Conceptual development of a harmonised method fortracking change and evaluating policy in theagri-environment: The Agri-environmental Footprint Index

Gordon Purvis a,*, Geertrui Louwagie a, Greg Northey a, Simon Mortimer b, Julian Park b,Alice Mauchline b, John Finn c, Jørgen Primdahl d, Henrik Vejre d, Jens Peter Vesterager d,Karlheinz Knickel e, Nadia Kasperczyk e, Katalin Balazs f, George Vlahos g,Stamatios Christopoulos g, Jukka Peltola h

aAgriculture and Food Science Centre, School of Biology and Environmental Sciences, University College Dublin,

Belfield, Dublin 4, Irelandb School of Agriculture, Policy and Development, The University of Reading, Earley Gate, PO Box 237, Reading RG6 6AR, UKcTeagasc, Johnstown Castle, Environment Research Centre, Wexford, IrelanddDanish Centre for Forest, Landscape and Planning, University of Copenhagen, Rolighedsvej 23, DK-1958 Frederiksberg C, Denmarke Institute for Rural Development Research at Johann Wolfgang Goethe University, Zeppelinallee 31, 60325 Frankfurt am Main, Germanyf Institute of Environmental and Landscape Management, Szent Istvan University, Godollo, HungarygDepartment of Agricultural Economics and Rural Development, Agricultural University of Athens, GreecehMTT Agrifood Research Finland, Jokioinen, Finland

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7

a r t i c l e i n f o

Published on line 12 February 2009

Keywords:

Agriculture

Environmental quality

Index-based evaluation

Multiple criteria analysis

Participatory approach

Natural resources

Biodiversity

Landscape

a b s t r a c t

An aggregated farm-level index, the Agri-environmental Footprint Index (AFI), based on

multiple criteria methods and representing a harmonised approach to evaluation of EU agri-

environmental schemes is described. The Index uses a common framework for the design

and evaluation of policy that can be customised to locally relevant agri-environmental

issues and circumstances. Evaluation can be strictly policy-focused, or broader and more

holistic in that context-relevant assessment criteria that are not necessarily considered in

the evaluated policy can nevertheless be incorporated. The Index structure is flexible, and

can respond to diverse local needs. The process of Index construction is interactive,

engaging farmers and other relevant stakeholders in a transparent decision-making process

that can ensure acceptance of the outcome, help to forge an improved understanding of

local agri-environmental priorities and potentially increase awareness of the critical role of

farmers in environmental management. The structure of the AFI facilitates post-evaluation

analysis of relative performance in different dimensions of the agri-environment, permit-

ting identification of current strengths and weaknesses, and enabling future improvement

in policy design. Quantification of the environmental impact of agriculture beyond the

stated aims of policy using an ‘unweighted’ form of the AFI has potential as the basis of an

ongoing system of environmental audit within a specified agricultural context.

# 2009 Elsevier Ltd. All rights reserved.

available at www.sciencedirect.com

journal homepage: www.elsevier.com/locate/envsci

* Corresponding author.E-mail address: gordon.purvis@ucd.ie (G. Purvis).

1462-9011/$ – see front matter # 2009 Elsevier Ltd. All rights reserved.doi:10.1016/j.envsci.2009.01.005

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7322

1. Introduction

Since the 1990s, the reform process of the Common Agricul-

tural Policy (CAP) of the EU has increasingly incorporated an

environmental dimension. The Agricultural Council Integra-

tion Strategy and subsequent Helsinki European Council (1999)

formally integrated the environmental dimension into the

CAP (CEC, 2000, 2006a). The Strategy sets integration objectives

for water, agro-chemicals, land use and soil, climate change

and air quality, as well as landscape and biodiversity. Such

policy is implemented through a mixture of instruments

ranging from regulatory measures specified by agricultural

and environmental legislation and directives, to agri-environ-

mental incentive schemes with voluntary participation

(Pearce, 2005). The latter are intended to encourage envir-

onmentally sensitive farming and environmental manage-

ment practices in agriculture that go beyond the requirements

of legislative controls, which are a means to enforce minimum

environmental standards and prevent environmental degra-

dation beyond agreed reference points (OECD, 2001; Bromley,

1997; Pearce, 2005). The implementation of such a wide range

of instruments is expected to result in improved, maintained

and/or protected environmental conditions as formulated in

the policy objectives. The 1992 CAP reforms included provi-

sions for Member States to establish agri-environment

schemes (the Agri-environmental Regulation, Council Regula-

tion (EEC) No. 2078/92). Following Agenda 2000 reform, the

Rural Development Regulation (Council Regulation (EEC) No.

1257/1999) combined several policy measures, including the

adoption of specific agri-environmental incentive measures.

As a result of these initiatives, approximately 25% of

agricultural land in the EU-15 is now managed under

dedicated incentive schemes (EEA, 2006) (henceforth termed

AE-Schemes), with specified management packages describ-

ing farming obligations included in a contract (usually of 5 or

10-year duration) signed by individual farmers.

AE-Schemes are co-financed by the EU and the Member

States, whereby since the 2003 CAP reform, the EU contributes

a maximum of 85% of scheme payments in Objective 1 areas

(regions specified as economically underperforming) and 60%

in other areas. Between 1999 and 2003, the European

Community spent close to two billion euros per year in this

way (EC, 2005a). Understandably with this level of public

funding expenditure, there is a need to justify payments by

evaluation and establishment of the environmental and wider

social benefits achieved. All Member States must make

provision for effective implementation and evaluation of

their schemes and assessment of environmental impact are

important elements of the required evaluation process (CEC,

2000, 2005, 2006b; EC, 2007a,b). Mid-term reviews (MTRs) of

Rural Development Programmes (EC, 2004, 2005b) have not

clearly established and effectively demonstrated the environ-

mental benefits of adopted policy. In 2006, the Common

Monitoring and Evaluation Framework (CMEF) was published

to guide the evaluation process (EC, 2006). Whilst the CMEF

provides a strategic and comprehensive approach to AES

evaluation, it does not provide specific examples of successful

evaluations, and there is little consensus on how to set,

monitor and validate performance targets for AE-Schemes.

Even more importantly, there are no agreed methodologies for

‘tracking’ the effects of constant ongoing change in environ-

mental, agricultural and rural socio-economic conditions

within European farming. Such change can result from a

much wider range of factors, including climate change,

urbanisation, changing patterns of land use, technological

innovation and new market opportunities. The research

reported here directly addresses the need for a harmonised

methodology for assessment of the environmental perfor-

mance of AE-Schemes, and the more general need for wider

agri-environmental monitoring and assessment.

2. Existing AE-Scheme evaluation

In practice, past evaluations of individual AE-Schemes have

largely concentrated on administrative and implementation

issues, in particular, measurement of farmer participation

rates. However, participation in AE-Schemes per se does not

guarantee the actual delivery of environmental goods and

services, and only the monitoring of actual scheme outcomes

can demonstrate their true impact (Lee and Bradshaw, 1998).

