymour and Ridley, 2005; Galdeano-Gomez et al., 2006). The main

1 An environmental aspect is defined as an “element of an organization’s activitiesor products or services that can interact with the environment” which has or canhave environmental impacts. An environmental impact is then “any change to theenvironment, whether adverse or beneficial, wholly or partially resulting from anorganization’s environmental aspects” (AFNOR, 2004,2000).

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f activities within a specific study site, the environmental charac-eristics of the area and the way they influence EP are usually notntegrated into its assessment.

Fewer works have been published on the improvement and spa-ial optimization of EP (Färe et al., 1996; Tyteca, 1997; Callens andyteca, 1999). When applied in agriculture, they mainly consist inhe assessment of the EP for different farming systems (Hanegraaf,998; Pacini et al., 2003, 2004b), the farming system with the high-st EP should then be preferred to the others.

The objectives of this paper are:

to develop a methodology to optimize the EP of agricultural activ-ities pertaining to water and

to experiment the proposed approach on a case study: the LaBoulouze watershed.

The focus is on the methodological approach while the casetudy is mainly for illustrating how the method can be imple-ented.The method simulates scenarios taking into account the spa-

ial heterogeneity of the environment characteristics (i.e. soilypes and slopes). Its originality lies in the coupling of two dif-erent approaches: agro-hydrological modeling and multicriteriaptimization. The aim is to locate different farming systems atatershed scale and thus to provide a new land use plan mini-izing impacts on water regarding three main criteria: nitrates,

ediments (water quality factors) and water yields (water quantityactor). Gross margins are added as an objective in the optimiza-ion in order to obtain a new land use which would be economicallycceptable to the different stakeholders who could be involved, andore especially the farmers.The first part of the paper provides a literature review on the

oncept of EP. Secondly, we examine the different methods usedor its evaluation and optimization and then display the proposed

ethod. Thirdly we present some results of the implementation ofhe method on the La Boulouze watershed (South West of France).inally, we discuss the strengths and weaknesses of this method.

. Concept of environmental performance

.1. Environmental performance as a part of economicerformance in industry

The main studies on EP proceed from industry. Tyteca (1996)onducted a substantial review of the EP concept but providedo specific definition. However, it can be deduced from what iseasured that EP is the potential impacts an industry have on the

nvironment—the negative ones being called undesirable outputs.P would allow us to compare analogous units, plants, firms or evenndustrial sectors or to monitor the behavior of one unit studiedver time, i.e. to compare the current impacts of this unit to its pastnes. The method described by Tyteca (1996) is the total produc-ivity factor approach which, using distance functions, calculatesifferent ratios including inputs, desirable outputs and undesir-ble outputs. Afterwards, this method was developed by differentuthors but EP has still not been defined precisely (Färe et al., 1996)ven though some have emphasized that it is part of a more globalramework, sustainable development (Tyteca, 1997; Callens andyteca, 1999). Olsthoorn et al. (2001) did not define EP preciselyither, but the object of the measurement is again the impacts – orndesirable outputs – of a plant or a company on the environment.

onsiders EP as a purpose in itself but speak for integrating the envi-onmental dimension into the economic one. These papers seem to

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include EP as a constraint that must be taken into account but notas the main objective.

Gray and Shadbegian (2007) add the importance of localization,as the EP of an industrial plant is “spatially related”, i.e. it dependson the specific characteristics of its localization and on the EP of theplants close to it.

As a conclusion, EP is widely used in industry but almost neverdefined. In most cases the definition is not explicit and has to bededuced from parts of the text contents of the papers.

1.2. Environmental performance as the compliance withregulations in agriculture

Unlike EP in industry, EP in agriculture received scarce attentionuntil a few years ago. Carpentier and Ervin (2002) explain it notablyby the fact that diffuse pollutions from farms are less visible to thepublic than larger industries’ pipes or stacks and by a regulatoryframework which is less strict than in industry.

Following – amongst others – Tyteca (1996, 1997) and Färe et al.(1996), Galdeano-Gomez et al. (2006) adapted the total factor pro-ductivity method at the scale of horticultural cooperatives. They didnot define EP, while it is measured as compliance with standardscoming from regulations. EP is not an aim but studied through itsinfluence on the productivity of cooperatives.

Grolleau (1998) adapted the ISO 14001 norm to agriculturealthough he kept its definition as the measurable results of anorganization’s management of its environmental aspects. The orga-nization is then the farm. Seymour and Ridley (2005) discussed thepossibility of using ISO 14001 at a bigger scale than the farm, i.e.the catchment. They do not provide any explicit definition thoughone can be deduced from their work. Paraphrasing ISO 14001, EPwould then be the measurable results, at catchment scale, of themanagement by the farms included in the catchment of their envi-ronmental impacts. The definition from ISO standards is still toogeneral to be used directly in agriculture with its specificities. How-ever, it is one of the few definitions available and can be used as abasis for a specific one for agricultural activities.

