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# Characterization of agricultural spaces engaged in proximity-based alternative food networks: The southeastern France case study

Juliette Benedetti, Stéphane Bouissou, Karine Emsellem

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Juliette Benedetti, Stéphane Bouissou, Karine Emsellem. Characterization of agricultural spaces engaged in proximity-based alternative food networks: The southeastern France case study. Land Use Policy, 2025, 158, pp.107767. 10.1016/j.landusepol.2025.107767. hal-05266599

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# Characterization of agricultural spaces engaged in proximity-based alternative food networks: The southeastern France case study

Juliette Benedetti*, Stéphane Bouissou, Karine Emsellem

Université Côte d'Azur, CNRS, Aix-Marseille Université, Avignon Université, ESPACE, Nice, France

## ARTICLE INFO

**Keywords:**

Proximity

Local food

Alternative food networks

Agricultural model

## ABSTRACT

Alternative Food Networks (AFN) are food commercial strategies based on geographical proximity. In Europe, these alternative strategies are emerging in a context of increasing productivity and the globalization of food markets. This article characterizes the forms of agricultural spaces involved in AFN within the PACA (Provence-Alpes-Côte d'Azur) region of France, and assesses the potential for agricultural spaces to engage in such alternative food chains. Based on interviews and semi-automatic data collection, the article develops a machine learning classification model to assess the likelihood of AFN engagement for agricultural spaces. The measurement of LISA (Local Indicators of Spatial Association) coefficients, based on the model's output, identifies differences between spatial clusters of conventional and alternative agricultural spaces. Alternative agricultural spaces are found to be more isolated than conventional compact regions. The model's analysis highlights the exclusion of alternative agricultural spaces from large production regions and their geographical proximity to urban areas. The results also underline smaller sizes, lower specialization, and more complex morphological characteristics for these alternative agricultural spaces, making them less suited to intensive forms of production. In light of strong public policy interest in food system relocalization, this article highlights the relationship between agricultural spaces characteristics and their engagement in AFN, and emphasizes the importance of considering the agricultural model when designing strategies to support and preserve 'local' agriculture.

## 1. Introduction

The contemporary European agri-food system relies on intensive production and spatial specialization (Gasselin, et al., 2021; Poulot, 2014), commonly referred to as ‘conventional’ or ‘industrial’ agriculture in academic literature (Galliano, et al., 2017). Such agri-food system shapes agricultural activities (Preux, et al., 2015). In France, approximately 40 % of the country’s agricultural land is occupied by ‘large’ farms, generating over 250,000 euros in Standard Gross Production (Chambres d’agriculture, 2022). These farms are characterized by their large size, substantial capital, and crop specialization (Poulot, et al., 2024). The industrial-scale agricultural production is conducive to specific commercial strategies, defined as the food chain choices made by farmers to sell their food products. With large production volumes, industrial agriculture tends to engage in mass distribution strategies (Callois, 2020). Referred to as ‘conventional’ food chains (Gori and Castellini, 2023; Malak-Rawlikowska, et al., 2019), such commercial

strategies are characterized by long chains involving multiple intermediaries, and resulting in increased transport distances and times (Song, et al., 2021).

On the other hand, commercial alternatives based on geographical ‘proximity’ are emerging (Renting, et al., 2003). Alternative Food Networks (AFN)<fnref n="1" /> offer an alternative to conventional distribution methods (ibid.), questioning rationales of productivity, standardization and industrial organization (Mastronardi, et al., 2019). Indeed, AFN address a growing societal demand for transparency in food sourcing, particularly in response to mistrust regarding the use of agricultural inputs (Lagane, 2011). In this sense, many AFN are grounded in direct interactions between farmers and consumers (Praly, et al., 2014), and carry an objective of re-spatialization (Renting, et al., 2003; Marsden, et al., 2000), implying a geographical proximity between production and consumption spaces (Benedetti and Araldi, 2024; Paül and McKenzie, 2013). This paper examines agricultural spaces adopting such alternative commercial strategies, to identify their spatial characteristics. Driven by a

<footnote>* Correspondence to: Campus Carlone, 98 bd Édouard Herriot, BP 320, Nice 06204, France.</footnote>

<footnote>E-mail addresses: juliette.benedetti@univ-cotedazur.fr (J. Benedetti), stephane.bouissou@univ-cotedazur.fr (S. Bouissou), karine.emsellem@univ-cotedazur.fr (K. Emsellem).</footnote>

<footnote n="1">¹ All acronyms are defined in Appendix 4.</footnote>
thematic research focus, this approach employs quantitative modeling and spatial analysis methods, to investigate the likelihood of AFN engagement for agricultural spaces within the French region Provence-Alpes-Côte d'Azur (PACA) case study.

## 2. Literature review

### 2.1 Contemporary agri-food models and the associated agricultural forms

Emerging in the 20th century, the European agricultural model is characterized by mechanization, the use of synthetic fertilizers, non-renewable resources, and phytosanitary products (Gasselin, et al., 2021; Temple, et al., 2018). The agri-food sector thus relies on productivity goals, driven by intensive practices (Temple, et al., 2018) to reduce unit production costs (Veysset, et al., 2017). Referred to as 'industrial' agriculture in the literature (Galliano, et al., 2017), this model remains relevant today: the 2023–2027 Common Agricultural Policy (CAP) pursues objectives of 'productivity gains' and 'competitiveness' for the agricultural sector (European Commission, 2023). Indeed, agricultural practices intensification enabled farmers to expand into international markets (Poulot, 2015) through mass distribution strategies. In 2020, the European Union was the world's third-largest importer and leading exporter of food (European Commission, 2021). Such international trade relies on 'long' food chains, involving multiple intermediaries. Referred to as 'conventional', these food chains increase distances between production and consumption locations (Song, et al., 2021). Driven by both agricultural productivism and international trade over the 20th century, the food supply model has shaped contemporary agriculture and the spatial organization of agricultural activities.

Literature highlights the influence of production massification and global food trade on agricultural activities. The evolving food system over time has influenced urban-rural relations. As early as the 19th century, Von Thünen (1826) developed an economic model describing the concentric organization of agricultural activities around a central market to optimize costs. Historical evidence supports this model, as seen in 19th-century Parisian food supply patterns, which exhibited a concentration of productive spaces within a restricted perimeter (Billen, et al., 2012). In the second half of the twentieth century, urban-rural relations underwent significant 'rearrangements' (Poulot, 2014). First, literature highlights the obsolescence of the spatial constraints described in Von Thünen's model in industrialized countries, driven by reduced transport costs and expanded international trade (Sinclair, 1967). The 'urban metabolism' concept illustrates that consumption spaces now depend on 'dispersed' supply areas (Barles, 2018). Additionally, the literature underscores the evolution of agricultural 'patterns' in urban peripheries (Sinclair, 1967). Peri-urban agriculture is indeed often regarded as a land reserve (Poulot, 2014), prioritizing the immediate value of the land sale over the long-term gain of its agricultural use (Jaillet and Jababert, 1982). Furthermore, agricultural areas are 'specializing' into productive regions in rural spaces (Ministère de l'agriculture, 2024). Such specialization dynamics lead to a spatial concentration of farms whose production is mostly commercialized through international food chains (Levers and Müller, 2019; Wästfelt and Zhang, 2016), referred to as a production 'basin' (Poulot, 2014; Rieutort, 2009).

