agri-environmental indicators

ECOLEC-03064; No of Pages 15

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w w w. e l s e v i e r. c o m / l o c a t e / e c o l e c o n

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METHODS

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Lúcio André de O. Fernandesa,⁎, Philip J. Woodhouseb

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Family farm sustainability in southern Brazil: An application of agri-environmental indicators

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Lecturer in Economy and Ecology, Universidade Católica de Pelotas(UCPel), Felix da Cunha, 412, 96010 000, Pelotas, RS, Brazil Senior Lecturer in Environment and Rural Development, School of Environment and Development, Institute for Development Policy and Management (IDPM), University of Manchester, UK b

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Article history:

This paper investigates the sustainability of agroecological and conventional agriculture on

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Received 16 October 2007

small farms in southern Brazil. A methodology was developed to identify agri-environmental

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Received in revised form

indicators of the environmental, economic and social dimensions of the farming systems.

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The criteria used for selecting sustainability indicators were policy relevance, measurability,

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Accepted 24 January 2008

validity/analytical soundness, level of aggregation/communication to the user. Based on

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these criteria indicators were selected for natural, financial, physical, human, and social

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capital assets. The research identified ‘external’ indicators, those relevant to researcher/

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Keywords:

policy makers, and ‘internal’ indicators, those relevant to the resource users. The two sets

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Peasant farming

were combined and data relevant to the selected indicators were collected from secondary

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Agroecology

sources and also direct from farmers through a small-scale sample survey. After analysing

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Sustainability indicators

the data, indicators were selected for each capital asset to generate a multi-criteria

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Brazil

assessment of sustainability at three scales: farm, local and regional. The analysis provided

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evidence of ecological and social advantages of ecological farms, but financial disadvantages

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associated with the cost of alternative marketing arrangements for agroecological produce.

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The paper draws conclusions about the usefulness of indicators in the assessment of farming

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sustainability and possibilities for wider application.

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Since the 1992 Rio Earth Summit established the Commission on Sustainable Development (CSD) to monitor the progress towards sustainable development, agri-environmental indicators have become widely regarded as an important means to assess the contribution of farming systems to “sustainable development” goals. There has been a corresponding growth in attempts to identify appropriate indicators both conceptually and empirically (Syers et al., 1995; Gomez et al., 1996; Bockstaller et al., 1997; Müller, 1997; Dumanski et al., 1998;

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Introduction

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© 2008 Elsevier B.V. All rights reserved.

Masera et al., 1999; Rigby et al., 2000; Hess et al., 2000; OECD, 1999a, 1999b, 2000, 2001; Herendeen and Wildermuth, 2002; Rasul and Thapa, 2003; Stoorvogel et al 2004; Belcher et al., 2004). However, although sustainable development has acknowledged social and economic, as well as ecological, dimensions, analysis that integrates them effectively remains elusive. A recent review of sustainability indicators (Hezri and Dovers, 2006: 91) underlines the challenges of “disciplinary and methodological heterogeneity” which require engagement between environmental and social sciences and methods to aggregate “raw and incongruent sustainability variables”.

⁎ Corresponding author. Tel.: +55 53 2128 8291; fax: +55 53 2128 8298. E-mail addresses: [email protected], [email protected] (L.A.O. Fernandes), [email protected] (P.J. Woodhouse). 0921-8009/$ – see front matter © 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ecolecon.2008.01.027

Please cite this article as: Fernandes, L.A.O., Woodhouse, P.J., Family farm sustainability in southern Brazil: An application of agri-environmental indicators, Ecological Economics (2008), doi:10.1016/j.ecolecon.2008.01.027

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Despite the large literature on the subject of indicators of sustainability, it remains difficult to identify a clear consensus, and, in order to advance empirical study without becoming trapped in an endless conceptual discussion, it is necessary to accept an operational definition of indicators, such as:

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2.2.

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Policy relevance: the analytical framework

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A key requirement in determining the relevance of indicators 146 to a particular decision-making context is a framework that 147 both establishes the range of factors that are relevant to the 148

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Methodology for agri-environmental indicators

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Agri-environmental indicators (AEI) are estimators of the impact of agricultural practices on the agroecosystem. They can be used at different levels of decision-making. At farm level they can help farmers to adapt their practices to be environmentally sound, for example, while at broader regional planning or national levels they orientate policy decisions and evaluate policy effects (Glenn and Pannel, 1998). One of the most comprehensive proposals developed for agri-environmental indicators is that of the OECD (Organization for Economic Cooperation and Development) (OECD 1999a, 1999b, 2000). However, the list of potential indicators offered by the OECD is very large. In practice, it is necessary to work with a more limited set of indicators selected for their relevance to a given set of sustainability goals in a given context. Of more universal applicability, however, are the OECD criteria for the selection of agri-environmental indicators (OECD, 1999a; Doyle, 1999), summarised in Table 1. These criteria have not, to our knowledge, been challenged nor are they inconsistent with recent writing on methodological aspects of developing and applying sustainability indicators (Hezri and Dovers, 2006; Reed et al., 2006). Consequently, in this research these four selection criteria are applied as the basis of the methodology developed to identify and measure agri-environmental indicators. In the following sections we discuss in turn how each of these criteria inform the design of the research.

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Yet, until recently, few authors of studies on ecological indicators have given any consideration to how sustainability goals or objectives are chosen, and therefore to the validity of “sustainability indicators” that address those goals (van der Werf and Petit, 2002). This paper takes as its point of departure that the selection of sustainability indicators is essentially a political process (Rudd, 2004; McCool and Stankey, 2004). This implies reconciling “expert-led” and “community-led” perspectives on sustainable development priorities (Reed et al., 2006). It also follows that, while universal principles of agri-environmental indicators may be widely agreed, selection of specific indicators will reflect local political, as well as ecological, contexts. The paper reports research undertaken in the state of Rio Grande do Sul, in southern Brazil, where, between 1999 and 2002, agricultural policy was explicitly designed to achieve “sustainable rural development”, understood in both ecological and socio-economic terms, through the promotion of “agro-ecological” production on small-scale “family” farms. The paper first sets out a methodology developed to identify indicators to monitor the impact of this policy on farming sustainability. It then uses social, economic, and ecological indicators to compare sustainability of agro-ecological and conventional farms, including soil measurements on old and new fields to determine trajectories of environmental change. The paper draws two sets of conclusions. The first suggests there are ecological benefits, but also financial costs and social investment, involved in adoption of agro-ecological farming. The second underlines the significance of ideological factors in farmers' adoption of agro-ecological methods, and hence the importance of understanding the political and institutional processes through which policy discourse is conducted on alternative pathways for sustainable rural development.

