Agritech Policy Landscape: Insights from Relevant Stakeholders on Policy Issues and Strategic Plans in Italy Ahmed Moussaoui*1, Rino Ghelfi1, Davide Viaggi1 1University of Bologna, *Corresponding author: ahmed.moussaoui2@unibo.it This article has been accepted for publication and undergone full peer review but has not 1 been through the copyediting, typesetting, pagination and proofreading process, which may 2 lead to differences between this version and the Version of Record. 3 Please cite this article as: 4 Moussaoui A., Ghelfi R., Viaggi D. (2025). Agritech Policy Landscape: Insights from 5 Relevant Stakeholders on Policy Issues and Strategic Plans in Italy, Bio-Based and Applied 6 Economics, Just Accepted. DOI:10.36253/bae-17356 7 Abstract 8 Agricultural practices face growing challenges, including climate change, resource 9 constraints, meeting sustainability goals and food security. This study examines 10 stakeholder perspectives on smart farming technologies and their integration into policy 11 frameworks. A mixed-method approach, using triangulation of qualitative and quantitative 12 data, combines an online survey (targeting experts from academia, industry, and 13 policymaking) distributed through the Agritech project network and face-to-face 14 interviews (engaging key stakeholders with in-depth knowledge of agricultural policy and 15 technology implementation). Key findings reveal significant optimism about the potential 16 of smart technologies to enhance efficiency, sustainability, and productivity in agriculture. 17 However, widespread adoption is hindered by barriers such as high initial investment costs 18 mailto:ahmed.moussaoui2@unibo.it and a lack of technical knowledge. The study identifies policy gaps and provides actionable 19 recommendations, including financial incentives, capacity-building initiatives, and 20 improved infrastructure, to support the integration of these technologies. The findings 21 underscore the critical need for adaptive policies that align with the evolving landscape of 22 agricultural innovation, ensuring equitable access and long-term sustainability. 23 Keywords 24 Agritech, technology adoption, European agricultural policy, sustainability, stakeholders’ 25 perspectives. 26 JEL codes : Q16, Q18 1. Introduction 27 The global agricultural sector faces increasing challenges in balancing productivity, 28 sustainability, and environmental responsibility. Climate change and resource constraints 29 are putting increasing pressure on agricultural systems, whereas food security remains a 30 multifaceted challenge that goes beyond production. Ensuring stable access to affordable, 31 nutritious food also depends on market structures, distribution networks, and social 32 inclusion (FAO, 2021). While technological innovation can support more efficient and 33 sustainable production, it must be embedded within broader strategies that address 34 systemic barriers to food security (FAO, 2021; IPCC, 2023). Given the limitations of arable 35 land and the growing demand for sustainable food production, smart agriculture 36 technologies are gaining recognition as a key driver of transformation. These technologies, 37 encompassing sensor-based systems, IoT configurations, AI applications, and renewable 38 energy solutions, offer advanced tools for precision farming, real-time monitoring, and 39 resource optimization (Basso and Antle, 2020; Finger et al., 2019; Knierim et al., 2019). 40 However, their adoption remains low and uneven despite their potential, primarily due to 41 high initial costs, limited technical knowledge, and inadequate infrastructure (Akimowicz 42 et al., 2021). These barriers are particularly pronounced for small and medium-sized farms, 43 which often lack the necessary resources and institutional support to implement such 44 technologies effectively. 45 Recent research by Menozzi et al. (2023) also highlights that farmers’ decisions to engage 46 in sustainability practices are shaped not only by economic incentives but also by 47 behavioral drivers, such as perceived control and peer influence. In the case of digital 48 agriculture, these behavioral aspects, especially regarding trust in digital systems and ease 49 of use, are equally important and deserve policy attention. 50 Complementing this view, Giampietri et al. (2020) emphasize the role of trust in 51 intermediaries and institutional transparency in shaping farmers’ willingness to adopt 52 CAP-subsidized risk management tools. While their study addresses instruments like 53 insurance and mutual funds, our work extends this behavioural framing to digital 54 agriculture, where trust also involves confidence in data systems and algorithm-based 55 decision-making. While these behavioral dynamics were not the primary focus of our 56 empirical study, they provide a valuable conceptual lens through which to interpret 57 stakeholder concerns around adoption. 58 A well-structured policy environment is critical in facilitating the adoption of smart 59 agriculture technologies. Policies that support financial incentives, training programs, and 60 rural infrastructure development can significantly enhance accessibility and encourage 61 broader implementation among diverse farming operations (Détang-Dessendre et al., 62 2018). While existing frameworks, such as the Common Agricultural Policy (CAP), the 63 Green Deal, and the Farm to Fork Strategy, emphasize the role of innovation in agricultural 64 sustainability, they exhibit notable gaps in addressing key adoption barriers. For instance, 65 the CAP’s current funding mechanisms primarily benefit large-scale farms with greater 66 financial capacity, leaving smallholders with limited access to grants and subsidies 67 necessary for adopting high-cost digital technologies (Lovec et al., 2020). Additionally, 68 despite the Green Deal and Farm to Fork Strategy highlighting the need for sustainable 69 agriculture, they fall short in prioritizing investments in rural digital connectivity, an 70 essential component for integrating smart technology, particularly in remote agricultural 71 regions (Ehlers et al., 2022). There is a need for proactive and adaptive policy approaches 72 that address both financial and technical barriers while fostering stakeholder collaboration 73 and long-term sustainability. 74 This study aims to examine stakeholder perspectives on the adoption challenges and 75 opportunities of smart agriculture technologies and identify policy interventions that can 76 facilitate their broader integration. Using a mixed-method approach, the research combines 77 qualitative interviews with key stakeholders and a quantitative online survey to gather 78 diverse insights on the policy landscape, adoption barriers, and potential solutions. The 79 analysis applies triangulation between the qualitative and quantitative findings to 80 strengthen the interpretation of results and ensure that policy recommendations are 81 grounded in multiple sources of evidence. The findings contribute to the existing literature 82 by bridging the gap between technological advancements and policy implementation, 83 providing evidence-based recommendations to enhance the diffusion of technology in 84 agriculture. 