1. Introduction
Hydrogeological risk is one of main environmental risk categories, alongside weather, volcanic, and seismic risks, that can significantly harm people and economies during natural events [1]. Hydrogeological instability refers to detrimental changes in the land’s hydrogeological balance, primarily triggered by extreme weather events such as heavy rainfall, prolonged droughts, and ongoing climate change. These events can lead to surface erosion, landslides, floods, water stagnation, mudslides, and other forms of ground instability.
Specifically, floods are one of the most dangerous environmental hydrogeological risks we face today [2,3]. Their unpredictable nature makes them particularly alarming as it often leads to misjudging their occurrence [2,4]. Acknowledging and addressing this threat is crucial to better prepare and protect communities from devastating consequences. Indeed, hydrogeological instability poses a significant threat to communities globally, impacting not only the environment but also the population, cultural heritage, infrastructure, and economic and productive sectors [5,6,7,8].
The Mediterranean climate, characterized by rainy winters and dry summers, increases the likelihood of hydrogeological instability [1]. According to the latest report by the Istituto Superiore per la Protezione e la Ricerca Ambientale (ISPRA), Italy is one of the European countries most exposed to the risk of hydrogeological instability [9]. Given the seriousness of the problem, Europe has allocated 2.49 billion euros to Italy through the PNRR (National Recovery and Resilience Plan) for investment that includes hydrogeology management interventions.
Gisotti [10] defined hydrogeological instability as a disease of civilization: humanity accelerated the speed of natural events that otherwise would have been slower, making them natural disasters caused by humans. Over the last few years, losses from floods have significantly increased due to increased economic activity in flood zones in conjunction with heavier rainfall in parts of Europe [11]. Additionally, due to changing socioeconomic factors and climate conditions, the impacts of flooding are expected to worsen in the future [12].
Therefore, understanding the use of ecosystems is crucial for protecting our planet’s delicate balance and achieving sustainable resource use. The Goal 15 of Agenda 2030 aims for sustainable ecosystem use that protects the environment while providing economic and social benefits. In effect, ecosystems contribute to human well-being by providing benefits known as ecosystem services (ESs) directly and indirectly [13]. The extent of these services relies on complex interactions between ecosystem and socioeconomic systems [14].
In this context, it behooves us to acknowledge the significant impact of the primary sector on our society. Indeed, most ecosystems result from the interaction between nature and human activities, such as providing agricultural landscapes, which rely on managing agricultural and forestry land. In other words, several ESs arise directly from ecosystems that are affected by farming practices [15].
Agriculture is not only about producing food and other products for our daily needs; it also involves the careful and responsible management of our land and natural resources [16], performing functions related to maintaining and caring for the landscape, protecting territories and plant and animal biodiversity and mitigating the effects of climate change [17,18,19]. In the Mediterranean region, agricultural landscapes have been historically shaped by human influence, which has been crucial for ensuring food security, preserving biodiversity and maintaining a sweeping range ESs [20,21].
In effect, over the years, there has been a growing recognition of agriculture’s diverse roles in delivering ESs essential for enhancing human well-being. Scientists and policymakers leverage ecosystem services to underscore the vital role of the environment in sustaining human livelihoods, highlighting that a healthy ecosystem is essential for our well-being and economic prosperity. Consequently, the term is widely utilized, and environmental science research has progressed in various fields, ranging from theoretical concepts to practical applications also in the primary sector [22,23].
Notably, certain ESs provided by farming practices can play a crucial role in mitigating hydrogeological hazards, particularly in regions susceptible to landslides and floods [15]. For instance, cultivating specific crops, implementing contour farming techniques, and maintaining wetlands can promote soil health, improve water retention, and reduce runoff. As a result, these agricultural strategies support food security and foster resilience in vulnerable landscapes, helping protect communities from the adverse impacts of extreme weather events and geological instability. Previous research highlights the environmental functions of agriculture and livestock farming that result in regulating water and preventing soil erosion, hydrogeological instability, and biodiversity loss [24,25]. The impacts of agricultural land abandonment on hydrology and geomorphology have been previously studied (i.e., [26,27,28,29]). This phenomenon is often linked to the abandonment of marginal lands and traditional agricultural practices, the destruction and degradation of traditional irrigation systems, the removal of boundary walls, hedges, and tree lines, and, ultimately, imbalances in ecosystems.
Forest management is also significant, particularly in Italy, where a network for water regulation and slope stabilization has been created to combat erosion in the diverse landscape [30].
On the contrary, in fragile areas, such as mountainous regions, inadequate forest management can destabilize wooded slopes, leading to hydrogeological instability and endangering the livability of nearby populated areas [26].
In sum, the primary sector is recognized as both a consumer and a provider of ESs [31], and its land use management affects the level of hydrogeological risk since appropriate land management delivers ESs, which in turn bolsters resilience to challenging climate conditions [15,26,32].
However, the “second job” of the primary sector-the careful maintenance of the territory-is not directly compensated [16]. This largely stems from the fact that the externalities of primary activities that do not fall into the provisioning services category are “public” services. Due to this, they are taken for granted or often unrecognized by the final user, and their economic value is often invisible, overlooked, and ‘lost’ across the value chain [33,34,35].
Therefore, thorough research on the relationship between the primary sector and the rural landscape (From a geographical standpoint, the traditional rural landscape is a concentrated area that is intimately connected to agriculture, both geographically and functionally [36]. From the perspective of landscape ecology, rural landscapes provide a variety of landscape services that directly or indirectly fulfil human needs, such as food production, pollination, water regulation, and recreation [37]. Therefore, in this context, we refer to rural landscape as an area of land used by farmers, ranchers, forestry, and other agricultural activities and from which services are derived that directly or indirectly fulfil human needs.) in Mediterranean regions is essential and proper consideration of these services is fundamental for several reasons nowadays: (i) it allows sustainable management of natural resources and enables the understanding of how primary sector activities affect ESs [35], including preventing hydrogeological risk; (ii) current and future well-being depends on these services provision; (iii) the valorization of the ESs offered by the activities of the primary sector is functional to their support and compensation for their provision of public goods. Consequently, it is crucial to understand whether and which ESs associated with primary activities the society recognizes, appreciates, and values [33,38,39].
It must be pointed out that to assign an economic value to ESs emerging from agricultural activities— necessary to more accurately define the costs and benefits of various policy options, correct market failures, and identify the best strategies for enhancing the balance of economic development, human well-being, and environmental protection [40]-ESs must first be recognized, then appreciated, and finally deemed necessary or requested by the community [35,41,42]. Therefore, the first step is to understand how society perceives ESs [33,43]-that is, how people understand the usefulness and contribution to the well-being of some landscape [44]-that, in turn, is the first step towards developing a shared understanding of the agro-landscape [35] and what brings awareness of the agricultural landscape’s value [45].
The discussion about which ES can be considered more important than others—or, in other words, the value of an ES—depends mainly on the stakeholders’ opinions, needs, perceived benefits, knowledge, and awareness [46,47,48].
Although several studies investigated how the concept of ESs can be used in the primary sector [35], the focus was more on theoretical reflections, and there are still numerous aspects not yet investigated but necessary to support decision-makers [49,50,51]. There have been few studies in the scientific literature on the flood regulation provided by agroecosystems, mainly focusing on factors such as land use patterns and intermediate indicators [52]. As far as we know, no previous study explicitly refers to the perception evaluation of ESs provided by agriculture, livestock, and forestry businesses in a hydrogeological risky environment, particularly in flood-prone regions.
