Promoting the Transition towards Agriculture 4.0: A Systematic Literature Review on Drivers and Barriers

Abstract

In the modern era, the imperative of digitalisation to enhance competitiveness spans various sectors, with agriculture being no exception. Agriculture 4.0, strategically positioned to address challenges like climate change, food security, and resource preservation, holds the potential to increase productivity, profitability, and sustainability in agriculture. Despite the global accessibility to digital technologies, their adoption within the agriculture sector, especially among small and medium-sized farms, encounters obstacles. Realising the full potential of Agriculture 4.0 requires understanding the factors influencing technology adoption. To address this, the study conducts a systematic literature review using the PRISMA method, focusing on identifying the primary drivers and barriers associated with the implementation of Agriculture 4.0 technologies. The study is complemented by a network analysis of the identified drivers and barriers. A total of 42 articles from 2011 to 2023, sourced from the Scopus database, are examined. Individual and farm-related factors play a crucial role in driving the adoption of smart farming technologies, along with social trust and influence. Economic constraints and lack of infrastructure, such as internet access, emerge as significant barriers. The identified drivers and barriers can inform the development of strategies to promote the transition to Agriculture 4.0. Farmers stand to benefit from insights into potential advantages, required skills, and challenges, aiding informed decision-making in the adoption of Agriculture 4.0 technologies.

1. Introduction

The rising global population, projected to reach 9 billion by 2050, coupled with demography ageing, rural-to-urban migration, and the impacts of climate change and soil degradation, poses serious threats to the agricultural sector [1], requiring a greater focus on the sustainable use of natural resources [2]. Agriculture is the world’s largest industry; it employs over one billion people and produces food valued at more than $10 trillion yearly [3]. However, agriculture faces challenges due to unsustainable farming practices, which have severe consequences for both people and the environment [1]. The extensive cultivation of crops is a major cause of deforestation and ecological damage, adversely impacting natural habitats and biodiversity [4]. Unsustainable farming practices also lead to soil erosion, resulting in the depletion of half of the arable land over the last 150 years [5]. Moreover, agriculture is one the main sources of pollution in many countries, with pesticides, fertilisers, and chemicals compromising water, soil and air quality. These practices are responsible for 70% of the planet’s freshwater usage and further contribute to the accumulation of greenhouse gases [6].

Investing in digital technologies is essential to accelerate the transition to more sustainable agriculture systems to reduce the use of production inputs, minimise input costs, and preserve the environment [7]. Digital technologies can upgrade the agriculture sector, reduce the environmental footprint, preserve natural resources, encourage entrepreneurial innovation, and provide economic opportunities [8,9]. In this regard, in accordance with the FAO definition, Agriculture 4.0 is “agriculture that integrates a series of innovations in order to produce agricultural products. These innovations englobe precision farming, IoT and big data in order to achieve greater production efficiency” [10].

Several terms are used to denote Agriculture 4.0, coined as an analogy to the term Industry 4.0, signifying the implementation of digital technologies into the agricultural sector. Scholars have labelled this digital transformation of agriculture through various terms, such as ‘digital agriculture’, ’smart farming’, and ’smart agriculture’ [11].

These terms refer to the utilisation of advanced technologies in the collection and analysis of agricultural data [12]. The confluence of these tools and methods creates a powerful approach that improves agricultural practices by increasing their efficiency and productivity [13]. These technologies can provide guidance to farmers in decision-making related to their actions and relationships with supply chain partners [11], targeting the sustainable economic, environmental, and social progress of agricultural practices.

Historically, the agricultural sector has undergone significant transformations across different industrial revolutions. The First Industrial Revolution, starting in 1780, fostered mechanisation through the utilisation of human or animal power alongside plain tools. The Second Industrial Revolution, starting in 1870, switched to the integration of electrical technology, combustion engine tractors, and agrochemicals. The Third Industrial Revolution, emerging in 1969, was characterised by automation and widespread computer usage across various domains [11]. Since 2011, the Fourth Industrial Revolution has embraced an array of technologies, including the Internet of Things (IoT), cloud computing, Artificial Intelligence (AI), Big Data, augmented reality, robotics, sensors, 3D printing, Machine Learning (ML), digital twins, blockchain, and cyber-physical systems, among other emerging technologies [11]. In this phase, intelligent and interconnected devices possess autonomous decision-making capabilities, interconnected through physical or wireless systems. These technologies are linked to sensors that measure physical quantities in the environment and convert them into readable data [14].

More recently, Agriculture 4.0 has established a pathway towards the next stage of farming, involving operations without human intervention and autonomous decision-making systems. Agriculture 5.0 builds upon Agriculture 4.0 by assimilating the principles of Industry 5.0, aiming to provide healthy and affordable food while preventing ecosystem degradation [15,16,17].

Digital solutions have a significant impact on sustainable development worldwide. Digital agriculture is a profitable farming technique that minimises labour and environmental impacts. It can help farmers create safe, sustainable, high-quality food by offering smart solutions, improving food quality and safety, reducing waste and energy consumption, and promoting healthier, sustainable food options [18,19,20,21].

Agriculture 4.0 could also serve as a crucial connection between agricultural growth and the Sustainable Development Goals (SDGs) outlined in Agenda 2030. Fourth-generation technologies optimise natural resources, enhance water management, reduce food waste, and bolster biodiversity [22]. Helping farmers’ effective utilisation of digital technologies, such as digital agriculture, to increase food production while respecting the environment is imperative for fostering more sustainable and resilient agricultural systems [23]. Therefore, the Agriculture 4.0 paradigm holds the potential to advance several SDGs, including Zero Hunger (SDG 2), Clean Water and Sanitation (SDG 6), Responsible Production and Consumption (SDG 12), Life Below Water (SDG 14), and Life on Land (SDG 15) [24]. Despite the substantial impact of emerging technologies on the food and agriculture sector, there remains an evident delay and insufficient adoption of 4.0 digital technologies among farmers, especially smallholders, resulting in disparate adoption rates between developed and developing countries [22,25].

Recent empirical research suggests that the decision to adopt 4.0 technologies is influenced by a multitude of key factors [12,26,27]. Despite the abundance of literature on this subject, there exists, to the best of our knowledge, a limited body of work that effectively synthesises research on the drivers and barriers to adoption [28,29]. Varied results have emerged in diverse technical and socioeconomic settings, highlighting the need for more integrative reviews that systematically capture and consolidate these findings.

By building upon the preceding context, this research is primarily focused on conducting a rigorous systematic literature review to identify the decisive factors influencing the adoption of Agriculture 4.0 technologies. The focus is on emphasising theoretical gaps, specifically in areas not addressed in the current research stream and classifying these factors as drivers and barriers intricately linked to the implementation of 4.0 technologies. Understanding the objective factors shaping the adoption of 4.0 technologies is crucial for decision-makers, technology providers (firms), and researchers studying the determinants of growth in this domain. Notably, the surge in interest in digitalisation and the transition to a digital economy in recent years, intensified by the COVID-19 pandemic, underscores the significance of this research. Public authorities need to understand the approaches by which farmers adopt innovations to formulate a strategy for the progression of digital agriculture, aiming to decrease agriculture’s environmental impact while simultaneously increasing or maintaining agricultural output and productivity.

Therefore, this literature review aims to investigate factors influencing the adoption of 4.0 technologies in farming and classify these factors as drivers and barriers linked to the implementation of these technologies.

The paper is structured as follows. Section 2 outlines the methodology used. Section 3 presents the results of the review of relevant papers. Section 4 discusses the findings and provides suggestions for future research, and Section 5 concludes the paper.

2. Methodology

2.1. Data Collection Procedure

This literature review follows the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [30], employing an evidence-based checklist connected to a four-phase flow diagram: identification, screening, and inclusion [31]. Figure 1 shows the flow chart of the research article selection by PRISMA.

This structured approach ensures clarity and transparency in the review process, facilitating a rigorous evaluation of studies and the inclusion of high-quality, relevant articles. PRISMA was chosen for its widespread use and recognition across various research domains, although its limitations have been acknowledged in certain fields [32]. The PRISMA Checklist is provided as Supplementary Materials. Consistent with the PRISMA statement, an exhaustive search of papers and articles was conducted on Scopus (www.scopus.com) to evaluate the existing literature. In line with previous research [33,34], Scopus was chosen for its wide coverage across diverse disciplines, extending beyond agriculture alone, and its inclusion of scientific journals covering different ranking levels rather than only focusing on top-tier journals. Previous studies comparing different databases for literature reviews and bibliometric analysis found that Scopus coverage is almost 60% greater than Web of Science (WoS), which only includes ISI-indexed journals and may limit the number of articles [35,36].

