Title 1 Assessing the social impacts of Digital Agriculture Technology Solutions: a practical tool 1 Bianchi A.1, Corbo C.1, Parigi F.1, Cesari de Maria S.1, Renga F. M.1, Tumino A.1 2 1 Department of Management, Economics and Industrial Engineering, Politecnico di Milano, 3 Milano, Italy 4 This article has been accepted for publication and undergone full peer review but has not been through 5 the copyediting, typesetting, pagination and proofreading process, which may lead to differences 6 between this version and the Version of Record. 7 Please cite this article as: 8 Bianchi A., Corbo C., Parigi F., Cesari de Maria S., Renga F. M., Tumino A. (2025). Assessing the 9 social impacts of Digital Agriculture Technology Solutions: a practical tool. Bio-Based and Applied 10 Economics, Just Accepted. DOI:10.36253/bae-16929 11 12 Abstract 13 14 Digital Agriculture Technology Solutions (DATSs) can improve the sustainability of the agricultural 15 sector. While most of the research on the impacts of DATSs is focused on the economic and 16 environmental dimensions of sustainability, this work aims to understand the social benefits that 17 DATSs have on farmers. Integrating top-down and bottom-up approaches, a Social Sustainability 18 Assessment Framework for DATSs adoption was developed and subsequently applied in the form of 19 a Social Self-Evaluation Tool, a questionnaire tested on 60 farmers across 20 European countries, 20 with a heterogeneous composition in terms of sector, types of DATSs, agronomical context, and socio-21 economic background. The Framework and the Social Self-Evaluation Tool allowed for a deep 22 investigation of the social impacts of DATSs in terms of labour evolution, education and learning, 23 and generational change. The results demonstrated the positive effects of DATSs on the social sphere 24 of sustainability, as well as the importance of integrating this type of social analysis in the evaluation 25 of digital technologies in agriculture. 26 27 Keywords: social sustainability, social framework, agriculture 4.0, digital agriculture 28 29 JEL Classification codes: O330, Q12, Q160 30 31 1. INTRODUCTION 32 33 Agriculture faces significant economic, environmental, and social challenges and a range of 34 megatrends - including climate change, environmental degradation, geopolitical instability, 35 demographic dynamics, changing supply chains and evolving consumer demand - are increasingly 36 putting pressure on the sector at both local and global levels. According to FAO (2025), between 638 37 and 720 million people may have faced hunger in 2024 (7.8 – 8.8% of the global population), with 38 projections indicating that 512 million people will be chronically undernourished by 2030. The global 39 population is expected to reach 9.7 billion by 2050, demanding increased agricultural productivity 40 while preserving natural resources, ecosystems, and biodiversity (UN, 2022). Climate change is 41 adversely affecting agriculture, leading to significant economic and productivity losses, complicating 42 efforts to meet human needs (IPCC, 2022). In this scenario, digital technologies can play a pivotal 43 role in increasing the sustainability, productivity, and resilience of agriculture (European 44 Commission, 2023a). It has already been extensively highlighted how technologies like Internet of 45 Things (IoT), Data Analytics and Cloud Computing, Artificial Intelligence (AI) and Machine 46 Learning (ML), Satellites, Geographic Information Systems (GIS), Drones and Robots, could enable 47 2 a wide range of Digital Agriculture Technology Solutions (DATSs) with the potential to transform 48 agriculture, increasing productivity while reducing impacts on natural resources and alleviating the 49 labour-intensive work of farmers (Papadopoulos et al., 2024; Maffezzoli et al., 2022; Balafoutis et 50 al., 2020). 51 While a lot of work has been done to assess and prove the environmental and economic 52 sustainability of DATSs, there is a lack of studies in the literature focusing on the social impacts of 53 digital innovation in agriculture. The research presented in this paper stems from the need to deeply 54 investigate the social impacts of DATSs adoption on farmers. Therefore, this paper aims to present 55 the development, application, and testing of a framework to analyse the benefits and impacts of 56 DATSs on farmers from a social perspective. 57 This paper is structured as follows. Section 2 will briefly introduce the concept of social 58 sustainability and describe its relationship with DATSs, setting out the background and the objectives 59 of the present work. Section 3 will explain the research methodology that led to the Framework 60 presented in Section 4. A discussion on results is presented in Section 5. Lastly, Section 6 will offer 61 a general assessment of the findings, along with the limitations of the current study and potential 62 future developments. 63 2. THEORETICAL BACKGROUND AND OBJECTIVES 64 2.1. Social Sustainability and DATSs 65 Despite its frequent use in academic literature and public discourse, the concept of “social 66 sustainability” lacks a universally accepted definition (McGuinn et al., 2020), as most attention is 67 often focused on economic and environmental sustainability (Janker & Mann, 2020). The 2030 68 Agenda for Sustainable Development frames social sustainability as a multidimensional objective 69 encompassing social equality, poverty eradication, and decent living standards for all (UN, 2015). 70 According to the World Bank, “Social sustainability increases when more people feel part of the 71 development process and believe that they and their descendants will benefit from it”, but the concept 72 remains elusive due to complex socio-cultural factors that are difficult to analyse empirically (Barron 73 et al., 2023). The social sustainability of agriculture is increasingly gaining relevance in scientific 74 discussions as well as within institutional and agribusiness sectors, despite the research focus on 75 farming sustainability having been predominantly centred on environmental aspects so far (European 76 Commission, 2023; Nowak et al., 2019; McGrath et al., 2023). In agriculture, social sustainability 77 mainly refers to the possibility of maintaining or improving farmers’ lives and working conditions 78 (Trivino-Tarradas et al., 2019), encompassing various aspects such as fair income, social inclusion, 79 decent living standards, and physical and emotional well-being (Zanin et al., 2020). Jenker et al. 80 (2019), applying Maslow's hierarchy of needs, define social sustainability in agriculture as improving 81 farmers' satisfaction with their physiological, security, social, esteem, and self-actualisation needs. 82 Latruffe et al. (2016) distinguish social sustainability at two levels: the farm community level, 83 focusing on farmers' well-being, working conditions, education, and quality of life; and the society 84 level, involving rural development, employment, ecosystem services, quality products, 85 intergenerational continuity, and acceptable agricultural practices. 