“Mapping Out” Sustainable Social Farming Paths in Italian Municipalities

Abstract

Social farming in Italy has not developed homogeneously. In view of this, this article adopts a multivariate analysis approach to analyse the heterogeneity and the similarities in the development paths of social farming in Italian municipalities. The article takes into account the information from a representative sample of 410 interviews. The results suggest that the offer of social farming activities is highly correlated with the distinct nature of the enterprises and with the interest of local actors, who in many cases finance these activities. Regarding the characteristics of social farms, the results of principal component analysis show that the consolidated experience of offering social services and the continuation of activities are the most important organisational elements. Concerning the territorial distribution, the findings of a hierarchical cluster analysis show that Italian municipalities have distinct experiences according to the level of maturity of the social farms in each cluster, with differences in location, the agricultural system, the organisational culture and the social services provided. Assessing enterprise characteristics and recent development paths for social services in Italy can have far-reaching implications for policy. The latter should provide information and training to farmers and users to increase understanding of the social value of social farming and foster a collaborative and sustainable approach to social farming practice.

1. Introduction and Theoretical Framework

Social farming (SF) is both a traditional and an innovative activity for farmers [1,2]. It involves the use of agricultural resources for rehabilitation and social inclusion, depending on the local circumstances and the territorial specificities [3,4,5,6]. Social agriculture encapsulates a variety of responses to specific local problems and needs [7,8,9]. In general, as identified by Dalla Torre et al. (2020) [10], Italian social farms are oriented towards social and healthcare services, education, work inclusion and co-therapy. Indeed, the main purpose of social agriculture, as highlighted by the European Economic and Social Committee (CESE), is “to create the conditions within a farm that allow people with specific needs to take part in the daily activities of a farm, in order to ensure the development and individual realisation, helping to improve their well-being” [11].

In Italy, these activities are mainly carried out by cooperatives, which represent one of the most widespread forms of social enterprise [12]. Moreover, social cooperatives (SCs) perform a central role in the production of welfare services [13]. Social co-operation is a particular form of social enterprise regulated in Italy by state law (L: 381/91), which recognises its public importance [14,15]. Furthermore, the term SF has recently entered the field of rural development in the European Union (EU), embracing a wide constellation of different emerging practices; experiences that, in many cases, started as bottom-up initiatives and have been in the shadows for a long time [16,17,18].

The first experiences arose in the 1980s and 1990s on the initiatives of groups of individuals who saw traditional rural activities as a way to respond to the therapeutic needs of people with a disadvantage, most of whom were from urban areas [19,20].

SF activities involve a large number of target groups, both in urban and rural areas (children, the elderly, adolescents, disabled people, migrants, detainees and more) [16].

As a field of study and community investment, SF is at the centre of these issues. However, it involves a particularly complex area of policy since it requires integration between the various social, health, economic and welfare fields in addition to the environmental and ecological spheres [3].

Moreover, SF is connected to a large number of issues related to rural development such as the organisation of local services, the evolution of farmers’ attitudes in their relationships with local communities, the reputation of farmers, the re-organisation of the local economy and the introduction of new elements of solidarity and reciprocity [7]. The theme of multifunctional agriculture should, therefore, also be seen in terms of these lesser-known aspects.

The paths of re-organisational campaigns in response to the diversification and evolution of the urban market may vary, but they generally seek to satisfy demand more effectively. A preliminary analysis identified, firstly, an agricultural path that links the presence of production processes that are vital for external requirements with the agricultural, culinary, educational, environmental and social needs of the local community [21].

A second pathway is one that seeks to meet the needs of urban citizens in order that they can have access to fresh and healthy food, contact with nature, a chance for recreation and good use of free time, all while weaving new social relations. The results include community gardens, and urban social gardens, peri-urban agriculture, direct sales and greater accessibility [22].

Finally, a third path to explore is that made by agricultural land confiscated from the mafia, in order to understand how the same may be used for social purposes [23,24].

Despite the growing interest in the topic, a database of existing practices is still lacking. Nevertheless, SF initiatives are increasing in Italy and at the international level, and scientific research has highlighted a considerable variety of experiences in several countries [25,26,27,28,29]. Furthermore, the potential for SF to develop multifunctionality within the agricultural sector remains only partially explored, especially with regard to its ability to promote local development in disadvantaged areas. The focus of academic attention has remained limited to the ability of SF to combine the production of foodstuffs with the provision of services aimed at improving the health and well-being of particular categories of disadvantaged people. However, another important aim is to create job opportunities for the long-term unemployed or individuals with greater difficulties accessing the work sphere [30,31,32].

Within this scenario, SF has appeared as a multifunctional innovative strategy. It gives a return to society through the production and processing of agricultural products by incorporating direct social benefits through employment, training, therapy and the rehabilitation of groups at risk of social exclusion [33]. SF offers social cohesion, the empowerment of vulnerable groups, local development in rural and peri-urban settings and an equitable balance between revenues and costs to society [34,35].