2.2. Identification Criteria

The query used to identify the articles is outlined below:

(TITLE-ABS-KEY (“smart agr*”) OR TITLE-ABS-KEY (“smart farm*”) OR TITLE-ABS-KEY (“agriculture 4.0”) OR TITLE-ABS-KEY (“agriculture 5.0”) OR TITLE-ABS-KEY (“agr* adopt*”) OR TITLE-ABS-KEY (“farm* adopt*”)) AND PUBYEAR > 2010 AND PUBYEAR < 2024 AND (LIMIT-TO (SUBJAREA, “AGRI”) OR LIMIT-TO (SUBJAREA, “BUSI”) OR LIMIT-TO (SUBJAREA, “ECON”) OR LIMIT-TO (SUBJAREA, “SOCI”))

Keywords were searched within titles, abstracts, and keyword lists in the articles to ensure complete coverage of the sample.

Additionally, keywords with the ending “*” allowed for varying endings (e.g., adopt* retrieved both ’adoption’ and ‘adopted’). The search criteria included year and subject limitations, consisting of agriculture, business, economic, and social science papers.

The chosen query structure and the inclusion of the keywords ‘farm* adopt*’ and ‘agr* adopt*’ guarantee that no articles are excluded. Several combinations of the search terms were tested based on the research questions, and this one was ultimately selected because it yielded the fewest repetitions.

In April 2023, the Scopus query resulted in 3722 studies published from 2011. The selection of articles from 2011 onwards was intended to focus on the most recent literature on Agriculture 4.0. Furthermore, according to [37], the Industry 4.0 paradigm was first introduced in Germany at the end of 2011, which is why we chose to start the review in 2011.

2.3. Screening and Selection Criteria

The study adhered to the PRISMA guidelines and involved screening records in two stages: (1) an initial screening based on the title and (2) a subsequent screening of the abstract.

During the title screening process, 2601 articles with non-relevant content were excluded. Many articles outside the scope of this review were included in the Scopus list of results. These papers were excluded because they covered a wide range of topics, including the design and development of technological innovations, investigation of agricultural practices or techniques, diagnosis of soil health or crop disease, or conducting reviews. After screening the abstracts, 535 articles were excluded due to their focus. These documents were excluded because they focused on the development of technologies, innovative practices or techniques, and conceptual discussions.

After excluding papers with titles not pertinent to the study, further evaluation was conducted of relevant papers, which involved reading their abstracts and removing those that did not discuss technology adoption factors.

2.4. Eligibility and Inclusion Criteria

The 586 remaining articles underwent full-text evaluation to ensure practical applicability. To be considered for inclusion, studies had to meet the following criteria:

• be written in English
• have been published in peer-reviewed scientific journals
• focused solely on the adoption of digital tools in agriculture
• reported qualitative or quantitative data related to technology acceptance
• contain full text/DOI available
• not be a review article.

The emphasis was on papers containing detailed data related to factors for digital technology adoption. Ultimately, 42 articles met the final criteria and were included in the analysis (Figure 1).

Table 1. Systematic review summary: analysed technologies, countries (sample size), productions, drivers and barriers to adoption.

Authors	Technologies	Countries (Sample Size)	Type of Production	Drivers to Adoption	Barriers to Adoption
Aubert et al. (2012) [38]	GPS, GIS, yield monitors and maps, remote sensing, variable rate application systems, navigation systems	Canada (438)	Cereal, oleaginous	Innovativeness, education, support, knowledge, relative benefits, ease of use, usefulness	Perceived availability, perceived trialability, perceived voluntariness, compatibility, complexity, information
Mittal and Mehar (2016) [39]	Information and Communication Technology (ICT)	India (1200)	Crop farming	Age, education, farm size
Paustian and Theuvsen (2017) [40]	Precision farming	Germany (227)	Crop farming	Farm size, education, experience, innovativeness, additional farm business branch, employers’ management	Cost
Tamirat et al. (2018) [41]	Precision farming	Denmark, Germany (260)	Tilled crops	Farm size, age, demonstration, networks
Pambudy (2018) [42]	Agricultural innovation	Madura island (92)	Cattle	Managerial skills, failure tolerance, communication channel	The constant change in technology, lack of entrepreneurship characteristics, ineffective communication processes
Pivoto et al. (2018) [14]	Telemetry, automation system, data collection system, georeferenced soil sampling	Brazil (180)	Crop farming		Compatibility, education, ability, soft skills, data management
Pivoto et al. (2019) [37]	Georeferenced soil sampling, automatic spray, variable rate fertiliser application, management software	Brazil (119)	Livestock, dairy, grain	Relative benefits, costs, farm needs, need to store information about soil and climate crop management, education, farm size	Cost, Lack of a skilled workforce, lack of knowledge, network influence, interest, Internet, data management, trust
Thompson et al. (2019) [28]	Variable rate fertiliser-seed application, yield monitor, autosteer, precision soil sampling, unmanned aerial vehicle (UAV), aerial imagery	USA (837)	Corn, soybean, wheat, cotton producers	Farm size, financial profitability, farm management, cost savings, yield improvement, convenience	Lack of understanding or knowledge, cost
Caffaro and Cavallo (2019) [43]	FMIS, robotic automation system	Italy (310)	Crop farming	Farm size	Education, cost, work alone
Vollaro et al. (2019) [44]	Agricultural innovation	Italy (300)	Cattle, arable crops, fruit	Farm size, mechanisation, labour, production type, education, experience, income	Cost, bureaucracy, risks, managerial skills
Das et al. (2019) [45]	Cloud computing	Ireland (332)	Diary, beef, sheep, arable	Age	Cost, lack of awareness, lack of allowance, unavailability, ease of use, data ownership, farm size
Chuang et al. (2020) [46]	Internet of Things (IoT)	Taiwan (241)	Fruit, tea, vegetable	Age, gender, usefulness, ease of use, trust, willingness to pay, support, turnover
Skevas and Kalaitzandonakes (2020) [47]	unmanned aerial vehicles (UAV)	Missouri (964)	Livestock and crop farmer	Economic and environmental gains, age, farm successors, cooperation with other farmers, income	Potential privacy from neighbouring
Kernecker et al. (2020) [48]	Autonomous machines, recording-mapping, GPS-connected tools, FMIS apps	France, Germany, Greece, Netherlands, Serbia, Spain, United Kingdom (287)	Arable crops, orchards, field vegetables and vineyards	Farm size, income, arable crop	Cost, complexity of use, data interpreting, compatibility, connectivity of device, lack of use demonstrations, lack of access to reliable information and independent advisory service, lack of cost-benefit and added value
Li et al. (2020) [49]	Precision soil sampling, yield mapping, GPS guidance and unmanned aerial vehicles (UAVs or drones)	China (449)	Crop farming	Perceived need, perceived benefits, facilitating conditions, perceived risks of adoption, knowledge, training, support	Cost, farm size, finding providers, lack of accessible and affordable resources, trust
Caffaro et al. (2020) [50]	Apps, drones, sensors for data acquisition and automatic download, robots, autonomous machines	Italy (314)	Arable and horticultural farming	Usefulness, information
Ronaghi and Forouharfar (2020) [51]	Internet of Things (IoT)	Iran (392)	Crop farming	Performance expectancy, effort expectancy, social influence, individual factors, facilitating condition, behavioural intention	Age, experience, income, region droughts, imposed sanctions
Michels et al. (2020) [52]	Smartphone apps	Germany (207)	Arable farming	Performance expectancy, effort expectancy, social norm, facilitating conditions	Lack of mobile internet coverage, suitability of phone, complexity
Bolfe et al. (2020) [53]	Internet, apps, GPS, digital maps, field sensors, remote sensors, embedded electronics, telemetry, automation, deep learning, Internet of things (IoT), cloud computing, big data, blockchain and cryptography, AI	Brazil (504)	Rural producers	Perceived increase in productivity, better process quality, reduced costs, greater knowledge of cultivated areas.	Cost, connectivity, accessing credit, lack of knowledge
Von Veltheim and Heise (2021) [54]	Autonomous Field Robots (AFR)	Germany (490)	Crop farming	Expected benefits, perceptions of advantage, trust, efficiency, productivity	Lack of knowledge, lack of experience, complexity to use, lack of reliability, operational requirements, compatibility
Kaňovská (2021) [55]	Remote and proximal sensing sensors, robots	Czech Republic (21)	Winemakers	Adjustment of the product portfolio, savings, consulting, organisation	Information, conservative approaches, ignorance of SFT, financial demands, low state support, age
Schukat and Heise (2021) [56]	Smart farming	Germany (422)	Chicken, layers, piglet, pig, sows, dairy, beef, horse farmers	Trust in smart products, technology readiness, ease of use	Limited internet, data security, trust, lack of technology readiness and facilitating conditions
Marescotti et al. (2021) [57]	Information and Communication Technology (ICT)	Italy (63)	Dairy farmers	Age, education, farm size, smartphone use for work, optimistic behaviour towards the future of the farm	Cost, trust, time to use, internet connection
Quan and Doluschitz (2021) [58]	Machinery technologies	China (4165)	Maize farmers	Farm size, crop type, family labour, subsidy, support, economies of scale	Number of fields
Zuo et al. (2021) [59]	Drone	Australia (991)	Crop farmers	Education, farm size, farm successor, organic	Financial stress from bank, income
Silvi et al. (2021) [60]	Precision farming	Brazil (378)	Dairy farmers	Available technical support, return on investment (ROI), user-friendliness, upfront investment cost, compatibility	Need of investment in other factors, uncertainty ROI, lack of integrations, lack of knowledge
Zheng et al. (2022) [61]	Internet	China (1449)	Crop farmers	Internet, training	Lack of information, education, information asymmetry, market price fluctuations, the proportion of non-agricultural income
Agussabti et al. (2022) [62]	Autonomous machines, recording-mapping, GPS-connected tools, FMIS apps	Indonesia (258)	Rice, corn, potato	Farm size, access to funds	Cost, support, lack of demonstrations, loan or capital debt burden, unclear added value
Arjune and Kumar (2022) [63]	Automation, robots, big data, recognising images, applications, Internet of Things (IoT), drones, climate change sensing	India (254)	Crop farming	Education, income, land holding, irrigation facilities, extensive contacts, exposure towards mass media, information-compulsive behaviour, economic motive, inventive proclivity, decision-making capability, scientific orientation, perception	Cost, less knowledge, security issues, improper guidance, risk, age
Ulhaq et al. (2022) [64]	Information and Communication Technology (ICT)	Vietnam (206)	Shrimp farmers	Usefulness, networks, confident to learn	Lack of effective support, risk
Kendall et al. (2022) [65]	Precision agriculture	China (27)	Arable farmer	Importance of regulations, observability, trialability, support, information exchange	Cost, lack of information, farm size, land fragmentation and farming discontinuous plots, risk
Giua et al. (2022) [66]	Smart farming technologies	Italy (474)	Arable crops, fruit, vegetables, viticulture	Individual intention-to-use, arable sector, farm size	Individual attitudes, farm characteristics, sector-specific challenges, support services, performance expectancy, complexity
Gerli et al. (2022) [67]	Smart farming technologies	Belgium, Italy, UK (29)	Livestock and crop farmer		Negative emotions, low attitude to learn, cost, lack of financial resources, social influence, information, fear of job loss and loss of control over data, technophobia.
Makinde et al. (2022) [68]	Feeding and herd management technologies	Canada (24)	Beef farming	Age, education, farm size	Cost, return on investment, technology usability, lack of awareness of technologies and their capabilities, perceived relevance
Ammann et al. (2022) [69]	Precision agriculture	Switzerland (34)	Outdoor vegetable farming	Financial support	Cost
Han et al. (2022) [70]	Smart farming technologies	China (11547)	Crop farmers	Social networks, trust, social participation	Demographic change
Suroso et al. (2023) [71]	Mobile internet	Indonesia (119)	Palm oil producer	Education, ease of use, productivity	Training size
Omar et al. (2023) [72]	Agri-finance app	Malaysia (337)	Oil palm, rubber, cocoa, pepper	Freedom of mobility, more productivity	Risk, security issues
Khanna et al. (2023) [26]	Agricultural sensors, drones, GPS	Punjab (342)	Crop farming	Financial aspect
Jaroenwanit et al. (2023) [27]	Smart farming technologies	Thailand (400)	Organic farming	Cost-effectiveness, convenience, sustainability, compatibility, optimism, interest in SFT, risk, government support, social influence
Da Silveira et al. (2023) [12]	Internet of Things (IoT), artificial intelligence (AI), blockchain, machine learning	Brazil (347)	Maize, rice, soybeans, wheat, fruit		Lack of infrastructure, cost, need to foster R&D and innovative business models, age, lack of efficacy in the data on the rural environment.
Al-Ammary et al. (2024) [73] *	Information and Communication Technology (ICT)	Kingdom of Bahrain (100)	Crop farmers		Lack of knowledge and awareness