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Agri-environmental indicators (AEIs)

Table 1 – Criteria for useful sustainability indicators (OECD, 1999a)

Validity or analytical soundness

“Conceptually, indicators are symptoms of behaviour in complex systems, and they are used as diagnostics of underlying status of the systems. Whereas indicators of human health (such as blood pressure) and of economic status (such as Gross Domestic Production) are widely understood, environmental indicators which reflect ecosystem condition and status have been less rigorously defined and less comprehensively researched” (Syers et al., 1995, p. 424). A core characteristic of an indicator, therefore, is its ability to summarize the main aspects of the system in focus. While indicators are primarily considered to be quantitative, since one of their main functions is to quantify change, it is accepted that qualitative indicators are also useful tools (Gallopín, 1997; Rigby et al., 2000; Woodhouse et al., 2000).

t1:3

Criteria for indicator selection Policy relevance

Accessibility to users at an appropriate scale

Measurability

t1:1 t1:2

Indicators should address issues considered of importance for policy making Sustainability may be viewed from a variety of perspectives, including those of scientists, farmers, rural residents, and consumers. Each may contribute different, but legitimate, criteria of sustainability. A valid indicator must therefore be able to reconcile the need for sound scientific analysis with a requirement to be recognised as legitimate by other, non-scientist, stakeholders An indicator may have policy relevance at only a particular scale, and thus selection must match an indicator to the scale that is appropriate to those decision-makers who will use it In the context of an immediate need for indicators to monitor policy impact, the key question is the availability, or easy acquisition of data


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achievement of “sustainability”, and also locates a particular indicator in relation to those factors. Such a framework should therefore be used as the basis for choice and use of indicators (Rigby et al., 2000): which indicators, at what level, and how they should be monitored (Segnestman et al., 2000). In practice, generic frameworks, such as “pressure-stateresponse” (see below) linking ecological, social and economic factors, tend to suggest large numbers of factors are potentially relevant, and connected in complex ways. If data demands of sustainability indicators are not to become very large, there is a need to refine the relevance of such frameworks. One important factor determining the relevance of a particular framework is the policy context. In Rio Grande do Sul this was characterised by a political initiative to reverse

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Fig. 1 – The Sustainable Livelihood (SLF) (DFID, 1999) and Driving Force–State-Response DSR) (OECD, 1999a) frameworks combined.

the agricultural policy in Brazil that, since the 1960s, had promoted modernization and increasing scale in agriculture (Almeida, 1998; Caporal, 1998), and had resulted in 28.5 million people leaving the Brazilian countryside (Graziano da Silva, 1999). The initiative, by the government of the state of Rio Grande do Sul in the late 1990s, emerged from a convergence of three principal political dynamics. The first was an essentially urban political movement that associated opposition to political repression during the 1960s and 1970s with a growing rejection of capitalism as economically unfair and anti-nature. By the 1980s, ecological concerns were prominent in the agenda of this movement organised principally through non-government organisations, many of whom – as elsewhere in Latin America (Altieri et al., 1996) – identified “agroecology”


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as an alternative basis for agriculture. One of its most influential proponents characterises agroecology as: “The application of ecological concepts and principles to the design and management of sustainable agroecosystems. [Sustainable agroecosystems are]… in a most general sense a version of sustained yield — the condition to harvest biomass from a system in perpetuity because the ability of the system to renew itself or be renewed is not compromised” (Gliessman, 1997, p.13).

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The identification of agroecology as a central idea in the “alternative politics” of Brazil generated urban interest in traditional agricultural systems that were perceived to have been developed in harmony with natural ecological systems (Altieri, 1987; Gliessman, 1997). This created a link between urban political groups and a second political movement constituted by small farmer or “peasant” (camponês) organisations attempting to protect farm incomes in the face of falling prices for farm produce as markets in Latin America were liberalised during the 1990s (Costa, 2000; Navarro, 2002). This alliance of radical environmentalism (Sachs, 1993) with neo-populist views of a “peasant path” of agricultural development (Bernstein and Byres, 2001), had a strong influence on the Partido dos Trabalhadores (Workers Party) elected to the state government of Rio Grande do Sul (RS) in 1999. As a result, the RS government committed itself to policies in support of an alternative path to sustainable agricultural and rural development through strengthening agroecological production in “family farm” agricultural systems (EMATER, 2000; Almeida, 2003). In southern RS, the policy focused on small farms growing beans, maize, tobacco, fruit, and horticulture, for the most part occupied by descendents of immigrants from Italy and Germany who settled on the forested upland areas in the nineteenth century. This effort to improve sustainability in terms of agriculture-based livelihoods formed the policy context for the selection and evaluation of agri-environmental indicators in this research. A framework that explicitly addresses the livelihood strategies of particular types of natural resource users and questions of livelihood sustainability is that proposed by Scoones (1998), now more commonly referred as “Sustainable Livelihoods Framework” (SLF) (Bebbington, 1999, Ashley, 2000; Ellis, 2000; Goldman et al., 2001; Adato and Meinzen-Dick, 2002). It has been proposed as the basis for qualitative analysis that covers the full diversity and richness of livelihoods, and the dynamic effects of these on the environment (DFID, 1999). The SLF was not originally proposed as a framework for sustainability indicators, but, in Fig. 1, can be seen to overlap substantially with “Driving-Force/State/Response (DSR) Framework” (Pieri et al., 1995) used to identify sustainability indicators for agriculture (OECD, 1999a,b). The DSR is, itself, a variant of the “Pressure State Response Framework” widely applied as a framework for organising information on environmental impacts from different economic sectors, such as energy and transport (OECD, 1999a, McCool and Stankey, 2004). The overlap between DSR and SLF frameworks (Fig. 1) shows the “state” category of DSR corresponding to the category of “capital assets” in the SLF. Both frameworks see “driving-forces” in terms of social, economic and political (legal, institutional, policy) forces, as well as “natural” ones. Similarly, both frameworks see

