Regional Determinants of the Development of Short Food Supply Chains in Poland

Abstract

This study investigates the regional drivers shaping the development of short food supply chains (SFSCs). SFSCs are increasingly recognized as sustainable alternatives to industrial food systems; however, their development dynamics at the regional level remain poorly understood. Drawing on structured interviews and surveys with regional agricultural institutions, combined with official statistical data, we applied Spearman’s rank correlation to test five hypotheses related to structural, institutional, historical, and demand-related factors across 16 provinces in Poland. The results revealed weak or statistically non-significant associations between most of the analyzed factors and the development of SFSCs, with the notable exception of a strong correlation with urban population size. These findings challenge conventional assumptions about the role of agrarian or policy conditions in supporting localized food systems and suggest that regional food policy should focus more on enhancing urban–rural linkages and consumer engagement to foster sustainable food supply models.

1. Introduction

Short food supply chains (SFSCs), based on the physical, organizational, and social proximity of market participants, have gained increasing importance over recent decades as an instrument supporting the sustainable development of rural areas and strengthening the economic, social, and environmental resilience of local food systems [1,2,3,4,5,6]. Their role has also been emphasized in European Union policies, most notably in the European Green Deal, which sets new directions for the agri-food economy towards 2050 [7,8].

Despite extensive evidence of the benefits generated by SFSCs [9,10,11,12], their rate of development across EU member states remains uneven [13]. This differentiation is determined by a set of mutually reinforcing and complementary factors, such as geopolitical, structural, cultural, and institutional conditions [14]. Research in this area, however, remains largely unsystematic, focusing mainly on local case studies and selected socio-economic or environmental dimensions [15,16,17].

As Raftowicz [9] notes, comprehensive analyses capable of capturing regional variation in the development of SFSCs are largely absent in Central and Eastern Europe. The regional dimension can be considered a key factor in the analysis of SFSCs, as agricultural structures, the degree of farm fragmentation, market access, soil quality, and social capital differ substantially across provinces [18,19,20]. Regional determinants may shape not only the pace of development but also the forms taken by SFSCs—from open-air markets and direct sales systems to more advanced models of cooperatives and producer–consumer partnerships [21,22].

In Poland, the problem of regional differentiation in SFSCs is particularly pronounced. Existing research has mainly focused on selected regions and specific forms of sales, which narrows their analytical value and hampers the formulation of generalizations at the national level [23]. International comparisons [24,25] provide valuable references, yet due to divergent socio-economic and institutional contexts, they cannot be directly transferred to the Polish case. Consequently, a research gap emerges with regard to systematic nationwide analyses that would allow for a precise understanding of how regional factors influence the intensity and trajectories of SFSC development in Poland.

The main objective of this article is to address this gap by identifying and empirically verifying the regional determinants of SFSC development in Poland. The study covers all 16 provinces and applies Spearman’s rank correlation analysis to assess the strength and direction of relationships between SFSC development and structural, environmental, institutional, and sociohistorical factors.

Based on this approach, five research hypotheses have been formulated:

H1. 

The development of SFSCs depends on farm structure—regions with more fragmented agriculture tend to exhibit a higher number of initiatives.

H2. 

The development of SFSCs is associated with the quality of agricultural production space—in regions with poorer soils, where extensive agricultural production is less profitable, a higher number of alternative initiatives can be observed.

H3. 

The involvement of agricultural support institutions fosters the development of SFSCs—regions with stronger institutional backing display a greater number of initiatives.

H4. 

Historical and social regional conditions influence the development of SFSCs—regions with strong traditions of family farming are more likely to adopt this model than areas dominated by large-scale industrial agriculture.

H5. 

The development of SFSCs depends on local demand—a higher concentration of consumers interested in this purchasing model fosters the intensification of initiatives.

2. Literature Review

The issue of short food supply chains has received increasing attention in the scientific literature, particularly in the context of sustainable development [17,26,27]. As argued by Raftowicz [26], this concept highlights the need to integrate economic, social, and environmental dimensions in order to ensure the long-term sustainability of food systems. SFSCs fit into this paradigm by promoting local production, reducing the negative environmental effects of transport, and shortening the distance between producers and consumers, thereby contributing to the rebuilding of social relations and trust. Nevertheless, an approach based solely on the theory of sustainable development proves insufficient to fully explain the mechanisms of SFSC development, especially from a regional perspective [28,29,30].

In addition to European studies, recent research from outside Europe provides complementary perspectives on the transformation of agri-food systems. Shadkam et al. [31] have offered a comprehensive review of simulation and optimization methods in agricultural supply chains, emphasizing the transition towards intelligent, digitalized frameworks for Agriculture 4.0. Their findings highlight the growing importance of adaptive and technology-based institutional mechanisms, which can also strengthen local and regional food systems. Panciszko-Szweda [32] discussed short food supply chains as a policy tool for sustainable rural development, stressing that SFSCs can contribute to smart territorial governance when supported by coherent policy frameworks. These global perspectives confirm that institutional adaptability and technological innovation are key drivers of regional agri-food resilience, consistent with the conceptual framework adopted in this study.

Research that takes regional differentiation into account requires a broader theoretical framework. Particularly useful are perspectives derived from (1) endogenous development theory, (2) social network theory, (3) the theory of embeddedness, and (4) new institutional economics. Each of these frameworks highlights different aspects of the functioning and development of SFSCs depending on local social, historical, economic, and institutional conditions.

First, endogenous development theory [33,34,35,36,37] posits that territorial development should rely primarily on the internal resources of a given region—both tangible and intangible—such as knowledge, innovation, human capital, and local cultural identity. In the context of SFSCs, this means that local and regional initiatives, agricultural potential, entrepreneurship, and production traditions are of greatest importance. SFSCs provide a practical tool for implementing the endogenous approach: they enable the use of local raw materials, reduce dependence on global distribution networks, and strengthen regional food identity. Importantly, regions with different agrarian structures, levels of urbanization, or natural resources may draw on this potential in different ways, which challenges the applicability of universal development policies.

