Promoting natural capital conservation: a bet for socioeconomic 1 development of marginal areas 2 Angelo Martella (a.martella@unitus.it), Francesca Pietrangeli(f.pietrangeli89@unitus.it), 3 Elisa Biagetti (elisa.biagetti@unitus.it), Barbara Pancino (bpancino@unitus.it), Silvio 4 Franco (franco@unitus.it) 5 Corresponding author: Francesca Pietrangeli, f.pietrangeli89@unitus.it 6 Affiliations: Università degli studi della Tuscia 7 This article has been accepted for publication and undergone full peer review but has not 8 been through the copyediting, typesetting, pagination and proofreading process, which may 9 lead to differences between this version and the Version of Record. 10 Please cite this article as: 11 Martella A., Pietrangeli F., Biagetti E., Pancino B., Franco S. (2025). Towards Digital 12 Farming: Exploring Technological Integration in Agricultural Practices of a sample of 13 Italian livestock farms, Just Accepted. DOI: 10.36253/bae-16911 14 15 Abstract 16 The aim of this paper is to enhance the well-being of marginalized areas by improving their 17 local economy, considering the correlation between socioeconomic marginalization and 18 environmental sustainability. These two objectives are at the core of the international and 19 European policy agenda, but they are not often merged in one action. Within this study, we 20 selected a marginal area in central Italy and assessed its environmental sustainability, using 21 the method of the ecological balance. The results show that this territory has the capacity to 22 provide an amount of natural capital greater than the ecological footprint generated by local 23 production activities, thus the value of the ecological balance is positive. Then, we discussed 24 how local policies can favor processes focused on agricultural products coming from areas 25 recognized as sustainable. Environmental sustainability can be work as a branding strategy, 26 which can raise the market value of the products and the farmers’ income, thus supporting 27 mailto:franco@unitus.it mailto:f.pietrangeli89@unitus.it the economic development of marginal areas and promoting the protection of their natural 28 capital. 29 Keywords: sustainability, marginality, ecological balance, agriculture, rural policy 30 1. Introduction 31 The socioeconomic development of marginal areas is one of the most important targets of 32 European policies, due to their generalized diffusion. About 45% of European territory 33 consists of regions presenting strong marginality characteristics (De Toni et al., 2021). 34 In countries where a large part of the territory is covered by marginal areas, as in the case of 35 Italy, the policy makers are engaged in facing the implementation of specific interventions to 36 fill the development gaps that often characterize such areas (Kang et al., 2013a). 37 Socioeconomic marginality, indeed, is associated with negative characteristics such as 38 isolation, low income, depopulation, old age of residents, weak connections with urban areas, 39 few infrastructures and a significant share of the local income being based on agricultural 40 production (Arshad et al., 2021). 41 However, there are also some positive aspects in marginal areas, such as immaterial assets, 42 which are important to promote their development: biodiversity, varied agroecosystems, 43 wide availability of natural resources, just to name a few (Ahmadzai et al., 2021; Balzan et 44 al., 2018). This evidence has led to the general and often implicit perception that marginality 45 is associated with sustainability. The point is not whether this perception is true; rather, what 46 do we mean by (socioeconomic) “marginality” and (environmental) “sustainability”. 47 This study, carried out within the Spoke 7 of PNRR Agritech project (“Integrated models for 48 the development of marginal areas to promote multifunctional production systems enhancing 49 agro-ecological and socio-economic sustainability”), aims to face and discuss this point. 50 More specifically, this paper has three main objectives. 51 The first one is to give a clear definition of socioeconomic marginality and select a marginal 52 area inside the province of Viterbo (Italy) that matches such definition. 53 The second one is to adopt a definition of environmental sustainability and assess if the 54 selected marginal area may be declared as sustainable. To accomplish this objective the focus 55 has been placed on the agricultural sector rather than on the whole local economic system. 56 This choice is justified by two main reasons. First, agriculture is the only economic sector 57 that directly manages natural resources, represented by farmland. Second, the idea of food 58 from a sustainable production system mainly refers more to agricultural practices than to the 59 other stages of the supply chain. 60 The third one is to discuss how specific actions supported by local institutions can favor 61 processes to promote agricultural products coming from an area recognized as sustainable to 62 raise their market value, thus increasing the farmers’ income and supporting the economic 63 development of a marginal area. 64 As asserted by Basile and Cavallo (2020), regional and cohesion policies of national 65 governments and the European Union typically address internal or marginal areas. However, 66 the objective of this paper is not to discuss the national or regional measures more suitable 67 for the development of marginal areas. This work looks at valorization process of local 68 economy as a possible "bottom-up" action, able to identify a territorial brand linked to 69 specific characteristics of marginal areas (Banini and Pollice, 2015). This appears to be an 70 interesting topic also considering that, despite its significance, research on the connections 71 between place branding and sustainable development is still limited in the scientific literature 72 (Aguilera-Cora et al., 2024). 73 To achieve these objectives the paper is structured as follows. 