Spatial Differentiation of the Competitiveness of Organic Farming in EU Countries in 2014–2023: An Input–Output Approach

Abstract

Organic agriculture is a production system based on environmentally friendly practices that promote the conservation of natural resources, biodiversity, and the production of high-quality food. Its tenets are linked to the concept of sustainable development, which integrates environmental, social, and economic goals. In the face of global competition and changes in food systems, studying their competitiveness of organic agriculture is essential. It is key to assessing its potential for long-term development and competition with conventional agriculture. The purpose of this study is to identify and assess the spatial differentiation in the competitiveness of organic agriculture in EU countries. This study assessed the level of input and output competitiveness of organic agriculture in selected EU countries using the author’s synthetic taxonomic indicators consisting of several sub-variables. The competitiveness of organic farming in twenty-three countries (Cyprus, Latvia, Portugal, and Finland were not included due to a lack of statistical data) was analysed using one of the linear ordering methods, i.e., a non-pattern method with a system of fixed weights. The research has shown significant spatial differentiation in both the input competitiveness and the outcome competitiveness of organic agriculture in EU countries. In 2023, Estonia had the highest level of input competitiveness, followed by Austria, the Czech Republic, and Sweden. In 2023, Estonia had the highest synthetic indicator of outcome competitiveness, followed by The Netherlands and Denmark. In addition, an assessment was made of changes in EU organic agriculture in 2014–2023 by analysing the direction and dynamics of changes in selected measures of the development potential of organic agriculture in all member states (27 countries). This sector is characterised by high growth dynamics, including both the area under cultivation and the number of producers and processors of organic food. This study identified several important measures to support the development of organic farming (especially in countries where this type of activity is relatively less competitive) through targeted support mechanisms, such as policy and regulatory measures, financing, agricultural training and advisory services, scientific research, encouraging cooperation, and stimulating demand for organic products.

1. Introduction

Organic agriculture is the subject of intense scientific debate in the context of its potential role in ensuring long-term global food security while reducing the negative environmental impacts of agriculture [1,2,3,4]. It is treated as a holistic production system based on natural resource management, which reduces the use of nonrenewable resources on farms. In this context, they represent a specific quality system linked to protected designations of origin and geographical indications. At the same time, its operation is subject to mandatory inspection and certification [5], which is an important element to ensure transparency, credibility, and high quality of organic products. This strengthens consumer confidence and the stability of this sector in the long term. Proponents of this food production system emphasise its beneficial effects on the environment, biodiversity, and soil quality [6,7,8,9]. Others believe that this production system is and will continue to be of little relevance [10,11,12] and that its relatively lower production efficiency compared to conventional agriculture may limit its ability to meet the growing global demand for food in the face of population growth and climate change [13,14,15].

Researchers’ interest in organic agriculture is also because it is nowadays seen as an important element in achieving the goals of sustainable development [16,17,18,19,20] and an important component of the European Union’s agricultural policy [6,21,22]. In addition, the past decades have seen a dynamic increase in the importance of this sector in the economies of many countries, which is reflected in the growing area of organic crops. This is related, among other things, to the development of global agricultural trade, which is stimulated by the development of transport and communications infrastructure and food marketing and distribution systems, as well as by the increase in the wealth of society and the convergence of consumption patterns [23]. Among the factors stimulating the development of this type of production and consumption, we can also mention the increasing role of pro-environmental farming methods (also in the context of the implementation of the Sustainable Development Goals), the subsidisation of this type of production, and the growing importance of sustainable food consumption [24], including the growing interest in healthy lifestyles manifested, among other things, in the increase in demand for organic food. Growing consumer interest in organically grown products has recently been highlighted by Chaudhary [25] and Kalyani and Prabhavathi [26], among others.

It should be noted, however, that despite the observed growth, the share of organic production in the world’s total agricultural land area remains relatively small compared with conventional agriculture [27]. According to Willer et al. [28], organic agriculture occupies just over 2% of the global arable land. This raises questions about its real potential on a macroscale and its possible further development, which is likely to be determined by its productivity and economic competitiveness against intensive models of conventional agricultural production. In this context, it is particularly important to conduct in-depth comparative analyses, taking into account not only production and economic aspects but also environmental aspects, to realistically assess the potential of organic agriculture as part of global food security strategies.

Owing to the multidimensionality of this phenomenon, new models and techniques for measuring agricultural competitiveness are being systematically developed [29], which can form the basis for creating policies and strategies aimed at improving the competitive position of countries in the global market. The complexity of agricultural competitiveness, while lacking a uniform definition and a coherent concept for its measurement, indicates the need for in-depth, systematic research that considers a variety of theoretical perspectives and practical assessments of this phenomenon. This study attempts to fill the research gap identified in the literature on the competitiveness of organic agriculture in EU countries.

The purpose of this study is to identify and assess the spatial differentiation of the competitiveness of organic agriculture in European Union countries. In connection with the research problem, the following research questions were identified:

Research question 1 (RQ 1): What were the trends of changes in the development potential of organic agriculture in selected EU countries from 2014 to 2023?

Research question 2 (RQ 2): What was the level of input competitiveness of organic agriculture in selected EU countries during 2014–2023?

Research question 3 (RQ 3): What was the level of outcome competitiveness of organic agriculture in selected EU countries in 2020–2023?

The arguments in this study are structured as follows: theoretical, methodological, and empirical. The theoretical section analyses the issues of competitiveness in light of selected economic theories and discusses measures of agricultural competitiveness. In addition, the peculiarities of organic farming are presented, and the issues of competitiveness of this type of activity are analysed against this background. This was followed by a detailed discussion of the research methods used in this study. The empirical part of the article assesses the spatial differentiation of the level of input and output competitiveness of organic agriculture in selected EU countries as well as assesses changes in the development potential of organic agriculture. The research area was selected EU countries (23 countries), which were dictated by the availability of statistical data. The research period was basically 2014–2023, followed by a discussion of the results. The article ends with the conclusions of the analysis and a bibliography.

2. Theoretical Background

2.1. Competitiveness as an Economic Phenomenon

We have been dealing with the competition since the dawn of time. The modern sense of the term boils down to competition between rivals in pursuit of analogous goals in various areas of life [30] and achieving and maintaining a competitive position over others [31]. It can be analysed from different perspectives, such as products, companies, sectors, regions, countries, or groups of countries [32]. Winning rivalry with competitors always brings some benefit, and the arena of competition can be not only a specific market but also a market sector or strategic group, and the rivals (the subject of competition) can be national economies, blocs of states, companies, business units, or individuals in the company. To compete effectively, one must be competitive, that is, gain an advantage and/or competitive position. Thus, competitiveness is a characteristic of entities that operate in a competitive environment. In classical theories, the starting point for evaluating and forming definitions of competitiveness is the factor resources (labour and capital). This is also noted by Stankiewicz [33], who argues that the objects of competition are mainly resources in the broadest sense, as they are limited in quantity and quality and affect the market offer of products and, above all, their acceptance by buyers. Smith [34], considered the founder of classical economics, first presented an analysis of the functioning of competitive markets. Using the metaphor of the “invisible hand of the market”, he described competition as a force that self-regulates the market and prices of goods, reconciles private and social interests, and ensures the efficient allocation of resources in the economy. The opposite view was held by Marx, who believed that competition causes the misallocation of capital, leading to the accumulation of capital and ultimately to the formation of a monopoly and throwing the market out of equilibrium. According to the neoclassical school, competition was equated with perfect competition, and Keynesianism rejected the classical school’s assumptions about the self-regulating effect of competition and advocated for state interventionism. In neotechnological theories, the resources of individual countries were expanded to include knowledge (technical progress), and in the trend of evolutionary economics, knowledge was considered a source of competitive advantage [30]. In modern studies and assessments of competitiveness, factors determining competitive potential and competitive position also include natural resources, product disposition, production scale effects, institutional environment, marketing and promotion, and access to information [35]. Cournot, a representative of the neoclassical school, linked the number of entrepreneurs to market price, claiming that the greater the number of firms in the market, the lower the selling price, which corresponds to marginal costs [30,36].

Porter [37] proposed one of the first definitions of competitiveness, presenting it as a diamond model of competitiveness and treating it as the ability of a nation to create conditions that enable companies to achieve sustainable competitive advantages. This term, although often criticised (because the competitiveness of companies is not analogous to the competitiveness of countries), is taken into account, especially in the analysis of sectors subject to global trends and trade liberalisation [38]. Porter, the author of the five-forces model and the theory of competitive strategy, treated the phenomenon of competitiveness multidimensionally, in dynamic terms, taking into account the synergistic effects of interrelated factors. He believed that building a competitive advantage based on one or two factors was unsustainable. He considers the following to be the main factors of competitiveness included in the concept of the diamond of competitive advantages: the position occupied by countries in terms of the quality of factor resources; the size, structure, and growth rate of domestic demand; the presence in the country of modern related industries supporting the various branches of the economy; the conditions for the formation, organisation, and management of companies; and the nature of domestic rivalry. According to the author, equally important is investment activity and the introduction of innovations, which indicate the importance of the human factor in building competitive advantages. In its report on competitiveness, the European Commission also states that the main drivers of human productivity and well-being are research and innovation [39].

