Digital agriculture offers the ability to utilize technology to convert precise data into actionable knowledge to drive and support complex decision-making on-farm and along the value chain. The promise is that, whilst past sources of knowledge were based on general knowledge often derived from research experiments, smart technologies will be able to offer on-farm, local-specific information to farmers (Poppe et al., 2015). As such, digital agriculture reflects a shift from generalized management of farm resources toward highly optimized, individualized, real-time, hyper-connected and data driven management (Van Es and Woodard, 2017).

Of the three pillars of digital agriculture: robotics, sensors, and Big Data analytics platforms, the latter is critical. The large amounts of data being currently generated on farms by, for example, yield monitors, are of little value unless they can be turned into useful decision support tools for farmers (Janssen et al., 2017; Weersink et al., 2018).

However, some scholars suggest that our capacity to collect large amounts of data outstrips our ability to convert it into usable information. Data analytics³ and decision support are fundamental for fully-enabled digital agriculture, but to date the interpretation and use of data from smart technologies is not matching expectations (Leonard et al., 2017; Weersink et al., 2018) and the capability to effectively analyse these data to achieve promised improvements is limited.

Whilst there is evidence of uptake of GPS technologies that simplify the work (e.g., auto-steer systems) or passively collect data (e.g., yield monitors) (Lowenberg-DeBoer and Erickson, 2019), they signify “embodied-knowledge technologies” (Griffin et al., 2017) that require no additional skills to capture their value; in other words, they rely on the knowledge that farmers already possess regarding how to operate their machinery. This is distinct from the information-intensive technologies which use data collected from the farm as input into a decision support system that generates a prescription for the variable inputs. This distinction, some argue, explains the low uptake of variable-rate (VR) technologies which require new skills and decision-making models compared to the widespread adoption of GPS automated steering systems, yield monitors, and grid soil sampling (Weersink et al., 2018). Barnes et al. (2019) for example in a recent European survey noted this distinction in adoption patterns. Capalbo et al. (2017) point to the many cases where VR application of nutrients continues to be based on simple rule-of-thumb or empirical approaches.

Overall, it is felt that the difficulty of constructing, maintaining, analyzing, and sharing such data limits the opportunity to derive effective decision rules with high information value to producers Weersink et al. (2018). Given this, there is still a heavy reliance on the user to interpret the data. Studies have also found an increased learning load for farmers from using digital agriculture tools and the need to invest in human capital (Van Es and Woodard, 2017; Eastwood et al., 2019).

Apart from the difficulty of providing decision support, farmers, advisers and researchers are finding it hard to manage, interpret, or make use of their data as a result of their volume and complexity (Van Es and Woodard, 2017). Typically farmers do not need high frequency and precise data for every decision (Robertson et al., 2018) and have limited capacity to deal with data complexity (Lioutas et al., 2019).

Despite these challenges, there are multiple examples of technologies available, from farm management software solutions (e.g., AGERmetrix and FieldView^TM in USA and Agrivi in UK) to decision support tools (e.g., FieldNET Advisor^TM in the USA) (Kamilaris et al., 2017; Saiz-Rubio and Rovira-Más, 2020), that show how analytic capabilities are advancing.

However, limited decision support continues to reduce farmers' ability to meet the new demands of digital agriculture and can present significant adoption hurdles (Pierpaoli et al., 2013; Knierim et al., 2018). Whilst we cannot characterize the complex implementation problems of digital agriculture as solely due to limited capabilities in analytics and data use (Lowenberg-DeBoer and Erickson, 2019), it is evident that the optimism for digital agriculture is not yet matched by analytic capability within the AKIS.

Although there is a suggestion that skilled agricultural workers have the highest probability of automation compared to other workers (Nedelkoska and Quintini, 2018), the extent to which this will support or replace decisions in farming depends on the technology. Sensors provide raw data (e.g., weather data), and smart devices (robotic vehicles, drone mounted cameras) will allow sophisticated farm management advice (Walter et al., 2017), while smart systems have the capability to execute autonomous actions (Budaev et al., 2019). For the former, human interpretive skills for decision making are still important, but for the latter the role of humans in analysis and planning is increasingly assisted by machines.

The nature and extent to which the human role shifts in the “sense–analyse–act” cycle in achieving actionable knowledge is debated. Whilst many agree that farmers' knowledge is not about to be replaced by algorithms, it is suggested that their involvement will be at a much higher intelligence level, leaving most operational activities to machines (Wolfert et al., 2017). This distinction between strategic and tactical action releases the farmer from mundane day-to-day monitoring although it also removes the opportunity for observational knowledge which contributes to experiential learning.

There is a perceived risk of increasing reliance on technical experts and the technology resulting in a loss of tacit knowledge if the cognitive processing of information is delegated to machines or algorithms (Jago et al., 2013; Shepherd et al., 2018). Arguably the farmers' experiential knowledge acquired over the years is at risk (Moschitz and Stolze, 2018). However, the opportunity for farmers to acquire a better knowledge of their production sites and thus gain greater certainty when making decisions increases (Rösch and Dusseldorp, 2007). The use of digital technologies, such as sensors for monitoring animal behavior, can arguably also replace the lost knowledge of older generations (Moschitz and Stolze, 2018). Furthermore, new systems are expected to support handling a higher complexity as well as an increased local adaptation which may be beyond individual experiential knowledge (Aubert et al., 2012).

More fundamentally, decision-making and experiential processing commonly applied on farm have been supported in the past with descriptive and diagnostic tools and models explaining what and why things have happened. Digital agriculture heralds an era where these learning opportunities will be potentially diminished, in which the “what is known” is prioritized over the “capacity to know.”

