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The use of socio-economic and environmental indicators in assessing
the effectiveness of EU agri-environmental policy
Geoff A. Wilson (*) and Henry Buller (**)
* Department of Geography, King’s College London, Strand, London, WC2R 2LS; e-mail:
geoff.wilson@kcl.ac.uk
** Countryside and Community Research Unit, Cheltenham and Gloucester College of HE, Francis
Close Hall, Cheltenham, GL50 4AZ; e-mail: hbuller@chelt.ac.uk
Abstract
Since the mid-1980s, ‘indicators’ have been increasingly suggested as a possible
means to evaluate the effectiveness of various policy mechanisms. In this paper, we
shed some critical light on the use of indicators, by investigating in detail the
advantages and disadvantages of using the indicator approach in the assessment of
agri-environmental policy effectiveness in the European Union. We review the
theoretical and conceptual bases of the indicator approach, highlighting the
complexity of identifying appropriate indicators and the difficulties in identifying
means to ‘measure’ them. We analyse four levels of indicators (uptake, output, actorrelated and procedural indicators), and argue that while indicators continue to have
a central role to play in the evaluation of European agri-environmental policy, that
role is essentially limited and the notion of what constitutes an indicator needs to be
thoroughly re-examined.
Key words
Policy indicators; agri-environmental indicators; policy success; policy effectiveness;
Regulation 2078
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1. Introduction
Since the mid-1980s, agri-environmental policies (AEPs) have been implemented in
all European Union (EU) countries (Whitby, 1996; Potter, 1998; Buller et al., 2000).
Most of these have been put in place as a response to EU regulations (e.g. 797/85;
2078/92; Agenda 2000), and many share the common aims of extensification of
agriculture, farm income support, and environmental protection. Yet, although
hundreds of agri-environmental schemes are now operating in various EU localities,
costing European taxpayers about EURO 4 billion/year (with predicted budgets of
about EURO 8 billion/year by 2006), information on the ‘effectiveness’ of these
schemes has remained relatively sparse. This has been partly a result of the fact that
most EU policies were put in place without adequate and compulsory monitoring
frameworks attached to their implementation (CEC, 1997; Potter, 1998), but it also
reflects the diversity of rationales underlying AEP as a whole leading to, in many
cases, a notable lack of incentive to measure scheme success (Brouwer, 1999; Lowe
et al., 1999; Buller et al., 2000; Wilson and Wilson, 2001).
During the 1990s, increasing concern was voiced from different quarters about the
lack of evaluation of AEP success, particularly in view of the Agenda 2000
propositions which initially sought to substantially increase the agri-environmental
part of the Common Agricultural Policy (CAP), and in view of the fact that
agricultural support policies have multiple and sometimes contradictory
environmental effects (e.g. BirdLife International, 1994; CEC, 1997; Baldock, 1997;
Potter, 1998; Parris, 1999). Brouwer and Crabtree (1999b, 2), for example, argued
that “methods are … needed to indicate the effectiveness of policy response through
the agri-environment programmes … Indicators therefore are required to judge
whether a reduction in production-linked agricultural support would be beneficial or
harmful to the environment”, while Parris (1999, 27) has reminded us that “for most
OECD countries the systematic collection of basic agri-environmental data and
measurement of agri-environmental indicators is only beginning”.
Although the European Commission put in place mechanisms such as Regulation
746/96 that laid down the rules for the monitoring of schemes implemented under
Agri-environment Regulation 2078, there has been considerable debate surrounding
the means with which to ‘measure’ AEP success with regard to both socio-economic
and ecological parameters (Brouwer, 1995). While most monitoring has included
limited ecological assessments on the relative recovery/re-establishment of lost or
degraded wildlife habitats, discussions in the agriculture and environment sector in
the mid 1990s were also strongly influenced by general debates on how to measure
the effects of wider environmental policy strategies aiming at ensuring sustainable
development through the use of ‘environmental indicators’ (see in particular OECD,
1993, 1997, 1998; Moldan and Billharz, 1994; Hammond et al., 1995; Moxey et al.,
1998; Parris, 1999). Hence, the notion of ‘agri-environmental indicators’ to measure
the effectiveness of AEP and as a tool for regulation increasingly gained ground
among national and transnational policy-makers.
The aim of this paper is to discuss the use of such
effectiveness of EU AEP. The paper is divided into two
more broadly the notion of ‘indicators’ and consider
debates surrounding the utility, accuracy and general
indicators in assessing the
parts. First, we will discuss
conceptual and theoretical
applicability of indicators.
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Second, we will discuss empirical evidence to discuss problems and opportunities of
the indicator approach in assessing the effectiveness of AEPs. Following both Moxey
et al.’s (1998) and Lowe et al.’s (1999) calls for improved discussions of indicators
that take human agency and institutional structures into account, we will focus
specifically on indicators aimed at monitoring the response of the agricultural sector
to AEP policy targets, with particular reference to how farmers have adjusted farming,
farm management practices and environmental thinking on the basis of agrienvironmental scheme participation. Based on our own experience with attempting to
use indicators to ‘measure’ policy effectiveness, we will cast a relatively critical eye
on the utility and applicability of the indicator approach, and we will conclude the
paper by arguing for and against the use of indicators as a future tool for AEP
evaluation.
