Assessing Agri-Food Digitalization: Insights from Bibliometric and Survey Analysis in Andalusia

Abstract

The agri-food sector is going through a massive digital transformation thanks to new technologies such as the Internet of Things (IoT), big data, and Artificial Intelligence (AI). Regional disparities and implementation barriers prevent widespread uptake despite significant research advances. Drawing on bibliometric and survey data collected up to the end of 2023, this study examines global research trends and stakeholder perceptions in Andalusia (Spain) to identify challenges and opportunities in agricultural digitalization. Bibliographic analysis revealed that research has moved from early remote sensing to precision agriculture, IoT, robotics and big data, and that AI has recently taken over in predictive analytics, automation, and decision-support systems. However, our survey of Andalusian stakeholders highlighted a limited adoption of cutting-edge tools such as AI, blockchain, and predictive models due to economic constraints, technical challenges, and skepticism. Participants emphasized the importance of trust-building, as well as the use of simple tools that require minimal input and provide immediate benefits. Priorities for the responders were also improving market transparency, optimizing resource use, and system interoperability. The findings show that closing the gap between research and practice requires developing digital solutions that are user-centered, simplified, and context-adapted, especially when dealing with complex technologies like AI and predictive systems. This must be supported by targeted public policies and collaborative innovation ecosystems, all essential elements to accelerate the integration of smart agricultural technologies and align scientific innovation with real-world needs.

1. Introduction

Agriculture, a cornerstone of societal development, is experiencing a profound digital transformation that has the potential to improve efficiency and productivity [1]. The integration of digital solutions has become a strategic response to global challenges such as food security, climate change, sustainability, and supply chain instability [2]. Building on Industry 4.0 principles, Agriculture 4.0 [3,4] incorporates advanced tools such as the IoT [5], big data analytics [6], and blockchain [7]. However, the evolution of technology is now moving towards Agriculture 5.0 [8], which is characterized by human-centric AI, autonomous making decisions, and sustainable precision farming methods [9]. These innovations are intended to convert the exponential growth of agriculture data into actionable insights, improving resource management, optimizing production processes, and enhancing real-time decision-making capabilities in farming operations [10].

The research landscape on digital agriculture has developed over recent decades. Earlier studies concentrated on remote sensing and Geographic Information Systems (GIS) applications in agriculture [11]. The field exploded in the early 2000s with the advent of precision agriculture, which represented a turn towards more data-driven and technology-intensive farming methods [11,12]. Over the past two decades, research has moved into climate-smart agriculture, data-driven analytics, machine learning, and blockchain-based supply chain management [7,13]. Rapid advancements in these fields have been highlighted by recent studies, particularly in automation, robotics, and big data applications, emphasizing the sector’s digital innovation [14].

Despite advancements in digital agriculture research, the integration of these innovations remains uneven across regions [13]. Stakeholder perceptions critically influence uptake, with studies revealing regional disparities in priorities and concerns regarding digitalization [15,16,17]. Connectivity barriers are a dominant challenge in developing countries [18], while the selection of appropriate tools is a key factor in more technologically advanced regions [19]. Globally, persistent concerns include economic feasibility, technical knowledge gaps, and skepticism about the benefits of digital solutions [20,21,22].

Spain’s agri-food sector is considered one of the main pillars of the national economy and the overall European economy. Estimated at 8.94% of the Gross Value Added (GVA) of the country in 2023 (EUR 119.14 billion), it ranks fourth among the EU-27 countries’ agri-food economies [23]. The country leads the bloc in producing key commodities such as pork, fresh fruits, olive oil, citrus fruits, and small ruminants [23]. Spain’s most agricultural region, Andalusia, generates over 30% of the nation’s agricultural GVA and employment [24]. The region dominates primary production, leading in olive oil and greenhouse horticulture, and ranking second in herbaceous crops and citrus fruits [24]. Given the socioeconomic importance of this sector, stakeholder perspectives on digitalization in Andalusia are critical for setting policy, determining research priorities, and directing private-sector investments.

Although regional, national, and supranational policy initiatives, such as RIS3-S4 Andalucía [25], Hub Iberia Agrotech [26], the Digitisation Strategy for the Spanish Agri-Food sector [27], Horizon Europe [28], and AgrifoodTEF [29], aim to foster digital adoption, a significant gap persists between scientific advancements and real-world implementation [30,31,32]. Bridging this divide is essential for the effective deployment of digital agriculture solutions. While previous studies have independently examined research trends [11,33,34] and perceptions of agri-food actors [15,16], integrated approaches remain largely underexplored.

To address this gap, this study employed two methodologies to achieve three interrelated objectives. Firstly, a bibliometric analysis was conducted to map global research trends in agricultural digitalization, with a focus on thematic priorities, influential institutions, and emerging technologies. Secondly, stakeholder perceptions of technological transformation in Andalusia were evaluated to identify uptake patterns and barriers, contextualizing these findings within both national and international frameworks. Thirdly, research trends were compared with practical adoption levels to identify gaps, misalignments, and opportunities for an enhanced synergy between scientific innovation and industry needs. Collectively, these findings provide a comprehensive understanding of digitalization dynamics in the Andalusian agri-food sector, informing targeted policy frameworks and equipping private-sector initiatives with evidence-based strategies for successful implementation.

2. Materials and Methods

2.1. Bibliometric Analysis

A bibliometric analysis was used to explore the development of scientific publications, investigate keyword and thematic trends, and examine institutional affiliations and authors’ countries of origin [35,36,37]. This methodology was chosen because it uses quantitative techniques to identify, evaluate, and interpret published research within a specific domain [38,39,40]. Bibliometric tools also provide a holistic perspective of the field, allowing for the identification of research gaps, underlying challenges, and opportunities for future investigations [13,41]. In this study, the focus was on analyzing the academic literature relating to agricultural digitalization and associated technologies, with the aim of understanding the field’s progression and identifying key trends and priorities.

This study adopted the methodological framework proposed by Öztürk et al. [42], structuring the bibliometric investigation into four stages: (1) defining the research objective; (2) collecting the literature data; (3) analyzing and visualizing the data; and (4) interpreting the findings and results. While presented as different phases, this process aligns with methodologies that have previously been applied in bibliometric research within the agri-food sector [11,43,44,45].

2.1.1. Search Strategy

To ensure a comprehensive keyword coverage, terms were selected through a com-bination of techniques, including an extensive literature review, collaborative brain-storming sessions with field experts, and the snowballing approach [41,46,47].

Two keyword groups were defined to evaluate the intersection of agriculture, digital transformation, and emerging technologies (

Table 1. The dentification and grouping of keywords.