86 Since the 1990s, concepts such as Precision Farming, Digital Agriculture, Smart Agriculture, 87 and, more recently, Agriculture 4.0 have emerged, with digitalisation increasingly recognised as a 88 key driver in addressing global and local agricultural challenges, promoting sustainable, inclusive, 89 and equitable agricultural development (Bertoglio et al., 2021; Schroeder et al., 2021; Hernandez et 90 al., 2024). Agriculture 4.0, defined as the evolution of Precision Farming through automated data 91 collection, integration, and analysis from various sources, aims to transform traditional farming 92 systems into digitalised ones, enhancing benefits, reducing costs, and promoting environmental and 93 social sustainability (Maffezzoli et al., 2022). The adoption of DATSs can also improve social 94 sustainability at both the farm and society levels. However, understanding the interactions between 95 technologies, people, and society, along with their associated risks and impacts, remains difficult yet 96 3 essential (Gardezi et al., 2022). Policy and strategic EU documents and framework, such as the 97 European Union's Farm to Fork Strategy, the Green Deal, the CAP, the EU Food2030 and the EU 98 Vision for Agriculture and Food, all recognize digitalization and digital connectivity as crucial factors 99 to promote social sustainability, improving quality of life and economic prosperity in rural areas 100 (European Commission, 2023a, b, c; European Commission, 2025). However, digital innovation in 101 agriculture has lagged due to several interrelated factors such as solution complexity, limited 102 scalability, and structural barriers such as education, technological proficiency, and connectivity 103 (Dutta et al., 2019). Moreover, farmers often lack clear evidence of the tangible benefits these 104 technologies provide, as well as their actual return on investment, making it difficult to justify their 105 adoption, especially on smaller farms, while larger farms are more likely to adopt DATSs due to the 106 economies of scale they can leverage (Castle et al., 2015). This highlights the need for a thorough 107 investigation of the benefits of DATSs, particularly in the understudied social dimension. The social 108 impacts, benefits, and risks resulting from digitalisation can be various, depending on the type of 109 DATSs, the productive sector, the agronomic, cultural, and socio-economic context, and the way each 110 specific technology solution is used and integrated within farm management. Among the most 111 immediate and relevant benefits of implementing DATSs are the reduction of farmers' workloads and 112 working hours, and the substantial increase in work productivity and flexibility (Khanna & Kaur, 113 2019; Sri Heera et al., 2019; Tsouros et al., 2019), as well as the reduction in heavy labour activities, 114 injuries, and accident rates (Balafoutis et al., 2020). A reduction in workload and more flexible 115 working hours could mean a better work-life balance for farmers, allowing for more time with family, 116 friends, or leisure activities (McGrath et al., 2023). This, combined with the support that DATSs 117 provide in decision-making, management, monitoring, and labour-intensive tasks, can result in lower 118 work-related stress. However, the use of DATSs may also generate stress, particularly during the 119 initial phase of technology adoption, due to the steep associated learning curve (Gaber et al., 2024), 120 the need to change traditional farm management (Butler & Holloway, 2016; Driessen & Heutinck, 121 2015), and issues related to information processing and technology calibration or malfunctioning 122 (Balafoutis et al., 2020). 123 Naturally, there are also areas of impact that extend beyond the personal realm of individual 124 farmers and concern labour rights, women’s empowerment, gender gaps, social interactions, rural 125 communities, territorial development, youth engagement in farming, and many others (Rolandi et al., 126 2021; Ali et al., 2016). The technical knowledge, hard skills and training required to implement 127 DATSs in farms could also impact the local and regional labour market, leading to a higher demand 128 for qualified workers with ICT and digital skills, leading to possible digital skills gaps, especially in 129 certain socio-economic contexts (Pogorelskaia and Várallyai, 2020). When farmers cannot acquire 130 the appropriate technical knowledge and fail to update their skills, a digital divide can arise between 131 those who can take advantage of the benefits of technology and those who cannot, a process further 132 exacerbated by factors such as age, gender, language, or socio-economic background (Trendov et al., 133 2019). This digital divide can play not only locally but also globally, accentuating socio-economic 134 differences (FAO, 2023) or enhancing power disparities among food systems actors (Gardezi et al., 135 2022). According to FAO (2023), DATSs have proven to be capable of reducing the gender gap in 136 agriculture, strengthening women’s livelihoods and empowerment, but only if women’s access to 137 education, financial services, decision-making power and technologies is ensured, all things for which 138 women still lag behind men, particularly in low- and middle-income countries (Rodgers and Akram-139 Lodhi, 2019; Ali et al., 2016). DATSs can also radically change the role and social identity of the 140 farmer, undermining his traditional agronomic techniques and knowledge, and shifting their work 141 from the field to the office, to the extent that a future of farms without farmers can be envisaged 142 (Gardezi et al., 2022). As already happened in the history of agriculture with the introduction of 143 disruptive technologies (e.g. the tractor, the combine harvester, chemical pesticides, new genetically 144 modified varieties), the adoption of DATSs seems to evoke the fear that technology might reduce 145 human labour in both manual and intellectual tasks and lead to a decline of workers in the fields and 146 farms (Rotz et al., 2019). On the contrary, DATSs can solve the problem of labour shortage in 147 4 agriculture, particularly in agricultural systems where matching labour supply and demand is difficult, 148 such as in Western and Southern Europe, the US, and Canada. These regions are heavily reliant on 149 seasonal migrant workers, and exploitative and illegal labour practices are frequently reported in both 150 media and academia (Caxaj et al., 2023). In this context, DATSs can be a valuable tool to reduce the 151 reliance on exploited workers, increase supply chain transparency, improve working conditions, and 152 ensure respect for workers' rights. DATSs could also create new job opportunities and drive the 153 creation of new professional roles and actors involved in the digital transformation of food supply 154 chains (Bampasidou et al., 2024). 155 The multiplicity of social aspects, risk factors, and critical issues related to the use of DATSs 156 makes it necessary to thoroughly investigate how DATSs change, for better or worse, the lives of 157 farmers, and requires that such assessments are integrated into every analysis on the benefits and costs 158 of digitisation. The assessment of social sustainability in agriculture encompasses various levels of 159 analysis and can be approached through multiple methodologies, often derived from social sciences 160 and based on interviews and surveys for farmers (Packer & Zanasi, 2023). However, the 161 multidimensional and qualitative nature of social sustainability makes its assessment in farming 162 systems more challenging compared to the economic and environmental dimensions (Latruffe et al., 163 2016). Through this work, we aim to close this gap and explore the social impacts of DATSs on 164 farmers, seeking to better understand the benefits, but also the risks, that digital technologies bring to 165 the social sphere of sustainability. 166 2.2. Research objectives 167 The objective of this study is to provide a framework for assessing the social sustainability of 168 DATSs: the Social Sustainability Assessment Framework. This Framework will serve as the basis for 169 the development of a practical tool - the Social Self-Evaluation Tool - to enable a comprehensive 170 assessment of the social implications of DATSs adoption by farmers. This objective arises from the 171 recognition that commonly proposed frameworks and indicators in the literature, which often focus 172 on the economic and environmental domains of sustainability, are insufficient to fully examine the 173 social impacts of DATSs. Additionally, the study illustrates a first application of the Social Self-174 Evaluation Tool to a sample of farmers adopting DATSs in order to assess the practical relevance of 175 the Framework. However, the utility and applicability of the developed Framework and Tool extend 176 beyond the scope of this research, offering potential for broader use in future studies and practical 177 applications. The approach and structure underlying the conception and development of the Social 178 Sustainability Assessment Framework and the Social Self-Evaluation Tool will enable their 179 adaptation to other agricultural contexts and facilitate their use in future practical applications, 180 making them a valuable resource for assessing the social sustainability of digital innovation in various 181 farming systems. 