Telemetry in digital agriculture: Adoption drivers and interoperability barriers among German farmers

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Despite their technical potential, empirical evidence on the practical use of telemetry in agriculture remains limited. This study investigates the adoption of telemetry technology among German arable farmers using the Unified Theory of Acceptance and Use of Technology (UTAUT). The analysis is based on sixteen semi-structured expert interviews with farmers and agricultural contractors conducted in autumn 2024, evaluated through qualitative content analysis. Results show that telemetry is primarily used for administrative tasks such as work monitoring, automated documentation, and fleet management. While farmers associate telemetry with improved efficiency and reduced workload, measurable economic benefits are rarely quantified. A major barrier is the limited interoperability between manufacturer-specific platforms, leading to fragmented data environments and additional costs. Improving interoperability and strengthening advisory support may be crucial for realising the full potential of telemetry-based farm management. Agricultural Engineering Telemetry German Farmers UTAUT Qualitative Analysis Precision Farming Smart Farming Figures Figure 1 1 Introduction Over the last decade, agriculture has been increasingly digitized (Rotz et al., 2019 ; Muangprathub et al., 2019 ). Digital technologies are frequently discussed as an important tool for improving farm management efficiency, resource use (Walter et al., 2017 ; Wolfert et al., 2017 ; Finger et al., 2019 ; Saiz-Rubio & Rovira-Más, 2020 ) and food security (Gebbers & Adamchuk, 2010 ). Over the past two decades, a wide range of digital technologies have been introduced into agricultural practice, including GNSS-based guidance systems, drone-based monitoring, application maps, and digital farm management systems (Kliem et al., 2022 ; Papadopoulos et al., 2024 ). However, the adoption of these technologies varies considerably across farms and technologies (Kehl et al., 2021 ; Michels & Mußhoff, 2025 ; Michels et al., 2026 ). Their increasing availability has been accompanied by a growing integration of digital data into agricultural decision-making processes (Wolfert et al., 2017 ; Saiz-Rubio & Rovira-Más, 2020 ). A key prerequisite for many of these applications is the availability of operational machine data that can be collected, transmitted, and analysed within digital management systems. According to Hillerbrand et al. ( 2019 ), such data infrastructures form an essential basis for the further development of automation and autonomous processes in agriculture. These machine-generated data streams are commonly referred to as telemetry data (Clasen, 2021 ; Gscheidle, 2022 ). The term telemetry is derived from the Greek words tēle (distant) and métron (measure) and refers to the wireless transmission of measurement data from one location to another spatially separated location (Heuberger & Gamm, 2017 ). In agriculture, telemetry systems enable the real-time transmission of sensor data from agricultural machinery to external servers via mobile networks. The transmitted data may include basic positional information from GNSS receivers as well as machine-specific parameters such as engine speed, fuel consumption, and operating temperature. These data are typically timestamped, allowing them to be assigned to specific field operations during subsequent analysis (Ayaz et al., 2019 ; Kehl et al., 2021 ). Access to the machine’s CAN bus is a prerequisite for capturing detailed operational parameters beyond positional data (Meroth & Sora, 2021 ; Streicher, 2019 ). In arable farming, telemetry is mainly used for work monitoring, automated documentation of field operations, fleet management, and remote machine diagnostics. More advanced applications include the transmission and analysis of process data, such as yield maps generated during harvest via the ISOBUS interface and transferred to farm management information systems for further analysis (Kehl et al., 2021 ). The increasing availability of telemetry data creates new opportunities for agricultural management. Telemetry enables real-time monitoring of machinery operations, automated documentation of field activities, improved fleet coordination, and remote machine diagnostics (Halberstadt et al., 2022 ; Kehl et al., 2021 ). In addition, telemetry data can be integrated with other digital data sources such as weather data, soil sensors, or yield maps, thereby supporting more data-driven farm management and operational decision-making (Wolfert et al., 2017 ). However, several challenges still limit the practical use of telemetry systems. One of the most frequently discussed obstacles concerns the limited interoperability between manufacturer-specific telemetry platforms. Many agricultural machinery manufacturers operate proprietary digital ecosystems that lack standardised interfaces for data exchange, which complicates the integration of machine data across heterogeneous machinery fleets (Bartels et al., 2021 ; Dörr et al., 2023 ). Additional concerns relate to data governance, data security, and the handling of large volumes of machine-generated data (Kutter et al., 2011 ; Wickramainghe, 2024 ). Despite these technical capabilities, empirical evidence on the actual adoption and use of telemetry in German agriculture remains limited. Recent survey results indicate that only around 14% of German farmers currently use telemetry technologies and that only 75% are familiar with the concept, placing it among the least recognised digital agricultural technologies in a comparison of 32 digital tools (Michels & Mußhoff, 2025 ; Michels et al., 2026 ). While a considerable body of research has investigated the adoption of other digital technologies in agriculture, including smartphones (Michels et al., 2020a ), drones (Michels et al., 2020b ), and alternative fuel tractors (Michels et al., 2024 ), telemetry as a distinct technological infrastructure has not yet been examined in a dedicated acceptance study. Nevertheless, understanding the adoption process is at the forefront for research in digital agriculture (Ingram et al., 2022 ). This lack of research is noteworthy because telemetry differs conceptually from broader notions such as precision farming or smart farming. Whereas these concepts describe management approaches and application domains, telemetry primarily represents a data transmission infrastructure that enables the collection and exchange of machine-generated data. Understanding how farmers perceive and integrate this infrastructure into their operational routines is therefore essential for assessing the broader potential of digital agriculture. Furthermore, the existing literature on technology adoption in agriculture is predominantly based on quantitative survey approaches that analyse behavioural intentions using standardised models. While these studies provide valuable insights into general adoption patterns, they offer limited understanding of how farmers experience and evaluate technologies in everyday practice. Qualitative studies that explore the practical use and perceived benefits of telemetry technology remain largely absent. This study addresses this gap by examining the factors influencing the adoption and use of telemetry data among German arable farmers. The Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. ( 2003 ) serves as the theoretical framework. UTAUT combines performance-related expectations, perceived ease of use, social influences, and structural conditions that may affect technology adoption (Venkatesh et al., 2003 ). These dimensions are particularly relevant in the context of agricultural decision-making, where adoption decisions are influenced not only by expected economic benefits but also by operational complexity, social networks, and the availability of technical infrastructure (e.g. Michels et al., 2020; Michels et al., 2024 ), which is expected to hold for telemetry too. In contrast to previous quantitative applications of the UTAUT in the agricultural context, this study employs a qualitative research design based on semi-structured expert interviews with sixteen farmers and agricultural contractors in autumn 2024. The study contributes to the literature in three ways. First, it provides the first empirical investigation specifically focused on the acceptance of telemetry technology in German agriculture. Second, it offers a qualitative application of the UTAUT framework in this context, thereby complementing the predominantly quantitative evidence base. Third, by interviewing farmers who are already using telemetry, the study captures not only adoption factors but also post-adoption experiences, including perceived benefits, unmet expectations, and barriers to deeper utilisation. The remainder of the article is organized as follows: In the second section, used material and methods are described. Results are presented and discussed in the third section. The article closes with some conclusions in the fourth section. 2 Material and Methods 2.1 Unified Theory of Acceptance and Use of Technology The UTAUT was developed by Venkatesh et al. ( 2003 ) through a synthesis of eight established models of technology acceptance, including the Technology Acceptance Model (TAM), the Theory of Planned Behaviour (TPB), and the Theory of Reasoned Action (TRA). The UTAUT model has been applied for numerous studies regarding precision and smart farming (e.g. Adrian et al., 2005 ; Michels et al., 2020; Rübcke von Veltheim et al., 2022) as well as agricultural machinery (e.g. Michels et al., 2024 ). The model identifies four core constructs that determine technology acceptance and use behaviour (Fig. 1 ), which are explained and set in the context of telemetry in the following. Performance Expectancy is defined as the degree to which an individual believes that using a technology will help achieve gains in job performance. In the context of telemetry adoption, this construct captures farmers' expectations regarding improvements in work efficiency, cost reductions, yield increases, and operational facilitation through telemetry data use. Effort Expectancy refers to the degree of ease associated with the use of a technology. Applied to this study, it encompasses the perceived user-friendliness of telemetry platforms, the complexity of the applications, and the effort required to integrate the technology into existing farm routines. Social Influence is defined as the degree to which an individual perceives that important others believe they should use the technology. This includes the influence of professional colleagues, family members, advisors, dealers, and other actors on the adoption decision. Facilitating Conditions refer to the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of the technology. In the present study, this construct covers aspects such as internet connectivity, the availability of compatible hardware, technical skills, risk tolerance, and concerns regarding data security. The original UTAUT model additionally includes four moderating variables (age, gender, experience, and voluntariness of use) that are hypothesised to moderate the relationships between the core constructs, Behavioural Intention to Use, and actual Usage (Venkatesh et al., 2003 ). In quantitative applications of the model, these variables are typically incorporated to test interaction effects statistically. As the present study follows a qualitative research design based on semi-structured expert interviews, these moderating variables were not operationalised in a statistical sense. Instead, the four main constructs of the UTAUT framework served as a deductive structure for the development of the interview guide and the subsequent qualitative content analysis. 2.2 Interview design The interview guide was structured according to the four core constructs of the UTAUT framework: performance expectancy, effort expectancy, social influence, and facilitating conditions. Each construct was operationalised through a dedicated block of open-ended questions addressing farmers’ experiences with telemetry technology. Questions related to Performance Expectancy explored farmers’ expectations and perceived benefits of telemetry use, including potential effects on yields, operating costs, work efficiency, workload reduction, and overall economic benefits. Respondents were also asked about perceived weaknesses of telemetry technology and their expectations regarding future developments. The construct Effort Expectancy was addressed through questions concerning the ease of use and practical integration of telemetry systems. These questions focused on the usability of telemetry platforms, the perceived complexity of the technology, obstacles encountered during implementation, and the technical tools required for data use. Questions related to Social Influence examined the potential impact of professional networks and social environments on adoption decisions. Interviewees were asked about the opinions of professional colleagues, the role of agricultural advisors or associations, and whether their own telemetry use influenced other farmers. Finally, Facilitating Conditions were explored through questions addressing the organisational and technical prerequisites for telemetry use. These included the availability of digital infrastructure, required hardware and machinery, internet connectivity, individual technical skills, risk attitudes toward technological adoption, and concerns related to data security. In addition to these thematic blocks, the interview guide included a second section collecting sociodemographic and farm-level background information. Ethic approval was obtained by the University’s ethics committee. The full questionnaire is given in the Appendix. 