1 Addressing needs for the diffusion of digital greenhouse farming. Insights from Living Labs in the 1 Mediterranean basin 2 Oriana Gava1*, Sara Sturiale2, Marisa Gallardo3, Dolores Buendía Guerrero4, Dursun Buyuktas5, 3 Gulcin Ece Aslan5, Asma Laarif6, Thameur Bouslama6, Alejandra Navarro7, Luca Incrocci2, Fabio 4 Bartolini8 5 1. Research Centre for Agricultural Policies and Bioeconomy, Council for Agricultural 6 Research and Economics, Viale della Toscana 21, 50127 Firenze, Italy 7 (oriana.gava@crea.gov.it) 8 2. Department of Agriculture, Food and Environment, University of Pisa, Via del Borghetto 9 80, 56124 Pisa, Italy (sara.sturiale@phd.unipi.it, luca.incrocci@unipi.it) 10 3. Department of Agronomy, University of Almería, Carretera de Sacramento s/n, La 11 Cañada de San Urbano, 04120 Almería, Spain (mgallard@ual.es) 12 4. Research Station of the Cajamar, Paraje Las Palmerillas 25, 04710 El Ejido, Spain 13 (doloresbuendia@fundacioncajamar.com) 14 5. Department of Agricultural Structures and Irrigation, Faculty of Agriculture, Akdeniz 15 University, Antalya, Turkey (dbuyuktas@akdeniz.edu.tr, ecebacalan@akdeniz.edu.tr) 16 6. University of Sousse, LR21AGR03, Regional Research Centre on Horticulture and Organic 17 Agriculture (CRRHAB), 57, Chott Mariem, TN-4042 Sousse, Tunisia 18 (laarif.asma@iresa.agrinet.tn, thameurbouslama@gmail.com) 19 7. Research Centre for Vegetable and Ornamental Crops, Council for Agricultural Research 20 and Economics, Via Cavalleggeri 51, 84098 Pontecagnano – Faiano, Italy 21 (alejandra.navarrogarcia@crea.gov.it) 22 8. Department of Chemical, Pharmaceutical and Agricultural Sciences, University of Ferrara, 23 Via L. Borsari 46, 44121 Ferrara, Italy (fabio.bartolini@unife.it) 24 * Corresponding author at: oriana.gava@crea.gov.it 25 26 This article has been accepted for publication and undergone full peer review but has not been 27 through the copyediting, typesetting, pagination and proofreading process, which may lead to 28 differences between this version and the Version of Record. 29 Please cite this article as: 30 Gava O., Sturiale S., Gallardo M., Buendía Guerrero D., Buyuktas D., Ece Aslan G., Laarif A., Bouslama 31 t., Navarro A., Incrocci L., Bartolini F. (2025). Addressing needs for the diffusion of digital greenhouse 32 farming. Insights from Living Labs in the Mediterranean basin, Bio-Based and Applied Economics, 33 Just Accepted. DOI:10.36253/bae-17755 34 mailto:oriana.gava@crea.gov.it mailto:sara.sturiale@phd.unipi.it mailto:mgallard@ual.es mailto:ecebacalan@akdeniz.edu.tr mailto:laarif.asma@iresa.agrinet.tn mailto:thameurbouslama@gmail.com mailto:alejandra.navarrogarcia@crea.gov.it mailto:oriana.gava@crea.gov.it 2 Abstract 35 Agriculture 4.0 represents a huge opportunity for the transformation of agrifood sectors. However, 36 its adoption (and diffusion) in real-world farming contexts faces multiple challenges. This study 37 focuses on greenhouse farming within the Mediterranean basin. It aims to assess the needs of actors 38 involved in the uptake of Agriculture 4.0 and define enabling conditions to support achieving these 39 needs, focusing on the introduction of an innovative decision support system in real-world 40 greenhouses for tomato production. A qualitative and comparative approach is implemented, using 41 participatory data collection methods with cross-disciplinary experts from four case studies across 42 the Mediterranean Basin. Data are collected through one-to-one open discussions, supported using 43 context and SWOT analyses to stimulate reflection and recall. The findings highlight the need to 44 improve digital literacy among farmers and advisors, build trust through tailored education 45 conditions and mentorship, and support young farmers with financial incentives and training. 46 Market dynamics are relevant as well, pinpointing the need for stronger product images and 47 increasing consumer awareness through certification and labelling. Great interest and technology 48 potential emerges from the possibility to enable (partial) remote work thereby benefiting a work-49 life balance. Simplifying bureaucratic processes and enhancing policy support for cooperation and 50 farmer unions are also essential for encouraging farmers to adopt digital technology. 51 Keywords: Decision Support System (DSS), agricultural digitalisation, qualitative research, multi-52 actor engagement, actor needs, enabling 53 54 3 1. Introduction 55 1.1 Background 56 Agriculture 4.0, also known as digital agriculture, leverages precision and data-driven 57 technologies such as the Internet of Things, data analytics, artificial intelligence, and machine 58 learning and is viewed as a promising approach to sustainably enhance food production. The use of 59 these technologies is particularly relevant in intensive farming systems like greenhouse production 60 (Maffezzoli et al., 2022; Mondejar et al., 2021). However, while technology-driven solutions can be 61 appealing, resources may be wasted if technologies are not developed responsibly by aligning with 62 the actual needs of actors1 directly involved in technology adoption and diffusion (Rose et al., 2021). 63 Large literature acknowledge this issue and suggest that a variety of needs exist ranging from 64 developing user-friendly digital applications, reducing access costs and enhancing digital literacy, to 65 improving policy and governance (Klerkx et al., 2019; McFadden et al., 2022; UNESCO, 2018; Wolfert 66 et al., 2017; Yuan and Sun, 2024). These needs often cannot be met, and enabling conditions should 67 be created to facilitate the successful implementation and adoption of specific actions, policies, or 68 technologies (Huber-Stearns et al., 2017). Creating enabling conditions for Agriculture 4.0 requires 69 technical and financial measures and coherent governance frameworks that reconcile productivity, 70 environmental sustainability, and social objectives (Coderoni, 2023). It also requires adopting a 71 responsible research and innovation (RRI) approach through the engagement of a variety of actors 72 (e.g., farmers, researchers, policymakers) directly involved in the technology adoption and its 73 diffusion, prioritising their actual needs and considering the diversity of contextual features at the 74 territorial level (Eastwood et al., 2019; Rose et al., 2021). These actors should be part of the 75 innovation process to ensure that Agriculture 4.0 technologies are implemented in a way is socially 76 1 The term "actor" is used consistently throughout this study (instead of “stakeholder”) to ensure homogeneity of wording and to better reflect the actor-centred nature of the presented research. 4 beneficial, inclusive, and equitable to all the affected actors while minimising negative impacts 77 (Fielke et al., 2022; Klerkx et al., 2019; McGrath et al., 2023; Rose and Chilvers, 2018). This 78 engagement can also lead to improved design of agricultural policies in the frame of Agriculture 4.0 79 that foster fair and equitable working conditions (da Silveira et al., 2021; Maffezzoli et al., 2022). 80 Living labs (LLs) are increasingly recognised as suitable settings for operationalising RRI, by serving 81 as collaborative platforms for inclusive, reflexive, and context-sensitive innovation and enabling 82 research approach centred on the perspectives of those directly involved in agriculture and 83 innovation (Campos and Marín-González, 2023; Owen et al., 2012). 