DOI: 10.3303/CET24114163 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Paper Received: 4 July 2024; Revised: 15 August 2024; Accepted: 29 November 2024 
Please cite this article as: Kacz K., Koltai J.P., Varga A.M., Troján S., Vinkóczi T., 2024, Sentinels of Sustainability: Practices of Agricultural 
Farms Clustered in Hungary’s Farmers’ Markets, Chemical Engineering Transactions, 114, 973-978  DOI:10.3303/CET24114163 
  

 CHEMICAL ENGINEERING TRANSACTIONS  
 

VOL. 114, 2024 

A publication of 

 

The Italian Association 
of Chemical Engineering 
Online at www.cetjournal.it 

Guest Editors: Petar S. Varbanov, Min Zeng, Yee Van Fan, Xuechao Wang 

Copyright © 2024, AIDIC Servizi S.r.l. 

ISBN 979-12-81206-12-0; ISSN 2283-9216 

Sentinels of Sustainability: Practices of Agricultural Farms 

Clustered in Hungary’s Farmers’ Markets 

Károly Kacz, Judit Petra Koltai*, Anita Miklósné Varga, Szabolcs Troján, Tamás 

Vinkóczi 

Széchenyi István University, Egyetem tér 1. 9026 Győr, Hungary 

koltaij@sze.hu 

The analysis of local farmers' markets (FMs) is an important part of sustainability issues, as the products they 

offer can guarantee both the proper functioning of local farms and healthy nutrition for inhabitants. The aim of 

this research is to identify the most important characteristics of FMs small-scale businesses related to 

agricultural activities based on a convenience sample in Hungary. The database obtained by the questionnaire 

survey was compiled based on the answers of a total of 220 farm owners/managers. The results were subjected 

to analysis using a K-means clustering method, which identified four distinct groups of farms with the highest 

weighting of turnover from agricultural activity. The clustering distinguished beekeeping, arable crop production, 

animal husbandry, and vegetable and fruit production, which were treated as a separate cluster. The observed 

differences between the groups indicate that farms primarily engaged in beekeeping and livestock farming have 

the highest turnover. These findings may be manifested by the group’s member farms in terms of higher 

professional representation and high-quality products. A noteworthy distribution-related finding is that livestock 

products exhibit the greatest average distance from the point of origin to the FM, which can be attributed to the 

sparse geographical location of the production sites. The results could have a considerable consequence for 

policymakers in informing them about the design of support opportunities and the identification of beneficiary 

groups. 

1. Introduction 

With the rise of intensive production methods and traditional agriculture, small-scale producers have been 

increasingly excluded from the central arena of trade and production has been concentrated in the larger market 

players. This has increased the physical distance between producer and consumer, with many actors involved 

in the value chain, creating sustainability problems. However, agricultural production is inseparable from 

sustainability (Koltai and Gombkötő, 2023). Short Food Supply Chains (SFSCs) provide solutions to many 

problems. They reduce the physical distance between producers and customers and, unlike long supply chains, 

often integrate personal connections as a guarantee for quality, trust and organic qualification in the system, 

especially for locally produced products mainly made from natural resources (Réthy and Dezsény, 2013). 

According to Benedek (2014), this term covers a wide range of marketing/supply channels. Generally, the 

geographical, social and cultural distance between producers and consumers is typically small, and they often 

require environmentally friendly production methods. Marsden et al. (2000) emphasise the type of relationship 

between producers and consumers in these supply chains, stating that these relationships are not only the 

products themselves but also the role of value and meaning. Renting et al. (2003) also stated that the SFSC 

concept involves the interrelationship between actors directly involved. In addition, the European Commission's 

joint research centre developed a common definition of SFSCs based on some decisive literature on the topic: 

“The foods involved are identified by, and traceable to a farmer. The number of intermediaries between farmer 

and consumer should be ‘minimal’ or ideally nil” (Santini and y Paloma, 2013). 
The adaptation capability of SFSCs to new situations and consumer needs is rather high. Acella et al. (2024), 

examining supply chains, found that consumers consider product authenticity as a key criterion and prioritise 

factors that benefit society when evaluating products. This finding is confirmed by the research of Milford et al. 

