Technical ReportPDF Available

Abstract and Figures

Farming systems in Europe face a vast range of environmental, economic, social and institutional challenges. Examples include more volatile producer and input prices, higher probability of extreme weather events, increasing dependence on land owners and financial institutions, organizational change within value chains, competing policy objectives and increasing administrative demands, and new societal concerns and changing consumer preferences. In this paper we define resilience as maintaining the essential functions of EU farming systems in the face of increasingly complex and volatile economic, social, environmental and institutional challenges. A farming system is a system hierarchy level above the farm at which properties emerge as a result of the formal and informal interactions and interrelations among farms, available technologies, stakeholders along the value chain, citizens in rural and urban areas, consumers, policy makers, and the environment. Existing resilience frameworks do not sufficiently capture the regional interplay of the multiple processes and stakeholders apparent in farming systems. In order to capture the described developments in EU agriculture, and in order to proactively address those challenges, we propose a framework to analyse the resilience of EU farming systems. The integrated framework can be applied by public and private decision makers to formulate differentiated strategies across EU farming systems depending on context-specific challenges and available resources.
Content may be subject to copyright.
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Project acronym: SURE-Farm
Project no.: 727520
Start date of project: June 2017
Duration: 4 years
Report on resilience framework for EU agriculture
Work Performed by P1 (WU) in cooperation with all partners
– Wim PAAS
– Thomas SLIJPER
– Isabeau COOPMANS
– Eewoud
– Willemijn VROEGE
– Sina
– Robert FINGER
– Yann DE MEY
– Peter
– Mauro VIGANI
– Damian MAYE
– Alfons BALMANN
– Franziska APPEL
– Peter FEINDT
– Jeroen CANDEL
– Muriel TICHIT
– Francesco ACCATINO
– Saverio SENNI
– Isabel BARDAJÍ
– Bárbara SORIANO
– Katarzyna
– Helena HANSSON
– Mariya
– Pytrik REIDSMA
(Contact: Miranda Meuwissen)
Due date 31 January 2018
Version/Date 31 January 2018
Work Package
WP 1
Task T. 1.1
Task lead
Dissemination level Public
Business Economics, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, the Netherlands,
Plant Production Systems, Wageningen University, the Netherlands
Strategic Communication, Wageningen University, the Netherlands
Public Administration and Policy, Wageningen University, the Netherlands
Agricultural and Farm Development, Institute for Agricultural and Fisheries Research (ILVO), Belgium
Institute of Rural and Agricultural Development, Polish Academy of Sciences, Poland
Division of Bioeconomics, KU Leuven, Belgium
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Universitetet i Bergen, Norway
Agricultural Economics and Policy Group, ETH Zurich, Switzerland
Georg-August-Universität Göttingen, Germany
Aberystwyth Business School, Aberystwyth University, UK
Countryside and Community Research Institute, University of Gloucestershire, UK
Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Germany
Albrecht Daniel Thaer Institute, Humboldt University, Germany
Agroecology, INRA, France
Department of Agricultural and Forestry Sciences, Università degli Studi della Tuscia, Italy
Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), Universidad Politecnica de Madrid, Spain
Department of Economics, Sveriges Lantbruksuniversitet, Sweden
Department of Natural Resources Economics, University of National and World Economy, Bulgaria
Institute of Agricultural Economics, Romania
Introduction ................................................................................................................................. 1
The resilience concept ................................................................................................................. 2
Three main processes .......................................................................................................... 2
Stages of adaptive cycle processes ..................................................................................... 3
Processes are interrelated ................................................................................................... 6
The resilience framework and its components .......................................................................... 7
Characterising the farming system (resilience of what) .................................................... 8
Key challenges (resilience to what) ..................................................................................... 9
Essential functions of the farming system (resilience for what purpose) ....................... 10
Resilience indicators ........................................................................................................... 11
Resilience attributes ........................................................................................................... 13
Discussion and conclusions ....................................................................................................... 18
References .................................................................................................................................. 19
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
1 Introduction
Farming systems in Europe face a vast range of environmental, economic, social and institutional challenges.
Examples include more volatile producer and input prices, higher probability of extreme weather events, increasing
dependence on land owners and financial institutions, organizational change within value chains, competing policy
objectives and increasing administrative demands, and new societal concerns and changing consumer preferences
(Rosin et al., 2013; Maggio et al., 2014; Gertel and Sippel, 2016). Farming system dynamics determine how systems
respond and cope with such risks. Resilience theory provides an integrated framework to investigate the ability of
complex social-ecological systems to cope with changing environments (Folke et al., 2010; Bullock et al., 2017). The
theory emphasises change, uncertainty, interconnectivity and adaptability of complex systems (Senge, 1990; Holling
and Gunderson, 2002). In this paper we define resilience as maintaining the essential functions of EU farming
systems in the face of increasingly complex and volatile economic, social, environmental and institutional challenges.
A farming system is a system hierarchy level above the farm (Giller, 2013) at which properties emerge as a
result of the formal and informal interactions and interrelations among farms, available technologies, stakeholders
along the value chain, citizens in rural and urban areas, consumers, policy makers, and the environment (Ge et al.,
2016). For instance, many EU farms are particularly vulnerable at the point of intergenerational hand-over due to a
decrease in the attractiveness of farming when compared to other employment sources, which can lead to lack of
interested successors or new entrants (Happe et al., 2009; Fischer and Burton, 2014; Chiswell and Lobley, 2015; Van
Vliet et al., 2015). This is not only affecting the farm, and hence entrepreneurial and employment opportunities in
the agricultural sector, but also the landscape. Cultural and environmental implications of farming practices have
significant implications for the attractiveness and demographic stability of rural areas (Copus et al., 2006).
Furthermore, standard business practices that produce a competitive income for farmers are often based on
increasing farm size and on agricultural techniques that contribute to accumulated societal concerns and
environmental risks, in particular related to water, soil and biodiversity (Hazell and Wood, 2008). Such business
practices are increasingly perceived with reservation by consumers and retailers (Spiller and Nitzko, 2015). As most
farming systems in Europe are regional and specialised, these risks and uncertainties therefore differ across regions,
subsectors, different types of farms, and different farming systems.
Existing resilience frameworks do not sufficiently capture the regional interplay of the multiple processes
and stakeholders apparent in farming systems. For instance, Walker et al. (2004) and Folke et al. (2010)
conceptualise resilience in broadly defined socio-ecological systems, Darnhofer (2010) discusses resilience
enhancing strategies at farm level, while Tendall et al. (2015) and Waters (2011) focus on the role of value chain
actors in regional and global food systems. Also the holistic food security paper by Bullock et al. (2017) mainly
stresses field and farm strategies. The same is true for metrics to assess resilience. Although indicators have been
defined, a review by Quinlan et al. (2016) shows that indicators mostly focus on a specific issue, such as biophysical
measures (Carpenter et al., 2001), a different scale, e.g. watersheds (Carpenter et al., 2001), or do not distinguish
between robustness, adaptability and transformability (Cabell and Oelofse, 2012).
In order to capture the described developments in EU agriculture, and in order to proactively address those
challenges, we propose a framework to analyse the resilience of EU farming systems. The integrated framework can
be applied by public and private decision makers to formulate differentiated strategies across EU farming systems
depending on context-specific challenges and available resources.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
2 The resilience concept
The resilience framework explained here builds on the concept of adaptive cycles (Holling et al., 2002) as a heuristic.
Adaptive cycles represent different stages (growth, equilibrium, collapse, reorientation) through which systems pass
in response to changing environments and internal dynamics (Fath et al., 2015). The sequence, direction and speed
with which farming systems proceed through these adaptive cycles are empirical questions. While a system might
remain in one stage for a long time, and the sequence of stages is not fixed, transition from one stage to another is
always a possibility if circumstances change. Reorientation is generally preceded by so-called ‘tipping points’ which
illustrate thresholds beyond which systems may collapse or change drastically (Ge et al., 2016).
Most analyses of agricultural production systems have confined their conceptual vision to the growth and
equilibrium phases, and have neglected the possibility of collapse and reorientation phases. A consequent
shortcoming of this limited vision is that conditions for system continuation have either been taken for granted, or
have been confined to notions of status quo stability or incremental change. Accordingly, the stages of collapse and
reorientation have been framed out of consideration and widely ignored. We use the adaptive cycles as a conceptual
metaphor to understand change in farming systems. While in practice it may be difficult to observe all phases in
farming systems (Van Apeldoorn et al., 2011), understanding adaptive cycles improves understanding of resilience
(Carpenter et al., 2001). For instance, while many agricultural sectors seem persistent, drastic system changes
(regime shifts) within one generation (Cumming and Peterson, 2017) may be the result of a tipping point.
2.1 Three main processes
Studies assessing the resilience of agricultural systems mostly focus on agricultural production processes (e.g. Porter
et al., 2013) and generally find that diverse systems are more resilient to variability (e.g. Reidsma and Ewert, 2008;
Gil et al., 2017 ). However, in practice, changes in among others technology, markets and policy resulted in larger
and more specialised farms (Andersen et al., 2007; Mandryk et al., 2012). Hence, developments in agriculture can
only be understood if multiple processes are considered simultaneously. We emphasize three main adaptive cycle
processes that are essential for EU farming systems: next to agricultural production processes, these are farm
demographics, and governance processes (Fig. 1).
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Agricultural production is defined as the agricultural and multifunctional activities undertaken by farms leading to
the provision of private and public goods, such as the provision of food and fibre, regulating services for climate
change mitigation and clean water as well as cultural services such as landscapes (e.g. Reidsma et al., 2015). Farm
demographics concern the provision of labour to farming systems, capturing both farm populations and hired labour
force. Governance is defined as the organization and process of societal steering
through a mixture of
communicative, and regulatory steering mechanisms, ultimately aimed at the realization of collective goals (Kjaer,
2004). In our framework, governance embraces elements of the Common Agricultural Policy (CAP) and its national
transpositions, public and private regulations affecting agricultural production chains, and public and private risk
management strategies. Other processes affecting the performance of farming systems, such as local infrastructure
and culture, are incorporated as farming system characteristics.