84 Research centred on the actors’ needs that respond to priority issues at the territorial level 85 requires significant improvement and expansion, especially developed towards LLs (Mgendi, 2024; 86 Ogunyiola et al., 2024). Understanding these needs would yield grounded recommendations to 87 bridge the research-practice gap while supporting the improvement of existing interventions to 88 foster responsible and sustainable agricultural digitalisation (McFadden et al., 2022; Wanner et al., 89 2018). Also, bridging the research–practice gap is essential for ensuring that digitalisation 90 contributes to sustainability, a challenge long recognised in agricultural policy research (Matthews, 91 2021). The literature shows that Agriculture 4.0 can help mitigate the strain on limited resources, 92 address climate change, reduce water and agrochemical usage, improve soil health, and boost 93 biodiversity, while maintaining or increasing yields, lowering input costs, and enhancing food safety 94 through better traceability (MacPherson et al., 2022). However, the observed impacts are not 95 without controversy, and there is an ongoing debate regarding the social implications of agricultural 96 digitalisation, with some studies highlighting benefits such as improved working conditions and 97 community well-being, while others point to issues like social inequality, data privacy concerns, and 98 the potential to widen the gap between large and small-scale farmers (Carolan, 2024; Klerkx et al., 99 2019). To maximize the positive impacts while minimising the negative consequences and fully 100 5 realize the potential of Agriculture 4.0, it is essential to expand and deepen knowledge about the 101 perspectives and requirements of actors on the ground to understand how to sustain them in 102 addressing the issues they experience (Ingram et al., 2022). The research implications need to be 103 practically useful beyond the case study level by offering a broader perspective on the researched 104 problem (Yin, 2014), although there is a notable lack of research that provides such a varied 105 perspective. Particularly, more studies are needed that incorporate evidence from a wide range of 106 geographical and socio-economic contexts (Fasciolo et al., 2024; Hinson et al., 2019; Klerkx and 107 Rose, 2020; Maffezzoli et al., 2022). 108 1.2 Aim and contribution of the research 109 Against this background, the aim of this study is to assess actor needs and propose enabling 110 conditions to foster the diffusion of Agriculture 4.0 in the Mediterranean basin, through qualitative 111 research. The research adopts a qualitative approach framed within a RRI framework, 112 operationalised through LLs established at the project level in each case study. The four RRI 113 dimensions (anticipation, reflexivity, inclusion, and responsiveness) guided research activities and 114 actor engagement from the project’s inception through to the generation of findings and policy 115 recommendations. LLs supported innovation implementation and adoption through processes of 116 mutual learning and co-creation across domains of expertise and disciplinary boundaries. Their 117 composition reflects the diversity of actor perspectives in real-world agricultural contexts and 118 constitutes the sample for this study. The research follows a stepwise approach involving: (i) the 119 prioritisation of key socio-economic issues affecting the adoption and diffusion of the DSS (and more 120 broadly, Agriculture 4.0 innovations) in the greenhouse sector; (ii) the identification of priority actor 121 needs, i.e. those directly linked to the identified issues; and (iii) the elaboration of enabling 122 conditions to support the fulfilment of these needs. A real-world situation is examined, i.e. the 123 introduction of an innovative decision support system (DSS) in greenhouses of the Mediterranean 124 6 basin. Four case studies are considered, i.e. Almería (Spain), Antalya (Turkey), Monastir (Tunisia), 125 and Tuscany (Italy), hosting each of them a real-world pilot farm for testing the DSS in commercial 126 greenhouses (Sturiale et al., 2024a). All case studies are important players in the international 127 greenhouse vegetable market are representatives for the greenhouse sector at the territorial level 128 (Sturiale et al., 2024a). The data were collected from cross-disciplinary experts engaged within LLs. 129 This research advances knowledge in several ways. First, it centres on actors and their needs, 130 which are essential for identifying priority issues and enabling conditions for digital technology 131 adoption (Soriano et al., 2023). Second, it bridges the science-practice gap by offering grounded yet 132 theoretically sound recommendations, consistent with responsible research and innovation 133 principles (Lajoie-O’Malley et al., 2020; MacPherson et al., 2022). Third, it provides a cross-country 134 perspective across the Mediterranean, a region marked by shared greenhouse technologies and 135 climate concerns but diverse socio-economic contexts, allowing for implications beyond the case 136 study level (Bocean, 2024). 137 The findings offer a holistic view of Agriculture 4.0 challenges and opportunities, based on 138 diverse Mediterranean case studies (Xu et al., 2024). They clarify actors’ prioritised needs and the 139 enabling conditions for digital agriculture uptake. Although focused on Mediterranean greenhouses 140 and a specific DSS, the insights are relevant to broader agricultural sectors globally. The inclusion of 141 varied case studies enriches the analysis and extends its relevance (Bocean, 2024). The 142 operationalisation of RRI through Living Labs (LLs) shaped knowledge co-production and 143 interpretation, revealing socially relevant dynamics and innovation trajectories beyond standard 144 metrics (Stilgoe et al., 2013). LLs facilitated joint reflection, contextual adaptation, and integration 145 of diverse knowledge systems, including those of smallholders, women, and migrant workers 146 (Campos and Marín-González, 2023; Ehlers et al., 2025). This responsiveness aligned innovation with 147 evolving societal needs and values (Kokotovich et al., 2021), positioning LLs as boundary 148 7 infrastructures where technical, social, and normative dimensions are negotiated. Finally, the 149 research highlights the social implications of agricultural digitalisation, particularly equity and 150 inclusion, which merit greater attention in academic and policy debates (Hundal et al., 2023; 151 Maffezzoli et al., 2022). 152 153 2. Theoretical framework 154 Actor needs are the requirements, expectations, and preferences of those who have an interest 155 or stake in a particular project, process, or system, including a wide range of operational, economic, 156 social, and environmental aspects that are deemed critical for ensuring the successful adoption and 157 implementation of innovations. Identifying and addressing key actor needs is essential for aligning 158 project outcomes with the interests and priorities of all involved parties, thereby enhancing the 159 overall effectiveness and sustainability of the initiative (Feng et al., 2024; Littau et al., 2010). These 160 needs respond to issues experienced not only by farmers but also by other actors, such as e.g., 161 advisors, which are generally context-specific and can negatively affect the uptake and widespread 162 use of digital agriculture in rural areas (Dibbern et al., 2024). Research indicates that real-world 163 issues are barriers to Agriculture 4.0 and can create lock-in situations that hinder the achievement 164 of sustainability goals through digital transformation. Especially, these issues can prevent the full 165 adoption and integration of digital technologies in agriculture, thereby limiting the potential 166 benefits in terms of productivity, profitability, and sustainability (da Silveira et al., 2023a, 2023b). 167 The literature identifies drivers and barriers of Agriculture 4.0 (da Silveira et al., 2021; Dibbern 168 et al., 2024). Drivers include, e.g., the potential for increased productivity, profitability, and viability 169 of farming through the optimisation of resource use, cost reduction, and enhancement of crop 170 yields (Fragomeli et al., 2024). Other drivers encompass education, age, and farm size; for instance, 171 younger and more educated farmers managing larger, capital-intensive enterprises are more likely 172 8 to adopt Agriculture 4.0 technologies (Kroupová et al., 2024). Barriers include economic constraints, 173 such as the high initial costs and limited access to capital, which can deter adoption, particularly 174 among small and medium-sized farms (Dibbern et al., 2024). Other examples of barriers are the lack 175 of technical literacy and insufficient information about the benefits and profitability of digital 176 agriculture that hinder farmers' willingness to invest in new technologies (Kroupová et al., 2024). 177 Identifying enabling conditions to support the realisation of actors’ needs is of particular relevance 178 to improve the sustainability of farming through digital tools, by removing the barriers and then 179 overcoming lock-in situations (da Silveira et al., 2023b). 