The contemporary agriculture and its associated shapes are widely discussed in the literature. Scholars highlight the increasing farm sizes (Poulot, et al., 2024; Mohr, et al., 2023; Dallimer, et al., 2009), benefitting from higher direct CAP aid (Pe'er and Lakner, 2020), and facilitating economies of scale (Heinrichs, et al., 2021). Beyond their size, morphological factors influence farm productivity (Puech and Mignolet, 2022; Looga, et al., 2018; Latruffe and Piet, 2013; Demetriou, et al., 2013), and economic competitiveness. Farm fragmentation refers to the spatial dispersion of plots belonging to the same farm (Van Dijk, 2003; King and Burton, 1982). According to literature, such dispersion

leads to higher production costs (Janus, et al., 2023; Hartvigsen, 2014; Latruffe and Piet, 2013; Niroula and Thapa, 2005), constrains mechanized and industrialized processes (Demetriou, et al., 2013; Niroula and Thapa, 2005), and limits crop specialization (Lusho and Papa, 1998). Moreover, the literature indicates that plot morphology affects productivity. Irregularly shaped plots complicate tillage or fertilizer application, increase labor time per unit area (Puech and Mignolet, 2022; Ntihinyurwa and de Vries, 2020), and restrict row cropping (Demetriou, et al., 2013). In this perspective, morphological indexes are developed to measure the impact of plot shape on farm productivity (Puech and Mignolet, 2022; Looga, et al., 2018; Latruffe and Piet, 2013; Demetriou, et al., 2013).

This literature review underscores how spatial characteristics of agricultural spaces, such as farm size, plot morphology, and specialization, have evolved alongside food supply strategies throughout the 20th century, and influence farm productivity and economic performance. However, some studies suggest that unfavorable characteristics may, in fact, drive innovation by encouraging new economic strategies (Ntshangase, et al., 2018; Latruffe and Piet, 2013). For instance, farms may develop new economic activities such as agrotourism (Andrea, et al., 2013; Rieutort, 2009), or engage in alternative commercial strategies (Malak-Rawlikowska, et al., 2019). The following section focuses on the emergence of alternative food chains.

### 2.2. Emerging alternative food networks and food systems re-spatialization

Alternative food chains based on 'proximity' have emerged since the second half of the 20th century (Renting, et al., 2003), such as Japanese teikei (Parker, 2005), *Community Supported Agriculture* (CSA) (Morgan, et al., 2018), or the French *Associations pour le Maintien de l'Agriculture Paysanne* (AMAP) in 2001 (Guiraud, 2019). The concept of 'proximity' encompasses geographical, relational, or functional proximities between locations and stakeholders involved in food production and consumption (Benedetti, et al., 2023), and seeks a limited number of intermediaries along the supply chain (Praly, et al., 2014). The term 'alternative food networks' (AFN) encompasses these various initiatives. In opposition to the mass distribution strategies of the contemporary food system, characterized by excessive productivity, standardization, and industrialized production processes (Higgins, et al., 2008), AFN address consumer demand for greater transparency regarding product origin and quality (Mastronardi, et al., 2019). Additionally, they offer economic benefits to farmers (Malak-Rawlikowska, et al., 2019; Galt, et al., 2011). First, consumers favor food products providing clear information on production methods, environmental sustainability, and geographical origin (Galt, et al., 2011). For instance, the AMAP Charter in France requires products to be organic (Charte des AMAP, 2014). Second, a limited number of intermediaries enables fairer remunerations for farmers (Malak-Rawlikowska, et al., 2019; Praly, et al., 2014).

Beyond the socio-economic proximities facilitated by AFN, the scientific literature places particular emphasis on geographical proximity, aiming at 're-spatializing' food systems (Renting, et al., 2003; Marsden, et al., 2000). While conventional food chains generally rely on large spatial distances from production to consumption spaces, direct sales reintroduce the significance of transport costs, which are borne by both farmers and consumers (Wästfelt and Zhang, 2016). Moreover, the literature highlights that the spatial context of agricultural activities plays an important role in shaping commercial strategies of agricultural spaces. Studies indicate that agriculture engaged in AFN is often located in peri-urban areas (Brinkley, 2018; United States Department of Agriculture, 2012), which explains the prevalence of metropolitan case studies in AFN research (Guiraud, 2019; Zasada, et al., 2017; Corsi, et al., 2015; Darrot, 2014). In this regard, the degree of urbanization surrounding agricultural spaces emerges as a key indicator of their potential integration into alternative food chains.

Finally, the literature insists on the relationship between plot
structure and AFN engagement. Research interested in the impact of plot morphology on productivity (Puech and Mignolet, 2022), suggest that morphologies poorly suited to the industrial production model and conventional food chains, may instead favor new economic strategies (Ntshangase, et al., 2018; Latruffe and Piet, 2013). Few authors have yet explored this research topic, identifying specific morphological features for farms engaged in AFN. First, AFN have been widely recognized as essential outlets for small and medium-sized farms (Prosser and Lane, 2021; Yacamán Ochoa, et al., 2019; Cicia, et al., 2011; Galt, et al., 2011). In particular, their limited production volumes make them less suited to the mass commercialization typical of conventional supply chains (Malak-Rawlikowska, et al., 2019). Moreover, AFN bring higher revenues to smaller farms, as the margins are redistributed among a lower number of intermediaries (Praly, et al., 2014). Second, the literature suggests specific plot morphologies for farms engaged in AFN. Indeed, while simpler, more elongated, and less fragmented morphologies would be factors for increasing productivity (Puech and Mignolet, 2022; Looga, et al., 2018; Corbelle-Rico, et al., 2012) and thus more suited to conventional chains, more complex parcel shapes are likely to encourage AFN engagement. For instance, complex plots shapes (Mouléry, et al., 2022) or elevated mean shape indexes (MSI) (Benedetti and Araldi, 2024) have been proven conducive to AFN engagement.

## 2.3. Research hypothesis and case study

This first section highlights the spatial characteristics of agricultural spaces in the context of changes within the food supply model. Specifically, the productivity dynamics and the internationalization of trade observed over the last century have significantly transformed agricultural spaces in terms of farm size, morphology, and specialization. However, emerging commercial strategies such as alternative food networks, moderate these spatial logics. Agricultural spaces that are poorly adapted to the globalized and productivity-driven model may find economic opportunities through AFN, allowing them to remain competitive. This paper explores the idea that certain spatial characteristics of agricultural spaces could influence their commercial strategies. Using the specific case study of the PACA region (France), this work examines whether it is possible to predict the likelihood of AFN engagement for various agricultural spaces based on their land-use contexts, sizes, or plot morphologies.

Over the past decade, public policies supporting local agriculture have been developed in France (Lulovicova and Bouissou, 2023). The PACA (Provence-Alpes-Côte d'Azur) region in southeastern France presents a suitable case study for AFN. This region pioneered the AFN development in France, with the first AMAP established in the early 2000s (Guiraud, 2019). By 2020, 42 % of farms in the PACA region commercialize through short food chains, in particular for honey (73 %) and vegetable (60 %) productions (DRAAF PACA, 2024). In total, 549, 600 ha of agricultural land are identified within the study area in the OCSOL land use database (2019), accounting for approximately 17 % of the region's surface area.

This case study encompasses a wide diversity of agricultural landscapes in terms of farm sizes, crop types, and engagement with alternative food chains. The region's geographical diversity ranges from alpine to Mediterranean landscapes: the northern part is characterized by mountains and the southern parts features large alluvial plains and coastal areas (DREAL PACA, 2022). The case study region diverse profiles also result from a differentiated urbanization, the coast often being highly artificialized compared to the natural alpine areas. This landscape diversity impacts the spatial distribution of crop types: livestock farming is common in the Alps, while vegetables, fruit crops, and viticulture dominate the coastal and western areas. Average farm sizes vary across the region's six subregions (Chambre d'agriculture PACA, 2024). Furthermore, involvement in short food chains is geographically uneven, with the highest participation occurring in densely populated peri-urban areas (DRAAF PACA, 2024). Given this spatial diversity, this

article examines the hypothesis that certain agricultural spaces are more likely to engage in AFN. Specifically, it explores the notion that AFN strategies are associated with particular agricultural forms, characterized by particular economic, landscape and morphological contexts. The following section presents the main approach of this paper, the data collection and the methods employed.