Other authors consider EP to be the environmental pillar of sus-tainable development but still do not define it. From the methodsused for its measurement, it is the compliance of environmentalimpacts vis-à-vis objectives (van der Werf and Petit, 2002; Paciniet al., 2003, 2004a,b; Vlahos and Beopoulos, 2003). Dealing withsustainability in agriculture, Caporali et al. (1989) suggest minimiz-ing the negative environmental impacts of agriculture by aimingfor an “environmentally sound agriculture” and “environmentallycompatible agroecosystems”. As the concept of agroecological per-formance is used to achieve these environmentally compatibleagroecosystems, we can conclude that it is the distance betweenthe state of the agroecosystem (defined for instance by a farmboundaries) and an environmentally sound agroecosystem. Thisenvironmentally sound agroecosystem is defined as an ecosystemwhere agricultural activities are compatible with environment pro-tection.

Using the notion of distance allows EP to be defined as an opera-tional concept, usable for our purpose as it facilitates its assessmentand optimization.

1.3. An operational definition based on the notion of distance

After the review of the literature on EP, the conclusion isthat there was no widely accepted definition. Therefore, basedon notions commonly linked to it such as distance, environmen-

Y. Darradi et al. / Ecological Indicators 22 (2012) 27–37 29

Table 1Different methods for evaluating EP in agriculture.

Method Author/Date Scale Object of the study Remarks/observations

Total productivity factor Galdeano-Gomez et al.(2006)

Horticulturalcooperativesconsidered as firms

Link between EP and productivity Adapted from the method developed by Tytecaand Färe (Färe et al., 1996; Tyteca, 1997; Callensand Tyteca, 1999; Tyteca et al., 2002) in industry.Measure the “undesirable outputs” in a normativeframework, i.e. the distance between the valuemeasured and “the minimum standards requiredby the environmental controls”.

ISO 14001 Grolleau (1998) Farm Adaptation of ISO 14001 inagriculture

ISO norms provide a method to measure EP whichcan be used in agriculture given the requiredchanges.

Seymour andRidley (2005)

Catchment Possibility of using EMS(Environmental ManagementSystem, based on the ISO 14000series of standards) which aredesigned at the farm scale and at abigger scale: the catchment.

“ISO 14001 does not set requirements forenvironmental performance beyond commitment,compliance, and continual improvement. Thus, ISOis not a guarantee that acceptable environmentalperformance is actually occurring”.As ISO 14001 was designed for a smaller scale,there is no guarantee that the changes wanted atthe catchment scale will happen.Link between the farm practices and the outcomesat the catchment scale: actions to improve EP atfarm scale impact the catchment scale.

Indicators de Snoo (2006) Farm Creation of a tool to benchmark theEP so farmers can compare witheach other

It deals with pesticides with 2 indicators: theamount used and the potential impact of that use.

Environmental performanceindicators

Hanegraaf(1998)

Farm Measure the EP concerning nitratesfor two different farming systems

Measure the environmental impacts of farming.2 indicators: nitrate surplus and emission ofnitrous oxide.

parising s

s, at fi

aaeotfblFrago

Ecological-economic modelbased on linear programming

Pacini et al.(2003, 2004a,b)

Farming systems atfarm and field scale

Comfarmone

ctivities would be: the environmental performance of agriculturalctivities within an area is defined by the distance between thenvironmental state of the relevant ecosystems at a specific timer over a specific period and the environmental state to reach forhese ecosystems, considered to be of a “high environmental per-ormance level”. If the environmental state of the system is definedy n criteria, EP is then a n-dimensional Manhattan distance (abso-

ute deviation between the value obtained and the targeted one).ig. 1 presents a schematic representation of EP when three crite-ia are considered. Criteria may be the concentrations of pollutinggents in the rivers or the soils, or the quantity of water used to irri-ate the fields. In this example, the criteria are the concentrationsf nitrates, the concentrations of sediments and the water flows.

. Method for the optimization of environmentalerformance

.1. Assessment of environmental performance through the use of

Tyteca (1996) pointed out the need for tools to assess the EPf firms with objectivity and outlined a need for a new method.

ig. 1. Schematic representation of environmental performance when defined by 3riteria.

on of EP of organicystems and conventionaleld and farm levels

Environmental impacts have to comply withthresholds (e.g. 50 mg/L when studying nitrateleaching) while the gross margins are maximized.

Existing methods such as life cycle analysis (LCA) do not inte-grate the impacts of the different products taken into account intoone or more indicators and environment impact assessment isnot adapted for comparing a large number of units. Therefore, theauthor suggested the total productivity factor method.

Afterwards, this approach was developed by differentauthors—including Tyteca. Some developed models to createenvironmental performance indicators (EPI) and suggested newones, using a distance function to improve the environmentalperformance of firms (Färe et al., 1996; Tyteca, 1997; Callensand Tyteca, 1999; Tyteca et al., 2002). This method follows aneconomic logic as the environmental impacts are studied throughtheir connection with the costs generated and not as the mainpurpose.

Olsthoorn et al. (2001) suggest that most evaluation methodsare based on indicators and do not directly measure the environ-mental quality of the system studied. Two reasons are outlined.Firstly, data related to environmental impacts are usually unavail-able. Secondly, the costs for acquiring data when dealing directlywith environmental impacts are usually high.