* The study was published online on 15 March 2023.

reported each included study and a summary of its characteristics. Additional details are provided in Appendix A.

3. Results

3.1. Overview of Selected Articles

The Scopus search results indicate a notable increase in literature related to digital agriculture over the past five years. This demonstrates the growing importance of the field, as evidenced by the trend of over eight hundred publications registered on the site in 2022, as shown in Figure 2.

Following the implementation of the PRISMA protocol, the final subset consisted of 42 articles that met the inclusion criteria. To examine the obstacles and drivers to the adoption of smart farming technologies, as listed in Table 1, an in-depth analysis of the selected studies is conducted to discuss the findings relevant to the research questions.

Approximately 58% of the articles were published between 2020 and 2022, with a noteworthy 24% of the papers specifically published in 2022. This surge indicates a substantial increase in research activity in this field in recent times. Moreover, the selected papers exhibit diversity, covering multiple continents. The selected articles are broken down as follows: 35% from Europe, 37% from Asia, 21% from the Americas, and 7% from Indonesia. The analysis of various technologies in the field of digital farming reveals a heterogeneous landscape. Mobile applications emerge as highly investigated, likely due to the widespread usage of smartphones and the abundance of accessible agriculture-related applications. Other noteworthy technologies include Unmanned Aerial Vehicles (UAVs) or drones, Geographic Positioning Systems (GPS), robots, autonomous machines, IoT, mapping systems, Information and Communication Technologies (ICT), and Farm Management Information Systems (FMIS). Agricultural sensors, used for climate change, remote sensing, and field monitoring, are widely featured in various agricultural fields. This variety of tools indicates the complexity of technologies examined in the field of Agriculture 4.0. The target groups of these studies are also heterogeneous, with a predominant focus on crop farming, constituting about 70% of the publications. Arable crops, like grain, soybean, rice, and corn, are the most analysed, followed by fruit crops, orchard crops, and vineyards. Livestock producers are also investigated, with dairy and beef producers being the most studied, succeeded by cattle, chicken farming, horses, and shrimp producers.

Researchers employed various theoretical frameworks and analytical methodologies in their studies. The number of participants, who were mostly farmers along with agricultural experts and lecturers, ranged from 4 to 11,547, with an average of 713, all at least 18 years old (see

Table A4. List of selected articles, objectives, sample, and number of citations.