In recent years, SF has undergone a significant development, mainly thanks to the adoption of law n. 141/2015 (Repubblica Italiana, 2015) implemented by a Ministerial Decree in 2018 [36], that provided a framework of principles and procedures to recognise SF practices in a homogenous way [24]. Indeed, this law identified the following typologies of SF:

• Social and workplace inclusion of people from the weakest sectors of society, as well as those who are disabled and disadvantaged, as per the current legislation;
• Social, socio-sanitary, rehabilitative, therapeutic, training and educational services for families, seniors, the disadvantaged and disabled people;
• Social activities to support local communities, which make use of material and immaterial agricultural resources to provide services useful for everyday life, as well as promoting, supporting and achieving actions of social and occupational inclusion, recreation and education;
• Educational activities aimed at vulnerable people.

In addition, in Italy, SF has supported a novel welfare system concept through a multitude of practices and activities [37]. For example, in agricultural production focused mainly on organic and high-quality natural products, sustainable growth and services has played a primary role [38]. Such production has been aimed at empowering disadvantaged groups such as individuals with a physical or mental disability, people recovering from drug addiction, ex-detainees, young people, the elderly and the victims of domestic violence [39].

Despite this, there is still a gap between the widespread trend of SF at the national level and research into this phenomenon. The latter has typically been based on qualitative research due to the absence of quantitative information. To address this lack of data, the Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria (CREA) conducted a survey in 2020 to collect data on various aspects of SF in Italy [40].

This survey, conducted by means of a questionnaire, recorded responses from 410 operators distributed over 351 Italian municipalities. It gathered information on the characteristics of the operating realities, the agricultural and social activities that are carried out, and the networks of relations and other important organisational characteristics. However, this investigation had some limitations. Therefore, it cannot be taken as a systematic and reliable information source of quantitative importance. Nevertheless, it provides the basis upon which to draw a new and up to date picture of the SF phenomenon.

In this article, given that the SF sector suffers from a lack of data on its organisational and economic characteristics [41], the CREA database has been analysed in order to carry out some statistical elaborations [40].

The main objective is to find, as suggested by Dell ‘Olio et al. (2017) [24], new and more recent development paths for social services in Italy, to investigate SF in Italian municipalities and to focus on integrated social activities and the strategies of ecosystem services.

In light of the above, the research hypotheses are as follows:

Hypothesis 1 (H1).

The success of SF development in Italian municipalities is attributed to the organisational elements of enterprises.

Hypothesis 2 (H2).

The development of different SF paths in Italian municipalities is more influenced by the presence of social networks than by the interest of local actors.

Hypothesis 3 (H3).

SF represents a form of resilience in Italian agriculture.

To test these hypotheses, the identification of different development paths for SF is required.

2. Materials and Methods

2.1. Data Collection

As already stated, at present in Italy, available statistics on SF at the municipality level are few and incomplete. The CREA open access database is the source of the data used in this analysis. From here, the answers given by 410 social farm actors that operate in 351 Italian municipalities are extrapolated. As shown in

Table 1. The presence of enterprises that carry out SF activities in Italian municipalities.

Italian Regions	(A) ISTAT Data	(B) CREA Data	(C) Tot Municipalities	(D) Municipalities with SF (CREA)	B/A	D/C
Abruzzo	21	15	305	14	71.43	4.59
Basilicata	14	7	131	7	50.00	5.34
Calabria	37	30	404	27	81.08	6.68
Campania	49	19	550	17	38.78	3.09
Emilia-Romagna	78	28	330	26	35.90	7.88
Friuli-Venezia Giulia	36	17	215	15	47.22	6.98
Lazio	52	30	378	14	57.69	3.70
Liguria	13	17	234	10	130.77	4.27
Lombardy	94	47	1503	45	50.00	2.99
Marche	32	12	225	12	37.50	5.33
Molise	13	10	136	9	76.92	6.62
Piedmont	77	31	1180	27	40.26	2.29
Puglia	52	25	257	21	48.08	8.17
Sardinia	33	11	377	11	33.33	2.92
Sicily	53	34	391	32	64.15	8.18
Tuscany	108	35	273	32	32.41	11.72
Trentino Alto Adige	41	5	282	5	12.20	1.77
Umbria	25	8	92	5	32.00	5.43
Valle D’Aosta	5	2	74	2	40.00	2.70
Veneto	71	27	563	20	38.03	3.55
Total	904	410	7900	351	45.35	4.44

Source: Author’s elaboration of data from the CREA open access database, 2020.

, these municipalities belong mainly to northern and central regions (Lombardy, Tuscany, Sicily, Calabria, Piedmont and Emilia Romagna).

The data analysis was carried out in two stages. In the first stage, the coding of the answers through the creation of a database of the 410 archived interviews conducted by CREA made it possible to identify the organizational elements that characterize each case. Twenty-three variables were codified (

Table 2. List of starting variables and their coding.