2. Agri-environmental indicators: some insights into the conceptual debates
Indicators are generally described as “a parameter [a property that is measured and
observed], or a value derived from parameters, which points to, provides information
about, describes the state of a phenomenon/environment/area, with significance
extending beyond that directly associated with a parameter value” (OECD, 1998,
107). Environmental indicators are, therefore, a vehicle for summarising, or otherwise
simplifying, and communicating information about something that is of importance to
environmental decision-makers (Moxey et al., 1998). They are a conceptual attempt at
identifying optimal amount of information under conditions of high uncertainty (i.e.
incomplete information settings on the state of the environment or human
environmental behaviour, or relating to the policy ‘implementation gap’; Barrett and
Fudge, 1981; Hogwood and Gunn, 1984; Winter, 1990; Adriaanse, 1993; Romstad,
1999). Indicators, thus, reduce the number of measurements and parameters that
normally would be required to give an ‘exact’ presentation of the situation, and they
simplify the communication process through which results of measurements are
provided to users. They should (in theory at least) provide managerially significant
information about patterns or changes in the state of the environment or changes in
human activities that affect the environment (Dabbert et al., 1999), and should enable
better targeting of information and action, particularly with respect to AEP (e.g.
Wilson, 1997a; Potter, 1998; Buller et al., 2000). Moxey et al. (1998) emphasise that
the increasing need for indicators also reflects broader shifts in society, with
increasing pressures to measure and assess the performance of public authorities by
external references in relation to actual policy outcomes, i.e. improved interorganisational control within increasingly complex systems of governance.
Before providing specific examples of the utility of the indicator approach in the
assessment of AEP in the EU in the second half of this paper, this section will discuss
four conceptual issues that have to be considered when using the indicator approach
in environmental policy evaluation. First, we need to acknowledge that indicators are
based on the belief of quantifiable environmental impacts and human environmental
behaviour. Second, the direct link between indicators and policy design and
implementation needs to be addressed. Third, conceptual debates surrounding the
choice of appropriate indicators need to be considered, and fourth, the issue whether it
is possible to develop a hierarchy of indicators in the effort to pick the ‘right’
indicators for policy assessment needs to be discussed.
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2.1 A quantifiable environment?
Indicators are based on quantitative notions that assume that policy success can be
‘measured’ (Rose, 1991). As a result, indicators have been widely used in disciplines
such as economics that rely largely on the quantification of human action (e.g.
environmental accounting; physical natural resource accounts) or in natural science
disciplines such as ecology or climatology that largely assume that environmental
effects can be modelled and quantified (see McKenzie et al., 1992; OECD 1998). As
a result, much of the theoretical work on indicators has come from these disciplines,
including contributions by economists (e.g. Norgaard, 1990; Farrow and
Krautkraemer, 1991; Asheim, 1994) focusing on issues of resource scarcity and
assuming rational and measurable human decision-making processes, or by natural
scientists preoccupied with developing suitable physical environmental indicators to
measure changes in the state of the environment (e.g. McKenzie et al., 1992; Dabbert
et al., 1999).
Conceptual approaches for the development of environmental indicators by the
Organisation for Economic Cooperation and Development (OECD) highlight this
preoccupation with ‘the measurable’. The OECD (1998) sees indicators as a tool for
decision-making and for assessing countries’ environmental performance to be used
for quantitative environmental performance reviews, in the EU often using data
compiled by EUROSTAT questionnaires on the state of the environment and on
pollution abatement and control expenditure provided by different member states. The
OECD adopts the (arguably simplistic) view that the main environmental concerns
related to agriculture can be reduced to nitrogen and phosphorus run-off from
excessive commercial fertiliser and pesticide use and intensive livestock farming
(OECD, 1993, 1997, 1998; see also MAFF, 1998). As a result, the OECD places great
emphasis on quantifiable indicators such as changes in the intensity of use of N and
P2O5 fertilisers, livestock densities, or the intensity of pesticide use (see Technical
Annex ‘Agriculture’ in OECD, 1998; Brouwer, 1999).
There are, however, evident problems related to the notion of quantifiable
environmental change. Indicator approaches focus on measurable entities that can be
expressed in numbers where data is more readily available. As we will discuss below,
this may lead to the neglect of more complex indicators where immediate quantitative
data may not be available, such as ‘loss of scenic beauty of agricultural landscapes’,
‘changes to nature and extent of wildlife habitats’ or, arguably most importantly,
‘changing environmental management practices and attitudes of farmers’. In this
context, the Council for the Protection of Rural England has recently emphasised that
some environmental characteristics and human environmental responses are
inherently better suited than others to quantification (CPRE, 1995). This leads Lowe
et al. (1999, 271) to question the OECD approach by arguing that “the OECD-type
indicator framework perpetuates a policy outlook which, by abstracting farming from
its social and environmental context, is part of the fundamental problem” (see also
IEEP, 1998).
Indicators can, therefore, never be a perfect quantification of an agri-environmental
problem. Although the belief in quantifiable environmental change may give the
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appearance of objectivity, it renders invisible the social choices their collection and
collation entail, leading Lowe et al. (1999, 264) to further argue that “the focus on
environmental outcomes, which is reinforced by the preoccupation with quantitative
indicators, may lead to a disregard of the lessons being learned from regulatory policy
by target groups and the need for wider institutional reforms”. In the agrienvironmental context discussed below, this means that measurable indicators (e.g.
changes in fertiliser and pesticide use) and quantifiable farm uptake data have been
overused as possible means to measure the ‘success’ of agri-environmental schemes,
while intangible socio-cultural and qualitative indicators1 have been neglected (for the
UK see, for example, MAFF, 1993, 1994, 1997; National Audit Office, 1997; or
House of Commons Agriculture Committee, 1997).
