Identifying target areas for agroforestry in European agricultural landscapes based on environmental pressures and socioeconomic contexts

Identifying target areas for agroforestry in European agricultural landscapes based on environmental pressures and socioeconomic contexts V. Anthony Gabourel-Landaverde a,* , Susanne Schnabel a , J. Francisco Lavado-Contador a , Jo Smith b , Jo˜ ao H.N. Palma b a INTERRA Research Institute, Universidad de Extremadura, C´ aceres, Spain b Moinhos de Vento Agroecology Research Centre, M´ ertola, Portugal ARTICLE INFO Dataset link: Spatial distribution of accumulated environmental pressures related to soils, biodiversity, water and climate change (0-14) in the potential agricultural area to introduce agroforestry (Original data), Regional hotspots of environmental pressures identified in the European Union 27 Member States, United Kingdom and Switzerland at NUTS 2 level (Original data), Target areas to introduce agroforestry (6-14) accumulated environmental pressures (Original data), Spatial distribution of accumulated soil pressures (0-3) in the potential agricultural area to introduce agroforestry in the European Union 27 Member States, United Kingdom and Switzerland (Original data), Spatial distribution of accumulated biodiversity pressures (0-5) in the potential agricultural area to introduce agroforestry in the European Union 27 Member States, United Kingdom and Switzerland (Original data), Spatial distribution of accumulated climate change pressures (0-4) in the potential agricultural area to introduce agroforestry in the European Union 27 Member States, United Kingdom and Switzerland (Original data), Spatial distribution of accumulated water pressures (0-2) in the potential agricultural area to introduce agroforestry in the European Union 27 Member States, United Kingdom and Switzerland (Original data), Ratio of young farm managers (<40 years old) to elderly farm managers (>40 years old) in the NUTS 2 regions for the years 2020 (EU27 and Switzerland) and 2016 (UK) (Original data), Socio-economic contexts (C1, C2, C3) in the European Union 27 Member States, Switzerland and United Kingdom at NUTS 2 ABSTRACT Agroforestry is a practice where the intentional combination of trees and shrubs, crops and livestock occur on the same land to generate environmental, economic and social benefits. This study identifies target areas in the European Union 27 Member States, United Kingdom, and Switzerland where introducing agroforestry can further enhance environmental benefits and climate change resilience. Using a spatial approach, the methodology involved four steps: selecting suitable agricultural areas, analysing environmental pressures, defining target areas for agroforestry introduction, and characterising the socio-economic context. Fourteen environmental indicators across soil, biodiversity, water, and climate change were analysed using defined threshold values to identify areas where sustainability is compromised. Heat maps highlighted high-pressure areas (6–14 accumulated environmental pressures) as target areas. Socio-economic context was described using six indicators related to demography, farmer training and willingness to change, and economy at the NUTS 2 regional level, defining high, medium, and low-profile regions. Results indicated biodiversity and climate change pressures affected larger areas than soil and water pressures, with hotspots in France, Spain, and Romania. Regions facing greater socio-economic challenges (low-profile) also experienced more environmental pressures. The study concludes by defining suitable locations with high environmental pressures, along with their socio-economic contexts, for agroforestry introduction, emphasizing its importance for climate resilience. Identifying target areas for agroforestry in European agricultural landscapes based on environmental pressures and socioeconomic contexts * Corresponding author at: INTERRA Research Institute, Universidad de Extremadura, 10071 C´ aceres, Spain. E-mail address: [email protected] (V.A. Gabourel-Landaverde). Contents lists available at ScienceDirect Trees, Forests and People journal homepage: www.sciencedirect.com/journal/trees-forests-and-people https://doi.org/10.1016/j.tfp.2025.100961 Trees, Forests and People 21 (2025) 100961 Available online 26 July 2025 2666-7193/© 2025 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/bync-nd/4.0/ ). scale (Original data), Share of organic farming holdings (%) in the NUTS 2 regions for the years 2020 (EU27 and Switzerland) and 2016 (UK) (Original data), Degree of urbanisation in the NUTS 2 regions for the years 2020 (EU27 and Switzerland) and 2016 (UK) (Original data), Unemployment rates of the NUTS 2 regions for the years 2021 (EU27 and Switzerland) and 2016 (UK only) (Original data), Mean economic size of farms (Euros) of the NUTS 2 regions for the years 2020 (EU27 and Switzerland) and 2016 (UK) (Original data), Ratio of farm