Second, a significant complement to this perspective is social network theory [38,39], which focuses on the role of interpersonal relationships in shaping market structures. In the case of SFSCs, this implies that the durability and efficiency of local food markets depend on the quality of ties between producers, consumers, and support institutions. Strong social networks facilitate the exchange of information, foster coordination, and build trust—essential in semi-formal or informal transactions typical of SFSCs. Conversely, in regions with weak social capital, the active role of public institutions may be necessary to stimulate cooperation and build relationships.

Third, the theory of embeddedness [40] is also crucial for understanding the mechanisms underlying SFSCs, as it assumes that economic processes are embedded in social and cultural structures. Unlike neoclassical approaches, which treat markets as autonomous entities, embeddedness theory emphasizes that social relations, cultural norms, and historical context play a decisive role in shaping economic processes. In the case of SFSCs, three key dimensions of embeddedness can be distinguished: (1) social embeddedness, referring to relationships between producers and consumers, built on trust, knowledge of product origin, and shared values; (2) institutional embeddedness, encompassing the role of public policy, regulations, and the availability of support instruments; and (3) historical embeddedness, linked to the economic past of regions—such as the legacy of partitions, the degree of agricultural collectivization, or agrarian structures following the post-socialist transformation. From this perspective, regional differences in SFSC development may result not only from current economic conditions but also from long-term historical processes that have shaped local social and economic structures.

Finally, the institutional approach complements the above perspectives by drawing attention to the formal and informal rules governing markets. According to new institutional economics [41,42], the development of economic systems—including SFSCs—is strictly dependent on the quality of institutions, such as the legal framework, support mechanisms, bureaucratic procedures, and the level of social trust. Institutions influence transaction costs, market access, and the distribution of public resources. In the context of SFSCs, they can foster local models (e.g., by simplifying sanitary regulations) or hinder them (e.g., through overregulation or the absence of support infrastructure).

These four theoretical perspectives are not mutually exclusive but rather complementary in explaining the development of short food supply chains. The endogenous development theory emphasizes the role of local resources and community-based initiatives as drivers of rural sustainability. The social network theory adds the relational dimension, highlighting trust, cooperation, and knowledge exchange between producers and consumers. The concept of embeddedness explains how economic actions remain rooted in social and cultural contexts, shaping market interactions and consumer preferences. Finally, the new institutional economics focuses on the formal and informal rules that structure these interactions and influence transaction costs. Taken together, these frameworks provide a multidimensional understanding of SFSCs, where local assets, social relations, and institutional frameworks interact to shape regional development trajectories. Based on these theoretical premises, the study formulates five hypotheses linking structural, institutional, historical, and demand-related factors with the regional dynamics of SFSC development.

In summary, there is a clear research gap concerning a comprehensive, nationwide analysis of the regional determinants of SFSC development. This applies in particular to the relationships between agrarian structures, the quality of agricultural production space, institutional support, and the actual intensity of SFSC initiatives. Understanding these relationships is crucial not only for theory but also for the design of effective public policies—aligned with the objectives of the European Green Deal and rural development strategies. This article seeks to address this gap by applying an empirical approach based on statistical data from all 16 provinces and a correlation analysis between selected variables and the number of SFSC initiatives across regions.

The hypotheses presented in the Introduction were developed based on the main theoretical approaches discussed in this section. The endogenous growth perspective provides the rationale for assuming that regions with a more favorable agrarian structure and resource endowment are more likely to develop SFSC (H1). Environmental and resource-based theories explain the expected relationship between soil quality and SFSC development (H2). Institutional economics supports the assumption that the involvement of local institutions stimulates the growth of SFSCs (H3), while the embeddedness approach justifies the inclusion of historical and socio-cultural legacies as factors influencing local food networks (H4). Finally, the market-based sustainability perspective underpins the demand-oriented hypothesis, according to which urban population size is a major driver of SFSC development (H5).

3. Materials and Methods

3.1. Data Sources

This study focuses on Poland—the largest agricultural producer among the new member states of the European Union, possessing the third-largest agricultural land area. The country is characterized by a highly fragmented agrarian structure (74% of farms are below 10 ha), which favors the development of SFSCs by enabling direct sales of products and fostering closer relationships between producers and consumers.

The main sources of data include:

• Official registers: statistical data from the Ministry of Agriculture and Rural Development, the Chief Veterinary Inspectorate, and the State Sanitary Inspectorate covering the years 2004–2024. These data concern the number of entities operating within three forms of short food supply chains: rolniczy handel detaliczny (RHD, i.e., agricultural retail trade), działalność marginalna, lokalna i ograniczona (MOL, i.e., marginal, local, and restricted activity), and direct sales (SB). Table 1 presents the number of entities engaged in SB, MOL, and RHD in Poland from 2004 to 2024.

Table 1. Number of entities engaged in SB, MOL, and RHD in Poland from 2004 to 2024.

Year	SB	MOL	RHD	Total
2024	16,178	2229	22,449	40,856
2023	16,492	2353	22,237	41,082
2022	15,264	2339	18,040	35,643
2021	14,576	2291	15,343	32,210
2020	13,613	2216	12,212	28,041
2019	11,630	2284	7713	21,627
2018	10,020	2241	3271	15,532
2017	9570	2264	1633	13,467
2016	8688	2253	-	10,941
2015	7678	2203	-	9881
2014	7065	2117	-	9182
2013	6466	2042	-	8508
2012	5856	1920	-	7776
2011	5139	1867	-	7006
2010	4271	1817	-	6088
2009	3791	1363	-	5154
2008	2758	1167	-	3925
2007	1790	906	-	2696
2006	847	-	-	847
2005	709	-	-	709
2004	610	-	-	610

Source: Author’s elaboration based on [43]. Note: The category RHD refers to the Polish legal form introduced by the Act on Agricultural Retail Trade of 16 November 2016 (in force since 1 January 2017) [44]. Data for the years 2004–2016 are therefore not missing but unavailable, as this type of activity was not yet legally recognized or statistically recorded before 2017. Additionally, data for MOL are unavailable for 2006 due to the absence of centralized registration. Although the legal framework was introduced in 2006, national statistical reporting began only several years later after the system was standardized by the Chief Veterinary Inspectorate [45].

