Agroforestry systems measures in Italy: an explorative analysis of policy effectiveness

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However, despite active policy support since 2007 under the Common Agricultural Policy (CAP), their adoption in Italy remains limited. This study investigates the reasons behind the low uptake of agroforestry measures across three CAP programming periods through a sequential mixed-method design, starting with a financial and policy analysis to outline the implementation framework, followed by survey-based econometric modeling to explore behavioral and institutional factors. The financial analysis reveals a limited implementation framework (25%) and low expenditure levels, with only a few regions effectively activating the agroforestry systems measures. To better understand the underlying reasons for this low participation, an econometric model was developed based on a questionnaire administered to agricultural and forestry entrepreneurs. The model identified four variables significantly associated with the willingness to apply for agroforestry measures. The results suggest that awareness, technical capacity, and direct experience are key factors for adoption, while limited knowledge, bureaucratic complexity, and insufficient economic incentives constitute the main barriers. Strengthening training, communication, and policy coherence could therefore promote agroforestry adoption in future CAP cycles. Agroforestry systems Common Agricultural Policy Policy implementation Rural development Italy Figures Figure 1 1. Introduction Agroforestry (AF) is among the oldest land-use systems, originally practiced as a subsistence strategy integrating trees, crops, and livestock within the same landscape (Nair 1985). Over time, the scientific community has systematically defined agroforestry systems (AFS) as diverse land-use configurations encompassing multiple typologies, whose structure and composition vary according to ecological, cultural, and socioeconomic contexts, as well as the specific tree and crop species cultivated across regions (Jose 2009; Mosquera-Losada et al. 2018). Despite their long-standing tradition, AFS have experienced a sharp decline in Europe since the early decades of the 20th century, primarily driven by the spread of extensive agriculture, the Green Revolution, and the abandonment of marginal lands, which favored simplified and specialized agricultural systems aimed at maximizing crop yields (King 1987; Ferrario 2021; Varela et al. 2022; Watson et al. 2020). This shift contributed to significant territorial and social challenges, including the loss of biological and landscape diversity, increased vulnerability to erosion and hydrogeological instability, and the erosion of traditional knowledge and cultural heritage (Vang Rasmussen et al. 2024). However, in recent decades, AFS has regained prominence in research and policy agendas due to their capacity to deliver multiple ecosystem services, such as carbon sequestration, biodiversity conservation, soil protection and fertility, animal welfare, and the provision of diversified and aesthetically valuable rural landscapes (Paris et al. 2019; Hernandez-Morcillo et al. 2018; Mosquera-Losada et al. 2018; Torralba et al. 2016; Smith et al. 2022; Mele et al. 2019). AFS are increasingly recognized as a pathway toward sustainable and resilient forms of intensive agriculture (Graves et al. 2017; Kay et al. 2019), playing a critical role in both climate change mitigation and adaptation. Their ability to withstand and recover from natural and anthropogenic disturbances (Viñals et al. 2023) and their contribution to “sustainable intensification” make them a strategic land-use option for the future of agriculture (European Parliament 2020). Furthermore, AFS can enhance the economic and cultural value of rural and inner areas by supporting high-quality local products and fostering territorial cohesion (EIP-AGRI 2017; FAO 2022). By diversifying farm income, they strengthen rural economies and improve farm resilience to yield variability, while promoting multifunctionality (Paris et al. 2019; Lin 2011). For these many reasons, at the European level AFS have gained renewed political relevance, being explicitly acknowledged in major policy frameworks such as the EU Biodiversity Strategy for 2030, the European Green Deal, and the Nature Restoration Law (European Commission 2019; 2020). Within the Common Agricultural Policy (CAP), financial support for AFS has been available since the 2007–2013 programming period and continues under the current CAP 2023–2027, which provides funding for both the establishment and maintenance of AFS (Mosquera-Losada et al. 2018; EU CAP Network 2023). Nevertheless, this support has not achieved the expected success, both at the European level and in Italy (Lawson 2023; Rivieccio 2023). In Italy, AF occupies a unique position at the intersection of historical tradition and innovation yet remains marginal within agricultural policy frameworks (Le et al. 2025). Current estimates indicate that AFS cover approximately 1.6 million hectares (Chiarabaglio et al. 2023) out of a Utilized Agricultural Area (UAA) of 12.4 million hectares (ISTAT 2024). Pioneering studies have highlighted the potential of silvoarable systems within the framework of the CAP, while also pointing to uncertainties related to management constraints, investment costs, and reductions in direct payments associated with tree presence on agricultural land (Pisanelli et al. 2012). Stakeholders acknowledge the broad productive and environmental benefits of AFS but emphasize management complexity and bureaucratic burdens as major obstacles (Camilli et al. 2018). Paris et al. (2019) further note that CAP measures often favor monoculture systems at the expense of integrated practices such as AF. Recent case studies on innovative systems -such as olive trees intercropped with wild asparagus- demonstrate advantages in terms of overall productivity and profitability, although they require higher labor inputs (Rezgui et al. 2024). Overall, interest in AF as a tool for sustainable agriculture is growing, particularly in rural areas; however, its diffusion remains constrained by economic, administrative, and political barriers. Despite increasing recognition of AF as a key component of sustainable land management, empirical evidence on the implementation and effectiveness of CAP support measures remains limited, particularly in Mediterranean contexts such as Italy. Previous studies have primarily focused on the ecological or technical benefits of AFS, whereas studies on the policy and behavioral factors explaining limited adoption have received little attention (Tranchina et al. 2024; Mosquera-Losada et al. 2023). Against this backdrop, the present study pursues a sequential mixed-method design with two main objectives: (i) to provide an updated overview of the level of economic and financial support allocated to AFS under the CAP and their implementation across the Italian territory; and (ii) to investigate, in light of the limited uptake recorded nationwide, the underlying reasons for the modest success of these support measures through surveys with two stakeholder groups, aiming to identify latent barriers and outline potential adjustments for future programming. 2. Research design The research design was structured to respond directly to this twofold objective through a sequential mixed-method approach (Creswell and Plano Clark 2018). In the first phase, an updated and systematic overview of the economic and financial support allocated to AFS under the CAP, as well as their implementation across the Italian territory, was developed by organizing and analyzing available administrative and program-level data. This step enabled the quantification of both the intensity and territorial distribution of support measures over time. In the second phase, the study explored the reasons behind the limited uptake of these measures through primary data collected via surveys and interviews with two stakeholder groups. This qualitative and quantitative evidence was used to identify latent barriers, implementation bottlenecks, and perception gaps that may have constrained the diffusion of AF practices. By triangulating these complementary sources of information, the research design ultimately aimed to generate actionable insights to inform adjustments to support schemes in future programming cycles. 2.1. Agroforestry systems and Rural Development support The analysis involved the three CAP programming periods with fundings for AFS: 2007-2013 (Reg. (EC) No. 1698/2005), 2014-2022 (Reg. (UE) 2220/2020), 2023-2027 (Reg. (UE) n. 1305/2013 and Reg. UE n. 2021/2116). All calls for tenders issued by the 21 Italian Regional Authorities (19 Regions and 2 Autonomous Provinces) were collected and examined for the periods in which AFS measures were active: Measure 2.2.2 in 2007–2013, Sub-measure 8.2 in 2014–2020/22, and Intervention SRA28.3 and SRD05.3 in the current programming period 2023-2027. Based on data from the Annual Implementation Reports (AIR), the analysis examines variations in subsidy types and intensity, selection criteria, and eligibility requirements across regions, with the purpose of evaluating how regional authorities adapted EU guidelines to local conditions, including territorial, environmental, and socio-economic contexts. 2.2. Data collection for the survey Considering the limited success of AFS support measures, this study aimed to investigate more deeply the underlying reasons for the lack of participation, as well as the conditions that could foster greater future engagement. To this end, an empirical approach was developed based on two distinct questionnaires, addressed respectively to regional officers (Regional Questionnaire, RQ) and to agricultural and forestry entrepreneurs (Entrepreneurs Questionnaire, EQ), with the aim of obtaining a multilevel understanding of the phenomenon. Preliminarily, some exploratory qualitative semi-structured interviews with regional administrators, AF experts, and farmers were carried out in two different steps in order to set the questionnaires. Following the approach provided by Graneheim and Lundman (2004), the interviews were registered upon the consent of the interviewees, transcribed, and content analysis was performed. The two questionnaires have some parts in common such as: personal details, AF knowledge, interest and actual presence, and future perspective for AF. Instead, the special part is different for the two targets, as described in the paragraph below. 2.2.1. Regional administrators Questionnaire (RQ) The branching and semi-structured questionnaire addressed to regional administration representatives was designed to collect both descriptive information (e.g., regional regulations, support instruments, the role of Rural Development programs) and subjective evaluations (e.g., perceived obstacles, strategic priorities, future opportunities). The questions included both closed-ended items as well as open-ended questions aimed at gathering opinions and qualitative comments. Regional authorities were asked to describe their approach to AFS across the different CAP programming periods (I: 2007–2013, II: 2014–2022, III: 2023–2027) in order to better understand why and how each region acted to promote the diffusion of AS, and how these actions evolved over time. In particular, when AF measures had been activated in a given region, respondents were invited to provide details on the resources allocated, the perceived outcomes, and the main limitations encountered. Conversely, in cases where AF measures had not been activated, they were asked to explain the reasons underlying this decision. 