17.2.1 A Toolbox for Resilience Assessment

In SURE-Farm we assembled an IA toolbox with complementary qualitative and quantitative methods (see Herrera et al., Reference Herrera, Kopainsky and Appel2018). The qualitative methods consisted of two participatory workshops with representatives of different stakeholder groups (e.g., farmers, food chain actors, NGOs, government) and were conducted in each farming system. The first workshop (FoPIA-SURE-Farm 1; Paas et al., Reference Paas, Accatino and Antonioli2019, Reference Paas, Coopmans, Severini, van Ittersum, Meuwissen and Reidsma2021a) was focused on the resilience of current systems: we assessed the main challenges, the perceived importance and performance of functions, the strategies adopted to cope with past challenges, and the resilience attributes. The second workshop (FoPIA-SURE-Farm 2; Accatino et al., Reference Accatino, Paas and Herrero2020; Paas et al., Reference Paas, San Martín, Soriano, van Ittersum, Meuwissen and Reidsma2021b; Reference Paas, Accatino and Bijttebier2021c) was focused on the resilience of future, hypothetical, systems. The quantitative methods included the assessment of current and future ecosystem services based on data and models and the simulation of farming system behavior based on system dynamics modelling.

17.2.2 Assessing Challenges (Resilience to What)

During the activities of the SURE-Farm projects (participatory workshops, focus groups, interviews), we identified and discussed key challenges in interaction with stakeholders in the case studies. For future resilience we assessed in interaction with stakeholders the closeness of the most important challenges to critical thresholds, whose exceedance would have a drastic impact on farming system functioning. This assessment was done in all case studies except two (BE-Dairy and FR-Beef, due to the COVID-19 crisis). Closeness of challenges to critical threshold was classified into ‘not close’, ‘somewhat close’, ‘close’, and ‘at or beyond’.

17.2.3 Assessing Functions (Resilience for Which Purpose)

Eight farming system functions were identified and categorized as providing private or public goods. The provision of private goods includes (1) producing food, (2) producing other bio-based resources, (3) ensuring economic viability and (4) providing quality of life for people involved in farming. The provision of public goods includes (1) maintaining natural resources (2) maintaining biodiversity in good condition, (3) ensuring animal welfare and (4) ensuring that rural areas are attractive places for residence and tourism. In participatory workshops, stakeholders were asked to individually assess the importance and performance of the eight functions. Importance was assessed by letting stakeholders divide 100 points over the functions. Assessing performance was based on stakeholders’ scores on a scale: (1) very low, (2) low, (3) moderate, (4) good and (5) very good performance.

The participatory assessment of functions was complemented with an ecosystem services assessment based on quantitative data, mostly related to the biophysical components of the system. For ecosystem services, the considered private goods were food crop production, fodder crop production, energy crop production, grazing livestock density, and timber removal. The considered public goods were carbon storage, habitat quality, atmospheric pollutant deposition, topsoil organic matter concentration, relative pollination potential, recreation potential, soil erosion control, and water retention. The assessment was based on gridded ecosystem service maps at the European scale made publicly available by the Joint Research Centre of the European Commission (see Maes et al., Reference Maes, Fabrega and Zulian2015). We calculated the average grid value of the ecosystem services in the farming systems as well as in the sub-national regions surrounding them (Nomenclature of Territorial Units for Statistics 3; NUTS 3). This allowed for comparing each farming system with the surrounding region in terms of ecosystem service provision.

In order to explore future sustainability and resilience, stakeholders were asked to determine critical thresholds for main challenges, functions, and resilience attributes, and, next, assess system performance in case critical thresholds would be exceeded (Accatino et al., Reference Accatino, Paas and Herrero2020; Paas et al., Reference Paas, San Martín, Soriano, van Ittersum, Meuwissen and Reidsma2021b; Reference Paas, Accatino and Bijttebier2021c). Impacts on performance were classified as strongly negative (−2), moderately negative (−1), no trend (0), moderately positive (+1), and strongly positive developments (+2). As a baseline reference, researchers also assessed the development of farming system performance based on current levels and trends of functions and resilience attributes. Subsequently, stakeholders identified possible alternative configurations of the farming systems. Alternative systems were generated based on individual input and elaborated in small group discussions of three to eight stakeholders moderated by a researcher. In these discussions, stakeholders were invited to elaborate how an alternative system would perform regarding system functions and resilience attributes.