Group 1: Digitalization and New Technologies	Group 2: Agriculture	Others
“5.0”, “smart”, “digital*”, “4.0”, “sensors”, “predictive model”, “machine learning”, “deep learning”, “iot”, “sensors”, “precision”, “big data”, “robotic”, “blockchain”, “artificial intelligence”, “remote sensing”, “DSS”, “new technologies”, “internet of things”, “LLM”, “digital twins”, “drones”, “automation”	“farm*”, “agri*”, “crop*”	“agritech” “agtech”

* Represents the wildcard symbol used to capture variations in keyword terms.

). Group 1 comprises digitalization-related terms, while Group 2 covers agricultural concepts. Each term from Group 1 was combined with every term in Group 2 using the “W/0” operator. This proximity operator ensures that matches are found, regardless of the sequence in which the terms from the two groups appear. Wildcard symbols (*) were applied to selected Group 1 terms and all Group 2 terms to account for word variations. The combined use of the proximity operator (“W/0”) and Wildcard symbol (*) enabled the extraction of documents containing diverse keyword combinations (e.g., “digital agriculture” or “agricultural digitalization”). The Boolean “OR” operator was applied across all the previous combinations, incorporating also the terms “agtech” and “agritech” to ensure a broader coverage of documents that were relevant to the study.

2.1.2. Data Collection

The data collection was conducted using the Scopus database (Elsevier), a comprehensive repository of peer-reviewed research that is aligned with this study’s objectives [44,46,48]. A preliminary cross-database comparison of the search string in Scopus and Web of Science (WoS), the two leading academic databases [11], revealed that 50% of the retrieved documents were common to both, with 41% being exclusive to Scopus and 9% to WoS. This confirmed Scopus’s near-complete coverage of relevant works, justifying its selection.

The search was restricted to the “Title, Abstract, and Keywords” field, encompassing all document types published up to the end of 2023. The complete search protocol is available in the Supplementary Materials (Supplementary Material S1). Conducted in August 2024, the initial search returned 29,750 documents. Non-English publications were excluded, and metadata (including citations, abstracts, and keywords) were exported in BibTeX and CSV formats, yielding 28,014 records. Duplicate entries, incomplete records (e.g., missing authors, abstracts, or keywords), and corrupted files were removed, resulting in a final dataset of 27,182 documents for analysis.

2.1.3. Data Analysis and Visualization

The bibliographic data were analyzed using the Bibliometrix library, version 4.1.3 [49], and the VOSviewer software, version 1.6.20 [50]. Additionally, the Python programming language (version 3.9) [51] was employed for preprocessing, cleaning, and formatting the data to ensure compatibility with both tools. The Bibliometrix library, designed for scientometric research, integrates diverse analytical methods, while its web-based interface, Biblioshiny, enables an interactive exploration of the results [49]. The exported BibTeX database was converted into a structured dataset for analysis, enabling the extraction of insights through descriptive indicators and graphical outputs. The Biblioshiny interface was used to generate visualizations of the annual scientific production, citation trends, frequent keywords, and outputs by authors, affiliations, and countries.

VOSviewer complemented this analysis by constructing bibliometric networks to visualize conceptual relationships in the literature, such as keyword co-occurrences and overlay visualizations, enabling the identification of thematic and structural linkages and their evolution over time.

2.2. Stakeholder Survey

2.2.1. Study Design

An online survey was developed using Microsoft Forms to explore stakeholders’ perspectives on digital transformation and technology adoption in the agri-food sector of Andalusia. The data collection took place between November 2022 and March 2023.

The initial version of the questionnaire was designed based on an extensive literature review on agricultural digitalization and emerging technologies, integrating insights from recent studies [52,53,54]. To enhance the questionnaire’s relevance for the target audience, semi-structured interviews were held with four key players from various areas of the agri-food sector. This group included a farmer, an academic researcher specializing in agricultural technology, a technical consultant from an agri-tech company, and an executive from the food industry. Each interview lasted about one hour and aimed to evaluate, refine, and validate potential survey items.

To ensure the validity and reliability of the instrument, the preliminary survey was reviewed by the same stakeholders. During the testing phase, participants were encouraged to report technical issues, flag unclear or confusing items, and evaluate the questionnaire’s usability. The collected feedback highlighted challenges, ambiguities, and areas for improvement, leading to iterative refinements that enhanced the survey’s clarity and functionality.

The complete questionnaire is available in the Supplementary Materials (Supplementary Material S2). Although it has been included in English, the original questionnaire was conducted in Spanish. The final version consisted of seven sections. Section S1 outlined the survey’s objectives, significance, and estimated completion time. Section S2 gathered sociodemographic data, educational backgrounds, and professional profiles. Section S3 evaluated participants’ previous exposure to digital tools, any relevant training, and their perceptions of existing technologies. Section S4 investigated how digital tools are integrated into daily operations and the role of stakeholders in the implementation process. Section S5 identified economic, social, and infrastructural barriers to technology adoption. Section S6 assessed strategies and tools for managing operational data within enterprises. Section 7 examined participants’ views on the potential impact of digitalization on the future of the agri-food sector.

2.2.2. Sampling Strategy

A convenience sampling approach, a non-probabilistic method that is often used when random sampling is not feasible due to logistical or resource constraints [55], was employed to gather insights on digitalization and technology adoption among various stakeholders in Andalusia’s agri-food sector. The sample included farmers, researchers, students, agribusiness professionals, and providers of agricultural services and supplies. To enhance representation, the survey was distributed through agricultural cooperatives, universities, professional associations, and agribusiness forums, encouraging participation from different actor groups. Efforts were also made to include representatives from Andalusia’s major crop sectors, such as cereals, olive groves, horticulture, and oilseeds. Out of the 102 responses collected, 79 were kept for analysis after excluding incomplete submissions (missing one or more responses) and participants who were not based in Andalusia. The recruitment aimed to ensure a sufficient number of respondents from each stakeholder category to achieve data saturation [56].

Convenience sampling was employed to achieve typological diversity (across professional roles and crop sectors) despite resource constraints, aligning with approaches used in analogous studies. For instance, Soon-Sinclair et al. [57] and Schulze Schwering et al. [54] utilized convenience sampling to explore food fraud perceptions and the adoption of digital tools, respectively, among farmers, showcasing its effectiveness in resource-limited, exploratory studies. Likewise, Damanik and Khaliqi [58] employed this method to ensure geographic diversity in their research on Indonesia’s food waste reduction efforts, while Brown [59] pointed out its ability to balance accessibility with methodological constraints. As highlighted by Etikan [55], convenience sampling is particularly valuable in exploratory studies, as it facilitates the collection of rich, context-specific data while acknowledging their inherent limitations.