85 This study is part of the Agritech project, a national research initiative funded by the Italian 86 National Recovery and Resilience Plan (PNRR) that brings together universities, research 87 institutions, and industry stakeholders to foster innovation in precision agriculture, AI, and 88 sustainable farming. Conducted within Spoke 3, which focuses on policy frameworks and 89 governance for smart agriculture adoption, this research builds on prior project activities 90 that mapped key actors in the innovation ecosystem and developed targeted engagement 91 strategies (AGRITECH, 2023). The stakeholder database, created in the framework of the 92 project, enabled the distribution of our questionnaires through a trusted and well-informed 93 network, ensuring policy-relevant insights from diverse, experienced participants across 94 academia, industry, and policymaking. 95 The paper first describes the methodological framework, detailing the qualitative and 96 quantitative data collection and analysis approaches. It then presents key findings, 97 highlighting stakeholder perspectives on the benefits and challenges of smart agriculture 98 technologies. The discussion explores the broader implications for policy and practice, 99 focusing on the need for strategic policy interventions to overcome adoption barriers. 100 Finally, the study concludes with recommendations for future research and actionable 101 policy measures to foster a more supportive environment for smart agriculture innovation. 102 2. Methodology 103 2.1. Overview 104 To comprehensively assess stakeholder perspectives on smart agriculture technologies, this 105 study employed a mixed-method approach, integrating qualitative and quantitative data 106 collection techniques. This methodological choice is well-suited for exploring complex 107 issues such as technology adoption in agriculture, as it allows for in-depth insights from 108 expert stakeholders while also capturing broader trends in the sector (Creswell & Clark, 109 2017; Fielke et al., 2020). The combination of qualitative interviews and a structured online 110 survey aims to strengthen the study's analytical depth by triangulating stakeholder 111 perceptions across different backgrounds and levels of expertise. 112 Given the exploratory aim of this research and considering the quantitative sample size, 113 the survey quantitative data primarily serve to identify general trends and perceptions 114 rather than provide statistically robust conclusions. This quantitative approach is 115 complemented by the qualitative interviews, which offer deeper, context-rich insights. By 116 combining both qualitative and quantitative data, we follow an established methodological 117 practice known as triangulation, enhancing the reliability and validity of our findings 118 through cross-verification (Fetters et al., 2013). 119 The review of the existing literature revealed that previous research has often examined 120 technology adoption in agriculture from either a purely economic or behavioral 121 perspective. The focus of this study is to integrate policy dimensions and directly involve 122 stakeholders from multiple sectors, including academia, technology providers, policy 123 institutions, and farmers' associations. This holistic approach, which explicitly links 124 technological innovation with policy development, represents a novel contribution to the 125 existing body of literature. 126 The study focused on stakeholders in Italy. While Emilia-Romagna, one of Italy’s most 127 technologically advanced agricultural regions, was the starting point of the stakeholders’ 128 mapping, the survey distribution and interviews also involved participants from other key 129 agricultural areas such as Puglia, Lombardia, and Veneto. This broader geographical 130 engagement allowed the research to capture a more representative view of the national 131 smart agriculture policy landscape. 132 Both qualitative and quantitative components of the study shared a common core of 133 thematic focus, centering on: 134 • The barriers and drivers of smart agriculture technology adoption. 135 • The role of existing policies in shaping adoption trajectories. 136 • The perceived needs for policy innovation to facilitate broader uptake. 137 These dimensions were used both to frame the design of the survey and interviews and to 138 guide the interpretation of findings in the results and discussion sections. Rather than 139 formal hypotheses, they function as thematic pillars for an exploratory investigation into 140 how policy, behavior, and technology interact in the current agricultural innovation 141 landscape. 142 This methodological design aims to ensure a holistic assessment of the policy landscape 143 surrounding smart agriculture technologies, while providing valuable insights for both 144 academic discourse and policy formulation. 145 2.2. Qualitative data collection 146 The qualitative phase focused on gathering comprehensive insights from experts with 147 extensive knowledge of smart agriculture technologies and policies. It was essential to 148 understanding the barriers and opportunities surrounding the adoption of these 149 technologies. A semi-structured interview format was used to ensure a structured approach, 150 allowing for a mix of predefined questions and open-ended discussions. This approach 151 provided a comprehensive view of stakeholder experiences, enabling the identification of 152 key themes related to technology adoption and policy needs. 153 In-depth qualitative interviews were conducted with carefully selected experts in smart 154 agriculture technologies and policy. These interviews were designed to elicit rich, detailed 155 insights from highly experienced individuals. Although the final sample comprised five (5) 156 participants, The decision to proceed with these interviews was taken based on the principle 157 of thematic saturation, that is, the point at which no substantially new insights emerge from 158 additional interviews (Guest et al., 2006). Given the specificity and expertise of our 159 respondents, the interviews provided consistent and robust information across key themes. 160 This approach aligns with accepted qualitative research standards, where small, 161 purposively selected samples are typical and appropriate for exploratory, expert-based 162 investigations (Creswell, 2013). 163 The questionnaire was designed based on the Agricultural Knowledge and Innovation 164 System (AKIS) framework, which highlights the importance of multi-actor collaboration 165 in agricultural innovation. It was structured into five main sections: (1) the respondent’s 166 background and expertise, (2) their perspectives on smart agriculture technologies, (3) 167 challenges related to adoption, (4) awareness and evaluation of current policies, and (5) 168 recommendations for improving policy support. This structured design ensured that 169 responses covered both technical and policy-related dimensions, making this phase a 170 crucial foundation for the overall study. 171 Participants were selected through a purposive sampling approach, ensuring that only 172 individuals with significant expertise and direct involvement in the field were included. 173 The selection process was based on a stakeholder mapping exercise carried out earlier in 174 the Agritech project. Experts were identified from three key groups: public sector 175 representatives involved in agricultural policy, academic researchers specializing in 176 precision agriculture and rural policy, and industry professionals working with smart 177 agriculture technologies and farmer cooperatives. This targeted selection process ensured 178 a diverse yet highly relevant sample, strengthening the credibility of the findings. 