Based on the preceding, this study takes a demand-side approach and aims to investigate the perception of the ESs provided by agricultural, livestock, and forestry activities with particular focus on those furnished in areas subject to hydrogeological instability, events that occur with increasing frequency in the Italian territory and which are linked in a one-to-one way with anthropic activity.
Consequently, concerning an Italian area, this study intends to answer the following research questions:
RQ1: “Which ESs provided by agriculture, livestock, and forestry in alluvial areas are most recognized by individuals?”.
RQ2: “Is the perception of an ES provided by agriculture, livestock, and forestry in alluvial areas somehow linked to the perception of all other Ess?”
Specifically, the research focused on Sardinia, an Italian region located in the Western Mediterranean sub-basin and characterized by a territory prone to hydrogeological instability phenomena, particularly alluvial events, that are manifestations of natural events closely linked to climate change and whose extent can be significantly amplified by human action [53].
To address the research questions, a quantitative survey was conducted among both residents and non-residents to gather their perceptions of ESs provided by agriculture, livestock, and forestry in flood-prone areas. The data were initially analyzed using descriptive statistics. This was followed by a Multiple Correspondence Analysis (MCA) to group perceptions of the relevance of ESs for each activity typology. Finally, we assessed the relationship between preferences expressed for each ESs and sociodemographic variables using a set of Multinomial Logit Regression (MLR).
The research will provide significant contributions to the literature. This work intends to delve into the literature’s little study of evaluating ESs provided in primary sector in hydrogeological risk areas: by analyzing the perception of agroforestry’s role in ESs provided in flood areas, this research responds to the call for more scientific studies to improve forest ecosystem functions [54]. Furthermore, to our knowledge, no previous studies explicitly refer to the ESs provided by such activities in an environment at hydrogeological risk, particularly in areas at risk of flooding.
The research will also offer important contributions to policymakers and decision-makers. The inclusion of results in the decision-making process will help to (i) build the legitimacy of strategies and decisions, satisfying the interests of multiple stakeholders, (ii) anticipate probable reactions, behaviors, and compliance with new regulations and strategies, and (iii) identify areas of agreement [44,55,56].
The paper is structured as follows: Section 2 describes the study area, sample, and research methodology; Section 3 presents the results; Section 4 discusses the results; and Section 5 concludes by outlining implications for practitioners, academics, and policymakers, along with recommendations for future research.
2. Materials and Methods
2.1. Study Area
The area chosen for this study is Sardinia, the second-largest island in the Mediterranean, which characterizes its climate. The island features hilly and rugged terrain and is prone to slope instability and soil degradation, particularly in the mountainous and hilly regions. Along the coastline, several small valleys correspond with river estuaries and coastal wetlands. In these valleys, recent urban development—often driven by the tourism sector—has replaced traditional agricultural and grazing practices [57]. Hydrogeological hazards have historically played a significant role in the study area, and recent history has seen extreme and flash flooding events occur on the island, causing significant changes in channel morphology and sediment distribution, damage to private and public infrastructure, and even fatalities [58,59]. The region is among those most exposed to hydrogeological risk and is ranked 7th among all Italian regions, both for the surface area at risk of landslides and for that at risk of floods. According to the estimates of the Report on hydrogeological instability in Italy by ISPRA, 14.4% of Sardinia is at risk of floods and inundations. The data shows that 89.7% of Sardinian municipalities have areas in their territories characterized by a high or very high risk of landslides or a medium hydraulic risk, with a risk for over 58,000 buildings, almost 11,000 productive activities and over 28,000 employees [60]. These conditions underscore the urgent need for interventions that protect vital agricultural practices, essential for sustainable management and the preservation of the landscape and environment [61].
2.2. Selection of Relevant Ecosystem Services
To explore societal awareness and appreciation of the positive externalities from Sardinia’s primary sector, we first identified a set of Ecosystem Services (ES) deemed relevant for the activities investigated (agriculture, livestock, and forestry). The list of ESs was based on the Common International Classification of Ecosystem Services (CICES) 5.2 classification while maintaining the division into the four categories identified by the Millenium Ecosystem Assessment (MEA) [13]. The CICES classification expands and elaborates on the four categories previously defined by MEA: provisioning, regulating, cultural, and supporting services. In the CICES framework, ecosystem services are described in terms of what ecosystems provide for people, distinguishing these services from the goods and benefits that individuals derive from them. The three main categories identified, namely supply, maintenance and regulation services and cultural and social services (the support services defined previously in the MEA are included in the processes and functions that characterize ecosystems), are in turn divided into types and subtypes that identify services in an increasingly more hierarchically limited manner.
This phase involves two steps. Based on the CICES 5.2 classification, the indications from the report provided by the European Environment Agency [62] that offers a list of the primary ESs in floodplains, and previous literature on the research topic, two researchers independently identified the ESs that were relevant to the study. If there were any uncertainties regarding the inclusion criteria, a third researcher conducted an independent evaluation. This process resulted in the selection of a list of 40 ESs. Next, we organized and simplified this list to ensure it was understandable for individuals who may not be experts in the field or had not previously encountered the concept of ecosystem services. The final list includes 22 ESs shared by agricultural and livestock activities and 20 for forestry activities (Table 1).
2.3. Questionnaire Design
A structured questionnaire was drawn up in Italian for residents and non-residents of Sardinia, consisting of four sections.
As respondents were welcomed into the study and assured that the data provided was reliable and anonymous, they received a brief overview:
“Sardinia’s territory is prone to “geological-hydraulic instability” phenomena, particularly alluvial ones, representing the expression of natural events. These can be strongly amplified by both anthropic factors and those connected with climate changes in recent decades. Agriculture, livestock farming, forests, and Mediterranean scrub can mitigate the effects of climate change. However, such events can also put them at risk, compromising the performance of some of their important material and immaterial functions. These are environmental, social, and cultural functions that-if preserved and well-valued-not only contribute to safeguarding the sustainability of the systems but generate well-being in the populations and vitality of the territories involved”.
In the second part of the survey, we collected respondents’ sociodemographic data, including age, gender, education level, income, and geographic location. Following this, we gathered data on various constructs that could affect respondents’ perceptions such as working in the environmental fields and primary sector, coming from a family of peasant origins, level of knowledge of the territory and of the agricultural, livestock, and forest issues of Sardinia, level of knowledge of agriculture, level of information on climate change issues, importance attributed to environmental issues and level of worry on climate change effects. Details about previous professional or family experience and knowledge significantly influence an individual’s self-confidence regarding their understanding (Table 2). In the context of environmental behavior, this self-assurance is crucial for shaping the individual’s perceptions and behaviors [63,64,65,66].
In the last part, respondents were asked to express their appreciation for the ESs for each Sardinian primary sector investigated, indicating how important (1 = not very important, 2 = moderately important, 3 = very important) the role of primary activities in providing ESs that affect the well-being of the population in the Sardinian floodplains. In short, respondents evaluated ESs according to their personal opinions, which were influenced by the specific group they belong to, the time, and the location.
