An indicator framework for assessing livelihood ...

Global Environmental Change 28 (2014) 109–119

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Global Environmental Change journal homepage: www.elsevier.com/locate/gloenvcha

An indicator framework for assessing livelihood resilience in the context of social–ecological dynamics Chinwe Ifejika Speranza *, Urs Wiesmann, Stephan Rist Centre for Development and Environment, Institute of Geography, University of Bern, Hallerstrasse 10, CH-3012 Bern, Switzerland

A R T I C L E I N F O

A B S T R A C T

Article history: Received 31 March 2013 Received in revised form 13 June 2014 Accepted 16 June 2014 Available online 16 July 2014

Livelihood resilience draws attention to the factors and processes that keep livelihoods functioning despite change and thus enriches the livelihood approach which puts people, their differential capabilities to cope with shocks and how to reduce poverty and improve adaptive capacity at the centre of analysis. However, the few studies addressing resilience from a livelihood perspective take different approaches and focus only on some dimensions of livelihoods. This paper presents a framework that can be used for a comprehensive empirical analysis of livelihood resilience. We use a concept of resilience that considers agency as well as structure. A review of both theoretical and empirical literature related to livelihoods and resilience served as the basis to integrate the perspectives. The paper identifies the attributes and indicators of the three dimensions of resilience, namely, buffer capacity, self-organisation and capacity for learning. The framework has not yet been systematically tested; however, potentials and limitations of the components of the framework are explored and discussed by drawing on empirical examples from literature on farming systems. Besides providing a basis for applying the resilience concept in livelihood-oriented research, the framework offers a way to communicate with practitioners on identifying and improving the factors that build resilience. It can thus serve as a tool for monitoring the effectiveness of policies and practices aimed at building livelihood resilience. ß 2014 Elsevier Ltd. All rights reserved.

Keywords: Livelihood resilience Buffer capacity Self-organisation Learning Adaptation Vulnerability

1. Introduction Resilience is increasingly becoming a key concept in social science-oriented environmental research analysing human-nature interactions in social–ecological systems (SES) and exploring how to deal successfully with climatic, economic or social change. Although much has been written about ecosystem and social– ecological resilience (Holling, 1973; Carpenter et al., 2001; Folke et al., 2002; Berkes et al., 2003), the few studies addressing resilience from a livelihood perspective (e.g. Marschke and Berkes, 2006; Sallu et al., 2010; Obrist et al., 2010), do so from different perspectives. Capturing how much a livelihood practice maintains or increases an actor’s capacity (agency) to affect societal structures and processes (structure) and maintain the actor’s livelihood, especially during periods of crisis, needs to be made more operable by integrating these perspectives.

* Corresponding author. Tel.: +41 31 631 88 22; fax: +41 31 631 85 44. E-mail addresses: [email protected] (C. Ifejika Speranza), [email protected] (U. Wiesmann), [email protected] (S. Rist). http://dx.doi.org/10.1016/j.gloenvcha.2014.06.005 0959-3780/ß 2014 Elsevier Ltd. All rights reserved.

Resilience thinking is implicit in the Sustainable Livelihood (SL) approaches, for example, the SL approach of the United Kingdom Department for International Development (DFID), that focusses on how people’s capabilities, assets and activities, as well as transforming structures and processes lead to positive outcomes like more income, increased wellbeing or improved food security (Obrist et al., 2010; op. cit. 286). Adger (2000) refers to livelihood stability as one aspect of social resilience, but operationalization and assessments of livelihood resilience are few (e.g. Marschke and Berkes, 2006). Obrist et al. (2010) also note that social resilience remains neglected especially from an actor or practice theory perspective. The authors define social resilience as ‘‘the capacity of actors to access [livelihood] capitals in order to – not only cope with and adjust to adverse conditions (i.e. reactive capacity) – but also search for and create options (i.e. proactive capacity), and thus develop increased competence (i.e. positive outcomes) in dealing with a threat’’ (Obrist et al., 2010, p. 289). Linking livelihood approaches to resilience thinking can enhance understanding of livelihood dynamics, of how households maintain and enhance their livelihoods in the face of change, including stresses and shocks (Marschke and Berkes, 2006; Scoones, 2009; Sallu et al., 2010). Following Obrist et al. (2010),

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we specially consider that resilience means – at the same time – increasing the capabilities (agency) to respond to adverse external conditions and to develop collective action aimed at changing the part of external societal structures that constrain resilience-related agency. Another added value is that resilience can be used to characterise a livelihood system’s ability to deal with change and recover from adverse consequences. Marschke and Berkes (2006) identified resilience-building strategies and used local perspectives of wellbeing as a surrogate of resilience. Sallu et al. (2010) used livelihood strategies and principal component analysis to determine households’ resilience through time. Considering that a livelihood has various dimensions at the individual level in the form of capacities (livelihood assets and strategies) and at the structural level in the form of transforming structures and processes and the vulnerability context, these dimensions need to be considered when conceptually and empirically integrating livelihood and resilience. However, there is a lack of such a framework with which livelihoods can be assessed for resilience. While a resilience assessment includes characterising and assessing the exposure to shocks and stresses (resilient of what to what?), the question of ‘‘what constitutes livelihood resilience’’ needs more conceptualisation (cf. Sallu et al., 2010; Bahadur et al., 2010). Answering the question, ‘‘How can livelihood resilience conceptually be defined and how can it be made more operable?’’ is thus the focus of this paper. In a first step we attempt to fill the research gap regarding the question of how to conceptually link resilience with the various livelihood dimensions. In a second step we develop an indicator framework and address how to make the framework operational by identifying attributes and indicators