# 3. Data collection and methods

## 3.1. Approach overview and databases

This article explores the capacity of modeling methods to predict the commercial strategies of agricultural spaces, based on their spatial characteristics. This modeling approach first relies on a data collection step (Fig. 1a). Several classification models are trained to predict the commercial strategies of agricultural spaces (b). In the subsequent step, the developed model is analyzed to identify the influence of independent variables - i.e., the characteristics of agricultural spaces - on their engagement in AFN (c). Finally, the model is applied to all agricultural spaces within the case study region (d), and spatial analysis techniques are employed to investigate the spatial structures emerging from the model's output (e). Fig. 1 details the methodological steps developed in this paper.

The first step involves building a database that associates the commercial strategies of agricultural spaces (dependent variable) with their spatial characteristics (independent variables) (Fig. 1a). The independent variables are derived from a literature review (sections 1.1 and 1.2) and depend on the existing spatialized datasets. Three datasets were identified to describe agricultural contexts. The OCSOL land-use database (2019), produced by CRIGE PACA, characterizes land use. This first database enables the construction of independent variables related to agricultural types, contextual land covers, and crop type diversity (see Appendix 1). The level 2 Registre Parcellaire Graphique (RPG) database (2020) is produced by DRIAFF. This second database is used to build all organizational variables related to CAP funding, farm size, fragmentation, and road accessibility. Finally, the Plan Cadastral Informatisé (2019), is a cadastral dataset produced by the Direction Générale des Finances Publiques (DGFiP), and characterizes plot morphology. This third database provides information to build all morphological variables specific to the agricultural plots, including convexity, elongation, complexity and shape indices, and parcel size (Appendix 1).

The dependent variable describes the commercial strategies of agricultural spaces. There is currently no spatial database available that identifies the commercial strategies of agricultural spaces. In France, the RGA census is the only source identifying farms that sell at least part of their production through short food chains. Although exhaustive, this census provides low-quality spatialization: only farm headquarters are located, with a maximum resolution of 1 km² as of 2020. Therefore, this approach relies on collecting spatialized data labelling the AFN engagement of agricultural spaces. Acquiring the dependent variable requires data collection through a combination of surveys (Benedetti, et al., 2023) and semi-automatic methods. This data collection gathers spatial point data, whose commercial strategies are labeled. However, most independent variables relate to the spatial contexts of these points. For example, the proportion of urban land use can only be measured by analyzing the surrounding area. Therefore, the proposed method relies on contextual analysis, as applied in urban studies (Araldi and Fusco, 2019; Serra, et al., 2018). In this paper, a grid is deployed across the case study region to characterize agricultural spaces in their context, with each observation corresponding to a mesh rather than a farm. To address the Modifiable Areal Unit Problem (Openshaw, 1983) related to data collection scales, we test five grids of different dimensions to identify the mesh size that optimizes model performance.

This initial data collection stage is used to train several supervised classification models (Fig. 1b) using labeled data. The aim is to explore the ability of these models to discriminate between different commercial
<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Approach overview.</figcaption></figure>

strategies based on the characteristics of agricultural spaces. Four machine learning models are trained on an initial subset of the collected database (70 % of observations), and their performance is assessed using a second subset (30 % of observations). The third step (Fig. 1c) focuses on identifying the discriminating variables of the classification model. An agnostic tool (Viana et al., 2021; Lundberg and Lee, 2017) is employed to evaluate the contribution of each independent variable to the model's output and to identify the most influential variables.

Subsequently, the model is applied to all agricultural spaces in the PACA region, including unlabeled areas (Fig. 1d), in order to spatially identify regions with potential for AFN engagement. Finally, spatial analyses of the model output are conducted to explore the spatial organization of areas with varying likelihoods of AFN engagement within the case study region. Spatial autocorrelation of the model output is assessed through both global Moran's I (Moran, 1950) and Local Indicators of Spatial Association (LISA) coefficients (Anselin, 1995). Finally, LISA clusters are used as input for a spatial neighborhood analysis to estimate the dimensions of each type of agricultural basin (Fig. 1e). All five steps of this protocol are outlined in the following sections, beginning with data collection.

## 3.2. Data collection

The first step in data collection is two-fold: (1) identification and

construction of the model's independent variables, and (2) collection of the dependent variable. Initially, the discriminating features of agricultural spaces used as independent variables for the model were identified from the literature (sections 1.1 and 1.2), as well as from available spatialized datasets (Section 2.1). Appendix 1 provides a summary of this literature review, listing the 59 potential independent variables identified and their corresponding spatialized datasets.

While most indicators can be measured using simple spatial statistics, some take the form of composite indexes, with their respective formulas detailed in Appendix 2. Schmook and Januszewski indexes describe farm fragmentation (Demetriou, et al., 2013) and are calculated using the level 2 RPG database (Appendix 2). Diversity indicators such as the Simpson (Simpson, 1949) and Shannon-Weaver indexes (Ortiz-Burgos, 2016) characterize crop diversity within each mesh. The literature also references various morphological indexes, such as the Gravelius index (Mouléry, et al., 2022) and the Mean Shape Index (MSI), which measure perimeter-area ratios for plots. These morphological indexes are complemented by indicators of circularity, convexity, or elongation (Benedetti and Araldi, 2024), which are also described in Appendix 2. Finally, the literature develops indicators of plot shape and complexity, such as the Nseg, N50, and P2 indexes (Puech and Mignolet, 2022). These are defined as follows: the Nseg index represents the number of segments shaping the plot, the N50 index refers to the minimum number of segments required to reach half of the total plot
perimeter, and the P2 index measures the proportion of the total plot perimeter represented by the two largest segments. Independent variables were built using 2019 datasets, the most recent year available for land use data. All 59 independent variables were measured across five mesh sizes: 300, 450, 600, 750, and 900 m high. Hexagonal meshes were selected for their better spatial resolution with the same number of meshes and the isotropy of neighborhoods (De Sousa, et al., 2006).

As a categorical variable, the dependent variable classifies agricultural spaces as either engaging in proximity-based AFN (Alternative class) or as random agricultural spaces (Random<fnref n="2" />). The Alternative population was identified through both surveys and semi-automatic data collection. Surveys, conducted as part of previous research between March and June 2022 (Benedetti, et al., 2023), targeted a variety of outlets involved in AFN sales (e.g., farm sales, markets, AMAP, grocery stores), identifying 215 AFN farms within the PACA region. Semi-automatic data collection, carried out in January 2024, was based on two websites listing AFN farms in the region (see Appendix 3). In total, 853 agricultural spaces were labeled as 'engaged in AFN commercialization strategy', constituting the Alternative population. The Random population was built through random selection of an equivalent number of agricultural spaces from the OCSOL database. The final dataset thus consists of approximately 1700 observations from across the region, all collected as point entities. These observations were then spatially linked to the five grids deployed across the case study area.

### 3.3. Method

This work explores the capacity to predict the commercial strategies of agricultural spaces based on their spatial characteristics. Therefore, this paper employs supervised classification machine learning model to assign input data into classes (Ali, et al., 2019). Machine learning methods have gained considerable attention in recent years for their applications in agricultural and land use research (Wang, et al., 2022). For example, such modeling tools are used to characterize land use (Levers, et al., 2015), detect and understand dynamics and their determinants (Viana et al., 2021), evaluate the performance of different scenarios (Stetter, et al., 2024), and identify areas at risk of agricultural abandonment (Meijninger et al., 2022). In this paper, a supervised classification model is developed to predict a categorical dependent variable describing the commercial strategy of agricultural spaces. Several machine learning models are trained and compared (step b).

