Analysis of Factors Influencing the Formation of Bioregions

Abstract

Bioregions are examples of true sustainable development. The desire for sustainability within local communities leads to agreements and the formation of bioregions in which sustainable development is based on practical action. This paper analyzes the concept of bioregions, emphasizing holistic approaches applied to, and relationships with, economic, environmental and social factors in pathways and the pace of specialization for the development of territories, thereby complementing the agricultural dimension and paying special attention to the application of organic farming techniques. The formation of bioregions is based on the desires of local communities, but at the same time, we believe that there are objective factors that influence the development of bioregions. Thirteen factors that could affect the creation of bioregions were selected using factors referred to in research papers, as well as by adding original ones. These factors can be divided into the sustainable agriculture and tourism groups, in which high values indicate a high potential for the formation of bioregions, as well as the intensive agriculture factor group, where high values indicate the benefits of large-scale economies that hinder the formation of bioregions. Cluster analysis identified six potential bioregions in Latvia, each with distinct socio-economic, environmental, and agricultural characteristics: the metropolitan region (dominant indicator-PIT per capita, value 890 EUR), the tourism cluster (dominant indicator—tourists served as a % of the total population, value 28%), the extensive agriculture cluster (dominant indicator—organically certified UAA, value 14,645 ha), the nature and education cluster (dominant indicator—protected areas, value 7587 ha), the intensive agriculture cluster (dominant indicator—profit from productive land, value 278 EUR ha⁻¹), as well as a non-specialized cluster (no strongly dominant indicators). This paper describes each cluster and discusses its potential for bioregion development.

1. Introduction

Regional economic growth, traditionally seen as an indicator of prosperity, is increasingly constrained because quantitative expansion faces economic, social and environmental limits and is therefore no longer sustainable [1]. It is therefore more appropriate to consider the sustainable development of rural areas to prevent their depopulation and degradation [2]. Rural areas, especially in industrialized countries, become increasingly important not only in the area of agricultural production but also in the areas of recreation and preservation of biodiversity, natural areas and local culture [3]. This multifunctionality allows rural areas to supply ecosystem services that meet the needs of both urban and rural households and contribute to the transition to rural sustainability [4]. Rural areas need to meet the growing societal demand for high-quality food, local products, a circular economy, a natural environment and other services [5]. The European Union’s Common Agricultural Policy and Green Deal have reinforced the need for an agro-ecological transition and sustainable development. The creation of bioregions is one of the most promising approaches to achieving sustainable rural development. The concept of bioregions is often referred to in order to highlight the territorial dimension of sustainable development. This usually applies to geographical areas with similar conditions that affect their economic, social and environmental performance and opportunities for agricultural production systems. In such areas, agricultural production combines with various other elements, e.g., handicrafts, food, tourism and recreation, as well as the protection of soil, water, air, habitats and landscapes and the preservation of biodiversity and cultural aspects, e.g., the preservation and development of local community traditions [6]. At the same time, as a rule, the core of bioregions is agricultural, usually organic production, which is the basis for a sustainable food system that, as a result of consumer preferences and political conditions, faced challenges posed by global trade, meaning the creation of new concepts of local food [7]. This leads to an emphasis on prosocial values in the local community and action for the creation of bioregions. For example, efforts in Italy’s Cilento to provide local school meals with quality local food through Public School Food Procurement (PSFP) faced a relatively high price and a problem of insufficient quantity [8]. In addition to the two basic factors: local organic, biodynamic or agroecological food production and local initiative, objective preconditions are needed for the creation of bioregions, ensuring that their formation is based on systematic analysis rather than ad hoc initiatives.

Based on a literature review and an analysis of bioregion and bio-district examples in European countries as well as other parts of the world, the authors of the present study have defined a bioregion as follows. In a narrow sense, a bioregion is an area where organic farming practices are applied. More broadly, it is a territory where public, private and non-governmental actors collaborate through voluntary agreements to preserve and enhance organic, socio-economic, cultural and landscape values, bringing about farming and consumption practices that respect local biodiversity and balance the interests of stakeholders in local development planning, sustainable use and management of local resources. This definition, developed by the authors, is included in the Landscape Policy Implementation Plan for 2024–2027 [9].

