Enabling Technologies in Citrus Farming: A Living Lab Approach to 1 Agroecology and Sustainable Water Resource Management 2 Giuseppe Timpanaro 1, Giulio Cascone 1*, Vera Teresa Foti 1 3 1 Department of Agriculture, Food and Environment, University of Catania, Via S. Sofia 100, 95123, 4 Catania, Italy. giuseppe.timpanaro@unict.it; giulio.cascone@phd.unict.it; v.foti@unict.it 5 6 This article has been accepted for publication and undergone full peer review but has not been through 7 the copyediting, typesetting, pagination and proofreading process, which may lead to differences 8 between this version and the Version of Record. 9 Please cite this article as: 10 Timpanaro G., Cascone G., Foti VT (2025). T Enabling Technologies in Citrus Farming: A Living 11 Lab Approach to Agroecology and Sustainable Water Resource Management, Bio-Based and Applied 12 Economics, Just Accepted. DOI:10.36253/bae-17357 13 14 Highlights 15 • Enabling technologies accelerate agroecological transition in inland agriculture. 16 • Sensors, DSS, and digital tools reduce water consumption on citrus farms. 17 • Digital technologies boost yield per hectare and increase net profit. 18 • Living Labs foster knowledge transfer, reducing resistance to innovation. 19 • Monte Carlo simulation reveals key drivers affecting economic outcomes. 20 21 Abstract 22 This study examines the role of enabling technologies in the agroecological transition, focusing on 23 sustainable water management in citrus farming through the participatory approach of a Living Lab 24 in the Inner Area of Calatino in Sicily. The analysis is based on a comparison of two citrus farms: 25 one equipped with advanced digital tools (sensors, decision support systems, and real-time 26 monitoring), and one with a traditional management approach. Through the joint application of 27 mailto:giuseppe.timpanaro@unict.it mailto:giulio.cascone@phd.unict.it mailto:v.foti@unict.it economic analysis, Monte Carlo simulation and sensitivity analysis, it was possible to estimate the 28 effects of technology adoption. Findings reveal that enabling technologies reduce water consumption 29 by 33%, increase yield per hectare by 16%, and boost net profit by 25% (+€2,780/ha), enhancing 30 resource efficiency and lowering operational costs. Additionally, the Living Lab facilitated 31 knowledge transfer, fostered collaboration, and mitigated resistance to innovation, highlighting the 32 need for targeted training and institutional support to promote broader adoption. 33 These results provide valuable insights for policymakers and stakeholders, demonstrating how digital 34 solutions can drive sustainability, economic viability, and resilience in agriculture, but also for 35 farmers, providing operational tools to improve farm efficiency and profitability. 36 37 Keywords 38 Agroecology, Enabling Technologies, Living Lab, Water Management, Citrus Farming 39 40 1. Introduction 41 In recent decades, agroecology has become a key strategy to tackle sustainability challenges in 42 agriculture. It combines ecological, economic, and social principles to address problems like soil 43 degradation, biodiversity loss, climate change, and economic inequality This paradigm not only 44 protects the environment but also offers economic advantages by fostering local markets, short supply 45 chains, and more equitable and resilient food systems (Van der Ploeg et al., 2019; D’Annolfo et al., 46 2017; Poux and Aubert, 2018). 47 Agroecology successfully integrates environmental sustainability with agricultural productivity 48 through practices that enhance soil fertility, promote crop diversification, and reduce reliance on 49 chemical inputs. Studies have demonstrated that agroecological systems can achieve yields 50 comparable to those of conventional agriculture while delivering significant benefits in terms of lower 51 environmental impact and increased resilience to climate change (D’Annolfo et al., 2017; Poux and 52 Aubert, 2018). Moreover, adopting agroecological practices improves the quality of food produced, 53 contributing to human health and the well-being of farming communities (Belliggiano and Conti, 54 2019). 55 Other studies have highlighted how agroecological systems can generate economic benefits for 56 farmers by reducing dependence on external inputs and increasing long-term profitability (Van der 57 Ploeg et al., 2019; D’Annolfo et al., 2017). However, the agroecological transition requires adequate 58 support from public policies, including instruments that promote the adoption of agroecological 59 practices and facilitate market access for small-scale producers (Gava et al., 2022; Schiller et al., 60 2020). Agroecology not only promotes more sustainable and resilient farming practices but also 61 represents a comprehensive approach to agri-food governance, fostering farmers' autonomy, food 62 sovereignty, and social justice (Van der Ploeg et al., 2019). 63 A key factor in accelerating the agroecological transition is the integration of Key Enabling 64 Technologies (KETs), such as digital tools, Internet of Things (IoT) sensors, artificial intelligence, 65 and precision agriculture systems, which optimize resource management and reduce waste (Chollet 66 et al., 2023; Bellon-Maurel et al., 2022). These technologies provide real-time data on soil and crop 67 status, boosting efficiency and reducing environmental impact (Fischetti et al., 2025; Ewert et al., 68 2023). By adapting practices to local conditions, KETs offer agroecology a practical path to greater 69 sustainability (Ewert et al., 2023). 70 71 However, the integration of KETs into agroecology has sparked debate within the agroecological 72 community, dividing the sector into two opposing perspectives. Traditionalists argue that 73 agroecology should preserve traditional practices and local knowledge, avoiding reliance on 74 technological tools that could disrupt the ecological and social balance of agricultural systems. 75 Modernizers see innovation as an opportunity to improve sustainability and efficiency. They support 76 the responsible integration of new technologies to make farming models more resilient (Bertoglio et 77 al., 2021; Menozzi et al., 2015; Arata and Menozzi, 2023). 78 Despite these concerns, the synergy between agroecology and enabling technologies offers significant 79 potential for sustainable development, particularly in inner areas. These territories can benefit from 80 agroecological innovation to revitalize agricultural activity and enhance local natural resources (Gava 81 et al., 2025; Verharen et al., 2021). Moreover, inner areas offer unique opportunities for 82 agroecological innovation due to the presence of traditional farming systems and the availability of 83 high-quality natural resources (Verharen et al., 2021). The integration of modern technologies into 84 agroecological production systems—through decision-support tools, knowledge-sharing platforms, 85 and mobile applications for farm management (Espelt et al., 2019; Emeana, 2021)— represents a 86 concrete opportunity to facilitate the transition to more sustainable models. These tools can help 87 reduce barriers to the adoption of agroecological practices and strengthen producers' competitiveness 88 in the market (Maurel and Huyghe, 2017). 89 In this context, Living Labs emerge as essential tools for promoting an integrated system that 90 combines technology and agroecology. These participatory innovation spaces engage farmers, 91 researchers, policymakers, and other agri-food system stakeholders, fostering the experimentation of 92 innovative solutions and facilitating knowledge transfer at the local level (Larbaigt et al., 2024; 93 Berghez et al., 2019; Giampietri et al., 2020; Ouattara et al., 2024). Living Labs serve as a bridge 94 between scientific research and agricultural practice, allowing technologies to be tailored to specific 95 territorial needs, thereby improving farmers' acceptance of new practices and enhancing the 96 effectiveness of transition strategies (Giagnocavo et al., 2022; Belliggiano and Conti, 2019). 