- The k-Nearest Neighbors (KNN) (Cover and Hart, 1967) is an algorithm that assigns an output based on the value of the k nearest neighbors in a multidimensional space. The predicted class is determined by measuring the distances between the observation and the training set, typically using Euclidian distances (Ali, et al., 2019);

- The Random Forest (RF) (Breiman, 2001) is an algorithm that assigns an output based on a set of simultaneously trained decision trees. Random Forests models' output depends on the aggregation of several decision trees results to input data (Maças, et al., 2024);

- Gradient Boosting models generate predictions through an ensemble of successively trained decision trees, each one correcting the errors of its predecessor. Two such models were trained and tested: Gradient Boosting Classifier (GBC) (Friedman, 2002) and Light Gradient Boosting (LGB) (Ke, et al., 2017).

As the independent variables were calculated from five grids, a total of twenty model/mesh combinations are trained, tested, and compared. The Shapley Additive exPlanations (SHAP) agnostic approach

(Lundberg and Lee, 2017) is used to evaluate the importance of each independent variable to the model's output and to analyze the direction of their relationships (step c). The trained model is then applied to all unlabeled meshes within the region to predict their probability to belong to the Alternative population (step d). In the absence of existing data on regional commercial strategies, applying the model to the entire case study region makes it possible to spatially identify areas with potential for engagement in AFN.

The final step involves analyzing the spatial structures of the model output (step e). The global Moran's I coefficient (Moran, 1950) is measured to determine whether the potential AFN engagement of agricultural spaces exhibits spatial autocorrelation within the case study region. Such method allows identifying whether values of model's output among nearby locations tend to be similar or not. Additionally, Local Indicators of Spatial Association (LISA) (Anselin, 1995) are used to visualize spatial clusters of similar potentials for AFN engagement, identifying subspaces with stronger and weaker spatial autocorrelation. The LISA clusters discriminate between coherent agricultural spaces with similar values of model output (either High-High or Low-Low clusters), and spaces with contrasted values of model output (either High-Low or Low-High clusters). From an operational perspective, such spatial analysis methods allow identifying spaces of high potential for AFN engagement.

Finally, a spatial neighborhood analysis is conducted to assess the spatial extent of each cluster and quantify the size of agricultural basins. For each cluster, neighborhood areas are defined using buffers ranging from 0-6 kilometers, measuring the respective proportions of each cluster in the vicinity. This last step allows quantifying the dimensions of agricultural spaces with high potential for AFN engagement, and determines whether these spaces are organized into agricultural 'basins,' similar to conventional agriculture.

## 4. Results

### 4.1. Characteristics of agricultural spaces engaging in AFN

First, twenty model type/mesh combinations have been trained. Table 1 compares their statistical performance at predicting the output of the test ensemble, on both accuracy and sensitivity performance metrics. Accuracy measures the model's ability to correctly classify all commercial strategies. Sensitivity, on the other hand, specifically measures the model's performance on the target class only (Alternative commercial strategy). The best accuracy was achieved with the Random Forest model trained on a 300 m mesh size, with 85 % of correctly classified observations. The highest sensitivity was achieved by the Gradient Boosting Classifier (GBC) trained on a 600-meter mesh size. The 600 m GBC model correctly identified 76 % of Alternative observations in the test set, compared to 73 % for the 300 m Random Forest model. Since this study focuses on the Alternative class, the 600 GBC

<caption>**Table 1**</caption>

Accuracy and sensitivity of all model/grid combinations. In red, the best performances indicating the selected combination. Note: sensitivity is preferred over specificity as it describes the proportion of true Alternative over real Alternative.

<table><thead><tr><th>Mesh width</th><th>Performance indicator</th><th>KNN</th><th>RF</th><th>GBC</th><th>LGB</th></tr></thead><tbody><tr><td rowspan="2">300</td><td>Accuracy</td><td>0.72</td><td><strong>0.85</strong></td><td>0.82</td><td>0.81</td></tr><tr><td>Sensitivity</td><td>0.53</td><td>0.73</td><td>0.74</td><td>0.70</td></tr><tr><td rowspan="2">450</td><td>Accuracy</td><td>0.71</td><td>0.83</td><td>0.78</td><td>0.81</td></tr><tr><td>Sensitivity</td><td>0.53</td><td>0.72</td><td>0.68</td><td>0.70</td></tr><tr><td rowspan="2">600</td><td>Accuracy</td><td>0.70</td><td>0.81</td><td>0.81</td><td>0.78</td></tr><tr><td>Sensitivity</td><td>0.55</td><td>0.68</td><td><strong>0.76</strong></td><td>0.69</td></tr><tr><td rowspan="2">750</td><td>Accuracy</td><td>0.71</td><td>0.80</td><td>0.78</td><td>0.79</td></tr><tr><td>Sensitivity</td><td>0.54</td><td>0.61</td><td>0.64</td><td>0.68</td></tr><tr><td rowspan="2">900</td><td>Accuracy</td><td>0.69</td><td>0.80</td><td>0.80</td><td>0.76</td></tr><tr><td>Sensitivity</td><td>0.51</td><td>0.63</td><td>0.67</td><td>0.65</td></tr></tbody></table>

<footnote n="2">² No public dataset gathers spatially explicit data on farms supplying long conventional chains, that would have been used to collect a proper *Conventional* class. In this context a *Random* class was created instead, grouping agricultural spaces gathering all commercial strategies.</footnote>
model was chosen for its higher sensitivity (Table 1).

The impact of independent variables on model output was analyzed using the SHAP model-agnostic tool (Fig. 2). Fig. 2 illustrates each variable's contribution to the model output (horizontal axis) and ranks them by importance (vertical axis). Independent variables characterizing land-use contexts of agricultural spaces - features identified by '(O)' in Fig. 2 - appear to play a key role in predicting commercial strategy. Specifically, SHAP values indicate that *Alternative* observations are less likely to be located within large agricultural contexts (low feature values associated with positive SHAP values). Conversely, artificialized areas are more prevalent within *Alternative* agricultural spaces (high feature values associated with positive SHAP values), though this pattern is not consistently observed (see Discussion section). Furthermore, *Alternative* spaces exhibit greater crop diversity, as reflected by lower Simpson index values associated with positive SHAP values. In terms of production types, *Alternative* observations are associated with a higher proportion of greenhouses, while vineyards are underrepresented. This suggests that viticultural productions are less likely to be commercialized through proximity-AFN.

The independent variables derived from the cadastral database allow for the distinction between the two classes (*Alternative* and *Random*) in terms of plot morphology (features identified by '(C)' in Fig. 2). The *Alternative* class (positive SHAP values) is associated with a large number of cadastral plots within the mesh, resulting in higher total cadastral perimeter values (sum of the perimeters of all parcels within the mesh) and lower plot area values (median) as well as lower plot perimeter values (mean). Agricultural spaces with smaller plot sizes are therefore more likely to engage in alternative chains. Similarly, *Alternative* observations tend to be less elongated and more complex, as indicated by the higher 'Mean N50 index' value. The level 2 RPG database (features identified by '(R)' in Fig. 2) provides economic information, such as farm size, fragmentation or CAP fundings) on the classes. SHAP values reveal that the total areas of the farms tend to be lower for *Alternative* observations (positive SHAP values), suggesting that these farms are smaller and therefore receive fewer subsidies from CAP direct payments. Furthermore, *Alternative* observations are characterized by smaller average distances between plots from the same farm, indicating a stronger spatial grouping of plots belonging to each farm.

The impact of other independent variables on the model output are more challenging to assess. In particular, the 'Mean P2 index value' variable does not explicitly differentiate between the two classes: for instance, high feature values can be found associated to both positive and negative SHAP values. Similarly, although larger artificialized areas within the mesh consistently push the model output towards the *Alternative* class, certain positive SHAP values are linked to lower shares of artificialized land. These observations highlight the ambiguous impact of these variables on the model output and suggest that multiple phenomena may be at play. Empirical analysis of *Alternative* agricultural spaces with varying profiles across these variables - for instance lower shares of artificialized land - could provide further insight into these issues.

