Economic Resilience in Intensive and Extensive Pig Farming Systems

Abstract

European pig farmers are challenged by increasingly stringent EU regulations to protect the environment from pollution, to meet animal welfare standards and to make pig farming more sustainable. Economic sustainability is defined as the ability to achieve higher profits by respecting social and natural resources. This study is focused on the analysis of the economic resilience of intensive and extensive farming systems, based on data collected from 56 farms located in Denmark, Poland, Italy and Spain. Productive and economic performances of these farms are analyzed, and economic resilience is assessed through a survey including a selection of indicators, belonging to different themes: [i] resilience of resources, [ii] entrepreneurship, [iii] propensity to extensification. The qualitative data from the questionnaire allow for an exploration of how production systems relate to the three dimensions of resilience. Different levels of resilience were found and discussed for intensive and extensive farms. The findings suggest that intensive farms benefit from high standards and greater bargaining power within the supply chain. Extensive systems can achieve profitability through value-added strategies and generally display good resilience. Policies that support investment and risk reduction are essential for enhancing farm resilience and robustness, while strengthening farmer networks can improve adaptability.

1. Introduction

Pork is the second largest source of animal protein in the world after chicken meat—accounting for 34% of global meat consumption [1]—and its production has increased by 38% from 2000 to 2023, with the 27 EU Member States contributing 15% to world production in 2023 [2].

However, European pig farmers have been facing a number of challenges in running their business in recent decades due to more and more strict EU rules to protect the environment against the air, soil and water pollution that originate from pig farming, to comply with animal welfare standards and to make pig farming socially and economically sustainable [3]. The increasing market orientation of the common agricultural policy (CAP), the fragmentation and weakening of marketing agencies and—last but not least—the growing societal demand for a more sustainable agriculture have led many farmers to rethink their farm development strategies and to rediscover farm diversification as one way of reducing market risks, as well as improving the efficiency of the farm’s organization and resource use [4].

This paper proposes the exploration of an innovative approach to economic performance, which incorporates information relating to resilience, as well as financial and technical performance indicators. The concept of resilience has been included in the European Union upcoming agenda, after becoming very popular in recent decades and being accepted as a key parameter in international discussions for sustainable development [5,6]. Nevertheless, the precise semantics of the word resilience is not univocal and the debate on its definition is still open [7,8,9]. The main and most recent definition, as well as the one adopted in this paper, is called “evolutionary resilience”. In this type of vision, a shock event that occurs sets in motion a phenomenon of transformation which leads the farm to evolve and improve. If, before, the “engineering definition” described an economic system able to cope with a shock event and return to the previous equilibrium regime [10,11,12,13], the ecological definition hints at an initial process-oriented approach, which emphasizes the will to find a new balance point, different from the previous one, but still static [14].

In the evolutionary approach, shock becomes a factor that does not lead the farm to move toward a new equilibrium situation but toward an ongoing process of virtuous mutation and change [14,15,16].

Central to the evolutionary approach is the ability to go beyond adaptability and engage in transformation. Transformability refers to the ability to cross critical thresholds and embark on new development pathways. In the context of agricultural development, this concept can be applied in various ways—for example, by choosing to raise native breeds that are more resilient and capable of generating greater added value, thereby enhancing economic performance. It can also involve integrating emission mitigation practices into production systems or investing in human capital through targeted training and education. In the present study, extensification is a central element, reflecting a farm’s capacity to undergo significant structural and strategic changes.

Over the years, various concerns have led researchers to adopt an evolutionary perspective, emphasizing adaptability as the key to continuous development. Discussing resilience allows for an analysis not only of how local economies recover from shocks but also of the underlying capacity to withstand them. Adaptability, followed by the ability to transform, are essential to the evolution of territorial economies.

This perspective gains further relevance when situated within the VUCA framework—volatility, uncertainty, complexity, and ambiguity—which captures the defining characteristics of today’s socio-economic environment [17,18].

Volatility reflects the speed and magnitude of change in markets and supply systems, particularly in sectors such as agro-industry, where global shocks (e.g., pandemics or epidemics) can abruptly alter supply chains [19].

Uncertainty highlights the growing difficulty of making reliable forecasts, especially concerning raw material procurement and export planning. Complexity stems from the increasing number of interdependent variables and technological demands, which complicate decision making and strategic planning across agro-industrial value chains. Ambiguity refers to the lack of clarity in problem identification and solution development, often due to contradictory or incomplete information, which undermines efforts to guide transformation effectively.

In this light, fostering adaptive and transformative skills becomes essential not only for short-term recovery but for the long-term sustainability and competitiveness of economic systems operating under VUCA conditions. In this perspective, resilience can be a powerful strategy to face these VUCA challenges as it is connected to coping with social, economic and environmental shocks. In the course of the paper, the concept of resilience will be divided into its three main components, with the aim of improving farm viability after disruptions in the short term ([i] robustness), medium term ([ii] adaptation) and long term ([iii] transformation), respectively [20,21]. The synergy of the above-mentioned farm skills is evident: short- and medium-term responses to disturbance can affect long-term viability and the sustainability of the activity [22].

The primary objective of this paper is to offer a new perspective on the economic sustainability of pig farming systems by framing the analysis within the broader concept of economic resilience. Specifically, the study explores how different farm management models—intensive and extensive—demonstrate resilience in the face of economic pressures, contributing to the wider academic discourse on the economic viability of livestock systems. To address this aim, the following research question is investigated: what are the most important factors influencing the economic resilience of intensive and extensive farms?