Authors	Objectives	Main Findings	Cit. 10/23
Aubert et al. (2012) [38]	To explore the adoption of precision agriculture technology as a type of information systems innovation, which integrates information system innovation with core business technology.	The factors that have the strongest effect on technology adoption are the compatibility among PA technology components, farmers’ expertise, and the perceived extent of resources available.	280
Mittal and Mehar (2016) [39]	To analyse the factors that influence the likelihood of farmers in five states of India adopting different agriculture-related information sources and understand how socioeconomic factors impact farmers’ behaviour in selecting various sources of agricultural information.	Farmers use multiple information sources, which may be complementary or substitutes for each other. The selection of these sources is influenced by factors such as age, education level, and farm size.	119
Paustian and Theuvsen (2017) [40]	To investigate the operational and sociodemographic factors that significantly influence the adoption of precision farming by German crop farmers.	The adoption of precision farming is positively affected by agricultural contractor services, such as an additional farming business, for those with under 5 years’ experience in crop farming and more than 500 ha of arable land.	174
Tamirat et al. (2018) [41]	To analyse the factors that affect the adoption of innovation steps and how this adoption can influence the formation of an entrepreneurial status among Madura cattle farmers.	The characteristics of entrepreneurs and the communication process influence the adoption of innovations by farmers.	3
Pambudy (2018) [42]	To characterise the scientific knowledge about smart farming based on the main factors of development by country and over time and to describe smart farming prospects in Brazil from the perspective of experts in this field.	Education, ability, and skills of farmers to understand and handle SF tools are prominent limiting factors.	180
Pivoto et al. (2018) [14]	To identify the primary socioeconomic factors that influence the adoption of precision agriculture in Denmark and Germany.	The decision of farmers to adopt is significantly influenced by farm size, farmer age, and participation in demonstration and networking events such as attending workshops and exhibitions.	51
Pivoto et al. (2019) [37]	To assess producers’ views on four main precision agriculture technologies and evaluate the benefits of these technologies among US farms.	Farmers’ perceptions of the benefits derived from various precision agriculture technologies are heterogeneous. Yield improvement or cost savings are primary reasons to adopt precision agriculture.	43
Thompson et al. (2019) [28]	To identify the barriers and determining factors that influence grain farmers’ decisions to adopt smart farming technologies in the Brazilian grain sector.	There is no strict pattern in farmers’ profiles in terms of socioeconomic characteristics. Adopting smart farming requires farmers to be open and receptive to this concept of agriculture.	41
Caffaro and Cavallo (2019) [43]	To investigate the impact of sociodemographic variables and subjective factors, such as farmers’ perceived barriers, on the use of smart farming technologies among a sample of Piedmonts’ farmers.	Low levels of education and working on-farm alone were positively associated with perceived economic barriers. These barriers, in turn, were negatively associated with the adoption of SFTs. On the other hand, farm size had a positive direct effect.	29
Vollaro et al. (2019) [44]	To analyse the determinants of farmers’ adoption of innovations and examine the effect of the source of information and the link to agricultural research on the contribution of innovation to farm performance.	Structural characteristics of the farms, such as farm size, mechanisation, labour and farm specialisation play a relevant role in the innovation adoption. Motivations for innovation adoption are largely related to the combination of cost reduction and production increases.	4
Das et al. (2019) [45]	To identify the reasons for the slow adoption of smart farming technologies in Ireland, in order to recognise the barriers to adoption and find ways to improve the current system.	The adoption of technology among young farmers is comparatively higher than among older farmers. Non-adopters cited age, high cost, and lack of awareness as the main barriers, while adopters faced barriers such as lack of allowance, unavailability, ease of use, and data ownership.	46
Chuang et al. (2020) [46]	To investigate the intention of young farmers to use the Internet of Things systems for field-level management of Taiwanese farms.	The intention of young farmers to use innovative technologies is primarily influenced by perceived organisational support, followed by average annual turnover, perceived usefulness, perceived ease of use, and trust in the system supplier.	10
Skevas and Kalaitzandonakes (2020) [47]	To study the actual and expected adoption of UAVs among a diverse group of crop farmers and investigate the factors that influence this adoption in Missouri.	The primary factor driving adoption is farmers’ expectations of potential economic and environmental benefits from using UAVs. Adoption decisions of individual farmers are also influenced by their socioeconomic and farm characteristics.	9
Kernecker et al. (2019) [48]	To understand what motivates European farmers from different farming systems to adopt SFT, what factors support adoption, what barriers farmers and experts perceive to SFT adoption, and what suggestions farmers and experts must make to improve SFT.	Farmers and experts consider peer-to-peer communication to be an important source of information and express concern about the lack of impartial advice. Experts are generally more convinced of the advantages of SFT and are optimistic about the long-term trends of technological development.	79
Li et al. (2020) [49]	To explore the factors that determine Chinese farmers’ adoption of precision agriculture technologies in cropping systems.	Facilitating conditions (e.g., knowledge, resources and access to consultant services) play a substantial role in improving the willingness to adopt precision agriculture technologies.	34
Caffaro et al. (2020) [50]	To predict farmers’ intention to adopt Sustainable Farming Technologies based on their personal and impersonal (formal and informal) information sources.	The intention of farmers to adopt a technology is influenced by their perception of its usefulness, which in turn is influenced by both formal and informal sources of information.	63
Ronaghi and Forouharfar (2020) [51]	To identify the factors that influence the adoption and application of IoT in smart farming by farmers in Iran, a typical Middle Eastern country, using a contextualised approach.	Performance expectancy, effort expectancy, social influence and facilitating conditions positively affect the intention to use IoT technology.	59
Michels et al. (2020) [52]	To analyse the use of mobile decision support apps using the Unified Theory of Acceptance and Usage of Technology.	Not all farmers who perceive a crop protection smartphone app function as useful actually use a corresponding app. If farmers perceive the usage of crop protection apps as relatively effortless, they are more likely to use them.	35
Bolfe et al. (2020) [53]	To collect information from rural producers through an online survey regarding the current use, applications, challenges, and prospects of digital technologies in Brazil.	The perception of increased productivity is the main perceived benefit of digital technology adoption, while the acquisition costs of machines, equipment, software, and connectivity are the main challenges.	46
Von Veltheim and Heise (2021) [54]	To identify distinct clusters and strategic groups based on farmers’ attitudes towards the adoption of autonomous field robots. The study aims to contribute empirical evidence to the existing research.	Three groups of farmers are identified according to their response behaviour. Although these three groups differ, an overall attitude for autonomous field robots is observed.	10
Kaňovská (2021) [55]	To identify the obstacles to and advantages of implementing smart farming technologies among small and medium-sized winemakers. This includes the use of sensors and weather stations to gather site-specific data for viticulture purposes.	The drivers identified are the adjustment of the product portfolio, savings, consulting, and organisation of activities. Barriers include a low need for information, alternative sources of information, conservative approaches, ignorance of SFT, financial demands, low state support, and the age of winemakers.	1
Schukat and Heise (2021) [56]	To evaluate the attitudes of German livestock farmers towards smart products by categorising them into groups using factor and cluster analysis.	The social environment, the expected effort for implementation, the general trust in smart products, and the technology readiness of the farms are the main distinguishing factors among the four clusters identified.	7
Marescotti et al. (2021) [57]	To examine the impact of farmers’ attitudes and farm characteristics on the adoption of technological devices, such as smartphones, tablets, and computers in the rural area of Valtellina in the Italian Alps.	The attitudes towards new technologies are affected by a farmer’s age, education level, farm size, actual smartphone usage for professional duties, and optimistic behaviour towards the future of the farm.	14
Quan and Doluschitz (2021) [58]	To identify the factors that influence maize farmers’ adoption of four machinery technologies: seeding, ploughing, harvesting, and pesticide spraying, and to analyse the interrelation between these adoption decisions.	Arable land area, crop diversity, family labour, subsidy, technical assistance, and economies of scale have positive effects on machinery adoption, while the number of discrete fields in the farm has a negative impact.	2
Zuo et al. (2021) [59]	To investigate the factors influencing the future adoption of drone technology in agriculture among Australian irrigators in the southern Murray-Darling Basin.	Future drone adoption is positively associated with human, financial, and farm capital factors. However, financial stress from the bank, as well as the percentage of net farm income and off-farm income beyond a certain level, act as barriers to adoption.	6
Silvi et al. (2021) [60]	To characterise Brazilian dairy farms based on different factors such as their use of technology, willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes.	The need for investment in other sectors of the farm, the uncertainty of ROI and the lack of integration with other farm systems and software are the most important factors precluding investment in precision dairy technologies. Increasing technical support may have a positive impact on the adoption.	5
Zheng et al. (2022) [61]	To investigate the influence of Internet usage on the adoption of agricultural production technology by smallholder farmers in China.	The use of the Internet can significantly promote the adoption of technology. The effect of Internet use is heterogeneous, with a greater impact on smallholder farmers who have low levels of education, limited training, and high incomes.	20
Agussabti et al. (2022) [62]	To analyse the adoption readiness of using smart farming technology for three food commodities in Aceh Province, Indonesia, namely rice, maize, and potatoes, and explore the perceptions of farmers and agricultural extension workers in Aceh concerning the potential benefits of adopting SFT.	Both farmers and extension workers perceive the application of SFT positively. However, farmers have a lower readiness level compared to extension workers due to their limited capacity.	2
Arjune and Kumar (2022) [63]	To identify the key attributes and knowledge of farmers that lead to the adoption of smart agriculture and to discover the significant challenges farmers face in adopting Smart Agriculture practices.	The primary barriers to adopting smart agriculture practices are the initial cost of adaptation and the lack of funds. It is important to address these challenges in order to fully embrace the benefits of smart agriculture.	0
Ulhaq et al. (2022) [64]	To analyse the perceptions and attitudes of Vietnamese intensive shrimp farmers towards adopting information and communication technology for shrimp monitoring.	The adoption of shrimp farming technologies is significantly influenced by perceived ease of use, perceived usefulness, and subjective norms. It is likely that farmers who feel confident in their ability to learn a new technology will find it easier to use.	20
Kendall et al. (2021) [65]	To investigate the perspectives of Chinese family farmers in order to gain a nuanced understanding of their perceptions and attitudes towards precision agriculture, as well as to identify the factors that either facilitate or hinder its adoption.	The socio-political landscape, farming culture, agricultural challenges, adoption intentions and practical support mechanisms are likely to influence the level and rate of adoption of PA technologies amongst family farmers in China.	12
Giua et al. (2022) [66]	To investigate the factors that influence the intention to use and the actual adoption of Smart Farming Technologies in the agricultural sector.	Intention to use SFT is driven by performance expectations and social influence. The adoption decision is affected by farmers’ intentions and farm characteristics.	11
Gerli et al. (2022) [67]	To investigate the impact of psychological factors on skill development in the adoption of smart farming technology.	Psychological factors affect the development of digital skills and the adoption of smart technologies.	6
Makinde et al. (2022) [68]	To analyse the perceptions, level of awareness, and experiences of two different stakeholders, farmers and veterinarians, with digital precision livestock farming technologies used on Canadian beef farms.	Costs and return on investment, technology usability, lack of awareness of technologies and their capabilities, and perceived relevance of the technology are the main technology adoption barriers.	3
Ammann et al. (2022) [69]	To identify the key drivers and barriers to technology adoption in Swiss outdoor vegetable production, determine the most promising technologies for adoption in vegetable production, and provide measures to support technology adoption in the agricultural sector.	Economic factors are important drivers and barriers to technology adoption. Furthermore, the practical relevance of new technologies provided through communication and education holds further potential in terms of their adoption.	6
Han et al. (2022) [70]	To test the impact of social capital, including social networks, social participation, and social trust, on farmers’ willingness to adopt agricultural technology. To investigate the moderating effect of demographic changes on social capital and farmers’ willingness to adopt new agricultural technology.	Social trust has a significant positive impact on farmers’ willingness to adopt new agricultural technologies. On the contrary, while social participation has no significant impact, while social networks influence farmers’ technology adoption behaviour differently.	1
Suroso et al. (2023) [71]	To investigate the adoption of mobile internet and its impact on palm oil productivity and determine the factors that influence mobile internet adoption and its implications for the productivity of palm oil farmers.	Education, training size, ease of use, and productivity are determinants of mobile internet adoption. However, mobile internet adoption has not significantly affected the productivity of palm oil farmers.	0
Al-Ammary and Ghanem (2024) [73]	To investigate the preparation, acceptance, and adoption of information and communication technologies in agriculture in the Kingdom of Bahrain, focusing on soil sensors, remote sensing, artificial intelligence (AI), and big data.	Farmers are not ready to adopt sophisticated devices and complex applications such as crop sensing tools, the Internet of Things (IoT) and AI mainly due to insufficient knowledge and awareness.	0
Omar et al. (2023) [72]	To investigate the impact of the two dimensions of technology readiness, namely motivator and inhibitor, on the behavioural intention of farmers in Sarawak, Malaysia, to adopt the e-AgriFinance app, a mobile agricultural finance application.	Motivator dimensions have a relatively stronger positive effect in predicting the farmers’ behavioural intention, while inhibitor dimensions have a relatively weaker negative effect.	9
Khanna et al. (2023) [26]	To gain a clear understanding of farmers’ mindsets and perceptions towards the adoption of precision agriculture techniques and to capture farmers’ attitudes towards the obstacles they face when adopting precision agricultural practices in Punjab, India.	Overall farmers across the state are keen on adopting new-age farming practices. However, they feel hesitant due to several associated issues	4
Jaroenwanit et al. (2023) [27]	To provide a better understanding of risk management in the adoption of smart farming technologies by farmers in rural areas.	Government support variables have the most significant influence in adopting smart farming to risk management	0
Da Silveira et al. (2023) [12]	To validate the barriers that hinder the development of Agriculture 4.0 in Southern Brazil’s agricultural production chain and contribute to the development of a framework to overcome these barriers and facilitate the expansion and dissemination of Agriculture 4.0 in Brazil.	Lack of infrastructure, lack of solutions accessible to farmers, need to foster R&D and innovative business models, age group risk, and lack of efficacy in the data on the rural environment are the most important barriers to the dissemination of Agriculture 4.0.	0

