Agriculture 4.0: Technological Adoption, Drivers, Benefits and Challenges in Italy. A Descriptive Survey Pacciani, C. a, Catellani, E. a, Bacchetti, A. b, Corbo, C. a, Ciccullo, F. a and Ardolino, M. b a Department of Management Engineering, Politecnico di Milano, Via Lambruschini, 4b 20156 - Milano - Italy (cosimo.pacciani@polimi.it, eleonora.catellani@polimi.it, chiara.corbo@polimi.it, federica.ciccullo@polimi.it) b Department of Mechanical and Industrial Engineering, University of Brescia, Via Brianze, 38 25123 - Brescia - Italy (andrea.bacchetti@unibs.it, marco.ardolino@unibs.it) This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as: Pacciani, C., Catellani, E., Bacchetti, A., Corbo, C., Ciccullo, F. Ardolino, M (2025). Agriculture 4.0: Technological Adoption, Drivers, Benefits and Challenges in Italy. A Descriptive Survey, Bio- Based and Applied Economics, Just Accepted. DOI:10.36253/bae-17382 Abstract: This study aims to examine the current state of awareness regarding Agriculture 4.0 (A4.0) among Italian agricultural enterprises and to analyse variations in adoption levels, expressed needs, perceived benefits, challenges and barriers to digitalisation. Drawing on data from a descriptive survey conducted among Italian farms in 2024, this study presents findings from 1,248 respondents. The results indicate varying levels of adoption of A4.0 solutions, with monitoring systems and connected vehicles being the most widely implemented. The primary drivers for A4.0 adoption include farm management, operational control, and the enhancement of production efficiency, all of which are associated with significant perceived benefits. However, challenges such as limited interoperability and skill shortages hinder A4.0 implementation, while financial and structural constraints remain major barriers for farms seeking to transition to A4.0. This study offers valuable insights to inform policymakers, industry stakeholders, and researchers in fostering a more effective and inclusive digital transformation in the Italian agricultural sector. Keywords: Agriculture 4.0, Smart Farming, Digital Agriculture, Survey JEL codes: M15, O32 1. Introduction 1 2 Agriculture 4.0, also known as “digital agriculture”, “smart farming” or “smart agriculture”, is defined 3 as the integration of advanced digital technologies - such as the Internet of Things (IoT), robotics, 4 Artificial Intelligence (AI), and Big Data analytics - into the agricultural sector (Fragomeli et al., 2024). 5 This concept is grounded in the broader framework of Industry 4.0, which is responsible for the 6 transformation of manufacturing processes (Da Silveira et al., 2021). Agriculture 4.0 (hereby A4.0) 7 represents a significant departure from both traditional and precision agriculture by leveraging 8 automated, interconnected, and data-driven systems (Sharma et al., 2022). 9 The transition to digitalised agricultural systems is increasingly considered as pivotal for addressing the 10 global challenges facing society today. Rapid population growth, urbanization, industrialization, loss of 11 arable land, freshwater scarcity, and environmental degradation have escalated concerns regarding food 12 security (Abbasi et al., 2022). To meet the rising global demand for food, agricultural practitioners must 13 enhance productivity while minimising pressure on natural resources such as water, land, and energy 14 (Sharma et al., 2022). 15 This highlights the urgent need for efficient, data-driven agricultural practices that optimise resource 16 usage and improve productivity (Fragomeli et al., 2024). Moreover, agriculture is both a major 17 contributor to greenhouse gas emissions and a sector vulnerable to the impacts of climate change (Sott 18 et al., 2020). Integrating digital technologies offers the potential to mitigate the environmental footprint 19 of agricultural practices while bolstering farmers' resilience to climate change (Balasundram et al., 20 2023). 21 Technologies such as robotics, smart irrigation and IoT sensors can promote more sustainable 22 agricultural practices by reducing emissions, optimising resource use, and enabling real-time monitoring 23 of crop conditions (Assimakopoulos et al., 2024). The environmental benefits of A4.0 are closely tied to 24 economic advantages, as digital solutions improve operational productivity, reduce resource waste, and 25 generate cost savings (Zul Azlan et al., 2024). Additionally, from a social perspective, the digitalisation 26 of agriculture empowers farmers by providing better decision-making tools and improving working 27 conditions (Zhai et al., 2020). 28 According to Papadopoulos et al. (2024), for instance, recording and mapping technologies, combined 29 with guidance and controlled traffic farming technologies, could lead to reductions of up to 80% in 30 fertiliser use. Furthermore, VRT (Variable Rate Technologies) could achieve a 60% decrease in fertiliser 31 consumption and an 80% reduction in pesticide use, while also potentially boosting yields by 62%. 32 Additionally, robotic systems and smart machines could reduce labour by 97% and diesel consumption 33 by 50%. 34 Thus, A4.0 represents a transformative approach that addresses environmental, economic, and social 35 challenges, contributing to the development of more sustainable and resilient agricultural systems 36 (Maffezzoli et al., 2022b). 37 Despite the promising role that A4.0 solutions could play in mitigating sustainability challenges while 38 improving productivity, their uptake remains limited and fragmented (Osrof et al., 2023). Literature 39 relates the uneven adoption rate to different factors. 40 Recent empirical contributions confirm that farmers’ intentions to adopt new solutions go beyond purely 41 economic considerations and are shaped by a combination of personal attitudes and perceived obstacles. 42 For instance, Giampietri et al. (2020) emphasize the role of farmers’ trust, experience and knowledge in 43 the adoption of risk management practices, highlighting the importance of transparency about costs and 44 benefits in adoption incentivization. Menozzi et al. (2015) highlight the relevance of farmers’ attitudes 45 and perceived control in adopting sustainable farming practices, stressing the need for better 46 communication and collaboration within the agricultural supply chain to increase A4.0 adoption. 47 Meanwhile, data from farm-level surveys show how age, gender, education and farm size remain 48 significant influencing factors for choices regarding, for example, climate change adaptation 49 strategies (Onyenekwe et al., 2023). 50 Despite the ongoing discussions in the literature regarding the factors that favour or hinder the spread of 51 A4.0, the influence of specific contexts, as countries and types of farms and farmers, remains a 52 compelling area of investigation (Fragomeli et al., 2024; Da Silveira et al., 2023). Therefore, the authors 53 emphasise the need for a country-specific investigation on: i) farmers’ awareness of A4.0; ii) the main 54 challenges and barriers in the adoption as well as iii) the sustainability benefits perceived. We believe 55 that building a comprehensive knowledge around the gap between A4.0 technologies, their promised 56 technical advantages and the actual implementation along with the feasibility of realising the related 57 sustainability benefits, is fundamental to inform key decision makers (e.g., policy makers). This 58 knowledge can help in shaping proper strategies which place farmers and their context-specific needs at 59 the centre. 60 To this end, a survey was conducted targeting Italian farms to assess the current level of digitalisation in 61 the agricultural sector, with a specific focus on the key dimensions influencing the adoption and 62 implementation of A4.0 solutions. The following research questions were formulated to explore the 63 state-of-the-art of A4.0 in Italy: 64 - RQ1: What is the level of adoption and awareness of A4.0 solutions in Italy? 65 - RQ2: What are the primary factors driving agricultural enterprises to adopt A4.0 solutions? 66 - RQ3: To what extent have the achieved benefits aligned with the expressed needs? 67 - RQ4: What are the most significant hindering factors to farmers' adoption of A4.0 solutions? 68 This study reveals that, while A4.0 awareness is high, adoption is uneven, with greater uptake of A4.0 69 solutions such as monitoring systems and connected vehicles. Adoption is mainly driven by 70 improvements in farm management rather than operational efficiency. 71 Benefits generally meet or exceed expectations, particularly in optimizing technical inputs and water 72 use, which yield both economic and environmental gains. Social sustainability effects remain debated, 73 with some evidence of labor market benefits, though concerns persist over potential job displacement. 74 Despite the benefits, adoption is hindered by challenges such as interoperability, lack of skills, uncertain 75 return on investments and limited technical support. Financial and structural barriers - especially for 76 small farms - and poor connectivity in remote areas further constrain A4.0 uptake. This study 77 recommends targeted policy support, training, and agrifood supply chains stakeholder collaboration to 78 overcome these barriers and accelerate digital transformation in Italian agriculture. 79 The remainder of the paper outlines as follows: the first section develops a review of the existing 80 literature on main A4.0 solutions and applications along with the factors connected to their spread, 81 section 2 presents a literature review covering the evolution of technologies in agriculture, the main 82 driving technologies and their applications, challenges and benefits. Section 3 explains the methodology 83 adopted, while results are described in section 4. Finally, sections 5 and 6 discuss the main results and 84 draw conclusions from the authors’ work. 85 86 87 88 2. Literature Review 89 2.1 The evolution of agricultural technologies 90 Over the years, agriculture has evolved through distinct technological phases, progressing from 91 Agriculture 1.0 to Agriculture 4.0 (Zhai et al., 2020). Traditional agriculture, Agriculture 1.0, relied 92 heavily on manual labour and animal power. The transition to Agriculture 2.0 began in the 19th century 93 with the introduction of mechanised farming and steam engines, which significantly increased the 94 efficiency of agricultural activities. This second phase was also characterised by an extensive use of 95 chemical fertilizers and pesticides, leading to environmental degradation and resource overexploitation. 96 In the 20th century, Agriculture 3.0 emerged, leveraging advancements in computing and electronics to 97 automate processes and enhance precision, also reducing dependency on chemicals. Today, A4.0 marks 98 the era of smart farming, integrating digital technologies to create highly interconnected and data-driven 99 agricultural systems (Fragomeli et al., 2024). 