that can be used to measure or assess resilience in livelihoods, and illustrating it with examples from empirical literature. 2. Resilience as a conceptual and analytical lens Various fields use the concept of resilience, interpreting resilience in different ways and emphasising different dimensions (Table 1). Resilience is widely used in research on human–nature interactions particularly that which uses a social–ecological lens, encompassing the social, economic, cultural, political and environmental factors and their interactions, which together shape vulnerability, adaptive capacity, and development outcomes. The increasing multiplicity of global challenges of which climate change is one and the difficulties of finding lasting solutions to variable climatic challenges raise interest on adopting resilience as a concept in livelihoods research. Resilience refers to the capacity of individuals, social groups or SES to accommodate stresses and disturbances, to self-organise, and to learn in order to maintain or improve essential basic structures and ways of functioning (cf. Berkes and Folke, 1998; Carpenter et al., 2001; Walker et al., 2002; Berkes et al., 2003; Folke, 2006; Adger, 2003, 2006; IPCC, 2007, 2012). This definition encompasses the system-oriented approaches characterising resilience as linked to human agency, as well as to social structures or systems (Bohle et al., 2009; Obrist et al., 2010). The essence of the resilience concept is that it captures the factors that enable functioning under adverse conditions. Cumming (2011) argues that many disciplinary concepts and approaches are relevant in the study of resilience and certain

Table 1 Definitions and measures of resilience. Disciplines and authors

Definitions

Measures of resilience

Ecological resilience (Holling, 1973: 14, 17)

‘‘A measure of the persistence of systems and of their ability to absorb change and disturbance and still maintain the same relationships between populations or state variables’’.

Holling, 1973; Carpenter et al., 2001; Gunderson and Holling, 2002; Walker et al., 2002 Population ecology; Resilience as an element of stability; as a central feature of population dynamics (Pimm, 1984, 1991: 3, 13) Social resilience (Adger, 2000: 347; cf. Obrist et al., 2010: 289)

The magnitude of disturbance a system tolerates (can tolerate) before moving into a different state space and set of controls. Resilience is ‘‘how fast a variable that has been displaced from equilibrium returns to it. Population resilience is the rate at which populations recover their former densities’’. ‘‘Social resilience as the ability of groups or communities to cope with external stresses and disturbances as a result of social, political, and environmental change’’.

Ex-ante and ex-post: ‘‘the overall area of the domain of attraction’’ and ‘‘the height of the lowest point of the basin of attraction above equilibrium’’; ‘‘probabilities of extinction’’ (p. 20); ‘‘capacity to absorb and accommodate future events’’ (p. 21). Capacities (a) to absorb disturbances (b) for selforganisation, and (c) to learn and adapt (Carpenter et al., 2001; Walker et al., 2002) Ex-post: ‘‘The return time, the amount of time taken for the displacement to decay to some specified fraction of its initial value. Long return times mean low resilience, and vice versa.’’ Resilience as a rate of change. Ex-ante and ex-post: Coping and adaptive capacity.

Economic value of resilience (Walker et al., 2010)

Resilience as distance to a threshold; this distance is a stock variable, where the level of the stock is equivalent to the systems resilience. Resilience to a specific disturbance or event involves identifying a particular threshold effect such that the system will not recover its earlier pattern of behaviour if this threshold is crossed. Resilience as maintaining identity over time: ‘‘maintenance of key components and relationships and the continuity of these through time’’. ‘‘If resilience is low, identity may be lost and if identity is lost, resilience was low’’ (Cumming, 2011: 13; Cumming and Collier, 2005). Resilience reflected in ‘‘lives lived well despite adversity’’. Under exposure to significant adversity, ‘‘resilience is both the capacity of individuals to navigate their way to the psychological, social, cultural, and physical resources that sustain their wellbeing, and their capacity individually and collectively to negotiate for these resources to be provided and experienced in culturally meaningful ways (Ungar, 2008: 225; Ungar, 2011).

Social–ecological resilience (Resilience Alliance, 2010: 34)

Spatial resilience (Cumming, 2011: 13)

Social ecology of resilience; – psychology, social anthropology (Ungar, 2005)

Ex-ante measure of current and future resilience. The bigger the distance from a critical threshold, the bigger the system’s resilience. Ex-ante and ex-post: Identify important system variables and their thresholds; If threshold is crossed, system loses resilience. Ex-ante and ex-post: ‘‘Quantifying identity and assessing the potential for changes in identity’’.

Ex-ante and ex-post: Capacity, associated factors and processes.