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“response” at both local level (“farm behaviour”, “livelihood strategies”) in terms of farmers/resource user decisions (and, in DSR, also in terms of consumers' decisions), and also at national government scale (changes in legislation and policy). Indicators may thus be identified under each of the three broad categories of “driving-force”, “state” and “response” of the combined framework. However, in the DSR the relationship between these categories is not explicit, and is likely in practice to be complex. This complexity is highlighted by the combined framework (Fig. 1), that suggests that between the categories “driving-force” and “response” there are always overlaps: a “response” to a particular environmental condition or “state” becomes a “driving-force” that will influence future conditions of state. Thus indicators of “pressure” or “response” suffer from the disadvantage that they can only be interpreted in the light of a defined mechanism which links them to the rest of the model. In contrast, indicators derived from capital stocks or assets do not need a prior definition of such mechanisms, but can be selected on the basis of being consistent with desirable (sustainable) outcomes. Following these considerations, the simplest course is to choose, in the first instance, indicators of “state” or of “stocks”. These reflect most closely the outcomes of existing “drivingforces” and also the effectiveness of current “responses”. The categories within SLF that most clearly correspond to indicators of “state” are those of “livelihood assets”. Moreover, in this respect the SLF offers an important advantage over the DSR in that livelihood assets cover social and economic, as well as ecological aspects of sustainability, whereas the DSR, in the OECD formulation, is restricted to ecological and human health dimensions only. Livelihood assets, typically organised under categories of “capital” (Table 2) are indicators of outcomes of past and present livelihood strategies but can also be interpreted in terms of potential for (sustainable) future livelihoods.

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2.3. Analytical validity: a consultative process of indicator 270 selection 271 In order to find valid indicators of family farm sustainability, a 272 number of stakeholders likely to influence decisions over farm 273 Table 2 – Categories of livelihood assets in the Sustainable Livelihoods Framework (adapted from Ellis, 2000, pp32–37)

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Land, water and biological resources that are utilised by people to generate means of survival Capital (e.g. infrastructure, housing) created by economic production processes Community and other social claims on which individuals and households can draw by virtue of their belonging to social groups of various degrees of inclusiveness in society Stocks of money to which the household has access Labour available to the household: education, skills, and health


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concerns, such as market and social issues, as well as ecological impacts. At farmer and extension/research meetings the focus was more on farm management, and economic viability of family farms. However, as farmers and their organisations were, to a large extent, promoters of agroecology, they were also heavily influenced by “co-evolutionary” ideas linking ecology and society in development (Norgaard, 1994). A number of more general issues, such as marketing, access to health and education, and credit appeared at various levels. The outcomes of this workshop stage of the research (see Table 4) provided a starting point from which to identify specific indicators, using criteria of measurability (availability of data) and appropriateness to specific scale of decisionmaking, as described below.

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Scale-relevance of sustainability indicators

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To be relevant to an assessment of sustainability, indicators need to be related to a specific scale of decision-making (Müller, 1997; Rigby et al., 2000), such as regional policy, or farm management. The region selected for the empirical research was the “southern region” of the state of Rio Grande de Sul, an administrative division that encompasses 26 municipalities,

Fig. 2 – Flow diagram of research design.

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t3:1 Theme

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RS state agricultural policy on family farmers and agroecology Agroecological production and the work of ARPASUL

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Changes and trends, strategy to cope with changes. Indicators of perceived success and failure Workshop with Agroecological farmers Changes and ecological group — Glória, Canguçu: trends, strategy to farmers 10 family farmers cope with changes. participating Indicators of perceived success and failure Workshop with EMBRAPA family farm Identifying researchers research group: six indicators: analysis researchers of a proposal for environmental, social and economic agri-environmental indicators Workshop with EMATER regional staff: 12 Identifying extension agents extension agents indicators: analysis of a proposal for environmental, social and economic agri-environmental indicators

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Focus group or workshops

Group participants

Focus group with RS state agricultural policy makers programme coordinators

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management strategy were consulted, including farmers themselves, extension agents, agricultural scientists and state-level policy makers. This approach is based on the idea of recognising, and reconciling where possible, “external” (“expert-led” or “top-down”) and “internal” (“community-led” or “bottom-up”) viewpoints on what constitutes sustainability in farming (Rigby et al., 2000; Reed et al., 2006). The steps of the methodology are summarised in Fig. 2. Initially, an identification of “external” indicators, those relevant to researchers' assessment of sustainability, was oriented by considering the five SLF asset categories in the light of a review of available literature. A second, consultation, stage identified “internal” indicators, those relevant and valid to the resource users. The consultation was conducted through a variety of participatory methods, including semistructured interviews with key RS state government officials responsible for land reform, agricultural marketing, and technical advice, and through a series of six workshops and focus groups. Of these, three were with farmers: two with ecological groups, and one with non-ecological farmers. A further three were with extension workers in the government extension service (EMATER), with regional staff of the Brazilian Agricultural Research Corporation (EMBRAPA), and with agricultural policy-makers of the state government (Table 3). The goal at this stage was to identify with the stakeholders an agreed set of factors contributing to the sustainability of the farming systems, and reconciling scientific and farm-management perspectives. The five categories of livelihood assets were used to ensure that the scope of these consultations included broader

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Table 3 – Focus groups and workshops

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Focus group with agroecological farmers association Workshop with non-ecological farmers

ARPASUL Board: five farmers (group coordinators) and two technical staff Smallholding farmers association — Santa Clara, Canguçu: 15 family farmers participating


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Table 4 – Summary of methodology in identifying agri-environmental indicators

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Policy relevance

Natural capital Land

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Soil

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Water

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Per capita farm size

‘Effort’ indicators Farm management practices: Fallow, crop rotation, in tercropping, green manure, soil constant cover ‘Effect’ indicators Visible erosion. Depth of soil horizon A. Levels of OM, P, K, Ca, Mg. Crops harvest historical data compared with climate data. Weeds as Indicators. Farmers own evaluation of soil condition ‘Effort’ indicators Availability: Amount invested in dams, irrigation, etc. Quality: Amount invested to protect water for human consumption from slurry, fertilizer, pesticides ‘Effect’ indicators Availability: historic incidence of scarcity of water for crops, livestock, and human use Quality: Risk of disease, or pollution ‘Effect’ indicators Biodiversity improvement/ decline Agricultural diversity: number of different crops/livestock Intensity of nitrogen fertilizer and fossil fuels consumption in relation to farm production