Many farmers abandon the dominant paradigms based on material accumulation through ownership, as well as violence, endless economic growth, the imperative to make a profit and reduce humans and nature to the level of a commodity [10]. Critical consumption practices have become the basis for building sustainable communities in Italy through the promotion of Sustainable Community Movement Organizations (SCMOs)—experiences that include fair trade, the growth movement, alternative food supply networks, ecovillages, etc. In Italy, the most extensively examined SCMOs are solidarity procurement groups (GAS), which have gradually established relationships with producers and distributors at the local level since the 1990s, thus creating networks of economic solidarity and districts and bioregions [11]. The autonomy that arises from self-production and self-sufficiency, and the interaction between producers and consumers, is an example of how the transfer of knowledge and individual and collective practices creates and strengthens relationships between the participants, while making them self-determined and emancipated [12]. This also applies to Community Supported Agriculture (CSA), which is based on building trust between producers and consumers and raising awareness of the complexity of the production process [13]. CSA was conceived and developed in Germany and Switzerland and introduced in the USA in the 1980s [14]. CSA is an agricultural system in which consumers and farmers jointly assume the risks and benefits of agricultural production. In a simple CSA model, consumers individually purchase shares to provide financial support to an organic farm before the harvest season and periodically receive fresh produce from the farm during the season, so farmers can focus on production and have no trade-related costs [15]. Similar reasoning can be applied to Participation Guarantee Schemes (PGS) created as an alternative to third-party certification, currently the only EU-recognized guarantee scheme for organic food [16]. These instruments, adopted mainly through socio-political and economic initiatives, clearly manifest their pedagogical and transformative nature aimed at strengthening small producers economically [13]. In the long run, an alliance between the members of an initiative group and the convergence of objectives are needed to maintain the movement that fosters productive and creative activity in the creation of a bioregion. Individual initiatives need to be combined with collective action to provide the necessary ecological knowledge and social ties in the long run [17]. Regional trends in various European countries should be assessed separately, determined by the different historical, climatic and economic conditions and the size of the countries. However, it should be recognized that, due to the impact of COVID-19 and general geopolitical instability, population migration trends, especially in Eastern Europe, show that urban dwellers often move to rural areas as a permanent place of residence or have several places of residence—urban and rural. In Estonia, population dynamics have shifted. While many former industrial and rural centers are shrinking, some previously sparsely populated areas are experiencing a resurgence, often driven by young and educated people [18]. Urban and rural areas are interdependent, yet disparities between them have increased, especially in rural areas, where insufficient development and social exclusion are exacerbated by the spatial distance of the areas from economic centers. As regards perceptions and value, it has been found that, in urban areas, the main elements are housing and family, whereas in rural areas these are complemented by the elements of natural values and landscapes and the elements of close social contact and community. The functional possibility rating of a place is lower in rural areas than in cities [19]. Opportunities for bioregions closely relate to the availability of land, finance, innovation and social services. Rural entrepreneurship can reduce poverty and migration, as well as increase employment in rural areas—a source of income and job creation—yet entrepreneurs in rural areas have more difficulty in accessing technological, financial and human resources than those in urban areas, while also finding it difficult to make a profit due to low population density and remote markets [20]. The main difficulties for young businesspersons on family farms are a lack of loans and finance and the high cost of production, a lack of skilled labor and low production prices [21].

Examining the creation of bioregions can identify the preconditions, internal incentives that drive action, potential strategies and expected results, which are summarized in

Table 1. Opportunities and challenges for the creation of bioregions in rural areas.

Context	Economic	Social	Environmental	Political and Legal
Preconditions	Organic farming; crafts; tourism	Community approach; education Employment	Nature protection; sustainable use of resources	EU Green Deal; EU sustainability strategies CAP
Internal incentives	Low economic potential; access to finance and innovation	Population; lack of knowledge; public services	Intensive production; brownfield areas	Lack of representation in city administrations; production policy
Strategy	Cooperation; protection and development of a new or traditional market segment	Educating and involving communities in decision-making	Conservation and sustainable use of ecosystem services	Development and adoption of laws and regulations and support mechanisms
Results	Self-producing and self-sufficient autonomy	Conscious consumption practices; prosperity and well-being	Biologically diverse and ecologically stable environment	Sustainable development of rural areas

.

The preconditions and internal incentives directly relate to the aim of the present research, since they allow for the identification of objective and analyzable factors that determine the potential of territories for the creation of bioregions. Some of the factors, such as the availability of organically managed or protected natural areas, political goals and regulatory frameworks, are relatively easy to identify, whereas social factors are much more complicated, as they include the desire to act, or motivation, which is the reason for self-organization. The internal incentives often result from the identification of specific problems at the local or regional level, e.g., low economic activity, limited access to public services and the need to protect the environment or even particular environmental objects. In the Cilento region of Italy, problems with local school catering—the high cost and insufficient quantity of local food—became a catalyst for community action to create a bioregion [8]. Such cases indicate that seemingly local challenges can prompt strategic changes in the development of an area initiated by the local community. The strategies put forward as a result of such actions include activities that fit into the concept of bioregions and can serve as a basis for unity among the local community, e.g., by signing a bioregion memorandum, as well as through cooperation within governing bodies. The strategies are largely focused on cooperation, educational work in communities, the sustainable use of local resources and strengthening local identity. The expected outcomes, such as a self-sustaining economic structure, environmentally friendly production, public participation and community resilience, can provide a response to the negative impacts and challenges posed by internal incentives, while ensuring the sustainable development of the area. Although the creation of each bioregion is unique and based on the will of the local community, similar preconditions and internal incentives can serve as the basis for the identification of functionally similar strategies across various areas. Such similarities allow us to compare regions, group them and develop bioregional strategies to facilitate their cooperation, which allows us to analyze clusters of potential bioregions. In this aspect, the role of cluster analysis is important, which allows for the structured identification of areas with similar features and development preconditions, thereby allowing us to assess the potential for bioregions, strategic cooperation and regional policymaking.

The aim of this study is to identify objective factors influencing the creation of bioregions and to apply cluster analysis to determine potential bioregions in Latvia. This approach enables a systematic assessment of regional preconditions and incentives, highlighting areas with similar socio-economic and environmental characteristics. Of course, this approach alone cannot provide a legal basis for the creation of bioregions, since the creation of bioregions is based on the initiative of the local community and voluntary agreement. However, it can serve as an essential support tool, thereby helping to identify the potential of areas, build local identity and encourage communities to negotiate common values, goals and courses of action. The potential bioregions identified can serve as a basis for a community memorandum, cooperation initiatives or a sustainable development strategy.

2. Materials and Methods

The present study suggests a methodological approach to analytical identification of potential bioregions. The research performed an analysis of factors in the creation of bioregions and hierarchically clustered factor indicators to assess the prospects for the creation of bioregions in the regions and municipalities of Latvia. The technological map of the research methodology is presented in Figure 1.

2.1. Research Design and Data Sources

This study applied a quantitative multidimensional statistical approach to assess the potential for bioregional development in Latvia. The analysis was performed at the municipal level, based on data on 36 municipalities of Latvia in 2021. Riga was excluded because of its highly urbanized structure. The data were obtained from official sources, including the Central Statistical Bureau (CSB), the Rural Support Service (RSS) and the State Land Service (SLS). The dataset included indicators of environmental, agricultural and socio-economic aspects of regional development.