2.2.2. Agricultural and Forestry Entrepreneurs Questionnaire (EQ) This questionnaire was administered to farm owners as well as to technicians, consultants, and collaborators of Italian agricultural and forestry enterprises. The objective was to investigate the factors influencing the willingness to apply for public calls under the current CAP programming period (2023–2027), with particular reference to measures supporting the establishment and management of AS. The first section of the questionnaire included sociodemographic variables (gender, role within the enterprise), territorial variables (region, predominant farm morphology, minimum and maximum altitude), and other socioeconomic characteristics. The second section focused on assessing the perceived benefits of AF, measured through questions concerning the restoration of traditional systems, production diversification, income diversification, climate change mitigation, biodiversity conservation, increased soil fertility, and erosion control. Additional questions explored previous experience with AF adoption, membership in professional associations, participation in training or information initiatives on AF, and opinions regarding the need for public intervention to support the sector. 3. Empirical strategy For the analysis of data related to farmers, an econometric approach was adopted to identify the main factors that have influenced - and may continue to influence - the propensity to adopt AF measures, thereby providing useful insights to improve the effectiveness of public policies and to design support tools more closely aligned with the needs of the farmers. Specifically, a binary Logit model was applied (using STATA software), given the dichotomous nature of the dependent variable, which was constructed based on the question: “If, in the new 2023–2027 programming period, your Region were to activate measures supporting the establishment and management of agroforestry systems, would you consider applying for the calls?”. In this model, the probability of willingness (or unwillingness) to apply is linked to a set of explanatory variables. In the literature, similar approaches have been used by Neupane et al. (2002) and Tega and Bojago (2023), who investigated the determinants of AF practices adoption by farmers in Nepal and Ethiopia, respectively. The description of the variables included in the econometric model and the corresponding basic descriptive statistics are presented in Tables 1 and 2, respectively. This analysis serves as an exploratory tool to identify the individual, farm-related, and experiential characteristics that could influence the likelihood of participation in AFS support measures. Tab. 1 Description of the variables included into econometric model Variable name Description Variable type Dependent variable Willing_to_apply_AF Willingness to apply calls for AF measures Dummy (“No” = 0; “Yes” = 1) Explanatory variables gender Indicate the gender of the respondents Dummy (“Male” = 0; “Female” = 1) training Indicate if the respondent has participated in training/information activities on AF Dummy (“No” = 0; “Yes” = 1) perceived_benefits Mean of responses to the 7 Likert-scale items on knowledge of AF benefits Continuous (Likert scale 1 - 4) experience The farm had or currently has land managed under AS Dummy (“No” = 0; “Yes” = 1) association Membership in a farmers’ association Dummy (“No” = 0; “Yes” = 1) Control variables macroarea Geographical area where the respondent from (North, Central, South) Categorical (i.macroarea) morphology Predominant morphology of the area where farm managed by the respondents is located (Mountain, Hill, Plain) Categorical (i.morphology) altitude Mean altitude of the farm area, calculated as the average of the minimum and maximum elevations indicated by the respondents (m a.s.l.) Continuous (numeric) Tab. 2 Descriptive statistics of the variables used in the econometric model Variable Mean Std. dev Gender 0.29 0.46 Training 0.39 0.49 Experience 0.65 0.48 Association 0.64 0.48 Perceived benefits restoring traditional systems 2.38 1.20 production diversification 2.62 1.16 income diversification 2.46 1.17 climate mitigation 2.40 1.22 biodiversity conservation 2.85 1.08 increase in fertility 2.54 1.24 erosion defense 2.60 1.18 Macroarea North 0.28 0.45 Center 0.41 0.49 South 0.31 0.46 Morphology Mountain 0.28 0.45 Hill 0.48 0.50 Plain 0.24 0.43 Altitude 443.28 372.90 With regard the Perceived_benefits variable, it is a latent construct capturing the perceived benefits of AF. It was derived from seven Likert-scale items included in the questionnaire, each assessing the perception of respondents about a specific benefit of AF: restoration of traditional systems, diversification of production, diversification of income, climate mitigation, biodiversity conservation, improvement of soil fertility, and erosion control. The construct was calculated as the mean score across the seven items (Likert scale 1-4), thus providing a synthetic measure of the overall perception of AF benefits, where higher values indicate a more positive perception. 4. Results and Discussion The following sections outline the results corresponding to the dual aim of this study, providing insights into both the financial and perceptual factors influencing AFS adoption. 4.1. Financial analysis The promotion of AFS under the CAP began in 2007, when Measure 2.2.2 was introduced within the Rural Development Programmes, providing grants exclusively for the establishment of AFS. These grants supported farmers in creating systems that combine silviculture and extensive agriculture on the same land. During the 2007–2013 programming period, only planting costs were covered, with a co-financing rate of 70%, rising to 80% in disadvantaged areas. No subsidies were provided for maintenance. This innovative measure remained largely unapplied, likely due to limited interest and awareness within the agricultural sector. Only four regions (Lazio, Marche, Sicilia, Umbria) activated the measure, while Veneto implemented it later during the period. Initially, Italy allocated €8.2 million under this measure to establish 6,737 hectares of AFS. However, these resources were significantly reallocated to other CAP measures. By the end of the period, only 0.3% of the planned resources had been spent, with Veneto being the sole region to support just two applications, covering a total of 20 hectares. In the 2014-2022 programming period, AFS support was reintroduced through Measure 8.2, which, unlike the previous measure, also covered maintenance costs by providing an annual premium for up to five years. Each region was required to specify in its Rural Development Programme the tree density, eligible species, and conditions for sustainable land use, with the aim of diversifying farm production and income. Implementation approaches, however, were heterogeneous: in some regions the measure was framed primarily with economic objectives, while in others environmental aims prevailed. The degree of detail also varied considerably, ranging from generic provisions to specific prescriptions on tree species. Initially, five regions activated the measure (Basilicata, Marche, Puglia, Umbria, Veneto), allocating about €9 million-less than 1% of the total forestry resources under Measure 8. Implementation showed modest improvement compared to the previous period, although payments only started in 2020 due to mismatches between programming and local needs. Overall, €1,281,429 were spent on 1,204 hectares, with only two regions (Puglia and Veneto) making use of the measure. Nevertheless, actual spending remained far below expectations: by the final year, expenditure corresponded to just 20% of planned resources in Puglia and 27% in Veneto, while at the national level only about 13% of allocated resources had been spent until 2022. Veneto eventually used nearly all its available resources to establish 1,201 hectares of AFS, even increasing allocations during the period, while Puglia sharply reduced them in the last year. The application of the N+3 rule-where N denotes the commitment year and +3 refers to the additional three years granted for expenditure-extended the eligibility of payments for the 2014–2020 programming period (including transitional years) until 2024. This allowed the Umbria Region to absorb a portion of the reprogrammed funds, contributing to an increase in Italy’s aggregate expenditure to 31.95% of the reprogrammed allocation after the 2023 adjustment. Table 3 reports the amount of allocated funding (programmed and spent resources) for the 2007-2013 and 2014-2022 periods, including only regions where measures were activated. Tab. 3 Allocation of resources in agroforestry systems for the programming periods 2007-2013 (Measure 2.2.2.) and 2014-2022 (Measure 8.2). Values are expressed in euros (€), rounded to the nearest unit. n.a. = not activated Programming period 2007-2013 2014-2022 (2024) Measure 2.2.2 Measure 8.2 Resources (€) Regions Programmed Spent Programmed Spent Spent 2007 2013 2013 2014 2023 2022 2024 Basilicata n.a. n.a. n.a. 826.446 826.446 0 0 Latium 616,093 0 0 n.a. n.a. n.a. n.a. Marche 2,270,000 2,500 0 2,000,000 2,000,000 0 0 Apulia n.a. n.a. n.a. 5,000,000 3,169,402 1.280.196 1,886,826 Sicily 4,540,000 0 0 n.a. n.a. n.a. n.a. Umbria 760,068 0 0 1,000,000 187,400 0 86,563 Veneto n.a. 30,000 27,544 231,911 4,638 1.233 3,623 Total 8,186,161 32,500 27,544 9,058,357 6,187,886 1.281.429 1,977,012 In the current 2023–2027 programming period, according to the Italian CAP Strategic Plan (approved in November 2022, CCI 2023IT06AFSP001), AFS support is delivered through two interventions: SRA28 – Support for the maintenance of afforestation/reforestation and agroforestry systems, SRD05 – Afforestation/reforestation and agroforestry systems on agricultural land. Both interventions include Action 3, which specifically targets agroforestry systems: SRD05.3 – Establishment of agroforestry systems on agricultural land, funding plantation costs; SRA28.3 – Maintenance of agroforestry systems, covering maintenance costs for environmental purposes. Each Action 3 is further divided into two sub-actions: 3.1 silvoarable systems and 3.2 silvopastoral systems, designed to deliver multiple productive and environmental functions. As of today, these two interventions remain only partially implemented: approximately 25% of regional authorities have activated them, while about half of the regions have never activated any measure or intervention related to agroforestry systems. Specifically, SRA28.3 is operational in five regions, while SRD05.3 is active in six regions (Piedmont, Apulia, Tuscany, Umbria, Veneto), with Sicily activating only SRD05.3 for establishment without maintenance. However, no region has reported any expenditure during the first two years (2023–2024). Figure 1 summarizes the activation status of measures and interventions related to agroforestry systems across the three programming periods, including regions that have activated them and those that have incurred expenditure. 