A pilot study was conducted to assess the questionnaire’s suitability prior to the actual fieldwork. We wanted to ensure that the short questionnaire presentation did not influence the respondents (absence of imbuing personal opinions and language simple to avoid misinterpretation to avoid biases) and that each question was formulated objectively and clearly, even for those who were not experts.
Seventeen hard copies were distributed face-to-face, and upon completion, feedback was requested regarding the clarity of the questions, the length of the questionnaire, and the perceived influence of the initial descriptive text.
The researcher asked each interviewee whether the introduction of the questionnaire had in any way influenced their answers. No interviewee revealed such an influence. Concerning the questions formulation, no respondents raised any issues, making these questionnaires eligible for inclusion in the overall sample.
Subsequently, we transferred the questionnaire to a Google Forms and shared it across social networks for three months (15 May–15 August 2024). Online surveys are increasingly utilized for research data collection because they are cost-effective, quick, and efficient in gathering valid information while overcoming geographic barriers and expenses [67,68].
Respondents were recruited through non-random criteria and social networks, creating a potential selection/sampling bias due to their beliefs, interests, and feelings towards a particular topic that can affect their willingness to participate in a survey [69,70]. Nevertheless, no respondents were compensated for participating in this study. Therefore, we could identify individuals with strong opinions on ESs and understand their authentic appreciation.
By the response deadline, 270 valid answers had been recorded (all questions in the questionnaire were mandatory, and only those filled out completely could be submitted).
2.4. Data Analysis
Data analysis was conducted using descriptive statistics, Multiple Correspondence Analysis (MCA), and Multinomial Logit Regression (MLR).
First, descriptive statistics were computed to summarize the dataset and provide essential background information about the variables involved.
Subsequently, the MCA was employed to cluster respondents’ preferences based on their perceptions of the relevance of ESs, thus identifying the most recognized ESs in crops, livestock, and forest activities and assessing the relationships among their amenities. MCA, an extension of correspondence analysis and principal component analysis, is an exploratory multivariate analysis technique well-suited for analyzing categorical data [71,72,73,74]. It allows for the assessment of relationships among multiple categorical variables. It involves creating a lower-dimensional representation of quantitative variables that captures the most significant patterns and relationships among the categories, making the analysis more interpretable [75].
MCA is performed by applying a standard correspondence analysis on an indicator matrix (X) structured as J × M, where Jk represents the vector of the perceived level of importance of ESs (low, moderate, or elevated attributed importance) for each K nominal variable (with ∑Jk = J and where k is nominal variables represented by the ESs, that are 22 in case of crops and livestock activities, or 20 in case of forest activities) and M = 270 (the number of observations).
MCA on X yields two sets of factor scores, one for each row and column, which are typically scored to ensure their variance matches the associated eigenvalue. The analysis will demonstrate the relationship between the ESs identified for each primary activity by expressing them in a low-dimensional space to define distinct clusters or profiles.
We employed an MCA biplot [76] to visualize the relationships among several categorical dependent variables in a two-dimensional space. Biplots offer a visual representation of data that can uncover patterns, relationships, and structures that might be hidden in complex, high-dimensional data sets. By projecting the data onto a two-dimensional plane, biplots enable us to visualize the geometry of the data, providing insights into both the observations and the variables that characterize them. Specifically, biplots simplify complex data by transforming it into orthogonal components that serve as the axes for plotting data points and capture the most variance (an orthogonal rotation of the principal axes was conducted to enhance interpretation).
According to Khangar and Khangar [77], “biplots show the following quantities of a data matrix in one display. (i) The variance-covariance structure of the variables, i.e., the inner product between two variables and the cosine of the angle between them, approximates their correlation with equality if the fit is perfect. (ii) It explores the relationship (interrelationship) among (between) rows and columns. (iii) The Euclidean distances between observations in the multidimensional space are shown.”
The correlation between two variables is visible by looking at the cosine of the angle between lines which can be traced on each pair of axes and points of the biplot: a slight angle between two vectors suggests that the variables are highly correlated.
Ultimately, we analyzed how the preferences expressed for each ESs and primary sector activity relate to sociodemographic variables through an MLR model set.
MLR allows modeling relationships between a set of independent variables (dichotomous or continuous) and a dependent variable that can have more than two categories. It extends binary logistic regression to handle outcome variables with three or more levels (in this case, there are three levels that are low, moderate, or elevated attributed importance). Using MLR, we can examine the impact of sociodemographic, sociodemographic, past professional and family experience, and subject knowledge variables on trends in the appreciation of ESs derived from crops, livestock, and forestry activities in floodplain areas of Sardinia.
3. Results
3.1. Sample Profile
The 270 Italian respondents were primarily residents (90%) and females (67.7%), mainly in the 50 to 59 age group, followed by those aged 49 to 59. Only 8.51% have a lower secondary school diploma, and over 60% of the interviewees have at least a university degree. These percentages may be attributed to the voluntary response sample. Most respondents had an office job (28%), followed by those who do unidentified work and retired people and freelancers. Over a third of respondents were not employed in the agricultural and livestock (79.6%) or environmental (75.9%) sector and did not come from a family with a peasant tradition (65,9%). The knowledge of the Sardinian Territory is just above sufficiency (4.4), whereas the knowledge of agriculture and livestock issues in Sardinia is not so high. In fact, on a scale of 1 to 7, just about 33% answered from 5 to 7, and 47.8 gave an vote lower than 4. As regards the environmental dimension and the problems connected to it, the interviewees showed that they highly value environmental issues (80% gave a vote of 6 and 7), are well informed about climate change (over 75% gave a vote between 5 and 7), and are worried about it (over 88% gave a vote between 5 and 7) (Table 3).
3.2. Descriptive Analysis
The average responses for each ecosystem service (ES) indicated a high awareness of the externalities created by crop activities in flood-prone areas (see Table 4). The slight standard deviation concerning the mean value suggested that the data were closely clustered around the average. The frequency analysis revealed a strong tendency to acknowledge substantial benefits, except in three specific areas: other goods and services (such as pharmaceuticals and textiles), recreational activities, and hiking opportunities.
Regarding livestock activities (Table 5), respondents highly perceive provisioning services, particularly about food supply. In other instances, their evaluations tend to be moderate. For the supporting and regulating services, perceptions are predominantly high for all ecosystem services (ESs) provided by farms in flood-prone areas, except for the service focused on preserving livestock practices. Additionally, all cultural services, except for safeguarding culture and identity, are perceived moderately.
Looking at the perception of ESs provided by forestry activities (Table 6), an almost parity between moderate and high evaluation of provisioning services emerges. In all other ESs provided by forestry in areas subject to flooding, the perception is considerably high, with the highest result recorded precisely about the regulation of flood risk service.
Finally, the synoptic Table 7 indicates the mean and standard deviation of the four categories of ESs to represent better the differences in the perception of the ESs provided by the three types of primary activities examined.
3.3. Multiple Correspondence Analysis
Two primary dimensions were identified to represent the results on the Cartesian plane. The biplot is an efficacious tool that helps us analyze how variables effectively differentiate between groups of objects. Figure 1, Figure 2 and Figure 3 display the component loadings for the survey data related to agriculture, livestock, and forestry activities. As shown in Table 8, the first two-component model explained approximately 50% of the variability overall in all three activities.
