Digitalization and skills in agriculture

Abstract

The effects of digital agriculture on the knowledge and skills are widely debated. This exploratory literature review disentangles “digital agriculture” and explores its impacts from five perspectives: (1) the farmers, (2) the farm, (3) the farm workers, (4) the agricultural value chain, and (5) the agricultural knowledge and innovation systems (AKIS). Taking these interrelated but distinct perspectives allows for a holistic exploration of broader implications, including how impacts on knowledge and skills reshape power dynamics along value chains. Disentangling “digital agriculture” shows that the digitalization–skills nexus is as relevant in the Global North and Global South. Moreover, it shows that sweeping statements and generalizations about knowledge and skill effects can be simplistic and misleading, as the impacts depend on several factors: the capabilities of the specific form of digital agriculture in terms of sensing, analytics, and automation; its design, specifically, whether it positions users as passive recipients or active participants; the underlying business model, which is linked to who developed the tool; the digital proficiency and literacy of users; social and environmental contexts; and broader institutional framework conditions. Studies tend to emphasize either the risks of deskilling (e.g. erosion of experiential knowledge, reduced decision-making capacities over time) or the opportunities for reskilling and upskilling (e.g. access to new data, powerful analytical aids, learning opportunities) depending on whether they take a more constructivist perspective, which highlights the importance of experiential, traditional knowledge, or positivist, objectivist perspective, which values scientific approaches to knowledge. While valuable empirical studies exist, there is a need for further research, including quantitative studies. There is a need for policies and regulations as well as efforts within AKIS to promote digital proficiency and literacy, and initiatives related to responsible innovation and emancipatory smart farming, to ensure that digital agriculture contributes to skilling.

Introduction

Certain periods in agricultural history were associated with such transformative changes that they are referred to as “agricultural revolutions” as they fundamentally reshaped the nature of agricultural production—and the knowledge and skills associated with it. Many argue that the rise of digital agriculture—that is the use of digital data to support decision-making and action—marks the beginning of a new revolution (e.g. Birner et al., 2021; Rose and Chilvers, 2018; Shepherd et al., 2020). In its most advanced form, digital agriculture combines data sensing, analytics, and automation, giving rise to terms such as Agriculture 4.0, smart farming, or digital factory farming (Birner et al., 2021; Prause, 2025; Wolfert et al., 2017). In images of such farming systems, farmers, farm managers, and farm workers are often relegated to field margins or offices, where they interact with farm management software on tablets or computers (Prause, 2021). This raises critical questions about how digital agriculture will impact their knowledge and skills, as well as the broader implications of this (Ingram et al., 2022; Klerkx et al., 2019).

The impacts of digital agriculture on knowledge and skills are widely debated (Hackfort, 2021; Zscheischler et al., 2022). Critical scholars argue that digital agriculture—particularly (but not only) in its most advanced forms—threatens to lead to an erosion of farmers’, managers’, and workers’ experiential knowledge and skills such as those related to decision-making (e.g. Brooks, 2021; Carolan, 2020; Moschitz and Stolze, 2018). In contrast, other scholars emphasize that agricultural history is full of examples of reskilling and upskilling in response to technological change. In this view, digital agriculture may lead to a loss of some forms of knowledge and skills, but also require new skills related to the use of digital technologies (e.g. Goller et al., 2021), improve managerial and strategic skills by improving access to data and analytical aides (e.g. Evans et al., 2017; Ingram and Maye, 2020), and create opportunities for learning and creative thinking (e.g. Evans et al., 2017; Ogunyiola, 2024).

There is a deeper political dimension of this debate. Digital agriculture has the potential to reshape power dynamics on farms and along value chains by influencing who has which capabilities and which types of knowledge matter, and who is in control (e.g. Hackfort, 2021; Legun et al., 2023; Prause, 2021, 2025). This concern connects to a long-standing debate in the social sciences: the deskilling debate (see introduction to this special issue). Braverman (1974) argued that new technologies often break complex tasks into smaller, simpler parts—thereby reducing workers need for specialized knowledge and skills as well as autonomy and shifting expertise from workers to machines and managers. The deskilling thesis has been widely debated since, with arguments and evidence for deskilling as well as reskilling and upskilling (Heisig, 2009). While much of the debate has focused on industrial contexts, there is a significant literature on agricultural deskilling and skilling, with examples from both the Global North and Global South (e.g. Flachs and Stone, 2019; Marshak et al., 2021; Stone, 2007). Authors such as Prause (2021, 2025) argue that digitalization—following several waves of industrialization, commodification, and farmland simplification related to the evolution of biotechnology and mechanization—transforms farms into digital factories. From this perspective, digital agriculture’s effects on knowledge and skills could alter the relationships between farmers and workers—and between farmers and value chain actors that offer digital agriculture products (i.e. agricultural machinery and input companies).