4.2. Data collection for the survey The survey collected a total of 121 questionnaires, including 28 from regional administrators and 93 from agricultural and forestry entrepreneurs. The geographical distribution of responses was uneven across the Italian territory, with some areas showing higher participation rates than others. The spatial distribution provides a nationwide overview; however, the limited sample size does not allow for robust conclusions regarding territorial differences in the responses obtained. 4.2.1. Regional administrators questionnaire Analysis of the responses indicates that at least one officer from each region completed the questionnaire, thereby ensuring full national coverage and the representation of all Italian regions. While respondents generally reported good knowledge of AF and acknowledged its relevance - especially for biodiversity conservation and climate change mitigation - both interest and technical know-how remain limited. These factors appear to be among the main reasons for the low uptake of AFS measures, as information on environmental and economic benefits is not readily accessible. Other barriers mentioned include the lack of available land and the persistence of preconceived ideas. Institutional intervention in AFS is considered crucial by 86% of respondents, primarily for educational purposes. Knowledge is deemed essential to determine whether the grant is suitable for specific needs, for the territory and the farm, how to apply, and how to invest the subsidy. Respondents also called for increased financial support, not only to fund AF practices but also to recognize the ecosystem services they provide. Bureaucratic assistance is also needed, to better promote calls for applications and simplify administrative procedures. Those who considered institutional interventions unnecessary viewed AFS as a niche and low-value sector, not warranting the use of public funds. According to those who implemented AFS measures in the previous CAP programming periods (2007–2013 and 2014–2022), results were generally poor and the scale of interventions inadequate, with one exception: the Veneto region, which met all requests for Measure 8.2, although results still fell short of expectations. The shortcomings were attributed, in the first period, to the novelty of the intervention (with excessive overall funding but insufficient amounts per grant), and in the second period, to the use of AFS measures to finance other types of works, such as windbreak barriers, rather than AF plantations. Regions that did not activate AFS measures cited limited budgets, lack of demand, and absence of available land. Looking ahead, the outlook appears more positive: although only a few regions have activated the two interventions (SRD05 and SRA28) in the 2023-2027 programming period, a larger number plan to do so. Expectations include the establishment of new AFS, with allocated funds ranging from €0.2 to €9.0 million per region, reflecting growing interest in AFS. 4.2.2. Agricultural and Forestry Entrepreneurs questionnaire The empirical analysis was conducted on a sample of 93 observations, which calls for caution in interpreting the results and limits the generalizability of the conclusions. Nevertheless, the findings provide valuable insights into the factors influencing farmers’ propensity to adopt AFS supported by CAP 2023–2027 measures. The the binary logit model results (Table 4) reveal that four variables exibit a statistically significant association with farmers’ willingness to apply for future AFS measures. Geographical macro-area, farm morphology, and altitude were included as control variables. Tab. 4 Logit model results Dependent variable: Willing_to_apply_AF Odds Ratio (OR) Robust Standard Error (RSE) Perceived Benefits 2.17 ** 0.74 Experience 3.80 * 2.88 Training 5.37 ** 4.23 Gender 4.02 * 3.18 Association 0.37 0.32 Macroarea Yes - Morphology Yes - Altitude Yes - Note: Significance codes: ∗p < 0.10, ∗∗ p < 0.05, ∗∗∗ p χ² = 0.0008 In particular, perceived benefits, previous experience with AF practices, participation in specific training, and gender all show a positive and statistically significant relationship with the dependent variable. Among these factors, training exhibits the strongest association, suggesting that targeted educational initiatives can substantially enhance the intention to adopt AS. Farmers with a more positive perception of AF benefits such as environmental improvement, production diversification, or landscape preservation, are more than twice as likely to express willingness to participate in future calls compared to those who do not perceive such benefits. This result is significant at the 5% level, indicating a robust relationship between a positive perception of benefits and the propensity to participate. Similarly, respondents who have had direct experience with the implementation of AFS areas on their farms show a higher inclination to apply for AFS calls. Although this result is only marginally significant, it is consistent with the hypothesis that concrete experience strengthens interest in such measures. A distinctive finding concerns gender: women appear more inclined to participate than men, a result that deserves further investigation to better understand the underlying motivations. Conversely, membership in a farmers’ association does not show a significant effect. Although some coefficients display relatively large robust standard errors - likely due to the limited sample size - the direction and statistical significance of the main effects remain consistent with theoretical expectations. Therefore, it is preferable to focus on the direction of the effects rather than on their magnitude, which may be imprecise. Overall, the model demonstrates a good explanatory capacity (Pseudo R² = 0.2660) and a strong overall significance (Prob > χ² = 0.0008), supporting the reliability of the identified relationships despite the inherent data limitations. Tali evidenze possono fornire spunti concreti per l’orientamento delle politiche future, pur nella consapevolezza che l’analisi è stata condotta su un campione ridotto e non di certo rappresentativo dell’intera popolazione di imprenditori agricoli e forestali italiani. 5. Conclusions The findings of this study confirm that, despite the opportunities offered by the Common Agricultural Policy (CAP), the adoption of agroforestry systems (AFS) by Italian farms remains limited. In previous programming periods (2007-2013 and 2014-2022), measures designed to support the establishment and management of AFS were only partially implemented and, even when available, recorded low participation rates among farmers. This limited uptake reflects structural and institutional challenges that have persisted over time, including fragmented policy frameworks and insufficient alignment between EU requirements and local farming conditions. The current CAP 2023-2027 programming period introduces higher financial allocations and new interventions specifically targeting agroforestry systems. While this could represent an opportunity for revitalization, implementation remains uneven and slow. Regional disparities persist, with significant differences in capacity, awareness, and commitment. Regional officers acknowledge the importance of agroforestry for biodiversity conservation and climate change mitigation, yet they consistently point to limited technical expertise, lack of accessible information, and bureaucratic complexity as major obstacles to effective adoption. These structural barriers suggest that, despite increased resources, the actual uptake of agroforestry measures will depend on targeted support, capacity building, and simplification of administrative procedures rather than financial incentives alone. Institutional intervention is therefore considered essential, particularly in three areas: (i) strengthening technical training and advisory services; (ii) simplifying administrative procedures to reduce transaction costs; and (iii) increasing financial support, including mechanisms to recognize and remunerate the ecosystem services provided by AFS. Lessons learned from previous programming periods can play a crucial role in guiding the design and implementation of future measures, ensuring greater coherence and responsiveness to local needs. From the perspective of agricultural and forestry entrepreneurs, the results indicate that perceived benefits of AF - such as improved soil fertility, biodiversity enhancement, and diversified income streams - represent a key factor in fostering adoption. However, this perception appears strongly linked to direct experience: farmers who have already experimented with AF practices tend to recognize their advantages more readily. This finding underscores the importance of targeted communication and awareness-raising initiatives to disseminate knowledge of AF benefits. Equally critical is the provision of clear and transparent information on initial investment costs, expected economic returns, and long-term profitability, enabling farmers to make informed decisions. Future research should expand the sample size and explore additional behavioral and contextual variables to deepen understanding of the dynamics influencing adoption. Such evidence would provide valuable guidance for policymakers in designing more effective support schemes and promotion strategies. Moreover, integrating AF into broader value chains and fostering synergies with other CAP measures and forestry programs could enhance its socioeconomic impact, particularly in marginal and inner rural areas affected by depopulation and land abandonment. In these contexts, appropriate land management and infrastructural support - such as road networks and water systems - are crucial to preserving the multifunctional benefits of AF. Finally, awareness of AFS remains limited among both farmers and the general public. Many farmers still perceive agroforestry as a non-productive, environmentally oriented system with low profitability and limited mechanization potential. Overcoming these misconceptions requires a comprehensive approach that combines technical training, professional capacity building, and the recognition of the economic value of ecosystem services. Only through coordinated efforts at institutional, technical, and communicative levels can agroforestry transition from a marginal practice to a cornerstone of sustainable land management in Italy. Declarations Contributions Rosa Rivieccio: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Visualization, Writing – original draft, Writing – review and editing; Martina Agosta: Data collection, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing; Erica Mazza: Conceptualization, Data curation, Data collection, Investigation; Raoul Romano: Conceptualization, Data collection, Investigation, Project