Soil analysis measurements of ‘new’ and ‘old’ plots — levels of OM, P, K, Ca, Mg

‘Effort’ indicators Health risks to farming families from pesticide contamination ‘Effect’ indicators Lost working days due to illness General health of farm families Capability to provide schooling to children Level of schooling of farmers

Investment in water infrastructure Final destination of pesticide containers Biological and chemical analyses of water sources (pesticides, nitrates, phosphates)

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Land sufficiency

Indicators selected (obstacles to measurement)

Number of members of farm household per ha of land: “inverted spatial impact”: ISI (interpretation of ‘effect’ indicators is more straightforward than ‘effort’ indicators) Change in soil nutrient levels: “organic matter conservation”: OMC

Sustainability dimension

Regional

Ecology

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Ecology

(interpretation of ‘effect’ indicators is more straightforward than ‘effort’ indicators) (identifying source of contamination of water may be complex)

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Information measurable

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Valid Sustainability Indicators identified at “workshop stage” — stakeholder consultation

Scale relevance

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‘Sustainable Livelihoods’ (SL) assets/issues

Measurability

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Validity

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Biodiversity

Energy balance

Human capital Health

Education

Percent area in fallow, amount of vegetation regrowth; percent forest

(limited comparability between different farming systems)

Industrial energy used in nitrogen fertilizer and in fossil fuel energy consumption (incl tractor hire)

Degree of independence from industrial energy “Inverted Energy Consumption”: IEC

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Ecology

Pesticide hazard index: usage of pesticides weighted by toxicity Survey of lost working days due to illness ISMA-health index Survey of farm family level of schooling ISMA-Education index

Degree of absence of pesticide hazards: “Pesticide Avoidance Index” PAI (difficult to attribute causes of illness) “Combined health and education index”: ISMA “Average schooling”: ASC “Combined health and education index”: ISMA

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Table 4 (continued) Policy relevance

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‘Sustainable Livelihoods’ (SL) assets/issues

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Social capital Information access/ institutional support to farming families

Validity

Food security

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Family perceptions

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Political representation

t4:43 Physical capital Access to energy and infrastructure

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Farm infrastructure and equipment Financial capital Household income

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Farm and non-farm income Farm Gross and net Margins Revenue/cost from other activities Other incomes Family living expenditures Trends input/output price ratio Market price/average farm gate price Marketing costs ratio Output produced/output commercialized ratio

Input and output market trends

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Access to electricity Availability/proximity/ of roads, services household goods, farm buildings and machinery

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Level of satisfaction or ‘well-being’ Perspective of continuity of farming business for the family (children) Participation/power in the municipal agricultural councils Priority in state government participatory budgets

Indicators selected (obstacles to measurement)


Farm survey data State and NGO records: Pronaf, Procera, RS rural

“Technical Networking”: TN “Amount of credit”: CRED

Child nutrition measures Beneficiary of food aid programmes Farm survey data

(data unavailable)

Measures of social inequality: Gini index

“Progressive Gini Index”: PGI

Regional

Society

Electricity index Infrastructure index

“Electricity Index”: EI “Infrastructure Index”: INI “Physical capital replacement”:PRC

Regional Local

Economy Economy

Farm

Economic

“Farm Gross margin”: FGM “Total household per capita income”: TPI

Farm Local

Economy Economy

“Input price index”: IPI

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Scale relevance

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‘Effort’ indicators: Frequency of access to extension services/advice Participation in farmers' organizations ‘Effects’ indicators: Access to subsidised/low interest credit) Access to special markets (collective buying, farm street market, agroecological market, etc.) Adequacy of food consumption

Information measurable

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Valid Sustainability Indicators identified at “workshop stage” — stakeholder consultation

Replacement cost of farm equipment and buildings

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Measurability

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Farm survey: Farm costs and revenues Other household income Family consumption expenditures Trends in input prices: adapted Laysperies index

approximately 950 000 habitants and an area of 48222 km2 (ITEPA, 1998). Primary data was collected in the municipalities of Canguçu (population 51 427, area 3520 km2), where family farming predominates, and Pelotas (population 323034, area 1647 km2), an important regional market and institutional centre. The two municipalities are classified in different agroeocological regions, although with quite similar agroclimatic conditions (mean annual rainfall 1200–1600 mm,

mean temperature 16–19 °C). The significant decision-making 334 scales identified were at farm, local and regional level. 335 1. The farm level was an obvious choice as it is still where most decision making about farming and the use of agroecosystem resources takes place. Users of indicators at this scale are therefore farmers, and technical and advisory workers who work closely with them.


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The initial “workshop” phase of consultation with stakeholders identified a wide range of sustainability concerns that we then sought to represent in the form of indicators. These could be classified as indicators of “effort”, such as implementation of soil conservation measures, or “effects”, such as measured soil quality. It was decided that, as far as possible, the selection of “effects” indicators was more consistent with ecological, economic and social “state”. Ideally, indicators ought to be obtainable from routinely available secondary data. In practice this is seldom the case, and specific indicators were identified not only from a range of secondary statistical sources but also from primary research, including a farm survey and soil sample analysis. In selecting specific indicators it was necessary to take account of considerations of cost, and clarity of interpretation. The goal was to identify a limited number of indicators at each scale or decision-making level that could be derived from data available at low cost over a short timeframe.

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Data was collected directly from farmers through a survey of 84 farms, including 20 “ecological” farmers linked to the South Region Agroecological Association, ARPASUL, together with 53 of their “non-ecological” neighbours using conventional methods, and 11 farmers who had given up ecological farming. To be a member of ARPASUL, and hence permitted to market their produce as “agroecological”, farmers had to formally declare adherence to agroecological principles (e.g. avoiding chemical fertiliser use; using green manure, composting, non ploughing). Membership of the Association required a year of preliminary registration, followed by a process of “participatory certification” involving a visit to the farm by the ARPASUL committee to confirm the farmer's implementation of agroecological methods. The farms included a mixture of enterprises, including milk, poultry, tobacco, onions, cereals and fruit (peaches). Not all enterprises were found on every farm. On “ecological” farms not all production was “agroecological”, although such farmers were explicitly committed to using agroecological approaches in certain enterprises (notably horticulture), and certain principles (e.g. avoiding pesticide use) more generally. The sample of farms was split between the municipalities of Canguçu (59 farms) and Pelotas (25 farms). In addition to a questionnaire

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Measurability: indicator values and aggregation

2.5.2.