In the agricultural sector, dimension one showed a Cronbach alpha of 0.66 and explained 32.8% of the total variance, while dimension two showed a Cronbach alpha of 0.27 and explained 13.5% of the total variance.
The MCA for the livestock sector showed a Cronbach alpha of 0.66 and explained 33.2% of the total variance, while size two showed a Cronbach alpha of 0.41 and explained 20.7% of the total variance.
The MCA for the forestry sector, dimension one showed a Cronbach alpha of 0.75 and explained 37.8% of the total variance, while dimension two showed a Cronbach alpha of 0.29 and explained 14.5% of the total variance.
The biplot allows you to study the extent to which variables divide groups of data.
The findings showed that observations were grouped according to the respondents’ relevance level. Upon examining the graph, it became clear that the first dimension (represented by the horizontal axis) distinguished between respondents who provided moderate evaluations of ESs and those who expressed more polarized opinions—either moderate important or very important. The vertical axis distinguishes respondents who perceived poor contribution from the primary activities to the ESs and those who recognized a significant contribution. The graphs demonstrate that the first and second dimensions achieved flawless differentiation among the variables. This indicates that recognizing the importance of a single service inherently involves an appreciation for all services delivered by the primary sector.
The results show that the observations are grouped according to the importance attributed by the interviewees. Examining the graphs, in all three cases, ES provided in agriculture, livestock, and forestry, it emerges that when the importance of service is recognized, the same value is also recognized for all the other ES. The synoptic analysis shows that the averages of the ES recognized by sector are almost similar. The correlation analysis also shows that the results obtained in the three sectors are positively correlated.
3.4. Set of Logit Analysis
Before conducting a series of MLRs, we performed a multicollinearity test on the sociodemographic variables. All 11 variables demonstrated a Variance Inflation Factor (VIF) of less than 2 and a tolerance level greater than 0.5. Consequently, there is no concern regarding multicollinearity or highly correlated independent variables that convey similar information in this research [78,79].
The average appreciation of the ESs (the dependent variable) provided by each activity of the primary sector was regressed on the set of socio-demographic variables (identified as independent variables) (see Table 9, Table 10 and Table 11). The findings reveal that three independent variables consistently hold significance across all three regression analyses: the level of education, employment in the environmental sector, and knowledge about climate change. The climate change concern is relevant only to agriculture and forestry. At the same time, the knowledge of territory was found to have a role only in appreciating the benefits provided by livestock activity. None of the other independent variables demonstrated any significant relationship with the dependent variables.
4. Discussion
Flooding accounts for one-third of Europe’s economic losses from natural hazards, making it one of the most common hazards [80]. In Italy, where the vast majority of municipalities (93.9%) are exposed to the risk of landslides, floods, and coastal erosion, the significant increase in these phenomena has severe repercussions for the environment, particularly affecting fragile territories and local populations, with economic losses as well as human lives lost [81].
Floods and landslides are affected by natural factors and human activities, with their frequency and impact linked to the current development model [15]. However, actions taken by farmers that enhance ESs can help reduce hydrogeological risks. Refocusing research on ecosystem services is urgent [82] and can significantly contribute to sustainable development, especially as time to address sustainability challenges grows short [83].
This study focused on ESs economic valuation, a key research theme linking ESs to decision-making [83] as raises awareness about the costs of ES loss and investigates the people’s perceptions of ESs that affect environmental management choices [84]. Consequently and since making the evaluation of ESs perceptions crucial for fostering paths in terms of sustainability [49], this research investigated how society perceive ESs provided by the primary sector, particularly in flood-prone areas.
Although 90% of the interviewees are Sardinian and 86% declared to have worked in the environmental sector, the level of subject knowledge of the Sardinian territory is only 4.4 (on a scale from 1 to 7). However, it must be remembered that the territory is an entity arising from the interconnected dynamics between nature and society [85,86]—and, therefore, also includes productive activities—. Its subjective knowledge is essential not only for those who govern it but also for those who live there since it implies that one knows how to give meaning to various territorial features, recognize their historical, cultural, physical, and environmental values, and become aware of local issues and conditions [87]. Also, the knowledge of the problems that affect the primary sector is shallow (3.66). This result should not be interpreted solely because only 2.2% of the sample engages in agricultural or livestock activities. Instead, it should be understood in the context of society’s still limited awareness regarding issues in this sector. On the contrary, a picture emerges in which great importance is given to environmental issues, and the level of attention and concern regarding climate change is high.
Concerning the appreciation of ESs, respondents appear to recognize and appreciate the provision of ESs provided by crops more than the other two primary activities.
Looking at crop activities, the supporting ESs appear to obtain greater appreciation, slightly exceeding the regulation services. This data is relevant because it highlights that people’s perception of agriculture goes beyond providing tangible natural things that meet human needs (including food, clean water, fuel, and genetic resources). They recognize the importance of agricultural activities in providing supporting ES and demonstrate an awareness of their role within riverine areas, particularly concerning the preservation of pollinating insects, soil fertility, biodiversity, and water quality. A similar argument can be made for livestock activities, though to a lesser extent, as the appreciation for such services decreases from 2.56 in crop activities to 2.39 in livestock activities.
Among the regulatory services provided by agricultural activities, the awareness of their role is most prominent in fire risk management (2.60), a significant concern for the Sardinian territory. This is followed by the regulation of flood risk (2.57). This information is crucial for this research as it highlights the interviewees’ recognition of the positive externalities associated with agricultural activities, underscoring the importance of preserving these practices. Even in this case, a similar argument can be made for livestock activities: the appreciation for such services decreases from a rating of 2.53 in crop activities to 2.249 in livestock activities; awareness of livestock farming’s role in fire regulation is recorded first, while flood risk regulation, on par with climate change mitigation, is placed in third place in importance, recording only 2.23. It should be noted that livestock are frequently criticized for their negative impact on the environment due to the greenhouse gas emissions that directly contributing to climate change [88]. However, the impact of livestock on the environment varies depending on the production system used and it can help in saving natural resources [88,89,90,91]. Our results can be interpreted in two ways. First, respondents may be uninformed about the positive externalities associated with livestock farming. This lack of awareness might explain the consistently lower positive responses recorded for livestock farming than crop activities. If respondents are poorly informed, they may ’unperception’ the ESs and overlook their importance [92]. Alternatively, respondents may not believe livestock farming can generate significant positive externalities, particularly in reducing flood risks. In both circumstances, according to Dumont et al. [93], livestock, as part of this sustainable future, requires further communication of the services provided by livestock farming to society.
When examining cultural ESs, they have been deemed the least important overall across crop externalities. This data raises particular reflections because cultural ESs ‘are usually directly experienced and intuitively appreciated’ [94]. The most recognized crop’ (but also livestock) activities’ externality concerns the culture and identity safeguard, followed by the landscape safeguard. Actually, the overall perception of the ES culture is diminished by the moderate perception of ES recreation. It highlights the need for better management and promotion of agricultural and livestock areas. The durability and sustainable development of these activities and the areas in which they are present can be achieved by leveraging sustainable tourism, especially that based on natural and cultural assets [35]. This approach can help maximize the potential of the territorial capital, which includes historic and artistic heritage, agricultural, pastoral, and forestry systems, as well as traditional crafts and local expertise. Leveraging these assets will possibly revitalize these areas [95,96] and foster local development [97,98]. However, if ’only human activities and presence can maintain this huge capital’ [98], the role of policymakers is crucial in ensuring that these activities are not abandoned, adopting policies that generate attractiveness and change the trend among the primary sector. Currently, an increasing part of the population moves along the coasts, where regional tourism is most concentrated [99], increasingly depleting the number of workers in inner island areas [100] and primary activities.