). Surveys were spread by mail or distributed at agricultural events, while interviews were conducted through phone or video calls. Frequently used theoretical frameworks include the Technology Acceptance Model (TAM) [74], the Unified Theory of Acceptance and Use of Technology (UTAUT) [75], and Roger’s Diffusion of Innovations theory (1983) [76], sometimes combined to build more comprehensive adoption models. Several researchers have also integrated these theories to provide a more specific model of the drivers and barriers to adoption (e.g., [66]). Investigations into digitisation in agriculture have employed both quantitative and qualitative methods, with the structural equation model (SEM) frequently appearing in quantitative analysis, followed by cluster analysis and logit/probit regression. Qualitative methods included SWOT-TOWS analysis and coding approaches. It is noteworthy that researchers abstained from using mixed methods combining qualitative and quantitative analyses.

3.2. Factors Affecting 4.0 Technology Adoption

The analysis of 42 selected papers has identified 35 variables that potentially affect farmers’ intention to adopt 4.0 technologies. Variables were grouped according to their common features to simplify analysis and determine patterns. For example, variables such as age, gender, and educational level were grouped together, facilitating an in-depth evaluation of sociodemographic trends. This approach improves data comprehension and deepens knowledge regarding the connection between the key variables. After grouping all variables, they were classified into seven categories, serving as explanatory factors. These categories include traits related to (i) individual, (ii) economic, (iii) environmental, (iv) farm-related, (v) institutional, (vi) technological, and (vii) psychological aspects (Figure 3).

Table A1. Individual, economic and environmental factors affecting farmers’ technology adoption by authors.

AUTHORS	INDIVIDUAL								ECONOMIC			ENV
	Age	Education	Digital Skills	Entrepreneur Status	Experience	Gender	Innovativeness	Family Size	Cost	Convenience	Income	Environment
Aubert et al. (2012) [38]	X	X	X				X			X	X
Mittal and Mehar (2016) [39]	X	X
Paustian and Theuvsen (2017) [40]	X	X	X		X	X	X					X
Tamirat et al. (2018) [41]	X	X			X						X
Pambudy (2018) [42]	X			X		X
Pivoto et al. (2018) [14]		X	X
Pivoto et al. (2019) [37]	X	X	X		X
Thompson et al. (2019) [28]									X	X	X
Caffaro and Cavallo (2019) [43]		X							X	X
Vollaro et al. (2019) [44]		X		X							X
Das et al. (2019) [45]	X	X	X		X	X	X		X
Chuang et al. (2020) [46]	X	X			X	X			X		X
Skevas and Kalaitzandonakes (2020) [47]	X	X				X			X		X	X
Kernecker et al. (2019) [48]									X		X	X
Li et al. (2020) [49]			X
Caffaro et al. (2020) [50]	X				X
Ronaghi and Forouharfar (2020) [51]	X			X	X	X					X
Michels et al. (2020) [52]	X	X				X
Bolfe et al. (2020) [53]					X		X		X
Von Veltheim and Heise (2021) [54]	X	X	X		X	X			X			X
Kaňovská (2021) [55]	X						X		X		X
Schukat and Heise (2021) [56]	X					X						X
Marescotti et al. (2021) [57]	X	X				X
Quan and Doluschitz (2021) [58]										X
Zuo et al. (2021) [59]	X	X				X	X				X	X
Silvi et al. (2021) [60]	X		X				X
Zheng et al. (2022) [61]	X	X	X	X	X						X	X
Agussabti et al. (2022) [62]	X	X			X				X		X
Arjune and Kumar (2022) [63]	X	X	X		X	X	X	X		X	X
Ulhaq et al. (2022) [64]	X	X	X	X	X	X
Kendall et al. (2021) [65]	X	X			X				X
Giua et al. (2022) [66]	X	X									X
Gerli et al. (2022) [67]			X
Makinde et al. (2022) [68]	X		X		X				X	X
Ammann et al. (2022) [69]			X						X	X	X	X
Han et al. (2022) [70]	X		X			X			X		X
Suroso et al. (2023) [71]	X	X	X		X			X
Al-Ammary and Ghanem (2024) [73]			X						X
Omar et al. (2023) [72]	X	X			X	X					X
Khanna et al. (2023) [26]			X						X
Jaroenwanit et al. (2023) [27]	X	X				X
Da Silveira et al. (2023) [12]	X	X	X						X	X		X
Total	30	25	19	5	17	16	8	2	17	8	17	9
Group total	122								42			9

provides a detailed overview of the results by authors.

Table 1 shows the main findings derived from 42 research articles. The identified factors were further analysed and classified into drivers and barriers, aiming to provide a clearer picture of the literature on the adoption of 4.0 technologies.