Summary reports (CEC, 2000; EC, 2005a) have concluded that

very few scheme evaluations specifically measure environ-

mental outcomes and following a very detailed analysis,

Oreade-Breche (2005) stressed the need for ‘‘. . .monitoring and

evaluation procedures and tools that are less oriented towards

implementation and more oriented towards impact, and

adapted to the variety of issues concerned’’.

In a comprehensive review of independent studies that

have attempted to assess the environmental impact of

European AE-Schemes on biodiversity, Kleijn and Sutherland

(2003) concluded that, with the possible exceptions of the UK

and the Netherlands, ‘‘. . .there is a lack of research examining

whether agri-environment schemes are effective’’. In another

study, Primdahl et al. (2003) conducted interviews with 789

farmers participating in AE-Schemes across 22 case study

areas in nine EU Member States and Switzerland and with 211

non-participating farmers. Using 12 agricultural indicators,

they showed that participant farmers undertook more agri-

environmental activities that might be expected to maintain or

improve environmental quality than non-participants.

Knickel and Schramek (1998) and Knickel (2000) found similar,

indirect evidence of likely environmental benefits. A number

of studies have independently sought to evaluate the effec-

tiveness of specific AE-Schemes, particularly with respect to

biodiversity (e.g., Kleijn et al., 2006; Albrecht et al., 2007;

Carvell et al., 2007; Reid et al., 2007; Verhulst et al., 2007;

Wretenberg et al., 2007). Others have sought to assess the

wider impact of farming specifically on biodiversity (e.g.,

Jeanneret et al., 2006; Holzschuh et al., 2007; Billeter et al.,

2008), whilst Henle et al. (2008) reviewed the wider relation-

ship between agriculture and biodiversity in Europe, propos-

ing a number of novel monitoring and management strategies

based on recognition and reconciliation of apparent ‘conflicts’.

Whilst evaluation of measures for the protection of

biodiversity within the EU agricultural sector has received

relatively widespread attention, integrated assessment of

policy responses to other agri-environmental issues, such as

the protection of soil and water, and limitation of greenhouse

gas emissions, have received less attention until relatively

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7 323

recently (GFA, 2006; Hudec et al., 2007). These latter reports

largely collate information about the Measures taken by EU

Member States that are likely to have positive environmental

effects, but provide very limited information about actual

environmental state. Indeed, with very few exceptions

(Solagro, 2000), holistic, integrated methods for the assess-

ment of actual environmental conditions on European

farmland have yet to be developed. Currently, there is no

accepted common evaluation methodology for the specific

evaluation of AE-Schemes, either ex ante in relation to scheme

design and approval, or ex post as part of ongoing scheme

improvement.

3. Challenges of agri-environmentalevaluation

3.1. Degrees of environmental quality

The objectives of a policy need to be specific and measurable,

and thus lend themselves to assessment by using suitable

agri-environmental indicators. OECD (2001) outlined a hier-

archical arrangement of three indicator categories on the

policy continuum between legislative restrictions and AE

incentives:

(i) Agri-environmental standards; which represent measurable

levels of environmental quality that are legally enforce-

able and might be considered ‘minimum acceptable

environmental conditions’, e.g., the concentration of

nitrate permitted in groundwater as specified by the

Nitrates Directive (91/676/EEC: Annex I).

(ii) Agri-environmental reference levels; which are specified

measurable levels of environmental performance that

might be expected of a conscientious farmer before the

payment of specific incentives is deemed appropriate, e.g.,

the achievement of requirements associated with ‘Good

Agricultural Practice’ (GAP) or ‘Good Agricultural and

Environmental Condition’ (GAEC) (Council Regulation (EC)

No. 1782/2003: Article 5, Annex IV).

(iii) Agri-environmental performance targets; which are measur-

able levels of enhanced environmental quality intended to

be achieved and maintained into the future. To achieve

such targets, specific agri-environmental payments are

deemed appropriate as an incentive mechanism, and

compensation for specifically incurred additional costs

and/or commensurate ‘loss’ in farm income.

In the view of the OECD (2001), the setting and imple-

mentation of agri-environmental performance targets is

strongly dependent on natural conditions, local farming

and production systems, agricultural structures, traditions

and social perceptions; and their application is frequently

highly customised, regional or even farm-specific. Any

harmonised system of evaluation for Europe’s AE-Schemes

therefore needs to be highly customisable, facilitating a

flexible context-specific process that is able to reflect the great

diversity of bio-geo-climatic, agronomic and cultural condi-

tions and resulting agri-environmental priorities across

Europe.

3.2. The changing background

The very diversity of current applications of the Rural

Development Regulation, presents a significant challenge to

the development of a harmonised approach to evaluation.

However, this challenge is also set against a background of

significant recent, and likely future change, including:

� EU enlargement, with the addition of new Accession States

that bring with them an even greater diversity of farming

types and regional differences in environmental and rural

development issues.

� The 2003 CAP reform, which resulted in the installation of

the Cross Compliance principle (Council Regulation (EC) No.

1782/2003). This decoupled most direct payments from

agricultural production per se (the Luxemburg Agreement),

and linked future support to observance of a number of

Statutory Requirements regarding the environment, food

safety, etc., and the maintenance of agricultural land in

Good Agricultural and Environmental Condition, whilst

maintaining a parallel development of additional incentive

payments specifically modulated towards agri-environmen-

tal improvement.

� An ongoing process of market liberalisation within the WTO,

which continues to strongly influence the CAP reform

process affecting the likely framework of future AE-

Schemes, and the issues and instruments that will continue

to be permitted within the context of the so-called Green Box

(Potter and Burney, 2002; Swinbank and Daugbjerg, 2006).

� Recent recognition at the highest EU levels of likely

significant ‘disruption scenarios’ in European Agriculture,

which are likely to include: climate shock; energy crises and/

or food crises (SCAR, 2007).

The major challenge is therefore to devise a common agri-

environmental evaluation framework that accommodates not

only the current variety of agri-environmental circumstances,

but also has the flexibility to cope with likely changing

priorities in European agricultural policy.

3.3. Considerations regarding effective policy evaluation

Evaluation of agri-environmental policy essentially involves

measurement of actual, or likely environmental conditions on

farms under the influence of a particular policy implementa-

tion. Ultimately, understanding why a policy is working or not

requires an understanding of the cause-and-effect linkages in

a complex chain of relationships between policy design,

implementation, farmer’s decision-making, resultant prac-

tice, and consequent change in environmental conditions.

Evaluation needs to assess both the appropriateness and

technical validity of the adopted policy measures. Two

questions need to be answered; firstly, are the adopted

measures appropriate in the circumstances? Secondly, is

the actual implementation of the measures efficient in

achieving the desired result? Here, we describe a methodology

with an even more fundamental aim linked to both these

questions; that of determining whether, under the influence of

a particular policy, there is any discernible change in environmental

performance/quality at farm level. The focus at farm level is

NR: Aspects of soil quality and stability;

Aspects of water quantity and quality;

Aspects of air quality.

B: Conservation of wildlife species and habitats;

Protection and utilisation of functional biodiversity

within farming systems;

Maintenance of genetic diversity, particularly crop

varieties and livestock breeds.