2.2.3. Data Analysis

Following data cleaning, 79 valid survey responses were exported to Excel via Microsoft Forms. These responses served as the basis for the subsequent analysis. The data were processed and analyzed using the Python programming language (version 3.9) [51], employing libraries such as Pandas (version 1.4.2) for data manipulation, NumPy (version 1.21.5) for numerical analysis, and Seaborn (version 0.11.2) and MatplotLib (3.5.1) for visualization. These tools facilitated a detailed exploration and the creation of informative visualizations to summarize key findings [60].

3. Results and Discussion

3.1. Global Research Trends in Agri-Food Digitalization

Our bibliometric analysis covered publications from 1967 to 2023, encompassing 27,182 documents from 6932 different sources. Compared with previous bibliometric studies within the field of digital agriculture and emerging technologies [7,11,13,38,61], this study incorporated a substantially larger volume of documents, enabling a more comprehensive and robust analysis of global research trends since the field’s inception. Figure 1 illustrates the annual evolution of scientific output in this domain, showing the number of documents published per year. This temporal distribution serves as a critical foundation for our subsequent analysis of how research activity has intensified over time, highlighting key turning points in the field. Figure 2 presents the cumulative occurrences of the top 10 most important keywords across the analyzed documents. It provides a clear view of the prominence, evolution, and temporal emergence of research priorities within the digitalization of the agri-food sector.

Following Figure 1 and Figure 2, the earliest research on technological applications in agriculture emerged in the late 1960s [62], with subsequent studies in the 1980s and 1990s exploring remote sensing methodologies for agricultural monitoring [63,64]. A sustained interest in digital innovations began in the early 2000s, driven by advancements in remote sensing and especially the integration of new technologies into precision agriculture (Figure 2).

The University of Wageningen (Netherlands) and four U.S. institutions (University of California, University of Florida, Purdue University, and Iowa State University) were pioneers in this research area. As indicated in

Table 2. The top 10 affiliations ranked by publications.

Rank	Institution	NP 1	Country
1	China Agricultural University	436	China
2	Wageningen University and Research	337	Netherlands
3	University of Florida	334	USA
4	University of California	274	USA
5	Purdue University	249	USA
6	Nanjing Agricultural University	227	China
7	University of Bonn	205	Germany
8	Iowa State University	202	USA
9	Zhejiang University	191	China
10	University of Nebraska–Lincoln	160	USA

¹ NP: Number of publications.

, these institutions remain among the top 10 contributors in terms of publication volume, reflecting their enduring influence. Notably, nearly half of the publications in this ranking originate from U.S. institutions, one-third from Chinese institutions, and 20% from institutions within the European Union.

The bibliometric analysis revealed key milestones in the evolution of research on the digital transformation of the agri-food sector. As illustrated in Figure 1, the annual publication output steadily increased between 2010 and 2016, with a total growth of 121% during this period. This was followed by exponential expansion post-2016, where the number of publications skyrocketed, resulting in an overall growth of 265% from 2016 to 2020, with an average annual growth rate of 38%. This pronounced acceleration aligned with the maturation and emergence of transformative technologies such as the IoT, robotics, big data, and AI, as well as improvements in spatialization and remote sensing for precision agriculture. Notably, three of the five most cited papers that we found in the bibliometric analysis were published between 2017 and 2018 [6,65,66], significantly contributing to this turning point. These studies explored the integration of big data, machine learning, and deep learning in agriculture, emphasizing their potential to revolutionize decision-making, enhance productivity, and improve sustainability. These innovations signify the emergence of Agriculture 4.0, representing a significant shift towards the integration of advanced digital tools in agriculture [4].

During this period, government initiatives, such as the European Union’s Horizon 2020 framework [28] and U.S. programs supporting precision agriculture technologies [67], played a pivotal role in advancing agri-food innovation. As emphasized by Dibbern et al. [13], public funding and network collaboration emerged as critical drivers, facilitating cross-disciplinary progress and partnerships. After 2010, China also emerged as a key player, with the China Agricultural University greatly increasing its research output. This trend is illustrated in Figure 3, which depicts the evolution of publications from the top ten countries of origin based on authorship. Table 2 and Figure 3 also highlight the significant presence of Indian researchers in international universities, who have made substantial contributions to global research on agricultural digitalization in recent years. Other countries, including Australia, Brazil, and several EU nations, began contributing significantly after 2015, reflecting the globalization of research efforts.

The COVID-19 pandemic underscored the importance of digitalization in agriculture, marking another critical inflection point in the upward trajectory of research. Logistical challenges during this period were addressed through the rapid adoption of digital tools such as remote advisory platforms, automated monitoring systems, and digital marketplaces [68].

In recent years, there has been a clear shift toward integrating cutting-edge innovations in the agri-food sector, with scientific output reaching its peak in 2023 (Figure 1). Figure 4 helps illustrate how research priorities have evolved over time, offering a chronological view of keyword co-occurrence. The color gradient, from blue (older) to yellow (more recent), shows when specific terms gained prominence [69].

The analysis reveals a clear progression from foundational and well-established topics, such as precision agriculture, precision farming, and spatial variability (appearing predominantly in blue and violet tones) towards sustainability-oriented concepts like climate-smart agriculture, sustainability, and climate change, which appear in green tones. These shifts indicate the growing importance of environmental concerns in digital agriculture research.

More recently, the thematic landscape has expanded toward advanced digital solutions, with yellow-highlighted nodes representing emerging topics such as deep learning, machine learning, agricultural robotics, and blockchain. These cutting-edge technologies have become increasingly prominent since 2020, reflecting a strong push towards automation, predictive analytics, and enhanced data-driven decision-making.

A co-occurrence analysis of commonly used keywords was conducted to illustrate the main thematic areas of digital agriculture research, as well as the relationship between them (Figure 5). Author keywords that appeared at least 20 times were analyzed using VOSviewer. The generated network reveals the field’s thematic structure, with the node size reflecting the keyword frequency and the connecting line thickness indicating the co-occurrence strength. Nodes and lines are color-coded by cluster, identifying distinct research themes through shared co-occurrence patterns. This clustering not only visualizes the structure of scientific production but also highlights key technological areas and stages of digital maturity. These elements will serve as a conceptual backbone for the subsequent analysis, providing a structured lens both for comparing research trends with stakeholder perceptions and for designing targeted strategies to enhance digital adoption.

The analysis revealed six major thematic clusters (Figure 5). Cluster 1 (red) is about precision agriculture, remote sensing, and geospatial analysis. Keywords such as yield prediction, crop monitoring, and vegetation indices refer to the use of satellite imagery, drones, and sensing technologies for productivity enhancement and resource management. Cluster 2 (green) is about sustainability and smart agriculture, using common terms such as climate change, and sustainable development. Research here focuses on climate resilience and resource efficiency, and digitalization is seen as an important driver for environmentally sustainable practices.