100 These innovations enable farmers to make real-time, data-informed decisions, improving 101 productivity, sustainability, and resource efficiency while minimising environmental impact. Several 102 terms are used to denote A4.0, such as “digital agriculture”, “smart farming” and “smart agriculture” 103 (Albiero et al., 2020). 104 As outlined by Sponchioni et al. (2019) and Maffezzoli et al. (2022b), Agriculture 4.0 can be defined 105 as the evolution of precision farming, realised through the automated collection, integration, and 106 analysis of data from the field, equipment sensors, and other third-party sources. While precision 107 farming serves as a management system that aims at optimising crop production inputs at the field 108 level (Bongiovanni and Lowenberg-Deboer, 2004; Pierce and Nowak, 1999; Gebbers and Adamchuk, 109 2010), A4.0, facilitated by the smart and digital technologies inherent in Industry 4.0, transforms 110 previously isolated data silos into actionable knowledge, supporting farmers in decision-making both 111 within their enterprises and across the broader agrifood supply chain. This shift from traditional to 112 digital systems ultimately aims to enhance cost efficiency and profitability, fostering the transition to 113 more sustainable agricultural systems from an economic, environmental and social perspective. 114 Recent advancements in A4.0 are marked by emerging trends that are shaping the future of farming, 115 with a particular focus on enhancing efficiency, sustainability, and resilience. A key forthcoming 116 development is the transition toward Agriculture 5.0, which extends the foundations of A4.0 by 117 incorporating human-centric, sustainable, and resilient principles derived from Industry 5.0 (Abbasi 118 et al., 2022). This shift refines the collaboration between humans and machines, aiming to improve 119 efficiency while reducing environmental impact through circular economy strategies (Fragomeli et 120 al., 2024). Alongside this evolution, digital twin technology has gained prominence as a tool for 121 optimising agricultural operations (Peladarinos et al., 2023; Escribà-Gelonch et al., 2024), creating 122 real-time virtual replicas of farms that enable monitoring, predictive analytics, and improved system 123 integration (Polymeni et al., 2023). By simulating real-world agricultural processes, digital twins can 124 support decision-making in areas such as crop growth, soil composition, and climate adaptability 125 (Peladarinos et al., 2023). At the same time, the increasing challenges posed by climate change have 126 accelerated the adoption of climate-smart agricultural (CSA) practices, which focus on building 127 resilience against environmental concerns, reducing greenhouse gas emissions, and ensuring long-128 term food security through adaptive resource management (Balasundram et al., 2023). 129 130 131 2.2 Key technologies and applications 132 There are various ways to categorize the key technologies driving A4.0, as different literature studies 133 highlight several aspects of innovation in the field. Internet of Things (IoT) enables the connection 134 of agricultural devices and machinery, allowing real-time monitoring and automation of farm 135 operations (Assimakopoulos et al., 2024; Abbasi et al., 2022). Sensors and wireless sensor networks 136 collect critical data on soil conditions, weather, and crop health, supporting precision farming (Ahmed 137 et al., 2024). Artificial Intelligence (AI) and Machine Learning process large datasets to optimise 138 resource use, detect plant diseases, and predict yields, making farming more data-driven and efficient 139 (Ahmed et al., 2024; Balyan et al., 2024). AI-driven systems are increasingly capable of autonomous 140 decision-making, on-farm reinforcement learning, and real-time adaptation, significantly 141 transforming how decisions are made at the farm level (Khanna et al., 2024). Robotics and automation 142 include autonomous machines and drones that assist in tasks such as planting, harvesting, and 143 spraying, reducing labour dependency and increasing precision (Ahmed et al., 2024; Balyan et al., 144 2024). Data analytics and Big Data play a crucial role in processing vast amounts of information 145 collected from farms, offering insights for better decision-making (Abbasi et al., 2022). Cloud and 146 edge computing ensure that agricultural data is processed efficiently and securely, reducing latency 147 and enabling real-time responses in smart farming systems (Abbasi et al., 2022). Blockchain 148 technology enhances transparency and traceability in the agricultural supply chain by securely 149 recording transactions and ensuring data integrity (Ahmed et al., 2024). 150 While the technologies discussed above form the foundations of A4.0, they are not typically deployed 151 in isolation. Instead, they are combined into integrated digital solutions, translating technological 152 capabilities into practical tools for farming and therefore addressing different agricultural needs. Such 153 integrated solutions include Decision Support Systems (DSS) (Araujo et al., 2021), monitoring 154 systems (Dayıoğlu and Turker, 2021), mapping solutions (Karunathilake et al., 2023), Variable Rate 155 Technologies (VRT) (Dayıoğlu and Turker, 2021), connected vehicles (Karunathilake et al., 2023), 156 telemetry systems (Papadopoulos et al., 2024), robotics and drones (Araujo et al., 2021). These 157 solutions are further described in the methodology section, where their identification, based on a 158 review of scientific and grey literature, forms a key step of the survey design. Investigating adoption 159 at the solution level, rather than at the level of individual technologies, better reflects how farmers 160 actually implement digital tools in practice. 161 As with key technologies and solutions, the applications of A4.0 have been categorised in different 162 ways, reflecting the broad range of domains in which digital technologies can support agricultural 163 practices. Water and irrigation management involves smart irrigation systems, IoT-based sensors, and 164 climate monitoring tools to optimise water use, ensuring efficient irrigation and drought adaptation 165 (Ahmed et al., 2024; Javaid et al., 2022). Soil and crop health monitoring utilizes remote sensing, 166 drones, and AI-driven diagnostics to assess soil fertility, detect diseases, and manage agrochemical 167 and fertilizer use with precision (Yousaf et al., 2023). Predictive analytics for climate adaptation and 168 yield forecasting apply Machine Learning and Big Data analytics to anticipate weather patterns, pest 169 outbreaks, and crop productivity, helping farmers make data-driven decisions to mitigate risks 170 (Kumar Kasera et al., 2024). Autonomous machinery and robotics enhance efficiency by using 171 automated tractors, drones, and harvesting robots to perform tasks such as soil preparation, planting, 172 and harvesting with minimal human intervention (Oliveira et al., 2021). Controlled-environment 173 agriculture includes greenhouse cultivation, hydroponics, and aquaponics, which optimise growing 174 conditions and reduce dependency on natural weather cycles, ensuring year-round food production 175 (Maffezzoli et al., 2022b). Livestock monitoring and regulation employs wearable sensors, automated 176 feeding systems, and AI-based health tracking to improve animal welfare, optimise breeding, and 177 prevent diseases (Ahmed et al., 2024). Finally, supply chain optimisation focuses on product tracking, 178 storage management, and food processing, incorporating blockchain and automation to enhance 179 traceability, reduce waste, and streamline logistics from farm to consumer (Kumar Kasera et al., 180 2024). 181 To summarise, these technological solutions, applied in a diverse range of domains, can result in a 182 set of improvements for farmers. Such improvements, later investigated through a survey, encompass 183 different dimensions. A4.0 solutions can support farmers with improved forecasting capabilities and 184 improved farm management and control; support planning and scheduling activities, while also 185 facilitating and streamlining workforce processes; optimise the use of technical inputs (water, 186 pesticides, fertilizers), enhance efficiency and reduce losses due to pests and diseases. Finally, 187 through monitoring and measurement, they enable increased awareness on farm operations and 188 improve the quality of agricultural products. 189 All these enhancements can lead to substantial economic, environmental and social benefits. 190 2.3 Sustainability benefits 191 A4.0 yields significant economic, social, and environmental benefits, thereby fostering a profound 192 transformation of the agricultural sector. Economically, it enhances resource use efficiency by 193 optimising the application of water, fertilizers, and pesticides, reducing waste, and increasing 194 agricultural yields. This leads to greater profitability for farmers and more cost-effective farming 195 practices (Zul Azlan et al., 2023; Abbasi et al., 2022). Additionally, the automation and digitalisation 196 of farm operations, such as harvesting, sowing, and irrigation, result in time and cost savings, 197 improving operational efficiency (Pradel et al., 2022). The introduction of predictive models and real-198 time data analysis can help farmers forecast adverse conditions like disease outbreaks or extreme 199 weather, thereby improving the resilience of agricultural systems and ensuring production even in 200 challenging circumstances (Zul Azlan et al., 2023). From an environmental standpoint, smart farming 201 practices significantly reduce agriculture’s ecological footprint. Precision agriculture technologies, 202 AI-driven crop monitoring, and automated machinery facilitate the efficient use of resources, leading 203 to reduced fuel consumption, lower greenhouse gas emissions, and improved water conservation 204 (Cambra Baseca et al., 2019). Moreover, the deployment of technologies such as drones and IoT-205 based environmental monitoring systems supports soil health management, optimises nutrient use 206 efficiency, and strengthens climate resilience (Abbasi et al., 2022). By minimising waste and 207 promoting environmentally responsible practices, A4.0 emerges as a key driver of sustainable 208 agricultural development (Zul Azlan et al., 2023). 209 From a social perspective, A4.0 plays a crucial role in enhancing the well-being of rural communities, 210 agricultural workers, and consumers. By promoting more efficient and sustainable farming practices, 211 A4.0 strengthens food security, mitigates food shortages, and reduces waste (Jin et al., 2020). 