administration, Supervision, Validation, Writing – original draft, Writing – review and editing. All authors reviewed and revised the manuscript and approved the final manuscript Funding This research was carried out within the framework of the Council for Agricultural Research and Analysis of Agricultural Economics (CREA), Research Centre for Policies and Bioeconomy, and funded by the Italian Ministry of Agriculture, Food Sovereignty and Forests (MASAF) under the Rete PAC Programme, Project WP 1, CR 01.11 – Agroforestry Systems. Informed Consent Statement Informed consent was obtained from all subjects involved in the study. All subjects were anonymized, and no subject can be identified. Data Availability Statement: All relevant data are included in this article. Acknowledgments The authors wish to thank all respondents to the questionnaire for this study, including officers from Italian regional authorities and managers of agricultural and forestry enterprises. Special thanks go to Jacopo Goracci of Tenuta di Paganico (GR, Italy) for his support in disseminating the questionnaire. The authors have edited the final output and take full responsibility for the content of this publication. Conflicts of Interest The authors declare no conflicts of interest. References Camilli F, Pisanelli A, Seddaiu G, Franca A, Bondesan V, Rosati A, Burgess PJ (2018) How local stakeholders perceive agroforestry systems: an Italian perspective. Agroforestry Systems 92(4):849–862 Chiarabaglio et al. (2023) Agroforestazione in Italia: una opportunità per le aziende agrarie. Rete Rurale Nazionale 2014–2020, Consiglio per la ricerca in agricoltura e l’analisi dell’economia agraria, Roma. ISBN 9788833852690 Chiozzotto F (2018) Agroforestazione e sviluppo rurale: un potenziale ancora inespresso. 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Agricultural Systems 72(3):177–196 Paris P, Camilli F, Rosati A, Mantino A, Mezzalira G, Dalla Valle C, Burgess PJ (2019) What is the future for agroforestry in Italy? Agroforestry Systems 93(6):2243–2256 Pisanelli A, Perali A, Paris P (2012) Potentialities and uncertainties of novel agroforestry systems in the European CAP: farmers’ and professionals’ perspectives in Italy. L’Italia Forestale e Montana/Italian Journal of Forest and Mountain Environments 67(3):289–297 Rezgui F, Rosati A, Lambarraa-Lehnhardt F, Paul C, Reckling M (2024) Assessing Mediterranean agroforestry systems: agro-economic impacts of olive wild asparagus in central Italy. European Journal of Agronomy 152:127012 Rivieccio R (ed) (2023) Sistemi agroforestali: una misura poco attivata [in Italian]. Pianeta PSR. https://www.pianetapsr.it/flex/cm/pages/ServeBLOB.php/L/IT/IDPagina/2899 Smith LG, Westaway S, Mullender S, Ghaley BB, Xu Y, Lehmann LM, Smith J (2022) Assessing the multidimensional elements of sustainability in European agroforestry systems. Agricultural Systems 197:103357. https://doi.org/10.1016/j.agsy.2021.103357 Tega M, Bojago E (2024) Determinants of smallholder farmers’ adoption of agroforestry practices: Sodo Zuriya District, southern Ethiopia. Agroforestry Systems 98(1):1–20 Torralba M, Fagerholm N, Burgess PJ, Moreno G, Plieninger T (2016) Do European agroforestry systems enhance biodiversity and ecosystem services? A meta-analysis. Agriculture, Ecosystems & Environment 230:150–161. https://doi.org/10.1016/j.agee.2016.06.002 Tranchina M, Reubens B, Frey M, Mele M, Mantino A (2024) What challenges impede the adoption of agroforestry practices? A global perspective through a systematic literature review. Agroforestry Systems 98:1817–1837. https://doi.org/10.1007/s10457-024-00993-w Vang Rasmussen L, Grass I, Mehrabi Z, et al. (2024) Joint environmental and social benefits from diversified agriculture. Science 384(6651):eadj1914. https://doi.org/10.1126/science.adj1914 Varela E, Olaizola AM, Blasco I, Capdevila C, Lecegui A, Casasús I, et al. (2022) Unravelling opportunities, synergies, and barriers for enhancing silvopastoralism in the Mediterranean. Land Use Policy 118:106140. https://doi.org/10.1016/j.landusepol.2022.106140 Watson CA, et al. (2020) Agroforestry in Europe: a land management system to combat climate change. Advances in Agronomy 159:1–45. https://doi.org/10.1016/bs.agron.2020.07.001 Additional Declarations No competing interests reported. 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1","display":"","copyAsset":false,"role":"figure","size":167123,"visible":true,"origin":"","legend":"\u003cp\u003eActivation status and expenditure of forestry measures and interventions by Italian regions across the three CAP programming periods (2007–2013, 2014–2022, 2023–2027). Legend codes: A1–A3 indicate the number of activations (once, twice, three times); NE = no expenditure; E1/E2 = expenditure once or twice; NA = never activated any measure or intervention\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8290861/v1/5dd93791eaab61ae1011ac56.jpeg"},{"id":100421984,"identity":"25335e5e-de5d-4dd4-966a-0f12b33ac0c1","added_by":"auto","created_at":"2026-01-16 14:04:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":936597,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8290861/v1/40e61c60-7085-4eb6-abb0-e2220c434888.pdf"},{"id":100414599,"identity":"66df2fbc-4d26-490d-a79c-2cf9becc0a6c","added_by":"auto","created_at":"2026-01-16 13:19:44","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":259735,"visible":true,"origin":"","legend":"","description":"","filename":"GraphicalAbstract.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8290861/v1/fd01b995dbd5a575f27e56e3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Agroforestry systems measures in Italy: an explorative analysis of policy effectiveness","fulltext":[{"header":"1.\tIntroduction","content":"\u003cp\u003eAgroforestry (AF) is among the oldest land-use systems, originally practiced as a subsistence strategy integrating trees, crops, and livestock within the same landscape (Nair 1985).\u003c/p\u003e\n\u003cp\u003eOver time, the scientific community has systematically defined agroforestry systems (AFS) as diverse land-use configurations encompassing multiple typologies, whose structure and composition vary according to ecological, cultural, and socioeconomic contexts, as well as the specific tree and crop species cultivated across regions (Jose 2009; Mosquera-Losada et al. 2018).\u003c/p\u003e\n\u003cp\u003eDespite their long-standing tradition, AFS have experienced a sharp decline in Europe since the early decades of the 20th century, primarily driven by the spread of extensive agriculture, the Green Revolution, and the abandonment of marginal lands, which favored simplified and specialized agricultural systems aimed at maximizing crop yields (King 1987; Ferrario 2021; Varela et al. 2022; Watson et al. 2020). This shift contributed to significant territorial and social challenges, including the loss of biological and landscape diversity, increased vulnerability to erosion and hydrogeological instability, and the erosion of traditional knowledge and cultural heritage (Vang Rasmussen et al. 2024).\u003c/p\u003e\n\u003cp\u003eHowever, in recent decades, AFS has regained prominence in research and policy agendas due to their capacity to deliver multiple ecosystem services, such as carbon sequestration, biodiversity conservation, soil protection and fertility, animal welfare, and the provision of diversified and aesthetically valuable rural landscapes\u0026nbsp;(Paris et al. 2019; Hernandez-Morcillo et al. 2018; Mosquera-Losada et al. 2018; Torralba et al. 2016; Smith et al. 2022; Mele et al. 2019).\u0026nbsp;AFS are increasingly recognized as a pathway toward sustainable and resilient forms of intensive agriculture (Graves et al. 2017; Kay et al. 2019), playing a critical role in both climate change mitigation and adaptation. Their ability to withstand and recover from natural and anthropogenic disturbances (Viñals et al. 2023) and their contribution to “sustainable intensification” make them a strategic land-use option for the future of agriculture (European Parliament 2020). Furthermore, AFS can enhance the economic and cultural value of rural and inner areas by supporting high-quality local products and fostering territorial cohesion (EIP-AGRI 2017; FAO 2022). By diversifying farm income, they strengthen rural economies and improve farm resilience to yield variability, while promoting multifunctionality (Paris et al. 2019; Lin 2011).\u003c/p\u003e\n\u003cp\u003eFor these many reasons, at the European level AFS have gained renewed political relevance, being explicitly acknowledged in major policy frameworks such as the EU Biodiversity Strategy for 2030, the European Green Deal, and the Nature Restoration Law (European Commission 2019; 2020). Within the Common Agricultural Policy (CAP), financial support for AFS has been available since the 2007–2013 programming period and continues under the current CAP 2023–2027, which provides funding for both the establishment and maintenance of AFS (Mosquera-Losada et al. 2018; EU CAP Network 2023). Nevertheless, this support has not achieved the expected success, both at the European level and in Italy (Lawson 2023; Rivieccio 2023).\u003c/p\u003e\n\u003cp\u003eIn Italy, AF occupies a unique position at the intersection of historical tradition and innovation yet remains marginal within agricultural policy frameworks (Le et al. 2025). Current estimates indicate that AFS cover approximately 1.6 million hectares (Chiarabaglio et al. 2023) out of a Utilized Agricultural Area (UAA) of 12.4 million hectares (ISTAT 2024).\u003c/p\u003e\n\u003cp\u003ePioneering studies have highlighted the potential of silvoarable systems within the framework of the CAP, while also pointing to uncertainties related to management constraints, investment costs, and reductions in direct payments associated with tree presence on agricultural land (Pisanelli et al. 2012).\u0026nbsp;Stakeholders acknowledge the broad productive and environmental benefits of AFS but emphasize management complexity and bureaucratic burdens as major obstacles (Camilli et al. 2018). Paris et al. (2019)\u0026nbsp;further note that CAP measures often favor monoculture systems at the expense of integrated practices such as AF.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eRecent case studies on innovative systems -such as olive trees intercropped with wild asparagus- demonstrate advantages in terms of overall productivity and profitability, although they require higher labor inputs (Rezgui et al. 2024).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, interest in AF as a tool for sustainable agriculture is growing, particularly in rural areas; however, its diffusion remains constrained by economic, administrative, and political barriers.\u003c/p\u003e\n\u003cp\u003eDespite increasing recognition of AF as a key component of sustainable land management, empirical evidence on the implementation and effectiveness of CAP support measures remains limited, particularly in Mediterranean contexts such as Italy. Previous studies have primarily focused on the ecological or technical benefits of AFS, whereas studies on the policy and behavioral factors explaining limited adoption have received little attention (Tranchina et al. 2024; Mosquera-Losada et al. 2023).