Forestry activities must be discussed separately. Although Provision ESs register a lower value than the other two primary activities, this is due to the low perception of food production. This data supports the earlier findings of Paletto et al. [101], who found that the provision of timber and fuelwood is the least important benefit reported by a sample of Italian visitors to a Tuscany forest. In contrast, biomass production is higher than the other activities. The perception of externalities inherent in regulating ESs slightly exceeds those of supporting.
The results indicate that the value assigned to flood regulation is notably high, at 2.66, making it the highest value in absolute terms. This finding is not surprising, as the ESs provided by forests that are most widely recognized by the public include the mitigation of adverse events such as floods and droughts, the reduction of noise and air pollution, the purification and maintenance of water quality, the containment of toxic substance dispersion, the preservation of soil fertility, and the control of erosion [102].
Actually, among all the ESs that the three activities have in common, forestry has consistently registered a higher degree of perception. In recent decades, woodlands and forestry have gained increasing importance to society [103], which is also reflected in how people perceive the services derived from these activities.
Another important point is MCA’s results, which revealed the ’one for all’ tendency, confirming the previous study of Madau et al. [35]. Respondents recognize the importance of ESs as a whole, and appreciating one ES necessitates acknowledging all ESs. These data are invaluable for policymakers who seek to enhance public awareness of the ESs generated by those activities in the primary sector. Recognizing these contributions is essential for fostering the durability and resilience of those practices and promoting the benefits they offer to society. Policymakers can take action to raise awareness of only a few ESs since this will increase awareness of all positive externalities the primary sector generates, thereby raising overall awareness with minimal effort.
Examining the relationships between socio-demographic variables and the average appreciation of the ESs provided by each primary sector activity, it is important to note the irrelevance of age and gender. This data can be read in light of the flood risk perception, a subjective concept that reflects how individuals assess the potential negative impacts of floods [104,105]. This perception is not uniform [106]; it varies from person to person and is shaped by demographic and psychological factors, along with personal flood experiences. Previous research focused on how demographic variables such as age, gender, education level, and income significantly influence how people perceive this risk are contrasting [107]. In this study, we hypothesize that, as Oasim et al. [108] and Armas et al. [109], age and gender do not influence flood risk perception and, therefore, not even the perception of the role of primary activities in mitigating such risk. Therefore, policymakers aiming to enhance risk communication—which is designed to improve risk perception and awareness among at-risk populations and promote preventive measures for risk reduction [110], including a greater awareness of stakeholders and operators in the primary sector—don’t have to target only on specific age groups or gender.
The situation is different when it comes to education. Previous studies [108,111,112,113] confirm that education influences risk perception, making individuals more aware of environmental hazards. Anılan and Yüksek [113] found that people with higher education levels proposed more environmentally friendly solutions for mitigating flood damage and demonstrated a greater awareness of their responsibilities. Therefore, we can conclude that a higher level of education may enhance the recognition of positive externalities generated by primary activities that help reduce the risk of floods. Working in the environmental sector is also a discriminant factor in recognizing the role of the primary sector in all three types of activities examined, as well as being informed about climate change. In this case, the interviewees probably have more knowledge about humanity’s tools to combat climate change, including good agricultural, livestock, and forestry practices.
It is important to note that while the average importance attributed to environmental issues is 6.22 on a scale of 1 to 7, this general figure is irrelevant for our research purposes. Discussing environmental issues in general is insufficient to effectively raise awareness about the primary sector’s role in producing ESs; instead, it is necessary to specifically emphasize the primary sector’s role in mitigating the effects of climate change.
Knowledge of the territory only affects the perception of the positive externalities generated by livestock farming, while working in the primary sector or coming from a peasant family is irrelevant for all primary activities. This last data suggests that sector operators may lack awareness of their role in improving the territory’s landscape and flood resilience, and human well-being. In an extension of what Mattalia says [18], according to which shepherds are unaware gardeners, the results lead us to believe that farmers, breeders, and foresters are unaware custodians of the territory.
The importance of these results stems from the fact that understanding how communities perceive and value ESs is crucial because, although they are difficult to measure, they can be estimated based on evaluations of people’s preferences [33]. Such estimation supports policymakers in making informed decisions and justifying investments and actions, also in light of the fact that the maintenance of farming activities is viewed as a means to achieve environmental goals [15]. Moreover, this knowledge can help decision-makers develop communication strategies to inform communities about the positive externalities of the primary sector, in this case, in flood-prone areas such as those of Sardinia.
Our research demonstrates the potential for a transformative approach, moving away from traditional subsidies towards a system of payments for ES provision. By aligning these payments with market demands—reflecting the values society places on ESs—it is possible create more targeted and effective policies that focus on the specific ESs that matter most to communities s [114].
Our study is not without limitations. While the sample size meets Kline’s dictates [115], we cannot generalize the findings from our study due to the limited area of investigations to only Sardinia. Additionally, the issue of self-selection must be addressed [116], as participants had the freedom to engage in the survey. As a result, it is probable that mostly individuals with an interest in the topic, whether positive or negative, chose to take part. Furthermore, the respondents in this study were highly educated, which limits the applicability of our findings to the general population. Still, the overall positive perception of flood regulation ES provided by the primary sector among respondents underscores the critical importance of integrating these services into decision-making. Although our findings may not be broadly generalizable, they offer valuable insight into the perspectives of those with strong opinions.
5. Conclusions
Flooding is the most critical natural hazard worldwide [117], and Italy is no different since, for centuries, it has grappled with hydrogeological instability. Addressing these issues is essential for safeguarding communities and ensuring a sustainable future. In this context, significant support arises from the primary sector due to its careful maintenance of the territory [16].
Primary sector, through cultivation and the maintenance of meadows, pastures and forests, performs crucial environmental functions in regulating water and preventing hydrogeological and slope instability [25].
In response to the first research question, this study aimed to analyze the perception of ESs provided by the primary sector, highlighting a significant sensitivity in recognizing and appreciating these services. Additionally, the research aimed to investigate whether the perception of an ES provided by primary activities in alluvial areas is somehow linked to the perception of other ESs. The results support previous findings in the Mediterranean region and Sardinia, particularly in the context of dairy sheep breeding [35] and indicate a tendency to recognize the importance of ESs as a whole. It means that decision-makers can only focus on increasing the perception of some ESs, as these will drive the perception of all the others.
Furthermore, the findings indicate that age and gender do not significantly influence how important people consider Ess. However, the level of education and awareness about climate change emergencies do impact the perceived importance of these services.
Recognizing the significance of this research field is essential to guiding both public and private stakeholders in efforts to ensure the survival of the primary sector in Mediterranean regions, which are especially vulnerable to hydrogeological instability. Therefore, further research is necessary to explore the relationship between the primary sector and the rural landscape in the Mediterranean regions. This research could provide valuable evidence to support rural policies designed to protect primary sector activities in these areas. Future research can concentrate on the second step, which involves evaluating the economic value of ecosystem services (ES) provided by the private sector. This includes monetarily assessing key ESs as well as examining the preferences and willingness to pay among various demographic groups.
