Economic resilience in European dairy farms: Trends, determinants and challenges

Wilczyński, Artur; Koloszycz, Ewa Article Economic resilience in European dairy farms: Trends, determinants and challenges Amfiteatru Economic Provided in Cooperation with: The Bucharest University of Economic Studies Suggested Citation: Wilczyński, Artur; Koloszycz, Ewa (2025) : Economic resilience in European dairy farms: Trends, determinants and challenges, Amfiteatru Economic, ISSN 2247-9104, The Bucharest University of Economic Studies, Bucharest, Vol. 27, Iss. 69, pp. 587-605, https://doi.org/10.24818/EA/2025/69/587 This Version is available at: https://hdl.handle.net/10419/319826 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Food Market Shifts – Challenges for Food Chain Actors AE Vol. 27 • No. 69 • May 2025 587 ECONOMIC RESILIENCE IN EUROPEAN DAIRY FARMS: TRENDS, DETERMINANTS AND CHALLENGES Artur Wilczyński1 and Ewa Kołoszycz2 1)2) West Pomeranian University of Technology in Szczecin, Poland Please cite this article as: Wilczyński, A. and Kołoszycz, E., 2025. Economic Resilience in European Dairy Farms: Trends, Determinants and Challenges. Amfiteatru Economic, 27(69), pp. 587-605. DOI: https://doi.org/10.24818/EA/2025/69/587 Article History Received: 15 October 2024 Revised: 4 February 2025 Accepted: 6 March 2025 Abstract In the face of increasing economic challenges and changing market conditions, farm resilience is becoming a key issue to ensure its development. The aim of this paper is to analyse the level of economic resilience of farms specialising in dairy farming in the ten largest dairy-producing countries of the European Union between 2004 and 2021. The research applies a self-modified multidimensional index of economic resilience, which includes elements such as vulnerability, intensification, biodiversity, diversification, and performance. The results show a decrease in economic resilience in the analysed farm groups, regardless of their economic size. It was also found that farms with a larger economic size did not demonstrate higher economic resilience compared to farms with a smaller size. The research results indicate a downward trend in the economic resilience index from 2004 to 2021. The key dimensions that affected this indicator were intensification and diversification. This suggests that, to increase their resilience, the dairy farms studied must address challenges such as reducing input use intensity, minimising dependence on hired labour, and diversifying income sources. Keywords: measuring resilience, multidimensional index, comparative analysis, European dairy farms, farm groups JEL Classification: Q12, Q13  Corresponding author, Artur Wilczyński – e-mail: artur.wilczyns[email protected] This is an Open Access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. © 2025 The Author(s). AE Economic Resilience in European Dairy Farms: Trends, Determinants and Challenges 588 Amfiteatru Economic Introduction Agriculture, a primary economic sector, relies on natural resources and supplies raw materials for other industries. However, it faces a contradiction: while necessary to meet growing food demand, it also contributes to climate change. This drives a constant search for solutions that balance productivity with environmental protection and sustainable resource use. (Klein et al., 2014; Scuderi et al., 2021; Dhanaraju et al., 2022). In addition to environmental conditions, agricultural activity is determined by several other factors. Risk factors are of particular importance, comprising frequently recurring situations that may expose the farm to harm or loss. In the case of dairy farms, these include weather conditions, common plant and animal diseases, restricted access to markets, consumer preferences, changes in agricultural policy, and changes in the labour market (Wolf and Karszes, 2023). Moreover, farms are also exposed to the occurrence of shocks. These are sudden and unpredictable situations that determine the economic performance of farms. There are two types of shocks: external and internal. External shocks include sudden natural hazards (e.g., droughts, floods, frosts, fires, infectious animal diseases), demand and supply shocks affecting price fluctuations, and unexpected changes in the political environment. On the other hand, internal shocks consist of the health problems of farm owner and workers (Berchoux et al., 2019). Specific shocks for dairy farms include infectious animal diseases, volatility of milk and input prices, lack of payment for sold products, unexpected decline in milk quality, and sudden political decisions (e.g., embargo on exports of milk products). Emerging shocks lead to long-term stress, impacting animal welfare, low milk prices, rising production costs, labour shortages, fake news (e.g., regarding the quality of milk products, the environmental impact of milk production), milk substitutes, deteriorating consumer relations, and legal changes in the dairy sector. (Popp and Nowack, 2020). Due to the