212 Furthermore, the integration of advanced technologies equips farmers with improved decision-213 making tools, contributing to higher living standards by lowering labour costs and enhancing working 214 conditions (Da Silveira et al., 2021). Additionally, A4.0 enhances product quality and traceability, 215 ensuring food safety and addressing consumer concerns (Zul Azlan et al., 2023). The integration of 216 advanced digital monitoring technologies can in fact support the verification of environmental and 217 social standards along the food supply chain (Meemken et al., 2024). These systems not only 218 strengthen sustainability management but also offer new avenues for accountability and trust in food 219 systems. However, they further raise important questions about equity and data access, which merit 220 further attention as digital monitoring expands (Meemken et al., 2024). Despite such promising social 221 benefits, scholars have also drawn attention to the danger of overly optimist narratives that see these 222 innovations as universal solutions. Klerkx et al. (2020) emphasize the need to account for the social 223 and ethical implications of A4.0 transitions, particularly in terms of labor displacement, rural 224 depopulation, power concentration, and the marginalization of alternative, potentially more 225 accessible technologies. 226 In fact, while A4.0 promises numerous benefits, its impacts are not unilaterally positive. Muhl et al. 227 (2022) stress how digital agriculture may reinforce existing inequalities and that social issues like 228 food insecurity, often driven by broader social injustices, will not be solved by technological 229 development alone. The sustainability debate thus calls for an inclusive and responsible approach to 230 the use and development of these technologies, ensuring accessibility across different contexts (Muhl 231 et al., 2022). 232 2.4 Challenges and barriers 233 The adoption of A4.0 technologies is hindered by a range of significant challenges and barriers, as 234 highlighted by (Assimakopoulos et al., 2024, Da Silveira et al., 2021; Da Silveira et al., 2023; 235 Fragomeli et al., 2024). An interesting classification of challenges is provided by Lezoche et al. 236 (2020), where a distinction is made between organizational, social and technological challenges. 237 Among organization challenges, one of the most frequently associated with A4.0 adoption is the high 238 cost connected to the technology adoption, including the initial investment required for the 239 implementation of the components, the ongoing maintenance costs, and the cost of skilled labour (Da 240 Silveira et al., 2023). These financial challenges are particularly burdensome for small-scale farms, 241 which often lack the necessary capital or access to financing options to invest in such innovations 242 (Assimakopoulos et al., 2024). Additionally, from a more social perspective, the complexity of 243 modern agricultural technologies and the advanced skills required for their operation present further 244 obstacles (Fragomeli et al., 2024). These barriers are not unique to the Italian context; similar 245 challenges have been widely observed in other regions, particularly among smallholder farmers. For 246 instance, Mhlanga et al. (2023) highlight the digital transformation obstacles in African agriculture, 247 where factors such as limited infrastructure, insufficient digital literacy, lack of funding mechanisms, 248 and farmer resistance significantly constrain adoption. In general, farmers with limited technological 249 proficiency - especially older individuals or those with lower levels of formal education - may 250 struggle to integrate digital tools into their daily operations (Assimakopoulos et al., 2024). It can be 251 stated that, beyond costs, adoption is shaped by a complex interaction of operator characteristics (such 252 as age, education, and digital skills), farm-level attributes (including size, income, and specialization), 253 and the perceived attributes of the technologies themselves - such as their trialability, ease of 254 integration, and perceived utility (Khanna et al., 2024). 255 From an organizational perspective, uncertain regulatory aspects and complex legal frameworks often 256 hinder adoption (Lezoche et al., 2020), highlighting the role of manufacturers and governmental 257 bodies as critical in mitigating these challenges. 258 Looking at technological challenges, a further barrier is often recognized in inadequate 259 infrastructures, particularly in rural areas, where poor internet connectivity and restricted access to 260 technical support networks hinder the full utilization of digital technologies (Da Silveira et al., 2023; 261 Fragomeli et al., 2024). Moreover, farmers already managing extensive daily responsibilities may 262 perceive these new technologies as overly time-consuming or complex, together with concerns about 263 lack of interoperability and issues about data security and privacy (Lezoche et al., 2020). Moreover, 264 many farmers report a lack of accessible training programs, technical guidance, and support services, 265 which prevents them from fully understanding and implementing digital tools (Da Silveira et al., 266 2023). 267 These financial, educational, infrastructural, and institutional barriers underscore the multifaceted 268 challenges associated with adopting A4.0 technologies. Addressing these issues through targeted 269 policies, improved infrastructure, and comprehensive training initiatives is essential for promoting 270 widespread and equitable adoption of digital farming solutions. 271 272 3. Research Methodology 273 274 The primary objective of this research is to assess the current state of digitalisation within the Italian 275 agricultural sector, with a specific focus on different key dimensions that shape the adoption and 276 implementation of A4.0 technologies. To evaluate the state-of-the-art of A4.0 in Italy, the following 277 research questions have been formulated: 278 - RQ1: What is the level of adoption and awareness of A4.0 solutions in Italy? 279 - RQ2: What are the primary factors driving agricultural enterprises to adopt A4.0 solutions? 280 - RQ3: To what extent have the achieved benefits aligned with the expressed needs? 281 - RQ4: What are the most significant hindering factors to farmers' adoption of A4.0 solutions? 282 To address these research questions, the study examines the following dimensions: 283 Adoption and awareness of A4.0 solutions: assessing the extent to which identified A4.0 solutions have 284 been implemented across the sector and the level of awareness that Italian farms have regarding these 285 technologies. 286 Drivers of digitalisation: identifying the factors motivating farms to explore and implement the proposed 287 A4.0 solutions, highlighting key needs and expectations. 288 Benefits achieved: evaluating the advantages achieved through the adoption of A4.0 solutions with 289 regards to the specific needs expressed. 290 Challenges encountered by farmers adopting A4.0 technologies: examining obstacles that farms 291 encountered during the adoption and implementation process of A4.0 solutions. 292 Inhibiting factors for non-adopting farmers: investigating the underlying reasons for the hesitation or 293 inability of non-user farmers to adopt A4.0 solutions. 294 The last two categories are drawn from the literature on "challenges and barriers", which typically does 295 not distinguish between adopters and non-adopters. However, based on the authors' experience and 296 discussions with farmers and technology providers, this distinction was deemed necessary to better 297 reflect the obstacles faced by Italian agricultural enterprises in uptaking and using A4.0 solutions. 298 To address these objectives systematically, the research followed a structured methodology comprising 299 the following steps: 300 Sample development. The research referenced data from the 7th General Census of Agriculture of the 301 Italian National Institute of Statistics (ISTAT)1 to identify a representative sample of Italian agricultural 302 enterprises. The sampling framework accounted for critical variables, including farm size, production 303 type, and geographic distribution, ensuring a diverse and comprehensive representation of the Italian 304 agricultural sector. The sample was drawn from three perspectives: (1) geographic distribution: Italian 305 farms were grouped in four main regions to capture macro-regional variations in farms geographical 306 distribution (Table 1). (2) Primary crop production: Italian farms have been classified based on their 307 primary agricultural products, determined by the proportion of Utilised Agricultural Area (UAA) 308 allocated to specific cultivations (Table 2). (3) Farm size: Italian farms have been categorised according 309 to their UAA size, allowing for an analysis of adoption patterns by operational scale (Table 3). A 310 proportionate stratified random sampling approach was employed, whereby the total population, as 311 defined by ISTAT, was divided into mutually exclusive strata. Each stratum was sampled in proportion 312 to its representation within the overall population. Within each stratum, participants were selected using 313 a random sampling method. 314 315 Identification of a set of A4.0 solutions. A tailored set of A4.0 solutions was developed in alignment with 316 the operational characteristics of the agricultural sector based on an analysis of scientific and grey 317 literature on this topic (Araújo et al., 2021; Dayıoğlu and Turker, 2021; Karunathilake et al., 2023; 318 Papadopoulos et al., 2024). This set comprises the following A4.0 solutions: 319 1 https://www.istat.it/statistiche-per-temi/censimenti/agricoltura/7-censimento-generale/ https://www.istat.it/statistiche-per-temi/censimenti/agricoltura/7-censimento-generale/ DSS – Decision Support Systems, that assist farmers in decision-making by optimising management and 320 agronomic choices based on field data, environmental, weather and soil data, and information provided 321 by the farmer. These systems can directly provide both management and agronomic advice to the users. 322 Monitoring systems, enabling the monitoring, often remotely and automatically, of environmental 323 conditions or other parameters related to crops. 324 Mapping solutions, allowing the mapping of soil and crops, providing spatial variability in soil, crop, 325 and hydrological characteristics, among others. These spatialised datasets can be used for various 326 purposes such as variable rate input applications, agronomic decision-making support, and operational 327 management. 328 Variable Rate Technology (VRT) solutions that enable field operations and the distribution of inputs 329 based on the spatial variability detected in the field and the needs of the soil and crop systems. 330 Connected vehicles, i.e. digitally connected machinery that is equipped with integrated digital 331 technologies, such as assisted driving, precision navigation systems, and auto-steering systems. 332 Telemetry systems and solutions for vehicle and equipment monitoring, that can locate, monitor and 333 provide assisted control of agricultural machinery, including auto-steering systems and telematic 334 solutions for fleet monitoring, predictive maintenance, and machinery efficiency improvement. 335 Robotics, i.e. solutions involving autonomous machinery capable of movement, decision-making, and 336 performing specific tasks and crop operations with little or no operator intervention. 337 Drones, i.e. solutions and services involving the use of drones for mapping crops and land through 338 cameras and sensors, monitoring crop health, and applying products or biological control agents. 