N.	Variables	Code	Likert Scale
1	Legal form	LF	From 1 to 15
2	Year of establishment	YE	From 0 to 5
3	Business year	BY	From 0 to 5
4	Membership of representative bodies	MRB	From 0 to 5
5	Physical size	FS	Hectares
6	Cultivation method	CM	From 0 to 4
7	Productive activities affected by SF	PAASF	From 0 to 5
8	Related activities affected by SF	RAASF	From 0 to 11
9	Activities carried out	ACO	From 0 to 4
10	Continuity of activities of SF	CASF	From 0 to 4
11	Services offered by SF	SOSF	From 0 to 12
12	Purpose of the SF for people with disabilities	PSFPD	From 0 to 7
13	Sales channels	SC	From 0 to 8
14	Recipients of SF activities	RSFA	From 0 to 8
15	Type of network agreements	TNA	From 0 to 13
16	Start-up of SF with funding	SUSFF	0 = no; 1 = yes
17	Type of financing invested in SF	TFISF	From 0 to 10
18	Type of investment in SF	TISF	From 0 to 8
19	External subjects involved in SF	ESISF	From 0 to 11
20	Occupational profiles with social skills	OPSS	From 0 to 12
21	Target audience involvement	TAI	From 0 to 8
22	N. persons involved in SF by type of disability	N.PISFTD	Number
23	Role played by people with disabilities	RPPD	From 0 to 11

Source: Author’s elaboration of data from the CREA open access database, 2020.

). The answers vary on a Likert scale from 0 to 15, from a value of nil to a maximum value of 15. If no answer is given, 0 is recorded, and 1–15 are recorded according to the number assigned to the answer chosen. These two types of coding are appropriate statistical treatments for these data [42].

2.2. Data Analysis

To view the emerging empirical differences between the selected cases in order to identify the profiles of SF operators based on their prevalent activities, 23 variables were added for the application of multivariate analysis (MA). This choice allowed the explained variance to be maximised. In the MA, each qualitative variable was transformed into a quantitative variable using the Likert scale and ordinal-scale responses. Furthermore, the approach to MA (PCA and HCA) allows an analysis of complex problems by comparing the variables involved in order to identify the latent components capable of explaining the correlations.

In particular, the statistical analysis was carried out in three stages: firstly, descriptive statistics; then, principal component analysis; and finally, hierarchical cluster analysis.

(1) Descriptive analysis allows for the transformation of raw data into a format that is simple to comprehend and interpret. It involves rearranging, organizing, and manipulating data to generate descriptive information [43]. The descriptive analysis was utilized to provide information on the main characteristics of the enterprises that perform SF activities. The means, standard deviation and coefficient of variation (CV) were calculated for the 23 informative variables.

(2) Principal component analysis (PCA) was employed as a tool to reduce dimensionality [44]. It facilitated the reduction of quantitative variables to a more manageable set of factors or principal components (PCs). The number of principal components will always be less than or equal to the number of original variables. Due to the different units of measurement of the starting variables, PCA was carried out on the standardised values:

$$zij=\frac{xij-\mu j\left(X\right)}{\sigma j\left(X\right)}$$

where xij is the generic entry of the data matrix X, with ‘n’ rows (farms) and ‘p’ columns (variables); µj(X) and σj(X) are the mean and standard deviation of the j-th variable, respectively, and zij is the (dimensionless) entry of the standardised matrix Z, having µj(Z) = 0 and σj(Z) = 1. The simplifying components summarise and explain the observed scores to interpret the solution obtained. PCs were extracted in such a way as to maximise the proportion of variance explained. Several studies have used similar approaches [45,46].

(3) Hierarchical cluster analysis (HCA), based on Euclidean distances, was conducted on the first 9 PCs, explaining a significant part of the initial variance. Ward’s classification algorithm was utilised. This method is unique because it utilizes an analysis of variance approach to evaluate the distances between clusters. We can refer to Ward (1963) [44] for details concerning this method, which is considered to be very efficient; it tries to find the partitions Pn, Pn − 1, …, P1 in order to minimize the loss associated with each grouping and quantify it in an easily interpretable form. The Ward method is based on the well-known decomposition: T = W + B, where T is the total sum of squares (SS) of the observations, W is the within-clusters SS, and B is the between-clusters SS. In general, passing from k + 1 to k clusters, W tends to increase (less homogeneity in the new cluster with the addition of new units), while of course B decreases: at each step of the Ward procedure, the clusters joined together are the two with the minimum increase in W.

In relation to the subsequent choice, the optimal number of clusters was chosen based on the cubic clustering criterion (CCC) statistic [47]. The HCA was carried out using Clusters 1–10 and the maximisation of CCC was obtained with 6 clusters.

3. Results

3.1. Descriptive Statistics to Identify the Overriding Characteristics of SF Activities

In order to ascertain the validity of the first research hypothesis, the organisational elements that have a positive effect on the highly diversified phenomena of SF in Italian municipalities were evaluated. Microsoft Excel version 365 was used to perform a general analysis. The objective was essentially to give an overview of the data gathered from the CREA interviews, to identify the traits of social farms, to understand which ones are most active, and to evaluate their experience in this field. The types of services and duration of farmer participation in SF were also evaluated.