179 Interviews were carried out face-to-face whenever possible, allowing for detailed 180 discussions and clarifications. In cases where in-person meetings were not feasible, remote 181 interviews were held. Five key experts participated in this qualitative survey. Thematic and 182 textual analysis was used to process the responses, identifying recurring themes and key 183 insights. The results from this phase informed the refinement of the quantitative survey in 184 the next stage of data collection, ensuring that the study captured both broad trends and in-185 depth perspectives. 186 2.3. Quantitative data collection 187 The second data collection phase involved an online questionnaire to capture broad 188 stakeholder perspectives on smart agriculture technologies, their adoption, perceived 189 benefits, policy awareness, and associated challenges. 190 This structured survey was designed to complement the qualitative insights gathered in the 191 first phase by providing quantifiable data to identify patterns and validate expert opinions. 192 The integration of both qualitative and quantitative methods was an attempt to ensure a 193 comprehensive and balanced understanding of the key factors influencing the adoption of 194 smart agriculture technologies. 195 The online questionnaire was adapted from the qualitative questionnaire, and structured 196 into multiple sections, each addressing a critical aspect of technology adoption and policy 197 implications. The first section focused on general respondent information, including their 198 professional background, sector of activity, and geographic location, allowing for an 199 analysis of how perspectives varied across different stakeholder groups. The second section 200 examined familiarity and involvement with smart agriculture technologies, prompting 201 respondents to indicate their level of knowledge and direct engagement with specific 202 technologies, such as robotics, IoT, AI, renewable agri-systems, and spectral technologies. 203 The third section examined the perceived contributions of these technologies, evaluating 204 opinions on their potential to improve agricultural productivity, resource efficiency, 205 environmental sustainability, and labor optimization. 206 A key component of the questionnaire was its focus on policy awareness and barriers to 207 adoption. Respondents were asked whether they were aware of existing policies that 208 support smart agriculture technologies, providing insights into the effectiveness of current 209 policy communication and identifying gaps where improved dissemination of information 210 might be needed. Additionally, the survey investigated major obstacles preventing the 211 widespread adoption of these technologies, including financial constraints, technical 212 knowledge gaps, regulatory barriers, and infrastructure limitations. The final section 213 solicited policy recommendations, encouraging respondents to suggest changes to existing 214 policies or propose new policy instruments that could facilitate the integration of smart 215 agriculture technologies into mainstream agricultural practices. 216 The questionnaire was strategically distributed across multiple channels to ensure a high-217 quality and representative dataset. It was shared within the Agritech project network, 218 reaching academics and researchers with expertise in agricultural policy, technology, and 219 innovation. It was also circulated among stakeholders from the previously established 220 project stakeholders’ network, including policymakers, industry representatives, farmers' 221 associations, and technology developers, potentially reaching over 90 persons. This 222 distribution strategy was designed to maximize diversity in respondent backgrounds while 223 maintaining a high level of expertise in the responses collected. 224 The sampling approach was purposive, targeting individuals with direct experience and 225 informed perspectives on adopting smart agriculture technologies. Rather than aiming for 226 a large random sample, the focus was on obtaining high-quality responses from 227 knowledgeable stakeholders whose input could provide valuable insights into policy needs 228 and adoption challenges. A total of 35 responses were collected, and after applying validity 229 criteria, 20 responses were retained for final analysis. While this sample size may appear 230 modest for a quantitative survey, it is consistent with expert-elicitation methods in policy 231 and innovation research, where depth of knowledge and professional insight are prioritized 232 over statistical representativeness (Baker et al., 2013). 233 The criteria for inclusion ensured that responses were complete, internally consistent, and 234 provided by individuals with relevant expertise in the field of smart agriculture. Validity 235 was assessed based on completeness, consistency, and relevance to the research topic. 236 Responses that were incomplete, contained inconsistencies, or came from participants with 237 no clear connection to smart agriculture were excluded. Both the online questionnaire and 238 the qualitative interviews were conducted in parallel in the same period of time. 239 Rather than claiming statistical generalizability, the primary goal of the quantitative data 240 is to highlight general patterns, stakeholder perspectives, and areas needing policy 241 attention. These quantitative insights are therefore exploratory and are critically supported 242 and contextualized through the qualitative findings obtained from in-depth expert 243 interviews, ensuring that the interpretations are robust and contextually meaningful. 244 While the sample size of five qualitative interviews and 20 valid quantitative responses 245 may appear limited, it is justified by the methodological rigor applied in the selection and 246 analysis processes. The qualitative interviews were conducted with carefully selected key 247 stakeholders representing different sectors of agriculture, including policy, research, and 248 industry, ensuring expert-driven insights. Thematic saturation was reached, as no 249 significantly new themes emerged in later interviews, suggesting that the core challenges 250 and opportunities had been effectively captured (Baker et al., 2013). 251 For the quantitative survey, although the response count is modest, it reflects targeted 252 participation from experienced stakeholders within the Agritech project network and a pre-253 established stakeholder database. The respondents’ expertise ensured high-quality, 254 informed perspectives, making the findings valuable for understanding adoption trends and 255 policy needs. Future research could expand the sample size to further validate the findings. 256 2.4. Data analysis 257 The analysis of the collected data followed a structured multi-step approach, integrating 258 both qualitative and quantitative methodologies to ensure a comprehensive interpretation 259 of stakeholder perspectives on the adoption of smart agriculture technology and policy 260 needs. Given the mixed-methods nature of the study, different analytical strategies were 261 applied to the qualitative and quantitative datasets to maximize the depth and reliability of 262 insights. 