There are many criteria for classifying the phenomenon of competitiveness, such as activities or effects, moment of evaluation, area of occurrence, parties to market relations, time of observation, and level of competitiveness. According to one of them, namely, activities or effects, input competitiveness and outcome competitiveness can be distinguished. Input competitiveness determines the ability of entities to create a basis for effective competition, and outcome competitiveness determines the results of competition, that is, the achievement of a market position. Input competitiveness is determined by the skilful use of existing resources, speed of response to changes in the environment, and ability to take advantage of the configuration of the environment and others, which makes this type of competitiveness similar to competitive advantage. Outcome competitiveness is determined, among other things, by market share or financial performance, which brings it closer to the market position achieved [33].

Thus, competitiveness is a multidimensional concept that is analysed in national, sectoral, and enterprise contexts. In macroeconomic terms, competitiveness refers to a country’s ability to achieve sustainable economic growth and improve its living standards in an open global economy [40].

2.2. The Essence and Measurement of Agricultural Competitiveness

An important issue in socioeconomic development is the competitiveness of agriculture, especially in countries where it is an important sector of the economy. There is a lack of clarity in the literature on how to define and measure the competitiveness of agriculture [41], including organic farming. According to Latruffe [42], agricultural competitiveness can take the form of price competitiveness, non-price competitiveness, structural competitiveness and institutional competitiveness. Studies often analyse the regional competitiveness of agriculture, which, according to Nowak [43], manifests itself in the sector’s ability to successfully operate and develop under conditions of existing rivalry and with the use of local potential. The agri-food sector is based on the interdependence of the food industry and agricultural entities, with external factors and horizontal ties playing a key role in economic outcomes and overcoming supply-scale barriers in exports [44]. Ball et al. [45] used trade-related measures of competitiveness and prepared a ranking of agricultural competitiveness in 11 EU member states compared to the United States. According to researchers, competitiveness is expressed by relative changes in productivity and market position [46] and is the most reliable long-term indicator of competitiveness [47]. The measures often used in competitiveness studies are TFP indicators of total factor productivity. The most popular of these are cited in the studies of Nowak and Różanska-Boczula [47], Kijek et al. [48], and Melfou et al. [49]. These are the Malmquist productivity index, Hicks-Moorsten productivity index, and Tornqvist index.

The competitiveness of agriculture can be analysed through its ability to maintain and increase its market share in domestic and international markets in an economically viable and sustainable manner [29]. Factors such as comparative advantage, cost efficiency, production efficiency, international markets, food quality and safety, government policy, technological innovation, and environmental sustainability can be considered in assessing competitiveness [47,50]. Some researchers have expressed the opinion that competitiveness studies should use comprehensive approaches instead of isolated indicators, which do not accurately reflect the spectrum of determinants of competitiveness [51]. One model used to describe competitiveness is the competitiveness pyramid. This concept presents the factors that determine the level of competitiveness and proposes a systematic approach to their classification. This model, adapted to the specifics of the agricultural sector, taking into account the aspect of international competitiveness, was used in the research of Nowak and Różanska-Boczula [47] to determine the competitiveness of agriculture in EU countries and in the research conducted by Kołodziejczak and Kossowski [52], among others. This concept considers the relationship between the factors shaping competitiveness in the long term and performance, including welfare growth.

2.3. The Specifics of Organic Agriculture

Following EU legislation on organic production, as outlined in Regulation (EU) 2018/848 [53], organic farming is defined as a general system of farm management and food production based on practices that favour the environment and climate, a high level of biodiversity, the protection of natural resources, and the application of strict animal welfare standards. It responds to growing consumer demand for food created from natural processes and substances. Organic farms abandon the use of genetically modified plants, mineral fertilisers, and chemical pesticides, replacing them with organic fertilisers and natural protection methods. They also use organic seeds, practise waste recycling, and diversify crop rotations to improve soil fertility and support natural biological processes. Such practices help reduce pressure on the environment, promote the conservation of biodiversity, reduce the risk of degradation of natural resources, and simultaneously have a positive effect on increasing the diversity of agricultural production space, raising the biological activity of soils, and shaping the landscape. In addition, they reduce greenhouse gas (GHG) emissions and the risk of water and soil contamination from residual chemical inputs. Therefore, they can be an important part of food systems that promote sustainability [13]. Thus, the peculiarity of this system is the use of nature’s mechanisms throughout the production cycle [54].

The literature on organic farming, in its broadest sense, often emphasises its alternative character to the dominant model of conventional agriculture [55,56,57]. In this context, organic agriculture is gaining importance as a form of farming that considers the principles of sustainable development and environmental protection, which justifies the need to deepen knowledge of its specifics and potential to counteract the negative effects of conventional agriculture.

An important aspect that determines the direction of agricultural development is the availability of agricultural land resources, of which the best-quality land is already largely exhausted. In this context, organic farming appears to be the most acceptable alternative to conventional production models [55]. In addition, according to Smith et al. [58], organic farming systems are more beneficial to pollinators, have a positive impact on the environment, and allow the production of food with comparable or higher nutritional value and lower pesticide residue content. Its use is justified not only environmentally but also economically, as pointed out by Jaenicke, Carlson [59], Shadbolt et. al. [60], and Gamage et al. [55], due to the possibility of obtaining higher prices for products labelled as organic. Nevertheless, the system is not without its difficulties. Organic farmers face lower and more variable yields, difficulties in maintaining soil fertility, costly and time-consuming certification procedures, and limited market access [13,55,58,61]. In response to these constraints, the combination of organic farming with new technologies that can increase efficiency while maintaining sustainability is being increasingly advocated. Innovative approaches, consistent with agroecology principles, promote productivity growth in an environmentally friendly manner [55]. Similarly, Sazonska et al. [61] and Smoluk-Sikorska [62] discussed organic farming and its key aspects. In this context, modern digital technologies are particularly significant, as they provide practical solutions to the challenges faced by organic producers. Blockchain technology, QR codes, and digital seed traceability ensure the quality and traceability of organic products and help to build consumer trust. Research by Efremova [63] has demonstrated that digitalisation can generate a multiplier effect across the entire agricultural sector, enhancing food security and export potential. Similarly, Zaruk et al. [64] emphasise the potential of information systems, automation, and artificial intelligence to optimise organic production processes. However, they also highlight existing barriers, such as implementation costs. Despite these challenges, the authors argue that cloud technologies, big data and mobile applications will be a key area for development. Likewise, Hussein [65] and Sarkar et al. [66] emphasise that modern technologies support product quality and productivity as well as biodiversity conservation, reduced chemical inputs, and the development of local communities. Furthermore, all the authors stress the importance of investing in farmer education to enable the effective use of available technological tools.

Organic agriculture focuses on healthy soils, complex crop rotations, ecosystem dynamics, and closed nutrient cycles. It aims to reduce external inputs (e.g., by banning and replacing mineral nitrogen fertilisers and synthetic pesticides) and relies on biological plant protection. Farmers wishing to produce organically must adhere to publicly (at the national or regional level) or privately codified regulations on management practices at the farm or crop level and undergo a certification process to label their products as organic [67].

2.4. Competitiveness of Organic Agriculture

According to Stankiewicz’s concept [33], competitiveness should be understood as a systemic arrangement of four interrelated elements: competitive potential, competitive advantage, competitive instruments, and competitive position. Concerning organic farming, this approach allows a comprehensive assessment of its ability to develop by comparing it with intensive agricultural production systems in terms of production and economic efficiency, as well as environmental and social impacts.

When analysing the problem of production efficiency, it is worth emphasising again that organic farming, compared to conventional systems, is usually characterised by lower yields in the short term, whereas in the long term, it can lead to higher yields [68,69]. According to Feledyn-Szewczyk and Kopinski [70], Ponisio et al. [14], Rigby and Cáceres [71], and a meta-analysis by Yadav et al. [68] and Seufert et al. [15], among others, the average yield decrease in organic systems compared to conventional farming methods is approximately 20%, which is mainly due to the abandonment of synthetic fertilisers and pesticides in favour of natural mechanisms to promote plant growth and soil fertility. In contrast, Yadav et al. [68], Reganold and Wachter [13], and Martini et al. [72] found that in the long term, organic farming can lead to improved soil quality, increased biodiversity, and consequently increased and stable yields. It should be noted, however, that most of the comparisons of organic and conventional agriculture to date in terms of production efficiency have been based on short-term yield analyses, often of single crops, neglecting to evaluate the performance of entire production systems under selected climatic and geographic conditions and their impact on yields in the long term, as pointed out by, among others, Bautze et al. [73], Riar et al. [74], Li et al. [75], Knapp and van der Heijden [69], and Seufert et al. [15].

Although, as already mentioned, organic production systems are often characterised by lower yields than conventional agriculture, higher prices for organic products [76,77,78], lower production costs [70,78,79], and subsidised production [70,74,76] can compensate for these differences and ensure the profitability of production, increasing the competitiveness of organic agriculture [80]. For years, producers in EU countries have been able to financially count on support for food production, including organic methods, which are implemented to stimulate development and increase the competitiveness of this sector. Concurrently, it is considered that competitiveness analyses are believed to be most meaningful when used to measure the price effect [81]. A few studies have measured the impact of support on competitiveness [82,83,84]. They showed that when agricultural support increases, the strength of competition decreases; therefore, more attention should be paid to the type of support implemented than to the amount of support. It will be more effective and beneficial for farmers to have support that raises the prices of agricultural products. In the EU, subsidies under the Common Agricultural Policy (CAP) are also an effective instrument for promoting the transition to organic farming, as they bridge the disparity in profitability between organic and conventional production systems [76,77,85]. Appropriate financial support encourages farmers to transition to a more sustainable agricultural management model, despite the higher costs and greater production risks associated with organic farming.