managers with full training to farm managers with basic knowledge and practical experience only in the NUTS 2 regions for the years 2020 (EU27 and Switzerland) and 2016 (UK) (Original data) Keywords: Landscape planning Agroforestry European agriculture Land use modelling Climate change 1. Introduction Agricultural land in Europe is facing multiple challenges driven by climate change (Dmuchowski et al., 2022), the intensification and mechanisation of farming practices, and the abandonment of traditional systems like agroforestry, which have been in decline since the last century (Nerlich et al., 2013; Rubio-Delgado et al., 2023). These challenges involve different degrees of pressure on the elements that are part of agricultural systems, such as biodiversity, soil, water and climate. In this context, agroforestry (AF) has emerged as a promising strategy for mitigating climate change (Dmuchowski et al., 2022) and offers an opportunity to diversify and enhance farming operations (Rois-Díaz et al., 2018). Agroforestry is a practice where the intentional combination of trees and shrubs, crops and livestock occurs on the same land to generate environmental, economic and social benefits (Mosquera-Losada et al., 2018; USDA, 2025), enhancing ecosystem services such as carbon sequestration, increased soil fertility, and providing habitat for wildlife (Jose, 2009). As such, there is increasing interest from policy makers in encouraging wider uptake of agroforestry across Europe (EU, 2021; Mosquera-Losada et al., 2023), highlighting the need to identify target areas to focus policy support mechanisms in regions where agroforestry implementation is most effective and appropriate. To support agroforestry uptake, various methods have been employed to map agroforestry areas in Europe. Some efforts have concentrated on identifying new areas for potential agroforestry systems through suitability analysis (Reisner et al., 2007) or by assessing environmental pressures (Kay, Rega, et al., 2019). Meanwhile, other studies have focused on quantifying the extent of agroforestry systems (den Herder et al., 2017; Mosquera-Losada et al., 2018; Rubio-Delgado et al., 2024) and changes in these areas over time (Rubio-Delgado et al., 2023; Rubio-Delgado et al., 2025). In terms of socio-economic aspects, some studies have explored the future megatrends affecting European agriculture at the regional scale (Debonne et al., 2022), which is relevant for understanding the interaction between the socio-economic and the environmental domains in time and space, given that agricultural systems are complex socio-ecological systems that involve many components (Scown et al., 2019). However, few studies have explored the interaction between environmental and social aspects in agricultural systems, which is crucial for the development of agricultural policy that is implemented mostly at the regional level (Debonne et al., 2022; Quandt et al., 2023). In this regard, environmental and socio-economic aspects are considered in this work, aiming at the definition of target areas to introduce agroforestry. Key environmental factors include variables related to soils, water, biodiversity and climate change. Additionally, socio-economic factors related to economic conditions, demography and farmers’ willingness to adopt agroforestry practices, which are related to the context indicators used in the Common Agricultural Policy (CAP) (EC, 2024) were considered as important to characterise target areas. Regarding the role of agroforestry on soils, the improvement of soil physical, chemical and biological properties (Centeno-Alvarado et al., 2023) by increasing effective soil depth and water-use efficiency, has been reported in agroforestry systems over the last 40 years (Dollinger and Jose, 2018), as well as the positive impact on soil carbon storage (Kay, Rega, et al., 2019). Moreover, a higher diversity and activity of soil microbiota in agroforestry systems has been described (Rolo et al., 2023), which could be translated into improved soil functioning and plant health. From the biodiversity point of view, several studies have evidenced that agroforestry have the capacity to enhance biodiversity (Edo et al., 2023; Santos et al., 2022), are important for reducing species loss and protecting endangered species (Torralba et al., 2016), for pollination support (Centeno-Alvarado et al., 2023) and for pest control (Samal et al., 2024). Furthermore, the European Union (EU) Biodiversity Strategy for 2030 promotes increasing the uptake of agroforestry support measures due to the multiple benefits it provides for biodiversity, people and climate (EU, 2021). From a water use and quality perspective, reduced water stress and increased water use efficiency are some of the benefits reported by agroforestry systems, as compared with monocultures that require a high demand for water (Rolo et al., 2023). Additionally, the improvement of surface and groundwater