182 3. RESEARCH METHODOLOGY 183 184 The research is conducted within the Horizon Europe QuantiFarm project, which aims to support the 185 development and adoption of DATSs across EU countries as a key element for improving the 186 sustainability performance of the agricultural sector. The QuantiFarm project encompasses 30 Test 187 Cases (TCs), each involving one or more farmers who have adopted DATSs on their commercial 188 farms and other stakeholders, such as agronomists and technology providers. As shown in Figure 1, 189 the research methodology that led to the development of the Social Sustainability Assessment 190 Framework and the Social Self-Evaluation Tool integrated a top-down approach - through a literature 191 review aimed at examining existing frameworks for the assessment of social aspects in agriculture, 192 particularly those applicable to the impact of digital technologies - with a more bottom-up 193 perspective. To this end, semi-structured interviews were conducted with the TCs to incorporate into 194 the Framework the most relevant impact areas and social issues related to the implementation of 195 digital solutions on farms. The feedback collected through ongoing interaction with farmers and other 196 5 stakeholders served to refine, improve and validate the Framework for its application. This integrated 197 approach led to the development of the Social Sustainability Assessment Framework, which was then 198 translated into a questionnaire for farmers, the Social Self-Evaluation Tool. The first application of 199 the Tool was conducted with the sample of farmers who adopted digital solutions within the TCs of 200 the QuantiFarm project. The experience gained from this first application, combined with the 201 feedback collected through ongoing interaction with the TCs, served to test the future applicability of 202 the Framework and the Tool in other agricultural contexts. 203 204 Figure 1: An overview of the methodology for the development of the framework 205 206 207 3.1. Literature review 208 A literature review was conducted to explore the application of the social sustainability concept 209 in agriculture, particularly in relation to the digital innovation process and the use of Agriculture 4.0 210 technologies. More specifically, the goal of the literature review was the identification of the main 211 areas of impact of DATSs on social sustainability, thanks to the analysis of existing frameworks, tools 212 and KPIs for assessing the social impacts in farming. The review provided a deeper understanding of 213 how digital technologies interact with the social aspects of farmers’ lives and daily work activities, 214 including work-life balance, working conditions, skills development, farm management, workplace 215 culture, and workforce development. Given the predominantly practical outcomes of the research - 216 namely, the development of a framework that could be easily used with farmers - a pragmatic 217 approach took precedence over a purely theoretical literature review. Therefore, a balance was sought 218 between narrowing the research focus to the main areas of impact and maintaining the ability to 219 account for all the nuances and types of influence that the technology could exert on farmers. 220 Consequently, rather than a systematic review, our work more aligns with the concept of 221 scoping/mapping review (Paré et al. 2015), or integrative review, defined by Torraco (2005) as: “…a 222 form of research that reviews, critiques, and synthesizes representative literature on a topic in an 223 integrated way such that new frameworks and perspectives on the topic are generated.” As underlined 224 by Elsbach & Knippenberg (2020), an integrative review could consolidate evidence but also generate 225 new insights to advance a specific field of study. 226 For the literature review, the PRISMA (Preferred Reporting Items for Systematic reviews and 227 Meta-Analyses) guidelines were followed, as described in Figure 2 (Page et al., 2020). This approach 228 allowed for a transparent and structured identification, selection, and evaluation of relevant studies, 229 6 ensuring the reliability and validity of the review process. Thus, a four-step review methodology was 230 followed, consisting of: 1) the definition of an appropriate search strategy, including keywords and 231 databases, 2) the delineation of boundaries and inclusion and exclusion criteria, 3) a first screening 232 and selection of papers, and 4) the full-text analysis of papers and extraction of relevant information. 233 Based on the authors’ expertise and previous literature analysis, a set of keywords related to 234 digital technologies in agriculture was selected. The keyword “Agriculture 4.0”, which refers to a 235 wide array of digital technologies and solutions used in agriculture (Maffezzoli et al. 2022), has been 236 complemented by other keywords frequently used as synonymous terms in academia and industry: 237 “Precision Farming”, “Precision Agriculture”, “Smart Farming”, “Smart Agriculture” and “Digital 238 Agriculture”. This allowed for the inclusion of studies published before the widespread adoption of 239 the Agriculture 4.0 concept, encompassing digital agricultural technologies not explicitly categorised 240 under the Agriculture 4.0 paradigm by the authors. The selected keywords were combined with the 241 keyword “Social”: “Agriculture 4.0” AND “Social”, “Precision Farming” AND “Social”, “Precision 242 Agriculture” AND “Social”, “Smart Farming” AND “Social”, “Smart Agriculture” AND “Social”, 243 “Digital Agriculture” AND “Social”. These queries were considered sufficient to allow the retrieval 244 of research articles relevant to our objective, enabling a thorough investigation of the main areas of 245 social impact of DATSs. 246 The literature search was undertaken through Scopus, the largest abstract and citation database, 247 selected for its international recognition, multidisciplinary coverage, and comprehensiveness. The 248 database search was carried out using each query individually. To narrow the scope of the analysis 249 and ensure that the papers retrieved from Scopus were relevant to the most recent digital technologies 250 - falling under the umbrella of Agriculture 4.0 - the search was limited to publications from the last 251 ten years, i.e., from 2013 onwards. Moreover, since a preliminary analysis of the papers found in Scopus 252 using the first two queries, “Agriculture 4.0” AND “Social” and “Precision Farming” AND “Social”, 253 returned many articles without a clear focus on the social dimension, the search strategy for the 254 subsequent queries was refined by applying a “Subject area” filter in Scopus, limiting results to those 255 in the “Social Sciences” domain. 256 After the elimination of duplicated papers - that is, those retrieved in Scopus by more than one query 257 - the screening process involved the analysis of the title, keywords and abstract of each paper. This 258 led to the exclusion of the articles that did not have a clear and substantive focus on the social impacts 259 of digital technologies on farmers. The number of papers identified for each query is shown in Figure 260 2, along with the total number of papers subjected to full-text analysis after excluding duplicates and 261 those not aligned with the research objectives. 