The largest modeling errors were found to correspond with N treatment simulations representing plots located

TABLE 4 | Mean annual yield and ROI error estimates generated by the APSIM model framework based on spatially averaged precision yield monitor data.

in areas with historically low productivity and economic return. Yield in "No-cost" zones were found to have an RMSE of 4313.6 kg ha−1 (21.3% ROI) compared to 1449.6 kg ha−1 (7.1% ROI) and 1667.6 kg ha−1 (8.4%) associated with "Expense-limited" and "Revenue" zones, respectively (**Figure 7**; **Table S6**).

## Objective 3: Simulated Environmental and Economic Impacts Associated With Variable Rate Seeding and N Fertilizer

Multiple seeding and N-fertilizer rate combinations were simulated across all subfield soils within the North-central Iowa field during 2015 and 2016 to identify combinations of seed and N-fertilizer inputs predicted to result in agronomic and economic optimums. A total of 4774 APSIM simulations were processed to estimate yield and ROI responses across a management decision space of 1–15 seeds per square meter and 0–150% of the fieldscale N-fertilizer rate. Simulations showed a range of variability in maize yield and ROI estimates across the different soil types and years (data not shown). Modeled yield and ROI response surfaces were generated, and agronomic and economic optimums were identified from the different combinations of seeding density and N-fertilizer rates (e.g., **Figure 8**).

Modeled annual crop productivity estimates for 2015 and 2016 ranged from 2812.4 to 14055.2 kg ha−1 across all plots, seeding densities, and N-fertilizer rate combinations. The range of yields corresponded with a minimum ROI of −75.3% and a maximum of 1.6% (data not shown). Initial soil conditions set using SSURGO data were found

FIGURE 5 | Scatter plot of simulated and spatially averaged yield monitor data corresponding with each experimental N-treatment plot located in the northcentral Iowa field.

to directly influence the magnitude of the simulated maize yields, particularly at lower N-fertilizer rates (0 and 50% of field-scale). Due to relatively high input costs and low commodity prices during 2015 and 2016, a majority of modeled yield values were estimated to result in an ROI below break-even (0% ROI).

FIGURE 7 | Range of ROI values within each experimental zone during 2015 and 2016. No-cost zones were found to have the greatest range of variability resulting from the alternative N-treatments within each zone. Revenue zones were found to have the least variability compared to the No-cost and Cost-limited zones.

In 2015, maximum yield estimates consistently corresponded with the highest seeding density (15 seed m−2 ) and N-application rate (150% of field-scale rate; 336.8 kg ha−1 ) for all soil types (data not shown). The economic optimum seeding density (EOSD) was also consistent across a majority of soils ranging from 8 to 9 seeds m−2 (**Figure 9**). Economic optimum N-fertilizer rates (EONR) in 2015 showed increased variability across the field ranging from 110 to 180 kg ha−1 . Although the EONR varied across the field, the maximum predicted ROI potential associated with the different soils was found to be relatively consistent, varying by 4.1%. Similar to 2015, maximum crop yields in 2016 corresponded with the greatest N-fertilizer rate (150% of fieldscale rate; 168.4 kg ha−1 ) considered within the decision space for the spring application. However, the agronomic optimum seeding rates in 2016 were more variable than 2015, ranging from 9 to 14 seeds m−2 . This indicated higher seeding rate scenarios were likely N-limited at the 150% field-scale application rate. As in 2015, the predicted 2016 EOSD was consistent (8–9 seed m−2 ) and showed little variation from EOSD predictions from the previous year. N-fertilizer rates were again shown to be the main regulating input for maximizing ROI across the field.