2.2 Stages of adaptive cycle processes
In reality, the processes depicted in Fig. 1 are not as regular as this conceptual representation suggests. However,
in the domain of each constituent adaptive cycle, indications of the main stages can be distinguished. Adaptive
agricultural production cycles have been analysed since the recognition of the hog cycle and consequent
development of the cobweb model (Hanau 1927, Ezekiel 1928). However, the emergence of adaptive cycles goes
far beyond these patterns of price and supply responses that are found in farm sectors with delayed supply
responses. An illustrative example is the pig production sector in North-Western Europe, which saw enormous
growth in the past due to increasing connectedness to global input and output markets (Assefa et al., 2017a).
However, over recent years conventional pig production lost connectedness with markets and society due to a
variety of factors, including a higher valuation of animal welfare, environmental problems and political
developments (e.g., the Russian embargo). As a result, many farms quit the market through loss of perspectives or
even bankruptcy (collapse) or more orderly professional reorientation . This phenomenon extends not just to
Figure 1: The resilience concept for farming systems.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
“factory farms”, but also to farms which use relatively traditional production techniques. On the other hand, the EU
ban on battery cages for laying hens in 2012 and the delisting of eggs from such facilities by major retailers in
countries such as the Netherlands and Germany several years before led to the exit of “battery egg” production on
a massive scale, whereas many family-based businesses used this opportunity for reorientation towards alternative
technologies (Spiller et al., 2015). Besides such involuntary developments, farmers in various branches of
production, along with value chain actors, have transformed their production systems by endowing their products
with credence characteristics (such as organic and local food), thereby finding new ways to connect to markets and
society, and new potential for growth.
On the global scale, agricultural price spikes in 2008 and 2011/12, led to increasing concerns that the process
of the technological treadmill may have slowed down, and agricultural production may no longer grow as fast as
demand (e.g., von Witzke, 2008). These price spikes were accompanied by substantial fluctuations of prices of
energy and fertilizers . Whilst these increased production costs, they also revealed new potential markets for non-
food agricultural products, including bioenergy and other alternative feedstock sources . Equally, recent subsequent
declines in energy and food prices show that farmers as well as the up- and downstream sectors need to be aware
of and able to respond rapidly to substantial uncertainties. While these uncertainties require appropriate risk
management strategies, the price fluctuations may nevertheless indicate that in the medium or long term the
bioeconomy may provide particular new growth perspectives.
Regarding demographics, the process of the farm population as well as the labour force is also affected by
several overlapping cycles at various scales. The most obvious is the generational cycle within the farm family.
Employed labour force and the management of corporate farms are also affected by similar processes of
generational renewal. In every new generation of a family or turnover of employees (especially managers) of a
corporate farm, decisions are necessary on whether to continue and how to adapt the organisation of the farm to
changing needs and abilities, especially as farming is often perceived as bound to limiting conditions (or push factors
of farm exit), such as low income, long working hours, remote locations and often high personal financial risks
(Huber et al., 2015). Farm demography is also affected by technological cycles, both within the sector and outside.
Cochrane’s (1958) model of the technology treadmill describes how farmers have either to adopt a new technology
(growth) or suffer from decreasing incomes that might finally lead to market exit that occurs in extreme cases
through bankruptcy (collapse) while in others through involuntary or consciously planned professional reorientation
(push factor). A conscious reorientation is more likely when wages outside agriculture are attractive (a pull factor)
and farm employees have convertible skills. At the farming system level, technological progress not only reduces
total labour input, but also results in an increasing capital to labour ratio, which in turn requires beyond necessary
financial resources and more efficient use of labour, specialised operator skills and improved farm management
capacities. Such a development can enable growth of production and per capita income. However, accumulation of
push and pull factors in combination with demands for highly specialised skills may result in a structural deficit of
farm successors and skilled farm labour, which could trigger reorientation or even collapse of regional farming
systems. Such a reorientation can include seasonal and permanent migration of farm labour and farmers, such as
the establishment of new farms in East Germany and other former socialist countries after 1990 by farmers
migrating from other EU regions and countries, such as West Germany, the Netherlands and Denmark. Farm
structural change has also led to a decrease in medium-sized and mixed farms towards more large-scale and
specialised farms (Mandyk et al., 2012; Van Vliet et al., 2015).
Regarding governance, processes occur and interact at different spatial levels from local/regional to national,
EU and international. At the EU level, we can distinguish two adaptive cycles. The first one applies to the European
Union’s CAP as a system of farm support (see Feindt, 2010; Knudsen, 2009; Termeer et al, 2015). The establishment
of the CAP scheme in 1962 enabled a period of growth. The mixture of import levies, export subsidies, and
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
guaranteed prices led to a modernisation of production systems, increased production, sufficient farmers’ income
and enough food at affordable prices. However, the high guaranteed prices also resulted in overproduction, which
required ever more extensive market interventions, causing an increasing budget deficit. By the mid-1980s,
repeated budget overruns even threatened a collapse of the CAP. A sequence of piecemeal adjustments measures
of the CAP– milk quotas, budget stabilisers and set-aside – resulted in an equilibrium stage, although not one that
was self-stabilising. It bought time but did not provide a structural solution for the vicious cycle of overproduction,
market intervention and budget deficits. Furthermore the CAP became increasingly criticized for its distorting
effects on international markets and its negative side effects such as landscape distortion and environmental
pollution. The decreasing public legitimizing of tax money flowing to farmers threatened a collapse of the CAP again.
Since 1992, the CAP entered a phase of reorientation, through the sequence of serious CAP reforms in 1992
(McSharry), 1998 (Agenda 2000), 2003 (Fischler) and 2013 (Ciolos), interspersed with periods of equilibrium. Political
pressure on the large CAP budget and questions about its effectiveness in delivering sustainable and resilient
farming systems suggest that another stage shift towards reorientation of the CAP is not unlikely.
The second adaptive cycle in governance at EU-level pertains to the regulatory framework. Here EU
agricultural sectors experienced an equilibrium with state-led food safety regulation and light-touch environmental
regulation until the 1980s. This was followed by several stages of regulatory growth: the adoption of diverse
environmental directives (the Birds, Habitats and Nitrates Directives, respectively), food traceability regulation,
genetically-modified organisms regulation and cross-compliance mechanisms. This growth path was punctuated by
instances of reorientation, in particular the rise of private standards (e.g., GlobalGAP), public-private co-regulation
and the move towards a comprehensive system of food risk management. Rising concerns about high transaction
costs, competitiveness, and the effectiveness and efficiency of the regulatory framework make a shift towards
another stage of reorientation likely.
With regard to risk management, strategies at EU level mainly pertain to the management of price and
production risks. For normal risks, which can be dealt with at farm level, risk management historically focussed on
farm diversification with farms having both crop and livestock activities (equilibrium). At the end of the 19
in North-West Europe this was augmented with mutual insurance schemes covering specific risks such as cattle
disease and hail damage (Interpolis, 1976), and, mostly after the 1930s, cooperatives which enabled to pool price
risks among members (Fernández, 2013). With post-war increasing levels of farm specialisation, risk-sharing
strategies became increasingly important, including contracts, financial leverage, commercial insurance and
exchange of farmland (Meuwissen et al., 2001) (growth). From the 1980s onwards, vulnerability of specialised farms
was among the reasons for certain farms to ‘reinvest’ in farm diversification (reorientation), initially focussing on
multifunctional activities, such as agri-tourism and nature conservation (Van der Ploeg and Roep, 2003), and
currently further stimulated towards the production of energy and processing of waste (DG Agri, 2017a). Also
diversification through off-farm income plays a vital role for several farms (e.g. Mishra and Goodwin, 1997,
McNamara and Weiss, 2005, Lien et al, 2010, Meraner and Finger, 2018). Alongside, specialised farms showed
increasing interest in risk-sharing agreements, both with other farmers, e.g. through price pools (Van Asseldonk et
al., 2016) and disease mutuals (Meuwissen et al., 2013), as well as with other chain actors (Assefa et al., 2017b)
(growth). Factors driving this interest included decreasing intervention prices (DG Agri, 2017b), decreasing support
from governments in case of epidemic disease outbreaks (Meuwissen et al., 2003), and increasing concentration
rates in other stages of the chain (AMTF, 2016). In addition, since 2009, the CAP has introduced various risk
management measures to support farmers’ uptake of insurance and mutual funds (DG Agri, 2017b), e.g. with
subsidies for multi-peril crop insurance being extended to 70% as of 2018 (EC, 2017). Yet, as uptake of risk
management instruments is expected to stay relatively low, some reorientation is likely, incentivized by smart
technology, e.g. to reduce transaction costs (Meuwissen et al., 2018), joint learning and co-creation to activate
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
farmers to engage in joint initiatives such as producer organisations and mutual funds, and promoted capacity
building, e.g. with regard to insurance and futures contracts (DG Agri, 2017a). With regard to the management of
catastrophic risks, the adaptive cycle includes the already mentioned changes of intervention prices and CAP risk
management measures over time, but also pertains to the provision of ad-hoc disaster aid. With regard to the latter,
large differences exist between member states depending on political context and risk environment (OECD, 2009).
However, given the growing support for risk management instruments in the CAP, member states increasingly
develop public-private schemes to deal with catastrophic risks, e.g. in Finland (Liesivaara et al., 2017), indicating a
phase of reorientation. Some member states even completely abandoned the provision of disaster relief for risks
which can be insured through public-private partnerships, as illustrated during the 2017 extreme weather events in
the Netherlands (Van Asseldonk et al., 2018).
2.3 Processes are interrelated
Phase shifts in one cycle can either suppress or accentuate the dynamics of the other cycles. For example, farm
demographics are directly influenced, not only by agricultural policies, such as early retirement or new entrant
schemes, but also indirectly by regulations on international labour migration and differing national taxation rules
for the capital transfer involved in intergenerational hand-over. Correspondingly, the seasonality of agricultural
production links with farm demographic processes, especially peak labour requirements driving the (seasonal)
movements of the labour force. In turn, agricultural production processes also interrelate with policy. For instance,
local contexts cause differences in local transpositions of agro-environmental and risk management tools of Rural
Development Programmes.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
3 The resilience framework and its components
The framework essentially aims at understanding the dynamics of a farming system’s provision of its essential
functions while facing a number of challenges, i.e. resilience does not reflect separate properties of a system, but
describes the dynamics of its sustainable performance and the attributes contributing to these dynamics. Following
Folke et al. (2010) we interpret the dynamics of the adaptive cycle stages described in Section 2.2 (growth,
equilibrium, collapse, reorientation) along a scale of the following resilience types: robustness, adaptive capacity
(adaptability) and capacity to transform (transformability). Robustness is the ability to maintain desired levels of
outputs despite the occurrence of perturbations (Urruty et al., 2016). Adaptability is the capacity to adjust responses
to changing external drivers and internal processes and thereby allow for development along the current trajectory
while continuing important functionalities (stability domain) (Folke et al. 2010). Transformability is the capacity to
create a fundamentally new system when environmental, economic, or social structures make the existing system
untenable in order to provide important functionalities (Walker et al. 2004). Transformability is less about planning
and controlling but more about preparing for opportunity or creating conditions of opportunity for navigating the
transformations (Folke et al. 2010, citing Chapin et al. 2010). Socio-ecological systems can sometimes be trapped in
very resilient but undesirable regimes in which adaptation is not an option. Escape from such regimes may require
large external disruptions or internal transformations to bring about change.