increasing incidence of shocks and stresses, the concept of farm resilience (agricultural system resilience) has emerged. Meuwissen et al. (2019) define farm resilience as “its ability to ensure the provision of the system functions in the face of increasingly complex and accumulating economic, social, environmental, and institutional shocks and stresses, through capacities of robustness, adaptability, and transformability”. Our article focuses on one type of resilience, namely, the economic resilience. It is defined multidimensionally as resistance (the ability to cope with economic shocks), absorption (the ability to absorb an economic shock), recovery (the ability to return to a previous state of economic equilibrium), and reorientation (if present, the ability to make structural changes and return to economic equilibrium at a higher level than the initial state) (Martin, 2011). The aim of the research was to determine the level of economic resilience of farms specialising in dairy farming (dairy farms) located in ten largest milk-producing countries in the European Union (EU). The choice of dairy farms as the subject of the study was motivated by the fact that they are particularly exposed to shocks and stresses in managing crop and livestock production. Economic resilience was the subject of the study and was measured using a multidimensional index. The time horizon of the study was 18 years (2004-2021). The study is divided into five parts. The first part provides an analysis of the literature on farm resilience, focusing on its definition and measurement. The second part outlines the research methodology, with particular emphasis on the calculation of the economic resilience index. The third part describes the farm groups based on production and economic indicators. The next section presents the results, analysing trends in economic resilience over the Food Market Shifts – Challenges for Food Chain Actors AE Vol. 27 • No. 69 • May 2025 589 examined period and the structure of the economic resilience index, which helps identify the challenges faced by farms. The final part discusses the findings in the context of previous research and provides a summary of the conclusions. 1. Review of the scientific literature 1.1. The concept of economic resilience The term resilience originates from the Latin resiliō, meaning to rebound or resist. It was first used in the 1940s in psychology, where it was studied how people cope with misfortune and hardship (Hanisch, 2016). Over time, the study of resilience has become interdisciplinary (Alexander, 2013) and has emerged in the context of different fields (Brown, 2014; Quinlan et al., 2016). Resilience can be defined as the ability of a system to absorb disturbances and adapt, maintaining its core functions, structure, identity, and regulatory mechanisms (Walker et al., 2004). In economic sciences, resilience has gained prominence in analyses of economic crises and is understood as the ability to recover quickly from a shock (Rose, 2004; Martin and Sunley, 2015; Sánchez et al., 2016). Resilience can be either static, related to maintaining key functions and stability, or dynamic, involving the effective use of resources for recovery, leading to a new state of equilibrium (Fuchs and Thaler, 2018; Xie et al., 2018). It is important to emphasise that both the speed and effectiveness of actions taken after a shock are crucial for the system’s return to stable growth (Fingleton et al., 2015; Morkūnas et al., 2018). The key capabilities determining resilience – robustness, adaptability, and transformability – have been extensively discussed in the works of Meuwissen et al. (2019) and form the foundation for the operationalisation of dairy farm resilience adopted in this study. In the context of agriculture and farms, economic resilience takes on more specific characteristics. Darnhofer et al. (2010) described it as the ability of a farm to adapt its practices in response to various economic shocks and pressures to ensure long-term viability. Béné et al. (2012), in their definition of economic resilience, combined short-term responses with long-term planning and structural adaptation, enabling farms not only to survive, but also to thrive. More recent studies describe economic resilience as the ability of farms to maintain current profitability in the face of perturbations, such as market and production risks and policy changes, and ultimately to move to a new equilibrium (Tendall et al., 2015; Vigani and Berry, 2018). 