Table 1. Number of entities engaged in SB, MOL, and RHD in Poland from 2004 to 2024.

Year	SB	MOL	RHD	Total
2024	16,178	2229	22,449	40,856
2023	16,492	2353	22,237	41,082
2022	15,264	2339	18,040	35,643
2021	14,576	2291	15,343	32,210
2020	13,613	2216	12,212	28,041
2019	11,630	2284	7713	21,627
2018	10,020	2241	3271	15,532
2017	9570	2264	1633	13,467
2016	8688	2253	-	10,941
2015	7678	2203	-	9881
2014	7065	2117	-	9182
2013	6466	2042	-	8508
2012	5856	1920	-	7776
2011	5139	1867	-	7006
2010	4271	1817	-	6088
2009	3791	1363	-	5154
2008	2758	1167	-	3925
2007	1790	906	-	2696
2006	847	-	-	847
2005	709	-	-	709
2004	610	-	-	610

Source: Author’s elaboration based on [43]. Note: The category RHD refers to the Polish legal form introduced by the Act on Agricultural Retail Trade of 16 November 2016 (in force since 1 January 2017) [44]. Data for the years 2004–2016 are therefore not missing but unavailable, as this type of activity was not yet legally recognized or statistically recorded before 2017. Additionally, data for MOL are unavailable for 2006 due to the absence of centralized registration. Although the legal framework was introduced in 2006, national statistical reporting began only several years later after the system was standardized by the Chief Veterinary Inspectorate [45].

• Qualitative research: in-depth interviews and surveys conducted in 2023 across all 16 provinces, involving employees of regional marshal offices (provincial government offices), agricultural departments, and agricultural advisory centers.
  The qualitative component of the study comprised 32 structured interviews and 32 survey responses collected between June and October 2023 across all 16 Polish provinces. Respondents represented regional institutions responsible for agricultural development, including Marshal Offices and Agricultural Advisory Centres. The interview protocol addressed issues such as the functioning and evolution of SFSCs, institutional forms of support, barriers to development, and the effectiveness of regional policies (see Appendix A).
• Spatial and statistical data: including data from Statistics Poland (GUS), the Agricultural Census [46], and the Agricultural Production Space Valorization Index (WWRPP) provided by IUNG-PIB [47].

3.2. Methods of Analysis

The analysis was carried out at the level of 16 provinces, applying Spearman’s rank correlation test to assess the strength and direction of the relationships between SFSC development and five independent variables.

The selection of explanatory variables was guided by both theoretical and empirical considerations. Previous studies have shown that the development of SFSCs is influenced by a combination of structural, institutional, social, and market-related factors [1,2,9,16,22,24]. Therefore, five indicators were selected to capture these dimensions: (1) farm structure, representing the agrarian base of production; (2) the quality of agricultural production space, reflecting environmental and resource conditions; (3) institutional involvement, denoting the level of public and organizational support; (4) historical and social legacies, expressing long-term path dependencies; and (5) urban demand, indicating the scale of consumer markets and purchasing power. Together, these variables form a multidimensional framework for assessing the regional determinants of SFSC development in Poland.

Spearman’s rank correlation coefficient was calculated as follows:

$$\mathsf{\rho } \mathbf{=} 1 \mathbf{-} {\displaystyle \frac{6\Sigma D^2}{N(N^2-1)}}$$

(1)

Note: ρ = Spearman’s rank correlation coefficient. Given N = 16, correlations above |0.50| approximately correspond to p < 0.05.

where

ρ (Spearman’s rho) = Spearman’s rank correlation coefficient;

D = x_i − y_i, the difference between the ranks of corresponding values of the variables x_i and y_i (i = 1, 2, …, n);

x_i = rank of the i-th object in the first ordering;

y_i = rank of the i-th object in the second ordering;

N = number of objects studied = 16, i.e., the number of provinces in Poland.

The direction of the coefficient depends on the adopted ranking order. In this study, the difference D was defined as D = xi − yi, where xi represents the ranking of SFSC entities and yi represents the ranking of the independent variable. This convention determines the sign of the coefficient without affecting the interpretation of its strength.

Given the small sample size (N = 16), the correlation analysis was treated as exploratory, focusing on the direction and relative strength of relationships rather than on strict statistical inference. The statistical power of the tests is therefore limited, and significance levels should be interpreted cautiously. Approximate p-value thresholds corresponding to ρ values were used to guide interpretation but without claiming formal significance testing.

The analysis was deliberately limited to bivariate rank correlations because of the small number of regional observations (N = 16). This approach was chosen to avoid model overfitting and to maintain transparency of the exploratory results. More advanced multivariate techniques—such as generalized linear models or LASSO-based variable selection—were considered beyond the scope of this preliminary investigation but are recommended for future research.

In the literature, the correlation coefficients reported in

Table 2. Interpretation of Spearman’s correlation coefficients.