The drive towards the broader implementation of ecological farming practices is also reflected in the legislative and strategic actions undertaken by the European Union within the framework of the Common Agricultural Policy (CAP). On 30 May 2018, the European Commission adopted Regulation (EU) 2018/848 on organic production and labelling of organic products. This regulation aims to harmonise production standards, enhance transparency, and build consumer trust in organic labelling. Furthermore, the EU’s strategies set an ambitious target: by 2030, at least 25% of the agricultural land in each EU [86] member state should be farmed organically. The CAP plays—and will continue to play—a significant role in shaping the landscape of organic agriculture in the EU. Consequently, there is a clear need for further in-depth research into the effectiveness and long-term impact of CAP instruments that support the development of organic farming at national and EU levels.

In addition, it should be noted that production efficiency and thus the profitability of production often depend on the technological approach adopted in cultivation and the knowledge and experience of the farmers themselves, as well as the species of crops grown [14,87,88] and the area of cultivation. According to Walsh et al. [89], larger farms can benefit from economies of scale, potentially increasing their profitability compared with smaller farms.

One important factor in the competitiveness of organic farming is the rising interest in a healthy lifestyle among end-buyers/costumers, who associate organic products with unprocessed, high-quality food. Such consumers are willing to pay a higher price for higher-quality foods. This interest translates into the popularisation of both organic food and consumer trends linked to concerns about health, the environment, and sustainability. In this context, organic food is part of the broader trend of responsible consumption, becoming part of the strategy for building the competitive advantage of organic agriculture. In the long term, improving the competitiveness of the sector is influenced by the growing demand for organic food [13,90].

Food from organic farms is perceived as healthier and safer for consumers, partly because of the ban on synthetic pesticides, herbicides, and mineral fertilisers [91,92,93,94]. Studies have shown that organic produce contains lower levels of pesticide residues but often has a higher content of certain bioactive ingredients [95,96]. These features represent an essential competitive advantage in the context of growing consumer health awareness and demand for high-quality food. Combined with organic certification, which ensures that certain production standards are met, organic products can successfully compete in the market, not only in terms of nutritional value but also in terms of reputation and public trust [97].

Assessing the competitive potential of organic farming requires considering not only production and economic aspects but also the environment. Important in this context are the differences in production technology, which, in the case of organic farming, differ significantly from the methods used in the conventional system, as mentioned earlier. The importance of growing conditions and environmental benefits in social, economic, health, and even ethical and aesthetic dimensions resulting from organic farming has been pointed out by many authors, among others such as Varma et al. [98], Zieliński et al. [99], Nowak et al. [100], Das et al. [101], Smith et al. [102], Nguyen et al. [103], Kirdar [104], Głodowska and Gałązka [8], Aceleanu [105], and Crowder and Reganold [76]. This framing of organic agriculture promotes its perception as a fully-fledged alternative to intensive production systems, which is part of the drive for sustainable rural development.

3. Materials and Methods

This study uses statistical data from EUROSTAT and reports from the Research Institute of Organic Agriculture FiBL and IFOAM—Organics International from 2016 to 2025 [28,106,107]. The statistical analysis of the collected data was processed using MS Excel spreadsheet from Microsoft Office 365. The research area generally covered EU countries, with 27 EU countries included in the study of changes in EU organic farming between 2014 and 2023, while 23 countries were analysed in terms of organic farming competitiveness (Cyprus, Latvia, Portugal, and Finland were not included due to a lack of statistical data). The research period generally covered the years 2014–2023. We would also like to add that GenAI was used to translate the text.

The empirical section consists of two parts. The first evaluates the changes in EU organic agriculture in 2014–2023, analysing the direction and dynamics of changes in selected components of the development potential of organic agriculture in individual EU countries, that is, crop acreage, number of producers, and processors, which was the answer to RQ 1. Changes in the mentioned components of organic agriculture were determined using the trend line established by the least-squares method (y = ax + b) and single-basis indices. The directional coefficient of the trend equation (a), which illustrates how the dependent variable (y) changes over time, and the coefficient of determination (R2), which indicates the degree of fit of the empirical variables to the trend line, were used to assess the direction and degree of change. Trend lines were drawn for the relative and absolute values calculated against the average of the years analysed. This allowed for horizontal and vertical comparisons by eliminating the absolute levels of individual quantities. In the absence of data on the analysed phenomena in some years of the 2014–2023 period, the principle of using the arithmetic average of adjacent years was adopted, or in the absence of data from the years beginning and ending the analysed time horizon, that is, 2014 or 2023, the principle of duplicating data from adjacent years was adopted. The analyses made it possible to observe the general tendencies of changes in organic agriculture in the EU and its member countries.

The second subsection assesses the level of input and output competitiveness of organic agriculture in the selected EU countries using the author’s synthetic taxonomic index consisting of several sub-variables (diagnostic variables and features). In the case of the analysis of input competitiveness of organic agriculture, data from 2014 and 2023 were included, whereas the study of output competitiveness used data from 2020 and 2023. The use of values from 2020 was dictated by the lack of statistical data available for this area in 2014. The selection of indicators was directed by the established research objective, that is, the assessment of spatial differentiation of the level of input and output competitiveness of organic agriculture in EU countries as well as the availability of statistical data and literature studies, e.g., [6,21,108,109,110]. It should be noted that there are problems with the availability of statistical data related to organic farming, which is a research limitation of this study. In the case of missing a lot of data for specific objects of analysis (countries), objects with missing values were excluded [111]. The research area initially covered all EU countries, but due to the lack of statistical data on the competitiveness of organic farming, Cyprus, Latvia, Portugal, and Finland were excluded from this study. In the case of missing individual data for a specific country and a given year of analysis, imputation was used, which consists in replacing the missing data with the average value from the two adjacent years for which data were available [112].

With regard to the input competitiveness analysis of organic agriculture, the above-mentioned index was calculated for two years (i.e., 2014 and 2023) and consisted of five sub-variables (what is the answer to RQ 2):

• X1—Share of total organic area (fully converted and under conversion to organic farming excluding kitchen gardens) in total utilised agricultural area UAA (%).
• X2—Share of agricultural organic producers in total number of agricultural holdings (%).
• X3—Average organic farm area (Total organic area in ha/number of organic producers) (ha).
• X4—Food area (Total organic area in ha per 10 000 inhabitants) (ha/10 000 inhabitants).
• X5—Financial inputs for agricultural science per hectare of organic area (euro/ha).

In order to answer RQ 3 and assess the level of output competitiveness of organic farming in EU countries, a synthetic taxonomic index was constructed, which was calculated for two years (i.e., 2020 and 2023) and consisted of four diagnostic variables:

• Y1—Export of organic products (t) per total organic area (fully converted and under conversion to organic farming, 1000 hectare) (t/1000 ha).
• Y2—Expenditure on organic products per person (Euro per capita).
• Y3—Cereal yield (t/ha).
• Y4—Export of organic product export (t) per number of organic producers and processors (t/number of organic producers and processors).

The variables were assessed using formal criteria: measurability, completeness, and comparability. The correlation strength among the diagnostic indicators was analysed, confirming that all variables were suitable for further research.

Table 1 and Table 2 present the statistical characteristics of the analysed diagnostic variables for input and output competitiveness in 2014, 2023, 2020, and 2023, respectively. Disparities among countries were identified by focusing on the minimum and maximum values and coefficient of variation. The relative measure of dispersion, the classical coefficient of variation (V_j), was used to assess variability:

$$V_j={\displaystyle \frac{{AV}_j}{S_j}}$$

(1)

where:

• AV_j—arithmetic mean of indicator x_j
• S_j—standard deviation of indicator x_j

Table 1. Descriptive statistics of diagnostic variables in 2014 and in 2023 year—input competitiveness.

Descriptive Statistics	2014
	X1	X2	X3	X4	X5
Arithmetic mean	6.80	4.44	63.27	273.74	943.66
Standard deviation	5.16	4.16	87.11	257.91	2108.41
Maximum	19.35 (Austria)	15.80 (Austria)	447.41 (Slovakia)	1182.23 (Estonia)	9764.71 (Malta)
Minimum	0.29 (Malta)	0.11 (Malta)	3.40 (Malta)	0.79 (Malta)	0.00 (Luxembourg)
Coefficient of variation	0.76	0.94	1.38	0.94	2.23
Descriptive statistics	2023
	X1	X2	X3	X4	X5
Arithmetic mean	10.61	7.11	54.24	436.02	747.24
Standard deviation	6.68	6.17	38.28	374.08	1707.95
Maximum	25.69 (Austria)	23.84 (Austria)	151.86 (Slovakia)	1734.58 (Estonia)	7787.88 (Malta)
Minimum	0.62 (Malta)	0.33 (Malta)	2.64 (Malta)	1.27 (Malta)	0.00 (Luxembourg)
Coefficient of variation	0.63	0.87	0.71	0.86	2.29

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 1 March 2025) [113].

Table 1. Descriptive statistics of diagnostic variables in 2014 and in 2023 year—input competitiveness.