Cluster 3 (blue) shows IoT and smart farming integration. Terms like wireless sensor networks, big data and cloud computing show how connectivity and automation enable real-time decision making. Cluster 4 (yellow) is about AI and image processing, with keywords such as machine learning, weed detection, and crop classification indicating the use of AI-driven tools for pest control, disease detection, and yield optimization. Cluster 5 (purple) examines robotics and automation, with keywords including autonomous navigation and agricultural robots. This cluster is researching robotic systems and autonomous vehicles to reduce labor dependency and increase precision in agricultural operations. Lastly, Cluster 6 (turquoise) is devoted to cyber-physical systems, digital twins, and urban agriculture aiming at advanced systems for controlled environments such as vertical farming and hydroponics for urban sustainability problems.

For consistency with the stakeholder survey period, only the literature published up to the end of 2023 was included in the analysis. However, research in digital agriculture has continued to evolve rapidly. Recent studies have highlighted the emergence of Generative Artificial Intelligence (GenAI) and Large Language Models (LLMs) as transformative tools for the agri-food sector, enabling new forms of decision support, knowledge extraction, and automation [70,71]. For example, LLMs are now being integrated into farm management platforms to provide real-time, context-aware recommendations [70], while digital twins are increasingly used for disease identification and classification, enhancing predictive capabilities and operational efficiency [72]. Additionally, there is growing attention to data rights and privacy, with recent research emphasizing the need for clear data ownership agreements, mechanisms for farmers to control consent, and the development of fair value distribution models for agricultural data [73,74].

3.2. Digital Adoption: Stakeholder Insights from Andalusia

3.2.1. Survey Findings

The results of our survey involving 79 stakeholders across the Andalusian agri-food sector were analyzed to capture diverse perspectives on digitalization. Figure 6 and Figure 7 provide an overview of the participant profiles included in the empirical sample. Figure 6 highlights the presence of various actor types across the value chain, including primary producers, processors and distributors, researchers, technology providers, and advisory services. This distribution reflects the complexity of the sector and ensures that the study captures perspectives from a broad range of functional roles. Such diversity is critical for analyzing the opportunities and barriers to technology integration from multiple angles, and strengthens the representativeness of the results across the different components of the agri-food system.

Figure 7 complements this by showing the distribution of stakeholders according to their main crop or production activity. It confirms the inclusion of agents working with a wide range of crops, with particular emphasis on the most representative in Andalusia such as olive trees, cereals, oilseeds, and horticulture, which together account for the majority of the region’s cultivated area [24]. Together, both figures reflect the heterogeneity of the sector and guarantee a comprehensive representation of its main typologies and crop specializations, thus reinforcing the empirical robustness of the study.

The sociodemographic characteristics of the 79 respondents are summarized in

Table 3. The sociodemographics of the respondents included in this study.

Variable	Options	Number	%
Age	<23 years	1	1.3%
	23–35 years	34	43.0%
	36–50 years	18	22.8%
	51–65 years	24	30.4%
	>65 years	2	2.5%
Gender	Male	69	87.3%
	Female	10	12.7%
Educational level	Primary or Secondary School	11	13.9%
	High School or Technical degree	6	7.6%
	University degree	62	78.5%
Work status	Employed	48	60.8%
	Self-employed	26	32.9%
	Student	5	6.3%

. The majority of respondents were male (87%), with only 13% identifying as female. In terms of age, the present study predominantly representing individuals aged 23 to 35 years, followed by participants aged 51–65 years and 36–50 years. Younger participants under the age of 23 and those older than 65 years were minimally represented in the sample. The educational background of respondents indicates a predominance of university-educated individuals, with 79% holding a university degree. Other educational levels included primary or secondary education (14%) and high school or technical degrees (8%). This highlights a generally high level of formal education among the stakeholders surveyed. Regarding employment status, 61% of respondents were employed, 33% were self-employed, and 6% were students.

The respondents were asked to evaluate the perceived positive impact of new technologies and digitalization across various dimensions, using a scale from 0 (no positive impact) to 5 (great positive impact). As shown in Figure 8, the highest-rated impacts were observed in “helping people make decisions”, “increasing productivity”, and “optimizing the use of resources”, with most respondents attributing a significant positive effect (scores of 4 and 5). Conversely, the dimensions with comparatively lower scores included “improving animal welfare” and “environmental protection”, which were still considered impactful but with a slightly lower emphasis from participants.

The perceived importance of barriers to digital technology adoption, as reported by stakeholders, is illustrated in Figure 9. Among the most critical challenges identified are the economic justification of the investment and the lack of knowledge and training for the use of the tools, both of which received the highest ratings of the perceived importance (level 5) by a significant proportion of respondents. Additionally, issues such as the uncertainty about the tool’s benefits and the lack of advisory support, were consistently marked as highly relevant, emphasizing the necessity for focused capacity-building initiatives. Interestingly, while infrastructure and connectivity deficits remain a concern, they are not as prominently emphasized as cognitive and economic factors, suggesting that human and financial dimensions pose more immediate barriers than technical ones. Moreover, the perception of “no perceived need” appears to be the least relevant obstacle, suggesting that most stakeholders recognize the potential value of digitalization but face practical and financial limitations to its implementation.

Complementing this perspective, Figure 10 delves into the perceived importance of various groups and entities as drivers of digitalization. Stakeholders attributed a very high level of importance to actors such as public research institutions, and government administrations. These results point to a clear demand for public-sector involvement and structured funding mechanisms. At the same time, the role of internal organizational structures was highlighted, with company departments and farmers’ associations or cooperatives seen as essential for fostering practical implementation and trust in digital tools. Conversely, financial institutions and informal networks were seen as less influential, reinforcing the need for structured, institutional support and strategic partnerships to overcome current barriers.

In addition, our study brings attention to aspects that were mostly overlooked in national and international studies [15,16,17,18,19,75,76]. When assessing scenarios that require decision-support tools, respondents placed a high priority on comparative market data analyses, monitoring crop growth, and forecasting market trends. The importance of farm management tools, especially predictive models for optimizing operational planning, was also highlighted.

The utility of digital platforms for agricultural production management and confidence in predictive models of crop performance were also assessed. Among the respondents, 34% viewed digital tools as highly useful and 32% saw them as moderately useful. Similarly, 30% expressed strong confidence in predictive models and 25% reported moderate trust. However, skepticism persisted, with 19% expressing limited trust and a small minority reporting no confidence.