263 The qualitative data obtained from face-to-face interviews were manually analyzed using 264 a combination of textual synthesis and thematic analysis. This approach was chosen to 265 extract detailed insights from expert responses while maintaining the depth and context of 266 qualitative feedback. In particular, thematic analysis involved identifying recurring 267 patterns in the responses related to technology adoption, policy gaps, financial constraints, 268 and regulatory needs (Kiger & Varpio, 2020). While the analysis was primarily descriptive, 269 it provided structured insights into the challenges and opportunities surrounding each 270 specific smart technology developed in the Agritech project. The responses were 271 synthesized into key themes aligned with the study’s focus, ensuring stakeholders’ 272 perspectives on technology diffusion, policy barriers, and suggested interventions were 273 effectively captured. 274 To ensure a structured interpretation of the qualitative data, insights were categorized into 275 two main dimensions. The first focused on technology-specific insights, where each smart 276 technology of the Agritech project, namely: IoT, AI, sensor-based systems, and robotics, 277 was examined separately. Responses highlighted perceived benefits, adoption challenges, 278 and policy needs unique to each innovation. The second dimension analyzed the broader 279 policy environment, capturing stakeholder views on existing policy frameworks, gaps in 280 regulatory support, and recommendations for improving policy measures. This approach 281 ensured that the qualitative findings were systematically organized, aiming to understand 282 stakeholder perspectives. 283 Given the exploratory purpose and the sample size, the quantitative data obtained from the 284 online survey were analyzed in XLSTAT using basic descriptive statistical methods 285 (frequencies, percentages, and cross-tabulations) to highlight general trends and 286 stakeholder perceptions regarding smart technology adoption, rather than conducting in-287 depth statistical tests. Frequency distributions were used to summarize categorical 288 variables such as familiarity with specific technologies, perceived benefits, policy 289 awareness, and adoption challenges. Cross-tabulations were applied to compare 290 stakeholder perspectives across different professional sectors. Additionally, mean and 291 standard deviation calculations were used to analyze responses on Likert-scale questions, 292 assessing attitudes toward policy effectiveness, investment challenges, and knowledge 293 dissemination needs. 294 The findings from the quantitative analysis provided a broad overview of key trends in 295 technology adoption and policy perceptions. These insights were cross-referenced with the 296 qualitative findings to ensure that the study’s conclusions were supported by both in-depth 297 expert opinions and a wider range of stakeholder perspectives. 298 3. Results 299 The presentation of results follows the dual structure of our research design, distinguishing 300 between general (cross-cutting) trends observed across stakeholders from the online survey 301 (Section 3.1) and technology-specific insights derived from expert qualitative interviews 302 (Section 3.2). 303 3.1. Cross-Cutting Perspectives on Smart Technology Adoption 304 3.1.1. Geographic Distribution and Professional Sectors of the Online Survey 305 The geographic distribution of online respondents shows a balanced representation from 306 Italy’s major agricultural regions (figure 1), with the highest representation from Emilia 307 Romagna (46%), followed by Puglia (36%), and smaller contributions from Lombardia 308 and Veneto (9% each). This distribution indicates a blend of perspectives from key 309 agricultural areas, offering insights into potential regional variations in technology 310 adoption and policy needs within the smart technologies sector. 311 312 Figure 1: Geographic distribution of stakeholders 313 In terms of professional sectors, the respondents represented a broad spectrum within the 314 agricultural and smart technologies domains (figure 2). Approximately 33.33% of 315 participants were involved in agricultural technology, including roles related to software 316 development and research in precision agriculture. Another 33.33% came from academic 317 backgrounds, emphasizing the importance of research-driven insights in advancing smart 318 technologies solutions. Direct farming operations accounted for 12% of respondents, 319 ensuring representation of the practical, on-ground perspective crucial to understanding 320 adoption barriers. The remaining participants were involved in diverse areas, including 321 professional training, technological transfer, manufacturing, and viticulture. This 322 multifaceted representation highlights the need for cross-sectoral collaboration to create 323 comprehensive and inclusive smart technology adoption policies. 324 46% 36% 9% 9% Emilia Romagna Puglia Lombardia Veneto 325 Figure 2: Professional Sector of the stakeholders 326 327 The level of involvement with specific smart agriculture technologies varied among online 328 respondents (figure 3). Sensor-based technologies emerged as the most familiar, with 329 31.82% of respondents indicating familiarity. Autonomous systems, AI, IoT, and nature-330 based renewable systems each garnered attention from 13%-18% of respondents, reflecting 331 a broad interest in diverse smart agricultural innovations. Novel spectral interface 332 technologies were the least familiar, with only 4.55% of respondents indicating 333 involvement or interest, which could be attributed to limited applications or high 334 implementation costs. 335 33% 33% 12% 22%Agricultural technology companies Academic Research Direct Farming Operations Others 336 Figure 3: Key stakeholders' familiarity with Agritech project innovative technologies 337 338 Online Respondents identified several primary contributions of smart technologies to the 339 agricultural sector (figure 4). The leading perceived benefit was resource waste reduction, 340 cited by 25.81% of participants as a crucial advantage. Closely following was the potential 341 for reducing environmental impact, highlighted by 22.58% of respondents as a key benefit. 342 Improved crop yields were also a prominent contribution, recognized by 19.35% of 343 participants as a fundamental outcome of adopting smart technologies. Enhanced pest, as 344 well as disease detection and increased labor efficiency were both identified as significant 345 benefits, with each selected by 16.13% of respondents. Interestingly, none of the 346 respondents chose the “Others” option, suggesting that the primary contributions listed 347 were comprehensive enough to cover stakeholders’ perceptions of the benefits of smart 348 technologies. 349 18.18% 13.64% 18.18% 31.82% 13.64% 4.55% Autonomous and Robotic Systems IoT Technologies Artificial Intelligence / Machine Learning and Modelling Sensor-based technologies/ Remote Sensing/Geospatial technologies Nature-based/Innovative Renewable Agri-Systems / Water, soil, wastewater, and nutrients reuse / Organic Agriculture Novel Spectral Interface Technologies 350 Figure 4: Contributions of innovative technologies to the agricultural sector, according to key stakeholders 351 352 3.1.2. Policy Awareness and Integration 353 The survey revealed varied levels of policy awareness among respondents. A substantial 354 portion, 50%, expressed uncertainty regarding whether smart agriculture technologies are 355 acknowledged within existing policy frameworks, suggesting a need for clearer 356 communication on policy provisions. In contrast, 37.50% of respondents believed that 357 relevant policies do exist, while 12.50% indicated an absence of any supportive policy. 