74 The background is divided into two subsections. In the first one we discuss what a marginal 75 area is, what does marginality mean, which the marginality indicators are and why the idea 76 of sustainability is so often linked to marginal areas. In the second one, we present the 77 definition of environmental sustainability as the spatial condition where the natural capital 78 maintenance is ensured. The methodology section is also split in two parts. One describes the 79 process adopted to identify a marginal area as a group of municipalities in a province. The 80 other explains how the environmental sustainability of an agricultural system can be 81 evaluated by means of an ecological balance. The results section reports on the outcome of 82 the marginal area selection process and the description of its main characteristics. The 83 ecological balance assessment of the selected area’s agricultural system follows. The results 84 obtained are then discussed to underline their coherence with previous studies and in terms 85 of policy implications of sustainability conditions. The paper ends with some final 86 considerations related to the opportunity of valorizing the environmental dimension of the 87 marginal areas. 88 89 2. Background 90 2.1. The concept of marginality and marginal areas characteristics 91 Rural areas cover a wide part of the Italian territory. Many of these areas face the issue of 92 overcoming the developmental gaps with urban areas and the consequent negative 93 phenomena such as depopulation, ageing and loss of primary services. This topic is 94 particularly relevant in a country like Italy, where the internal areas suffer isolation and 95 socioeconomic problems. 96 For this reason, these areas are specifically considered as the target of many European (Oecd 97 and European Commission, 2020) and national policies, such as the “National Strategy for 98 Inner Areas” (SNAI), to support their revitalization with a “place-based” approach, where 99 the focus of the policies are the specific qualities of each area and its local production system 100 (De Toni et al., 2021). 101 In addressing these issues, the regions with the above characteristics are often defined as 102 marginal areas, yet it is not so common to find a clear meaning of what “marginality” really 103 is. Therefore, a definition of marginality is needed for the task of identifying and shaping the 104 boundaries of a marginal area. 105 The general idea underlying the concept of marginal areas, a notion frequently used by 106 researchers and policy makers, is often associated with a complex condition of disadvantage 107 according to both geographic characteristics and socioeconomic aspects. Sometimes this term 108 is also used like a synonym for abandoned, degraded, unused or under-used area (Sallustio 109 et al., 2018). 110 To provide a more comprehensive definition, it should be noted that there is a widely shared 111 understanding that socio-economic marginality refers to situations of territorial disadvantage 112 that undermines the vitality, competitiveness and development potential of a region (Arshad 113 et al, 2021). These are areas with a very low population density and a negative demographic 114 trend, along with a minimal presence of basic services (Malikova et al., 2016). Furthermore, 115 a region is considered marginal because it is significantly distant from essential services and 116 is also characterized by geomorphological conditions that lead to structural deficiencies in 117 transportation routes, which in turn affect the location of settlements and productive activities 118 (Lucas, 2012; Vendemmia et al., 2021). A territory far from primary services and weakly 119 connected to infrastructures and networks is marginal because these conditions affect the 120 establishment of farms and industries (Meini et al., 2017). Marginality also involves a 121 structural weakening of the local system’s capacity to react, caused by the simultaneous 122 occurrence of a combination of recessive effects (Carrosio and Osti, 2017). 123 Due to the uneven geographical distribution of development conditions, such as structures, 124 activities resources, knowledge and so on, resources available to develop local systems do 125 not operate everywhere with the same intensity, increasing the risk of socioeconomic 126 marginality (Sotte, 2016). On the other hand, if we associate the territorial conditions of 127 marginality to the specific characteristics of the economic local production system, the 128 economic system itself can represent a development driver, provided that is possible to 129 promote its positive environmental dimension (Ahmadzai et al., 2021). 130 Given a definition of marginality, it is important to clarify that, as a phenomenon, it can be 131 measured, detected, and quantified based on coherent economic, social, territorial, 132 environmental, and demographic indicators within a specific area. As well as the possible 133 definitions, even the indicators of socioeconomic marginality are varied and often 134 interrelated. Commonly used indicators include income levels, employment status, education 135 and access to basic services such as healthcare, housing and social protection. Other 136 indicators may include participation in informal labor markets, vulnerability to economic 137 shocks and social isolation (Vendemmia et al., 2021). Indicators help both with identifying 138 the area at risk of marginalization and providing a framework for assessing the effectiveness 139 of policies aimed at reducing inequality and promoting social and economic development 140 (De Toni et al., 2021). 141 In choosing indicators it is important to consider that marginality is both a relative and 142 dynamic condition: relative because it can only be defined through comparison with different 143 situations, either spatial or socio-economic; dynamic because various causes can influence 144 its level, as well as changes in terms of comparison or in the factors considered important in 145 defining it (Ahmadzai et al., 2021). To identify marginal areas as a group of municipalities, 146 data related to selected indicators should be collected at municipal level. 