Range of Correlation Coefficient	Interpretation
|0.0–0.2|	very weak correlation
|0.2–0.4|	weak correlation
|0.4–0.6|	moderate correlation
|0.6–0.8|	strong correlation

Source: Author’s elaboration based on [48].

are commonly interpreted as follows:

To construct the variable “institutional involvement,” data from qualitative interviews and surveys were coded and aggregated. Each respondent rated the perceived activity of key institutions supporting SFSCs (Marshal Offices, Agricultural Advisory Centres). on a 5-point Likert scale (1 = very weak involvement, 5 = very strong involvement). The average score for each province was then calculated and converted into an ordinal ranking from 1 (lowest involvement) to 16 (highest). This procedure enabled the quantification of relative institutional support across regions while ensuring methodological transparency and comparability.

To operationalize the variable “historical legacy,” we adapted the borders of the former partitions of Poland (Prussian, Austrian, Russian, and Western/Northern Lands) to the current administrative division into 16 provinces. Each province was assigned a categorical value (1–4) corresponding to its dominant historical affiliation. This classification reflects long-term differences in institutional and agrarian trajectories, such as land ownership patterns, levels of collectivization, and the persistence of household farming traditions [18,49]. In the analytical model, this categorical variable was subsequently transformed into an ordinal ranking (1 = lowest, 16 = highest persistence of traditional household farming). This approach allowed the inclusion of qualitative historical characteristics in a quantitative framework while maintaining theoretical consistency with the concept of historical embeddedness [40].

The use of Spearman’s rank correlation was justified by the ordinal and non-parametric nature of the analyzed variables, as well as the small sample size (N = 16). Unlike regression models, which assume linear relationships and normal data distribution, Spearman’s coefficient allows for identifying monotonic associations without imposing strict parametric assumptions. This approach is particularly suitable for exploratory studies based on aggregated regional data, where variables represent rankings or composite indices rather than continuous measurements. Moreover, the analysis was conducted at the voivodeship level, which limits the degree of spatial precision and may introduce scale-related biases known as the Modifiable Areal Unit Problem (MAUP). Although potential spatial autocorrelation between neighboring regions cannot be fully ruled out, the small number of spatial units prevented the reliable application of spatial econometric models. These limitations are acknowledged and discussed in the Section 6, where directions for future spatially explicit analyses are suggested.

3.3. Analytical Framework

The dependent variable was the number of entities operating within SFSCs in individual provinces (2024).

Table 3. Dependent and independent variables used in Spearman’s rank correlation test.

Variable	Description	Type of Variable	Mean	Min	Max
Agrarian structure	Share of farms up to 10 ha in the total number of farms	Independent	71.4	48.4	94.2
Quality of agricultural space	Average WWRPP (Agricultural Production Space Valorization Index)	Independent	67.5	55.0	81.6
Institutional involvement	Institutional support ranking (based on interviews)	Independent	8.5	1	16
Historical legacy	Degree of embeddedness of agricultural traditions (regions: historical partitions)	Independent	8.5	1	16
Demand potential	Population of provincial capital cities	Independent	460	11,4000	1,862,402
Number of SFSC entities	Registered RHD, MOL, and SB producers in 2024	Dependent	10,600	610	40,856

Source: Author’s elaboration based on [43,46,47,49,50].

summarizes both the dependent and independent variables and includes basic descriptive statistics (mean, minimum, and maximum values) to enhance the transparency of the dataset and facilitate the interpretation of regional variability.

Although the analysis was based on the absolute number of SFSC entities per province, potential scale bias related to population size and agricultural structure was qualitatively assessed. Proportional relationships between the number of farms, population size, and SFSC entities were examined to ensure that regional differences did not artificially influence the correlations. This qualitative assessment confirmed that the identified relationships—particularly the strong link with urban population—remain consistent and robust.

While the model assumes that the independent variables influence SFSC development separately, it is acknowledged that potential interactions among them may occur. In particular, urban demand and institutional support may reinforce each other: stronger institutional engagement can amplify the market effects of urban consumer preferences, while well-developed transport and digital infrastructure can further mediate this relationship. Due to the limited number of observations (N = 16 provinces) and the risk of overfitting, interaction terms were not included in the quantitative model. Nevertheless, these possible synergies are explored in the Discussion Section 5.

Detailed calculations of Spearman’s rank correlation coefficient are provided in Appendix B.

3.4. Study Limitations

Spearman’s correlation test allows for the identification of relationships, but:

• it does not indicate causal relationships,
• it does not account for interaction effects (e.g., the simultaneous influence of agrarian structure and demand potential),
• it assumes monotonicity in relationships, which limits the detection of more complex patterns.

Due to the limited availability of comparable data at the municipal level, the analysis was carried out at the provincial scale, which may obscure local variations.

Given the small number of regional observations (N = 16), the results should be interpreted as exploratory. The limited sample size reduces statistical power and restricts the generalizability of findings beyond the provincial level, although the observed relationships remain theoretically consistent and directionally robust.

4. Results

Using data obtained from the Ministry of Agriculture, the provinces recording the highest number of farms registered within SFSCs are Wielkopolskie, Dolnośląskie, and Małopolskie. The lowest numbers of registered farms are found in Świętokrzyskie, Podlaskie, and Opolskie.

Figure 1 illustrates the variation in the dependent variable, i.e., the number of farms registered in SFSCs in Poland. The intensity of the shading reflects the concentration: the province with the highest number of farms is marked with the darkest shade, while the province with the lowest number is shown in white.

Based on the data presented, the research hypotheses were analyzed.

Hypothesis 1:

The development of short food supply chains in Poland depends strongly on farm structure. In regions with more fragmented agriculture, a greater number of SFSC initiatives can be observed.

The study found a positive but weak relationship between farm structure and the development of SFSCs. A comparative analysis was conducted between provinces with the largest number of registered SFSC entities and those with the highest degree of farm fragmentation (up to 70 ha), based on the data presented in

Table 4. Number of farms (in thousands) and the share of farms in Poland by province.