180 Enabling conditions include financial support, technological infrastructure, policy frameworks, 181 and capacity-building initiatives that collectively create a conducive environment to harness the 182 potential of digital tools for enhancing farmers' productivity, resource efficiency, and decision-183 making capabilities. For instance, financial support through subsidies and incentives can reduce the 184 initial cost burden, making these technologies more accessible to smaller farms (Fragomeli et al., 185 2024). Public or private support to investment in physical assets in rural areas can address 186 inadequate infrastructure, facilitating the effective and widespread use of Agriculture 4.0 187 technologies (Derakhti et al., 2023). Implementing training programs to enhance technical expertise 188 among farmers can bridge the knowledge gap and ease the integration of digital tools on farm 189 (Wang et al., 2020). Additionally, creating knowledge-sharing initiatives and fostering a culture of 190 innovation can help overcome resistance to change and build social trust in Agriculture 4.0 191 (Ganeshkumar et al., 2023). 192 193 3. Methodology and data 194 3.1 The living lab approach in a RRI framework 195 9 This study is grounded in a broader project that adopts a RRI approach to balance economic, 196 socio-cultural, and environmental dimensions in addressing complex societal challenges (Owen et 197 al., 2012). RRI offers a normative framework for guiding innovation toward socially desirable 198 outcomes, structured around four interrelated dimensions: anticipation, reflexivity, inclusion, and 199 responsiveness. RRI principles call for early and continuous involvement of diverse actors to ensure 200 that innovation processes align with societal values and needs (Gremmen et al., 2019). LLs have 201 emerged as a promising methodology for operationalising RRI in agricultural digitalisation. They 202 provide collaborative, real-world environments where diverse stakeholders (e.g., farmers, 203 researchers, policymakers, civil society) can co-create, test, and evaluate technologies (Campos and 204 Marín-González, 2023; Ehlers et al., 2025). LLs are particularly suited to addressing the social 205 dimensions of Agriculture 4.0, enabling dialogue, trust-building, and the negotiation of trade-offs 206 between technological promise and lived experience (Cascone et al., 2024; Compagnucci et al., 207 2021; Gardezi et al., 2022). The LL approach enables the integration of multiple knowledge systems 208 and interests that both shape and are shaped by digital agricultural transitions (Kamilaris et al., 209 2017; Wolfert et al., 2017). The engagement of locally embedded experts can provide grounded 210 insights into local needs and priorities as and the potential impacts of Agriculture 4.0, supporting 211 knowledge exchange across diverse socio-economic and cultural settings (da Silveira et al., 2021; 212 Regan, 2019; Zhai et al., 2020). 213 LLs were established in 2021 using a socio-technical systems approach, which recognises 214 that technological innovation is embedded in broader institutional, economic, and cultural contexts 215 (Rijswijk et al., 2021). They are implemented in four Mediterranean regions, i.e. Almería (Spain), 216 Antalya (Turkey), Monastir (Tunisia), and Tuscany (Italy), each hosting a commercial-scale pilot farm 217 for testing a Decision Support System (DSS) for tomato greenhouse production, i.e. the studied 218 innovation. The DSS was specifically developed to optimise input use (water and nutrients), support 219 10 integrated pest and disease management, and enhance productivity in tomato greenhouses. It uses 220 climate and cultivation data to run simulation models for fertigation and outbreak prediction. 221 Accessible online via Wi-Fi and managed through a mobile app, the DSS is low-cost and compatible 222 with existing farm infrastructure. It provides farmers with tailored guidance on irrigation and 223 fertilisation schedules, along with alerts for pest and disease development to support timely 224 biological control interventions. 225 The case studies were selected as they meet the criteria of typicality (Mediterranean-type 226 greenhouses, generally low-tech, well-developed greenhouse sector, important market position) 227 and diversity (contextual specificity: socio-economic, cultural, geographical) with respect to a series 228 of relevant sustainability issues related to the low diffusion of Agriculture 4.0 in greenhouse farming 229 (Gong and Tan, 2021; Sovacool, 2011) (Table 1).230 11 231 Table 1. Implementation of living labs in the case studies and key case study features. 232 Case studies Living lab participants DSS modules Economic Social Environmental Level of digital technology Agribusiness Knowledge creation/transfer Policy Almería (Spain) 13 8 7 Water, Fertiliser, Pest Management High labour costs, decreasing margins, competition from other countries Predominantly immigrant workforce, labour conditions, specialization, contract stability Limited adoption of advanced techniques (e.g. closed-loop systems), use of biological control and drip irrigation Moderate Antalya (Turkey) 16 4 1 Water, Fertiliser High production costs, insufficient government support Predominantly immigrant workforce, labour conditions, specialization, contract stability Limited adoption of advanced techniques, lack of data on sustainable systems Low to moderate Monastir (Tunisia) 4 7 3 Water, Fertiliser, Pest Management Low financing capacity, misuse of inputs, lack of control over costs and prices National, predominantly unqualified workforce, reluctance of older farmers, fragmented ownership High chemical use, limited adoption of sustainable practices and advanced techniques Low Tuscany (Italy) 5 28 3 Water, Fertiliser, Pest Management High labour costs, low market power, poor generational turnover Predominantly immigrant workforce, low confidence in new technologies Public concerns about food naturalness, taste, and environmental impact Low to moderate 233 12 The selection rationale informed the LL design and actor engagement strategies, ensuring 234 that the innovation process was locally relevant and socially responsive. For instance, Almería faces 235 high labour costs and market competition; Antalya struggles with limited government support and 236 high input costs; Monastir is affected by low financing capacity and fragmented farm structures; and 237 Tuscany deals with poor generational turnover and low confidence in digital tools. These contextual 238 differences also shaped the implementation decisions made by farmers regarding DSS modules. 239 Participants were selected through purposive sampling based on their capacity to offer 240 informed insights into the specific challenges and dynamics surrounding digital technology adoption 241 (Patton, 2023; Potters et al., 2022). The sampling strategy aimed to engage individuals 242 knowledgeable about the innovation and committed to sustainability improvements in their local 243 greenhouse sectors. Actor selection was guided by local knowledge and aimed to ensure 244 representation across the agricultural value chain, including producers, advisors, policymakers, 245 technology providers, and civil society. Willingness and capacity to engage across all LL phases, i.e. 246 from problem framing to evaluation, were also considered. Actors participating in each LL constitute 247 the sample for this research (Figure 1). 248 13 249 Figure 1. Living lab actor demographics. Software: Python libraries matplotlib (Hunter, 2007), seaborn (Waskom, 2021). 250 LL actor composition across the case studies shows diversity in terms of gender, age, 251 education level, and agricultural background. However, some cases reveal uneven representation 252 in specific categories, such as a predominance of male participants or limited variation in education 253 levels, reflecting local stakeholder networks, actor availability, and broader socio-institutional 254 dynamics. 255 LLs were established at the project level as vehicles for embedding RRI principles throughout 256 the innovation process. The established LLs brought together a diverse range of actors operating at 257 both farm and territorial levels. While actor representation differed across case studies, reflecting 258 local technical, social, and cultural contexts, the categorisation of participants aimed to balance 259 inclusivity with operational feasibility, ensuring comparability across cases. 260 14 LL structure and activities were explicitly aligned with the four RRI dimensions (anticipation, 261 reflexivity, inclusion, responsiveness), ensuring that the development and diffusion of the DSS were 262 technically sound, ethically grounded, and socially responsive (Ehlers et al., 2025; Stilgoe et al., 263 2013) (Table 2). 264 Table 2. Implementation of living labs (LL) under the dimensions of responsible research and innovation. 