\u003c/p\u003e\n\u003cp\u003eAgainst this backdrop, the present study pursues a sequential mixed-method design with two main objectives: (i) to provide an updated overview of the level of economic and financial support allocated to AFS under the CAP and their implementation across the Italian territory; and (ii) to investigate, in light of the limited uptake recorded nationwide, the underlying reasons for the modest success of these support measures through surveys with two stakeholder groups, aiming to identify latent barriers and outline potential adjustments for future programming.\u003c/p\u003e"},{"header":" 2. Research design","content":"\u003cp\u003eThe research design was structured to respond directly to this twofold objective through a sequential mixed-method approach (Creswell and Plano Clark 2018). In the first phase, an updated and systematic overview of the economic and financial support allocated to AFS under the CAP, as well as their implementation across the Italian territory, was developed by organizing and analyzing available administrative and program-level data. This step enabled the quantification of both the intensity and territorial distribution of support measures over time. In the second phase, the study explored the reasons behind the limited uptake of these measures through primary data collected via surveys and interviews with two stakeholder groups. This qualitative and quantitative evidence was used to identify latent barriers, implementation bottlenecks, and perception gaps that may have constrained the diffusion of AF practices. By triangulating these complementary sources of information, the research design ultimately aimed to generate actionable insights to inform adjustments to support schemes in future programming cycles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.1. Agroforestry systems and Rural Development support\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe analysis involved the three CAP programming periods with fundings for AFS: 2007-2013 (Reg. (EC) No. 1698/2005), 2014-2022 (Reg. (UE) 2220/2020), 2023-2027 (Reg. (UE) n. 1305/2013 and Reg. UE n. 2021/2116).\u003c/p\u003e\n\u003cp\u003eAll calls for tenders issued by the 21 Italian Regional Authorities (19 Regions and 2 Autonomous Provinces) were collected and examined for the periods in which AFS measures were active: Measure 2.2.2 in 2007–2013, Sub-measure 8.2 in 2014–2020/22, and Intervention SRA28.3 and SRD05.3 in the current programming period 2023-2027.\u003c/p\u003e\n\u003cp\u003eBased on data from the Annual Implementation Reports (AIR), the analysis examines variations in subsidy types and intensity, selection criteria, and eligibility requirements across regions, with the purpose of evaluating how regional authorities adapted EU guidelines to local conditions, including territorial, environmental, and socio-economic contexts.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. Data collection for the survey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsidering the limited success of AFS support measures, this study aimed to investigate more deeply the underlying reasons for the lack of participation, as well as the conditions that could foster greater future engagement. To this end, an empirical approach was developed based on two distinct questionnaires, addressed respectively to regional officers (Regional Questionnaire, RQ) and to agricultural and forestry entrepreneurs (Entrepreneurs Questionnaire, EQ), with the aim of obtaining a multilevel understanding of the phenomenon.\u003c/p\u003e\n\u003cp\u003ePreliminarily, some exploratory qualitative semi-structured interviews with regional administrators, AF experts, and farmers were carried out in two different steps in order to set the questionnaires.\u003c/p\u003e\n\u003cp\u003eFollowing the approach provided by Graneheim and Lundman (2004), the interviews were registered upon the consent of the interviewees, transcribed, and content analysis was performed.\u003c/p\u003e\n\u003cp\u003eThe two questionnaires have some parts in common such as: personal details, AF knowledge, interest and actual presence, and future perspective for AF. Instead, the special part is different for the two targets, as described in the paragraph below.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2.1. Regional administrators Questionnaire (RQ)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe branching and semi-structured questionnaire addressed to regional administration representatives was designed to collect both descriptive information (e.g., regional regulations, support instruments, the role of Rural Development programs) and subjective evaluations (e.g., perceived obstacles, strategic priorities, future opportunities).\u003c/p\u003e\n\u003cp\u003eThe questions included both closed-ended items as well as open-ended questions aimed at gathering opinions and qualitative comments.\u003c/p\u003e\n\u003cp\u003eRegional authorities were asked to describe their approach to AFS across the different CAP programming periods (I: 2007–2013, II: 2014–2022, III: 2023–2027) in order to better understand why and how each region acted to promote the diffusion of AS, and how these actions evolved over time. In particular, when AF measures had been activated in a given region, respondents were invited to provide details on the resources allocated, the perceived outcomes, and the main limitations encountered. Conversely, in cases where AF measures had not been activated, they were asked to explain the reasons underlying this decision.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.2.2. Agricultural and Forestry Entrepreneurs Questionnaire (EQ)\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThis questionnaire was administered to farm owners as well as to technicians, consultants, and collaborators of Italian agricultural and forestry enterprises. The objective was to investigate the factors influencing the willingness to apply for public calls under the current CAP programming period (2023–2027), with particular reference to measures supporting the establishment and management of AS.\u003c/p\u003e\n\u003cp\u003eThe first section of the questionnaire included sociodemographic variables (gender, role within the enterprise), territorial variables (region, predominant farm morphology, minimum and maximum altitude), and other socioeconomic characteristics. \u003c/p\u003e\n\u003cp\u003eThe second section focused on assessing the perceived benefits of AF, measured through questions concerning the restoration of traditional systems, production diversification, income diversification, climate change mitigation, biodiversity conservation, increased soil fertility, and erosion control. Additional questions explored previous experience with AF adoption, membership in professional associations, participation in training or information initiatives on AF, and opinions regarding the need for public intervention to support the sector.\u003c/p\u003e"},{"header":" 3. Empirical strategy","content":"\u003cp\u003eFor the analysis of data related to farmers, an econometric approach was adopted to identify the main factors that have influenced - and may continue to influence - the propensity to adopt AF measures, thereby providing useful insights to improve the effectiveness of public policies and to design support tools more closely aligned with the needs of the farmers.\u003c/p\u003e\n\u003cp\u003eSpecifically, a binary Logit model was applied (using STATA software), given the dichotomous nature of the dependent variable, which was constructed based on the question: \u003cem\u003e\u0026ldquo;If, in the new 2023\u0026ndash;2027 programming period, your Region were to activate measures supporting the establishment and management of agroforestry systems, would you consider applying for the calls?\u0026rdquo;.\u003c/em\u003e\u003cbr\u003e\u0026nbsp;In this model, the probability of willingness (or unwillingness) to apply is linked to a set of explanatory variables. In the literature, similar approaches have been used by Neupane et al. (2002) and Tega and Bojago (2023), who investigated the determinants of AF practices adoption by farmers in Nepal and Ethiopia, respectively.\u003c/p\u003e\n\u003cp\u003eThe description of the variables included in the econometric model and the corresponding basic descriptive statistics are presented in Tables 1 and 2, respectively. This analysis serves as an exploratory tool to identify the individual, farm-related, and experiential characteristics that could influence the likelihood of participation in AFS support measures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e Description of the variables included into econometric model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"661\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eVariable name\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eDescription\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eVariable type\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eDependent variable\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eWilling_to_apply_AF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eWillingness to apply calls for AF measures\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDummy (\u0026ldquo;No\u0026rdquo; = 0; \u0026ldquo;Yes\u0026rdquo; = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eExplanatory variables\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003egender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eIndicate the gender of the respondents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDummy (\u0026ldquo;Male\u0026rdquo; = 0; \u0026ldquo;Female\u0026rdquo; = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003etraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eIndicate if the respondent has participated in training/information activities on AF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDummy (\u0026ldquo;No\u0026rdquo; = 0; \u0026ldquo;Yes\u0026rdquo; = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eperceived_benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean of responses to the 7 Likert-scale items on knowledge of AF benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eContinuous (Likert scale 1 - 4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eexperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eThe farm had or currently has land managed under AS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDummy (\u0026ldquo;No\u0026rdquo; = 0; \u0026ldquo;Yes\u0026rdquo; = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003eassociation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMembership in a farmers\u0026rsquo; association\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eDummy (\u0026ldquo;No\u0026rdquo; = 0; \u0026ldquo;Yes\u0026rdquo; = 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003cem\u003eControl variables\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003emacroarea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eGeographical area where the respondent from (North, Central, South)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eCategorical (i.macroarea)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 192px;\"\u003e\n \u003cp\u003emorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ePredominant morphology of the area where farm managed by the respondents is located (Mountain, Hill, Plain)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 217px;\"\u003e\n \u003cp\u003eCategorical (i.morphology)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003ealtitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eMean altitude of the farm area, calculated as the average of the minimum and maximum elevations indicated by the respondents\u0026nbsp;\u003cbr\u003e\u0026nbsp;(m a.s.l.)