Table 2. Main methodological stages of the research (PCA-based approach).

Step	Description	Data and Methods	References and Tools
1	Data collection	Thirteen socio-economic, environmental and agricultural indicators for 36 municipalities (2021); CSB, RSS, SLS, ADC, LASAM, GIS OZOLS	[22,23,24]
2	Data preparation	Standardization of variables; multicollinearity check (correlation matrix); normality testing (Mardia test)	RStudio 4.3.3; psych, ggcorrplot
3	Principal Components Analysis (PCA)	Principal Axis Factoring with Varimax rotation; factor extraction, naming and interpretation	RStudio psych::principal
4	Regression analysis	Multiple regression to assess the impact of factor structures on young farmer support (A006.01)	RStudio, lm
5	Cluster analysis	Ward’s method with Euclidean distance; classification of municipalities; validation of clusters	RStudio, cluster
6	Visualization	Spatial mapping of clusters and regression results	ArcGIS Pro; RStudio graphics (ggcorrplot, base plotting)

depicts the steps of the study, which required data sources and references.

The methodological process was streamlined into six main stages, which are presented in Figure 1 and summarized in Table 2.

2.2. Selection of Variables

Initially, 18 variables were selected based on their relevance to the bioregion concept and the availability of data. They included, for example, the share of certified organic farmland, the coverage of protected areas, the average income level, the unemployment rate, the intensity of tourism and the number of educated farmers. After the initial assessment considering dispersion and multicollinearity, 12 variables were selected for the analysis (p < 0.05). All the variables were standardized prior to the analysis.

2.3. Statistical Analysis

All the statistical operations were performed in an RStudio environment version 4.3.3 (2024-02-29 ucrt).

The following steps were performed.

2.3.1. Testing Normality and Correlations

The multidimensional normality of the data was tested by a Mardia test (psych package), which revealed significant asymmetry (498.84) and kurtosis (0.57), thereby indicating that the data did not correspond to the normal distribution. Therefore, exploratory methods were employed. A correlation matrix was created using the ggcorrplot package to identify multicollinearity and latent structures.

2.3.2. Factor Analysis

An analysis of the factors was performed using Principal Axis Factoring with Varimax rotation (fa() function, psych package). The conformity factor for the KMO samples was 0.58, and the Bartlett sphericity test was statistically significant (p < 0.001), thereby confirming the appropriateness of the factor analysis.

The factors with eigenvalues above 1 were retained, and the scree graph confirmed the choice of three factors:

-

Factor 1: Sustainable agriculture

-

Factor 2: Intensive agriculture

-

Factor 3: Tourism and service potential

The models were used for further analyses.

2.3.3. Regression Analysis

A multifactorial linear regression was performed to assess the impact of the identified factor structures on regional involvement in sustainable development. The dependent variable was the number of support requests under measure A006.01 Start-up support for young farmers (EAGF and EAFRD).

The regression model showed a significant positive correlation between the sustainable agriculture factor (β = 1.7101, p < 0.0001) and the number of young farmers, while the tourism factor showed a negative correlation (β = −7185, p = 0.0389). The factor of intensive farming was excluded because of its multicollinearity and statistical insignificance.

2.3.4. Cluster Analysis

For the classification of municipalities by type of bioregions, a hierarchical cluster analysis was performed, employing Ward’s method and the Euclidean distance method based on the results of factor rotation. Based on the dendrogram structure and cluster validation, four distinct clusters were identified, which represent different types of potential bioregions with different ecological, economic and social characteristics.

2.4. Spatial Mapping and Visualization

The results of the cluster and regression analyses were visualized using ArcGIS Pro. The maps were created to show the cluster affiliation of municipalities and the intensity of support for young farmers, thus highlighting territorial differentiation and development potential.

2.5. Software and Reproducibility

All the statistical analyses were performed in an RStudio environment using the following packages: psych, ggcorrplot and cluster.

Factor analysis of potential bioregions

To identify potential bioregions in Latvia, the study performed a factor analysis, selecting 13 variables by municipality (

Table 3. Factors in the creation of potential bioregions in Latvia.

Variable	Data Source	Symbol RStudio
Organically certified UAA, ha	CSB	FA1
UAA as a % of the total area in the municipality	CSB	FA2
Organically certified UAA as a % of the total UAA	CSB	FA3
Number of organic production operators	ADC	FA4
EAGF and EAFRD beneficiaries under initiative A019.40 Ensuring the functioning of a LAG and activating the territory	LAD	FA5
Profit from productive land, EUR ha−1	LASAM	FA6
Habitat quality, points ha−1	LASAM	FA7
PIT revenue per capita, EUR	RAIM	FA8
Unemployment rate among the economically active population aged 15–74, %	RAIM	FA9
Amateur art collectives as a % of the total population	Ministry of Culture	FA10
Tourists served as a % of the total population	CSB	FA11
Number of agricultural enterprise managers with higher and professional agricultural education	CSB	FA12
Specially protected areas, ha	GIS OZOLS	FA13

). For an analysis, given the multifaceted nature of the concept of bioregions and the complex structures that covers all the dimensions of territorial development, it is important to select factors that include all indicators of preconditions and prerequisites for bioregions. However, it should be acknowledged that there was a lack of statistical data for a number of industries important for the development of bioregions, e.g., the number of home producers and artisans in municipalities, as well as local food consumer initiative groups. The case of an analysis of 29 municipalities in the northern regions of Italy [6] served as a basis for the choice of factors, which were then adapted and improved according to the situation in Latvia.