265 *Commitment letters are confidential. **Project deliverables report across RRI dimensions. 266 Engagement Anticipation Reflexivity Inclusion Responsiveness Actors Practice partners (farmers) Research team and LL actors LL actors Research team and LL actors Type of involvement Early engagement Iterative learning and feedback Actor mapping and continuous engagement Adaptation of methods and approaches; identification of impact indicators Timing Since project proposal Throughout project Throughout project Throughout project Activities Commitment letters*; co- definition of focal questions Harmonised guidelines; joint interpretation of impact results Activity protocols; ethical/legal compliance; diverse representation Context analysis, SWOT, needs assessment (and other sustainability assessment exercises); co-creation and sharing sessions (workshops, training), policy recommendations Process documentation** (Bartolini et al., 2021; Incrocci et al., 2024; Laarif et al., 2024a, 2024b; Navarro Garcia and Lupu, 2021; Sturiale et al., 2024c, 2024b) 267 15 268 Anticipation was embedded from the proposal stage, with early engagement of practice 269 partners to co-define focal questions and explore potential impacts and trade-offs of DSS adoption 270 days (see (Bartolini et al., 2021; Fernández et al., 2024)). Reflexivity was fostered through iterative 271 learning cycles, joint interpretation of impact results, and continuous reflection on the assumptions 272 and values shaping the innovation process experiences (see (Laarif et al., 2024b; Sturiale et al., 273 2024b)). Inclusion was ensured by mapping and engaging a diverse set of actors across agribusiness, 274 policy, and knowledge domains, with attention to ethical and legal compliance and the 275 representation of marginalised voices (see (Navarro Garcia and Lupu, 2021)). Responsiveness was 276 demonstrated through the adaptation of methods, indicators, and engagement strategies based on 277 contextual feedback, informing both DSS implementation and policy recommendations see 278 (Bartolini et al., 2021; Incrocci et al., 2024; Laarif et al., 2024b, 2024a; Sturiale et al., 2024c)). 279 280 3.2 Data collection process and analysis 281 All data collection and reporting activities were designed and conducted by the research 282 team, with local members operating within their respective LLs. Activities were supported by 283 centrally harmonised guidelines, jointly agreed upon and prepared. These included methodological 284 instructions and templates for data collection and reporting, reflecting best practices in LLs that 285 emphasize structured actor engagement, harmonized protocols, and context-sensitive 286 implementation. Case study-specific findings were initially analysed by the lead author and 287 subsequently reviewed by all co-authors, with the final output discussed and validated collectively. 288 This collaborative and iterative approach aligns with established LL methodologies that promote 289 inclusive and responsible innovation through interdisciplinary co-creation and collective validation 290 (Forbat et al., 2025; Gardezi et al., 2022; Hossain et al., 2019) 291 16 Actor engagement was facilitated through one-to-one open discussions aimed at prioritising 292 context-specific issues and identifying corresponding needs and enabling conditions. These 293 interviews were conducted via video call, allowing participants to interact with visual materials and 294 texts as they were developed during the conversation. 295 The discussions were informed by in-depth context analyses conducted at the case study 296 level as part of related research activities (see (Sturiale et al., 2024c)). These analyses framed the 297 unique circumstances of each agricultural setting and helped identify the factors influencing the 298 adoption and effectiveness of digital technologies (Rijswijk et al., 2021). They included a broad range 299 of information: physical and technological attributes of greenhouse farming (Klerkx et al., 2019); 300 economic aspects such as financial performance, cost structures, and incentives (Metta et al., 2022); 301 social dimensions including workforce demographics, labour conditions, and public perceptions 302 (Eastwood et al., 2019); and environmental considerations related to sustainability practices and 303 impacts (Rose et al., 2021). Before the interviews, respondents received the context analysis along 304 with a clear explanation of the exercise’s aims and procedures. The sessions employed SWOT 305 analysis (see Supplementary materials) as a boundary object, leveraging its accessibility and 306 familiarity to facilitate structured dialogue (Spee and Jarzabkowski, 2009). This approach enabled 307 experts with diverse perspectives to collaboratively identify barriers and drivers of digital 308 technology uptake and to prioritise issues relevant to local contexts (Helms and Nixon, 2010; Pagot 309 and Andrighetto, 2024). Respondents were explicitly invited to elaborate through recall and 310 brainstorming with research team members, following a three-step process: 311 1) Reflect on priority issues that should be addressed in the greenhouse farming sector at 312 the territorial level to foster agricultural digitalisation, based on their experience in the 313 LL and knowledge of the DSS, but not limited to it; 314 17 2) Identify barriers and drivers to solving these issues, derived from SWOT items—315 specifically, barriers from weaknesses and threats, and drivers from strengths and 316 opportunities(Pagot and Andrighetto, 2024); 317 3) Highlight priority needs that could help overcome barriers or leverage drivers to address 318 the identified issues. 319 Enabling conditions for these priority needs were defined through discussion during the final 320 project workshop, which included all scientific partners and LL actors. These conditions were 321 informed by the presentation of project outcomes and refined through collective input. 322 323 4. Results and discussion 324 Findings indicate similarities among case studies, particularly regarding needs related to 325 knowledge, farmers’ behaviour and bargaining power, and remote work. However, contextual 326 differences highlight specific territorial needs to foster the uptake and diffusion of the DSS and, 327 more broadly, to enable the wider use of digital tools in agriculture (Table 3).328 18 Table 3. Prioritised needs and enabling conditions related to priority issues and SWOT items in the case studies. 329 Case studies Priority issues SWOT items Priority needs Enabling conditions Almería Knowledge and practical skills Unskilled labour Improving technical skills of farmers and advisors Create and/or improve education and foster knowledge transfer about digital tools Tuscany Unskilled labour Antalya Low level of knowledge Monastir Low level of specialisation Tuscany Reluctance to change Propensity to innovate; Aging farmers Building acceptability and trust Create and/or improve education and foster knowledge transfer about digital tools Almería Aging farmers Antalya Aging agricultural population Support for young farmers’ entrepreneurship Monastir Low profitability Monastir Abandonment of farming activities Farm exit; Low profitability Reducing farm exits Create and/or improve education and knowledge, and foster knowledge transfer about digital tools; Support for young farmers’ entrepreneurship Tuscany Farm exit; Economic viability Almería High market competition and low consumer awareness Market competition Creating product identity Product branding Antalya Market conditions Monastir Too low margin of product sale Market competitiveness Increasing farmer margins Certification and labelling schemes; Policy support for sustainable products Almería Unfair distribution of value added along the value chain Weak bargaining power; Many middlemen Increasing farmer bargaining Promote collective approaches (e.g., cooperatives, unions); Organising demand-driven production Antalya Many middlemen Monastir Lack of collective organisation Tuscany Low bargaining power; Level of cooperation Tuscany Slow and complex bureaucracy for public incentives Burdensome bureaucracy Simplifying bureaucracy Simplified paperwork for public incentives Antalya Low profitability Production-support policy 19 Case studies Priority issues SWOT items Priority needs Enabling conditions Monastir Insufficient supply of greenhouse- grown food Water shortages Developing land and crop production planning Antalya High production costs Rising energy costs; High input costs Increasing liquidity for new technology uptake Support for investment in digital technology Monastir High production costs Tuscany Heavy workload and difficult work-life balance Work-life balance; Climate change Facilitating remote farming operations Education and knowledge transfer; Public/private investment in broadband infrastructure Almería Workload; Work-life balance Antalya Many working hours Monastir Difficult management of personal life 330 331 332 20 4.1 Improving technical skills of farmers and advisors and knowledge transfer 333 The widespread deficiency in knowledge and practical skills related to digital tools among 334 agricultural workers presents a significant issue, which can become a barrier to the effective 335 implementation of digital solutions. This was consistently observed across the case studies. Advisors 336 often possess skills in digital technologies, but they lack the time to test or explain them to farmers. 