147 Moreover, it is appropriate that such aggregation corresponds to an administrative authority. 148 This is important because only in the presence of an authority that aggregates the 149 municipalities it is possible to apply policies to develop the marginal area, involving the 150 participation of economic actors and the engagement of local administrations and 151 stakeholders (Covino R., 2017). 152 153 2.2. Definition and assessment of environmental sustainability 154 Agriculture, like all productive activities, uses natural resources to obtain the material and 155 energy inputs needed to carry out production processes and to absorb residues, such as waste 156 and greenhouse gas emissions. However, unlike other sectors, agriculture has the potential to 157 directly contribute to the supply of natural resources with different types of farmlands. The 158 property of agricultural system of managing natural resources and economic activities at the 159 same time offers the opportunity of directly comparing availability and consumption of 160 natural capital in a certain area. It is thus possible to assess a real ecological balance (Franco, 161 2021a). 162 In this regard, there is a broad consensus in the scientific community that ecological footprint 163 (Wackernagel and Rees, 1996), is a suitable methodology to compare supply and demand of 164 natural resources, therefore assessing the environmental sustainability of economic processes 165 (Moldan et al., 2012; Neumayer, 2013). The environmental sustainability discussed here, 166 refers to the ability of the ecosystems to support given economic effects. Specifically, the 167 approach used in this study, evaluates a type of environmental sustainability defined as 168 “strong”, which assumes the conservation of natural capital as a basic condition, where the 169 use of natural resources must avoid the deterioration of their reserve. This methodology is 170 not limited to the analysis of single economic activities, but it is also applicable for entire 171 regions, thus providing the possibility to operate at different scales, as highlighted in many 172 studies focused on agricultural sector (Niccolucci et al., 2008; Cerutti et al., 2013; Galli et 173 al., 2014; Martella et al., 2023; Biagetti et al., 2023a). 174 To proceed with the assessment of an ecological balance in agriculture, the first step is 175 represented by the delimitation of the space involved in the assessment. Then, it is essential 176 to define the key indicators for calculation. The availability of natural resources present in 177 the defined area is measured by Biocapacity (BC), while Ecological Footprint (EF) quantifies 178 the impact of economic activities carried out in the same area. EF translates the natural capital 179 required by cultivation and livestock management in terms of biologically productive areas, 180 in a way that such demand can be compared with the carrying capacity of the agricultural 181 ecosystem (Franco, 2021b). This makes it possible to determine the condition of 182 environmental sustainability or unsustainability as the result of the Ecological Balance (EB 183 = BC - EF). 184 BC, EF and EB are expressed using a standardized unit of measurement, defined as global 185 hectare (gha). This metric represents the average biological productivity of one hectare of the 186 world’s surface, calculated by dividing the planet’s total capacity to provide natural resources 187 by the number of hectares of land surface (Kitzes and Wackernagel, 2009; Galli, 2015). The 188 Yield Factor (YF), specific to each country, and the Equivalence Factor (EQF), are the two 189 parameters used to convert physical land hectares to final global hectares, with values varying 190 by both land type and year considered (Borucke et al., 2013). 191 The ecological balance, and consequently the validation of the sustainability condition, can 192 be applied to different typologies of agricultural systems, from a single crop to a farm, up to 193 an entire region (Biagetti et al., 2023b). In this study, which focuses on the assessment of the 194 sustainability of the agricultural sector in marginal areas, the boundary within which the 195 supply and demand of natural capital must be assessed is represented by the total agricultural 196 area inside a predefined group of municipalities. 197 198 3. Methodology 199 3.1. Delimitation of marginal areas within a region 200 To identify marginal areas within a region, as pointed out in the background section, many 201 approaches can be applied. In this study, considering the focus on the socio-economic 202 dimension and the outcome of a literature overview on this topic, three indicators were 203 chosen: old-age index, depopulation rate and per capita income. These indicators synthetize 204 the marginality condition in terms of aging and decline of the local population and residents’ 205 impoverishment (De Toni et al., 2021; Vendemmia et al., 2021). In this study such indicators 206 for each municipality in the selected region are calculated as follows: 207 - Old-age index (2023): resident population older than 65, divided by resident population 208 younger than 14 (ISTAT, 2024a); 209 - Population variation (2001-2023): resident population variation between 2023 and 2001 210 divided by resident population in 2001 (ISTAT, 2024a); 211 - Per capita income (2023): total taxable income divided by resident population (Ministero 212 dell’Economia e delle Finanze 2024). 213 For every municipality in the province of Viterbo, the indicator values are classified in the 214 range 1 (low marginality) to 5 (high marginality) coherently with the categories reported in 215 table 1. In this way three new marginality indicators, respectively IAGE, IDEP and IINC, are 216 defined. 