339 For the purpose of this research, Farm Management Information Systems (FMIS) have been excluded 340 from the analysis, as they are classified as enabling technologies rather than core components of the 341 A4.0 paradigm. As Industry 4.0 evolves and digital technologies continue to expand and mature into 342 practical solutions for farmers, it becomes crucial to distinguish between core components of the 343 paradigm and enabling technologies. While enabling technologies play a vital role in supporting A4.0, 344 they are considered complementary rather than fundamental elements of the paradigm itself. 345 Survey design and implementation. An online survey was developed and distributed targeting farms 346 identified through the sampling framework. The online format was chosen for its cost-efficiency, ease 347 of administration, and ability to minimise errors associated with manual data collection, as also reported 348 by van Selm and Jankowski (2006) and Maffezzoli et al. (2022a). 349 This survey consisted of seven sections: 350 1. General information, collecting foundational and demographic details about the respondent and 351 their agricultural enterprise. 352 2. A4.0 awareness and implementation, assessing the level of familiarity and the extent of adoption 353 of the proposed set of A4.0 solutions. 354 3. Needs, benefits, and challenges, exploring the specific needs driving the adoption of A4.0 355 solutions, the benefits achieved, and the challenges encountered during their implementation. 356 4. Data management capabilities, evaluating the farms’ ability to collect, store, analyse, and utilize 357 data effectively to inform decision-making processes. 358 5. Digital skills, assessing the competences and level of expertise of farm operators in relation to 359 A4.0 solutions. 360 6. Investments, reviewing past investments, current expenditures, and anticipated future 361 investments in A4.0 solutions. 362 7. Inhibiting factors, identifying the barriers and constraints preventing or limiting the adoption of 363 A4.0 solutions. 364 The full set of questions included in each section of the survey is provided in Appendix A, located at the 365 end of this manuscript. 366 367 Data collection. Data collection was conducted from September 2024 to December 2024. The process 368 yielded a total of 1,248 valid responses, providing a robust dataset for detailed analysis. Tables 1, 2 and 369 3 report the sample of responses collected according to the critical sampling variables and table 4 370 provides a summary of the main descriptive statistics on collected data. 371 372 Table 1. Total population and sample size and their distribution across Italian regions (number of farms) 373 Pop. size distr. Pop. size distr. (%) Sample size distr. Sample size distr. (%) North-west 113,972 10% 304 24% North-east 187,429 16% 319 26% Centre 179,230 16% 328 26% South and Islands 652,392 58% 297 24% Total 1,133,023 100% 1,248 100% 374 Table 2. Total population and sample size and their distribution across primary crop productions (UAA - Utilised Agricultural Area) 375 Pop. size distr. Pop. size distr. (%) Sample size distr. Sample size distr. (%) Cereal crops 3,054,288 34% 31,923 19% Vineyards 742,926 8% 92,693 56% Fruit crops 444,805 5% 7,310 4% Fodder crops 2,564,217 28% 3,469 2% Olive cultivation 1,114,593 12% 4,723 3% Vegetable crops 445,966 5% 4,490 3% Legumes 85,007 1% 132 0.1% Citrus fruits 149,863 2% 21,353 13% Industrial plants 477,091 5% 562 0.3% Total 9,078,756 100% 166,655 100% 376 Table 3. Total population and sample size and their distribution across farms’ size (number of farms) 377 Pop. size distr. Pop. size distr. (%) Sample size distr. Sample size distr. (%) 0 hectares 12,499 1% 23 1% Up to 0.99 hectares 228,481 20% 19 2% From 1 to 1.99 hectares 209,662 18% 61 5% From 2 to 2.99 hectares 128,381 11% 55 4% From 3 to 4.99 hectares 147,320 13% 123 10% From 5 to 9.99 hectares 160,133 14% 209 17% From 10 to 19.99 hectares 109,545 10% 262 21% From 20 to 29.99 hectares 45,118 4% 104 8% From 30 to 49.99 hectares 41,167 4% 109 9% From 50 to 99.99 hectares 32,487 3% 120 10% From 100 onwards 18,230 2% 163 13% Total 1,133,023 100% 1,248 100% The tables presented above highlight a discrepancy between the sample distribution and that of the 378 overall population, resulting in an overrepresentation of farms located in Northern Italy and an 379 underrepresentation of those in the South and Islands. This imbalance may introduce a geographical 380 bias into the analysis. Moreover, the average Utilised Agricultural Area (UAA) of the sampled farms, 381 amounting to 22 hectares, is notably higher than the national average of 11.1 hectares reported by 382 ISTAT2, indicating a sample skewed toward more structured and capital-intensive farming 383 operations. The sample also includes a disproportionately large share of vineyard farms, a sector 384 typically associated with higher profitability and investment capacity, which may further influence 385 the study's results. 386 However, these deviations do not necessarily compromise the validity of the findings. The research 387 primarily aims to investigate the adoption and perceived benefits of A4.0 solutions, an area where 388 more structured and technologically advanced farms are expected to play a pioneering role (Giua, 389 2022). Consequently, focusing on more innovative and capitalised enterprises allows for a more 390 detailed understanding of current trends, challenges, and potential impacts, which can serve as a 391 reference point for the broader agricultural sector as it transitions toward digitalisation. 392 Prior to presenting the results of the survey data analysis, the authors provide a table outlining the key 393 descriptive statistics of the collected dataset. 394 395 Table 4. Summary of main descriptive statistics of survey collected data 396 Unit Mean Median Std Min Max Farm size Ha 22.21 38.50 1,718.2 1 0 40,000 Farm annual turnover EUR Less than €50,000 share 0.35 Between €50,000 and €250,000 share 0.38 Between €250,000 and share 0.12 2 https://www.istat.it/it/files/2022/06/censimento_agricoltura_gismondi.pdf https://www.istat.it/it/files/2022/06/censimento_agricoltura_gismondi.pdf €500,000 Between €500,000 and €1,000,000 share 0.06 Over €1,000,000 share 0.09 Employees in farm no. 3.69 11.75 6.60 0 950 A4.0 solutions adopted in farm no. 2.68 4.00 1.65 0 8 Total amount spent on A4.0 solutions by farm EUR Less than €5,000 share 0.23 Between €5,000 and €15,000 share 0.17 Between €15,000 and €30,000 share 0.13 Between €30,000 and €50,000 share 0.09 Between €50,000 and €75,000 share 0.08 Between €75,000 and €100,000 share 0.07 More than €100,000 share 0.23 397 The descriptive statistics in the table above highlight that farm size distribution is skewed. This 398 asymmetry is commonly observed across many countries, as both very large and very small farms 399 coexist, often with significantly different spending capacities, as also noted by the OECD (Bokusheva 400 and Kimura, 2016). 401 402 4. Results 403 404 4.1 A4.0 awareness and adoption level 405 The initial findings of this analysis focus on the current levels of adoption of A4.0 solutions among 406 survey respondents. A summary of these results is presented in Figure 1. To assess awareness of A4.0 407 solutions, a four-point scale was employed, ranging from low to high familiarity, following the approach 408 outlined by Maffezzoli et al (2022a). This scale effectively distinguishes varying levels of awareness 409 and facilitates cross-tabulation, allowing for the identification of patterns across different respondent 410 groups. The four levels of awareness are defined as follows: (a) Unknown, representing a complete lack 411 of familiarity, indicating no awareness of the existence of the proposed solution; (b) Known, denoting 412 limited familiarity and implying that the respondent has heard of the solution, but possesses only a 413 superficial understanding; (c) Used in the past, not anymore, indicating theoretical familiarity and 414 suggesting that the respondent has a solid understanding of the solution despite no longer using it; and 415 (d) In use, representing practical familiarity, meaning the respondent not only knows about the solution, 416 but also employs it. 417 Figure 1. Agriculture 4.0 awareness level. Sample: 1,248 respondents 418 419 420 The data reveal varying levels of adoption and awareness of A4.0 solutions. Key findings show that 421 approximately 26% of respondents currently implement monitoring systems and connected vehicles, 422 making these among the most widely adopted A4.0 solutions. Meanwhile, 20% of respondents adopted 423 mapping solutions and 19% employed telemetry systems and solutions for vehicle and equipment 424 monitoring. 425 Adoption rates for Decision Support Systems (DSS) and Variable Rate Technology (VRT) solutions are 426 notably lower, at 7% and 6% respectively. Robotics and drones show the lowest adoption rate, standing 427 at only 3%, likely due to constraints related to cost, technical expertise, or perceived necessity. 428 Disaggregated data by farm size reveal that only 23% of farms with less than 10 hectares of UAA 429 have adopted at least one A4.0 solution. Similarly, among farms with annual revenues below EUR 430 50,000, the adoption rate stands at 21%. However, adoption increases substantially with scale: 66% 431 of farms with a UAA between 100 and 199.9 hectares have adopted A4.0 technologies, and this figure 432 rises to 82% for farms exceeding 200 hectares. A comparable trend is evident with respect to 433 economic size, where adoption reaches 74% among farms with annual revenues between EUR 434 500,000 and EUR 1,000,000, and rises further to 81% for those with revenues above EUR 1,000,000. 435 In contrast, our findings do not reveal substantial differences in A4.0 adoption based on the age or 436 education level of farm managers. The only exception is among managers over the age of 65, who 437 show a lower adoption rate (30%). Similarly, post-graduate degree holders are the only educational 438 group exhibiting higher-than-average adoption rates (48%). 439 With regard to agricultural production types, enterprises primarily engaged in cereal cultivation report 440 higher adoption rates of A4.0 solutions (49%), alongside vineyard and fodder farms (both at 43%). 441 The relatively higher A4.0 adoption among cereal and fodder producers can be attributed to the 442 extensive nature of these cropping systems, which can be particularly well-suited to the application 443 of A4.0 solutions in optimizing operations over wide areas. Conversely, vineyard enterprises, 444 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Robotics Drones Variable Rate Technology (VRT) solutions Decision Support Systems (DSS) Telemetry systems and solutions for vehicle and equipment monitoring Mapping solutions Connected vehicles Monitoring systems In use Used in the past, not anymore Known Unknown typically characterized by higher revenue margins, tend to possess greater financial capacity to invest 445 in innovation, thereby facilitating the uptake of digital solutions and innovative technologies. 