ROI, NO3 − leaching, and N2O emissions differences between the agronomic and economic optimum seeding density and N-fertilizer rate scenarios were calculated to determine the range of economic and environmental outcomes that separate maximum economic return and maximum yield potential. Yield differences between the economic and agronomic maximums associated with each subfield soil polygon ranged from 423.1 to 830.5 kg ha−1 in 2015 and from 99.5 to 897.1 kg ha−1 in 2016. The yield differences corresponded with ROI gains ranging from 10.0 to 12.1% in 2015 and 0.3 to 8.0% in 2016, respectively (**Figure 10**). The yield and ROI differences between the agronomic and economic optimums were found to be driven by both changes in seeding density as well as N inputs. In cases where seeding density was the main factor separating the agronomic and economic optimum management scenarios, the shift in ROI was minor. These relatively small shifts in ROI from changes in seeding density were primarily the result of a reduced yield impact compared to the simulated changes in N-fertilizer. Additionally, the relative cost savings from the reduced seed density were minimal compared to the cost of N. An average reduction of 5 seeds m−2 in 2015 equated to reduced input cost of \$44.40 ha−1 . In terms of N, a reduction of 43.0 kg ha−1 would be needed to achieve an equivalent cost reduction, however, proportionally larger differences in Nfertilizer rate were estimated. An average N-fertilizer difference of 157.3 kg ha−1 was predicted to separate economic and agronomic optimum managements in 2015 (\$161.99 ha−1 in cost savings based on the average N-price in 2015), followed by an estimated 19.5 kg ha−1 (\$20.13 ha−1 ) difference in N-fertilizer in 2016. The economic optimum combination of inputs was found to have an average decrease NO3 − leaching across all soils with an average reduction of 3.7 and 1.4 kg ha−1 in 2015 and 2016, respectively (**Figure 11**). In addition to NO3 − leaching, N2O emissions showed a relative decrease when comparing the seeding and Nfertilizer rates associated with maximum yield to those associated with maximum ROI. In 2015, differences between the optimum management scenarios accounted for an average change in N2O emissions of 14.8 kg ha−1 . In 2016, the N2O emissions difference between economic and agronomic optimum scenarios was 0.5 kg ha−1 (**Figure 11**).

## DISCUSSION

Optimizing the use of input resources within cropping systems is critical to improving sustainability and increasing economic returns from farm fields. However, predicting how to best allocate input resources such as seed and N-fertilizer to maximize ROI is difficult due to many dynamic factors influencing crop productivity and variability within the cropping systems (Scharf et al., 2005; Jaynes et al., 2011; Dhital and Ruan, 2016). As a result, subfield seeding density and N-fertilizer application guidance is needed prior to upcoming cropping seasons. However, preseason methods of determining the EONR and EOSD, such as a yield-goal approach, rely heavily on historical data and often involve estimates of interdependent factors (Sela et al., 2017). Regional MRTN tools that incorporate N-fertilizer prices and use empirical data to predict yield response to variable N-fertilizer rates provide field-scale approximations of optimums, but do not provide site-specific subfield recommendations or adjust for year-to-year variability (Sawyer et al., 2006).

Cropping system models such as APSIM are capable of predicting such site-specific subfield yield responses, however determining how these models can best be applied to provide land managers with actionable information to use within their existing management operations is difficult. The framework presented here was developed to determine if a cropping system model, coupled with publicly available data sources, could be used as a decision support tool for estimating sitespecific subfield economic optimum seeding density and Nfertilizer rates. Ultimately to be adopted, the framework will need to be practical to use, requiring further development of a user-friendly interface that may need to be catered to specific regions. This study was focused on maize grown in the Midwest U.S., a region where the adoption of precision agriculture has spread rapidly (Schimmelpfennig, 2016), and hence a logical initial focus for framework development. However, the SSURGO and Daymet data sources provide coverage across the conterminous U.S. allowing the framework to be applied to a variety of cropping system locations with varying weather and soil conditions. Extending beyond our study region, there are analogous tools such as Yield Prophet (Hochman et al., 2009; McCown et al., 2009) which have been successfully implemented in wheat, barley, canola, and oat cropping systems in Australia. Additionally, alternative data sources which provide alternate global weather and soil characteristics could be integrated with the framework to further extend its use in different regions (e.g., the European Centre for Medium-Range Weather Forecasts Reanalysis (ERA) products and the European Soil Database).