Descriptive Statistics	2014
	X1	X2	X3	X4	X5
Arithmetic mean	6.80	4.44	63.27	273.74	943.66
Standard deviation	5.16	4.16	87.11	257.91	2108.41
Maximum	19.35 (Austria)	15.80 (Austria)	447.41 (Slovakia)	1182.23 (Estonia)	9764.71 (Malta)
Minimum	0.29 (Malta)	0.11 (Malta)	3.40 (Malta)	0.79 (Malta)	0.00 (Luxembourg)
Coefficient of variation	0.76	0.94	1.38	0.94	2.23
Descriptive statistics	2023
	X1	X2	X3	X4	X5
Arithmetic mean	10.61	7.11	54.24	436.02	747.24
Standard deviation	6.68	6.17	38.28	374.08	1707.95
Maximum	25.69 (Austria)	23.84 (Austria)	151.86 (Slovakia)	1734.58 (Estonia)	7787.88 (Malta)
Minimum	0.62 (Malta)	0.33 (Malta)	2.64 (Malta)	1.27 (Malta)	0.00 (Luxembourg)
Coefficient of variation	0.63	0.87	0.71	0.86	2.29

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 1 March 2025) [113].

Table 2. Descriptive statistics of diagnostic variables in 2020 and in 2023 year—output competitiveness.

Descriptive Statistics	2020
	Y1	Y2	Y3	Y4
Arithmetic mean	9.43	85.16	2.80	1.27
Standard deviation	20.72	105.55	0.90	2.71
Maximum	91.76 (Romania)	384.13 (Denmark)	4.57 (The Netherlands)	10.55 (Romania)
Minimum	0.01 (Lithuania) 0.00 (Czechia Denmark, Hungary)	0.73 (Slovakia) 0.00 (Malta)	1.00 (Malta)	0.01 (Belgium, Bulgaria, Croatia) 0.00 (Czechia, Denmark, Ireland, Lithuania, Malta Luxembourg, Hungary, Poland, Sweden Slovenia, Slovakia)
Coefficient of variation	2.20	1.24	0.32	2.13
Descriptive statistics	2023
	X1	X2	X3	X4
Arithmetic mean	16.47	80.90	2.75	1.27
Standard deviation	39.95	98.85	1.23	4.03
Maximum	192.76 (Estonia)	363.04 (Denmark)	6.00 (The Netherlands)	20.31 (Estonia)
Minimum	0.01 (Lithuania) 0.00 Czechia, Denmark)	0.68 (Malta)	0.81 (Cyprus)	0.01 (Belgium) 0.00 (Czechia, Croatia, Cyprus, Denmark, Ireland, Lithuania Luxembourg, Hungary, Poland, Slovenia)
Coefficient of variation	2.43	1.22	0.45	3.18

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 15 March 2025) [113] and reports from the Research Institute of Organic Agriculture FiBL and IFOAM—Organics International [28,106,107].

Table 2. Descriptive statistics of diagnostic variables in 2020 and in 2023 year—output competitiveness.

Descriptive Statistics	2020
	Y1	Y2	Y3	Y4
Arithmetic mean	9.43	85.16	2.80	1.27
Standard deviation	20.72	105.55	0.90	2.71
Maximum	91.76 (Romania)	384.13 (Denmark)	4.57 (The Netherlands)	10.55 (Romania)
Minimum	0.01 (Lithuania) 0.00 (Czechia Denmark, Hungary)	0.73 (Slovakia) 0.00 (Malta)	1.00 (Malta)	0.01 (Belgium, Bulgaria, Croatia) 0.00 (Czechia, Denmark, Ireland, Lithuania, Malta Luxembourg, Hungary, Poland, Sweden Slovenia, Slovakia)
Coefficient of variation	2.20	1.24	0.32	2.13
Descriptive statistics	2023
	X1	X2	X3	X4
Arithmetic mean	16.47	80.90	2.75	1.27
Standard deviation	39.95	98.85	1.23	4.03
Maximum	192.76 (Estonia)	363.04 (Denmark)	6.00 (The Netherlands)	20.31 (Estonia)
Minimum	0.01 (Lithuania) 0.00 Czechia, Denmark)	0.68 (Malta)	0.81 (Cyprus)	0.01 (Belgium) 0.00 (Czechia, Croatia, Cyprus, Denmark, Ireland, Lithuania Luxembourg, Hungary, Poland, Slovenia)
Coefficient of variation	2.43	1.22	0.45	3.18

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 15 March 2025) [113] and reports from the Research Institute of Organic Agriculture FiBL and IFOAM—Organics International [28,106,107].

From the list of potential diagnostic indicators, those for which |V_j| < 0.1 are eliminated. All indicators were characterised by sufficient variability.

All variables describing input and output competitiveness were stimulants, for which low values were undesirable from the point of view of the phenomenon under study.

This study uses one of the methods of multidimensional data analysis, which deals with the study of objects described by many features. The primary goal of these methods is to construct a synthetic (aggregate) measure that allows for the comparison of elements of a set (objects) described by many diagnostic variables (features). The synthetic index is a latent variable because its values are not directly observed. In order to determine the value of the synthetic measure, linear ordering methods are used, which are applied in economic research to establish the order or classification of objects (e.g., countries, regions, companies, products, etc.). There are two basic groups of methods used to determine the value of a synthetic variable: non-pattern methods and pattern methods [114,115,116].

This study uses a non-pattern method with a system of fixed weights. The research shows that linear ordering using this method, together with the zero-unitarisation method for normalising variables, gives relatively objective results compared to other methods used [117]. The zero-unitarisation method is based on the range between the maximum and minimum values of the examined feature. The literature emphasises the high adequacy of the method in normalising both quantitative and qualitative variables [118]. The zero-unitarization method is particularly useful in empirical research on various complex phenomena occurring in different spheres of human activity, as well as for constructing rankings of objects and then dividing them into groups [119,120,121]. This method may be applied in the construction of rankings of different objects such as countries, provinces, counties, districts, and similar. Numerous examples of the widespread use of the zero-based unitarisation method for the comparative analysis of various multi-feature objects can be found in the literature [122,123,124,125,126,127,128]. This method is also used in agricultural research [129,130], including research on the competitiveness of agriculture [32].

The research procedure aimed at constructing a synthetic index consisted of several steps, which are described [131,132,133] as follows:

• Construction of a matrix of sub-variables (diagnostic variables) for each of the objects (countries) studied:

$$X=\left[x_{ik}^t\right],$$

where

• x^t_ik is the original value of the k-th variable in the i-th object at time t.

The following diagnostic variables (features) were used: five variables for input competitiveness (X1–X5) and four variables for output competitiveness (Y1–Y4). The objects were EU countries—23 countries were ultimately included in this study. Separate matrices were determined for each year of analysis (for input competitiveness—for 2014 and 2023, and for output competitiveness—for 2020 and 2023).

Each object (country) is characterised by a vector of diagnostic variables:

X_i^t = [X^t_i1 X^t_i2 … X^t_in]

2.

Normalising the variables to make them comparable using the zero-unitarisation method:

–

For stimulants:

$$Z_{ik}^t={\displaystyle \frac{x_{ik}^t-{\min \{x}_{ik}^t\}}{{\max \{x}_{ik}^t\}-{\min \{x}_{ik}^t\}}}$$

–

For destimulants:

$$Z_{ik}^t={\displaystyle \frac{{\max \{x}_{ik}^t\}-x_{ik}^t}{{\max \{x}_{ik}^t\}-{\min \{x}_{ik}^t\}}}$$

where:

• Z^t_ik—normalised value of the k-th variable in the i-th object at time t (t = 1, 2, …, T).

Due to the fact that all variables describing input and output competitiveness were stimulants, only the formula for stimulants was used.

Note that the normalised diagnostic variable values range from <0;1>, with values closer to one indicating higher levels of the analysed feature.

Normalised diagnostic variables form a matrix:

$$Z=\left[Z_{ik}^t\right]$$

Each object (country) is described by a vector of normalised features: Z_i^t = [Z^t_i1 Z^t_i2 … Z^t_in].

3.

Determination of the value of the synthetic metrics (this is a synthetic measure characterising the i-th object) according to the formula:

$$Z_i^t={\displaystyle \frac{1}{k}}\times \sum _{k=1}^m{Z}_{ik}^t$$

Among the methods for creating synthetic metrics, the non-pattern method with a fixed weight system was selected. In the literature, when there are no expert recommendations regarding weights, it is usually recommended to use indicators based on fixed variable weights [134]. The synthetic metrics take values from the range <0; 1>.

Based on the value of the synthetic indicator, the countries studied were classified into one of four groups in terms of the level of competitiveness of organic agriculture (input or output). Group I included countries with the highest levels, whereas Group IV had the lowest. The class ranges were determined using left-closed intervals: Group I, Av + S(x); Group II, Av; Group III, Av-S(x); and Group IV, 0 (where Av is the arithmetic mean and S(x) is the standard deviation) [134].

4. Results

4.1. Organic Farming in the European Union Countries—Analysis of Changes in the Area, Number of Producers, and Processors

The results of the analysis based on Eurostat data show the dynamic growth of organic farming in the European Union (EU). As seen from the data in

Table 3. Direction and dynamics of change in selected elements of organic agriculture in EU countries from 2014 to 2023 in relative terms (trend equation, index 2014 = 100).