358 Several specific frameworks were noted among those who confirmed policy awareness, 359 including PAC 2023-27, Agenda 2030, and precision farming policies. Additionally, 360 respondents mentioned partial policy alignment with broader frameworks such as the 361 Green Deal, Farm to Fork, and Soil and Biodiversity Strategies. This feedback highlights 362 a fragmented policy environment where existing frameworks recognize the importance of 363 innovation in agriculture but lack specific support for smart agriculture technologies. 364 19% 26% 16% 16% 23% Improved crop yields Reduction of resource waste Enhanced pest/disease detection Increase in labor efficiency Reduction of environmental impact Survey participants identified significant barriers impacting the adoption of smart 365 agriculture technologies, primarily focusing on high initial investment costs and limited 366 technical knowledge. 45% of respondents cited each of these factors, emphasizing the need 367 for financial strategies and educational initiatives to address these challenges. Additionally, 368 10% of respondents noted limited infrastructure as an obstacle, highlighting the importance 369 of developing robust infrastructure to support connected technologies like IoT. None of the 370 respondents considered regulatory barriers an issue, suggesting that financial and 371 knowledge-based obstacles are the most immediate concerns. These findings imply that 372 while policies supporting smart agriculture technologies exist, they are not tailored to 373 alleviate farmers' specific challenges, particularly small and medium-sized operations with 374 limited capital and expertise. 375 Participants offered a range of recommendations for policy adjustments that could facilitate 376 the adoption of specific smart agriculture technologies. For autonomous and robotic 377 systems, respondents suggested financial incentives, such as non-repayable grants, and the 378 diddemination of broader information to raise awareness. IoT technologies were identified 379 as requiring targeted training programs, while AI and machine learning would benefit from 380 a structured data-sharing framework and technical support to aid users in navigating 381 complex algorithms. Sensor-based technologies require policies that focus on transforming 382 raw data into actionable information, enabling farmers to make informed decisions based 383 on real-time insights. For renewable agri-systems, respondents suggested training vouchers 384 and regulatory adjustments to support organic and sustainable practices. These policy 385 recommendations emphasize the importance of tailoring support mechanisms to the 386 distinct requirements of each smart agriculture technology, thus enhancing both 387 accessibility and usability. 388 Online survey respondents prioritized several key research questions to guide future policy 389 development regarding smart agriculture technologies. Approximately 44.44% of 390 participants identified “How can government policies foster innovation in agriculture?” as 391 the most pressing question, signaling strong interest in government's direct role in driving 392 technological advancements. Equally prioritized was “How can smart agriculture 393 technologies be integrated into the existing agricultural system?” indicating that the 394 practicalities of implementing new technologies within current systems are of critical 395 concern alongside policy considerations. The importance of understanding the impact of 396 existing policies on the adoption of smart agriculture technologies was also noted, with 397 11.11% ranking it as the primary concern and 44.44% ranking it as the second most 398 important concern. Lastly, the collaboration between government and private sector 399 stakeholders was noted as an area for future exploration, even if with lower priority. The 400 diversity of opinions on this question suggests a balanced focus on government-led and 401 collaborative initiatives. 402 The online survey also identified key stakeholders essential to the development of smart 403 agriculture technologies policy, including farmers and academia (each cited by 25% of 404 respondents), smart technologies companies (17.86%), public agencies, and large retailers 405 (14.29% each). This distribution underscores the necessity of engaging diverse participants 406 to create policies that address practical needs, market demands, and technological 407 feasibility. 408 3.2. Technology-Specific Insights 409 The qualitative data gathered from the qualitative expert interviews provide a deeper 410 understanding of stakeholder perspectives on specific smart agriculture technologies, their 411 potential contributions, and the barriers that may hinder their adoption. The insights gained 412 through these interviews underscore the diversity of challenges and recommendations 413 within the smart agriculture technologies domain, offering nuanced perspectives that 414 supplement the survey findings. 415 3.2.1. Perspectives on Robotic Systems 416 Stakeholders frequently highlighted the transformative potential of robotic systems in 417 addressing labor shortages, a pressing issue particularly in labor-intensive areas such as 418 fruit and vegetable production. Robotic technologies allow for precise management of 419 tasks, from field crop monitoring to harvesting, which can significantly improve efficiency 420 while reducing reliance on manual labor. This technological precision supports a shift 421 toward sustainable practices, as robots can optimize resource allocation, minimize wastage, 422 and even carry out tasks with environmental sensitivity in mind. However, stakeholders 423 pointed out that the high costs associated with robotic systems pose substantial barriers to 424 adoption, especially for small and medium-sized farms. The financial outlay required for 425 these technologies and their technical complexity presents a formidable challenge for 426 farmers without specialized knowledge or resources to support this transition. 427 To address these issues, stakeholders suggested targeted financial incentives, such as non-428 repayable grants or tax relief for farms adopting robotic systems. Furthermore, they 429 advocated for broader policy adjustments to ease the learning curve associated with these 430 technologies. Suggestions included on-site training programs, community equipment-431 sharing initiatives, and educational workshops that demystify the use of robotics in 432 farming. From a policy perspective, interviewees indicated that while overarching 433 strategies like the Green Deal and Farm to Fork acknowledge the importance of agricultural 434 innovation, they lack specific provisions to support the adoption of robotics. By expanding 435 precision farming policies to include robotics, policymakers could foster a more 436 comprehensive approach to integrating these technologies into agricultural systems. 437 3.2.2. IoT for Resource Optimization 438 IoT technologies were recognized by stakeholders as essential for optimizing resource use, 439 particularly in water management. By integrating IoT-enabled devices, farmers can collect 440 real-time data on soil moisture, crop health, and environmental conditions, allowing for 441 precise irrigation adjustments that conserve water and reduce costs. Beyond individual 442 farm benefits, stakeholders noted that the data generated by IoT systems could support 443 broader agricultural analytics, improving forecasting and resource management on a 444 regional or even national level (Weersink et al., 2018). 