Secondary data

A number of potentially useful sources of secondary data were identified, particularly for the regional decision-making level. Indicators of the provision of education and health services to rural households can be interpreted as a proxy for human capital and may serve as an indicator of social dimension of sustainability of rural livelihoods. An indicator available for this purpose was the ISMA index (Índice Social Municipal Ampliado) of housing, education, health and income compiled for each municipality by the Economics and Statistic Foundation (FEE, 2002) of the state of Rio Grande do Sul (Winckler et al., 2002). Similarly, data for the percentage of households connected to electricity services in each municipality (ITEPA, 2001) provides an indicator of physical capital, and the Gini (inequality) index for each municipality (FEE, 2003) is an indicator of social capital. Unfortunately, these secondary data only supply indicators at the level of the municipality and thus do not discriminate between ecological and non-ecological farmers. However, they may be useful when taken together with other indicators in comparing farm sustainability in different municipalities in the state. Secondary data for agricultural output and input prices were obtained from the planning department (DEPLAN) of the state extension agency, EMATER. The output price data comprise a state-wide index (weighted average farm gate price) generated as an average of regional prices weighted according to the output of particular agricultural products in each region. This was supplemented with price data from local industry and traders and by farmers' records of prices for agroecological products sold through street markets. In practice, however, output price data were not available over a sufficient number of years. Therefore, input price data were used to estimate an adapted Laspeyres index (Wonnacott and Wonnacott, 1990) for ecological and non-ecological farms. In this index, the weighting of individual commodities (inputs in this case) was based not on their quantities, but on their percentage contribution to total input cost. Thus the cost

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administered face-to-face with farmers on all the farms in the sample, paired soil samples were taken from 17 of the surveyed farms where it was possible to identify comparable fields recently cultivated from fallow (“new” fields) and having undergone several years of continuous cultivation (“old” fields). Data from ecological and non-ecological farms were compared (see Section 3, below) to identify variables that could be transformed into meaningful indicators of livelihood assets, and values of those variables were compared for the ecological and non-ecological farms through statistical analysis. For variables with a normal distribution, comparison between means of two different samples was made using the t-test. For variables with a non-normal distribution, comparison between two different samples was made using nonparametric tests (Kolmogorov–Smirnov and Mann–Whitney test). Where soil samples had been collected, routine laboratory soil analyses at the Universidade Federal de Pelotas were used to compare basic soil fertility measures (acidity, aluminium, organic matter, phosphorus, potassium) for “old” and “new” fields.

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2. The local level, corresponding to the neighbourhood or “community”, and – very approximately – to the “district” subdivision of a municipality, is very influential in the farming process. Farmers tend to judge themselves by comparison with other local farmers' performance, and it is at this scale that farmers groups and local associations are formed. Users of indicators at this scale are government agencies at state or municipal level (the democraticallyelected levels) and also farmers' and other lobby groups. 3. The regional level is an explicit planning entity for the government, and would expect to capture macro-level trends and their effects on farming and households, including those from higher levels, such as national policies or international agreements.

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Primary data collection


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Livelihood asset category


Scale

Natural Natural Natural Human Human Human Social Social Social Physical Physical Physical Economic Economic Economic

Ecology Ecology Ecology Ecology Society Society Society Society Society Society Economy Economy Economy Economy Economy

Farm Local Regional Farm Local Regional Local Local Regional Regional Local Farm Farm Local Regional

OMC organic matter conservation IEC inverted energy consumption ISI inverted spatial impact PAI pesticide avoidance index ASC average schooling ISMA health and education index TN technical networking CRED credit access. PGI ‘progressive Gini index’ EI electricity index INI infrastructure index PCR physical capital replacement FGM farm gross margin TPI total per capita income IPI input price index

weight of input x in the total cost becomes its weight in the index. The input price variation was estimated as the average price variation of the most used inputs (responsible for more than 70% of farming costs) in non-ecological farming (Fertilizer, pesticides and fuel) and in the ecological farming system (Fuel (for marketing) and organic fertilizers): Input price Index ¼

X

PtCo=

X

ð1Þ

PoCo:

467

2.5.3.

468

A key challenge to the use of indicators of sustainability is the problem of aggregation of indicators of economic, social and ecological conditions that many argue to be incommensurable (Martinez-Alier, 2002), in order to provide an overall assessment of sustainability. The approach adopted here follows that of Gomes et al. (1996) and Bockstaller et al. (1997), using polygons or “cobwebs” to show indicators of different sustainability dimensions on separate axes without having to aggregate their values. It is similar to the “AMOEBA”1 approach to assessing water management in the Netherlands (Bell and Morse, 1999), and has become common in studies of agriculture (e.g. Dumanski et al., 1998; Masera et al., 1999; Rigby et al., 2000; Giampietro and Pastore, 2001) and rural livelihoods (Ellis, 2000). Summarising indicators in this graphical form requires transformation of indicator values such that all indicators can be plotted on a positive scale (“more is better”). Thus, variables for which increasing value connotes negative change were inverted to give positive indicators: e.g. Gini index was inverted to become a “Progressive Gini Index” (PGI); and the rate of change (decline) in soil organic matter, was transformed to provide an indicator of “organic matter conservation” (OMC).

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5.02 8.27 5.49 6.57 4.91 3.90 12.5 7.92 4.02 7.0 5.29 6.04 4.63 4.73 4.3

4.99 4.61 4.70 3.68 5.03 3.90 1.66 4.28 4.02 7.0 4.70 4.71 5.10 5.08 5.7

1 An acronym which in Dutch stands for ‘general method for ecosystem description and assessment' (Bell and Morse, 1999, p. 47).

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3.

Results

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3.1.

Summary of indicators selected

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The indicators selected and their values for “ecological” and “non-ecological” farms in the research area are summarised in Table 5, and graphically in Fig. 3. As explained above, a number of the indicators were derived from existing secondary data. This was particularly

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Indicator values and aggregation

“non-ecol”

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“ecol”

Similarly, the axis scales of indicator values were standardised by setting a mean or median value of each indicator as the middle of each scale. For indicators derived from secondary data the middle of the scale was given by the average state or regional variable value. For data derived from primary sources (e.g. farm survey) the median (variables with non-normal distribution of values) or mean (variables with normal distribution of values) of the whole sample were used as the middle of the scale. Details of the selected indicators are presented below.