aspects in the social sciences have parallels with ecology (cf. Table 1). Brand and Jax (2007) examined definitions of resilience based on their degree of normativity, showing that resilience can be a descriptive concept, a normative concept or a hybrid of both. Our interest in this paper is to identify how livelihood resilience can be conceptually defined as a precondition for assessing it based on existing and new conceptualisations and measures. Table 1 shows that resilience can be interpreted to mean the ‘‘magnitude of a disturbance’’, ‘‘a rate of change’’, ‘‘rate of recovery’’, ‘‘a threshold’’, ‘‘persistence/continuity’’, ‘‘identity’’, ‘‘ability’’ or ‘‘capacity’’. As human strategies are key in livelihood research, it is important to draw on schools of thought that allow linking the individual actor’s agency with the social structure of which they are part. Thus, resilience also emphasises ‘‘protective enabling factors’’, defined by Obrist et al. (2010, p. 286) as supportive factors of a social structure. By asking what protective enabling factors make livelihoods prevail (e.g. good harvests) despite adversity (e.g. drought), resilience addresses long-term capacity to cope with and adapt to change and indirectly addresses the underlying structural causes of vulnerability. Thus in chronic poverty literature, resilience is generally ascribed to three characteristics: (a) good outcomes despite high-risk, (b) sustained competence under conditions of threat, and (c) recovery from shocks and stresses (Boyden and Cooper, 2007, p. 1). While Table 1 shows the various ways resilience is conceptualised, it also indicates that for resilience measures to be meaningful, resilience needs to be contextualised. In the following we elaborate how this can be done for livelihoods. 3. Livelihood approaches, agency, structure and livelihood resilience A livelihood approach describes the resources that people have and the strategies they adopt to make a living, thereby not only focussing on monetary but also on other assets such as social networks. A livelihood approach can help improve the understanding of people’s adaptive capacities and how to reduce poverty as it puts ‘‘people’s livelihood concerns’’ (Ashley, 2000, p. 9), their differential capabilities to cope with shocks and how to enhance people’s livelihoods at the centre of analysis (Allison and Ellis, 2001). According to Chambers and Conway (1992), a livelihood comprises the capabilities, assets (including both material and social resources) and activities that contribute to a means of living. A livelihood is sustainable when it can cope with and recover from stresses and shocks, maintain or enhance its capabilities and assets while not undermining natural resources. A livelihood approach focusses on the following components and their interactions: the livelihood context, the livelihood capitals (assets), the institutions and processes mediating/influencing livelihood strategies (the chosen combination of assets and activities), and the livelihood outcomes and trade-offs (Scoones, 1998). A livelihood context is the vulnerability context, in which people live, which is characterised by certain conditions, trends, shocks and seasonality. Within such contexts, people have different resources (livelihood assets) at their disposal, which they use to achieve their goals. These assets can be human, social, natural, physical and financial capital. The asset bundle (amount, diversity and balance between assets) positively influences livelihood strategies – the more assets individuals or households have at their disposal, the wider are the options available to them to secure their livelihoods (DFID, 2000). The societal structures represented by the transforming structures and processes such as institutions, organisations, policies and legislation, shape livelihoods and outcomes like increased food production or crop loss. They are crucial for livelihoods as they operate at all levels and determine access to resources (entitlements, Sen, 1984), affect

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terms of exchange between different types of capital and influence livelihood strategies (Shankland, 2000; Keeley, 2001; Scoones, 2009). However, Scoones (2009) argues that although ‘‘livelihoods analysis that identifies different future strategies or pathways provides one way of thinking about longer-term change’’ (p. 190), livelihood approaches have failed to capture systemic transformation and long-term change. The author proposes to enhance livelihoods thinking especially in relation to sustainability through incorporating the concept of resilience. Hence examining the trade-offs between vulnerability and resilience perspectives in livelihoods theory can enhance the scholarship in this field (Scoones, 2009; Sallu et al., 2010). Consequently, while livelihood approaches centre on human actors combining their assets, making choices and taking action, understanding agency and actor-perspectives, it is also important to integrate into the analysis how actors influence social structure and how in turn social structures influence actors; e.g. by drawing on Bourdieu’s theory of practice (Bourdieu, 1977, 1984, 1997), Giddens’ structuration theory (1984) and Wiesmann et al.’s human actor model (2011); they all help to understand actors, their rationales and agency, and how the social structure in the form of institutions (norms, values, rules) interact with agency to result in livelihoods that are strong or weak in resilience. Such theories provide insights on how social structuration processes build or erode resilience (cf. Fuchs, 2002b, 2003). Understanding social structuration processes and actor-perspectives is thus important for building livelihood resilience. For instance, smallholder farmers may perceive most social–ecological conditions ‘‘as uncertainties and risks rather than opportunities’’ (Wiesmann, 1998; Wiesmann et al., 2011). They thus diversify their strategies, an important attribute of resilience (cf. Ifejika Speranza et al., 2008; Ifejika Speranza, 2013). The actor perspective also connects the social ecology of resilience, which is captured in the following points: first, how the social environment facilitates or inhibits resilience, second, the interactions between actors and their social environment and third, the individual capacities to build resilience (Ungar, 2011). Based on the above discussions, we identify the following measures of resilience (Table 1) as proxies of livelihood resilience: ‘‘capacity’’, ‘‘persistence’’ and ‘‘rate of recovery’’. Livelihood resilience thus refers to the capacity of livelihoods to cushion stresses and disturbances while maintaining or improving essential properties and functions. Livelihood resilience is characterised by actors’ assets and strategies to maintain and increase assets, to self-organise and to learn. A livelihood is thus resilient if it can maintain its key functions (food, income, insurance, poverty reduction, etc.) and absorb the impacts of disturbances without causing major declines in production and wellbeing. Livelihood resilience thus depends on how well a livelihood functions, on actors’ capacity and agency, and on the social, institutional and natural conditions. 4. Indicators for measuring resilience The resilience concept can help to understand the factors that enable actors to protect their livelihoods from the adverse consequences of change (e.g. climate change and climate variability). Major questions are: What are the key protective and enabling factors for agricultural-based livelihoods in the context of climate change and to what extent and how do actors’ practices contribute to these? How do policies enhance or hinder actors’ capacities to deal better with climatic hazards? From the adopted definition of resilience, three major attributes, which can further be decomposed into various proxy indicators, are usually identified (cf. Carpenter et al., 2001;

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Fig. 1. A conceptual and analytical framework for characterising livelihood resilience.