262 From a total of 857 publications found on Scopus, 15 were excluded because they were duplicates, 263 while 687 were excluded because they addressed the topic of social impacts only superficially or 264 failed to provide impact areas, indicators, or tools related to the social aspects of digital technologies 265 in agriculture. A total of 155 papers were included in an in-depth analysis to extract relevant 266 information, such as existing frameworks, tools and KPIs on social impacts of DATSs. Each paper 267 was thoroughly reviewed, and those demonstrating a direct relationship with social impacts on 268 farming were entered into an Excel database, where key information and insights were systematically 269 recorded. 270 271 7 Figure 2: Flow diagram of literature review (adapted by the authors based on PRISMA 2020) 272 273 274 The analysis of the scientific literature was complemented by grey literature, specifically focusing on 275 reports from government agencies, NGOs, international institutions (e.g. World Bank), UN Agencies 276 (e.g. FAO, IFAD), and industry associations working on the social aspects of sustainability in 277 farming. These sources were carefully reviewed and cross-referenced with the scientific literature. 278 Including grey literature allowed us to capture a broader range of perspectives, enriching the evidence 279 beyond what is available in peer-reviewed journals. This approach also made it possible to integrate 280 context-specific insights and practical knowledge from real-world applications, leading to a more 281 holistic understanding of the social implications of DATSs adoption. 282 3.2. Integration of a bottom-up approach 283 Given the practical goal of the research, the top-down literature review was complemented with 284 a bottom-up approach. The importance of combining top-down and bottom-up methods in social 285 science and social sustainability assessment is increasingly recognised (Ochsner et al., 2017), not 286 only to improve the comprehensiveness of analyses but also to ensure a more holistic understanding 287 of complex societal dynamics and the inclusion in the assessment process of all the issues that truly 288 matter on the ground (Magee et al., 2013). As highlighted in Yin's seminal work on Case Studies 289 (Yin, 2017), the value of interviews lies in their ability to provide direct and context-specific insights 290 into complex situations, allowing for the collection of detailed information about the experiences of 291 the interviewees. Interviews not only offer an immediate understanding of social dynamics but also 292 enrich the analysis and offer a deeper understanding of the phenomenon under study (Yin, 2017). 293 Thus, 30 semi-structured interviews were organised with the Test Cases (1 interview for each TC), 294 involving farmers using DATSs, but also agronomists and technology providers engaged in each TC. 295 All the interviews were conducted remotely through the Microsoft Teams platform and lasted 296 approximately one hour each. While the focus of the interviews was on farmers, agronomists and 297 8 technology providers were also involved, as they served as contact points between the farmers and 298 the project and played a crucial role in facilitating communication, as many of the farmers did not 299 speak English, the language of the interviews. Moreover, agronomists and technology providers gave 300 valuable insights into the social implications of DATSs, offering a complementary perspective to that 301 of the farmers. A semi-structured set of open-ended questions was used to conduct the interview in 302 order to balance consistency with the research objectives, but also to ensure enough flexibility for 303 participants to elaborate on their experiences and perspectives. The question addressed: 1) the 304 business and agronomical context of the farms involved in the TC; 2) the reasons behind the DATSs 305 adoption; 3) the main impacts of DATs in terms of agronomic practices and farm management; 4) the 306 barriers, problems and limitations to DATSs use; 5) the main benefits of DATs; 6) the impacts that 307 DATSs had on the social dimension, including the implication the digitalisation process had on 308 farmers lives and farm management. Each interview has been recorded and the audio transcribed to 309 ensure accurate capturing of all information. The transcriptions of all the interviews were analysed 310 using a “manual approach”, due to the relatively small scale of the sample and the need to maintain 311 a more flexible, nuanced understanding of the social impacts and issues mentioned by each 312 stakeholder. As underlined by Mattimoe et al. (2021), a manual approach “can facilitate a closeness 313 to the qualitative data” and “facilitate the identification of themes in an organic manner”. Also, 314 Maher et al. (2018) suggest that a manual approach “encourages more meaningful interaction with 315 the data, compared to a technological approach”. Recurring themes, issues and concepts regarding 316 the social impacts of DATSs were extracted from the text and collected to be compared and integrated 317 with the outputs of the literature analysis. By doing so, the bottom-up process enriched the top-down 318 analysis (literature review), offering direct and concrete insights into the social implications of 319 DATSs, specifically from the perspectives of farmers and relevant stakeholders involved in field 320 activities. This approach not only ensured that real-world context was incorporated in the research 321 but also helped identify the key aspects around which to structure the framework, focusing on the 322 issues that most directly affect farmers in their daily work and lives. 323 324 3.3. Framework application to Test Cases 325 To test and apply the framework, we used the sample of 30 Test Cases involved in the QuantiFarm 326 project, comprising commercial farms operating in 20 European countries, and 7 agricultural sectors, 327 including 20 different crops and animals. Each TC includes one or more farmers who have recently 328 adopted one or more categories of DATSs, providing a valuable sample for the application and testing 329 of the Framework and Tool. In Table 1, the sector of each Test Case is reported, together with the 330 type of crop or animal production, the category of DATSs implemented in the farm, the country, the 331 total production managed through technology (expressed in terms of ha or total number of animals), 332 and the number of farmers using DATSs. The categories of DATSs adopted across the 30 TCs are: 333 • Decision Support Systems (DSS) 334 • Farm Management Systems 335 • Variable Rate Technologies (VRT) 336 • Precision Irrigation Systems 337 • Digital Pest Control Systems 338 • Automated Greenhouses 339 • Feeding robots 340 • Milking robots 341 • Sensors for quality assessment (for the aquaculture sector) 342 • Automated monitoring, activity sensors, heat and calving detectors (for the meat and 343 dairy sector) 344 The final sample consists of 60 farmers, as some TCs involved more than one farmer. While the 345 sample size may seem limited, the heterogeneity in agronomic, socio-economic contexts, and 346 9 technological settings represents a strength of this research, allowing for a more comprehensive 347 investigation of the social impacts of DATSs across diverse backgrounds and environments. 348 Moreover, this sample of farmers enabled the evaluation of the Framework's and Tool's applicability 349 in various agronomic and socio-economic settings, thus paving the way for future applications in 350 different contexts. 