L: Aesthetic appearance and cultural-historic value of the

countryside;

Multifunctional (amenity, recreational and educational)

value and use of the countryside.

1 The control of natural hazards is probably ultimately inter-pretable in terms of one or more of the three identified AE-Issues(NR, B and L). Food quality and safety issues, however, and animalwelfare are probably best classified amongst a relatively smallnumber of ‘other’ issues that were identified as targeting asocio-economic rather than environmental dimension of ruraldevelopment. All such objectives fall outside the strictly environ-mental focus of the AFI as we currently conceive it.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7324

important because AE-Schemes strategically target the man-

agement actions of individual farmers.

3.4. The need for multidimensional assessment

By commonly agreed definition, the agri-environment is a

complex, integrative and multidimensional concept. It has

been argued that agri-environmental assessment must there-

fore be based on a holistic integration of assessment across the

multiple dimensions of agri-environment (Wascher, 2002).

This is the only way that unintended side-effects of a policy,

whether beneficial or otherwise, can be identified. Jones (2005)

argues that multiple objectives should logically, and generally

do, complement each other; but recognises that they can

sometimes conflict internally within a mix of instruments

targeting a common objective, or externally with other

apparently unrelated policies. In a similar vein, Montague

and Allerdings (2005) considered it a major challenge to

identify the relationships between performance in different

environmental dimensions, and the consequent potential

tensions, trade-offs and complementarities between different

aims. Multiple criteria methods provide a means to address

inherent complexity by assembling and quantifying multiple

indicators on a common, directly comparable scale of

measurement (Zeleny, 1982; Belton and Stewart, 2002; Nardo

et al., 2005). Such methods have become widely used in

environmental analysis (Funtowicz et al., 1990; Thomassin,

1999; Hayashi, 2000; Bartolini et al., 2005) as they allow for

simultaneous measurement of multiple objectives, and

expression of their individual contribution and relative

importance in achieving an overall goal.

4. Developing a common descriptiveframework for the agri-environment

4.1. A survey of the varied structure and aims of EU agri-environmental schemes

The variation in current AE-Scheme design, structure and

objectives across Europe is considerable. In a survey of 244 EU-

funded schemes conducted in 2005–2006 (selected more-or-

less at random from a total population probably in excess of

350 within the former 25 EU States), we found that schemes

varied widely in structure, scope and focus (Table 1). Roughly

two-thirds of the sample had a broad (holistic) range of

environmental objectives targeting multiple dimensions of

the agri-environment, whereas one-third could be considered

‘narrow’, with sometimes only a single, environmental

objective.

4.2. Identifying a common framework for schemestructure

As any harmonised methodology has to be able to address any

imaginable agri-environmental context within the existing EU

27 (and future Accession States), its structure needs to be both

universal and very flexible. Three paramount areas of agri-

environmental concern (major AE-Issues) were clearly identi-

fiable in our survey of existing AE-Schemes. These AE-Issues

relate to the maintenance/protection/conservation/enhance-

ment of:

(i) Natural Resources (NR).

(ii) Biodiversity (B).

(iii) Landscape Quality (L).

Whilst an exhaustive listing of the specific AE topics

expressed in all current implementations of the Rural

Development Regulation is clearly not feasible, typical sub-

Issues that frequently feature within each of the above AE-

Issues include:

Not surprisingly, these almost universal AE-Issues repre-

sent widely understood, ‘higher level’ European policy

objectives and correspond closely to the concerns elaborated

by the EC (EC, 1999, 2000, 2005a, 2006, 2007a). In our survey,

almost 90% of the issues expressed, and specifically addressed

in associated scheme documentation, were readily assignable

to one, or more of these categories (Table 2). Only rarely were

‘other’ environmental concerns, such as contributing to food

quality & safety, public health and animal welfare, or

controlling natural hazards (floods, avalanches, fires),

included in scheme objectives.1

Further analysis of the 244 AE-Schemes examined in our

survey made it possible to discern three quite distinct aspects

of a farmer’s management that are routinely targeted by policy

measures for the improvement of agri-environmental condi-

tions:

(i) Crop and Animal Husbandry (CAH);

(ii) Physical Farm Infrastructure (PFI);

(iii) Natural and Cultural Heritage features on farms (NCH).

These practical management domains can be more

specifically defined as follows:

Table 1 – Summary of collated information concerning the structure, objectives and scope of 244 agri-environmental schemes in 25 EU Member States.

Country Number ofschemes

characterised

Scheme structure Geographic scale ofimplementation

Spatialfocus of

implementation

Requiredscale of

implementation

Targetedfarm types

Environmentalobjectives

Menu-driven

Singlepackage

National Regional Horizontal Designatedareas

Wholefarm

Partfarm

A I E M O Broad(holistic)

Specialised

AT 1 1 0 1 0 1 0 Mainly – 1 0 0 0 0 1 0

BE 12 5 7 0 12 8 4 5 7 8 0 0 0 4 9 3

CY 4 0 4 4 0 4 0 4 0 3 0 0 1 0 4 0

CZ 9 7 2 9 0 7 2 7 2 8 0 0 0 1 5 4

DE 3 2 3 0 3 2 1 2 3 2 0 1 0 0 3 0

DK 4 – Mainly 4 0 3 1 3 1 4 0 0 0 0 3 1

EE 7 0 7 5 2 3 4 4 3 4 0 0 2 1 6 1

ES 12 0 12 9 3 8 4 5 4 8 1 1 0 2 12 0

FI 11 4 7 11 0 3 8 4 7 11 0 0 0 0 0 11

FR 12 11 1 1 11 11 1 2 10 8 0 0 0 4 7 5

GB 12 8 8 0 12 9 3 8 5 8 0 0 0 4 12 0

GR 12 0 12 12 0 4 8 11 1 8 1 1 2 0 7 5

HU 30 0 30 30 0 22 8 0 30 13 0 8 8 12 22 8

IE 1 Limited Mostly 1 0 1 0 1 0 1 0 0 0 0 1 0

IT 12 12 0 0 12 12 0 0 12 12 0 0 0 0 10 2

LT 7 0 7 7 0 7 0 1 6 7 0 0 0 0 5 2

LU 11 10 1 11 0 10 1 3 8 7 0 0 0 4 10 1

LV 4 3 1 4 0 2 2 1 3 2 1 1 0 0 3 1

MT 3 1 2 3 0 3 0 3 0 3 0 0 0 0 0 3

NL 9 2 2 7 2 4 5 1 4 9 0 0 0 1 9 0

PL 10 0 10 10 0 4 6 5 5 9 0 1 0 0 3 7

PT 12 3 9 5 7 4 8 11 1 5 0 4 0 3 9 3

SE 14 0 14 14 0 8 6 3 10 13 0 0 0 1 9 5

SI 22 0 22 22 0 17 5 0 22 7 1 10 9 13 13 9

SK 10 0 10 10 0 10 0 2 8 5 0 3 3 2 4 6

Total 244 69 171 180 64 167 77 86 152 166 4 30 25 52 167 77

% 100 28 70 74 26 68 32 35 62 68 2 12 10 21 68 32

Notes: States: AT = Austria, BE = Belgium, CY = Cyprus, CZ = Czech Republic, DE = Germany, DK = Denmark, EE = Estonia, ES = Spain, FI = Finland, FR = France, GB = United Kingdom, GR = Greece,

HU = Hungary, IE = Republic of Ireland, IT = Italy, LT = Lithuania, LU = Luxemburg, LV = Latvia, MT = Malta, NL = The Netherlands, PL = Poland, PT = Portugal, SE = Sweden, SI = Slovenia, SK = Slovak

Republic. Targeted farm types: A = all farms, I = intensive, farms E = extensive farms, M = marginal farms, O = other special farm types, i.e., specialised production systems such as flower cultivation,

viticulture, pig breeding, etc.