Before analysing a system’s dynamics and resilience attributes, one must first specify the system boundary
and its configuration (‘resilience of what’), the challenges of interest to the system (‘resilience to what’), and the
essential functions of the system (‘resilience for what purpose’), see also Carpenter et al. (2001) and Herrera
(2017). The various steps of the framework, including example indicators describing the dynamics (resilience
indicators), and attributes contributing to these dynamics (resilience attributes) are outlined in Fig. 2. The lower
part refers back to the main processes outlined in Section 2 and provides the basis for the attributes. Example
attributes are derived from cycle phases described in Section 2.2 and from the general resilience attributes
described by Cabell and Oelofse (2012). The various phases of the framework are explained below, i.e. (1)
characterising the farming system, (2) appraising key challenges affecting the system, (3) framing the essential
functions of the system, (4) assessing resilience along the scale of robustness, adaptability and transformability, and
(5) identifying resilience attributes which contribute to the robustness, adaptability and transformability of the
farming system.
System characteristics, challenges, and interpretation of attributes are dynamic concepts (complex systems
continually change). Even the essential functions of farming systems change over time. Such dynamics complicate
the analyses of resilience indicators and the identification of resilience enhancing attributes. To provide meaningful
suggestions towards resilience enhancing attributes for EU farming systems it is therefore important to first describe
and understand phases (1) to (3) for a specific trajectory before proceeding to the analysis of resilience indicators
and attributes of the trajectory.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
3.1 Characterising the farming system (resilience of what)
The type of challenges a system is facing, as well as its response are largely affected by the characteristics of the
system. Characterising the system is therefore the first step in our framework presented in Fig.2. This entails a
description of key system characteristics such as farm types (Andersen et al., 2007; Andersen 2017), institutions in
place, the agro-ecological context, (dis)connects related to the system’s essential functions (Cumming et al., 2014),
and the identity of the system (Cumming and Peterson, 2017).
Key actors within the system boundary are identified using the following selection criteria, i.e. the boundary
of a farming system is such that we include actors who influence farms, and, conversely, farms also influence these
actors. In contrast, we exclude actors who influence the farming system, but who are themselves scarcely influenced
by the system. Fig.3 provides an example farming system for a specific trajectory. In this example financial
institutions (banks, insurers etc.) will influence the farming system, but the farming system barely influences the
Figure 2. Framework to analyse the resilience of farming systems,
including example resilience indicators and attributes.
1. Farming
Robustness Adaptability Transformability
4. Resilience
5. Resilience
Recovery rat e; degree
of return
Scope for changing identity
Safe operating spac e
Public goods
Private goods
3. Essential
realignment Learning
Resources to implement
sustainable production Farm heterogeneity
Flexibility wrt
markets Diversity of institutions
2. Challenges
Impact on
Institutions (Dis)connects
Expo sed to
Resilience enhancing
Agro- ecological
context Identity
Region diversity and
Redundant sto ck Multiple sources of risk
High levels of natural, s ocial,
human and financial capital
Opportunities for
Open attitude to
Financial support Human capital and
knowledge management
Flexibilit y wrt norms and
regulatory frameworks
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
bank – so, the bank is not included in the system. In contrast, the local credit union is strongly affected by the farms
so is included in the farming system boundary. Also, in this example, farm households are part of the farming system
as is mostly true for family farms, i.e. family members play an important role in the provision of capital, labour and
land to the farm. Farm households’ influence would be less if family members including the farmer provide most of
their resources outside the farm. The pluri-active nature of farm households was described by e.g. Hansson et al.
(2013). The example also illustrates that actors in farming systems can also refer to actors who influence and
develop policies, such as ministries, EU institutions, lobbyist, farmers’ organisations, and environmental
Figure 3: Selection criteria to identify actors within the system boundary of a farming system,
incl. example actors.
With regard to the agro-ecological context, key characteristics at system level include climatic conditions (e.g.
Metzger et al. 2005; Van Wart et al. 2013) and soil conditions (e.g.Hazeu et al. 2010; Hijbeek et al. 2014; ESDAC,
2017). Regarding the identity of the system, Cumming and Peterson (2017) refer to a system’s identity as “key
actors, system components, and interactions”. They also mention the subjective nature of it (“[..] although
subjective, it is not arbitrary; it requires establishment of key criteria [..]”).
3.2 Key challenges (resilience to what)
To identify the variety of challenges farming systems are confronted with, we categorise the challenges along four
dimensions, i.e. economic, environmental social and institutional risks. Also, we distinguish two ways of how these
Policy makers
(food policy)
Indirect influence
Actors who influence
the farming s ystem,
but who are t hemselves
scarcely influenced
by the sy stem
Actors who influence
farms, and, c onversely,
farms also i nfluence
these actors
Sele ction criteria:
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
challenges affect farming systems: as shocks, or as a long-term pressure with inherent uncertainties. Adapted from
Zseleczky and Yosef (2014), we define a shock as a sudden change in the risk environment of a farming system that
influences (part of) the farming system on the short term through negative effects on people’s current state of
well-being, level of assets, livelihoods, or safety, or their ability to withstand future shocks. Examples of shocks are
extreme price drops (economic risk), extreme weather events (environmental risk), sudden changes to on-farm
social capital due to illness, divorce, or stress regarding ownership or succession (social risk), and geopolitical issues
such as the Russian boycot (institutional risk). In contrast, long-term pressures refer to stressors slowly changing
the context of a farming system, inherently leading to new uncertainties (adapted from Zseleczky and Yosef, 2014).
There are ample examples of long-term pressures affecting farming systems, such as reduced availability of finance
(economic risk), hydro-geological disturbances (environmental risk), demographic changes including rural
outmigration (social risk), and changing policy objectives (institutional risk). Future impact studies often focus on
long-term pressures (e.g. Porter et al. 2013 for climate change), while shocks may have more severe impacts (e.g.
Schaap et al. 2013 for extreme climate events in the Netherlands). Distinction between various dimensions and
sub-classifications (shock, long-term pressure) is somewhat arbitrary, but the classification can be useful as a
‘checklist’ (Annex 1).
3.3 Essential functions of the farming system (resilience for what purpose)
Depending on a system’s location (e.g. close to a city centre, or remote), system functions may differ. Furthermore,
institutional discourses on sustainable development espouse different sustainable development principles even
though the general consensus as quoted in the Brundtland Report (1987), i.e. ‘development that meets the needs
of the present without compromising the ability of future generations to meet their own needs’ (quoted in
Robinson, 2004: 227) may be the same. This is useful to recognise at a farming system level to understand the
variety of ‘essential functions’. In general, functions can be subdivided towards the provision of private goods and
public goods. Private goods refer to (i) the availability of healthy and affordable food products, (ii) the availability of
other bio-based resources for the processing sector, including fuels and fibres, (iii) the economic viability of farm as
viable farms contribute to balanced territorial development, and (iv) improved quality of life by providing
employment and offering decent working conditions. Public goods refer to (i) maintaining natural resources in good
condition, (ii) protecting biodiversity of habitats, genes and species, (iii) ensuring that rural areas are attractive
places for residence and tourism, and (iv) ensuring animal health and welfare. We define these functions at the level
of farming systems (not farms), implying that the framework is not primarily aimed at preserving individual (family)
farms. Although the interaction between the provision of various functions can provide significant synergies for
farming systems, they are not always mutually supportive as there can be conflicts between e.g. social and economic
dimensions and there are often trade-offs involved. Thus, the level of interdependency can vary according to the
farming system and its system boundary. This means that each farming system has a level of sustainability which is
relative to its own target functions and depending on system-specific interactions.
Multiple indicator frameworks exist to asses a system’s performance regarding the essential functions (e.g.
Alkan Olsson et al. 2009, Van Asselt et al. 2014). We use EC (2001) and SAFA guidelines (FAO, 2013) as a basis,
augmented with own elaborations as indicated in Annex 2. In order to select the indicators measuring the
performance of farming systems the first step is to identify and prioritise functions related to the provision of private
and public goods and, as a second step, combine the functions with the relevant indicators, which are function and
farming system specific. Then for the further selection of indicators, three principles can be applied. The first
principle to consider is the type of challenge affecting the farming system. Different challenges can have different
durations of impact (short- or long-term) therefore different indicators are needed to assess the potential
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
sustainability impact. For example, for shocks such as extreme weather events an indicator such as productivity
(t/Ha) can be appropriate, while for a long-term pressure such as climate change an indicator on soil erosion and/or
water quality might be more appropriate. The second principle that can be considered is linked to the use of
resources (human, natural and economic). Such resources are farming system specific. For example, for the large
scale production of arable crops in the East of England key resources are labour, land and technology. Each can be
assessed from different functions point of view, hence using different indicators. For instance, regarding land,
indicators could refer to landscape maintenance (to reflect attractive rural areas), water and soil quality (as an
indicator for maintained natural resources), and % land tenure (as an indicator for economic viability). Finally, a
third principle is the efficiency with which the outcome of the farming system is obtained. In the case of arable
crops, efficient outcomes for the resilience of the farming system can be the following: number of jobs created
(quality of life), populations of key farmland animal and plant species, e.g. birds, butterflies, meadow plants
(biodiversity), and liquidity and profitability (economic viability of farms). Essential functions may change over time.
Also, there may be functions which could be provided by other systems.
3.4 Resilience indicators
Upfront classification of a system or nested subsystem into stages of robustness, adaptability or transformability is
not straightforward. Instead, it seems better to start exploring (i) the dynamics of the essential functions
(robustness) [Fig4.], (ii) the relation between risks (shocks, long-term pressures) and responses (adaptability) [Fig.5],
and (iii) the occurrence of tipping points (drastic system changes, regime shifts within one generation, changed
identity) (transformability) [Fig.6].