The diverse conclusions drawn by scholars often reveal interconnected factors influencing the multifaceted process of technology introduction.

While some attributes highlighted by authors may not decisively impact digital technology adoption, common barriers include limited knowledge of smart farming technologies’ benefits and functionality, the financial burden associated with the initial investment, and insufficient funds. The role of education, farm size, and farmer age remains ambiguous, with varying outcomes reported among authors. The provision of technical support from manufacturers or governmental entities plays a crucial role in understanding how smart technologies work.

The subsequent sections provide an analysis of the most important characteristics that emerged from the analysis of the selected 42 studies. These characteristics are grouped in line with the established categories.

3.2.1. Individual Factors

The adoption of 4.0 technologies seems significantly influenced by farmers’ age, with younger farmers exhibiting greater inclination. There appears to be greater willingness among farmers under the age of 50 years old for adoption [41]. In particular, farmers aged from 30 to 40 years old demonstrate considerable familiarity with 4.0 technologies and knowledge of the advantages of 4.0 technologies [55]. On the contrary, elderly farmers commonly resist change, perceiving the integration of advanced tools and processes related to Agriculture 4.0 as having limited value [12], owing to their heavy dependence on customary practices [45]. Furthermore, older persons are often viewed as technophobes [57]. Senior citizens may fail to recognise prospective long-term financial gains, potentially due to retirement apprehensions [41] and shortened planning horizons [47]. Arjune and Kumar (2022) [63] also highlight a negative correlation between age and the adoption of smart technologies.

The impact of gender on the adoption of 4.0 technologies remains uncertain. The majority of farmers are men, likely reflecting their higher participation in the agricultural sector. The findings of Chuang et al. (2020) [46] suggest that young male farmers show a greater inclination to use smart technologies for field management and agricultural problem-solving compared to their female counterparts. The implementation of digital technologies is also frequently influenced by farmers’ educational attainment. Higher levels of education are deemed necessary for understanding and effectively implementing these intelligent technologies [37]. Farmers with more extensive education are more inclined to embrace such technologies in their fields [37]. Professionally trained young farmers who have successfully completed their agricultural university education possess the essential expertise, drive, and strategic foresight required for effective long-term planning [40]. Highly educated individuals may serve as early adopters of emerging smart technologies [59,68]. Educated farmers tend to exhibit reduced dependence on other farmers and a greater inclination to acquire knowledge from diverse sources, including digital ones [39]. Low levels of education can impede technological adoption. Farmers over 40 years old may have had reduced exposure to digital technologies [37], while less educated individuals may perceive increased economic and trade barriers [43]. However, Zheng et al. (2022) [61] found that less educated individuals are more likely to adopt digital technologies than their more educated counterparts, who may be engaged in non-farm work.

Work experience is a crucial component in the uptake of Agriculture 4.0 technologies. Paustian and Theuvsen (2017) [40] find that individuals with under five years of experience and those with 16 to 20 years of experience in agriculture are more likely to implement digital technologies. Indeed, older farmers with less work experience are more inclined to apply fewer technologies [51]. The efficacy of technology, as perceived by farmers, is oftentimes influenced by their subjective experiences or those of others [54].

The utilisation of Agriculture 4.0 technologies demands advanced digital skills, which encompass proficiency in 4.0 technologies and comprise both fundamental education and essential finesse in managing the equipment [14]. A lack of knowledge and information is seen as an obstacle [45,55,63]. Indeed, Pivoto et al.’s (2019) [37] research finds that a lack of technological proficiency or reluctance to use technology are the primary reasons why many companies fail to adopt it. On the other hand, Gerli et al. (2022) [67] highlight the positive impact of curiosity and open-mindedness, especially in young people, regarding their willingness to acquire new skills. However, Suroso et al. (2023) [71] suggest that technologies which require excessive learning time have a detrimental effect on the adoption of digital 4.0 technologies. Adoption is additionally impacted by the level of innovativeness, i.e., the farmer’s perceived ability to innovate. Aubert et al. (2012) [38] suggest that the innovative capabilities of business leaders have a positive impact. The acquisition of innovative technologies can be influenced by multiple factors, including managerial skills. The usage of digital technologies by farmers is more likely to occur when they hold higher expectations for the technology’s impact on their performance [51]. Furthermore, improving farm management yields a positive influence on adoption [28]. The research conducted by Vollaro et al. (2019) [44] indicates that the lack of managerial skills is a hindrance to the acquisition of innovative technologies.

3.2.2. Economic and Environmental Factors

The primary economic obstacles to adopting digital technologies include expenses and insufficient agricultural profitability [65]. The adoption of such technology is hindered by high prices and a lack of viable solutions [26]. In addition, the initial investment costs are high and deter potential users, as confirmed by Arjune and Kumar (2022) [63]. For small firms, which struggle to find comparable alternatives, these costs are especially prohibitive [49]. To ensure economic viability, it is necessary to spread these costs over several years [37]. However, acquisition costs remain unaffordable regardless of farm size [53]. Farmers, as entrepreneurs, should consider the long-term benefits of technological investments, but significant expenses and uncertainty regarding return on investments hinder their adoption [68]. Decision-making is largely influenced by profitability [48]. Farmers willing to pay high prices [46] and those with greater purchasing power [12] exhibited a strong inclination to adopt.

Effective cost and resource management, along with the minimisation of unnecessary expenses, positively impacts the adoption of digital technologies [27]. Return on investment plays a significant role in the decision-making process of technology adoption. Therefore, it is essential for entrepreneurs and farmers to consider the long-term benefits of any technology investment [68]. Moreover, economies of scale can also positively influence the adoption of digital technologies [58]. There is a positive relationship between yearly income and the use of Agriculture 4.0 technologies [63]. Farmers with high incomes have more money to invest and can tolerate the risks associated with adopting digital technologies better [61]. Additionally, they can secure loans more effortlessly [59]. Even if their high incomes are dependent on family incomes, they are more equipped to manage the initial expenses [47]. Furthermore, households with higher incomes typically possess a greater awareness of smart technologies when compared to those with less favourable economic circumstances [61]. Nevertheless, if non-farm income becomes the primary source of income, technology investments may not be directed towards the farm [59].

From an environmental perspective, farmers who perceive the benefits of digital technologies in reducing the environmental impact of agricultural production are more inclined to adopt them [12,47]. Additionally, storing crop, soil, and climate management data has demonstrated that farmers are keen on using data to gain valuable insights into previous harvests. This growing concern may be attributed to climate impacts, increased yield variability, and greater climate fluctuations [37]. On the other hand, agricultural technologies with a smaller environmental footprint that may hinder farm productivity and profitability are less likely to be embraced [37].

3.2.3. Farm-Related Factors

Kendall et al. (2021) [65] have identified farm size, land fragmentation, and discontinuous plot management as significant obstacles to the implementation of digital technologies. Farmers with larger farms exhibit greater enthusiasm towards the adoption of smart technologies, as they can afford to bear the entire costs and relative risks [39,40]. In contrast, small-scale farmers often struggle to adopt innovative technologies due to a scarcity of investment capital or knowledge [43], including those from family farms [58]. Small and medium farmers are likely to manage farm operations in a traditional manner and may not require the utilisation of 4.0 technologies for farm management [62]. Moreover, research suggests that they may have a greater likelihood of experiencing technophobia [57]. Considering the overall investment of time and money required for setting up, learning, and maintaining digital technology, it may not be justifiable on a relatively small scale [68]. Furthermore, it is worth noting that the implementation of advanced technologies may not be appropriate for small-scale farming operations [65]. A study conducted by Zuo et al. (2021) [59] reveals a positive correlation between the adoption of intelligent technologies and farm size. However, this correlation decreases as the size of the farms continues to increase.

The selection and implementation of smart technologies are influenced by the type of production. Kernecker et al. (2019) [48] suggest that arable and orchard growers have the same level of adoption but adopt different types of technologies. Furthermore, Quan and Doluschitz (2021) [58] find that increasing crop variety on farms may incentivise farmers to implement more sophisticated technology across a range of crops, thereby enhancing their efficiency. Giua et al. (2022) [66] reported that the adoption of multiple Agriculture 4.0 tools is positively influenced by specialisation within the arable sector, whereas non-adopters appear unaffected by sectoral specialisation.