2.3 Data collection and analysis Data were collected through semi-structured expert interviews with individuals actively involved in agricultural production. The interview partners included farm managers, managing directors, and employees working on arable farms and contracting businesses that had already implemented telemetry technology. In total, sixteen interviews were conducted with representatives of farms located in different regions of Germany. Prior to the main data collection, a pretest was conducted with one farmer to evaluate and refine the interview guide (Weichbold, 2022 ). The interviews took place between October and December 2024. All interviews were recorded with the informed consent of the participants and subsequently transcribed using the audio transcription software NoScribe . To ensure comparability across interviews, a standardized set of transcription rules following Kuckartz and Rädiker ( 2022b ) was applied. The interview material was analysed using qualitative content analysis following the summarising approach proposed by Mayring ( 1994 ), complemented by procedural elements described by Kuckartz & Rädiker ( 2022a ). The coding framework was deductively derived from the four main constructs of the UTAUT model and informed by existing UTAUT codebooks (Gruzd et al., 2024 ) as well as the structure of the interview guide. During the analysis, a small number of categories that lacked sufficient discriminatory clarity were merged, and selected subcategories were added inductively to capture themes emerging from the interview material that were not fully anticipated by the deductive framework (Kuckartz & Rädiker, 2022a ). Coding and data organisation were conducted using the qualitative data analysis software MAXQDA . Relevant text segments were defined as coding units and assigned to thematic categories. These segments were subsequently paraphrased and condensed to their essential content, while non-substantive passages were discarded. All retained segments were standardised to a consistent grammatical and linguistic level to improve comparability (Mayring, 1994 ). The entire coding process was carried out by a single researcher. While this limits the possibility of assessing inter-coder reliability, consistency was supported by the use of a deductively derived coding framework (Gruzd et al., 2024 ) and iterative revision of category assignments throughout the analysis. 3 Results and Discussion 3.1 Sample characteristics Sixteen expert interviews were conducted with farmers and agricultural contractors from Germany between October and December 2024. A pretest was carried out in July 2024. The interview partners were selected based on their active use of telemetry technology in their operations. The farms are distributed across Lower Saxony, Schleswig-Holstein, Mecklenburg-Western Pomerania, and Saxony-Anhalt. An overview of all interview partners is provided in Table 1 . Table 1 Sample characteristics (N = 16) ID Farmers’ age in years Farming experience in years Position Farm type Telemetry since Arable land in ha Agricultural qualification ID1 54 18 Managing director Contractor 2018 – Vocational training ID2 26 4 Employee / management support Arable 2020 1,300 Vocational training ID3 26 8 Employee / management support Arable 2016 780 Master craftsman ID4 63 32 Farm owner and manager Arable n.a. 1,600 None ID5 40 22 Farm manager Mixed 2021 250 Master craftsman ID6 25 3 Farm manager Arable 2017 7,500 M.Sc. Agr. Sciences ID7 58 38 Farm manager Arable 2020 90 Dipl.-Ing. Agr. Sciences ID8 25 7 Employee / management support Mixed 2019 600 Vocational training ID9 32 4 Managing director Contractor 2020 – M.Sc. Agr. Sciences ID10 51 33 Managing director Contractor 2016/2021 – None ID11 27 11 Owner and managing director Arable 2021 750 B.Sc. Agr. Sciences ID12 31 3 Data analyst and controller Arable 2020 6,500 B.Sc. Agr. Sciences ID13 57 35 Farm manager Arable 2014/2022 600 Dipl.-Ing. Agr. Sciences ID14 23 6 Junior manager Arable 2021 ca. 1,000 Vocational training ID15 64 56 Farm manager Arable 2010 780 Dipl.-Ing. (FH) ID16 32 7 Farm manager Arable 2009 1,450 B.Sc. Agr. Sciences Farm type: Arable = arable farm; Contractor = contracting business without own arable land; Mixed = arable farm with additional contracting services. Arable land is reported for arable and mixed farms only; contracting businesses are indicated by “–”. Where two years are listed under “Telemetry since”, the first refers to initial use and the second to intensified use. n.a. = not available. At the time of the interviews, participants ranged in age from 23 to 64 years and reported between 3 and 56 years of professional experience in agriculture. The arable land managed by the farms ranged from 90 to 7,500 hectares. Telemetry data had been in use on the surveyed farms for periods ranging from approximately three to fifteen years, though the majority had intensified their use within the past six years. Educational backgrounds ranged from vocational training to master's-level university degrees in agricultural sciences. The sample includes five contracting businesses, two of which also operate their own arable land. All interview partners were male. 3.2 Interview results 3.2.1 Performance expectancy The interview partners associated telemetry technology primarily with reductions in administrative and organisational workload rather than with direct improvements in agronomic production processes. The most frequently reported benefits were real-time work monitoring, automated documentation of field operations, and—on larger farms and contracting businesses—fleet management. Real-time monitoring was described as a substantial relief for farm managers, as it enables continuous oversight of machine locations and ongoing operations without requiring physical presence in the field. Several respondents emphasised that errors can be detected and corrected earlier and that automation systems on machines can be adjusted remotely from the office (ID1, ID4, ID6, ID12). Farm managers also reported a reduction in mental workload because completed tasks are automatically recorded and can be reviewed at any time (ID13). In contracting businesses, real-time location data were additionally used to inform clients about expected arrival times (ID2, ID12). However, several respondents noted that real-time data transmission was perceived by some employees—particularly older workers—as a form of surveillance, which in some cases resulted in resistance or threats of resignation (ID3, ID4, ID11, ID15). Automated documentation was unanimously regarded as a major improvement. Respondents described the digital records as comparable to an instantly accessible logbook of past activities (ID7, ID11, ID15). The accuracy of fertiliser and crop protection documentation was reported to have improved significantly, and automated data capture eliminated the need for manual data entry into documentation software, resulting in considerable time savings (ID4, ID8, ID11, ID13). Fleet management was highlighted primarily by larger operations. During harvest or other coordinated work chains, telemetry allows managers to identify excessive idle times and adjust the number of deployed vehicles accordingly. Employees also reported benefits, as the technology facilitated self-organisation within work teams (ID4, ID10, ID12). For smaller farms with few machines, however, the relevance of fleet management was considered limited (ID7, ID11). Machine maintenance was perceived as beneficial on farms that had integrated telemetry into their servicing routines. Automated maintenance alerts and proactive contact by service partners were reported to improve the predictability of routine servicing (ID2, ID4, ID5, ID15). Working time recording was regarded as a secondary application rather than a core telemetry function. While some respondents valued the transparency it provided for billing purposes (ID9, ID10, ID12), others raised concerns regarding data protection and employee trust (ID14, ID15). Regarding efficiency gains, respondents consistently distinguished between agronomic production processes—where no direct efficiency improvements were attributed to telemetry—and overall farm organisation, where benefits were more evident. Improvements were most frequently reported for invoicing in contracting businesses, order dispatching, record keeping, and internal error analysis through remote workshop access (ID4, ID7, ID10, ID11, ID12). Approximately half of the respondents reported no or only marginal efficiency improvements so far, although most expected gains in the future. None of the respondents reported yield increases attributable to telemetry data use. Several interviewees questioned whether telemetry could plausibly influence yields and instead pointed to other precision farming technologies as drivers of yield improvements. Similarly, expectations regarding cost reductions were largely unmet. While some farms recognised the potential for savings, the costs of external telemetry providers were perceived as high and often offset expected benefits (ID1, ID10). Only two farms reported concrete cost reductions: one through improved irrigation planning using telemetry and soil sensor data (ID13) and another through the elimination of an administrative position (ID9). The most frequently cited weakness of telemetry technology was the lack of interoperability between manufacturer-specific platforms. Farms operating heterogeneous machinery fleets reported being forced to use multiple portals simultaneously because manufacturers do not allow data sharing across systems (ID7). Although standardised formats such as ISO-XML exist, their implementation differs between manufacturers, leading to inconsistencies and data transfer errors (ID2, ID5, ID7, ID9). Some farms attempted to overcome these limitations through bridging solutions or external telemetry providers, though at considerable additional cost (ID6, ID8). Additional weaknesses included insufficient data quality on farms cooperating with contractors (ID13), employee resistance or sabotage of telemetry modules (ID1, ID4, ID6, ID9, ID10, ID12, ID14), the vulnerability of web-based platforms to outages (ID5, ID10), and the considerable time required for full system implementation during peak season (ID1, ID13). Sustainability effects were perceived as limited by most respondents. The most commonly mentioned effect was the reduction of unnecessary trips and associated fuel savings (ID8, ID9, ID12). One farm reported improvements in irrigation efficiency through telemetry-supported monitoring (ID13). However, reductions in fertiliser and crop protection inputs were primarily attributed to guidance systems, section control, and other precision farming technologies rather than to telemetry itself (ID4, ID11, ID16). Regarding future expectations, respondents expressed strong demand for improved interoperability and manufacturer-independent platforms. Suggested solutions included uniform data formats (ID2, ID8, ID10, ID16), cross-manufacturer aggregation platforms (ID6), and licensing models to compensate manufacturers for data sharing (ID11). Additional requests concerned simpler system implementation and greater flexibility for integrating externally generated data (ID12, ID13, ID16). Overall, respondents expected telemetry to reduce administrative workload further and to improve overall farm efficiency in the future (ID1, ID4, ID14). 3.2.2 Effort expectancy The user interfaces of the telemetry platforms used by the respondents were generally assessed as user-friendly. The systems mentioned included both manufacturer-owned platforms—such as John Deere Operations Center, Claas Connect, Fendt Connect, and Kverneland Farm Center—and external solutions including 365 FarmNet, Agrarmonitor, Exatrek, and RTK Clue. No single platform was unanimously preferred, as evaluations were strongly influenced by the respondents’ familiarity with their respective systems (ID5). The John Deere Operations Center was mentioned most frequently and was valued for its ability to share coverage maps in real time (ID6, ID8), although its mobile application was criticised for usability issues (ID3, ID5). Despite generally positive assessments of usability, respondents reported several obstacles to telemetry use. These varied considerably across farms and included high acquisition costs, limited time for training, and the age and willingness of employees to engage with digital technologies (ID1, ID4, ID6, ID10, ID12, ID14, ID16). The most critical concern related to the operational reliability of telemetry platforms. Full integration into farm management systems creates strong dependencies on platform availability, and several respondents reported experiencing system failures during field operations (ID5). Additional concerns included the potential for manufacturers to exploit collected data for commercial purposes (ID11) and the risk of losing access to sensitive farm data if a platform were discontinued or the provider became insolvent (ID10, ID14). The overall complexity of telemetry applications was generally rated as high to very high. However, respondents emphasised that perceived complexity depends strongly on the depth of use. Standard applications such as monitoring and documentation were considered manageable, whereas comprehensive farm-level data management involving multiple manufacturers, telemetry portals, and contractor data was described as significantly more demanding (ID13, ID14). Most respondents considered their own skills sufficient for routine telemetry use. Limitations were reported primarily in relation to user age (ID4, ID14, ID15). Basic monitoring tasks were described as relatively straightforward, while the configuration of complex work orders required more technically experienced users (ID4). Several respondents noted that after a short familiarisation period, the technology becomes accessible to most users (ID8). Older and middle-aged users, however, emphasised the need for continuous engagement with the systems in order to maintain their competence (ID9, ID11, ID15). The technical requirements for telemetry use were described as relatively modest. The main devices used were tablets, smartphones, and desktop computers, together with SIM cards and software licences for the respective telemetry platforms. Some external telemetry providers required additional hardware such as transmission boxes or beacons (ID11, ID12). In individual cases, additional equipment was installed to overcome technical limitations, such as CAN bus readers used to obtain more detailed machine data (ID9). One respondent also combined telemetry data with soil sensors and weather stations for irrigation management (ID13). The integration of telemetry technology into daily work routines was described as largely unproblematic for farm managers after a short adjustment period (ID8, ID10, ID14, ID16). The implementation of manufacturer-owned systems was considered considerably simpler than the adoption of external platforms, as manufacturer portals typically require only a registration to access machine data from newer models (ID6, ID12). Farms operating homogeneous machinery fleets reported particularly smooth implementation processes (ID3, ID4). In contrast, the introduction of external systems such as Agrarmonitor was described as more demanding, as it requires comprehensive digital mapping of farm operations and affects all employees (ID6, ID9, ID10). Respondents highlighted the importance of employee training, which should not be limited to the initial implementation phase (ID1, ID3). One interviewee also noted a lack of technical expertise among machinery dealers, which occasionally delayed support (ID7). The most complex aspect of implementation was reported to be the definition of internal data interfaces linking telemetry platforms with other software systems (ID2, ID5). 