351 Table 1: Overview of the Test Cases (TC) to which the framework was applied 352 TC Sector Production DATSs category Country DATSs management n. of farmers using DATSs 1 Arable Potatoes DSS Greece 0.85 ha 2 2 Arable Corn Precision Irrigation system, VRT Portugal 29.2 ha 2 3 Arable Barley, Wheat DSS Spain 30.6 ha 1 4 Arable Cotton VRT Greece 5.1 ha 3 5 Arable Wheat DSS Turkey 105 ha 8 6 Arable Wheat, Onion, Potatoes DSS Netherlands 3.5 ha 1 7 Arable Potatoes DSS Poland 98 ha 2 8 Arable Wheat, Rapeseed, Rye, Barley DSS Latvia 1 silo 1 9 Arable Corn, Wheat DSS Slovenia 17 ha 1 10 Arable Wheat DSS Romania 553 ha 1 11 Horticulture Olives DSS Greece 8.6 ha 5 12 Horticulture Apples DSS; Digital pest control System Poland 1 ha 1 13 Horticulture Grapevine DSS Italy 1.1 ha 1 14 Horticulture - Indoor farming Strawberries and Blueberries DSS Serbia 3.4 ha 3 15 Horticulture Olives DSS Cyprus 5.1 ha 5 16 Horticulture Apples DSS; Digital pest control System Netherlands 1 ha 1 17 Horticulture Grapevine DSS Romania 14 ha 1 18 Horticulture Tomatoes DSS Italy 60.5 ha 9 19 Horticulture - Indoor farming Tomatoes Automated Greenhouses Netherlands 6 ha 1 20 Horticulture Bananas Precision Irrigation System Spain 2.2 ha 1 10 21 Horticulture - Indoor farming Tomatoes Automated Greenhouses Finland 1.2 ha 1 22 Meat Poultry Farm management system UK 64,000 birds 1 23 Meat Cows Feeding robot; Heat and calving detectors France 302 cows 1 24 Meat Pigs Farm management system Belgium 682 pigs 1 25 Dairy Cows Feeding robotics + Activity Sensors France 207 cows 1 26 Dairy Cows Milking Robot Ireland 180 cows 1 27 Dairy Cows Automated monitoring Germany 250 cows 1 28 Dairy Cows Milking Robot; Feeding robotics Romania 803 cows 1 29 Apiculture Bees Automated Monitoring Lithuania 10 beehives 1 30 Aquaculture Oysters Sensors for quality assessment Croatia 5,000.0 m2 1 353 4. RESULTS 354 4.1. The Social Sustainability Assessment Framework and the Social Self-355 Evaluation Tool 356 357 Based on the findings from the literature analysis (section 3) and the semi-structured interviews 358 with the Test Cases (section 3.2), the areas of the social dimension most impacted by the adoption of 359 DATSs were identified. Despite the widespread use and established nature of some questionnaires 360 and indicators for social impacts and working conditions assessment found in literature, such as those 361 proposed by Eurofound (2016) or Horodnic et al. (2019), there is a notable scarcity of questionnaires 362 tailored to the primary sector and, more specifically, focused on the adoption of innovative 363 technologies by farmers, failing to incorporate certain social indicators that are significantly impacted 364 by the implementation of DATSs. Thus, the first result of this work has been the identification of the 365 9 key social impact areas associated with DATSs adoption, listed in Table 2. 366 367 Table 2: Key social impact areas of DATSs 368 Social impact area Definition Some references 11 Data Every aspect related to data collection, usage, ownership, sharing and privacy Rotz et al., 2019; McGrath et al., 2023; Wisemana et al., 2019 Food Quality/Safety The influence of DATSs on the quality and safety of food delivered to consumers, including traceability and transparency issues Guruswamy et al., 2022; Food Availability The influence of DATSs on the productivity of farms and the consequent availability of food in local, regional and global contexts Benfica et al., 2023 Labour Evolution Every aspect related to the farmers' activities and farm management impacted by DATSs Rotz et al., 2019; Salvia, 2019 Inclusive Growth The influence of DATSs in creating equitable opportunities for all individuals Hernandez et al., 2024 Education and Learning All the aspects concerning skills, education, and technical competences required to adopt and use DATSs, but also the impacts that new technologies can have on reskilling processes and learning opportunities for farmers Lundström et al., 2018; Gardezi et al., 2022 Gender Equality The impacts that DATSs could have on the creation of opportunities for women to gain power and leadership in the sector, but also the possible digital and economic divide that new technologies can create Ofisi & Lukamba, 2020; Abdulai 2022; Huyer, 2016; Hernandez et al., 2024 Ruralisation All those processes concerning the revitalisation of rural areas and the impacts that DATSs could have on the creation of new job opportunities for young people outside urban areas European Commission, 2023; Rolandi et al., 2021 Generational Change The role of young farmers in innovation, the attractiveness of farming for young people and the generational change within farm companies Kabadzhova, 2022; Afere et al., 2019 369 Although the adoption of digital technologies has both positive and negative effects on farmers 370 across all the identified social areas, the focus of the Framework was placed on those areas that, 371 according to the literature and the information gathered directly from the Test Cases, can be 372 considered the most directly impacted: Labour Evolution, Education and Learning, Generational 373 Change, and Gender Equality. Food Safety is more closely related to analyses involving downstream 374 actors in the food value chain and was not included in the Framework. Similarly, since the areas 375 related to Food Security, Inclusive Growth and Ruralisation go beyond impact assessments at the 376 individual farm level and require territorial or regional approaches, they were excluded from the 377 Framework. The area of Data Concerns was also excluded from the Framework, as it is more closely 378 related to technical issues rather than social ones. 379 Labour evolution is a central topic in the literature on the social impact of DATSs, representing 380 the area most directly affected by digitalisation and the one in which farmers experience the greatest 381 impact from the transformative role of digital technologies (Rotz et al., 2019). A substantial part of 382 the Framework’s development focused on this area of impact, within which three specific sub-areas 383 were identified: Work Dynamics and Activities, Work-related Stress, and Work-life Balance. 384 Work Dynamics and Activities refer to the role that DATSs play in transforming the day-to-385 day work of farmers in terms of monitoring, automation, decision-making, resource optimisation and 386 farm management (McGrath et al., 2023; Rotz et al., 2019). In this regard, it is relevant to understand 387 whether DATSs help the farmer in his daily activities and how his tasks and workload change. 388 12 Work-related Stress embeds the physiological, psychological, and behavioural responses that 389 individuals may experience when the demands of their job exceed their ability to cope effectively 390 (Michie, 2002). Nevertheless, the impact of DATSs on work-related stress remains uncertain 391 according to the scrutinised papers. On one hand, the solutions have the potential to reduce farmers' 392 workload, thereby providing them with more relaxed working schedules. On the other hand, the 393 adoption of new technologies may introduce additional stress and intensify work demands as 394 individuals strive to familiarise themselves with the technology (Smith & Carayon, 1995). 395 Work-life Balance does not imply only an equal distribution of time between work and personal 396 life but rather entails the ability to effectively manage and harmonise these two domains, ultimately 397 enhancing both the quality of life and work outcomes. When successfully achieved, work-life balance 398 can generate positive spill-over effects, benefiting not only the individuals directly involved but also 399 all other stakeholders. In this regard, the adoption of digital solutions has shown promise in 400 facilitating this delicate equilibrium by enabling more efficient task completion and promoting 401 conscious utilisation of data (Wolor, 2020; Esguerra, 2020; Čehovin & Kohont, 2017). 