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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.
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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
Miranda MEUWISSEN
1
– Wim PAAS
1,2
– Thomas SLIJPER
1,3
– Isabeau COOPMANS
4
– Anna CIECHOMSKA
5
– Eewoud
LIEVENS
6
– Jo DECKERS
7
– Willemijn VROEGE
8
– Erik MATHIJS
6
– Birgit KOPAINSKY
7
– Hugo HERRERA
7
– Sina
NITZKO
9
– Robert FINGER
8
– Yann DE MEY
1
– P. Marijn POORTVLIET
3
– Phillipa NICHOLAS-DAVIES
10
– Peter
MIDMORE
10
– Mauro VIGANI
11
– Damian MAYE
11
– Julie URQUHART
11
– Alfons BALMANN
12
– Franziska APPEL
12
Katrien TERMEER
13
– Peter FEINDT
13,14
– Jeroen CANDEL
13
– Muriel TICHIT
15
– Francesco ACCATINO
15
–Simone
SEVERINI
16
– Saverio SENNI
16
– Erwin WAUTERS
4
– Isabel BARDAJÍ
17
– Bárbara SORIANO
17
– Katarzyna
ZAWALIŃSKA
5
– Carl-Johan LAGERKVIST
18
– Gordana MANEVSKA-TASEVSKA
18
– Helena HANSSON
18
– Mariya
PENEVA
19
– Camelia GAVRILESCU
20
– Pytrik REIDSMA
2
(Contact: Miranda Meuwissen)
Due date 31 January 2018
Version/Date 31 January 2018
Work Package
WP 1
Task T. 1.1
Task lead
WU
Dissemination level Public
1
Business Economics, Wageningen University, P.O. Box 8130, 6700 EW Wageningen, the Netherlands, miranda.meuwissen@wur.nl
2
Plant Production Systems, Wageningen University, the Netherlands
3
Strategic Communication, Wageningen University, the Netherlands
4
Public Administration and Policy, Wageningen University, the Netherlands
5
Agricultural and Farm Development, Institute for Agricultural and Fisheries Research (ILVO), Belgium
6
Institute of Rural and Agricultural Development, Polish Academy of Sciences, Poland
7
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
8
Universitetet i Bergen, Norway
9
Agricultural Economics and Policy Group, ETH Zurich, Switzerland
10
Georg-August-Universität Göttingen, Germany
11
Aberystwyth Business School, Aberystwyth University, UK
12
Countryside and Community Research Institute, University of Gloucestershire, UK
13
Leibniz Institute of Agricultural Development in Transition Economies (IAMO), Germany
14
Albrecht Daniel Thaer Institute, Humboldt University, Germany
15
Agroecology, INRA, France
16
Department of Agricultural and Forestry Sciences, Università degli Studi della Tuscia, Italy
17
Research Centre for the Management of Agricultural and Environmental Risks (CEIGRAM), Universidad Politecnica de Madrid, Spain
18
Department of Economics, Sveriges Lantbruksuniversitet, Sweden
19
Department of Natural Resources Economics, University of National and World Economy, Bulgaria
20
Institute of Agricultural Economics, Romania
INDEX
1
Introduction ................................................................................................................................. 1
2
The resilience concept ................................................................................................................. 2
2.1
Three main processes .......................................................................................................... 2
2.2
Stages of adaptive cycle processes ..................................................................................... 3
2.3
Processes are interrelated ................................................................................................... 6
3
The resilience framework and its components .......................................................................... 7
3.1
Characterising the farming system (resilience of what) .................................................... 8
3.2
Key challenges (resilience to what) ..................................................................................... 9
3.3
Essential functions of the farming system (resilience for what purpose) ....................... 10
3.4
Resilience indicators ........................................................................................................... 11
3.5
Resilience attributes ........................................................................................................... 13
4
Discussion and conclusions ....................................................................................................... 18
5
References .................................................................................................................................. 19
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
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.
2
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).
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Report on resilience framework for EU agriculture
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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
economic,
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.