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Table 2 – Incidence of the three major identified AE-Issues in the survey of 244 agri-environmental schemes within theexisting 25 EU Member States.

Country Number of schemescharacterised

Number of schemes targeting AE-Issues relating to

NR B L ‘Other’

AT 1 1 1 1 1

BE 12 11 10 5 2

CY 4 2 3 1 2

CZ 9 5 7 4 0

DE 3 3 3 3 1

DK 4 4 3 0 1

EE 7 4 7 4 0

ES 12 10 11 4 2

FI 11 5 3 1 1

FR 12 7 9 5 2

GB 12 5 11 8 2

GR 12 9 8 4 2

HU 30 16 24 18 1

IE 1 1 1 1 1

IT 12 12 7 9 4

LT 7 1 6 4 1

LU 11 9 9 10 0

LV 4 2 4 1 0

MT 3 0 1 2 0

NL 9 6 9 9 2

PL 10 6 6 0 1

PT 12 4 9 5 3

SE 14 9 7 6 0

SI 22 17 13 13 0

SK 10 5 6 3 0

Total 244 154 178 121 29

% 100 63 73 50 12

Notes: Issues: maintenance/protection/conservation/enhancement of: NR = abiotic natural resources, B = biodiversity, L = landscape. States:

AT = Austria, BE = Belgium, CY = Cyprus, CZ = Czech Republic, DE = Germany, DK = Denmark, EE = Estonia, ES = Spain, FI = Finland,

FR = France, GB = United Kingdom, GR = Greece, HU = Hungary, IE = Republic of Ireland, IT = Italy, LT = Lithuania, LU = Luxemburg, LV = Latvia,

MT = Malta, NL = The Netherlands, PL = Poland, PT = Portugal, SE = Sweden, SI = Slovenia, SK = Slovak Republic.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7326

CAH—Husbandry practice relating to crop and/or livestock

production systems, i.e., management of production pro-

cesses.

PFI—Functional design, construction and maintenance of

physical farm infrastructure, e.g., layout and type of

permanent field boundaries,2 drainage systems, nutrient

and manure storage facilities, etc., i.e., management of

production facilities.

NCH—Management of areas and resources on the farm

that are not a part of the formal production process, e.g.,

natural habitats, watercourses, historical, archeological

and aesthetically important countryside features that

occur outside production units (fields or other production

facilities), i.e., management of the wider countryside.

Conceptually, management of Physical Farm Infrastruc-

ture (PFI) can be thought of as the means to provide an

appropriate physical ‘interface’ between the production

2 Note, in some contexts where traditional field boundariesrepresent a significant wildlife habitat and heritage element inthe wider countryside, it may be more appropriate to assignquestions of their management to the Natural and Cultural Heri-tage (NCH) Issue, rather than Physical Farm Infrastructure (PFI);this being an example of the required flexibility in designing aharmonised approach, valid in all EU circumstances.

process (CAH) and valued heritage features of the wider

landscape (NCH) in which farming occurs. The identified AE-

Management Strategies target universal and practical sub-

divisions of agro-ecosystem management that nest within

each of the major AE-Issues as the primary means to deliver

policy objectives (Fig. 1). The resulting hierarchical framework

effectively defines a universal template for the agri-environ-

ment comprising nine individual AE-Dimensions. Whilst all

nine dimensions in this framework are not necessarily

relevant in all AE contexts, this structure can be used to

identify and classify the specific priorities and most appro-

priate agri-environmental management strategies for any

specified farming type and context (Fig. 2).

5. The Agri-environmental Footprint Index(AFI)

Taking into account all of the above considerations, the Agri-

environmental Footprint Index (AFI) provides a multiple

criteria-based methodology for the ‘context-dependent’ eva-

luation and validation of locally specific objectives within the

common framework of wider EU policy aims (CEC, 2000). It

does this by providing both a standardised structure for a

multidimensional index using the universal evaluation frame-

work illustrated in Figs. 1 and 2, and a defined, interactive

Fig. 1 – A common AE framework (major AE-Issues T AE-Management Strategies for description of EU AE-Schemes.

CAH = Crop and Animal Husbandry, PFI = Physical Farm Infrastructure, NCH = Natural and Cultural Heritage.

Fig. 2 – Use of the identified universal AE framework to create a descriptive Assessment Criteria Matrix (ACM), in which agri-

environmental priorities and appropriate management strategies can be formulated as specific evaluation criteria for any

agri-environmental context; this example illustrates only a limited number of generic assessment criteria for a purely

hypothetical farming system.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7 327

process by which a customised form of the Index can be

designed for a particular evaluation.

5.1. Scope of the AFI

The AFI has been designed to quantify environmental impact

at the level of individual farms within a particular AE context,

and to provide an integrated measure of the mean, and over

time, the changing environmental impact of a particular type

of farming when the Index is calculated for large, statistically

representative samples of farms.

5.2. Building blocks for the AFI

The first requirement for calculation of an AFI, is the

creation of a customised template, or Assessment

Criteria Matrix (ACM) as illustrated in generic form in

Fig. 2. The ACM describes the evaluation task in terms

of criteria relevant to the specified evaluation background.

The evaluation background will include, but is not

necessarily restricted to, specific objectives of the

AE-Scheme, or AE management package being evaluated.

The ACM is essentially a means to describe the local

agri-environmental priorities and farming practices

relevant to these priorities, and provides a basis for

subsequent development of an Indicator Matrix (IM) con-

taining indicators appropriate for the evaluation of the

identified assessment criteria within each AE-Dimension.

AE-Indicators, representing measurable parameters rele-

vant to assessment criteria are the third and lowest level

within the hierarchical structure of the AFI framework

(Fig. 3).

Fig. 3 – An Indicator Matrix created by the selection and specification of indicators appropriate for assessment of criteria

defined in the Assessment Criteria Matrix (ACM) (see Fig. 2).