The 13 factors mentioned are described using descriptive statistics in

Table 4. Descriptive statistics of 13 indicators across 36 municipalities.

Indicator	Min	Max	Mean	SD	Range
FA1	0.00	25,197.00	8393.81	6674.84	25,197.00
FA2	11.09	56.22	32.46	11.07	45.13
FA3	0.00	24.96	12.74	7.68	24.96
FA4	0.00	260.00	95.69	76.31	260.00
FA5	0.00	2.00	0.89	0.62	2.00
FA6	39.00	300.00	110.44	61.56	261.00
FA7	4.00	10.00	5.80	1.18	6.00
FA8	321.00	1164.00	594.44	213.31	843.00
FA9	5.10	22.70	9.14	4.11	17.60
FA10	0.07	0.61	0.31	0.12	0.54
FA11	0.83	39.44	9.80	8.64	38.60
FA12	10.00	1359.00	567.47	373.83	1349.00
FA13	0.00	45,835.31	5833.00	8285.05	45,835.31

.

Descriptive statistics of 13 indicators in 36 municipalities (Table 4) reveal significant differences. Regarding agricultural variables, the area of organically certified UAA (FA1) ranges from 0 ha to 25,197 ha, with an average of 8394 ha, while its relative share of the total UAA area (FA3) varies from 0% to 25%, indicating uneven implementation of organic farming. The number of organic production operators (FA4) varies significantly, ranging from 0 to 260, and social indicators—the proportion of amateur art collectives in the population (FA10)—also vary significantly, indicating differences in community cultural involvement. The economic statistics of Latvian municipalities show sharp differences—personal income tax revenue per capita (FA8) ranges from 435 euros to 996 euros, while the unemployment rate (FA9) ranges from 5.1% to 13.6%. Structural differences are also visible in the size of specially protected areas (FA13), which ranges from 191 ha to 8683 ha. These results highlight the heterogeneity of Latvian municipalities and provide a basis for further factor and cluster analysis.

Given the fact that the development of bioregions is largely linked to agriculture, including organic farming, which ensures sustainable management of the environment and the production of high-quality local food products, the selection of several variables was based on agricultural data:

• To identify the spread of organic farming, variables were selected to find the organically certified UAA in absolute terms (ha) and as a % of the total UAA area (%): FA1 and FA3. The factors provide an opportunity to assess the contribution of organic production to the total agricultural production in municipalities.
• The UAA as a % of the total area in the municipality: FA2 indicates the degree of specialization of municipalities in the agricultural industry. An important indicator of the economic development of municipalities in Latvia is agricultural output.
• The number of organic production operators: FA4 was selected to identify the attitude of agricultural operators towards organic production and the associated food quality.
• Profit from productive land (EUR per ha⁻¹) indicates the efficiency of agricultural production. The factor (FA6) has been selected to identify the intensity of agricultural production in municipalities.
• The factor “number of agricultural enterprise managers with higher and professional agricultural education” (FA12) was selected to identify the motivation and competence of owners and managers of agricultural enterprises in the field of sustainable development of agricultural production. Educated business managers understand the importance of multifaceted development of rural areas, both in the socio-economic and environmental domains. The number of such specialists in certain areas provides the prospect of successful development of bioregions. The organic farming segment can be considered a basic prerequisite conducive to the creation of bioregions; however, initiatives by local communities are a sine qua non because it is impossible to create a bioregion without public support and authorization. One of the factors in the creation of Italian bioregions was solidarity procurement groups, which ensured the availability of ethical consumer networks; this was considered in the factor analysis of the suitability of 29 municipalities for the creation of bioregions in the north of Italy—the Bologna Apennines region [6]. Such groups could be the most direct factor in the readiness of local communities to take the next step in creating a bioregion. In Latvia, such groups represent consumers who want to buy fresh and high-quality food. Such groups can be found on social media; however, there are also groups that have united based on mutual contact, e.g., a person has relatives in rural areas and supplies the food to a group in the city (coworkers, other relatives, neighbors, etc.). Given that objective statistical data are not available for groups of this type, the category of social factors includes data on the prosperity of individuals and local initiative groups contributing to the preservation of traditions and culture and other territorial development issues supported by European Union funding programs.
• An essential component of the concept of bioregions is the activity of local communities and the desire to develop their municipality and neighborhood areas. One of the available statistical datasets is the number of EAGF and EAFRD beneficiaries under initiative A019.40 Ensuring the functioning of a LAG and activating the territory. This factor (FA5) has been selected to identify the readiness of local communities to play an active role in solving territorial development problems.
• PIT revenue per capita (EUR)—FA8 indicates the standard of living in municipalities, which is one of the most important socio-economic indicators for projecting any trends, having a motivating character, e.g., in the desire of the population for self-sufficiency in their material lives.
• The unemployment rate among the economically active population aged 15–74—this factor (FA9) was selected to identify the potential of municipalities in training and attracting the workforce, as well as possibly to motivate the part of the population looking for an opportunity to work within the limits of their abilities and to make a living for themselves based on the opportunities provided by bioregions.
• Amateur art collectives as a % of the total population shows the most active part of the local population, since participation in such collectives is an intangible desire to improve one’s personality and socialize, as well as preserve the culture, traditions and history of the municipality. At the same time, such an assumption is based on the specific conditions of Latvia. There is a very strong choral and folk dance movement in Latvia, and this movement unites 40,000 people [25]. Self-organizing groups are a strong movement, and their members are highly motivated to organize and participate in various social campaigns. At the same time, they form a discussion platform where people meet three to four times a week and can discuss local current affairs. Such collectives involve not only the participants but also family members and lovers of amateur art, as well as spectators. The events featured by such collectives are usually accompanied by fairs with the participation of masters of fine crafts, craftsmen and home producers. This factor (FA10) is important in the process of creating bioregions as an aspect of social activity in local communities. The diversification of the economy in rural areas and the concept of bioregions contribute to the development of other industries that provide opportunities for the population to engage in the development of the territory and obtain financial self-sufficiency by exploiting available resources. Such industries include tourism, e.g., agritourism, which can be easily combined with the activities of organic farms and involves offering tours and, at the same time, educating visitors about organic farming, local traditions and culture. Another kind of tourism could be ecotourism, which is associated with visiting natural landscapes and attractions. The organizers of this kind of tourism have an opportunity to perform an educational function related to environmental protection, nature and biodiversity. Another segment is home production and crafts, on which, unfortunately, objective and complete statistics could not be found; therefore, it was not included in the present research, but it should definitely be taken into account in future research studies because it is an important indicator, and many years of experience show that crafts are very popular in Latvia.