217 218 Table 1 – Categories associated with different levels of marginality 219 Level of Marginality Old-age index Population variation Income per capita (€) 1 < 1.5 > +15% > 14,000 2 1.5 - 2.0 +5% - +15% 13,000 - 14,000 3 2.0 - 2.5 –5% - +5% 12,000 - 13,000 4 2.5 - 3.0 –15% - –5% 11,000 - 12,000 5 > 3.0 < –15% < 11,000 220 Categories in table 1 were set considering the values distribution of the three indicators in the 221 municipalities of Viterbo province. It follows that IAGE, IDEP, IINC measure a relative (local) 222 level of marginality. Furthermore, considering that the Italian old-age index is 1.93, the 223 population variation is 3.5% and the income per capita is 14,590 €, what is marginal within 224 Viterbo province is even more marginal in a national perspective. Therefore, the highest 225 values of IAGE, IDEP, IINC can be associated with an absolute marginal condition. 226 Then, for each municipality (i) the index of marginality IMARi is calculated using the following 227 relation: 228 IMARi = round ((IAGEi + IDEPi + IINCi)/3) (1) 229 Once each municipality is characterized with an individual marginality score, it’s necessary 230 to define a marginal area where to assess the environmental sustainability of the local 231 agricultural system. Such delimitation should consider at least two criteria: all the 232 municipalities included in the area have a high enough level of marginality; the municipalities 233 aggregation corresponds to an administrative body (or, at least, to a recognized legal entity) 234 that is in the position to define and implement actions at local level. Therefore, the second 235 step of the methodology is to border the marginal area that will be the object of sustainability 236 evaluation and, at the same time, to identify the related governance institution that can 237 promote specific actions for supporting the valorization of local products. 238 For this purpose, after identifying all the possible k municipality aggregations in the selected 239 region (the province of Viterbo in our case), a variable t is defined. Such variable can have 240 only two values: tij = 1 if the municipality i belongs to aggregation j; tij = 0 if the municipality 241 i does not belong to aggregation j. 242 Using this variable, it is possible to assess the overall marginality of the aggregation j as 243 Mj = ∑ (IMARi x tij) n i=1 nj (2) 244 Once measured the marginality for each territorial aggregation, the one with the highest Mj 245 can be chosen. This will be the area of which the sustainability of the agricultural system will 246 be assessed. 247 248 3.2. The Ecological Balance for sustainability assessment 249 To evaluate the condition of deficit/surplus of natural capital in a delimited agricultural 250 system a comparison between its supply and demand may be established. Such an ecological 251 balance (EB) is calculated as the difference between the system’s overall biological 252 productivity (BioCapacity - BC) and the impact of production activities (Ecological Footprint 253 - EF): EB = BC – EF (Martella et al., 2023) 254 The amount of natural capital in the area (BCTOT) is determined by the bio-productivity of 255 different types of farmland: cropland (BCCROP), forest land (BCFOR), semi-natural areas 256 (pastures, shrublands, …) (BCASN), other surfaces (buildings, roads, …) (BCOTH), water 257 (BCWAT). 258 The values of BCFOR, BCASN, BCOTH are determined following the standard ecological 259 footprint methodology (Wackernagel and Rees, 1996) by converting the relative area through 260 standard coefficients YF (yield factor) and EQF (equivalence factor) provided by Global 261 Footprint Network (GFN, 2024). 262 The biocapacity associated with cropland is also calculated following the standard 263 methodology, but the overall value is assessed by summing up the bio-productivity of each 264 one (i) of the n crops cultivated in the area, as suggested by Passeri et al. (2013): 265 BCCROP = ∑ Aj × Ypj Ywj × EQF CROP n i=1 (3) 266 where Aj indicates the cultivated area, Ypj the crop average yield in the area, Ywj the crop 267 world average yield (FAO, 2024). 268 Concerning the term BCWAT, is quite difficult to carry out a detailed analysis on the 269 watersheds inside the farms within the area under analysis. Therefore, the biocapacity of this 270 typology of land cover is included in BCOTH. 271 The demand for natural capital, measured by the total ecological footprint (EFTOT) is 272 calculated through the sum of three farm activities impacts: cultivations (EFCROP), livestock 273 (EFLIV), others (EFOTH). 274 EFCROP can be calculated following the methodology proposed by Passeri et al. (2013), which 275 identifies two sources of impact: overproduction (EFovp) and use of inputs (EFinp). The first 276 one is related to the bias between the observed yield and the yield that can be obtained in 277 natural conditions, i.e., without using inputs. The second one expresses the area of bio-278 productive land required to absorb the effects of input use. The assessment of these two 279 components, both referring to calculation methodology and coefficients used, is explained in 280 detail in Blasi et al. (2016) and Franco (2021b). 281 The impact of livestock farming activities in terms of natural capital demand can be estimated 282 as suggested in Biagetti et al. (2023b). The impact for each (j) of the m types of livestock 283 farms in the area (EFlivj) is calculated in terms of bio-productive land needed for absorbing 284 the emissions caused by input utilization and by enteric fermentation and management of 285 animal manure, making use of specific conversion coefficients available in the literature 286 (Coderoni et al., 2013; Mancini et al., 2016). 287 Finally, EFOTH includes the impact of labor use, energy and fuel consumption for operation 288 and maintenance of farms areas not directly affected by production processes. 