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where Po is the price at the start (1996) and Pt is price at the end (2000) of the period, and C is a weighting derived from the relative cost of inputs.

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Value

F

t5:5 t5:6 t5:7 t5:8 t5:9 t5:10 t5:11 t5:12 t5:13 t5:14 t5:15 t5:16 t5:17 t5:18 t5:19

Indicator

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t5:3 t5:4

Table 5 – Summary of indicators selected

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t5:1

Fig. 3 – Summary diagram of 15 agri-environmental indicators.


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Farming Sample Household Farm Mean age of system size size size members of (ha) household (years)

t6:3

(mean)

(mean)

(mean)

Land owned per capita (ha) (median)

84

4.21

21.2

35.1

4.31

20 64

4.40 4.16

16.6 22.7

29.4 36.9

2.54 3.71

t6:4 t6:5 t6:6 t6:7

508

Whole sample Ecological Nonecological

514

the case for indicators considered to be of significance to regional decision-makers (ISMA, PGI, EI). Since these data are aggregated to municipal level, they cannot contribute to comparisons between “ecological” and “non-ecological” farm sustainability within the same municipality. For these comparisons, the primary focus of this study, data were primarily drawn from the farm survey.

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3.1.1.

516

Table 6 shows that the “ecological” farms were slightly smaller, averaging 16.6 ha, than the ‘non-ecological’ (mean 22.7 ha) but with larger and younger families who owned less of their land than their “non-ecological” neighbours. A sustainability indicator of land resources used by the farms was selected as the inverse of “land per capita” (i.e. the number of household members per hectare of land farmed), which provided median values of 0.24 for “ecological” and 0.21 for “non-ecological”. The final values for the “Inverted Spatial Impact” (ISI) indicator (Table 5) were estimated relative to the value 5 given to the overall sample median. Three further indicators of the ecological dimension of sustainability were derived from the farm survey data: soil organic matter conservation (OMC), inverted energy consumption (IEC), and pesticide avoidance index (PAI). Soil organic matter conservation (OMC) was derived from soil analysis results that suggested systematic differences between soils from older and more recently cultivated fields (Table 7). Statistically significant reductions were measured in organic matter (OM), Potassium (K), and in Cation Exchange Capacity (CEC) in samples from older fields, while available

512 513

517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536

Indicators of the ecological dimension of sustainability

t7:1

Table 7 – Soil analysis results

t7:2

Chemical attribute and unit

t7:3 t7:4 t7:5 t7:6 t7:7 t7:8 t7:9 t7:10 t7:11 t7:12 t7:13

546

Annual percentage change : PRC ¼ ðRCY4100Þ=ðOMnew Þ

548

OM Conservation ¼ ð100k PRCÞ:

550

As before, indicator values are expressed relative to scale value 5 for the mean of the whole sample. The inverted energy consumption indicator (IEC) was selected to compare the wider impact of farming in terms of its use of energy from sources outside the farm. Farm survey supplied data on the principle direct and indirect forms of energy use (Pervanchon et al., 2002): fuel consumption, tractor hiring hours and inorganic nitrogen fertilizer. Total energy consumption was estimated as the sum of the energy (MCal) of various fuels used and the energy in industrial nitrogen (Table 8). Inversion of these total energy consumption values was used to obtain an indicator with scale value 5 for the median of all farms. Avoidance of health hazards from pesticide use was commonly mentioned by farmers as a motive for having adopted agroecological methods. Thus, while pesticide use is understood as forming part of the ecological dimension of sustainability, it was originally introduced in the workshop stage of this research as an element of “human capital”, rather than “natural capital”. A pesticide index was constructed using the farm survey data on quantities of pesticides used, aggregated using a weighting from the toxicological classification of the Brazilian Ministry of Agriculture (Andrei, 1996). Indicator values were obtained by inverting the pesticide index values to provide a ‘pesticide avoidance index' (PAI = 1 / 1 + pesticide index) with value 5 for the median of the surveyed farms. Unsurprisingly, PAI values were significantly lower for “ecological” farms than for their conventional neighbours (Table 9), but the index allows a more nuanced analysis of

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RCY ¼ ðOMold OMnew Þ=ðField ageÞ

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t6:2

phosphorus (P) was found to have increased (reflecting net additions in fertilizer and manure). Differences in Aluminium (Al), acidity (pH) and Clay were not significantly different. Although statistical significance is lost when the effect is analysed separately for the two types of farms, a consistent advantage of “ecological” farms is visible when compared with “non-ecological” (Fig. 4). The sustainability indicator OMC was estimated from the annual rate of change of soil organic matter (RCY) as follows:

OO

Table 6 – Farm sample characteristics

PR

t6:1

OM%⁎⁎

P mg/dm3⁎ K mg/dm3⁎ CEC cmolc/dm3 ⁎

Plot pair

New Old New Old New Old New Old

Whole sample

Ecological

Non-ecological

Mean

N

Mean

N

Mean

N

3.19 2.56 11.2 19.6 117 78 10.6 9.5

17 17 17 17 17 17 17 17

3.20 2.75 14.1 24.7 99 69 11.0 10.0

7 7 7 7 7 7 7 7

3.19 2.43 9.3 15.9 130 83 10.4 9.1

10 10 10 10 10 10 10 10

Statistical significance of differences (whole sample only): ⁎⁎P b 0.01, ⁎P b 0.05.


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Table 9 – Statistics from farm survey used in indicator selection

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3.1.2.

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The farm survey data were examined to identify possible indicators of “human capital” and “social capital”. Fewer clear ideas on these aspects had emerged from the consultative workshop phase of the research, and the choice of indicators was consequently more influenced by the reliability and interpretation of the data itself. Three indicators selected were the “average schooling” of the household (ASC), giving a simple measure of investment in human capital, “credit access” (credit received over the past 5 years - CRED) as a measure of social support to farming families from state and non-government organisations, and “technical networking” (TN). The last of these derived from farm survey data showing that the number of contacts with agencies of technical assistance was significantly higher for “ecological” farmers, as a result of their NGO affiliations, than for their “nonecological” neighbours (Table 9). We considered this to be a measure of “social capital” in terms of the Sustainable Livelihoods Framework, but it also constitutes an important indicator of social capacity to apply technical knowledge in the pursuit of sustainability. A fourth indicator in this

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t8:1 t8:2

t8:3

t9:3

b 0.10

9.0

16.7

t9:4

b 0.05

15.0

2.00

t9:5

N 0.10

Years

4.55

4.66

t9:6

b 0.05 N 0.10

R$ R$

3740 8510

2020 6638

t9:7 t9:8

6.00

t9:9

b 0.05

Km

b 0.10

R$

1862

2353

t9:10

N 0.10

R$

4330

4771

t9:11

N 0.10

R$

1424

1627

t9:12

N 0.10

R$

802

824

t9:13

N 0.10

R$

531

715

t9:14

4.00

Indicators of the social dimension of sustainability

a

For variables with a Normal distribution a t-test was applied, and for non-Normal distributions a Mann–Whitney test was used.

dimension of sustainability is the ‘electricity index’(EI) adopted as a regional-level proxy for social investment in infrastructure to improve living standards. In each case, the median values of these variables were converted to indicator values relative to a value of 5 for the median of the whole survey.