445 Despite these advantages, stakeholders expressed concerns over the cost and 446 interoperability of IoT systems, which can make adoption challenging, particularly for 447 smaller farms. The lack of standardized protocols for data sharing among different IoT 448 devices presents another barrier, as farmers often require an integrated view of data across 449 multiple devices and systems. To address these issues, stakeholders recommended policy 450 interventions to promote data-sharing standards and compatibility protocols to enable 451 seamless integration across IoT platforms. Additionally, they advocated for reducing 452 bureaucratic complexities surrounding IoT implementation, which could encourage more 453 farms to adopt IoT configurations and benefit from their potential efficiencies. 454 3.2.3. Sensor Platforms and Remote Sensing Technologies 455 Sensor technologies, particularly those designed for unmanned or automated 456 configurations, were identified as having significant potential to enhance agricultural 457 efficiency. These technologies allow for precise management of resources like water and 458 nutrients and provide real-time monitoring that supports effective disease control and 459 overall crop health management. For example, by using soil moisture sensors, farmers can 460 optimize irrigation schedules, reducing water use without compromising crop quality. 461 Additionally, the environmental benefits of sensor-based systems are considerable, as they 462 minimize the need for excess inputs, thereby lowering the environmental footprint of 463 agricultural operations. 464 However, stakeholders noted that sensor platforms face barriers similar to those of other 465 advanced technologies, including high installation costs, technical limitations, and the need 466 for specialized training. Furthermore, respondents pointed out that the absence of a unified 467 data platform for sensor integration complicates data interpretation, making it challenging 468 for farmers to convert raw data into actionable insights. To support the adoption of sensor 469 technology, stakeholders suggested policy adjustments that include infrastructure 470 investments, such as broadband expansion to rural areas and establishing public-private 471 partnerships for data platform development. These initiatives could facilitate real-time data 472 aggregation and analysis, allowing farmers to maximize the benefits of sensor platforms 473 for sustainable agriculture. 474 3.2.4. Role of Artificial Intelligence and Machine Learning in Agriculture 475 Artificial Intelligence (AI) and Machine Learning (ML) technologies hold transformative 476 potential for agriculture, enabling real-time analysis and predictive insights that enhance 477 decision-making and resource allocation. AI-driven applications allow farmers to monitor 478 crop health, predict yield outcomes, and optimize input use, making farm management 479 more efficient and responsive. Stakeholders believe that AI could streamline processes 480 across the agricultural value chain, from planning and planting to harvest and market 481 delivery, thereby adding value at each production stage. 482 Despite this promise, AI adoption in agriculture is restricted by several challenges. First, 483 the high costs associated with AI solutions can be prohibitive, particularly for smaller 484 operations. Second, data interoperability presents technical challenges, as different AI 485 applications often require diverse data inputs that may not be readily compatible with each 486 other. Lastly, stakeholders highlighted the complexity of using AI solutions, which often 487 require advanced technical knowledge that may be inaccessible to many farmers. 488 Recommendations for policy interventions included establishing open data systems, which 489 could facilitate data sharing across AI platforms, and government-supported training 490 programs that simplify the use of AI. Additionally, respondents advocated for technical 491 support mechanisms to help farmers navigate AI applications and fully realize their 492 potential benefits. 493 3.2.5. Nature-Based Solutions and Renewable Agriculture 494 Stakeholders emphasized the growing importance of nature-based solutions, such as water 495 and soil reuse, nutrient recycling, and organic farming practices, as essential components 496 of sustainable agriculture. These renewable systems reduce environmental impact by 497 reducing reliance on synthetic inputs and fostering a more balanced relationship between 498 agriculture and the environment. Nature-based solutions promise healthier soils, improved 499 crop resilience, and long-term sustainability, making them an attractive alternative for 500 farmers aiming to minimize their ecological footprint. 501 However, the transition to renewable agri-systems is not without challenges. Stakeholders 502 noted that high initial investment costs, limited expertise, and regulatory inconsistencies 503 are significant barriers. To address these challenges, respondents recommended that 504 policies provide financial incentives, such as subsidies for transitioning to organic farming 505 and grants for infrastructure investments. Training programs focused on sustainable 506 farming practices and more robust certification systems were also suggested to ensure 507 market recognition of organic and nature-based products. By supporting these transitions, 508 policymakers can promote a more sustainable agricultural model that aligns with 509 environmental goals. 510 3.2.6. Novel Spectral Interface Technologies 511 While novel spectral interface technologies, including microwave and THz radiation 512 applications, were less familiar to many respondents, some stakeholders acknowledged 513 their potential for non-invasive agricultural monitoring. These technologies allow for 514 detailed analysis of crop health, soil composition, and other critical indicators without 515 physical contact, which could prove valuable for precision agriculture. However, the 516 application of spectral technologies faces unique challenges, including high costs, safety 517 concerns related to radiation use, and the need for specialized expertise to interpret 518 complex data. 519 Stakeholders recommended targeted policy interventions to address these challenges. 520 Suggestions included funding for research focused on agricultural applications of spectral 521 technologies, safety standards to ensure that radiation use does not pose health risks, and 522 farmer training programs to build competence in spectral data interpretation. Additionally, 523 respondents expressed interest in exploring integrating spectral data with AI, which could 524 improve data analysis and support more efficient agricultural decision-making. 