## 4.2. Assessing coherent spatial behaviors of commercial strategies within PACA region agricultural spaces

In the second step, the selected model was applied to non-labeled agricultural spaces within the region. Fig 3 presents the model output, which indicates the probability of each observation being predicted as *Alternative*, in other words its likelihood of engaging in AFN commercialization. In the absence of existing data on regional commercial strategies, this visualization enables the spatial identification of areas with potential for engagement in AFN. Areas represented in red indicate a high potential for AFN engagement, while those in blue highlight regions with low potential, as suggested by the model outputs.

Fig. 3 suggests a spatial structure in the model’s output, revealing a regionalization of agricultural spaces’ potential to commercialize through AFN. To further explore this regionalization, the overall spatial autocorrelation of the model’s output was assessed using Moran’s I. The results indicate a moderate positive correlation (+0.46) with a significant p-value (0.001), allowing us to reject the null hypothesis of no spatial autocorrelation. This result stresses that potentials for AFN engagement within the case study region are spatially clustered. To identify and visualize such clusters, local spatial autocorrelation is measured using LISA coefficients. This method allows an identification and mapping of clusters describing various spatial behaviors in terms of AFN engagement potentials. Fig. 4 maps the LISA clusters identified

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** SHAP values of main classification model features. Lower feature values are indicated in blue, while higher values are in red. Horizontal axis indicates the SHAP value (impact on model output), a higher SHAP value impacting the model output towards *Alternative* class. The databases used are indicated as follows: (O) for OCSOL (land use contexts); (C) for cadastral database (plot morphology); and (R) for RPG (farm fundings, fragmentation and size).</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig 3. Model applied to all agricultural meshes within PACA region.</figcaption></figure>

with a significant p-value at a 0.05 risk threshold. The results reveal clustered areas displaying similar potentials for AFN engagement, with a clear regionalization of conventional and alternative agricultural spaces.

The four LISA clusters identify areas with strong (positive or negative) spatial autocorrelation, while other areas show no significant spatial autocorrelation. Two clusters feature negative spatial autocorrelation, in which the values of observations are negatively correlated with those of their surroundings. The *Resistant Proximity Agriculture* (RPA) cluster encompasses observations with a high probability of AFN engagement, surrounded by conventional agricultural spaces (High-Low). The *Isolated Conventional Agriculture* (ICA) cluster consists of conventional agricultural spaces located within AFN-conducive environments (Low-High). These negative spatial autocorrelation clusters are underrepresented (8 % and 5 % of significant observations, respectively), making them barely visible in Fig. 4.

The other two LISA clusters display positive spatial autocorrelation and correspond to agricultural regions with similar commercial strategies potentials. On the one hand, alternative agricultural regions, referred to as *Proximity Agriculture Regions* (PAR), exhibit a high probability of AFN engagement (High-High, represented in dark green in Fig. 4). On the other hand, *Conventional Agriculture Regions* (CAR) show a low probability of AFN engagement (Low-Low, represented in blue in Fig. 4). These positive spatially autocorrelated clusters are prevalent,

accounting for 34 % and 53 % of observations respectively (Fig. 4). Specifically, conventional regions are clearly identified, such as the Valensole plateau in Alpes-de-Haute-Provence and Sault plateau in Vaucluse, the wine-growing areas of central Var and the northern edge of Vaucluse (Côtes du Rhône), and the rice production and animal breeding activities of Camargue in Bouches-du-Rhône. In contrast, alternative agricultural regions are predominantly found along the coast near the cities of Marseille, Toulon, and Nice, in particular within urban-adjacent alluvial plains (respectively Huveaune, Gapeau and Var rivers), around the urban area of Avignon (Vaucluse), and in the Alpine subregion of Hautes-Alpes (for instance near the Durance alluvial plain). Moreover, from an operational perspective, these results can guide institutional decisions regarding the preservation and (re)development of proximity agriculture, by identifying whether agricultural spaces are conducive (proximity clusters) or not (conventional basins) to AFN development.

To further explore the dimensions of these production basins, a final section focuses on characterizing the size of each cluster. For each cluster, Fig. 5 presents the proportions of all clusters in the 0–6 km vicinity. For the RPA and ICA clusters (Figs. 5c and 5d), the proportion of meshes assigned to the same cluster decreases beyond the first kilometer. The same cluster is represented in the 1st degree neighborhood in around 15 % of cases, and this applies in only 3 % of cases for the 2nd
<figure><img src="image_7.png" /><figcaption>Fig. 4. Significant LISA clusters (Low-Low, Low-High, High-High, High-Low) for alternative prediction, within the six sub-regions of the case study: Alpes-de-Haute-Provence, Hautes-Alpes, Alpes-Maritimes, Bouches-du-Rhône, Var, and Vaucluse.</figcaption></figure>

degree. This finding is consistent with the geographically isolated nature of these resistance areas: at a 1st degree neighborhood, 70–75 % of meshes are non-agricultural land uses for ICA and RPA, respectively. When the 1st degree is agricultural, 10–15 % of meshes belong to the opposite cluster (CAR for RPA and PAR for ICA).

The proportion of non-agricultural land use in the neighborhoods of positive spatial autocorrelation clusters (CAR and PAR) is considerably lower compared to the negative spatial autocorrelated clusters. However, CAR and PAR exhibit different patterns. First, the proportion of non-agricultural meshes around PAR clusters exceeds 70 % in the 2nd degree neighborhood (1.2 km), whereas this percentage is only reached at the 6th degree (3.6 km) for CAR clusters. Proximity regions (PAR) are indeed frequently surrounded by non-agricultural neighborhoods, particularly urban or natural areas. Second, these clusters differ in terms of compactness (neighborhood characterized by meshes from the same cluster). For conventional CAR regions, the neighborhood remains coherent in around 40 % of cases at 3rd degree neighborhood (over 1.8 km), compared to only 20 % of cases for PAR proximity agricultural regions. At 6 km, the neighborhood remains coherent in about 25 % of cases for CAR, versus 12 % for PAR. These observations underscore the much larger dimensions of conventional agricultural regions (CAR),

which are more compact and less fragmented than alternative regions (PAR), supporting the concept of production 'basins' supplying conventional food chains (Fig. 5).

## 5. Discussion

### 5.1. Limitations and improvement perspectives

Several hypotheses guide the thematic and methodological choices made in this paper, leading to several limitations and improvement perspectives. From a thematic perspective, this work explores the potential to predict the commercial strategy of agricultural spaces based on their spatial characteristics. The goal of discriminating between either conventional or alternative commercial strategies requires a binary classification. However, the commercial strategies of a single farm are often hybrid (Malak-Rawlikowska, et al., 2019). Thus, rather than predicting the commercial strategy of an agricultural space, this study develops a method for characterizing the adaptation of agricultural spaces to AFN engagement.

Furthermore, this approach assumes that the spatial characteristics of agricultural spaces influence their commercial strategies. This strictly
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Neighborhoods of the four significant LISA clusters: (a) Conventional Agriculture Region (CAR), (b) Proximity Agriculture Region (PAR), (c) Resistant Proximity Agriculture (RPA), (d) Isolated Conventional Agriculture (ICA).</figcaption></figure>

spatial approach is motivated by the lack of available public socio-economic data, which also justifies the use of spatial meshes. Therefore, the approach developed in this paper excludes other potential factors, such as local governance initiatives or the socio-economic characteristics of the farm, which have been shown in literature to be important drivers of AFN engagement. In particular, AFN are favored by younger, highly educated farmers (Cicia, et al., 2011), with specific land tenure arrangements, such as leasing under agreements (Galt, et al., 2011). Literature also highlighted various farm labors within structures engaged in AFN (Nost, 2014), such as farmer-only, wage labor (Janssen, 2010), or sharer volunteer labor (Cone and Myhre, 2000). In terms of practices, farms engaged in AFN often promote sustainable farming practices (Yacamán Ochoa, et al., 2019), such as agroecological cultivation, organic certification and environmental conservation endeavors (Galt, et al., 2011). This broad socio-economic literature on AFN highlights that even within highly AFN-conducive agricultural spaces, the presence of unfavorable socioeconomic characteristics may nevertheless limit the potential for AFN development. Moreover, cooperation between civil organizations and public authorities through the development of urban food policies is crucial for strengthening local food chains (Duvernoy, 2018), highlighting the critical role of both governmental and non-governmental actors in fostering the development of AFN (Poças Ribeiro, et al., 2021). While the model results suggest that spatial characteristics have a significant impact on commercial strategies, the approach could be further improved by incorporating governance and socio-economic data.