289 Therefore, the negative term of the ecological balance is calculated as follows: 290 EFTOT= ∑ (EFovp i + EFinp i )n i=1 + ∑ EFlivj m j=1 + EFOTH (4) 291 EB, as well as all the components of BC and EF, are measured in global hectares (gha). 292 However, to explain the outcome of the ecological balance in clearer terms, it is possible to 293 convert the EB value in “real” hectares of a specific land cover. If we choose forest as land 294 cover and consider the average biological productivity of an average Italian forest, an index 295 of ecological performance (IEP) may be defined. Such an index expresses the hectares of 296 Italian forest, in terms of natural capital, that is made available (if EB>0) or subtracted (if 297 EB<0) for each hectare of the study area. 298 299 4. Results 300 4.1. Delimitation of the marginal area 301 According to the methodology presented in the previous section, the old-age index, 302 population variation and per capita income were calculated for all the 60 municipalities in 303 the province of Viterbo. All data were extracted from Italian bureau of census (ISTAT, 304 2024a) database, except the taxable income that was obtained from the Ministry of Finance 305 (MF) website. 306 Then the indicators IAGE, IDEP, IINC were assessed applying the classes reported in table 1. For 307 each municipality i these values were combined to obtain the marginality index IMARi. All the 308 values of variable, indicators and IMAR are listed in table A1 of the Appendix. 309 The next step was to identify the territorial aggregations within the province of Viterbo. 310 These are the following: Inner area Alta Tuscia, Biodistrict of Bolsena lake, Biodistrict of 311 Maremma, Biodistrict of Amerina road and Forre, Local Action Group Alto Lazio, Local 312 Action Group Teverina, Local Action Group Etrusco Cimino, Local Action Group Amerina 313 Agro Falisco, Local Action Group Tuscia Romana, Mountain community Alta Tuscia 314 Laziale, Mountain community of Cimini (rural district). 315 Table 2 lists the names of territorial aggregation, the number of the municipalities and the 316 marginality level Mj. 317 The aggregation with the highest level of marginality is the Local Action Group (LAG) Alto 318 Lazio with a Mj equal to 4.27 followed by mountain community Alta Tuscia Laziale (4.13). 319 All municipalities that are also included in the LAG Alto Lazio belong to this second 320 aggregation. Considering this point and, moreover, the peculiarities of a LAG with respect 321 to a mountain community in terms of policy intervention, the LAG Alto Lazio was preferred. 322 Therefore, this area was chosen to calculate the environmental sustainability of the 323 agricultural system and verify the possibility of increasing the value of local products through 324 an environmental claim. 325 Figure 1 shows the values of IMAR for the Viterbo province municipalities and the boundaries 326 of the LAG Alto Lazio. Table 3 lists the municipalities which belong to LAG Alto Lazio and 327 their main characteristics. 328 329 Table 2 – List of municipality aggregation in Viterbo province 330 Aggregation Code Aggregation Name Municipalities (nj) Mj A1 Inner area Alta Tuscia 22 3.91 A2 Biodistrict of Bolsena Lake 17 3.59 A3 Biodistrict of Maremma 2 2.50 A4 Biodistrict Via Amerina and Forre 13 3.08 A5 LAG Alto Lazio 15 4.27 A6 LAG in Teverina 11 3.55 A7 LAG Etrusco Cimino 9 3.22 A8 LAG Amerina Agro Falisco 10 2.80 A9 LAG Tuscia Romana 6 2.83 A10 Mountain community Alta Tuscia Laziale 8 4.13 A11 Mountain community of Cimini 10 3.10 Source: Our elaboration 331 332 Figure 1 – IMAR of Viterbo province municipalities and LAG Alto Lazio boundaries 333 334 335 Table 3 – Main characteristics of LAG Alto Lazio municipalities 336 Municipality Old-age index Depopul. Rate Income p.c. (€) IMAR Area (km2) Agricultural area (km2) Population Acquapendente 3.11 -8.9% 13,117 4 131.71 46.08 5,271 Arlena di Castro 3.21 -2.9% 11,001 4 21.87 14.88 842 Canino 2.44 -0.7% 10,806 4 123.50 76.27 5,036 Cellere 4.02 -17.7% 11,138 5 32.70 25.46 1,071 Farnese 4.25 -19.4% 11,924 5 52.38 23.78 1,393 Gradoli 4.49 -16.3% 11,607 5 37.50 7.11 1,252 Grotte di Castro 3.82 -20.2% 13,389 4 33.42 16.88 2,369 Ischia di Castro 2.92 -13.2% 10,966 4 104.95 55.94 2,138 Latera 6.23 -25.0% 12,063 4 22.66 9.24 767 Onano 3.33 -23.6% 10,215 5 24.60 11.29 893 Piansano 3.69 -13.2% 11,898 4 26.61 18.39 1,928 Proceno 5.89 -17.6% 12,032 4 41.90 26.36 521 San Lorenzo Nuovo 3.21 -2.6% 12,001 4 27.99 8.51 2,013 Tessennano 5.39 -33.1% 11,961 5 14.60 11.40 281 Valentano 2.91 -5.7% 13,297 3 43.29 26.75 2,768 Total 739.68 378.34 28,543 Source: Our elaboration on ISTAT and MF (2024a) 337 338 It could be useful to point out that a Local Action Group is a public-private partnership set 339 up as an association with recognized legal personality, that has the mission to implement a 340 strategic local development plan as defined in measure 19 of the 2014-2020 Lazio Regional 341 Development Plan. The LAG decision-making is managed by the Board of directors 342 composed of a maximum number of five members. This kind of administrative local authority 343 has the advantage of bringing decision making processes closer to the territory, enhancing 344 the real strengths and allowing broad and direct participation of local communities. 345 This feature fits with the area selection requirement, because only by involving local 346 authorities, stakeholders’ environmental policies and actions are more effective in developing 347 a marginal area. 348 349 4.2. The Ecological Balance of LAG Alto Lazio 350 Primary data on the agricultural sector in LAG Alto Lazio area was obtained through the 7th 351 General Census of Agriculture (ISTAT, 2024b). The information collected includes data on 352 the crop mix, in terms of major cultivation types and dimensions, and livestock, in terms of 353 species and number of animals. Processing ISTAT data is a key element for obtaining 354 detailed information on land use, such as crops and bio productive areas, as well as statistics 355 on the number and type of livestock. Then, all information on the cultivation techniques 356 (yield, work, machinery, inputs) was obtained from the FADN database for the year 2021 357 and 2022 (FADN, 2024). The technical information contained in the FADN provides a solid 358 basis for the analysis of different agronomic practices and their consequent implications on 359 the LAG territory. 