97 A concrete example of such integration is the experimental initiative focused on citrus farming in the 98 inner area known as the "Calatino," aimed at demonstrating its economic feasibility. This territory 99 encompasses nine municipalities in central-eastern Sicily (Caltagirone, Grammichele, Licodia Eubea, 100 Mazzarrone, Mineo, Mirabella Imbaccari, San Cono, San Michele di Ganzaria, and Vizzini) all within 101 the Metropolitan City of Catania. The area represents 1.6% of the regional population and spans 102 approximately one thousand square kilometres. 103 In this Living Lab a range of integrated systems have been installed, incorporating weather stations, 104 sensors, and decision-support systems, with the aim of optimising water usage. This initiative is 105 expected to enhance resource use efficiency, while concurrently improving the resilience and 106 economic viability of the production system (Fischetti et al., 2025; Ewert et al., 2023; Rocchi et al., 107 2024). 108 Citrus farming was selected for this study because it represents one of the most relevant agricultural 109 sectors in Sicily, with more than 30 % of national citrus production, and oranges covering more than 110 60 % of the total supply (Scuderi et al., 2022). While remaining a leading global player, Italy has lost 111 leadership in the last decade due to structural criticalities in strategic areas such as Sicily (Rapisarda 112 et al., 2015), which nevertheless maintains 55 % of the national area dedicated to citrus (about 61 000 113 ha) (Istat, 2022). 114 The research was based on the hypothesis that adopting an integrated system (weather station, 115 sensors, and decision-support system) enables a more sustainable management of water resources, 116 reducing waste (water consumption) and environmental costs while positively impacting operational 117 costs, revenues, and farm economic efficiency. 118 Therefore, the following research questions were formulated: 119 • Q1. How can the integration of enabling technologies accelerate the agroecological transition 120 in inner areas? 121 • Q2. What are farmers' perceptions and resistances regarding the adoption of digital tools and 122 precision agriculture systems in the agroecological context? 123 • Q3. What economic and environmental impacts result from combining agroecological 124 practices with innovative technologies, particularly in the citrus sector? 125 • Q4. To what extent do Living Labs facilitate the creation of an integrated system that merges 126 technology and agroecology, fostering sustainability in inner areas? 127 128 2. Materials and Methods 129 2.1. Study Area 130 The Inner Area of Calatino covers approximately 982 km² and includes nine municipalities in the 131 province of Catania: Caltagirone, Grammichele, Licodia Eubea, Mazzarrone, Mineo, Mirabella 132 Imbaccari, San Cono, San Michele di Ganzaria, and Vizzini. The area has a population of 133 approximately 70,606 inhabitants. It is characterized by an economy strongly linked to agriculture, 134 with a significant presence of farms and specialized crops, as well as artisanal activities primarily 135 related to ceramics and small-scale industry. 136 The utilized agricultural area (UAA) of the Inner Area of Calatino amounts to 56,330 hectares, of 137 which approximately 4% is allocated to organic farming. Organic production is particularly 138 concentrated in the municipalities of San Cono (11%) and Vizzini (9.9%). Overall, the Calatino 139 region hosts 279 organic farms, primarily cultivating citrus fruits, vineyards, olive groves, and 140 herbaceous crops, representing a growing sector. 141 One of the most representative sectors in terms of income and employment in Calatino is citrus 142 production, particularly concentrated in the municipality of Mineo, which hosts vast plantations 143 dedicated to the cultivation of oranges and mandarins (Table 1). 144 145 Table 1. Agricultural land and crops in the Calatino region. 146 Municipality Area (km²) Farms Utilised agricultural area (ha) Citrus groves (ha) Vineyards (ha) Olive groves (ha) Herbaceous crops (ha) Caltagirone 383,37 2.368 20.437 615 892 1.469 10.659 Grammichele 32,07 511 1.698 480 21 176 665 Licodia Eubea 112,45 823 6.132 68 956 342 2.660 Mazzarrone 34,78 352 1.905 17 865 160 375 Mineo 245,27 1.859 15.423 3.000 30 952 5.573 Municipality Area (km²) Farms Utilised agricultural area (ha) Citrus groves (ha) Vineyards (ha) Olive groves (ha) Herbaceous crops (ha) Mirabella Imbaccari 15,3 214 990 4 9 117 419 San Cono 6,63 100 278 1 4 33 58 San Michele di Ganzaria 25,81 217 904 4 45 139 535 Vizzini 126,75 463 8.563 170 48 296 4.080 Total Calatino 982 6.907 56.330 4.359 2.870 3.684 25.024 Source: Elaboration on ISTAT data, 2022. 147 148 Additionally, other municipalities in the area, such as Caltagirone and Vizzini, also feature extensive 149 citrus orchards, although integrated with other agricultural productions. Mazzarrone is renowned for 150 its PGI table grapes, while San Cono stands out for its PDO prickly pear (Figure 1). 151 152 153 Figure 1. Production characteristics of the study area (our elaboration). 154 155 Local agriculture is characterized by a combination of herbaceous crops (cereals, legumes, forages) 156 and tree crops (vineyards, olive groves, citrus orchards, and fruit trees), with a huge portion of the 157 area dedicated to organic or transitioning farming methods. 158 The University of Catania has launched a Living Lab with the aim of fostering the transition towards 159 sustainability and a circular economy. The initiative involves farmers, local institutions, 160 environmental organisations and consumers, and is focused on establishing the Calatino Bio-district. 161 Among the various crops present, citrus cultivation was chosen as the focal crop for the Living Lab 162 project because of its significant economic weight in the Calatino area and its sensitivity to water 163 resource management issues. Citrus fruits represent one of the main sources of local agricultural 164 income and require particularly efficient water management, making them an ideal case for 165 experimenting with innovative strategies in line with agroecological principles. 166 The primary objectives are to promote: 167 • the transition to organic farming and organic certification to enhance the competitiveness of 168 local products; 169 • the adoption of sustainable agricultural practices, such as crop rotations, organic fertilizers, 170 and integrated pest management, in line with agroecological principles; 171 • short supply chains, through local markets and the creation of a food hub for the distribution 172 and valorization of organic products; 173 • social inclusion and cooperation among producers, processors, and distributors. 174 Through these strategies, the Bio-district aims to enhance the environmental sustainability of local 175 agriculture and promote economic development based on circularity and biodiversity, positioning 176 Calatino as a model for agroecological transition in Sicily. 177 2.2. Study Design 178 The Calatino Living Lab serves as a participatory platform where farmers, researchers, technical 179 experts, and institutional representatives collaborate to facilitate the agroecological transition of the 180 region. This large-scale transition is often hindered by regulatory constraints, economic challenges, 181 and technological limitations (Toffolini et al., 2021; Beaudoin et al., 2022; Potters et al., 2022; 182 Yousefi and Ewert, 2023; Timpanaro et al., 2024; Gardezi et al., 2024). In Sicily, the recent regional 183 legislation on agroecology (Regional Law No. 21 of 29/07/2021, "Provisions on Agroecology, 184 Biodiversity Protection, Sicilian Agricultural Products, and Technological Innovation in 185 Agriculture") establishes strict criteria for farms, highlighting the need for an in-depth analysis of its 186 practical implications and potential areas for improvement. 