Further to this, Tsoulfas and Pappis (2008) suggest that there isno single set of indicators which could permit an evaluation of theEP but use EPI – amongst many others – in a model based on mul-ticriteria decision making (MCDM) in order to evaluate differentscenarios taking into account the environmental issues.

Table 1 summarizes EP-based articles which share the commonaims of (i) comparing farms or farming systems with each other asregards environmental criteria or (ii) minimizing the impacts oneconomic performance of compliance with environmental regula-tions. The methods presented are designed to be implemented at asmaller scale than the catchment except for the work by Seymour

In addition to the methods presented in Table 1, it can be notedthat van der Werf and Petit (2002) review the literature basedon the evaluation of environmental impacts at farm level through

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welve indicator-based methods. While only one of these methodsims to assess EP (as the “compliance with codes of good agricul-ural practice”), the object of the assessment is the same in fivether methods out of the twelve they study. As the goal of theseve methods is to measure environmental impacts, the authorsonclude that evaluating EP or environmental impacts is the same.he remaining six methods aim at evaluating environmental sus-ainability by assessing the environmental impacts of agriculture.hese methods define targets for these impacts, and the evalua-ion attempts to assess their level of achievement. The evaluationan be based on means, effects or both and the authors underlinehe advantages of effect-based indicators because “the link to thebjective is more direct”, but they are “more costly”.

The methods used for the evaluation of sustainability were alsoeviewed as its link with EP (EP being then the environmental pillarf sustainability) was outlined before: they are also based on indi-ators while the scale of the study is the farm and/or the farmingystems (Caporali et al., 1989; Yiridoe and Weersink, 1997; Tellarinind Caporali, 2000).

These methods presented above are designed to measure EP buto not deal with its optimization. They are mostly based on indi-ators. Indicators provide a global assessment of the EP of farmingystems, mainly at the farm scale, but they are mostly based on theeans implemented and their potential effects rather than on the

ctual effects. Moreover, farming systems are scattered in the areastudied and indicators are not spatialized. Thus, an indicator-basedethod would not meet the requirements of our study.Besides, we define EP as a distance to a desired state. As this

tate is described by three main criteria – nitrates, sediments andater yields – EP is a multidimensional distance. The optimiza-

ion method must then take into account the different criteria asell as the uneven nature of the environment where farming sys-

ems are located. Thus focus is now on a multicriteria optimizationntegrating spatial distribution.

.2. Multicriteria optimization as a tool to optimizenvironmental performance

Romero (1997) proposed a multicriteria approach to addressasic problems in environmental economics when dealing withppraisal and management of environmental assets, based on mul-iple criteria decision making (MCDM). Outlining the difficulties ofsing cost–benefit analysis, as it involves giving a monetary valueo all assets, he chose MCDM as it “has revealed itself as a power-ul methodology to address problems related to the managementnd planning of natural and environmental resources” (Romero,997). He sought a compromise between environmental and eco-omic objectives and used compromise programming (CP), whichaximizes (or minimizes) the objectives simultaneously in relation

o two different vectors of values, Anchor and Nadir. The first oneorresponds to the “best values” for each objective, not consideringhe others, while the second corresponds to the “worst values”. The

ain characteristic of CP is that there are no target-values: Anchornd Nadir values differ between areas studied depending on theirpecific characteristics and farming systems.

Sumpsi et al. (1997) outline that when optimizing several objec-ives, the main methods are the multi-objective programmingMOP) model (optimization of several objectives, possibly in con-ict with each others) and the goal programming (GP) model. Inoth cases the target-values are introduced by the user.

Linares and Romero (2002), studying electricity planning, alsomphasize that there are two main approaches when dealing with

eria to one expressed in monetary terms”, which is not totallyatisfactory due to “large uncertainties underlying the monetaryaluation of health and environmental impacts”. The second is

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based on MCDM which can be viewed as too subjective but isalso “more transparent and flexible”. The authors then adapt a goalprogramming model developed by González-Pachón and Romero(2001). Goal programming is a model aiming to satisfy as far aspossible a set of goals while the target-values are introduced bythe user.

GP has been used in numerous studies over the years andAouni and Kettani (2001) reviewed a number of them. GP has beenimplemented in several sectors and for different issues, from themanagement of a reservoir watershed to vehicle park managementvia agriculture and forestry. Its flexibility and simplicity of use makeit one of the most popular MCDM methods.

The main difference between CP and GP lies in the definitionof the objectives to reach. The CP model defines by itself the vec-tors of Nadir and Anchor for the area of study considering its owncharacteristics. On the other hand, GP aims to reach target valuesdefined a priori by users for each criterion. The state to reach fromthe EP definition can thus be either the Anchor values vector or a“normative” framework created from a priori defined targets. Asthe choice is made to refer to a normative framework, GP appearsmore suitable for this study.

Many variants exist for GP, from weighted goal program-ming to lexicographic goal programming, and Romero (2004)discussed them by comparing their achievement function. Thegeneral structure is an achievement function integrating the cri-teria to be optimized while abiding by constraints. These elementsare presented in the next part of this paper where we propose amethodology for optimizing EP.

