Periurban Agriculture and Organic Farming: Investigating Synergies and Policy Implications

Abstract

One of the most successful on-farm diversification activities in Italy is the adoption of organic farming: a bona fide entrepreneurial approach to differentiating products for specific economic targets as opposed to merely a survival strategy to avoid decline and abandonment. The main objective of this paper is to assess the positioning of organic farming in periurban areas as defined in Rural Development Programmes (RDPs). Using Italian FADN data and running a logit regression model, we compare urban and periurban farms to other groups of farms identified in the RDP to assess their propensity to switch to organic farming. The assumption is that periurban farmers are more oriented to supplying organic products than farmers in other locations, given their proximity to urban populations who are keen on consuming organic products and are willing to pay a premium price for them. This, in turn, activates other on-farm functions such as the supply of public goods and services. This synergy is also relevant for the design and targeting of specific policies in line with the type of area considered in RDPs.

1. Introduction

There has been a growing interest in urban and periurban agriculture in recent years due to their growing role in providing food and other services for city dwellers [1,2,3,4]. The motivations behind urban and periurban agriculture are very different; however, they share the use of specific spaces directly in contact with urban areas, and they offer more than simply food, including many social and environmental services [4]. Recently, academic debate and policy actions have paid more attention to food security and food policy; in this context, urban and periurban farms have gained space, given their potentially significant role in providing food for citizens and public entities (canteens) as well as private businesses (e.g., restaurants and hotels) [4,5,6]. Food policy has shifted its attention from farmers and farming activities to consumers, food quality, food equity, and food security. This in turn has assigned a new and rather more complex role to urban and periurban areas, not only as suppliers of food and services but also as stakeholders in the action for more sustainable and just food policies [7]. From this perspective, the interest in organic farming and consumption is a response to the new demand for sustainability, the greening of practices, and quality, creating opportunities to access primary sector support [8].

From a geographical perspective, periurban agriculture can be defined as a farming system realised in a defined space surrounding an urban area [1]. In Europe, especially in countries with low land endowments and high populations, agriculture has progressively filled interspaces around urban settlements and neglected green areas, shaping the periurban landscape and creating a unique mosaic of activities and land use [9]. Although there is no universally shared definition of periurban agriculture, many agricultural practices occur within and around the boundaries of cities, filling the gaps that have been left empty of other human and more properly urban activities. Such activities serve multiple purposes that depend directly on the requirements of the urban population [9], and they also compete amongst themselves for alternative uses of resources in urban contexts (land, water, energy, and labour).

The long-run environmental, social, and economic crisis—particularly exacerbated by the recent faster pace of climate change, the continuous decline of agricultural areas and the risk of abandonment, and unpredictable external shocks such as the recent pandemic and wars—is prompting farmers to look for new strategies and sources of income, such as on-farm non-agricultural income-generating businesses, including organic farming, according to various entrepreneurial patterns. These new activities then become part of these farmers’ income-generation portfolios, thus allowing them to diversify and generate new entrepreneurial skills [2,3,10]. Changes and strategies in agriculture can be looked upon as a reaction to integrated production aiming to support global value chains and as an attempt to create new modes of production based on product differentiation and activity diversification [2,3,11,12,13].

Since the 1990s, there has been a progressive abandonment of a productivist approach to agricultural activities, emphasising the plurality, multiplicity, and co-existence of concepts rather than the homogeneity, generality, comprehensiveness, or universality of productivism [14,15,16,17]. Post-productivism and multifunctionality require a shift in focus from economic growth to sustainable development [18].

Multifunctional farming also helps to define agricultural diversity, expanding on-farm activities beyond a strictly primary sector production to the provision of environmental and social services, including recreational, educational, economic, and ecosystem facilities [19,20]. A deeper exploration of agricultural diversity could shed light on diversification practices in different contexts and for different purposes. This, in turn, could also help to better define and target policy interventions in agricultural activities and rural areas alike [21,22].

In this line of reasoning, farmers’ diversification of their sources of income to spread the risk over more activities can be seen as a prevention strategy [23]. Organic farming in particular has often been seen as a form of product diversification that has allowed farms to survive despite declining agricultural income by tapping into the sustainable and health food markets. In fact, recent studies have shown that organic farming should not be seen merely as a strategy of income diversification but rather as a distinct entrepreneurial model that could have different motivations that would lead to a differentiated policy approach [16,24].

The main objective of this paper is to assess the role of organic farming in periurban areas—as defined in the Rural Development Programmes (RDPs) of the Common Agricultural Policy (CAP)—and to identify the key factors that prompt farmers to undertake organic production¹. We ran a logit regression model moving from the Italian FADN data organised on the basis of the functional areas identified in the RDP. In other words, we compared urban and periurban farms to other groups of farms identified in the RDP to assess their propensity to switch to organic farming. The assumption is that periurban farms are more motivated than farms in other locations to supply organic products since these products are particularly appealing to the urban population; thus, it is an entrepreneurial choice of product differentiation and income diversification [24].

The analysis of income diversification and entrepreneurship in agriculture and rural areas is theoretically grounded in new entrepreneurship theory and the post-productivist paradigm [10,11,12]. They both build on the concept of the multifunctional role of agriculture, supplying new and different functions to society and also innovative use of land, in which it is not necessarily devoted to the mere production of agricultural goods [14,15,25,26]. The multifunctional role of agriculture is grounded in a new generation of skilled entrepreneurs who reposition the production factors within their farms without necessarily relocating them off-farm [10]. This process attracts new capital and a more skilled non-agricultural labour force in the sector, in the farms, and in the rural areas. In the case of periurban farms, their location has directed their entrepreneurial skillset towards the needs of the urban population by aiming to supply specific services and goods, including organic products.

Periurban agricultural areas have often been neglected by both rural and urban studies [27]. For the former, those areas did not fully fit the conceptualisation of rural areas since they endured the influence of the urban models of production and consumption. For the latter, periurban agriculture was just a transitional phase of rural areas being progressively surrounded by urban gentrification. In fact, urban and rural areas are increasingly integrated both physically and functionally, with mutual benefits [28].

Coming to the territories analysed in this paper, RDP areas offer an original approach with respect to the recent literature because it directly links results and design and targets specific policies proposed by the RDPs. It must be underlined that the method of classification by RDPs does not refer to a common shared definition of urban and periurban areas, and it mainly considers such empirical elements as population density, natural hindrances (mountains, special constraints, and so on), and the proportion of agricultural land to total land. Despite its limits, it is functional for a policy-orientated paper since policy interventions are designed and implemented, especially in countries where policies are managed at the sub-national level, using this classification [29,30,31].