From a methodological perspective, the choice to employ a machine learning model in order to enhance its predictive performance comes

with limitations. The interpretation of machine learning models is more limited than that of traditional modeling approaches, as frequently highlighted in the literature (Guidotti, et al., 2018; Lipton, 2016). Learning from data derived from the PACA region case study constrains the generalization of the model interpretation, both in terms of the relationships among independent variables and their respective influence on the dependent variable. In this perspective, using agnostic tools to characterize the *Alternative* class in the developed model (step (c)) only allows for an understanding of the phenomena at stake within the scope of our case study. In future research endeavors, adopting a more traditional modeling approach could improve the interpretability of the underlying phenomena, with the aim of formalizing a theoretical model that governs the interactions between independent and dependent variables. Furthermore, the interpretation of the model-agnostic approach proves more challenging for certain variables. While most independent variables differentiate between the two classes, some variables, such as 'Share of artificialized land' or 'Mean P2 index of cadastral plots', exhibit unclear class separation. This suggests that multiple phenomena may be at play. In this regard, an empirical analysis of *Alternative* agricultural spaces with distinct profiles regarding these opaque variables could provide additional insights.

Finally, another limitation stems from the choice of model. Model selection was based on maximizing sensitivity to focus on the *Alternative* class, thereby prioritizing this metric over others. To further explore such modeling approaches, future research endeavors might compare the variability of model outputs across several models, selected according to their performance on different metrics. Moreover, the decision to select the model based on maximum sensitivity depended on the
available input data. Semi-automatic data collection was employed to identify spaces engaged in AFN, but such data collection was not feasible to identify spaces supplying proper conventional chains. Consequently, the *Random* class does not exclusively contain observations from a *Conventional* class, but instead represents a mix of both categories. Assuming such data becomes available, replicating this method with a formally identified *Conventional* class would allow for a more precise refinement of the model's results, thereby enhancing its reliability.

## 5.2. Discussing the results and further research directions

The analysis of the classification model highlights several characteristics specific to agricultural spaces engaged in AFN. The results corroborate the idea of existing links between agricultural spaces' characteristics and their commercial strategies. First, agricultural spaces engaged in AFN tend to be excluded from areas dominated by agriculture and exhibit greater crop diversity. While existing literature identifies contemporary trends of agricultural specialization into production 'basins' and monocultures (Gasselin, et al., 2021; Poulot, 2014; Rieutort, 2009), the results support the notion that agriculture engaged in AFN constitutes an exception to the dominant agricultural model. Specifically, the findings show that certain crops, such as viticulture, tend to be less represented within the *Alternative* class. In line with existing literature, the wine sectors in the PACA region are indeed most often oriented toward export (Agreste PACA, 2019). Viticulture represents a emblematic agricultural sector for the region, associated with origin and quality-labeled wines of international renown, such as Côtes du Rhône AOC and Côtes de Provence (Chambres d'agriculture PACA, 2022).

Moreover, the results also indicate that *Alternative* observations are more frequently located near artificialized spaces, in line with literature on AFN in peri-urban areas (DRAAF PACA, 2024; Guiraud, 2019; Brinkley, 2018; Zasada, et al., 2017; Corsi, et al., 2015; Darrot, 2014). This is consistent with previous research works highlighting that AFN rely on regular interactions between farmers and consumers (Paül and McKenzie, 2013), with both parties bearing transportation costs (Wästfelt and Zhang, 2016). However, the results presented in Fig. 2 reveal a more complex relationship for the 'Share of artificialized land' variable. Specifically, certain high SHAP values (which influence the model towards the *Alternative* class) are associated with low proportions of artificialized land. This observation introduces nuance to the analysis: while the strong presence of artificialized spaces within a mesh consistently influences a shift toward an alternative commercial strategy, not all *Alternative* spaces exhibit high proportions of artificialized areas. Therefore, agriculture engaged in AFN is not necessarily dependent on immediate proximity to consumption areas.

Farm size emerged as another discriminator between the two populations, with alternative agriculture being associated with smaller and more numerous plots within the mesh. From an economic theoretical standpoint, such results strongly align with the existing literature. Small-scale farms are generally poorly suited to specialized (Poulot, et al., 2024) and large-scale production models (Terres, et al., 2015; Latruffe and Piet, 2013; Niroula and Thapa, 2005), and may therefore need to adopt alternative commercial strategies. The results revealed that farms located in alternative spaces had smaller total areas and, as a result, received less support from CAP aid. This observation supports the idea that small and medium-sized farms are more likely to adopt alternative commercial strategies, as noted in the literature (Prosser and Lane, 2021; Yacamán Ochoa, et al., 2019; Cicia, et al., 2011; Galt, et al., 2011). Additionally, this study highlights certain morphological characteristics of agricultural areas involved in AFN. These include less elongated shapes and more complex plot structures, which are align with existing research (Benedetti and Araldi, 2024; Mouléry, et al., 2022). These findings are consistent with prior studies indicating that irregularly shaped plots tend to have lower productivity, which limits mechanization possibilities (Puech and Mignolet, 2022) and the ability to engage in series cultivation (Demetriou, et al., 2013). Overall, this study highlights

that agricultural spaces characterized by dimensions, morphological features, and contextual conditions that are poorly aligned with a productivity agricultural model are more inclined to adopt alternative market strategies. These findings open several avenues for further research. First, certain morphological criteria (such as the P2 index) do not clearly differentiate between the two classes. In this respect, exploring agricultural areas with varied profiles for these variables could help better interpret their influence on the model. Additionally, the methodological choices made in this study limit the extent to which the model's findings can be generalized. Future research could investigate whether the drivers of AFN engagement identified in this paper are consistent across other regions or only reflect the specificities of the case study.

In the specific PACA region case study, a French pioneer in AFN development (Guiraud, 2019), the classification model applied to all agricultural spaces revealed spatial autocorrelation related to alternative commercialization potential. A spatially differentiated commitment to short circuits had already been highlighted in the 2020 general agricultural census (DRAAF PACA, 2024). The overall spatial autocorrelation observed in the model's output is primarily explained by the strong heterogeneity of agricultural spaces within the region (DREAL PACA, 2022). Urbanized coastal areas are dominated by market gardening and viticulture, while Alpine areas are primarily characterized by extensive livestock farming, with farm sizes varying across subregions. For example, the topographic variability in the Alpes-Maritimes subregion limits farm sizes and has constrained agricultural modernization throughout the 20th century, such as the implementation of irrigation infrastructure (Kayser, 1958). In contrast, in areas better suited to agricultural intensification, the segmentation of conventional agricultural spaces into 'basins' can be associated with the dynamics of agricultural specialization throughout the 20th century (Poulot, 2014). These high-production agricultural spaces, largely intended for export (Levers and Müller, 2019; Wästfelt and Zhang, 2016), arose in a context of diminishing distance to consumption markets (Sinclair, 1967). In this context, local autocorrelation coefficients (LISA) identified basins of conventional agriculture in the PACA region, including the Plateau de Valensole, wine-growing areas of central Var and northern Vaucluse, and agricultural zones of Camargue.