2.2 Indicators and policy
Indicators have to relate directly to stated environmental policy goals and objectives
(OECD, 1998; Jesinghaus, 1999). Indicators can, therefore, only be conceptualised in
the context of policy design and implementation. As such, they have become an
integral assessment tool of EU agricultural policy reform (Brouwer and Crabtree,
1999b), and the main objective of indicators is to assist policy-makers in their
evaluation of policies and, in particular, to gain an understanding of the
‘implementation gap’ between policy formulation ‘at the top’ and policy acceptance
at the grassroots level that often hinders successful policy implementation (Barrett
and Fudge, 1981; Hogwood and Gunn, 1984; Winter, 1990). This is important insofar
as so-called ‘policy relevance’ has become a key criteria for selecting environmental
indicators by various organisations (e.g. MAFF in the UK; OECD; United Nations).
However, this implies that policy goals and objectives have to be clear. As we will
discuss in detail below, this is rarely the case with respect to many environmental
policies such as Agri-environment Regulation 2078/92/EEC. With regard to research
on this specific policy, indicators have, therefore, been most commonly linked to
explicit quantitative objectives of schemes implemented under the Regulation (e.g.
uptake targets; cost-benefit; environmental thresholds; set-aside targets), but have so
far been insufficiently linked to less clearly prescribed qualitative policy objectives
such as the influence of agri-environmental schemes on various actors’ action and
thought (e.g. ‘ways of using agricultural land which are compatible with protection
and improvement of the environment’ [Article 1b of Agri-environment Regulation
2078] or ‘education and training of farmers in types of farming compatible with the
requirements of environmental protection and upkeep of the countryside’ [Article
1g]).
If we accept the notion that indicators and policy are a continuously reinforcing
feedback system (OECD, 1997, 1998), three policy-related problems emerge. First,
the indicator approach may reinforce the design and implementation of policies that
are easily quantified and may, therefore, increase the often existing implementation
gap. In other words, more easily quantifiable physical and economic environmental
targets may be over-emphasised to the neglect of less easily prescribable sociocultural policy goals. Second, any published indicator exerts a pressure on policy1
For example, alternative behaviourally-grounded conceptions of scheme success have emphasised
changes in landholders’ environmental actions as equally important indicators to measure policy
‘success’ (e.g. Morris and Potter, 1995; Wilson, 1996, 1997a; Lobley and Potter, 1998).
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makers to improve the figure but not necessarily to improve the underlying problem
(Jesinghaus, 1999). Regulation theorists such as Campbell (1987) or Romstad (1999),
therefore, argue that indicator relevance should go beyond simple policy relevance to
include broader socio-cultural needs and expectations. Third, while indicators might
reveal trends, they do not necessarily explain them (Lowe et al., 1999). Measuring
AEP ‘effectiveness’ may just become a simple description of change without
addressing the issue of causality. If it were possible to estimate the possible policy-off
situation, then the comparison between observed outcomes and the policy-off
situation may offer the possibility to quantify policy effects (Lehmann and Schmid,
1997).
2.3 Criteria for choosing appropriate indicators: the ‘5 Rs’
That indicators are based on notions of quantifiable environmental change and that
they have to relate directly to overtly stated or hidden policy goals and objectives has
important implications for the selection of ‘appropriate’ indicators. Indicators are
expected to be consistent, reliable, unambiguous, transparent, and they should have
predictive capacities and analytical soundness (OECD, 1998; Moxey et al., 1998;
Romstad, 1999; Peco et al., 1999). As the discussion above illustrates, they should
particularly be measurable and policy relevant. These different demands and
expectations on indicators can be summarised into five categories (the ‘5 Rs’):
indicators should be reliable, relevant, reproducible, representative and realisable.
As indicators are simplifications of often complex processes, they need to remain
reliable with regard to what they are aiming to ‘measure’ (Hammond et al., 1995;
Romstad, 1999). While organisations such as the OECD argue that this reliability
criteria is crucial for indicator measurability (in other words, that indicators should
lend themselves to being linked to economic modelling, forecasting and information
systems), such measurability still varies greatly among indicators and across different
geographical areas (Brouwer and Crabtree, 1999a). In addition, the nonstraightforward links between agriculture and the physical environment, the resulting
multi-causality of environmental parameters, and that linkages between agriculture
and environment are far more complex than many other economic activities, suggest
that the reliability criteria for indicators is often the most difficult to achieve.
A common claim is that indicators have to be relevant, especially in their requirement
for policy performance analysis. Policy-makers’ preference for certain indicators can
be explained as a trade-off between quality and cost. As a result, indicator relevance
varies from one country to another (or even from one region to another), and often
they have to be interpreted in the appropriate context, taking into account different
ecological, geographical, social, economic and institutional features. With regard to
physical environmental indicators, for example, the relevance of an indicator may
depend on the site-specific environmental state, and the optimal indicator set will
most likely differ from one location to another. In particular, indicator relevance may
vary from actor to actor, as different actors in the AEP implementation process seek
different goals and objectives with regard to scheme outcomes (e.g. CPRE, 1995;
Department of the Environment, 1996; OECD, 1997). Hence, while environmental
NGOs, for example, may seek to promote environmental sustainability through AEP
(e.g. FoE, 1992; Environmental Challenge Group, 1995; World Resources Institute,
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1995), agricultural economists generally emphasise the importance of financial
benefits of schemes with a resulting focus on cost-benefit indicators and ‘value for
money’ (e.g. Garrod et al., 1994; Garrod and Willis, 1995).