The analysis of organic farming in periurban areas was conducted using the Farm Accountancy Data Network (FADN) on Italian farms. The FADN database is the sole source of farm-level data on agricultural structures, production, and economic outcomes. Over time, additional information, both accounting and non-accounting (such as environmental and social data), has been incorporated into it. Although originally designed to support agricultural policies, the FADN has evolved into a key resource for diverse economic analyses, often addressing topics beyond its original scope. Numerous studies now leverage FADN data to investigate various facets of the agricultural sector. For example, Forleo et al. (2021) [32] used FADN data to assess the efficiency of Italian agricultural businesses engaged in other gainful activities. Likewise, Casolani et al. (2021) [33] examined the impact of European funds on organic farms under the 2014–2020 Rural Development Programme. Rega et al. (2022) [34] illustrated how FADN data can be utilised to evaluate environmental performance at different levels, from individual farms to regions. Similarly, Cisilino et al. (2019) [35] compared the environmental performance of organic farms to conventional farms and also assessed the impact of subsidies on organic farming. In addition, Coppola et al. (2022) [36] studied the economic sustainability of Italian farms using FADN data. These examples highlight how researchers have used FADN data to assess agricultural structures, economic viability, environmental impact, and much more. Building on these applications, the present study focuses on analysing organic farming practices at the farm level. Specifically, we have aimed to identify the structural and economic factors that drive the adoption of organic farming techniques, excluding any motivational and philosophical considerations that might influence farmers’ decisions.

Figure 1 shows the theoretical framework of the paper and the logical steps followed. The post-productivistic paradigm has enhanced the multifunctional role of agriculture and the production of new products, services, and processes in agriculture, including organic farming. At the same time, multifunctionality has assigned a renewed role to periurban farms that react to their urban surroundings and develop their activities as suppliers of new services, products, and processes to urban citizens. Our paper focuses on the synergies between periurban agriculture and organic farming, which are generated by the same common roots in new prevailing approaches to agriculture in the contemporary world. Following that, the structure of the paper is as follows: Section 2 offers an overview of the recent literature linking periurban agriculture and organic farming to new approaches in the post-productivist mode of production, narrating the theoretical framework within which to present our results. Section 3 focuses on the methodology followed to analyse the synergies between periurban agriculture and organic farming, while Section 4 provides the main results. Section 5 presents a discussion, and, lastly, Section 6 offers conclusions and some policy implications of our analysis.

2. Background and Literature Review

Organic farming is expanding rapidly: the global organic farmland area grew by over 104% between 2010 and 2020, and between 1999 and 2020 there was an almost seven-fold increase [29], despite significant variability between regions. According to The World of Organic Agriculture: Statistics and Emerging Trends [37], in 2022, 188 countries were carrying out organic activities on a total of 96.4 million hectares (in 2000 there were “only” 15 million hectares), 26.6% more than the previous year, of which 53% were in Australia, followed by India (4.7%) and Argentina (4.1%). In terms of the percentage of total national agricultural land, however, the countries with the highest organic share were Liechtenstein (43.0%), Austria (27.5%), and Estonia (23.4%). Globally, organic farming accounts for 2.0% of total agricultural land. In Europe, this figure rises to 3.7%, covering 18.5 million hectares and involving over 480,000 producers. In the European Union (EU) specifically, organic agriculture represents 10.4% of the total agricultural area, with 16.9 million hectares and more than 419,000 producers. The leading countries in terms of organic areas are France (2.9 million hectares), Spain (2.7 million hectares), and Italy (2.3 million hectares).

The EU has made notable progress in its financial policies, particularly for the upcoming period [38], by prioritising the production of organic agriculture in areas that face specific natural constraints, such as mountainous regions, while shifting the income support payments from larger farms to smaller ones to promote a more equitable distribution of resources in the agricultural sector. As a matter of fact, organic farming in the EU has always been considered a successful policy in terms of resource allocation, and funds have steadily increased over the last two decades [39].

However, organic farming is not distributed evenly across all EU countries; this depends on how each country implements the specific measures supplied by the CAP as well as many other factors that influence farmers’ behaviour at a local level.

Indeed, the efficacy of policy instruments depends on contextual specificities such as governance capacity and the socio-economic system. Many challenges were highlighted in a recent comparative analysis of policies for promoting organic farming and biodiversity conservation [40]: tackling socio-cultural barriers to adopting organic agriculture, building common agendas among different parts of policy administrations, and securing stable funding. Regarding territorial coverage, spatial planning can act as a tool for synchronising organic agriculture with biodiversity objectives in developed and emerging countries. In any case, the effectiveness of this kind of policy depends mostly on how agricultural subsidies are allocated but also on robust land-use mapping, stakeholder engagement, and the capacity to monitor effective compliance [40].

The production of organic products in periurban areas raises interesting research issues related to periurban agriculture’s ability to improve the welfare of the urban population by bettering the level of food safety and food security, mainly in the Global South. A systematic review of the outcomes of urban and periurban agriculture [41] highlights, through a thematic analysis, that periurban agriculture responds to several SDGs: accessible and affordable food to reduce hunger (SDG 2), diverse, healthy, and nutritious food for good health (SDG 3), equitable access to vulnerable communities (SDG 5, SDG 10), urban risk management and climate mitigation through reduced food miles (SDG 11, SDG 13), sustainable production and consumption benefits through minimising synthetic inputs and recycling waste to produce food (SDG 12), better water and nutrient recycling, improved soil health and biodiversity (SDG 15), and improved environmental awareness and pro-environmental values (possibly SDG 17). Indeed, periurban areas can play a significant role in promoting organic production given their potential as an important part in providing primary products and food to cities [41]. Specifically, they can be useful for the development of canteens that prioritise organic food and the implementation of green public procurement practices, as demonstrated by Copenhagen, the first city to achieve 100% organic public canteens supplied by approximately 25,000 ha of organic farmland, and Vienna, which has developed a network of around 860 ha of organic urban gardens, primarily supplying public canteens [42]. Public procurement can be considered not only as the exchange of products and services but also as the exchange of ideas and values; in this regard, procurement policies can play a role in steering companies towards more sustainable practices [43]. By way of example, the city of Copenhagen in 2019 developed a strategy to increase organic product consumption, focusing on training and upskilling food professionals of canteens with the support of consultants. The municipality’s Food and Meal Strategy [44] has the ambition to have 90% of the food in the meals prepared in each kitchen be organic by 2025.

The spatial distribution of organic agriculture can be a result of various factors, including proximity to markets of larger urban centres [45]. Additionally, Malek et al. (2019) [46], in their study on the distribution of organic crop farmers, highlighted that organic farms tend to be located in more densely populated areas with lower levels of poverty and better market access. In this context, SFSCs [47] play a special role in organic production by strengthening farmers’ economic positions and meeting consumer demands, creating positive relationships between producers and consumers. At the European level, developing shorter supply chains represents a key pillar of the “Farm to Fork” strategy [48] towards a more sustainable food system. However, reducing the chain requires producers to maintain appropriate standards and pay close attention to relationships within the supply chain [49].

That said, some of the current environmental alarms and pressure on natural resources can be perceived in a different manner in cities and in periurban areas than in rural contexts. Air quality, solar radiation, climate change, and other urban conditions that are inherently different from rural environments may act as a barrier to production and the adoption of farming in urban and periurban areas [50].

These issues are particularly significant in Mediterranean areas, where environmental conditions often reach extremes and ties between urban and rural areas are also historically significant. For these reasons, short(er) local supply chains may play a key role in creating synergies between organic farming and periurban agriculture [51,52]. That said, there is a need and scope for more research on the links between periurban agriculture and organic farming.

By now, spatial analysis of organic agriculture is a consolidated method, even though different approaches, datasets, indicators, and regionalisation criteria have been used, with rather different results. Ilbery et al. (2016) [53] analysed the development of organic agriculture in South West Wales and South East England and suggested that physical, structural, and socio-cultural factors, including periurban farms’ location, can affect the regional concentration of organic farming. In this case, differences between territories are explained in terms of regional demand for organic food, organic heritage, and the development of an organisational infrastructure.