Furthermore, to our knowledge, no previous studies explicitly refer to the perception of flood regulation ES provided by agriculture, livestock, and forestry businesses in an environment at hydrogeological risk, particularly in flood-prone regions. To reinforce our conclusions, further research could utilize stratified sampling. Additionally, conducting comparative studies will deepen our understanding of how perceptions evolve and how these changes connect to the characteristics of the agricultural landscape and its users.
Conceptualization, B.A., R.F., P.P., A.B. and F.A.M.; methodology, B.A. and F.A.M.; software, B.A. and F.A.M.; validation, B.A. and F.A.M.; formal analysis, B.A. and F.A.M.; investigation, B.A., A.B. and F.A.M.; resources, B.A., A.B. and F.A.M.; data curation, B.A., R.F., P.P., A.B. and F.A.M.; writing—original draft preparation, B.A. and F.A.M.; writing—review and editing, B.A., R.F., P.P., A.B. and F.A.M.; visualization, B.A. and F.A.M.; supervision, R.F. and P.P.; project administration, F.A.M.; funding acquisition, F.A.M. All authors have read and agreed to the published version of the manuscript.
Ethical review and approval were waived for this study. In accordance with EU Regulation no. 2016/679 (GDPR), this study was based on a fully anonymous online survey with no personally identifiable information being collected, ethical review and approval were waived.
Relating to ethical questions, in accordance with EU Regulation no. 2016/679 (GDPR), all personal data collected during this study were processed based on the informed consent of the participants (Article 6(1)(a)). Data collection was limited to the specific purposes outlined in the study, and all personal information was anonymized where possible (Article 5(1)(b)). Appropriate security measures were implemented to ensure the confidentiality and integrity of the data (Article 32). Participants were informed about their rights to access, rectify, and request the deletion of their data at any time (Articles 13 and 17).
The data that support the findings of this study are available from the corresponding author, upon reasonable request.
The authors declare no conflicts of interest.
Footnotes
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List of ecosystem services.
| Crops | Livestock | Forestry |
|---|---|---|
| Provisioning | Provisioning | Provisioning |
| Food production | Food production | Food production |
| Feed production | Feed production | Timber and biomass |
| Other goods and services | Other goods and services | |
| Biomass production | Biomass production | |
| Supporting | Supporting | Supporting |
| Preservation of pollinating insects | Preservation of pollinating insects | Preservation of pollinating insects |
| Biodiversity safeguard | Biodiversity safeguard | Biodiversity safeguard |
| Preservation of soil fertility | Preservation of soil fertility | Preservation of soil fertility |
| Water quality safeguard | Water quality safeguard | Water quality safeguard |
| Preservation of agricultural practices | Preservation of livestock practices | |
| Regulating | Regulating | Regulating |
| Sequestration of plant | Sequestration of plant | Sequestration of plant |
| Regulation of odors and noise | Regulation of odors and noise | Regulation of odors and noise |
| Regulation of soil erosion risk | Regulation of soil erosion risk | Regulation of soil erosion risk |
| Regulation of landslide risk | Regulation of landslide risk | Regulation of landslide risk |
| Regulation of flood risk | Regulation of flood risk | Regulation of flood risk |
| Regulation of fire risk | Regulation of fire risk | Regulation of fire risk |
| Regulation of air pollutants | Regulation of air pollutants | Regulation of air pollutants |
| Climate change mitigation | Climate change mitigation | Climate change mitigation |
| Regulation of greenhouse gas | Regulation of greenhouse gas | Regulation of greenhouse gas |
| Cultural | Cultural | Cultural |
| Landscape safeguard | Landscape safeguard | |
| Recreational activities supply | Recreational activities supply | Recreational activities supply |
| Hiking activities supply | Hiking activities supply | Hiking activities supply |
| Culture and identity safeguard | Culture and identity safeguard | Culture and identity safeguard |
| Paths and villages safeguard | ||
| Monumental trees safeguard |
List of the socioeconomic variables and variables that can influence perception.
| Variable | Description | Value |
|---|---|---|
| Gender | Gender of the respondent | Female = 0; Male = 1 |
| Age | Age of the respondent | 18–29 = 1; 30–39 = 2; 40–49 = 3; |
| Education | Level of education | Middle school = High school = 3; Degree = 3; Post-graduate = 4 |
| Work Environment | Working in the environmental fields | No = 0; Yes = 1 |
| Work Agriculture | Working in agriculture | No = 0; Yes = 1 |
| Family | Agricultural family | No = 0; Yes = 1 |
| Knowledge Territory | Level of knowledge of the territory | From 1 to 7 |
| Knowledge of primary sector issues | Level of knowledge of issues concerning crops, livestock, and forestry activities in Sardinia | From 1 to 7 |
| Knowledge Climate change | Level of information on climate change | From 1 to 7 |
| Relevance Environment | Importance attributed to environmental issues | From 1 to 7 |
| Climate change worry | Level of worry about climate change effects | From 1 to 7 |
Profile of the sample.
| Variable | Total n = 270 | % |
|---|---|---|
| Gender | ||
| Male | 180 | 66.67 |
| Female | 90 | 33.33 |
| Age | ||
| ≤17 | 2 | 0.74 |
| 18–29 | 25 | 9.26 |
| 30–39 | 38 | 14.07 |
| 40–49 | 92 | 34.07 |
| 50–59 | 64 | 23.70 |
| ≥60 | 49 | 18.15 |
| Resident | ||
| In Sardinia | 242 | 89.63 |
| In other regions | 28 | 10.37 |
| Education | ||
| Lower middle school | 23 | 8.52 |
| High school | 82 | 30.37 |
| University | 96 | 35.56 |
| Post University | 69 | 25.56 |
| Occupation | ||
| Breeder | 3 | 1.11 |
| Desk Job | 76 | 28.15 |
| Director | 9 | 3.33 |
| Farmer | 3 | 1.11 |
| Freelance | 25 | 9.26 |
| Househusband/housewife | 10 | 3.70 |
| Others | 51 | 18.89 |
| Pensioner | 25 | 9.26 |
| Student | 22 | 8.15 |
| Teacher | 35 | 12.96 |
| Unemployed | 11 | 4.07 |
| Prior employed in the agricultural or livestock sector | ||
| Yes | 55 | 20.37 |
| No | 215 | 79.63 |
| Prior employed in the environmental sector | ||
| Yes | 65 | 24.07 |
| No | 205 | 75.93 |
| Coming from a family with a peasant tradition | ||
| Yes | 92 | 34.07 |
| No | 178 | 65.93 |
| Subject knowledge of the Sardinia territory | ||
| 1 | 8 | 2.96 |
| 2 | 21 | 7.78 |
| 3 | 44 | 16.30 |
| 4 | 59 | 21.85 |
| 5 | 76 | 28.15 |
| 6 | 44 | 16.30 |
| 7 | 18 | 6.67 |
| Subject knowledge of agriculture and livestock issues in Sardinia | ||
| 1 | 36 | 13.33 |
| 2 | 38 | 14.07 |
| 3 | 55 | 20.37 |
| 4 | 52 | 19.26 |
| 5 | 45 | 16.67 |
| 6 | 30 | 11.11 |
| 7 | 14 | 5.19 |
| Importance given to environmental issues | ||
| 1 | 0 | 0.00 |
| 2 | 0 | 0.00 |
| 3 | 2 | 0.74 |
| 4 | 17 | 6.30 |
| 5 | 36 | 13.33 |
| 6 | 64 | 23.70 |
| 7 | 151 | 55.93 |
| Climate change information level | ||
| 1 | 0 | 0.00 |
| 2 | 6 | 2.22 |
| 3 | 17 | 6.30 |
| 4 | 44 | 16.30 |
| 5 | 73 | 27.04 |
| 6 | 69 | 25.56 |
| 7 | 61 | 22.59 |
| Level of concern about climate change | ||
| 1 | 1 | 0.37 |
| 2 | 4 | 1.48 |
| 3 | 7 | 2.59 |
| 4 | 19 | 7.04 |
| 5 | 42 | 15.56 |
| 6 | 66 | 24.44 |
| 7 | 131 | 48.52 |
Descriptive analysis of the level of perception of ESs provided by crop activities.