A secondary objective is to explore how intensive and extensive systems differ in their performance and to assess whether factors that enhance sustainability and resilience in a farm system can be transferred to the other one. In this context, the approach will be descriptive rather than hypothesis testing, focusing on identifying items that could potentially shift between systems.

Following the presentation of the methodological approach, the paper introduces the sample used for the research, providing contextual information, national statistics and preliminary insights into sectoral performance. The main findings on economic resilience have been presented, structured around three key dimensions: resilience of resources, entrepreneurship and propensity to extensification. These factors are subsequently examined in relation to the current context of uncertainty, with reference to the VUCA framework. The conclusions summarize the main outcomes and offer reflections for future research.

The most common studies on the economic evaluation of agricultural enterprises in modern times have focused on surveying and verifying the economic performance of financial results. With the aim of assessing the stability of the farm’s activities and monitoring management, the most frequently analyzed parameters have so far been income and gross margin (understood as the difference between revenue and operational costs) [23,24]. This approach reflects the vision of the modern farmer, who tends to identify the two above-mentioned values as the two objectives to be pursued above all others, since they are the primary necessity for a producer to be able to continue his business [4,25].

Profitability is a fundamental theme in economic evaluation and can be described as the difference between the value of the goods produced and the cost required to produce them [26]. However, this tends to describe a static situation and returns a sustainability statement applied to the short term. Several researchers, over time, have questioned which parameters have a relevant influence in the field of economic performance analysis. Firstly, two fundamental aspects to be considered are efficiency and the price of outputs [27]. Assessing efficiency, in particular, requires the inclusion, within the economic assessment process, of an analysis concerning the technical values of a farm [28].

Feed efficiency, animal health and fecundity are crucial parameters in the definition of a farm’s performance, as they have a major impact on production costs.

This approach, proposed and validated for the entire agricultural sector, is strategic for the pig sector, where slender profit margins require the farmer to increase efficiency in order to maintain his level of competitiveness. Some studies [29,30,31] have emphasized the link between technical and economic parameters, including, for example, the feed conversion rate [32]. This is logical, considering that healthy animals have less need for veterinary and animal care, struggle less to gain weight and are healthier for human nutrition [33].

The evolution of this approach describes the most commonly used method for the economic evaluation of farm activity, which then compares the technical–economic performance of a farm with a benchmark. A broader view, which is proposed in this research, refers to the use of resilience factors. The vision proposed is shared with the SusPigSys project. Within both projects, the indicators and themes covered refer to the FAO-SAFA publication, which proposes a list of themes, sub-themes and indicators for economic resilience [34]. These elements have been revised, in line with the vision of the project.

In the course of this paper, data on resilience of resources, entrepreneurship and propensity to extensification were incorporated into the classical economic analysis. This last parameter, explained in more detail below, is specific to the mEATquality project, which aims to investigate how resilience factors relate to extensification characteristics.

The evolutionary resilience approach highlights the management objective over the long term. This means that, once a shock event occurs, the farm begins a process of evolution which allows it to be able to face challenges in the long term, without stagnating in equilibrium points which can easily expose it to new shock events. For example, transformability may involve restructuring the farm and adopting innovative strategies that enhance its performance from multiple perspectives: economically (e.g., increasing product value through high-quality indigenous breeds or changing the farming system), socially (e.g., improving training and strengthening human capital) and environmentally (e.g., implementing emission mitigation strategies) [14,35].

In this way, the issue of economic resilience became part of the sustainability analysis. The concept of sustainability was defined in 1987 by the United Nations Brundtland Commission Report as the capability of “meeting the needs of the present without compromising the ability of future generations to meet their own needs” [36]. By analogy, sustainable farming must meet the needs of present and future generations, while ensuring its three key functions: profitability, environmental health and social health [37]. Hence economic sustainability is a part of a healthy agri-food chain and can be defined as the ability of achieving higher profitable returns by using social and natural resources sustainably [38]. According to Malak-Rawlikowska et al. [39], economic resilience has to be considered a key theme for the assessment of economic sustainability of farms, next to evaluation of technical performances. Likewise, the estimation of economic resilience can be broken down into different components: robustness, adaptability and transformability. Reinterpreting the investigation tool developed by Malak-Rawlikowska et al. (2021) [39], this paper proposes a list of specific indicators for the qualitative assessment of economic resilience, with reference to a sample of farms involved in the mEATquality project. This project aims to link extensive husbandry practices to the intrinsic quality of pork meat and to understand how to provide the increasingly demanding consumer with a quality product that is environmentally, socially and economically sustainable [39].

The identification of the three variables—robustness, adaptability and transformability—widely recognized in the literature, aligns with the themes of resource resilience, entrepreneurship and extensification propensity. This study also aims to take up the challenge posed by the application of resilience within economic sciences, following the path of those who are integrating it into the agricultural sector. It seeks to establish a correspondence between these three well-established variables and practical parameters applicable to the agricultural context. Resource resilience, entrepreneurship and the propensity toward extensification enrich a growing area of study that will be crucial for the future of sustainable development analysis.

2. Materials and Methods

The study proposed in this paper is a [i] descriptive (information obtained through direct interviews), [ii] fundamental (overview of reality and formulation of a theory) and [iii] qualitative analysis (looking in depth at non-numerical data) [40].