The framework was applied to determine subfield EONR and EOSD combinations for a Butler, County, Iowa, U.S. cropping system field in continuous maize and demonstrated the ability of the tool to capture subfield variability of yield and ROI across multiple years (2012–2017) and weather conditions (**Figures 2**, **3**). The initial demonstration of the framework required making some assumptions about the farming operations (e.g., selling grain at peak annual price) that could be incorporated into the user interface as customizable settings. Results of the Butler County field analysis showed fair agreement with annual subfield soil-based observations obtained from area-averaged yield monitor data for the 2012–2017 growing seasons. The greatest sources of modeling error corresponded with several small-area soils along the field boundary (**Figure 2**). Yields and

ROI associated with these soils were all over-estimated compared to the observations derived from yield monitor data. Plant stresses from non-simulated factors (e.g., standing water and soil compaction) could explain model over estimation in these areas. However, due to the small size (4% of field area) and the proximity of the soils to the field boundary the increased error

by one or more soil types sharing the same optimal combination of inputs are shown.

might be expected. Furthermore, yield monitor measurements may be artificially low near field borders (Luck and Fulton, 2004). Excluding the identified soils improved model fit to observations associated with the remaining 96% of the field area (r 2 = 0.48 to r 2 = 0.65) and reduced yield RMSE from 2171.8 to 1645.0 kg ha−1 .

Normalizing the RMSE by the mean observed yield resulted in a relative RMSE (RRMSE) of 15.5% (**Table S2**), which was similar in magnitude to a previous study examining the ability of the APSIM cropping system model to capture yield response to variable N-fertilizer rates in an Iowa maize system (Puntel et al., 2016). The RRMSE between 15 and 30% represents moderate model performance based on Yang et al. (2014). However, it is important to note the framework simulation approach was characterized by a high degree of difficulty as it runs a continuous simulation between 2012 and 2017, avoiding re-initialization of the soil data to capture year-on-year impacts on N-cycle dynamics (Constantin et al., 2011; Basso and Ritchie, 2015) related to the variable weather conditions and prior management practices. Such a modeling approach may propagate and accumulate error associated with a particular year during the full simulation period (Salo et al., 2016; Puntel et al., 2018). Based on this high level of difficulty and the resulting model performance, we believe the ability of the framework to capture subfield-scale patterns outside of a calibrated experimental setting indicates the framework to be a viable basis for an assessment tool when using public soils and weather data.

The model outputs corresponding with varying combinations of seeding density and N-fertilizer rates showed, in addition to increased ROI, managing for maximum economic return vs. maximum yield, likely provides an environmental cobenefit by reducing N-losses from NO3 − leaching and N2O emissions. Daily outputs from the analysis showed modeled N2O emissions were driven by interannual conditions that resulted in several periods during the growing season each year in which conditions favored denitrification (data not shown). Alternatively, the daily outputs showed a majority of NO3 − leaching to have occurred during the fall of 2015 into the spring of 2016. This indicated the fall manure application in 2015 was susceptible to high rates of NO3 − leaching. The economic and environmental co-benefit associated with strategically targeting management practices to subfield zones supports similar findings in Muth (2014) and Brandes et al. (2016), which have noted a correlation between environmental and economic performance in cropping systems. Such a relationship presents a financial incentive for adopting the economically optimum inputs, even at the cost of potential yield. A comparison of the predicted economic and agronomic optimums performed in this analysis showed the importance of managing the balance between seeding density and reduced Nfertilizer rates. The dual benefit of increased economic return and reduced environmental impacts may be sufficiently appealing to incentivize adoption by land managers, especially relative to regulatory actions by governments (Arbuckle, 2013; Kalcic et al., 2014).