Country	Organic Area			Producers			Processors
	Trend Equation y = ax + b	R2	Index 2014 = 100	Trend Equation y = ax + b	R2	Index 2014 = 100	Trend Equation y = ax + b	R2	Index 2014 = 100
EU 27	6.3665x + 64.984	0.993	175.93	5.9627x + 67.244	0.980	169.11	10.084x + 44.651	0.940	224.40
BE	5.0746x + 72.09	0.952	155.07	5.488x + 70.717	0.950	166.54	8.3506x + 54.072	0.956	207.97
BG	2.0621x + 88.658	0.053	308.47	−2.6843x + 114.76	0.162	114.00	11.404x + 37.276	0.942	290.15
CZ	2.263x + 87.554	0.985	121.14	3.0809x + 83.055	0.957	138.31	7.2621x + 60.058	0.954	186.17
DK	6.9589x + 61.726	0.882	181.00	4.5494x + 74.978	0.784	161.35	2.0331x + 89.607	0.348	124.90
DE	6.7507x + 62.871	0.927	178.97	4.9165x + 72.959	0.933	153.84	6.5738x + 63.844	0.933	192.12
EE	4.4102x + 75.744	0.875	144.80	2.8182x + 84.5	0.734	127.63	5.1462x + 71.696	0.728	155.96
IE	9.5209x + 47.685	0.520	344.42	8.9807x + 50.606	0.502	319.69	−4.3224x + 123.77	0.383	79.84
GR	11.501x + 36.744	0.824	254.90	12.584x + 30.787	0.779	268.49	1.4522x + 92.013	0.611	105.87
ES	4.7543x + 73.851	0.971	156.41	6.792x + 62.644	0.949	182.51	7.0251x + 61.362	0.938	187.31
FR	9.8816x + 45.651	0.968	247.35	10.043x + 44.765	0.938	231.12	17.9x + 1.5482	0.855	384.86
HR	7.1327x + 60.77	0.850	239.49	10.155x + 44.148	0.964	307.10	4.2993x + 76.354	0.762	177.64
IT	5.6058x + 69.168	0.957	176.93	5.3304x + 70.683	0.935	173.01	6.8737x + 62.195	0.957	196.19
CY	8.8199x + 51.491	0.838	269.36	4.8375x + 73.394	0.764	203.90	4.9283x + 72.894	0.749	178.43
LV	3.7715x + 79.257	0.842	146.04	−0.0315x + 100.17	0.000	97.24	26.924x − 48.085	0.652	790.00
LT	3.7246x + 79.515	0.767	155.90	0.0768x + 99.578	0.002	106.18	12.127x + 33.303	0.886	259.70
LU	7.7123x + 57.582	0.922	184.01	7.3183x + 59.75	0.901	203.80	3.2675x + 82.029	0.483	115.28
HU	10.228x + 43.747	0.905	256.74	12.067x + 33.631	0.945	370.16	3.521x + 80.634	0.371	184.25
MT	10.215x + 43.817	0.846	194.12	10.439x + 42.583	0.875	250.00	−0.7682x + 104.23	0.017	77.78
NL	6.001x + 66.994	0.984	162.91	4.471x + 75.409	0.982	144.82	2.7019x + 85.14	0.667	122.32
AU	2.923x + 83.924	0.866	129.39	1.8254x + 89.96	0.805	118.33	2.883x + 84.144	0.288	112.09
PL	−1.0959x + 106.03	0.127	84.30	−1.6978x + 109.34	0.302	85.33	8.0111x + 55.939	0.866	157.02
PT	17.977x + 1.1289	0.731	405.41	18.331x − 0.8201	0.796	481.47	10.729x + 40.992	0.293	230.56
RO	12.558x + 30.933	0.875	222.82	0.2744x + 98.491	0.002	89.03	5.406x + 70.267	0.914	163.71
SI	3.0199x + 83.391	0.976	129.02	1.3788x + 92.417	0.741	114.85	−5.8346x + 132.09	0.2259	90.68
SK	4.5454x + 75	0.867	140.40	19.384x − 6.613	0.763	413.65	13.6x + 25.201	0.859	291.07
FI	5.4802x + 69.859	0.952	161.15	1.8672x + 89.73	0.706	116.44	−2.4964x + 113.73	0.162	65.98
SE	1.4331x + 92.118	0.371	109.59	−1.253x + 106.89	0.435	90.23	0.6188x + 96.597	0.014	111.11

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 20 March 2025) [113]. In grey are marked the most important changes in the direction and dynamics of selected components of the development potential of organic agriculture across individual EU countries.

and

Table 4. Direction of change in selected elements of organic agriculture in EU countries from 2014 to 2023 in absolute terms (trend equation, index 2014 = 100).

Country	Organic Area		Producers		Processors
	Trend Equation y = ax + b	R2	Trend Equation y = ax + b	R2	Trend Equation y = ax + b	R2
EU 27	862,683x + 9 × 106	0.994	20,227x + 227,483	0.980	8302.8x + 36,735	0.94
BE	4506.2x + 64,014	0.952	127.04x + 1554.7	0.956	121.55x + 787.07	0.956
BG	2415x + 103,830	0.053	−144.64x + 6183.8	0.162	28.973x + 94.7	0.942
CZ	11,804x + 456,690	0.985	141.24x + 3807.7	0.957	54.873x + 453.8	0.974
DK	17,502x + 155,239	0.992	168.4x + 2775.4	0.784	20.318x + 882.3	0.353
DE	91,499x + 852,152	0.927	1560.3x + 23,155	0.933	1106.4x + 10,745	0.933
EE	8907x + 152,976	0.875	53.339x + 1599.3	0.734	8.2545x + 115	0.728
IE	8188x + 40,971	0.520	179.05x + 1008.9	0.502	−10.158x + 290.87	0.383
GR	63,995x + 204,456	0.824	4181.1x + 10,229	0.779	23.667x + 1499.5	0.611
ES	108,423x + 2 × 106	0.971	2917.8x + 26,912	0.949	334.16x + 2918.7	0.938
FR	206,332x + 953,225	0.968	4491.2x + 20,020	0.938	4931.8x + 426.57	0.855
HR	7184.2x + 61,209	0.841	469.34x + 2040.5	0.964	15.282x + 271.4	0.762
IT	109,999x + 1 × 106	0.957	3659.1x + 48,521	0.935	1373.9x + 12,432	0.957
CY	564.28x + 3294.3	0.838	57.818x + 877.2	0.764	3.2182x + 47.6	0.749
LV	10,321x + 216,887	0.842	−1.2333x + 3920.3	0.000	42.406x − 75.733	0.652
LT	8717x + 186,100	0.767	1.9212x + 2492.1	0.002	13.206x + 36.267	0.886
LU	461.21x + 3443.5	0.922	8.1818x + 66.8	0.901	3.0061x + 75.467	0.483
HU	24,436x + 104,522	0.905	511.63x + 1425.9	0.945	16.133x + 369.47	0.371
MT	5.0667x + 21.733	0.846	1.9939x + 8.1333	0.875	−0.0545x + 7.4	0.017
NL	3912.8x + 43,682	0.984	80.394x + 1355.9	0.982	28.891x + 910.4	0.667
AU	18,416x + 528,762	0.866	459.87x + 22,664	0.805	54.073x + 1578.2	0.288
PL	−5951x + 575,780	0.127	−354.32x + 22,818	0.302	46.448x + 324.33	0.866
PT	74,928x + 4705.2	0.731	1393.4x − 62.333	0.796	98.2x + 375.2	0.936
RO	51,211x + 126,147	0.875	29.673x + 10,652	0.002	9.5091x + 123.6	0.914
SI	1445.8x + 39,923	0.976	50.152x + 3361.5	0.741	−12.83x + 290.47	0.226
SK	9562.9x + 157,788	0.867	163.76x − 55.867	0.763	14.355x + 26.6	0.859
FI	15,675x + 199,812	0.952	89.176x + 4285.3	0.706	−11.818x + 538.4	0.162
SE	8222.5x + 528,519	0.371	−68.782x + 5867.6	0.435	6.6848x + 1043.5	0.015

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 20 March 2025) [113]. In grey are marked the most important changes in the direction of selected components of the development potential of organic agriculture across individual EU countries.

, the acreage of organic crops in the EU from 2014 to 2023 has increased by 76% (an average annual increase of 6.37% or 862,683 ha). Over the years, the share of individual countries in total EU organic crop acreage has also changed. Between 2014 and 2023, the share of the five largest countries in terms of organic crop acreage in the EU decreased from 66.38 to 61.95%. The largest producers of organic food in the EU in 2014 were Spain, Italy, France, Germany, and Poland, and by 2023, France, Spain, Italy, Germany, and Greece. During the period under review, the highest growth rates in crop acreage were recorded in Portugal (305% to 860,878 hectares), Ireland (244% to 86,009 hectares), and Bulgaria (208% to 147,798 hectares). In relative terms, the largest average annual increase in organic acreage was in Portugal at 17.97% (74,928 hectares). However, the fastest average annual increase in cultivated area was recorded in countries that are already leaders in organic production, namely, France (by 206,332 ha), Italy (109,999 ha), Spain (108,423 ha), and Germany (91,499 ha). Relatively rapid acreage growth was also recorded in Romania and Greece, with average annual increases of 12.56% (51,212 ha) and 11.50% (63,995 ha), respectively. Poland was the only country to record a decline in acreage during the period under review (by 16%), which meant an average annual decrease of 1.1% (5951 ha).