Province	Number of Agricultural Holdings in Thousands						Share of Agricultural Holdings
	Total	Up to 10 ha	10–30 ha	30–50 ha	50–100 ha	Above 100 ha	Up to 10 ha	10–30 ha	30–50 ha	50–100 ha	Above 100 ha
Dolnośląskie	53	37	10	2	2	1	70.5	19.6	4.5	3.7	2.8
Kujawsko-Pomorskie	60	33	18	4	2	1	54.8	0.0	7.3	4.1	1.8
Lubelskie	161	126	29	4	2	1	78.3	18.4	2.5	1.2	0.4
Lubuskie	20	14	4	1	1	1	65.0	32.1	5.7	5.1	4.3
Łódzkie	117	90	23	2	1	0	76.8	17.7	2.1	0.9	0.3
Małopolskie	127	119	6	1	0	0	94.2	19.8	0.6	0.4	0.1
Mazowieckie	208	152	48	6	2	1	72.9	20.0	2.7	1.2	0.4
Opolskie	25	17	5	2	1	1	64.6	4.7	6.3	4.8	2.7
Podkarpackie	114	107	5	1	1	0	92.9	22.8	0.9	0.6	0.3
Podlaskie	77	42	29	5	2	0	53.4	21.7	5.9	2.5	0.6
Pomorskie	39	24	11	2	2	1	58.1	5.0	6.1	4.3	2.7
Śląskie	50	43	5	1	1	0	85.4	37.6	2.0	1.5	0.7
Świętokrzyskie	80	69	9	1	0	0	87.0	28.7	1.3	0.6	0.2
Warmińsko-Mazurskie	43	20	15	4	3	1	48.4	10.4	8.8	5.9	3.1
Wielkopolskie	116	71	33	6	3	2	62.2	10.9	5.1	2.7	1.4
Zachodniopomorskie	29	16	7	2	2	2	54.5	33.8	7.1	8.1	6.6
Total	1319

Source: Author’s elaboration based on [46].

.

The most fragmented farm structures are found in the provinces of Małopolskie, Podkarpackie, and Świętokrzyskie, while the least fragmented are in Zachodniopomorskie, Podlaskie, and Warmińsko-Mazurskie, as illustrated in Figure 2.

$${\mathsf{\rho }}_{\mathbf{1}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6\Sigma D^2}{N(N^2-1)}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6·440}{16({16}^2-1)}} \approx \mathbf{0.35}$$

Spearman’s rank correlation coefficient was 0.35, indicating a weak and statistically non-significant relationship. Therefore, this hypothesis can be considered only weakly supported rather than confirmed. While farm fragmentation may support the development of SFSCs, the relationship is weak and cannot be regarded as either strong or unequivocal.

Hypothesis 2:

The development of short food supply chains in Poland depends on the quality of agricultural production space. In regions with lower-quality agricultural production space, where extensive agricultural production is less profitable, the number of SFSC initiatives increases.

A similar procedure was applied to verify Hypothesis 2. For this purpose, the index that most comprehensively accounts for natural conditions affecting yield quality and levels was used. This index, developed as early as the 1970s at the Institute of Soil Science and Plant Cultivation—State Research Institute (IUNG-PIB) in Puławy, is known as the Agricultural Production Space Valuation Index (WWRPP) [47].

The index is based on the assessment of several site-related factors: soil quality and agricultural suitability, water relations, land relief, and agro-climate. Each factor was assigned a weight reflecting its significance in shaping yields:

• Soil conditions: soil quality classes and complexes of agricultural suitability (18–95 points),
• Agro-climate: based on long-term meteorological data (precipitation, temperature, length of the growing season) (1–15 points),
• Water relations: based on the amount of water retained in the soil profile, taking into account granulometric composition and land relief (1–5 points),
• Land relief: based on slope classification and type of relief (0.1–5 points).

Detailed data are presented in

Table 5. Summary of provincial WWRPP values and agricultural potential classes.

No.	Province	Total WWRPP	Class of Agricultural Potential	Potential Rank
1	Dolnośląskie	74.9	High	2
2	Kujawsko-pomorskie	71.0	High	4
3	Lubelskie	74.1	High	3
4	Lubuskie	62.3	Medium	13
5	Łódzkie	61.9	Medium	14
6	Małopolskie	69.3	Medium	6
7	Mazowieckie	59.9	Low	15
8	Opolskie	81.6	High	1
9	Podkarpackie	70.4	High	5
10	Podlaskie	55.0	Low	16
11	Pomorskie	66.2	Medium	9
12	Śląskie	64.2	Medium	12
13	Świętokrzyskie	69.3	Medium	7
14	Warmińsko-mazurskie	66.0	Medium	10
15	Wielkopolskie	64.8	Medium	11
16	Zachodniopomorskie	67.5	Medium	8

Source: Author’s elaboration based on open data from IUNG-PIB [47]. Note: Classification based on the following thresholds—Low < 60; Medium = 60–70; High > 70.

.

A graphical illustration of the variation in the Agricultural Production Space Valuation Index is presented in Figure 3. The lowest index values are observed in the provinces of Podlaskie, Mazowieckie, and Łódzkie, while the highest values occur in Lubelskie, Dolnośląskie, and Opolskie.

$${\mathsf{\rho }}_{\mathbf{2}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6\Sigma D^2}{N(N^2-1)}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6 ·632}{16({16}^2-1)}} \approx \mathbf{0.07}$$

The Spearman rank correlation coefficient was 0.07, indicating that there was virtually no correlation. These results indicate that soil quality does not have a meaningful effect on the number of SFSC entities.

Hypothesis 3:

The involvement of agricultural support institutions has a significant impact on the development of short food supply chains (SFSCs). Regions with stronger institutional support are characterized by a higher number of initiatives promoting short food supply chains.