Figure 4: Exploring robustness of farming systems, i.e. ability to maintain desired levels of outputs despite the
occurrence of perturbations (Urruty et al., 2016). The figure is adapted from Scheffer et al. (2012). The
recovery rate shows that system functions (e.g. aggregate amount of food produced, or consumer trust)
recover in 3 time steps after the shock, while degree of return indicates that system functions come back to
their original level.
System s tate
Reco very r ate Degree of return
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Figure 5: Exploring adaptability of farming systems, i.e. the capacity to adjust responses to changing external
drivers and internal processes thereby allowing for development along the current trajectory while continuing
important functionalities (stability domain) (Folke et al. 2010). Figures are adapted from Scheffer et al. (2015)
and Carpenter et al. (2017). A local response that is currently at a safe level (I) needs to be adjusted to a lower
value to keep the system within the safe operating space in a future more risky environment (II). The response
may need to be adapted again once risks further increase (III). For instance, a high degree of individualistic
behaviour of farmers (I) may suffice for a viable sector income if market power of upstream and downstream
actors is relatively low. However, with increasing market power and reducing mechanisms of risk-sharing, the
high degree of individualism becomes untenable and needs to be adapted to lower levels (II), implying more
connectedness among the actors in the system. Too much connectedness may however suffocate innovations
therefore requiring yet another response (III) once market power further increases.
Respo nses
Respo nses
Continuing important sy stem functions
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Due to complexity of farming systems (e.g. Fig. 3), cycles can also occur at smaller scale (nested subsystems). It is
therefore also meaningful to explore robustness, adaptability and transformability (Fig 4, 5 and 6 respectively) at
e.g. farm or farm household level.
3.5 Resilience attributes
Resilience attributes contribute to the resilience of farming systems; they improve the resilience indicators. For
instance, they determine the speed of recovery in Fig. 4, the variety of responses in the safe operating space (Fig.
5), or the pace at which a system can transform after a collapse (Fig. 6). Cabell and Oelofse (2012) identified 13
general attributes contributing to the resilience of agroecosystems, i.e. (i) socially self-organised networks of e.g.
farmers, consumers and the community, (ii) ecological self-regulation, e.g. by farmers maintaining plant cover and
incorporating more perennials, (iii) appropriately connected, e.g. crops planted in polycultures and collaboration
Figure 6: Exploring transformability of farming systems, i.e. the capacity to create a fundamentally new
system when ecological, economic, or social structures make the existing system untenable in order to
provide important functionalities (Walker et al. 2004). The figure is adapted from Cumming and Peterson
(2017). System functions after transformation (II) may diverge from functions in the previous system (I). A
system is characterised as transformed if its identity (II) differs from the previous identity (I
). Transformation
may be the result of collapse (III) after a tipping point, or more gradual change (IV). Collapse is subjectively
defined as loss of identity and persistent change in one or more key capitals, which could encompass social,
financial, natural, built, or other forms of capital (Cumming and Peterson, 2017). Collapse is often preceded
by vulnerability of the system (V).
System state
Tipping point
System is
are unstable
functions after
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
between chain actors; (iv) functional and response diversity, e.g. by heterogeneity within landscapes and farms; (v)
optimal redundancy, i.e. planting multiple varieties of crops, keeping equipment for various crops, and retrieving
nutrients from multiple sources; (vi) spatial and temporal diversity, e.g. by a mosaic pattern of managed and
unmanaged land and diverse cultivation practices; (vii) exposed to disturbance, dealt with by e.g. pest management
and positive selection; (viii) coupled with local natural capital, e.g. by not depleting soil organic matter, and little
need to import nutrients or export waste; (ix) reflective and shared learning, e.g. by record keeping and knowledge
sharing between farmers; (x) globally autonomous and locally interdependent, e.g. by less reliance on commodity
markets and reduced external inputs, more reliance on local markets, and shared resources such as equipment; (xi)
honors legacy, e.g. by incorporating traditional cultivation techniques with modern knowledge; (xii) building human
capital, e.g. by investing in infrastructure for education, and support for social events in farming communities, and
(xiii) reasonably profitable, implying that farmers and farm workers earn a liveable wage, and the agricultural sector
does not rely on distortionary subsidies. These 13 attributes are built on >50 references discussing resilience at
various scales including farm (Darnhofer, 2010) and socio-ecological systems (Folke et al., 2010).
In our framework we aim to further specify how these attributes contribute to specific types of resilience,
i.e. robustness, adaptability and transformability. In addition, although links between some resilience indicators and
attributes have been proven (e.g. Gil et al., 2017; Cabell and Oelofse, 2012), this does not imply that such links are
always and universally applicable. We therefore suggest that such links need to be empirically tested under different
conditions. We focus on attributes closely fitting to the 3 main processes in this paper, i.e. agricultural production,
farm demographics and governance processes. We expect that attributes are relatively more complex and intense
in case of transformability compared to robustness. This is illustrated in Fig. 2 by a number of example attributes.
For instance, for learning, an important component for resilience building (Cundill et al. 2015; de Kraker 2017). In
different stages of the adaptive cycle, learning plays different roles, including incremental innovation towards
further growth (‘single-loop learning’), more radical innovation in response to crises in the system (‘double-loop
learning’) and transition to a different state or major realignment, such as transforming to organic farming practices
(‘triple-loop learning’). (Compare with aggregate attribute [ix] of Cabell and Oelofse (2012), i.e. reflective and shared
learning). The second illustration in Fig. 2 refers to the way agricultural production is organised, i.e. as having
resources for implementing sustainable production, with farms being diverse among themselves and having
different production systems, technologies and products (farm heterogeneity), or with the whole region being
biodiverse and having different sources of ecosystem services and redundancy of species (region diversity and
redundancy). The third example relates to networks, ranging from opportunities for collaboration reflecting a
situation in which relationships among farmers support collaboration and exchange of resources, stakeholder
engagement in which farmers are willing to collaborate among themselves and with government institutions, and
open attitude to innovation with stakeholders willing to innovate, trying new processes and participating in new
markets. The fourth example in Fig.2 is on governance, with attributes ranging from financial support to ensure that
farmers have access to cash when needed with government and financial institutions working together in offering
credits and subsidies, human capital and knowledge management through formal and informal institutions
fostering dialogue and helping to spread knowledge among farmers, and flexibility in which norms, legislation and
regulatory frameworks are flexible enough for allowing experimentation and innovation. The fifth example is on
diversity, with attributes ranging from enterprise diversification to increase firms’ ability to cope with shocks,
flexibility e.g. regarding markets, and diversity of institutions, enabling the generation of new goods and services.
The final example is on resources, varying from keeping redundant stock to cope with supply disruptions, availability
and use of multiple sources of risk management including joint risk prevention and risk pooling initiatives, and high
levels of natural, social and human capital to foster reorientation towards new identity after collapse. The selection
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
of attributes will always reflect aspirations for the specific type of farming system and resilience in a given case
While resilience attributes might be studied in isolation, we argue that the complexity of farming
systems requires an integrated consideration. Fig. 7 presents an integrated analysis of selected attributes from Fig.
Figure 7: Interconnections and synergies between selected resilience attributes.
Note: Green (Farm demographics), Orange (Agricultural Production), Blue (Governance)
An integrated assessment allows to understand synergies among attributes. For instance, landscape heterogeneity
in organic farming contributes to enhancing biodiversity (Rader et al., 2014). Simultaneously, genetic diversity offers
farmers opportunities for incorporating different techniques, introducing different species and overall for
configuring their farms in ways that suit better their particular landscape. These potential synergies are not
restrictive to natural resources but also include social, organizational and economic attributes. For instance,
participatory processes and stakeholder engagement result in the creation of knowledge and human capital that
can be used to experiment and transform traditional farming systems. At the same time, flexibility also requires
new knowledge and closer interaction between farmers (Sherman, 2014).
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
An integrated approach also demonstrates trade-offs between different attributes and resilience indicators. For
example, an increase in farm heterogeneity might have adverse effects on farm productivity and consequently on
farmers’ financial liquidity. While heterogenic farms might be able to adapt better they will probably be less robust
and more likely to show some visible changes in their own dynamics during a shock.
Retrieving attributes through co-creation
Although resilience attributes might be retrieved from retrospective (time series) analyses within specific
trajectories and boundaries (Reidsma et al., 2010; Martin et al., 2017), the changing risk environment faced by EU
farming systems may also require new responses to enhance robustness, adaptability and transformability fitting
the new situation. Due to the importance of diversity and stakeholder engagement in many of the proposed
resilience attributes, co-creation seems a suitable approach to identify such attributes. Co-creation refers to the
participation of end-users in the product and services definition (Von Hippel, 1987), more recently also applied by
companies to design value adding strategies (Prahalad and Ramaswamy, 2004) and by the public sector in the policy
making process (Voorberg and Tummers, 2014). The Resilience Alliance (2010) proposed a participatory approach
to assess resilience, including the identification of resilience attributes. In our framework, this approach will be
merged with the Framework for Participatory Assessment (FoPIA; Morris et al., 2011; Konig et al., 2013) in order to
not only assess resilience, but also the impact on public and private goods.
The principle of co-creation is the process of creating new products, strategies and policies with people
affected by certain challenges and not for them. The co-creation call is addressed to a small group of people with
specialized skills and shared interests on the challenge to deal with. Different strategies have been developed and
proven successful to co-create: (i) organizing face to face workshops; (ii) planning big date events, and (iii) creating
virtual platform/community. Regarding the latter, the development of new technologies facilitates developing
virtual communities to bring together skilled people to discuss on topics related to their knowledge and expertise.
The virtual platforms provide an excellent climate to enable the members of the community to submit contributions,
comments on contributions from other participants, rate and vote.
In the context of farming systems a virtual co-creation platform is developed. The access to the virtual
platform is private and limited to a selection of stakeholders. Between 40 and 60 key senior opinion stakeholders
with proven experience in agricultural production, farm demographics, and governance issues are convened to
participate in the virtual platform to co-create breakthrough solutions from a broad approach. A balanced selection
of all the stakeholders - farmers’ organizations, insurance companies, banks, value chain actors, policy makers,
environmental and consumers’ organizations and research institutes - is ensured as well as the geographical origin
from EU Member States. The co-creation process follows the design thinking approach that comprises five phases:
(i) gather information (which is the need); (ii) generate ideas (get breakthrough ideas); (iii) Make ideas tangible
(learn how to make ideas better) and (iv) share the story (inspire other towards action) (Brown and Wyatt, 2010).