While there is growing recognition of the need to understand the impacts of digital agriculture on knowledge and skills (e.g. Klerkx et al., 2019), research on this topic has been slow to develop (e.g. Carolan, 2020; Ingram and Maye, 2020) and the empirical evidence base remains limited (Hackfort, 2021) and/or biases toward the most advanced forms of digital agriculture (Ingram and Maye, 2020). As such, open questions remain on in what forms and under what conditions digital agriculture leads to de-skilling (reducing the specialized knowledge and skills needed to perform a job), re-skilling (changing the type of knowledge and skills needed), or up-skilling (improving knowledge and skills).

Drawing on conceptual considerations and the emerging literature, I analyze how different types of digital agriculture tools affect knowledge and skills of farmers, farm managers, and workers. For this, I first unpack the concept of digital agriculture, which is often used as a broad, catch-all category. This shows that there are significant variations in the capabilities of different types of digital agriculture, translating to distinct impacts on knowledge and skills. I then look at knowledge and skills effects from five different perspectives: (1) the farmers themselves, (2) the farm as such, (3) the farm workers, (4) the agricultural value chain, and (5) the agricultural knowledge and innovation systems (AKIS). These perspectives are interrelated but also distinct in important ways, and together they allow for a more holistic analysis and better exploration of potential implications.

Disentangling digital agricultures

Digital agriculture is an umbrella term encompassing a wide range of technologies (Birner et al., 2021). While some scholars define digital agriculture by emphasizing its most advanced forms—those that integrate data sensing, analytics, and automation—this risks overlooking forms of digital agriculture in less mechanized farming systems, particularly in the Global South. As a first step, it is therefore useful to distinguish between embodied and disembodied digital agriculture.

Embodied digital agriculture refers to technologies that are physically integrated into farm machinery or equipment and directly interact with the farm environment by collecting data from it (using sensors) and/or performing physical actions in it (using actuators). Examples include autonomous tractors, weeding robots, and sensor-based irrigation systems. Disembodied digital agriculture consists of software-based tools such as advisory apps and farm management systems. In non-mechanized farming systems, some forms of embodied digital agriculture can be difficult to use (e.g. Daum et al., 2022; Njuguna et al., 2025). Many forms of digital agriculture combine embodied and disembodied elements.

Digital agriculture can be applied at various levels. Some forms focus on specific farming activities such as weeding or pest and disease control (e.g. weeding robots or smartphone applications such as Plantix that help farmers to diagnose pests and diseases). Other forms focus on all activities related to a particular crop or field (e.g. RiceAdvice by AfricaRice or FIELD MANAGER by agro-chemical giant BASF). At the broadest level, farm management systems such as AGRIVI 360 Farm Insights and PTx Trimble focus on the whole farm (Tummers et al., 2019).

While digital agriculture always involves the use of digital (non-analog) data, its capabilities vary (e.g. Birner et al., 2021; Daum et al., 2022). The “sense–analyze–act” cycle is often used to explain the fundamental principles of digitalization and automation, and this cycle also serves as a useful starting point for classifying digital agriculture, as different tools place varying emphasis on and employ different methods for data sensing (collection), data analytics (processing and interpretation), and action (implementation, such as in automation). ¹

Degree and type of data collection

Some forms of disembodied digital agriculture require no or limited farm-level data such as apps that provide farmers with market price updates or weather forecasts. Those tools can be used in non-mechanized farming systems, where the use of embodied sensors is challenging, however, they often provide generic, non-tailored information only (Birner et al., 2021; Daum et al., 2022). To address this, some tools try to use “humans as sensors”. For example, Indian farmers can obtain advise on how to optimize livestock production based on regularly, manually entered data (e.g. on milk yields, feeding regimes, animal health) using Farmtree (Daum et al., 2022). In many cases, digital agriculture does involve the use of remote (e.g. satellite imaging) and/or proximal sensors (e.g. sensors embedded in farm machinery). There are examples of sensor-based digital agriculture in non-mechanized farming systems such as AfriScout, which advises pastoralists on grazing routes using satellite data, and SoilScanner, which provides recommendations on soil fertility management using soil scanners in Kenya (Machado et al., 2020; Njuguna et al., 2025). However, the use of sensors is most widespread in mechanized farming systems and indoor systems (i.e. greenhouses, barns). In such systems, a variety of sensors can be used to continuously collect real-time data, leading to “big data” in terms of volume, velocity, variety, veracity, and valorization (Kamilaris et al., 2017). These data can be used for monitoring (e.g. of soil parameters or machinery or worker performance), controlling (to remotely adjust operations), optimization (to improve performance), and automation (Porter and Heppelman, 2014; see below).

Degree and type of analytics

From the farmers’ perspective, digital agriculture is associated with decision support systems, typically in the form of apps or computer software. Decision support tools can focus on different farm levels (see above). At the farm level, they are referred to as farm management software or information systems (Tummers et al., 2019). Decision support can be more “passive” or “descriptive” (e.g. in the form of maps, reports, or alerts based on remote or proximal sensors monitoring fields, pastures, animals, and machinery) or more “active”—diagnosing problems, predicting possible futures, and suggesting—or prescribing—ways for optimization (Daum et al., 2022). Farm management systems are not new but today's systems can draw on internal and external data collected through hyper-connected systems in real time, and have higher analytical capabilities using computer modeling, machine learning, or artificial intelligence (Evans et al., 2017; Ingram and Maye, 2020).