The choice of methodology was based on an overview of the most recent and relevant literature available, conducted within the framework of the mEATquality project. This was not a systematic review but rather an analysis of the most up-to-date sources pertinent to the research focus. Figure 1 shows the flowchart of the research process.

Following the formulation of the research question, the study sample was selected. Data collection took place between 2022 and 2023, during activities carried out within the mEATquality project, which are closely related and preparatory to the current analysis of economic resilience.

Data were collected through structured individual interviews and then elaborated through content analysis, an approach that involves classification and interpretation of information. These data were subsequently processed and analyzed in order to conduct the resilience assessment.

The administration of a structured questionnaire during the farm visit is part of the approach previously validated by the Era-Net SusAn project “Sustainable pig production systems” (SusPigSys) [41]. The questionnaire was administered in parallel with the survey on the farm’s technical–economic data and allowed the collection of qualitative data about resilience, in the absence of valid and agreed quantitative indicators for measuring these aspects.

This type of data, which is closely linked to the subjective perception of the farmer and their specific business reality and operating context, is difficult to quantify and can be detected through direct interviews. In this sense, it requires an effort of objectivity on the part of the farmer and the interviewer. This topic could be quite complex, especially since it is possible for bias to occur during interviews when talking about qualitative data. Survey rigor and dealing with bias are the main challenges for qualitative researchers who employ interviewing as a method of data generation in their studies [42].

2.1. Sample Description

A total of 80 pig farms were involved in the overall study, belonging to different production systems and equally divided among Denmark, Poland, Spain and Italy whose national pig populations represent 48% of the total pig population in the 27 EU Member States in 2023 [43]. Within each country context, multiple production systems were considered, but for the purposes of this research, only one production system per country was selected. This decision was dictated by the need to highlight characteristics that could be specifically associated with each management system. Keeping the sample in its entirety would not have produced analytically meaningful insights for the objectives of this study.

As a result, the sample presented in this research has a different composition in each of the four geographic areas. In Denmark and Poland, most of the farms surveyed operate under an intensive model, and thus this production system was chosen as a reference. Italy and Spain were selected for an in-depth exploration of extensive management systems. At the project level, the criteria for including farms were as follows:

• 20 farms per country;
• Belonging to one of three main production systems:

  ○

  intensive, conventional: a system characterized by high animal density, standardized feeding practices, use of industrial feed, indoor housing and use of veterinary technologies and medicines;

  ○

  extensive, organic or conventional: a low-density model in which animals have access to pasture or open areas, with management practices geared toward animal welfare. In organic systems, this also involves compliance with specific regulations on feeding and medication;

  ○

  intensive, with outdoor access, conventional or organic: a hybrid model that combines high production intensity with regular access to fenced or paddocked outdoor areas.

To build a consistent sample for the present analysis, the most common production system in each country was selected and data from farms operating under other models were excluded. Hybrid systems and farms classified as outliers—whose data were inconsistent or distorted by errors that could not be identified at the collection stage—were excluded. The sub-set of the original project sample includes 56 farms and consists of 16 Polish, 10 Danish, 19 Spanish and 11 Italian farms.

In Poland and Denmark, the sample is made up of predominantly intensive farms mainly focused on commercial breeds. Both conventional and organic certified farms in the Spanish and Italian samples include commercial and local breeds, as well as free range or extensive farms.

Table 1. Pig farming overview in Denmark, Poland, Spain and Italy in 2023.

Descriptive Parameters of National Pig Farming 1	DK	PL	ES	IT
Pig farms 2	2131	51,798	85,197	25,177
Pigs (×1000)	11,368	9770	33,803	9171
Average nr. of pigs per farm	5335	189	397	364
Breeding pigs (×1000)	1142	674	2790	673
Piglets up to the LW of 20 kg (×1000)	4141	2007	10,731	1680
Pigs (between 20 and 50 kg LW × 1000)	3527	2836	7112	1761
Fattening pigs of LW ≥ 50 kg (×1000)	2557	4253	13,171	5057

¹ Source: Eurostat [43], except for pig farms. ² Source: Statistics Denmark, ARiMR, Ministerio Agricultura, Pesca y Alimentacion; Banca Dati Nazionale [44,45,46,47,48].

shows some national statistics from each of the four reference areas, in order to give context to these regional economies, without the goal of making a comparison.

Most pigs in these four countries are kept intensively. In Denmark the high level of pig farm specialization is shown by the relatively low number of pig farms (i.e., 2131) with a high number of pigs per farm (i.e., 5335 pigs), whereas in Poland the average number of pigs per farm is much lower (i.e., 189 pigs), showing the presence of many pig farms (i.e., 51,798), including very small ones. Spain produces more pigs than any other European country, mainly intensively but 1% of them are acorn-fed Iberian pigs and raised extensively during the finishing phase through autumn–winter grazing on oak wood pastures. This type of fattening is called montanera. However, the production of Iberian pigs in Spain accounts for around 10% of the national pig production [49] but only 32% of them are kept outdoors (15% on montanera and 17% free range) [50]. The Italian pig sector is focused particularly on the intensive production of heavy pigs for PDO hams and PGI charcuterie; however, pigs of six Italian native breeds (Cinta senese, Mora romagnola, Nero siciliano, Apulo-calabrese, Casertana, Sarda) are also raised on small farms, often outdoors, for the production of high-quality cured products. In Italy, 1.5% of pigs are free range on 16% of pig farms, with an average of 30 pigs per farm [48] and organic pigs account for 0.7% of the pig population [44].