337 This disconnect between research and practice hampers the adoption of Agriculture 4.0 338 technologies and the DSS under study, potentially leading to suboptimal farm management and 339 productivity. 340 These findings suggest a pressing need for comprehensive educational reforms and targeted 341 training programs at both national and international levels. Interviewees emphasised the 342 importance of integrating digital skills into agricultural education to ensure that current and future 343 generations of farmers are equipped to use Agriculture 4.0 technologies effectively. They also 344 highlighted the need for robust knowledge transfer mechanisms to bridge the gap between research 345 and practice. This includes fostering partnerships between research institutions and agricultural 346 practitioners to facilitate the dissemination of innovative practices and technologies. 347 These findings are supported by the literature, which similarly identifies the lack of digital 348 literacy as a systemic issue in agriculture. Studies indicate that enhancing technical skills through 349 targeted educational programs is essential for bridging the gap between research and practical 350 application (Dibbern et al., 2024; Fragomeli et al., 2024). The need for improved knowledge transfer 351 mechanisms is also emphasised, as effective communication of research findings can lead to better 352 farm management practices (Rose et al., 2021). Furthermore, peer-to-peer learning initiatives are 353 recognised as valuable tools for fostering a supportive environment for skill development, enabling 354 farmers to leverage digital tools effectively (da Silveira et al., 2023b). 355 356 21 4.2 Building acceptability and trust 357 All case studies emphasize the general lack of acceptability and trust in digital technology 358 among farmers. Farmers, particularly older ones, may be reluctant to adopt digital tools due to 359 resistance to change, perceived risks, and a lack of digital literacy. Those who have relied on 360 traditional methods for decades may be sceptical about the benefits of Agriculture 4.0 and prefer 361 to stick with familiar practices. They may see the initial investment and learning curve associated 362 with digital tools as risky. Providing tailored education and training, including mentorship programs 363 that demonstrate the tangible benefits and offer hands-on sessions with digital tools, can help build 364 trust and encourage adoption. 365 The literature suggests that tailored education and mentorship programs can alleviate 366 farmers’ aversion towards new technologies by demonstrating their tangible benefits (da Silveira et 367 al., 2023b; Ganeshkumar et al., 2023). Building trust through hands-on training and engagement is 368 crucial for overcoming scepticism and encouraging adoption (da Silveira et al., 2023a). Additionally, 369 more support for young farmers' entrepreneurship can drive innovation and the adoption of digital 370 tools, as younger farmers may be more open to integrating the DSS and other Agriculture 4.0 371 technologies into their practices. Younger farmers tend to be more receptive to digital innovations, 372 indicating that fostering entrepreneurship among youth can drive broader acceptance of Agriculture 373 4.0 technologies (Klerkx and Rose, 2020). 374 The reluctance of older generations to adopt digital tools highlights the need for tailored 375 educational initiatives that address specific concerns and barriers. Policymakers should consider 376 implementing mentorship programs that pair experienced farmers with younger, tech-savvy 377 individuals to foster trust and facilitate knowledge exchange. The role of young farmers as change 378 agents in the adoption of digital technologies should be recognised and supported through targeted 379 entrepreneurship programs (Bocean, 2024; Shamshiri et al., 2024). Furthermore, cultivating 380 22 communities of support through collaborative platforms can empower all farmers, including those 381 beyond the greenhouse sector. These platforms encourage peer-to-peer learning, creating a 382 collaborative environment that is also beneficial for enhancing trust and confidence in technology 383 use (Derakhti et al., 2023; Gumbi et al., 2023; Petraki et al., 2025) (Derakhti et al., 2023; Gumbi et 384 al., 2023; Petraki et al., 2025). 385 386 4.3 Reducing farm exits 387 In Monastir and Tuscany, a key issue is the gradual abandonment of farming activities, 388 primarily due to low profitability and very limited generational turnover. This trend poses a 389 significant threat to the agricultural sector, as it may hinder the adoption and diffusion of Agriculture 390 4.0 technologies, which are essential for modernising farming practices and improving productivity. 391 Therefore, there is a need to reduce farm exits. Supporting young farmers' entrepreneurship 392 through tailored policy initiatives, such as access to training programs, financial incentives, and 393 mentorship opportunities, is vital for revitalising the sector (Derakhti et al., 2023). By making 394 farming more attractive to younger generations, the sector can ensure a continuous influx of new 395 entrants and ideas, which is essential for the adoption of innovative practices (Eastwood et al., 396 2019). 397 To reduce farm exits, especially by attracting and retaining young farmers to ensure a 398 continuous influx of new entrants and ideas, several enabling conditions should be established. 399 Providing financial support through subsidies, grants, and low-interest loans can reduce the initial 400 cost burden for young farmers, making farming more attractive and viable (Derakhti et al., 2023). 401 Implementing training programs that focus on new digital tools and how they can support 402 sustainable practices can enhance the technical skills of young farmers, enabling them to adopt and 403 integrate Agriculture 4.0 technologies effectively (Eastwood et al., 2019). Establishing mentorship 404 23 programs where experienced farmers guide and support young farmers can facilitate knowledge 405 transfer and build confidence. 406 The trend of farm abandonment due to low profitability and limited generational turnover 407 is a critical issue that requires urgent attention. Attracting and retaining young farmers is essential 408 for the sustainability of the agricultural sector, including its modernisation through digital tools. 409 Incentives, such as access to affordable land, financial support, and training programs focused on 410 digital tools, can create a conducive environment for youth by making farming more appealing to 411 younger generations. In turn, the agricultural sector can benefit from fresh ideas and innovative 412 practices that are needed for the uptake and widespread use of Agriculture 4.0 technologies 413 (MacPherson et al., 2022; Petraki et al., 2025). 414 415 4.4 Creating product identity 416 Meeting market requirements is perceived as a major issue in Almería and Antalya, 417 highlighting the importance of a strong product image to stand out against competitors. The 418 emerging need is for product differentiation in the market and greater consumer awareness, 419 especially by creating a unique identity for greenhouse-grown vegetables, distinguishing them from 420 other horticultural products, e.g. grown elsewhere or using different practices. This involves 421 developing elements that resonate with consumers, such as e.g. product denomination and origin 422 and logo, as well as emphasising the environmental and human health benefits of agricultural 423 products, while ensuring transparency in the production system. This can be achieved by 424 highlighting unique attributes of the products, especially focusing on eco-friendly practices achieved 425 through DSS use, to attract consumers who are increasingly concerned about environmental 426 sustainability and health. Greater consumer awareness is essential to inform and educate the public 427 about sustainability attributes, thus driving specific demand. 