Considering the above, one indicator of tourism was selected for factor analysis, tourists served as a % of the total population, which indicates the attractiveness of a municipality from the perspective of tourists and the quality of tourism services supplied there. The factor (FA11) was selected to identify the activity of municipalities regarding the supply of tourism services, which is one of the pillars of economic activity of bioregions. The number of available accommodation facilities and bed places was not considered, unlike in the Italian study on the ability of municipalities to create bioregions [6], because, given the short distances between the municipalities of Latvia, there is a large number of tourists who make one-day tours or visit tourist attractions of several municipalities during a day, thus staying overnight in one of them. This study is limited by the availability of comparable social and cultural statistics at the municipal level. Although indicators such as amateur art collectives (FA10) and tourism-related activity (FA11) were included, other dimensions of cultural vitality, community participation and social capital could not be systematically measured due to data limitations. As a result, the current analysis may under-represent the social and cultural aspects of bioregional development. Future research should address this gap by integrating survey-based indicators, qualitative case studies and big data sources (e.g., mobility or social media datasets) to provide a more comprehensive understanding of the socio-cultural drivers of bioregions.

When assessing environmental conditions, it is necessary to consider not only territorial characteristics and geographical data but also environmental and ethical indicators that are subject to changes in sustainable environmental policies and directly relate to environmental protection. From the available data on specially protected areas, the research selected natural objects that are governed by the law On Specially Protected Nature Territories, which lays down the general rules for the protection and use of protected areas [22].

This group of factors can be extended in future research on the specialization of bioregions. The present research analyzed two environmental factors:

• Habitat quality (points ha⁻¹) as a factor (FA7) measures the biodiversity of habitats in rural areas and relates to environmental factors.
• Specially protected areas (ha) as a factor (FA13) indicates the possibilities of exploiting an area; the larger the area, the more logical the creation of bioregions in municipalities. This factor involves aspects of nature, environment and landscape quality.

The data were obtained from sources in which they were aggregated according to a common methodology for all municipalities of Latvia in 2021. Five statistical datasets were obtained from the CSB. The data on the education levels of agricultural enterprise managers were obtained from the CSB Agricultural Census and Survey for 2020. Two statistical datasets were obtained from the RDIM (Regional Development Indicator Module) database and two from the LBTU Bioeconomy and Sustainable Resources Management Centre (LASAM) database. The data on organically certified operators were obtained from the Agricultural Data Centre database, while the data on the beneficiaries of European funding under initiative A019.40 Ensuring the functioning of a LAG and activating the territory were available on the RSS website. The data on Latvian amateur art collectives were available on the Latvian cultural data portal Culture Data. The areas of specially protected nature territories were calculated by municipality using maps from the GIS OZOLS database.

3. Results

The distribution of factors shows the specifics of each factor category, which helps to explain the interaction of the factors both within a category and between the categories. As shown in

Table 5. Breakdown of the factors by category.

Symbol	Name	Factor
FG1	Sustainable agriculture	Organically certified UAA, ha
		Organically certified UAA as a % of the total UAA
		Number of organic production operators
		Unemployment rate among the economically active population aged 15–74, %
		Amateur art collectives as a % of the total population
		Number of agricultural enterprise managers with higher and professional agricultural education
		Specially protected areas, ha
FG2	Intensive agriculture	UAA as a % of the total area in the municipality
		Profit from productive land, EUR ha−1
FG3	Tourism	Habitat quality, points ha−1
		PIT revenue per capita, EUR
		Tourists served as a % of the total population

, the factor categories were assigned symbols and names that described each of them.

The factor groups (FG1–FG3) include a different number of indicators: for FG1, seven, for FG2, two and for FG3, three. These disproportion numbers reflect the empirical structure of the data: some dimensions of regional development, such as sustainable agriculture and socio-environmental aspects (FG1), are inherently multidimensional, while a smaller, more targeted set of indicators characterizes others, such as intensive agriculture (FG2). The difference in the number of indicators does not affect the reliability of the categorization, since PCA summarizes intercorrelated variables into latent factors regardless of the initial size of indicators. Rather, it shows that some areas of bioregional potential are based on a broader range of indicators, while others are based on a narrower defined basis. The first factor category (FG1) includes the factors selected based on organic farming indicators, which indicate the beneficial impact of the factors on territorial development in the context of sustainability: organically certified UAA (ha) as a % of the total UAA, as well as the number of organic production operators. The factor “agricultural enterprise managers with higher and professional agricultural education” interacts with the above-mentioned factors and is equivalent in terms of numerical value significance. This explains the role of knowledge-based agricultural practices in sustainable and competitive production, the production of high-value-added food products and the preservation of biodiversity, since organic farming is inherently more complex than intensive farming. Business managers need an in-depth knowledge of the basic principles and philosophy of organic farming, which involves minimizing the use of pesticides and artificial fertilizers and exploiting natural resources, to ensure the profitable operation of their enterprises. Given that the first category also includes significant factors such as “amateur art collectives as a % of the total population” and “specially protected areas, ha”, it could be concluded that the factors of this category are significant and have a positive impact on prospects for the creation of bioregions, as they correspond to the concept of bioregions: harmonious interaction between environmentally friendly agriculture, strong local communities and the quality of the environment, as well as the ethical use of ecosystems. The factor “unemployment rate among the economically active population aged 15–74, %”, in the opinion of the authors, indicates the socio-economic situation of the municipalities of Latvia, i.e., where the unemployment rate is higher or where industrial production and intensive agriculture are not developed. This situation could be perceived as an opportunity to involve the population in the transformation of rural areas to move towards socio-economic development based on the principles of the bioeconomy.