As Table 3 and Table 4 show, the number of organic producers in the EU also increased by 69.11% over the same period, with an average annual growth of 6.00%, implying an increase of 20,227 producers each year. In 2014, the countries with the highest number of producers included Italy, Spain, France, Poland, and Austria, and in 2023, they were Italy, France, Greece, Spain, and Germany. The share of these countries in the total number of organic producers in the EU has increased from 59.71% (2014) to 68.52% in 2023. The highest growth rates in the number of organic producers were observed in Portugal (up 381.47% to 16,028), Slovakia (up 313.65% to 1667), and Hungary (up 270.16% to 6189). The largest average annual increase in the number of organic producers was in Slovakia (19.38%), Portugal (18.33%), and Hungary (12.07%). However, in real terms, the number of producers grew the fastest in countries with already dominant producer numbers, such as France, Greece, Italy, Spain, and Germany (identical to the increase in legal acreage). Relatively fast growth in the number of producers in real terms was also recorded in Croatia, Malta, and Ireland, with annual average increases of 10.16, 10.44, and 9.00%, respectively, in absolute terms; however, this meant an increase of only 469, 2, and 179 producers, respectively. Rapid growth in the number of producers was also recorded, in addition to countries with already dominant numbers, in Portugal, Hungary, and Austria. During the period under review, an average annual decline in the number of producers was recorded in Sweden (by an average of 1.3%, or approximately 69 producers), Bulgaria (by 2.7%, or 145 producers), Poland (1.70%, or 354 producers), and Lithuania (by 0.03%, or approximately one producer).

The growth in organic production in the EU is also evidenced by an increase in the number of processors. Between 2014 and 2023, their number in the EU increased by 124.4% (Table 3) to 111,447. This represented an average annual increase of 10.1%, or 8303 processors (Table 3 and Table 4). During the review period, the highest number of organic processors was recorded in France, Italy, Germany, Spain, and Austria. The share of these countries in the total number of organic processors in the EU has increased from 81.8% to 88.2% between 2014 and 2023. An increase in the number of processors was recorded in all EU countries except Malta, Ireland, Slovenia, and Finland. In absolute terms, the number of processors grew the fastest in France, Italy, and Germany, averaging 4931, 1374, and 1106 processors per year, respectively. The average annual growth rates were 17.9%, 6.9%, and 6.6%, respectively. Significant growth in the number of organic processors in relative terms was also observed in Lithuania (26.9%) and Bulgaria (11.4%), although in absolute terms, this meant an average annual increase of 42 and 29 processors, respectively.

4.2. Input and Outcome Competitiveness of Organic Farming in the European Union Countries

In response to RQ 2, the value of the synthetic indicator of the input competitiveness of organic farming in 2014 and 2023 was determined. According to this study, the value of the synthetic index of input competitiveness of organic agriculture ranged from 0.041 to 0.517 in 2014 and from 0.093 to 0.681 in 2023 (

Table 5. Synthetic index of input competitiveness of organic farming and ranking of EU countries in 2014 and 2023.

Country Ranking Position	Country	Synthetic Index of Input Competitiveness in Organic Farming in 2014	Country	Synthetic Index of Input Competitiveness in Organic Farming in 2023	Change in Position in 2023 Compared to 2014
1	Austria	0.517	Estonia	0.681	1
2	Estonia	0.510	Austria	0.522	−1
3	Czechia	0.455	Czechia	0.468	0
4	Sweden	0.410	Sweden	0.462	0
5	Slovakia	0.373	Slovakia	0.387	0
6	Denmark	0.249	Denmark	0.346	0
7	Italy	0.225	Lithuania	0.326	4
8	Germany	0.215	France	0.317	6
9	Malta	0.200	Greece	0.299	6
10	Spain	0.199	Italy	0.275	−3
11	Lithuania	0.196	Germany	0.273	−3
12	Slovenia	0.186	Spain	0.246	−2
13	Belgium	0.184	Belgium	0.223	0
14	France	0.177	The Netherlands	0.206	2
15	Greece	0.167	Malta	0.200	−6
16	The Netherlands	0.153	Luxembourg	0.179	1
17	Luxembourg	0.118	Slovenia	0.177	−5
18	Poland	0.107	Hungary	0.168	2
19	Croatia	0.091	Croatia	0.161	0
20	Hungary	0.081	Romania	0.140	2
21	Ireland	0.062	Ireland	0.103	0
22	Romania	0.055	Bulgaria	0.095	1
23	Bulgaria	0.041	Poland	0.093	−5

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 1 March 2025) [113].

). The highest value of the indicator in question, which indicates the level of a country’s input competitiveness, was achieved by Austria in 2014, where despite the increase in the value of the synthetic index from 2014 to 2023, it fell to second place in the ranking of countries in 2023. This was due to a strong increase in the synthetic index for Estonia, which rose from second place in 2014 to top position in 2023. Austria’s top position in terms of competitive potential in 2014 was undoubtedly due to the highest percentage of organic acreage in the total UR and the percentage of organic food producers in the total number of food producers in the country. In addition, as noted earlier, Austria was one of the five countries with the highest number of organic food producers. It is worth noting that the next four places (after Austria and Estonia) in the ranking of countries in terms of input competitiveness in both years under study were obtained by the same countries: the Czech Republic, Sweden, Slovakia, and Denmark. This indicates a continuing trend in these countries to strengthen the competitive potential of organic farming, particularly in terms of the share of organic land in the country’s total agricultural area, food area, and share of organic farms in the country’s total number of farms. In 2014, the last ranked countries were Bulgaria, Romania, and Ireland, for which the synthetic index value did not exceed 0.062.

In 2023, Estonia had the highest value for the synthetic index of the input competitiveness index for organic agriculture (Table 5). This was due to the highest value of the food area index (i.e., the total area of organic farms per 10,000 residents) as well as a relatively high share of organic land area in the total agricultural area. Second, Austria, which, as in 2014, was characterised by the highest share of organic land area in total farmland and the highest share of organic farmers in the total number of farms. The third and fourth places in the ranking of countries according to the level of input competitiveness of organic farming in 2023 were the Czech Republic and Sweden, respectively (the value of the synthetic index was 0.468 and 0.462, respectively, in 2023), with relatively high indicators of the average area of organic farmland and the share of organic agricultural producers in the total number of farms. Poland recorded the lowest value of the synthetic index of input competitiveness of organic agriculture in 2023, followed by Bulgaria and Ireland, for which the value of the calculated synthetic input competitiveness index ranged from 0.093 to 0.103. Considering the comparisons in the years under analysis, France’s and Greece’s positions in the ranking of EU countries increased most strongly (by six positions), while the greatest decline was characterised by Malta (by six positions), Slovenia, and Poland (by five positions). In the case of Malta, the aforementioned decline can be partly explained by the relatively small size and growth of organic acreage and number of producers compared to the “production giants” in this area. In comparison, the increase in the number of producers and acreage under cultivation during the period under review in Malta averaged 2 people and 5.10 hectares per year, respectively. However, in France, the increase was 4491 producers and 206,332 hectares, respectively. In the Slovenian situation, this can also be attributed to the relatively small increase in the organic area and the number of producers during the analysed period. In Poland, this was undoubtedly influenced by a 15.70% decrease in the area under cultivation and thus a decrease in the number of producers.

In response to RQ 3, the value of the synthetic indicator of the outcome competitiveness of organic farming in 2020 and 2023 was determined. The synthetic index of outcome competitiveness for organic farming in the European Union countries oscillated between 0.041 and 0.551 in 2020, while it ranged from 0.020 to 0.593 in 2023 (

Table 6. Synthetic index of output competitiveness of organic farming and ranking of EU countries in 2020 and 2023.

Country Ranking Position	Country	Synthetic Index of Outcome Competitiveness of Organic Farming in 2020	Country	Synthetic Index of Outcome Competitiveness of Organic Farming in 2023	Change in Position in 2023 Compared to 2020
1	Romania	0.551	Estonia	0.593	11
2	The Netherlands	0.427	The Netherlands	0.449	0
3	Denmark	0.415	Denmark	0.368	0
4	Italy	0.386	Austria	0.326	1
5	Austria	0.342	Sweden	0.325	2
6	Luxembourg	0.302	Luxembourg	0.303	0
7	Sweden	0.300	Belgium	0.294	1
8	Belgium	0.293	Germany	0.244	1
9	Germany	0.286	Greece	0.175	2
10	France	0.269	Romania	0.172	−9
11	Greece	0.237	Bulgaria	0.161	7
12	Estonia	0.237	France	0.152	−2
13	Croatia	0.229	Italy	0.149	−9
14	Spain	0.205	Croatia	0.149	−1
15	Ireland	0.141	Ireland	0.097	0
16	Lithuania	0.133	Czechia	0.092	3
17	Slovakia	0.132	Slovenia	0.083	5
18	Bulgaria	0.120	Slovakia	0.083	−1
19	Czechia	0.115	Hungary	0.074	1
20	Hungary	0.115	Poland	0.068	2
21	Slovenia	0.099	Spain	0.067	−7
22	Poland	0.078	Lithuania	0.044	−6
23	Malta	0.041	Malta	0.020	0

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database [113] (accessed on 25 March 2025) and reports from the Research Institute of Organic Agriculture FiBL and IFOAM—Organics International [28,106,107].