The assessment of future systems was completed with system dynamics modelling, which is based on a causal-loop diagram able to represent the cause–effect relationship present in the farming systems (Richardson, Reference Richardson2011). The advantage of this approach is that cause-effect relationships can be mapped coherently, which is otherwise challenging during a participatory assessment due to the limits of mental capabilities of researchers and stakeholders (e.g., bounded reality). Therefore, in a sense, the modelling approach can extend the reach of our mind (Sterman, Reference Sterman2000).

17.2.4 Assessing Resilience Attributes (What Enhances Resilience)

Resilience attributes are characteristics of the farming system or its surrounding environment which enhance resilience (Cabell and Oelofse, Reference Cabell and Oelofse2012; Paas et al. Reference Paas, Coopmans, Severini, van Ittersum, Meuwissen and Reidsma2021a; see a complete list in Chapter 1). An example of a resilience attribute is ‘ecologically self-regulated’ which promotes resilience because it is argued that a system relying on natural regulation processes is more likely to withstand shocks due to input shortage. Based on input from FoPIA-SURE-Farm workshops, we assessed the presence of resilience attributes in the farming systems by looking at the strategies that farming system actors have already adopted and the strategies that are proposed to realize potential future systems. Strategies are linked to resilience attributes (see Reidsma et al., Reference Reidsma, Paas and Accatino2020a) as we argue that a strategy can be seen as a concrete example of supporting a certain resilience attribute. For example, if farmers aim to diversify their production, they are supporting the attribute ‘functional diversity’. In this chapter we specifically reflect on those resilience attributes that were supported in the past and those that are likely to be supported in potential future systems. In addition to this, we used system dynamics to explore the relationships between functions and resilience attributes. Based on the results of the participatory workshops we built causal-loop diagrams, describing cause–effect relationships among system components, including system functions and resilience attributes. More details are available in Reidsma et al. (Reference Reidsma, Paas and Accatino2020a).

17.4.1 Functions in Current Systems

Beyond the marked differences among case studies (Figure 17.1), we observed some common elements. First, in all case studies functions were perceived to have different performances and were assigned different importance. Second, stakeholders perceived food production to perform moderate to high in all case studies, while functions related to the social domain (‘Quality of life’ and ‘Attractiveness of the area’) performed consistently low to moderate. Two exceptions were observed in which policy changes led to lower economic viability and finally to lower food production: for ES-Sheep the decoupling of payments from production pushed non-land-owning farmers to rent hectares for maintaining payment rights; for PL-Horticulture the access to the EU provoked a lowering of product prices. Third, stakeholders tended to assign a higher importance to economic viability but, at the same time, they considered this function to perform poor to moderately. Exceptions were IT-Hazelnut and RO-Mixed: for IT-Hazelnut the reason was found in the high profitability of hazelnuts, produced in high quantities in the region. For RO-Mixed the reason was found in the subsidies, which covered an important share of costs in small farms.

Figure 17.1 Perceived performance and importance of functions as assessed by stakeholders in the SURE-Farm case studies. Perceived performance is indicated on both the x- and y-axis to allow comparability among functions within a case study (vertically), and among case studies for a function (horizontally). The radius of the circles is proportional to the importance assigned.

Source: Elaborated from Reidsma et al. (Reference Reidsma, Meuwissen and Accatino2020b).

Different relative performances were observed, across farming systems, among ‘Food production’; ‘Economic viability’ and environment-related functions (‘Natural resources’ and ‘Biodiversity and habitat’). In the arable systems the perceived performance of ‘Food production’ and ‘Economic viability’ was on average higher than in other systems, while environment-related functions were perceived to perform lower. The studied arable systems of western Europe (NL-Arable, UK-Arable, DE-Arable&Mixed) have historically invested more in the improvement of food production and economic viability, than in the improvement of public functions. In BG-Arable, stakeholders stated that food production was generally perceived not compatible with the conservation of natural resources in general, and nature conservation was considered more under the responsibility of policy-making rather than being a farmers’ goal. In other cases, both ‘Food production’ and environment-related functions performed relatively well. In RO-Mixed this was favoured by the access of Romania to the EU in synergy with local policies and awareness about the importance of public goods. For FR-Beef the farming system is built upon a synergy between extensive beef production and maintenance of the landscape. In the ES-Sheep, the low performance of private functions, especially ‘Food production’, linked to the reduction of the number of sheep in the region, has decreased the contribution of the sector to nature conservation.