187 The methodological approach adopted is summarized in Figure 2. The establishment of a 188 collaborative ecosystem is imperative for the co-design of innovative solutions for sustainable water 189 resource management, agroecology, and the adoption of enabling technologies by farmers, 190 institutions, researchers, businesses, and consumers. A preliminary study involved the identification 191 of key stakeholders and the definition of local challenges. This was followed by structuring the Living 192 Lab as a participatory platform for research and experimentation. Stakeholders were selected using a 193 targeted approach, favoring organic or in-conversion farmers operating in the citrus sector who 194 expressed interest in adopting agroecological practices and innovative technologies. Institutional 195 representatives, technicians and local associations with a key role in promoting agricultural 196 sustainability in the Calatino area were also involved. Stakeholder engagement was achieved through 197 preliminary meetings, thematic focus groups, interactive workshops, and demonstration visits to pilot 198 farms, with invitations disseminated via email, social media, and local networks. 199 Although this targeted selection ensured the active participation of motivated and competent actors, 200 it is important to recognise that it may have introduced a certain degree of bias into the selection. 201 Specifically, the inclusion of stakeholders already inclined towards innovation and sustainability may 202 limit the generalisability of the results to broader agricultural populations that may be more hesitant 203 or resistant to adopting digital technologies. 204 205 206 Figure 2. Methodological framework adopted in the Calatino Living Lab. 207 208 The first step of the Living Lab was an in-depth analysis of regional regulations to understand the 209 criteria for recognizing agroecological farms and the potential barriers to their adoption. Through 210 participatory discussions among stakeholders several critical issues were identified, including: 211 • high initial requirements, such as the obligation to allocate 20% of farmed land to native 212 varieties and to replant 20% of the area with indigenous tree species; 213 • management difficulties, due to the requirement for complex environmental certifications and 214 the high costs of compliance; 215 • limited technological support, as no incentives are provided for adopting innovative tools that 216 could facilitate the agroecological transition; 217 • commercial constraints, including the obligation to sell 20% of production in local markets, a 218 requirement that could disadvantage farms located in more remote areas. 219 The stakeholder discussions within the Living Lab also highlighted a shared need to leverage 220 technological innovations to support farms in resource management, improve production efficiency, 221 and ensure economic sustainability. A key concern among stakeholders was water resource 222 management, one of the main challenges for Sicilian agriculture. Multiple focus groups were 223 organized to explore issues such as: 224 • how can water management be improved in agroecological farms? 225 • which technologies can promote water conservation without compromising productivity? 226 • what strategies can be adopted to make irrigation more efficient and less dependent on 227 intensive water use? 228 The focus groups revealed that many organic farms lack advanced tools for water monitoring, relying 229 instead on empirical practices that often lead to waste or water shortages. 230 Based on the discussions and emerging needs, two organic citrus farms in the Calatino region were 231 selected as pilot cases to assess the impact of enabling technologies applied to irrigation management 232 (one implementing Key Enabling Technologies and the other without KETs). These farms align with 233 the agroecological principles defined by FAO (2018) and were equipped with (Table 2): 234 • weather stations for real-time monitoring of temperature, humidity, and precipitation; 235 • soil sensors to measure moisture levels and optimize irrigation; 236 • Decision Support Systems (DSS) based on climatic and agronomic data to enhance resource 237 management. 238 The choice of these technologies was guided directly by the critical issues identified during the focus 239 groups. Soil sensors and weather sheds allow accurate monitoring of environmental parameters, 240 enabling more efficient irrigation management tailored to actual crop needs. The DSS system 241 provides farmers with decision support based on objective data, reducing uncertainty in irrigation 242 planning and helping to limit water wastage. Table 2 summarizes the comparison between the 243 principles of agroecology (FAO, 2018), the corresponding enabling technologies, and their practical 244 application in traditional agroecology, precision agriculture, and the two pilot farms within the Living 245 Lab. The structure of the table allows for a direct comparison of how different approaches integrate 246 technology to address agroecological goals. Reading across each row, one can observe the 247 progressive transition from traditional practices to precision and digitally-supported agroecological 248 farming. Each principle – such as biodiversity, resource efficiency or co-creation of knowledge – is 249 linked to specific digital tools (e.g. soil sensors, DSS platforms) and corresponding practices observed 250 in the field. For example, while the traditional approach relies on experience-based decisions, the 251 digitised farm uses real-time data to manage irrigation and nutrient input more precisely. This 252 alignment between agroecological objectives and enabling technologies illustrates how innovation 253 can improve sustainability and productivity without compromising ecological integrity. 254 255 256 257 Table 2. Comparison between Agroecology, Precision Agriculture and the two pilot citrus farms for experimentation 258 within the Calatino Living Lab. 259 FAO Principles Enabling Technologies Agroecology Precision Agriculture Farm with Technologies Farm without Technologies 1. Diversity GIS (Geographic Information Systems) Biodiversity mapping Irrigation and fertilization zoning Mapping cover crops and water retention Traditional cultivation without mapping 2. Synergy Big Data Local agroecological planning Optimization of production efficiency Weather and soil data analysis for crop synergy Experience- based management and traditional rotations 3. Efficiency IoT (Internet of Things) Sensors for water conservation Automated irrigation and fertilization Targeted irrigation sensors and DSS for water management Scheduled irrigation without monitoring 4. Resilience Drones Monitoring of natural resources Detection of infestations and targeted irrigation Decision-support system for mitigating water and climate stress Reactive response to climate change without predictive tools 5. Recycling Sensors Natural measurement of soil nutrients Advanced soil and crop monitoring Nutrient monitoring to reduce chemical inputs Fertilizers and compost application based on experience 6. Knowledge Sharing Big Data and digital platforms Shared access to environmental and agricultural data AI-driven process optimization Software for comparison between agroecological farms Limited knowledge exchange within local cooperatives 7. Human and Social Values Mobile applications for farmers Digital training for social inclusion Agricultural workforce automation Decision-making support based on digital data Dependence on personal experience and manual labor 8. Food Traditions Blockchain for traceability Protection of local production Monitoring of production chains Traceability of farm sustainability Traditional sales without digital certification 9. Responsible Governance Open data and GIS Active participation in agricultural management. Automated data collection for agricultural policies Use of platforms for farm monitoring Participation limited to local cooperatives 10. Circular Economy IoT and AI for agricultural waste management Recycling and reuse of agricultural by- products Waste reduction through optimization Crop residue recovery and reuse of wastewater Traditional disposal without optimization 260 261 262 2.3. Elaboration Method 263 The comparison between citrus farming with and without innovative technologies was based on the 264 analysis of total costs and net benefits for each system, including water savings, production yield, and 265 profitability increase, as extensively explored in the literature (Alston, 2010; Pardey et al., 2010; 266 Lubell et al., 2011; Alston et al., 2021; Medici et al., 2021; Jamil et al., 2021). 