The analysis of the data revealed highly diversified phenomena. Five main types of services on offer were identified:

• Socio-occupational integration of disadvantaged persons, support for medical treatment or rehabilitation and support for families with disabled members;
• Education of minors, support for the socialisation of socially excluded people and assisted therapies with animals;
• Guidance for disadvantaged persons or those at risk of social exclusion;
• Training of disadvantaged persons or persons at risk of social exclusion, social integration of disadvantaged persons and schoolwork experience;
• Reception for women who are victims of violence or in difficulty and education for minors.

Regarding the characteristics of the enterprises, they are small- and medium-sized (the size ranges from 0 to 1500 ha) and exhibit an average surface area equal to 21 hectares of the utilised agricultural area (UAA) versus the national average farm size of 11 hectares. It is worth noting that there are only 16 enterprises with more than 100 ha and this implies, as shown by the CV, a great heterogeneity among social farms at the territorial level (

Table 3. Descriptive analysis of the enterprise characteristics.

Variables	Means	Std. Dev.	Min	Max	CV
LF	5.607	3.541	1	15	0.632
YE	2.995	1.485	0	5	0.496
BY	3.639	1.462	0	5	0.402
MRB	1.834	1.296	0	5	0.707
FS	20.873	94.164	0	1500	4.511
CM	1.68	1.091	0	4	0.649
PAASF	2.334	1.556	0	6	0.667
RAASF	2.671	2.63	0	11	0.985
ACO	1.285	0.856	0	4	0.666
CASF	1.437	1.066	0	4	0.742
SOSF	3.510	2.322	0	12	0.662
PSFPD	0.759	1.206	0	7	1.589
SC	1.222	1.606	0	8	1.314
RSFA	2.689	2.532	0	11	0.942
TNA	3.673	3.030	0	13	0.825
SUSFF	0.249	0.433	0	1	1.739
TFISF	2.300	2.901	0	10	1.261
TISF	1.739	2.228	0	8	1.281
ESISF	8.98	2.600	0	11	0.290
OPSS	2.310	2.392	0	12	1.036
TAI	2.488	2.739	0	11	1.101
N.PISFTD	3.771	8.118	0	60	2.153
RPPD	1.395	2.323	0	8	1.665

Source: Author’s work based on collected materials.

). However, their production system and years in business vary considerably (some are new; others have been in business for 10–20 years). Most of the farms (55%) started their activities less than 20 years ago and only 12% were established in the period 2014–2023. The number of years in business is crucial because it can influence entrepreneurial capital (e.g., experience and knowledge) and social capital (e.g., relationships within the local territory).

The farms implementing SF activities are mostly SCs (49%), followed by those with sole proprietorship (19%), while the remainder are associations, corporations or other types of organisations such as local authorities, religious bodies, foundations, health institutes or companies, universities and other research entities. The adoption of organic farming methods by 53% of farms indicates their attention to environmental issues. In addition to agricultural production, they provide a variety of services, including work placements, educational and rehabilitative activities and retail. The core services provided by farms revolve around three main spheres: socio-employment inclusion (53%), the guidance of disadvantaged persons or persons at risk of social exclusion (52%) and rehabilitative actions (12%).

The main target groups are minors (26%), people with disabilities (17%), school/work alternating students, the elderly aged over 65 (13%), people with disabilities, the unemployed with socioeconomic problems, people with addiction issues, detainees (current and ex) and subjects in medical rehabilitation therapy (9%). The work sphere involves disadvantaged individuals (such as those with drug addictions or ex-detainees) and/or disabled people through the use of training internships and job placements. The educational and didactic field offers activities and lectures on biodiversity and the rural world to raise awareness and sensitize individuals. Children and teenagers of middle and high school age are the primary target in this sphere; they are encouraged to develop and reinforce eco-friendly behaviour starting in their early years.

From the analysis, it is possible to observe how the topic of SF is hybrid in nature, encompassing a great variety of resources, skills, operating practices and individuals who mobilize different sectors and activities.

Hypothesis 1 investigates the links between the particularities of enterprises and SF activities. The findings of the analysis indicate that the offer of SF activities is highly correlated with the peculiarities of the enterprises and with the interest of local actors, who in many cases finance these activities.

3.2. PCA Analysis

The variables used for PCA are reported in Table 2. Following Hair et al. (1998) [48] only nine of 23 PCs that had eigenvalues greater than 1.0 were retained for the successive HCA (Figure 1).

The eigenvalues of these PCs ranged from 1.03 to 3.62 and overall, they explained 63% of the total original variation (

Table 4. Eigenvalue analysis.