Kujala et al. (2022) [54] considered a variety of potential factors that can affect the regional distribution of organic farming, such as a long organic heritage, agricultural sectors, and market diversity. Their results showed that one of the pathways of organic development identified urban markets as an important driver of the development of organic products. Other studies have highlighted that organic farming is characterised by regional clustering in economically developed areas with favourable soil and climate conditions [46,47,48,49,50,51,52,53,54,55,56]. Conversely, a recent spatial analysis of organic agriculture was carried out in Croatia [57], where there has been a notable increase in areas of organic production after accession to the EU, mainly in the Slavonia and Lika regions. This study identified a combination of economic reasons and personal beliefs as the key factors.

Lu and Cheng (2023) [58], studying certified rice in Taiwan, showed a “neighbourhood effect” on certified areas per farm, indicating that a geographical spillover effect can confer a natural advantage on organic cultivation.

Some authors have highlighted that high-organic areas are more likely than low-organic areas to have sizeable populations located in or near metropolitan centres [59], or, in other words, that organic farming tends to be found in more populous areas characterised by a lower poverty rate and easier access to markets based on a global regime [46]. In contrast, other studies have shown the negative impact of population density on organic areas [54,57]. Additionally, organic farming is often seen as one of the innovations stimulated by the condition of periurbanity, together with other agronomic, social, and environmental innovations (less often technological) aimed at adapting agricultural practices to local conditions [60].

Urban, periurban, and rural differences in the development and distribution of organic agriculture were also found by Tosun et al. (2023) [61] using the 2020 Special Eurobarometer Survey involving 24,328 individuals living in 27 European Union member states. This study highlighted that individuals living in urban and periurban areas were more likely than rural residents to consider the fight against environmental degradation and climate change as a priority in agricultural policy; this could mean a greater likelihood of finding both consumers and producers of organic products in (peri)urban areas.

From the literature review supporting this paper, it emerges that studies linking urban, periurban, and organic farming do not analyse periurban areas in comparison to other functional areas within RDPs, which is especially relevant in terms of targeted policy design and the enhancement of the specific features of periurban multifunctional farms. For this reason, this paper will try to fill this specific gap in the literature.

3. Materials and Methods

3.1. Data Source

The empirical analysis is based on the Italian dataset from the Farm Accountancy Data Network (FADN), a primary European source for farm income, structure, and economic data. This annual economic and accounting survey is conducted by EU member states following a standardised EU methodology².

The Italian FADN sample includes approximately 11,000 farms representing those with a standard output (SO) of at least EUR 8000 since 2014, thus excluding smaller farms. It is a stratified random sample based on the Agricultural Census, which is representative of regions, economic size, and type of farming. Farms are selected using an equiprobabilistic method and allocated to sample strata using Neyman and Bethel methods to minimise expected errors for key variables like SO, utilised agricultural area (UAA), and livestock units (LSU).

The FADN collects extensive data on the economic, financial, structural, environmental, and social aspects of farms. These data are widely used for analysing agricultural income trends, evaluating EU and regional agricultural policies, and assessing environmental impacts.

In the present research, farms in the FADN sample were classified according to their location, based on the territory classification outlined by EU RDPs, which identify four distinct areas:

A.

Urban and periurban areas;

B.

Rural areas with intensive and specialised agriculture;

C.

Intermediate rural areas;

D.

Rural areas with development problems.

Specifically, farms were divided into two sub-samples:

-

Urban and periurban areas: this includes all farms located in provincial capitals that are urban in the strict sense and groups of municipalities with a rural population of less than 15% of the total population;

-

All other areas: this includes farms located in the three other areas identified by RDPs.

The use of this criterion for territorial distribution is essential for the analysis presented here. It enables an assessment of the impact of agricultural policies across the various areas outlined in the RDPs. RDPs include a range of measures aimed at enhancing agricultural competitiveness, fostering sustainable natural resource management, and addressing climate change, with the goal of promoting balanced development in rural economies and communities. While the scope of a rural development policy extends beyond the coverage of the FADN, certain RDP measures are specifically targeted at (or primarily benefit) farmers. Many of these environmental incentives are delivered through grants and annual payments, which support investments and agricultural practices that contribute to climate action and the sustainable management of natural resources. This includes promoting organic farming and the responsible use of such inputs as pesticides and fertilisers.

Using FADN data for the 2014–2021 accounting years, we identified 3350 farms located in urban and periurban areas and 83,354 farms located in other areas. In terms of agricultural area, the former covers just over 74,000 hectares of utilised agricultural area (representing only 3% of the total UAA), while farms in the other areas cultivate approximately 2.5 million hectares (

Table 1. Number of farms, utilised agricultural areas (UAAs), and corresponding percentages per sub-sample and region.

Region	Urban–Periurban Areas				Other Areas
	Farms (n.)	%	UAA (ha)	%	Farms (n.)	%	UAA (ha)	%
Abruzzo	31	0.7	378	0.4	4417	99.3	84,344	99.6
Basilicata	-	-	-	-	2997	100.0	111,364	100.0
Calabria	345	8.9	4780	10.3	3531	91.1	41,788	89.7
Campania	86	1.8	462	0.8	4723	98.2	60,673	99.2
Emilia-Romagna	974	16.1	22,558	11.1	5081	83.9	180,939	88.9
Friuli-Venezia Giulia	109	3.1	1982	2.0	3396	96.9	98,140	98.0
Lazio	164	3.9	7128	5.6	4037	96.1	120,926	94.4
Liguria	219	6.3	1624	6.0	3230	93.7	25,568	94.0
Lombardia	181	3.2	5793	2.5	5550	96.8	22,5587	97.5
Marche	174	5.2	3347	2.8	3200	94.8	116,790	97.2
Molise	10	0.4	328	0.4	2650	99.6	74,867	99.6
Piemonte	251	3.4	12,146	3.8	7218	96.6	308,103	96.2
Puglia	69	1.4	1478	1.3	4952	98.6	111,421	98.7
Sardegna	-	-	-	-	4210	100.0	86,627	100.0
Sicilia	52	1.0	1173	0.7	5104	99.0	162,421	99.3
Toscana	119	2.7	4322	3.2	4266	97.3	130,545	96.8
Trentino Alto Adige	269	5.9	1234	2.4	4321	94.1	50,729	97.6
Umbria	-	-	-	-	3603	100.0	148,314	100.0
Valle D’Aosta	101	5.2	1638	1.3	1844	94.8	122,212	98.7
Veneto	196	3.8	3700	2.1	5024	96.2	172,086	97.9
Italy	3350	4.4	74,072	3.0	83,354	96.1	2,433,443	97.0

Source: our elaboration on FADN data, 2014–2021.

).

3.2. Econometric Analysis

In this work, we used the logit model, or logistic regression, which is a statistical tool that is particularly suitable for analysing the outcomes of a categorical dependent variable as a function of one or more independent variables. Specifically, it is designed to handle situations in which the dependent variable takes on only two values, typically coded as 0 and 1. This makes it useful for analysing yes/no decisions, such as, in our case, the decision to adopt organic farming techniques.