The design thinking process is led by an expert who is in charge of encouraging the participation of the community
members. With this purpose different activities can be performed in the co-creation platform in addition to the
debates, like sharing videos, pictures, documents and participating in gamification features like questionnaires,
points and levels and weights.
The results obtained through the virtual co-creation platform are discussed at local
level by organizing local co-creation workshops in the case study regions. The stakeholders identified within the
farming system boundaries in case study regions (Figure 3) are invited to participate in the local co-creation
workshops. With this double-level co-creation process, the feasibility of the virtual co-creation platform results is
tested at case study level.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Lack of the type of attributes described in Fig 2. may not necessarily lead to system collapse. Nevertheless, Cumming
and Peterson (2017) describe among others the following mechanisms contributing to vulnerability (Fig. 6) and
collapse: ecological degradation and excessive resource consumption, too much complexity, sunk cost effects, lack
of diversity, and external disruptions. Sunk cost effects were also described by Williamson (1987): a high level of
asset specificity could reduce the scope for adaptation and transformation. As it well known, economic agents
commonly have a choice between special purpose and general purpose investments. While the former could permit
larger cost savings than the latter form of investment, special purpose investments are also risky. This is because, if
new conditions arise, specialised assets cannot be redeployed without sacrifice of productive value (Williamson,
1987). This causes imperfect resource mobility and reduces the room for transformation.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
4 Discussion and conclusions
Farming systems are complex systems characterised by among others institutions and agro-ecological context.
Previous resilience frameworks do not sufficiently capture this complexity. Also, previous frameworks do not
distinguish between different types of resilience thereby diminishing the spectrum of solutions to enhance
resilience. We distinguish 3 main adaptive cycle processes pertaining to farming systems, i.e. agricultural, farm
demographics and governance processes, and build our framework around three types of resilience: robustness,
adaptability and transformability. Exploring resilience is preceded by defining spatial, functional and temporal
Preliminary applications of the framework in the SURE-Farm case study regions illustrates that the
framework can be applied in a farming system context enabling to explore robustness, adaptability and
transformability to specific risks. Preliminary case work however also illustrates the difficulties in applying the
framework. For instance, key risks and essential functions depend on stakeholder perspectives and e.g. the
occurrence of recent shocks, thereby reinforcing the need for clarity on spatial, functional and temporal boundaries
of the system under consideration. A further challenge of the framework is the identification of meaningful
indicators to reflect the performance of a selected system function over time. Single parameter indicators likely
forego the complexity of farming systems including historical context. We may therefore need to move to composite
indicators or simultaneous analysis of multiple indicators. A further issue is that example attributes shown in Fig. 2
‘start to live their own live’, i.e. they are taken for granted without being verified yet. For example, it has been often
shown that the attribute diversity contributes to improved resilience to climate variability and change (Gil et al.,
2017). However, this is not the case in all situations, and resilience to climate variability and change does not
necessarily imply that systems are also resilience to technological change. Although these example attributes are
useful for prototyping, exact interpretations in different context need further elaboration and evidence. In addition,
this elaboration will be challenging in itself as most attributes (especially under adaptability and transformability)
are slowly changing variables (Carpenter et al. 2011). “[..] Learning about slow variables takes a long time, so it is
easy to miss important processes or focus attention on the wrong hypotheses”. Also, preliminary case work
illustrates that different types of resilience can occur simultaneously. Before concluding on ‘the resilience of a
system’, it needs to be understood which types of resilience best suit certain risks and system functions.
In current literature the word of ‘resilience’ is used abundantly. Our framework provides structure and
definition to this multi-facetted concept. It also stipulates a structure to discuss sensitive topics among stakeholders,
such as professional reorientation, negative feedbacks between attributes, stakeholder awareness and action plans
regarding resilience of farming systems, and disputes about short-term versus long-term solutions. Due to its
multiple entry points (e.g. top-down and bottom-up) we expect that the framework can be used by researchers to
retrospectively understand the dynamics of sustainability in farming systems, and by decision makers to pro-actively
identify differentiated attributes, i.e. resilience-enhancing strategies, across EU farming systems depending on
context-specific risks and available resources.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
5 References
AMTF (Agricultural Market Task Force) (2016). Improving market outcomes; enhancing the position of farmers in the supply chain.
Andersen, E. (2017). The farming system component of European agricultural landscapes. Eur. J. Agron.82, 282–291.
Andersen, E., Elbersen, B., Godeschalk, F., Verhoog, D. (2007). Farm management indicators and farm typologies as a basis for
assessments in a changing policy environment. J. Environ. Manage. 82, 353-362.
Assefa, T.T., Meuwissen, M.P.M. and Oude Lansink, A.G.J.M. (2017b). Price risk perceptions and management strategies in selected
European food supply chains: An exploratory approach. NJAS-Wageningen Journal of Life Sciences,
Assefa, T.T., Meuwissen, M.P.M., Gardebroek, C. and Oude Lansink, A.G.J.M. (2017a). Price and Volatility Transmission and Market
Power in the German Fresh Pork Supply Chain. Journal of Agricultural Economics 68(3), 861–880.
Balmann, A., Dautzenberg, K., Happe, K., and Kellermann, K. (2006). On the Dynamics of Structural Change in Agriculture; Internal
Frictions, Policy Threats and Vertical Integration. Outlook 35(2), 115-121.
Binder, C.R., Feola, G., Steinberger, J.K., 2010. Considering the normative , systemic and procedural dimensions in indicator-based
sustainability assessments in agriculture. Environ. Impact Assess. Rev. 30, 71–81. doi:10.1016/j.eiar.2009.06.002
Brown, T., and Wyatt, J. (2010). Design thinking for social innovation IDEO. Development Outreach, 12(1), 29-31.
Brundtland, G. (1987) Our Common Future: The World Commission on Environment and Development. Oxford University Press, Oxford.
Bullock, J. M., Dhanjal-Adams, K. L., Milne, A., Oliver, T. H., Todman, L. C., Whitmore, A. P., & Pywell, R. F. (2017). Resilience and food
security: rethinking an ecological concept. Journal of Ecology, 105(4), 880-884.
Cabell, J. F., and Oelofse, M. (2012). An indicator framework for assessing agroecosystem resilience. Ecology and Society 17(1), 18.
Carpenter, S. R., Brock, W. A., Hansen, G. J., Hansen, J. F., Hennessy, J. M., Isermann, D. A., Pedersen, E.J., Perales, K.M., Rypel, A.L., Sass,
G.G., Tunney, T.D., and Vander Zanden, M.J. (2017). Defining a Safe Operating Space for inland recreational fisheries. Fish and
Fisheries, 18(6), 1150-1160.
Carpenter, S., Walker, B., Anderies, J. M., & Abel, N. (2001). From metaphor to measurement: resilience of what to
what? Ecosystems, 4(8), 765-781.
CBS (2018). Statline: Landbouw; gewassen, dieren en grondgebruik naar hoofdbedrijfstype, regio. Retrieved on 23-01-2018.
Chiswell, H.M., and Lobley, M. (2015). A recruitment crisis in agriculture? A reply to Heike Fischer and Rob JF Burton's understanding
farm succession as socially constructed endogenous cycles. Sociologia Ruralis, 55(2), 150-154.
Copus, A., Hall, C., Barnes, A., Dalton, G., Cook, P., Weingarten, P., Baum, S., Stange, H., Lindner, C., Hill, A., and Eiden, G. (2006). Study
on employment in rural areas: final deliverable. SAC.
Cumming, G. S., & Peterson, G. D. (2017). Unifying Research on Social–Ecological Resilience and Collapse. Trends in Ecology &
Evolution, 32(9), 695-713.
Cumming, G. S., Buerkert, A., Hoffmann, E. M., Schlecht, E., von Cramon-Taubadel, S., & Tscharntke, T. (2014). Implications of agricultural
transitions and urbanization for ecosystem services. Nature, 515(7525), 50.
Cundill, G. (2010) Monitoring social learning processes in adaptive comanagement: Three case studies from South Africa. Ecology &
Society, 15(3) p. 28.
Darnhofer, I. (2010). Strategies of family farms to strengthen their resilience. Environmental policy and governance, 20(4), 212-222.
De Kraker, J. (2017) Social learning for resilience in social-ecological systems. Current Opinion in Environmental Sustainability, 28, 100-
Demeter, R.M., Meuwissen, M.P.M., Oude Lansink, A.G.J.M. and Van Arendonk, J.A.M. (2009). Scenarios for a future dairy chain in the
Netherlands. NJAS 56(4), 301-323.
DG Agri, 2017a. Modernising and simplifying the CAP. Background Document Economic challenges facing EU agriculture.
DG Agri, 2017b. Risk management schemes in EU agriculture; dealing with risk and volatility.
EC (2017). EU farm policy rules to be further simplified.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
European Commission (2001). A Framework for Indicators for the Economic and Social Dimensions of Sustainable Agriculture and Rural
Development. DG Agriculture, available at
European Commission (2017). “Future of food and farming”, Communication of European Commission.
European Soil Data Centre (ESDAC),, European Commission, Joint Research Centre.
FAO (2013). SAFA guidelines, Sustainability Assessment of Food and Agriculture systems, version 3.0, available at
Fath, B. D., Dean, C.A., and Katzmair, H. (2015). Navigating the adaptive cycle: an approach to managing the resilience of social systems.
Ecology and Society 20(2), 24.
Feindt, P. (2010). Policy-learning and environmental policy integration in the common agricultural policy, 1973-2003. Public
Administration, 88, 296-314.
Fernández, E. (2013). Selling agricultural products: farmers' co-operatives in production and marketing, 1880–1930. Business History,
56, 547-568.
Fernández, E. (2013). Selling agricultural products: farmers' co-operatives in production and marketing, 1880–1930. Business History,
56, 547-568.
Fischer, H. and Burton, R.J. (2014). Understanding farm succession as socially constructed endogenous cycles. Sociologia Ruralis, 54(4),
Folke, C., Carpenter, S.R., Walker, B., Scheffer, M., Chapin, T., and Rockström, J. (2010). Resilience thinking: integrating resilience,
adaptability and transformability. Ecology and Society, 15(4), 20.
Ge, L., Anten, N.P.R., Van Dixhoorn, I.D.E., Feindt, P.H., Kramer, K., Leemans, R., Meuwissen, M.P.M., Spoolder, H., and Sukkel, W. (2016).