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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
5
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
th
century
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
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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.
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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.
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Report on resilience framework for EU agriculture
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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
system
Governance
Farm
demographics
Robustness Adaptability Transformability
4. Resilience
Indicators
5. Resilience
Attributes
Recovery rat e; degree
of return
Scope for changing identity
Safe operating spac e
Public goods
Private goods
Social
Environmental
Economic
3. Essential
functions
Social
Incremental
innovation
Major
realignment Learning
Networks
Resources to implement
sustainable production Farm heterogeneity
Diversity
Enterprise
diversification
Flexibility wrt
markets Diversity of institutions
Understand
dynamics
Radical
innovation
Agricultural
production
2. Challenges
Impact on
performance
Institutions (Dis)connects
Expo sed to
challenges
Resilience enhancing
strategies
Agro- ecological
context Identity
Region diversity and
redundancy
Redundant sto ck Multiple sources of risk
management
High levels of natural, s ocial,
human and financial capital
Production
Opportunities for
collaboration
Stakeholder
engagement
Open attitude to
innovation
Governance
Financial support Human capital and
knowledge management
Flexibilit y wrt norms and
regulatory frameworks
Resources
Farms
Other
actors
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Report on resilience framework for EU agriculture
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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
organisations.
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)
FARM
x
FARM
1
FARMING SYSTEM
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:
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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
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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.
0
20
40
60
80
100
120
System s tate
Time
Reco very r ate Degree of return
Shock
System
functions
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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
Risks
III
Respo nses
Risks
III
Continuing important sy stem functions
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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).
Risks
System state
Tipping point
System is
vulnerable
I
V
III
II
System
functions
System
functions
are unstable
System
functions after
transformation
IV
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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
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of attributes will always reflect aspirations for the specific type of farming system and resilience in a given case
study.
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.
2.
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).
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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.
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Non-resilience
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.
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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
boundaries.
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.
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Agreement No. 727520
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Agreement No. 727520
ANNEX 1: Examples of environmental, economic, social and institutional challenges, subdivided into shocks and
long-term pressures.
Environmental Economic Social Institutional
Shocks
-
(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
crisis
- Changes in interest
rates
-
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
farm)
- Insufficient availability of
seasonal labour
-
Changes in access to
markets (e.g. Brexit in
the UK, Russian
embargo)
Long
-
term
pressures
-
Reduced soil fertility (soil
mining, depletion of soils
nutrients)
- Climate change
- Deforestation
- Pollution by heavy metals
- Hydro-geological
disturbance
- Impacts on drinking water
- Species extinction
- Decline of pollinators
- Antimicrobial resistance
- Loss of habitats
- Altered phosphorous cycle
- Altered nitrate cycle
-
Reduced access to bank
loans
- Higher speed of
information-sharing and
inherent lack of time for
verification
- Changes in buying
strategies of
downstream actors
- Increased cost of hired
labour
- New competitors in
internationalised and
liberalised markets,
competition on and
reallocation of
resources
- Upstream and
downstream market
power along the value
chain
- ‘Financialisation’ of
agricultural and land
markets
- 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
stakeholders
-
Reduced trust and
commitment towards
cooperatives
- Remoteness, reduced
access to social services
(housing, education,
health), less developed
infrastructure
(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,
migration)
- Ageing of rural areas (lack
of generational renewal)
- Changing attitude
towards farm
employability
(succession, hired labour,
part-time farming)
-
Chang
ing national and
EU environmental
policy
- Changes in
government support
for agriculture
- Changes in regulations
in destination markets
(non-tariff barriers).
- Restrictive standards
(e.g. GM-free
standards and
regulations)
- Intellectual property
(‘biopatents’)
- Changing policy
objectives and
administrative
demands
- Changes in land tenure
regulations
- Changes in food safety
regulations
- Changes in production
control policies
(quota)
- Land-grabbing
- Other countries
agricultural policies
(e.g. American Farm
Bill, ASEAN policies,
BRICS policies)
- Trade and WTO
reforms
- Wars and conflicts
(African wars,
Ukraine)
- Changes in quota
25
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Agreement No. 727520
ANNEX 2: Functions of farming systems subdivided into private goods and public goods, including indicators.