In our ecosystem service data assessment, we compared the multifunctionality of farming systems with the multifunctionality of their surrounding regions. We identified two groups: (i) farming systems that enriched the multifunctionality of the region providing a relatively rich array of ecosystem services and (ii) farming systems that were mostly focused on food production and reduced the diversity of ecosystem services provided in the region. Within group (i), IT-Hazelnut brought ecosystem services intrinsically connected to the presence of permanent crops (e.g., carbon storage, recreation potential); extensive livestock systems (ES-Sheep and FR-Beef) provided ecosystem services related with recreation potential and, in the case of FR-Beef, also erosion control. Within group (ii), BG-Arable was formed by monocultures poor in habitat quality and decreasing organic matter in soils; RO-Mixed decreased most of the public goods of the surrounding region (especially carbon storage, pollutant removal, habitat quality); NL-Arable, PL-Horticulture, DE-Arable&Mixed removed public goods to the surrounding region already poorly multifunctional; and SE-Poultry was clearly disconnected from the surrounding region which was mostly occupied by forests; UK-Arable was classified into group (ii) according to data analysis, but in participatory workshops stakeholders reported practices aimed at increasing some ecosystem services, such as carbon sequestration (e.g., practices of no-tillage, cover crops).

Participatory workshops and the ecosystem service assessment provide complementary information. For FR-Beef and RO-Mixed the multifunctionality of ecosystem services is confirmed by stakeholder perception, while this is not the case for ES-Sheep. For SE-Poultry, stakeholders perceive good performance of ‘Natural resources’; however, elaboration of data suggests a separation between the broiler system and the surrounding forest. In DE-Arable&Mixed, data indicate poor performance of ecosystem services, but from a local stakeholder perspective, the presence of mixed crop–livestock systems are argued to ensure the maintenance of natural resources. For IT-Hazelnuts the system performs well with ecosystem services, but the intensive character of the system causes concerns among system actors. Results from the two methods align for PL-Horticulture and the arable systems. Overall, the participatory workshops provided information that is missing in satellite data about ecosystem services such as management practices that might contribute to ecosystem services provision.

17.4.2 Alternative Systems

For each farming system, three particular cases were considered in interaction with stakeholders: maintenance of the status quo, system decline when critical thresholds would be exceeded, and alternative systems for the future that could enhance sustainability and resilience. For each case study, at least one type of proposed alternative system was characterized by an increased use of technology. Examples are the investment in precision agriculture for NL-Arable, shelter farming in PL-Horticulture, technological innovation in IT-Hazelnut, use of robots in SE-Poultry, precision farming in BG-Arable, and advanced practices of pasture management in ES-Sheep. In many cases, the proposed alternative systems were related to organic and/or nature-inclusive agriculture (NL-Arable, UK-Arable, DE-Arable&Mixed, RO-Mixed, PL-Horticulture, IT-Hazelnut) and enhancement of diversification such as diversification of crops in BG-Arable, development of alternative crops in NL-Arable and RO-Mixed, and the achievement of fodder self-sufficiency in SE-Poultry. Other alternative systems were mostly specific to case studies, such as different forms of collaborations within the farming system (BG-Arable, NL-Arable, RO-Mixed), the valorization of products (BG-Arable, IT-Hazelnut), improvement of the attractiveness of the region (DE-Arable&Mixed), and valorization of the products locally processed and transformed (IT-Hazelnut).

17.4.3 Functions in Future Systems

In case the status quo is maintained in the future, no significant improvements were expected in functions’ performances and some function indicators were perceived to likely decrease, such as ‘Quality of life’ (UK-Arable) and ‘Economic viability’ (BE-Dairy, ES-Sheep). However, in some case studies (IT-Hazelnut, SE-Poultry, NL-Arable), a moderate improvement was expected for the functions that are already performing moderately to well (especially ‘Food production’). Stakeholders indicated that when critical thresholds are exceeded, most of the functions might worsen their performance. The most critical function was ‘Economic viability’: it was seen as the most urgent to improve, as in the longer term it may cause lower ‘Attractiveness of the area’ and therefore decrease the availability of labor to realize ‘Food production’. In regard to ‘Natural resources’, UK-Arable and NL-Arable were perceived close to thresholds concerning soil quality, which directly affects the production of food.