The tables and graphs presented in the second part of the paper were then constructed by clustering the data collected into the categories mentioned above. Production and economic data were collected from sampled farms for nine main indicators: Live Weight at Slaughter (LWS), Average Number of Sold Pigs (ASP), Live Weight Produced per Pig (LWP), Average Daily Gain (ADG), Fattening Duration (FD), Fattening Cycles per Year (FCY), Feed Conversion Ratio (FCR), Mortality (M) and Gross Margin per Live Weight Produced per Labor Unit (GM).

2.2. Design of Indicator Framework

The assessment of economic resilience is performed through the selection of precise indicators belonging to different themes of interest summarized as follows: [i] resilience of resources, [ii] entrepreneurship, [iii] propensity to extensification. Each of these themes included indicators relating to different areas that have an impact on the farm’s economic performance. The incidence of each indicator is calculated from the data collected at the farm level.

This paper proposes a list of specific parameters for the qualitative assessment of economic resilience, based on the farmer’s perception of their role within the value chain, as well as their power within the respective market. The technical and economic performance is presented, and the main key indicator chosen is the gross margin per kg live weight produced per labor unit.

The first category of assessment is related to the resilience of resource that implies the ability to create optimal preconditions for exploitation of investment and innovation potential of the farm. To examine this aspect, a series of questions to assess how modern the farms are, their capacity to invest and the quality of their human capital were made. The modernity index was evaluated by assessing the age of buildings and equipment, while the investment potential was examined based on factors such as ease of access to financing, the technological level of the farm and the resources available to foster innovation and further investments.

Entrepreneurship is defined as the capacity of creating bargaining power in the supply chain. The horizontal cooperation among pig farms, the ability of selling and purchasing at the right moment with reliable suppliers and capacity of developing direct sales strategies related to the specific qualities of meat are analyzed.

The third theme is the propensity for extensification which is included in the case of managerial risk and seeks to analyze the opinion of the farmers involved in the survey on possible added value from switching from a conventional to extensive farming system (e.g., from integrating some factors of extensification in conventional farming). Risk management relates to a sustainable integration in the markets of inputs and outputs to be less vulnerable to price fluctuations. A questionnaire was used to interview sampled farms, including 22 questions: 10 for resilience of resources, 9 for entrepreneurship and 3 for propensity to extensification (

Table 2. Structure of the survey for data collection.

Theme	Indicator	Goal of the Question	Question	Answer
Resilience of resources	Degree of modernity	Assessment of the robustness of enterprises. The degree of modernity is a parameter chosen to assess the ability of an enterprise to withstand shocks.	Average age of pig production buildings.	Text
			Average age of pig production equipment.	Text
			How modern and technologically advanced is the farming system?	Score 1–5
			How soon are you used to adopt new products, technologies or practices once they are developed?	Score 1–5
	Investment potential	Checking the investment readiness of firms is important to understand how they are positioned toward uncertainty. Is there a basis for the farmer to make forecasting and investment efforts?	Does the financial position allow for large investments?	Score 1–5
			How easy would it be for you to receive a bank loan to keep your farm updated?	Score 1–5
			How many suitable resources (i.e., money, land) are available to your farm to extensify or further extensify the housing and management system for fattening pigs in your farm?	Score 1–5
	Human capital	Evaluating the human capital of a business is fundamental to assess the competences and make projections on the type of evolution the firm will be able to sustain.	Have you undertaken any training related to your business in the past 2 years?	Yes/No
			Have you undertaken any training related to extensive pig production in the past 2 years?	Yes/No
			If you have workers (including family members), are there any opportunities for them to undertake training to enhance their career progression?	Yes/No
Entrepreneurship	Flexibility in replacement of suppliers	Understanding the farmer’s flexibility in navigating between suppliers or buyers is critical to understanding the adaptability and the type of response in the event of a shock.	How easy would it be for you to replace your feed supplier?	Score 1–5
			How easy would it be for you to replace the meat processor you deliver your pigs to?	Score 1–5
	Bargaining power in the supply chain	How the farm positions itself in the market and supply systems. This aspect is fundamental to assessing the volatility typical of the agri-food industry.	To which extent can you influence input prices (i.e., feed, labour, energy, etc.)?	Score 1–5
			To which extent can you influence sale price of pigs?	Score 1–5
			To which extent can you influence the quantity of pigs that you can produce and deliver?	Score 1–5
			To which extent can you influence the quality of pork meat that you produce or can produce?	Score 1–5
			To which extent can you influence the way of payment?	Score 1–5
	Cooperation between farmers	Evaluate the degree of cooperation of farmers, who could thus better cope with market uncertainty and volatility.	Are you member of a pig farmer association?	Yes/No
			Are you member of an organization (producer group) of purchasing feed, piglets or using machinery?	Yes/No
Propensity to extensification	Propensity to extensification	Assess the propensity of farms for a possible transition to more extensive models.	To which extent your switch to a more extensive pig production would or will result into a greater market access and bargaining power?	Score 1–5
			To which extent your switch to a more extensive pig production would or will result into a greater farm profitability?	Score 1–5
			To which extent are you trained or prepared to extensify the housing and management systems for fattening pigs in your farm?	Score 1–5

).