402 Education and Learning are a prerequisite for the dissemination of DATSs, but at the same time, 403 can be promoted by their adoption. Agriculture 4.0 technologies require hard skills and technical 404 competencies to fully exploit their potential. This means that a certain learning effort and re-skilling 405 processes are often required from farmers. Therefore, it is important to analyse the effort that farmers 406 had to exert to use a new technology, the difficulties related to understanding how it works and the 407 possible stress generated by the learning process. 408 Generational Change refers to the attractiveness of the agricultural sector for young people and 409 the impacts that new digital technologies could have on the business succession to the new generation 410 of farmers. Understanding the perceptions of young individuals regarding agriculture as a viable and 411 appealing career choice is essential for addressing the challenges associated with attracting and 412 retaining young talent and promoting economic development and employment in rural areas. 413 Historically, agriculture has struggled to attract young individuals, largely due to perceived factors 414 such as low prestige, manual labour, and limited opportunities for growth and innovation 415 (Kabadzhova, 2022; Afere et al., 2019). Our goal is to investigate whether the integration of digital 416 solutions and the resulting increased entrepreneurial opportunities make the sector more attractive to 417 young people and farmers’ sons. 418 Gender Equality and the issues related to the gender gap in agriculture encompass the disparities 419 and unequal treatment experienced by men and women within the agricultural sector (OECD, 2018). 420 This gap is apparent in multiple dimensions of agriculture, such as land ownership and tenure, 421 availability of credit and financial services, control over productive assets, involvement in decision-422 making processes, access to education and training, and representation within agricultural 423 organisations and institutions (Fremstad & Paul, 2020). Various social, cultural, economic, and 424 institutional factors contribute to the perpetuation of the gender gap in agriculture (Ali et al., 2016). 425 While several studies have examined the impact of gender on technology adoption in agriculture, 426 there remains a lack of research exploring the influence of digital solutions on the gender gap. Despite 427 this being a central topic in today's debate, especially in a traditionally male-dominated sector like 428 agriculture, the issue will be explored in more depth during the second year of the project, and it is 429 not included in the framework presented below. 430 Building on the identified key social impact areas, the Social Sustainability Assessment Framework 431 was developed, as illustrated in Figure 3. For each key social impact area, a set of relevant themes 432 and indicators was identified to be included in the Framework, the evaluation of which is essential 433 for assessing the real social impact of DATSs. Several studies in the literature (e.g. Eurofound 2016; 434 Horodnic et al., 2019; Boxal & Macky, 2014) have provided a robust foundation, having undergone 435 rigorous testing and demonstrating efficacy in identifying relevant social indicators. However, these 436 studies were not specifically designed to assess the impacts of DATSs. To address this gap and ensure 437 a comprehensive examination of the distinctive aspects of the agricultural sector, the Social Self-438 Evaluation Framework incorporates certain indicators already present in the literature with novel 439 13 indicators focusing on work-life balance (Wiradendi Wolor, 2020; Esguerra, 2020), work-related 440 stress (Persechino et al., 2013), and the attractiveness of the sector for young individuals (Afere et 441 al., 2019). 442 Building on the Social Sustainability Assessment Framework, the Social Self-Evaluation Tool was 443 developed as a questionnaire consisting of 23 rating scale items to be administered to farmers. The 444 rating scale system is used to measure the respondent’s opinion and attitude towards each item. Rating 445 scales are frequently employed in the social sciences to measure attitudes. One commonly used 446 instrument is the Likert-type scale (Tanujaya et al. 2022). Likert scales assess respondents’ attitudes 447 by asking them to indicate their level of agreement or disagreement with a series of statements related 448 to a specific topic (Croasmun & Ostrum, 2011). Some researchers argue that increasing the number 449 of points makes the scale more representative and closer to a universal system. However, others 450 believe that increasing the number of items beyond the minimum needed does not significantly 451 improve reliability, and more response options can decrease response quality and consistency due to 452 increased mental effort (Croasmun & Ostrom, 2011). Some research indicates that answer quality 453 declines with more than eleven options and that there are no additional psychometric benefits beyond 454 six options, with the optimal number being between four and six (Tanujaya et al., 2022). 455 456 Figure 3:the Social Sustainability Assessment Framework 457 458 459 A Likert-type scale was therefore used for the Social Self-Evaluation Tool with an ordinal data 460 type and five answer choices for each question: strongly disagree (SD), disagree (D), neither agree 461 nor disagree (N), agree (A), and strongly agree (SA). The questions are divided as follows: 462 • 15 questions on Labour Evolution – 10 on Work Dynamics and Activities, 3 on Work-463 related Stress and 2 on Work-life Balance 464 • 5 questions on Education and Learning 465 • 3 questions on Generational Change 466 14 The Social Self-Evaluation Tool was distributed to the sample of farmers for completion, with the 467 data being collected and returned through the contact points of each TC. These contact points 468 facilitated communication and ensured the farmers' participation in the process, allowing for a 469 systematic collection of the social impact data generated through the application of the tool. 470 4.2. First application of the Social Self-Evaluation Tool 471 472 The analysis of the data collected through the Social Self-Evaluation Tool administered to the 473 farmers shows that digital solutions seem to contribute positively to the social sustainability of 474 farming in line with what has been found on economic and environmental aspects by other researchers 475 (Papadopoulos et al., 2024). Indeed, considering the responses obtained from farmers from the first 476 application of the Tool, it appears that DATSs positively impact social aspects concerning all three 477 key impact areas identified within the Framework: Labour Evolution, Education and Learning, and 478 Generational Change. The overall farmers’ perception of DATSs seems to be quite positive, even in 479 cases where the technology has been adopted quite recently and farmers are still getting accustomed 480 to it. 481 In Figures 4, 5, 6 and 7, the results obtained from the Tool application are shown for each item. 482 To facilitate an immediate interpretation of results, data are expressed in terms of the percentage of 483 all the responses for each Likert class. Concerning the dimension of “Labour Evolution”, specifically 484 the “Work Dynamics and Activities” in Figure 5, the most evident positive impact is the enhanced 485 ability to make decisions more consciously and efficiently, a factor observed in over 90% of cases, 486 considering the sum of the farmers who strongly agree and those who agree with that sentence. This 487 aligns with the fact that more than half of the implemented DATSs fall into the DSS category, hence 488 solutions that are specifically designed to support the decision-making process of farmers. Moreover, 489 most of the other technologies incorporate sensors, IoT and monitoring systems that can valorise the 490 large amount of data generated in fields, stables or greenhouses and support day-to-day farm 491 management. DATSs also appear to contribute significantly to tackling complex tasks and addressing 492 unforeseen problems, with 72% and 64% of respondents agreeing or strongly agreeing, respectively. 