PC	Eigenvalue	Difference	% of Variance	Cumulative %
PC1	3.621	1.619	0.157	0.157
PC2	2.002	0.331	0.087	0.245
PC3	1.671	0.186	0.073	0.317
PC4	1.484	0.190	0.065	0.382
PC5	1.295	0.164	0.056	0.438
PC6	1.130	0.031	0.049	0.487
PC7	1.099	0.030	0.048	0.535
PC8	1.069	0.043	0.047	0.581
PC9	1.026	0.075	0.045	0.626

Source: Author’s work based on collected materials.

).

The eigenvectors of the weights of the original variables on the new standardised variables are reported in

Table 5. Correlations among starting variables and PCs.

Variables	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9
LF	−0.057	0.137	0.201	0.024	0.185	−0.177	0.603	0.131	−0.388
YE	−0.006	−0.303	−0.029	−0.162	0.352	0.115	0.070	−0.478	0.142
BY	0.317	−0.354	−0.100	0.138	0.102	−0.031	0.056	−0.079	−0.015
MRB	0.177	0.151	−0.119	0.029	−0.057	0.320	−0.113	−0.226	−0.418
FS	−0.012	−0.084	0.196	−0.127	−0.061	0.324	0.080	0.627	0.137
CM	0.292	−0.161	0.189	−0.232	0.311	0.003	0.035	−0.045	0.084
PAASF	0.295	0.043	0.089	−0.276	0.327	0.023	−0.048	0.286	−0.121
RAASF	0.240	−0.071	0.144	−0.286	0.283	−0.114	−0.308	0.069	−0.211
ACO	0.183	−0.303	−0.312	0.136	−0.102	−0.114	0.048	0.177	−0.156
CASF	0.153	−0.479	−0.181	0.084	−0.207	0.029	0.001	0.173	0.101
SOSF	0.282	0.015	−0.217	0.170	−0.072	0.055	−0.010	0.084	−0.376
PSFPD	0.243	0.194	−0.328	−0.185	−0.065	−0.101	0.113	−0.100	0.303
SC	0.148	0.007	0.158	0.367	0.100	−0.026	0.371	−0.080	0.008
RSFA	0.253	−0.076	0.409	−0.080	−0.433	−0.112	−0.016	−0.111	−0.010
TNA	0.269	0.164	−0.193	−0.096	−0.175	0.263	0.107	−0.104	−0.195
SUSFF	0.138	0.102	0.056	0.352	0.119	0.401	−0.178	0.115	0.223
TFISF	0.244	0.242	0.143	0.341	0.155	0.004	−0.286	0.047	0.165
TISF	0.183	0.150	0.171	0.387	0.145	−0.196	−0.018	−0.099	0.052
ESISF	0.048	0.233	−0.015	−0.188	−0.016	0.488	0.231	−0.072	0.065
OPSS	0.194	−0.084	−0.037	0.042	0.016	0.085	0.422	0.029	0.337
TAI	0.243	−0.026	0.432	−0.119	−0.432	−0.042	−0.006	−0.158	0.022
N.PISFTD	0.152	0.277	−0.216	−0.067	−0.021	−0.384	−0.003	0.224	0.088
RPPD	0.219	0.281	−0.170	−0.201	−0.056	−0.174	0.044	−0.037	0.239

Source: Author’s work based on collected materials.

. The relevance of every PC is determined by the initial variables that correlate with it. The first component, which we can define as “Enterprises with consolidated experience in offering social services”, explains 16% of the cumulative variance, i.e., it identified the typical variables of experience in the social sector. A variance of 9% concerned the second component, namely “Enterprises with continued social farming activities”: enterprises that carry out activities of social work inclusion and training for disadvantaged subjects and those with fragile or no employment (unemployed, immigrants, detainees, ex-detainees and subjects with addictions) during the whole year. Regarding component 3, the “Target factor”, is highly correlated (0.43) with the TAI. This indicates that target audience involvement is the most important factor that drives the enterprises. Therefore, in order to measure the involvement of the target audience, one must take into account the target factor. The fourth component represents the “Enterprises with high social investments”. It explains 7% of the total variance and is linked to the variables relating to the type of investment in SF and to the services that revolve around the management of channels for the selling of the companies’ own products. The fifth component identifies, based on its highest correlations with the variables RSA and TAI, the “Enterprises with a high content of SF activities”, as they have a strong demand for the acceptance of different categories of vulnerable people. Following this, the sixth component shows a strong link with the variables ESIAS and SUASF and identifies “Enterprises established thanks to public funds”. The seventh connotes “Enterprises with sale channels for their own products”. The eighth characterises “Large and historical enterprises” and finally, the ninth and last component connotes “Enterprises that have a strong link with territorial organisations”.

3.3. Retained Clusters and SF Development Paths

The HCA was carried out using Clusters 1–10 and the maximisation of CCC [48] was obtained with six clusters (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7). Statistics of the HCA are summarised in

Table 6. Main statistics of the HCA.

Cluster	Freq.	Percent	Distance between Cluster Centroids
1	66	16.10	4.45
2	49	11.95	5.47
3	106	25.85	4.20
4	120	29.27	6.12
5	32	7.80	5.39
6	37	9.02	4.75

Source: Own work based on collected materials.