| Variables | Score | Frequency (%) | |||
|---|---|---|---|---|---|
| Mean | S.D. | Low | Mod. | Elevate | |
| Provisioning | |||||
| Food production | 2.65 | 0.48 | 0.37 | 34.07 | 65.56 |
| Feed production | 2.53 | 0.55 | 2.59 | 41.85 | 55.56 |
| Other goods and services | 2.37 | 0.63 | 7.78 | 47.04 | 45.19 |
| Biomass production | 2.39 | 0.60 | 6.30 | 48.89 | 44.81 |
| Supporting | |||||
| Preservation of pollinating insects | 2.61 | 0.53 | 1.85 | 35.56 | 62.59 |
| Biodiversity safeguard | 2.58 | 0.54 | 2.22 | 37.41 | 60.37 |
| Preservation of soil fertility | 2.56 | 0.54 | 2.22 | 39.63 | 58.15 |
| Water quality safeguard | 2.62 | 0.52 | 1.48 | 35.19 | 63.33 |
| Preservation of agricultural practices | 2.44 | 0.57 | 4.07 | 48.15 | 47.78 |
| Regulating | |||||
| Sequestration of plant pollutants | 2.54 | 0.55 | 2.59 | 40.74 | 56.67 |
| Regulation of odors and noise | 2.39 | 0.63 | 7.78 | 45.56 | 46.67 |
| Regulation of soil erosion risk | 2.52 | 0.59 | 4.81 | 38.15 | 57.04 |
| Regulation of landslide risk | 2.54 | 0.59 | 5.19 | 35.19 | 59.63 |
| Regulation of flood risk | 2.57 | 0.58 | 4.44 | 34.44 | 61.11 |
| Regulation of fire risk | 2.60 | 0.56 | 3.33 | 33.70 | 62.96 |
| Regulation of air pollutants | 2.55 | 0.56 | 2.96 | 39.26 | 57.78 |
| Climate change mitigation | 2.53 | 0.57 | 3.70 | 39.63 | 56.67 |
| Regulation of greenhouse gas | 2.55 | 0.52 | 1.11 | 42.96 | 55.93 |
| Cultural | |||||
| Landscape safeguard | 2.44 | 0.60 | 5.93 | 44.44 | 49.63 |
| Recreational activities supply | 2.30 | 0.65 | 10.37 | 49.63 | 40.00 |
| Hiking activities supply | 2.31 | 0.62 | 8.52 | 51.48 | 40.00 |
| Culture and identity safeguard | 2.51 | 0.58 | 4.07 | 40.37 | 55.56 |
Descriptive analysis of the level of perception of ESs provided by livestock activities.
| Variables | Score | Frequency (%) | |||
|---|---|---|---|---|---|
| Mean | S.D. | Low | Mod. | Elevate | |
| Provisioning | |||||
| Food production | 2.48 | 0.58 | 4.07 | 43.70 | 52.22 |
| Feed production | 2.28 | 0.64 | 10.37 | 51.48 | 38.15 |
| Other goods and services | 2.18 | 0.69 | 16.30 | 49.63 | 34.07 |
| Biomass production | 2.41 | 0.59 | 5.19 | 48.15 | 46.67 |
| Supporting | |||||
| Preservation of pollinating insects | 2.36 | 0.59 | 15.56 | 38.15 | 46.30 |
| Biodiversity safeguard | 2.36 | 0.74 | 2.59 | 42.22 | 55.19 |
| Preservation of soil fertility | 2.31 | 0.73 | 9.26 | 40.37 | 50.37 |
| Water quality safeguard | 2.53 | 0.55 | 15.93 | 32.59 | 51.48 |
| Preservation of livestock practices | 2.41 | 0.65 | 5.56 | 52.96 | 41.48 |
| Regulating | |||||
| Sequestration of plant pollutants | 2.27 | 0.75 | 17.78 | 37.04 | 45.19 |
| Regulation of odors and noise | 2.18 | 0.76 | 21.48 | 38.89 | 39.63 |
| Regulation of soil erosion risk | 2.19 | 0.79 | 23.33 | 34.44 | 42.22 |
| Regulation of landslide risk | 2.20 | 0.80 | 23.70 | 32.59 | 43.70 |
| Regulation of flood risk | 2.23 | 0.80 | 22.96 | 30.74 | 46.30 |
| Regulation of fire risk | 2.38 | 0.73 | 14.44 | 33.33 | 52.22 |
| Regulation of air pollutants | 2.22 | 0.78 | 21.48 | 34.81 | 43.70 |
| Climate change mitigation | 2.23 | 0.75 | 19.26 | 38.15 | 42.59 |
| Regulation of greenhouse gas | 2.22 | 0.78 | 21.48 | 35.19 | 43.33 |
| Cultural | |||||
| Landscape safeguard | 2.33 | 0.66 | 10.74 | 45.56 | 43.70 |
| Recreational activities supply | 2.16 | 0.73 | 20.00 | 43.70 | 36.30 |
| Hiking activities supply | 2.19 | 0.71 | 17.04 | 46.67 | 36.30 |
| Culture and identity safeguard | 2.45 | 0.57 | 3.70 | 47.78 | 48.52 |
Descriptive analysis of the level of perception of ESs provided by forestry activities.