1.2. Measuring the economic resilience of dairy farms The development of methods to measure economic resilience in farms has progressed in parallel with the evolution of the resilience concept itself. Initially, the research focused on qualitative methods, such as case studies and ethnographic research, which allowed an indepth understanding of adaptive strategies and decision-making processes in farms. Although these approaches provided valuable insights into the mechanisms through which farms respond to crises, their limitation was the difficulty in generalising findings to a larger population (Quinlan et al., 2016). AE Economic Resilience in European Dairy Farms: Trends, Determinants and Challenges 590 Amfiteatru Economic As the need for comparative analysis increased, researchers began to develop quantitative approaches based on economic indicators, econometric models, and multi-criteria assessment methods. Financial indicators, such as liquidity, profitability, technical efficiency, and debt levels, enable an assessment of farm resilience based on accounting data and production outcomes (Shadbolt et al., 2017). Econometric models, in turn, allow for the analysis of external factors affecting farm stability (Cradock-Henry, 2021). The specific nature of dairy farms means that their resilience cannot be assessed solely on the basis of conventional financial indicators. Milk production is characterised by a high intensity of resource use and sensitivity to market fluctuations and regulatory changes. Therefore, increasing attention has been given to additional factors, such as income diversification, feed autonomy, and the ability to cooperate within agricultural knowledge and innovation systems (AKIS) (Vigani and Berry, 2018; Thorsøe et al., 2020; Kuipers et al., 2024). The literature increasingly focuses on different dimensions of resilience: robustness (resistance to shocks), adaptability (adjustment capabilities), and transformability (potential for structural change) (Meuwissen et al., 2019). Slijper et al. (2022) proposed a model for quantifying dairy farm resilience, incorporating return on assets (ROA), shock magnitude and depth, and recovery time to assess financial stability. Their approach complements earlier models that focused on liquidity, efficiency, and solvency metrics in the context of farms’ ability to respond to shocks (Shadbolt et al., 2017). This study adopts a multi-dimensional Economic Resilience Index (ERI), developed by Vigani and Berry (2018) but adapted to the specific characteristics of dairy farms. It consists of five main dimensions: vulnerability, intensification, biodiversity, diversification, and performance. A key adaptation in this study was the modification of the biodiversity dimension — rather than using the traditional Simpson’s Diversity Index (SID) based on cultivated land area, we applied a SID based on the value of plant production. This adaptation reflects the critical role of on-farm feed production in dairy farms, offering a more precise measurement of the impact of crop structure on farm income stability and economic resilience (Sen et al., 2017; Singh et al., 2023). Despite advancements in resilience measurement, there is still a lack of comprehensive comparative studies covering dairy farms in different European Union countries, operating under varied institutional and economic systems (Meuwissen et al., 2019; Thorsøe et al., 2020). Contemporary studies primarily focus on economic performance, competitiveness, and sustainability of production (Poczta et al., 2020; Parzonko et al., 2024; Savickienė and Galnaitytė, 2024). These findings indicate that larger-scale dairy farms, with higher technical efficiency and more diversified income sources, tend to exhibit greater economic resilience and greater capacity to adapt to external shocks. However, the influence of agricultural policy on dairy farm resilience cannot be overlooked. The Common Agricultural Policy (CAP), through direct payment schemes and risk management instruments, enhances farm robustness, but does not always support long-term adaptation and transformation (Buitenhuis et al., 2020). Therefore, it is crucial to consider the role of state policies in developing effective resilience monitoring tools and advisory support systems, as emphasised by Morkūnas et al. (2018). Based on the identified research gaps and previous studies on economic resilience, the following hypotheses have been formulated: Food Market Shifts – Challenges for Food Chain Actors AE Vol. 27 • No. 69 • May 2025 591 H1: The economic resilience of the analysed groups of dairy farms increases in the following years of the conducted analysis. H2: Dairy farms with the largest economic size are characterised by higher economic resilience compared to farms with lower economic size. H3: There is a high variability in the structure of the economic resilience index of the studied groups of dairy farms, based on the economic size criteria. These hypotheses are derived from prior research on economic resilience in agriculture and analyses of farm size effects on financial stability. Their verification will provide information on how the components of the Economic Resilience Index and farm size influence adaptability and transformation capacity in response to market and policy changes. 2. Research methodology The research covered farms with the publicly available European Farm Accountancy Data Network (FADN) code 45 (Specialist milk). The FADN is a survey that collects accountancy data each year from around 80,000 farms located in the EU, representing approx. 