Degree and type of automation

Some forms of digital agriculture integrate data sensing, analytics, and automation, allowing them to make and execute decisions with minimal or no human intervention. Porter and Heppelmann (2014) distinguish between smart products, smart product systems, and systems of systems. Examples of smart products are autonomous tractors with pesticide sprayers that detect weeds via sensors and automatically adjusts spray valves, or a weeding robot. While smart products are often linked to precision farming (Wolfert et al., 2017), they can also be used for broadcasting inputs (Daum et al., 2022). Smart product systems refer to multiple smart products working together within an interconnected system. Systems of systems take this integration further, connecting smart products (or systems) with a farm management system to optimize operations using internal and external data. For example, an automated barn may integrate milking robots, automated feeders, and climate control systems, all connected to a farm management system that also considers external data such as market trends.

Disentangling knowledge and skills

Most definitions suggest that knowledge is theoretical (knowing what), while skills are practical (knowing how), and that the mastery of both is expertise or competence. Thornhill-Miller et al. (2023) define skills as “the ability to solve problems in context and to perform tasks using appropriate resources at the right time and in the right combination” (p. 3). Lundström and Lindblom (2018) suggest that farmers must possess the “skills to know that action is required, know what to do, and also know how to do it” (p. 10). Pitt (2021) suggests that skills combine “thought and practice, as bodily abilities of ‘thinking through doing’” (p. 64).

Knowledge and skills can reinforce each other, and they overlap to some extent (Pitt, 2021). While “explicit” knowledge is theoretical, formal, and transferable, “tacit” knowledge is experiential, informal, and non-transferable—“it is in the minds of those who know” (Evans et al., 2017: 73)—and hence closely linked to skills. “Tacit” knowledge refers to the “intellectual or corporeal capabilities and skills that the individual cannot fully articulate, represent or codify … [it] is developed from direct experience, observation or interaction in which one largely learns by doing” (Williams and Baláž, 2008: 55). Over the years, farmers accumulate “knowledge from within” and become “experts on their own farm,” giving them the ability to make intuitive decisions (Evans et al., 2017; Lundström and Lindblom, 2018; Nuthall and Old, 2018; von Diest et al., 2020).

While knowledge is primarily gained through formal education (except for “tacit” knowledge), skills primarily develop through experiential learning in the forms of training, experience, and practice. Lundström and Lindblom (2018) suggest that farmers obtain skills by learning-in-action “through a kind of life-long longitudinal case study set-up,” for which “being embodied and situated in” is essential (p. 10). Brown and Duguid (1991) suggest that skilling processes involve “interpretive sense-making, congruence finding, and adapting” (p. 53) in interaction with the physical world (e.g. environments, materials). Brooks (2021) highlights the social nature of skilling in agriculture, describing it as “a dynamic, hybrid, group process integrating environmental and social learning” (p. 387).

There is a debate about the relative importance of knowledge and skills and their different forms (Higgins et al., 2017; Legun et al., 2023; von Dienst et al., 2020). von Dienst et al. (2020) argue that the dominant paradigm in agriculture focuses on explicit, formal, and transferable knowledge. This is linked to positivism, which means relying on scientific methods, experiments, and data—much like physics or engineering. In contrast, they argue, scholars adopting a more constructivist paradigm that is linked to experience and traditions emphasize tacit, informal, and experiential knowledge. Likewise, Legun et al. (2023) differentiate between technical and embodied expertise, the latter being “experiential, place-based knowledge held by a particular person,” which “accumulates, is tied to place, and is non-transferable” (p. 508). Sørensen et al. (2021) argues that agroecology-oriented farming requires different knowledge and skills than agro-industrial farming.

Farmers, farm managers, and farm workers require various competencies to support operative, tactical, and strategic decision-making and action at different farm levels: from diagnosing plant diseases at the crop level to selecting soil fertility management strategies at the field level and making investment decisions at the farm level. While farm workers require more hands-on, practical skills related to specific tasks, farmers and farm managers also need strong planning and management competencies (e.g. related to task management, budgeting, record-keeping, and making strategic investment decisions) (e.g. Lundström and Lindblom, 2018). To make and execute decisions, farmers, managers, and workers rely on both theoretical and practical knowledge related to agriculture (e.g. its biological, chemical, and technological aspects), alongside broader cognitive skills such as systems thinking and analytical reasoning, as well as non-cognitive competencies like communication and collaboration.