To verify Hypothesis 3, the relationship between the voivodeships with the highest institutional engagement in SFSC development and those with the largest number of registered entities operating within short food supply chains was examined. Figure 4 presents the variation in the level of involvement of institutions supporting SFSC development in Poland. The most active institutions in fostering SFSCs are located in the Kujawsko-Pomorskie, Małopolskie, and Podlaskie voivodeships, whereas the lowest level of engagement is observed in the Śląskie, Mazowieckie, and Zachodnio-Pomorskie voivodeships.

$${\mathsf{\rho }}_{\mathbf{3}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6\Sigma D^2}{N(N^2-1)}} \mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6·818}{16({16}^2-1)}} \approx \mathbf{0.2}$$

Spearman’s rank correlation coefficient was 0.20, indicating a weak and statistically non-significant relationship. Therefore, the hypothesis is not empirically supported. Although institutional actors may influence certain regional initiatives, their overall involvement does not appear to be a decisive factor differentiating the development of SFSCs across provinces.

Hypothesis 4:

The development of short food supply chains (SFSCs) depends on historical and social regional conditions. Regions with strong traditions of family farming rooted in history demonstrate a higher number of initiatives promoting short food supply chains compared to regions dominated by large-scale industrial agriculture.

To verify Hypothesis 4, the relationship was examined between the voivodeships strongly shaped by historical and social regional conditions and those with the highest number of registered entities operating within short food supply chains. The development of agriculture in Poland has been significantly influenced by historical and social regional factors stemming from the partitions of Poland and subsequent political transformations. Each of these areas developed under distinct institutional conditions until 1989, as illustrated in

Table 6. Institutional determinants of land development in Poland.

Aspects	Western and Northern Regions	Prussian Partition Territories	Austrian Partition Territories	Russian Partition Territories
Historical development path	Departure from the previous development trajectory, shaped by reforms after 1989.	Institutions supporting the development of large agricultural enterprises.	Granting voting rights to peasants, with gradual consolidation of holdings.	“Land hunger,” division of farms among children, limited modernization.
Demographics and settlement patterns	Population shifts, internal migration, and challenges related to settlement.	Consolidation of land and regulatory frameworks.	Large settlements and a dense network of urban centers.	Small settlements with dispersed structures.
Farm characteristics	Small-scale farms (7–15 ha), with the emergence of larger agricultural enterprises.	Dominance of large, commercially focused, and productive farms.	Peasant-worker farms with a dual livelihood.	Prevalence of medium-sized farms, gradual reduction in smallholdings.
Institutions and organizations	Weakened informal structures, notable state ownership (e.g., state farms).	Well-established peasant organizations and collaborative efforts.	Early implementation of mandatory education.	Abolition of feudal obligations, limited institutional backing.
Social capital	A considerable proportion of state farms, with developing social cohesion.	High human and social capital, with early institutional advancements	Dual occupations (peasant-worker), promoting resilience.	Low social capital due to fragmented reforms and delayed access to education.

Source: Author’s elaboration based on [49].

.

By adapting the borders of the former partitions and historical regions to the current administrative boundaries of Polish voivodeships, the following regionalization can be established:

• Western and Northern Lands (ZZiP): Dolnośląskie, Opolskie, and Warmińsko-Mazurskie voivodeships,
• Prussian Partition: Wielkopolskie, Kujawsko-Pomorskie, Lubuskie, and Pomorskie voivodeships,
• Austrian Partition: Małopolskie, Świętokrzyskie, and Podkarpackie voivodeships,
• Russian Partition: Mazowieckie, Łódzkie, Lubelskie, and Podlaskie voivodeships,
• West Pomeranian Voivodeship (transitional area).

In the Western and Northern Lands, settled after 1945 by relocated populations, the previous economic model was disrupted, facilitating the emergence of new institutions and social structures. State ownership predominated, and after 1989, the restructuring of state farms (PGRs) and land privatization contributed to the development of modern, large-scale farms.

In the Prussian Partition, strong formal and informal institutions supported the development of large commercial farms. Gradual peasant enfranchisement, land reforms, and well-developed agricultural organizations fostered cooperation and modernization. A high level of education and strong civic activity among the population enabled more efficient agricultural management and the implementation of joint economic initiatives.

In contrast, in the Austrian Partition, small farms predominated, often fragmented due to inheritance laws and limited access to credit. This situation led to land fragmentation and low agricultural productivity, forcing rural populations to seek employment outside agriculture and shaping the so-called peasant-worker farm model.

In the territories of the Russian Partition, the settlement structure was dispersed, and the urban network remained underdeveloped. The late introduction of compulsory education and the delayed fight against illiteracy hindered the development of human capital. Despite peasant enfranchisement, many agrarian issues remained unresolved, and agriculture continued to be characterized by low productivity.

The agrarian reform of 1944 and subsequent collectivization had varying impacts across regions. In the south, where small farms predominated, the changes were limited, whereas in the west, new ownership structures emerged, influencing the subsequent economic development of these areas. After 1989, privatization policies and the restructuring of the state sector further reinforced regional disparities rooted in the legacy of the former partitions.

Figure 5 graphically presents the degree of dependence on historical and social regional conditions in Poland. The voivodeships most influenced by historical and social factors are Małopolskie, Podkarpackie, and Świętokrzyskie, while the least affected are the Pomorskie, Warmińsko-Mazurskie, and Opolskie voivodeships.

$${\mathsf{\rho }}_{\mathbf{4}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6\Sigma D^2}{N(N^2-1)}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6 ·476}{16({16}^2-1)}} \approx \mathbf{0.3}$$

The Spearman rank correlation coefficient was 0.3, indicating a weak correlation.

Hypothesis 5:

The development of short food supply chains (SFSCs) depends on local demand for this type of purchasing model, which stems from a high concentration of consumers interested in such products within a given area.

The final analysis aimed to verify Hypothesis 5. The study involved calculating the correlation between the ranking of the number of entities engaged in SFSCs and the ranking of the number of inhabitants in the capitals of individual voivodeships. The analysis was based on the assumption that SFSCs are influenced by the cores of urban centers, as illustrated in Figure 6.