3 A separate Panel of Technical Specialists including membersdrawn from the wider Stakeholder Group may be constituted forthis purpose, but the agreement of the wider Stakeholder Groupshould always be obtained.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7328

5.3. The AFI evaluation process

To gain a truly multidimensional perspective in policy

evaluation, it is necessary to engage a wide range of

stakeholder views. Several approaches have in a number of

ways sought to make use of and recruit ‘expert opinion’ to this

process (e.g., Carey et al., 2003, 2005; Morris, 2004). To be

effective, such consultation needs to facilitate both the

contributions of ‘experts’, and the effective integration of

non-specialist stakeholder views. The interactive AFI meth-

odology presented here has benefited considerably from

having been developed through a series of 15 worked case

study applications done in 7 EU Member States, which has

resulted in a carefully designed, interactive process described

at length in an AFI Users Manual (Mortimer et al., 2009). In

broad outline, this process includes nine defined steps (Fig. 4).

5.3.1. Defining the basis of a specific applicationAt the outset of an evaluation, evaluators initially define the

scope of the AFI application in terms of its farming context

(farming type and geo-climatic region) and the AE-Scheme

being evaluated. At this initial stage, it is also necessary to

identify an appropriate group of stakeholders (Beopoulos and

Vlahos, 2005; Hein et al., 2006) to assist with the application,

and to define the farm sampling strategy and the compar-

ison(s) to be made using the quantified Index (e.g., AE-Scheme-

participating vs. non-participating farms over an agreed

timescale).

5.3.2. Creating an Assessment Criteria Matrix (ACM)Evaluators with the help of the stakeholders must create a

customised and context-relevant ACM by identifying and

classifying specific assessment criteria within the universal

ACM framework (Fig. 2). This process can include assessment

criteria that are additional to the explicit aims of the scheme

being evaluated. The inclusion of such additional criteria

creates a more holistic ACM, and ultimately an evaluation tool

with the potential to look beyond the possibly narrower

objectives of stated AE policy and identify any unintended

side-effects of policy implementation, whether positive or

negative.

5.3.3. Weighting the relative importance of AE-Issues, andAE-Management StrategiesIt is also necessary for evaluators and stakeholders to reach a

consensus, or arithmetically averaged view regarding their

perception of the context-dependent relative importance of

the three major AE-Issues (WNR, WB, WL), and the three AE-

Management Strategies (WCAH, WPFI, WNCH) within each AE-

Issue, such that, at each level of the Index, relative weights

always sum to unity (Fig. 6).

At this stage, it may well be concluded that some AE-Issues,

or Management Strategies within specific AE-Issues have very

little, or even no relevance within the evaluation context,

giving such a component an effective weighting of zero. This

may be appropriate for a strictly limited assessment of stated

scheme objectives. However, if a more ‘holistic’ evaluation of

environmental conditions is required, it is highly desirable

that a comprehensive ACM is created to achieve this.

5.3.4. Creating an Indicator Matrix (IM) with appropriatefarm-scale indicatorsEvaluators, with the specific assistance of environmental

specialists from within the stakeholder group3 and the

subsequent agreement of that wider group, select or

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7 329

specifically design4 farm-level indicators likely to be suitable

for evaluation of the criteria agreed within relevant dimen-

sions of the ACM. Indicators can relate to, but should not

directly seek to assess actual compliance with contractual

obligations stipulated in the scheme being evaluated.5 As

assessment seeks to track change in agri-environmental

quality over time, indicators that express actual environ-

mental state would normally be preferred (OECD, 1996;

Smeets and Weterings, 1999; EC, 2000). However, such data

can be difficult, frequently impossible, and nearly always

prohibitively expensive to collect. Proxy ‘driver’ indicators

relating to farming practice that have a clearly understood

relationship with actual environmental conditions are fre-

quently the only available cost-effective option. The temporal

and spatial dimensions inherent in the relationships between

farming practice and environmental state (i.e., the lags

between cause-and-effect) can also mean that driving force

indicators relating to changing management practices can be

a more practical means for documenting environmental

improvement (CEC, 2000; Oltmer et al., 2000; Onate et al., 2000;

Primdahl et al., 2003). The validity of such proxy measures,

however, greatly depends on there being well-established

and accurate underlying impact models established within

the relevant farming context, and a body of well documented

evidence for the assumed relationships underpinning their

use (Thomassin, 1999; CEC, 2006a).

In an Indictor Matrix, the same indicator may validly be

used in more than one AFI-Dimension. For example, a

reduction in nutrient inputs may be a useful indicator for

the CAH dimension of both Natural Resources (specifically

water quality) and Biodiversity objectives; however, such an

indicator may very likely be assigned quite different weights

(relative importance) within different dimensions. Each

populated AFI-Dimension must have at least one, and

probably a maximum of five indicators, depending on the

number and complexity of assessment criteria specified for

that dimension (Fig. 3). In order to avoid double counting and

redundancy, multiple indicators within the same AFI-Dimen-

4 In many cases, identified assessment criteria will relate torelatively complex environmental issues, e.g., an importantassessment criterion within the NR � CAH dimension might relateto a farmer’s use of animal manures; the environmental hazardassociated with land spreading has multiple facets, including thetype of manure (solid or slurry), the application technology usedand the time (season) of application – and the pollution risk from Nand P contamination of water sources will differ in differentseasons. In such cases, it is unlikely that a single statistic relatingto manure use can realistically evaluate environmental hazard,and so it will be necessary for evaluators to design a ‘multi-metric’indicator that aggregates multiple individual statistics relating tomanure disposal in order to effectively measure the environmen-tal hazard associated with a particular farmer’s practice.

5 This is important, since the selection of management indica-tors that merely evaluate current contractual obligations willprovide only an evaluation of policy implementation, and willnot necessarily indicate actual environmental benefit. Indicatorsshould be chosen on the basis that they are independent arbiterslikely to reflect actual environmental conditions, or the level ofperceived environmental risk. With time and use, the adoption ofwell-founded proxy indicators for evaluation is likely positively tofeed back into better-specified contractual obligations.

sion should address clearly different assessment criteria, or be

complementary and necessary for accurate evaluation of a

single criterion. Indicator selection is a very important part of

the AFI methodology and further guidance, including the

development of multi-metric indicator functions to assess

complex issues is given in the AFI Users Manual (Mortimer

et al., 2009).

5.3.5. Collection of indicator dataWhen the essential building blocks defining a specific AFI are

in place (i.e., an ACM and a matching IM), the evaluators need

to collect the required farm-scale data, either from existing

data sources (European, national or regional databases), or

drawn from an application-specific survey and/or monitoring

of sample farms.

5.3.6. Characterising relationships between indicator valuesand indicator scoresTo address the issue of common scaling, absolute measured

values of indicators need to be transformed onto a standar-

dised AFI scoring scale ranging from 0 to 10, whereby higher

scores correspond to ‘better’ environmental conditions.

Examples of possible theoretical relationships between indi-

cator values and AFI scores are given in Fig. 5. For indicators

expressed on a continuous scale of original measurement

X! Y (e.g., nutrient application rate), the relationship

between original indicator values and AFI score may be linear,

or non-linear. For categorical indicators (e.g., type of applica-

tion equipment used in manure application), the relationship

is somewhat more judgmental involving expert allocation of

appropriate scores on the standardised AFI scale (0! 10) to

actual categories.