428 24 Related research highlights the importance of transparency and sustainability in agricultural 429 practices, which can be achieved through digital tools like the DSS examined in this study, enhancing 430 consumer trust and demand (Fragomeli et al., 2024; Maffezzoli et al., 2022). Certification and 431 labelling schemes play a critical role in communicating the value of sustainably produced goods, 432 thereby attracting consumers who prioritise environmental and health benefits (da Silveira et al., 433 2023b). Certification provides formal recognition of adherence to specific standards, such as organic 434 farming or sustainable practices, which can enhance the credibility and marketability of the 435 products. Labelling schemes offer a clear and accessible way for consumers to identify and trust 436 these certified products. This aligns with the need for greater consumer awareness regarding the 437 attributes of agricultural products (da Silveira et al., 2023a). 438 The importance of a strong product image in meeting market demands is a key finding that 439 has implications for marketing strategies and consumer education. Farmers should prioritise 440 transparency and sustainability in their practices to enhance consumer trust and demand. This can 441 be achieved through the widespread uptake of effective certification and labelling schemes that 442 communicate the environmental and health benefits of agricultural products. However, initiatives 443 that promote consumer awareness regarding sustainable practices are needed as well to drive or 444 enhance demand for responsibly produced goods (McFadden et al., 2022; Xu et al., 2022). 445 446 4.5 Increasing farmer margins 447 Actors in Monastir highlight the issue of low profit margins in agricultural sales, which 448 discourages investment in new technology. To address this, there is a critical need to allow for a 449 price premium on agricultural products. This can be achieved by differentiating products based on 450 their sustainability and quality attributes, such as environmental and health benefits, appealing to 451 consumers willing to pay more for sustainably produced goods (Derakhti et al., 2023). 452 25 Enabling conditions for this need include robust certification processes that ensure 453 transparency and trust in the sustainability claims. Certification and labelling schemes can play a 454 crucial role in communicating the value of sustainable products to consumers, justifying the price 455 premium. Effective marketing campaigns are also essential to educate consumers about the 456 benefits of sustainable food and to increase their willingness to pay for it. Additionally, financial 457 mechanisms like subsidies, grants, or other incentives can support farmers in adopting new 458 technologies by offsetting the costs of DSS uptake and related changes in sustainable agricultural 459 practices and inputs, thereby enhancing their economic viability (Eastwood et al., 2019). 460 The importance of a strong product image in meeting market demands is a key finding that 461 has implications for marketing strategies and consumer education. Farmers should prioritise 462 transparency and sustainability in their practices to enhance consumer trust and demand. This can 463 be achieved through the widespread uptake of effective certification and labelling schemes that 464 communicate the environmental and health benefits of agricultural products. However, initiatives 465 that promote consumer awareness regarding sustainable practices are needed as well to drive or 466 enhance demand for responsibly produced goods (McFadden et al., 2022; Xu et al., 2022). 467 468 4.6 Increasing farmer bargaining 469 All case studies highlight the issue of unfair value distribution along the food value chain. 470 This imbalance results in farmers receiving a disproportionately small share of the profits compared 471 to other downstream actors, such as distributors and retailers. In some regions, like e.g. Almería 472 and Antalya, this problem is exacerbated by the relatively high number of intermediaries. Inequity 473 in value distribution can lead to financial instability for farmers, discouraging the adoption of 474 innovations such as the DSS and sustainable practices, and may result in low market responsiveness. 475 26 Strengthening farmers' bargaining power is then crucial for achieving sustainability objectives 476 through digitalisation. 477 Key enabling conditions to address this need involve the promotion of collective approaches, 478 such as cooperation initiatives, including second tier-cooperatives, and fostering stronger farmer 479 unions. Additional benefits can be realised by enhancing efficiency through the organisation of 480 demand-driven production. By organising into cooperatives or unions, farmers can pool their 481 resources, share knowledge, and collectively negotiate better prices and terms with downstream 482 actors (Ganeshkumar et al., 2023; Klerkx and Rose, 2020). Example cooperation initiatives include 483 marketing cooperatives that help farmers sell their products collectively or supply cooperatives that 484 enable farmers to purchase inputs at lower costs. For example, second-tier cooperatives, i.e. union 485 of smaller, first-tier cooperatives, proved successful especially in Almeria, working together to 486 provide services, support, and resources (including training) to their cooperative members 487 (Giagnocavo et al., 2014). Also, farmer unions can advocate for policies that support fairer farmer 488 prices, provide legal assistance, and offer entrepreneurial training. These collective actions among 489 farmers can lead to improved market access and enhanced resilience against market fluctuations 490 (Rose et al., 2021). The organisation of demand-driven production can be achieved through the 491 adoption of dedicated digital tools, such as predictive technology and analytics. These tools link 492 supply with demand, helping growers mitigate unexpected risks and challenges by predicting 493 market demand and maximising productivity (Eastwood et al., 2017; Suksa-ngiam and Bechor, 494 2024). The DSS developed in this research represents a farm-level step towards organising demand-495 driven production. It equips greenhouses with sensors and IoT devices that provide on-site 496 information useful for predictive models. However, dedicated tools for market predictions are still 497 needed. Cooperation initiatives may help distribute the costs of these additional technologies, 498 enabling their widespread adoption at the territorial level. 499 27 Strengthening farmers’ bargaining power is essential for improving their economic viability 500 and enabling the adoption of Agriculture 4.0 technologies. Promoting collective approaches, such 501 as cooperatives and unions, and organising demand-driven production are key strategies. These 502 approaches empower farmers to negotiate better terms, access markets more effectively, and share 503 the costs and benefits of digital innovation. 504 505 4.7 Simplifying bureaucracy 506 In Tuscany, stakeholders emphasise that slow and complex bureaucracy often discourages 507 farmers from applying for public incentives, hindering the sustainable upgrade of farm practices, 508 including the adoption of new digital tools. The complexity and lengthy processes involved in 509 paperwork can be particularly daunting, leading to frustration and disengagement among farmers. 510 Complex bureaucratic processes can deter farmers from applying for public incentives (McFadden 511 et al., 2022). To address these issues, there is a need for simpler bureaucracy. 512 Enabling conditions for this simplification include implementing streamlined application 513 processes that reduce unnecessary bureaucratic steps and increase assistance to farmers and 514 advisors throughout the application process (Eastwood et al., 2019). Simplified application 515 procedures and targeted support can enhance farmer engagement and participation, making it 516 easier for them to access the support they need for adopting digital tools and sustainable practices. 