3.2.3 Social influence Social influences played only a limited role in the adoption decisions reported by the respondents. The majority of interview partners stated that they had neither significantly influenced the telemetry use of professional colleagues nor been influenced by colleagues in their own adoption decisions (ID2, ID4, ID6, ID12, ID13). In a few cases, respondents reported occasional interest from neighbouring farms, although this interest remained largely passive (ID16). Two farmers reported actively recommending telemetry technology to colleagues: One successfully through an existing business cooperation, while the other observed little effect (ID1, ID7). Two contractors noted that their clients were indirectly exposed to the technology through observing its use during contracted work, which may have contributed to later adoption decisions. However, this influence was described as incidental rather than intentionally promoted (ID9, ID10). External actors, including agricultural advisors, professional associations, and trade publications, were generally perceived as having only a minor influence on adoption decisions. In isolated cases, respondents reported that their initial awareness of telemetry technology had been generated through trade publications, although the decision to adopt was ultimately attributed to individual initiative (ID11). The most notable external influence came from machinery dealers and manufacturers, who actively promoted telemetry-enabled machinery (ID8, ID13, ID16). In several cases, telemetry use followed directly from the acquisition of new machinery rather than from a deliberate decision to introduce telemetry as a separate technological system (ID3, ID5). Within the social environment of the farm, reactions to telemetry use were mostly positive. In family contexts, the technology was perceived favourably when it reduced workload and created additional time for family life (ID13, ID14). Employees were generally reported to recognise the benefits of telemetry after an initial period of use (ID2, ID5, ID8, ID14, ID16). Some respondents also noted that employees appreciated being integrated into a modern digital working environment and that coordination within teams improved through the use of telemetry systems (ID10). External observers were often described as impressed by the technological capabilities (ID6, ID7). Negative reactions were limited to a small group of employees. In particular, older workers sometimes perceived real-time data transmission as a form of surveillance, which in several cases led to open resistance (ID3, ID6, ID8). On individual farms, employees resigned or threatened to resign following the introduction of telemetry systems (ID11, ID15). 3.2.4 Facilitating conditions The technical skills required for basic telemetry use were generally considered sufficient among the respondents. Younger interview partners described themselves as digitally proficient and reported resolving technical issues quickly (ID2, ID3, ID6, ID8). Respondents aged approximately 30 and above indicated that they were able to handle the technology but needed to engage with it actively to maintain their competence (ID9, ID11). Older respondents, particularly those born before 1970, reported greater difficulties and acknowledged that younger family members were considerably faster in adopting new applications (ID7, ID15). Internet connectivity had improved in recent years according to most respondents. More than half reported no remaining connectivity issues (ID2, ID3, ID10). Three further respondents described coverage as largely satisfactory, estimating it at approximately 90 per cent (ID6, ID9, ID12). For roughly a quarter of the sample, however, mobile network coverage on farm premises remained insufficient, though three of these four farms were able to use wired or wireless LAN connections in offices and workshops as an alternative (ID4, ID8, ID13, ID15). Overall, connectivity was considered adequate for telemetry use across all surveyed farms, as most telemetry modules buffer data locally during connection gaps and transmit them as data packages once a connection is re-established (ID7). No respondent had acquired new agricultural machinery solely for the purpose of telemetry use. Rather, when purchasing new machines for other operational reasons, farmers had ensured that telemetry features were included or activated as part of the transaction. Only a small number of farms used bridging technologies to enable uniform telemetry data access across heterogeneous fleets (ID6, ID8). Additional equipment acquired specifically for telemetry included tablets for employees with corresponding mobile contracts (ID14), external transmission modules for farms using third-party telemetry providers (ID9, ID11, ID12), and CAN bus readers to enable automated data capture from older machines lacking manufacturer interfaces (ID9). 3.2.5 Further aspects Beyond the facilitating conditions described above, respondents also discussed broader attitudes toward technological innovation. Most interview partners expressed general interest in new technologies but preferred to observe market developments for two to three years before making substantial investments (ID4, ID5, ID13, ID16). Two respondents emphasised the perceived downside risk of new technologies: while the financial exposure of a low-cost digital platform such as 365 FarmNet was considered negligible, the investment required for an autonomous field robot was regarded as prohibitively high (ID9, ID11). Approximately half of the respondents reported no particular concerns regarding telemetry use. Three respondents acknowledged data protection considerations but assessed their telemetry data as not particularly sensitive, as they are often incomplete and unprocessed (ID11, ID13, ID15). More substantial concerns related to the web-based storage of farm data. Several respondents feared that platforms might become temporarily unavailable or that data could be permanently lost due to provider insolvency or cyberattacks (ID5, ID10). One respondent noted that continuous digital documentation rendered his entire farm operation transparent to external actors (ID14). Finally, the cost–benefit ratio of telemetry was questioned by some interview partners: while the technology offers recognised advantages, the costs, particularly those of external providers, were sometimes perceived to exceed the measurable financial returns (ID7, ID10). 3.3 Discussion Although telemetry is widely described in the literature as a key component of data-driven agriculture and smart farming systems, enabling real-time machine monitoring, automated documentation, and improved resource management (Ayaz et al., 2019 ; Kehl et al., 2021 ; Halberstadt et al., 2022 ), the interviewed farmers associated the technology mainly with improvements in documentation, work monitoring, and organisational coordination. This suggests that the practical application of telemetry currently differs from the broader expectations described in the smart farming literature, where telemetry is frequently presented as an enabling infrastructure for comprehensive data-driven farm management. A central finding of this study concerns the discrepancy between high performance expectations and the limited ability of farmers to demonstrate measurable economic benefits. While respondents widely expected telemetry to improve operational efficiency and reduce workload, only a small number of farms reported clearly quantifiable financial gains, which are only marginal. Similar gaps between expected and realised benefits of digital agricultural technologies have been reported for smart farming technologies across European cropping systems, where economic advantages are often difficult to isolate and measure at farm level (Kernecker et al., 2020 ). In the present study, many respondents reported lacking the time or analytical capacity to systematically evaluate the data generated by telemetry systems, which may further limit the identification of potential efficiency gains. This also raises the question of whether Performance Expectancy, consistently identified as a predictor of Behavioural Intention to Use in quantitative UTAUT applications in German agriculture (e.g. Michels et al., 2020; Michels et al., 2024 ; Rübcke von Veltheim, 2022), captures evidence-based assessment or rather anticipatory optimism when applied to technologies whose benefits are diffuse and difficult to quantify. Another important factor influencing the practical use of telemetry is the limited interoperability between manufacturer-specific platforms. The fragmentation of telemetry ecosystems forces many farms to operate several parallel portals and complicates the integration of machine data into farm management systems. Interoperability problems and incompatible data formats have also been identified in previous studies as major obstacles to the effective use of digital agricultural technologies (e.g. Drewry et al., 2019 ; Dörr et al., 2023 ), which is very crucial as most farms in Germany use more than one manufacturer (Bernhardt et al., 2021 ). The present study extends this evidence by showing that farmers develop individual workaround solutions, including bridging technologies, external telemetry providers, and CAN bus readers, to address data fragmentation. These solutions are inherently fragile and dependent on continued manufacturer support, which may partly explain why telemetry data are often used only for basic monitoring and documentation rather than for more complex analytical applications. Furthermore, these plattforms are costly (e.g. Bartels et al., 2021 ). Social influence played only a minor role in the adoption decisions reported by the respondents. Most farmers described their decision to use telemetry as largely independent from professional networks or advisory structures. This contrasts with quantitative UTAUT studies in the German agricultural context, where social influence has been identified as a predictor of Behavioural Intention to Use, for instance regarding alternative fuel tractors (Michels et al., 2024 ). A possible explanation is that telemetry, unlike highly visible technologies such as field robots or drones, operates largely in the background of farm operations and is therefore less subject to peer observation and social comparison. Instead, telemetry use was often linked to the acquisition of new machinery equipped with integrated digital technologies. However, social dynamics within farms became more relevant after implementation. In particular, some employees perceived the transparency created by real-time machine monitoring as a form of surveillance, which occasionally led to internal tensions. This contributes to the on-going discussion regarding privacy in digital farming (e.g. Linsner et al., 2021 ). Effort expectancy did not appear to represent a major barrier to telemetry use. The interviewed farmers generally described telemetry systems as manageable after a short familiarisation period, which is consistent with the increasing digitalisation of agricultural machinery and the widespread availability of GNSS-based systems in modern farm operations (Ayaz et al., 2019 ; Kehl et al., 2021 ). Similarly, facilitating conditions such as internet connectivity, technical infrastructure, and digital skills were generally not perceived as major obstacles. Although connectivity problems still occur in some rural areas, most farms reported sufficient infrastructure for basic telemetry applications. This is also supported by Michels & Mußhoff ( 2025 ) who conclude that technologies that are easily to integrate and use are mostly likely to be adopted. 