525 4. Discussion 526 The findings of this study reinforce the well-documented potential of smart agriculture 527 technologies to address pressing challenges in the agricultural sector, such as resource 528 efficiency, climate adaptation, and sustainability. These technologies, when the right 529 conditions are met, also play a growing role in building food system resilience by 530 improving productivity and reducing losses, particularly under climate stress, as reported 531 by Gemtou et al., (2024). Despite this potential, adoption remains limited due to financial, 532 technical, and infrastructural constraints. These results align with previous research, which 533 emphasizes that economic barriers and knowledge gaps are among the most significant 534 obstacles to the adoption of technology in agriculture (Basso & Antle, 2020; Finger et al., 535 2019). However, the findings also highlight a critical gap in policy awareness, which has 536 received less attention in the existing literature but emerged as a key concern among 537 stakeholders in this study. 538 One of the particularities of this research lies in its mixed-methods approach, which 539 combines qualitative depth with exploratory quantitative insights. While the number of 540 responses in the survey is modest, the alignment between the survey trends and the 541 interview narratives provides a form of triangulation that enhances the robustness of the 542 results. This integration allowed us to validate emerging patterns, ensuring that the insights 543 are not reliant on a single data source but are reflected across multiple forms of stakeholder 544 engagement (Fetters et al., 2013; Creswell & Plano Clark, 2017). The triangulation design 545 was particularly valuable for assessing the adoption barriers and policy dynamics around 546 smart technologies, where numerical trends were consistently reinforced by expert 547 perspectives. 548 This convergence of evidence across the two methods strengthens confidence in the 549 relevance of the results. One of the most striking of these results is the widespread lack of 550 clarity regarding the role of existing policies in supporting smart agriculture technologies. 551 Many respondents expressed uncertainty about whether current frameworks, such as the 552 Common Agricultural Policy (CAP) 2023–2027, the Green Deal, and Farm to Fork, 553 sufficiently address the specific needs of technological adoption in agriculture. This 554 reflects findings from previous studies indicating that while sustainability and innovation 555 are often mentioned in high-level policies, their implementation at the farm level is often 556 fragmented and unclear (Candel, 2022; Rose et al., 2021). A key implication of this study 557 is that policymakers must improve communication strategies to ensure that farmers, 558 technology developers, and other stakeholders are well-informed about existing policy 559 instruments and funding opportunities. 560 This lack of clarity is also linked to a broader issue of trust and how farmers perceive these 561 policies. For instance, Giampietri et al. (2020) show that trust in intermediaries plays a 562 critical role in adoption of CAP-subsidized risk management tools. Our findings suggest 563 that in the context of smart farming, this trust must extend to digital service providers and 564 data systems, highlighting the need for transparency, digital literacy, and certification 565 mechanisms that can build farmers’ confidence in technological tools. 566 Consistent with earlier research (Long et al., 2016; Weersink et al., 2018), this study also 567 confirms that high initial investment costs remain a fundamental barrier to technology 568 adoption. This is particularly problematic for small and medium-sized farms, which 569 struggle to access capital for automation, AI-driven decision support tools, and IoT-enabled 570 monitoring systems. The exploratory quantitative results highlighted the widespread 571 concern about financial and technical barriers, and likewise, these survey insights were 572 strongly supported by qualitative findings, where experts repeatedly emphasized similar 573 barriers such as high upfront costs, limited access to financial resources, and difficulties 574 accessing technical support. This cross-analysis between survey data and expert interviews 575 strengthens the validity of our observations and highlights the need for targeted policy 576 responses that directly address these barriers. While financial incentives, such as grants, 577 tax credits, and low-interest loans, are already part of some policy frameworks, 578 stakeholders expressed concerns that these incentives are often complex, difficult to access, 579 or insufficient to offset adoption costs. Policymakers should consider simplifying 580 administrative procedures for funding applications and targeting financial assistance 581 toward the most impactful technologies identified in this study, such as sensor-based 582 monitoring, AI-driven decision-making, and precision irrigation systems. 583 Additionally, as reinforced by both datasets, cost-sharing and infrastructure emerged as 584 cross-cutting themes, underscoring their significance regardless of methodological lens. 585 Stakeholders recommended public-private partnerships to support cost-sharing initiatives, 586 particularly for expensive infrastructure investments, such as rural broadband expansion. 587 These findings reinforce recent discussions on the role of co-financing mechanisms and 588 innovation clusters in mitigating the risk associated with technology adoption for farmers 589 (Ehlers et al., 2022). 590 A consistent finding across both data sources was the importance of technical knowledge 591 and training in shaping adoption outcomes, consistent with previous studies (Charatsari & 592 Lioutas, 2013; Lovec et al., 2020). Smart agriculture technologies often require specialized 593 skills, yet many farmers have limited access to training programs that could help them 594 integrate these innovations effectively. Stakeholders emphasized the need for structured, 595 hands-on training initiatives that focus on technology usability, data interpretation, and 596 integration into existing farming systems. 597 This highlights an important policy gap: while some funding exists for technology 598 development, there is often insufficient investment in farmer education and capacity 599 building. Policymakers should consider expanding agricultural extension services to 600 provide in-person training, online courses, and demonstration farms where farmers can 601 experience the benefits of digital agriculture firsthand. Knowledge transfer partnerships 602 between research institutions and farming communities could also play a crucial role in 603 reducing this barrier. This aligns with Menozzi et al. (2023), who emphasize that perceived 604 behavioural control and attitudes are pivotal in shaping adoption decisions, especially when 605 practices are unfamiliar or technically demanding. Similarly, our respondents stressed the 606 difficulty of using AI or IoT platforms, reinforcing the need for support measures that go 607 beyond finance to include training, usability, and peer-to-peer learning networks. 608 The study also highlights infrastructure limitations, particularly concerning internet 609 connectivity in rural areas. Technologies such as IoT-based monitoring, remote sensing, 610 and AI-driven decision support tools rely on high-speed internet and cloud computing, yet 611 many agricultural regions lack the necessary broadband infrastructure. This issue is 612 consistent with prior research, which emphasizes that the digital divide between urban and 613 rural areas is a significant barrier to the diffusion of technology (Ehlers et al., 2022). 