In the literature, there are several instances where the logit model is used in agricultural research to analyse the factors influencing the adoption of innovative agricultural practices, including organic farming (e.g., [62,63,64]).

Mathematically, the logit model can be described as follows:

$$\mathrm{l}\mathrm{o}\mathrm{g}\mathrm{i}\mathrm{t}\mathrm{t} (\mathrm{p}) = \mathrm{l}\mathrm{n} \left({\displaystyle \frac{p}{1-p}}\right) = {\beta }_0 + {\beta }_{1\times 1} + {\beta }_2{\mathrm{X}}_2+ \dots + {\beta }_{\mathrm{n}}{\mathrm{X}}_{\mathrm{n}}$$

(1)

where

✓

p is the probability that the event of interest occurs (the adoption of organic farming);

✓

β₀, β₁, β₂, …, β_n are the coefficients of the model;

✓

X₁, X₂, …, X_n are the independent variables.

The logit model uses the logistic function to ensure that the probability p always remains within the range between 0 and 1. The logistic function is defined as follows:

$$\mathrm{P}(\mathrm{Y} = 1|\mathrm{X}) = {\displaystyle \frac{1}{1+e^{-({\beta }_0 + {\beta }_1{\mathrm{X}}_1 + {\beta }_2{\mathrm{X}}_2 + \dots + {\beta }_{\mathrm{n}}{\mathrm{X}}_{\mathrm{n}})}}}$$

(2)

where P(Y = 1|X) is the probability that the dependent variable (Y) is equal to 1 given the set of predictors (X) and β0, β1, β2, …, βn are the model parameters. The logit function is the natural logarithm of the odds ratio, where the odds ratio is the probability of success divided by the probability of failure. The higher the value of the odds ratio, the greater the probability that the dependent variable (adoption of organic farming) will take on the value 1. Predictor variables are used to estimate the log probabilities of the binary outcome. The outcome variable is the prediction or inference drawn from this probability. Therefore, the marginal effects (the impact of the predictors on the adoption of organic agriculture) are also defined. In logistic regression, these marginal effects measure the probability that a change in the dependent variable occurs following a change in one of the independent variables. In fact, the interpretation of the coefficients (β) in the logit model is such that an increase of one unit in (Xi) leads to a change in the log-odds (logarithm of the odds ratio) of (βi).

In this work, to understand binary decisions in the agricultural context and provide useful information for the formulation of targeted agricultural policies, two logistic regression models were used: one for farms located within the areas defined by RDPs as urban and periurban and another for farms located in all other areas. The variables used in the logit and the regression models are summarised in

Table 2. Description of the variables used in the logit and regression models.

Variables	Description
Dependent variable (logit model)
Y	Dummy, 1 if farms adopt organic farming; 0 if farms do not adopt organic farming
Independent variable
Year	Accounting years
Altimetry	Breakdown of farms according to altitude: mountains, hills, plains
Region	Administrative region where the farms are located (NUTS 2 region)
Type of farming (TOF)	Production specialisation of the farm
Economic size group (ES)	Economic size of farms, measured through the standard output
Utilised agricultural area (UAA)	Area used for farming, measured in hectares
Livestock units (LSU)	Aggregation of livestock from various species and ages, as per convention
Power of machines (KW)	Power of the machines available per farm, measured in kW
Family working units (FWU)	Amount of family work performed in the year, which is equal to 2200 h per year
Strategic profile	Reclassification of the farms in the Italian FADN
Management form	Type of farm management
Legal form	Type of legal form of the farms (e.g., individual, cooperative, company)
Age	Farmer’s age, in years
Gender	Farmer’s gender (male, female)
Level of education	Farmer’s education level (primary school, secondary school, high school, degree)
Total farm revenue (TFR)	Value of all of the farm’s production
GSP	Gross saleable production (GSP) (EUR)
GSP crops	Gross saleable production (GSP) related to the crop activity (EUR)
GSP livestock	Gross saleable production (GSP) related to the livestock activity (EUR)
GSP renewable energy	Gross saleable production (GSP) related to the renewable energy activity (EUR)
GSP quality	Gross saleable production (GSP) related to the quality production activity, i.e., protected designation of origin, protected geographical indication, organic production, etc. (EUR)
GSP processed products	Gross saleable production (GSP) related to the transformed products activity (EUR)
GSP direct sales	Gross saleable production (GSP) related to the direct sales activity (EUR)
Subsidies	Public support received by farms, in EUR
Agritourism	Revenues related to agritourism activities (EUR)
Hire of machinery (contract labour)	Revenues related to machinery hire activities (EUR)
Active rent	Revenues related to active rent activities (EUR)
Specific costs	Sum of the expenses for the purchase of non-farm consumption factors, other miscellaneous expenses, and third-party services (EUR)
Multi-year costs	Costs incurred for the purchase of goods that exhaust their usefulness in several financial years; only the quota pertaining to the year is considered (EUR)
Distributed incomes	Sum of the expenses for wages, social security charges, and passive rent (EUR)
Dependent variable (OLS model):
Farm net income (FNI)	The overall economic result of the farm, which identifies the ability to remunerate all the production factors used in the farm. Represents the dependent variable (EUR)

. Among the FADN variables used, we highlighted the use of the so-called “strategic profile”. The availability of information in the Italian FADN database on the diversification and differentiation of agricultural production allows a reclassification of farms in the FADN sample, defining a new farm typology that considers the intensity of the qualitative differentiation activity of the product and production diversification. This is a method of aggregating farms into homogeneous groups, both in terms of levels of gross saleable production and of the intensity of revenue deriving from non-strictly agricultural activities carried out by farms (agritourism, processing, other related activities) and the qualitative differentiation of agricultural production (processes and products with quality certification), defined as the strategic profile³ [65,66].

Once the factors that influence the decision to adopt or not to adopt organic farming techniques were defined, we examined which variables influence the formation of a farm’s net income. In this regard, a linear regression model (ordinary least squares, OLS) was used. OLS is a statistical method used to estimate the relationships between a continuous dependent variable (in our case, the farm net income) and one or more independent variables.

The multiple linear regression model can be expressed mathematically as follows:

Y = α + β₁X₁ + β₂X₂ + β₃X₃ + … + β_nX_n + ε_t

(3)

The OLS regression estimates the relationship between multiple independent variables (X) and a dependent variable (Y). Their relationship is described through the equation of a line, where α indicates the value of Y when X is equal to zero (intercept) and β indicates the slope of the line (regression coefficient). The regression coefficient β describes the change in Y associated with a unit change in X, while controlling for the other explanatory variables in the model. Finally, ε_t represents the statistical error, which is the error committed in defining the value of the variable Y through a linear function of X.

The coefficient β of each independent variable reflects both the strength and type of relationship that the independent variables have with the dependent variable. When the sign of the coefficient is negative, the relationship is negative. Conversely, when the sign is positive, the relationship is positive.

The OLS regression model also finds various applications in the agricultural sector. For example, it has been used to analyse relationships between continuous variables, such as crop yield, input costs, and productivity measures, to analyse the factors influencing the net income of agricultural holdings, or even to analyse the impact of the adoption of sustainable agriculture practices on farm income (e.g., [67,68,69,70,71]).