If binary (yes/no) indicators are assigned extreme (0 and 10)

scores, their inclusion in the Index will produce strongly

polarised, ‘black and white’ results. Unless their importance is

considered very significant within the context being assessed,

the use of such indicators should be avoided, or alternatively,

they should be assigned less extreme scores on the standar-

dised scale commensurate with their perceived environmen-

tal significance.

The specifics of indicator transformations will be very

highly dependent on the evaluated context and it is

essential that indicator scaling is independently determined

within each particular application of the Index. The scaling

of a particular indicator may also differ if it is used in more

than one AFI-Dimension; e.g., using nutrient input level as

an example of a likely indicator for both Natural Resources

and Biodiversity objectives, a given (modest) level of

nutrient application in a botanically sensitive pastoral

system may have negligible consequences in terms of water

quality, but very severe consequences for sward botanical

diversity. Knowledge of the actual range of indicator values

following data collection (Step 5) will help to inform the

process of calibrating scoring scales. As a guiding principle,

evaluators would normally seek to ensure that the ‘average’

value of an indicator for a sample of non-scheme farms

equates to a score close to the centre of the standardised

0! 10 scale. In this way, when subsequently used to

compare scheme and non-scheme farm samples, or when

used in the longer term monitoring of change in such

Fig. 4 – Diagrammatic representation of the consultative process to create and quantify an AFI for a specified agri-

environmental context. The Stakeholder Group should include technical specialists with environmental knowledge, who

are particularly involved in the process of indicator development and scaling. Unless otherwise stated, all stakeholders are

involved at each consultative step.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7330

samples, the developed Index will be maximally sensitive to

either improvement or degradation in environmental con-

ditions.

5.3.7. Weighting the indicators used within individual AFI-DimensionsBefore the resulting Index can be calculated, it is also

necessary for the evaluators to obtain a consensus, or

arithmetically averaged view regarding Stakeholder percep-

tion of the relative importance of the agreed indicators (WI

(1. . .n)) within each cell (AFI-Dimension) of the Indicator Matrix.

When assigning indicator weights, such that within each AFI-

Dimension, relative weights always sum to unity, it is

important that the Stakeholders reflect on the relative

importance of the original assessment criteria within the

particular AFI-Dimension being evaluated. Considerations of

indicator quality should only influence initial indicator

selection (Step 4), and should not affect the subsequent

Fig. 5 – Examples of potential transformation relationships

between original indicator values and indicator scores

expressed on the standardised AFI scale of 0–10.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7 331

weighting given to their actual use within a particular

dimension.6

5.3.8. Calculation and reporting of the AFIThere are essentially two basic components that need to be

integrated into the final calculation of the AFI:

(i) Agri-environmental Scores, i.e., transformed indicator

values that express their measurement on the standar-

dised (0–10) AFI scale.

(ii) Agri-environmental Weights, which allow for expression

of the relative importance of the various components of

the Index at each hierarchical level.

In policy evaluation, weighting provides a means to

express and quantify the trade-offs between different

concerns, which sometimes inevitably conflict. Evaluators

therefore calculate a mean overall AFI score for a specified

sample of farms by an aggregative process of multiplying

indicator scores for individual farms by their agreed weights,

and summing at each successive level of the Index7 (Fig. 6).

Substitutability is inevitable in the calculation of a weighted

sum, and means that the logic of indicator selection and

weighting needs very careful attention to ensure that the

6 If this recommendation is not followed, the resulting aggre-gated index is likely to be distorted by perceived limitations inmethodology, and so provide a less accurate evaluation withrespect to expressed agri-environmental priorities. While limita-tions in methodology cannot be ignored, they should inform, butnot affect the outcome of the evaluation.

7 Note: this process results in aggregated scores at all levels ofthe Index that are expressed on the standardised scale of 0–10. Forsimplicity, and to avoid the need to use decimals in the finalexpression of an overall AFI score, it is suggested that at its highestlevel the Index should be multiplied by a factor of 10 giving it amaximum theoretical value of 100.

‘averaging’ of weighted indicator scores is ecologically

meaningful and valid.

Multiple criteria methods are best suited for assessments

based on between 4 and 20 indicators (Hayashi, 2000; ODPM,

2005). If, in a holistic assessment of a very broad

AE-Scheme, the AFI structure contains more than about

20–25 indicators, the weighted sums of scores should

probably not be aggregated to the highest (total AFI)

level. The outcome of the calculation process can then be

reported as a set of component AFI scores quantifying lower

levels of the Index, e.g., scores for each major AE-Issue, for

Management Strategies (aggregated across AE-Issues), or

scores for individual AE-Dimensions (see Mortimer et al.

(2009) for examples of AFI output using radar charts to

plot relative scheme performance in different AE-Dimen-

sions). In all cases, the constituent scores at lower levels of

the index (down to the level of individual indicators) are

likely to aid in-depth analysis and interpretation of the

outcome.

5.3.9. Post-calculation sensitivity analysisAs standard good practice, it is usual to undertake an

analysis of the sensitivity of any evaluation system.

Sensitivity analysis normally involves testing the degree

to which the output variable (aggregated AFI scores at each

hierarchical level) changes in response to changes in a

single input variable. Sensitivity analysis of the AFI is

complicated by the fact that a single input variable cannot

be changed in isolation because of the use of proportional

weighting at each level of the Index. It is suggested that

the variation in weightings expressed by stakeholders (prior

to their reaching a consensus, or averaged view), can be

used to explore the sensitivity and properties of the tool

created.

6. Evaluation strategy using the AFI

6.1. The type and scope of evaluation

The AFI can integrate evaluation of multiple and complex

environmental issues. It is therefore well suited to either

a holistic, or a strongly policy-focused assessment of

the majority (approx. 70%) of EU AE-Schemes that have

wide-ranging (broad) environmental objectives (Table 1).

The ability of the AFI method to integrate complex

evaluations is a major strength, but this may be unneces-

sary for a strongly policy-focused evaluation of a ‘narrow’

scheme with limited (or highly targeted) environmental

objectives (Table 3). In such a case, a more focused and less

elaborate approach concentrating on the stated objective

of policy is likely to be more efficient. However, a more

holistic evaluation that goes beyond the stated objectives of

policy can facilitate the identification of unforeseen side-

effects of policy, which might otherwise be neglected

(Lehtonen, 2005). Use of the AFI for evaluation of a scheme

with very narrowly specified environmental objectives

may therefore be justified if policy designers have not

already carefully considered the wider agri-environmental

perspective.

Fig. 6 – Calculation of the AFI by an aggregative process of multiplying weights by scores, and summing at each successive

level of the index: indicators within AE-Dimensions, Management Strategies within Issues, and finally Issues within the

overall Index. This example uses a notional NR T NCH score and notional weights for Indicators, Management Strategies

and AE-Issues.