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 217px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eContinuous (numeric)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eTab.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e2\u003c/strong\u003e Descriptive statistics of the variables used in the econometric model\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"352\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 211px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariable\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. dev\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eAssociation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003ePerceived benefits\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003erestoring traditional systems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.20\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eproduction diversification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eincome diversification\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eclimate mitigation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003ebiodiversity conservation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eincrease in fertility\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eerosion defense\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e2.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eMacroarea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eCenter\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eMorphology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eMountain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003eHill\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003ePlain\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 211px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAltitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e443.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 71px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e372.90\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eWith regard the \u003cem\u003ePerceived_benefits\u0026nbsp;\u003c/em\u003evariable, it is a latent construct capturing the perceived benefits of AF. It was derived from seven Likert-scale items included in the questionnaire, each assessing the perception of respondents about a specific benefit of AF: restoration of traditional systems, diversification of production, diversification of income, climate mitigation, biodiversity conservation, improvement of soil fertility, and erosion control. The construct was calculated as the mean score across the seven items (Likert scale 1-4), thus providing a synthetic measure of the overall perception of AF benefits, where higher values indicate a more positive perception.\u003c/p\u003e\n\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e"},{"header":"4.\tResults and Discussion","content":"\u003cp\u003eThe following sections outline the results corresponding to the dual aim of this study, providing insights into both the financial and perceptual factors influencing AFS adoption.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.1. Financial analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe promotion of AFS under the CAP began in 2007, when Measure 2.2.2 was introduced within the Rural Development Programmes, providing grants exclusively for the establishment of AFS. These grants supported farmers in creating systems that combine silviculture and extensive agriculture on the same land. During the 2007\u0026ndash;2013 programming period, only planting costs were covered, with a co-financing rate of 70%, rising to 80% in disadvantaged areas. No subsidies were provided for maintenance.\u003c/p\u003e\n\u003cp\u003eThis innovative measure remained largely unapplied, likely due to limited interest and awareness within the agricultural sector. Only four regions (Lazio, Marche, Sicilia, Umbria) activated the measure, while Veneto implemented it later during the period. Initially, Italy allocated \u0026euro;8.2 million under this measure to establish 6,737 hectares of AFS. However, these resources were significantly reallocated to other CAP measures. By the end of the period, only 0.3% of the planned resources had been spent, with Veneto being the sole region to support just two applications, covering a total of 20 hectares.\u003c/p\u003e\n\u003cp\u003eIn the 2014-2022 programming period, AFS support was reintroduced through Measure 8.2, which, unlike the previous measure, also covered maintenance costs by providing an annual premium for up to five years. Each region was required to specify in its Rural Development Programme the tree density, eligible species, and conditions for sustainable land use, with the aim of diversifying farm production and income. Implementation approaches, however, were heterogeneous: in some regions the measure was framed primarily with economic objectives, while in others environmental aims prevailed.\u003c/p\u003e\n\u003cp\u003eThe degree of detail also varied considerably, ranging from generic provisions to specific prescriptions on tree species. Initially, five regions activated the measure (Basilicata, Marche, Puglia, Umbria, Veneto), allocating about \u0026euro;9 million-less than 1% of the total forestry resources under Measure 8. Implementation showed modest improvement compared to the previous period, although payments only started in 2020 due to mismatches between programming and local needs.\u003c/p\u003e\n\u003cp\u003eOverall, \u0026euro;1,281,429 were spent on 1,204 hectares, with only two regions (Puglia and Veneto) making use of the measure. Nevertheless, actual spending remained far below expectations: by the final year, expenditure corresponded to just 20% of planned resources in Puglia and 27% in Veneto, while at the national level only about 13% of allocated resources had been spent until 2022. Veneto eventually used nearly all its available resources to establish 1,201 hectares of AFS, even increasing allocations during the period, while Puglia sharply reduced them in the last year. The application of the N+3 rule-where N denotes the commitment year and +3 refers to the additional three years granted for expenditure-extended the eligibility of payments for the 2014\u0026ndash;2020 programming period (including transitional years) until 2024. This allowed the Umbria Region to absorb a portion of the reprogrammed funds, contributing to an increase in Italy\u0026rsquo;s aggregate expenditure to 31.95% of the reprogrammed allocation after the 2023 adjustment.\u003c/p\u003e\n\u003cp\u003eTable 3 reports the amount of allocated funding (programmed and spent resources) for the 2007-2013 and 2014-2022 periods, including only regions where measures were activated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab. 3\u003c/strong\u003e Allocation of resources in agroforestry systems for the programming periods 2007-2013 (Measure 2.2.2.) and 2014-2022 (Measure 8.2). Values are expressed in euros (\u0026euro;), rounded to the nearest unit. n.a. = not activated\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"628\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgramming period\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2007-2013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2014-2022 (2024)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 218px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure 2.2.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 326px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMeasure 8.2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eResources (\u0026euro;)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eRegions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 144px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgrammed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 162px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProgrammed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSpent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 82px;\"\u003e\n \u003cp\u003e2022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e2024\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eBasilicata\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e826.446\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e826.446 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eLatium\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e616,093\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eMarche\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e2,270,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e2,500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e2,000,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e2,000,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eApulia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 69px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e5,000,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e3,169,402\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.280.196\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1,886,826\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eSicily\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e4,540,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003en.a.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 82px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eUmbria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e760,068\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1,000,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e187,400\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e86,563\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003eVeneto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003en.a.