267 The baseline assumptions for the comparison are reported in Table 3. The analyzed parameters 268 highlight the potential impact of digital innovations on irrigation, climate monitoring, decision-269 making processes, water-use efficiency, management costs, and agronomic yield. 270 271 Table 3. Comparison parameters adopted in the evaluation of KETs in citrus fruit growing (*) Aspect Farm with Technology Farm without Technology Irrigation Uses precise data (soil moisture, weather forecasts) to optimize water requirements Irrigation based on experience and traditional fixed irrigation cycles (not optimized) Climate Monitoring Weather station and sensors provide real- time data on temperature, wind, and rainfall Based on visual observations and generic weather forecasts Decision-Making User-friendly application suggests irrigation timing and quantity Subjective decisions based on intuition and experience Water Efficiency Greater water control with reduced waste High risk of water excess or deficit, leading to higher-than- necessary consumption Management Costs Initial investment in technology, but lower variable costs (e.g., energy for irrigation) Constant costs due to inefficient resource use Agronomic Yield Optimized water requirements and reduced plant stress, leading to higher productivity Yield affected by irrigation mismanagement or unexpected climatic conditions *Our elaboration. 272 As for the total costs (C) for each agricultural system, these are calculated as the sum of the costs of 273 water, fertiliser, labour, cover crops and technology (for the innovative system only), as shown in 274 Table 4. 275 276 Table 4. Data determination methodology for evaluating the cost-effectiveness of adopting KETs technology for water 277 savings. 278 Variables Farm with Technology Farm without Technology Total costs (C) 𝐶𝑡 = 𝐴 ∗ (𝑊𝑡 ∗ 𝐶𝑤 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑡 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟) 𝐶𝑐 = 𝐴 ∗ (𝑊𝑐 ∗ 𝐶𝑤 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟) Total revenue (R) 𝑅𝑡 = 𝐴 ∗ 𝑃𝑡 ∗ 𝑝 𝑅𝑐 = 𝐴 ∗ 𝑃𝑐 ∗ 𝑝 Net profit (Π𝑐) Π𝑡 = 𝑅𝑡 − 𝐶𝑡 = 𝐴 ∗ (𝑃𝑡 ∗ 𝑝 − (𝑊𝑡 ∗ 𝐶𝑤 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑡 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟)) Π𝑐 = 𝑅𝑐 − 𝐶𝑐 = 𝐴 ∗ (𝑃𝑐 ∗ 𝑝 − (𝑊𝑐 ∗ 𝐶𝑊 + 𝐶𝑓 + 𝐶𝑝 + 𝐶𝑒 + 𝐶𝑐𝑐 + 𝐶𝑜𝑡ℎ𝑒𝑟)) The variables considered were the following: A = Cultivated area (ha); Pc = Production per hectare in agriculture without innovative water-saving technologies (t/ha); Pt = Production per hectare in agriculture with innovative water- saving technologies (t/ha); p = Sales price per tonne (€/t); Wc = Water consumption per hectare in agriculture without innovative water-saving technologies (m³/ha); Wt = Water consumption per hectare in agriculture with innovative water saving technologies (m³/ha); Cw = Water cost per m³ (€/m³); Cf = Fertiliser cost per hectare (€/ha); Cp = Pesticide cost per hectare (€/ha); Ct = Technology cost (installation + maintenance per hectare) (€/ha); Ccc = Cover crop cost per hectare (€/ha); Ce = Energy cost per hectare (€/ha); Cother = Other costs (€/ha). 279 280 The additional benefit of farming with innovative technologies over conventional farming is given 281 by: 282 Δ𝐵 = Π𝑡 − Π𝑐 283 Expanding 284 Δ𝐵 = 𝐴 ∗ ((𝑃𝑡 − 𝑃𝑐) ∙ 𝑝 − [(𝑊𝑡 − 𝑊𝑐) ∗ 𝐶𝑤 + 𝐶𝑡 + 𝐶𝑐𝑐]) 285 Where: 286 (Pt - Pc) * p = represents the increase in profitability due to increased production. 287 (Wt - Wc) * Cw = represents the water savings in terms of costs. 288 Ct + Ccc are the additional costs for the adoption of technologies and cover crops. 289 If: 290 ΔB>0 → adoption of the technologies is cost effective. 291 ΔB<0 → the additional costs outweigh the benefits, making the transition uneconomic without 292 incentives. 293 ΔB≈0 → Profitability is similar in the two models, but there may be indirect environmental benefits. 294 The economic evaluation was completed with a sensitivity analysis, hypothesising alternative 295 scenarios on a possible rent for the KETs plant and equipment (necessary to have up-to-date and 296 enhanced decision support systems with links to meteorological databases), and with a Monte Carlo 297 modelling to focus the analysis on the other variables (water consumption, operating costs, 298 production) that present uncertainty and that most influence the difference in profit between the two 299 pilot companies. 300 Monte Carlo modelling assumes that: 301 ∆Π𝑖 = Π𝑖 𝑡𝑒𝑐ℎ − Π𝑖 𝑛𝑜𝑛𝑡𝑒𝑐ℎ 302 At the end of N iterations we estimate 303 • the average profit for each company 304 Π̅𝑡𝑒𝑐ℎ = 1 𝑁 ∑ Π𝑖 𝑡𝑒𝑐ℎ𝑁 𝑖=1 and Π̅𝑛𝑜𝑛𝑡𝑒𝑐ℎ = 1 𝑁 ∑ Π𝑖 𝑛𝑜𝑛𝑡𝑒𝑐ℎ𝑁 𝑖=1 305 • the average difference 306 ΔΠ̅̅ ̅̅ = 1 𝑁 ∑ ΔΠ̅̅ ̅̅ 𝑖 𝑁 𝑖=1 307 • the distribution (and dispersion) of ∆Π, which makes it possible to assess the probability that 308 the technology will lead to a higher profit. 309 The final Monte Carlo model used was as follows: 310 ΔΠ = [400 ∗ 𝑄𝑡𝑒𝑐ℎ − (𝑤 ∗ 𝑐𝑤 𝑡𝑒𝑐ℎ + 𝑐𝑐𝑜𝑣𝑒𝑟 𝑡𝑒𝑐ℎ + 𝑐𝑓𝑒𝑟𝑡 𝑡𝑒𝑐ℎ + 𝑐𝑝𝑒𝑠𝑡 𝑡𝑒𝑐ℎ + 𝑐𝑒𝑛𝑒𝑟𝑔𝑦 𝑡𝑒𝑐ℎ + 𝑐𝑡𝑒𝑐ℎ + 𝑐𝑜𝑡ℎ𝑒𝑟)]311 − [400 ∗ (𝑤 ∗ 𝑐𝑤 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑐𝑜𝑣𝑒𝑟 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑓𝑒𝑟𝑡 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑝𝑒𝑠𝑡 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑒𝑛𝑒𝑟𝑔𝑦 𝑛𝑜𝑛𝑡𝑒𝑐ℎ + 𝑐𝑜𝑡ℎ𝑒𝑟)] 312 where each uncertain parameter is sampled from a specified distribution. Repeating this calculation 313 for many iterations yields the profit difference distribution, which provides a comprehensive 314 assessment of the economic sensitivity to the adoption of the innovative technology. 315 3. Results 316 3.1. Living Lab approach and case study characteristics 317 The two citrus farms analyzed were identified as pilot sites within the Living Lab of the Calatino 318 Inner Area, a collaborative ecosystem aimed at testing and validating innovative solutions for 319 regenerative citrus farming and sustainable water resource management. The objective is to develop 320 scalable strategies for other farms seeking to integrate regenerative practices with technological 321 innovations. 322 The selection of the farms (Table 5) was based on: 323 • Representation of the citrus sector within the region and the study area. 324 • Diversity in management practices, as one farm adopted enabling technologies, while the 325 other relied on a traditional agroecological approach. 326 • Entrepreneurs’ willingness to engage in the co-experimentation and training process. 327 The two pilot farms are in Mineo (Catania province) and share the same production identity (5 328 hectares of blood oranges, organic certification, and a commitment to regenerative agriculture). Their 329 differing agricultural management approaches make them suitable case studies for assessing the 330 impact of enabling technologies compared to a system based solely on traditional agronomic 331 experience. 