Why we need resilience thinking to meet societal challenges in bio-based production systems COSUST Current Opinion in
Environmental Sustainability 23, 17–27.
Gertel, J., and Sippel, S.R. (2016). The financialisation of agriculture and food. In: Shucksmith, M., and Brown D.L., (eds), Routledge
International Handbook of Rural Studies.
Gil, J.D.B., Cohn, A.S., Duncan, J., Newton, P., Vermeulen, S. (2017). The resilience of integrated agricultural systems to climate change.
Wiley Interdisciplinary Reviews: Climate Change 8.
Giller, K. E. (2013). Guest Editorial: Can we define the term 'farming systems'? A question of scale. Outlook on Agriculture, 42(3), 149-
Hansson, H., Ferguson, R, Olofsson C. and Rantamäki-Lahtinen, L. (2013). Farmers’ motives for diversifying their farm business – the
influence of family. Journal of Rural Studies, 32, 240-250.
Happe, K., Schnicke, H., Sahrbacher, C., and Kellermann, K. (2009). Will they stay or will they go? Simulating the dynamics of single-
holder farms in a dualistic farm structure in Slovakia. Canadian Journal of Agricultural Economics/Revue canadienne
d'agroeconomie, 57(4), 497–511.
Hazell, P., and S. Wood. (2008). Drivers of change in global agriculture. Philosophical Transactions of the Royal Society B, 363(1491),
Hazeu, G., Elbersen, B., Andersen, E., Baruth, B., van Diepen, K. and Metzger, M., 2010. A biophysical typology in agri-environmental
modelling. In Environmental and Agricultural Modelling (pp. 159-187). Springer Netherlands.
Herrera, H. (2017). Resilience for Whom? The Problem Structuring Process of the Resilience Analysis. Sustainability, 9(7), 1196.
Hijbeek, R., Wolf, J. and van Ittersum, M. (2014). Compatibility of agricultural management practices and types of farming in the EU to
enhance climate change mitigation and soil health. A typology of farming systems, related soil management and soil degradation in
eight European countries. CATCH-C Deliverable D, 2, p.242.
Holling, C. S., & Gunderson, L. H. (2002). Resilience and adaptive cycles. In: Panarchy: Understanding Transformations in Human and
Natural Systems, 25-62.
Holling, C.S., Gunderson, L.H., and Peterson, G.D. (2002). In: Gunderson L.H. and Holling C.S. (eds.): Panarchy: understanding
transformations in human and natural systems. Island Press, 63-102.
Huber, R., Flury, C., Finger, R. (2015). Factors affecting farm growth intentions of family farms in mountain regions: Empirical evidence
for Central Switzerland. Land Use Policy 47: 188–197.
Ilbery, B. and Maye, D. (2005) Food supply chains and sustainability: evidence from specialist food producers in the Scottish/English
borders. Land Use Policy, 22, 331-334.
Interpolis (1976). Van Onderlinge Waarborgmaatschappij tot Interpolis 1898-1978, Gedenkboek uitgegeven ter gelegenheid van 80 jaar
brandverzekering. Interpolis Tilburg, pp. 36.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Kirwan, J., Maye,
D. and Brunori, G. (2017)
Acknowledging complexity in food supply chains when assessing their performance and
sustainability. Journal of Rural Studies, 52, 21-32., A., Van Criekinge, T.,
and Malingreau, J.P. (2014). Global food security 2030. Assessing trends with a view to guiding future EU policies. EU Joint Research
Centre – Foresight and Behavioural Insights Unit.
Kjaer, A.M. (2004) Governance. Governance. Cambridge: Polity.
Knudsen, A. C. L. (2009). Farmers on welfare: The making of Europe’s common agricultural policy. Ithaca, NY: Cornell University Press.
König, H.J., Uthes, S., Schuler, J., Zhen, L., Purushothaman, S., Suarma, U., Sghaier, M., Makokha, S., Helming, K., Sieber, S., Chen, L.,
Brouwer, F., Morris, J., Wiggering, H. (2013). Regional impact assessment of land use scenarios in developing countries using the
FoPIA approach: Findings from five case studies. J. Environ. Manage. 127, S56-S64.
Ledogar RJ, Fleming J. (2008). Social Capital and Resilience: A Review of Concepts and Selected Literature Relevant to Aboriginal Youth
Resilience Research. Pimatisiwin. 2008 Summer 6(2), 25-46.
Lien, G., Kumbhakar, S. C., & Hardaker, J. B. (2010). Determinants of off-farm work and its effects on farm performance: the case of
Norwegian grain farmers. Agricultural Economics, 41(6), 577-586.
Liesivaara, P., Meuwissen, M.P.M., Myyrä, S. (2017). Government spending under alternative yield risk management schemes in Finland.
Agricultural and Food Science 26(4), 223-232.
Mandryk, M., Reidsma, P. and van Ittersum, M. K. (2012). Scenarios of long-term farm structural change for application in climate change
impact assessment. Landscape Ecology, 27(4), 509-527.
Martin, G., Magne, M.-A., Cristobal, M.S. (2017). An Integrated Method to Analyze Farm Vulnerability to Climatic and Economic
Variability According to Farm Configurations and Farmers’ Adaptations. Front. Plant Sci. 8. doi:10.3389/fpls.2017.01483
Maye, D. and Duncan, J. (2017) Understanding sustainable food system transitions: practice, assessment and governance. Sociologia
Ruralis, 57 (3), 267-273.
McNamara, K. T., and Weiss, C. (2005). “Farm household income and on-and off-farm diversification.” Journal of Agricultural and Applied
Economics, 37(1), 37-48.
Meraner, M, Finger, R. (2018). Risk perceptions, preferences and management strategies: Evidence from a case study using German
livestock farmers. Journal of Risk Research. In Press
Metzger, M.J., Bunce, R.G.H., Jongman, R.H.G., Mücher, C.A., Watkins, J.W. (2005). A climatic stratification of the environment of Europe.
Global Ecology and Biogeography, 14, 549-563.
Meuwissen, M.P.M., Assefa, T. and Van Asseldonk, M.A.P.M. (2013). Supporting insurance in European agriculture; experience of
mutuals in the Netherlands. EuroChoices 12(3), 10-16.
Meuwissen, M.P.M., De Mey, Y., and Van Asseldonk, M.A.P.M., (2018). Prospects for agricultural insurance in Europe. Agricultural
Finance Review, forthcoming.
Meuwissen, M.P.M., Hardaker, J.B., Huirne, R.B.M. and Dijkhuizen, A.A. (2001). Sharing risks in agriculture: principles and empirical
results. NJAS Wageningen Journal of Life Science 49, 343-356.
Meuwissen, M.P.M., Van Asseldonk, M.A.P.M. and Huirne, R.B.M. (2003). Alternative risk financing instruments for swine epidemics.
Agricultural Systems 75(2-3), 305-322.
Mishra A., and Goodwin B. (1997). “Farm Income Variability and the Supply of Off-Farm Labor.” American Journal of Agricultural
Economics, 79, 880–887.
Morris, J.B., Tassone, V., de Groot, R., Camilleri, M., Moncada, S. (2011). A Framework for Participatory Impact Assessment: Involving
Stakeholders in European Policy Making, a Case Study of Land Use Change in Malta Ecol. Soc. 16, 12.
OECD (2009). Managing Risk In Agriculture: A Holistic Approach. OECD, Paris.
Paracchini, M.L., Petersen, J.E., Hoogeveen, Y., Bamps, C., Burfield, I., van Swaay, C. (2008). High nature value farmland in Europe. An
estimate of the distribution patterns on the basis of land cover and biodiversity data. JRC Scientific & Technical Report EUR 23480
EN, Publications Office of the European Union. Luxembourg. 87 pp.
Phuong, L.T.H., Biesbroek, G.B., and Wals, A.E.J. (2017). The interplay between social learning and adaptive capacity in climate change
adaptation: A systematic review. NJAS - Wageningen Journal of Life Sciences 82, 1-9.
Poppe, K., and Termeer, C. (2018). The development of the livestock industry in Brabant – a factsheet. Working document WUR.
Porter J R, Xie L, Challinor A, Cochrane K, Howden M, Iqbal M M, Lobell D B, Travasso M I (2014). Food security and food production
systems In: Field, C B, Barros, V R, Dokken, D J, Mach, K J, Mastrandrea, M D, Bilir, T E, Chatterjee, M, Ebi, K L, Estrada, Y O, Genova,
R C, Girma, B, Kissel, E S, Levy, A N, MacCracken, S, Mastreandrea, P R, White, L L (Eds) Climate Change 2014: Impacts, Adaptation
and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the
Intergovernmental Panel on Climate Change (Cambridge, UK and New York, USA: Cambridge University Press) pp 485-533. See also:
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Prahalad C. K. and Ramaswamy, V. (2004). Co-creation experiences: The next practice in value creation. Journal of interactive marketing,
18 (3), 5-14.
Provincie Noord-Brabant (2010). Traditional and Technology: The Brabant Agenda. Retrieved on
Quinlan, A. E., Berbés-Blázquez, M., Haider, L. J., and Peterson, G. D. (2016). Measuring and assessing resilience: broadening
understanding through multiple disciplinary perspectives. Journal of Applied Ecology, 53(3), 677-687.
Rabobank (2017). Annual Report. Retrieved on 23-01-2018.
Ramaswamy, V. and Gouillart. F. J (2010). The power of co-creation. Build it with them to boost growth, productivity and profits. Simon
and Schuster.
Reidsma, P., Ewert, F. (2008). Regional farm diversity can reduce vulnerability of food production to climate change. Ecology and Society
13 (1): 38.
Reidsma, P., Ewert, F., Oude Lansink, A., Leemans, R. (2010). Adaptation to climate change and climate variability in European
agriculture: The importance of farm level responses. Eur. J. Agron. 32, 91-102.
Resilience Alliance (2010). Assessing resilience in social-ecological systems: Workbook for practitioners. Version 2.0. Online:
Robinson, G. (2004) Geographies of agriculture: globalisation, restructuring and sustainability. Pearson, Harlow.
Rosin, C., Stock, P., and Campbell, H. (2013). Introduction: shocking the global food system. In: Rosin, C., Stock, P., and Campbell, H.
(eds), Food systems failure; the global food crises and the future of agriculture, pp. 1-14.