446 447 4.2 Needs expressed and benefits perceived from A4.0 implementation 448 449 To comprehensively analyse the key drivers that motivated respondents to adopt and implement A4.0 450 solutions, this study focuses on the specific needs expressed by farmers. These needs reflect both 451 strategic and operational priorities, ranging from farm management and control to the optimisation of 452 input consumption. 453 Figure 2 reveals a substantial level of awareness among respondents regarding the broad and 454 multifaceted nature of the A4.0 paradigm. Rather than being perceived merely as an extension of 455 precision agriculture - whose primary goal is to deploy technological solutions in the field to optimise 456 input consumption and reduce costs - A4.0 appears to be increasingly recognised as a comprehensive 457 framework for enhancing overall farm management and control, with positive effects along the overall 458 agrifood supply-chain. This paradigm shift suggests that farmers view A4.0 not only to refine specific 459 agricultural practices, but also as an integral component in fostering a more efficient and data-driven 460 agricultural enterprise. 461 Among the ten most frequently expressed needs related to farm management and control, the most 462 prominent include enhancing forecasting capabilities (41%), particularly in areas such as disease 463 outbreaks, crop requirements, plant growth and yield projections, improving control and management 464 processes within the farm enterprise (38%) with a focus on better decision-making and operational 465 efficiency, optimising the planning and scheduling of agricultural activities (32%) and increasing 466 awareness of ongoing farm activities and operations (31%). Similarly, in relation to the optimisation of 467 input consumption, respondents identified key areas where A4.0 solutions could bring significant 468 improvements, including optimising the use of technical inputs such as fertilisers and agrochemicals 469 (28%) and enhancing the efficiency of machinery and equipment utilisation (26%), contributing to both 470 cost reductions and operational sustainability. Furthermore, respondents expressed the need to 471 streamline and optimise workforce processes (26%), ensuring that operators can perform tasks with 472 efficiency and effectiveness, and to maximize water-use efficiency (24%), which is particularly critical 473 in the context of recent meteorological events in Italy: in 2024, the country experienced heavy rainfall 474 in the northern regions, while facing severe droughts in the south3. 475 Furthermore, respondents reported adopting A4.0 solutions for additional objectives, including reducing 476 losses due to diseases, pests, and infestations (35%), a critical aspect of maintaining both yield stability 477 and crop health, and improving the quality of the final agricultural product (20%) to meet regulatory 478 requirements. 479 Figure 2. Needs expressed by respondents. Sample: 511 respondents who adopted at least one of the proposed Agriculture 4.0 solutions. Respondents could 480 choose a maximum of 5 options. 481 3 Agro-meteorological Monitoring INDices (AgroMIND) map on Agricultural Drought (SPEI6) (https://wonderful-bush- 09061f403.5.azurestaticapps.net/AgroMIND.html) https://wonderful-bush-09061f403.5.azurestaticapps.net/AgroMIND.html https://wonderful-bush-09061f403.5.azurestaticapps.net/AgroMIND.html 482 483 Figure 3 illustrates the perceived benefits derived from the adoption of A4.0 solutions, as evaluated in 484 relation to the specific needs previously expressed by respondents. The findings indicate that, on average, 485 the implementation of A4.0 technologies resulted in outcomes that aligned with initial expectations for 486 most adopters (74% on average). Additionally, a subset of respondents (8% on average) reported that 487 the benefits they experienced exceeded their initial expectations. 488 These results suggest that most farmers who invested in A4.0 solutions perceived their adoption as a 489 successful means of addressing their agricultural needs, with reported benefits generally meeting 490 anticipated outcomes. However, a smaller proportion of respondents indicated that the benefits they 491 obtained were either below their expectations (14% on average) or entirely absent (4% on average), 492 highlighting potential limitations in implementation effectiveness, technology adoption challenges, or 493 contextual constraints that may have hindered the full realisation of expected advantages. 494 Furthermore, the analysis reveals that the perceived benefits were more pronounced in activities related 495 to the optimisation of input consumption compared to those associated with farm management and 496 control. Specifically, an average of 11% of respondents reported experiencing benefits that exceeded 497 their expectations in the domain of input consumption optimisation. In contrast, only an average of 4% 498 of respondents indicated that benefits surpassed expectations for farm management and control 499 activities. This suggests that A4.0 solutions may be particularly effective in enhancing input efficiency, 500 resource utilisation, and operational streamlining, whereas their impact on broader management and 501 control functions may be more variable or dependent on additional contextual factors. 502 503 0% 5% 10% 15% 20% 25% 30% 35% 40% 45% Improve the quality of the final agricultural product Maximize water-usage efficiency Streamline and optimize worforce processes Enhance the efficiency of machinery and equipment Optimize the use of technical inputs Increase awareness on farm activities and operations Optimize the planning and scheduling of activities Reduce losses due to diseases, pests, and infestations Improve farm control and management processes Enhance forecasting capabilities Figure 3. Benefits perceived by respondents. Sample: 511 respondents who adopted at least one of the proposed Agriculture 4.0 solutions 504 505 506 Moreover, Italian farmers who have already adopted A4.0 solutions exhibit a significantly higher 507 propensity to invest further in these technologies compared to non-adopters. Specifically, 20% of current 508 users reported their intention to invest more than EUR 50,000 in A4.0 technologies within the next year, 509 whereas only 3% of non-users indicated an equivalent investment plan. Furthermore, 27% of adopters 510 expected to allocate between EUR 5,000 and EUR 30,000, compared to just 18% among non-adopters. 511 Notably, 55% of non-users were unable to estimate their future investments, in contrast to only 26% of 512 current users. These findings suggest that A4.0 adopters, having already perceived benefits (often 513 exceeding expectations) are more inclined to pursue further technological advancement and exhibit a 514 clearer strategic orientation toward digital transformation. 515 516 4.3 A4.0 implementation challenges and factors inhibiting A4.0 adoption 517 518 This study also aims to assess the challenges encountered by respondents who have adopted at least one 519 of the proposed A4.0 solutions, as well as the barriers faced by those who either could not or chose not 520 to adopt any of these solutions. 521 Figure 4 presents the challenges encountered by farms that have implemented A4.0 solutions. The 522 findings indicate that one of the most significant issues - reported by 36% of respondents - is limited or 523 non-existent interoperability among the adopted solutions. Many farmers, indeed, struggle with 524 integrating different digital tools within their existing farm management systems, leading to 525 inefficiencies and operational difficulties (Khanna et al., 2024). 526 Following interoperability concerns, other notable challenges include the lack of appropriate skills to 527 effectively utilise A4.0 solutions (30%) and the perceived inadequacy of return on investment (26%), 528 suggesting that respondents may not see immediate or sufficient financial benefits from their A4.0 529 investments, potentially discouraging further technological adoption. Furthermore, 26% of respondents 530 indicate insufficient or unreliable technical assistance, which further limits A4.0 effectiveness together 531 with operational challenges (20%) and inadequate connectivity (16%). 532 Interestingly, only 6% of respondents reported that they did not face any challenges during their A4.0 533 implementation. This finding suggests that most adopters have encountered at least some difficulties in 534 integrating and implementing A4.0 solutions, emphasising the need for targeted interventions to enhance 535 0% 20% 40% 60% 80% 100% Improve the quality of the final agricultural product Maximize water-usage efficiency Streamline and optimize worforce processes Enhance the efficiency of machinery and equipment Optimize the use of technical inputs Increase awareness on farm activities and operations Optimize the planning and scheduling of activities Reduce losses due to diseases, pests, and infestations Improve farm control and management processes Enhance forecasting capabilities Above the expectations In line with the expectations Below the expectations None system compatibility, improve user experience and provide better support mechanisms for farmers 536 transitioning to digital technologies. 537 538 Figure 4. Challenges encountered by respondents. Sample: 511 respondents who adopted at least one of the proposed Agriculture 4.0 solutions. 539 Respondents could choose more than one option. 540 541 542 Figure 5 illustrates the key barriers that have prevented farms from adopting A4.0 technologies. One of 543 the most frequently cited limitations is farm size, with 68% of respondents indicating that their farms 544 are too small to justify investment in A4.0 solutions. This is not surprising, as Table 3 shows that in the 545 Italian context, most farms (77%) are small or medium-sized. 546 Further constraints concern the possible exploitation of A4.0 solutions, with 59% of respondents 547 believing that they would not fully exploit these solutions and 50% stating that their current agricultural 548 technologies and management practices adequately meet their business needs, thereby reducing the 549 perceived necessity of implementing A4.0 solutions. 550 Financial concerns also play a significant role, as 45% of respondents believe that the anticipated benefits 551 do not justify the required investment, while 41% struggle to see the potential economic advantages of 552 incorporating digital tools into their operations. Additionally, financial constraints further limit adoption, 553 with 38% of respondents citing their inability to spread investment costs over time and 36% highlighting 554 the difficulty of sharing these costs across multiple enterprises. Bureaucratic challenges also emerge as 555 a deterrent, as 36% of respondents report difficulties in accessing financial incentives due to stringent 556 requirements and administrative burdens, while 22% point to restricted access to credit lines as a further 557 impediment. 