360 361 Table 4 - Subdivision of farmland among land cover typologies in LAG “Alto Lazio” 362 Municipality Total (ha) Crop land Forest Land Semi-Natural Areas Other surfaces Acquapendente 7,127 64.7% 31.0% 1.3% 3.0% Arlena di Castro 1,634 91.1% 5.9% 0.4% 2.6% Canino 10,039 76.0% 12.9% 1.0% 10.1% Cellere 2,925 87.0% 9.8% 0.6% 2.6% Farnese 2,997 79.4% 16.4% 2.3% 1.9% Gradoli 941 75.7% 19.0% 1.9% 3.4% Grotte di Castro 2,165 78.0% 17.1% 2.4% 2.5% Ischia di Castro 8,499 65.8% 28.5% 1.1% 4.6% Latera 1,361 67.9% 28.7% 0.8% 2.6% Onano 1,394 81.0% 15.1% 1.6% 2.3% Piansano 2,003 91.8% 3.6% 1.5% 3.1% Proceno 3,132 84.2% 11.9% 1.5% 2.4% San Lorenzo Nuovo 1,113 76.6% 17.7% 1.7% 4.0% Tessennano 1,395 81.8% 14.4% 2.2% 1.6% Valentano 3,182 84.1% 12.3% 1.8% 1.8% Total 49,907 75.8% 18.4% 1.3% 4.4% Source: ISTAT, (2024b) 363 364 Figure 2 – Tree map of LAG Alto Lazio distribution of UAA 365 366 Source: Our elaboration on ISTAT (2024b) 367 368 Table 5 - Composition of livestock (number of heads) in LAG Alto Lazio 369 Livestock Heads Cattle Dairy cow 348 Other cattle 5,794 Equines Horses 293 Other equines 29 Sheep 68,109 Goats 763 Pigs Sows 489 Other pigs 7,033 Poultry Laying hens 77,395 Broilers 14,591 Source: Our elaboration on ISTAT (2024b) 370 371 The data analysis revealed that total farmland of the LAG Alto Lazio covers 49,905 hectares, 372 which is about 67% of the whole area. As shown in table 4, the total farm area is allocated 373 76% to cropland, 18% to forest land, and the remaining 5% to semi-natural areas (identified 374 with grazing land) and other surfaces. 375 Focusing on cropland, the LAG area shows a general heterogeneity of cultivations, which 376 can be divided into three main categories (Figure 2): leguminous fodder and temporary 377 pastures (42.6%), mainly linked to livestock practices; a group of three major crops: olive 378 trees, durum wheat, barley (28.5%); a group of many minor crops, including other cereals, 379 vegetables and fruit trees, characterized by low-incidence surfaces (28.9%). 380 Regarding livestock (see table 5), the main categories include laying hens and broilers, which 381 constitute 52.6% of the total, followed by sheep, representing 39%. The remaining portion is 382 divided between pigs (4.3%) and other species, such as cattle, horses and goats (4.1%). 383 The analysis performed on the LAG Alto Lazio farmland made it possible to evaluate the 384 environmental sustainability of the local agricultural system. The assessment, based on the 385 comparison between biocapacity (BC) of different types of land use and the ecological 386 footprint (EF) of agricultural activities, was carried out following the methodology explained 387 in the previous section and using the data elaborated from ISTAT and FADN databases and 388 the coefficient described in the cited literature. The results are synthetized in table 6. 389 390 Table 6 - Ecological balance of LAG Alto Lazio 391 BioCapacity gha Ecological Footprint gha Cropland 104,353 Crops 93,521 Forested land 19,785 Livestock 16,229 Semi-natural areas 3,512 Other activities 7,669 Other surfaces 3,730 Total BC 131,380 Total EF 117,419 Ecological Balance (EB) + 13,961 Source: Our elaboration 392 393 As can be seen, the LAG agricultural system has the capacity to make a natural capital amount 394 (BCTOT=131,370 gha) greater than the ecological footprint generated by local production 395 activities (EFTOT = 117,419 gha) available. The positive value of the ecological balance (EB 396 = 13,961 gha) signifies a condition of environmental sustainability where available natural 397 resources are not exploited beyond their regenerative capacity. 398 The BC of cropland represents the most significant component of the overall BC (almost 399 80%), and it is able to fully sustain the impacts generated by the cultivation activities. A 400 significant aspect that emerged from the analysis is the ability of forests and grazing land to 401 offset approximately 97% of the impact generated by livestock (EFLIV) and other activities 402 (EFOTH). 403 To explain the outcome of ecological balance more explicitly, the IEP was assessed. Its value, 404 equal to 0.13, shows that every hectare of the LAG agricultural system hectares made 405 available to the community, beyond its agricultural productivity, the natural resources 406 equivalent to 0.13 ha of average Italian forest. This result emphasizes the area’s ability to 407 maintain a positive balance between used and regenerated resources and to actively 408 contribute to the improvement of overall environmental quality. The IEP represents a key 409 parameter for assessing the efficiency with which natural resources are managed by the 410 farming systems as a whole, thus providing a clear picture of the overall sustainability of 411 agricultural practices in the LAG Alto Lazio territory. 412 413 5. Discussion 414 Although the topic of socioeconomic marginality has sparked extensive scientific and 415 political debate in recent years, accurately and comprehensively assessing the phenomenon - 416 especially in terms of extent and location - is not an easy task. Moreover, the concept of 417 marginality is highly subjective and, together with the variety of definitions, indicators and 418 methodological approaches used (Peterson and Galbraith, 1932; Csikós and Tóth, 2023), 419 complicates comparisons with similar studies. Marginal areas can emerge in a wide range of 420 socio-ecological systems, from deserts to rainforests, and thus vary in their conceptualization 421 (Lipper et al., 2006). These areas are generally perceived as “limited” in their ability to 422 sustain human activities due to persistent biophysical and/or socioeconomic constraints. 