The information collected in the questionnaire reported responses in scales of agreement with the statement from 1–5, Yes/No responses and short multiple responses. This information was transformed into low, medium and high incidence indices (Figure 2) and then the incidence was calculated for each country. This information, combined with the country context analysis, provides interesting insights to contextualize the participants’ perceptions of extensive livestock farming.

Production and economic data from the 56 pig farms selected for analysis were processed statistically. Farmers were recruited on a voluntary basis; before and at the beginning of each farm visit, the farmer (i.e., person(s) to be interviewed) was informed in speech and writing about the project, including information about anonymity, why the research was being conducted, how data were being used and if there were any risks associated, and were asked to return a signed informed consent form before the start of data collection. Statistical analysis of all data was performed with SPSS Statistics 29. The data were tested for normal distribution with the Kolmogorov–Smirnov test and the distribution of the values was defined as non-normal, so the mean does not appear in the table. Non-parametric analysis (Mann–Whitney U test) was applied for non-normally distributed data of the single continuous variables of the production and economic indicators. In addition, the parameter r was added, which provides information on the sample size [51].

3. Results

3.1. Production and Economic Indicators

The survey conducted across these four national samples aims to analyze the outcomes on two different levels. First, the technical results related to production performance and sample characteristics are presented. This initial level of analysis provides the context for a deeper exploration of economic resilience, understood as a system’s capacity to absorb perturbations, adapt to new conditions and transform [17,18]. In the second stage, the analysis is focused on economic resilience, examining in depth the various responses provided by entrepreneurs. The discussion is structured around the themes outlined in the methodology (i.e., [i] resilience of resources, [ii] entrepreneurship, [iii] propensity to extensification) [4].

Table 3. Statistics for productive and economic indicators of Polish and Danish farms.

Indicators		Polish Farms (16)			Danish Farms (10)
Acronym	Unit of Measure	Q25	Mdn	Q75	Q25	Mdn	Q75	r	p-Value
LWS	kg/pig	116.3	125.0	128.8	115.0	118.5	123.0	0.34	0.087
ASP	no. of pigs	508	845	2,601	4209	6065	12,029	0.75	<0.001
LWP	kg	86.3	95.0	98.8	84.8	88.2	91.4	0.32	0.109
ADG	kg/day per pig	0.78	0.92	1.02	1.04	1.07	1.13	0.60	0.001
FD	no. of days	90	95	124	79	81	87	0.73	<0.001
GM	EUR/kg L.W. per L.U.	0.02	0.12	0.44	0.14	0.21	0.40	0.12	0.551

LWS, Live Weight at Slaughter; ASP, Average number of Sold Pigs; LWP, Live Weight produced per Pig; ADG, Average Daily Gain; FD, Fattening Duration; GM, Gross Margin per live weight produced per labor unit (GM). Median (Mdn), lower quartile (Q25) and upper quartile (Q75) values for continuous variables of productive and economic indicators. p = result of Mann–Whitney U test, r = rank-biserial correlation coefficient.

presents the results of the technical parameters related to farm performance and production. Data are organized into country pairs, based on the type of farming system considered. For each parameter, the table shows the sample median values along with the corresponding quartiles, providing more detailed understanding of data variability. It can be observed that, within the intensive farming framework, pigs are sold at a lower weight, around 120 kg. Weight gain performance is average in the Polish sample and it reaches optimal levels in the Danish sample. The duration of the cycle is highly variable and depends precisely on the pig’s final live weight at slaughter and on its ADG. The strong focus on research and innovation in the Danish pig farming sector allows for more than four production cycles per year, whereas Poland completes three. Mortality and feed conversion rate (FCR) were calculated according to the median, as data were not collected for every farm in the sample. Mortality in the Danish sample is 2.8%, and in the Polish ones it is 2.5%. The FCR amounts to 2.7 in Polish farms and 2.5 in the Danish ones. These results highlight the high productivity levels of Denmark, which has the lowest production costs in Europe [52].

In the case of the three parameters for which the Mann–Whitney U test showed statistically significant differences, the test supports the result by returning high r values.

Regarding the extensive farming sample, the values of productive and economic indicators differ significantly from those of intensive farms previously presented (p-value < 0.001). In

Table 4. Statistics for productive and economic indicators of Italian and Spanish farms.

Indicators		Italian Farms (11)			Spanish Farms (19)
Acronym	Unit of Measure	Q25	Mdn	Q75	Q25	Mdn	Q75	r	p-Value
LWS	kg/pig	160.0	170.0	187.5	160.0	172.5	184.0	0.16	0.735
ASP	no. of pigs	49	120	190	85	125	205	0.03	0.370
LWP	Kg	110.5	130.0	157.5	112.4	122.8	155	0.10	0.420
ADG	kg/day per pig	0.22	0.44	0.52	0.34	0.39	0.44	0.07	0.899
FD	no. of days	240	365	545	270	305	455	0.18	0.641
GM LU	EUR/kg L.W. per L.U.	0.06	0.80	3.35	0.46	0.99	2.05	0.01	0.966

LWS, Live Weight at Slaughter; ASP, Average number of Sold Pigs; LWP, Live Weight produced per Pig; ADG, Average Daily Gain; FD, Fattening Duration; GM, Gross Margin per live weight produced per labor unit (GM L.U.). Median (Mdn), lower quartile (Q25) and upper quartile (Q75) values for continuous variables of productive and economic indicators. p = result of Mann–Whitney U test, r = rank-biserial correlation coefficient.

it is possible to observe that pigs are sold at a significantly higher live weight, exceeding 170 kg, while the ADG is lower. This is because extensive farming does not allow for strict control over feeding, relying mainly on grazing with eventual supplementary feeding.