5 million farms. Microeconomic data is collected based on a standardised methodology, and the data are representative (European Commission, 2022). The study area covered dairy farms from Germany, France, the Netherlands, Poland, Italy, Ireland, Spain, Denmark, Belgium, and Austria. These are the largest milk-producing countries in the EU, collectively accounting for about 84% of the milk production in the EU. Groups of farms were classified into economic size classes (ES6 typology). This classification is based on the value of the Standard Output (SO) calculated by Member States per hectare or per head of livestock, using basic data for a reference period of five successive years (European Commission, 2015). The study’s time horizon covered the years 2004-2021, and the condition for including a country in the analysis was the access to national data for the entire 18-year period. The year 2021 is the most recent for which data has been collected in the FADN database for all EU countries. The study utilised the publicly available FADN database. Table no. 1 shows the study area by farm groups based on economic size. Only large dairy farms included all ten countries in the study. In the medium-large group, seven countries had full 18-year data, while in the very large group, only four did. Germany and Italy were present in all three groups. Table no. 1. The way of comparing results Medium-large dairy farms 50k EUR ≤ SO < 100k EUR Large dairy farms 100k EUR ≤ SO < 500k EUR Very large dairy farms SO ≥ 500k EUR Germany, France, Poland, Italy, Ireland, Spain, Austria Germany, France, Netherlands, Poland, Italy, Ireland, Spain, Denmark, Belgium, Austria Germany, Netherlands, Italy, Denmark The measurement of economic resilience (ER) was based on a multidimensional index developed and validated by Vigani and Berry (2018). The index is constructed from five dimensions: Vulnerability (V), Intensification (I), Biodiversity (B), Diversification (D), and Performance (P). Vigani and Berry (2018) used Simpson’s Diversity Index (SID), based on a matrix of crop types and crop areas for each farm, to measure the Biodiversity dimension. AE Economic Resilience in European Dairy Farms: Trends, Determinants and Challenges 592 Amfiteatru Economic In our study, we utilised the same index (SID), but based on the output value of each crop produced. This approach has been used by Sen et al. (2017), Debasis et al. (2018), Singh et al. (2023). Table no. 2 provides details on calculating the economic resilience index (ERI). Table no. 2. Calculation method for the Economic Resilience Index (ERI) dimensions Name of the ERI index dimension Description of the ERI index dimension Vulnerability (V) 𝑉 =Total liabilities Total assets ×100 A farm that is ‘overloaded’ with liabilities should be expected to have less financial capacity to absorb unexpected shocks. The desirable level of the indicator is to minimise it (smaller is better). Intensification (I) 𝐼 =Seeds+Fertilisers+Crop protection+Feed+Energy+Contract work Total Utilised Agricultural Area ×100 An inputand labour-intensive farm has a reduced ability to adapt to changing financial and production conditions. The desirable level of the indicator is to minimise it (smaller is better). Biodiversity (B) Simpson's Diversity Index (SID) 𝑆𝐼𝐷 =(1−∑ 𝑃𝑖2)×100 𝑛 𝑖=1 𝑃𝑖=value of output for ith crop Total output The SID indicator takes values between 0 and 100. A higher value of the indicator signifies a higher biodiversity. Greater crop biodiversity is expected to lead to increased farm resilience. It helps protect the farm against biotic or abiotic stresses, thereby reducing the risk of high variability in production values. The desired level of the indicator is to maximise it (bigger is better). Diversification (D) 𝐷 = Other output Total output ×100 A farm that diversifies its sources of income compensates for low income from selected agricultural activities with higher income from other activities, thus stabilising its income. This has a positive impact on the economic resilience of the farm. The desired level of the indicator is to maximise it (bigger is better). Performance (P) 𝑃 =Total output Total Inputs ×100 Farms with better economic results can benefit from higher profits and greater liquidity, allowing them to manage periods when unfavourable business conditions (e.g., shocks) arise. The goal is to maximise the indicator (bigger is better). The calculation of the individual dimensions of economic resilience (Table no. 2) allows the synthetic index (ERIi) to be calculated. This is carried out in two steps according to the formulas below (Vigani and Berry, 2018): ERi = (-) Vi + (-) Ii + Bi + Di + Pi (1) ERIi = (ERi-ERmin) / (ERmax-ERmin) (2) Food Market Shifts – Challenges for Food Chain Actors AE Vol. 27 • No. 69 • May 2025 593 where:  ERi – Economic Resilience;  ERIi – Economic Resilience Index;  ERmin – Minimum value of the ER in each economic size group of farms;  ERmax – Minimum value of the ER in each economic size group of farms;  i=1.