Milestad and Darnhofer, 2003; Milestad, 2003), namely buffer capacity, self-organisation and capacity for learning. Resilience is maintained when buffer capacity exists and is not declining, selforganisation exists and is promoted, and learning occurs. These major attributes and their indicators are captured in the following conceptual framework (Fig. 1) that serves as a basis for elaborations in the following sections. 4.1. Buffer capacity Buffer capacity has been described as the amount of change (disturbance) a system can undergo (absorb) and still retain the same structure, function, identity, and feedbacks on function and structure (Carpenter et al., 2001; Resilience Alliance, 2010). From an actor and livelihood perspective, capacity as portrayed by livelihood capitals and their dynamics reflect buffer capacity. We thus adopt an operational definition of buffer capacity as the capacity to cushion change and to use the emerging opportunities to achieve better livelihood outcomes such as reduced poverty (Ifejika Speranza, 2013). The indicators include the following: a. Endowments and entitlements – Endowments refer to resources owned by an actor while entitlements refer to actors’ access to resources. Both can cushion change. Endowments are usually captured with livelihood capitals (or assets), which can be human (e.g. skills, health condition, knowledge), financial (e.g. incomes, savings), physical (e.g. technological innovations), social (benefits through group memberships) or natural (e.g. soil organic carbon content) (cf. DFID, 2000). Livelihood capitals, like Bourdieu’s capitals, and entitlements, determine human agency. Entitlements are ‘‘the set of alternative commodity bundles that

a person can command in a society using the totality of rights and opportunities that he or she faces’’ (Sen, 1984, p. 497). Devereux (2001, p. X), drawing on Sen (1984), describes ‘‘a person’s ‘entitlement set’ as the full range of goods and services that he or she can acquire by converting his or her ‘endowments’ (assets and resources, including labour power) through ‘exchange entitlement mappings’ (e.g. production, trade, social security provisions and subsidies). Entitlements thus determine capabilities, that is, ‘what people can do or be with their entitlements’’’ (Sen, 1984). A SES, group or individual with more endowments and entitlements is often more likely to buffer adverse impacts than those with lesser or none. Adger and Kelly (1990) show that the human access to and use of resources are determinant factors for the people’s ability to cope with and adapt to stress (see also Adger, 2003; Berkes et al., 2003; Smit and Wandel, 2006; Marschke and Berkes, 2006; Ifejika Speranza et al., 2008). The site-specificity of knowledge; e.g. through the level of experience a land user has in a specific environment (cf. Atran et al., 2002; Boillat and Berkes, 2013) can help ensure that livelihood strategies are adapted to the SES. Some studies show that migrant farmers often do not have adequate knowledge of the agro-ecosystems to which they have migrated (Elbers, 2002). They tend to carry the same seeds and use the same farming techniques, which often do not fit the new location’s ecological conditions. In addition, buffer capacity also depends on how livelihood strategies deplete or enhance capacities. For example, stewardship embodies a co-management of environmental resources to achieve long-term sustainability, and is an ethic common in many


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Table 2 An assessment framework for analysing the buffer capacity dimension of livelihood resilience. Dimensions of resilience

Indicators (changes in)

Resilience check – indicator-variablesa

Endowments/entitlements

Ownership and access to resources – assessing levels and changes in conditions of and access to livelihood capitals Level of education – the higher the education the more literate

Human capital – literacy level Knowledge (experience) Skills Health condition Financial capital – income/yields Savings Labour income Buffer capacity through a rural livelihood lens

Expenditure Dependency ratio (DR)

Social capital

Physical capital Natural capital

Number of years in farming Other non-farm skills being practiced Ability to use household labour; presence of a disabled household member Crop yields as proxy – e.g. kilogram per hectare produced last season and last drought affected season Context specific – e.g. livestock Number of days of labour sale multiplied by income per man day Recall consumption expenditure in the last week The higher the DR the higher the financial burden for a household: Sum of 0–14 years and >64 years old divided by Number of 15–64 years old; household members not earning an income divided by those earning an income Increase in other assets due to membership or participation in social networks; Labour support from group members Income gained through membership in groups Use of group tools, equipment and infrastructure Machinery, buildings, equipment, water ponds, granary – their financial equivalents. Soil fertility (nutrients), soil organic carbon, agroforestry and tree carbon, soil moisture content, biomass, runoff/erosion, pests, diseases – observations and measurements

Individual/ household

Group/village/ district

X

X

X X X

X X X

X

X

X X

X

X X

X X

X

X

X X X

X X X

X

X

Source: Own design based on literature mentioned in this section. a A 5-point Likert measurement scale can be used to capture the contributions to resilience: 0: none; 1: very low; 2: low; 3: average; 4: high; 5: very high.

4.2. Self-organisation

macroscopic structures by microscopic interrelations’’ (Fuchs, 2002c, p. 229). Holling (2001, p. 394) refers to this as ‘‘the internal controllability of a system; that is, the degree of connectedness between internal controlling variables and processes’’, which can be interpreted as the degree to which a social network can direct its own actions and outcomes. Social self-organisation connotes certain levels of autonomy, freedom to act, collective action, self-help, self-reliance, independence, power and control of own action, all of which can foster identity, trust and confidence and contribute to empowerment. Accordingly, Milestad (2003) defines self-organisation of farming systems as the ability of a group of farms to form flexible networks as well as the ability to be involved with the social, economic and institutional environment on other scales than the local. Self-organisation highlights how human agency, adaptive capacities, power and social interactions shape social resilience (cf. Obrist et al., 2010). An emerging common feature is that endogenous interactions and processes are the core for selforganisation (cf. Di Marzo Serugendo et al., 2004; Cumming, 2011). The attributes include:

Conscious of other definitions (cf. Nicolis and Prigogine, 1989; Maturana and Varela, 1992; Camazine et al., 2001), we follow Fuchs’ (2002a,b,c, 2003, 2004) conceptualisations of social self-organisation, which particularly draws on Bourdieu’s and Giddens’ work. Self-organisation in social systems can be used in two senses: in a general systemic sense and in a specific autonomous sense. General self-organisation in social systems refers to the spontaneous emergence/re-creation of society (rules, norms, values, and organisation) through a dialectic of social structures (top-down processes) and human actions (bottom-up processes), without explicit control or constraints from outside the system (Di Marzo Serugendo et al., 2004; Holland, 1994; quoted in Cumming, 2011, p. 17). Autonomous self-organisation refers to a state where actors determine their own rules. Under conditions of crisis and instability, social self-organisation ‘‘denotes that the individuals affected by the emerging structures determine and design, the occurrence, form, course and result of this process all by themselves. They establish

a. Institutions, which refer to societal norms and rules (Ostrom, 1990) as well as formal institutions like groups, organisations and government bodies. Existing norms and rules can indicate self-organisation in a SES (Pretty and Smith, 2004; Brondizio et al., 2009; Cabell and Oelofse, 2012). They can enhance or limit actors’ adaptive capacities and are crucial for building resilience. Questions include how and to what extent institutions foster or hinder livelihoods and how much an actor’s livelihood practices contribute to building institutions conducive to coping with and adapting to stresses and shocks. b. Cooperation and networks are good bases for self-organisation and refer to interactions between actors in the SES resulting in the creation of own rules, norms and values (institutions), building trust and decreasing dependence on external actors for information, innovation and capital (Ifejika Speranza, 2010). An isolated actor that does not interact with his/her social environment misses opportunities to gain knowledge, build

indigenous knowledge and management systems (cf. Berkes et al., 2000). In some cases in Sub-Sahara Africa, smallholder farmers mine soil nutrients, cultivating the same plots without adequately fertilising the soils to ensure long-term productivity (Ifejika Speranza et al., 2008). Therefore, an issue of concern is to examine in what ways and how much farm practices reflect actors’ capacity to act in an environmentally friendly manner. Based on the foregoing, we identified variables for capturing buffer capacity (Table 2), which can be transformed into a livelihood resilience measurement scale (Table 2). Drawing from the human actor model (Wiesmann et al., 2011), the assets of value to the community and to actors’ livelihoods have to be identified and quantified (where possible) together with the community. To measure contributions to resilience, a 5-point Likert scale (e.g. 0: none; 1: very low; 2: low; 3: average; 4: high; 5: very high contributions) that captures scores for the three dimensions of resilience can be employed.


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trust and increase social capital that can sustain livelihoods when shocks or stresses occur (Pretty and Smith, 2004; Ifejika Speranza, 2010). Social capital, that is, valued relations (Bourdieu, 1984, 1986) or ‘‘the benefits derived from participation and membership in social networks or other social structures’’ (Cumming, 2011, p. 190ff), is crucial for building and maintaining resilience as it can be transformed to other livelihood capitals. Social capital can be measured using proxies such as the number and types of groups in which an actor is a member, the degree of participation in groups and networks, trust, reciprocity and social cohesion (cf. Narayan and Pritchett, 1997, p. 270). Lack of cooperation among actors is a poor basis for self-organisation (Ifejika Speranza, 2010). c. The network structure of a SES can influence system dynamics and management outcomes, for example, by facilitating or impeding processes such as information sharing, access to resources and collaboration opportunities (Cumming, 2011). Network structure may cushion livelihoods from shocks in the larger system or transmit shocks (diseases, epidemics) faster through the system. Depending on the SES, protection from shocks in the larger system can be a desirable attribute but this would require loosening the livelihood’s link to the larger system (cf. Rochlin, 1997). In case of negative impacts, existing ‘‘buffer capacities’’ may be adequate to cushion shocks. Conversely, to rapidly spread positive outcomes across a livelihoods system (e.g. agricultural innovations), tight feedbacks within the system may be the desired attribute. Analysing the SES’s level of connectivity and centrality and considering the types and degrees of relations between actors in a livelihoods system can help explain how network structure facilitates resilience (Janssen et al., 2006). d. Reliance on own resources reduces dependency on external inputs and saves time for prompt local-level action. This refers to resources that are mainly but not exclusively locally available, local knowledge, culture and leadership, along with openness to integrating external knowledge and practices (COMPAS, 2007). We present the variables that capture self-organisation in Table 3. 4.3. The capacity for learning The capacity for learning connotes adaptive management, implying that a resilient SES is a learning system that incorporates

previous experiences into current action and thus has memory (ibid). General dictionary definitions refer to learning as the acquisition of knowledge or skills. Learning ability at individual livelihood and system levels is crucial for building resilience. The organisational learning and social learning literature offer various important insights for understanding individual and societal capacity for learning. We thus borrow from this literature in operationalising the capacity for learning at an individual actor level and at societal level. Learning is not just acquiring knowledge and skills but also translating the knowledge into action (Argyris and Scho¨n, 1978). Fiol and Lyles (1985, p. 811) define learning as ‘‘the development of insights, knowledge, and associations between past actions, the effectiveness of those actions, and future actions’’. In this vain, Kim (1993, p. 2) argues that ‘‘learning encompasses two meanings: (1) the acquisition of skill or knowhow, which implies the physical ability to produce some action, and (2) the acquisition of know-why, which implies the ability to articulate a conceptual understanding of an experience’’, that is, connecting ‘‘thought and action’’. As the concern here is with capturing learning and the capacity to learn, we do not dwell on these typologies but note that learning is evidenced when behaviours change because of acquired knowledge (Argyris and Scho¨n, 1978). Although the following definition has been applied to learning organisations, we can draw some analogy for a learning individual or system. Adapted from (Garvin et al., 2008, p. 110) a learning actor (e.g. a farmer) can be understood as one who excels at ‘‘creating, acquiring and transferring knowledge’’ and is skilled at modifying his/her livelihood activities ‘‘to reflect new knowledge and insights’’ with a view to improving performance (Garvin, 1993, p. 80; Jerez-Go´mez et al., 2005). Various levels of learning are distinguished for a better understanding of the learning process: zero learning, single, double and triple loop learning (see Bateson, 1973; Argyris and Scho¨n, 1978; Romme and van Witteloostuijn, 1999; Pahl-Wostl, 2009 for details). As learning is not an automatic process, various conditions that facilitate learning have been studied in the organisational learning and learning organisation literature as well as in social learning literature. We draw analogies from measures of organisational learning capability (cf. Jerez-Go´mez et al., 2005; Chiva et al., 2007; Li et al., 2008), learning organisations (Watkins and Marsick, 1997; Moilanen, 2005; Song et al., 2009) and social learning (Schusler et al., 2003; Rist