quality has been associated with some types of agroforestry, such as riparian buffers or alley cropping (Schultz et al., 2021; Udawatta et al., 2002). Agroforestry also plays an important role in controlling nitrogen loss, further contributing to water quality improvements (Elrys et al., 2023; Kim and Isaac, 2022). In the context of climate change, agroforestry is considered a natural climate solution due to its ability to mitigate climate change while promoting food security and enhancing biodiversity, and its potential is comparable to other strategies such as reforestation and reduced deforestation, offering significant opportunities for broader adoption and expansion (Terasaki Hart et al., 2023). From an adaptation perspective, agroforestry could play a key role in response to extreme weather events (Stetter and Sauer, 2024) and have the capacity to reduce temperature variation (Palma et al., 2018). V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 2 However, various challenges have been identified in the technical, economic, education and policy fields for the implementation of agroforestry across Europe (Mosquera-Losada et al., 2023; Rois-Díaz et al., 2018; Sollen-Norrlin et al., 2020; Tranchina et al., 2024). In terms of productivity, lower yields per-area under high tree density or reduced staple output in small farms due to resource competition have been reported (Ivezi´ c et al., 2021). Economically, the viability of agroforestry depends on factors such as the valuation of its ecosystem services (ES) (Giannitsopoulos et al., 2020; Thiesmeier and Zander, 2023). As Kay, Graves, et al. (2019) demonstrated across European landscapes, incorporating the economic value of ES like carbon sequestration and reduced environmental externalities is crucial for making agroforestry systems economically competitive. In this sense, it is necessary to understand the socio-economic context in regions where agroforestry could be introduced, along with other complex technical and policy-related components (Tranchina et al., 2024), as a baseline to guide future measures. In particular, the objectives of this work are to 1) map environmental pressure indicators related to soils, biodiversity, water and climate change, 2) identify regions in Europe under several environmental pressures, 3) define target areas where the introduction of agroforestry could reduce the impact of those pressures and 4) characterise the socioeconomic context of the target regions to facilitate the implementation of agroforestry, in regards with the current challenges that pose the establishment and maintenance of agroforestry. 2. Materials and methods The selection of the target areas consisted of four steps (Fig. 1). Firstly, potential agricultural areas were selected in the European Union 27 (EU27) Member States, United Kingdom (UK) and Switzerland (CH). Secondly, environmental pressure indicators were analysed. Thirdly, target areas were defined. Finally, socio-economic contexts were identified. The methodology was based on the spatial approach proposed by Kay, Rega, et al. (2019) to identify priority areas to introduce agroforestry in Europe. Similarities with this work are found in the inclusion of 7 common land cover classes, the exclusion of nature protected sites from the potential agricultural areas, and the selection of 8 common environmental pressure indicators. 2.1. Selection of suitable potential agricultural area For the estimation of the total agricultural area in the EU27, UK and CH, the Land-Use based Integrated Sustainability Assessment (LUISA) base map from 2018 (Batista and Pigaiani, 2021) was used. The LUISA map is a modified and improved version of the CORINE land cover map of 2018 with a spatial resolution of 100 m, which enhances the resolution of the included classes, facilitating more accurate estimation of the total agricultural area. Suitable areas for implementing agroforestry were estimated from the total agricultural land, considering temporary crops (irrigated arable land, non-irrigated arable land, rice fields), permanent crops (fruit trees and berry plantations, olive groves, vineyards), and pastures (pastures, natural grasslands) (see Table S1 and Fig. S1 in supplementary material). Since nature conservation areas are subjected to specific rules and regulations, due to legal agreements and conventions aimed at preserving biodiversity and natural habitats, the nature conservation sites were identified and, therefore, not considered as potential areas for introducing agroforestry. To this, the following maps, with a spatial resolution of 100 m, were used: Natura 2000 Network (EEA, 2022b), RAMSAR sites (SISR, 2022) and the Emerald Network for Switzerland (FOEN, 2018). Furthermore, the already existing agroforestry areas were determined based on the LUISA map. Those areas can only be considered as an approximation to the real agroforestry surface because not all agroforestry areas in Europe are