Paustian and Theuvsen (2017) [40] find that farmers engaged in multiple agricultural activities beyond only growing crops or raising animals are more inclined to adopt technology. This is due to their desire to enhance their overall production, in comparison to farmers who solely focus on one activity. Certified organic farmers are found to be more willing to adopt, as per the findings of Kaňovská (2021) and Zuo et al. (2021) [55,59]. They identify conservative practices as a hindrance to adoption. Farmers who displayed optimism regarding their business’s future tend to be younger and more educated, leading to a higher propensity for technology adoption [57]. Moreover, the implementation of a succession strategy [59] or the existence of a successor on the farm has been linked to an increased probability of adopting agricultural technologies 4.0 [47].

Experienced employees have a positive impact on farmers’ views regarding the ease of technology use [38]. Similarly, the probability of adoption increases when family dependents and members are more involved [58]. However, Paustian and Theuvsen (2017) [40] note that farms without family employees are significantly more inclined to adopt. In addition, operating as an individual farmer imposes significant time and financial constraints on the individual, necessitating more innovative approaches to complete tasks [43].

3.2.4. Institutional Factors

Farmers consider adequate infrastructure as a conducive factor for adopting 4.0 tools [56]. However, the lack of such infrastructure poses a significant barrier to adoption [12]. Indoor devices enable interconnectivity, providing farmers with swift access to technical knowledge and information [61]. In addition, the spread of smartphones has facilitated internet access [14]. However, the absence of connectivity may inhibit farmers’ access to crucial data and information [52]. Indeed, slow or absent internet connectivity can impede the adoption of such technologies [57], and infrastructure-related factors may have adverse effects, particularly in rural regions [45].

Technical support plays a positive role in the adoption of digital technologies in farming, enabling farmers to acquire and apply agricultural innovations, thus fostering adoption [58]. Giua et al.’s (2022) [66] findings suggest that farmers receiving inadequate external support during the intention-to-use phase are less likely to adopt technological advances. The availability and quality of support from technology providers influence the perception of ease of use [38,60]. When technologies seem complex and challenging to use [62], participation in demonstration projects can enhance the likelihood of adoption [49]. Instead, distrust in training programmes hampers adoption [64]. Experiences shared by family members and other farmers can support the adoption of digital technologies in agriculture [46]. Policymakers and service providers should provide farmers with support services, such as training programmes and helplines, to enhance their digital knowledge and skills [49]. Financial assistance positively impacts the acceptance of digital technologies [69], especially when subsidies are directed towards the procurement of government-approved digital technology [65].

The presence of laws aimed at increasing agricultural production efficiency and reducing environmental impacts positively influences the adoption of 4.0 technologies [65]. Access to finance requirements can be facilitated through appropriate policies [51]. Disseminating information to a large audience or individuals can be achieved through the adoption of communication channels. According to research by Pambudy (2018) and Arjune and Kumar (2022) [42,63], the implementation of digital technologies can be facilitated by the use of extension contact tools and exposure to mass media.

3.2.5. Technological Factors

The adoption of agricultural technologies 4.0 is boosted by information derived from both formal and informal channels [67]. Formal information, obtained from sources like training sessions and farmers’ groups, has a direct impact on the inclination to embrace 4.0 tools [50]. Peer-to-peer communication is a key source of information for farmers [55]. Informal acquisition of information can occur through unstructured circumstances and situations [67]. However, insufficient and restricted access to financial information, including grants and low-interest credit, hinders adoption [65].

Farmers are more likely to use a technology if it is compatible with existing equipment [38]. Compatibility is crucial as farmers tend to purchase from different companies instead of sticking to one brand [14], and incompatible technologies restrict adoption [48].

The implementation of digital technologies in agriculture requires advanced skills [38]. Farmers who perceive smart technologies as complicated or troublesome to operate are less likely to adopt them [45]. Regular software updates can increase the complexity, but if technologies are perceived as easy to operate, farmers are more likely to adopt them [52,68]. The use of technologies like smartphones for professional purposes positively impacts the integration of 4.0 tools [52,57]. Technophobic farmers show a lower frequency of digital technology usage, indicating a negative stance towards innovation [57]. Internet usage is suggested to aid adoption [61]. Limited capital and a lack of expertise in using digital communication devices result in low utilisation among farmers [42].

The adoption of 4.0 technologies offers farmers several benefits, including increased profitability [37], cost reduction, which makes farms, especially small farms, less dependent on subsidies [54], and non-financial benefits [28]. Kaňovská (2021) [55] argues that several factors influence the decision to adopt, including timesaving, reduced gasoline costs, and lower costs for fertilisation, irrigation, and pest control, as well as reduced personnel needs. Additionally, adopting new technologies can result in improved mobility and productivity in daily life [72], simplified life and work [45], increased production [44], and improved convenience and sustainability [27]. However, these benefits may not always be immediately visible [67]. Challenges to adoption include difficulties in processing and organising sensor data due to a lack of knowledge of data utility and the absence of suitable software [14]. The manual entry of data restricts the use of management software [37]. Concern about cyber theft contributes to a lack of trust in digital technology [56]. Some farmers perceive that these technologies may lack precision compared to manual labour and create a disconnect between farmers and the crops and animals they work with [57].

3.2.6. Psychological Factors

Behavioural intention refers to an individual’s cognitive disposition to be persuaded to use technology [51]. The adoption of technologies in digital agriculture has positively correlated with interest in the field [27]. However, the utilisation of these technologies on farms seems to depend solely on the perception of their benefits [49]. Gerli et al. (2022) [67] demonstrate that agricultural producers have a greater propensity to adopt technologies resulting in higher productivity, cost-efficiency, and sustainability, which are user-friendly and receive support from their social networks (trusted individuals, colleagues, and other farmers). However, Aubert et al. (2012) [38] indicate that these advantages are minor. If farmers are already interested in smart farming, they need to be stimulated, and it will be easier for them to accept smart farming [27].

Negative emotions, such as fear and annoyance, can generate scepticism and reinforce a conservative mindset, while positive emotions can encourage the adoption of 4.0 technologies [67]. Trust appears to be a significant factor in technology adoption [56] and can be defined as trust in the reliability and personal integrity of technology performance [54]. Social trust also positively influences adoption [71] because farmers are more likely to adopt if a technology has been tested by others in the field [67]. An open and positive mindset leads farmers to keep up with 4.0 technologies [27]. Negative emotions related to farmers’ substitution with machines [67], safety issues, and associated risks can be barriers to adoption [63]. Farmers with a high level of inhibition prefer to stay in their comfort zone and are unwilling to try innovative technologies as they find them useless and prefer not to depend on them [72].

Adopting technological innovations is a strategic choice that could decrease production risks and enhance productivity [64]. In addition, 4.0 technologies can assess feasibility and manage resources to reduce climate change-related risks [27]. For firms engaged in other economic pursuits, perceived risks may hinder adoption, particularly when connected to financial and technical obstacles [49].

Social influence, defined as connections with friends, colleagues, and family members, can have a favourable impact on farmers’ acceptance of digital technologies [51]. Furthermore, Aubert et al. (2012) [38] suggest that external pressures also play a part in the uptake of smart technologies. Collaboration among farmers has a positive impact on the uptake of digital devices, particularly if this equipment has already undergone testing by colleagues [64]. Collaborative efforts signify a forward-thinking, receptive approach that can stimulate communication and knowledge sharing [47]. The exchange of experiences and ideas among farmers and coworkers can notably enhance both hardware and software capabilities, as well as provide organisational support [51]. Negative responses to colleagues’ use of technology can hinder adoption [47]. Being a member of cooperatives [47] and taking part in workshops and exhibitions [41] have been found to be positively linked with an elevated desire to adopt, particularly if they encourage a switch of knowledge.

3.2.7. Summary of Drivers and Barriers

Common factors affecting adoption are shared by farmers worldwide. The primary barriers to adoption are the high initial and maintenance costs, small farm size, and lack of support from manufacturers and governments. European farmers are more inclined towards the use of drone or robot technologies on their farms, but they face limitations in adoption due to a lack of support, knowledge, and connectivity among digital technologies. European arable crop farmers appear to be increasingly utilising digital technologies. On the other hand, American farmers tend to use sensors in their farming activities, but they also face limitations in adoption due to a lack of digital skills. The absence of connectivity and compatibility, especially in rural areas, is also a problem for farmers. However, technologies are preferred, particularly when they can facilitate the farming process and are easy to use. Asian farmers tend to use sensors, GPS, or IoT technologies on their farms. They may face obstacles to adoption due to a lower propensity for risk-taking, fear of failure, and scarcity of information. However, more educated and younger farmers are more likely to adopt these technologies regardless of their country of origin.