3.4 Implications 3.4.1 Practical implications The findings of this study provide several implications for agricultural practice. First, the results indicate that telemetry technologies are currently used primarily for administrative functions such as work monitoring, documentation, and fleet management. More advanced applications, particularly machine optimization and data-driven process control, were reported by only two of the sixteen interview partners. This suggests that further efforts are needed to integrate telemetry data more systematically into operational farm management and decision-making processes beyond routine administrative tasks. Second, the study highlights the importance of interoperability between machinery manufacturers and digital platforms. Many interview participants reported substantial difficulties arising from fragmented telemetry ecosystems, incompatible data formats, and the absence of standardized interfaces. These issues were identified as the most frequently cited weakness of the technology. Improving interoperability and developing more open data architectures could therefore significantly increase the practical value of telemetry systems, particularly for farms operating heterogeneous machinery fleets or cooperating with contractors. Third, the results underline the relevance of training and advisory services. Although the basic technical infrastructure for telemetry use is already available on most farms, effective utilization beyond standard applications requires additional digital competencies and a better understanding of the analytical possibilities these systems offer. Several participants noted a lack of expertise among dealers and service providers, which delays support and limits adoption depth. Targeted training programs, not only during initial implementation but also after an introductory period, and improved advisory support could facilitate a deeper and more efficient use of telemetry technologies. Fourth, the introduction of telemetry technology should be accompanied by transparent internal communication. The interviews revealed that real-time data transmission was perceived by some employees, particularly older workers, as a form of surveillance, leading to resistance and, in individual cases, to resignations or threats thereof. Farms that addressed these concerns proactively and communicated the purpose of data collection, traceability rather than permanent monitoring, reported a gradual reduction in tensions. This suggests that change management processes deserve explicit attention when implementing telemetry systems. 3.4.2 Research implications The findings also point to several directions for future research. First, the observed discrepancy between high performance expectations and the limited ability of farmers to quantify actual benefits warrants further investigation. Quantitative studies could examine whether this gap reflects measurement difficulties on the part of the farmers, an actual shortfall in realized benefits, or a combination of both. Second, this study focused exclusively on farms already using telemetry technology. Investigating farms that have consciously decided against adoption would provide complementary insights into rejection factors and perceived barriers. Third, the influence of farm size and organizational structure on adoption depth, from basic monitoring to comprehensive data-driven management, could not be systematically assessed with the present sample and merits dedicated quantitative analysis with larger and regionally more diverse samples. 3.4.3 Limitations The findings of this study should be interpreted in light of several limitations. First, the analysis is based on a relatively small sample of 16 interview partners from German agricultural businesses with focus on the northern regions of Germany. Although the qualitative design allows for in-depth insights into farmers’ perceptions and experiences with telemetry technologies, the results cannot be considered statistically representative for the entire population of German farms. Second, the study relies on qualitative expert interviews and a qualitative content analysis. While this methodological approach enables a detailed exploration of complex attitudes and decision-making processes, it inherently involves a certain degree of subjectivity in the interpretation and coding of interview data. Third, the coding process was conducted by a single researcher. Consequently, intercoder reliability could not be assessed, which may limit the robustness and reproducibility of the categorization process. Finally, the study focuses on the current stage of telemetry implementation on farms. As digital technologies in agriculture continue to evolve rapidly, the perceived benefits, barriers, and usage patterns may change over time. 4 Conclusions This study examined the factors influencing the adoption and use of telemetry technology among German arable farmers using a qualitative application of the UTAUT framework. Based on sixteen expert interviews, the results indicate that telemetry data are already used regularly on the surveyed farms. However, their application remains largely concentrated on administrative functions, particularly work monitoring, documentation, and, on larger farms, fleet management. While most farmers recognize the broader potential of telemetry data for operational optimization and data-driven decision-making, these possibilities have so far been only partially realized in practice. A central structural barrier identified in the study is the limited interoperability between manufacturer-specific telemetry platforms. The resulting data fragmentation and the need to operate multiple parallel systems create additional complexity and costs, particularly for farms with heterogeneous machinery fleets. At the same time, farmers generally associate telemetry technology with high expectations regarding efficiency gains and potential cost reductions. However, concrete and quantifiable economic benefits remain difficult for most farmers to demonstrate. This discrepancy between high expectations and limited measurable outcomes represents an important finding and highlights the need for further empirical investigation. Social influence was found to play only a minor role in adoption decisions, which were primarily driven by individual initiative or linked to the acquisition of new machinery. Nevertheless, internal social dynamics following implementation, especially employee resistance related to perceived surveillance, emerged as a relevant factor that may affect the depth of technology use within farms. Overall, the findings suggest that telemetry technology in German agriculture has progressed beyond the stage of initial adoption but has not yet reached a phase of comprehensive utilization. Unlocking the full potential of these systems will likely require improved data interoperability, targeted training and advisory services, and a clearer empirical evidence base regarding the economic benefits of telemetry-based farm management. Declarations Acknowledgements We thank all interview partners for taking part in the interviews and Hannes Meyer for his support. Marius Michels acknowledges financial support by the German Research Foundation (DFG). This research was partially supported by funds of the Federal Ministry of Food and Agriculture (BMLEH) based on a decision of the Parliament of the Federal Republic of Germany. The Federal Office for Agriculture and Food (BLE) provides coordinating support for artificial intelligence (AI) in agriculture as funding organisation, grant number FKZ 28DE401B23. References Adrian AM, Norwood SH, Mask PL (2005) Producers’ perceptions and attitudes toward precision agriculture technologies. Comput Electron Agric 48(3):256–271. https://doi.org/10.1016/j.compag.2005.04.004 Ayaz M, Ammad-Uddin M, Sharif Z, Mansour A, Aggoune EHM (2019) Internet-of-Things (IoT)-based smart agriculture: Toward making the fields talk. IEEE access 7:129551–129583. https://doi.org/10.1109/ACCESS.2019.2932609 Bartels N, Dörr J, Fehrmann J, Gennen K, Groen EC, Härtel I, Walter LS (2021) Abschlussbericht Machbarkeitsstudie: Machbarkeitsstudie zu staatlichen digitalen Datenplattformen für die Landwirtschaft. 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Technol Forecast Soc Chang 203:123360. https://doi.org/10.1016/j.techfore.2024.123360 Michels M, Mußhoff O (2025) Status Quo der Digitalisierung auf Ackerbaubetrieben in Deutschland. Berichte über Landwirtschaft-Zeitschrift für Agrarpolitik und Landwirtschaft, 103(2). https://doi.org/10.12767/buel.v103i2.543 Michels M, Twietmeyer J, Musshoff O (2026) Farm typologies and precision agriculture technology co-adoption in German agriculture: A combined cluster and network approach. Smart Agricultural Technol 101994. https://doi.org/10.1016/j.atech.2026.101994 Muangprathub J, Boonnam N, Kajornkasirat S, Lekbangpong N, Wanichsombat A, Nillaor P (2019) IoT and agriculture data analysis for smart farm. Comput Electron Agric 156:467–474. https://doi.org/10.1016/j.compag.2018.12.011 Papadopoulos G, Arduini S, Uyar H, Psiroukis V, Kasimati A, Fountas S (2024) Economic and environmental benefits of digital agricultural technologies in crop production: A review. Smart Agricultural Technol 8:100441. https://doi.org/10.1016/j.atech.2024.100441 Rotz S, Gravely E, Mosby I, Duncan E, Finnis E, Horgan M, Fraser E (2019) Automated pastures and the digital divide: How agricultural technologies are shaping labour and rural communities. J Rural Stud 68:112–122. https://doi.org/10.1016/j.jrurstud.2019.01.023 von Rübcke F, Theuvsen L, Heise H (2022) German farmers’ intention to use autonomous field robots: A PLS-analysis. Precision Agric 23(2):670–697. https://doi.org/10.1007/s11119-021-09854-3 Saiz-Rubio V, Rovira-Más F (2020) From smart farming towards agriculture 5.0: A review on crop data management. Agronomy 10(2):207. https://doi.org/10.3390/agronomy10020207 Streicher G (2019) Nutzen und Vorteile von Telemetrie . ABL Bayern – Arbeitsgemeinschaft Landtechnik und Landwirtschaftliches Bauwesen in Bayern e. V. Online available at https://www.alb-bayern.de/De/Technik/SatellitengestuetzteLandtechnik/Telemetrie/aufgabe-nutzen_Aufgabe Venkatesh V, Morris MG, Davis GB, Davis FD (2003) User acceptance of information technology: Toward a unified view1. MIS Q 27(3):425–478. https://doi.org/10.2307/30036540 Walter A, Finger R, Huber R, Buchmann N (2017) Smart farming is key to developing sustainable agriculture. Proceedings of the National Academy of Sciences , 114 (24), 6148–6150. https://doi.org/10.1073/pnas.1707462114 Wickramainghe S (2024) Telemetrie - eine umfassende Einführung in die Fernmessung . Online available at: https://www.splunk.com/de_de/blog/learn/was-ist-telemetrie.html Weichbold M (2022) Pretests. In N. Baur & J. Blasius (Eds.), Handbuch Methoden der empirischen Sozialforschung (pp. 443–451). Springer Fachmedien Wiesbaden. https://doi.org/10.1007/978-3-658-37985-8_28 Wolfert S, Ge L, Verdouw C, Bogaardt MJ (2017) Big data in smart farming–a review. Agric Syst 153:69–80. https://doi.org/10.1016/j.agsy.2017.01.023 Additional Declarations The authors declare no competing interests. Supplementary Files Appendix.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9455683","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":625364409,"identity":"ae6b1bce-71ce-4a69-ae6e-a67bdd123b61","order_by":0,"name":"Marius Michels","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-4391-4457","institution":"Georg-August-Universität Göttingen","correspondingAuthor":true,"prefix":"","firstName":"Marius","middleName":"","lastName":"Michels","suffix":""},{"id":625364410,"identity":"65bb0ae7-0aeb-4efd-a86e-ef504b5971e1","order_by":1,"name":"Hinrich Ilse","email":"","orcid":"","institution":"Georg-August-Universität Göttingen","correspondingAuthor":false,"prefix":"","firstName":"Hinrich","middleName":"","lastName":"Ilse","suffix":""}],"badges":[],"createdAt":"2026-04-18 09:15:01","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9455683/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9455683/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107407263,"identity":"787b567a-9869-4578-be0a-b67243300f32","added_by":"auto","created_at":"2026-04-21 08:35:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88041,"visible":true,"origin":"","legend":"\u003cp\u003eUTAUT Model following Venkatesh et al. (2003)\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-9455683/v1/14be130e1326641b19f74fe6.png"},{"id":107489431,"identity":"d039b541-a196-4248-aba9-135f1842213a","added_by":"auto","created_at":"2026-04-22 02:47:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":449253,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9455683/v1/f6f97b21-f873-40f9-9eb7-32541d4336cd.pdf"},{"id":107407262,"identity":"d4bfbbe4-70af-4dbc-80f3-873a518c208c","added_by":"auto","created_at":"2026-04-21 08:35:21","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":16150,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.docx","url":"https://assets-eu.researchsquare.com/files/rs-9455683/v1/357699ffeba75cc7df6ca8ed.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eTelemetry in digital agriculture: Adoption drivers and interoperability barriers among German farmers\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eOver the last decade, agriculture has been increasingly digitized (Rotz et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Muangprathub et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Digital technologies are frequently discussed as an important tool for improving farm management efficiency, resource use (Walter et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Wolfert et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Finger et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Saiz-Rubio \u0026amp; Rovira-M\u0026aacute;s, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and food security (Gebbers \u0026amp; Adamchuk, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Over the past two decades, a wide range of digital technologies have been introduced into agricultural practice, including GNSS-based guidance systems, drone-based monitoring, application maps, and digital farm management systems (Kliem et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Papadopoulos et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). However, the adoption of these technologies varies considerably across farms and technologies (Kehl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Michels \u0026amp; Mu\u0026szlig;hoff, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Michels et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). Their increasing availability has been accompanied by a growing integration of digital data into agricultural decision-making processes (Wolfert et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Saiz-Rubio \u0026amp; Rovira-M\u0026aacute;s, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). A key prerequisite for many of these applications is the availability of operational machine data that can be collected, transmitted, and analysed within digital management systems. According to Hillerbrand et al. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), such data infrastructures form an essential basis for the further development of automation and autonomous processes in agriculture. These machine-generated data streams are commonly referred to as telemetry data (Clasen, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gscheidle, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe term telemetry is derived from the Greek words \u003cem\u003etēle\u003c/em\u003e (distant) and \u003cem\u003em\u0026eacute;tron\u003c/em\u003e (measure) and refers to the wireless transmission of measurement data from one location to another spatially separated location (Heuberger \u0026amp; Gamm, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In agriculture, telemetry systems enable the real-time transmission of sensor data from agricultural machinery to external servers via mobile networks. The transmitted data may include basic positional information from GNSS receivers as well as machine-specific parameters such as engine speed, fuel consumption, and operating temperature. These data are typically timestamped, allowing them to be assigned to specific field operations during subsequent analysis (Ayaz et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kehl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Access to the machine\u0026rsquo;s CAN bus is a prerequisite for capturing detailed operational parameters beyond positional data (Meroth \u0026amp; Sora, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Streicher, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn arable farming, telemetry is mainly used for work monitoring, automated documentation of field operations, fleet management, and remote machine diagnostics. More advanced applications include the transmission and analysis of process data, such as yield maps generated during harvest via the ISOBUS interface and transferred to farm management information systems for further analysis (Kehl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The increasing availability of telemetry data creates new opportunities for agricultural management. Telemetry enables real-time monitoring of machinery operations, automated documentation of field activities, improved fleet coordination, and remote machine diagnostics (Halberstadt et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Kehl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, telemetry data can be integrated with other digital data sources such as weather data, soil sensors, or yield maps, thereby supporting more data-driven farm management and operational decision-making (Wolfert et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). However, several challenges still limit the practical use of telemetry systems. One of the most frequently discussed obstacles concerns the limited interoperability between manufacturer-specific telemetry platforms. Many agricultural machinery manufacturers operate proprietary digital ecosystems that lack standardised interfaces for data exchange, which complicates the integration of machine data across heterogeneous machinery fleets (Bartels et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; D\u0026ouml;rr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Additional concerns relate to data governance, data security, and the handling of large volumes of machine-generated data (Kutter et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wickramainghe, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite these technical capabilities, empirical evidence on the actual adoption and use of telemetry in German agriculture remains limited. Recent survey results indicate that only around 14% of German farmers currently use telemetry technologies and that only 75% are familiar with the concept, placing it among the least recognised digital agricultural technologies in a comparison of 32 digital tools (Michels \u0026amp; Mu\u0026szlig;hoff, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Michels et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). While a considerable body of research has investigated the adoption of other digital technologies in agriculture, including smartphones (Michels et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020a\u003c/span\u003e), drones (Michels et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020b\u003c/span\u003e), and alternative fuel tractors (Michels et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), telemetry as a distinct technological infrastructure has not yet been examined in a dedicated acceptance study. Nevertheless, understanding the adoption process is at the forefront for research in digital agriculture (Ingram et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis lack of research is noteworthy because telemetry differs conceptually from broader notions such as precision farming or smart farming. Whereas these concepts describe management approaches and application domains, telemetry primarily represents a data transmission infrastructure that enables the collection and exchange of machine-generated data. Understanding how farmers perceive and integrate this infrastructure into their operational routines is therefore essential for assessing the broader potential of digital agriculture. Furthermore, the existing literature on technology adoption in agriculture is predominantly based on quantitative survey approaches that analyse behavioural intentions using standardised models. While these studies provide valuable insights into general adoption patterns, they offer limited understanding of how farmers experience and evaluate technologies in everyday practice. Qualitative studies that explore the practical use and perceived benefits of telemetry technology remain largely absent.\u003c/p\u003e \u003cp\u003eThis study addresses this gap by examining the factors influencing the adoption and use of telemetry data among German arable farmers. The Unified Theory of Acceptance and Use of Technology (UTAUT) developed by Venkatesh et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) serves as the theoretical framework. UTAUT combines performance-related expectations, perceived ease of use, social influences, and structural conditions that may affect technology adoption (Venkatesh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These dimensions are particularly relevant in the context of agricultural decision-making, where adoption decisions are influenced not only by expected economic benefits but also by operational complexity, social networks, and the availability of technical infrastructure (e.g. Michels et al., 2020; Michels et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), which is expected to hold for telemetry too. In contrast to previous quantitative applications of the UTAUT in the agricultural context, this study employs a qualitative research design based on semi-structured expert interviews with sixteen farmers and agricultural contractors in autumn 2024.\u003c/p\u003e \u003cp\u003eThe study contributes to the literature in three ways. First, it provides the first empirical investigation specifically focused on the acceptance of telemetry technology in German agriculture. Second, it offers a qualitative application of the UTAUT framework in this context, thereby complementing the predominantly quantitative evidence base. Third, by interviewing farmers who are already using telemetry, the study captures not only adoption factors but also post-adoption experiences, including perceived benefits, unmet expectations, and barriers to deeper utilisation.\u003c/p\u003e \u003cp\u003eThe remainder of the article is organized as follows: In the second section, used material and methods are described. Results are presented and discussed in the third section. The article closes with some conclusions in the fourth section.\u003c/p\u003e"},{"header":"2 Material and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Unified Theory of Acceptance and Use of Technology\u003c/h2\u003e \u003cp\u003eThe UTAUT was developed by Venkatesh et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) through a synthesis of eight established models of technology acceptance, including the Technology Acceptance Model (TAM), the Theory of Planned Behaviour (TPB), and the Theory of Reasoned Action (TRA). The UTAUT model has been applied for numerous studies regarding precision and smart farming (e.g. Adrian et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Michels et al., 2020; R\u0026uuml;bcke von Veltheim et al., 2022) as well as agricultural machinery (e.g. Michels et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). The model identifies four core constructs that determine technology acceptance and use behaviour (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which are explained and set in the context of telemetry in the following.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePerformance Expectancy\u003c/span\u003e is defined as the degree to which an individual believes that using a technology will help achieve gains in job performance. In the context of telemetry adoption, this construct captures farmers' expectations regarding improvements in work efficiency, cost reductions, yield increases, and operational facilitation through telemetry data use.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEffort Expectancy\u003c/span\u003e refers to the degree of ease associated with the use of a technology. Applied to this study, it encompasses the perceived user-friendliness of telemetry platforms, the complexity of the applications, and the effort required to integrate the technology into existing farm routines.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSocial Influence\u003c/span\u003e is defined as the degree to which an individual perceives that important others believe they should use the technology. This includes the influence of professional colleagues, family members, advisors, dealers, and other actors on the adoption decision.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFacilitating Conditions\u003c/span\u003e refer to the degree to which an individual believes that an organisational and technical infrastructure exists to support the use of the technology. In the present study, this construct covers aspects such as internet connectivity, the availability of compatible hardware, technical skills, risk tolerance, and concerns regarding data security.\u003c/p\u003e \u003cp\u003eThe original UTAUT model additionally includes four moderating variables (age, gender, experience, and voluntariness of use) that are hypothesised to moderate the relationships between the core constructs, Behavioural Intention to Use, and actual Usage (Venkatesh et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). In quantitative applications of the model, these variables are typically incorporated to test interaction effects statistically. As the present study follows a qualitative research design based on semi-structured expert interviews, these moderating variables were not operationalised in a statistical sense. Instead, the four main constructs of the UTAUT framework served as a deductive structure for the development of the interview guide and the subsequent qualitative content analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Interview design\u003c/h2\u003e \u003cp\u003eThe interview guide was structured according to the four core constructs of the UTAUT framework: performance expectancy, effort expectancy, social influence, and facilitating conditions. Each construct was operationalised through a dedicated block of open-ended questions addressing farmers\u0026rsquo; experiences with telemetry technology.\u003c/p\u003e \u003cp\u003eQuestions related to Performance Expectancy explored farmers\u0026rsquo; expectations and perceived benefits of telemetry use, including potential effects on yields, operating costs, work efficiency, workload reduction, and overall economic benefits. Respondents were also asked about perceived weaknesses of telemetry technology and their expectations regarding future developments.\u003c/p\u003e \u003cp\u003eThe construct Effort Expectancy was addressed through questions concerning the ease of use and practical integration of telemetry systems. These questions focused on the usability of telemetry platforms, the perceived complexity of the technology, obstacles encountered during implementation, and the technical tools required for data use.\u003c/p\u003e \u003cp\u003eQuestions related to Social Influence examined the potential impact of professional networks and social environments on adoption decisions. Interviewees were asked about the opinions of professional colleagues, the role of agricultural advisors or associations, and whether their own telemetry use influenced other farmers.\u003c/p\u003e \u003cp\u003eFinally, Facilitating Conditions were explored through questions addressing the organisational and technical prerequisites for telemetry use. These included the availability of digital infrastructure, required hardware and machinery, internet connectivity, individual technical skills, risk attitudes toward technological adoption, and concerns related to data security.\u003c/p\u003e \u003cp\u003eIn addition to these thematic blocks, the interview guide included a second section collecting sociodemographic and farm-level background information. Ethic approval was obtained by the University\u0026rsquo;s ethics committee. The full questionnaire is given in the Appendix.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Data collection and analysis\u003c/h2\u003e \u003cp\u003eData were collected through semi-structured expert interviews with individuals actively involved in agricultural production. The interview partners included farm managers, managing directors, and employees working on arable farms and contracting businesses that had already implemented telemetry technology. In total, sixteen interviews were conducted with representatives of farms located in different regions of Germany. Prior to the main data collection, a pretest was conducted with one farmer to evaluate and refine the interview guide (Weichbold, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The interviews took place between October and December 2024.