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(2023), which mapped the main motivations for local food production and distribution, with environmental 

sustainability, community building, supply chain transparency, direct support to farmers, and transfer of 

knowledge about food and agriculture to consumers as key factors. SFSCs include several distribution channels 

of which a given producer generally takes advantage of more than one – even conventional – at the same time 

(Brown and Miller, 2008). Within their research, Martinez et al. (2010) also came to similar conclusions; for the 

interest of increasing their profits, in many cases, the producers distribute their goods through several channels 

at a time. Local products are increasingly being marketed not only through direct marketing strategies such as 

the growing number of farmers’ markets (FMs) and various forms of community-supported agriculture, but also 

through small and large retail stores, from farmers’ cooperatives to supermarkets, and through new consumer 

demand and market potential initiatives (Dunne et al., 2010). 
SFSCs have an undeniable role in the promotion and stimulation of the sustainable development of local 

communities in rural areas (Paraušić et al., 2023). In line with the Sustainable Development Goal (SDG), 2.3 

addresses this situation with the aim of improving market access and value-addition opportunities for small-

scale food producers as well as family farms, while SDG 2.c intends to secure the correct functioning of food 

commodity markets (United Nations, 2017). The European Union is also demonstrating a clear intent to continue 

supporting short supply chains in the Common Agricultural Policy 2023-27 to enhance farmers' position in the 

value chain and improve long-term food security (European Union, 2021). 
FMs could be considered the historical form of SFSCs, and they could be the key to rebuilding local food 

systems. An FM is generally a temporary or permanent physical marketplace intended to sell goods directly by 

farmers to consumers. FMs may encourage the production of a greater diversity of products, which would be 

needed for a more localised food system. This larger diversity attracts a wide variety of shoppers and 

helps strengthen local farm operations. Economic interactions in FMs are also combined with various social 

interactions (Brown and Miller, 2008). According to Juhász (2012), although the importance of neo-traditional 

forms continues to increase, the market is the most essential direct distribution channel for producers in terms 

of income. The FMs' direct-to-consumer marketing approach allows urban consumers to connect with farmers 

while shopping for produce and products (Polimeni et al., 2022). This way of selling local products is highly 

popular among consumers. Wang et al. (2022), analysing their research results, also found that their 

respondents prefer the FM among SFSCs. In their research, Curtis and Hand (2024) collected and systematised 

the alternative food movement in the United States and identified 6 main forms of the movement, including FMs. 

They found their popularity to be unbroken, especially in urban areas. 
The dominant role of FMs in the various SFSC solutions for the trade of produced and sold local products in 

Hungary is undeniable. Like the simplified rules for small-scale production, the conditions for setting up local 

FMs are less complicated than those for traditional markets. Due to the simplifications, not all requirements for 

setting up and maintaining local FMs need to be met, but the distance from the market, the products to be sold, 

and the range of distributors are limited. Thus, in Hungary, as in other Central and Eastern European countries 

in terms of numbers, traditional SFSC types play a decisive role, particularly the local FMs. The number of such 

organisations indicates an increasing tendency in Hungary in accordance with their ever-rising role in the trade 

of local products. Based on the available data it can be concluded that FMs appear in almost all parts of the 

country, but it is true that it is not prevalent in the most backward regions. Since their legal recognition in Hungary 

(2012), local FMs have gained momentum, and their number has been increasing year by year. However, the 

pandemic period temporarily halted the growth trend. In terms of the distribution of the number (307 units in 

2023) of FMs, the central region of Hungary (Budapest and Pest County) is the largest, with approximately 20 % 

of them operating in this area, in line with the distribution of consumers (Hungarian Chamber of Agriculture, 

2024). 
Based on the relevant literature, the research questions were formulated as follows: What are the characteristics 

of typically small-scale businesses operating and selling on local FMs, with different activity directions, in the 

Hungarian agricultural market? What types of future-oriented mindsets can define farmers with different 

characteristics? The importance of this research direction is reinforced by the fact that the lack of a 

comprehensive analysis of the definition of characteristics and different future perceptions related to the main 

agricultural activity is a research gap in Hungarian studies. However, this type of research can provide useful 

insights and recommendations for policymakers, both in the field of support and contribution. This study attempts 

to fill this research gap with a properly designed methodology, contributing to the scientific potential of measuring 

agricultural activities in the framework of FM. 