558 While digital skills were identified as a notable challenge among those who have already adopted A4.0 559 solutions, they appear to be a less pressing concern for non-adopters: only 25% of respondents cited a 560 lack of necessary competencies as a barrier, while an equal proportion stated that their collaborators also 561 lacked the required skills. Such discrepancy in how digital skills are perceived between adopters and 562 non-adopters reflects an experience gap in A4.0 implementation: non-adopters seem to not yet 563 acknowledge the digital skills challenge because they have not engaged with A4.0 deeply enough, 564 whereas adopters have firsthand knowledge of the difficulties and their impact on agricultural activities. 565 Furthermore, 24% of non-adopters indicated that they did not know where to access basic information 566 about A4.0 solutions, underscoring the need for better dissemination of knowledge and educational 567 resources. 568 Beyond financial and technical barriers, several other factors have contributed to the reluctance to adopt 569 A4.0 technologies. A lack of applicability to specific agricultural production areas was cited by 34% of 570 0% 5% 10% 15% 20% 25% 30% 35% 40% Other No issues detected or identified Inadequate or unavailable connectivity Operational challenges Insufficient or unreliable technical assistance Inadequate return on investment Lack of appropriate skills and expertise Limited or non-existent interoperability respondents, suggesting that certain farming sectors or operational models do not align with the 571 capabilities offered by the proposed A4.0 solutions. Connectivity issues also play a role, with 18% of 572 respondents identifying poor internet access as a constraint, particularly where digital infrastructure may 573 be insufficient. Additionally, concerns related to data security and privacy were reported by 15% of 574 respondents, indicating a degree of hesitation regarding the management and protection of sensitive farm 575 data in digital systems. 576 These findings highlight the multifaceted nature of the barriers impeding A4.0 adoption, encompassing 577 economic, technical, infrastructural, and informational challenges. Addressing these concerns through 578 targeted policies, financial support mechanisms, improved access to training, and enhanced digital 579 infrastructure could facilitate broader adoption and ensure that a wider range of farms can benefit from 580 the efficiencies and advancements offered by A4.0 solutions. 581 Figure 5. Inhibiting factors faced by respondents. Sample: 737 respondents who have not adopted any of the proposed Agriculture 4.0 solutions 582 583 584 5. Discussion 585 586 This study examines the adoption and awareness levels of Agriculture 4.0 (A4.0) solutions, the drivers 587 influencing technological adoption, the benefits obtained, as well as the challenges faced by A4.0 users 588 and the inhibiting factors expressed by A4.0 non-adopters. A comprehensive understanding of these 589 aspects is essential for policymakers, researchers, and industry stakeholders to identify obstacles and 590 develop strategies aimed at facilitating the widespread integration of digital technologies in the 591 agricultural sector. Such integration holds the potential to enhance productivity, efficiency, and 592 sustainability within Italian agriculture. 593 The findings indicate that while there is widespread awareness of A4.0 solutions among Italian farmers, 594 adoption levels vary significantly. These discrepancies are closely associated with the structural 595 0% 20% 40% 60% 80% 100% Concerns about data privacy and cybersecurity of A4.0 Inability to utilize the solution due to connectivity challenges Limited access to credit lines Lack of clarity on where to obtain the necessary information Insufficient technical or operational expertise Insufficient skills among operators to implement 4.0 solutions Solutions not applicable to specific production activities Incentives hindered by requirements or bureaucracy Investment viability relies on cost-sharing across enterprises Investment viability relies on spreading costs over time Challenges in evaluating the potential investment benefits Benefits perceived as insufficient to justify the investment Existing tools considered adequate to meet current needs Low expected utilization of the solution Company size deemed too small to justify the investment Agree Neither agree nor disagree Disagree characteristics of farming enterprises, particularly the size of the Utilised Agricultural Area (UAA) and 596 the level of annual turnover. Existing literature has consistently highlighted that the uptake of digital 597 agricultural technologies is contingent upon several structural and socio-economic factors, including 598 farm scale, crop specialization, farmer age, and educational background (Giua, 2022). At the national 599 level, our results corroborate this evidence, demonstrating that adoption rates tend to increase 600 proportionally with both the physical and economic size of farms. This trend is further reflected in 601 specific production types - such as cereals, fodder crops, and vineyards - where the extensive nature of 602 the former two may necessitate technological support, while the relatively higher revenue margins 603 typical of vineyard operations may facilitate investment in A4.0 solutions. Certain solutions, such as 604 monitoring systems and connected vehicles, have achieved higher acceptance, whereas others remain 605 unexploited. The primary motivation for adopting A4.0 solutions is predominantly associated with 606 macro-level farm management improvements, including enhanced forecasting capabilities and more 607 effective control and management processes, rather than in-field operational efficiencies, such as 608 optimising technical inputs and increasing machinery and equipment efficiency. 609 The analyses presented in this manuscript, which focus on the Italian agricultural sector, are broadly 610 aligned with the findings of international research. For instance, as reported by the United States 611 Department of Agriculture4, in 2023, 27% of U.S. farms or ranches employed A4.0 solutions for crop 612 management. Among the most widely adopted A4.0 solutions for crop management were automated 613 guidance systems (covering 58% of planted acres), yield mapping (44%), Variable Rate Technology 614 (37%), soil maps (22%) and drones and satellite imagery (7%) (United States Government 615 Accountability Office, 20245). Similarly, in Germany, a survey conducted on Bavarian farmers reported 616 that the most widely adopted digital tools included weather and pests forecast models and apps (38%), 617 digital field records (21%), automated steering systems (21%), maps from satellite data (14%), with an 618 overall adoption rate estimated around 62% of the sampled agricultural enterprises (Gabriel and 619 Gandorfer, 2023). 620 This study also underscored the benefits of A4.0 solutions, which were generally perceived as aligning 621 with expectations, with some exceeding initial anticipations. This suggests a largely successful 622 implementation among adopters. Notably, the areas where respondents reported the greatest benefits 623 surpassing expectations were related to the optimisation of technical inputs and water management. 624 Consistent with the findings of Zul Azlan et al. (2023), Abbasi et al. (2022), and Pradel et al. (2022), 625 A4.0 solutions have demonstrated the potential to assist farmers in reducing input and water 626 consumption, thereby generating both economic advantages through cost reduction and environmental 627 benefits. Regarding the potential social sustainability benefits, Italian farmers have identified 628 “streamline and optimise workforce processes” among the ones more in line with expectations, with a 629 small share of farmers pointing out that A4.0 solutions disappointed their expectations. The broader 630 social sustainability implications of this perceived benefit remain debated in literature. Some studies 631 suggest a positive evolution in the agricultural labour market, potentially improving farmers' livelihoods 632 and creating new employment opportunities (e.g., Rotz et al., 2019). Other contributions, instead, 633 underline the need for specific studies on the yet unexplored consequences on the agricultural labour 634 market originated from the optimisation of farming activities, potentially reducing the demand for 635 unskilled workers (Rotz et al., 2019; Rose et al., 2021). 636 Nevertheless, despite the perceived benefits of A4.0 solutions, their implementation remains constrained 637 by several challenges. These include interoperability issues, lack of adequate skills, return on investment 638 concerns and technical assistance limitations, which hinder correct A4.0 solutions implementation and 639 their benefits. In addition, several financial and structural constraints emerge as significant deterrents for 640 4 https://downloads.usda.library.cornell.edu/usda-esmis/files/h128nd689/4j03fg187/fj237k64f/fmpc0823.pdf 5 https://www.gao.gov/assets/d24105962.pdf non-adopters. Among these, the lack of trust in A4.0 solutions appears to be the most critical barrier. 641 This skepticism is often linked to a perceived low utility of A4.0, a belief that existing tools are sufficient 642 to meet current needs, difficulties in assessing the potential benefits, and the generally small size of 643 agricultural enterprises - factors that collectively slow digital adoption in Italian agriculture. Economic 644 and financial obstacles seem to be less relevant: these include doubts about the feasibility of investments 645 that depend on cost-sharing over time or across multiple farms, as well as limited access to incentives - 646 often constrained by bureaucratic complexity (Cisilino and Licciardo, 2022). These financial constraints 647 pose a fundamental challenge particularly for small and medium-sized farms that may lack the capital 648 required for initial investments in A4.0 solutions. This issue is further exacerbated by the uncertainty 649 surrounding return on investment, making it difficult for farmers to justify the adoption of these solutions 650 without clear and measurable long-term economic benefits. In contrast, technical challenges appear to 651 be less influential: only a minority of non-adopters cite inapplicability to specific production processes, 652 lack of technical skills, or insufficient expertise as reasons for avoiding A4.0 solutions. Moreover, 653 connectivity issues emerge as a challenge for non-adopters, especially in marginal areas and on hills 654 across Italian regions, thus limiting the implementation of A4.0 solutions, as highlighted by Sozzi et al. 655 (2021). As also emphasised by Fragomeli et al. (2024) and Da Silveira et al. (2023), such obstacles 656 significantly impede the broader adoption of A4.0 solutions by limiting both the willingness and ability 657 of farmers to integrate these tools into their production systems. Furthermore, as highlighted by 658 Gonzales-Gemio and Sanz-Martín (2025), the inequality in access to A4.0 solutions could hinder the 659 adoption of sustainable agricultural practices. Digital platforms and monitoring solutions, for instance, 660 have the potential to substantially enhance the efficiency of carbon farming initiatives and contribute 661 more broadly to agricultural sustainability. 