| Variables | Score | Frequency (%) | |||
|---|---|---|---|---|---|
| Mean | S.D. | Low | Mod. | Elevate | |
| Provisioning | |||||
| Food production | 2.17 | 0.74 | 20.37 | 42.22 | 37.41 |
| Timber and biomass | 2.43 | 0.59 | 4.81 | 47.41 | 47.78 |
| Supporting | |||||
| Preservation of pollinating insects | 2.60 | 0.54 | 2.22 | 35.93 | 61.85 |
| Biodiversity safeguard | 2.59 | 0.51 | 1.11 | 38.52 | 60.37 |
| Preservation of soil fertility | 2.59 | 0.53 | 1.85 | 37.41 | 60.74 |
| Water quality safeguard | 2.59 | 0.52 | 1.48 | 38.15 | 60.37 |
| Regulating | |||||
| Sequestration of plant pollutants | 2.60 | 0.54 | 2.59 | 34.44 | 62.96 |
| Regulation of odors and noise | 2.49 | 0.64 | 8.15 | 35.19 | 56.67 |
| Regulation of soil erosion risk | 2.63 | 0.51 | 1.11 | 34.44 | 64.44 |
| Regulation of landslide risk | 2.65 | 0.49 | 0.74 | 33.70 | 65.56 |
| Regulation of flood risk | 2.66 | 0.50 | 1.11 | 32.22 | 66.67 |
| Regulation of fire risk | 2.50 | 0.63 | 7.41 | 34.81 | 57.78 |
| Regulation of air pollutants | 2.60 | 0.53 | 2.22 | 35.56 | 62.22 |
| Climate change mitigation | 2.64 | 0.50 | 1.11 | 34.07 | 64.81 |
| Regulation of greenhouse gas | 2.62 | 0.52 | 1.85 | 34.44 | 63.70 |
| Cultural | |||||
| Recreational activities supply | 2.46 | 0.61 | 5.93 | 42.59 | 51.48 |
| Hiking activities supply | 2.53 | 0.56 | 2.96 | 40.74 | 56.30 |
| Culture and identity safeguard | 2.52 | 0.58 | 4.07 | 39.63 | 56.30 |
| Paths and villages safeguard | 2.57 | 0.55 | 2.59 | 37.41 | 60.00 |
| Monumental trees safeguard | 2.57 | 0.57 | 4.07 | 34.81 | 61.11 |
Summary of the level of perception of ESs provided by agriculture, livestock, and forestry activities.
| Ecosystem Services | Crops | Livestock | Forestry | |||
|---|---|---|---|---|---|---|
| Mean | S.D. | Mean | S.D. | Mean | S.D. | |
| Provisioning | 2.50 | 0.46 | 2.34 | 0.50 | 2.30 | 0.49 |
| Supporting | 2.56 | 0.45 | 2.39 | 0.53 | 2.60 | 0.47 |
| Regulating | 2.54 | 0.48 | 2.24 | 0.70 | 2.60 | 0.46 |
| Cultural | 2.40 | 0.53 | 2.28 | 0.59 | 2.53 | 0.51 |
| Total | 2.50 | 0.43 | 2.31 | 0.52 | 2.53 | 0.44 |
Cronbach alpha and eigenvalue of plots’ dimensions of crop, livestock, and forestry activities.
| Crop | Livestock | Forestry | |
|---|---|---|---|
| Cronbach alpha dimension 1 | 0.66 | 0.66 | 0.76 |
| Eigenvalue dimension 1 | 32.8 | 33.2 | 37.8 |
| Cronbach alpha dimension 2 | 0.27 | 0.41 | 0.29 |
| Eigenvalue dimension 2 | 13.4 | 20.7 | 14.5 |
| Total variance | 46.2 | 53.9 | 52.3 |
Multinomial regression of crop activities.
| Variable | Coeff. | s.e. | Z-Score | p-Value | |
|---|---|---|---|---|---|
| Gender | −0.045 | 0.070 | −0.639 | 0.522 | |
| Age | 0.003 | 0.029 | 0.112 | 0.911 | |
| Education | 0.171 | 0.033 | 5.189 | 0.000 | *** |
| Work Environment | 0.296 | 0.085 | 3.479 | 0.001 | *** |
| Work Agriculture | −0.066 | 0.096 | −0.682 | 0.496 | |
| Family | −0.066 | 0.072 | −0.910 | 0.364 | |
| Knowledge Territory | 0.014 | 0.032 | 0.429 | 0.668 | |
| Knowledge Agriculture | 0.018 | 0.030 | 0.602 | 0.548 | |
| Knowledge Climate change | 0.249 | 0.035 | 7.092 | 0.000 | *** |
| Relevance Environment | 0.010 | 0.034 | 0.298 | 0.766 | |
| Climate change worry | 0.059 | 0.032 | 1.825 | 0.069 | * |
| Log-likelihood | −202.269 | ||||
| R-square | 0.959 | ||||
| p-value (F) | 0.000 |
* Statistically significant for p < 0.1; ** Statistically significant for p < 0.05; *** Statistically significant for p < 0.001.
Multinomial regression of livestock activities.
| Variable | Coeff. | s.e. | Z-Score | p-Value | |
|---|---|---|---|---|---|
| Gender | −0.027 | 0.078 | −0.350 | 0.727 | |
| Age | 0.022 | 0.038 | 0.566 | 0.572 | |
| Education | 0.114 | 0.037 | 3.082 | 0.002 | *** |
| Work Environment | 0.407 | 0.096 | 4.239 | 0.000 | *** |
| Work Agriculture | 0.041 | 0.110 | 0.368 | 0.713 | |
| Family | 0.004 | 0.086 | 0.042 | 0.966 | |
| Knowledge Territory | 0.082 | 0.034 | 2.383 | 0.018 | ** |
| Knowledge Agriculture | −0.039 | 0.033 | −1.163 | 0.246 | |
| Knowledge Climate change | 0.277 | 0.036 | 7.636 | 0.000 | *** |
| Relevance Environment | 0.026 | 0.037 | 0.703 | 0.483 | |
| Climate change worry | −0.018 | 0.033 | −0.546 | 0.586 | |
| Log-likelihood | −237.337 | ||||
| R-square | 0.939 | ||||
| p-value (F) | 0.000 |
* Statistically significant for p < 0.1; ** Statistically significant for p < 0.05; *** Statistically significant for p < 0.001.
Multinomial regression of forestry activities.
| Variable | Coeff. | s.e. | Z-Score | p-Value | |
|---|---|---|---|---|---|
| Gender | 0.102 | 0.070 | 1.458 | 0.146 | |
| Age | −0.007 | 0.032 | −0.225 | 0.823 | |
| Education | 0.201 | 0.034 | 5.840 | 0.000 | *** |
| Work Environment | 0.274 | 0.089 | 3.079 | 0.002 | *** |
| Work Agriculture | −0.074 | 0.098 | −0.756 | 0.450 | |
| Family | −0.043 | 0.074 | −0.588 | 0.557 | |
| Knowledge Territory | 0.008 | 0.032 | 0.260 | 0.795 | |
| Knowledge Agriculture | −0.008 | 0.030 | −0.266 | 0.790 | |
| Knowledge Climate change | 0.262 | 0.034 | 7.707 | 0.000 | *** |
| Relevance Environment | 0.006 | 0.034 | 0.192 | 0.848 | |
| Climate change worry | 0.054 | 0.032 | 1.696 | 0.091 | * |
| Log-likelihood | −204.482 | ||||
| R-square | 0.957 | ||||
| p-value (F) | 0.000 |
* Statistically significant for p < 0.1; ** Statistically significant for p < 0.05; *** Statistically significant for p < 0.001.
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; Furesi, Roberto 2 ; Pulina, Pietro 2
; Bardi, Antonietta 2
; Madau, Fabio A 3
1 Department of Soil, Plant and Food Sciences (DISSPA), University of Bari Aldo Moro, Bari, Via Amendola 165/A, 70126 Bari, Italy
2 Department of Agriculture, University of Sassari, 07100 Sassari, Italy;
3 Department of Agriculture, University of Sassari, 07100 Sassari, Italy;