Indicators
1
Private goods
-
De
liver healthy and affordable food
products
-
Productivity (e.g. ton/ha)
a.b
- Price differentials (domestic price/international market price)
b
- Nutritional quality
d
- Loss of crops/livestock due to pests/disease
d
- Food quality (e.g. share of food produced that successfully passes a quality control)
b
-
Deliver other bio
-
based resources for the
processing sector
-
Productivity (e.g. ton/ha)
d
- Use of agricultural waste (e.g. straw for energy production)
d
-
Ensure economic viability (viable farms
help to strengthen the economy and
contribute to balanced territorial
development).
-
Net farm income (level, downside risk)
a,b
- Cost of production
b
- Distribution of profit (i.e. share of producers’ price on the sale price)
d
- % farms that are owned/tenanted
a
- Age structure
a
- Debt/asset ratio
a,b
- Added value of the whole supply chain
a
- Farmer associations and platforms for learning
d
- Number of forced farm exits
a
- Share of farms that are locked-in (due to high sunk costs)
d
-
Improve quality of life in farming areas by
providing employment and offering decent
working conditions.
-
Income for agricultural workers (wage level)
a,b
- Number of on-farm and agribusiness jobs (annual working units/ha)
b
- Unemployment rate in the area
b
- Work quality (absence of labour force due to sickness)
b
- Right to quality of life (% of workers and producers with a good quality of life)
a,b
- Capacity development (trainings and opportunities for workers to grow professionally)
b
- Fair access to means of production
b
- Employment relations
b
- Non-discrimination
b
- Gender equality
b
- Health coverage
b
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)
b
- Water quality (e.g. pesticides and nitrates in rivers)
b
- Nutrient balance (Nitrogen Use efficiency (kg N output/ k N input); N surplus (kg N/ha);
P surplus (kg P/ha))
b
- GHG balance (Mg CO
2
e M kcal
-1
)
b
- Use of pesticides (tons per 1,000 ha)
d
- Waste management
d
- Energy efficiency
b
- Share of total energy coming from renewable resources
b
-
Protect b
iodiversity of habitats, genes, and
species
-
Diversity and abundance of key farmland animal, plant and insect species (e.g. birds,
butterflies, meadow plants)
b
- Woodland cover
d
- Agri-environmental payments
d
- % agricultural land with commitment to environmental conservation
d
; % committed to
organic agriculture
a
; % high nature value farm land
c
- % agricultural land providing wildlife corridors/habitat connectivity
d
- Use of pesticides (tons per 1,000 ha)
d
- Use of GMO (for animal feed)
d
- Habitat connectivity
b
- Diversity of production (to promote diversity of crops and breeds and protection of
genetic diversity in domestic species)
b
-
Ensure that rural areas are attractive
places for residence and tourism
(countryside, social structures)
-
House prices
d
- Broadband coverage
d
- Happiness index (OECD) of rural populations
d
- In- versus out-migration
d
- Landscape maintenance and preservation budgets
d
- Rate of pluri-active farms
d
- Regional agri-tourism offered
c
26
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-
Rate
of alternative farming systems (e.g. CSA farming
, org
anic farming
)
d
- Extent of public access (e.g. footpaths, bridleways etc.)
d
- Planning policies that protect the rural nature of the countryside
d
-
Ensure animal
health &
welfare
2
-
Evidenced compliance with animal welfare regulation
b
- Enrolment in certification schemes
d
- Market share of products with certified higher levels of animal welfare
d
- % animals not requiring medical treatments
b
- %
animals free from stress/pain/discomfort
b
(e.g. based on physiological measures
(cortisol level) or behavioural indicators (biting/stinging behaviour)
d
- Use of antibiotics (e.g. average number of dairy cows treated per year)
d
1
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).
2
In FAO (2013) included under ‘environmental integrity’.
Technical Report
Full-text available
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