Table 3 An assessment framework for evaluating the self-organisation dimension of livelihood resilience. Dimensions of resilience



Institutions

E.g. policies, rules, local norms; existing rules and regulations governing land and water use Enforcement of rules and regulations governing land and water use (e.g. applied sanctions for non-compliance?); government encourages collective action (e.g. government support to/partnerships with farmer organisations) Current group memberships: Number and type of groups in which farmer is a member Number of times a farmer missed the meetings of his/her main group in the last 12 months Village members can generally trust each other in matters of lending and borrowing money Number of households in labour exchange Context specific attributes of the SES’ network-structure that are desirable for maintaining and improving resilience (e.g. network size, density, degree, bonding, proximity, homogeneity, connectivity levels, centrality, and network ties). Major source of farm inputs (farm/non-farm); duration or distance to input source – the shorter the time/distance required to access inputs the better the livelihood resilience

Cooperation and networks Participation Self-organisation

Trust Reciprocity Network structure

Reliance on own resources


Group/village/ district X X

X

X

X

X

X

X

X

X X

X



et al., 2006) to capture the capacity to acquire and act on knowledge. In an inter-linked SES, assessing the capacity to act on knowledge can be at the individual actor level, at the multipleactor level (e.g. resource user group) or at the system level. At the individual actor level, assessing the capacity for learning entails examining whether learning and learning outcomes (at a particular point in time as an outcome of various learning processes) are fostered. Examining how the capacity for learning contributes to resilience requires identifying the capacities needed for learning and how to foster these capacities or how adaptations enhance these capacities. In addition, at the multiple-actor level and at the system level the focus is on the existence of processes that foster learning (cf. Wenger, 1998). How individual face-to-face learning processes are linked to organisational and societal learning processes; e.g. how face to face learning can help transform societal structures, is therefore important for assessing livelihood resilience. Armitage et al. (2008) show that this capacity can be captured by examining whether face-to-face learning is triggering single, double or triple loop learning; this means to trace back to what degree cognitive learning turns into social learning by not only identifying institutional and structural obstacles, but developing collective action aimed at changing constraining structures (Rist et al., 2006). Since a SES is dynamic, in temporal and spatial terms, actors are constantly adjusting their livelihoods strategies and learning from what other actors (e.g. farmers) are doing to maintain and increase production. Adaptive management and sustainable governance are crucial as they emphasise understanding feedback from the SES, their management and the involved actors. In this sense, management is a tool not only for changing the system but also for learning about the system (Milestad and Darnhofer, 2003; Rist et al., 2007). Assessing resilience includes examining ways and the extent to which actor-practices have contributed to acquiring and using new knowledge. Investigating how much the SES promotes learning (a desirable state) among actors focusses on variables that ¨ rtenblad, indicate whether and how a SES fosters learning (cf. O 2001). If the issue is to understand learning processes within the SES, then we need to identify variables that capture/describe the nature and processes of learning (cf. Easterby-Smith and Araujo, 1999). Hence, the capacity for learning does not depend on the individual alone but also on the interactions between the individual and the wider SES. For assessing capacity for learning, we adapt Li et al.’s (2008) construct of learning capability, and we also draw on Watkins and Marsick (1997), Milestad (2003), Jerez-Go´mez et al. (2005) as well as Rist et al.’s (2006) dimensions of social learning processes: a. Knowledge of threats and potential opportunities refers to the extent to which an actors’ knowledge relates to the issue of concern (cf. Li et al., 2008) – for example, the ability to analyse threats and potential opportunities to a livelihood. As actors’ knowledge can be mainly tacit, evaluating this dimension requires proxies that capture what the actors actually do. b. Shared (collective) societal vision about a livelihood or livelihoods system can contribute to transformation for resilience. Collective vision is indicated by the extent to which existing institutions and conditions promote the exchange of information and service, shared knowledge, perceptions, beliefs and collective action that empower individual actors (Jerez-Go´mez et al., 2005) in their livelihoods. This attribute also includes the extent to which individual actors’ visions in a SES fit one another. Without a shared vision, individual actions do not contribute to learning on a wider scale (Kim, 1993), and therefore shared vision is important for livelihood transitions.