Function performances were perceived to be different depending on the alternative systems. Still, some commonalities were observed: (i) Stakeholders were aware of the existence of trade-offs, i.e., not all functions could be improved at the same time. (ii) In many alternative systems, food production was expected not to change or only moderately improve, meaning that this function was not targeted as a priority to improve. (iii) ‘Economic viability’ and, when discussed, also ‘Other bio-based resources’, ‘Attractiveness of the area’, ‘Animal health & welfare’ were often expected to improve from moderately to strongly; ‘Natural resources’ and ‘Biodiversity & habitat’ were often expected not to change or moderately improve.

17.5.1 Resilience Attributes and Capacities in Current Systems

When linking the strategies implemented in the current systems to resilience attributes, we observed that 38% of the strategies positively contributed to the resilience attribute “Reasonably profitable” (Figure 17.2). Many strategies also contributed to “Building human capital”, “Socially self-organized”, “Infrastructure for innovation”, “Response diversity”, “Functional diversity” and “Coupled with local and natural capital (production)”. There seems to have been a lack of attention for improving “Optimal redundancy of crops, nutrients, and water”, and for the “Spatial heterogeneity at landscape level”.

Figure 17.2 The contribution to resilience attributes of the identified strategies implemented and proposed in farming systems. The darker line shows the ratio of (past) strategies implemented for current systems contributing to an attribute, and the lighted line the ratio of future strategies for alternative systems contributing to an attribute. Attributes are ordered, starting with the attribute to which most past strategies contributed.

(based on Reidsma et al., Reference Reidsma, Paas and Accatino2020a)

17.5.2 Resilience Attributes and Capacities in Future Systems

The strategies identified by stakeholders to reach alternative systems were relatively more focused on strengthening “coupled with local and natural capital”, both regarding production and legislation. Strategies to improve these resilience attributes include improving soil quality, improving circularity, reducing inputs, using varieties adapted to local climatic conditions, local branding, and policies that support this. The following attributes were more often strengthened when compared to strategies already implemented: “diverse policies” (although on average not mentioned often), “coupled with local and natural capital (legislation)”, “appropriately connected with actors outside of the farming system”, “coupled with local and natural capital (production)”, “functional diversity”, and “ecologically self-regulated”.

17.7.1 Sustainability Dimensions Are Currently Not Addressed in a Balanced Way

Our assessment revealed that the main focus of our case studies was on food production, whereas other environmental, economic, and social functions were often overlooked. Strategies implemented in the past revealed that much attention was given to the attribute “reasonably profitable”: while this led to an increase in food production, it did not improve economic viability for the farmers. The strategies ensured a certain robustness in the past, but economic viability remained close to perceived critical thresholds in many farming systems. The need to ensure economic viability induces myopia among farming system actors, as long as performance in the environmental and social domain is considered to be acceptable.

17.7.2 Lack of Sustainability Corresponds to a Lack of Resilience

Insights from our assessment suggested that the unbalanced attention to sustainability dimensions corresponds to poor resilience. First, according to stakeholders’ input, the status quo is not resilient: if it is maintained for the future, most functions would likely not improve or deteriorate; in the case of exceeding critical thresholds, most functions are expected to strongly worsen. Second, in a current situation where sustainability dimensions are not equally addressed, challenges are currently present and most of them are close to critical thresholds (Table 17.1). The existence of common challenges raises concern about the resilience of European agriculture: current farming systems are under stress. Above all, economic issues are perceived as extremely critical by stakeholders. In addition, some of the challenges are internally generated (e.g., lack of successors and workforce), meaning that the current configurations generate problems. Third, the system dynamics analysis assessed that focusing on production and economic functions would erode resilience attributes.

17.7.3 Sustainability and Resilience for the Future

The view of stakeholders for future systems was clearly characterized by a joint improvement of sustainability and resilience, especially in regard to environmental and social aspects. The analysis performed with system dynamics showed that functions promote resilience attributes and vice versa. This suggests that there are pathways towards the joint improvement of sustainability and resilience. In the literature some studies highlighted the linkage among environmental and social functions and resilience. According to Altieri et al. (Reference Altieri, Nicholls, Henao and Lana2015), resilience to extreme climate events is higher for systems that integrate ecological processes in their configuration and practices via, e.g., diversification, polycultures, crop-livestock systems, and organic soil management. Concerning the social component, studies highlight the importance of, e.g., creating a learning environment, enhancing the capacity of community self-organization (Berkes, Reference Berkes2007), and ensuring a good quality of life (Darnhofer, Reference Darnhofer2010) to promote resilience.