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7332

6.2. Dealing with multiple co-incident change

A major challenge in the use of any evaluation system is the

time lag that can occur between the implementation of policy

measures and any manifestation of environmental outcomes,

and the difficulties that can be created by other factors that

influence environmental conditions over the intervening time

period (Jones, 2005). It is therefore important to be able to

distinguish policy effects, using the concept of additionality to

identify environmental trends that are unlikely to have taken

place in the absence of the policy (Pearce, 2005). The most

obvious way to achieve this is to document environmental

change relative to a directly comparable baseline measured in

the absence of the policy (Bartolini et al., 2005). The necessary

baseline can have both spatial and temporal dimensions.

Establishing the former might involve the assessment of mean

AFI scores for separate samples of ‘Scheme’ and ‘non-Scheme’

Table 3 – Suitability of the AFI method for ‘holistic’, ormore strictly ‘policy-focused’ evaluation of AE-Schemeswith ‘broad’ or ‘narrowly targeted’ environmental ob-jectives.

AE-Scheme objectives

‘Broad’ ‘Narrow’

Evaluation ‘Holistic’ HHH HHObjective ‘Policy-focused’ HH X

farms at a fixed point in time; and the latter by a ‘before’ and

‘after’ comparison of scores for farms as they join a particular

scheme. The ideal methodology would combine both temporal

and spatial dimensions by monitoring change in mean AFI

scores for samples of both ‘Scheme’ and ‘non-Scheme’ farms

across a time-series; thereby also allowing exploration of the

relationship between duration of scheme participation and

environmental performance. Ultimately, there is need for a

close practical integration of evaluation methodology with

organised monitoring (Carels and Van Gijseghem, 2005;

Oreade-Breche, 2005), such that changes in mean AFI scores

can be related to policy implementation.

7. Advantages of the AFI methodology

7.1. Use of a common evaluation framework

The AFI provides a means to summarise local agri-environ-

mental priorities (the ACM), and to quantify environmental

performance at farm scale (the IM) relevant to those priorities.

From an ecological viewpoint, the ACM sub-divides the agro-

ecosystem into universally applicable AE-Dimensions in a

matrix defined by environmental priorities and management

drivers. Girardin et al. (2000) used a similar concept in relating

farming practices to environmental themes/issues by creating

an interaction matrix. King et al. (2004) suggested a similar

framework in proposing a national farm management indicator

matrix linking farm management practices with environmen-

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7 333

tal outcomes, arguing that the merit of such a matrix lies in its

ability to summarise the complexity of relationships between

farm practice, farm capacity and environmental outcomes.

The AFI method has been developed for use at the scale of

individual farms, because current AE-Schemes are largely

focused at this level through their use of contracts with

individual farmers. Some AE-Issues, however, might be more

obviously addressed at a larger scale, e.g., water quality at a

catchment scale. We see this as a future challenge for the

further development and refinement of the method.

7.2. Advantages of ‘looking beyond’ current policy

As policy often ‘evolves’ in a somewhat piecemeal fashion, the

ability to undertake a fresh, and perhaps more holistic and

integrated review of the local agri-environmental perspective

is a very important advantage of the AFI methodology. Indeed,

if the weighting process is removed from the AFI calculation,

the resulting simplified Index can be used as a possibly more

objective longer term monitoring tool for environmental audit

and assessment. Whilst, the weighting process undoubtedly

introduces an element of greater subjectivity to the concept of

objective monitoring, within the context of policy evaluation

weighting provides a highly desirable means to recognise and

reconcile policy priorities and potential trade-offs at all levels

of the Index. Theoretically, for use as an ongoing policy

monitoring and evaluation tool, such weighting should be done

only once during the initial development of the index.

However, if subsequent AE policy priorities change very

radically the weighting might need to be revised and the

Index back-calculated using previously collected data to

standardise the analysis of temporal trends.

7.3. Engagement in a consultative process

The second core feature of the AFI methodology is its use of a

consultation process with local stakeholders and technical

expertise in order to elaborate a customised form of the Index

(Fig. 4). This is of considerable benefit as it encourages a wider

debate, and facilitates compromise regarding often complex,

local agri-environmental issues. If the Index is developed in a

holistic way, our case studies have shown that the process can

encourage the development of an improved collective under-

standing of the local issues and more effective policy

development—lessening the perception of fundamental con-

flict between environmental and agricultural interests (Henle

et al., 2008).

7.4. Stimulation of agri-environmental data collection

Use of the AFI method requires careful consideration and

selection of the most appropriate indicators with which to

assess the identified evaluation criteria. In most circum-

stances, this process is likely to be frustrated by the limited

availability of pre-existing data. This in itself, however, can

provide a useful insight into what kinds of data should be

available, and help in the process of identifying existing

information gaps and in developing systems of agri-environ-

mental data collection. Use of the AFI in our 15 case study

applications of the method across a range of EU contexts has

resulted in the identification of a wide range of potential farm-

level indicators. It is envisaged that as the AFI becomes more

widely used, the further development of this indicator

resource could facilitate a harmonised approach to data

collection systems across Europe. Such a resource would

strongly support development of new, and improve existing,

farm monitoring systems (CEC, 2006a), particularly with

regard to the collection of environmentally relevant farm

management data.

8. Summary comments and conclusions

The Common Monitoring and Evaluation Framework (CMEF)

(EC, 2007a) provides a single framework for monitoring and

evaluation of all rural development interventions for the

programming period 2007–2013. The Commission’s Evaluation

Unit clearly regards the CMEF as providing a relevant pan-

European set of indicators. Given this broad scale, however, it

can be anticipated that the CMEF will need to be supplemented

by evaluations that are more context-specific within the

varied circumstances in particular Member States. The AFI

concept presented here offers exactly such an approach, the

practical feasibility of which has been tested in small-scale

case study applications across Europe. One of the method’s

major strengths is the involvement of stakeholders in the

process of revisiting the local agri-environmental priorities in

a holistic way, in order to customise the AFI framework. At

present our experience is not sufficient to give reliable advice

regarding sample numbers for a full-scale application, and for

this reason we would strongly recommend the involvement of

statisticians with experience of other farm survey exercises.

As a consequence of its hierarchical structure, the AFI can

be reported as a set of component scores for different levels of

the Index. This means that a forensic dissection of the overall

Index score to its various component levels can instantly

reveal important information regarding those parts of a

scheme (with respect to specific farm–environment interac-

tions) that are performing well, and those parts that should

receive priority attention for scheme improvement. Use of the

AFI as an evaluation tool can therefore facilitate identification

of the strengths and weaknesses of a scheme’s design in

different dimensions of the agri-environment and in a

feedback process, contribute to policy improvement. In

addition, provided that baseline data are available for

sufficiently large samples of farms, the AFI may be used to

evaluate the effectiveness of alternative scheme designs,

either ex post by actual, or ex ante by simulated implementation

of the alternatives.