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c. Commitment of the SES to ‘‘learning and developing a culture that promotes the acquisition, creation and transfer of knowledge as fundamental values’’ (Jerez-Go´mez et al., 2005, p. 717) is crucial for learning. This pertains to the extent to which government policies and regulations support actors’ livelihoods and involve actors in decisions related to their livelihoods. Existing learning platforms that provide opportunity for learning such as regular meetings between extension officers and farmers can create opportunities for combining different types of knowledge, fostering learning that is important in sustaining livelihoods (Rist et al., 2007). d. Knowledge identification capability (KIC) is ‘‘the ability to identify/scan the external environment for valuable knowledge for [livelihood] survival and development’’ (Li et al., 2008, p. 2533). KIC can be demonstrated by a farmer’s ability to monitor new farming technologies or to identify crucial knowledge for the farm’s future survival or development. Openness and experimentation through learning by doing and not considering one’s own values, beliefs and experiences to be better than the rest (Jerez-Go´mez et al., 2005), also reflects KIC. Experimentation, or ‘‘the degree to which new ideas and suggestions’’ are considered and tested (Chiva et al., 2007, p. 226), offers opportunities to adapt livelihood strategies to dynamic conditions. e. Knowledge sharing capability (cf. Li et al., 2008) refers to the extent to which an actor (e.g. a farmer) spreads knowledge to other farmers. This can be evaluated by examining whether other farmers have ‘‘copied’’ practices from a farmer. f. Knowledge transfer capability refers to the extent to which a farmer applies own knowledge or internalises external knowledge (e.g. other farmers’ knowledge) to serve farm production objectives (cf. Li et al., 2008). g. Functioning feedback mechanisms can spread knowledge and increase social memory through interactions among actors. Milestad (2003) notes that feedback mechanisms are crucial for learning as they allow farmers to monitor signals from the ecosystem, which they process, interpret and subsequently respond to with relevant changes in farm management. At a cross scale level, examining feedback between the various actor levels (for example between farmers, extension officers, district officers, ministry directors and politicians) provides information on how feedbacks shape a farmer’s livelihood resilience. This indicator closely relates to network structure in the selforganisation dimension of resilience. Table 4 elaborates the indicator variables for learning capacity. The elaborations above hint at interactions between the three resilience dimensions. Certain sub-components of the three main features of resilience relate to other components (e.g. endowments–human capital such as the level of education or skills can contribute to capacity for learning). 4.4. Diversity – a cross-cutting dimension The indicators of buffer capacity, self-organisation and learning can further be qualified in terms of their diversity. Diversity refers to differences in livelihood characteristics (e.g. livelihood diversification, crop diversity, biodiversity, social group diversity) and processes, and the multiple ways livelihoods function. Livelihoods that comprise different components with different characteristics and different functions are likely to be more robust to stress and shocks (Ifejika Speranza et al., 2008). Diversification in agricultural systems can be through genetic variety, species, and crop structural diversity, and across scales, within crop, within field, and at landscape level (Lin, 2011). Crop diversification can improve resilience through pest and disease suppression (e.g. Krupinsky


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Table 4 An assessment framework for evaluating the learning capacity dimension of livelihood resilience. Dimensions of resilience




Group/village/ district

Knowledge of threats and opportunities

Ability to analyse threats/potential opportunities (e.g. threats to farm production and opportunities to increase production over the last 12 months) Policies on farming and their fit with farmers practices, number of farmers with same/similar practices, frequency of discussing core practices in an extension platform in the last 12 months Public extension services organise open meetings regularly, access of all farmers in the community to extension services, frequency of discussing the performance of a last season with the extension service and with other farmers, time spent per month to access needed production information Knowledge of prices for inputs and products (at beginning, middle of farm season and after harvest); of the best time to purchase and sell; of new agricultural practices in the area in the last 12 months, frequency of consulting forecasts Farmer’s planned new practices in the next farm season Number of times a farmer attended information events in the last 12 months and farmer’s actions in those events (listening, discussing, etc.) New items/methods tested in the last 12 months and how many adopted or dropped, new items/methods used in current farming season Farm production/management problems, number of times farmer discussed farm production/management problems with other actors in the community during last 12 months Number of farmers a farmer gave information/new methods to in the last 12 months New ideas/practices a farmer learned from other farmers (and other actors) in the last 12 months Frequency of interaction with key actors in farm production in the community in the last 12 months (e.g. other farmers, extension officers, district agricultural officers, local politicians, ministry directors, researchers, input traders, others-specify), new ideas and practices farmers learnt from these actors in the last 12 months

X

X

X

X

X

X

Shared vision

Commitment to learning

Knowledge identification capability-monitoring

Capacity for learning

Planning Participation to access information Experimentation

Openness

Knowledge sharing capability Knowledge transfer capability Functioning feedback mechanisms

X

X X

X

X

X

X X X

X


et al., 2002; Armbrecht and Gallego, 2007), through the different characteristics of the crops that respond differently to climatic stress, thereby maintaining functional capacity compared to nondiverse agro-ecosystems (Altieri, 1999; Tengo, 2003; Altieri and Koohafkan, 2008; Lin, 2011). Diversity also provides flexibility in that a livelihoods system or an individual farmer can draw on different farm resources. Crop diversity increases resilience as droughts or floods do not affect all crops in a mixed cropping or agroforestry system to the same degree, hence diversity reduces the risk of crop loss or widespread disease infestation (Ifejika Speranza, 2006). The benefits of diversity can also be by complementing farm income with non-agricultural incomes (Moench and Dixit, 2004). However, this generally positive relationship depends on the context (Adger, 2000).

Wolmer, 2002), impacts of development interventions on livelihoods (Frost et al., 2007), long-term change in wellbeing (Ulrich et al., 2012) and future livelihood pathways (Pender, 2004). Central to the resilience concept is the question of whether and how livelihoods can continue to function despite shocks and stresses (Sallu et al., 2010). The general understanding that the dynamics of many ecological and human systems follow patterns/processes of growth, conservation, release/collapse and reorganisation (Gunderson and Holling, 2002) is applicable to livelihood resilience (Ifejika Speranza, 2011). From an actor and livelihoods perspective, Tables 2–4 each provide a lens for a systematic evaluation of livelihood assets and the contributions of livelihood strategies to buffer capacity, selforganisation and capacity for learning, respectively. The following points are critical to consider in empirical analysis:

5. Dynamics in livelihood resilience and analytical pathways Having identified what livelihood resilience constitutes, the following steps can explain livelihood dynamics and future possible trajectories (Bagchi et al., 1998; de Haan and Zoomers, 2005; Sallu et al., 2010; Ulrich et al., 2012): 1. Conducting historical (longitudinal) analysis of livelihoods 2. Identifying and characterising possible livelihood trajectories in the face of exposure to shocks and stresses 3. Analysing recovery pathways and implications for maintaining or enhancing livelihood resilience Research has focussed on long-term shifts in livelihood strategies (Mortimore, 2003), livelihood pathways (Scoones and

(1) Understanding the SES within which livelihoods practices occur: To analyse the resilience of agriculture-based livelihoods to climate change, it is important to understand the climatic shocks the SES has been exposed to and is likely to be exposed to, the agency of farmers and how the SES affects farmers’ agency. The SES influences whether and how farmers can adopt certain practices because it can make relevant knowledge and resources available and accessible (e.g. for institutional factors see Fraser and Stringer, 2009). It is also within such a SES that farmers give meaning and value to different practices (cf. Wiesmann, 1998). Since resilience is a normative concept and ‘‘a scientific construct that has to be inferred and cannot be directly observed or measured’’ (Obrist et al., 2010, 285), proxies and


indicators as in Tables 2–4 can be used to evaluate resilience and to measure the contributions of a practice to resilience. ‘‘Meanings of resilience also have to be negotiated’’ with actors in the SES (Obrist et al., 2010, p. 288), and Tables 2–4 provide a framework. The human-actor model (Wiesmann et al., 2011) can serve for selecting variables of ‘‘meaning’’ to the actors. (2) Understanding the positions of the farmers within a SES: This includes discussing the processes through which farmers’ practices contribute to resilience and analysing how much the SES supports farmers in their livelihoods practices. Relevant questions include: Does the SES make the resources (knowledge, skills, finance, etc.) needed available or accessible to actors? What opportunities and capacities do actors have to influence their SES (other stakeholders, institutions, etc.)? (3) Analysing individual actor/farm-level capacities and processes that shape resilience: At the individual actor level, the questions are: How much do farmer practices reduce vulnerability and contribute to the resilience of livelihoods? What are the capacities of actors to act, to access or create opportunities and innovation in dealing with climate change triggered agricultural production crises? It is also at this level that the biophysical outcomes of actors’ practices can be analysed in detail. Questions focus on how much a practice improves ecosystem functions in terms of soil fertility, soil nutrient content, carbon sequestration and increased yields. Farmer assessments or expert assessments can capture the contributions of practices to resilience (e.g. Tables 2–4). Data collected from such a measurement scale can serve for further analysis (e.g. factor analysis to identify important underlying dimensions) (cf. Ifejika Speranza, 2013), and for creating resilience indices or a resilience model. To retain detail, it may be more useful to create sub-indices for the three dimensions instead of creating one general resilience index. Where further detail is required, quantitative measures of biophysical farm properties (e.g. soil fertility, biodiversity, etc.) can be collected. As resilience is not fixed but changes over time and across contexts, the practices adopted can differ across locations and time. Periodic assessments, longitudinal studies and multi-level analysis can provide adequate insight for decisions on progress made in enhancing resilience through supporting certain livelihood practices. Although the frameworks in Fig. 1 and Tables 2–4 elaborate the conditions and processes of resilience, they do not indicate whether their valuation should be with regard to social, economic or ecological dimensions. These sustainability dimensions can be included by asking for the specific contributions of practices to each dimension (Ifejika Speranza, 2012). Further, as explaining causality is difficult, resilience related outcomes must be contextually and temporally specific. This limits the generalizability of findings (Ungar, 2011). Context matters in assessing resilience as atypical strategies or decisions may be adaptive and positive in a local context but not in a wider context (Ungar, 2011, cf. Wiesmann, 1998). Therefore, it makes sense to adapt resilience tools to the local situation by asking which practices are locally associated with resilience. In this sense, Tables 2–4, which have been informed by empirical literature, need to be further adapted to the specific context in which they are applied. Finally, as a practice can contribute to only one resilience dimension (buffer capacity, self-organisation or learning) and to only one sustainability dimension (social, economic and ecological), applying composite measures as suggested in Tables 2–4 can provide adequate approximations of resilience. Empirical applications of the proposed assessment framework in specific contexts will provide further insights.

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6. Conclusions This article conceptualises resilience for research that departs from a social actor and livelihood perspective, drawing on illustrations from farming systems. It discusses the ways resilience is operationalised in literature and identifies indicators for the three dimensions, namely buffer capacity, self-organisation and capacity for learning, and how they can be applied in assessing livelihood resilience. Due to the concept’s breadth and the comprehensiveness of operational measures of resilience, it may be difficult to assess all indicators in one empirical study. One approach to reduce the number of variables is to focus on variables relevant for a particular social–ecological context, drawing on insights from social theories and social ecology. The proposed measurement scale can serve as a tool for periodically monitoring the contributions of actors’ practices to maintaining livelihoods during stresses and shocks. The framework provides a basis for indepth empirical analysis of resilience from an actor and livelihood perspective. Such a resilience-check has the potential to contribute to planning and monitoring development projects and policies, including measures to adapt to climate change, and can thus positively influence the resilience outcomes of such interventions. Acknowledgements This work was funded by the Swiss National Science Foundation (SNSF) under its Ambizione grant [PZ00P1_137068] for the research project ‘‘Resilient agriculture-based livelihoods and resilient agricultural landscapes? Adaptation to climate change in African agriculture’’. We are grateful to Johanna Jacobi and Anne Catherine De Chastonay for their helpful comments on an earlier version of this paper. We thank the two anonymous reviewers for their constructive comments.

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