represented in the LUISA map. The following land uses were considered as representative of agroforestry systems: agroforestry areas (dehesas and montados mainly located in the Southwest of the Iberian Peninsula), annual crops associated with permanent crops (combination of temporary crops with a woody crop is considered agroforestry), complex cultivation patterns (combination of annual crops, pasture and/or permanent crops, including kitchen gardens, the latter considered agroforestry), and land principally occupied by agriculture, with significant areas of natural vegetation (mosaics of agricultural land combined with natural and semi-natural areas). Finally, for the estimation of suitable potential areas to introduce agroforestry the natural protected sites and the previously existing agroforestry areas were subtracted from the total agricultural area. The subsequent analysis of environmental indicators was carried out in the resulting areas. 2.2. Selection of environmental pressure indicators A set of 14 environmental indicators related to soils, biodiversity, water, and climate change were selected (Table 1), using European or Fig. 1. Methodology proposed for the identification of target areas to introduce agroforestry systems in the EU27, UK and CH. V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 3 national cartographic datasets. Due to data gaps for biodiversity and soil indicators in some countries (Croatia, Cyprus, and Switzerland), average values were calculated for European environmental zones (Metzger, 2018) and extrapolated to analogous zones in those countries. Threshold values were then established for each indicator to assess impacts and identify areas where sustainability is compromised (see Section S1 for detailed methodology about each indicator). Concerning soils, water erosion, wind erosion and loss of soil organic carbon were selected, as these are the most common types of soil degradation in Europe (Arias-Navarro et al., 2024). These processes have a significant impact on soil health, resulting in reduced crop productivity, increased soil losses, and degraded water quality (Panagos et al., 2020). In that sense, reducing soil erosion and increasing soil organic carbon stocks can enhance resilience by improving soil health, water and air quality, biodiversity, and crop productivity, which could be achieved by the implementation of agroforestry (Dollinger and Jose, 2018; Rolo et al., 2023). Regarding biodiversity, no consistent and detailed spatial data bases on species richness, diversity, or related direct indicators of biodiversity for the whole extent of the countries considered in this study were available. Therefore, other indicators expressing functional aspects of biodiversity were used as proxies for biodiversity related pressures which are available for Europe, such as natural pest control, pollinator potential, and potential threats to soil biodiversity. Natural pest control is important for crop productivity and food security, as it reduces crop losses and the need for pesticides (Rega et al., 2018). Pollinators are necessary for crop yield and quality as many crops are dependent on pollinating insects to produce food for human consumption (Vallecillo et al., 2020). Soil biodiversity is also essential for soil health, as it influences soil formation, nutrient cycling, and pest control (Orgiazzi et al., 2016). In this regard, the role of agroforestry in enhancing biodiversity has been widely recognised (Edo et al., 2023; EU, 2021; Santos et al., 2022; Torralba et al., 2016). Water issues were addressed using irrigated areas and nitrogen surplus as key indicators. Agriculture both contributes to and suffers from water scarcity, a problem highlighted by the European Environmental Agency (2021), particularly in southern and southwestern Europe, and expected to worsen. Across the EU27, Norway, and the UK, water abstraction is a significant environmental pressure (EEA, 2021), with agriculture being the largest consumer, primarily for irrigation. High nitrogen levels also degrade European water quality. Therefore, improving water-use efficiency and addressing nitrogen excess are crucial for agricultural resilience. Agroforestry can play a key role in these efforts (Elrys et al., 2023; Kim and Isaac, 2022; Udawatta et al., 2002). In terms of climate change, projected impacts (temperature, aridity, drought, heavy precipitation) were estimated. The IPCC (Lee et al., 2021) projects increased extreme weather (floods, droughts) across European subregions, posing significant agricultural risks (Olesen et al., 2011). While some regions may experience benefits like longer growing seasons (Olesen et al., 2011), studies emphasize climate change’s negative impact on agricultural productivity via temperature increases, altered water availability, and extreme events (Stetter and Sauer, 2024). Agroforestry is recognized as valuable for both climate change mitigation and adaptation (Mosquera-Losada et al., 2018; Quandt et al., 2023; Rolo et al., 2023; Stetter and Sauer, 2024; Terasaki Hart et al., 2023). 