\u003c/p\u003e \u003cp\u003eAll interviews were recorded with the informed consent of the participants and subsequently transcribed using the audio transcription software \u003cem\u003eNoScribe\u003c/em\u003e. To ensure comparability across interviews, a standardized set of transcription rules following Kuckartz and R\u0026auml;diker (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022b\u003c/span\u003e) was applied.\u003c/p\u003e \u003cp\u003eThe interview material was analysed using qualitative content analysis following the summarising approach proposed by Mayring (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1994\u003c/span\u003e), complemented by procedural elements described by Kuckartz \u0026amp; R\u0026auml;diker (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e). The coding framework was deductively derived from the four main constructs of the UTAUT model and informed by existing UTAUT codebooks (Gruzd et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) as well as the structure of the interview guide. During the analysis, a small number of categories that lacked sufficient discriminatory clarity were merged, and selected subcategories were added inductively to capture themes emerging from the interview material that were not fully anticipated by the deductive framework (Kuckartz \u0026amp; R\u0026auml;diker, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022a\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCoding and data organisation were conducted using the qualitative data analysis software \u003cem\u003eMAXQDA\u003c/em\u003e. Relevant text segments were defined as coding units and assigned to thematic categories. These segments were subsequently paraphrased and condensed to their essential content, while non-substantive passages were discarded. All retained segments were standardised to a consistent grammatical and linguistic level to improve comparability (Mayring, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1994\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe entire coding process was carried out by a single researcher. While this limits the possibility of assessing inter-coder reliability, consistency was supported by the use of a deductively derived coding framework (Gruzd et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and iterative revision of category assignments throughout the analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results and Discussion","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eSixteen expert interviews were conducted with farmers and agricultural contractors from Germany between October and December 2024. A pretest was carried out in July 2024. The interview partners were selected based on their active use of telemetry technology in their operations. The farms are distributed across Lower Saxony, Schleswig-Holstein, Mecklenburg-Western Pomerania, and Saxony-Anhalt. An overview of all interview partners is provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample characteristics (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFarmers\u0026rsquo; age in years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarming experience in years\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePosition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarm type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTelemetry since\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eArable land in ha\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eAgricultural qualification\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManaging director\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContractor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVocational training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployee / management support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVocational training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployee / management support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMaster craftsman\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm owner and manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003en.a.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMaster craftsman\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM.Sc. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDipl.-Ing. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEmployee / management support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVocational training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManaging director\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContractor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eM.Sc. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eManaging director\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eContractor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2016/2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026ndash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOwner and managing director\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e750\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB.Sc. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData analyst and controller\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6,500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB.Sc. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2014/2022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDipl.-Ing. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJunior manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eca. 1,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVocational training\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDipl.-Ing. (FH)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eID16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFarm manager\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1,450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eB.Sc. Agr. Sciences\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eFarm type: Arable\u0026thinsp;=\u0026thinsp;arable farm; Contractor\u0026thinsp;=\u0026thinsp;contracting business without own arable land; Mixed\u0026thinsp;=\u0026thinsp;arable farm with additional contracting services. Arable land is reported for arable and mixed farms only; contracting businesses are indicated by \u0026ldquo;\u0026ndash;\u0026rdquo;. Where two years are listed under \u0026ldquo;Telemetry since\u0026rdquo;, the first refers to initial use and the second to intensified use. n.a. = not available.\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAt the time of the interviews, participants ranged in age from 23 to 64 years and reported between 3 and 56 years of professional experience in agriculture. The arable land managed by the farms ranged from 90 to 7,500 hectares. Telemetry data had been in use on the surveyed farms for periods ranging from approximately three to fifteen years, though the majority had intensified their use within the past six years. Educational backgrounds ranged from vocational training to master's-level university degrees in agricultural sciences. The sample includes five contracting businesses, two of which also operate their own arable land. All interview partners were male.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Interview results\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1 Performance expectancy\u003c/h2\u003e \u003cp\u003eThe interview partners associated telemetry technology primarily with reductions in administrative and organisational workload rather than with direct improvements in agronomic production processes. The most frequently reported benefits were real-time work monitoring, automated documentation of field operations, and\u0026mdash;on larger farms and contracting businesses\u0026mdash;fleet management.\u003c/p\u003e \u003cp\u003eReal-time monitoring was described as a substantial relief for farm managers, as it enables continuous oversight of machine locations and ongoing operations without requiring physical presence in the field. Several respondents emphasised that errors can be detected and corrected earlier and that automation systems on machines can be adjusted remotely from the office (ID1, ID4, ID6, ID12). Farm managers also reported a reduction in mental workload because completed tasks are automatically recorded and can be reviewed at any time (ID13). In contracting businesses, real-time location data were additionally used to inform clients about expected arrival times (ID2, ID12). However, several respondents noted that real-time data transmission was perceived by some employees\u0026mdash;particularly older workers\u0026mdash;as a form of surveillance, which in some cases resulted in resistance or threats of resignation (ID3, ID4, ID11, ID15).\u003c/p\u003e \u003cp\u003eAutomated documentation was unanimously regarded as a major improvement. Respondents described the digital records as comparable to an instantly accessible logbook of past activities (ID7, ID11, ID15). The accuracy of fertiliser and crop protection documentation was reported to have improved significantly, and automated data capture eliminated the need for manual data entry into documentation software, resulting in considerable time savings (ID4, ID8, ID11, ID13).\u003c/p\u003e \u003cp\u003eFleet management was highlighted primarily by larger operations. During harvest or other coordinated work chains, telemetry allows managers to identify excessive idle times and adjust the number of deployed vehicles accordingly. Employees also reported benefits, as the technology facilitated self-organisation within work teams (ID4, ID10, ID12). For smaller farms with few machines, however, the relevance of fleet management was considered limited (ID7, ID11).\u003c/p\u003e \u003cp\u003eMachine maintenance was perceived as beneficial on farms that had integrated telemetry into their servicing routines. Automated maintenance alerts and proactive contact by service partners were reported to improve the predictability of routine servicing (ID2, ID4, ID5, ID15). Working time recording was regarded as a secondary application rather than a core telemetry function. While some respondents valued the transparency it provided for billing purposes (ID9, ID10, ID12), others raised concerns regarding data protection and employee trust (ID14, ID15).\u003c/p\u003e \u003cp\u003eRegarding efficiency gains, respondents consistently distinguished between agronomic production processes\u0026mdash;where no direct efficiency improvements were attributed to telemetry\u0026mdash;and overall farm organisation, where benefits were more evident. Improvements were most frequently reported for invoicing in contracting businesses, order dispatching, record keeping, and internal error analysis through remote workshop access (ID4, ID7, ID10, ID11, ID12). Approximately half of the respondents reported no or only marginal efficiency improvements so far, although most expected gains in the future.\u003c/p\u003e \u003cp\u003eNone of the respondents reported yield increases attributable to telemetry data use. Several interviewees questioned whether telemetry could plausibly influence yields and instead pointed to other precision farming technologies as drivers of yield improvements. Similarly, expectations regarding cost reductions were largely unmet. While some farms recognised the potential for savings, the costs of external telemetry providers were perceived as high and often offset expected benefits (ID1, ID10). Only two farms reported concrete cost reductions: one through improved irrigation planning using telemetry and soil sensor data (ID13) and another through the elimination of an administrative position (ID9).\u003c/p\u003e \u003cp\u003eThe most frequently cited weakness of telemetry technology was the lack of interoperability between manufacturer-specific platforms. Farms operating heterogeneous machinery fleets reported being forced to use multiple portals simultaneously because manufacturers do not allow data sharing across systems (ID7). Although standardised formats such as ISO-XML exist, their implementation differs between manufacturers, leading to inconsistencies and data transfer errors (ID2, ID5, ID7, ID9). Some farms attempted to overcome these limitations through bridging solutions or external telemetry providers, though at considerable additional cost (ID6, ID8). Additional weaknesses included insufficient data quality on farms cooperating with contractors (ID13), employee resistance or sabotage of telemetry modules (ID1, ID4, ID6, ID9, ID10, ID12, ID14), the vulnerability of web-based platforms to outages (ID5, ID10), and the considerable time required for full system implementation during peak season (ID1, ID13).\u003c/p\u003e \u003cp\u003eSustainability effects were perceived as limited by most respondents. The most commonly mentioned effect was the reduction of unnecessary trips and associated fuel savings (ID8, ID9, ID12). One farm reported improvements in irrigation efficiency through telemetry-supported monitoring (ID13). However, reductions in fertiliser and crop protection inputs were primarily attributed to guidance systems, section control, and other precision farming technologies rather than to telemetry itself (ID4, ID11, ID16).\u003c/p\u003e \u003cp\u003eRegarding future expectations, respondents expressed strong demand for improved interoperability and manufacturer-independent platforms. Suggested solutions included uniform data formats (ID2, ID8, ID10, ID16), cross-manufacturer aggregation platforms (ID6), and licensing models to compensate manufacturers for data sharing (ID11). Additional requests concerned simpler system implementation and greater flexibility for integrating externally generated data (ID12, ID13, ID16). Overall, respondents expected telemetry to reduce administrative workload further and to improve overall farm efficiency in the future (ID1, ID4, ID14).