The application of the OLS multiple regression model was preceded by the identification of any anomalous values or outliers in the distribution of the farms’ net income. In this regard, the methodology identified by Tukey (1977) [72] was adopted, which is based on data quartiles and the use of box and whisker plot diagrams. Mathematically, the quartiles of the distribution were first identified, and then the interquartile range was defined:

IQR = Q₃ − Q₁

(4)

where IQR is the interquartile range, Q1 is the first quartile, and Q3 is the third quartile. This allowed us to identify the following quantities:

✓

The lower adjacent value (LAV), i.e., the smallest (minimum) observed value that is greater than or equal to

LAV = Q1 − 1.5 × (IQR)

(5)

✓

The upper adjacent value (UAV), i.e., the largest (maximum) observed value that is less than or equal to

UAV = Q3 + 1.5 × (IQR)

(6)

If the values fall within the range between the LAV and UAV, there are no outliers in the collected data. On the other hand, if the values collected are outside these limits, they can be considered outliers. In this work, the anomalous values identified through the aforementioned procedure were excluded from the OLS regression.

Both the logit model and the OLS model are fundamental tools in agricultural research. The logit model is essential for studying binary decisions, such as the adoption of organic farming techniques, while the OLS model is valuable for analysing continuous variables, such as farm net income. The application of these models provides crucial information for the formulation of agricultural policies and the management of agricultural businesses.

In conducting the multiple regression analysis, a method known as “Backward Stepwise Regression” was employed. This approach involves initially using a complete (saturated) model that includes all available explanatory variables. Subsequently, at each step, the explanatory variables with the least significant regression coefficient, based on the t-test, are gradually eliminated. This process continues until the lowest t-test value remains significant, and it is no longer possible to eliminate additional explanatory variables. This gradual approach was chosen because it reduces the number of explanatory variables in the model, thereby mitigating multicollinearity and addressing overfitting.

Furthermore, the results of the OLS model were subjected to validation and verification tests. The White and Breusch–Pagan tests were used to check for heteroscedasticity, while the variance inflation factor (VIF) was calculated for each independent variable to assess multicollinearity. Potential endogeneity was also investigated using the RESET Ramsey test. Finally, the Testuhat test was used to verify the normality of the residuals. Analysing the residuals allows us to evaluate, retrospectively, whether the hypothesised regression model is correct.

For the logistic regression model, the model fit was also checked. Adding independent variables to this model typically increases the amount of variance explained in the log-odds (expressed as R²). However, indiscriminately adding explanatory variables can lead to overfitting, which reduces the model’s generalisability and data fit. The goodness of fit, which quantifies the accuracy of the estimated probabilities of the response variable, is often assessed using the Hosmer–Lemeshow (HL) test, introduced in Hosmer and Lemeshow (1980) [73]. This test is a measure of goodness of fit based on the chi-square test.

The discrimination capacity of the model, i.e., its ability to correctly distinguish between different categories or classes of output, was also verified. This information is obtained by constructing the receiver operating characteristic (ROC) curve, which is a graphical tool for evaluating the predictive capacity of the logistic model. The model’s predictive capacity is greater the higher the sensitivity, given a fixed specificity. The area under the ROC curve (AUC) ranges from 0 to 1. An AUC of 0.5 indicates poor predictive ability, represented by a segment connecting the origin to the point (1,1). Conversely, an AUC of 1 indicates excellent predictive ability.

4. Results

The analysis methodology used highlighted both the structural and organisational aspects of the various farms as well as the differences that characterise them across the various areas identified in the RDPs.

4.1. Results of Descriptive Analysis

Following the classification of rural areas of the RDPs, Italian FADN farms number slightly more than 4% in urban and periurban areas, about 26% in areas with intensive and specialised agriculture, 38% in intermediate areas, and 31% in areas with development problems (Figure 2).

Organic farms amount to 15,006, with a distribution that veers towards areas with development problems (40.7%, Figure 2).

There are 661 organic farms in urban and periurban areas (4.4%). They are located mostly in the south and centre, specifically in Calabria, followed by the Marches and Lazio (Figure 3). In contrast, organic farms in the other areas are mainly located in Northern Italy, with the Emilia Romagna region having the largest number (1087), followed by Piemonte with 334 farms. In Southern Italy, however, the greatest number of organic farms are found, once again, in Calabria (351) (Figure 3).

The analysis of the utilised agricultural area (UAA) of farms in the Italian FADN sample revealed that, in general, farms located in urban and periurban areas have a slightly lower average UAA compared to farms in other areas (33.6 hectares vs. 33.9 hectares). This trend is also observed when considering only conventional farms (31.4 hectares vs. 32.5 hectares). However, the situation is reversed when considering only organic farms. Organic farms in urban and periurban areas have a higher average UAA than those in other areas (43.8 hectares vs. 40.5 hectares). More generally, organic farms tend to have a larger average agricultural surface area than conventional farms (Figure 4).

Table 3. Conventional and organic farms in the FADN sample in the urban and periurban areas, by classes of economic size.

Classes of Economic Size	Conventional		Organic		Total
	n.	%	n.	%	n.	%
Small	684	23.1	134	20.3	818	22.6
Medium–small	692	23.3	129	19.5	821	22.6
Medium	584	19.7	186	28.1	770	21.2
Medium–large	728	24.6	163	24.7	891	24.6
Large	276	9.3	49	7.4	325	9.0
Total	2964	100.0	661	100.0	3625	100.0

and

Table 4. Conventional and organic farms in the FADN sample in all other areas, by classes of economic size.

Classes of Economic Size	Conventional		Organic		Total
	n.	%	n.	%	n.	%
Small	14,847	22.3	2798	19.5	17,645	21.8
Medium–small	14,485	21.8	3660	25.5	18,145	22.4
Medium	14,795	22.2	3442	24.0	18,237	22.6
Medium–large	17,299	26.0	3825	26.7	21,124	26.1
Large	5069	7.6	620	4.3	5689	7.0
Total	66,495	100.0	14,345	100.0	80,840	100.0

show the subdivision of farms according to their economic size classes, defined by the standard output (SO), and depending on their location in the two areas identified.

In this way, five classes of economic size of farms were defined:

-

Small: SO of up to EUR 25,000;

-

Medium–small: SO between EUR 25,000 and 50,000;

-

Medium: SO between EUR 50,000 and 100,000;

-

Medium–large: SO between EUR 100,000 and 500,000;

-

Large: SO exceeding EUR 500,000.

In urban and periurban areas, 28% of farms have a medium economic size, and approximately 25% are medium–large. Large organic farms in these areas make up just 7%.In other areas, the highest percentage of organic farms falls within the medium–large economic size class (26.7%), while the large economic size class has the lowest percentage of organic farms (4.3%).

The average percentage of utilised agricultural area (UAA) owned by farms in urban and periurban areas is approximately 48%, compared to 49.8% in other areas. However, urban and periurban farms have a higher proportion of rented surface area (40.7% vs. 39%), which is increasingly becoming a solution for expanding the cultivable surface area available to farmers. Additionally, the percentage of irrigated UAA is higher in urban and periurban areas (44.4%) than in other areas (33.1%) (

Table 5. Distribution of values of the main structural variables per areas.