332 333 Table 5. Structural characteristics of the pilot sites. Information Farm with technology Farm without technology Localization Mineo Mineo UAU, ha 5 5 Production address Blood orange Blood orange Organic certification Yes Yes Regenerative agriculture Cover crops + advanced water management Cover crops with traditional management Water use Sensor monitoring + DSS Manually programmed irrigation Nutrient management Soil analysis + targeted fertilisation Experience-based fertilisation Pest control Biological strategies + data monitoring Biological strategies without monitoring Market Selling to local supply chains and quality markets Selling to local supply chains and quality markets *Our elaboration 334 The farm utilizing innovative technology has integrated sensors, a decision support system (DSS), 335 and advanced soil analysis to optimize irrigation and plant nutrition. The goal is to achieve more 336 efficient water use, a more targeted nutrient management strategy, and continuous pest monitoring, 337 thereby reducing input usage and maximizing productivity. 338 The farm without innovative technology follows a more traditional approach, with manually 339 scheduled irrigation and fertilization based on the farmer’s experience. While it employs cover crops 340 and organic farming strategies, it lacks tools for real-time monitoring of soil and water conditions, 341 which can result in less precise management and higher resource consumption. 342 The intersection of three key elements—organic farming (a low-impact agricultural management 343 model aligned with agroecological principles, aiming for balanced and resilient production systems 344 while reducing dependency on external inputs), regenerative agriculture (cover crops contribute to 345 reducing erosion, improving water retention, and increasing soil organic matter, fostering a healthier 346 and more productive ecosystem in the long term), and enabling technologies (agroecology does not 347 exclude technology but leverages it to enhance sustainable resource management)—is represented by 348 agroecology. This guiding principle unites the two pilot farms of the Living Lab in the Calatino. 349 This integrated approach improves the sustainability, productivity, and resilience of agricultural 350 systems, turning environmental and economic challenges into opportunities for innovation (Niggli, 351 2015; Gascuel-Odoux et al., 2022; Bless et al., 2023; Domínguez et al., 2024). 352 3.2. Issues related to the management of irrigation resources 353 The discussion among stakeholders on the water emergency in citrus farming has highlighted how it 354 is the result of a combination of climatic, institutional and economic factors that negatively affect 355 production and farm sustainability. Figure 3 represents a visualization of the relationships between 356 the main factors characterizing this crisis, as they emerged during the focus group. The structure was 357 elaborated using MAXQDA software, through the exploration of co-occurrences between thematic 358 codes applied to text segments. The figure is organized hierarchically, starting from the main cause 359 (climate change) at the top, branching downward into its effects on water availability and plant health, 360 and further into institutional and economic consequences. Arrows represent causal links, while 361 mitigation strategies are shown as side branches connected to the specific problems they address. No 362 color coding was used; the structure is entirely based on logical connections and thematic clusters. 363 This approach made it possible to clearly highlight the connections between climatic, institutional 364 and economic variables, as well as the mitigation strategies adopted by citrus growers and sector 365 experts. 366 367 Figure 3. Cause-effect relationships in irrigation water management issues in citrus farming 368 369 The central element of the water crisis, as emerged from the discussion, is climate change, which 370 manifests through alterations in rainfall patterns. This results in two opposing but equally damaging 371 situations: water scarcity, caused by reduced precipitation and rising temperatures that intensify 372 evaporation and increase plant water demand, or water excess, with sudden and intense rainfall 373 leading to floods, water stagnation, and root damage. 374 These issues are compounded by institutional inefficiency, which worsens water resource 375 management. The lack of maintenance of watercourses, poor planning in water distribution, and the 376 bureaucratic rigidity of reclamation consortia make it difficult for citrus growers to access water when 377 they need it most. Additionally, the absence of a consumption-based pricing system leads to waste 378 and inefficient resource use. 379 To address the water crisis, citrus growers have adopted various technological and agronomic 380 solutions. These include innovations in irrigation, such as surface and subsurface micro-irrigation 381 systems to reduce water waste, or the use of regulated deficit irrigation systems to optimize water use 382 according to plant growth stages. Farmers have also experimented with alternative water resources, 383 such as treated wastewater, through phytoremediation processes, to reduce dependence on 384 conventional water sources. A common strategy is the selection of rootstocks resistant to water stress, 385 as well as the use of raised beds to improve drainage and controlled cover cropping. 386 According to stakeholders, a coordinated territorial approach involving public institutions, 387 reclamation consortia, and producer organizations is lacking. Additionally, a revision of irrigation 388 tariffs based on actual consumption could encourage more responsible water use, while increased 389 digitalization in water resource management (sensors, weather stations) could enable more precise 390 irrigation planning. 391 These results highlight not only the complexity of the water crisis, but also the proactive role of 392 farmers in experimenting with feasible solutions. The issues and strategies discussed in this section 393 have been translated into the visual structure shown in Figure 3, which helps to summarise the entire 394 problem-solving framework in a single view. This makes the figure particularly useful for better 395 understanding where to intervene and how to support adaptation efforts more effectively. 396 3.3. Cost-effectiveness assessment of KETs deployment 397 The calculations clearly show the positive impact of KET adoption on farm management, with 398 benefits reflected in water efficiency, operating costs, productivity and overall profitability. 399 Table 6 shows that the adoption of enabling technologies results in a significant improvement in farm 400 management, with water consumption reduced by 33% and a consequent annual saving of 420 €/ha, 401 without penalizing productivity. This implies greater sustainability in resource use and reduced 402 production costs. 403 The cost of energy for water withdrawal is reduced by 31%, confirming how energy efficiency is an 404 additional economic benefit of technological innovation. 405 Productivity increases by 6 tons/ha (+16%), translating into a revenue increase of €2,400/ha. This 406 result underscores how technological adoption not only improves efficiency, but also directly 407 contributes to strengthening the company's competitiveness. 408 At the same time, there is a reduction in the use of fertilizers (-15%) and a drastic decrease in 409 pesticides (-71%), reflecting the improvement in agronomic management and less dependence on 410 external inputs, with clear economic and environmental benefits. 411 Table 6. Parameters for comparing citrus fruit farms with and without KETs (*) Parameter Farm with technology Farm without technology Difference % Annual water consumption (m³/ha) 2,800 4,200 -33% Average cost of water (€/m³) 0.3 0.3 0% Water saving (€/ha) 420 € 0 € --- Water saving (%) 33% 0 --- Production per hectare (t/ha) 44 38 16% Sale price (€/t) 400 € 400 € 0% Revenues per hectare (€/ha) 17,600 € 15,200 € 16% Cost cover crops (€/ha) 250 € 250 € --- Fertiliser costs (€/ha) 720 € 850 € -15% Pesticide cost (€/ha) 40 € 140 € -71% Energy cost for irrigation (€/ha) 520 € 750 € -31% Technology investment (€/ha) 500 € 0 € --- Other cost 1,570€ 1,990€ -21% Total cost (€/ha) 3,600 € 3,980 € -10% *Our elaboration. 