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Nonecological

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agrochemical hazards than the simple ecological/non-ecological dichotomy would suggest. It was apparent, for example, that “ecological” farmers were using products for crop protection, although these were permitted under agroecological rules and considered to be non-toxic. These products were therefore classified as having the lowest toxicity risk. Similarly the PAI offers the possibility of identifying shifts towards using less hazardous agrochemicals among conventional farmers. This indicator thus allows sustainability analysis to discriminate between formal claims (e.g. membership of agroecological organisations) and farm practice.

Unit Ecological

OO

581

Pesticide Median index Technical Median advice Average Median schooling Total credit Median Physical Mean capital replacement cost Distance Median from schools Partial Median operational cost Farm gross Mean margins Per capita Median income Per capita Median living expenditures Per capita Median balances

PR

Fig. 4 – Soil mean chemical contents annual change.

Statistic P value a

F

Variable

Table 8 – Energy consumption — farm machinery and fertilizer nitrogen Farming system

t8:4 t8:5 t8:6

Ecological Non-ecological

t8:7

⁎⁎ P b 0.01; ⁎P b 0.05.

Fuel energy (MCal)

Energy industrial N (MCal)⁎⁎

Total energy consumption (MCal)⁎

Median

Median

Median

1155 973

1691 3789

2995 5234

t9:1 t9:2

3.1.3.

t9:15

613 614 615 616 617 618

Indicators of the economic dimension of sustainability 619

Four indicators relevant to economic dimensions of sustainability were derived from the farm survey. Two of these corresponded to “physical capital” and two more to “financial capital”. The measures of physical capital were the estimated cost of replacing farm buildings (physical capital replacement — PCR), and an infrastructure index (INI) calculated as the average of distances from a farm to school, hospital, and tarmac road, weighted by access to electricity and telephone. In principle, this index would include an element referring to access to water, but in the particular areas surveyed water was in every case obtained from farmers' own wells and no constraints in supply were encountered. Wider application of the index will require water availability to be taken into account, even within neighbouring municipalities of Rio Grande do Sul, where erratic rainfall distribution creates periodic drought conditions, in some instances including failure of water supply from wells. In neither set of variables were significant differences encountered between ecological and non-ecological farmers, except ecological farmers on average lived closer to schools. The two “financial capital” variables selected as indicators of sustainability from an economic standpoint were farm


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4.1.

Fig. 5 – Input price index.

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Comparison of ‘ecological' and ‘non-ecological’ farms 691

Fig. 3 summarises the values of all 15 indicators selected, for both ecological and non-ecological farms. It is a similar approach to that used by Giampietro and Pastore (2001), but groups indicators into three dimensions of sustainability relating to ecology, society and economy. The indicators may also be re-grouped (see Table 5) according to criteria of scale of decision-making, or type of “livelihood asset”, or “capital”: “An analysis of agricultural performance should be based on an integrated set of indicators that are able to:

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4. Indicator Interpretation and farming sustainability

• Reflect the various perspectives • Read the changes occurring in different hierarchical levels in parallel on space-time scales”. (Giampietro and Pastore, 2001, p.179). The indicators suggest a number of conclusions about the relative sustainability of the different farming systems. All five indicators of ecological sustainability showed advantages for the ‘ecological’ farms, particularly with respect to industrial energy use (IEC), pesticide toxicity hazards (PAI), and the degree of integration of individual farms into technical information networks (TN). Smaller advantages were also recorded for ‘ecological’ farms with respect to the number of people supported per unit of land (ISI), and the rate of conservation of soil organic matter (OMC). However, in terms of economic sustainability, ecological farms tend to suffer from lower farm gross margins (FGM), lower total per capita income (TPI), and less favourable input price trends (IPI). They differ little from non-ecological farms with respect to the replacement cost of farm buildings (PCR) or distance from services (INI). The indicators of social sustainability show little difference between the two farming systems in terms of social inequality, health and education (ISMA, ASC), or access to electricity (EI). However, the much higher access to credit (CRED) and technical networking (TN) for ecological farms suggests higher levels of social support than for non-ecological farms.

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gross margin (FGM) and total per capita income (TPI). FGM represents the balance of total farm revenue after deduction of production costs estimated over 1 year. It uses the notion of operational costs (Carmo and Magalhães, 1999), which include all variable costs and direct overhead costs such as rent and permanent labour, but exclude depreciation. It is consistent with conventions in economic analysis of the ability of a firm to continue operating in the short run (Maddala and Miller, 1989; Varian, 1999). The TPI is a broader measure of financial assets of the farming household that includes income from non-farming activities, and thus allows scope to consider income diversification and the strength of local labour markets. No statistically significant differences were found between the mean values of either of these variables for ecological and non-ecological farmers, although the latter appear to enjoy advantages in both cases (Table 9). More detailed analysis indicated that, while ecological farmers have significantly lower production costs, these are cancelled out by significantly higher costs of non-conventional marketing, notably farmers' use of their own vehicles to supply urban street markets. Conversely, the data suggest that, although ecological farmers have lower incomes, they appear more stable, as there were no negative gross margins among the ecological farms. In contrast, while non-ecological farmers as a group appear to have higher average income, expenditure and balances (incomes less expenditures), individual non-ecological farms are also more likely than ecological ones to have lower, or even negative, balances of expenditures over household incomes. A final indicator was sought to compare the vulnerability of the two different farming systems to market trends in input and output prices. Due to the constant variation in agricultural product price, and considering the relatively small amount of information on this issue, the research opted to compare different market effects by using input prices only. The input prices index was calculated by an adapted Laysperies index as described in the previous section. The input prices index varied for the ecological group from 100% in 1996 to 74.19% in 2000. The nonecological input prices index varied from 100% to 66.18% during the same period. Thus a negative input price change is a favourable index change of 0.26 for the ecological farmers and an even more favourable index change of 0.34 for the non-ecological farmers (Fig. 5), due largely to differences in the cost of marketing (fuel). All the other inputs had falling prices, measured in US dollars, during the period.