Areas	Average UAA (ha)	Owned UAA (%)	Rented UAA (%)	Irrigated UAA (%)	Livestock Units	Power of Machines (KW)
Urban and Periurban	33.6	48.1	40.7	44.4	229.9	232.8
All other	33.9	49.8	39.0	33.1	147.5	192.5

).

The size of the labour force employed on farms showed little difference between the two sub-samples analysed. In urban and periurban areas, the average is 2 annual work units (AWU) per year, compared to 1.9 AWU in other areas. In both areas, 1.3 AWU are represented by the farmer and their family (family work units—FWU). In fact, on almost all farms, agricultural work is primarily carried out by the farmer’s family, with over 70% in all years (

Table 6. Values of the main structural indices based on the identified area.

Areas	Mechanisation Index (KW/UAA)	Annual Working Units	Family Working Units	Labour Intensity (AWU/UAA)	Family Management Index (FWU/AWU)
Urban and Periurban	24.3	2.0	1.3	0.4	0.8
All other	15.4	1.9	1.3	0.2	0.8

).

The availability of UAA per employee (work intensity) averages 0.4 hectares in urban and periurban areas and 0.2 hectares in other areas (Table 6).

Finally, farms exhibit a high degree of mechanisation (Table 5). They possess machinery with an average power of around 233 kW in urban and periurban areas (compared to 193 kW in other areas) and an agricultural mechanisation degree (available power) of 24.3 kW per hectare, compared to 15.4 kW in other areas (Table 6). This reflects another typical characteristic of Italian farms, namely the possession of an excessive fleet of machines relative to the actual physical size of the farms.

Analysis of the types of farms shows a similar distribution in the two areas identified in this study. Notably, 88% of farms are specialised, with a focus on permanent crops (39.2% in urban and periurban areas and 30.2% in other areas) and arable land (30.8% in urban and periurban areas and 25.7% in other areas). Conversely, approximately 12% of farms practise mixed agriculture (Figure 5).

A more detailed breakdown of the type of farming reveals that, in urban and periurban areas, most permanent crops are fruit (46.4%), viticulture (35.3%), and olive growing (18.2%). In other areas, fruit growing and viticulture each account for 42% of the surface area dedicated to permanent crops, while olive growing occupies approximately 16% of the surface area.

4.2. Results of Statistical Analysis

In this study, two logistic regression models were employed to examine the adoption (conversion) of organic farming techniques on farms. Specifically, logistic regression was utilised to analyse the relationship between the dependent variable (i.e., whether the farm practices organic farming) and various independent (explanatory) variables that influence the decision to adopt organic farming techniques. This model directly estimates the probability of an event occurring based on a given set of independent variables, with the dependent variable being bound between 0 and 1. Additionally, two multiple regression models were used to identify factors influencing the formation of corporate net income.

The validity and reliability of the logit model used in this study were verified using a Hosmer–Lemeshow test (for goodness of fit) and the variance inflation factor (VIF) to check for multicollinearity. The results of these tests indicated no significant issues that could compromise the reliability of the logit model, thus confirming its robustness in the context of the analysis.

The linear regression model (OLS) was also subjected to verification tests, which indicated neither multicollinearity nor heteroscedasticity. However, the analysis revealed a slight deviation from the normal distribution, showing a leptokurtic distribution characterised by a higher peak and heavier tails than the normal distribution, indicating the presence of more extreme values. Nonetheless, this did not raise significant concerns. As highlighted by several scholars (e.g., [69,74,75,76,77]), in the case of large samples, deviations from normality in regression residuals do not impact the parameter estimates of the regression model. The central limit theorem ensures that the sampling distribution of the estimates approaches a normal distribution as the sample size increases.

The analysis revealed various factors which, to differing degrees and in diverse ways, exert influence on the adoption of organic farming techniques. The main results of the regression model are displayed in

Table 7. Results of the logistic regression model in urban and periurban areas.

Variables	Coefficients	Standard Error	z-Statistic	Odds Ratio
const	−143.05	44.80	−3.193
Year	0.0689	0.0222	3.104	1.076
Region	−0.0596	0.0116	−5.120	1.033
Type of farming	0.1316	0.0286	4.602	1.223
Age	−0.0119	0.0036	−3.310	0.990
Level of education	0.44586	0.0348	12.80	1.421
Utilised agricultural area	0.0109	0.0010	10.70	1.009
Livestock unit	−0.0029	0.0005	−6.383	0.998
Power machines	−0.0024	0.0003	−7.934	0.998
Annual working units	0.2156	0.0275	7.843	1.180
Altimetry	0.6293	0.0598	10.52	0.510
Economic size class	−0.0698	0.0408	−1.712	1.135
R2 McFadden	0.200		R2 adjusted	0.193
Log-likelihood	−1325.71		Akaike Criterion	2675.42
Schwarz Criterion	2749.71		Hannan–Quinn Criterion	2701.90

and

Table 8. Results of the logistic regression model in other areas.

Variables	Coefficients	Standard Error	z-Statistic	Odds Ratio
const	−197.18	8.759	−22.51
Year	0.0946	0.0043	21.78	1.099
Region	0.01365	0.0003	40.73	1.014
Altimetry	−0.2816	0.0147	−19.11	0.755
Type of farming	0.0815	0.0066	12.39	1.085
Economic size class	−0.0905	0.0103	−8.77	0.913
Management	0.2115	0.0109	19.35	1.236
Legal form	0.0640	0.0091	7.048	1.066
Utilised agricultural area	0.0028	0.00018	14.99	1.003
Livestock unit	−0.0012	0.0001	−11.28	0.999
Power machines	−0.0007	6.77414 × 10−5	−10.28	0.999
Family work units	−0.1164	0.0166	−7.012	0.890
Strategic profile	0.4187	0.0097	43.10	1.520
Other gainful activities	0.1171	0.0279	4.198	1.124
Age	−0.0117	0.0008	−15.16	0.988
Gender	−0.2022	0.0237	−8.535	0.817
Level of education	0.2219	0.0076	29.04	1.248

.

The results reveal that the decision to convert farms to organic farming is associated with geographical location at a regional level, the type of farming, altitude, and UAA. Specifically, the two logistic regressions indicate that the adoption of organic farming techniques is primarily linked to the structural aspects of the farm rather than purely economic factors.

After identifying the factors influencing the adoption of organic farming techniques, we proceeded to determine the elements that affect the formation of a farm’s net income using multiple regression models applied to the two areas under study (

Table 9. Results of the multiple regression models related to farm net income in the urban and periurban areas.