412 413 Despite an initial investment of €500/ha, the innovative company achieves a net profit of €14,000/ha, 414 compared to €11,220/ha for the traditional company, with a 25% increase in profitability (+€2,780/ha) 415 (Table 7). This highlights how the economic benefits far outweigh the costs of technology adoption. 416 417 Table 7. Comparison of economic benefits and adoption convenience between citrus farms with and without KETs (*) Parameter Farm with technology Farm without technology Difference % Revenues R (€/ha) 17,600 € 15,200 € 16% Total costs C (€/ha) 3,600 € 3,980 € -10% Net profit Π (€/ha) 14,000 € 11,220 € 25% Change in benefits (ΔB) +2,780 *Our elaboration. 418 419 3.4. Sensitivity analysis 420 Considering three scenarios based on complete enabling technologies to be acquired by annual 421 subscription, a sensitivity analysis can also be developed (Table 8): 422 • 200 €/ha/year → Basic Package (sensors + basic software); 423 • 400 €/ha/year → Intermediate (sensors + advanced DSS + local weather) 424 • 600 €/ha/year → Advanced (sensors + advanced DSS + weather integrated with weather 425 databases such as SIAS, ISPRA, SwissMetNet, etc.). 426 Sensitivity analysis on the different levels of technology subscription shows that even with a higher 427 fee (600 €/ha/year), the positive margin remains substantial (+2,180 €/ha compared to the farm 428 without technology). The intermediate package (400 €/ha/year) emerges as the one most balanced 429 between investment and economic benefit, suggesting a sustainable option for maximizing farm 430 profitability. 431 432 433 434 435 436 Table 8. Profit sensitivity with technology rent (*) Rental scenario Annual cost per hectare (€) Net new profit (€/ha) Difference vs. farm without technology (€) Convenience compared to the traditional model Rent 200 €/ha/year 200.00 € 13,800.00 2,580.00 Very affordable Rent 400 €/ha/year 400.00 € 13,600.00 2,380.00 Still profitable Rent 600 €/ha/year 600.00 € 13,400.00 2,180.00 Advantageous but low margin *Our elaboration. 437 438 To assess how net profit (€/ha) responds to key economic drivers, a Monte Carlo simulation was 439 conducted. The goal was to compare the farm adopting innovative technology with the one that does 440 not, highlighting how variations in certain parameters can either amplify or reduce the benefits 441 derived from technology adoption. 442 The model assumed that the product’s selling price (400 €/t) and non-specific fixed costs (e.g., general 443 expenses, logistics) remain constant, while variations in production and costs influenced by 444 technology were analyzed. Analysis considered water costs, expenses for cover crops, fertilizers, 445 pesticides, irrigation energy, and, for the technology-adopting farm, the technological investment. 446 The Monte Carlo simulation involves repeated iterations, where in each cycle, random values are 447 drawn for each parameter according to predefined distributions. In this study, uniform distributions 448 around baseline values were assumed. In particular, the unit cost of water was varied between 0.3 and 449 0.5 €/m³, while water consumption for the technological farm ranged between 2,520 and 3,080 m³/ha, 450 and for the non-technological farm, between 3,780 and 4,620 m³/ha. Similarly, production per hectare 451 and operating costs were defined within specific intervals to reflect real-world variability and 452 simulate a wide range of scenarios. 453 Table 9 shows that, on average, the farm adopting technology achieves a net profit of approximately 454 14,000 €/ha, while the non-technological farm reaches around 11,220 €/ha, resulting in an average 455 difference of +2,780 €/ha. These results indicate a significant average economic benefit from 456 adopting innovative technology. The standard deviations, 1,200 €/ha and 1,400 €/ha respectively, 457 highlight considerable variability. This suggests that while the average benefit is positive, in some 458 scenarios, the advantage may be lower or even more pronounced. 459 Table 9. Monte Carlo simulation results (*) Statistics Farm with technology (€/ha) Farm without technology (€/ha) Difference (Tech - NonTech, €/ha) Average profit 14.000 € 11.220 € 2.780 € Standard deviation 1.200 € 1.400 € 1.300 € Minimum Profit 11.000 € 8.500 € 2.500 € Maximum profit 17.000 € 15.500 € 3.500 € Median 14.100 € 11.300 € 2.800 € *Our elaboration. 460 461 The economic advantage is primarily driven by savings in operational costs. The technology enables 462 a substantial reduction in water consumption, leading to lower water expenses, and decreases costs 463 associated with fertilizers and pesticides, due to more efficient and sustainable farming practices. 464 These savings, combined with a potential increase in yield per hectare, contribute to a higher net 465 profit. 466 The simulation also highlights the model’s sensitivity to various parameters. For instance, an increase 467 in the unit cost of water shifts total costs to higher values, making water savings even more critical. 468 Similarly, variations in yield per hectare directly affect revenue and, consequently, net profit. The 469 ability to adjust multiple parameters simultaneously helps identify key drivers of economic success 470 and potential sources of risk. 471 The Monte Carlo simulation comparing farms with and without innovative technology demonstrates 472 that adopting technology leads to a significant average increase in net profit per hectare. These 473 findings provide essential support for strategic decision-making in a competitive and dynamic 474 environment, where operational efficiency and innovation are crucial for success. 475 4. Discussion 476 The analysis conducted within the Living Lab of the Calatino inner area has enabled an exploration 477 of the impact of enabling technologies on the agroecological transition in inner areas, highlighting 478 economic, environmental, and organizational benefits. Starting from the research questions, the 479 findings clearly show that the integration of enabling technologies enhances the efficiency of resource 480 management, particularly in terms of water and nutrient use, helping to improve productivity and 481 keep costs down. These findings align with those reported by Bellon-Maurel et al. (2022) and Maurel 482 and Huyghe (2017), who highlight how digital tools contribute to resource optimization and improved 483 sustainability in agricultural systems. Furthermore, Ajena et al. (2022) emphasize that digitalization 484 can break down traditional barriers fostering innovation in rural sectors, particularly in inner areas 485 where challenges are more pronounced. Therefore, the integration of technology accelerates the 486 agroecological transition by providing farmers with real-time data and decision-making tools that 487 enhance precision and sustainability in farm management. Regarding the second research question, 488 the comparison between the two pilot farms revealed a significant gap in farmers' perceptions. The 489 farm that adopted the innovative technology reported tangible benefits, such as reduced operational 490 costs and improved productivity. In contrast, the farm following a traditional approach relied on well-491 established methods and expressed skepticism toward digital tools. This resistance stems from a 492 perception of greater reliability associated with traditional methods, combined with limited 493 familiarity with innovative technologies and concerns about high initial costs and a steep learning 494 curve. These aspects are consistent with the findings of Anderson and Maughan (2021) and Schiller 495 et al. (2020), who describe the existing gap between innovation and tradition in agriculture. Literature 496 suggests that the lack of specific training and institutional support represents a major barrier to the 497 adoption of digital technologies (Timpanaro et al., 2023). 