….n – Means the farms of the country in the following year of analysis. The first step of the index calculation was to calculate the ER index according to formula (1). Due to the high variability of the values in their individual dimensions, following the approach of Vigani and Berry (2018), they were scaled using the natural logarithm. The next step was to convert the results of the Vulnerability (V) and Intensification (I) dimensions into negative values. This treatment is necessary because positive values of these dimensions have a negative impact on economic resilience. Finally, the ER index was standardised using the min-max method, resulting in an ERI index ranging from 0 to 1. 3. Research material Table no. 3 presents the basic parameters characterising the dairy farm groups of different economic sizes. The data indicates that as economic size increases, the average number of dairy cows also grows. For instance, German and Italian medium-large farms had average herd sizes of 24.3 and 28.2 cows, while in the very large group, the number was about ten times higher. There is also considerable variation in milk yield, with medium-large farms having yields about 15% lower compared to the large group. The stocking rate, representing the number of animals per hectare, showed notable differences; for example, Italian farms in the very large group exceeded 5 heads per hectare, indicating a higher level of intensification. Table no. 3. Selected indicators of dairy farms in the countries analysed (mean 2004 - 2021) Country Dairy cows (heads) Milk yield (tons) Share of unpaid labour input in total labour input (%) Total output per AWU (1000 EUR/AWU) Stocking density (LU/ha) Output/ input ratio (%) Medium-large dairy farms 50k EUR ≤ SO < 100k EUR Germany 24.3 6.2 97.5 58.3 1.63 114.1 Ireland 39.0 5.2 96.5 63.3 1.72 125.5 Spain 28.3 6.7 96.8 52.4 2.04 135.8 France 31.6 5.8 96.4 65.3 1.09 97.9 Italy 28.2 5.0 89.4 57.7 1.89 142.2 Austria 21.7 6.7 97.7 46.7 1.18 116.2 Poland 31.7 5.9 93.7 33.5 1.89 141.9 Large dairy farms 100k EUR ≤ SO < 500k EUR Belgium 66.1 7.0 97.6 110.9 2.28 125.5 Denmark 79.2 8.5 70.0 211.8 2.12 98.4 Germany 65.0 7.4 85.4 123.6 1.96 109.9 Ireland 90.3 5.7 79.7 119.0 2.14 127.6 Spain 71.1 7.2 83.5 97.1 2.92 130.5 France 65.4 6.9 87.7 104.0 1.50 103.0 AE Economic Resilience in European Dairy Farms: Trends, Determinants and Challenges 594 Amfiteatru Economic Country Dairy cows (heads) Milk yield (tons) Share of unpaid labour input in total labour input (%) Total output per AWU (1000 EUR/AWU) Stocking density (LU/ha) Output/ input ratio (%) Italy 77.8 6.0 79.7 114.9 3.09 153.6 Netherlands 75.4 8.0 90.5 158.9 2.34 113.1 Austria 44.6 7.5 95.2 76.9 1.58 118.6 Poland 69.0 7.0 68.4 58.7 1.85 137.5 Very large dairy farms SO ≥ 500k EUR Denmark 233.8 9.0 33.2 278.2 2.28 95.3 Germany 270.9 8.5 17.1 125.5 1.80 94.7 Italy 264.4 7.3 47.2 212.7 5.03 147.4 Netherlands 201.0 8.3 75.9 269.8 2.67 115.3 Employment characteristics reveal that very large German and Danish farms had the lowest share of family labour, at 17.1% and 33.2%, respectively, emphasising the role of hired labour. Labour productivity per AWU (Annual Work Unit) increased with economic size. For example, medium-large Polish farms had a labour productivity of €33.5k per AWU, while very large farms in Germany and Italy reached €123.6k and €114.9k per AWU, respectively. The highest productivity levels, exceeding €200k per AWU, were observed in very large farms in the Netherlands and Denmark (€269.8k and €278.2k, respectively). The ratio of production to inputs varies according to the scale and location of the farm. In Italy, for example, farms show very high efficiency, with ratio values ranging from 142.2% on farms in the medium-large group to 153.6% in the large group, suggesting an extremely favourable relationship between income generated and costs incurred. In contrast, Danish farms have an output/input ratio of less than 100%, indicating unprofitable production. 4. Results The research showed that in the medium-large and large farm groups, the highest average economic resilience index (ERI) calculated for the years 2004-2021 was achieved by Austrian dairy farms (Table no. 4). It exceeded the average levels of the ERI index in other farm groups by more than 0.3 points, with the average ERI index in the medium-large farms group of 0.62 and in the large farms group of 0.52. In both groups, the average economic resilience indices of Irish, Spanish, French, and Polish farms were below the average values. The dispersion of the ERI index over 18 years is the key to assessing economic resilience. In the medium-large group, Irish and Spanish farms had the highest dispersion, while in the large group, it was Dutch and Irish farms. This indicates a high variability in their ability to handle economic shocks, significantly affecting their economic results. Table no. 4. 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