Impacts of digital agriculture on knowledge and skills

In the following, I examine the impact of digital agriculture from five distinct perspectives: farmers, farm, workers, agricultural value chain, and AKIS. The first three perspectives focus on how digital agriculture influences the specific knowledge and skills required by farmers, managers, and workers by changing the technologies they use to sense, analyze, and act (section “A farmer's perspective”), by affecting the nature of farming itself (section “A farm perspective”), and by altering how the relations between farmers, managers, and workers are organized (section “A worker's perspective”). The fourth perspective sheds light on the factors shaping the form of digital agriculture and its broader impacts, particularly its effects power dynamics within value chains by changing which types of knowledge and skills are valued and who controls them (section “A value chain perspective”). The fifth perspective identifies potential entry points for policy action aimed at fostering more responsible approaches to digital agriculture and complementary knowledge and skills building efforts (section “An AKIS perspective”). Together, these perspectives are crucial for understanding how to ensure that digital agriculture leads to reskilling or upskilling, rather than deskilling.

A farmer's perspective

One can link farmers, managers, and workers decision-making and action to the “sense–analyze–act” cycle (Evans et al., 2017; Ingram and Maye, 2020). Indeed, similar frameworks have been used in fields focusing on human decision-making and action such has the OODA (observe, orient, decide, act) loop in military combat studies and the “perception–action cycle” in cognitive psychology and neuroscience (Evans et al., 2017). After all, humans need knowledge and skills to perform activities—whether routine farm operations, management duties, or long-term planning activities—along a similar cycle, and as they engage with it, they continuously acquire and refine their expertise at each stage:

Sensing: Just like digital tools, farmers need the ability to obtain relevant information. For this, they can draw on sources related to explicit forms of knowledge and “hard” data from their fields (e.g. soil reports) or on more tactic forms of knowledge arising from embodied experience while in the field.

Analyzing: Farmers need to be able to process and interpret this information and make informed decisions. Decision-making can be based on more structured, analytical reasoning or be more intuitive and experience-based and can include others such as peers and extension agents.

Acting: Farmers need to be able to implement decisions. This stage is linked to renewed sensing as observing the outcomes from previous actions generates new insights.

As digital agriculture increasingly affects the sense–analyze–act cycle, it inevitably reshapes what knowledge and skills are required—and farmers’, managers’, and workers’ modes of obtaining them.

Sensing

Given that many forms of digital agriculture emphasize (automated) data collection through sensors and reduce the need to be physically present in the field or the barn, scholars have raised concerns that these technologies undermining experiential, place-based learning and skilling (e.g. Heijting et al., 2011; Moschitz and Stolze, 2018; Zscheischler et al., 2022). However, digital agriculture can take different forms, with different emphasis on sensing.

Some forms of disembodied digital agriculture are likely to have limited impact on farmers’ presence and their sensory engagement with the physical world. This includes tools that do not use farm-specific data such as those providing market price updates (weather forecasts are a potential exception). Next, there are tools that use “humans as sensors,” which are unlikely to affect farmers’ presence in the field but may alter their sensory engagement. For example, Plantix requires farmers to take pictures with their smartphone cameras and SoilScanner requires them to take soil samples. While these tools require farmers to be in the field or barn, there are some concerns that they may affect the need and ability to read “natural” indicators—at least in the long term (e.g. Butler and Holloway, 2016; Hansen, 2015).

Embodied digital agriculture relies on remote and proximal sensors, and as farmers manage their farms from greater distances using sensor data, their physical presence in the field and sensory engagement might decline (Carolan, 2020). MacPherson et al. (2025) suggest that “digital tools may distance farmers from the hands-on aspects of their work, contributing to ‘de-skilling’” (p. 3) and Ingram and Maye (2020) speculate that they might reduce “the opportunity for observational knowledge which contributes to experiential learning” (p. 3). Physical presence and sensory engagement are likely to be most impacted by forms of digital agriculture involving automation, as evidenced by research on milking robots (e.g. Butler and Holloway, 2016; Hansen, 2015).

Several studies suggest that sensors do not necessarily replace human sensing but rather complement it. For example, farmers may deepen their understanding by seeing, feeling, and smelling soil—while also drawing on insights from digital soil maps and scans (e.g. Wolfert et al., 2017). In a case study on a decision support system in Sweden, Lundström and Lindblom (2018) explore “tool-mediated seeing” (Goodwin and Goodwin, 1996), arguing that a farmer can integrate “internal structure” (prior knowledge based on situated, embodied experience) with an “external structure” (in this case satellite-based soil maps), allowing him to “handle the variations that he is aware of, but unable to fully perceive with his eyes only” (p. 14). In cases where traditional, tacit knowledge has been lost, sensor-based agriculture may compensate or help to revive it. Similarly, such technologies may become essential when experiential knowledge loses relevance due to rapid changes such as the climate crisis.

Analyzing

Many forms of digital agriculture are designed to support decision-making, from operational and tactical decisions at the crop and field level to managerial and strategic decisions at the farm level. There is ongoing debate over whether some tools have crossed the fine line between supporting and replacing human decision-making.