). In 2023, the highest value of this indicator was recorded in Estonia (a change in position of 11 places), which was undoubtedly influenced by the results of two sub-variables: export of organic products per 1000 hectares and organic product export (t) per number of organic producers and processors. In 2023, for Estonia, the values of these indicators were the highest among the surveyed countries. In the second and third places, The Netherlands and Denmark, respectively, were analysed in both years. This was undoubtedly influenced in the case of The Netherlands by the highest value of the indicator, average grain yields, among the countries studied, and in the case of Denmark, the value of the indicator, per capita spending on organic products. According to reports by the Research Institute of Organic Agriculture FiBL and IFOAM Organics Europe, spending on organic food in Denmark has been the highest in the EU for many years, with nearly EUR 384 in 2020 and EUR 362 in 2023 [28,107]. In comparison, the average spending on organic food in Europe is EUR 66 per person in 2023, while in the European Union, it is EUR 104 [28]. Referring to organic cereal yields in The Netherlands, high yields are mainly due to modern cultivation technologies, high-quality soils, and high specialisation and efficient management of resources [135], which promote higher productivity compared to other EU countries. However, the lowest value of the synthetic index of outcome competitiveness of organic agriculture in 2023 was Malta (0.020), as well as countries such as Lithuania and Spain. The indices for the latter two countries are 0.044 and 0.067, respectively.

In 2020, the highest value of the synthetic index of organic farming’s competitiveness was observed in Romania (0.551), which was due to the highest value of the indicators: export of organic products per 1000 hectares and organic product export (t) per number of organic producers and processors among the countries studied. It can be noted that this indicator for the country has significantly decreased by 2023, causing the country to fall to tenth place in the ranking of European countries (Table 6). This is related to the large increase in the synthetic index for Estonia, which rose from 12th place in 2020 to first place in 2023. As already noted, the next places (second and third) in terms of the value of the synthetic indicator of output competitiveness among the analysed EU countries in 2020 were The Netherlands and Denmark. Over several years, these countries have maintained their positions in terms of the analysed indicators.

In 2020, countries such as Malta, Poland, and Slovenia ranked last on the list (Table 6). That is, the countries for which the largest declines in input competitiveness were recorded between 2014 and 2023. Analysing the changes, Estonia’s position increased the most (by 11 positions), while the largest declines were in Romania and Italy (by nine positions), Spain (seven positions), and Lithuania (six positions). It is also worth mentioning that in the period 2020–2023, the value of the synthetic indicator of organic farming’s output competitiveness decreased for three more EU countries, and for 10 countries besides Estonia, its value increased. Among these countries are Austria, Belgium, Germany, Hungary (all countries by one position), Greece, Sweden, Poland (by two positions), the Czech Republic (by three positions), Slovenia (by five positions), and Bulgaria (by seven positions).

In the next step of this study, the countries analysed were classified into one of four groups based on synthetic measures describing the level of input competitiveness of organic farming (

Table 7. Classification of EU countries based on the value of the synthetic index of input competitiveness in organic farming in 2014 and in 2023.

Group	Number of Countries in the Group	Level of Measurement	Countries
2014
I	5	Equal to or greater than 0.355	Austria, Estonia, Czechia, Sweden, Slovakia
II	2	From 0.216 to 0.354	Denmark, Italy
III	13	From 0.077 to 0.215	Germany, Malta, Spain, Lithuania, Slovenia, Belgium, France, Greece, The Netherlands, Luxembourg, Poland, Croatia, Hungary
IV	3	Less than 0.077	Ireland, Romania, Bulgaria
2023
I	4	Equal to or greater than 0.422	Estonia, Austria, Czechia, Sweden
II	5	From 0.276 to 0.421	Slovakia, Denmark, Lithuania, France, Greece
III	11	From 0.130 to 0.275	Italy, Germany, Spain, Belgium, The Netherlands, Malta, Luxembourg, Slovenia, Hungary, Croatia, Romania
IV	3	Less than 0.130	Ireland, Bulgaria, Poland

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 1 March 2025) [113].

) and the output competitiveness of this sector (

Table 8. Classification of EU countries based on the value of the synthetic index of output competitiveness in organic farming in 2020 and 2023.

Group	Number of Countries in the Group	Level of Measurement	Countries
2020
I	4	Equal to or greater than 0.364	Romania, The Netherlands, Denmark, Italy
II	8	From 0.237 to 0.363	Austria, Luxembourg, Sweden, Belgium, Germany, France, Greece, Estonia
III	8	From 0.110 to 0.236	Croatia, Spain, Ireland, Lithuania, Slovakia, Bulgaria, Czechia, Hungary
IV	3	Less than 0.110	Slovenia, Poland, Malta
2023
I	3	Equal to or greater than 0.337	Estonia, The Netherlands, Denmark
II	5	From 0.195 to 0.336	Austria, Sweden, Luxembourg, Belgium, Germany
III	13	From 0.053 to 0.194	Greece, Romania, Bulgaria, France, Italy, Croatia, Ireland, Czechia, Slovenia, Slovakia, Hungary, Poland, Spain
IV	2	Less than 0.053	Lithuania, Malta

Source: own elaboration based on EUROSTAT data, available online: https://ec.europa.eu/eurostat/data/database (accessed on 25 March 2025) [113] and reports from the Research Institute of Organic Agriculture FiBL and IFOAM—Organics International [28,106,107].

). Group I included countries with the highest level of synthetic index input competitiveness or outcome competitiveness, whereas Group IV included countries with the lowest level. When analysing input competitiveness in 2023, four countries were classified in the first group: Estonia, Austria, the Czech Republic, and Sweden. In 2014, Slovakia was classified as Group I, but in 2023, it was classified as Group II. Class II included two countries in 2014 and a total of five countries in 2023, with above-average levels of the phenomenon under study (in 2023, Group II included, apart from Slovakia, Denmark, Lithuania, France, and Greece). The countries promoted to this class in 2023 were Lithuania, France, and Greece. At the same time, Italy lost its position in Class II and fell into Class III. Class III consisted of 13 countries in 2014 and 11 countries in 2023. The lowest level of the synthetic index of input competitiveness in both years was characterised by three countries, namely, Ireland, Bulgaria, Romania (in 2014), and Ireland, Bulgaria, Poland (in 2023).

An analysis of the outcome competitiveness of organic agriculture in 2023 identified three countries that achieved the highest synthetic index values and were classified as Class I (Table 8). These included Estonia, The Netherlands, and Denmark. Compared to 2020, Italy and Romania, which were previously also in Class I, were missing from this group, but in 2023, they were moved down to Class III, which may indicate a relative weakening of their position compared to other countries. In 2020, Estonia, along with seven other countries, was in Class II, representing countries with above-average, but not the highest, levels of output competitiveness. By 2023, this number had decreased to five. Austria, Sweden, Luxembourg, and Belgium are included in this group. As mentioned earlier, Estonia recorded an improvement compared to 2020, which indicates progress in the outcome competitiveness of organic farming. Simultaneously, Greece, which previously belonged to class II, was classified as class III in 2023, which may suggest a deterioration in its competitiveness in relation to other countries surveyed in the analysed area. Apart from Greece, Class III, comprising countries with a below-average competitiveness index, consisted of eight EU member states in 2020, while in 2023, this number increased to 13. The lowest level of the synthetic index was characteristic of countries classified as Class IV. In both years analysed (2020 and 2023), Malta was included in this class. In 2020, Poland and Slovenia also belonged to this group, while Lithuania joined them in 2023, indicating a decline in that country’s position in output competitiveness in organic farming.

The Pearson correlation coefficient between the synthetic indicators of input and outcome competitiveness in 2023 was 0.524, which can be considered an average [136]. This indicates that there is a relationship between input and outcome competitiveness in the group of countries studied.