2.3. Methodology for optimizing environmental performance

2.3.1. Bio-physical modeling and thresholdsOptimization of the EP is carried out by coupling hydrological

modeling and multicriteria optimization as performed in previousstudies (Lescot et al., 2007; Pradel, 2007; Meyer et al., 2009). Mod-els can be spatialized, providing values for specific farming systemson a specific area defined by their bio-physical (geology, pedology,topography, land use, etc.) and climate characteristics. They canalso integrate interactions between hydrological entities, and thenbe used for assessing the initial situation of the area studied relat-ing to EP and for generating data on criteria affecting the EP. Theoptimization of the EP is then based on the relocation of farmingsystems in the watershed in order to fulfill the objectives set.

As we aim to reach a targeted environmental state of the water-shed system described by three criteria (concentrations – in mg/L –of nitrates [NO3

−] and sediments [SED], amount of water availablein mm), target-values are defined, based on standards that alreadyexist. Directive 91/676/EC (Council of the European Communities,1991) on nitrates, which aims to achieve a good ecological statusof waters, describes the level of drinkability at 50 mg/L in surfacewaters. However, French water agencies use another normativeframework, SEQ’EAU (Agences de l’Eau, 1999), which defines sev-eral thresholds for the different uses. A threshold of 25 mg/L isselected for [NO3

−] as it includes all the uses, while 2000 mg/Lis adopted for [SED] as it is the threshold defined by SEQ’EAUfor drinkability. Finally, the low-water flow was considered as thethreshold for water flows, but as it can be difficult to obtain thisvalue the average of the three lowest flows is calculated and usedinstead.

The standards define thresholds that should never be exceeded,so the time scale for assessment should be as short as possible, lead-ing to the choice of a daily period. However, as data is usually not

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ig. 2. Methodology for assessing the initial situation and generating data for opti-izing EP of agricultural activities within the watershed.

he EP, the soil and water assessment tool (SWAT, version 2005) issed, a physically based, continuous-time agro-hydrological modelWinchell et al., 2008). SWAT has been acknowledged in varioustudies and has been internationally recognized as a robust inter-isciplinary watershed modeling tool (Santhi et al., 2006; Bärlundt al., 2007; Ullrich and Volk, 2009). It models water flows, nutrientransport and turn-over and vegetation growth. A large number ofarameters can be changed by the user to calibrate the model.

.3.2. Assessment and optimization approachesThe method to optimize the EP of agricultural activities is

ivided into two main steps: (i) assessment of the initial situationf the watershed pertaining to the three criteria studied, then (ii)ptimization by relocating farming systems within the watershed.

.3.2.1. Assessment of the initial situation. The assessment of thenitial situation in the watershed considering the criteria studieds based on modeling (Fig. 2). The SWAT model begins by defining

atershed boundaries, followed by the subbasins and Hydrologi-al Responses Units (HRUs) (Flügel, 1996a,b). HRUs are the smallernits at which data is processed, and are created by intersectinghe land use, soil and slope geographical shapes.

As the next step, farming systems, including various man-gement scenarios, are implemented in each HRU. The cropsanagements entered consist of the different growing opera-

ions, i.e. sowing, harvesting, fertilization, mechanical operationsnd irrigation, with their temporal characteristics. To illustrate, itntegrates for instance the quantity and the composition of the fer-ilizers used, the characteristics of the crops (e.g. growth cycle) andhe quantity of irrigation applied. To take into account longer effectsf changes to farming systems, SWAT should be implemented over

minimum period of twenty years.Results are sediments and nitrate loads, water yields and crop

arvests at the HRU, subbasin and watershed scales. Concentrationsf nitrates and sediments in the water are calculated only at theubbasin and watershed scales, as the HRU scale does not integratell the processes occurring in the main stream and thus be unusable.verages for these values are calculated.

The initial status of the watershed (basis line run, cf. Fig. 2) isssessed by implementing farming systems corresponding to itsresent agricultural land use. Results obtained are [NO3

−], [SED],ater flows and crops harvests at the subbasin and watershed

cales.

icators 22 (2012) 27–37 31

2.3.2.2. The link between soil water assessment tool and optimization.The optimization aims to reach the target-values for all the criteriasimultaneously. In order to do so, the consequences of implement-ing a particular farming system on a particular HRU in terms ofnitrates, sediments, water and crop yields must first be described.Therefore, values are needed for these four criteria for each HRUdepending on the farming system (FS) implemented.

Loads are used for nitrates and sediments instead of concentra-tions because there would be no logic in using concentrations forHRUs whose area and water yields are different. Moreover, con-centration can easily be calculated from these loads and the wateryields. The values used are averages over the period studied.

One and only one farming system is implemented on the wholewatershed in order for SWAT to generate values for nitrate andsediment loads and water yields for this particular farming systemon all the HRUs. This step is repeated until simulations are run forall the farming systems existing initially in the watershed.

This method to generate nitrate and sediment loads, water andcrop yields for all the couples (HRU/farming system) existing ismade possible by the fact that SWAT processes the HRUs indepen-dently and therefore that the values obtained at the level of a HRUdo not pertain to the farming system implemented on the HRUsnearby.