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2 Effort expectancy\u003c/h2\u003e \u003cp\u003eThe user interfaces of the telemetry platforms used by the respondents were generally assessed as user-friendly. The systems mentioned included both manufacturer-owned platforms\u0026mdash;such as John Deere Operations Center, Claas Connect, Fendt Connect, and Kverneland Farm Center\u0026mdash;and external solutions including 365 FarmNet, Agrarmonitor, Exatrek, and RTK Clue. No single platform was unanimously preferred, as evaluations were strongly influenced by the respondents\u0026rsquo; familiarity with their respective systems (ID5). The John Deere Operations Center was mentioned most frequently and was valued for its ability to share coverage maps in real time (ID6, ID8), although its mobile application was criticised for usability issues (ID3, ID5).\u003c/p\u003e \u003cp\u003eDespite generally positive assessments of usability, respondents reported several obstacles to telemetry use. These varied considerably across farms and included high acquisition costs, limited time for training, and the age and willingness of employees to engage with digital technologies (ID1, ID4, ID6, ID10, ID12, ID14, ID16). The most critical concern related to the operational reliability of telemetry platforms. Full integration into farm management systems creates strong dependencies on platform availability, and several respondents reported experiencing system failures during field operations (ID5). Additional concerns included the potential for manufacturers to exploit collected data for commercial purposes (ID11) and the risk of losing access to sensitive farm data if a platform were discontinued or the provider became insolvent (ID10, ID14).\u003c/p\u003e \u003cp\u003eThe overall complexity of telemetry applications was generally rated as high to very high. However, respondents emphasised that perceived complexity depends strongly on the depth of use. Standard applications such as monitoring and documentation were considered manageable, whereas comprehensive farm-level data management involving multiple manufacturers, telemetry portals, and contractor data was described as significantly more demanding (ID13, ID14).\u003c/p\u003e \u003cp\u003eMost respondents considered their own skills sufficient for routine telemetry use. Limitations were reported primarily in relation to user age (ID4, ID14, ID15). Basic monitoring tasks were described as relatively straightforward, while the configuration of complex work orders required more technically experienced users (ID4). Several respondents noted that after a short familiarisation period, the technology becomes accessible to most users (ID8). Older and middle-aged users, however, emphasised the need for continuous engagement with the systems in order to maintain their competence (ID9, ID11, ID15).\u003c/p\u003e \u003cp\u003eThe technical requirements for telemetry use were described as relatively modest. The main devices used were tablets, smartphones, and desktop computers, together with SIM cards and software licences for the respective telemetry platforms. Some external telemetry providers required additional hardware such as transmission boxes or beacons (ID11, ID12). In individual cases, additional equipment was installed to overcome technical limitations, such as CAN bus readers used to obtain more detailed machine data (ID9). One respondent also combined telemetry data with soil sensors and weather stations for irrigation management (ID13).\u003c/p\u003e \u003cp\u003eThe integration of telemetry technology into daily work routines was described as largely unproblematic for farm managers after a short adjustment period (ID8, ID10, ID14, ID16). The implementation of manufacturer-owned systems was considered considerably simpler than the adoption of external platforms, as manufacturer portals typically require only a registration to access machine data from newer models (ID6, ID12). Farms operating homogeneous machinery fleets reported particularly smooth implementation processes (ID3, ID4). In contrast, the introduction of external systems such as Agrarmonitor was described as more demanding, as it requires comprehensive digital mapping of farm operations and affects all employees (ID6, ID9, ID10). Respondents highlighted the importance of employee training, which should not be limited to the initial implementation phase (ID1, ID3). One interviewee also noted a lack of technical expertise among machinery dealers, which occasionally delayed support (ID7). The most complex aspect of implementation was reported to be the definition of internal data interfaces linking telemetry platforms with other software systems (ID2, ID5).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3 Social influence\u003c/h2\u003e \u003cp\u003eSocial influences played only a limited role in the adoption decisions reported by the respondents. The majority of interview partners stated that they had neither significantly influenced the telemetry use of professional colleagues nor been influenced by colleagues in their own adoption decisions (ID2, ID4, ID6, ID12, ID13). In a few cases, respondents reported occasional interest from neighbouring farms, although this interest remained largely passive (ID16). Two farmers reported actively recommending telemetry technology to colleagues: One successfully through an existing business cooperation, while the other observed little effect (ID1, ID7). Two contractors noted that their clients were indirectly exposed to the technology through observing its use during contracted work, which may have contributed to later adoption decisions. However, this influence was described as incidental rather than intentionally promoted (ID9, ID10).\u003c/p\u003e \u003cp\u003eExternal actors, including agricultural advisors, professional associations, and trade publications, were generally perceived as having only a minor influence on adoption decisions. In isolated cases, respondents reported that their initial awareness of telemetry technology had been generated through trade publications, although the decision to adopt was ultimately attributed to individual initiative (ID11). The most notable external influence came from machinery dealers and manufacturers, who actively promoted telemetry-enabled machinery (ID8, ID13, ID16). In several cases, telemetry use followed directly from the acquisition of new machinery rather than from a deliberate decision to introduce telemetry as a separate technological system (ID3, ID5).\u003c/p\u003e \u003cp\u003eWithin the social environment of the farm, reactions to telemetry use were mostly positive. In family contexts, the technology was perceived favourably when it reduced workload and created additional time for family life (ID13, ID14). Employees were generally reported to recognise the benefits of telemetry after an initial period of use (ID2, ID5, ID8, ID14, ID16). Some respondents also noted that employees appreciated being integrated into a modern digital working environment and that coordination within teams improved through the use of telemetry systems (ID10). External observers were often described as impressed by the technological capabilities (ID6, ID7).\u003c/p\u003e \u003cp\u003eNegative reactions were limited to a small group of employees. In particular, older workers sometimes perceived real-time data transmission as a form of surveillance, which in several cases led to open resistance (ID3, ID6, ID8). On individual farms, employees resigned or threatened to resign following the introduction of telemetry systems (ID11, ID15).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4 Facilitating conditions\u003c/h2\u003e \u003cp\u003eThe technical skills required for basic telemetry use were generally considered sufficient among the respondents. Younger interview partners described themselves as digitally proficient and reported resolving technical issues quickly (ID2, ID3, ID6, ID8). Respondents aged approximately 30 and above indicated that they were able to handle the technology but needed to engage with it actively to maintain their competence (ID9, ID11). Older respondents, particularly those born before 1970, reported greater difficulties and acknowledged that younger family members were considerably faster in adopting new applications (ID7, ID15).\u003c/p\u003e \u003cp\u003eInternet connectivity had improved in recent years according to most respondents. More than half reported no remaining connectivity issues (ID2, ID3, ID10). Three further respondents described coverage as largely satisfactory, estimating it at approximately 90 per cent (ID6, ID9, ID12). For roughly a quarter of the sample, however, mobile network coverage on farm premises remained insufficient, though three of these four farms were able to use wired or wireless LAN connections in offices and workshops as an alternative (ID4, ID8, ID13, ID15). Overall, connectivity was considered adequate for telemetry use across all surveyed farms, as most telemetry modules buffer data locally during connection gaps and transmit them as data packages once a connection is re-established (ID7).\u003c/p\u003e \u003cp\u003eNo respondent had acquired new agricultural machinery solely for the purpose of telemetry use. Rather, when purchasing new machines for other operational reasons, farmers had ensured that telemetry features were included or activated as part of the transaction. Only a small number of farms used bridging technologies to enable uniform telemetry data access across heterogeneous fleets (ID6, ID8). Additional equipment acquired specifically for telemetry included tablets for employees with corresponding mobile contracts (ID14), external transmission modules for farms using third-party telemetry providers (ID9, ID11, ID12), and CAN bus readers to enable automated data capture from older machines lacking manufacturer interfaces (ID9).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5 Further aspects\u003c/h2\u003e \u003cp\u003eBeyond the facilitating conditions described above, respondents also discussed broader attitudes toward technological innovation. Most interview partners expressed general interest in new technologies but preferred to observe market developments for two to three years before making substantial investments (ID4, ID5, ID13, ID16). Two respondents emphasised the perceived downside risk of new technologies: while the financial exposure of a low-cost digital platform such as 365 FarmNet was considered negligible, the investment required for an autonomous field robot was regarded as prohibitively high (ID9, ID11).\u003c/p\u003e \u003cp\u003eApproximately half of the respondents reported no particular concerns regarding telemetry use. Three respondents acknowledged data protection considerations but assessed their telemetry data as not particularly sensitive, as they are often incomplete and unprocessed (ID11, ID13, ID15). More substantial concerns related to the web-based storage of farm data. Several respondents feared that platforms might become temporarily unavailable or that data could be permanently lost due to provider insolvency or cyberattacks (ID5, ID10). One respondent noted that continuous digital documentation rendered his entire farm operation transparent to external actors (ID14). Finally, the cost\u0026ndash;benefit ratio of telemetry was questioned by some interview partners: while the technology offers recognised advantages, the costs, particularly those of external providers, were sometimes perceived to exceed the measurable financial returns (ID7, ID10).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Discussion\u003c/h2\u003e \u003cp\u003eAlthough telemetry is widely described in the literature as a key component of data-driven agriculture and smart farming systems, enabling real-time machine monitoring, automated documentation, and improved resource management (Ayaz et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kehl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Halberstadt et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), the interviewed farmers associated the technology mainly with improvements in documentation, work monitoring, and organisational coordination. This suggests that the practical application of telemetry currently differs from the broader expectations described in the smart farming literature, where telemetry is frequently presented as an enabling infrastructure for comprehensive data-driven farm management.\u003c/p\u003e \u003cp\u003eA central finding of this study concerns the discrepancy between high performance expectations and the limited ability of farmers to demonstrate measurable economic benefits. While respondents widely expected telemetry to improve operational efficiency and reduce workload, only a small number of farms reported clearly quantifiable financial gains, which are only marginal. Similar gaps between expected and realised benefits of digital agricultural technologies have been reported for smart farming technologies across European cropping systems, where economic advantages are often difficult to isolate and measure at farm level (Kernecker et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the present study, many respondents reported lacking the time or analytical capacity to systematically evaluate the data generated by telemetry systems, which may further limit the identification of potential efficiency gains. This also raises the question of whether Performance Expectancy, consistently identified as a predictor of Behavioural Intention to Use in quantitative UTAUT applications in German agriculture (e.g. Michels et al., 2020; Michels et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; R\u0026uuml;bcke von Veltheim, 2022), captures evidence-based assessment or rather anticipatory optimism when applied to technologies whose benefits are diffuse and difficult to quantify.