\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e30,000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e27,544\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e231,911\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e4,638\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e1.233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e3,623\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8,186,161\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e32,500\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 74px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e27,544\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9,058,357\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6,187,886\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.281.429\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 82px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,977,012\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eIn the current 2023\u0026ndash;2027 programming period, according to the Italian CAP Strategic Plan (approved in November 2022, CCI 2023IT06AFSP001), AFS support is delivered through two interventions: SRA28 \u0026ndash; Support for the maintenance of afforestation/reforestation and agroforestry systems, SRD05 \u0026ndash; Afforestation/reforestation and agroforestry systems on agricultural land. Both interventions include Action 3, which specifically targets agroforestry systems: SRD05.3 \u0026ndash; Establishment of agroforestry systems on agricultural land, funding plantation costs; SRA28.3 \u0026ndash; Maintenance of agroforestry systems, covering maintenance costs for environmental purposes. Each Action 3 is further divided into two sub-actions: 3.1 silvoarable systems and 3.2 silvopastoral systems, designed to deliver multiple productive and environmental functions.\u003c/p\u003e\n\u003cp\u003eAs of today, these two interventions remain only partially implemented: approximately 25% of regional authorities have activated them, while about half of the regions have never activated any measure or intervention related to agroforestry systems. Specifically, SRA28.3 is operational in five regions, while SRD05.3 is active in six regions (Piedmont, Apulia, Tuscany, Umbria, Veneto), with Sicily activating only SRD05.3 for establishment without maintenance. However, no region has reported any expenditure during the first two years (2023\u0026ndash;2024). Figure 1 summarizes the activation status of measures and interventions related to agroforestry systems across the three programming periods, including regions that have activated them and those that have incurred expenditure.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e4.2. \u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eData collection for the survey\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe survey collected a total of 121 questionnaires, including \u003cstrong\u003e28\u003c/strong\u003e from regional administrators and \u003cstrong\u003e93\u003c/strong\u003e from agricultural and forestry entrepreneurs. The geographical distribution of responses was uneven across the Italian territory, with some areas showing higher participation rates than others.\u003c/p\u003e\n\u003cp\u003eThe spatial distribution provides a nationwide overview; however, the limited sample size does not allow for robust conclusions regarding territorial differences in the responses obtained.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2.1. \u0026nbsp; \u0026nbsp;Regional administrators questionnaire\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAnalysis of the responses indicates that at least one officer from each region completed the questionnaire, thereby ensuring full national coverage and the representation of all Italian regions. While respondents generally reported good knowledge of AF and acknowledged its relevance - especially for biodiversity conservation and climate change mitigation - both interest and technical know-how remain limited. These factors appear to be among the main reasons for the low uptake of AFS measures, as information on environmental and economic benefits is not readily accessible. Other barriers mentioned include the lack of available land and the persistence of preconceived ideas.\u003c/p\u003e\n\u003cp\u003eInstitutional intervention in AFS is considered crucial by 86% of respondents, primarily for educational purposes. Knowledge is deemed essential to determine whether the grant is suitable for specific needs, for the territory and the farm, how to apply, and how to invest the subsidy. Respondents also called for increased financial support, not only to fund AF practices but also to recognize the ecosystem services they provide. Bureaucratic assistance is also needed, to better promote calls for applications and simplify administrative procedures. Those who considered institutional interventions unnecessary viewed AFS as a niche and low-value sector, not warranting the use of public funds.\u003c/p\u003e\n\u003cp\u003eAccording to those who implemented AFS measures in the previous CAP programming periods (2007\u0026ndash;2013 and 2014\u0026ndash;2022), results were generally poor and the scale of interventions inadequate, with one exception: the Veneto region, which met all requests for Measure 8.2, although results still fell short of expectations. The shortcomings were attributed, in the first period, to the novelty of the intervention (with excessive overall funding but insufficient amounts per grant), and in the second period, to the use of AFS measures to finance other types of works, such as windbreak barriers, rather than AF plantations. Regions that did not activate AFS measures cited limited budgets, lack of demand, and absence of available land.\u003c/p\u003e\n\u003cp\u003eLooking ahead, the outlook appears more positive: although only a few regions have activated the two interventions (SRD05 and SRA28) in the 2023-2027 programming period, a larger number plan to do so. Expectations include the establishment of new AFS, with allocated funds ranging from \u0026euro;0.2 to \u0026euro;9.0 million per region, reflecting growing interest in AFS.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e4.2.2. \u0026nbsp; \u0026nbsp;Agricultural and Forestry Entrepreneurs questionnaire\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe empirical analysis was conducted on a sample of 93 observations, which calls for caution in interpreting the results and limits the generalizability of the conclusions. Nevertheless, the findings provide valuable insights into the factors influencing farmers\u0026rsquo; propensity to adopt AFS supported by CAP 2023\u0026ndash;2027 measures.\u003c/p\u003e\n\u003cp\u003eThe the binary logit model results (Table 4) reveal that four variables exibit a statistically significant association with farmers\u0026rsquo; willingness to apply for future AFS measures. Geographical macro-area, farm morphology, and altitude were included as control variables.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTab.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e4\u003c/strong\u003e Logit model results\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 409px;\"\u003e\n \u003cp\u003eDependent variable: Willing_to_apply_AF\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eOdds Ratio\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(OR)\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003eRobust Standard Error\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(RSE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003ePerceived Benefits\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e2.17 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eExperience\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e3.80 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e2.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eTraining\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e5.37 **\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e4.23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e4.02 *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e3.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eAssociation\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eMacroarea\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eMorphology\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 172px;\"\u003e\n \u003cp\u003eAltitude\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 152px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u0026nbsp;Note: Significance codes: \u0026lowast;p \u0026lt; 0.10, \u0026lowast;\u0026lowast; p \u0026lt; 0.05, \u0026lowast;\u0026lowast;\u0026lowast; p \u0026lt; 0.01. N = 93 observations. Pseudo R\u0026sup2;: 0.2660.\u0026nbsp;\u003cbr\u003e\u0026nbsp;Prob \u0026gt; \u0026chi;\u0026sup2; = 0.0008\u003c/p\u003e\n\u003cp\u003eIn particular, perceived benefits, previous experience with AF practices, participation in specific training, and gender all show a positive and statistically significant relationship with the dependent variable. Among these factors, training exhibits the strongest association, suggesting that targeted educational initiatives can substantially enhance the intention to adopt AS.\u003c/p\u003e\n\u003cp\u003eFarmers with a more positive perception of AF benefits such as environmental improvement, production diversification, or landscape preservation, are more than twice as likely to express willingness to participate in future calls compared to those who do not perceive such benefits. This result is significant at the 5% level, indicating a robust relationship between a positive perception of benefits and the propensity to participate.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSimilarly, respondents who have had direct experience with the implementation of AFS areas on their farms show a higher inclination to apply for AFS calls. Although this result is only marginally significant, it is consistent with the hypothesis that concrete experience strengthens interest in such measures.\u003c/p\u003e\n\u003cp\u003eA distinctive finding concerns gender: women appear more inclined to participate than men, a result that deserves further investigation to better understand the underlying motivations.\u003c/p\u003e\n\u003cp\u003eConversely, membership in a farmers\u0026rsquo; association does not show a significant effect.\u003c/p\u003e\n\u003cp\u003eAlthough some coefficients display relatively large robust standard errors - likely due to the limited sample size - the direction and statistical significance of the main effects remain consistent with theoretical expectations. Therefore, it is preferable to focus on the direction of the effects rather than on their magnitude, which may be imprecise.\u003c/p\u003e\n\u003cp\u003eOverall, the model demonstrates a good explanatory capacity (Pseudo R\u0026sup2; = 0.2660) and a strong overall significance (Prob \u0026gt; \u0026chi;\u0026sup2; = 0.0008), supporting the reliability of the identified relationships despite the inherent data limitations.\u003c/p\u003e\n\u003cp\u003eTali evidenze possono fornire spunti concreti per l\u0026rsquo;orientamento delle politiche future, pur nella consapevolezza che l\u0026rsquo;analisi \u0026egrave; stata condotta su un campione ridotto e non di certo rappresentativo dell\u0026rsquo;intera popolazione di imprenditori agricoli e forestali italiani.\u003c/p\u003e"},{"header":"5.