A further key requirement for indicators is that they are reproducible. An indicator
that can not be used by other researchers is meaningless. Ideally, they should be
theoretically well founded in technical and scientific terms, they ought to be based on
international standards and international consensus about their validity, they should be
adequately documented and of known quality (with an emphasis on the national and
international level rather than the local level; see below), be updated regularly in
accordance with ‘reliable’ procedures, and should be based on consistent (and
reproducible) methodologies. Indicators should also be representative, in that they
ought to provide the basis for inter-regional and international comparison. They
should, therefore, have a threshold or reference value against which to compare
changes in environmental state or human environmental activity, and such thresholds
should ideally be defined in relation to legal, scientific or policy-related norms (Parris,
1999). Finally, indicators should be realisable. In other words, they ought to be
simple and easy to interpret and able to show trends over time, and they should be
readily available at reasonable cost-benefit ratio (Romstad, 1999).
It is obvious that no researcher will ever achieve to fulfil the ideal ‘5 R’ criteria,
prompting Peco et al. (1999, 151) to argue that “it is impossible to construct a general
(common) system of agri-environmental indicators for the purpose of evaluating the
impact of the CAP on the environment”. As findings from our own research will show
(see Tables 1 and 2 below), selecting appropriate indicators, therefore, always has to
be a trade-off between what is feasible, affordable and manageable within a research
environment that is most usually restricted by limited time, money and personnel.
2.4 A hierarchy of indicators?
Selection of appropriate indicators is further hampered by the fact that the hierarchical
importance of indicators varies depending on the spatial focus of investigation, the
expected use of an indicator (or groups of indicators) in answering specific objectives,
and the policy context. Thus, some indicators are better able to reveal implementation
deficits than others (cf. Winter, 1990). Typological, spatial and conceptual indicator
hierarchies can be identified, that all need to be taken into account when interpreting
research results.
Typological indicator hierarchies refer to the pressure-state-response (PSR) model
developed by the OECD (1993, 1997, 1998) which corresponds well to the profile of
EU member states’ statistical services (see also United Nations, 1996; Eurostat,
1997). Pressure indicators are related to human activities, processes and patterns (e.g.
agricultural activities), and their importance is highlighted by current efforts to
establish a harmonised European System of Environmental Pressure Indices (ESEPI)
(Jesinghaus, 1999). State indicators, meanwhile, give information on the state of the
environment and natural resources arising from pressure indicators, while response
indicators provide information on policy responses and changes in awareness and
behaviour of communities and individuals. Human activities exert environmental
pressures that lead to changes in the state of the environment, and resulting response
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by society in form of policies and/or changed environmental management practices,
which, in turn, may change human pressures on the environment (Brouwer and van
Berkum, 1996; Lowe et al., 1999). Although the OECD (1998) has acknowledged that
there can be no universal set of AEP indicators, they nonetheless suggest to focus on
13 indicators that are believed to be sufficient core indications on changes in the
environmental state of agricultural landscapes and changes in human response
mechanisms. Thus, OECD pressure indicators include information on nutrients,
pesticides, water use and land use changes, state indicators focus on soil quality,
water quality, greenhouse gases, biodiversity, wildlife habitats, and landscape change,
while response indicators stress farm management practices, farm financial resources
and socio-cultural aspects (including, for example, education, training, or issues of
rural/urban migration).
The PSR typological model has been widely criticised for being too simplistic in its
assumptions of environmental causality, i.e. that the three types of indicators
(pressure-state-response) form discreet conceptual units that can be easily separated
from each other (Moxey et al., 1998). The problem of unequivocal causality relates to
the fact that, often, the multitude of environmental influences are only partly related
to processes measured through indicators (policy-on and policy-off scenarios and
trajectories of farmers’ decision-making processes in AEP being an obvious case-inpoint). The model has been further criticised for its positivist theoretical assumptions
that are based on the belief that both physical indicators and indicators of human
environmental action and thought can be quantified (Lowe et al., 1999; see also
Section 2.1 above). Particular problems are seen with state and response indicators.
For state indicators, no framework has been established yet for how to overcome
problems of lack of linearity and immediacy (Oñate et al., 2000), i.e. that the problem
may lag behind the source of disturbance (N leaching into soil and groundwater being
an obvious example). Although state indicators may be more closely linked to
environmental policy interests, they are more costly to produce and often suffer from
complex demands on the nature of measurement. Yet, the most severe shortfalls are
seen with regard to response indicators (Brouwer and Crabtree, 1999a). Parris (1999,
40) for example, concedes that “although the importance of socio-cultural issues in
the analysis of agriculture and the environment … is generally accepted, no precise
definition of the policy issues nor the relevant indicators have yet been established”.
Spatial indicator hierarchies highlight the problem of the need for multiple spatial
scales of indicator frameworks. In this context, Moxey et al. (1998) ask how it is
possible in indicator research to preserve the fine resolution detail required for
guiding local level actions, while avoiding information overload at the broader global
level? The OECD suggests to use international indicators in combination with specific
national indicators (i.e. moving away from the ‘5 R’ criteria mentioned above). This is
supported by Moldan and Billharz (1994) who argue that different indicators of policy
performance are required at different hierarchical levels, and that indicators useful for
international comparison will inevitably differ from those for monitoring regional or
local environmental change. While recent trends suggest that the focus is increasingly
on using national indicators for use in international work (e.g. ACANZ, 1993; IFEN,
1997; OECD, 1998) – a problem that we will also encounter in our analysis below –
recent work has also insisted that any list of indicators must be peculiar to each
system, and that the value of indicators must be calculated at the county or farm scale,
rather than at the national or international level (e.g. Peco et al., 1999). The debate on
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internationalisation or localisation of the indicator approach is, therefore, still ongoing.