2.3. Determination of target areas After combining the environmental pressure indicators related to soils, biodiversity, water, and climate, heat maps were generated to highlight the intensity of the 14 environmental pressures. Areas with 6 or more accumulated pressures were identified as target areas to introduce agroforestry. This threshold was determined based on the statistical distribution of pressure accumulation across potential areas. Specifically, only areas within the upper two quintiles of the distribution, representing regions with 6 to 14 environmental pressures, were selected. 2.4. Selection of the socio-economic factors After identifying target areas based on environmental pressures, a Table 1 Selection of environmental pressure indicators related to soil, biodiversity, water, and climate change variables. Pressure Group Indicator* Source Coverage Resolution Threshold Threshold source Soil Water erosion (FOAG, 2019; Panagos et al., 2020) EU27, UK, CH 100 m >2 t ha -1 yr -1 (Panagos et al., 2020) Wind erosion (Borrelli et al., 2017; FOAG, 2019) EU27, UK, CH 1 km >2 t ha -1 yr -1 (Borrelli et al., 2017) Soil Organic Carbon (SOC) saturation capacity (Lugato, Bampa, et al., 2014, 2014) EU27, UK (without CH) 250 m <0.4 Ratio between actual and potential SOC stock (De Rosa et al., 2024) Biodiversity Potential threats to soil biodiversity, 3 indicators: fauna, microorganisms, and biological functions (Orgiazzi et al., 2016) EU26, UK (without HR and CH) 500 m Upper two quintiles of the values’ distribution (Orgiazzi et al., 2016) Pest control index (Rega et al., 2018) All Europe (without CY) 100 m First two quintiles of the values’ distribution (Rega et al., 2018) Pollinator potential (Vallecillo et al., 2020) EU27, UK (without CH) 1 km First two quintiles of the values’ distribution (Vallecillo et al., 2020) Water Irrigated areas (Siebert et al., 2013) World 0.08◦>25 % irrigated land (Kay, Rega, et al., 2019) Nitrogen surplus (EEA, 2022a; FOEN, 2022) EU27, UK, CH 1 km (EU, UK) 100 m (CH) >50 kg N ha -1 yr -1 (Grizzetti et al., 2023) Climate change Annual mean temperature change (Berg, Photiadou, Simonsson, et al., 2021) All Europe 5 km Upper two quintiles of the values’ distribution Based on the values’ distribution Aridity index change (Berg, Photiadou, Bartosova, et al., 2021) All Europe 5 km Upper two quintiles of the values’ distribution Based on the values’ distribution Drought frequency change (EEA, 2019) All Europe 0.11◦Upper two quintiles of the values’ distribution Based on the values’ distribution Heavy precipitation days change (Nobakht et al., 2019) All Europe 0.50◦Upper two quintiles of the values’ distribution Based on the values’ distribution V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 4 socio-economic analysis assessed factors influencing agroforestry adoption. Six variables (Table 2) related to economy (farm size, unemployment), farmer training/willingness to change (manager training, organic farming percentage), and demography (young/elderly manager ratio, degree of urbanisation) were analysed across EU27, UK, and CH NUTS (Nomenclature of Territorial Units for Statistics) 2 regions. These indicators, used within the CAP and considered reliable (EC, 2024), were analysed individually and then spatially combined to identify regions with varying socio-economic contexts (for detailed information on these indicators, see Section S2 in supplementary material). To understand the European context of agroforestry implementation and maintenance, selected indicators were linked to five key challenges identified in a global stakeholder perception review by Tranchina et al. (2024). These challenges encompass: 1. Knowledge and experience gaps: Insufficient or low-quality knowledge and experience in technical and agronomic aspects of agroforestry. 2. Socioeconomic constraints: Issues related to market access, product marketing, supply chains, and job creation. 3. Labour demands: High labour requirements for establishment and maintenance of agroforestry. 4. Investment barriers: Significant financial investments needed for agroforestry establishment. 