Variables	Coefficients	Std. Error	t-Statistic	p-Value
const	−1886.48	885.85	−2.130	0.0333	**
Type of farming	617.46	208.73	2.958	0.0031	***
Organic	5372.73	864.34	6.216	<0.0001	***
UAA	−44.67	14.67	−3.047	0.0023	***
Livestock units	82.18	2.596	31.73	<0.0001	***
Power of machines	−5.08	2.08	−2.442	0.0147	**
Family working units	2833.72	493.40	5.743	<0.0001	***
GPV crops	0.78	0.0102	76.91	<0.0001	***
GPV livestock	0.67	0.012	56.71	<0.0001	***
GPV renewable energy	0.23	0.077	2.952	0.0032	***
GPV quality	0.006	0.018	0.3294	0.7418
GPV processed products	0.033	0.017	1.901	0.0573	*
GPV direct sales	0.0043	0.011	0.3715	0.7103
Subsidies	0.804	0.035	23.23	<0.0001	***
Agritourism	0.811	0.019	41.75	<0.0001	***
Hire of machinery (contract labour)	0.604	0.054	11.25	<0.0001	***
Active rent	0.42	0.097	4.309	<0.0001	***
Specific costs	−0.74	0.014	−52.59	<0.0001	***
Multi-year costs	−0.22	0.030	−7.274	<0.0001	***
Distributed incomes	−0.73	0.020	−37.02	<0.0001	***
Dependent variable’s mean	30,227.44		Std. Dev. Dep. Var	35,354.45
Square sum residues	1.06 × 1012		Std. Error regression	17,839.03
R2	0.746847		R2 adjusted	0.745403
F(19, 3330)	517.0578		p-value (F)	0.000000
Log-likelihood	−37,537.05		Obs.	3.350

*, **, ***: significant at 10%, 5% and 1%, respectively.

and

Table 10. Results of the multiple regression models related to farm net income in the other areas.

Variables	Coefficients	Std. Error	t-Statistic	p-Value
Constant	−21,304.1	1332.64	−15.99	<0.0001	***
Type of farming	634.80	247.31	2.567	0.0103	**
Management	4034.30	455.52	8.856	<0.0001	***
Organic	2134.44	1027.29	2.078	0.0377	**
Livestock units	80.30	1.17	68.88	<0.0001	***
Power of machines	32.26	2.32	13.90	<0.0001	***
Family working units	20,143.5	546.83	36.84	<0.0001	***
GPV renewable energy	0.46	0.0062	73.22	<0.0001	***
GPV quality	−0.12	0.0034	−35.51	<0.0001	***
GPV processed products	0.33	0.0032	104.8	<0.0001	***
GPV direct sales	0.10	0.0049	20.61	<0.0001	***
Subsidies	0.99	0.0156	63.70	<0.0001	***
Agritourism	0.23	0.0191	12.14	<0.0001	***
Active rent	0.34	0.098	3.469	0.0005	***
Specific costs	0.15	0.0021	67.75	<0.0001	***
Multi-year costs	−0.46	0.0223	−20.66	<0.0001	***
Distributed incomes	0.39	0.00977	40.37	<0.0001	***
Dependent variable’s mean	59,374.14		Std. Dev. Dep. Var	174,064.9
Square sum residues	1.01 × 1015		Std. Error regression	110,322.2
R2	0.598375		R2 adjusted	0.598298
F (19, 3330)	7760.168		p-value (F)	0.000000
Log-likelihood	−37,537.05		Obs.	83.354

**, ***: significant at 5% and 1%, respectively.

).

Two models to elucidate the determinants of corporate net income in both urban and periurban areas and other areas were used. The results of the OLS models are presented as regression coefficients. The independent variables used approximate the factors that influence the farms’ performance in terms of farm net income.

The signs of the estimated coefficients are significant and consistent with expectations. Specifically, Table 9 summarises the OLS regression model results related to farm net income in urban and periurban areas, which reveals that UAA has a negative value.

This issue is especially apparent when expansion requires investments in new equipment, facilities, or additional labour that do not generate returns that justify the expense. Furthermore, as farm size increases, management efficiency may decline, making the coordination of activities across larger areas more complex and less effective. This greater complexity often leads to higher fixed and variable costs, which are not always offset by a corresponding rise in revenue.

Analysis of results from the two logit models highlighted that among the factors influencing the probability of a farm adopting organic farming techniques are the “other gainful activities” (OGAs) related to the multifunctionality of a farm. This is particularly true for farms located in “other areas”. To further verify this result, the logit models were tested again by replacing the OGA variable with the detailed factors (variables) that comprise the OGA variable, such as direct sales, hospitality, catering, machinery rental, educational farms, renewable energy production, artisan activities, premises rental, environmental services, etc. This verification confirmed the initial findings. The various OGAs remain significant only for farms in other areas, while the level of significance was insufficient to include them in the results of the model applied to farms located in urban and periurban areas.

Lastly, cost items such as operating expenses, multi-year costs, distributed profits, and the constant (intercept) were considered.

5. Discussion

Global interest in organic agriculture has grown significantly, with a particular emphasis on urban and periurban areas in advanced economies. Urban settings offer unique opportunities for organic farming, such as increased demand for healthy and sustainable food, better access to local markets, and heightened environmental awareness among consumers. Periurban areas, situated on the outskirts of metropolitan areas, provide additional advantages by accommodating agricultural practices that may not be feasible in densely populated areas while serving as “buffer zones” for organic farming.

The proximity of farms to urban centres significantly impacts the adoption of organic farming. Urban environments see many consumers who value product quality and origin, thus driving the demand for organic products. Our research indicates that this demand stimulates farmers to adapt, especially when supported by local initiatives promoting sustainable and locally sourced food. Geographical and territorial factors also shape decisions. Periurban areas, often at the interface of urban expansion and agricultural activities, present both challenges and opportunities. These zones enable the development of organic farming for urban markets and also leverage ecosystem services, providing strong incentives for sustainable agricultural practices.

The results of the analysis are rather innovative since they go beyond the view shared in the literature that economic factors underlie the decision to switch to organic production [77,78,79]. However, certain economic factors, such as public subsidies (e.g., contributions from Pillar I of the CAP) or operational costs, were statistically significant but excluded from the logistic regression models given that their odds ratio was equal to 1. This indicates that these variables do not influence the decision to adopt organic farming.

Anyway, the adoption of organic farming is primarily linked to the structural aspects of the farm rather than purely economic factors. This suggests that only farms with certain characteristics can choose to convert to organic farming because of the difficulties connected with the related economic and organisational issues. In this regard, it is important to consider also the specific socio-economic context, specifically the high land costs in periurban areas and the competition with other productive sectors for using land.

With regard to farm manager characteristics, it appears that only age has a discernible impact on the decision to adopt organic farming, particularly for farms located in urban and periurban areas. These findings suggest that, in line with what emerges from the current literature, certain farmer characteristics (such as gender and education level) have a positive impact on the adoption of organic farming but only to a relatively minor extent affect the decision-making process for most farms [80,81].

That said, farms situated in other areas exhibit a broader range of variables influencing the agricultural entrepreneur’s decision to adopt organic farming. Notable among these are the management form, the legal form of the farm, the presence of other gainful activities⁴ (OGAs), and the strategic profile. Additionally, gender and level of educational attainment of agricultural entrepreneurs are also key factors alongside age, as already observed for agricultural enterprises in urban and periurban areas.

The multiple regression model results indicate that, in urban and periurban regions, an increase in agricultural surface area can lead to a reduction in farm net income. This phenomenon is often referred to in the literature as the “inverse relationship between farm size and productivity” (e.g., [82,83,84,85]).

The relationship between the physical size of agricultural holdings and farm net income has been extensively studied (e.g., [86,87,88,89,90,91,92,93,94,95]). Some of these papers indicate that the physical size of farms is a crucial factor in forming net income, with dynamics that vary significantly according to the geographical context and existing agricultural policies (e.g., [47,96,97,98,99,100]). Other studies suggest that expanding the agricultural surface area does not always result in a higher net income and, in some cases, may even lead to a reduction due to increased operational costs and management difficulties [100].