Various digital tools aim to enable farmers to discuss with peers and advisors. These are likely to enhance farmers’ decision-making capabilities. In fact, farmers have always engaged in discussions with others, what is new is the ability to share digital data—such as maps, images, and videos—and communicate more easily across time and space (Khan et al., 2025). Hackfort (2021) presents the example of WeFarm, which enables farmers to “share questions, information and advice, and might thus strengthen local agricultural knowledge rather than marginalize it” (p. 12). Farmers may use social media platforms, such as Facebook or WhatsApp groups, for example to share images of plant diseases, fostering discussions on diagnosis and treatment. Such tools can facilitate the exchange of place-based, experiential knowledge and enable social learning (Brooks, 2021; Khan et al., 2025; Prause et al., 2021).

A wide range of tools support farmers’ decision-making in passive ways by providing generic (e.g. market price updates, weather reports) or farm-specific information (e.g. soil tests and maps), thereby enhancing their situational awareness and understanding of their production sites (Evans et al., 2017; Goller et al., 2021; Wolfert et al., 2017). For instance, AfriScout provides pastoralists in Kenya with vegetation and water maps to aid in grazing decisions but does not suggest specific grazing routes (see section “Disentangling digital agricultures”). Such tools are referred to as “descriptive” tools as they mainly provide information—potentially more, easier, faster, better (Ingram and Maye, 2020). Some scholars argue that the reliance on digital information may displace traditional, analog forms of knowledge, while others contend that it can supplement or even strengthen it. There is no clear empirical evidence for either perspective (Evans et al., 2017; Ingram and Maye, 2020; Prause, 2021).

There are also tools that offer more “active” decision support by diagnosing problems, presenting alternative scenarios, suggesting optimization strategies, or even prescribing solutions (Daum et al., 2022). For instance, Plantix helps farmers identify pests and diseases, while Fodjan provides recommendations for optimizing livestock feeding (Bateki et al., 2021). There is ongoing debate over whether such tools support or risk replacing human decision-making. Some scholars argue that the reliance on such tools may gradually erode farmers’ knowledge and skills, such as reading natural indicators and making intuitive decisions, as they become increasingly accustomed to merely executing recommendations (e.g. Brooks, 2021; Carolan, 2020; Hackfort, 2021; Zscheischler et al., 2022). Hackfort (2021) warns that “the increasing reliance on technical experts and technology may result in a loss of tacit knowledge if the cognitive processing of information is delegated to machines or algorithms” (p. 11). However, while possible, there is little empirical evidence for this (Prause et al., 2021).

In fact, some scholars also argue that such tools may enhance farmers’ knowledge and decision-making skills, as users must still interpret, assess, and adapt the advice provided, or as they use it for exchange and learning (Goller et al., 2021; Ingram and Maye, 2020; Lundström and Lindblom, 2018). The impact likely depends on whether the tools position farmers as passive recipients of pre-packaged solutions or empower them to develop their own (e.g. Evans et al., 2017; Hackfort, 2021). For example, plant health apps can either strengthen or weaken farmers’ diagnostic skills and knowledge of plant health management. Tools that provide background information on pests and diseases, inform about integrated management strategies, and encourage the integration of farmers expertise are more likely to enhance knowledge and skills than those that offer prescriptive recommendations such as suggesting the most effective pesticide (Carolan, 2020, 2022).

Concerns are particularly pronounced regarding farm management tools (Carolan, 2020; Moschitz and Stolze, 2018; Zscheischler et al., 2022). Moschitz and Stolze (2018) caution that “the use of digital technologies for management decisions in individual branches and in the entire management system can lead to losing previously existing knowledge, since the cognitive processing of information is delegated to machines or algorithms” (p. 7). In contrast, Ingram and Maye (2020) suggest that while digital agriculture tools might take over operational and tactical decisions, human expertise remains essential to critically assess the advice provided by such tools and for higher-level strategic decision-making. Several studies also suggest that, for now, farmers do not use these tools in deterministic ways but rather as “learning tools” and as a nucleus that facilitates discussion with peers and advisors (Ingram and Maye, 2020; Lundström and Lindblom, 2018). Evans et al. (2017) suggest that “learning-oriented DSS can satisfy the need for autonomy and competence by providing the opportunity for independent decision-making and a means to master certain personal skills and knowledge” (p. 81). In turn, others believe that relying on such tools may nevertheless be a slippery slope: while farmers may critically deal with them at first based on existing expertise, this may gradually change over time, something that Zscheischler et al. (2022) refer to as a “use it or lose it problem.” Hybrid intelligence, where humans remain in the loop, has been proposed to address potential negative effects on farmers’ knowledge and skills or even foster upskilling (Rafner et al., 2021).

Acting

The highest level of capability in digital agriculture is autonomy, where automated systems take over farmers’ decisions and actions. However, it is important to recognize that automation can target different levels (see above). Smart products focus mainly on the farming activity level (e.g. an autonomous tractor, a weeding robot), smart products systems on the field level (e.g. automated barns and greenhouses), and systems of systems on the farm level (e.g. farm management system linked to smart products and product systems).