In summary, young people and those with high levels of education or experience are more likely to adopt agricultural 4.0 tools. Digital skills are crucial for utilising advanced tools, but they can be perceived as a deterrent if they require extensive training. Technologies that have a positive impact on the environment are more likely to be adopted. However, the cost of maintenance and initial investment remains the biggest obstacle to adoption. This barrier can be overcome if the farm is large, the family income is high, or if production or activities are diversified. Lack of support from manufacturers or the government is a significant obstacle, but the experience of other farmers, employees, or employers can facilitate adoption. Furthermore, the absence of an internet connection and adequate infrastructure may hinder adoption. Farmers typically favour technologies that provide benefits such as increased profitability or reduced chemical usage. However, if these technologies are complex or incompatible with existing tools, they are less likely to be adopted. Trust in digital technologies can influence adoption, while negative emotions can generate scepticism.

To provide a visual summary of the relationships between the analysed publications and the different drivers and barriers identified, a network analysis of terms was conducted using the VOSviewer software version 1.6.20. This allowed us to generate two network maps, presented in Figure 4 and Figure 5, which graphically synthesise the relationships among the factors. Each node in the graphs represents a factor, and their interactions are the relations among them. The network visualisation provides a clear overview of the determinants and shows how different factors are linked together. The centrality and occurrence of a node in the network are indicated by its size, with larger nodes indicating more interactions with other nodes. The thickness of the connecting lines indicates the strength of the relationship among terms. Related words, indicated by the same colour, frequently co-occur.

Figure 4 illustrates the relationships among the drivers presented in Table 1. The most prominent drivers in terms of both frequency and link power were farm size, education, and age. Cost saving, convenience, social influence, and facilitating conditions (all coloured green) were closely related and tended to occur together. Additionally, these drivers bridge compatibility and investment with the other drivers.

On the other hand, the identified barriers are depicted in Figure 5 of the network, which shows that the cost of adoption and ‘lack’ are central obstacles to adoption. ‘Lack’ refers to a deficiency in awareness, connectivity, compatibility, or ability, for example. Various clusters of barriers are identified, such as purple representing technological factors like complexity, compatibility, internet coverage, and farm characteristics, which are commonly identified together.

The clusters identified are typically not linked together but rather connected by the most significant obstacles, such as cost and lack. This is evident from the shape of the nodes and the connecting lines.

4. Discussion and Suggestions for Futures Research

Digital innovations have the potential to significantly contribute to achieving more sustainable and resilient agriculture [23]. Therefore, gaining a deep understanding of the factors influencing farmers’ adoption of these technologies is critical to ensuring the widespread and successful adoption of Agriculture 4.0 technologies [77].

This literature review aimed to enhance the understanding of these factors by providing an overview of common barriers and drivers to the implementation of Agriculture 4.0.

The detailed analysis of the 42 selected papers allowed us to identify the existence of 35 interrelated variables aggregated in seven categories, which require a holistic approach to identify patterns for promoting adoption at the farm level. While there is heterogeneity in producers’ perceptions and factors influencing the adoption of digital technologies, the results suggested the existence of certain similarities between barriers and drivers. Furthermore, this suggestion is corroborated by the explorations of terminological networks analysis carried out using VOSviewer, which reveals that some factors act simultaneously as both drivers and barriers. For example, age, farm size, and education are recognised as influencing both drivers and barriers.

The primary determinants identified in the literature are related to individual and farm-related factors, emphasising the central role of farmers and farms in adoption decisions [12,41,50]. This finding was also confirmed by the co-occurrence analysis, which shows how these factors play a key role as drivers in the adoption, as shown in Figure 4. However, the study highlighted that the impact of certain factors on adoption is not universally consistent, with variables such as gender, education, and experience not always being decisive [40,46].

On the other hand, the evidence highlighted that the current adoption of digital technologies is largely restricted to specific farms and crops (e.g., large farms and those engaged in multiple agricultural activities being more likely adopters). This mixed evidence suggests that investments and policies to promote widespread adoption of digital technologies should be context-specific, considering individual and farm conditions (e.g., type of farms, digital and managerial skills, existing infrastructure, and knowledge).

Regarding technological and economic factors, the findings indicate that the main barriers limiting farmers’ access to digital technologies are the lack of infrastructure, internet access, and compatibility, as well as economic constraints. These obstacles are also highlighted in the relationship network analysis presented in Figure 5, where the main nodes are related to cost and deficiency (“lack”). Addressing these issues is crucial for the digital transformation of the agricultural sector. Otherwise, their persistence will progressively contribute to the digital divide in agriculture. To prevent this divide and ensure equitable distribution of potential benefits, it is essential to improve farm infrastructure and enhance public policies to reduce costs for farmers seeking to implement Agriculture 4.0. With regard to institutional factors, the literature review revealed that lack of knowledge and skills is a barrier, suggesting the need to provide farmers with support services such as training programmes and financial assistance.

Meanwhile, the literature analysis suggests that the factors consistently identified as drivers are the relative advantages in terms of performance expectancy or perceived increase in productivity [55,56,71]. Therefore, it is essential for entrepreneurs and farmers to consider the long-term benefits of any technology investment [68]. As a result, policymakers should prioritise promoting the potential benefits of digital innovation while minimising implementation costs. Creating an enabling environment, strengthening digital infrastructure, and investing in information and education are crucial steps.

Considering environmental factors, results showed that farmers with higher environmental concerns are more inclined to adopt digital technologies in order to reduce their environmental impact [12,47].

While among the psychological factors, several articles in this review underscore the significance of social trust and social influence, stressing the value of friendly farmer relationships and agricultural cooperative ties over formal information sourced from technology developers. The exchange of experiences among those who have successfully implemented digital tools with a positive outcome has a favourable impact, especially if farmers receive it from their own network [37,70]. Social influence is a key factor in promoting positive experiences related to cost savings, convenience, and facilitating conditions, as shown in Figure 4. This confirms the importance of informal sources of information, such as word-of-mouth and peer-to-peer comparison, in shaping positive attitudes towards digital technologies [50,67]. This also suggests the need to integrate formal training and educational programmes with informal and interpersonal communication to encourage most farmers to adopt digital innovations. In this regard, policies should actively support the role of farmers’ organisations or associations in affecting farmers’ perceptions of the usefulness or ease of use of digital innovations.

The results from the current review allowed us to provide hints for further research developments. Concerning socio-demographics factors, results show that the impact of gender on the adoption of 4.0 technologies remains uncertain, highlighting that this variable deserves to be further investigated.

Furthermore, while constructs such as easiness of use and perceived usefulness were frequently explored, other crucial factors, including institutional, environmental, and psychological issues, received comparatively less attention. Therefore, future research should further explore how these factors may encourage the adoption of digital technology. Regarding environmental factors, further exploration is needed on the impact of environmental awareness, concerns, and perceived benefits due to the limited number of studies that have explored this dimension [12].

At the same time, a deeper comprehension of emotions and attitudes that affect farmers’ decision to adopt digital technologies is required. Unfavourable attitudes towards technology often stem from a doubtful and conservative mindset, leading to reluctance to abandon traditional methods in favour of innovative technologies [45]. Furthermore, Jaroenwanit et al. (2023) [27] find a constructive association between practical and financial government assistance and the adoption of smart agriculture. Advancing knowledge of farmers’ expectations with respect to possible aid and/or interventions provided by policy makers to support the adoption process is critical to facilitating the use of these technologies.

5. Conclusions

As digitalisation becomes essential in all sectors, including agriculture, understanding the mechanisms that drive farmers’ adoption of digital technologies will be crucial for successful implementation. Factors such as climate change and food scarcity may further accelerate the trend towards modernisation. The effectiveness of this process depends primarily on understanding the mechanisms that drive farmers to adopt and integrate digital technologies. This study sought to identify the main drivers and barriers associated with the adoption of Agriculture 4.0 technologies by conducting a systematic literature review using the PRISMA method. A total of 42 articles from 2011 to 2023 were reviewed, and 35 variables, grouped into seven macro-categories, were identified and discussed. Additionally, the relationship between the different factors that positively or negatively influence the adoption of digital technologies was visualised using VOSviewer. The findings show that age, education, and farm size are the most significant factors influencing adoption. Conversely, ‘lack’ and economic constraints are the primary obstacles. These findings allowed us to provide both practical implications to support stakeholders in the agricultural sector in promoting the diffusion and adoption of Agriculture 4.0 technologies, as well as suggestions for further research development in this field.

Finally, it should be stressed that the current review has some limitations. Firstly, due to the selection of only the Scopus database, it is possible that other publications in the field that are not included in this database have been omitted. Furthermore, by selecting articles written in English only, it is possible that relevant publications written in other languages have also been omitted.