2.3.2.3. Formulation of the optimization problem. The aim of theoptimization is to relocate farming systems in the watershedusing the data generated previously. The optimization method usesweighted goal programming (WGP) which is an extension of goalprogramming (GP). WGP is based on an achievement function com-pounded of the negative and positive deviations from the targetvalues. For q criteria, it can be written as follows (Lee and Clayton,1972; Romero, 2004):

q∑i=1

(˛ini + ˇipi) (1)

where ni and pi are, respectively, the negative and positivedeviations, i.e. the distances (absolute values) between the accom-plishment level of the objective i noted fi and the targeted levelnoted ti with:

∀i = 1, . . . , q fi(x) + ni − pi = ti

ni ≥ 0, pi ≥ 0 and x ∈ F, F being the feasible set of constraints.

˛i ={

kiif ni is unwanted

0 otherwise

ˇi ={

kiif pi is unwanted

0 otherwise

wi is the weight used to reflect the preference given to theachievement of the criteria i and ki the weight used for normalizingpurposes.

As the proposed methodology is based upon the concept of dis-tance, it can be remarked that the WGP model underlies a distancebased on metric p = 1 when GP is thought of as a special case of thegeneral distance-function model written as (Romero, 1985):

w∣∣t − f (x)

∣∣p]1/p

Thus, this model implies the minimization of the sum of weighteddeviations with respect to the targets established.

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Out of the three criteria used to define EP, only nitrate and sedi-ent loads are defined as objectives. On the contrary, water yields

re set as a constraint: the objective is only to keep them above theinimum value defined previously. Moreover, limited reduction of

ross margins at the watershed scale is set as an objective in orderot to take into account environmental stakes only.

As the goal is to decrease nitrate and sediment loads while hav-ng the best gross margins possible, the achievement function is asollows:

(˛∑

12∑im=1

vlP(ib, im)Lsbiniyear(ib)

+ ˇ∑

12∑im=1

vsedP(ib, im)SEDsbiniyear(ib)

+ �∑

vgmN(ib)GMsbiniyear(ib)

here ib is the index for the subbasins; im is the index for theonths; ̨ is the weight for the nitrate load objective; ̌ is theeight for the sediment load objective; � is the weight for the

ross margin objective; vlP(ib,im) is the positive deviation variableor overachievement of the nitrate load goal for the subbasin iburing the month im; vsedP(ib,im) is the positive deviation variableor overachievement of the sediment load goal for the subbasin iburing the month im; vgmN(ib) is the negative deviation variableor underachievement of the gross margin goal for the subbasin ib;sbiniyear(ib) is the initial average nitrate load for a year on the sub-asin ib; SEDsbiniyear(ib) is the initial average sediment load for aear on the subbasin ib; GMsbiniyear(ib) is the initial average grossargins for a year on the subbasin ib.The nitrate and sediment loads are optimized at a yearly scale

sing data obtained at a monthly scale while the gross margins areirectly defined at a yearly scale. Moreover, the deviation variablesre divided by values corresponding to the initial average one peronth in order not to have any more units and thus normalize the

ifferent components of this achievement function.As the model should incorporate the discrete and multi-

bjective nature of farming system allocation, the decision variables a binary variable vX(if,ib,ih) taking the value of 1 if the farming sys-em if is assigned to the spatial unit of the watershed and the valuef 0 if it is not (ih is the index for the HRUs). The model is written as

MILP (mixed-integer linear programming) using GAMS (generallgebraic modeling system), a modeling system for mathematicalrogramming and optimization consisting of a language compilernd solvers.

Constraints can be integrated into the optimization. Thus, oneas added to water yields, maintaining them, at the subbasin scale

nd for each month, equal to or greater than the sum of the averagesf the three minimum water yields at the HRU scale. Another onetates that one and only one farming system can be allocated to aRU. It is suggested to add a constraint to crop yields too in order

o keep the same kind of crop productions in the watershed. Fornstance, it can be considered that in each subbasin the productionor each crop must not decrease by more than X%.

Several spatial scales are taken into account in the method pro-osed. This outlines the fact that two hypotheses on changes topatial scales were introduced: (i) changes at HRU scale will impactP at subbasin and watershed scales and (ii) optimizing at sub-asin or watershed scale is not the sum of the optimizations at

In a nutshell, the optimization is at watershed scale with con-traints at subbasin scale and data at HRU scale. Moreover, theeviation variables used in the objective equation are not gener-ted directly at watershed scale, but at subbasin scale. Therefore,he optimization aims to minimize sums – at watershed scale – ofeviation variables calculated at subbasin scale.

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2.3.2.4. Expected outcomes. The main result of optimization is alocation variable indicating which farming system if has to beimplemented on the HRU ih from the subbasin ib in order tooptimize EP at the watershed scale. The changes outlined aremade to optimize the EP of the agricultural activities of thewatershed.

This method also provides complementary results correspond-ing to implementation of the new land use: optimized nitrate andsediment loads, optimized water and crop yields and optimizedgross margins. These results can be at HRU, subbasin and/or water-shed scales.