The second category, “intensive agriculture”, combines two factors in intensive production practices: “UAA as a % of the total area in the municipality” and “profit from productive land, EUR ha⁻¹”, which closely related to each other and indicate an opposite trend to the sustainable development model for rural areas—high profits are achieved in municipalities where conventional agricultural enterprises are common. It should be noted that this category does not include the factor “PIT revenue per capita, EUR”. This suggests that, in areas with intensive agriculture, enterprises have a small impact on positive changes in the financial well-being of the population. High-intensity agricultural enterprises use advanced technology, thereby achieving efficient management with a small number of employees. This factor category is specific to municipalities which, in the opinion of the authors, are not appropriate for bioregion development.

The category “tourism” includes a factor that indicates the number of tourists served as a % of the total population (%), PIT revenue per capita (EUR) and the quality of habitats (points ha⁻¹). The interaction of such factors shows that the diversification of economic activities in rural areas is possible and necessary through complementing them with other kinds of economic activity, e.g., tourism. This directly relates to the concept of bioregions, which is based on the idea of using environmental services for ecotourism.

The analysis of the factors and their interrelations allows us to conclude that the creation of bioregions could be based on the factors of knowledge-based organic farming. The activity and involvement of young farmers in economic activities is an indicator of dynamic development of territories in bioregions, since the status of a young farmer automatically implies appropriate education and motivation. A multifactorial linear regression analysis was performed to identify the impact of factors on the dependent variable “EAGF and EAFRD beneficiaries under initiative A006.01 Support for young farmers to start a business”.

When performing the regression analysis for the three factor categories, factor category 2 (intensive agriculture (FG2)) was excluded because the p-value was greater than 0.05 and, after excluding it, the coefficient of determination increased. This means that factor category 2 (FG2) and the dependent variable “EAGF and EAFRD beneficiaries under initiative A006.01—Support for young farmers to start a business” were not associated.

$$Y={2.9444}_{(p<0.0001)}+{{1.7101}_{\left(p<0.0001\right)}·X}_1-{{0.7185}_{\left(p=0.0389\right)}·X}_2$$

(1)

where $Y$—average number of beneficiaries of support among young farmers in 2021;

$X_1$—the sustainable agriculture factor category;

$X_2$—the tourism factor category.

The regression analysis revealed that the pathway parameter of 1.7101 (p < 0.0001) was statistically significant with P = 99%. This means that the factors of the sustainable agriculture category were relevant and, given that they were positively correlated with the dependent variable, it could be concluded that the motivation of young farmers to start a business and apply for support was higher in areas where the impact of the factors was stronger. The pathway parameter of 0.7185 (p = 0.0389) was statistically significant, with P = 95%. This indicates that the tourism factor was significant, but given that it negatively correlated with the dependent variable, it could be concluded that the motivation of young farmers to start a business and apply for support was greater in areas where the impact of the factors was stronger. In the opinion of the authors, this was due to the fact that the support programs did not apply a complex approach to motivating young agricultural specialists, offering them support specifically for agricultural activity, as well as the limited number of young specialists and their limited entrepreneurial capacity. The equation factor of 2.9444 (p < 0.0001) was statistically significant, with P = 99%, and was associated with the independent variables. The corrected coefficient of determination was R² = 0.4333. This means that 43.33% of the beneficiaries of support among young farmers related to the factors “sustainable agriculture” (FG1) and “tourism” (FG3).

An analysis of variance (ANOVA) was performed to determine whether the number of beneficiaries among young farmers had reached the municipality-level averages.

The number of applications for support among young farmers varied across the municipalities of Latvia Figure 2. The average was achieved in the Kurzeme region, except for South Kurzeme Municipality, as well as in some of the municipalities in the Latgale and Vidzeme regions. There were no applications at all in Jelgava Municipality and Dobele Municipality with intensive agriculture, whereas in Bauska Municipality, where there was also intensive agriculture, the number of applications reached the national average. There were also no applications in the municipalities of the Pieriga region, except for Ropazi Municipality, and in Gulbene Municipality and Valka Municipality in the Vidzeme region. The analysis revealed that an increase in the number of beneficiaries of support among young farmers was observed in municipalities with less developed tourism, while in those with intensive agriculture, the number of applications was low or even zero.

The factor categories determined by the factor analysis (FG1, FG2, FG3) were further used in a cluster analysis to analyze the municipalities of Latvia and assess the prospects for the creation of potential bioregions. The dendrogram of the clusters of potential bioregions in Latvia (Figure 3) identified six clusters as having the potential for bioregions.

The cluster analysis identified six clusters (Figure 4), which are characterized by distinctive features:

(1)

Intensive agriculture (cluster A): Bauska, Dobele and Jelgava Municipalities, in which, given the preconditions for agricultural development (terrain, soil quality, etc.), intensive agricultural production is traditional.