423 In our study, socioeconomic marginality was assessed using three key indicators, as indicated 424 in the methodology. The results show that Alto Lazio LAG is the area with the highest values 425 for the combination of such key indicators (old age index, population change and per capita 426 income), with a marginality index of 4.27 in the range 1 to 5. This suggests that the region is 427 characterized by a vulnerable economy and a significant social development gap. 428 In these conditions, especially for the marginal regions where the agricultural sector plays a 429 relevant role, the crops and livestock intensification could be seen as a possible strategy for 430 local socioeconomic development. Nevertheless, it is evident how a similar solution presents 431 significant environmental risks. Indeed, the practices for rising agricultural productivity with 432 the aim of greater profitability often lead to increase exploitation of natural resources, with 433 intensive use of pesticides and fertilizers, with harmful consequences for local ecosystems 434 (Hazell and Wood, 2008; Barbier, 2010). In this sense, marginal areas are not only land to be 435 exploited, but places with a precious biological capacity that must be used in a sustainable 436 way (Sallustio et al., 2018). Agricultural activity in marginal areas should be view from this 437 perspective, considering how productive techniques, crop selection and human-environment 438 relationships pose tangible problems in terms of environmental impact (Kanianska, 2016). 439 Referring to LAG Alto Lazio, the value of the ecological balance gives a positive result, 440 highlighting how the heterogeneity of land covers and the presence of forested and semi-441 natural areas within the agricultural land are essential not only biodiversity conservation but 442 also for ensuring the sustainability of farming systems. This outcome of our case study 443 suggests how marginal areas are often characterized by a land use pattern that can advantage 444 the environmental sustainability of local agricultural system. Several studies have discussed 445 the relationships between marginality and environmental issues, such as water quality, soil 446 degradation, biodiversity and climate change mitigation (Fisher et al., 2002; O’Connor et al., 447 2005; Searchinger et al., 2008; Kang et al., 2013b). Even if these studies raise concerns about 448 the environmental risks linked to some fragility in land use in marginal areas, the outcome of 449 our study clearly indicates that the combination of marginality and sustainable agriculture 450 should not be seen as separate concepts, but rather as two sides of the same coin. While 451 marginal areas present significant challenges, they also offer opportunities for more 452 ecological and resilient agriculture. Therefore, our study confirms that agriculture can play 453 two basic roles: as a producer of agricultural goods and as a custodian of the ecosystem. 454 By improving sustainable agricultural practices, production can be optimized while 455 minimizing environmental impact. This approach not only helps protect natural capital, but 456 also promotes economic and social development in rural areas, creating a virtuous circle that 457 benefits both local communities and the ecosystem at large. Furthermore, environmental 458 sustainability can be seen as a branding strategy, also at territorial level, with the result that 459 consumers will tend to perceive brands as adding value to the product and their place of origin 460 (Nakaishi and Chapman, 2024). The effects of this strategic leverage have been analyzed in 461 several studies, showing how integrating sustainability into branding increases consumers’ 462 willingness to pay a higher price for the product (Franco and Cicatiello, 2018). 463 464 6. Conclusions 465 466 The main aim of this study was to find out how to develop the local economy of marginal 467 areas and thus increase their well-being. We focus on the link between socio-economic 468 marginality and environmental sustainability and find that in the literature there are many 469 examples of marginal areas that are also environmentally sustainable. 470 As agriculture is often the main productive activity in marginal areas, promoting sustainable 471 agricultural practices can be the key both to increase the value of local food production and 472 strengthen the overall identity of the territory. This approach not only protects the natural 473 capital, but also promotes the resilience of rural communities. 474 We chose the Alto Lazio LAG as a case study due to its high socio-economic marginality 475 and the type of governance that allows political intervention. We then evaluated the 476 environmental sustainability of the LAG agricultural system using an environmental balance 477 approach and came to positive conclusions. Breeding activities, that generally have a high 478 impact on environmental sustainability, are present in this marginal area, but other 479 agricultural production combined with a good management system clearly compensate for 480 this and achieve a positive ecological balance. 481 Marginal areas possess the potential for sustainable production and self-development without 482 adversely affecting the ecosystem, particularly through the implementation of virtuous local 483 production systems exemplified by the Alto Lazio LAG. Consequently, it can be stated that 484 this agricultural model is commendable; if local authorities promote and enhance it, it could 485 serve as an effective mechanism to bridge the development gap in marginal areas while 486 simultaneously safeguarding the environment. 487 The study also focused on identifying opportunities for sustainability claims and certification 488 processes that could enhance the value of local agriculture and food production. This tool is 489 needed to effectively convey to consumers, in a clear and quantifiable manner, the beneficial 490 impact of local agricultural production systems on the environment. 