The purpose of introducing the subbasin scale into the optimiza-tion is to limit the specialization at this level by restricting potentialcompensations between subbasins. If not, the optimization wouldbe less constrained. However, it is considered that the new landuse obtained should be acceptable to the stakeholders who couldpotentially be involved in the environmental issues of agriculturalactivities. Thus, a specialization of the subbasins could appear inthe new land use. Farmers may then not agree on it as the changesin their farming systems might be too great. It would also be hardfor the people living in the watershed to accept a situation wherethe concentrations in nitrates and/or sediments in the water issome subbasins would be quite high in order to get better values inothers.

The integration of the subbasin scale would have no meaning ifan increase in nitrates and sediments was allowed where they arealready below the target-values. Indeed, the advantages would dis-appear when the decision variables are summed at watershed scalein the objective equation. Therefore, the target-values for nitrateand sediment loads on the subbasins are changed in this case andbecome the initial values. The aim is then to keep these loads equalto or below the ones in the initial situation.

3. Case study: La Boulouze watershed

3.1. Study area

The method has been implemented as an illustration in the LaBoulouze watershed, located in the southwestern part of France.This watershed is shaped as a 69.2 km2 rectangle (Fig. 3) and isintegrated in the Save river watershed (1150 km2). The watershedis very hilly with a peak at 331 m, the western side being veryslopy compared to the eastern side where there are approximatelya dozen tributaries. The average slope is 11.5% with values between0 and 43%. The water flows from the south to the north. This studyarea has been chosen as nitrates issues have been observed on sev-eral parts of the watershed. Moreover erosion is said to be an issue,especially on the more hilly parts.

The climate is said to be “toulousain”, integrating elements fromthe oceanic and Mediterranean ones and specificities due to theproximity of the Pyrenean mountains. On the 1985–2006 period,the mean annual precipitation is 698.32 mm per year while theaverage temperature is 13.1 ◦C with a temperature range relativelysmall as it is less than 20 ◦C. Whereas there are some periods ofaridity during some years, they are considered as exceptions andthe area is then said not to be prone to drought.

The geological substrate is molasse which is impermeable, thusthe run-offs are mainly superficial and increase the potential nitro-gen leaching and erosion (Paegelow, 1991; Lavie, 2005; Perrin,2008). There are mainly three types of soils: clay soils, which canbe separated between thin ones and over 40 cm ones, sandy soils

Fig. 4 presents the hydrological delineations within the LaBoulouze watershed as they are created by the SWAT model. Thereare then 33 subbasins and 2162 HRUs.

Y. Darradi et al. / Ecological Indicators 22 (2012) 27–37 33

Fig. 3. Location of the La Boulouze watershed.

Fig. 4. Hydrological delimitations in

This area is mainly agricultural. The farming systems modeledome from another research project – Concert’eau2 – as typicalarming systems in the area. An example of farming system is

2 Concert’Eau is a demonstration of a collaborative technological platform toupport an integrative management of agriculture to diminish impacts on waternd related aquatic ecosystems of the pilot river basin “Gascogne Rivers” (Adour-aronne river basin district, France), in accordance with the WFD requirements.

T: watershed, subbasins and HRUs.

presented in Appendix A. Eight farming systems are identified fora total of eleven different land uses (Table 2 and Fig. 5). The basisof most farming systems is the sunflower–wheat rotation.

The modeling of the initial situation using SWAT emphasizesthat the nitrates concentrations are higher than the standard cho-

34 Y. Darradi et al. / Ecological Indicators 22 (2012) 27–37

Table 2Land uses within the La Boulouze watershed.

Farming systems

Name Abbreviation

Corn–soft wheat–sunflower–durum wheat C–SW–S–DWCorn–soya–sunflower–durum wheat S–Soya–S–DWPea–soft wheat–sunflower–durum wheat P–SW–S–DWSunflower–soft wheat–sunflower–durum wheat (cf.

Appendix A)S–SW–S–DW

Sunflower–soft wheat–sunflower–durumwheat–sorghum

S–SW–S–DW–So

Sunflower–soft wheat–sunflower–durumwheat–sunflower–barley

S–SW–S–DW–S–B

Sunflower–soft wheat–canola–sunflower–durum wheat S–SW–Co–S–DWMeadow Meadow

Other land uses

liprtM

si4sws

tLdtTdit

ForestUrban areasWater surfaces

evel is well represented with water flows values twice as importantn June compared to August. As the state of “high environmentalerformance level” is considered to be reached when all the crite-ia are below the thresholds defined in Section 2.3.1 (or higher inhe case of the water flows), the objectives are not reached from

arch to August, and therefore they are not reached in general.

.2. Results

.2.1. New land use planThe new land use plan for the La Boulouze watershed is pre-

ented in Fig. 6. The main evolution is that the surface of meadowss twice as important as in the initial situation (from 1419 ha to511 ha). It seems that this increase is done to the detriment of theurface of S–SW–S–DW (sunflower–soft wheat–sunflower–durumheat, from 4511 ha to 2966 ha). The other land uses do not present

ignificant changes of surfaces.