614 A broader finding from this study is that smart agriculture policies must be adaptive, 615 responsive, and inclusive. Stakeholders reported that existing policies often fail to 616 differentiate between the needs of different types of farmers, particularly smallholders 617 versus large-scale agribusinesses. One-size-fits-all policy approaches may not be effective 618 in promoting equitable adoption, suggesting the need for targeted support mechanisms. 619 Additionally, stakeholder engagement must be prioritized in policy design and 620 implementation. The findings of the qualitative survey suggest that many policy 621 frameworks lack farmer representation in the decision-making process, leading to 622 misalignment between policy objectives and on-the-ground realities. To improve this, 623 policymakers should, according to the key expert stakeholders, incorporate participatory 624 approaches, such as co-design workshops, multi-actor innovation networks, and regional 625 consultation forums. 626 While this study aims to provide valuable insights into the adoption barriers and policy 627 needs of smart agriculture technologies, using triangulation, combining exploratory survey 628 findings with detailed expert interviews, to provide a balanced and credible approach, in 629 an attempt to make the insights more robust, certain limitations should be acknowledged. 630 The sample size, particularly for the qualitative interviews, was relatively small, which 631 may limit the generalizability of some findings. Additionally, the reliance on self-reported 632 data introduces the possibility of response biases, as participants' perceptions may not 633 always reflect objective realities. However, it is important to note that the study 634 purposefully targeted key stakeholders, namely: policy experts, researchers, and 635 technology developers, identified through a structured stakeholder mapping within the 636 Agritech project. As such, the participants likely represent some of the most informed 637 individuals on smart agriculture policy and technology in Italy, enhancing the relevance 638 and depth of the insights gathered. Future research should explore larger and samples to 639 validate these findings across different agricultural systems and geographic regions. 640 Comparative studies examining policy effectiveness in multiple countries could offer 641 deeper insights into best practices for supporting smart agriculture adoption. 642 5. Conclusion and Policy Implications 643 This study highlights the importance of policy frameworks in facilitating the adoption of 644 smart agriculture technologies while revealing key barriers hindering their widespread 645 implementation. The results emphasize stakeholders' strong optimism regarding these 646 technologies' role in improving agricultural efficiency, sustainability, and resilience. 647 However, the study also identifies three major obstacles: high investment costs, technical 648 knowledge gaps, and inadequate infrastructure, all of which must be addressed through 649 targeted policy interventions. 650 A critical takeaway from this research is the necessity for policy alignment and 651 accessibility. While existing frameworks acknowledge innovation, a disconnect exists 652 between policy provisions and stakeholder awareness. This highlights the need for 653 simplified policy regulations, better communication strategies, and stronger engagement 654 with the farming community. Policies should be designed to be practical, transparent, and 655 adaptable, ensuring that they effectively support farmers and technology adopters in 656 different agricultural settings. 657 Another key implication is the urgent need for financial instruments tailored to the realities 658 of smart agriculture, Such as differences in farm sizes, digital readiness and access to 659 broadband infrastructure, among others. Policies must focus on incentives such as 660 subsidies, tax relief, and low-interest loans to lower the entry barriers for farmers, 661 particularly small and medium-sized operations. At the same time, public-private 662 partnerships should be expanded to create co-financing models that distribute investment 663 risks across multiple stakeholders. 664 The role of education and technical training also emerges as a fundamental aspect of 665 successful adoption. Smart agriculture technologies require specialized skills that many 666 farmers currently lack. To address this, agricultural extension services should integrate 667 digital training programs, on-field demonstration projects, and mentorship initiatives. 668 Collaboration between universities, policymakers, and industry leaders can create 669 structured knowledge-sharing platforms that provide ongoing support to farmers. 670 Finally, this study underscores the importance of an inclusive and adaptive policy-making 671 approach. Engaging diverse stakeholders, from farmers to technology developers and 672 policymakers, is essential for crafting policies grounded in real-world needs. Multi-actor 673 governance structures, such as stakeholder consultation groups, regional innovation hubs, 674 and participatory policy platforms, should be institutionalized to ensure that agricultural 675 policies evolve in tandem with technological advancements. 676 In conclusion, smart agriculture technologies represent a transformative opportunity for the 677 agricultural sector; however, their full potential can only be realized with robust, well-678 coordinated, and forward-thinking policies. Policymakers can accelerate the transition 679 toward a more sustainable, productive, and resilient agricultural system by addressing 680 financial constraints, bridging the knowledge gap, expanding digital infrastructure, and 681 improving stakeholder engagement. Beyond economic and technological advancements, 682 the successful integration of these innovations has profound implications for long-term 683 sustainability and global food security. By improving resource efficiency, reducing 684 environmental degradation, and enhancing adaptive capacity to climate change, smart 685 agriculture technologies contribute to more resilient food systems that can meet the 686 demands of a growing population. However, ensuring equitable access to these 687 technologies is essential to prevent the widening of disparities between large-scale and 688 smallholder farmers. Future policy efforts should focus on fostering inclusive innovation, 689 integrating sustainability goals into technology adoption strategies, and aligning digital 690 agriculture with broader climate and food security policies. By doing so, agricultural 691 technologies can evolve in ways that not only drive economic growth but also ensure 692 environmental sustainability and food system resilience. 693 Acknowledgements 694 This study was carried out within the Agritech National Research Center and received 695 funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI 696 RIPRESA E RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, 697 INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022). This manuscript reflects 698 only the authors’ views and opinions, neither the European Union nor the European 699 Commission can be considered responsible for them. 700 6. References 701 Akimowicz, M., Del Corso, J.P., Gallai, N., and Képhaliacos, C. (2021). Adopt to adapt? 702 Farmers’ varietal innovation adoption in a context of climate change. The case of sunflower 703 hybrids in France. Journal of Cleaner Production, 279. DOI: 704 10.1016/j.jclepro.2020.123654 705 Basso, B. and Antle, J. (2020). 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