2. Material and methods 

The survey was conducted by questionnaire and consisted of two main stages. Statistical analyses were 

performed using the IBM SPSS Statistics 25 software package. In the first stage of the study, respondents were 

required to provide answers to 14 items pertaining to the companies in question. These questions were designed 

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to measure variables on a continuous scale and focused on the age of the owner, the establishment of the 

company, and questions concerning the operation of the company. Due to the disparate units of measurement 

utilised by the continuous scales, standard values were applied in the analysis. These variables formed the 

basis for the subsequent group construction. In the case of a high sample size, the use of the Two-Step 

clustering method (Artim-Esen, 2014) was recommended. However, the basic idea was to create five clusters, 

as this was the number of different types of activities (arable crop, fruit, vegetable, livestock, bees) distinguished 

in the questionnaire. The two-step method was implemented independently of the previous idea, with the 

objective of grouping the elements of the sample according to their similarity. The method forms sub-clusters in 

a single run and then gradually reads them into progressively larger clusters using hierarchical clustering to form 

the recommended number of clusters. The final cluster number was constructed using K-means clustering, 

which begins with randomly initialising a random cluster centre and then categorises all items to the nearest 

mean. Subsequently, the average values are continuously updated, and this process is repeated until the final 

clusters are formed. In the second stage, Likert-scale questions (25 items) were answered, focusing on future 

goals (e.g., producing high-quality products, supporting the environment by producing local products, using new 

technologies). The second stage questions were utilised to explore the differences between the groups' thinking 

about the future, based on the first stage. The paper's underlying premise is that the disparities between the 

means of the groups formed by K-means clustering can be quantified using HSD statistics. The selected method 

was Tukey's HSD test, which is well-suited for comparing means between groups within a sample (Toothaker, 

1993) by comparing the significance levels resulting from the Likert scale questions that guarantee each mean. 

The application of this method is recommended for continuous measurement (Sepanek et al., 2022), and for 

the present study, this should be treated as an analytical limitation due to the ordinal measurement scale of the 

second stage. The methodology provides an opportunity to determine for which of the established clusters the 

Likert scale variables in the questionnaire differ significantly from each other. 
The objective of this research is to cluster small-scale farmers to identify distinctive features that can be used 

to inform the implementation of their future plans. This group of farmers was selected for the purposive sample 

because it represents the most relevant area for selling at FMs. To achieve this, it was relevant to survey the 

member companies of the Hungarian Chamber of Agriculture (HCA), which, through its members, covers the 

entire domestic food chain, agricultural production and rural development. The organisation helped distribute 

the questionnaire in newsletter format to members, who could contribute anonymously by completing the survey. 

In addition, two other major agricultural institutions were involved, including the Hungarian National Rural 

Network and the Ministry of Agriculture, which shared the questionnaire with agricultural businesses on social 

media platforms and websites to reinforce the letter-based solution. The questionnaire was based on a previous 

survey (Kacz and Varga, 2018), supplemented with minor adjustments and a comprehensive review of the 

literature. The list of questions was finalised after a pilot survey with 10 respondents in September 2023 to 

ensure the integrity of the conversions. The questionnaire, which could be completed online via Google Forms, 

was sent by email to the members of the HCA. The questionnaire was to be completed between October and 

December 2023. A total of 220 responses were received during this period, which is considered a purposive but 

reasonable sample for a national survey, although its limitations must be considered. Three main stages of the 

data cleaning process were carried out: the first stage (exclusion of non-sellers at FMs), the second stage 

(exclusion of outliers) and the third stage (exclusion of identical responses and sample completions). For the 

continuous scales, values below 3 and above -3 were excluded from the standardised values due to their 

significant deviation from the mean. Following the data cleaning process, a sample of 195 respondents was 

obtained, with a validity rate of 88.64 %. The valid sample size with the relevant settings (f = 0.25; α err. prob.: 