493 The implementation of new digital technologies in farms has allowed more than half of the farmers 494 to have more time to focus on other tasks, likely due to a reduction in the workload in areas where 495 DATSs can assist. In nearly half of the cases, farmers perceive that DATSs reduce the intensity and 496 complexity of work activities, improve their planning, and allow them to better calibrate the speed of 497 execution. Despite the occasional perception that digital technologies might replace farmers and their 498 managerial and decision-making roles, only a small portion of respondents (12%) feel that their role 499 is being "replaced" by DATSs. 500 Currently, the perceived contribution of DATSs to certification and compliance with production 501 standards remains limited. However, this perception is likely to evolve in the coming years, given the 502 increasing importance of certifications related to sustainability, provenance, and traceability for both 503 consumers and food companies. Digitalisation has the potential to significantly enhance third-party 504 certification processes, thereby increasing the relevance and utility of DATSs in this domain. 505 Adopting new digital technologies demands that farmers acquire new skills to operate and maintain 506 innovative solutions they are not used to. In addition, steep learning curves and the time required to 507 achieve proficiency with these new technologies can be daunting, particularly for farmers less 508 accustomed to technology and more tied to “traditional” management. This could lead to work-related 509 stress, which in turn can reduce the acceptance of DATSs and curb their adoption. Despite these 510 considerations, it can be seen in Figure 4 that most farmers are not affected by stressful factors, from 511 a physical and emotional point of view. Instead, for 45% of respondents DATSs appear to mitigate 512 stress stemming from external factors like climate change. 513 A key component to consider in assessing farmers' well-being in terms of their work and their 514 quality of life, and consequently the impact that DATSs can have on these areas, is Work-life Balance 515 (Herrera Sabillòn et al., 2021). Our analysis suggests that farmers perceive DATSs as increasing their 516 15 available free time, providing more opportunities to spend quality time with family and friends. These 517 results stem not only from having more free time but also more regular working hours, better aligned 518 with those of other types of employment. 519 520 521 522 Figure 4: Impacts of DATSs on Work-related Stress and Work-life Balance. Values represent the percentage of respondents in each class for each item (sample: 60 farmers). Figure 5: Impacts of DATSs on Work Dynamics and Activities. Values represent the percentage of respondents in each class for each item (sample: 60 farmers). 16 It is well recognised how technical knowledge and skills, both soft and hard, are essential to facilitate 523 the adoption of DATSs (Geng et al., 2024) and can limit their successful use and resulting benefits 524 (Trendov et al., 2019). As can be seen in Figure 6, more than 4 out of 5 farmers had to learn new 525 things to use DATSs. Nevertheless, farmers perceive that they have engaged in an interesting and 526 motivating learning and upskilling process, and so, for more than 90% of respondents, the need to 527 acquire new knowledge is viewed entirely positively and not as a burden on their work. Contrary to 528 common assumptions, the learning process is not experienced as time-consuming, nor is it regarded 529 as complicated or stressful. 530 Digitalisation should not be regarded solely as a consequence of generational renewal in 531 agriculture; rather, it ought to be recognised also as a catalyst for this process. By making the sector 532 more appealing to younger generations, digital innovations can actively facilitate and accelerate 533 generational turnover within agricultural enterprises. (Farrell et al., 2021; Borda et al., 2023). This is 534 also what the farmers involved in this work perceive, as can be seen in Figure 7: 65% of them believe 535 that DATSs are capturing the interest of younger people in working on the farm or in the agricultural 536 sector, thereby facilitating the transition in company management (47% of respondents). All of this 537 helps half of the farmers become less uncertain about the future of their business. 538 539 Figure 7: Impacts of DATSs on Generational Change. Values represent the percentage of 540 respondents in each class for each item (sample: 60 farmers). 541 542 Figure 6: Impacts of DATSs on Education and Learning. Values represent the percentage of respondents in each class for each item (sample: 60 farmers). 17 5. DISCUSSION 543 544 The main contribution of this work lies in the development of a Framework for the analysis of 545 social sustainability impacts of digital agriculture solutions. This Framework identifies the key areas 546 of social impact and, within each area, delineates specific indicators for assessing social outcomes.. 547 The Framework was designed to be operationally usable for assessments and self-assessments, 548 providing a structured approach, simple yet effective, to evaluate the social implications of DATSs 549 adoption by farmers. To test its practical usability, we developed and implemented a first pilot 550 application in the form of a Social Self-Evaluation Tool, which enabled the collection and assessment 551 of social impacts experienced by farmers following the implementation of digital solutions in their 552 farms. Existing frameworks and indicators in the literature on social sustainability in agriculture do 553 not specifically focus on the effects of digitalisation, which is a growing area of interest within the 554 primary sector. Moreover, among the papers reviewed in the literature analysis that address the social 555 impacts of DATSs, a lack of structured frameworks emerges for understanding the positive and 556 negative repercussions of digital solution adoption by farmers. Some existing frameworks for 557 analysing the social aspects of digitalisation in agriculture, despite their significant scientific and 558 theoretical value, are often distant from the perspective of the individual farmer. These frameworks 559 address important issues, but they focus on broader societal impacts, which are not aligned with the 560 primary objective of this study: to understand the effects of digitalisation on the social sphere more 561 directly related to farmers' daily lives and work. 562 The Framework and the corresponding Tool were tested on a sample of 60 farmers who adopted 563 different types of DATSs. This allowed for an assessment of its usefulness and applicability, as well 564 as the collection of valuable insights about the group of farmers involved in the project. The analysis 565 of the results from the first application of the Framework has revealed a generally positive perception 566 of DATSs, with several beneficial impacts identified. DATSs were found to be effective in reducing 567 workload, increasing work productivity, and simplifying complex tasks. Although limited hard skills 568 and technical knowledge are recognised as common barriers to the widespread adoption of DATSs, 569 farmers were not deterred by the necessity for learning and did not perceive the required training as 570 stressful. Conversely, the process of learning to utilise DATSs was regarded as engaging and 571 intellectually stimulating. Furthermore, in most cases, farmers observed that the introduction of 572 digital solutions heightened the interest of younger generations in agriculture, reinforcing the notion 573 that digital innovations can facilitate