With respect to decision making, new sources of data are seen to create the opportunity to inform and drive a change in decision making from one that is typically characterized as being highly intuitive to one that is data driven and processed in real-time (Xin and Zazueta, 2016). This, many argue, requires a change in the mode of working for many farmers, transitioning from experiential decision-making to data-driven processes (Eastwood and Kenny, 2009; Nuthall and Old, 2018). However, in reality many farmers have been transitioning toward more data-driven decision-making processes for some time, integrating different information sources and drawing on different levels of analysis, using, for example, precision agriculture and DSS.

Insights from studies of DSS, reveal that they largely support, rather than replace, the decision maker; that farmers use DSS, not in a deterministic way to provide specific answers, but as learning tools (McCown, 2001; Baars, 2011; Lindblom et al., 2017). Experience with participatory design of DSS suggests that, a better appreciation of how farmers build tacit knowledge, the mind's store of decision rules and background information through repeated experience, may improve decision support for digital agriculture. In particular, understanding how this experiential processing can combine with analytical processing, where information is obtained through statistical description (Marx et al., 2007); Hansen et al. (2019), can help to overcome difficulties at the interface between data and decision-making. Working with farmers in developing technologies can also address the limited opportunities product developers have to ground truth information (Kamilaris et al., 2017). Although there are few examples yet of co-created digital technologies there is acknowledgment that farm management and information systems require a user-centric approach (Fountas et al., 2015; Van Es and Woodard, 2017).

Informal networks, between farmers and often including other actors, are one of main knowledge exchange mechanisms in farming communities which lead to learning and innovation (Leeuwis and Aarts, 2011; Ingram, 2015). The extent to which digital agriculture will disrupt or enable these network processes is an important consideration. Proposed smart systems, which promise to take and learn the best practices from advanced precise farmers, formalize and transfer their knowledge and support to other farmers in everyday decision making (Budaev et al., 2019) could arguably replace interpersonal networks. However, the potential for digital technologies to support collaborative knowledge creation has also been identified (Eastwood et al., 2012). ICT enables farmers to exchange information, benchmark their production against others, establish cooperation and peer review, and maybe even develop informal information systems that can complement more formal information systems (Wolfert et al., 2017). Many farmers have started to mobilize and organize themselves (e.g., in cooperatives, online communities) to create and share know-how, technologies and experiences, and big data understanding (Kamilaris et al., 2017; Carolan, 2018). Distributed sensing systems can form the basis for knowledge platforms for social learning (Robertson et al., 2018).

With respect to support services, farm advisers have always been important as interpreters of data and information. The digitalisation of expert knowledge into decision support tools or via artificial intelligence has the potential to disrupt advisory services and change the adviser's role (Wolfert et al., 2017). Digital agriculture tools can provide farmers with analytical power and access to information previously unavailable (Ayre et al., 2019). This may mean advisers need to reassess their capabilities, practices, services and skills as they respond to new demands (Eastwood et al., 2017; Rijswijk et al., 2018). They may also need to create new networks with technology providers and R & D (Lundström and Lindblom, 2018). Although there is a potential role for technology suppliers to take on greater advisory support for farmers and act as knowledge “translators,” they can often lack the farm systems expertise or knowledge networks to adequately support on-farm use (Eastwood et al., 2016). To address this problem, researchers propose a co-development approach for building the capability to use digital agriculture tools (Eastwood et al., 2019).

The emergence of new suppliers of equipment, software and services, business models and networks creates a new dynamic within the incumbent AKIS. The public and private sector generally operate together to establish a wide variety of data, knowledge and institutional arrangements that together constitute “a decision making infrastructure” that supports management in agriculture (Capalbo et al., 2017). This is evolving under digital agriculture with new disruptive entrants (e.g., digital technology companies) and various models of development and investment appearing, including new business models that challenge incumbent forms (Phillips et al., 2019). The changing roles of old and new software suppliers and the emerging landscape of data-driven initiatives, with the prominent role of big tech and data companies and research universities has been observed (Keogh and Henry, 2016; Van Es and Woodard, 2017; Wolfert et al., 2017). The dynamic between the new and established players is often framed by discussions of private-public data accessibility, ownership and governance (Bronson and Knezevic, 2016; Jakku et al., 2016); however, issues of knowledge are also key, and are redefining AKIS boundaries. The tensions and synergies between these new entrants and those in public bodies and universities are of particular interest. The former have limited understanding of agronomic principles but excellent market access, while the latter have the expertise and institutional learning which has provided the foundations for understanding the processes driving agricultural systems, through decades of experimental research and sophisticated modeling (enabling diagnostic understanding).

The opportunities for combining their different analytics are highlighted by Antle et al. (2017, p. 258) who point to synergies between the modeling community, which is strong on analytical capability, and the developers of user-related farm-level products. Harnessing big data and the analytical powers of models can also lead to what Capalbo et al. (2017) referred to as a virtuous circle which builds on both and will allow a new generation of models and decision support.

At a more fundamental level the arrival of new analytics raises the questions about knowledge and data-driven processes. Established R&D institutions applied analytical techniques associated with descriptive and diagnostic analytics which led to the effective application of what Sonka (2014) calls “Small Data.” An important question for Sonka is: “how can the best aspects of the Small Data system be linked to the application of Big Data technologies?” Whilst he acknowledges that knowing, at increasing levels of precision, “what” happened in the field or in animal facilities does have value, he argues that knowing “why” is also key to agricultural applications.

This shift in analytics also has the potential to significantly impact the AKIS in other ways, moving us from “hindsight” to “foresight” (Shepherd et al., 2018). Digitizing agriculture will take these systems to predictive (what will happen) and prescriptive (how can we make it happen) analytics, which are future focused. This development again raises questions about where our understanding of the mechanisms underpinning these predictions and prescriptions lies.