517 The complexity of bureaucratic processes can prevent farmers from accessing public 518 incentives. Simplifying these processes is crucial for enhancing farmer engagement and 519 participation in programs aimed at promoting digital agriculture. Policy improvement should 520 prioritise the streamlining of application procedures and the provision of targeted technical support 521 to farmers throughout the bureaucratic process, thereby facilitating access to the resources needed 522 for adopting new technologies (Martens and Zscheischler, 2022; Monda et al., 2023). 523 28 524 4.8 Developing land and crop production planning 525 Findings from Antalya indicate that the current supply of greenhouse-grown food is 526 insufficient to meet both domestic and foreign market demand. This production gap challenges the 527 region's agricultural sector, potentially leading to missed economic opportunities and reduced 528 competitiveness in both domestic and international markets. Therefore, strategic land and crop 529 production planning is needed to optimise the use of available agricultural land, ensuring that the 530 right crops are grown in the right quantities to meet market demands. This approach can stabilise 531 the market and ensure a steady supply of greenhouse-grown food. 532 Implementing policies that support effective production strategies is crucial for optimising 533 resource use and enhancing market competitiveness (Derakhti et al., 2023). Actors identify 534 production-support policies as crucial enabling conditions as they can provide the necessary 535 framework and resources to assist farmers in implementing effective land and crop production 536 strategies. These policies can encourage farmers to adopt best practices and invest in DSS or other 537 digital tools that enhance productivity and sustainability (Dibbern et al., 2024). 538 Addressing production gaps through strategic planning is essential for ensuring food system 539 resilience and competitiveness. Policy frameworks that support land and crop planning can help 540 align production with market needs, reduce inefficiencies, and promote the adoption of digital tools 541 that support data-driven decision-making in agriculture. 542 543 4.9 Increasing liquidity for new technology uptake 544 Farmers in Monastir and Antalya are struggling with rising production costs, making it 545 difficult to sustain their operations. In Tunisia, for instance, this issue arises because equipment like 546 greenhouses and agricultural inputs such as seeds, pesticides, and fertilisers are imported. To 547 29 overcome this issue, better access to liquidity is needed to invest in new technologies that can 548 enhance efficiency and productivity. Access to liquidity is critical to encourage farmers’ uptake of 549 Agriculture 4.0 technologies, such as the DSS, which can help reduce costs and increase yields in 550 greenhouses and other farming systems. 551 Specific support mechanisms for Agriculture 4.0, including grants and subsidies, can 552 encourage the modernisation of farm production and the adoption of Agriculture 4.0 (Eastwood et 553 al., 2019; Ganeshkumar et al., 2023). Support from public and private institutions is an important 554 enabling condition to help farmers adopt new digital technologies alongside more sustainable 555 practices. This financial backing is essential for enabling farmers to transition to more efficient and 556 sustainable practices (Klerkx and Rose, 2020). 557 Addressing liquidity constraints is essential for enabling farmers to invest in digital tools and 558 transition toward sustainable agricultural practices. Public and private financial support 559 mechanisms, such as subsidies, grants, and low-interest loans, can reduce the initial cost burden 560 and make digital technologies more accessible, particularly for small and medium-sized farms. 561 562 4.10 Facilitating remote farming operations 563 The heavy workload and difficulty in achieving a work-life balance for the workforce across 564 the case studies underscore the need for technology that facilitates remote farming operations. 565 These issues are particularly sensitive for women and young parents, who often juggle multiple 566 responsibilities. By reducing the need for constant physical presence on the farm, remote farming 567 operations can significantly alleviate the physical and time burdens on farmers, allowing them to 568 manage their greenhouses more efficiently and effectively from a distance (Finger, 2023; Lajoie-569 O’Malley et al., 2020). The DSS under study is designed to enable remote monitoring of greenhouse 570 conditions, such as climate and soil, and to perform tasks like fertilisation, irrigation, and diseases 571 30 onset and development. The need to facilitate remote farming operations closely aligns with the 572 overarching objective of the study and is intrinsically linked to other needs, such as improving the 573 technical skills of farmers and advisors, and knowledge transfer and building acceptability and trust. 574 Addressing the need for remote work likely requires most previously mentioned enabling 575 conditions. However, two more specific enabling conditions can be identified that complement 576 those already discussed. First, targeting public and private investments to establish robust digital 577 infrastructure in rural areas to ensure reliable internet connectivity is crucial. While some 578 specialised greenhouse districts in the investigated case studies may already have this 579 infrastructure, a digital divide still exists that must be bridged to enable agricultural digitalisation. 580 (Gumbi et al., 2023; Rose et al., 2021). Second, collaborative efforts are increasingly recognised as 581 vital for digital transformations in agriculture (Martens and Zscheischler, 2022; Wang et al., 2020). 582 Fostering a culture of knowledge sharing and collaboration among farmers can greatly improve their 583 ability and confidence to operate remote farming tasks. Peer-to-peer learning and mentorship 584 programs can play a significant role in enabling farmers to operate remote farming systems 585 confidently. This can be facilitated by developing collaborative platforms that foster a community 586 of support, where farmers can share resources, knowledge, and best practices (Fasciolo et al., 2024; 587 Fragomeli et al., 2024; Jayasiri et al., 2024). 588 Bridging the gap between urban and rural areas is closely linked to rural improvement: 589 investments in digital infrastructure are expected to offer manifold benefits, by enhancing individual 590 farms and improving the overall quality of life in rural communities. Improved internet connectivity 591 facilitates access to digital tools, which can enrich education, healthcare, and economic 592 opportunities for rural residents (Finger, 2023; Fragomeli et al., 2024). An additional important 593 aspect is the potential to offer partial remote work opportunities through the diffusion of DSS that 594 enable monitoring and operating tasks without the need to be on-site. This aligns the modality of 595 31 agricultural work with those seen in other economic sectors, making it more appealing. The 596 flexibility enabled by remote farming tasks reduces the physical toll typically experienced by farmers 597 and prioritises the balance between work and personal life, fostering inclusivity for women and 598 young parents in the agricultural workforce (Gabriel and Gandorfer, 2023; Gumbi et al., 2023; 599 Shamshiri et al., 2024; Yuan and Sun, 2024). 600 601 4.11 Critical assessment of the research 602 The limitations of the research should be acknowledged to support informed interpretation 603 and guide future research improvements. 604 The research is geographically limited to four case studies in the Mediterranean basin. 605 Although these regions represent significant players in the global greenhouse vegetable market, the 606 findings may not be fully generalisable to other regions worldwide. 607 LL actor selection aimed to ensure representation across the agricultural value chain, 608 however the number of participants per category varied by case study. For instance, in Antalya, only 609 one policy representative was involved, which may have constrained the diversity of policy 610 perspectives relevant to both the local territorial context and Turkey more broadly. 