the generational transition and ensure the continuity of the 574 agricultural sector. The present work attempted to define a tool for assessing the social impacts of 575 DATSs in real farming conditions, leading to good relevance of the collected data, but posing some 576 challenges. Indeed, the practical and time-related requirements of the project have necessitated a 577 compromise between the amount of information that could have been asked from the farmers and the 578 ability to thoroughly investigate and rigorously explain all the social issues related to the adoption of 579 DATSs. The research methodology, therefore, was focused on reviewing existing literature on the 580 social impacts of DATSs and subsequently developing a framework that could have been easily 581 interpreted by farmers. Since the Framework collected responses from DATSs adopters involved in 582 the QuantiFarm project, the total number of respondents was only 60, whereas a larger sample size 583 would have allowed for greater robustness. However, it is important to highlight the geographical 584 scope and the internal diversity of this sample. This heterogeneous group of farmers, albeit seemingly 585 not sizable, allowed us to assess the framework in different agronomical contexts, sectors (i.e. arable, 586 horticulture, livestock, apiculture, aquaculture), socio-economic, and technological backgrounds, 587 ensuring a more comprehensive understanding of the social impacts of DATSs in agriculture. To 588 obtain a more statistically robust analysis of the social impacts of DATSs, the Social Self-Evaluation 589 Framework will be applied throughout the remaining two years of the project. This longitudinal 590 approach will enable an examination of the evolution of social impacts over time, while also allowing 591 farmers to adapt to the technologies, become familiar with their usage, and more accurately assess 592 the perceived experiences and benefits resulting from their adoption. This will lead to a more robust 593 18 evaluation of the social impacts of DATSs. Furthermore, a tool like the Social Self-Evaluation 594 Framework could be applied in other contexts with a higher number of potential respondents, such as 595 the annual research on Agriculture 4.0 conducted by our research group, the Smart AgriFood 596 Observatory of the Politecnico di Milano, which involves the participation of hundreds of farmers 597 every year. 598 The TCs and farmers' active involvement with a bottom-up approach facilitated the successful 599 development of the Framework and contributed to a robust and inclusive data collection process. This 600 bottom-up approach has allowed for the validation of the questionnaire's robustness and the 601 identification of potential responses to impacts not yet analysed in the literature. Additionally, through 602 the continuous interaction with farmers, TCs and other stakeholders involved in the project, the Social 603 Self-Evaluation Framework will be integrated with new indicators, primarily focusing on the Gender 604 Gap and the role of DATSs in improving the condition of women in agriculture. The qualitative nature 605 of social indicators and the challenge of attributing impacts solely to digital solutions underscore the 606 need for a process of "data normalisation" in subsequent data collection rounds. This normalisation 607 aims to refine the analysis by excluding contingencies and external factors, providing a more accurate 608 view of the true impact of digital technologies on social dimensions. 609 6. CONCLUSIONS 610 611 This work addressed a critical gap in the existing literature by developing a Framework, 612 specifically tailored to the primary sector, to assess the impacts of digitalisation on a diverse range of 613 social aspects. Indeed, despite the existence of frameworks in scientific literature focusing on 614 assessing social sustainability and working conditions in companies, there is a lack of social 615 frameworks tailored to the primary sector and specifically focused on DATSs adoption. For these 616 reasons, the Social Sustainability Assessment Framework has been developed to offer an operational 617 tool to evaluate the social implications of DATSs adoption by farmers. 618 The approach followed to develop the Social Sustainability Assessment Framework, with the 619 integration of a literature analysis and a bottom-up process with semi-structured interviews with 620 farmers and other relevant stakeholders, has proven to be effective in identifying the most relevant 621 social issues related to DATSs adoption. The developed Framework offers an opportunity for further 622 exploration of critical aspects that can be impacted by the digital innovation process, such as the 623 evolution of work, generational change, and gender equality in the agricultural sector. In this way, 624 the Social Sustainability Assessment Framework not only offers a valuable initial contribution to the 625 social sustainability assessment of DATSs, but also lays the groundwork for future research aiming 626 to develop robust methodologies for assessing and addressing the social impacts of digital innovations 627 in the agricultural sector. 628 In order to test the practical applicability of the Social Sustainability Assessment Framework, 629 we developed and implemented a Social Self-Evaluation Tool, which enabled the collection and 630 assessment of social impacts experienced by a sample of 60 farmers following the implementation of 631 digital solutions in their farms. The overall results of the first year demonstrated the real benefits that 632 the adoption of DATSs could have on farmers and farm management, not only in terms of economic 633 and environmental sustainability (broadly demonstrated by other researchers), but also regarding 634 social impacts. Even more importantly, the application of the Framework allowed us to demonstrate 635 both its utility and its simplicity in use with farmers in assessing the social sustainability of DATSs. 636 It is increasingly evident that the need to consider social sustainability arises whenever 637 discussing and working on the digitalisation of agriculture. The social impact areas and issues 638 identified in our Social Sustainability Assessment Framework, along with the data collected through 639 the Social Self-Evaluation Tool, demonstrate that policymakers, technology providers, 640 agribusinesses, agronomists, and all stakeholders involved in the digital innovation of the primary 641 sector could benefit from using frameworks and tools like the one developed in this study to guide 642 the adoption of digital solutions in agriculture. By focusing on the social impact areas and issues 643 19 identified in this work, policies can help ensure that the digital transformation in agriculture not only 644 drives economic and environmental sustainability but also promotes social benefits, such as inclusive 645 growth and improvements in working conditions on farms. The adoption of such frameworks can be 646 instrumental in the design of targeted policy interventions aimed at addressing social issues in rural 647 areas, ensuring that technological advancements benefit all members of the farming community. 648 From the perspective of technology providers, it is equally crucial to consider the social implications 649 of DATSs that could be linked to the technical aspects of their development. The usability, ease of 650 use, and reliability of digital tools play a significant role in determining farmers' willingness to adopt 651 and effectively implement these technologies. By integrating social sustainability into the design and 652 development of their solutions, technology providers can ensure that their products promote 653 inclusivity, equity, and social benefits, without causing stress or introducing additional complications 654 to farm management. 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