0.05; sample size: 195; no. of groups: 4) had 0.838 power, which exceeded the literature suggested value of 

0.8 (Brooks and Johanson, 2011) and was deemed acceptable. The gender distribution of respondents revealed 

that 65.6 % were male and 34.4 % were female. In terms of education, 4.1 % had completed primary education, 

50.3 % had completed secondary education, and 45.6 % had completed tertiary education. In terms of age, the 

youngest respondent was 21 y old, while the oldest was 82 y old. The average age of respondents was 50.09 y. 

Based on descriptive statistics, sample units with quite different basic characteristics were analysed. The 

international nature of the research makes regional distribution important, although most responses come from 

the most agriculturally affected areas: Little Hungarian Plain and Great Hungarian Plain. Approximately 60 % of 

the responses were provided by owners of companies that were located in the indicated areas of high 

agricultural intensity. 

3. Results 

The two-step clustering process identified 3 clusters. However, subsequent research indicated that 4 clusters 

were more appropriate. (It should be noted that vegetable and arable crops are currently included in one cluster.) 

The five-cluster solution was also attempted, but the results were not acceptable. Accordingly, the four-cluster 

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solution was selected for further analysis. The separation of these clusters by F-values was primarily based on 

the main agricultural activity generating the turnover: Beekeeping (F = 515.49); Fruit production (F = 21.19); 

Vegetable production (F = 17.14) and Arable crop production (F = 150.63); Animal husbandry (F = 435.87). 

Consequently, the four clusters were named according to the activity generating the most revenue (Table 1). 

Table 1: Clusters formed based on the analysis (standardised values) 

Factors Beekeeping Arable corps 

production 
Vegetable and 

fruit production 
Animal 

husbandry 

Age of owner -0,27284 0,67419 -0,25772 -0,45506 
Y of foundation -0,21927 0,70312 -0,43509 -0,45352 
Land owned (ha) -0,34267 0,12107 -0,24311 -0,16735 
Size of leased land (ha) -0,15364 0,05207 -0,13584 -0,07079 
Turnover vegetable production -0,39858 -0,42209 0,49716 -0,45865 
Turnover from fruit production -0,52983 -0,48807 0,48207 -0,53203 
Turnover from livestock farming  -0,34687 -0,24790 -0,36028 2,69868 
Turnover from beekeeping 2,91503 -0,22581 -0,32758 -0,37388 
Turnover from arable crops -0,66873 1,18207 -0,55672 -0,64469 
Farmers' market distances -0,27087 -0,30994 -0,05952 0,17296 

The calibration was conducted using standardised values derived from data collected on a continuous scale. 

This approach ensures that a value of 0 represents the overall sample mean and that standard values represent 

the deviation from it. The results of the clustering analysis can be used to develop insights into the characteristics 

of the sample. The findings indicate that the owners of arable crop production farms are the oldest, and that 

these farms have been established for the longest time. In terms of land, members of the groups utilize a 

comparable amount of land. It is also typical for farmers to use their own land rather than leased land. In this 

respect, beekeepers are a special group, as their activity does not require their own land area. (The positive 

externalities of this activity mean that farmers welcome bees on their land, in most cases free of charge.) 

Nevertheless, the results indicate that farmers who are most involved in beekeeping in terms of turnover may, 

in fact, own land, but in this case, they typically also engage in other types of agricultural activity on this land. 

The area used for arable land is higher than the sample average, but this is a specific feature of Hungary, where 

almost 80 % of agricultural land is used for this type of agricultural activity. The distribution of turnover shows 

that farmers who are in the Arable crop production group do not only generate turnover from this activity but 

also generate more visible turnover from the other categories (fruit production: 9 %; animal husbandry and 

beekeeping: 4 - 4 %). In terms of the distance to the FM, it is essential to note that the following order of distance 

between the groups has been established: Arable crop production (AVG: 37.57 km), Beekeeping (AVG: 41.39 

km), Vegetable and fruit production (AVG: 62.04 km), Animal husbandry (AVG: 84.76 km). The sale of animal 