Smart products are likely to primarily impact knowledge and skills at the operational level (Carolan, 2020; Ingram and Maye, 2020; Legun et al., 2023)—although there could also be trickle up effects to higher levels. For instance, automated tractor guidance and steering systems or automated pesticide sprayers alter the skill sets required by machinery operators. Fully autonomous robotic solutions can eliminate farmers’ physical presence in the field or barn, which can affect sensory engagement and action-feedback learning (e.g. Butler and Holloway, 2016; MacPherson et al., 2025). However, studies on livestock farming—one of the most automated agricultural sectors—also indicate that automation at the activity level (i.e. milking, feeding) can relieve farmers, managers, and workers of labor-intensive routine tasks, many of which offer limited experiential learning. This, in turn, may create opportunities for deeper sensory engagement or more “meaningful work requiring creativity or other kinds of cognitive capacity” (Goller et al., 2021). Ingram and Maye (2020) argue that automation frees farmers mainly from work at the operational and tactical level, allowing them to upskill and focus on a “higher intelligence level.”

While smart products and product systems primarily impact operational and tactical knowledge and skills, systems of systems that integrate farm management tools could also affect competencies rated to the “higher intelligence level”. Legun et al. (2023) suggest that these systems could fundamentally reshape farmers’ roles, as “the farm space could be represented digitally and managed through AI”. Over time, this shift could have profound implications at the managerial and strategic level (e.g. Legun et al., 2023; Zscheischler et al., 2022). In this case, farmers would mainly need technical competencies such as data proficiency and literacy to effectively control and monitor digital agriculture as well as more office-related competencies such as on legal and marketing aspects (e.g. Carolan, 2020; Goller et al., 2021; Legun et al., 2023; Rotz et al., 2019b).

A farm perspective

In its most advanced forms, digital agriculture may drive a shift from viewing the farm as a living, analog space to perceiving it as a collection of abstracted data points and flows (e.g. Higgins et al., 2017; Zscheischler et al., 2022). Scholars whose thinking is rooted in constructivist thinking tend to highlight the challenges—or impossibility—of abstracting farming into digital models, given the importance of situated, experiential knowledge (e.g. Butler and Holloway, 2016; Lundström and Lindblom, 2018). From this perspective, digitalization—where attempted—leads to a loss of place-based and embodied knowledge and skills (Legun et al., 2023; Moschitz and Stolze, 2018) and reinforces simplistic, productivist farming systems as opposed to more agroecology-oriented approaches, with further implications on knowledge and skills (Hackfort, 2021; Higgins et al., 2017; Legun et al., 2023; Rotz et al., 2019a). Prause (2025) argues that precision farming represents “an attempt to monitor, control, and homogenize nature in industrial farming” (p. 10), and robots may also contribute to homogenization (e.g. Daum, 2021). According to Prause (2025), this simplification reduces complexity—along with the need for embodied expertise—leading to deskilling.

Scholars rooted in objectivist, scientific thinking, which emphasizes the importance of explicit, abstract, and transferable knowledge, often view digital agriculture more positively: tacit knowledge and skills are simply converted and integrated into digital formats or can be replaced (Legun et al., 2023). Ingram and Maye (2020) suggest that farmers can transition from making decisions based on experience to making them based on data. Similarly, Legun et al. (2023), in their discussion on various future visions of digital farming, describe one scenario in which “the farm space could be represented digitally and managed through AI”.

There are also several studies that suggest that digital agriculture is less about the substituting and more about integrating analog and digital farm worlds, hence arguing that both embodied and technical expertise is required (e.g. Goller et al., 2021; Legun et al., 2023). Lundström and Lindblom (2018) suggest that analog and digital representations of fields can supplement each other. Hackfort (2021) also points out that digitalization and agroecology are not antagonistic per se, referring to the emerging literature on “digital agroecology,” which emphasizes co-construction of knowledge. Similarly, Fraser (2022) highlights the potential of “emancipatory smart farming”.

A worker's perspective

Workers are at the core of the traditional “deskilling debate”, which focuses on whether technological change affects the specialized knowledge and skills needed by workers by simplifying tasks (see the “Introduction” section and introduction to special issue). Several scholars have argued that digitalization necessitates a fresh look at the deskilling debate, with Prause (2021) being the first to do so regarding agriculture. On many farms, particularly family farms and smallholder farms in the Global South the distinction between farmer (as management) and worker (as labor) is not very relevant. But workers are central to some types of agricultural production in both the Global North and Global South, particularly in the cultivation of high-value, labor-intensive crops such as fruits and vegetables. However, the high value and labor intensity of these crops—and the nature of their cultivation, often in controlled environments like greenhouses and barns—are all strong drivers for automation (e.g. Daum, 2021).