In both Spanish and Italian farms, the ADG is around 0.4 kg per day and production cycles are very long, lasting over a year. Mortality and feed conversion rate were estimated according to the median, as data were not collected for every farm in the sample. Mortality in the Italian sample is 2%, and in the Spanish one it is 5%. The FCR amounts to 5.0 in the Italian sample and 4.8 in the Spanish one.

In the case of the extensive sample, the differences between the two sub-samples are not statistically significant. These technical details and performance metrics of the farms provide a framework for developing a more qualitative assessment focused on the resilience of agricultural enterprises.

3.2. Economic Resilience Data

The in-depth examination of the production system and the context characterization of the samples of pig farms serve as a foundation for the qualitative exploration of the economic resilience themes, as outlined in the methodology.

The analysis of the incidences is presented through graphs, allowing for a more immediate visualization. Each graph includes a single theme and, for each topic, data that share the same production system are aggregated. Data were grouped under the components identified by the previous study of Meuwissen et al. [20] with the goal of studying and operationalizing theoretical aspects, such as robustness, adaptability and transformability, offering a new perspective. The organization into components, themes and indicators is summarized by Figure 3 below.

The category of resilience of resources can be associated with the concept of robustness, which is barely mentioned. The concept of resources’ resilience is concretized in this paper as an investigation of three different indicators: [i] degree of modernity, [ii] investment potential, [iii] human capital.

Robustness describes the capability to assimilate a perturbation without substantial changes on the farm and it is associated with the resources’ resilience. This has been measured by the evaluation of the age of buildings and the equipment, the ability to access loans for investments and the human resources (know-how). Figure 4 provides an initial insight related to the resilience of resources of the sample of extensive farming enterprises.

In the extensive farms, a moderate level of modernity is shown: moderately modern buildings are reported by the Spanish sample, whereas in Italian farms there is a diverse mix ranging from long-established, generational farms to newer, technologically advanced structures. The age of equipment and infrastructure could be considered as an indicator of the farm’s inclination or need to adopt technological innovation. When considered alongside factors such as access to training, investment capacity and other structural conditions, it provides valuable insight into the type of farming system and its overall efficiency.

Investments represent a transversal point, since they are fundamental both for providing good resistance to shock events to the farm and for future adaptability.

The investment potential largely depends on the type of farm and its context. In the sample, the possibility of investment is medium–high. One key differentiating factor might be the age of the farm, as some younger farms are more inclined to undertake investments, particularly technological ones. However, larger enterprises typically possess greater resources and market power [53].

The “intensive” farms show very different data, demonstrating great diversity between local economies (Figure 5).

Polish farms reveal a low average degree of modernity, and their financial condition does not allow for large investments. At the same time, they do not have easy access to loans and the resources held by the farmers are likely to be sufficient. Moreover, in this context, farms fail to adopt innovations in a timely manner. The Danish sample tends to be modern with farm resources allowing significant investments; usually, it is not complex for them to access financing, and the innovations are adopted within a short time frame. It is on average possible in both cases to participate in training activities.

Overall, when attempting to identify trends, it becomes evident that they generally have low structure and are highly case specific. This makes it difficult to pinpoint a replicable, large-scale pattern.

Once the shock is incurred, adaptability takes over. Adaptability is defined as the capacity to generate changes in inputs, outputs, marketing and risk management as a response to the stress event. Adaptability is linked to the theme of entrepreneurship, described through the ability to replace the suppliers, bargaining power along the value chain and propensity for cooperation among farmers (Figure 6) [54].

The topic of farmers’ bargaining power in the agri-food chain is broadly discussed: replacing one or more suppliers is definitely a challenge and requires good flexibility [55,56,57]. Crucial elements in defined farmers’ bargaining power in the value chain include not only traditional factors—such as resource endowment, product quality and location—but also qualitative dimensions, such as the nature of relationships within the value chain (vertical ties) and with other farmers (horizontal networks) [58,59].

This issue is particularly significant for small-scale farmers, who often lack sufficient capital and thus face substantial limitations in their bargaining capacity.

Italian interviewees have little influence over the cost of raw materials but they can somewhat affect other factors, even though the available quantity remains fairly rigid. In contrast, Spanish farmers report that both the quantity and quality can be modified, though altering payment terms and prices is more difficult. This competence is slowly learned and includes the organization of the farm itself or in collaboration with other farms or chain actors, for example, in the form of an association. Indeed, this allows bringing together skills, increases the power of the category and plays a positive role for the community. Such competence is crucial for farmers, who can decrease their dependence on external factors. In Italy, about half of the respondents participate in some form of cooperation, whereas in Spain, almost no respondent does the same. Figure 7 presents data from the intensive sample.