5. Limited technical support: Insufficient availability of technical assistance for farmers and landowners. In terms of economic viability, farm economic size is a key indicator of economic viability and reflects challenges related to socioeconomic constraints, labour/time demands, and investment barriers. Regions with higher agricultural output are better positioned for agroforestry adoption, while those with lower capacity could require financial support. Agroforestry can increase farm income in low-output rural areas by optimizing resource use and diversifying production, enhancing competitiveness and creating new market opportunities (Mosquera-Losada et al., 2023; Mukhlis et al., 2022). Agroforestry may require more labour than conventional agriculture, potentially creating jobs in high-unemployment areas and offering opportunities for women (Mukhlis et al., 2022). Additionally, its aesthetic value and cultural heritage association also offer rural employment opportunities through tourism (Rois-Díaz et al., 2018). Agroforestry systems are generally more complex and knowledgeintensive than conventional agriculture, involving complex interactions between crops, trees, and livestock (Mosquera-Losada et al., 2023; USDA, 2025) and requiring broader farmer skills than conventional agriculture, and knowledge gaps can be a barrier to adoption (Rois-Díaz et al., 2018). The ratio of trained farmers could indicate both the availability of knowledge/experience (challenge 1) and technical support (challenge 5), as trained farmers are more likely to possess the necessary knowledge for successful agroforestry management. Similarly, the percentage of organic farms could indicate the farmer’s willingness to adopt (challenges 1 and 5) due to shared principles between organic farming and agroforestry like biodiversity enhancement, complex system management, and improved soil health (Isaac et al., 2024; Rosati et al., 2021). Furthermore, agroforestry can improve productivity and help close the yield gap often associated with organic farming through ecological interactions (Sollen-Norrlin et al., 2020). Demographic indicators, such as the ratio of young to old farm managers (associated with challenge 3), reflect farmer population conditions. Regions with younger farmers are more likely to have generational renewal and be more innovative and risk-taking (García de Jal´ on et al., 2013) while regions with more elderly managers face greater risk of land abandonment. The degree of urbanisation also influences agroforestry feasibility by classifying regions as rural, intermediate, or urban. While agroforestry in rural areas can enhance socioeconomic and environmental outcomes, potentially improving incomes, food security, gender equality, and cultural activities (Mukhlis et al., 2022), its adoption is often hindered by limited policy attention and market access (Tranchina et al., 2024), relating to challenge 5, which hinders recognition of agroforestry as a solution for the climate crisis and rural livelihoods. 2.5. Definition of the socio-economic context European regions were classified into three socioeconomic contexts (C1, C2, and C3) based on combined economic, demographic, and farmer-related variables at the NUTS 2 level across the EU27, UK, and CH (classification rules detailed in Table S2). "High-profile" (C1) regions demonstrate positive characteristics like higher organic farming prevalence, younger and more trained farm managers, larger farms, lower unemployment, and a predominantly urban/intermediate character, suggesting a strong economic base for agroforestry. "Low-profile" (C3) regions face challenges such as lower organic farming adoption, fewer trained managers, an older farming population, lower economic output, higher unemployment, and a predominantly rural character, requiring targeted support. "Medium profile" (C2) regions encompass those not meeting C1 or C3 criteria (at least four of six socioeconomic factors), representing an intermediate category with unique attributes requiring a tailored approach to agroforestry implementation. Within each context, target areas suitable for agroforestry were identified, with "hotspots" defined as regions where the proportion of target area to total area fell within the highest quintile. These contextual factors significantly influence the type of support required. 3. Results 3.1. Estimation of the potential agricultural area The total estimated agricultural area for the EU27, UK and CH were 1,972,337 km 2 (Table 3). The most frequent classes were non-irrigated arable land, pastures, and natural grasslands, which, in combination, represented >80 % of the total agricultural area. Concerning the groups of analysis, temporary crops represented 54 %, pastures and grasslands 28 % and permanent crops 5.3 %. Regarding land cover classes considered as agroforestry and that were not considered as suitable potential areas occupied 12.6 % of the total agricultural area. Once nature conservation sites and agroforestry areas were Table 2 Selection of variables to characterise the socio-economic contexts. Topic Indicator*Temporal coverage Connection with challenges Training and willingness to change Ratio of farm managers with full training to farmers with basic knowledge and practical experience only 2020, 2016 Challenge 1 (knowledge and experience gaps) Challenge 5 (limited technical support)Share of organic farming holdings in proportion to total farms ( %) 2020, 2016 Economy Mean economic size of farms (Standard Output in Euros) 2020, 2016 Challenge 2 (socioeconomic constraints) Challenge 3 (labour and time demands) Challenge 4 (investment barriers) Unemployment