.2.2. An improvement of the environmental performanceThe consequences of the new land plan on the environmen-

al performance of the agricultural activities described within thea Boulouze watershed are presented in Table 3. We observe aecrease of the values of the sediments and nitrates concentra-ions, but the nitrates concentrations are still above the thresholds.

mportant at this level. Their analysis will be included in an articleo be published. The main point is that EP has been improved even

able 3ean values for the three criteria describing the environmental performance—initially an

Month [NO3−] (mg/L) [SED] (mg/L)

Initial situation After optimization Initial situation After optimizati

January 17.85 16.64 19.64 17.88

February 22.72 20.14 20.68 20.66

March 25.35 23.38 13.73 12.78

April 26.51 25.33 22.88 20.82

May 29.75 28.32 24.91 23.72

June 27.81 27.11 27.49 26.90

July 34.90 32.07 16.62 15.50

August 26.44 25.87 6.89 6.72

September 22.02 21.72 37.86 35.96

October 18.33 17.79 22.59 19.21

November 19.17 18.20 22.06 18.03

December 19.28 18.01 17.77 15.40

Mean value 24.18 22.88 21.09 19.47

Fig. 5. Farming systems in the La Boulouze watershed.

if the state of “high environmental performance level” has not beenreached.

4. Discussion and conclusions

In this paper we have proposed a definition of EP. However,it could be considered too general as it is based on the distance

d after optimization. Values above the thresholds are shaded.

Water flows (m3/s) “High environmental performancelevel” state

on Initial situation After optimization Initial situation After optimization

0.70 0.70 Reached Reached0.66 0.67 Reached Reached0.60 0.60 Not reached Reached0.59 0.59 Not reached Not reached0.72 0.72 Not reached Not reached0.82 0.84 Not reached Not reached0.60 0.62 Not reached Not reached0.37 0.40 Not reached Not reached0.49 0.52 Reached Reached0.43 0.46 Reached Reached0.52 0.53 Reached Reached0.63 0.64 Reached Reached

0.59 0.61

Y. Darradi et al. / Ecological Ind

bmtsitd

Etnkmoti

dmisddctl

Fig. 6. New land use plan in the La Boulouze watershed.

tate of a watershed depending on public policies), the character-stics of the watershed studied and the regulations of the country

here it is situated.The definition of EP was created as an operational one, aiming to

e used practically for the assessment and optimization of EP. Theethod proposed for its assessment is not quantitative but quali-

ative and no framework is provided in order to describe “differenttates” of EP, e.g. very good/good/medium/bad/very bad. This “gap”n the method is not considered to be a flaw because the goal is noto evaluate EP qualitatively. However, it can be a possible futureevelopment.

A method was then presented, created in order to optimize theP of agricultural activities in a watershed, based on the reloca-ion of farming systems at the HRU scale. The aim was to minimizeitrate and sediment loads while maximizing gross margins andeeping the water yields above a selected threshold. The achieve-ent function proposed was based on sums – at watershed scale –

f decision variables calculated at subbasin scale. In order to keephis subbasin scale, the target-values are changed not to permit anncrease of nitrate or sediment loads vis-à-vis the initial situation.

The choice of using sums of decision variables instead ofecision variables can be discussed, as the optimization is thenore restrained. Some may question the usefulness of this part as

t can be considered that the aim is to optimize EP at watershedcale and thus that solely the values obtained at this scale for theifferent criteria studied should be taken into account. The authors

icators 22 (2012) 27–37 35

added to the optimization, the subbasin scale was integratedto limit compensations and to facilitate potential compromisesbetween the different stakeholders who could be involved, andmore particularly the farmers.

However, if the subbasins are integrated into the optimizationin order not to obtain potentially unacceptably high rises in nitrateor sediment loads or decreases in gross margins in parts of thewatershed, the main scale is still the watershed. If not, the opti-mization would have been at subbasin scale and then the differentland uses obtained would have been aggregated to describe thewatershed. Optimizing at subbasin would represent a variation ofthe method proposed and a potential future development wouldbe to implement it and compare the results obtained.

The SWAT model uses data at HRU scale to calculate values atsubbasin level then at the watershed scale. While the values forwater and crops yields are just summed, SWAT also takes intoaccount the underlying processes regarding nitrates and sediments.However, these processes are not included in the optimization dueto the complexity of the equations involved (Neitsch et al., 2005;Winchell et al., 2008). Thus the target-values decided upon previ-ously cannot be used.

As we sum the values for nitrates and sediments loads during theoptimization, the optimized situation from GAMS pertaining thesetwo criteria is not the optimized one that would result from theimplementation of the optimized relocation of farming systems inSWAT. Thus, target values are replaced by reductions in the ini-tial values. The aim for nitrate and sediment loads has becomea decrease in their value by percentages decided from the initialsituation of the subbasins and the watershed.

The coupling between a model and WGP is the main strengthof this method but represents weaknesses too. Indeed, models ingeneral simplify reality in order to represent it. The results obtainedare an approximation of the “real” ones, depending on the equationsused. However, while few methods are available, this one has themerit of allowing not only an assessment of the initial situation, butalso the generation of data for the optimization.