To verify Hypothesis 5, a ranking of the populations residing in the capitals of individual voivodeships was developed, as illustrated in Figure 7. The largest populations are found in the capitals of the Mazowieckie, Małopolskie, and Dolnośląskie voivodeships, whereas the smallest are observed in the Warmińsko-Mazurskie, Lubuskie and Opolskie voivodeships [50].

$${\mathsf{\rho }}_{\mathbf{5}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6\Sigma D^2}{N(N^2-1)}}\mathbf{=}\mathbf{1}\mathbf{-}{\displaystyle \frac{6 ·224}{16({16}^2-1)}} \approx \mathbf{0.67}$$

The Spearman rank correlation coefficient was 0.67, indicating a strong correlation and thus confirming the hypothesis. This result suggests that the proximity of large urban centers and the concentration of consumers are key factors driving the development of short food supply chains.

5. Discussion

The results of the study enabled the verification of the hypotheses concerning the impact of regional factors on the development of short food supply chains (SFSCs). The analysis showed that although the correlation between farm fragmentation and the number of SFSC entities was positive (ρ = 0.35), it was weak and statistically non-significant, suggesting that structural factors play only a marginal role in SFSC development. This finding contradicts the conclusions of [51], which indicated that an increase in the size of commercial farms leads to a decline in interest in direct sales. This may suggest that other factors, such as market accessibility and consumer awareness, play a more significant role in shaping SFSCs. In this sense, the results confirm the observations of [1,2], who emphasized that the development of alternative food distribution networks is not a simple function of agrarian structure but rather results from the interaction of market, social, and cultural factors.

The absence of a significant correlation between the quality of agricultural production space and the number of SFSC entities suggests that agrarian factors are not decisive for the development of this sector. These results partially confirm earlier findings by [16], indicating that SFSCs are more dependent on socio-economic than on natural conditions. Similarly, Ref. [52] argued that high soil quality or favorable environmental conditions do not necessarily determine the development of alternative sales channels if there is a lack of infrastructure or sufficient market demand. Thus, it can be concluded that even in regions with less favorable natural conditions, SFSCs may develop successfully if producers have access to nearby urban markets.

A weak and statistically non-significant correlation (ρ = 0.20) was found between the involvement of agricultural support institutions and the development of SFSCs, confirming that institutional engagement alone does not explain regional differentiation in SFSC development. The low correlation coefficient (0.2) suggests that public policies and support programs may be poorly aligned with producers’ actual needs or that their implementation encounters bureaucratic barriers. This is consistent with the findings of [25,53], who, when analyzing food systems in Spain and Colombia, argued that the effectiveness of public support largely depends on its local adaptation. This aligns with our data from Poland, where the qualitative interviews suggest that the ineffectiveness of institutional support may be due to a lack of local adaptation, resulting in programs that are misaligned with producers’ actual needs.

Likewise, Ref. [23] demonstrated that in Poland, formal institutional mechanisms often fail to translate into tangible market development, mainly due to administrative burdens. From the perspective of new institutional economics [41,42], this indicates that institutional quality and the reduction in transaction costs are not yet sufficient conditions to generate multiplier effects.

The results concerning the influence of historical and social regional factors indicate a weak correlation (0.3), suggesting that agricultural traditions are not a key determinant of SFSC development. However, the persistence of household farming traditions observed in the Austrian and Russian partitions partly confirms the relevance of historical embeddedness, even if contemporary market mechanisms now play a more decisive role. Although the theory of embeddedness [40] assumes that strong agrarian traditions can foster such initiatives, the findings indicate that contemporary market forces have greater importance. Similar conclusions were reached by [49,54], who noted that in Central and Eastern Europe, historical institutional trajectories are increasingly overshadowed by current market processes, in contrast to Western European countries, where historical legacies continue to shape local food systems more strongly.

The potential interaction between institutional and market factors also deserves attention. Institutional engagement may strengthen the influence of urban demand by improving logistical capacity, marketing support, and policy incentives for local producers. Similarly, higher urban demand can stimulate greater institutional involvement through regional policy responsiveness. Although these relationships were not explicitly modeled due to data constraints, the observed correlations suggest that such synergistic effects could partially explain the regional differentiation in SFSC development.

The strongest relationship was observed between the population size of voivodeship capitals and the number of SFSC entities. The correlation coefficient (0.67) indicates that market size and urban demand are key determinants of the development of this supply model. This result aligns with the findings of [24], who demonstrated that the success of SFSCs largely depends on market proximity and consumers’ ecological awareness. Similarly, Ref. [12] highlighted the importance of a well-developed infrastructure for direct sales in major Western European cities. In this respect, the Polish case supports the observation of [38], suggesting that social networks based on relationships between consumers and producers can represent a key developmental resource, provided there is a sufficiently large concentration of consumers.

The practical implications of these findings are significant for both public policy and grassroots initiatives. First, SFSC support should be designed not only based on agrarian resources but primarily from a consumer market perspective—through the development of urban marketplaces, digital systems, and short logistics chains [9,26]. Second, local governments can play a mediating role in strengthening rural–urban linkages by supporting cooperative initiatives and territorial marketing. Third, agricultural producers should focus on joint brand building and collaboration networks to enhance their visibility and bargaining power vis-à-vis retail chains [52].

In summary, the findings shift the interpretative emphasis from endogenous and institutional factors toward demand- and market-driven ones. The development of SFSCs in Poland appears to be primarily a response to urban consumer demand rather than an outcome of soil quality, agrarian structure, or historical traditions. This calls for a shift in agri-food policy toward greater emphasis on consumer behavior and market mechanisms as the primary determinants of the sustainability of local food systems.