498 In this context, Living Labs serve as co-experimentation and training spaces that facilitate knowledge 499 transfer and help overcome initial resistance (Scuderi et al., 2023). Active participation and dialogue 500 among farmers, researchers, and technical experts contribute to demystifying new technologies and 501 highlighting their potential in sustainable resource management. Living labs show that they can 502 function as catalysts for change, fostering an agroecological transition that is not only technologically 503 advanced, but also socially inclusive (Cascone et al., 2024; Beaudoin et al., 2022). 504 The third research question led to a deeper analysis and reflection on the economic outcomes through 505 Monte Carlo simulation. From an economic perspective, the farm integrating enabling technologies 506 achieves higher per-hectare revenues due to increased production and more efficient cost 507 management. These findings align with the studies of Alston (2010) and Pardey et al. (2010), which 508 emphasize how agricultural innovation can generate substantial economic benefits. 509 From an environmental perspective, the adoption of innovative technologies promotes more 510 sustainable resource management and a reduction in chemical input use. The decrease in water 511 consumption and pesticide application, for example, contributes to minimizing environmental impact 512 and fostering more regenerative agricultural practices. These results are consistent with the evidence 513 provided by Domínguez et al. (2024) and D’Annolfo et al. (2017), who highlight the potential of 514 combining agroecological practices with technological innovation to promote sustainable and 515 resilient agriculture. Thus, the integration of technologies not only enhances economic efficiency but 516 also represents a successful approach to reducing environmental impact by encouraging a more 517 responsible use of resources. 518 Finally regarding Q4, the Living Lab model implemented in the Calatino context has proven to be an 519 effective environment for the co-creation and experimentation of innovative solutions. The two pilot 520 farms, despite sharing the same production identity and organic certification, differ in their 521 management approach: one integrates enabling technologies, while the other follows a traditional 522 method. This strategic choice has highlighted how the presence of digital technologies is not 523 contradictory to agroecological principles but rather enhances their effectiveness, improving the 524 sustainable management of resources and the resilience of the production system. 525 Living Labs play a crucial role in bridging the gap between technological innovation and traditional 526 agricultural practices. They provide a space where farmers, researchers, technologists, and 527 institutional stakeholders can experiment, exchange experiences, and validate solutions in real time 528 (Scuderi et al., 2024). In our case, the adoption of digital tools has improved irrigation monitoring 529 and management, leading to more efficient water use and lower operational costs. These results, 530 combined with the integration of regenerative practices such as the use of cover crops and targeted 531 nutrient management, contribute to creating an integrated system that addresses the environmental 532 and economic challenges of inner areas. Moreover, the active participation of farmers in Living Labs 533 fosters a bottom-up approach that stimulates responsible innovation and the dissemination of best 534 practices. 535 For example, during one of the demonstration sessions, an organic farmer had the opportunity to test 536 a low-cost soil moisture monitoring system, immediately noting its usefulness in reducing water 537 waste. This kind of direct experience helped turn initial prejudice into interest and openness. In 538 another case, a young farmer who initially showed skepticism toward the use of digital data for crop 539 management changed his perspective after sharing his needs with a group of experts within the Living 540 Lab and receiving support in interpreting the data collected. The opportunity to learn by doing, in a 541 nonjudgmental and co-creation-oriented context, proved essential to reduce cognitive barriers and 542 build confidence toward innovation. Recent studies (Gascuel-Odoux et al., 2022; Potters et al., 2022) 543 also highlight how collaboration and the engagement of local actors are essential for achieving 544 effective and sustainable agroecological transitions. 545 A critical issue that deserves attention concerns the economic implications related to the costs of 546 adopting enabling technologies, especially in vulnerable rural settings. While these technologies can 547 generate efficiency and reduced operating costs, they often entail significant upfront investments, the 548 need for technical maintenance, and increasing dependence on external suppliers. This can lead to an 549 imbalance in bargaining power between farms, which are often small or medium-sized, and 550 technology providers, which operate according to industrial and centralized market logics. 551 In the absence of adequate support and regulatory measures, this imbalance can produce regressive 552 effects: farms with greater economic capacity will be able to access technologies more easily and take 553 competitive advantage of them, while the more fragile realities risk being excluded from the 554 innovation process (Bissadu et al., 2025). 555 For this reason, it is crucial to accompany technology adoption with targeted policy strategies capable 556 of ensuring affordability, technical training, systems interoperability and open innovation models. 557 Living Labs, represent a possible lever to rebalance power dynamics through co-design and direct 558 involvement of farmers in technology selection and testing processes. To effectively address these 559 power imbalances and promote a more inclusive adoption of enabling technologies, several targeted 560 policy actions should be considered. Such measures can help rebalance contractual relationships 561 between farmers and technology providers, in line with the principles of responsible innovation 562 (Bellon-Maurel et al., 2022; Beaudoin et al., 2022; Gava et al., 2025). 563 First, public incentives for technology adoption should be conditional on the use of open standards 564 and interoperable systems to avoid technological lock-in, as discussed by Ditzler and Driessen (2022) 565 and Clapp and Ruder (2020). This approach strengthens farmers' autonomy and prevents dependence 566 on proprietary technologies controlled by a few large suppliers (Bissadu et al., 2025). 567 Second, it is essential to promote the creation of farmer-led cooperatives or technology consortia to 568 strengthen collective bargaining power in the purchase and negotiation of technology services. This 569 is in line with recommendations to strengthen agricultural innovation systems (Potters et al., 2022) 570 and enable bottom-up governance models (Gava et al., 2025). 571 Thirdly, the creation of public platforms dedicated to the collective procurement of technologies, 572 supported by technical advisory services and independent consultants, can further protect farmers 573 from unfavourable contractual conditions. The provision of advisory vouchers for access to third-574 party technical expertise would complement this strategy. 575 Furthermore, regulatory frameworks should explicitly recognise farmers' ownership of agricultural 576 data generated by digital systems, ensuring that technology providers cannot appropriate or monetise 577 such data without informed consent (Clapp and Ruder, 2020; Bellon-Maurel et al., 2022). 578 Living Labs themselves can be institutionalised as territorial “technology brokers”, acting as 579 independent intermediaries to ensure equitable access to innovation and promote co-created solutions 580 tailored to local needs (Beaudoin et al., 2022; Gardezi et al., 2024). This model of participatory 581 innovation is in line with the agroecological governance structures advocated by Gascuel-Odoux et 582 al. (2022), which support equitable access to technological innovation in rural areas. 