\tConclusions","content":"\u003cp\u003eThe findings of this study confirm that, despite the opportunities offered by the Common Agricultural Policy (CAP), the adoption of agroforestry systems (AFS) by Italian farms remains limited. In previous programming periods (2007-2013 and 2014-2022), measures designed to support the establishment and management of AFS were only partially implemented and, even when available, recorded low participation rates among farmers. This limited uptake reflects structural and institutional challenges that have persisted over time, including fragmented policy frameworks and insufficient alignment between EU requirements and local farming conditions. The current CAP 2023-2027 programming period introduces higher financial allocations and new interventions specifically targeting agroforestry systems. While this could represent an opportunity for revitalization, implementation remains uneven and slow. Regional disparities persist, with significant differences in capacity, awareness, and commitment.\u003c/p\u003e\n\u003cp\u003eRegional officers acknowledge the importance of agroforestry for biodiversity conservation and climate change mitigation, yet they consistently point to limited technical expertise, lack of accessible information, and bureaucratic complexity as major obstacles to effective adoption. These structural barriers suggest that, despite increased resources, the actual uptake of agroforestry measures will depend on targeted support, capacity building, and simplification of administrative procedures rather than financial incentives alone.\u003c/p\u003e\n\u003cp\u003eInstitutional intervention is therefore considered essential, particularly in three areas: (i) strengthening technical training and advisory services; (ii) simplifying administrative procedures to reduce transaction costs; and (iii) increasing financial support, including mechanisms to recognize and remunerate the ecosystem services provided by AFS. Lessons learned from previous programming periods can play a crucial role in guiding the design and implementation of future measures, ensuring greater coherence and responsiveness to local needs.\u003c/p\u003e\n\u003cp\u003eFrom the perspective of agricultural and forestry entrepreneurs, the results indicate that perceived benefits of AF - such as improved soil fertility, biodiversity enhancement, and diversified income streams - represent a key factor in fostering adoption. However, this perception appears strongly linked to direct experience: farmers who have already experimented with AF practices tend to recognize their advantages more readily. This finding underscores the importance of targeted communication and awareness-raising initiatives to disseminate knowledge of AF benefits. Equally critical is the provision of clear and transparent information on initial investment costs, expected economic returns, and long-term profitability, enabling farmers to make informed decisions.\u003c/p\u003e\n\u003cp\u003eFuture research should expand the sample size and explore additional behavioral and contextual variables to deepen understanding of the dynamics influencing adoption. Such evidence would provide valuable guidance for policymakers in designing more effective support schemes and promotion strategies. Moreover, integrating AF into broader value chains and fostering synergies with other CAP measures and forestry programs could enhance its socioeconomic impact, particularly in marginal and inner rural areas affected by depopulation and land abandonment. In these contexts, appropriate land management and infrastructural support - such as road networks and water systems - are crucial to preserving the multifunctional benefits of AF.\u003c/p\u003e\n\u003cp\u003eFinally, awareness of AFS remains limited among both farmers and the general public. Many farmers still perceive agroforestry as a non-productive, environmentally oriented system with low profitability and limited mechanization potential. Overcoming these misconceptions requires a comprehensive approach that combines technical training, professional capacity building, and the recognition of the economic value of ecosystem services. Only through coordinated efforts at institutional, technical, and communicative levels can agroforestry transition from a marginal practice to a cornerstone of sustainable land management in Italy.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRosa Rivieccio: Conceptualization, Data curation, Formal analysis, Methodology, Supervision, Visualization, Writing – original draft, Writing – review and editing; Martina Agosta: Data collection, Formal analysis, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing; Erica Mazza: Conceptualization, Data curation, Data collection, Investigation; Raoul Romano: Conceptualization, Data collection, Investigation, Project administration, Supervision, Validation, Writing – original draft, Writing – review and editing. All authors reviewed and revised the manuscript and approved the final manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was carried out within the framework of the Council for Agricultural Research and Analysis of Agricultural Economics (CREA), Research Centre for Policies and Bioeconomy, and funded by the Italian Ministry of Agriculture, Food Sovereignty and Forests (MASAF) under the Rete PAC Programme, Project WP 1, CR 01.11 – Agroforestry Systems.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eInformed consent was obtained from all subjects involved in the study. All subjects were anonymized, and no subject can be identified.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e All relevant data are included in this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to thank all respondents to the questionnaire for this study, including officers from Italian regional authorities and managers of agricultural and forestry enterprises. Special thanks go to Jacopo Goracci of Tenuta di Paganico (GR, Italy) for his support in disseminating the questionnaire. The authors have edited the final output and take full responsibility for the content of this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eCamilli F, Pisanelli A, Seddaiu G, Franca A, Bondesan V, Rosati A, Burgess PJ (2018) How local stakeholders perceive agroforestry systems: an Italian perspective. Agroforestry Systems 92(4):849\u0026ndash;862\u003c/li\u003e\n\u003cli\u003eChiarabaglio et al. (2023) Agroforestazione in Italia: una opportunit\u0026agrave; per le aziende agrarie. Rete Rurale Nazionale 2014\u0026ndash;2020, Consiglio per la ricerca in agricoltura e l\u0026rsquo;analisi dell\u0026rsquo;economia agraria, Roma. ISBN 9788833852690\u003c/li\u003e\n\u003cli\u003eChiozzotto F (2018) Agroforestazione e sviluppo rurale: un potenziale ancora inespresso. 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Agriculture, Ecosystems \u0026amp; Environment 230:150\u0026ndash;161. https://doi.org/10.1016/j.agee.2016.06.002\u003c/li\u003e\n\u003cli\u003eTranchina M, Reubens B, Frey M, Mele M, Mantino A (2024) What challenges impede the adoption of agroforestry practices? A global perspective through a systematic literature review. Agroforestry Systems 98:1817\u0026ndash;1837. https://doi.org/10.1007/s10457-024-00993-w\u003c/li\u003e\n\u003cli\u003eVang Rasmussen L, Grass I, Mehrabi Z, et al. (2024) Joint environmental and social benefits from diversified agriculture. Science 384(6651):eadj1914. https://doi.org/10.1126/science.adj1914\u003c/li\u003e\n\u003cli\u003eVarela E, Olaizola AM, Blasco I, Capdevila C, Lecegui A, Casas\u0026uacute;s I, et al. (2022) Unravelling opportunities, synergies, and barriers for enhancing silvopastoralism in the Mediterranean. Land Use Policy 118:106140. https://doi.org/10.1016/j.landusepol.2022.106140\u003c/li\u003e\n\u003cli\u003eWatson CA, et al. (2020) Agroforestry in Europe: a land management system to combat climate change. Advances in Agronomy 159:1\u0026ndash;45. https://doi.org/10.1016/bs.agron.2020.07.001\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Agroforestry systems, Common Agricultural Policy, Policy implementation, Rural development, Italy","lastPublishedDoi":"10.21203/rs.3.rs-8290861/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8290861/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgroforestry systems represent a multifunctional land-use approach capable of enhancing numerous ecosystem services, including biodiversity, soil conservation, and climate resilience, as well as promoting farm income diversification by fostering sustainable quality production. However, despite active policy support since 2007 under the Common Agricultural Policy (CAP), their adoption in Italy remains limited. This study investigates the reasons behind the low uptake of agroforestry measures across three CAP programming periods through a sequential mixed-method design, starting with a financial and policy analysis to outline the implementation framework, followed by survey-based econometric modeling to explore behavioral and institutional factors.\u003c/p\u003e \u003cp\u003eThe financial analysis reveals a limited implementation framework (25%) and low expenditure levels, with only a few regions effectively activating the agroforestry systems measures. To better understand the underlying reasons for this low participation, an econometric model was developed based on a questionnaire administered to agricultural and forestry entrepreneurs. The model identified four variables significantly associated with the willingness to apply for agroforestry measures. The results suggest that awareness, technical capacity, and direct experience are key factors for adoption, while limited knowledge, bureaucratic complexity, and insufficient economic incentives constitute the main barriers. Strengthening training, communication, and policy coherence could therefore promote agroforestry adoption in future CAP cycles.\u003c/p\u003e","manuscriptTitle":"Agroforestry systems measures in Italy: an explorative analysis of policy effectiveness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-16 11:40:18","doi":"10.21203/rs.3.rs-8290861/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-02-13T10:28:42+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-11T23:49:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-10T21:43:10+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-02-01T15:17:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"27230099479745426969133734932923346327","date":"2026-01-19T10:43:08+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229021788900734147533840093638315973923","date":"2026-01-18T18:10:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"73943909924099053526583088606274916998","date":"2026-01-15T15:40:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"310066908659421345326948828863535064789","date":"2026-01-15T14:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53311952021622125699649947358264650734","date":"2026-01-13T14:58:53+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-01-13T14:28:19+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-08T09:42:45+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T06:40:43+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2025-12-05T21:40:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"93be3342-12de-45e3-9e23-bc8045e6be3c","owner":[],"postedDate":"January 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-21T13:09:51+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-16 11:40:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8290861","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8290861","identity":"rs-8290861","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}