A third approach, and one that we adopted in the research programme from which this
paper is drawn (Schramek et al., 1999), is founded on a conceptual indicator
hierarchy that proposes four levels of increasing complexity (Table 1). Our analysis
of agri-environmental indicators presented in the following section, identifies the
strengths, weaknesses and potentials of these different levels in providing viable
assessments of the ‘success’ of AEP. However, before doing this, we need to consider
the broader presuppositions that underlie the use of indicators in evaluating,
specifically, AEPs.
Table 1:
Parameters for the evaluation of agri-environmental scheme
effectiveness
Level 1.
Uptake and outputs
- numbers of contracts/farms
- areas under contract
- expenditure
Level 2.
Outcomes and Effects
- effects upon farm management practices
- effects upon agricultural production
- effects upon the environment
- effects upon farm incomes
- effects upon agricultural labour
Level 3
Actors
- impact upon farmer attitudes and knowledge
- impact upon the social processes of action
- impact upon the activities of associated actors
Level 4
Procedures
- role of management institutions
- efficiency of the policy-making process
- impact upon regulatory procedures
(Source: adapted from Buller, 2000)
3. How effective at doing what? The strengths and limitations of agrienvironmental policy indicators
A recently completed research project funded by the DGVI of the European
Commission sought to explore appropriate methodologies for the implementation and
effectiveness of AEP in Europe (Schramek et al., 1999). In this part of the paper, we
consider the results of this research and, more specifically, what it tells us about the
use and misuse of agri-environmental indicators in informing and guiding policymaking. As we have stated above, the identification of a policy’s objectives is a
fundamental first step if effective evaluation procedures are to be put into place and
useful results are to be obtained. This presupposes, first, that the objectives are clearly
stated, second, that they are coherent and, third, that a relatively direct relationship
exists between the recognition of a given problem, the mechanisms evoked to deal
with it, and its eventual resolution. One of the problems of constructing a suitable
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evaluation strategy for AEP is that these presuppositions are not always wholly born
out.
Article 1 of Regulation 2078 lays down the three goals of EU AEP: to accompany the
changes to be introduced under the market organisation rules; to contribute to the
achievement of the Community’s policy objectives regarding agriculture and the
environment; and to contribute to providing an appropriate income for farmers (CEC,
1992). These reflect different concerns. As an accompanying measure to CAP reform,
agri-environmental aid schemes comply with the basic aim of that reform, namely to
reduce agricultural over-production within the EU and thereby lessen the overall costs
of agricultural support. The Agri-environmental Regulation also reflects growing
concern over the environmental consequences of intensification, notably with respect
to water pollution, biodiversity loss and landscape change. A third goal has been to
support and maintain extensive farming practices, not only against intensification but
also, particularly in upland and southern regions, against agricultural decline and the
environmental and social consequences of under-production. Here, then, are three
very different objectives that reflect macro-economic, environmental and socioeconomic concerns that seek de-intensification in certain cases and maintenance, even
intensification, in others, that ultimately relate to very different rural spaces and
agricultural systems.
Thus, any evaluation methodology is necessarily going to be very complex because
not only are the implementation procedures very different in the various EU member
states, and the types of farm system targeted and the range of landscape of
environmental problems encountered highly variable, but also the very objectives of
AEP are, themselves, multiple and by no means restricted to a single environmental
policy agenda. AEP cannot be seen simply as a strategy for achieving environmental
objectives within agriculture, particularly as such objectives might arguably be more
effectively secured by alternative strategies. Indeed, as a number of commentators
have suggested (see, for example, Buller et al., 2000), EU AEP has been singularly
unsuccessful in addressing the central integration issue of farm pollution. Its
‘success’, if it exists, lies elsewhere.
Despite the recent demand for effective instruments capable of evaluating AEP, two
operational problems to the development of a valid indicator framework for agrienvironmental measures exist. The first of these has been the consistent lack of baseline data on environmental ‘states’ prior to the start of contractual agri-environmental
agreements (i.e. referring to the issue of indicator reliability mentioned in Section
2.3). As most EU Member states only began to implement agri-environmental
measures in the 1990s, insufficient reflection was given to the eventual evaluation of
the policy some years further on (Buller, 2000). Second, although it is becoming
increasingly commonplace to evaluate the AEP implementation process in terms of
indicators, the vast bulk of these assume that successful implementation will engender
positive changes in environmental conditions. Yet, any analysis of the goals as well as
the implementation of EU-led AEP reveals that, for the most part, the resultant
schemes do not, in fact, seek changes, be they in agricultural practice or in
environmental quality. Rather, they seek to maintain existing practices considered
both to be environmentally friendly and necessary for the maintenance of sustainable
rural/agricultural communities by encouraging farmers not to make changes to
existing management techniques and thereby not to increase environmental pressures.
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Given that so much of the indicator rationale developed by the OECD (1993; 1997;
1998) and the European Environment Agency (e.g. EEA, 1998) (amongst others) is
guided by the desire to record the effects of agricultural de-intensification upon
environmental improvement, there is a risk here that the long-term and, ultimately
more profound, effects of environmental integration are being missed. The evaluation
of AEP thus needs to take into account alternative parameters of scheme success.