583 By adopting these integrated strategies, policymakers can help reduce asymmetries in bargaining 584 power, protect the interests of smallholder farmers, and promote an inclusive, resilient, and 585 participatory agroecological transition. 586 In summary, our research findings indicate that: 587 • The integration of enabling technologies accelerates the agroecological transition by 588 improving resource management and increasing profitability. 589 • Farmers’ perceptions are influenced by direct experience and the support provided by Living 590 Labs, which help overcome resistance to innovation. 591 • The combination of agroecological practices and innovative technologies generates positive 592 economic and environmental impacts, as evidenced by increased productivity and reduced 593 operational costs. 594 • Living Labs play a key role in facilitating the integration of technology and agroecology, 595 fostering the creation of integrated and sustainable systems in inner areas. 596 These findings not only confirm the existing literature but also provide an operational framework to 597 guide strategic decisions in complex agricultural contexts, where sustainability and innovation need 598 go hand in hand. The integrated and participatory approach promoted by Living Labs thus emerges 599 as an effective response to current and future challenges, helping to transform environmental and 600 economic challenges into opportunities for innovation and sustainable development. 601 602 5. Conclusions 603 The study conducted within the Living Lab of the Calatino Inner Area highlights how the integration 604 of enabling technologies can play a crucial role in accelerating the agroecological transition in rural 605 areas. The results, derived from a comparative analysis of two pilot citrus farms—one adopting 606 advanced digital tools and the other maintaining a traditional approach—demonstrate economic, 607 environmental, and managerial benefits, confirming the transformative potential of such innovations. 608 The farm that integrated sensors, decision support systems (DSS), and other digital technologies 609 achieved significant operational efficiency, including a 33% reduction in water consumption and a 610 16% increase in yield per hectare, leading to a 25% improvement in profitability. These findings not 611 only underscore the importance of more precise resource management but also confirm that the 612 adoption of enabling technologies can enhance environmental sustainability by reducing chemical 613 inputs and improving irrigation efficiency. The study also highlights some critical issues and concrete 614 challenges to be addressed. Among these, the affordability of technologies is a major obstacle, 615 especially for small companies with limited liquidity. Similarly, the technical complexity of the 616 systems and the costs associated with maintenance, software updates and staff training may limit 617 widespread adoption. Furthermore, the scalability of the tested solutions remains to be verified in 618 different contexts due to soil and climate conditions, farm size and crop type. 619 However, this study has some limitations. First, the small number of cases analyzed may limit the 620 generalizability of the results. Given the diversity of agronomic and socio-economic contexts, further 621 large-scale studies are needed to confirm the replicability of the observed benefits. Additionally, 622 while the methodology integrates an in-depth economic analysis and a Monte Carlo simulation, it 623 could be enriched by further long-term measurements to assess the economic and environmental 624 sustainability of these technologies over time. 625 Another limitation concerns the analysis of farmers’ perceptions. While the comparison between the 626 innovative and traditional groups highlighted resistance and scepticism toward digital tools, a more 627 extensive qualitative investigation—such as in-depth interviews or focus groups with a broader 628 sample of producers—could provide further insights into the dynamics of adoption and the training 629 needs required to support the transition. 630 Based on these considerations, several future research directions emerge. Expanding the Living Lab 631 model to other rural areas in Sicily and different agricultural sectors could help determine whether 632 enabling technologies can generate similar benefits in different contexts. Future studies could 633 implement comparative pilot projects in different production systems, such as viticulture or olive 634 growing, and monitor key indicators like water use efficiency, yield performance, and farmer 635 adoption rates over at least three growing seasons. 636 Further research could also explore the long-term impact of adopting digital tools, analyzing, for 637 example, how economic and environmental benefits evolve over multiple production cycles and 638 under changing climatic and market conditions. Longitudinal studies should be conducted, integrating 639 detailed farm accounting records, soil and water monitoring data, and farmer surveys, to track both 640 economic returns and resource use efficiency over a 5–10 year horizon. Another key area of interest 641 involves the development of training programs and institutional support mechanisms to facilitate the 642 dissemination of these technologies among farmers. Future initiatives should design modular, 643 practice-oriented training programs focused on digital literacy, irrigation management, and precision 644 agriculture tools, targeting different farmer profiles (smallholders, young farmers, cooperatives), 645 possibly through partnerships with vocational training institutes and local cooperatives. 646 Collaborations with universities and research centers to design dedicated training programs could 647 help overcome learning curve challenges and promote greater adoption of digital systems. 648 Finally, the study highlights the importance of targeted policy actions to mitigate power asymmetries 649 between farmers and technology providers. By introducing conditional incentives, promoting 650 collective procurement mechanisms, supporting open innovation models, and formalising the role of 651 Living Labs as technology intermediaries, policymakers can help ensure that the digital 652 transformation in agriculture promotes autonomy, inclusiveness, and long-term sustainability (Clapp 653 and Ruder, 2020; Bellon-Maurel et al., 2022; Gava et al., 2025). These measures are essential to 654 enable a fair and balanced agroecological transition, particularly in vulnerable rural contexts. 655 This study demonstrates that the integration of enabling technologies, supported by a participatory 656 model such as the Living Lab, represents a fundamental driver in accelerating the agroecological 657 transition in rural areas. Despite certain limitations, the findings provide a strong scientific and 658 operational contribution, suggesting that the combination of digital innovation and agroecological 659 practices can not only enhance economic efficiency and environmental sustainability but also foster 660 cultural and organizational change toward a more resilient and inclusive agricultural system. Future 661 research and targeted policy interventions will be essential to facilitate the broader adoption of these 662 models and contribute decisively to the transformation of the agri-food system. 663 664 Funding: This study was carried out within the Agritech National Research Center and received 665 funding from the European Union Next-GenerationEU (PIANO NAZIONALE DI RIPRESA E 666 RESILIENZA (PNRR) – MISSIONE 4 COMPONENTE 2, INVESTIMENTO 1.4 – D.D. 1032 667 17/06/2022, CN00000022). 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