rate ( %) 2021, 2016 Demography Ratio of young (<40 years old) to elderly (>65 years old) farm managers 2020, 2016 Challenge 2 (socioeconomic constraints) Challenge 3 (labour and time demands)Degree of urbanisation (three degrees: predominantly urban, intermediate, predominantly rural) 2014 * The geographical coverage of all indicators was the EU27, UK, and CH. The source of the data was Eurostat (EC, 2024), and the degree of urbanisation map was developed by de Beer et al. (2014b). V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 5 subtracted from the total agricultural land, potential areas for introducing agroforestry amounted to 1,537,281 km 2 which represented a 34.8 % of the total surface in the EU27, UK and CH (Table S3). 3.2. Extent and spatial distribution of target areas to introduce agroforestry Environmental pressures were consolidated into a composite map summarising the presence of up to 14 accumulated pressures (Fig. 2). Potential areas were classified into three categories: low (0–5), medium (6–10), and high (11–14) environmental pressures. The majority of affected land falls within the low-pressure (0–5) category, with progressively smaller proportions in the medium and high categories, as detailed in Table 4. For a detailed breakdown of the total area corresponding to each specific number of pressures, refer to Fig. S2 and Table S4. The areas impacted by individual environmental pressures are provided in Table S5. Additionally, separate accumulated pressure maps for soil, biodiversity, water, and climate change are available in Figures S3 to S6, respectively. For the definition of target areas to introduce AF, potential areas Table 3 Total agricultural area in the EU27, UK and CH. Agricultural land cover Total area km 2 Total area % Non irrigated arable land 1,018,692 51.6 Permanently irrigated land 39,860 2.0 Rice fields 6,370 0.3 Total Temporary crops 1,064,922 54.0 Pastures 447,854 22.7 Natural grassland 104,901 5.3 Total Pastures 552,755 28.0 Vineyards 34,385 1.7 Fruit trees and berry plantations 25,527 1.3 Olive groves 45,277 2.3 Total Permanent crops 105,189 5.3 Annual crops associated with permanent crops 3,901 0.2 Complex cultivation patterns 113,036 5.7 Land principally occupied by agriculture 102,424 5.2 Agroforestry areas 30,110 1.5 Total Agroforestry 249,472 12.6 Total 1,972,337 100.0 Fig. 2. Spatial distribution of accumulated environmental pressures in the potential agricultural area: low (0–5), medium (6–10) and high (11–14) environmental pressures. V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 6 reporting between 6 and 14 accumulated environmental pressures were selected. The resulting target areas amounted to a total of 601,617 km 2 , representing 39.1 % of the total potential area. Spain, France, Romania, Germany, Italy and UK showed the highest values. In contrast, Latvia, Croatia, Estonia, Malta and Switzerland reported very few areas with 6 or more cumulative pressures (Table S6). Regarding target area definition, Fig. 3 shows the percentage affected by various pressures. Biodiversity pressures were most widespread, impacting nearly two-thirds of the total area, with pest control alone affecting 75.7 %. Climate change (aridity and drought) impacted over half of the target area. Soil pressures were also significant: organic carbon loss (66.3 %), water erosion (40.1 %), and less so, wind erosion (6.9 %). Water pressures were less extensive: nitrogen excess (43 %) and irrigated areas (19.2 %). Target area proportions varied substantially across bioregions, from 94.8 % (Steppic) to 2.5 % (Boreal). The Steppic (94.8 %), Black Sea (86.6 %), Mediterranean (57.9 %), and Atlantic (40.2 %) bioregions had the highest proportions; the Continental (35.8 %), Pannonian (30.8 %), Alpine (4.7 %), and Boreal (2.5 %) the lowest. While soil organic carbon, temperature change, pest control, and threats to soil biological functions were generally key pressure indicators, regional variations existed. Biodiversity pressures were most relevant in the Atlantic region (followed by nitrogen surplus); soil variables in the Continental (followed by biodiversity); drought in the Mediterranean (followed by biodiversity and soil); and climate change in the Black Sea, Boreal, and Steppic. In the Pannonian region, soil organic carbon was most important (followed by biodiversity and temperature change). Irrigation was notably important in the Continental, Black Sea, and Steppic regions. Fig. 4 shows the relative importance of each variable across bioregions. 