662 These findings are consistent with an analysis published by the General Secretariat of the Council of the 663 European Union6, which emphasizes that - compared to other sectors - the pace of digital adoption in 664 agriculture has been slower. This lag is attributed to several interrelated factors, including inadequate 665 infrastructure, substantial upfront investment requirements, a widespread lack of digital skills, and the 666 inherent complexity of the agricultural sector. The latter includes considerable variability in climate 667 conditions, soil types, crop systems, and farming practices, all of which pose additional challenges to 668 the effective implementation of A4.0 solutions. 669 The findings of this study are also aligned with emerging academic literature on the barriers to A4.0 670 adoption within the Italian agricultural sector. For example, Addorisio et al. (2025), based on interviews 671 with Italian farmers, underscore the critical role of stakeholder cooperation and targeted training 672 initiatives in addressing key impediments to adoption. These include limited interoperability among 673 A4.0 solutions, insufficient digital competencies, and a lack of adequate technical support. Similarly, 674 Giorgio et al. (2024) explore perceived advantages and challenges associated with digitalisation in 675 Northern Italy. Reported benefits include enhanced environmental sustainability, improved input 676 efficiency, reduced labour requirements, and lower operational costs. However, the study also identifies 677 persistent barriers such as limited digital skills, inadequate data management practices and issues with 678 interoperability. These findings suggest that policies should not only support equipment acquisition, but 679 also promote the development of farmers’ human capital. 680 Addressing these challenges through targeted policy interventions, comprehensive training initiatives, 681 and improved system interoperability could substantially enhance A4.0 adoption rates, thereby ensuring 682 that a broader range of agricultural enterprises benefits from the efficiencies and advancements offered 683 by digital innovations. Moreover, collaboration among policymakers, technology providers, and industry 684 6 https://www.consilium.europa.eu/media/shxiaxmo/2024_971-art-agriculture-11-02-25.pdf stakeholders is crucial in fostering an ecosystem that supports seamless integration, mitigates adoption 685 barriers, and maximizes the impact of digital agricultural innovations. 686 Conclusions 687 This study offers valuable empirical insights into the current state of Agriculture 4.0 (A4.0) adoption in 688 Italy, shedding light on drivers influencing the uptake of A4.0 solutions, the perceived benefits, the 689 challenges met by farmers who adopted A4.0 solutions and the barriers that prevented other agricultural 690 enterprises from adopting A4.0 solutions. By disaggregating results according to critical variables related 691 to farms (size, primary crop production and geographical localisation), this research contributes to a 692 more nuanced understanding of how the A4.0 paradigm is taking root within the Italian agricultural 693 sector. These findings provide a strong empirical foundation for informing public policy, guiding 694 investment strategies and designing initiatives that are tailored to the needs of diverse farming profiles. 695 Specifically, the results highlight the importance of structural variables such as farm size, crop 696 production and turnover in shaping adoption patterns, suggesting that public support mechanisms should 697 be differentiated accordingly. Small farms, which tend to face greater barriers in terms of investment 698 capacity and technical know-how, may benefit from targeted subsidies, tax incentives, and digital 699 infrastructure improvements, particularly in under-served rural regions. Moreover, the limited adoption 700 of certain A4.0 solutions underscores the need for broader outreach, technical assistance and knowledge 701 transfer mechanisms to ensure that innovation diffuses beyond a small subset of more-structured farms. 702 Training programs should also be adapted to the varying levels of digital literacy across the sector, with 703 modular content suited to both entry-level and experienced users. 704 Moreover, by identifying which types of farms might be most likely to adopt A4.0 solutions and which 705 barriers inhibit the uptake of digital tools, technology providers can refine their product design, 706 marketing strategies, and sales services. Companies may, for instance, enhance interoperability and user-707 friendliness to address common usability challenges. 708 A promising avenue for future research would involve a comparative analysis of the levels of A4.0 709 adoption, associated needs, benefits, challenges, and barriers identified in this study with those observed 710 in other European countries and beyond. 711 Another potential research direction could focus on examining the impact of A4.0 solutions on 712 economic, environmental, and social sustainability to comprehensively assess the costs and benefits of 713 A4.0 implementation. This analysis could, in turn, contribute to bridging the gap between adopting and 714 non-adopting agricultural enterprises. 715 Nonetheless, these contributions should be considered in light of the following methodological 716 limitations arising from the survey administration method and the sample distribution compared to the 717 reference population. As with all Computer-Assisted Web Interviewing (CAWI) methods, this online 718 survey may exclude individuals without internet access or those less comfortable with technology. 719 Additionally, self-selection bias could skew the results, as participants are likely to be those with an 720 interest in the topic or familiarity with online surveys. Consequently, adoption rates of A4.0 solutions 721 reported in this study may be overestimated, while the perceived benefits and willingness to invest 722 further in digital technologies could reflect the attitudes of a smaller group of more innovation-oriented 723 farmers. Addressing these limitations in the future research would require efforts to reach less digitally-724 involved segments of the Italian agricultural sector to enhance the external validity of the findings. 725 Moreover, the discrepancy between the sample size distribution and the population size distribution leads 726 to an overrepresentation of farms in the North and an underrepresentation of those in the South and 727 Islands, potentially introducing a geographical bias. Furthermore, the average UAA (Utilised 728 Agricultural Area) of the sampled farms (22 hectares) is significantly higher than the figure reported by 729 ISTAT7 (11.1 hectares), suggesting a selection of more structured agricultural enterprises. Additionally, 730 the greater representation of the vineyard sector, which is characterised by higher-than-average 731 profitability and greater spending capacity, could influence this study’s findings. 732 This study was carried out within the Agritech National Research Center and within the Smart AgriFood 733 Observatory - Politecnico di Milano & University of Brescia and received funding from the European 734 Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E RESILIENZA (PNRR) – 735 MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 17/06/2022, CN00000022). This 736 paper reflects only the authors’ views and opinions, neither the European Union nor the European 737 Commission can be considered responsible for them. 738 739 References 740 741 Abbasi, R., Martinez, P., and Ahmad, R. (2022). The Digitization of Agricultural Industry – a Systematic 742 Literature Review on Agriculture 4.0. Smart Agricultural Technology, 2: 100042. 743 https://doi.org/10.1016/j.atech.2022.100042. 744 Addorisio, R., Casolani, N., Maesano, G., Coderoni, S., Perito, M.A., Mattetti, M., Canavari, M., 2025. Barriers 745 and drivers of digital agriculture adoption: Insights from Italian farming stakeholders. 746 https://doi.org/10.18461/ijfsd.v16i1o1 747 Ahmed, B., Shabbir, H., Naqvi, S. R., and Peng, L. (2024). Smart Agriculture: Current State, 748 Opportunities, and Challenges. IEEE Access, 12: 144456-78. 749 https://doi.org/10.1109/ACCESS.2024.3471647. 750 Albiero, D., De Paulo, R. L., Felix Junior, J. C., Da Silva Gomes Santos, J., and Melo, R. P. (2020). 751 Agriculture 4.0: A Terminological Introduction. Revista Ciência Agronômica, 51(5). 752 https://doi.org/10.5935/1806-6690.20200083. 753 Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., and Ramalho, J. C. (2021). Characterising the 754 Agriculture 4.0 Landscape - Emerging Trends, Challenges and Opportunities. Agronomy, 11(4): 667. 755 https://doi.org/10.3390/agronomy11040667. 756 Assimakopoulos, F., Vassilakis, C., Margaris, D., Kotis, K., and Spiliotopoulos, D. (2024). The 757 Implementation of “Smart” Technologies in the Agricultural Sector: A Review. Information, 15(8): 466. 758 https://doi.org/10.3390/info15080466. 759 Balasundram, S. K., Shamshiri, R. R., Sridhara, S., and Rizan, N. (2023). The Role of Digital Agriculture 760 in Mitigating Climate Change and Ensuring Food Security: An Overview. Sustainability, 15(6): 5325. 761 https://doi.org/10.3390/su15065325. 762 Balyan, S., Jangir, H., Tripathi, S. N., Tripathi, A., Jhang, T., and Pandey, P. (2024). Seeding a 763 Sustainable Future: Navigating the Digital Horizon of Smart Agriculture. Sustainability, 16(2): 475. 764 https://doi.org/10.3390/su16020475. 765 Bongiovanni, R., and Lowenberg-Deboer, J. (2004). Precision Agriculture and Sustainability. Precision 766 Agriculture, 5: 359-387. https://doi.org/10.1023/B:PRAG.0000040806.39604.aa. 767 Bokusheva, R., and S. Kimura (2016). Cross-Country Comparison of Farm Size Distribution. OECD 768 Food, Agriculture and Fisheries Papers, No. 94, OECD Publishing, Paris. 769 http://dx.doi.org/10.1787/5jlv81sclr35-en 770 7 https://www.istat.it/it/files/2022/06/censimento_agricoltura_gismondi.pdf https://doi.org/10.18461/ijfsd.v16i1o1 https://www.istat.it/it/files/2022/06/censimento_agricoltura_gismondi.pdf Cambra Baseca, C., Sendra, S., Lloret, J., and Tomas, J. (2019). A Smart Decision System for Digital 771 Farming. Agronomy, 9(5): 216. https://doi.org/10.3390/agronomy9050216. 772 Cisilino, F., Licciardo, F., 2022. Potential and Complexity of Implementing Financial Instruments in the 773 Framework of Rural Development Policies in Italy—The Friuli Venezia Giulia Revolving Fund. 774 Sustainability 14, 16090. https://doi.org/10.3390/su142316090 775 Da Silveira, F., Da Silva, S. L. C., Machado, F. M., Barbedo, J. G. A., and Amaral, F. G. (2023). Farmers’ 776 Perception of the Barriers That Hinder the Implementation of Agriculture 4.0. Agricultural Systems, 777 208: 103656. https://doi.org/10.1016/j.agsy.2023.103656. 