3.1 Output and outcome indicators
Referring back to Table 1 (above), we propose a hierarchy of indicator levels that take
us from ‘outputs’ through ‘outcomes’ and effects to a set of less tangible components
of policy ‘success’. The first of these levels, indicators of uptake, also known as
‘output indicators’, are commonly derived from the contracted area, numbers of
contractants and proportions of budgets spent and/or allocated (and combinations of
these), because of the relative simplicity and low cost of their construction as the only
really universal evaluation criteria across Europe (see also Section 2.3 above). This
has largely been the methodology employed by the Commission in their own initial
evaluation of the application of Regulation 2078/92 (CEC, 1997). As the Commission
itself claims in a later document (CEC, 1998, 123), “the most relevant indicator of
application is the area to which measures apply”. As a means of evaluating the
policy’s achievements, uptake figures provide a reassuringly high ‘success’ rate with
some 22.6 million hectares of European farmland being placed within agrienvironmental schemes and well over a million European farmers engaged, to greater
and lesser degrees, in the drive towards reconciling the needs of agricultural
production with those of environmental sustainability.
However, such an approach has a number of limitations in a cross-national context.
Output indicators based upon uptake rarely account for participant eligibility, usually
differentially defined in terms of farm, types of farm practice, specific geographical
locations or budgetary envelopes (see, for example, Wilson, 1997b). Further, broad
assessments, as presented by the European Commission’s own evaluation study
(CEC, 1997; 1998), do not account for different intensities or levels of participation
within individual schemes. Agri-environmental aid schemes currently operating are
often comprised of a number of ‘tiers’, with higher tiers often requiring, in exchange
for higher premiums, genuine changes in farm management practices, while the lower
tiers frequently require only the maintenance of existing practices (see, for example,
MAFF, 1993; 1996; Wilson, 1997a; and Lobley and Potter, 1998, for the UK context).
In many countries, including the UK, it is the lower tiers that attract the greatest
number of participants. An additional problem concerns the criteria for defining
successful uptake. Are schemes with high levels of uptake more ‘successful’? Clearly,
they attract proportionally more farmers and they succeed in placing significant areas
under contract. As such, their ‘impact’, in spatial as well as participation terms, is
undoubtedly greater. However, we cannot automatically assume that the generally low
take-up rates associated with, for example, arable schemes necessarily constitute
scheme failure. In assessing uptake, we need to be aware of the differential impact of
participant numbers. While the coherent protection of large areas of upland grasslands
might depend upon a high uptake rate, improving water quality, particularly around
sensitive areas, might be effectively achieved through the participation of only a small
12
number of highly targeted eligible farms. In such cases, the use of uptake indicators is
arguably of little value as a means of assessing scheme effectiveness.
A second level of indicators, requiring more precise temporal and spatial reference
points, is concerned with the outcomes and impacts of implementation in terms of
farm management techniques, farm income, farm labour, and, indeed, the wider
environment. Given that agri-environmental schemes specifically seek to engage
farmers in the employment of environmentally sound farm management practices,
such indicators are the common fare of agri-environmental scheme assessments
(CEC, 2000). Research carried out for DGVI by the current authors and others
specifically sought to identify and test indicators of this kind (Schramek et al., 1999;
Wilson and Hart, 2000). A summary of the most common indicators of this group,
drawn from various studies, is presented in Table 2.
Table 2:
Common agri-environmental indicators identified and tested
by recent research into agri-environmental scheme evaluation
Changes to stocking density
Changes to pasture land rotation
Quantity of pesticides used
Quantity of nutrients used
Change in farm usage of inputs
Proportion of land under grass
Yield changes for specified crops
Changes in use of farm land (arable to grassland….)
Changes in energy balance on farm
Changes in labour
Changes in gross profit per farm
Emissions to the atmosphere per farm
Number of livestock removed
Area of target land cover type
Dates and times of harvests and grass cutting
Hedgerow densities and changes to hedgerow densities
Changes to field margins
Levels of pesticide residue in produce and environment
Concentration on N in water (relation to soil depth and crop type)
Specific species of wild plant
Changes in vegetation density in grassland
Density of specific invertebrates
Bird nesting sites
Nesting pairs
Other identified animal species
Loss of soil under certain cultivation techniques
(Sources: CEC, 1998; Schramek et al., 1999; Oñate et al., 2000)
The application of outcome and impact indicators to the evaluation of agrienvironmental schemes has generally revealed positive environmental benefits arising
from contractualisation (Schramek et al., 1999). Oñate et al. (2000), whose work
derives from the same research project as our own, distinguish ‘improvement’ effects
and ‘protection’ effects and demonstrate, from a specific comparison of Spanish and
Danish agri-environmental schemes, that clear improvement effects were found and
that protection effects “may well be an important long-term result” (p. 278). Other
evaluations draw broadly similar conclusions. For example, the exercise undertaken
13
by the French Ministère de l'Agriculture (1998) reveals, first, a notable increase in
specific landscape management actions, such as hedgerow planting and water channel
clearing (particularly in those areas already noted for the quality of their agricultural
environments) and, second, some limited change in the use of pastures and chemical
inputs, though rarely in the major arable regions of the country.
However, despite their widespread use, such outcome and effect indicators contain a
number of significant limitations, over and above the operational issues associated
with agri-environmental scheme evaluation identified above. First and foremost,
many outcome indicators reveal little more than compliance to contractual
obligations, highlighting problems associated with the policy ‘implementation gap’
mentioned above (see also Peco et al., 1999; Buller et al., 2000; Oñate et al., 2000).
Indicators that reveal declining stocking densities or the maintenance of grasslands,
where farmers are receiving payments to undertake such actions, are ultimately of
little long-term value in assessing the degree to which environmental considerations
are being integrated into agricultural policy. Second, in many instances, the
environmental benefits of changes to farm management techniques are extrapolated
and assumed, rather than specifically revealed and documented. Hence, conversion of
arable land to grassland, like conversion from ‘conventional’ to organic farming, are
universally, and often uncritically, taken as being of positive environmental benefit.