products (cheese, smoked meat, etc.) occurs within the widest radius because these items have the highest 

added value during production, allowing their pricing to cover greater transportation distances. Consumers are 

willing to purchase such products, even at higher prices, because FMs often offer higher quality, less processed 

items that are typically free from the chemicals and additives used in industrial production. Furthermore, smaller 

farms often employ more sustainable practices, such as organic farming or free-range animal husbandry, which 

place less strain on the environment. The products of the first category do not require long-distance 

transportation due to their national importance (produced in many locations) and the typical assurance of their 

availability. For fruit and vegetables, long-distance transport is not recommended to preserve freshness and 

quality. In the case of honey, transportation may be challenging due to the difficulty of storage and movement 

for the farmer, and it may be advantageous and cost-effective for the farmer to sell at nearby markets. In the 

context of animal production, the transportation of products (e.g., meat, milk, eggs) is becoming increasingly 

important for the promotion of a healthy lifestyle and social acceptance. This is due to the growing societal 

demand for organic products. Consequently, the availability of these products in distant markets is becoming a 

crucial factor, which may motivate producers to transport them to more distant locations. 
In the first stage of the analysis, the potential groupings within the sample were identified. To illustrate the 

significant differences between the groups, the outcomes of Tukey's HSD test are presented in Table 2. It is 

noteworthy that no response on the Likert scale of 5 has a mean value below 3. This indicates that all 

respondents expressed a positive attitude. Nevertheless, there is a discernible discrepancy in the perceptions 

of ownership between the groups engaged in agricultural activities. The owners of the farms perceived vegetable 

and fruit production as a more pleasant activity than work with arable crops. This can be explained by the 

concrete physical embodiment of the work. The respondents perceived arable farming to be more monotonous 

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than fruit and vegetable production. As Pépin et al. (2022) stated, in general, vegetable farms are typically 

diverse. However, neither activity can be considered work done under constraint.  

Table 2: Tukey’s HSD test results (average values) 

Factor Group 1 Group 2 

Finding pleasure in work 
Vegetable and fruit production 

(4.47) 
Arable crop production (4.02) 

Favorable representation of the 

profession 

Vegetable and fruit production 

(3.86) 

Beekeeping (4.72) 

Animal production (4.67) 

Production of high-quality products Arable crop production 

(4.17) 

Beekeeping (4.89) 

Animal production (4.90) 

Support the protection of environment 

by producing local products 

Animal husbandry 

(4.71) 

Arable crop production (3.86) 

Vegetable and fruit production (3.90) 

In the case of livestock farming, it is worth noting that, according to the owners of most of these farms, this 

agricultural activity has the highest added value, both in terms of professional representation, quality and 

environmental protection. This could be due to the increasing global demand for animal products, making it a 

critical task for governments and society to improve the productivity of animal production. In many countries 

(especially in the least developed and low and middle-income countries), development policies do not provide 

equal treatment to livestock producers, making it difficult for them to adopt technological improvements and 

sustainable production practices (Pradere, 2014). This requires a commitment to basic agricultural activities, 

but it can also be an important factor in animal production, since selling on local markets does not mean factory 

farming, but rather the production of small quantities of good quality products, which reinforces a mindset 

compatible with sustainability. In addition, processed animal products are more durable products, and this type 

of production is territorially less frequent and more visible agricultural activity than the other groups, so that 

externalities that are less important for other producers are more important in this form. 

4. Conclusions 

The results confirm the success of the applied methodology, as 4 significantly distinguishable clusters 

(beekeeping, arable crop production, vegetable and fruit production, and livestock production) were created by 

considering the business-specific data of several FMs. These FM operators differed in several characteristics, 

which allowed to distinguish the dimensions specific to the activities and to identify the directions and attributes 

associated with the Hungarian-specific FMs. The results of the questionnaire survey should be treated with 

caution due to its limitations, lack of representativeness, and cross-sectional nature (opinions at one point in 

time). Although some of the questions were measured on a continuous scale and others on a Likert scale, the 

farmers' responses to the questionnaire survey cannot be considered generalisable. Consequently, the results 

are limited in their ability to broaden the perspectives of government decision-makers and help shape 

agricultural support and assistance programs and policies in line with the real FM framework, but it does provide 

a new perspective on agriculture. The extension of the research to a larger representative sample should be 

considered as a future plan, as this would be very important in terms of generalizability. In this context, it would 

be beneficial also to focus on the consumer side in parallel with the producer side. This could also contribute to 

a better understanding of the customer expectations of companies and governments towards agricultural 

activities. 