Income diversification activities, such as renewable energy, product quality (e.g., Protected Designation of Origin—PDO, Protected Geographical Indication—PGI, or organic products), processed products, and direct sales, were found to be statistically significant in both areas, although their impact varied. These strategies fall within the multifunctional paradigm, emphasising the enhancement and qualification of agricultural products [96]. Contracting was also statistically significant in urban and periurban areas, although it did not influence net income formation in other regions.

The regression analysis also highlighted the positive influence of political support on the net income of agricultural companies. This confirms the strategic role of policy intervention in the diffusion of organic agriculture due to its capability to integrate the farmers’ income, which is essential due to the major costs and minor productivity connected with this kind of agriculture.

6. Conclusions and Policy Implications

This study underscores the potential of tailored policies and institutional support to foster sustainable agricultural practices in urban and periurban settings while providing valuable insights for future research and policy development.

Our results demonstrate significant differences between organic farms located in periurban areas compared to those in other areas, as defined by the RDPs. However, in all the areas analysed, structural aspects are key and are shown to be more relevant than economic issues. This is a relevant result since it shows that some choices are determined less by financial opportunities than by the structural condition of farms, such as their dimension, the availability of a family labour force, and infrastructure endowments (buildings and other structures). For example, organic farms in Italy are physically larger than conventional farms on average, but for periurban farms, land can prove to be a critical constraint. This suggests the need for articulated strategies for supporting the diffusion of organic agriculture, including planning regulations to face the competition for land use, administrative simplification, logistics, and the promotion of public procurement initiatives aimed at offering organic products in schools, universities, and hospital canteens.

Another relevant issue is the public support granted to organic farms. Organic products obtain a premium price, so public subsidies from Pillars I and II of the CAP may not be as relevant to their choices. Similarly, the operational expenses can be offset by increased revenues. Nonetheless, a strategic territorial distribution of the subsidies and incentives (i.e., to cover certification costs) could support organic farming in areas with relevant environmental problems, such as urban and periurban areas.

Instead, human factors and the quality of human capital are significant in all cases, especially age and, to a lesser extent, gender and education, confirming the results of many studies on the factors influencing farmers’ adoption of organic farming [81]. Training, information, advice, and any other initiative aimed at increasing knowledge and innovation processes are needed. In this regard, the attention of the present CAP policy on intervention included in the Agricultural Knowledge and Innovation System (AKIS) could meet the need to improve human capital and accompany the transition toward a more sustainable system.

Particularly interesting is the role played by the OGAs in other areas but not in periurban areas. Given the role that agriculture has in periurban areas, especially in the direct relationship with consumers, this aspect was further explored to verify whether the different types of OGA, mainly direct sales and agritourism, are significant in these areas. However, these activities do not appear to significantly influence the choice to produce organically in urban and periurban areas, despite what we might have inferred from the results of other studies, such as those highlighting the key role of the short(er) local supply chains in linking organic farming and periurban agriculture [51,52].

Specifically, the analysis revealed that, for farms located in urban and periurban areas, the presence of OGAs within the same farm does not significantly influence farmers’ decisions to adopt organic farming techniques. However, the presence of OGAs does statistically significantly affect the formation of the farm’s net income. This result suggests that, although there is a relationship between OGAs and farmers’ environmental concerns, the strength of this association is relatively weak (statistically insignificant) compared to other factors such as the physical size of the farm, its economic size, land use type (type of farming), and total labour force employed.

In contrast, family labour on urban and periurban farms appears to have little impact on the decision to adopt organic farming. This is probably because both the farm manager and family members can easily engage in off-farm work and consequently contribute to additional income sources for the farming family. It is also worth underlining that, due to its proximity to urban areas, urban and periurban agriculture faces greater demographic pressure and stronger competition for natural resources compared to rural agriculture. However, that said, it also benefits from easy access to local markets and consumers.

This proximity enables urban and periurban farms to develop and strengthen a variety of diversification activities (such as offering social, recreational, educational, cultural, and agritourism services) and differentiation activities (such as direct sales, product transformation, and vertical integration). These efforts usually lead to a direct, trust-based connection with consumers and the surrounding socio-economic environment, thereby creating a crucial link between the farm and its community.

Similarly, there is a notable difference in the significance of the strategic profile when considering other areas alone. This variable aggregates FADN farms into five homogeneous groups, reported earlier in footnote 3, based on the incidence of different sources of revenue on the total gross saleable production and on product differentiation (quality).

The strategic profile plays a crucial role in determining the factors that influence the decision to adopt organic practices in non-urban and periurban areas (other areas). From our results, it seems that diversified and differentiated Italian farms, which mostly determine the strategic profile, are those with a greater tendency to embrace organic farming than micro- and conventional farms. In other words, organic farming becomes a viable choice within the possibilities of product differentiation and income diversification offered by the specific geographical conditions of periurban farms and by their more direct relationship with city-dwellers. It can also be observed that farms specialising in permanent crops are keener on adopting organic practices, while, in terms of economic size, medium-sized farms with an average economic size are more likely to adopt organic practices than the two extreme economic size classes (small and large). This certainly deserves further investigation, especially the relationships between “going organic” and having diversified activities and differentiated products.

The results of the analysis presented here offer valuable insights for potential future developments in the relationship between periurbanity, location, and organic farming. In particular, this study highlights that a farmer’s decision to convert to organic farming techniques primarily depends on the farm’s structural factors, while economic considerations play a negligible role.

Specifically, for farms located in urban and periurban areas, the age of the entrepreneurs significantly influences the decision to adopt organic practices. Thus, future research could explore youth participation in the primary sector along with policies aimed at promoting generational turnover. Notably, this present analysis examined agricultural policies under the previous programming cycle, as information on the new agricultural programming is currently unavailable.

Young farmers tend to be more attuned to environmental issues, and urban and periurban agriculture, focused on growing food near cities, provides them with the opportunity to cultivate local products, reduce environmental impact, and enhance food security. Therefore, future documents should delve into measures supporting generational turnover in Italian agriculture, particularly interventions outlined in the CAP National Strategic Plan (NSP) 2023-27. This plan serves as the framework for agricultural and rural development policy in Italy by identifying needs, priorities, and interventions related to both the first pillar (direct payments and sector interventions) and the second pillar (rural development).

Furthermore, the results indicate that, on farms located in non-urban areas, the strategic profile of the entrepreneur influences the decision to adopt organic farming. Therefore, further analyses could determine whether the individual strategic profiles identified within the FADN database are also relevant for farms in urban and periurban areas or whether it is possible to identify a new strategic profile specific to these types of farms.

In conclusion, it is important to note that a limitation of studies like this one lies in the unpredictability of transferring the analytical method to other European territories. Specifically, the use of the FADN database, being the only harmonised data source at a European level, ought to facilitate the extension of this type of analysis to other EU Member States. However, given that the Italian FADN collects significantly more information (with greater detail) than its European counterparts, enabling more in-depth analyses, this itself can sometimes pose a limitation—depending on the variables used—in applying the analytical methodology to other territorial contexts. Therefore, while the analysis presented here provides valuable insights for Italy in terms of rural policy objectives and recommendations, the challenge for future research lies in testing the method in other European contexts and ensuring comparable results and policy implications at the EU level.