Third, the employment of such indicators provides only a shallow and some would
say ‘optimally inaccurate’ (Moxey et al., 1998) evaluation of scheme effectiveness.
They are temporally and spatially limited and, in their uni-dimensionality, reveal little
of the underlying processes at work. In their assessment of such indicators, Andersen
et al. (1999) conclude that the farm management effects identified “cannot be linked
directly to specific stated targets or success criteria of individual schemes, mainly
because such criteria do not exist” (p.143).
While the two sets of indicators identified above have undoubtedly contributed to the
generation of an increasingly systematic and comparative evaluation methodology of
AEP, and have provided policy makers with an important tool in the legitimisation of
their intervention, they remain, within the context of an intended drive towards
achieving more sustainable forms of agriculture, limited in their ability to account
either for variations in implementation or for the ultimate durability of sustainable
practices. We have argued above (see Section 2.1) that critical for the understanding
of both of these are assessments of farmer participation and the sociological,
economic and institutional contexts and rationales that influence that participation (i.e.
socio-cultural and qualitative indicators). These constitute the third and fourth sets of
evaluation criteria that we wish to explore here.
3.2 Actor and procedural indicators
In looking at farmer participation as a means of assessing and of evaluating AEP
performance and effectiveness, how do we account for farmer participation? At one
level of analysis, we might seek to understand how and why farmers participate in
schemes (and why they do not participate) by considering their characteristics, their
motivations and their attitudes towards individual schemes, or indeed to AEP as a
whole. Such an approach commonly yields typologies of farmers and allows us to
differentiate likely participants from likely non-participants (see, for example,
14
Brotherton, 1989; Morris and Potter, 1995; Billaud et al., 1996; Wilson, 1996; 1997a;
CNASEA, 1997; Lemery et al., 1997; Schramek et al., 1999; Wilson and Hart, 2000).
Nevertheless, considerable care needs to be taken in drawing hard and fast
conclusions regarding the ‘success’ of schemes from these apparent participation
factors. While certain farm and farmer characteristics do correlate with participation
in schemes, this information is too often highly scheme specific and does not always
hold up when used in a cross-scheme or cross-national comparison (see our discussion
of the ‘5 Rs’ in Section 2.3). Further, statistical correlations, however significant, do
not in themselves indicate causality. While large farms might show a statistically
significant correlation with scheme participation in some areas, one cannot conclude
that having a larger farm is, in itself, a reason for, or indeed an influence on,
participation (Wilson and Hart, 2000). Many such variables are beset with
directionality contradictions. For some observers, youthful farmers are more likely to
participate, for others, older participants are the keener subscribers. For others,
financially buoyant farms are more likely to participate, while for others it is the
economically more marginal farmers that turn to agri-environmental measures as a
specific form of income support (see Morris and Potter, 1995; Wilson, 1996; 1997a;
Lobley and Potter, 1998; and Wilson and Hart, 2000, for detailed investigations of
these factors).
Second, few of the identified factors are independent, but are frequently part of a
complex social and/or economic cognitive system. To focus simply upon observable
farm/farmer characteristics is to ignore the context within which these characteristics
are developed and sustained. Third, the problem of scheme consistency2 makes the use
of structural indicators in creating an operational methodology for assessing agrienvironmental scheme effectiveness problematic (see also Section 2.4 above).
Further, there is the issue of appropriate levels of generalisation. To what degree can
one draw comparable and generalisable information from studies of farmer
participation when that participation, and indeed farming as an activity, is so
embedded in a whole array of national and sub-national social, technological,
political, cultural and territorial factors that ultimately make up the very diversity and
multifunctionality of rural Europe? Ultimately, while understanding farmers’ reasons
for participating (or not) in agri-environmental schemes may be of value in targeting
and designing schemes to achieve greater rates of take-up, it ultimately “offers little in
the way of accessible, comparative, operational and quantitative indicators for the
evaluation of scheme performance” (Wilson and Hart, 2000, p. 2182).
Another level of investigation focuses on the impact of schemes upon participants.
Critical to any evaluation of the sustainability of policy objectives is the degree to
which they lead to long-term changes in practices and/or attitudes – changes that
potentially stretch beyond shorter periods of compensation and financial support or
encouragement (Wilson, 1996). The drive towards more sustainable forms of
agriculture should not be founded in a short-term funding policy, but in a strategic and
jointly negotiated shift in, or re-emphasis of, values on the part of producers,
consumers and regulators. For, as Lowe et al. (1999) point out:
2
Seemingly similar schemes might, in fact, have very different objectives within different national or
regional contexts. A classic example is grassland protection which varies from being a clearly
ecological measure designed to protect highly sensitive zones (e.g. along rivers), to being a general
funding measure for extensive husbandry methods in high landscape value areas.
15
“Agricultural policy reforms in general, and agri-environment measures in particular,
should enhance farmers’ attitudes towards the environment and clarify their
understanding of their environmental responsibilities. It might reasonably be expected
that there would already be discernible changes in farmers’ attitudes, and even in farming
cultures, from participation in agri-environment schemes, even where the environmental
consequences could still not be gauged” (p. 271)
This, perhaps, is the ultimate challenge for evaluation strategies of public policy,
going beyond the quantitative realm of performance indicators to identify cognitive
and institutional shifts and responses among actors, organisations and juridical
regimes (Muller, 1998). The methodology for applying such an approach to AEP
remains, however, still largely undeveloped despite exhortations to go in this direction
(Moxey et al., 1998). The research from which this paper is drawn sought to address
this issue in two ways, first, by examining changing farmer attitudes towards agrienvironmental preoccupations as an indicator of scheme effectiveness (Buller, 1999;
Wilson and Hart, 2001) and, second, by considering the shifting role of farmers,
agricultural extension services and territorial management bodies to the provision of
agri-environmental advice and the local construction of agri-environmental schemes
(Buller and Lenormand, 1999; Buller and Brives, 1999).