Scheffer, M., Barrett, S., Carpenter, S.R., Folke, C., Green, A.J., Holmgren, M., Hughes, T.P., Kosten, S., Van de Leemput, I.A., Nepstad,
D.C. and Van Nes, E.H. (2015). Creating a safe operating space for iconic ecosystems. Science, 347(6228), 1317-1319.
Scheffer, M., Carpenter, S. R., Lenton, T. M., Bascompte, J., Brock, W., Dakos, V., ... & Pascual, M. (2012). Anticipating critical
transitions. Science, 338(6105), 344-348.
Schmitt, E., Galli, F., Menozzi, D., Maye, D., Touzard, J-M., Marescotti, A., Six, J., Brunori, G. (2017) Comparing the sustainability of local
and global food products in Europe. Journal of Cleaner Production, 165, 346-359.
Senge, P. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. Doubleday, 424.
Sherman, Mya H., and James Ford. "Stakeholder engagement in adaptation interventions: an evaluation of projects in developing
nations." Climate Policy 14, no. 3 (2014): 417-441.
Spiller, A., and Nitzko, S. (2015). Peak meat: the role of meat in sustainable consumption, In: Reisch, L.A. and Thogersen, J. (eds),
Handbook of research on sustainable consumption, pp. 192-208.
Steen, M., Manschot, M., and De Koning, N. (2011). Benefits of co-design in service design projects. International Journal of Design, 5(2),
Tendall, D. M., Joerin, J., Kopainsky, B., Edwards, P., Shreck, A., Le, Q. B., Kruetli, P., Grant, M., and Six, J. (2015). Food system resilience:
defining the concept. Global Food Security, 6, 17-23.
Termeer, C., Poppe, K., Feindt, P. Ge, L., Meuwissen, M.P.M., Verwaart, T., Hofstede, G., Mathijs, E. (2018). Reconnecting institutions for
the resilience of bio-based production systems. Working document WUR.
Termeer, C.J.A.M., Dewulf, A., Breeman, G.E., Stiller, S.J. (2015). Governance capabilities for dealing wisely with wicked problems
Administration and Society 47 (6), 680 – 710.
Urruty, N., Tailliez-Lefebvre, D., and Huyghe, C. (2016). Stability, robustness, vulnerability and resilience of agricultural systems. A
review. Agronomy for sustainable development, 36(1), 15.
Van Apeldoorn, D. F., Kok, K., Sonneveld, M.P.W., and Veldkamp, T.A. (2011). Panarchy rules: rethinking resilience of agroecosystems,
evidence from Dutch dairy-farming. Ecology and Society 16(1): 39
Van Asseldonk, M.A.P.M. et al., (2018). Does adoption of subsidized MPCI crowd out traditional market based hail insurance in the
Netherlands? Agricultural Finance Review, forthcoming.
Van Asseldonk, M.A.P.M., Tzouramani, I., Ge, L., and vrolijk, H.C.J. (2016). Adoption of risk management strategies in European
agriculture. Studies in Agricultural Economics 118(3), 154-162.
Van der Ploeg, J.D., and Roep, D. (2003). Multifunctionality and rural development: the actual situation in Europe. In: Van Huylenbroeck,
G., and Durand, G. (eds), Multifunctional Agriculture; A new paradigm for European Agriculture and Rural Development. Ashgate,
Hampshire, England, pp. 37- 53.
Van Vliet, J. A., A.G. Schut, P. Reidsma, K. Descheemaeker, M. Slingerland, G. van de Ven, and Giller, K. E. (2015). De-mystifying family
farming: features, diversity and trends across the globe. Global Food Security, 5, 11-18.
Van Vliet, J. A., A.G. Schut, P. Reidsma, K. Descheemaeker, M. Slingerland, G. van de Ven and Giller, K. E. (2015). De-mystifying family
farming: features, diversity and trends across the globe. Global Food Security, 5, 11-18.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
Van Wart J, van Bussel LGJ, Wolf J, Licker R, Grassini P, Nelson A, Boogaard H, Gerber J, Mueller ND, Claessens L, van Ittersum MK, KG
Cassman. 2013a. Use of agro-climatic zones to upscale simulated crop yield potential. Field Crops Research. 143, 44-55
Von Hippel, E. (1987). Cooperation between Rivals: Informal Know-How Trading. Research Policy, 16 (6), 291–302.
von Witzke, H. (2008). Agriculture, world food security, bio-energy and climate change: some inconvenient facts. Quarterly Journal of
International Agriculture, 47(1), 1-4.
Voorberg, W. H. , Bekkers V. J. J. M. and Tummers, L. G. (2014) A systematic review of co-creation and co-production: Embarking on the
social innovation journey Public Management Review, 17(9), 1333-1357.
Walker, B., Gunderson, L., Kinzig, A., Folke, C., Carpenter, S., & Schultz, L. (2006). A handful of heuristics and some propositions for
understanding resilience in social-ecological systems. Ecology and society, 11(1), 13.
Walker, B., Holling, C.S., Carpenter, S.R., and Kinzig, A. (2004). Resilience, adaptability and transformability in social–ecological systems.
Ecology and Society 9(2), 5.
Waters, D. (2011). Supply chain risk management; vulnerability and resilience in logistics. Kogan Page Limited, London, 256 p.
Zseleczky, L., & Yosef, S. (2014). Are shocks really increasing? A selective review of the global frequency, severity, scope, and impact of
five types of shocks. 2020 Conference Paper 5. May 2014. Washington, D.C.: IFPRI.
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
ANNEX 1: Examples of environmental, economic, social and institutional challenges, subdivided into shocks and
long-term pressures.
Environmental Economic Social Institutional
(droughts, excessive
precipitation, hail storms,
frost, floods)
- (Epidemic) pest, weed or
disease outbreaks
Price drops
for outputs
and price spikes for
inputs (volatility)
- Food or feed safety
- Changes in interest
Media attention to a food
safety or pest/disease
issue (food scares)
- Sudden changes to on-
farm social capital
(illness, death, divorce,
children deciding not to
go into farming, stress
regarding ownership and
the succession of the
- Insufficient availability of
seasonal labour
Changes in access to
markets (e.g. Brexit in
the UK, Russian
Reduced soil fertility (soil
mining, depletion of soils
- Climate change
- Deforestation
- Pollution by heavy metals
- Hydro-geological
- Impacts on drinking water
- Species extinction
- Decline of pollinators
- Antimicrobial resistance
- Loss of habitats
- Altered phosphorous cycle
- Altered nitrate cycle
Reduced access to bank
- Higher speed of
information-sharing and
inherent lack of time for
- Changes in buying
strategies of
downstream actors
- Increased cost of hired
- New competitors in
internationalised and
liberalised markets,
competition on and
reallocation of
- Upstream and
downstream market
power along the value
- ‘Financialisation’ of
agricultural and land
- High (start-up) costs
- Resource fixity leading
to ’locked-in situation’
- Changing quality and
frequency of
interactions between
farmers and suppliers,
financial institutions
and other direct
Reduced trust and
commitment towards
- Remoteness, reduced
access to social services
(housing, education,
health), less developed
(transportation, ICT)
- Gender gap
- Reduced access to
extension or advisory
services & skills training
- Changing societal
concerns about
agriculture (safety,
odour, animal welfare,
anti ‘factory farming’,
resource utilisation,
landscape issues)
- Changing consumer
preferences (local
produce, organic)
- Public distrust
- Demographic change
(increasing urbanisation,
rural outmigration,
- Ageing of rural areas (lack
of generational renewal)
- Changing attitude
towards farm
(succession, hired labour,
part-time farming)
ing national and
EU environmental
- Changes in
government support
for agriculture
- Changes in regulations
in destination markets
(non-tariff barriers).
- Restrictive standards
(e.g. GM-free
standards and
- Intellectual property
- Changing policy
objectives and
- Changes in land tenure
- Changes in food safety
- Changes in production
control policies
- Land-grabbing
- Other countries
agricultural policies
(e.g. American Farm
Bill, ASEAN policies,
BRICS policies)
- Trade and WTO
- Wars and conflicts
(African wars,
- Changes in quota
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
ANNEX 2: Functions of farming systems subdivided into private goods and public goods, including indicators.
Private goods
liver healthy and affordable food
Productivity (e.g. ton/ha)
- Price differentials (domestic price/international market price)
- Nutritional quality
- Loss of crops/livestock due to pests/disease
- Food quality (e.g. share of food produced that successfully passes a quality control)
Deliver other bio
based resources for the
processing sector
Productivity (e.g. ton/ha)
- Use of agricultural waste (e.g. straw for energy production)
Ensure economic viability (viable farms
help to strengthen the economy and
contribute to balanced territorial
Net farm income (level, downside risk)
- Cost of production
- Distribution of profit (i.e. share of producers’ price on the sale price)
- % farms that are owned/tenanted
- Age structure
- Debt/asset ratio
- Added value of the whole supply chain
- Farmer associations and platforms for learning
- Number of forced farm exits
- Share of farms that are locked-in (due to high sunk costs)
Improve quality of life in farming areas by
providing employment and offering decent
working conditions.
Income for agricultural workers (wage level)
- Number of on-farm and agribusiness jobs (annual working units/ha)
- Unemployment rate in the area
- Work quality (absence of labour force due to sickness)
- Right to quality of life (% of workers and producers with a good quality of life)
- Capacity development (trainings and opportunities for workers to grow professionally)
- Fair access to means of production
- Employment relations
- Non-discrimination
- Gender equality
- Health coverage
Public goods
Maintain natural resources in good
condition (water, soil, air)
Soil erosion (
physical, chemical and biological
quality of the soil, e.g.
% of area with
stable soil)
- Water quality (e.g. pesticides and nitrates in rivers)
- Nutrient balance (Nitrogen Use efficiency (kg N output/ k N input); N surplus (kg N/ha);
P surplus (kg P/ha))
- GHG balance (Mg CO
e M kcal
- Use of pesticides (tons per 1,000 ha)
- Waste management
- Energy efficiency
- Share of total energy coming from renewable resources
Protect b
iodiversity of habitats, genes, and
Diversity and abundance of key farmland animal, plant and insect species (e.g. birds,
butterflies, meadow plants)
- Woodland cover
- Agri-environmental payments
- % agricultural land with commitment to environmental conservation
; % committed to
organic agriculture
; % high nature value farm land
- % agricultural land providing wildlife corridors/habitat connectivity
- Use of pesticides (tons per 1,000 ha)
- Use of GMO (for animal feed)
- Habitat connectivity
- Diversity of production (to promote diversity of crops and breeds and protection of
genetic diversity in domestic species)
Ensure that rural areas are attractive
places for residence and tourism
(countryside, social structures)
House prices
- Broadband coverage
- Happiness index (OECD) of rural populations
- In- versus out-migration
- Landscape maintenance and preservation budgets
- Rate of pluri-active farms
- Regional agri-tourism offered
Report on resilience framework for EU agriculture
This Project has received funds from the European Union’s Horizon 2020 research and innovation programme under Grant
Agreement No. 727520
of alternative farming systems (e.g. CSA farming
, org
anic farming
- Extent of public access (e.g. footpaths, bridleways etc.)