(2)

Pieriga region (cluster B): Adazi, Kekava, Olaine, Ropazi, Salaspils and Saulkrasti Municipalities, the locations of which are in the vicinity of the capital, thus shaping their development. The direct impact of Riga is observed through the high level of employment and economic activity and the opportunity to work in the capital. This cluster is characterized by well-developed transportation infrastructure.

(3)

Tourism (cluster C): Cesis, Madona, Marupe and Sigulda Municipalities, which are characterized by tourism development, as well as organic agriculture and large areas of specially protected nature territories.

(4)

Extensive agriculture (cluster D): Aluksne, Augsdaugava, Balvi, Gulbene, Kraslava, Ludza, Preili and Rezekne Municipalities, which are characterized by organic farming and high social activity.

(5)

Nature and education (cluster E): Aizkraukle, South Kurzeme, Jekabpils, Kuldiga, Limbazi, Ogre, Smiltene, Talsi, Valmiera and Ventspils Municipalities, which are characterized by large areas of specially protected nature territories, strong educational backgrounds and social activity and organic farming above the national average.

(6)

General specialization (cluster F): Livani, Saldus, Tukums, Valka and Varaklani Municipalities, which are characterized by mediocre levels across all the factors.

The aggregated data on the average values of factors influencing the creation of bioregions (

Table 6. Average values for factors in the creation of bioregions by cluster, according to 2021 data.

The Factors in the Creation of Bioregions	Cluster
	A	B	C	D	E	F
Organically certified UAA, ha	2542.00	291.50	12,420.75	14,645.63	11,009.00	4312.40
UAA as a % of the total area in the municipality	53.15	16.70	30.80	37.33	30.88	38.11
Organically certified UAA as a % of the total UAA	2.60	4.52	16.72	19.14	14.35	12.57
Number of organic production operators	33.00	6.50	129.50	179.63	120.33	47.40
Profit from productive land, EUR ha−1	278.67	91.17	84.00	73.13	106.11	125.20
Habitat quality, points ha−1	4.13	7.02	6.18	5.30	5.54	6.02
PIT revenue per capita, EUR	575.33	890.25	733.75	392.00	578.03	500.20
Unemployment rate among the economically active population aged 15–74, %	7.63	5.65	7.00	14.71	7.51	10.04
Amateur art collectives as a % of the total population	0.26	0.15	0.25	0.43	0.30	0.37
Tourists served as a % of the total population	11.22	5.58	28.32	5.00	9.25	5.74
Number of agricultural enterprise managers with higher and professional agricultural education	652.33	49.67	527.75	919.25	710.22	386.40
Specially protected areas, ha	2789.71	688.85	14,534.46	4658.42	7587.38	3102.39

Note: Values highlighted in yellow represent the maximum for each factor across clusters, illustrating the distinct features of each cluster.

) show trends in the development of cluster areas.

An analysis of the highest average values for the factors revealed that three clusters–A, B and C—had clear specializations (Table 6).

Cluster A, which includes Bauska, Dobele and Jelgava Municipalities, with their highly developed intensive agriculture industries, had the smallest organically certified UAA as a % of the total UAA, at 2.6%, despite the fact that the UAA as a % of the total area in the municipalities had the highest average value across all the clusters, at 53.15%. In contrast, the profit from productive land (EUR ha⁻¹) was the highest. Based on the data, it could be concluded that the municipalities of cluster A were not suitable for the creation of bioregions.

The municipalities of the Pieriga region (cluster B) were distinguished by the highest PIT revenue per capita (EUR), which could be explained by the fact that a large segment of the population worked in Riga, which has a high socio-economic performance. The capital city had an impact on community activities, with amateur art collectives as a % of the total population being above the national average. This might be explained by the fact that the residents of these municipalities were members of amateur art collectives in Riga, so it could not be argued that the local communities were inactive. Despite the supposedly unfavorable preconditions for the creation of bioregions, cluster B included organic production operators and some organically certified UAA. Given that the mentioned UAA was relatively small, both the UAA as a % of the total area in the cluster area (16.70%) and the organically certified UAA as a % of the total UAA in the cluster area (4.52%) could be examined for the purpose of creating bioregions. The advantage of cluster B is the large number of buyers of organically certified food products in Riga and the Pieriga region with high buying power.

According to the basic principles of the concept of bioregions and the average values for the selected factors influencing the creation of bioregions, cluster C was the leader, with its specialization in tourism and well-developed organic farming and processing industry, as well as the largest area of nature reserves. A significant indicator of community activity—amateur art collectives as a % of the total population—was high enough, at 0.25%, which indicated good prospects for the creation and development of bioregions despite the fact that, in the municipalities of this cluster, the indicator of dynamic development of bioregions—the number of beneficiaries of support among young farmers—did not reach the national average, or there were no applicants for such support at all.

In the municipalities of cluster D, the values of significant factors influencing the creation of bioregions, which indicate trends in the development of organic farming and the activity of local communities, were the highest both within cluster D and in comparison with the averages of the other clusters. Based on this conclusion, it could be assumed that areas of these municipalities are suitable for the creation of bioregions and that the poor economic situation, low incomes and the large number of jobseekers could have a motivational effect if an appropriate territorial development policy were made.

Cluster E includes municipalities whose centers are state cities. This makes the effect of smaller-scale centralized territorial development similar to that in the municipalities of the Pieriga region (cluster B), where industrial centers with available jobs attracted residents from rural areas, which overall created a more favorable economic situation. This cluster also includes seaside municipalities, both those whose centers are state cities (South Kurzeme and Ventspils Municipalities), as well as other seaside municipalities, such as Talsi and Limbazi. The high performance of the municipalities in tourism could be explained both by the location of state cities and by the use of coastal areas. A significant contribution to the tourism industry was made by Kuldiga Municipality, which is a popular destination for local and foreign tourists. In the municipalities of cluster E, organic farming is well-developed, as the average area of organically certified agricultural land was the second largest (11,009 ha) among the clusters, the average number of agricultural enterprise managers with higher or professional education (710 managers) and the average area of specially protected nature territories (7587.38 ha) were also the second largest. This, overall, creates favorable conditions for the creation of bioregions.