3.3. Characterisation of the socio-economic indicators Socio-economic indicators were analysed across NUTS 2 regions. Farm size (standard output) (Table S7) ranged from very small ( € 3118–22,783, concentrated in Romania and Poland) to very large ( € 214,602–859,896, concentrated in France, Belgium, Netherlands, UK, and Germany). Unemployment rates (Table S8) ranged from 1.2 % to 28.4 %, with higher rates in Southern Europe (especially Spain and Greece) than in Central Europe and the UK, reflecting a north-south economic divide. Indicators related to farmers’ willingness to change also showed regional patterns. The ratio of fully trained to basically trained farm managers (Table S9) varied considerably (0.003–0.0046 to 0.510–8.167). Southern and Eastern Europe (e.g., Spain, Greece, Italy, Cyprus, Bulgaria) had lower ratios of trained managers, while Northwestern and Central Europe (e.g., France, Germany, Poland, Netherlands) had higher ratios. Organic farming prevalence (Table S10), another indicator of adoption of new practices, also showed regional disparities, being less common in Southern and Eastern Europe (e.g., Greece, Spain, Bulgaria, Romania) and more common in Northern, Central, and Northwestern Europe. Demographic characteristics revealed further regional differences. The degree of urbanisation was relatively balanced, with 34.8 % of regions classified as predominantly urban, 34.0 % as predominantly rural, and 27.7 % as intermediate (Table S11). The ratio of young to elderly farmers (0.061–5.959) (Table S12) revealed an aging farmer population Table 4 Extent of areas affected classified by the number of accumulated pressures (low, 0–5), medium (6–10) and high (11–14) and the proportion with respect to the total potential agricultural area. Number of pressures Area km 2 Area ( %) Low (0–5) 935,664 60.9 Medium (6–10) 592,479 38.5 High (11–14) 9,139 0.6 Total area 1,537,281 100.0 Fig. 3. Importance of environmental pressure indicators (soil, water, biodiversity, climate change) for defining target areas. Data represents the percentage of target area relative to the total target area, for each indicator. V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 7 Fig. 4. Importance of environmental pressure indicators in defining the target areas to introduce agroforestry across bioregions. SOC =Soil organic carbon stocks, WAE =Water erosion, WIE =Wind erosion, PTSM =Potential threats to soil microorganisms, PTSF =Potential threats to soil fauna, PTSB =Potential threats to soil biological functions, PCI =Pest control index, PP =Pollinator potential, NS =Nitrogen surplus, AEI =Area equipped for irrigation, TEM =Temperature change, ARI =Aridity index change, DRG =Drought risk change, HPD =Heavy precipitation days change. V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 8 in Southern and Northern Europe and the British Isles, compared to a higher proportion of young farmers in France and Central Europe (e.g., Austria, Germany, Switzerland, Poland, Czech Republic), highlighting distinct regional demographic trends. 3.4. Target areas to introduce agroforestry in different socio-economic contexts Considering each socio-economic context, low-profile (C3) regions comprised 36.7 % of the target area and had higher mean environmental pressures (5.9). High-profile (C1) regions comprised 20.4 % of the target area with mean pressures of 5.0. Medium-profile (C2) regions held the largest proportion (43 %) of the target area but had the lowest mean pressures (4.6). Thus, regions with greater socio-economic challenges (C3) also experienced higher environmental pressures and contained a larger portion of the target area than C1 and C2 regions (Fig. 5). In the low-profile context, most of the target areas were identified within the Mediterranean biogeographical region (62 %), with Spain emerging as the most affected country in this category and across Europe, despite having the second largest potential area overall. Other countries, such as Greece, Portugal, and Cyprus, had their entire target areas classified in low-profile regions, while Italy had only certain areas in this category. Outside the Mediterranean, Romania and Bulgaria were notably affected. High-profile regions were mainly concentrated in the Atlantic (54 %) and Continental (45 %) biogeographical regions, predominantly impacting France and Germany, as well as the Czech Republic, the Netherlands, and the UK. France was the most affected country in both the high-profile and medium-profile contexts and ranked second across the entire study area. In the medium-profile context, Italy, Spain, and the UK also had substantial target areas, primarily located in the Continental region (43 %), followed by the Atlantic (37 %) region. A total of 57 NUTS 2 regions were identified as hotspots (Fig. 6) covering a total area of 383,857 km 2 (Table 5). C3 regions were the most affected, with a total area of 162,077 km 2 . These regions face challenges such as an ageing population, fewer trained farmers, higher Fig. 5. Target areas for agroforestry introduction classified by environmental pressure levels: medium (6–10) and high (11–14). Target areas are further differentiated by socioeconomic context: high-profile (Context 1), medium-profile (Context 2), and low-profile (Context 3). V.A. Gabourel-Landaverde et al. Trees, Forests and People 21 (2025) 100961 9