Prause et al. (2021) argue that digital agriculture could trigger deskilling processes. They contend that digital agriculture tools such as management platforms could increase “workplace surveillance, control and worker deskilling, as well as of measurement, standardization and quantification of work” (p. 649, referring to Altenried, 2020). Several studies indicate that digital agriculture is indeed used to monitor workers. Prause et al. (2021) highlight a tool developed by agricultural machinery manufacturer John Deere that enables the tracking of farm machinery and, by extension, its operators. Additionally, Prause (2025) notes that tools for supply chain traceability—often mandated by downstream actors—can also contribute to surveillance and standardization, reducing knowledge and skills need.

However, while various studies raise theoretical and conceptual concerns about potential Braverman-type deskilling, empirical evidence is limited (e.g. Goller et al., 2021; Hackfort, 2021). Based on interviews conducted with seasonal and permanent farm workers in Germany, Prause (2021) did not find support for the claim of widespread deskilling (which she suggests might be due to the technologies used). Instead, workers appeared to acquire additional skills related to handling digital tools. Similarly, other studies suggest that digital agriculture may lead to a smaller yet more skilled workforce—one that needs knowledge and skills on how to operate digital systems and is able to integrate embodied expertise into digital decision-making and action (Legun et al., 2023; Rotz et al., 2019a).

A value chain perspective

Digital agriculture is developed by a range of actors, each with distinct objectives, which can shape business models, tool design, and user roles. Most tools are provided by companies that manufacture agricultural inputs such as seeds, fertilizers, and pesticides (e.g. BASF, Bayer, Syngenta) or agricultural machinery (e.g. AGCO, John Deere). These companies increasingly also form joint ventures to enhance their ability to obtain big data (Birner et al., 2021). Digital agriculture is also developed by tech companies new to agriculture (e.g. Google) and private start-ups (Birner et al., 2021). Moreover, the public sector (e.g. public extension) and farmers themselves can develop tools—either individually (e.g. Rotz et al., 2019b) or collectively, e.g. in the form of data cooperatives (e.g. Fraser, 2022; Hutchins and Hueth 2023).

There are concerns that effects on knowledge and skills at the farm level will also reshape power dynamics within agricultural value chains (Bronson, 2022; Carolan, 2020; Hackfort, 2021; Higgins et al., 2017). Echoing Braverman, digital agriculture may substitute farmers’ and farm managers’ embodied expertise with capital—in the form of digital technologies controlled by large agribusinesses. As noted by Birner et al. (2021), these companies increasingly shift from input-based business models (e.g. selling herbicides) to service-based models (e.g. providing solutions for “weed-free fields”).

Prause (2021) wonders whether agribusiness and technology companies will increasingly dictate farming practices through proprietary software and algorithms. Brooks (2021) even suggests that farmers may evolve into “cyborg” farmers—“human enough to continue farming (…) while sufficiently ‘non-human’ to function as a reliable market subject and data transmitter in a nudge world shaped by ‘sensors, devices, software, and data flows’” (p. 390, referring to Fraser, 2019). Likewise, Carolan (2020) warns of technological lock-ins as prescriptive digital tools systematically erode analog knowledge, creating a vicious cycle in which farmers become ever more dependent on digital agriculture and the companies behind. Clapp (2025) asks whether farmers could ultimately become “contract workers on their own land” (n.a.).

However, many of these concerns are speculative and reflect Wild West-type scenarios that assume a lack of clear governance and regulatory frameworks. As discussed above, the impact on farmers’ knowledge and skills depends on the capabilities of digital agriculture and some digital tools might even enhance farmers’ expertise and strengthen their position within agricultural value chains—such as those providing information on input prices and quality or facilitating farmer-to-farmer collaboration (see section “A farmer's perspective”). Moreover, digital agriculture can be designed in ways that contribute to knowledge and skills development to empower farmers, as discussed above (Evans et al., 2017; Fraser, 2022).

The extent to which digital agriculture influences not only farmers’ knowledge, skills but also power dynamics depends also on governance structures—specifically, who develops and controls these tools, as well as policies and regulations on things like data sovereignty and market concentration (e.g. Birner et al., 2021; Bronson, 2019; Fraser, 2019; Prause et al., 2021; Rotz et al., 2019a). Complementary efforts from AKIS can also help to ensure that digital tools do not undermine farmers knowledge and skills and power (next section).

An AKIS perspective

Ultimately, whether digital agriculture empowers or disempowers farmers depends largely on how effectively AKIS rise to the challenge. ² Several authors argue that large investments in education and extension are needed to equip farmers, managers, and workers with the necessary knowledge and skills to thrive in a digitalized environment (Goller et al., 2021; Ingram and Maye, 2020; Moschitz and Stolze, 2018).

Farmers must be supported to develop at least a basic understanding of digital agriculture technologies, along with digital proficiency and literacy, to navigate digitalized environments in an informed and independent manner (Goller et al., 2021; Hackfort, 2021; Higgins et al., 2017; Moschitz and Stolze, 2018; Zscheischler et al., 2022). This requires both “hard” digital skills for example in data management and “soft” skills such as critical thinking (Hackfort, 2021; Rotz et al., 2019b; Zscheischler et al., 2022). These skills are also essential for farmers to organize or participate in alternative forms of digital agriculture such as data cooperatives (see section “A value chain perspective”). Even as digital agriculture advances, analog knowledge remains crucial—or even gains new importance—as a counterbalance to ensure that farmers can critically deal with digital agriculture, including decisions about where and when they choose to use it (e.g. Evans et al., 2017; Goller et al., 2021; Rotz et al., 2019b). Farm managers and workers also need to be supported with knowledge and skill-building efforts (Goller et al., 2021; Rotz et al., 2019b).