. The number of enterprises per cluster ranged from 32 to 120. Two of the identified clusters showed a small number of farms (less than forty).

The distinct characteristics of each group thus reported, allowed for the identification of the different development paths for SF activated in Italian municipalities in relation to the second research hypothesis (H2).

3.3.1. Cluster 1: Social Farms Established Using a Bottom-Up Model

Cluster 1 is composed of 66 enterprises (16% of the total) located in northern and southern municipalities belonging mainly to the provinces of Campobasso, Vicenza, Bari and Pordenone (Figure 2). These enterprises were set up between the years 1994 and 2003, thanks not only to the strong motivation of the enterprisers involved and to their belief in the potential benefits they can offer to their users and to society as a whole, but also through association with social farm networks and those related to the distribution and sale of food products. This is certainly a good example of a social network of different actors. Indeed, the intense dialogue between the enterprises and local active associations not only facilitated the division and competence needed to organise the SF activities, but also compensated for the lack of initiatives from public institutions. Animal husbandry predominates as a productive activity. On average, the main social objective of these enterprises is the employment of vulnerable people who are involved as external collaborators, especially in SCs. The high number of network agreements can be positively associated with the strong relations with the territory and the higher number of people involved in the different activities carried out. Therefore, the types of networks organised at the territorial level represent the particularity of the enterprises of this first group.

Figure 2. Cluster 1 (66 enterprises).

Figure 2. Cluster 1 (66 enterprises).

3.3.2. Cluster 2: Small Social Care Farms

This second cluster, composed of 49 enterprises (12%) situated mainly in the municipalities of northern and central provinces (Pordenone, Roma and Lucca), includes enterprises with the smallest physical dimensions (6 ha vs. 21 ha)

Table 7. Mean values of the starting variables in the six cluster solutions, together with global means.

Variables	Cluster 1	Cluster 2	Cluster 3	Cluster 4	Cluster 5	Cluster 6	Global Means
Lf	6	6	5	5	7	6	6
Ye	3.364	2.204	2.651	3.192	3.813	3.027	3.032
BY	4.045	4.122	3.679	4.100	4.188	0.189	3.688
MRB	2.182	2.143	2.245	1.742	0.313	1.243	1.855
FS	9.636	6.082	16.016	11.453	124.214	15.595	21.281
CM	2.106	1.755	1.821	1.467	2.656	0.270	1.704
PAAAS	3.182	2.735	2.594	2.108	2.344	0.270	2.366
RAAAS	3.167	2.735	3.425	2.275	3.313	0.270	2.707
ACO	1.591	1.184	1.142	1.683	1.219	0.054	1.302
CAAS	1.561	1.163	1.387	1.883	1.750	0.000	1.455
SOAS	4.152	4.367	4.132	3.917	1.344	0.000	3.553
PASPD	2.242	0.857	0.764	0.317	0.063	0.000	0.769
SC	1.212	2.122	1.330	0.842	2.344	0.000	1.241
RASA	2.333	1.898	5.349	1.600	3.000	0.000	2.722
TNA	5.606	3.776	4.698	3.517	0.969	0.000	3.718
SUASF	0.227	0.612	0.283	0.167	0.188	0.027	0.252
TFIAS	2.136	6.122	3.075	1.192	1.031	0.000	2.333
TIAS	1.515	4.184	2.236	0.900	1.969	0.000	1.765
ESIAS	9.652	8.735	9.255	8.492	8.656	9.189	9.090
OPSS	3.621	2.673	2.236	1.883	3.563	0.000	2.344
TAI	1.879	1.469	5.519	1.208	2.938	0.000	1.278
NPIASTD	10.061	6.163	3.613	1.575	0.250	0.000	2.520
RPPD	3.652	1.980	1.708	0.425	0.063	0.000	3.824

Source: Author’s work based on collected materials.

and these are represented in Figure 3. The services offered are mainly for the guidance of disadvantaged people or those at risk of social exclusion. The activities of social agriculture in these territories came about thanks to donations and crowdfunding, which certainly allowed these businesses to increase their low incomes. Furthermore, the companies of this group have mainly made land improvement investments with arboretum plants.

Figure 3. Cluster 2 (49 enterprises).

Figure 3. Cluster 2 (49 enterprises).

3.3.3. Cluster 3: Social Farms with Social and Educational Functions

The 106 enterprises of this cluster, as shown in Figure 4, are spread mainly in the municipalities of different provinces across Italy (Como, Lecce, Bari, Bergamo, Torino, Bologna, Napoli and Roma). The main characteristic is the cultural character of the activities carried out. On average, most of the activities associated with the enterprises belonging to this group are educational farms aimed primarily at the elderly (over 65 years), refugees and asylum seekers.

Figure 4. Cluster 3 (106 enterprises).

Figure 4. Cluster 3 (106 enterprises).