778 Da Silveira, F., Lermen, F. H., and Amaral, F. G. (2021). An Overview of Agriculture 4.0 Development: 779 Systematic Review of Descriptions, Technologies, Barriers, Advantages, and Disadvantages. Computers 780 and Electronics in Agriculture, 189: 106405. https://doi.org/10.1016/j.compag.2021.106405. 781 Dayıoğlu, M., and Turker, U. (2021). Digital Transformation for Sustainable Future - Agriculture 4.0: 782 A Review. Tarım Bilimleri Dergisi, 27. https://doi.org/10.15832/ankutbd.986431. 783 Escribà-Gelonch, M., Liang, S., Van Schalkwyk, P., Fisk, I., Van Duc Long, N., and Hessel, V. (2024). 784 Digital Twins in Agriculture: Orchestration and Applications. Journal of Agricultural and Food 785 Chemistry, 72(19): 10737-52. https://doi.org/10.1021/acs.jafc.4c01934. 786 Fragomeli, R., Annunziata, A., and Punzo, G. (2024). Promoting the Transition towards Agriculture 4.0: 787 A Systematic Literature Review on Drivers and Barriers. Sustainability, 16(6): 2425. 788 https://doi.org/10.3390/su16062425. 789 Gabriel, A., Gandorfer, M., 2023. Adoption of digital technologies in agriculture—an inventory in a 790 European small-scale farming region. Precision Agric 24, 68–91. 791 Gebbers, R., and Adamchuk, V. (2010). Precision Agriculture and Food Security. Science, 327(5967): 792 828-31. https://doi.org/10.1126/science.1183899. 793 Giampietri, E., Yu, X. and Trestini, S. (2020) ‘The role of trust and perceived barriers on farmer’s 794 intention to adopt risk management tools’, Bio-based and Applied Economics, pp. 1-24 Pages. Available 795 at: https://doi.org/10.13128/BAE-8416. 796 Giorgio, A., Penate Lopez, L.P., Bertoni, D., Cavicchioli, D., Ferrazzi, G., 2024. Enablers to Digitalization in 797 Agriculture: A Case Study from Italian Field Crop Farms in the Po River Valley, with Insights for Policy 798 Targeting. Agriculture 14, 1074. https://doi.org/10.3390/agriculture14071074 799 Giua, C., Materia V., and Camanzi, L. (2022). Smart Farming Technologies Adoption: Which Factors 800 Play a Role in the Digital Transition?. Technology in Society 68: 101869. 801 https://doi.org/10.1016/j.techsoc.2022.101869 802 Gonzales-Gemio, C., and Sanz-Martín, L. (2025). Socioeconomic Barriers to the Adoption of Carbon 803 Farming in Spain, Italy, Egypt, and Tunisia: An Analysis Based on the Diffusion of Innovations Model. 804 Journal of Cleaner Production 498: 145155. https://doi.org/10.1016/j.jclepro.2025.145155 805 Javaid, M., Haleem, A., Singh, R. P., and Suman, R. (2022). Enhancing Smart Farming through the 806 Applications of Agriculture 4.0 Technologies. International Journal of Intelligent Networks, 3: 150-64. 807 https://doi.org/10.1016/j.ijin.2022.09.004. 808 Jin, X. B., Yu, X. H., Wang, X. Y., Bai, Y. T., Su, T. L., and Kong, J. L. (2020). Deep Learning Predictor 809 for Sustainable Precision Agriculture Based on Internet of Things System. Sustainability, 12(4): 1433. 810 https://doi.org/10.3390/su12041433. 811 Aggiungi intestazioni (Formato > Stili paragrafo) da visualizzare nel sommario. 812 813 https://doi.org/10.1126/science.1183899 https://doi.org/10.13128/BAE-8416 https://doi.org/10.13128/BAE-8416 Khanna, M. et al. (2024) ‘Economics of the Adoption of Artificial Intelligence–Based Digital 814 Technologies in Agriculture’, Annual Review of Resource Economics, 16(1), pp. 41–61. Available at: 815 https://doi.org/10.1146/annurev-resource-101623-092515. 816 Klerkx, L. and Rose, D. (2020) ‘Dealing with the game-changing technologies of Agriculture 4.0: How 817 do we manage diversity and responsibility in food system transition pathways?’, Global Food Security, 818 24, p. 100347. Available at: https://doi.org/10.1016/j.gfs.2019.100347. 819 Kumar Kasera, R., Gour, S., and Acharjee, T. (2024). A Comprehensive Survey on IoT and AI Based 820 Applications in Different Pre-Harvest, during-Harvest and Post-Harvest Activities of Smart Agriculture. 821 Computers and Electronics in Agriculture, 216: 108522. https://doi.org/10.1016/j.compag.2023.108522. 822 Lezoche, M. et al. (2020) ‘Agri-food 4.0: A survey of the supply chains and technologies for the future 823 agriculture’, Computers in Industry, 117, p. 103187. Available at: 824 https://doi.org/10.1016/j.compind.2020.103187. 825 Maffezzoli, F., Ardolino, M., and Bacchetti, A. (2022a). The Impact of the 4.0 Paradigm in the Italian 826 Agricultural Sector: A Descriptive Survey. Applied Sciences, 12(18): 9215. 827 https://doi.org/10.3390/app12189215. 828 Maffezzoli, F., Ardolino, M., Bacchetti, A., Perona, M., and Renga, F. (2022b). Agriculture 4.0: A 829 Systematic Literature Review on the Paradigm, Technologies and Benefits. Futures, 142: 102998. 830 https://doi.org/10.1016/j.futures.2022.102998. 831 Meemken, E.-M. et al. (2024) ‘Digital innovations for monitoring sustainability in food systems’, Nature 832 Food, 5(8), pp. 656–660. Available at: https://doi.org/10.1038/s43016-024-01018-6. 833 Menozzi, D., Fioravanzi, M. and Donati, M. (2015) ‘Farmer’s motivation to adopt sustainable 834 agricultural practices’, Bio-based and Applied Economics, pp. 125-147 Pages. Available at: 835 https://doi.org/10.13128/BAE-14776. 836 Mhlanga, D. and Ndhlovu, E. (2023) ‘Digital Technology Adoption in the Agriculture Sector: 837 Challenges and Complexities in Africa’, Human Behavior and Emerging Technologies. Edited by Z. 838 Yan, 2023, pp. 1–10. Available at: https://doi.org/10.1155/2023/6951879. 839 Mühl, D.D. and De Oliveira, L. (2022) ‘A bibliometric and thematic approach to agriculture 4.0’, 840 Heliyon, 8(5), p. e09369. Available at: https://doi.org/10.1016/j.heliyon.2022.e09369. 841 Oliveira, L. F. P., Moreira, A. P., and Silva, M. F. (2021). Advances in Agriculture Robotics: A State-842 of-the-Art Review and Challenges Ahead. Robotics, 10(2): 52. 843 https://doi.org/10.3390/robotics10020052. 844 Onyenekwe, C.S. et al. (2023) ‘Heterogeneity of adaptation strategies to climate shocks: Evidence from 845 the Niger Delta region of Nigeria’, Bio-based and Applied Economics, 12(1), pp. 17–35. Available at: 846 https://doi.org/10.36253/bae-13436. 847 Papadopoulos, G., Arduini, S., Uyar, H., Psiroukis, V., Kasimati, A., and Fountas, S. (2024). Economic 848 and Environmental Benefits of Digital Agricultural Technologies in Crop Production: A Review. Smart 849 Agricultural Technology, 8, 100441. https://doi.org/10.1016/j.atech.2024.100441 850 Peladarinos, N., Piromalis, D., Cheimaras, V., Tserepas, E., Munteanu, R. A., and Papageorgas, P. 851 (2023). Enhancing Smart Agriculture by Implementing Digital Twins: A Comprehensive Review. 852 Sensors, 23(16), 7128. https://doi.org/10.3390/s23167128 853 https://doi.org/10.1146/annurev-resource-101623-092515 https://doi.org/10.1146/annurev-resource-101623-092515 https://doi.org/10.1016/j.gfs.2019.100347 https://doi.org/10.1016/j.gfs.2019.100347 https://doi.org/10.1016/j.compag.2023.108522 https://doi.org/10.1016/j.compind.2020.103187 https://doi.org/10.1016/j.compind.2020.103187 https://doi.org/10.1016/j.compind.2020.103187 https://doi.org/10.1016/j.futures.2022.102998 https://doi.org/10.1038/s43016-024-01018-6 https://doi.org/10.1038/s43016-024-01018-6 https://doi.org/10.13128/BAE-14776 https://doi.org/10.13128/BAE-14776 https://doi.org/10.13128/BAE-14776 https://doi.org/10.1155/2023/6951879 https://doi.org/10.1155/2023/6951879 https://doi.org/10.1016/j.heliyon.2022.e09369 https://doi.org/10.1016/j.heliyon.2022.e09369 https://doi.org/10.3390/robotics10020052 https://doi.org/10.36253/bae-13436 https://doi.org/10.36253/bae-13436 https://doi.org/10.36253/bae-13436 Pierce, F. J., and Nowak, P. (1999). Aspects of Precision Agriculture. Advances in Agronomy, 67, 1-85. 854 https://doi.org/10.1016/S0065-2113(08)60513-1 855 Polymeni, S., Plastras, S., Skoutas, D. N., Kormentzas, G., and Skianis, C. (2023). The Impact of 6G-856 IoT Technologies on the Development of Agriculture 5.0: A Review. Electronics, 12(12), 2651. 857 https://doi.org/10.3390/electronics12122651 858 Pradel, M., De Fays, M., and Seguineau, C. (2022). Comparative Life Cycle Assessment of Intra-Row 859 and Inter-Row Weeding Practices Using Autonomous Robot Systems in French Vineyards. Science of 860 The Total Environment, 838, 156441. https://doi.org/10.1016/j.scitotenv.2022.156441 861 Rose, D. C., Wheeler, R., Winter, M., Lobley M., Chivers, C. (2021). Agriculture 4.0: Making it work 862 for people, production, and the planet. Land Use Policy, 100. 863 https://doi.org/10.1016/j.landusepol.2020.104933 864 Rotz, S., Gravely, E., Mosby, I., Duncan, E., Finnis, E., Horgan, M., LeBlanc, J., Martin, R., Tait 865 Neufeld, H., Nixon, A., Pant, L., Shall, V., Fraser, E. (2019). Automated pastures and the digital divide: 866 How agricultural technologies are shaping labour and rural communities. Journal of Rural Studies, 68, 867 112-122. https://doi.org/10.1016/j.jrurstud.2019.01.023 868 Sharma, V., Tripathi, A. K., and Mittal, H. (2022). Technological Revolutions in Smart Farming: Current 869 Trends, Challenges and Future Directions. Computers and Electronics in Agriculture, 201, 107217. 870 https://doi.org/10.1016/j.compag.2022.107217 871 Sott, M. K., Furstenau, L. B., Kipper, L. M., Giraldo, F. D., Lopez-Robles, J. R., Cobo, M. J., Zahid, A., 872 Abbasi, Q. H., and Imran, M. A. (2020). Precision Techniques and Agriculture 4.0 Technologies to 873 Promote Sustainability in the Coffee Sector: State of the Art, Challenges and Future Trends. IEEE 874 Access, 8, 149854–67. https://doi.org/10.1109/ACCESS.2020.3016325 875 Sozzi, M., Ahmed, K., Ferrari, G., Zanchin, A., Grigolato, S., and Marinello, F. (2021). Connectivity in 876 rural areas: A case study on internet connection in the Italian agricultural areas. IEEE International 877 Workshop on Metrology for Agriculture and Forestry (MetroAgriFor), Trento-Bolzano, Italy, 466-470. 878 https://doi.org/10.1109/MetroAgriFor52389.2021.9628665 879 Sponchioni, G., Vezzoni, M., Bacchetti, A., Pavesi, M., and Renga, F. (2019). The 4.0 revolution in 880 agriculture: A multi-perspective definition. XXIV Summer School “Francesco Turco” – Indust Syst Eng, 881 1, 143–149. 882 van Selm, M., and Jankowski, N. W. (2006). Conducting Online Surveys. Quality and Quantity, 40, 435-883 456. https://doi.org/10.1007/s11135-005-8081-8 884 Yousaf, A., Kayvanfar, V., Mazzoni, A., and Elomri, A. (2023). Artificial Intelligence-Based Decision 885 Support Systems in Smart Agriculture: Bibliometric Analysis for Operational Insights and Future 886 Directions. Frontiers in Sustainable Food Systems, 6, 1053921. 887 https://doi.org/10.3389/fsufs.2022.1053921 888 Zhai, Z., Martínez, J. F., Beltran, V., and Martínez, N. L. (2020). Decision Support Systems for 889 Agriculture 4.0: Survey and Challenges. Computers and Electronics in Agriculture, 170, 105256. 890 https://doi.org/10.1016/j.compag.2020.105256 891 Zul Azlan, Z., Hazim, Z. F., Junaini, S. N., Bolhassan, N. A., Wahi, R., and Arip, M. A. (2024). 892 Harvesting a Sustainable Future: An Overview of Smart Agriculture’s Role in Social, Economic, and 893 Environmental Sustainability. Journal of Cleaner Production, 434, 140338. 894 https://doi.org/10.1016/j.jclepro.2023.140338 895 https://doi.org/10.1016/j.jclepro.2023.140338