Reduction of agricultural pressures on the environment

and provision of agri-environmental (public good) services are

the two primary facets of the ‘multifunctional’ model of

European agriculture (CEC, 2000, 2006a). It is clear, however,

that restrictions on the intensity of husbandry systems are an

almost universal feature of current AE-Schemes. This clearly

acts as a very strong disincentive to voluntary farmer

participation in AE-Schemes in intensively farmed areas

(Kleijn and Sutherland, 2003). As a consequence, only

minimum regulatory thresholds in all aspects of agri-

environmental quality are likely to be attained in intensive

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7334

farmed regions (Downey and Purvis, 2005), and a valuable

opportunity to recruit farmers as managers of non-production

dimensions of the agro-ecosystem in such areas is likely to be

lost. However, wider adoption of the conceptual AFI frame-

work in AE-Scheme design, can encourage the development of

more innovative scheme structures, in which the relative

environmental significance (weighting) given to the different

agri-environmental dimensions can be customised to the very

different agronomic perspectives of intensive, extensive and

marginal farming regions when this is deemed to be in the

wider agri-environmental interest. Such a diversification of

scheme design would be more likely to retain the active

engagement of all farmers in incentive schemes, and greatly

benefit management and protection of wildlife habitats and

other valued landscape heritage features, even in intensive

farming areas where their practical conservation value may be

at least as great as in extensive farming regions.

Similar forces of polarisation in farming intensity are

influencing agriculture across the whole of Europe. We believe

that the AFI model is an appropriate starting point for the

design and evaluation of much more flexible agri-environ-

mental policies better suited to the increasingly wide variety of

agronomic conditions across Europe, the development of truly

multifunctional agriculture and the achievement of sustain-

able rural development.

Acknowledgements

This paper is based on the work of a multidisciplinary EU-

funded project (‘AE-Footprint’) to develop a common generic

methodology for evaluating the effectiveness of European

Agri-environmental Schemes (SSPE-CT-2005-006491). We

acknowledge the assistance of all members of the project

consortium (details available at: http://www.footprint.rdg.a-

c.uk/) and the inputs and comments of a number of European

environmental science and policy specialists who served on a

‘Panel of Other National Experts’ (the PONE), which helped us

considerably in the development of a pan-European evalua-

tion system applicable in all farming contexts.

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Gordon Purvis is a senior lecturer and academic co-ordinator ofthe BAgrSci Programme in Agri-environmental Sciences at Uni-versity College Dublin (UCD). He is the leader of the FarmlandEcology Research Group in UCD, and co-ordinator of the ‘Ag-Biota’project funded by the Irish Environmental Protection Agency. Dr.Purvis was a founder member of Ireland’s National Platform forBiodiversity Research Strategy (NPBR).

Geertrui Louwagie is a post-doctoral researcher at the Institute ofProspective Technological Studies at the Joint Research Centre ofthe European Commission in Sevilla, where she is involved insustainable agriculture and rural development projects. Sheobtained her MSc and PhD in soil science at Ghent University.Her research has focused on soils and land evaluation in agricul-tural, environmental and/or archaeological contexts.

Greg Northey is a PhD candidate in agri-environmental sciences atUniversity College Dublin (UCD). He holds a MSc degree in biolo-gical engineering from the University of Guelph and a BSc in forestengineering from the University of New Brunswick. His research isfocused on measuring the impacts of agriculture on the environ-ment and the continued development of the AE Footprint Index asa tool to design and evaluate agri-environmental schemes.

Simon Mortimer is Assistant Director of the Centre for Agri-Envir-onmental Research at the University of Reading, where he teachesecology and nature conservation. His research focuses on the rela-tionship between land management and biodiversity. Much of hisrecentwork concerns the impact ofmanagementpractices aimed atmaintaining or enhancing the diversity of agro-ecosystems.

Julian Park is a senior lecturer in agri-environmental systems atthe University of Reading with broad research interests in mea-suring the impacts of agriculture on the environment and explor-ing mechanisms for minimising these impacts.

Alice Mauchline is a post-doctoral researcher at the Centre forAgricultural Research at the University of Reading. Her research

e n v i r o n m e n t a l s c i e n c e & p o l i c y 1 2 ( 2 0 0 9 ) 3 2 1 – 3 3 7 337

has focused on the study of agricultural ecosystems. She has a BAin biological sciences and a PhD in the role of semiochemicals inplant–insect interactions.

John Finn is a researcher with Teagasc, which has responsibilityfor research advice and education for the Irish agri-food sector. Hisresearch focuses on the development of tools to assist the design,implementation and evaluation of environmentally effective agri-environmental schemes. He is also involved in experimentalinvestigation of the relationship between diversity and ecosystemprocesses, as well as agricultural management to protect andenhance habitats and species.

Jørgen Primdahl is professor of countryside planning at the DanishCentre for Forest, Landscape and Planning, University of Copen-hagen. His research focuses on rural landscape change and publicpolicy intervention in the change process. He has been involved ina number of EU and other research projects concerning agri-environmental policies and their effects on farming practiceand the environment.

Henrik Vejre is associate professor in landscape ecology andacademic co-ordinator of the MSc Programme in landscape man-agement at Forest and Landscape, Faculty of Life Sciences, Uni-versity of Copenhagen. His research interest is focused onlandscape functions and multi-functionality.

Jens Peter Vesterager holds a MSc degree in forestry from TheRoyal Veterinary and Agricultural University, Denmark, and iscurrently studying for a PhD at the University of Copenhagen,Faculty of Life Sciences, Forest and Landscape. His specificresearch interests include application of GIS, operations researchand impact modeling in policy design, implementation, and eva-luation.

Karl-heinz Knickel studied International agriculture at the Uni-versity of Kassel, subsequently undertaking a MSc at the Univer-sity of Reading, UK and a PhD in agricultural economics atCranfield University, UK. He is a senior researcher and Head of

the Department of ‘‘Sustainable Development/Multi-functionalityof Rural Space’’ at the Institute for Rural Development Research(IfLS).

Nadja Kasperczyk studied biology at the University of Frankfurt(1988–1994) and subsequently worked for 2 years at the BotanicalInstitute (Goethe-University) on plant diseases. In 2002, she joinedthe Institute for Rural Development Research at Goethe-UniversityFrankfurt (IfLS) as a research scientist, where she has participatedin projects on the integration of nature conservation objectives inagricultural policies and the evaluation of sustainability assess-ments tools.

Katalin Balazs is an agricultural engineer specialising in environ-mental management. She has a PhD in environmental sciencesand is an associate professor at the Department of EnvironmentalEconomics, Szent Istvan University, Hungary where she teachesagri-environmental policy, environmental farm planning andmanagement. Dr. Balazs has carried out research extensively onpolicy themes related to rural development, agri-environment andhigh nature value farmland.

George Vlahos is currently employed in the Agricultural Universityof Athens and has collaborated in various research projectsfocused in rural and agri-environmental policies.

Stamatios Christopoulos is an environmental scientist who stu-died ecology and environmental protection at Charles Universityin Prague and specialised in environmental impact assessment forhis MSc. He is currently a researcher at the Agricultural Universityof Athens, where his working for his PhD focused on the interfacebetween the environment and agriculture, and the evaluation ofagri-environmental policies.

Jukka Peltola finished his doctorate studies in 1997 at the Uni-versity of California – Davis, and now holds a post as senioreconomist at Agrifood Research Finland. His main research inter-ests include environmental and resource economics, the econom-ics of food safety and biosecurity.