3.3.4. Cluster 4: Social Farms with Environmental Projects

This fourth group brings together 120 companies (29%) that carry out projects mainly aimed at environmental and food education, the preservation of biodiversity and the dissemination of knowledge of the territory through the organisation of social and educational farms. These are businesses located mainly in the municipalities of the Sicilian provinces (Catania and Palermo) and Calabria (Cosenza, Reggio di Calabria) and are those that carry out their activities continuously (throughout the year) (See Figure 5).

Figure 5. Cluster 4 (120 enterprises).

Figure 5. Cluster 4 (120 enterprises).

3.3.5. Cluster 5: Large Social Farms with Eco-Friendly Agriculture

This group has only 32 businesses (7.8% of the total), the smallest number when compared to the other five. On average, it has the highest physical size in terms of hectares compared to the average of the 410 businesses (124 ha vs. 21 ha) Table 7. Compared to those belonging to the remaining groups, those in this group are more attentive to environmental issues as they generally use eco-friendly agricultural practices (organic and biodynamic). These enterprises, managed mainly by local authorities, rely on territories that are often confiscated from organised crime (Camorra, Mafia and Ndrangheta) in municipalities that mostly belong to the provinces of Catania, Ragusa, Rome, South Sardinia and Turin (Figure 6).

Figure 6. Cluster 5 (32 enterprises).

Figure 6. Cluster 5 (32 enterprises).

3.3.6. Cluster 6: New Entries in the SF Sector

In this last group there are 37 enterprises (9% of the total) that have been established more recently and that operate, as shown in Figure 6, mainly in the municipalities of the provinces of Bari, Messina, Reggio di Calabria, Rome and Turin (Figure 7). These are farms that possess small plots of land and sew annual crops by conventional methods (Table 7). The activities carried out are mainly related to the processing of the products they have grown.

Figure 7. Cluster 6 (37 enterprises).

Figure 7. Cluster 6 (37 enterprises).

4. Discussion

In Italy, SF has developed gradually and in a heterogeneous manner in the country’s municipalities. This is due to the heterogeneous Italian context [3], the increasing number of actors and vulnerable people involved in this work and to legislative measures [6]. In accordance with other scholars [4], these different development paths for SF offer many insights into the unique characteristics of enterprises that carry out social activities. To begin with, the analysis conducted on the territorial distribution has highlighted that social farms are mainly concentrated in the municipalities situated in northern and southern Italy. A second aspect worthy of note is that the majority of the SF experiences have come about thanks to their voluntary basis and the agreements made between farmers and local actors. For example, the intense dialogue between the enterprises of Cluster 1, labelled Social farms established using a bottom-up model, and active local associations, incentivised by the new legislative measures, have facilitated the division and competence needed to organise SF activities. In fact, the results of the analysis, in agreement with Moruzzo et al. (2022) [6], indicate that Italian municipalities have distinct experiences, according to the level of maturity of the SF in each cluster, with differences in location, the agricultural system, the organisational culture and the social services provided. Networking processes and the diverse levels of support and interest from universities, agricultural and SF organisations, policymakers and public institutions were also among the factors contributing to the differences between the enterprises included in the study.

The distinctive characteristics of SF in the six clusters identified are not only in the activities that are performed, but also in the manner in which they take shape and are executed in local contexts. These findings are in accordance with Nicolosi et al. (2021) [49] because most enterprises, such as those in Cluster 4 (Social farms with environmental projects) pay attention not only to educational aspects but also to those relating to the environment.

Furthermore, the results of the analysis show that social agriculture has developed along six different paths in terms of organisation, the services offered, the professionalism involved, the funding received and the regulatory framework of reference adopted. These paths are united by the fact that, in agreement with Senni (2007) [50], in most of them, with the exception of Cluster 1, the initiatives came about thanks to the boost provided by the social context and not by agriculture, as happened in other European countries (such as the Netherlands). Despite the identification of numerous benefits of SF in the six clusters, the economic, social, and sustainable dimensions differed significantly. In this regard, the significance of the “farm stage” path lies in its theoretical knowledge of technical aspects.

The presence of heterogeneous users involved in social and workplace activities or services in the same sector is noteworthy and contributes to the creation of quality SF. The study shows that SF is able to accommodate the weakest sections of the population, transforming disadvantage or disability into an ability to perform work functions. In line with Maino (2017) [51], the analysis highlighted that, thanks to public–private collaboration as seen in Cluster 1, alternative forms of welfare have developed, both locally and in an organised way, in an innovative and shared response to provide solutions to complex problems. The use of agricultural resources to provide services to individuals and communities is now a reality. Agriculture enterprises and small-scale farmers work with public services and the third sector to empower and engage local communities. In many Italian municipalities, SF experiences are initiated with funds from donations or crowdfunding, as is the case for small businesses included in Cluster 2 (Small social care farms). Moreover, in Cluster 5 (Large social farms with eco-friendly agriculture), a significant portion of these activities operate on land and in structures that were confiscated from organised crime. To this end, in line with Dell ‘Olio et al. (2021) [24], social cooperation has been explicitly recognised as one of the entities that can contribute to the re-use—for social purposes—of assets confiscated from criminal organisations. Furthermore, free-use concessions can act as an important deterrent to the spread of criminality, making policies to combat organised crime concrete.