The final part of this study focused on characterizing the neighborhoods of LISA clusters. Analysis of the negative spatial autocorrelation clusters, Low-High (Isolated Conventional Agriculture) and High-Low (Resistant Proximity Agriculture), confirmed the geographically isolated nature of these areas. Both are distanced from other agricultural spaces of the same cluster and are surrounded by non-agricultural land uses in 70-75 % of cases. In contrast, analysis of the neighborhoods of positive spatial autocorrelation clusters, High-High (Proximity Agriculture Region, PAR) and Low-Low (Conventional Agriculture Region, CAR), revealed differences in the size of conventional and proximity basins. Specifically, the PAR cluster had a significantly larger non-agricultural neighborhood than the CAR. This non-agricultural neighborhood can be attributed to the proximity to urban areas, as PAR are often located within peri-urban fringes described as a 'contested' space (Brinkley, 2018) and frequently used as a land reserve (Jaillet and Jalabert, 1982). Additionally, agriculture engaged in AFN in the PACA region is often located within predominantly natural Alpine areas with high topographic variability. As a result, PAR regions tend to be smaller and more fragmented. The CAR cluster, in contrast, has fewer non-agricultural neighborhoods and significantly larger dimensions, with neighborhoods remaining coherent in 25 % of cases at a 6 km distance. These observations align with existing literature, which describes the integration of farms into spaces specialized for agriculture (Poulot, 2014), reinforcing the spatial separation between productive and consumption spaces (Barles, 2018). These findings open up research perspectives in exploring the morphological, topographical, and crop-specific characteristics of these basins. Such investigations could further examine their similarities, morphological characteristics and specialization levels.
Finally, numerous public policies have emerged in recent years with the aim of relocalizing agriculture (Chambre d'Agriculture, 2024) and often promoting the sustainability of food systems. Many initiatives are being developed at the local level, including Territorial Food Projects in France, commitments such as the Milan Urban Food Policy Pact (Lulovicova and Bouissou, 2023), local action projects (Local Action Groups) or planning instruments, supported by European subsidies such as LEADER (Réseau Rural PACA, 2024; Vlontzos, et al., 2014; Arabatzis, et al., 2010). The results presented in this paper reinforce the connection between agricultural forms and commercial strategies, suggesting that certain agricultural structures are more conducive to such local food production. In a context of strong political will to relocalize agriculture for local supply purposes, this paper highlights the central question of which agricultural forms to preserve. In this regard, several operational research avenues could be pursued. First, the spatialized nature of the results may serve as a basis for developing territorial action plans that are geographically targeted - for example, to identify agricultural areas that should be preserved or may be suitable for the installation of young farmers (Vie publique, 2023). Second, this type of modeling could be downscaled to the territorial level, enabling the identification of plots most conducive to the development of AFN. Territorial application of the methodology developed in this paper could also help address the limitations in data availability at the regional level, by incorporating socio-economic and governance data specific to the study area. Finally, applying the model across multiple time scales would be valuable for analyzing the progressive evolution of a territory's potential for AFN engagement and its associated agricultural forms. Such a temporal approach could help spatially identify the vulnerability of agricultural areas with high potential for AFN engagement.

## 6. Conclusion

Alternative food networks (AFN) emerge in the late 20th century in a context of ambitions for productivity and competitiveness in the agricultural sector (European Commission, 2023), alongside with changes in agricultural spaces (Poulot, et al., 2024; Gasselin, et al., 2021; Rieutort, 2009) such as increasing spatial distances between urban and rural areas (Barles, 2018). In contrast to long food chains (Malak-Rawlikowska, et al., 2019), AFN are based on the re-spatialization of exchanges (Renting, et al., 2003; Marsden, et al., 2000), and involve geographical proximity between production and consumption (Paül and McKenzie, 2013). In France, the PACA region has been a pioneer in the development of alternative food chains (Guiraud, 2019). This article develops a

modeling approach to predict the potential for agricultural spaces in the PACA region to engage in AFN, based on their spatial characteristics.

Relying on quantitative methods, the resulting classification model show that agricultural spaces engaged in AFN exhibit certain morphological, economic, and contextual characteristics. Such characteristics are poorly aligned with the dominant agriculture maximizing productivity, and supplying long food chains. Specifically, the results highlight that AFN agriculture is often located near urban areas, consistent with the literature on peri-urban agriculture (Zasada, et al., 2017; Paül and McKenzie, 2013), features smaller farm sizes (Prosser and Lane, 2021; Galt, et al., 2011), and displays more complex geometric shapes (Benedetti and Araldi, 2024; Mouléry, et al., 2022). Furthermore, spatial analysis of the model's output revealed spatial structures of commercial strategies within the PACA region case study. Local autocorrelation coefficients (LISA) measured for AFN engagement potential enabled the identification of the dense nature of conventional agricultural regions, and conversely, more fragmented agricultural spaces for alternative agricultural regions.

In a context of strong public policy interest in the development of local food supply chains (Chambre d'Agriculture, 2024), this article questions the agricultural types to preserve. The results emphasize the need to protect small-scale, diversified peri-urban agriculture, maintaining social and geographical proximities between production and consumption areas. The approach developed in this paper is adaptable to various territorial scales and may serve as a foundation for decision-making and the formulation of public policies.

## CRediT authorship contribution statement

**Juliette Benedetti:** Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Resources, Project administration, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Karine Emsellem:** Writing – review & editing, Supervision, Project administration, Funding acquisition. **Stéphane Bouissou:** Writing – review & editing, Supervision, Project administration, Funding acquisition.

## Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Appendix 1. List of the independent variables retrieved from literature review and the available spatialized database

Note: certain variables are measured in two different ways; based on spatial data intersecting the grid (IM = 'intersecting mesh'); and based on spatial data clipped according to the grid (CM = 'clipped with mesh'). We opted for centrality indicators 'mean' and 'median', to calculate at a mesh level the indexes that are originally calculated at a plot level.

<table><thead><tr><td>Database name</td><td>Literature review</td><td>Built variables</td></tr></thead><tbody><tr><td>RPG niveau 2</td><td>Agricultural plot area</td><td>Sum of CAP funded plots areas (CM)</td></tr><tr><td>RPG niveau 2</td><td>Agricultural plot area</td><td>Sum of CAP funded plots areas (IM)</td></tr><tr><td>RPG niveau 2</td><td>Agricultural plot area</td><td>Median of CAP funded plots areas (IM)</td></tr><tr><td>RPG niveau 2</td><td>Agricultural plots number</td><td>CAP funded plots number (IM)</td></tr><tr><td>RPG niveau 2</td><td>Agricultural plots perimeter</td><td>Sum of CAP funded plots perimeters (IM)</td></tr><tr><td>RPG niveau 2</td><td>Agricultural plots perimeter</td><td>Median of CAP funded plots perimeters (IM)</td></tr><tr><td>RPG niveau 2</td><td>Farm fragmentation</td><td>Maximum distance between CAP funded plots with similar PACAGE (IM)</td></tr><tr><td>RPG niveau 2</td><td>Farm fragmentation</td><td>Minimum distance between CAP funded plots with similar PACAGE (IM)</td></tr><tr><td>RPG niveau 2</td><td>Farm fragmentation</td><td>Mean distance between CAP funded plots with similar PACAGE (IM)</td></tr><tr><td>RPG niveau 2</td><td>Farm size</td><td>Number of CAP funded plots with similar PACAGE (IM)</td></tr><tr><td>RPG niveau 2</td><td>Farm size</td><td>Sum of CAP funded plots areas with similar PACAGE (IM)</td></tr><tr><td>RPG niveau 2</td><td>Januszewski index</td><td>Median value of Januszewski indexes within the mesh (IM)</td></tr><tr><td>RPG niveau 2</td><td>Januszewski index</td><td>Mean value of Januszewski indexes within the mesh (IM)</td></tr></tbody></table>

