Sammanfattning

European farms face numerous complex and interrelated economic, environmental, social, and institutional shocks and stresses. In addition, farms face unanticipated crises, such as the COVID-19 pandemic. The impact of these shocks and stresses may limit farmers’ access to credit, constrain opportunities to invest, and reduce their willingness to continue farming. This may threaten the delivery of several farm functions, including food production, biodiversity, and the maintenance of natural resources. Resilient farms successfully cope with shocks and stresses and secure the delivery of desired farm functions. This likely requires adaptation and transformation. To this end, the European Commission calls for a better operationalisation and assessment of farm resilience.

The general objective of this thesis is to assess the resilience of European farms. Three building blocks are used to assess farm resilience: (i) understanding shocks and stresses, (ii) assessing the resilience capacities of robustness, adaptability, and transformability, and (iii) evaluating the performance of farm functions over time. These building blocks are investigated by perceived and indicator-based resilience assessments, which provide complementary insights. Perceived resilience assessments contribute to a better understanding of decision-making under risk and uncertainty. Indicator-based resilience assessments have a more objective character, allowing researchers to assess farm resilience using secondary datasets.

Chapter 2 connects risk theory and resilience thinking using survey data from 916 Dutch farmers. This chapter explores how risk perceptions, risk preferences, and risk management strategies are related to perceived robustness, adaptability, and transformability. The results of the Partial Least Squares Structural Equation Model (PLS-SEM) reveal the importance of a diverse portfolio of risk management strategies. More diverse risk management portfolios are associated with higher perceived adaptability and, in some cases, with higher perceived transformability. This underlines the importance of studying combinations of risk management strategies instead of optimising single strategies. Less risk-averse farmers perceive themselves as better able to adapt and transform while the relationship between risk-aversion and perceived robustness is heterogeneous across farms. Furthermore, higher perceived robustness, adaptability, and transformability are related to farmers who perceive themselves as more resilient.

Chapter 3 explores how learning and social networks contribute to farm resilience in terms of robustness, adaptation, and transformation. A combination of qualitative (semi-structured interviews, focus groups, expert interviews) and quantitative methods (farmer survey) is used to study the resilience of Dutch arable farmers from the Veenkoloniën and Oldambt. The results indicate that social networks and learning primarily enable farmers to adapt and, in some cases, contribute to robustness and transformation. Several strategies that enhance each of the resilience capacities are identified. Robustness-enhancing strategies are to build bonding social capital, strengthen financial management skills, and acquire agricultural knowledge. Adaptation-enhancing strategies include building bonding and bridging social capital and being an early adopter of innovation. Transformations are enhanced by the following strategies: building linking social capital from formal networks, learning radically new ideas, and critically reflecting on the status quo.

Chapter 4 assesses farm resilience in nine European countries. This chapter quantifies the resilience capacities of robustness, adaptation, and transformation. It uses the Farm Accountancy Data Network (FADN) panel dataset to study changes in inputs and outputs over time. Several indicators for each resilience capacity are aggregated into composite indicators. This chapter investigates which farm(er) characteristics and policy instruments affect the resilience capacities by estimating a correlated random effects fractional probit model combined with a control function approach. The results reveal that resilience-enhancing strategies are heterogeneous across regions and farm types. In most European regions, decoupled direct payments constrain robustness, while rural development payments enhance robustness. Both decoupled direct payments and rural development payments do not affect adaptation and transformation in most European regions.

Chapter 5 investigates if decoupled direct payments are an effective policy instrument to ensure short and long-term farm viability. The FADN panel dataset that contains farm-level data from eleven European countries is used. Dynamic correlated random effects probit models are estimated. A control function is employed to account for endogeneity caused by the non-random assignment of decoupled direct payments. The results indicate that 74.5% of the European farms is short-term viable, while less than half of the farms are long-term viable (42.5%). Decoupled direct payments increase the probability to be short-term viable in Southern and Eastern European countries while having no effect or even decrease the probability to be short-term viable for farms from Western and Northern European countries. Additionally, decoupled direct payments decrease the probability of being long-term viable in almost all countries.

Chapter 6 synthesises the results and identifies three common themes: (i) moving from risk analysis to resilience thinking deepens the understanding of farmer behaviour under shocks and stresses, (ii) assessing the contribution of risk management by adopting a portfolio view on risk management rather than focussing on single risk management strategies enhances the understanding of resilience, and (iii) reiterating the need to assess farm income as it contributes to multiple facets of farm resilience. Furthermore, Chapter 6 introduces policy and business implications. Policy instruments are suggested to foster a shift towards diverse risk management portfolios, build social capital through social networks and learning, facilitate the adoption of innovations, and focus on paying farmers for public good provision and eco-schemes. Business implications arise for farmers, other supply chain actors, innovation platforms, and banks and other credit suppliers. Farmers are recommended to adopt diverse risk management portfolios and be open to learn from their formal and informal networks. Food supply chain actors are recommended to foster cooperation and learning with farmers by creating joint innovation programmes. To enhance resilience, innovation platforms could host network events to facilitate social learning between farmers and their social network actors; for instance, concerning developments in precision agriculture and other innovations. For banks and other credit suppliers, being able to identify resilient and viable farms is important to grant loans to the least risky farms.

Publicerad i

ISBN: 978-94-6395-948-3
Utgivare: Wageningen University

UKÄ forskningsämne

Tvärvetenskapliga studier
Företagsekonomi


Publikationens identifierare

DOI: https://doi.org/10.18174/552175

Permanent länk till denna sida (URI)

https://res.slu.se/id/publ/127804