\u003c/p\u003e \u003cp\u003eAnother important factor influencing the practical use of telemetry is the limited interoperability between manufacturer-specific platforms. The fragmentation of telemetry ecosystems forces many farms to operate several parallel portals and complicates the integration of machine data into farm management systems. Interoperability problems and incompatible data formats have also been identified in previous studies as major obstacles to the effective use of digital agricultural technologies (e.g. Drewry et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; D\u0026ouml;rr et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is very crucial as most farms in Germany use more than one manufacturer (Bernhardt et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The present study extends this evidence by showing that farmers develop individual workaround solutions, including bridging technologies, external telemetry providers, and CAN bus readers, to address data fragmentation. These solutions are inherently fragile and dependent on continued manufacturer support, which may partly explain why telemetry data are often used only for basic monitoring and documentation rather than for more complex analytical applications. Furthermore, these plattforms are costly (e.g. Bartels et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSocial influence played only a minor role in the adoption decisions reported by the respondents. Most farmers described their decision to use telemetry as largely independent from professional networks or advisory structures. This contrasts with quantitative UTAUT studies in the German agricultural context, where social influence has been identified as a predictor of Behavioural Intention to Use, for instance regarding alternative fuel tractors (Michels et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). A possible explanation is that telemetry, unlike highly visible technologies such as field robots or drones, operates largely in the background of farm operations and is therefore less subject to peer observation and social comparison. Instead, telemetry use was often linked to the acquisition of new machinery equipped with integrated digital technologies. However, social dynamics within farms became more relevant after implementation. In particular, some employees perceived the transparency created by real-time machine monitoring as a form of surveillance, which occasionally led to internal tensions. This contributes to the on-going discussion regarding privacy in digital farming (e.g. Linsner et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEffort expectancy did not appear to represent a major barrier to telemetry use. The interviewed farmers generally described telemetry systems as manageable after a short familiarisation period, which is consistent with the increasing digitalisation of agricultural machinery and the widespread availability of GNSS-based systems in modern farm operations (Ayaz et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Kehl et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Similarly, facilitating conditions such as internet connectivity, technical infrastructure, and digital skills were generally not perceived as major obstacles. Although connectivity problems still occur in some rural areas, most farms reported sufficient infrastructure for basic telemetry applications. This is also supported by Michels \u0026amp; Mu\u0026szlig;hoff (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) who conclude that technologies that are easily to integrate and use are mostly likely to be adopted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Implications\u003c/h2\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.4.1 Practical implications\u003c/h2\u003e \u003cp\u003eThe findings of this study provide several implications for agricultural practice. First, the results indicate that telemetry technologies are currently used primarily for administrative functions such as work monitoring, documentation, and fleet management. More advanced applications, particularly machine optimization and data-driven process control, were reported by only two of the sixteen interview partners. This suggests that further efforts are needed to integrate telemetry data more systematically into operational farm management and decision-making processes beyond routine administrative tasks.\u003c/p\u003e \u003cp\u003eSecond, the study highlights the importance of interoperability between machinery manufacturers and digital platforms. Many interview participants reported substantial difficulties arising from fragmented telemetry ecosystems, incompatible data formats, and the absence of standardized interfaces. These issues were identified as the most frequently cited weakness of the technology. Improving interoperability and developing more open data architectures could therefore significantly increase the practical value of telemetry systems, particularly for farms operating heterogeneous machinery fleets or cooperating with contractors.\u003c/p\u003e \u003cp\u003eThird, the results underline the relevance of training and advisory services. Although the basic technical infrastructure for telemetry use is already available on most farms, effective utilization beyond standard applications requires additional digital competencies and a better understanding of the analytical possibilities these systems offer. Several participants noted a lack of expertise among dealers and service providers, which delays support and limits adoption depth. Targeted training programs, not only during initial implementation but also after an introductory period, and improved advisory support could facilitate a deeper and more efficient use of telemetry technologies.\u003c/p\u003e \u003cp\u003eFourth, the introduction of telemetry technology should be accompanied by transparent internal communication. The interviews revealed that real-time data transmission was perceived by some employees, particularly older workers, as a form of surveillance, leading to resistance and, in individual cases, to resignations or threats thereof. Farms that addressed these concerns proactively and communicated the purpose of data collection, traceability rather than permanent monitoring, reported a gradual reduction in tensions. This suggests that change management processes deserve explicit attention when implementing telemetry systems.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.4.2 Research implications\u003c/h2\u003e \u003cp\u003eThe findings also point to several directions for future research. First, the observed discrepancy between high performance expectations and the limited ability of farmers to quantify actual benefits warrants further investigation. Quantitative studies could examine whether this gap reflects measurement difficulties on the part of the farmers, an actual shortfall in realized benefits, or a combination of both. Second, this study focused exclusively on farms already using telemetry technology. Investigating farms that have consciously decided against adoption would provide complementary insights into rejection factors and perceived barriers. Third, the influence of farm size and organizational structure on adoption depth, from basic monitoring to comprehensive data-driven management, could not be systematically assessed with the present sample and merits dedicated quantitative analysis with larger and regionally more diverse samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.4.3 Limitations\u003c/h2\u003e \u003cp\u003eThe findings of this study should be interpreted in light of several limitations. First, the analysis is based on a relatively small sample of 16 interview partners from German agricultural businesses with focus on the northern regions of Germany. Although the qualitative design allows for in-depth insights into farmers\u0026rsquo; perceptions and experiences with telemetry technologies, the results cannot be considered statistically representative for the entire population of German farms.\u003c/p\u003e \u003cp\u003eSecond, the study relies on qualitative expert interviews and a qualitative content analysis. While this methodological approach enables a detailed exploration of complex attitudes and decision-making processes, it inherently involves a certain degree of subjectivity in the interpretation and coding of interview data.\u003c/p\u003e \u003cp\u003eThird, the coding process was conducted by a single researcher. Consequently, intercoder reliability could not be assessed, which may limit the robustness and reproducibility of the categorization process.\u003c/p\u003e \u003cp\u003eFinally, the study focuses on the current stage of telemetry implementation on farms. As digital technologies in agriculture continue to evolve rapidly, the perceived benefits, barriers, and usage patterns may change over time.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Conclusions","content":"\u003cp\u003eThis study examined the factors influencing the adoption and use of telemetry technology among German arable farmers using a qualitative application of the UTAUT framework. Based on sixteen expert interviews, the results indicate that telemetry data are already used regularly on the surveyed farms. However, their application remains largely concentrated on administrative functions, particularly work monitoring, documentation, and, on larger farms, fleet management. While most farmers recognize the broader potential of telemetry data for operational optimization and data-driven decision-making, these possibilities have so far been only partially realized in practice.\u003c/p\u003e \u003cp\u003eA central structural barrier identified in the study is the limited interoperability between manufacturer-specific telemetry platforms. The resulting data fragmentation and the need to operate multiple parallel systems create additional complexity and costs, particularly for farms with heterogeneous machinery fleets. At the same time, farmers generally associate telemetry technology with high expectations regarding efficiency gains and potential cost reductions. However, concrete and quantifiable economic benefits remain difficult for most farmers to demonstrate. This discrepancy between high expectations and limited measurable outcomes represents an important finding and highlights the need for further empirical investigation.\u003c/p\u003e \u003cp\u003eSocial influence was found to play only a minor role in adoption decisions, which were primarily driven by individual initiative or linked to the acquisition of new machinery. Nevertheless, internal social dynamics following implementation, especially employee resistance related to perceived surveillance, emerged as a relevant factor that may affect the depth of technology use within farms.\u003c/p\u003e \u003cp\u003eOverall, the findings suggest that telemetry technology in German agriculture has progressed beyond the stage of initial adoption but has not yet reached a phase of comprehensive utilization. Unlocking the full potential of these systems will likely require improved data interoperability, targeted training and advisory services, and a clearer empirical evidence base regarding the economic benefits of telemetry-based farm management.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eWe thank all interview partners for taking part in the interviews and Hannes Meyer for his support.\u003c/p\u003e \u003cp\u003eMarius Michels acknowledges financial support by the German Research Foundation (DFG).\u003c/p\u003e \u003cp\u003eThis research was partially supported by funds of the Federal Ministry of Food and Agriculture (BMLEH) based on a decision of the Parliament of the Federal Republic of Germany. 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Agric Syst 153:69\u0026ndash;80. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agsy.2017.01.023\u003c/span\u003e\u003cspan address=\"10.1016/j.agsy.2017.01.023\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Telemetry, German Farmers, UTAUT, Qualitative Analysis, Precision Farming, Smart Farming","lastPublishedDoi":"10.21203/rs.3.rs-9455683/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9455683/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eTelemetry systems enable the continuous transmission of machine-generated data and are an important component of digital agriculture. Despite their technical potential, empirical evidence on the practical use of telemetry in agriculture remains limited. This study investigates the adoption of telemetry technology among German arable farmers using the Unified Theory of Acceptance and Use of Technology (UTAUT). The analysis is based on sixteen semi-structured expert interviews with farmers and agricultural contractors conducted in autumn 2024, evaluated through qualitative content analysis. Results show that telemetry is primarily used for administrative tasks such as work monitoring, automated documentation, and fleet management. While farmers associate telemetry with improved efficiency and reduced workload, measurable economic benefits are rarely quantified. A major barrier is the limited interoperability between manufacturer-specific platforms, leading to fragmented data environments and additional costs. Improving interoperability and strengthening advisory support may be crucial for realising the full potential of telemetry-based farm management.\u003c/p\u003e","manuscriptTitle":"Telemetry in digital agriculture: Adoption drivers and interoperability barriers among German farmers","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-21 08:35:14","doi":"10.21203/rs.3.rs-9455683/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4df35fa5-9c0c-406f-b491-e1db362fba24","owner":[],"postedDate":"April 21st, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66567036,"name":"Agricultural Engineering"}],"tags":[],"updatedAt":"2026-04-21T08:35:14+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-21 08:35:14","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9455683","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9455683","identity":"rs-9455683","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}