WGP has many strong points, including its simplicity and easeof use. However, it also – as any tool – has feable points: the mainone in this method is the need to define weights. The user – wish-ing to optimize the EP of a given watershed – determines (i) if theremust be a difference in weight between nitrate and sediment con-centrations and (ii) which role is given to the economic criteria.The fact that EP is seen as a complement to economic performancein industry was underlined previously (Färe et al., 1996; Tyteca,1997; Callens and Tyteca, 1999; Dasgupta et al., 2001; Olsthoornet al., 2001; Gray and Shadbegian, 2007). This view of EP is alsosometimes found in agriculture (Galdeano-Gomez et al., 2006).Therefore, the weight for gross margins should not be too greatcompared to others. It is then suggested to give all weights the samevalue in order not to favor an optimization objective at the expenseof the others. As there are two environmental objectives against asingle economic one, the environmental dimension is still the mainone promoted. This has been the case for the implementation in theLa Boulouze watershed.

The outcome of this optimization is a new location of farmingsystems. Indeed, it shows which farming system must be imple-mented on which HRU as well as the nitrate and sediment loads,water and crop yields and gross margins characterizing it. However,the values for nitrates and sediments at subbasin and watershedscales are just sums of the ones at HRU scale. In order to gainbetter knowledge of the changes induced by the relocation of thefarming systems, the new locations are implemented in SWAT.

3 cal Ind

btaytomoacbtt

EwawtHmtroof

hey can obviously be discussed as they are mainly linked to Frenchegulations. These thresholds can be easily adapted to any situationhough. Moreover, as other criteria can be added, new target-valuesill have to be defined.

Finally, new environmental friendly farming systems can also bentegrated into the optimization. For instance, they could be basedn best management practices or organic industry requirements.

The strengths and weaknesses of this method were discussed,ut its weaknesses must not hide the fact that it is one ofhe few methods already existing in research. Moreover, EP waspproached from the angle of nitrate and sediment loads and waterields, but many other criteria can be used depending on the issueshe users wish to focus on. Indeed, criteria can be added as newbjectives in the achievement equation, assuming that they can beodeled and that the data needed can then be generated. Examples

f criteria that can be added are the concentrations of pesticides orny other concentrations of pollutants in the water. Other biologi-al indicators criteria or even criteria not related to water could alsoe added, but they are not modeled in SWAT. Another tool to obtainhe data needed for the optimization should thus be integrated inhe method.

This article presented a methodology created to optimize theP of agricultural activities within a watershed. The strong andeak points of this methodology have been emphasized, as well

s the fact that many choices were made without concertationsith all the stakeholders who could be involved in projects (e.g.

he criteria used or the weights for the optimization equation).owever, the authors believe that this method is an improve-ent in managing agricultural activities at watershed scale when

hey are known to have negative environmental impacts on wateresources. Using a coupling between a model and WGP allows tobserve the impacts of modifying farming systems or creating newnes. Therefore, integrating stakeholders concerns is made easieror potential applications.

The second objective of this paper was to implement thispproach, which was done in a French watershed. The first resultsbtained shows that EP has improved even if the high environmen-al performance state set as a goal is not reached. More completeesults and a deeper analysis of them are needed and will be theopic of an article to be published.

cknowledgments

This work has been carried out within the framework of IMAQUEnd INSOLEVIE projects and supported by funding from the FEDEREEC), CPER (Région Midi-Pyrénées) and the Régions Aquitaine and

idi-Pyrénées.

ppendix A. An example of farming system

Sunflower–soft wheat–sunflower–durum wheatManagementoperation date

Type of operation Nature/Quantity

Year 115-Mar Tillage operation Ploughing20-Mar Fertilization Two element fertilizer 60 kg P2O5,

65 kg K2O17-Apr Tillage operation Vibrocultor20-Apr Sowing Sunflower (SUNF)25-May Fertilization Urea 50 kg N25-Sep Harvest Sunflower25-Sep Tillage operation Stubble cleaning10-Oct Tillage operation Chisel

70 kg K2O25-Oct Tillage operation Circular spike harrow26-Oct Sowing Winter wheat (WWHT)

icators 22 (2012) 27–37

Appendix A (Continued )

Managementoperation date

Type of operation Nature/Quantity

Year 215-Jan Fertilization Three element fertilizer 50 kg

N + 65 kg P2O5 + 70 kg K2O20-Feb Fertilization Calcium ammonium nitrates 60 �N15-Mar Fertilization Urea 60 kg N5-Jul Harvest Winter wheat

Year 315-Mar Tillage operation Ploughing20-Mar Fertilization Two element fertilizer 60 kg P2O5,

65 kg K2O17-Apr Tillage operation Vibrocultor20-Apr Sowing Sunflower (SUNF)25-May Fertilization Urea 50 kg N25-Sep Harvest Sunflower10-Oct Tillage operation Cover-crop11-Oct Fertilization Two element fertilizer 65 kg

P2O5 + 50 kg K2O15-Oct Tillage operation Vibrocultor25-Oct Sowing Durum wheat (DWHT)

Year 420-Jan Fertilization Calcium ammonium nitrates 60 kg

N10-Mar Fertilization Urea 60 kg N15-Apr Fertilization Urea 70 kg N7-Jul Harvest Durum wheat (DWHT)

Year 1. . .

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