The qualitative feedback from participants provided additional insights into these perceptions. Those questioning the utility of digital tools emphasized the need for demonstrable ROI, reliable and localized data, and user-friendly interfaces to ensure a seamless integration into daily operations. Interoperability challenges between systems also emerged as a recurring concern, highlighting technical incompatibilities and the added complexity of integrating disparate platforms. Participants expressing a lack of confidence in predictive models cited concerns such as the variability of model outputs, challenges in accounting for uncontrollable factors, and the need for accurate, real-time input data. Additionally, the complexity of these models, which often require specialized knowledge and frequent updates, was identified as a significant barrier. Addressing these concerns will be critical for improving the adoption of and trust in these solutions.

Building on all of these concerns, participants were also asked to evaluate which factors they believe will be the most important for the agri-food sector in the coming years. Figure 11 presents stakeholders’ assessments of the medium- to long-term importance of a range of technologies and systemic elements. The highest-rated factors include soil, irrigation, and fertilizer management, as well as crop sensorization and monitoring, pointing to a sustained interest in tools that enhance efficiency and control. These results suggest that, despite existing challenges around adoption, there is a clear consensus on the central role of tools that help define operational goals, with benefits that are easily assessed and implementation feasibility more readily understood.

3.2.2. A Comparison with National and Global Contexts

Our stakeholder survey in Andalusia reveals a cautiously optimistic perception of digitalization, with a broad recognition of its benefits for decision-making, productivity, and resource efficiency, especially in technologies related to irrigation, fertilization, and crop monitoring and management. However, our results show that when it comes to more advanced digital technologies, the actual adoption is much more limited. This finding is consistent with recent regional studies by Parra-López et al. [32,77], which provide a more nuanced picture for specialized solutions such as digital traceability and phytosanitation systems in the olive sector in Andalusia. These works highlight that, while there is growing awareness of the potential of digital solutions to improve food safety, quality control, and value chain transparency, the real-world uptake remains low. Both our survey and these studies identify persistent barriers, including limited digital skills, a lack of targeted training, uncertainty about the profitability of these systems, and the high initial investment required. Notably, Parra-López et al. [32] emphasize that the perceived complexity and low evaluability of digital tools undermine user trust and slow down adoption, an observation that closely matches our own findings.

Our results also converge with studies by Parra-López et al. [78], and Santos et al. [31] regarding the importance of enabling environments, particularly the roles of cooperatives and public institutions, as critical facilitators of digital adoption in Andalusia. In our survey, these actors were identified as trusted intermediaries together with internal company departments, capable of mitigating cognitive and organizational barriers. While digital infrastructure was not perceived as a primary constraint in our study, cognitive and organizational factors were dominant, supporting the assertion by Reina-Usuga et al. [79] that advancing digital transformation in the region will require not just access to technology, but robust inter-organizational collaboration and investment in digital literacy.

At the national level, our results align closely with Spain’s broader digital agriculture landscape, as reported in recent national reports by MAPA [75,76]. Participants in our study highlighted productivity gains, decision-support benefits, and improved resource management as key advantages of digital tools, while also emphasizing the need for public support and collaboration among actors to ensure successful adoption. These findings are consistent with those of MAPA [75,76] and further supported by Abad-Segura et al. [80], who also identify sustainable technological innovation as a core driver of competitiveness and environmental performance in Spanish agriculture. Nonetheless, our findings also offer more granular insights into persistent barriers. In line with Romera et al. [81], concerns about interoperability and technological overload were prominent, indicating the need for more integrative and accessible digital ecosystems. Interestingly, despite the relatively high education level of our sample, digital literacy and training gaps remain acute, confirming arguments by Sadjadi and Fernández [30] that human capital development is as critical as technological access in Spain’s digital transition.

In the broader Southern European context, digital transformation in agriculture is progressing but remains uneven due to shared structural and institutional challenges. Italy and Spain, for example, face similar constraints such as aging rural populations, fragmented farm structures, and limited digital skills. However, Italy has advanced further in terms of coordinated policy experimentation and institutional support for innovation. Brunori et al. [82] and Arcuri et al. [83] argue that effective digitalization in rural areas hinges on inclusive governance models and farmer-centered data strategies. These priorities are consistent with our findings in Andalusia, where younger and more digitally engaged stakeholders emphasized the importance of user-friendly platforms and participatory technology development that integrates local knowledge systems.

Greece presents a more complex scenario, characterized by widespread digital illiteracy, smallholder dominance, and inadequate extension services [84,85]. These factors mirror some of the constraints reported in our survey, including limited support mechanisms for small-scale actors and usability concerns. Likewise, our respondents’ skepticism toward advanced digital technologies also aligns with observations by Papadopoulos et al. [86], who highlight that cultural resistance and limited hands-on experience hinder adoption in Mediterranean regions. In contrast, France has implemented structured innovation ecosystems such as #DigitAg and Occitanum, which aim to co-develop and scale digital solutions through living labs [87]. Yet, as in Andalusia, French stakeholders also report challenges in scaling these tools equitably due to issues of fragmentation and the limited interoperability of digital tools, suggesting that the institutional architecture alone is insufficient without deeper actor engagement and systemic alignment [87].

Globally, our survey findings are broadly consistent with the international literature. Studies conducted in Brazil [15], Canada [16], Nigeria [18], and Germany [19] similarly identified automation, precision farming, and smart tools as critical for efficiency and sustainability. However, regional variations are evident. For instance, infrastructural gaps and rural data management inefficiencies dominate in southern Brazil [15], contrasting with our survey’s findings. Conversely, studies in South Africa [17] and Nepal [88] emphasized the importance of public policies to support digital adoption, particularly for smallholder farmers. An international survey presented by McKinsey and Company [89] also highlighted regional differences in perceived barriers between stakeholders. In North America, the primary concern was the unclear return on investment; in Europe, the challenges of scalability were dominant; and in Latin America and India, the most significant barriers identified were the high costs and insufficient technical support.

Despite a shared recognition of digital technologies’ potential, the specific barriers to adoption vary by region and context. Our findings on high costs, uncertain return on investment, and limited technical skills reflect challenges documented across both developed and emerging economies [16,18,19,90]. Notably, we also observed a widespread distrust toward predictive models and a lack of perceived value among some stakeholders—factors echoed in global studies [91]. Additionally, a lack of technical knowledge and training continues to be a persistent hurdle, as observed in the research presented by Smidt and Jokonya [17], and Ezeaku et al. [18], where digital literacy was identified as a limiting factor. A distinctive contribution of our study is its emphasis on the role of internal organizational structures, such as cooperatives and company departments, in building trust and fostering adoption. Such an internal dynamic, often not taken into account in global assessments, may be key to closing the gap between the technological potential and real-world uptake in Andalusia. Thus, while digitalization is a global trend, its paths to implementation are shaped by local economic, organizational, and policy conditions.

3.3. Bridging Research and Practice: Challenges and Opportunities

3.3.1. Alignments and Divergences: Research vs. Stakeholder Priorities

Table 4. A matrix of the alignment between global research trends and Andalusian stakeholder priorities.