Beyond the European context, recent international research highlights how short food supply chains (SFSCs) operate within diverse socio-economic and institutional environments. In Africa, Moyo et al. [55] demonstrated that SFSCs contribute to improving food security and rural livelihoods, but their expansion remains constrained by infrastructural limitations, fragmented markets, and weak policy coordination. In Latin America, Souza et al. [56] showed that the School Feeding Program in Brazil’s Distrito Federal successfully integrates local producers into public procurement systems, proving that institutional support and coherent policy design are key to improving both food quality and rural incomes. In North America, Maas et al. [57] found that consumer engagement with SFSCs increased during the COVID-19 pandemic, reflecting growing awareness of food provenance, local trust, and community resilience. In Asia, the Chinese experience adds an important comparative dimension. Wang et al. [58] reported that only around 14% of commercial grain farmers participate in short grain supply chains, mainly through direct face-to-face sales. While such participation raises farmers’ income—especially when combined with cooperative or processing activities—systemic barriers persist, including small farm size, limited digital literacy, and exposure to market volatility. The expansion of e-commerce and community-based group-buying platforms has opened new opportunities but has not yet fully overcome structural and institutional constraints. Collectively, these examples demonstrate that, although contextual conditions differ, the fundamental drivers of SFSC development—such as institutional adaptability, consumer proximity, and technological innovation—are globally relevant. Compared with these regions, the Polish case represents a transitional model shaped by post-socialist legacies and EU-level policies, where urban market demand and institutional engagement interact to determine the scale and sustainability of local food systems.

These results should also be interpreted in the context of Poland’s specific socio-historical and institutional trajectory. As a post-socialist country that has undergone profound structural changes since EU accession, Poland represents a transitional case in which the mechanisms shaping SFSCs reflect both market liberalization and evolving institutional frameworks. The dominance of urban demand as a driving force is therefore partly conditioned by this context, where growing urban purchasing power intersects with relatively weak traditions of cooperative marketing. Nevertheless, similar dynamics can be observed in other Central and Eastern European countries, as well as in parts of Southern Europe, where fragmented agricultural structures and emerging local food systems reveal comparable patterns of adaptation.

It should also be noted that the analysis was based solely on bivariate correlations, which do not control for potential confounding factors such as infrastructure quality, cooperative density, or crop diversity. Although this limitation constrains causal inference, the results nevertheless provide meaningful insights into dominant regional patterns and serve as a sound basis for more advanced modeling in future research.

The results should be interpreted with caution due to the small sample size and correspondingly low statistical power, which limit the generalizability of the findings but do not undermine the consistency of the observed directional patterns.

6. Conclusions

The correlation analysis revealed that farm structure, the quality of agricultural production space, and institutional support showed only weak or statistically non-significant relationships with the development of short food supply chains (SFSCs) in Poland. These findings contradict the endogenous development approach [33,34,35,36,37], which emphasizes the importance of both tangible and intangible local resources as the foundation of regional development. This suggests that traditional factors, such as agrarian fragmentation or natural conditions, do not directly translate into a region’s capacity to organize alternative food distribution systems.

Similarly, the absence of significant correlations regarding institutional involvement indicates that the mechanisms described in the literature on new institutional economics [41,42]—such as regulatory quality or transaction costs—are not currently decisive factors in the development of SFSCs in Poland. This may result from a mismatch between support instruments and farmers’ actual needs or from bureaucratic barriers, as previously observed by [25].

The weak correlation between SFSC development and historical factors also challenges certain assumptions of the theory of embeddedness [40], which posits that long-term socio-cultural conditions should determine contemporary forms of cooperation and economic exchange. The results suggest that, in the Polish context, market factors—rather than historical legacies or agrarian traditions—play a more significant role in shaping alternative food networks.

The strongest relationship was found for local demand: the size of the urban population strongly correlates with the number of SFSC entities (r = 0.67). This highlights the crucial role of urban cores as growth poles for alternative food systems, confirming the premises of social network theory [38,39]. In cities with higher concentrations of consumers and more favorable conditions for building producer–consumer relationships, SFSCs develop most dynamically. This finding aligns with the observations of [12,24], who emphasized the importance of proximity to major markets and social capital for the success of short food supply chains.

In summary, the results indicate that the development of SFSCs in Poland is primarily driven by market and consumer factors rather than by endogenous agricultural resources or institutional support. Consequently, this study contributes to the existing literature by shifting the analytical focus from agrarian and historical determinants to demand- and urban-related factors. For public policy, this implies the need to incorporate the consumer perspective more comprehensively, expand urban infrastructure for direct sales, and strengthen rural–urban linkages—actions consistent with the principles of the European Green Deal.

This study has several limitations that should be considered when interpreting the results. First, the analysis was conducted at the voivodeship level, which limits the ability to capture local variations and the specific characteristics of individual municipalities or counties. Second, the Spearman rank correlation method allows for identifying interdependencies but does not explain causal relationships or multivariate effects. Third, data on institutional involvement and historical–social conditions are partly qualitative, which may have affected their comparability.

While the results are context-specific and reflect Poland’s post-socialist and EU integration conditions, they also provide insights relevant to other European regions undergoing similar socio-institutional transitions. The mechanisms identified—especially the interaction between urban demand and institutional support—may thus inform comparative analyses of SFSC development beyond Poland.

It should also be emphasized that the relatively small sample size (N = 16) limits the statistical reliability of the results and the possibility of drawing broader generalizations; nevertheless, the observed relationships provide a meaningful empirical basis for further research using larger datasets.

Future research should employ more advanced analytical methods, such as multiple regression models, spatial analyses, or panel approaches, to better capture the dynamics of SFSC development. Equally important is the inclusion of the consumer perspective through survey-based or qualitative studies addressing purchasing preferences, perceptions of food quality, and willingness to support local producers. Comparative analyses covering other Central and Eastern European countries would also be valuable to determine whether the observed patterns are universal or specific to the Polish context.