The research domain within AKIS can also help to ensure that farmers’ knowledge and skills are integrated rather than lost (Rijswijk et al., 2021). Several scholars argue that digital tools can facilitate knowledge exchange and continuous learning—provided they are designed with this goal in mind (Evans et al., 2017; Lundström and Lindblom, 2018). To ensure this, AKIS should foster the principles of responsible innovation and push developers to design tools that contribute to skilling (e.g. Eastwood et al., 2019b; Rose and Chilvers, 2018). These efforts should target both the development of tools by large companies and bottom-up, farmer-driven initiatives at regional and local levels (e.g. Rotz et al., 2019a). The later may play a key role in ensuring alternative forms of digital agriculture not linked to agro-industrial farming (Bronson, 2019).

Conclusion

Like past agricultural revolutions, the rise of digital agriculture is shaping farmers’ knowledge and skills. By disentangling “digital agriculture”, this explorative review shows that digital agriculture can take different forms, including both embodied and disembodied forms, making the digitalization–skills nexus as relevant for the Global South as the Global North. Moreover, it highlights that while digital agriculture must be accompanied by critical research, sweeping statements and generalizations about knowledge and skill effects can be overly simplistic and misleading, making it difficult to see actual opportunities and threats and ways to harness potentials and minimize risks. This review suggests that its effects depend on several factors, including the capabilities of digital tools in sensing, analytics, and automation; their design, particularly whether they position users as passive recipients or active participants; their underlying business model, which is closely linked to who develops and uses these tools; the extent to which users possess digital proficiency and literacy; and institutional framework conditions. Depending on these factors, digital agriculture can lead to all deskilling, reskilling, or upskilling.

In principle, the biggest threats to skilling come from the most advanced forms of digital agriculture that remove farmers, managers, and workers from the sense–analyze–acts cycles at the farm, reducing hands-on experiences and prescribing or taking over actions—particularly when applied at multiple farm levels. In contrast, tools that supplement actions along this cycle or are specifically designed for learning and exchange offer also unique potential for skilling. However, as noted above, the impact of digital tools depends heavily on their design and complementary efforts from the governance and AKIS perspective. For example, when designed responsibly with a focus on learning and hybrid intelligence, even advanced digital agriculture tools can lead to upskilling—especially when farmers are simultaneously empowered with critical digital and analog knowledge and skills.

The review suggests that scholarship on the effects of digital agriculture is partly divided into two camps: one grounded in constructivist thinking, which highlights the importance of experiential, traditional knowledge and one rooted in objectivist, positivist thinking, which values scientific approaches to knowledge. From the first perspective, digital agriculture is associated with risks of deskilling through the erosion of farmers’ experiential knowledge and a reduction in decision-making autonomy. From the second perspective, digital agriculture is instead associated with reskilling and upskilling. Neither perspective fully captures the reality of digital agriculture, which, in most cases—at least for now—involves an intertwining of the digital and analog worlds, a process in which both embodied and technical competencies are needed and potentially strengthen each other.

Thus far, qualitative studies have taken the lead exploring the impacts of digital agriculture. While these studies offer valuable insights, many studies primarily rely on theoretical and conceptual considerations or are based on perceptions and anticipations (Zscheischler et al., 2022). There are some excellent empirical studies using qualitative methods (e.g. Legun et al., 2023; Prause, 2021, 2025), but further case studies on a wider range of technologies and contexts are needed. Moreover, more quantitative research could help assess effects on a broader scale. However, it is important to recognize the likely challenges of quantitative studies, too, particularly in accounting for the complexities of digital agriculture such as the interplay between different technologies, the actual intensity of use, the influences of farmers prior knowledge and skills, the role of complementary efforts and contextual factors, the difference between short- versus long-term effects, and different forms or knowledge and skills.

Examining digital agriculture from five perspectives—farmers, farm, workers, value chain, and AKIS—has helped identify not only direct effects on knowledge and skills but also broader implications such as changing power relations along the value chain due to a potential shift of expertise from farmers to other value chain actors offering digital agriculture tools. This shows the need for clear governance and regulatory frameworks, particularly in areas like data governance and market concentration. Furthermore, at the AKIS level, investments are required to ensure digital proficiency and literacy, including both “hard” and “soft” digital skills (e.g. critical thinking). At the same time, efforts to build knowledge and skills related to traditional subject-specific expertise remain essential—and may even become more crucial. At the research side, supporting approaches linked to responsible digital innovation and hybrid intelligence are needed, potentially aligning with what Fraser (2022) calls “emancipatory smart farming.”