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4.2. Methodological issues in the use of agri-environmental 729 indicators 730 The methodology of indicator identification has been presented here in such a way to demonstrate the different component parts in a relatively schematic way. The specific choice of indicators and the values measured for them are clearly capable of further elaboration and refinement. The standardisation of indicator value scales requires adoption of a reference value for each indicator, which we set at value 5 for each indicator. An advantage of this approach is that it allows a simple visual interpretation of sustainability trends: an increase in all indicator values indicates an increase in sustainability. However, a more likely situation is one in which there will be trade-offs between different dimensions. For


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4.3.

Fig. 6 – Farm level sustainability cobweb.

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Policy implications for sustainable rural development 801

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The data we have presented in this paper suggests ‘agroecology’ in southern Brazil is being adopted by relatively younger farmers as a means to avoid environmental and health risks, such as those associated with pesticide use (PAI) This is consistent with the association of ‘agroecology’ with an alternative path of rural development. However, despite achieving lower operating costs (and hence greater efficiency), such farmers are currently, on average, achieving poorer financial results, although possibly more stable ones. Two key policy implications arise from the analysis. The first concerns the relatively high cost of marketing ‘ecological’ agricultural output. This appears to outweigh the higher prices that consumers are willing to pay for such agricultural produce, and suggests those promoting agroecology have a clear priority to intervene to bring down marketing costs, possibly by enlarging the market and integrating a wider range than the current horticultural products. It also suggests marketing is a bottleneck inhibiting new adopters of agroecology and persuading some existing ecological farmers that the financial penalties are too high. The prospect of environmentally sound farming being achievable only through lower incomes is unlikely to be attractive for the majority of farmers. The second policy implication arises from the important role that ‘social capital’ plays in the promotion of agroecological methods among farmers belonging to NGOs and other local (notably church-based) organisations. While this provides a model for intervention, it also suggests a limitation, in that ‘agroecology’ may be associated with an idealised conceptualisation of intrinsic ‘peasant’ values that prevents more commercially-minded farmers taking part in social organisations that deliver support to agroecological production in the form of technical assistance, support for organization and access to differentiated markets. Broadening this initiative promoting sustainable family farming would therefore need to examine whether there are other, non-material, dimensions of sustainability, such as those of culture and social identity (Bebbington, 1999). Although such factors were not included in this research, the methodology described in this paper could address them by identifying in its early stages ‘culture’ as relevant to a policy seeking to promote sustainable farming. The consultation (workshop) stage of the methodology would allow a discussion of what would constitute valid indicators of a cultural dimension of sustainability, and a list of possible indicators would then be assessed in terms of their practical measurability.

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In general terms we would argue that, using this model, it is possible to analyse different aspects that corroborate the sustainability of the farming system as well as of the farmers' livelihoods. The sustainability of the farming system is not measured by any of the scales alone, and much less by individual indicators, but repeated measurements of such ‘cobwebs’ to provide a trajectory of change in indicator values can contribute key elements for a sustainability analysis of such farming systems. The possibility of such repeated measurements is greatly enhanced by the use of relatively simple methods of primary data generation, based on questionnaire survey of farms and routine public service laboratory analysis (soil analysis).

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example, a deterioration in ecological indicators may result from efforts to improve economic aspects of livelihoods, as is implied in the comparison between indicators for ecological and non-ecological farms. In this paper, indicator values have been expressed in relative terms. That is, an average value for the sample or for the region or state was used to set a midpoint for the indicator value scale. However, it is equally possible to use target or “threshold” values of indicators to set the mid-point of the scales, with measured indicators for specific farming systems being expressed relative to these thresholds or target values. In practice, use of indicator values is likely to focus on comparisons over time. This allows decision-makers to monitor progress over time towards particular sustainability goals that have been agreed for particular types of farmers and farming systems. In this study we compared different technical strategies within essentially the same farming system, and using a common set of indicators. The application of this approach to compare farming livelihoods with very different characteristics and ecological constraints (e.g. temperate rainfed agriculture compared to semi-arid irrigated farming) implies a need to consider comparisons using different indicators, particularly in relation to the ecological dimension of sustainability. Such indicators are likely to relate to the most acute constraints felt locally. For example, water quantity and quality are more likely to feature as sustainability concerns in irrigated systems, and less so in rainfed systems. It is important to recognise that the immediate utility of this approach is in monitoring change over time within a specific farming system. However, as experience develops with such local monitoring, it would be worth exploring whether the approach outlined here, where indicator values are expressed relative to some mean or target figure would enable meaningful comparisons of different farming livelihoods based on the progress of each measured by systemspecific indicators of ecological sustainability. In practice, comparisons of sustainability of farming livelihoods in different agro-ecological zones are also likely to be made at larger scale, such as the “local” or “regional” scales proposed in this paper, for which indicators have a broader scope and specific ecological constraints are less prominent. These considerations underline the importance of identifying the relevant scale at which indicators are useful. Fig. 6, for example, presents a sub-set of indicators that we considered particularly relevant to decision-making at the scale of the farm (see also Table 5).

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The findings of this research were presented at three workshops, for academics, for EMBRAPA researchers, and for ARPASUL farmers. Despite casting some doubt on the sustainability of the existing local application of “agroecological” farming, the findings were received with interest by workshop participants. However, it is clear that to move beyond this initial problem identification will involve monitoring indicators over time to identify trends and impacts of specific interventions. The importance of the ‘measurability’ criterion used in selecting these indicators is to increase the likelihood that the identified indicators will be measurable using standard procedures understood by extension workers and farmers. In rehearsing these steps, however, we wish to underline that a process of designing agri-environmental indicators is not only, or even primarily, a matter of technical judgement, but a political process through which competing priorities and goals identified with ‘sustainable development’ are discussed and evaluated in terms of their trade-offs, and thus lead to choices about the explicit meaning of what may otherwise be a rather ambiguous area.

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