Replacing suppliers is generally not easy for Polish respondents, while it is slightly more feasible for Danish ones. In this kind of business relationship, actors aim to secure the most advantageous terms [60,61] and their bargaining power largely depends on the substitutability of partners within the supply chain [62]. When farms struggle to find alternative buyers or suppliers, their dependency increases, weakening their position [58,59]. According to this perspective, it is possible to hypothesize that farms with stronger contractual positions—able to leverage their power for better prices or financial gains—are more economically viable than those with weaker positions.

Influencing factors, such as production quantity and quality, are generally complex for the Polish sample, whereas in Denmark experiences vary. High levels of participation in cooperatives and producer associations are reported in both farm samples.

The final step is transformability, which in the context of this research refers not only to the necessity of change but also to the farmer’s willingness to adapt their structure and organizational practices. The trend toward transitioning to an extensive model was analyzed based on the farmers’ perceptions of this practice, particularly regarding the ease of accessing the market, increased profitability and greater power within the value chain (Figure 8).

The Danish sample context appears quite neutral to adopting an extensive approach, being convinced that such a change would hardly lead to increased profits or improved bargaining power. Polish farmers have fewer opportunities to achieve such a transformation, that they associate with an enhanced business performance.

The same questions were asked of extensive farms; this should be interpreted as a propensity to further extensification and/or recognition of the positive aspects of this method (Figure 9). In Spain, the opinion is generally very favorable, while Italy shows some cases of dissatisfaction.

4. Discussion

Economic resilience can be challenging in agriculture because it is not only about farmers’ ability to prevent and respond to unforeseen events but also to understand how to adapt with the perspective of continuous improvement, evolution and overcoming growing challenges in a highly unpredictable environment [63].

This study aims to assess the economic resilience of the pig sector by using technical production data on performance and contextual factors across regions. However, the analysis goes beyond country dynamics, seeking to identify the resilience level of the two examined production systems.

According to other research in this field, resilient farms should be endowed with three main components: robustness, adaptability and transformability [20].

In the previous section, these data were graphically represented for each reference context, revealing considerable variability across samples.

For the final comments, the distinction between countries within a single production system was eliminated by aggregating the data under two main categories. This step confirms the existence of differences between the two systems in terms of economic resilience. As illustrated in the following Figure 10, approximately 20 percent of extensive farms achieve high resilience values, while none exhibits low resilience levels. Conversely, the intensive system includes some cases with low resilience and no farms fall into the highest resilience category. In both systems, the majority of entries (>50%) are classified in the medium resilience category.

The factors composing this scenario are multifaceted, as highlighted in Figure 11, and each may be more or less pronounced in one system versus another. For example, the radar chart below shows that the two systems are comparable in terms of resilience.

Resilience of resources corresponds to robustness. This ability to withstand shocks and develop the capacity to prevent them is the primary competence that a farm should cultivate. Strengthening this competence reinforces the organizational structure and helps in anticipating future issues. Resource resilience across the three categories represents an advantage for extensive farms. This advantage is particularly evident in modernization values, though this may be more feasible given the reduced infrastructure requirements. Structural modernization aligns with the capacity for innovation and renewal investments, which can be complex in contexts involving structures, machinery and equipment. Intensive farming systems are hypothesized to be less constrained by these factors. Also, younger farms with future prospects and medium- to large-scale operations demonstrate greater innovation capacity [53]. This difference could be most likely due to the more complex structures in intensive farming, which requires significant investment to comply with updated legislation in the areas of animal welfare and environmental sustainability. It is also necessary to consider that access to bank loans is a variable closely linked to local policies and legislation.

Adaptability is defined as the farmer’s ability to quickly adjust to unforeseen changes, such as rapidly replacing suppliers or buyers, influencing the supply chain and successfully collaborating with other farmers through associations. Adaptability is a skill that has to be built with experience and that enables farmers to achieve and shape stability [64]. Adaptability is a highly individual aspect and it is difficult to identify a clear trend in this sector. It reflects the farmer’s ability to learn and respond to the specific context in which they operate. Some studies highlight the importance of promoting an “adaptive” management system that relies on a network of feedback mechanisms, continuous learning and the ability to anticipate future dynamics, based on past experience [65]. According to the radar graph (Figure 11), the role of extensive farming in supplier replacement flexibility is also significant, strengthened by cooperation among farmers. Intensive farms possess greater bargaining power within the supply chain. Cooperation, intended as vertical and horizontal, is a key factor in determining the competitiveness of the activity. According to some studies, the two types of cooperation are interconnected and generally have advantages for the actors along the entire line.

In some cases, including the example reported in the present study, the low level of horizontal cooperation (as happens in the case of the intensive sample) stimulates greater cooperation in the vertical direction and vice versa. In fact, extensive breeding presents a greater association between breeders, for example, for purchase or machinery utilization, in order to increase their weight in the value chain [66,67].

In line with the VUCA framework, adaptability is essential for effectively managing market volatility. Despite the existence of the EU common market, pork prices vary significantly across EU member states and exhibit strong seasonality—typically higher in summer and lower in winter—and this volatility affects both producers and consumers. In addition to seasonality, one of the factors that has contributed to the fluctuation of pork prices is the improvement of welfare conditions in finishing operations which, together with environmental sustainability strategies, promotes lower overall pork production and a reduction in greenhouse gas emissions. Such reductions could lead to higher market prices due to decreasing market supply. Understanding the nature and the frequency of price fluctuations is critical: if seasonal or long-term trends can influence strategic decisions, short-term or irregular fluctuations from expected prices expose the farm to risks. In some cases, intensive producers manage to influence market prices to some extent [68,69].