	High Stakeholder Demand	Low Stakeholder Demand
High Research Focus	Sensor-based monitoring Precision irrigation and fertilization Remote sensing tools ICT and IoT solutions	AI-driven analytics Blockchain Climate-smart technologies Robotics
Low Research Focus	Interoperable solutions Market insights platforms

provides a comparison between global research trends (Section 3.1) and the practical demands of Andalusian stakeholders (Section 3.2). Firstly, we identified the main technological trends in global research. Next, we compared these trends with stakeholder survey feedback regarding their perceived usefulness, importance, adoption, and trust. Each trend was then classified according to the level of stakeholder demand. In addition, if stakeholders considered a technology important or useful but it was not a major focus in the research literature, it was assigned to the ‘Low Research Focus/High Stakeholder Demand’ category. The ‘Low Research Focus/Low Stakeholder Demand’ quadrant is empty because we only analyzed technologies relevant in at least one dimension.

Technologies belonging to the identified Cluster 1 (soil management, irrigation control, fertilizer optimization, and crop monitoring through sensors and remote sensing tools) were prioritized by both global researchers and local stakeholders in Andalusia. These tools provide real-time data with a direct impact on productivity and resource efficiency. Local actors also value more established technologies, such as basic ICTs and IoT systems for data acquisition and sharing (part of Cluster 3), with which they are more familiar. The convergence of these technologies in both contexts points to a significant potential for scaling and technological collaboration.

In contrast, technologies such as AI-based analytics, big data, robotics, and blockchain, that occupy a central position in recent scientific literature (Clusters 3, 4, and 5), show low levels of adoption and trust among survey respondents. While there is latent demand for the decision-support capabilities these tools promise, even among a relatively tech-savvy sample, they are often perceived as complex, costly, and offering uncertain or delayed benefits. The lack of training, difficulty in selecting the right tool, and limited context-specific support further widen this gap, particularly for small and medium-sized farms that prevail in Andalusia and have limited investment capacity. A divergence was also identified in the area of environmental technologies. Even though global research increasingly promotes resilient and sustainable approaches (centered on Cluster 2), surveyed stakeholders prioritize profitability, operational efficiency, and short-term economic viability. While they recognize the environmental benefits, these are not seen as urgent unless they generate clear economic value.

Regarding the practice-driven priorities, there is a clear demand among Andalusian stakeholders for interoperable and pragmatic solutions. They express a need for tools that work seamlessly together, criticizing the current fragmentation in the technology market and the challenges of integrating different platforms, a complexity that sometimes leads to abandoning implementation efforts altogether. While recent research explores solutions and architectures for this issue [92], interoperability remains a major challenge and barrier in practice, making system integration difficult and reducing the effectiveness of digital farming tools [93]. This stands in contrast to a general trend in research often focused on developing powerful but sometimes isolated innovations, without sufficiently addressing the practical integration challenges faced by end users.

There is also strong interest in tools that enhance market and supply chain transparency, especially those providing real-time data on production volumes, market prices, and relevant trade trends. These platforms are seen as key to improving strategic decision-making, particularly in a context marked by price volatility and exposure to global markets. However, effective implementation requires clear regulatory frameworks and incentives for collaboration and data sharing among actors in the agri-food system. A major barrier identified is the lack of familiarity with practical data-sharing mechanisms and, in some cases, a lack of motivation or perceived direct benefit: almost half of respondents reported they had no experience with such processes and were therefore unsure of their value and concerned about losing control of strategic information. This finding is consistent with the MAPA report [94] and reveals an institutional and cultural dimension to the problem that has not been adequately addressed in the scientific literature, which tends to be more concerned with the technical feasibility of solutions than with the real-world adoption conditions. Although some studies have highlighted their importance, these platforms remain scarce, opaque, and largely inaccessible, falling short of meeting the high and diverse needs of stakeholders [95,96].

The observed discrepancies are not only related to general challenges in the sector but also to specific features of Andalusia that make research more distant from practice. Firstly, the region has an agricultural structure dominated by small, family-run farms, often focused on traditional crops such as olives or rainfed cereals, with low profit margins [97]. This size and type of operation make it difficult to invest in complex technologies that are expensive or require advanced management or major operational changes. Secondly, the key role of cooperatives in Andalusia presents both advantages and disadvantages. Cooperatives are essential for supporting technology adoption, through training, advice, and collective purchasing, but their ability to drive advanced innovation depends on their own modernization and resources. Unlike other regions where technology companies are the main drivers of innovation, Andalusia relies more heavily on cooperatives, which may require additional support to lead this transformation [31]. Thirdly, a long-standing dependence on subsidies has often fostered a farming culture more focused on stability than on innovation. While these subsidies provide a degree of security, their design does not always actively promote the adoption of digital tools. Lastly, the lack of practical digital skills is a significant obstacle in the region. Even among individuals with formal education, like those in our sample, there is often difficulty in using more advanced systems, leading to a sense of being overwhelmed by technology and hindering the use of different tools, especially if they are not interoperable.

This comparison highlights that, while global research is driving the technological frontier, effective adoption in Andalusia requires a careful adaptation to local realities. Economic justification, ease of use, user training, and platform interoperability are critical elements that must be integrated in the solutions. Both research and public policy need to better align with the operational logic of small producers, the coordinating role of cooperatives, and the need to build trust-based environments for data sharing. Clearly demonstrating the economic return of digital solutions and supporting their implementation with technical training will be crucial to turning existing willingness into effective adoption.

3.3.2. Actionable Strategies for Enhancing Digital Adoption

To effectively address these identified barriers to adoption and connect them with the main research areas structured in the six thematic clusters of the bibliometric analysis, this section proposes a set of concrete strategies. These strategies are organized around four key pillars: the regulatory and public policy framework, the promotion of public-private collaboration, the improvement of usability in advanced digital tools, and the interoperability of the digital ecosystem.

While public policies should support all four proposed strategic pillars, the following are some direct interventions suggested to remove key structural barriers:

• Tailored Financial Support: Our analysis shows that high initial costs are a significant barrier, especially for the small and medium-sized farms that predominate in Andalusia. This is why we recommend designing targeted and differentiated instruments such as tax deductions for digital investments (software, sensors, and machinery), direct grants, or low-interest loans for agricultural SMEs. Support for maintenance and technological upgrades is equally important to ensure the viability of investments and avoid premature obsolescence.
• Digital Literacy and Ongoing Training Programs: As shown in our study sample, the digital skills gap remains a major barrier to technology adoption, even among young and well-educated individuals. Policies should establish free and accessible training plans in collaboration with agricultural research centers, cooperatives, and professional organizations. These programs should cover not only the basic digital concepts addressed in Clusters 1 and 3 of the analysis, but also advanced tools such as crop management platforms and AI for agronomic decision-making, included in Clusters 4 and 5. Training should be practical, accessible in rural areas, and tailored to different socio-professional profiles.
• The Integration of Digitalization into Sectoral Policies: Digital transformation needs to be integrated into major agricultural policies, such as the Common Agricultural Policy (CAP). This could involve aligning digitalization goals with existing tools, for example, funding digital solutions through eco-schemes, explicitly supporting technological upgrades in investment aid, or linking priority access to new technologies with efforts to encourage generational renewal and a greater inclusion of women in the sector. In this context, the digital farm record, which will soon be introduced in Spain, offers a promising starting point for developing practical use cases and creating value among willing stakeholders.