491 The promotion of agricultural sustainability, fully respecting ecosystem limits, if 492 accompanied by a recognized certification and resulting from the active involvement of 493 stakeholders, can serve as a strategic lever for the economic development of local agricultural 494 production. This approach not only strengthens territorial competitiveness but also helps to 495 create a more inclusive and environmentally respectful growth model. 496 We are aware of the limits of our results, due to a partial accounting for the territory 497 sustainability, since we considered only the agricultural production system. Furthermore, 498 implementation challenges might arise due to potential discrepancies between the 499 overarching territorial vision and its practical application at the individual farm level. 500 Further investigations are necessary, incorporating a stakeholder engagement process that 501 involves local policymakers and relevant actors. 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New Society Publishers, Gabriola Island, Canada. 645 646 647 Table A1 – Marginality indicators of Viterbo province municipalities 648 Municipality Old-age index IAGE Depop. rate IDEP Income per capita IINC IMAR Acquapendente 3,11 5 -8,9% 4 13.117 2 4 Arlena di Castro 3,21 5 -2,9% 3 11.001 4 4 Bagnoregio 3,35 5 -6,7% 4 13.025 2 4 Barbarano Romano 3,55 5 5,5% 2 13.146 2 3 Bassano in Teverina 2,77 4 11,7% 2 12.075 3 3 Bassano Romano 2,50 4 7,7% 2 12.352 3 3 Blera 2,48 3 -7,1% 4 12.143 3 3 Bolsena 3,97 5 -9,7% 4 12.119 3 4 Bomarzo 2,26 3 4,0% 3 12.406 3 3 Calcata 1,99 2 6,6% 2 10.386 5 3 Canepina 2,22 3 -5,6% 4 11.665 4 4 Canino 2,44 3 -0,7% 3 10.806 5 4 Capodimonte 3,87 5 -0,9% 3 14.215 1 3 Capranica 1,91 2 13,1% 2 11.795 4 3 Caprarola 2,22 3 -0,7% 3 11.502 4 3 Carbognano 1,86 2 1,3% 3 10.980 5 3 Castel Sant’Elia 1,86 2 14,1% 2 10.578 5 3 Castiglione in Teverina 2,16 3 0,6% 3 11.933 4 3 Celleno 2,55 4 -2,2% 3 12.290 3 3 Cellere 4,02 5 -17,7% 5 11.138 4 5 Civita Castellana 2,23 3 0,5% 3 12.491 3 3 Civitella d’Agliano 3,33 5 -16,8% 5 12.100 3 4 Corchiano 1,86 2 7,5% 2 10.307 5 3 Fabrica di Roma 1,74 2 23,5% 1 11.716 4 2 Faleria 2,29 3 15,2% 1 11.778 4 3 Farnese 4,25 5 -19,4% 5 11.924 4 5 Gallese 2,65 4 -6,5% 4 12.105 3 4 Gradoli 4,49 5 -16,3% 5 11.607 4 5 Graffignano 2,67 4 -7,9% 4 11.759 4 4 Grotte di Castro 3,82 5 -20,2% 5 13.389 2 4 Ischia di Castro 2,92 4 -13,2% 4 10.966 5 4 Latera 6,23 5 -25,0% 5 12.063 3 4 Lubriano 2,70 4 -6,2% 4 11.970 4 4 Marta 2,83 4 -5,8% 4 12.261 3 4 Montalto di Castro 2,21 3 14,0% 2 12.649 3 3 Monte Romano 2,06 3 -3,5% 3 13.569 2 3 Montefiascone 2,41 3 2,6% 3 13.181 2 3 Monterosi 1,32 1 102,1% 1 11.677 4 2 Nepi 1,79 2 20,2% 1 11.762 4 2 Onano 3,33 5 -23,6% 5 10.215 5 5 Oriolo Romano 2,13 3 27,4% 1 12.464 3 2 Orte 1,71 2 16,6% 1 13.055 2 2 Piansano 3,69 5 -13,2% 4 11.898 4 4 Proceno 5,89 5 -17,6% 5 12.032 3 4 Ronciglione 2,32 3 13,2% 2 12.232 3 3 San Lorenzo Nuovo 3,21 5 -2,6% 3 12.001 3 4 Soriano nel Cimino 2,50 3 -2,8% 3 11.728 4 3 Sutri 2,40 3 32,0% 1 12.959 3 2 Tarquinia 2,32 3 5,7% 2 13.901 2 2 Tessennano 5,39 5 -33,1% 5 11.961 4 5 Tuscania 2,31 3 6,6% 2 11.788 4 3 Valentano 2,91 4 -5,7% 4 13.297 2 3 Vallerano 2,54 4 -3,7% 3 11.724 4 4 Vasanello 1,96 2 2,4% 3 12.322 3 3 Vejano 2,51 4 3,0% 3 12.715 3 3 Vetralla 2,09 3 11,6% 2 12.292 3 3 Vignanello 2,38 3 -8,8% 4 10.664 5 4 Villa San Giovanni in T. 2,97 4 3,4% 3 12.668 3 3 Viterbo 2,02 3 11,6% 2 14.440 1 2 Vitorchiano 1,18 1 63,2% 1 13.619 2 1 Source: Our elaboration on ISTAT (2024a) and MF (2023) 649 Table A2 – Membership of Viterbo province municipalities to single aggregations 650 Municipality IMAR A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 Acquapendente 4 1 1 0 0 1 0 0 0 0 1 0 Arlena di Castro 4 1 0 0 0 1 0 0 0 0 0 0 Bagnoregio 4 0 1 0 0 0 1 0 0 0 0 0 Barbarano Romano 3 0 0 0 0 0 0 0 0 1 0 0 Bassano in Teverina 3 0 0 0 0 0 0 0 0 0 0 0 Bassano Romano 3 0 0 0 0 0 0 0 0 1 0 0 Blera 3 0 0 0 0 0 0 0 0 1 0 0 Bolsena 4 1 1 0 0 0 1 0 0 0 0 0 Bomarzo 3 0 0 0 0 0 1 0 0 0 0 0 Calcata 3 0 0 0 1 0 0 0 1 0 0 0 Canepina 4 0 0 0 1 0 0 1 0 0 0 1 Canino 4 1 1 0 0 1 0 0 0 0 0 0 Capodimonte 3 1 1 0 0 0 1 0 0 0 0 0 Capranica 3 0 0 0 0 0 0 1 0 0 0 1 Caprarola 3 0 0 0 0 0 0 1 0 0 0 1 Carbognano 3 0 0 0 0 0 0 1 0 0 0 1 Castel Sant’Elia 3 0 0 0 1 0 0 0 1 0 0 0 Castiglione in Teverina 3 0 0 0 0 0 1 0 0 0 0 0 Celleno 3 0 1 0 0 0 1 0 0 0 0 0 Cellere 5 1 1 0 0 1 0 0 0 0 0 0 Civita Castellana 3 0 0 0 1 0 0 0 1 0 0 0 Civitella d’Agliano 4 0 0 0 0 0 1 0 0 0 0 0 Corchiano 3 0 0 0 1 0 0 0 1 0 0 0 Fabrica di Roma 2 0 0 0 1 0 0 0 1 0 0 0 Faleria 3 0 0 0 1 0 0 0 1 0 0 0 Farnese 5 1 1 0 0 1 0 0 0 0 0 0 Gallese 4 0 0 0 1 0 0 0 1 0 0 0 Gradoli 5 1 1 0 0 1 0 0 0 0 1 0 Graffignano 4 0 0 0 0 0 1 0 0 0 0 0 Grotte di Castro 4 1 1 0 0 1 0 0 0 0 1 0 Ischia di Castro 4 1 1 0 0 1 0 0 0 0 0 0 Latera 4 1 1 0 0 1 0 0 0 0 1 0 Lubriano 4 0 0 0 0 0 1 0 0 0 0 0 Marta 4 1 1 0 0 0 1 0 0 0 0 0 Montalto di Castro 3 1 0 0 0 0 0 0 0 0 0 0 Monte Romano 3 0 0 1 0 0 0 0 0 0 0 0 Montefiascone 3 1 1 0 0 0 1 0 0 0 0 0 Monterosi 2 0 0 0 0 0 0 0 0 0 0 0 Nepi 2 0 0 0 1 0 0 0 1 0 0 0 Onano 5 1 0 0 0 1 0 0 0 0 1 0 Oriolo Romano 2 0 0 0 0 0 0 0 0 1 0 0 Orte 2 0 0 0 1 0 0 0 1 0 0 0 Piansano 4 1 1 0 0 1 0 0 0 0 0 0 Proceno 4 1 0 0 0 1 0 0 0 0 1 0 Ronciglione 3 0 0 0 0 0 0 1 0 0 0 1 San Lorenzo Nuovo 4 1 1 0 0 1 0 0 0 0 1 0 Soriano nel Cimino 3 0 0 0 0 0 0 0 0 0 0 1 Sutri 2 0 0 0 0 0 0 1 0 0 0 0 Tarquinia 2 1 0 1 0 0 0 0 0 0 0 0 Tessennano 5 1 0 0 0 1 0 0 0 0 0 0 Tuscania 3 1 0 0 0 0 0 0 0 0 0 0 Valentano 3 1 1 0 0 1 0 0 0 0 1 0 Vallerano 4 0 0 0 1 0 0 1 0 0 0 1 Vasanello 3 0 0 0 1 0 0 0 1 0 0 0 Vejano 3 0 0 0 0 0 0 0 0 1 0 0 Vetralla 3 0 0 0 0 0 0 1 0 0 0 1 Vignanello 4 0 0 0 1 0 0 1 0 0 0 1 Villa San Giovanni in T. 3 0 0 0 0 0 0 0 0 1 0 0 Viterbo 2 0 0 0 0 0 0 0 0 0 0 0 Vitorchiano 1 0 0 0 0 0 0 0 0 0 0 1 Source: Our elaboration 651