References 

Acella M., Petrini A., Bulgari R., Ertani A., Sebők A., Devecchi M., Nicola S., 2024, Quality Perception of Short 

Food Supply Chains Products: From the Producer’s to the Consumer’s Point of View. International Journal 

of Food Studies, 13, 127-144, DOI: 10.7455/ijfs/13.1.2024.a10. 
Artim-Esen B., Çene E., Şahinkaya Y., Ertan S., Pehlivan Ö., Kamali S., Gül A., Öcal L., Aral O., Inanc M., 2014, 

Cluster analysis of autoantibodies in 852 patients with systemic lupus erythematosus from a single center. 

J Rheumatol, 41(7):1304–1310, DOI: 10.3899/jrheum.130984. 

Benedek Zs., 2014, The impact of short supply chains. A summary based on international literature and 

domestic experience. (in Hungarian) MTA Közgazdaság- és Regionális Tudományi Kutatóközpont 

Közgazdaság-tudományi Intézet, Budapest, Hungary, ISBN 978-615-5447-17-4. 

Brooks G.P., Johanson G.A., 2011, Sample Size Considerations for Multiple Comparison Procedures in 

ANOVA. Journal of Modern Applied Statistical Methods, 10(1), 97-109, DOI: 10.56801/10.56801/v10.i.526. 

977

https://doi.org/10.7455/ijfs/13.1.2024.a10


Brown C., Miller S., 2008, The Impacts of Local Markets: A Review of Research on Farmers Markets and 

Community Supported Agriculture (CSA). American Journal of Agricultural Economics, 90(5), 1296–1302, 

DOI: 10.1111/j.1467-8276.2008.01220.x. 

Curtis K., Hand G., 2024, First you need the farmers: The microfarm system as a critical intervention in the 

alternative food movement. Journal of Agriculture, Food Systems, and Community Development, 13(2), 

175–192, DOI: 10.5304/jafscd.2024.132.017. 

Dunne J.B., Chambers K.J., Giombolini K.J., Schlegel S.A., 2011, What does ‘local’ mean in the grocery store? 

Multiplicity in food retailers' perspectives on sourcing and marketing local foods. Renewable Agriculture and 

Food Systems, 26(1), 46-59, DOI: 10.1017/S1742170510000402. 

European Union, 2021, Regulation (EU) 2021/2115 of the European Parliament and of the Council of 2 

December 2021. Official Journal of the European Union, L 435(64): 6 December 2021, 

<data.europa.eu/eli/reg/2021/2115/oj>, accessed 31.05.2024. 

Hungarian Chamber of Agriculture (NAK), 2024, Local Farmers Market. (in Hungarian) 

<https://nak.amega.hu/termeloipiac-kereso>, accessed 01.05.2024. 

Juhász A., (Ed.) 2012, The role and potential of direct marketing in the market penetration of domestic food 

products. Life outside modern retail channels? (in Hungarian) Agrárgazdasági Tanulmányok, 

Agrárgazdasági Kutató Intézet (AKI), Budapest, Hungary, ISBN 978-963-491-579-9. 

Kacz K., Varga Á., 2018, A problem-based approach of community supported agriculture within short food 

supply chains based on a Western Transdanubian survey. Annals of the Polish Association of Agriculture 

and Agribusiness Economists, 20 (6), 118-123, DOI: 10.5604/01.3001.0012.7741. 

Koltai J.P., Gombkötő N., 2023, Environmental assessment of farm sustainability – A Hungarian case study. 

Chemical Engineering Transactions, 107, 409-414, DOI: 10.3303/CET23107069. 