In addition to public policy, effective collaboration between the private sector, cooperatives, research centers, and public administrations is essential for turning technological innovation into real, tailored solutions, bridging the gap identified between developers and end users. In this regard, governments should encourage the creation of local “agricultural digital ecosystems”, which are applied innovation environments where stakeholders can build trust while co-creating, testing, and adapting digital tools in a collaborative way. Regional pilot projects, Living Labs, and open innovation platforms are key instruments for fostering this kind of collaboration, especially for the more advanced technology included in Clusters 4, 5, and 6. In fact, there are already valuable examples in Andalusia showing how these approaches are working, as follows:

• Innovation Hubs and Networks: Initiatives such as the “Andalucía Agrotech DIH” hub [98] and its involvement in the HIBA (Hub Iberia Agrotech) [26] project have built networks connecting SMEs, startups, universities, and public administrations. These hubs offer advisory services, training, and promote co-creation through technology challenges like “Plataforma Iberia Conecta”.
• Sector-Specific Living Labs: Experiences such as the European ZeroW project (focused on reducing food waste in the fruit and vegetable sector) [99] or the territorial lab in Los Pedroches as part of the I-CISK project (digital water management in livestock farming) [100] illustrate the potential of participatory approaches. In these initiatives, cooperatives, research centers, and producers work together to develop technologies tailored to local conditions.
• Demonstration Pilot Projects: Initiatives such as Inverconec (integration of technologies in greenhouses in Almería) [101] and FerTICycle (application of IoT to manure management) [102] demonstrate how digitalization is being put into practice in real production contexts, generating both tangible benefits and greater trust among stakeholders.
• Catalytic Role of Cooperatives: The strategic capacity of cooperatives to drive innovation from the ground up is particularly crucial in Andalusia, given their central role and the modernization challenges previously identified [31]. They act as key players in the regional ecosystem, especially with more mature technologies like those included in Cluster 1 or in parts of Cluster 3. Examples such as COVAP’s investment in the RUMI system (livestock monitoring) [103], the RAVSA3 platform (the digital valorization of by-products) [104], and programs led by Cooperativas Agro-alimentarias de Andalucía (“Organización 5.0”, digital mentoring) [105] highlight the importance of continuing these initiatives, as they have proven effective in advancing digitalization.

Strengthening these collaborative ecosystems, built on territorial partnerships and the active participation of key groups that stakeholders value for driving transformation, is therefore essential for accelerating effective and sustainable change in Andalusia’s agri-food sector. A crucial task for these ecosystems will also be to generate solid evidence, through studies tailored to different production contexts and tools, that quantifies the real economic benefits of digitalization. This will help address the uncertainty around return on investment that concerns many stakeholders.

To create real value to Andalusia’s agri-food sector, strategies need to prioritize tools that best address the perceived needs and overcome identified adoption barriers. Our comparative analysis of global trends and local perceptions reveals clear patterns that should guide the focus and implementation of technological solutions:

• Leverage Areas of Strong Alignment: There is a strong alignment between global research and the priorities of Andalusian stakeholders regarding technologies related to Cluster 1 and part of Cluster 3. These focus on optimizing the management of key resources, such as soil management, irrigation and fertilizer control, and crop monitoring using sensors and remote sensing tools. Future actions should capitalize on this convergence by promoting adoption, while ensuring that it is done in a way that minimizes additional workload for farmers. The key lies in the seamless integration of these tools into intuitive Farm Management Software (FMS) that is centered on real-world operations. Value is maximized when the FMS is designed so that farmers mainly need to record the data they already generate in their daily routines (e.g., planting dates, treatments applied, work logs). The platform would then automatically incorporate and process information from sensors and other connected devices, presenting unified analyses and recommendations without requiring a constant, specific data entry for each new technology. Recent advances, such as the integration of LLMs into FMS platforms, can further streamline this process by enabling more natural, context-aware interactions and automating routine decision support tasks. Building on this familiarity, along with the perceived value of these technologies, can provide a strong entry point for more complex, progressive digitalization processes.
• Simplifying the Complex: In contrast to the previous point, our analysis reveals a clear divergence regarding the advanced technologies included in Clusters 4 and 5, such as AI, big data, and predictive analytics. Overcoming this barrier requires a conscious effort to simplify and adopt user-centered design. This includes integrating these technologies into intuitive interfaces (such as visual dashboards or voice commands), developing plug-and-play systems to enable easy interaction, promoting functional modularity (starting simple and scaling up later), ensuring models are tailored to local contexts (by validating with local data to build trust and relevance), and providing ongoing, targeted technical support and training for these tools. A promising strategy in this direction is to integrate predictive models (phenological models, yield predictions, disease identification, etc.) directly into existing FMS. The key is not just to display results, but to design a dynamic interaction: allowing users, for example, to easily input corrections or feedback if they notice a deviation in a model prediction. This user input could trigger an automatic adjustment of the model’s parameters, providing recalibrated predictions instantly. Such an approach would transform the model from an opaque ‘black box’ into a transparent, collaborative tool, where users actively participate in its calibration and continuous improvement, boosting trust, acceptance, and accuracy for their specific context.
• Linking Environmental Sustainability and Economic Profitability: A divergence was also identified in the environmental focus and the technologies covering the Cluster 2. Digital tools with an environmental focus (such as input optimization or water/carbon footprint tracking) should be framed and promoted by highlighting their direct or indirect economic benefits. It is essential to support studies and use cases that rigorously quantify the economic return of these technology-enabled sustainable practices, directly addressing the main concern of producers.
• Developing Market Transparency Tools and Promoting Data Sharing: In response to the clear demand from stakeholders for a greater market transparency (prices, volumes, and real-time trends), the strategy should focus on creating collaborative tools based on incentivized data sharing. The proposed model is one in which actors, such as producers and cooperatives, contribute certain data of their own (e.g., harvest forecasts and volumes sold) in order to access high-value, real-time aggregated information (such as average market prices or total volumes by region). This “give to get” approach directly addresses the barrier of lacking a perceived direct benefit, as identified in our analysis. Promoting the creation of this collective database, ideally through public-private collaboration projects, would not only provide an immediate market intelligence but also lay the groundwork for more advanced use cases, such as price prediction models or sector-wide production estimates. In addition, an even greater value could be realized if interoperable systems are built, allowing this collective intelligence to be visualized and integrated directly into each participant’s individual management systems (ERPs and business platforms).