Cluster F has a relatively high above-average organic farming area, indicating an essential structural basis for the development of a bioregion. In addition, the quality of habitats in this cluster was above average, which indicates a positive ecological background and the possibility of integrating environmental protection with agriculture. At the same time, cluster F faces a number of challenges: a lower level of income per capita, a higher unemployment rate and lower rate of professional education for agricultural enterprise managers. The performance of the tourism industry was low, which means untapped potential. Overall, cluster F is not ideal for the core of a bioregion; however, with targeted support, it could serve as an important complement to existing or emerging bioregions, providing some agricultural areas and an opportunity for socio-economic growth, especially through sustainable development and cooperation initiatives.

Table 7. Cluster characteristics by dimension.

Cluster	Dominant Indicators	Secondary Indicators	Typical Profile
A—Intensive agriculture	High FA2 (share of UAA in municipality), FA6 (profit from land)	Moderate FA8 (PIT revenue)	Municipalities with favorable soils and terrain, long-standing intensive farming tradition
B—Riga metropolitan area (Pieriga)	High FA8 (income), FA12 (educated managers), strong employment	Medium FA2 (UAA share), FA11 (tourism)	High socio-economic activity, strong commuting ties to Riga, developed infrastructure
C—Tourism	High FA10 (amateur art collectives), FA11 (tourists served), FA7 (habitat quality)	Moderate FA3 (organic UAA share)	Municipalities with cultural vitality, nature protection areas and tourism flows
D—Extensive agriculture	High FA1–FA3 (organic farming indicators), FA4 (organic operators)	High FA9 (unemployment), FA10 (cultural activity)	Rural municipalities with strong organic sectors, high social activity
E—Nature and education	High FA13 (protected areas), FA12 (educated managers), FA10 (cultural activity)	Moderate FA1–FA3 (organic land)	Large areas of protected land, strong educational and cultural background, above-average organic farming
F—Non-specialized (average values)	None strongly dominant; mid-level across FA1–FA13	Slightly higher FA8 (income)	Municipalities with average performance across factors

provides a summary of the bioregion cluster dimensions, highlighting their implications for bioregion development.

The clusters of potential bioregions show areas where there is unlikely to be an interest in creating bioregions (cluster A), since an intensive and conventional transformation of agriculture according to the requirements of agroecology would most likely face strong resistance from farmers. The potential of cluster F is also unclear; it does not show any specific indications of potential for bioregions. However, there is no reason to believe that it is not suitable for bioregions. Overall, consumers in Latvia appreciate the consumption of local traditional and organic foods [26]. Therefore, clusters E and D, which are generally characterized by high localism and organic farming, also have good potential. Interestingly, cluster C actually coincides with the Gauja National Park (GNP), the first bioregion in Latvia, which is most likely due to the popularity of specially protected areas and organic farming being appreciated by many tourists who visit this region. It should be noted that, in addition to these factors, the GNP bioregion has strong and motivated leaders who invest in it. Cluster B is affected by the capital, Riga, as it is characterized by a high level of income and relatively high requirements for goods, including food, which in turn allows us to hope for the creation of bioregions with the aim of providing school meals with organic food, especially if the experience of the GNP bioregion is to be adopted.

4. Conclusions

Overall, cluster analysis for determining the potential of bioregions is a useful method. The results provide material for discussion on the creation of bioregions for local authorities and community initiative groups. At the same time, it should be recognized that the inclusion of the social dimension, especially motivational factors, is debatable and should be developed. On the one hand, there is a desire to measure a factor that is difficult to measure (motivation and the ability of the community to agree), on the other hand, there is a lack of objective statistical or observational data. We assume, and this is consistent with other observations in Latvia, that people who participate in amateur collectives discuss developments in the territory, know the people of their community and establish trusting relationships, which are essential factors in the creation of bioregions. At the same time, agricultural education and readiness to start one’s own business as a young farmer also show some potential arising from university programs with courses on sustainable development, which allow young farmers to take the lead in the sustainable development of the territory. Motivation is important, and this factor should not be underestimated, but also should not be overestimated. A relatively small group of people is enough, which might not form a statistically significant group, but still can initiate and develop some kind of social movement.

The analysis revealed that regions with intensive agriculture benefit from global trade in the form of higher incomes provided by investment in agriculture, and it will be very difficult for the local community to change the development model of their territory. The areas bordering the capital can benefit from bioregions, as townspeople are more conscious buyers with higher buying power, which would contribute to the demand for goods and services in bioregions. It would be similar to smaller centers in the region, with tourism services playing an additional role. It is unlikely that the same combination of factors is possible for all potential bioregions; however, some factor categories, such as a good agricultural education, organic farming and organic processing, could contribute to the creation of a bioregion. The situation regarding organic farming, processing, rural tourism and specially protected nature areas is similar, as evidenced by the GNP bioregion in Latvia, as well as others in Europe. By being aware of these factors, policymakers and local communities can start a discussion on the creation of truly sustainable territories, also called bioregions. At the same time, the authors want to emphasize that the bioregion movement is a public initiative and municipalities have a supporting function. Therefore, in the first stage, support is needed for people to express their will and form bioregions. At the same time, it cannot become a formal institute, because then it would lose its essence and perform formal functions. At the same time, municipal institutes, such as education departments and tourism departments, can interact with bioregions by, for example, organizing school meals or organizing tourist attraction events.