611 The study relies on qualitative data collected through participatory methods involving a 612 diverse group of actors with interdisciplinary expertise. The sample size and composition of engaged 613 actors may not capture the full diversity of views within each region, and the findings might be 614 influenced by the perspectives and biases of the participants. 615 Data collection and reporting is based on internally developed procedures and protocols, 616 tailored to the LL approach and the relatively small sample size. In contexts where such internal 617 management is not feasible (e.g., studies involving randomised sampling, large sample sizes, 618 saturation-based sampling) widely recognised tools for reporting qualitative research should be 619 32 considered. For example, the COREQ checklist (Tong et al., 2007) ) offers a structured framework 620 for ensuring transparency and rigour in qualitative research. 621 Findings emphasise the importance of technical skills, trust, market dynamics, and policy 622 frameworks in fostering the adoption of digital technologies. However, other potential factors that 623 may also play an important role in technology adoption were not extensively explored, e.g., cultural 624 attitudes, social networks, economic incentives. 625 626 5. Recommendations for the science-policy-society interface 627 Findings highlight how technical skills, trust, market dynamics, and policy frameworks 628 interact to shape the adoption of Agriculture 4.0 technologies across Mediterranean regions. 629 Focusing on greenhouse farming, where remote management is relatively more feasible, this study 630 suggests that successful implementations may offer scalable models for broader agricultural 631 applications (Bocean, 2024; Yuan and Sun, 2024). These findings support the generation of 632 recommendations for the science-policy-society interface, emphasising the need for integrated 633 approaches to unlock the full potential of digital tools in agriculture. 634 There is a critical need for wider and enhanced collaboration among scientists, policymakers, 635 and agricultural practitioners to foster the successful adoption of Agriculture 4.0 technologies 636 (Matthews, 2021). Encouraging interdisciplinary research that integrates insights from agricultural 637 science, social sciences, and technology studies can provide a holistic understanding of the 638 challenges and opportunities associated with digital agriculture (Finger, 2023; Rotz et al., 2019). 639 Developing policy frameworks that are informed by empirical research and stakeholder input can 640 ensure that interventions are relevant and effective. Achieving synergies between digitalisation, 641 sustainability, and food security requires policy coherence and governance models that integrate 642 environmental and economic objectives through participatory, goal-based approaches Coderoni 643 33 (2023). Policy enhancement should include the engagement of farmers and agricultural advisors to 644 co-create policies that address their specific needs and concerns (Derakhti et al., 2023; Gabriel and 645 Gandorfer, 2023). Raising public awareness about the benefits of digital agriculture and the 646 importance of sustainable practices is essential for garnering societal support for agricultural 647 innovations. Educational campaigns should target not only farmers but also consumers, fostering a 648 culture of sustainability and responsible consumption (Gouroubera et al., 2025; Rose et al., 2021). 649 Establishing mechanisms for monitoring and evaluating the impact of digital agriculture initiatives 650 can provide valuable insights into their effectiveness and inform future policy decisions. Continuous 651 feedback loops between research, policy, and practice can enhance the adaptability and 652 responsiveness of agricultural interventions towards digitalisation (Fragomeli et al., 2024; Yang et 653 al., 2024). 654 The successful adoption of digital technologies extends beyond individual farms; it 655 empowers communities and enhances their economic resilience. As these technologies become 656 more widespread, rural areas may experience significant transformations that address longstanding 657 rural-urban disparities (Fragomeli et al., 2024; Yang et al., 2024). As the agricultural landscape 658 evolves, significant potential emerges from the adoption of remote farming technologies. These 659 technologies can improve work-life balance, foster community support, and create new job 660 opportunities, although strategic investment in digital infrastructure is still needed. This appeal can 661 enhance workforce diversity, ensuring that agriculture remains competitive and relevant in the 662 rapidly changing job market. Agricultural digitalisation serves not only ecological sustainability but 663 also uplifts rural communities by fostering a more equitable and diverse agricultural community 664 (Rose et al., 2021; Wolfert et al., 2017). 665 666 6. Conclusions 667 34 This study identifies key enabling conditions for the effective implementation of Agriculture 4.0 668 technologies in Mediterranean greenhouse farming. By following a RRI approach, findings from 669 participatory research across LLs case studies suggest that efforts should focus on improving digital 670 literacy, building trust in technology, leveraging market dynamics, and facilitating remote farming 671 operations. These strategies can support the digital transformation of agriculture while promoting 672 social inclusion, equity, and improved workforce conditions, particularly for women and youth. 673 To support evidence-based decision-making, the following policy recommendations are proposed: 674 • Invest in digital literacy and training: Tailored educational programs and knowledge transfer 675 mechanisms are essential to bridge the gap between research and farm-level application; 676 • Support inclusive technology adoption: Initiatives should consider generational and socio-677 economic differences to avoid inadvertently excluding older or less digitally literate farmers; 678 • Strengthen market incentives: Certification schemes, consumer awareness campaigns, and 679 simplified bureaucratic processes can enhance product value and encourage investment in 680 digital tools; 681 • Promote social equity and cooperation: Policies should reinforce farmer unions and 682 collaborative initiatives to improve bargaining power and ensure fair value distribution; 683 • Enable remote farming solutions: Digital tools that improve work-life balance and 684 operational efficiency can foster sustainability and attract new entrants to the sector. 685 Key limitations of this study include its context-specific nature and reliance on the socio-686 institutional dynamics of each territorial LL, which should be carefully considered when interpreting 687 the findings and assessing their broader applicability. Future research should expand the 688 geographical coverage, integrate quantitative methods, and explore additional factors, such as 689 cultural attitudes and social networks, that influence technology adoption. Also, in the context of 690 35 LLs, integrating Participatory Action Research principles could offer additional value, particularly in 691 enhancing actor agency and long-term impact, given the strong emphasis placed on collective action 692 and transformation led by participants. 693 Disclosure of potential conflicts of interest 694 Authors declare that they have no known competing financial interests or personal 695 relationships that could have appeared to influence the work reported in this paper. The funder had 696 no role in study design, data collection and analysis, decision to publish, or preparation of the 697 manuscript. 698 699 Data availability statement: Data will be made available upon request 700 Acknowledgements 701 This research was funded under the EU Partnership for Research and Innovation in the 702 Mediterranean Area (iGUESS-MED – ‘Innovative Greenhouse Support System in the Mediterranean 703 Region efficient fertigation and pest management through IoT based climate control’; Grant 704 Agreement number: 1916; Section 1, Topic 1.2.2: “Sustainability and competitiveness of 705 Mediterranean greenhouse and intensive horticulture”). The funder had no role in study design, 706 data collection and analysis, decision to publish, or preparation of the manuscript. The authors 707 thank all stakeholders in the living lab activities of the iGUESS-MED project for their contributions. 708 709 References 710 Bartolini, F., Incrocci, L., and Buendía Guerrero, D. (2021). 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