5. Discussion

The complexity of the phenomenon of agricultural competitiveness (including organic farming) and the lack of a uniform definition and concept for measuring it imply the need for systematic research that takes into account different points of view and assessments of agricultural competitiveness. This represents a research gap, which has been addressed by assessing the competitiveness of organic farming at the national level in the EU. Two separate synthetic measures were used to assess the input and output competitiveness of organic agriculture. The results of the analyses are difficult to relate directly to the findings of other authors due to the diversity of the partial indicators used, methodological approaches, and research contexts adopted. The existence of the research gap described above was confirmed by Kociszewski and Szubska-Włodarczyk [109], who pointed to the existence of a research gap concerning the lack of synthetic indicators of the efficiency and potential of organic farming on a macroeconomic scale. Therefore, based on Eurostat data and principal component analysis (PCA), the authors determined the efficiency of organic plant production and the potential of organic animal production on a macroeconomic scale in selected EU countries during 2012–2020. The results of the analyses allowed for a comparison of the economic situation of organic farming in different countries and the establishment of their rankings. Clear leaders in organic plant and animal production were The Netherlands, Belgium, and Denmark (only in animal production). In Poland, a relatively low crop production efficiency and one of the lowest animal production potentials were identified, which was also confirmed by the input competitiveness ranking presented in this study. Kukuła and Luty [108] analysed the level of development of organic farming in EU countries using the following characteristics: average size of organic farms, area of these farms per 1000 inhabitants, share of organic farmland in the total area of utilised agricultural land, retail value of organic crops per 1000 ha, and annual expenditure on organic food per capita. Countries were ranked using selected methods of linear ordering of a set of objects. The results of the analysis indicate that EU countries are generally characterised by an average or low level of organic farming development, with countries with a higher level of economic development occupying higher positions in the ranking. The authors emphasised that this applies primarily to countries where organic food is produced and purchased. This is consistent with the approach used in this study, where the output competitiveness assessment included the indicator ‘expenditure on organic products per person’ as one of the sub-variables. Nowak and Kobiałka [6] created a ranking of member states according to the level of development of organic farming, using a synthetic measure comprising seven sub-indicators: % of organic utilised agricultural area, % of all EU organic farms, changes in the area of organic agricultural land (total fully converted and under conversion to organic farming) in 2012–2021 (%), live organic bovine animals (% of all bovine animals), live organic sheep (% of all sheep), live organic goats (% of all goats), and organic cereals (excluding rice) for the production of grain (including seed) (tonne) (% of total production). Portugal ranked first in terms of the level of development of organic farming, followed by Croatia, France, Finland, and Estonia. Poland ranked last, which was confirmed by the input competitiveness ranking presented in this study. In other studies, an indicator of organic market development in EU-27 countries has been determined using index analysis [21]. In their analysis, the author used a synthetic index that reflected the level of development of the organic market in the agricultural sector in individual EU countries in 2012 and 2020. The value of this indicator is a measure of the advancement of the organic market: the higher its value, the more developed the organic processing market, and thus the greater the potential interest of farmers in taking up organic production. Consequently, countries with the highest index values, such as Luxembourg, The Netherlands, Belgium, Germany, and France, were classified at the top of the ranking. Romania, Poland, and Latvia were at the bottom of the ranking, which does not fully correspond with the research in this study and results from a different methodological approach. However, it should be noted that the research by Wrzaszcz [21], which ranks EU member states according to the level of organic farming development based on the ratio of organic land to total agricultural land, shows some convergence with the results of this study. It is worth emphasising that this indicator is one of the components of the synthetic taxonomic indicator used in the analysis of the input competitiveness of organic farming in EU countries created for the purposes of this study. The convergence of the results confirms the accuracy and consistency of the research methodology adopted and emphasises the importance of the indicator in assessing the competitiveness of the organic farming sector. This aspect was also highlighted by Krajewski et al. [110], who additionally emphasised that this indicator is important from the point of view of the EU’s goal of achieving a 25% share of organic farming in the total agricultural area by 2030. It should also be noted that the results of the research presented in this study, as well as the analysis carried out by Krajewski et. al. [110], indicate differences in the level of development and competitiveness of organic farming in European Union countries. This study reveals significant spatial differences in both the level of input competitiveness and output competitiveness of organic farming in European Union countries. The results of Krajewski et al. [110] further prove that the development of this sector does not show strong correlations with the basic socio-economic indicators of a given country. This suggests that organic farming develops in a multifactorial and heterogeneous manner, depending on the number of conditions and specific characteristics of a given country. Kijek et al. [48] found that various elements related to knowledge and human capital in agriculture were particularly important among the factors influencing changes in total factor productivity (TFP) in agriculture. External R&D expenditure exerted the strongest positive impact, which confirms the importance of investment in scientific development for improving the competitiveness of the sector and the validity of using at least one R&D-related indicator. Mitova [137] also drew attention to the need to address the complexity of organic farming competitiveness in a multifaceted manner, emphasising the need for a multidisciplinary approach.

The issue of organic farming competitiveness is also important from a practical point of view. This applies particularly to the approach to the principles of the Common Agricultural Policy (CAP), which, according to Krajewski et al. [110], should give priority to the problem of uneven development of organic farming in individual European Union countries. According to the authors, special support should be directed toward countries with a low share of organic farming and minimal growth in organic farmland. Therefore, the CAP policy on supporting organic farming must be differentiated and flexible to take into account the specific conditions of each member state. According to Zioło and Luty [138], for organic farming to develop in EU countries, emphasis should be placed on institutional and legal conditions at both the community and national levels, as well as on greater environmental awareness among consumers. According to the authors, state institutions in member states play and should continue to play an important role in shaping this development. Their goal should not only be financial support but also the continuous expansion of ecological knowledge among both producers and consumers, as well as the organisation of cooperation between producers and institutions involved in the distribution and promotion of organic food.

6. Conclusions

This study, based on the literature, points to the great importance of organic agriculture for sustainable development and the European Union’s agricultural policy, as well as the growing consumer interest in organic products, which implies the need to analyse the competitiveness of this sector. It is particularly important to consider both input and output approaches to assess its position and development potential in EU countries.

Analyses indicate the dynamic development of organic farming in the European Union. The area of organic farming in the EU increased by 76.0% between 2014 and 2023, and the number of organic producers by 69.1%. The largest EU countries in terms of area and number of organic producers in 2023 are France, Spain, Italy, Germany, and Greece. Between 2014 and 2023, the share of the five largest producers in terms of area in the EU’s organic farming area decreased by more than 4 percentage points to 61.9%, while the share of these countries in the total number of organic producers in the EU increased by almost 9 percentage points to 68.5%. Countries that are leaders in terms of area and number of producers also recorded the fastest growth in these elements of development potential. Apart from the leaders mentioned above, organic farming and the number of producers grew the fastest in Portugal. The increase in the number of processors also testifies to the development of organic production in the EU. The largest and growing number of organic processors has been recorded in France, Italy, Germany, Spain, and Austria. These issues answer RQ 1.

In response to RQ 2, based on a synthetic index of the input competitiveness of organic agriculture, it was found that there is significant spatial variation in the level of this type of competitiveness across EU countries. In 2023, Estonia had the highest indicator level, followed by Austria, the Czech Republic, and Sweden. These countries were classified as Group I, which included countries with the highest levels of synthetic indicators. It is worth noting that the next four places in the ranking in both years (after Austria and Estonia) were occupied by the same countries: the Czech Republic, Sweden, Slovakia, and Denmark. This indicates a continuing trend in these countries towards strengthening the competitive potential of organic farming. The lowest level of the synthetic index of input competitiveness in both years was recorded by three countries, namely, Ireland, Bulgaria, Romania (in 2014), and Ireland, Bulgaria, Poland (in 2023). In the years under review, France and Greece saw the strongest increase in their positions in the EU country ranking, while Malta, Slovenia, and Poland saw the largest decline.

Based on the synthetic index of output competitiveness of organic agriculture, significant spatial variation in the level of this type of competitiveness was found in the EU countries. In 2023, the highest value of this indicator was recorded in Estonia. The Netherlands and Denmark followed, together forming Group I, which includes countries with the highest level of organic farming competitiveness. In 2020, Romania and Italy also belonged to this group, but by 2023, they fell to Group III, which includes countries with a lower-than-average level of the synthetic indicator for the group under study. Malta has the lowest synthetic index of organic farming output competitiveness in 2023, followed by Lithuania and Spain. During the period under review, Estonia’s position improved the most, while Romania, Italy, and Spain saw the largest decline. The results provide answers to RQ 3.

This article proposes original synthetic taxonomic measures of input and outcome competitiveness of organic agriculture, which contribute to the development of science. The results of this study can help in shaping long-term policy toward organic farming at the EU level and in the individual countries studied. In addition, this article serves an informational and educational purpose in the field of agricultural competitiveness, including organic farming, for all entities interested in this issue (including agricultural producers, consumers, and public administration). Considering the importance of this type of activity for the achievement of the SDGs, as well as the health and well-being of the population, it seems important and justified to support the development of this type of activity, especially in countries with a relatively low level of competitiveness in organic agriculture. The lower level of competitiveness of organic farming in a given country indicates the need to increase institutional support for this activity, improve the effectiveness of public policy measures (i.e., agricultural, food, financial, and educational policies), and implement measures to support the growth of the market for organic products. The following several important measures can be identified to support the development of organic farming, particularly in countries where this type of activity is relatively less competitive:

-

Political and regulatory measures—increasing support under the Common Agricultural Policy for organic farming and creating national, regional, and local development strategies and support programmes for organic farming.

-

Financing—facilitating access to financing—including in relation to the conversion from conventional to organic production, investment activities in organic farming, and income support for farmers due to reduced productivity (e.g., through the creation of preferential credit lines and credit guarantees for organic farmers, and the introduction of long-term contracts—agreements with processors and retail chains can stabilise the financial situation of producers).

-

Agricultural training and advisory services—specialist advice, including on organic agrotechnology, marketing and farm management, and training programmes for new farmers, as well as for farmers wishing to convert from conventional to organic production.

-

Scientific research—financing research (e.g., in the field of disease- and pest-resistant varieties, agroecology, and improvement in organic farming methods) and support for knowledge transfer.

-

Encouraging cooperation—e.g., by creating platforms for the exchange of knowledge and experience between organic producers, R&D institutions, local governments, NGOs, and local communities, which will enable the sharing of best practices and the implementation of proven solutions in the field of organic farming.

-

Stimulating demand for organic products, including through educational campaigns, promotion of healthy eating, organic food subsidy programmes (e.g., in schools, hospitals, and public institutions), support for direct sales, short supply chains, and organic food fairs.

In the course of the research, problems with the availability of statistical data in public statistics on organic agriculture at the EU country level were identified, which constituted a research limitation in this study. Another research limitation is the complexity of factors influencing competitiveness and, consequently, the diversity of measures used to assess the level of competitiveness. Further research could include the analysis and assessment of spatial differences in the competitiveness of organic farming in individual countries (e.g., at the NUTS2 level).