Social farming, through the correct use of land resources, contributes to a positive change in the relationship between agriculture and society by improving the reputation of farms, building trust in local contexts and stimulating the entry of other actors in the sector. The six clusters of enterprises identified, particularly the enterprises of Cluster 4, present the typical features of generative welfare. The latter include aggregation and collaboration between actors from different economic fields relating to project proposals. In addition, as in Cluster 1, individuals operate in SF directly, externally, or through network relationships that enable them to utilise specific professional skills and/or projections that are useful for the development of actions. Finally, Cluster 6 is the group of New entries in the SF sector, which is creating local services to promote resilience in Italian agriculture. These farms usually involve unemployed individuals (who are often female), those with disabilities and refugees in their social activities. It is a proactive and innovative practice, a possible response to the needs of the population, both from a social, economic and environmental point of view.

In view of the findings obtained, the analysis also has important implications for policy, that in accordance with Nazzaro et al. (2021) [34], should promote information and training activities for farmers and users in order to increase the awareness of the social value of SF and encourage a collective approach to SF practices.

Regarding the third research hypothesis, the six clusters formed using multivariate analysis highlight different forms of resilience in Italian agriculture. Indeed, it emerged that, in the case of Cluster 1, the involvement of local administrations and the support of experts, in line with Olmedo et al. (2023) [52], are necessary because it created social networks that assist the social services on offer and creates an opportunity to supplement the low incomes of enterprises located mainly in rural municipalities. In Cluster 2, the impact of internal innovators was, at first, more on the socio-cultural side. This then attracted, or encouraged, the interest of new entrants into farming, as seen in Cluster 6, adding an economic relevance, which was later recognised by the municipalities. Furthermore, in accordance with Fazari and Musolino (2022) [53], the combination of new residents and a renewed interest in the agricultural sector has enabled “new farming” to become a potentially powerful engine to boost small-scale and locally based activities of economic and social significance.

Comparing the findings of this study with other previous analysis is quite difficult because there is little research that has focused on the municipal level to identify new development paths for SF in Italy. In this work, the lack of a database has been filled by transforming qualitative data into quantitative information in order to highlight what drivers determine different levels of development and management for enterprises with social activities in 351 Italian municipalities. This analysis has both strengths and limitations. One of the study’s strengths lies in the use of a Likert’s scale to transform the qualitative information into quantitative data. A limitation is due to the information utilised, which only provides the basis for identifying the differences in the development of SF in municipalities in Italy (Figure 2, Figure 3, Figure 4, Figure 5, Figure 6 and Figure 7).

5. Conclusions

The main objectives of this article were to explore new and more recent development paths for social services in Italy and to focus on integrated social activities and the strategies of ecosystem services.

The multivariate analysis approach was proposed to assess enterprise characteristics and analyse the heterogeneity and similarities in the development paths of social farming in Italian municipalities.

In conclusion, some important observations can be made.

Firstly, the offer of social farming activities is highly correlated with the distinct nature of the organizational elements of enterprises and with the interest of local actors, who in many cases finance these activities.

In fact, the results of the principal component analysis revealed that the most important organizational elements were the consolidated experience of offering social services and the continuation of activities. As these factors can influence both entrepreneurial capital (e.g., experience and knowledge) and social capital (e.g., relationships within the local territory) it is important to promote information and training activities for farmers and users in order to increase awareness of the social value of social farming and encourage a collaborative and sustainable approach to social farming practice.

Secondly, the findings of a hierarchical cluster analysis show that Italian municipalities have distinct experiences depending on the level of maturity of the social farms in each cluster, with differences in the location, agricultural system, organizational culture and social services provided. For this reason, specific policies are needed to address the dissemination of innovative SF initiatives in Italian municipalities, specifically targeting the primary sector.

Thirdly, the six new clusters created highlight different forms of resilience in Italian agriculture. For instance, the involvement of local administrations and the support of experts has created social networks that assist the social services on offer and create an opportunity for supplementing the low incomes of enterprises located mainly in rural areas. Moreover, the combination of new residents and a renewed interest in the agricultural sector has allowed “new farming” to become a potentially powerful engine to boost small-scale and locally based activities of economic and social significance.

This exploratory study acknowledges the importance of bottom-up initiatives in the primary sector designed to improve community wellbeing in Italian municipality areas lacking policy support.

Above all, the findings have highlighted the transversality of social farming activities that encompass different sectors (agriculture, welfare and the third sector). Indeed, it would be a great achievement if healthcare and agriculture were able to collaborate to develop innovative ways to provide effective assistance to vulnerable people in a sustainable and respectful way, while safeguarding animals and the environment.

To sum up, this study aims to contribute to academic research on the potential of social farming to develop multifunctionality, especially with regard to its ability to promote local and sustainable development in disadvantaged areas. Literature in this area remains scarce and therein lies the originality of this study.