What these various analyses have in common is the manner in which they have gone
beyond traditional quantifiable indicators. While each has, to a greater or lesser
extent, revealed shifts in farmer attitudes and in institutional and process responses to
the requirements of environmental policy integration, particularly as schemes have
matured and local consultation exercises have multiplied, specific indicators of such
change have proved more difficult to construct. Shifts in attitudes are highly
dependent on the attitudes of farmers before entering the scheme, on the types of
scheme proposed to farmers and upon the degree to which farmers are involved in the
construction and implementing of individual schemes alongside extension services,
countryside management structures and environmental interests (Wilson, 1996;
Buller, 1999; Wilson and Hart, 2001). Using such behavioural approaches, scheme
success can therefore be defined in three ways: in terms of the willingness of farmers
to adopt the management prescriptions, in which case extensive schemes involving
few changes to management practices might still be considered ‘successful’ as
farmers accept them more readily; in terms of the degree to which schemes change
farmers attitudes towards the agricultural environment, in which case different types
of schemes emerge that involve more or less profound changes to farm management
(e.g. conversion to organic methods); and, finally, in terms of the re-alignment of
local interests, stakeholders and institutional actors into new forms of partnership
based upon local and territorially specific agri-environmental management
frameworks (Buller and Lenormand, 1999; Buller and Brives, 1999).
4. Conclusions
It is clear from the above analysis that while indicators have a central role to play in
the evaluation of AEP, that role is essentially limited. Either that, or the notion of
what constitutes an indicator needs to be re-examined in light of conceptual caveats
mentioned in the first part of the paper. Thus, our analysis suggests that the
assumption that agri-environmental indicators may act as a vehicle for summarising,
16
or simplifying, and communicating information about policy parameters that are of
importance to agri-environmental decision-makers needs to be questioned – thereby
confirming Moxey et al.’s (1998) and Lowe et al.’s (1999) initial fears about the
fallacy of uncritically adopting indicator-based models in policy evaluation. We,
therefore, need to question whether indicators can provide managerially significant
information about patterns or changes in the state of the environment and human
activities as suggested in much of the literature based on positivist notions of humanenvironment interaction (e.g. Eurostat, 1997; OECD, 1997,1998; CEC, 1998;
Jesinghaus, 1999). Our discussion on measuring the ‘success’ of AEP has particularly
highlighted the simplistic assumptions about the measurability of human
environmental action and resulting environmental change that increasingly appear to
guide many policy evaluations, to the neglect of more complex indicators where
immediate quantitative data may not be available (especially actor and procedural
indicators; see above).
Ultimately, what is required is a shift away from simple information provision
towards the realm of public policy analysis. The limits of indicators in evaluating
agri-environment policy derive, in part, from the precipitate manner in which policymakers and others turned to them in the mid-1990s as a means of justifying and
legitimising an emerging, fast-growing and poorly monitored financial sector of the
CAP. Yet, while researchers were being asked to supply appropriate methodologies
for evaluating individual agri-environmental schemes, the central question over the
last few years has been that of the integration of environmental considerations, not
only into on-farm management practices but into the very architecture of the CAP.
Thus AEP constitutes, in many EU states, a significant part of the so-called ‘second
pillar’ of the post-Agenda 2000 CAP, even though this is still a poor cousin to the
‘first pillar’ of commodity support. The changes that the EU Rural Development
Regulation – as the mainstay of the ‘second pillar’ – may bring about are, in a certain
manner, more revealing of the advances made over the last ten years in the integration
of environmental concerns into agricultural policy (Lowe et al., 2001) than any set of
quantifiable agri-environmental indicators.
This paper has identified different types of indicator which we have regrouped into
four ‘levels’. These have different functions within the policy evaluation process.
‘Uptake’ or ‘output’ indicators are politically useful in that they provide a ready
assessment of scheme ‘success’ defined in the most simplistic of ways, although they
may shed little light on reasons for the implementation gap. ‘Outcome’ indicators,
meanwhile, show the effects of AEP, revealing whether or not individual schemes are
delivering their stated objectives. These indicators may be particularly useful to
policy-makers to address possible inconsistencies between policies developed at the
‘top’ and implementation failure at the grassroots level (cf. Winter, 1990; Adriaanse,
1993; Brouwer and Crabtree, 1999a). ‘Actor’ and ‘procedural’ indicators go beyond
this to display, first, the extent to which these objectives are the correct and most
relevant ones and, second, the overall position of AEP within a broader socio-political
and institutional agenda. Yet, as AEP moves on to become a component of a wider
rural development agenda, the use of output and outcome indicators will, we believe,
become less important, as alternative methodologies of evaluation currently employed
for integrated rural development schemes, LEADER territories, and the EU structural
funds, will gradually take their place.
17
Acknowledgements
The authors wish to acknowledge financial support from DGVI (European
Commission) for empirical aspects of this study as part of an EU-wide transnational
project (FAIR CT95-274). Many thanks also to all the teams and people from 10
European countries involved in this project, who made valuable conceptual and
practical contributions during discussions about the utility of the indicator approach in
assessing the effectiveness of European agri-environmental policy.
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