Marsden T., Banks J., Bristow G., 2000, Food Supply Chain Approaches: Exploring their Role in Rural 

Development. Sociologia Ruralis, 40(4), 424-438, DOI: 10.1111/1467-9523.00158. 

Martinez S., Hand M., Da Pra M., Pollack S., Ralston K., Smith T., Vogel S., Clark S., Lohr L., Low S., Newman 

C., 2010, Local food systems: concepts, impacts, and issues. ERR 97. U.S. Department of Agriculture, 

Economic Research Service, Washington, D.C., United States, May 2010. 

Milford A.B., Gillund F., Keech D., Reed M., 2023, Selling local food in the city. A comparative study of short 

food supply chains in Oslo and Bristol. Norsk Institutt For Bioøkonomi (NIBIO), <nibio.brage.unit.no/nibio-

xmlui/handle/11250/3052933?locale-attribute=en>, accessed 10.04.2024. 

Paraušić V., Kolašinac S., Dashi E., Šarić, B., 2023, Competencies of Western Balkan farmers for participating 

in short food supply chains: honey case study. New Medit, 4, 73-89, DOI: 10.30682/nm2304e. 

Pépin A., Trydeman Knudsen M., Morel K., Grasselly D., van der Werf, H.M.G., 2022, Environmental 

assessment of contrasted French organic vegetable farms. Acta Horticulturae, 1355, 209-216, 

DOI: 10.17660/ActaHortic.2022.1355.27. 

Polimeni J.M., Iorgulescu R.I., Albu L.L., Ionica A., 2022, Romanian Farmers’ Markets: Understanding the 

Environmental Attitudes of Farmers as an Instrument for Bioeconomy Development. Sustainability, 14, 

11553, DOI: 10.3390/su141811553. 

Pradere J., 2014, Links between livestock production, the environment and sustainable development. Revue 

scientifique et technique. International Office of Epizootics, 33, 765-781. 

Renting H., Marsden T.K., Banks J., 2003, Understanding alternative food networks: Exploring the role of short 

food supply chains in rural development. Environment and Planning A: Economy and Space, 35(3), 393-

411, DOI: 10.1068/a3510. 

Réthy K., Dezsény Z., 2013, Community Supported Agriculture. (in Hungarian) Ökológiai Mezőgazdasági 

Kutatóintézet Közhasznú Nonprofit Kft. (ÖMKi), Budapest, Hungary 

Santini F., Gomez y Paloma, S., 2013, Short food supply chains and local food systems in the EU. A state of 

play of their socio-economic characteristics. European Commission Joint Research Centre, Scientific and 

Policy Reports. Publications Office of the European Union, Luxembourg, DOI: 10.2791/88784. 

Sepanek L., Habarta F., Malá I., Marek L., 2022, A short note on post-hoc testing using random forests 

algorithm: Principles, asymptotic time complexity analysis, and beyond. Annals of Computer Science and 

Information Systems, 30, 489–497, DOI: 10.15439/2022F265. 

Toothaker L.E., 1993, Multiple Comparison Procedures, SAGE Publications, Thousand Oaks, California, USA, 

DOI: 10.4135/9781412985178. 

United Nations, 2017, Resolution 71/313: Work of the Statistical Commission Pertaining to the 2030 Agenda for 

Sustainable Development, A/RES/71/313, <ggim.un.org/documents/a_res_71_313.pdf>, accessed 

31.05.2024. 

Wang M., Kumar V., Ruan X., Saad M., Garza-Reyes J.A., Kumar A., 2022, Sustainability concerns on 

consumers’ attitude towards short food supply chains: an empirical investigation. Oper Manag Res, 15, 76-

92, DOI: 10.1007/s12063-021-00188-x. 

978

https://doi.org/10.1111/j.1467-8276.2008.01220.x
https://doi.org/10.1017/S1742170510000402
http://data.europa.eu/eli/reg/2021/2115/oj
https://doi.org/10.5604/01.3001.0012.7741
https://doi.org/10.1111/1467-9523.00158
https://doi.org/10.1068/a3510
https://doi.org/10.2791/88784