- Planning policies that protect the rural nature of the countryside
Ensure animal
health &
Evidenced compliance with animal welfare regulation
- Enrolment in certification schemes
- Market share of products with certified higher levels of animal welfare
- % animals not requiring medical treatments
- %
animals free from stress/pain/discomfort
(e.g. based on physiological measures
(cortisol level) or behavioural indicators (biting/stinging behaviour)
- Use of antibiotics (e.g. average number of dairy cows treated per year)
Sources are a: EC (2001); b: FAO (2013); c: Paracchini et al. (2008); and d: own elaboration. Indicators reflect practices (e.g. use of pesticides
per ha) or outcomes (e.g. pesticides and nitrates in rivers).
In FAO (2013) included under ‘environmental integrity’.
Technical Report
Full-text available
Die Ausgestaltung der Gemeinsamen Agrarpolitik (GAP) der Europäischen Union (EU) ist eine komplexe Policy-Design-Aufgabe. Die GAP verfolgt verschiedene Ziele, die mit unterschiedlichen Politikinstrumenten erreicht werden sollen. Bislang hat die GAP kaum dazu beigetragen, die Umweltwirkungen der Landwirtschaft großflächig zu verbessern. Die ökologische Wirksamkeit der bisherigen GAP-Instrumente ist als eher gering einzuschätzen. Deshalb stellt sich die Frage, welche Instrumente in welcher Zusammenstellung geeignet sein könnten, um die GAP stärker an den drängenden umweltpolitischen Herausforderungen auszugestalten. Im Forschungsprojekt „Verbesserung der Wirksamkeit und Praktikabilität der GAP aus Umweltsicht“ wurden die Politikinstrumente der GAP in der Förderperiode 2014 bis 2020 und die verschiedenen Vorschläge für eine GAP nach 2020 auf Basis einer systematischen Literaturanalyse bewertet, mittels einer Telefonbefragung die Positionierungen der unterschiedlichen agrarpolitischen Gruppen analysiert sowie Experteninterviews und Workshops zur Weiterentwicklung der GAP durchgeführt. Auf Grundlage der Forschungsergebnisse wurden Politikvorschläge entwickelt, wie die GAP aus Umweltsicht wirksam und praktikabel ausgestaltet werden kann. The design of the Common Agricultural Policy (CAP) of the European Union (EU) is a complex policy design task. The CAP addresses various policy objectives with numerous policy instruments. So far, the CAP has hardly contributed to improving the environmental impacts of agriculture on a large scale. The environmental effectiveness of the existing CAP policy instruments is considered to be rather low. The question therefore arises which instruments, in what policy mix, could be suitable to make the CAP more responsive to the urgent environmental challenges. The research project "Improving the effectiveness and feasibility of the CAP from an environmental perspective" carried out a systematic literature review to evaluate the policy instruments of the CAP in the funding period 2014 to 2020 and the proposals for a CAP after 2020 made by different groups, conducted a telephone survey to analyze the positions of the various agricultural policy groups, and run expert interviews and workshops on the future development of the CAP. Based on the findings, policy proposals were developed how the CAP could become more environmentally effective and more feasible for farmers and administrators.
Full-text available
The need for efficient risk management has increased in agriculture, as farmers are facing greater risks, for instance, due to climate change, price liberalisation and new plant diseases. The development of yield insurances is ongoing in many EU member countries. In Finland, the northernmost EU country, a government-financed crop damage compensation (CDC) scheme has been abolished. In this study, we analysed how the government´s expenditure would change due to the policy shift and provide insight into the tails of the loss distribution of a crop insurance scheme based on individual farm yields. According to a stochastic simulation model, the mean expenditures for the government as well as the variability in expenditure between years are expected to be lower as a result of the policy shift. The results obtained support the government's decision to terminate the CDC scheme.
Full-text available
The need to adapt to decrease farm vulnerability to adverse contextual events has been extensively discussed on a theoretical basis. We developed an integrated and operational method to assess farm vulnerability to multiple and interacting contextual changes and explain how this vulnerability can best be reduced according to farm configurations and farmers' technical adaptations over time. Our method considers farm vulnerability as a function of the raw measurements of vulnerability variables (e.g., economic efficiency of production), the slope of the linear regression of these measurements over time, and the residuals of this linear regression. The last two are extracted from linear mixed models considering a random regression coefficient (
Full-text available
We analyze the factors affecting farmers’ choice accounting for farm, farmer, and household characteristics as well as elicited risk perception and risk preferences. We consider three alternative hypothetical methods for assessing risk preferences to test the stability and behavioral validity of them. Our case study focuses on livestock farmers in the German region North Rhine-Westphalia. We find that risk preferences are context depending, i.e. differ across different fields of farm-level decision-making. Furthermore, our analysis shows that risk-averse farmers are more likely to prioritize on-farm risk management strategies over off-farm strategies. Moreover, higher risk perception, age, subjective numeracy, farm succession, farm size, and the proportion of rented land show a significant impact on farmers’ risk behavior
Full-text available
In the debate surrounding the sustainable future of food, claims like “buy local” are widespread in publications and the media, supported by the discourse that buying “local food” provides ecological, health and socio-economic benefits. Recognising the lack of scientific evidence for this claim, this paper aims to compare the results of sustainability assessments for 14 local and global food products in four sectors within four European countries. Each sector has been analysed independently using sustainability indicators across five dimensions of sustainability: environmental, economic, social, health and ethics. In order to determine if local products generally perform better, an outranking analysis was conducted to rank the products relative to their sustainability performance. Outranking is a multi-criteria decision aid method that allows comparison of alternatives based on quantitative and qualitative indicators at different scales. Each product is also characterized by a degree of localness in order to relate sustainability and localness. The results are given in the form of phi flows, which are relative preference scores of one product compared to other ones in the same sector. The rankings showed that global products consistently come last in terms of sustainability, even when the preference functions and weighting of the indicators were varied. The first positions of the rankings were taken either by the most local or an intermediary product. Moreover, detailed rankings at the attribute level showed the relative strengths and weaknesses of each food product along the local-global continuum. It appeared that the strength of local and intermediary products was mainly in health and socio-economic dimensions, particularly aspects of care and links to the territory such as biodiversity, animal welfare, governance or resilience. In relation to global food products, they presented substantial advantages in terms of climate change mitigation and affordability to consumers. This contrasts with the food-miles ecological claim. Thus, we conclude that distance is not the most critical factor in improving sustainability of food products, and that other criteria of localness (identity, governance or size) play a more critical role.
Full-text available
Successful implementation climate change adaptation depends to a large extent on the capabilities of individuals, organizations, and communities to create and mobilize the adaptive capacity (AC) of their socio-ecological system. Creating and mobilizing AC is a continuous process that requires social learning (SL). Although rich with empirical cases, the literature theorizing and empirically investigating the relationship between AC and SL is highly fragmented. This paper aims to critically examine the peer-reviewed literature that focusses on SL and AC in the context of climate change adaptation (CCA). Special attention is paid to the interplay between the two. Understanding this interplay can help improve our understanding of how CCA takes place in practice and advances theoretical debates on CCA. Systematic review methods are used to analyse 43 papers (1997–2016). Our findings reveal three perspectives that each play an important role in different contexts: an AC-focused perspective, a SL-focused perspective, and a hybrid perspective. These differences in conceptualizations of the relationship between SL and AC may seem trivial at first, but they have consequences for the design of learning-based interventions aimed at helping communities respond to climate change. It appears that such interventions need to be preceded by an analysis of the climate change context in order to decide whether to emphasize AC, SL or both simultaneously.
In 2007 the farm subsidies of the European Union's Common Agricultural Policy took over 40 percent of the entire EU budget. How did a sector of diminishing social and economic importance manage to maintain such political prominence? The conventional answer focuses on the negotiations among the member states of the European Community from 1958 onwards. That story holds that the political priority, given to the CAP, as well as its long-term stability, resides in a basic devil's bargain between French agriculture and German industry. In Farmers on Welfare, a landmark new account of the making of the single largest European policy ever, Ann-Christina L. Knudsen suggests that this accepted narrative is rather too neat. In particular, she argues, it neglects how a broad agreement was made in the 1960s that related to national welfare state policies aiming to improve incomes for farmers. Drawing on extensive archival research from a variety of political actors across the Community, she illustrates how and why this supranational farm regime was created in the 1960s, and also provides us with a detailed narrative history of how national and European administrations gradually learned about this kind of cooperation. By tracing how the farm welfare objective was gradually implemented in other common policies, Knudsen offers an alternative account of European integration history.
Ecosystems influence human societies, leading people to manage ecosystems for human benefit. Poor environmental management can lead to reduced ecological resilience and social–ecological collapse. We review research on resilience and collapse across different systems and propose a unifying social–ecological framework based on (i) a clear definition of system identity; (ii) the use of quantitative thresholds to define collapse; (iii) relating collapse processes to system structure; and (iv) explicit comparison of alternative hypotheses and models of collapse. Analysis of 17 representative cases identified 14 mechanisms, in five classes, that explain social–ecological collapse. System structure influences the kind of collapse a system may experience. Mechanistic theories of collapse that unite structure and process can make fundamental contributions to solving global environmental problems.
We investigate the relationship between the transmission of price volatility and market power in the German fresh pork supply chain. We use a theoretical model underpinning this relationship followed by an empirical application that uses monthly farm, slaughterhouse and retail pork price data for the period 2000–2011. We examine both the relationships of market power with price level transmission and price volatility transmission in the chain. We use a vector error correction model and least squares regressions to analyse price transmission and price volatility transmissions, respectively. Results show that retail market power limited both types of transmissions. Competition inducing policy measures coupled with measures that support price risk management initiatives of chain actors are suggested.