Beyond specific strategies for support, collaboration, or technological focus, interoperability stands out as a fundamental and cross-cutting requirement for successful digital transformation in the agri-food sector [92,93]. Without systems that can communicate and integrate effectively, any initiative, from sensor deployment to the use of management platforms or AI models, risks becoming isolated [106], limiting its real impact and perpetuating the frustration caused by fragmentation and integration challenges, as repeatedly identified by Andalusian stakeholders in our analysis. Addressing this structural challenge requires coordinated action on several fronts:

• It is essential to promote the use of open standards and shared data formats. Public administrations can help catalyze this by establishing regulations that encourage public APIs and compatible data exchange schemes, enabling different systems to work together [92].
• Interoperability should be addressed from the design phase, ensuring systems are modular, adaptable, and compatible with existing infrastructure. This requires close collaboration between developers and users, as well as possible compatibility certifications. This need is especially critical in Andalusia, given the diversity of technological solutions currently in use.
• Interoperability is also key to advancing the agri-food data economy, making it possible to build ecosystems where data can be reused securely and transparently, while respecting data sovereignty, privacy, and trust. Naturally, this requires the development of clear governance frameworks for ownership, privacy, and security [73,74].

Therefore, ensuring interoperability is not just a technical improvement, but a strategic investment that multiplies the value and sustainability of other actions. It facilitates scalability and creates a truly integrated and inclusive digital environment for Andalusia’s agri-food sector.

3.4. Limitations and Future Research

While this study offers valuable insights into the digitalization of the Andalusian agri-food sector, several methodological limitations should be acknowledged. The most significant constraint relates to the demographic composition of the survey sample. In comparison to the broader Spanish agricultural workforce [107], the sample notably overrepresents younger participants (43% aged 23–35), university-educated individuals (79%), and men (87%), while underrepresenting older farmers, particularly those aged 65 and above, women, and stakeholders with vocational or technical training. These imbalances are largely a result of the convenience sampling approach and the use of digital dissemination channels, which tend to attract participants who are more digitally literate, professionally connected, and actively engaged in innovation networks.

The reliance on online distribution channels, combined with a targeted outreach through professional networks and academic events, likely skewed participation toward younger, tech-oriented individuals already involved in digital transformation initiatives. While this approach enabled access to early adopters and key figures driving generational renewal, whose perspectives are vital for understanding future trends, it may also have excluded more traditional or disconnected profiles. Similar biases have been noted in related studies across Europe [54,57], suggesting this is a recurrent challenge in digital agriculture research relying on online instruments.

This sampling bias has clear implications for the interpretation of our results. Sensorization, decision-support, and digital platforms were consistently rated as highly useful, particularly for productivity, efficiency, and decision-making, in line with the more tech-savvy profiles in the sample. Nonetheless, reservations about the usability and reliability of complex tools were evident even within this group, and such skepticism is likely to be even greater among older or less digitally fluent stakeholders, who were underrepresented. Similarly, concerns regarding system interoperability and the relatively low attention paid to infrastructural or connectivity-related barriers may reflect respondents’ familiarity with digital environments rather than the broader challenges still confronting many rural areas. Another key implication is the emphasis placed on institutional drivers such as universities and public administration as primary enablers of digital transformation, potentially overlooking more informal, peer-based mechanisms that are often essential in conventional farming networks. Consequently, while our findings provide valuable insights into the digital vanguard of the sector, they should be interpreted with caution when extrapolating to the broader agri-food community.

To overcome these limitations, future research should adopt stratified sampling strategies that better reflect the demographic heterogeneity of the agricultural workforce. In addition, expanding recruitment beyond online channels, such as through in-person outreach, telephone surveys, or collaboration with local organizations, would help ensure a broader representation of all participant profiles. Such approaches would enable more robust comparisons across age, gender, education level, and stakeholder type. Advanced statistical techniques, including cluster analysis or factor-based segmentation, could also be applied to identify adoption profiles taking into account demographic characteristics as well as stakeholder typologies to support more customized technology diffusion strategies. Qualitative methods such as in-depth interviews or focus groups would complement these approaches and capture motivations, constraints, and context-dependent dynamics not captured in survey-based designs.

Expanding the scope of this study, future research could also benefit from adopting longitudinal designs to explore how stakeholder perceptions, adoption trajectories, and organizational structures evolve over time. This approach would be particularly valuable for capturing the transitional dynamics faced by less digitized actors and for assessing the sustained impact of digital policies, training programs, and institutional support mechanisms. Meanwhile, cross-regional comparative analyses within Spain and across other European agricultural regions might reveal how local conditions, policy environments and infrastructural differences affect the pace and nature of digital adoption. Those comparative perspectives would provide a richer context for understanding what drives and what blocks digital transformation in different agricultural contexts and would help shape more resilient and place-sensitive strategies for the sector’s digital future.

4. Conclusions

Agri-food digitalization is evolving with the ever-growing pace of new technologies. This study combined a bibliometric analysis of global research trends with an empirical survey of stakeholders’ perceptions in Andalusia, which revealed significant discrepancies between scientific progress and real-world implementation, as well as potentials to better align research with industry needs.

While advanced innovations such as AI and sustainability-oriented technologies are increasingly highlighted in global research, some stakeholders in Andalusia still express skepticism about such innovations. They valued intuitive, user-friendly tools that require a minimal user input and produce immediate economic returns, often at the expense of environmental considerations. Barriers like economic feasibility, trust, limited technical training and implementation complexity prevent such digital solutions from being adopted. Additionally, the paucity of collaborative data sharing frameworks in research and interoperability issues presents a window of opportunity for multistakeholder initiatives to increase supply chain transparency and operational efficiency.

These findings suggest the need for a holistic and realistic strategy to reconcile scientific progress with real-world reality. This requires supportive policy environments combining financial aid with easily accessible training, as well as encouraging collaboration among public institutions, private companies, and local actors to test and adapt technologies in real-life situations. Equally important is the development of modular, simplified, and user-friendly digital tools, including those that incorporate more advanced models, designed with a clear focus on usability and local relevance. In parallel, interoperability among systems and platforms is a prerequisite to make digital solutions scalable, useful and usable along the agri-food value chain. Aligning research priorities with sector-day-to-day challenges and capacities enables digital innovation to become an agent of inclusive and sustainable transformation.