Adaptability becomes crucial in contexts of uncertainty, where forecasting and decision making are hindered by a lack of clarity. In the absence of effective adaptation strategies, the EU pig sector is increasingly vulnerable to climate change due to both direct and indirect impacts. Direct effects (e.g., heat stress) can impair animal performance and health, while indirect effects include reduced feed availability, increased sanitary pressures and the spread of pathogens and vectors. Climate change introduces significant uncertainty into European pig production and farmers have to make complex decisions in this ambiguity.

Finally, transformability enables a farm to evolve its overall functioning in response to events that would render its current operations unfeasible. Transformability is the key tool for dealing with the complexity and ambiguity of the market.

Propensity to extensification shows ambivalent results. From the perspective of intensive systems, the propensity for transformation appears limited. This same pattern is observed among extensive farms, which should be interpreted as a propensity toward further extensification and/or recognition of the positive aspects of this method.

Applying these concepts to an enterprise is challenging, as pig farms are intrinsically complex entities, as they are involved in food production and closely linked to agriculture. This complexity is further amplified by their inclusion in an agroecosystem, where natural phenomena play a crucial role. The distinction becomes even more pronounced when comparing outdoor and indoor farms. By definition, indoor farms allow greater control over key factors such as animal nutrition, health and growth, which eludes extensive livestock management.

However, this analysis shows that different factors should be considered in assessing economic resilience. The qualitative approach allows for capturing a snapshot of a reality that, while not comprehensive or fully representative of the national context, is nonetheless valuable and requires this kind of insight.

Based on the research question that was posed and with reference to the results that emerged, the existence of various factors that influence economic resilience in the two farming systems is highlighted. Intensive and extensive farming differ on many levels, and each model can vary significantly depending on the context in which it is applied. These farmers’ competences largely depend on structural factors and on the unique nature of each farm’s ability to transform difficulties into opportunities for growth and evolution [64]. To put this into practice, a farm can deepen its understanding of the rhythms and peculiarities of its operating ecosystem, thereby increasing its readiness to identify and address issues arising from natural processes [69]. Moreover, stability is reinforced by diversifying activities, which helps redistribute risk and establish a safety network. Careful socio-economic management is essential to protect the enterprise from challenges related to its relationship with consumers.

5. Conclusions

The analysis of robustness, adaptability and transformability indicators reveals the diversity of characteristics that influence the economic resilience of intensive and extensive pig production systems.

The analysis of the sampled farms demonstrated a strong robustness in extensive farms and higher gross margins per labor unit, suggesting that extensification, despite its lower productivity, can be profitable through value-added strategies, such as the use of local pig breeds, differentiated products or quality certifications. Also, intensive farms show a good robustness, thanks to their high production standards.

Adaptability emerged as a key dimension of resilience, although it varied between pig farms. Greater flexibility in supplier relationships and stronger collective action were observed in the intensive sample, while influence within the value chain is stronger in the intensive sample. This suggests that strengthening farmer networks and promoting cooperative models could improve adaptability and reduce dependence on external actors.

Therefore, economic resilience in pig production may depend not only on internal farm characteristics. Measures that promote investment, encourage cooperation among farmers and reduce the risks associated with transitions can play a major role to strengthen resilience in both intensive and extensive systems. Future research should incorporate quantitative and longitudinal data to deepen the understanding of how economic resilience evolves over time.

The analysis reveals that, while intensive and extensive farming systems show overall comparable resilience levels, distinct differences emerge in specific factors. Extensive farms demonstrate advantages in resource resilience, particularly in structural modernization, benefiting from lower infrastructure requirements that facilitate innovation and renewal investments. Conversely, intensive systems exhibit superior supply chain flexibility and bargaining power.

The propensity for system transformation appears limited and intensive farms show resistance to extensification. These findings suggest that each system has developed specific strengths aligned with its operational characteristics, with transformation barriers rooted in structural and strategic factors rather than simple economic considerations.

This analysis indicates that resilience strategies should be tailored to each system’s inherent advantages rather than pursuing universal approaches, recognizing that the multifaceted nature of agricultural resilience requires system-specific solutions.

The main limitation of this research lies in the size of the sample and the spatial distribution of the companies themselves. The present study is to be interpreted as a suggestion for an innovative approach of analysis to be integrated with the more conventional verification of technical economic performance. A representative sample could provide more detailed trend analysis and suggest a correlation with the dynamics of regional economies and economic geography. These themes are not applicable to the present study, which has as reference a pool of farms participating in the project with selection criteria consistent with the needs of the project and subsequently adapted for the purpose of the paper.

This space is taken as an opportunity to offer further recommendations regarding future analyses to be conducted in this area. Economic resilience should be regularly incorporated as a key parameter within the assessment of economic sustainability, also serving as a bridge to social sustainability. It is recommended to use this study as a reference point for identifying relevant variables to include in resilience assessments and to consider developing a system of quantitative indicators for evaluation and scoring purposes.

Despite growing awareness, comprehensive assessments of climate impacts on the EU pig sector remain limited. The elaboration of forecasting models able to integrate technical, economical and resilience factors should be a priority for estimating medium- and long-term impacts. Future studies should also incorporate diverse socio-economic scenarios to better capture the complexity well described by the VUCA theory [70].