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<table><thead><tr><th>Database name</th><th>Literature review</th><th>Built variables</th></tr></thead><tbody><tr><td>RPG niveau 2</td><td>Schmook index</td><td>Median value of Schmook indexes within the mesh (IM)</td></tr><tr><td>RPG niveau 2</td><td>Schmook index</td><td>Mean value of Schmook indexes within the mesh (IM)</td></tr><tr><td>RPG niveau 2</td><td>Road accessibility</td><td>Median value of road accessibility for plots with similar PACAGE (IM)</td></tr><tr><td>RPG niveau 2</td><td>Road accessibility</td><td>Mean value of road accessibility for plots with similar PACAGE (IM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of irrigated land (excluding rice) (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of fruit trees and berry plantations (excluding olive trees) (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of vineyards (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of rice fields (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of non-irrigated arable land (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of pastures, meadows and grasslands (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of complex cultivation patterns (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of fragrant, aromatic, or medicinal crops (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of high density of greenhouses areas (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Olive groves (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of mixt agricultural and natural land (CM)</td></tr><tr><td>OCSOL</td><td>Agricultural types</td><td>Share of annual and permanent crops (CM)</td></tr><tr><td>OCSOL</td><td>Contextual land covers</td><td>Share of artificialized land within the mesh (CM)</td></tr><tr><td>OCSOL</td><td>Contextual land covers</td><td>Share of agricultural land within the mesh (CM)</td></tr><tr><td>OCSOL</td><td>Contextual land covers</td><td>Share of natural land within the mesh (CM)</td></tr><tr><td>OCSOL</td><td>Contextual land covers</td><td>Share of wetland within the mesh (CM)</td></tr><tr><td>OCSOL</td><td>Contextual land covers</td><td>Share of water bodies within the mesh (CM)</td></tr><tr><td>OCSOL</td><td>Crop types diversity</td><td>Simpson index (IM)</td></tr><tr><td>OCSOL</td><td>Crop types diversity</td><td>Shannon-Weaver index (IM)</td></tr><tr><td>Cadastral</td><td>Convexity index</td><td>Median convexity of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Convexity index</td><td>Mean convexity of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Elongation index</td><td>Median elongation of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Elongation index</td><td>Mean elongation of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>N50 index</td><td>Median N50 index of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>N50 index</td><td>Mean N50 index of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Nseg index</td><td>Median Nseg index of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Nseg index</td><td>Mean Nseg index of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>P2 index</td><td>Median P2 index of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>P2 index</td><td>Mean P2 index of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot number</td><td>Cadastral plots number</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Median MSI (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Mean MSI (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Median Gravelius (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Mean Gravelius (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Sum of cadastral plots perimeters (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Median perimeter of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Mean perimeter of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Median circularity of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Mean circularity of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot shape</td><td>Sum area of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot size</td><td>Median area of cadastral plots (IM)</td></tr><tr><td>Cadastral</td><td>Plot size</td><td>Mean area of cadastral plots (IM)</td></tr></tbody></table>

## Appendix 2. Measurement of indexes from the literature

$$ \text{Schmook index} = \frac{\text{area(convex hull}(x_1 + x_2 + \dots + x_K))}{\sum_{i=1}^{K} \text{area}(i)} $$

$$ \text{Januszewski index} = \frac{\sqrt{\sum_{i=1}^{K} \text{area}(i)}}{\sum_{i=1}^{K} \sqrt{\text{area}(i)}} $$

i corresponds to each of the K plots sharing the same PACAGE number (belonging to the same farm).

$$ \text{Simpson index} = \frac{\sum_{j=1}^{S} n_j(n_j - 1)}{N(N - 1)} $$

$$ \text{Shannon Weaver index} = - \sum_{j=1}^{S} (p_j * \ln(p_j)) $$

j corresponds to each of the S crop types identified within the mesh, N corresponds to the total cultivated area in the mesh; n_j corresponds to each crop type's cultivated area; and p_j corresponds to each crop type's proportion ($p_j = \frac{n_j}{N}$).
$$MSI(i) = \frac{\text{perimeter}(i)}{\sqrt{\text{area}(i)}}$$

$$Gravelius index(i) = \frac{\text{perimeter}(i)}{2 \sqrt{\pi * \text{area}(i)}}$$

i corresponds to each plot.

$$\text{Circularity index}(i) = \frac{4\pi * \text{area}(i)}{\text{perimeter}(i)^2}$$

$$\text{Convexity index}(i) = \frac{\text{area}(i)}{\text{area}(\text{convex hull}(i))}$$

$$\text{Elongation index}(i) = \frac{\max(d_i)}{\min(d_i)}$$

$d_i$ corresponds to all distances between the vertices of the polygon i's convex hull.

**N.B.:**

Nseg index represents the number of segments shaping the plot

N50 index refers to the minimum number of segments required to reach half of the total plot perimeter

P2 index measures the proportion of the total plot perimeter represented by the two largest segments

## Appendix 3. List of websites the dependent variable has been retrieved from

‘ Mon producteur’ census: http://www.mon-producteur.com/

AMAP census: https://www.lesamapdeprovence.org/

## Appendix 4. List of acronyms and their definitions (in alphabetic order)

<table><thead><tr><td>Acronyms</td><td>Meaning</td><td>Type</td><td>Definition</td></tr></thead><tbody><tr><td>AFN</td><td>Alternative Food Network</td><td>Food chain</td><td>Food chain conducive to spatial, functional, relational, economical proximities (Praly, 2014)</td></tr><tr><td>AMAP</td><td>Association pour le Maintien de l'Agriculture Paysanne</td><td>Organisation</td><td>French type of AFN (equivalent to CSA) (Guiraud, 2019)</td></tr><tr><td>CAP</td><td>Common Agricultural Policy</td><td>Policy</td><td>European public policy for agriculture and food sectors</td></tr><tr><td>CSA</td><td>Community Supported Agriculture</td><td>Organisation</td><td>Food chain for farmers to sell directly to consumers (Morgan, 2018)</td></tr><tr><td>GBC</td><td>Gradient Boosting Classifier</td><td>Machine learning model</td><td>Machine learning model relying on decision trees</td></tr><tr><td>KNN</td><td>K-Nearest Neighbors</td><td>Machine learning model</td><td>Machine learning model relying on multidimensional spaces</td></tr><tr><td>LGB</td><td>Light Gradient Boosting</td><td>Machine learning model</td><td>Machine learning model relying on decision trees</td></tr><tr><td>LISA</td><td>Local Indicators of Spatial Association</td><td>Spatial analysis method</td><td>Spatial analysis method to identify local autocorrelation (Anselin, 1995)</td></tr><tr><td>MSI</td><td>Mean Shape Index</td><td>Morphological index</td><td>Morphological index measured as: perimeter / sqrt(area)</td></tr><tr><td>OCSOL</td><td>Occupation des sols</td><td>Dataset</td><td>French dataset on land use</td></tr><tr><td>PACA</td><td>Provence-Alpes-Côte d'Azur</td><td>Region</td><td>Administrative region located in southern-easter part of France</td></tr><tr><td>PACAGE</td><td>/</td><td>Identifier</td><td>Identification number for farms benefitting from CAP fundings</td></tr><tr><td>RF</td><td>Random Forest</td><td>Machine learning model</td><td>Machine learning model relying on decision trees</td></tr><tr><td>RGA</td><td>Recensement Général Agricole</td><td>Dataset</td><td>French dataset on agricultural activities</td></tr><tr><td>RPG</td><td>Registre Parcellaire Graphique</td><td>Dataset</td><td>LPIS (Land Parcel Identification System)</td></tr><tr><td>SHAP</td><td>Shapley Additive Explanations</td><td>Agnostic method</td><td>Agnostic method used for machine learning models interpretation (Lundberg & Lee, 2016)</td></tr></tbody></table>

## Data availability

The authors do not have permission to share data.

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