The modeled relationship between maximum yield and maximum ROI was shown to reinforce aggregated empirical findings that are used in MRTN tools such as the Corn Nitrogen Rate Calculator (CRNC) (Sawyer et al., 2006). A small yield response was observed in 2015 when the primary source of N to the field was varied (224.5 kg ha−1 in the Fall 2014) indicating that some N-stress occurred at the lower rates of 0 and 50% of the field-scale N application. The long history of manure amendment prior to the 2012 start of the analysis has likely resulted in a large stock of organic N sources within the soil profile of the North-central Iowa field. A calibration approach, such as the one used in Puntel et al. (2016), based on previous knowledge of the system including the C:N ratio of the crop residues could be used to improve the predictive capability of the framework for future years.

The accuracy and usefulness of the tool could also be improved with more information and site-specific data sources. For example, relatively low yield observations in the southwest corner of the Iowa maize field were reported by the land manager to be the result of a broken tile drain and water infiltration issues (personal communication). Such information was not captured in the simulations when using only the public data sources to drive predictions. As a result, the variable N-treatment zone (No-cost 1) located in this area was found to correspond with the greatest modeling errors [yield and ROI RMSE of 4313.6 kg ha−1 and 21.3%, respectively (**Table S6**)], compared to the other variable N-treatment zones. For example, expense-limited zones showed the least error with a yield RMSE of 1449.6 kg ha−1 and ROI RMSE of 7.1%. Therefore, a real world application of the framework could be improved with some familiarity with the field being analyzed beyond crop, weather, and soil conditions. Calibration of the framework using historical precision yield data and initialization of simulated soil properties including initial NO3 − and NH+ 4 concentrations would also likely improve model performance. Such calibrations could be used to account for residual N within the soil in amounts adequate to offset any Nlimitations that may occur in lower N-treatment plots. However, the ability and motivation of the land manager to take the steps to obtain the necessary site-specific measurements may not be likely in some cases (Schimmelpfennig, 2016). In practice, there will be a range of potential users of the framework with varying levels of access to site-specific data and precision equipment/technology. Further calibration of the framework to provide more accurate predictions may represent a second phase in the analysis process after initially using the framework to highlight areas of the field that are not likely to respond to changes in management practices (e.g., "No-cost" zones). The second "calibrated" phase would then focus on areas of field where the model has shown to provide a yield response similar to historical observations. We envision the framework being applied in the following manner:

The result of this second phase would then be used to guide subfield management of seeding and N-fertilizer rates for the sub-field areas that have not been excluded in the "No-cost" zones. These zones should be prioritized for the enrollment of conservation programs or targets for perennial energy crops (Brandes et al., 2016). Further testing on additional independent sites will strengthen the predictive ability of the framework for supporting future application decisions.

## CONCLUSIONS

Optimizing the use of input resources within cropping systems is critical to reducing nutrient losses improving sustainability, and increasing economic return from cropping system fields. However, predicting how to best allocate input resources within a field is difficult due to the spatial and temporal variability of weather, soils, and management practices within the systems. By leveraging publicly available field-tosubfield data sources, cropping system models may provide a valuable decision support tool for predicting site specific yield, ROI, and environmental impacts on which farmers could base management decisions. The developed framework provides a basis for a subfield decision support tool for estimating economically optimum seeding densities and Nfertilizer rates. An application of the framework to predict annual yield and ROI outcomes in a maize cropping system found the framework effectively captured subfield variability of the observed crop productivity. These results support that this framework could potential be used to increase both economic and environmental performance in relatively well performing zones. Our analysis also indicated that poor performing zones are unlikely to be profitable at any realistic combination of the key inputs studied, suggesting that these areas may be targets for alternative cropping systems (e.g., perennial grasses). NO3 − leaching and N2O emissions differences between economic and agronomic optimum yields reinforces a correlation between maximum profitability and improved environmental performance. Further development of the integrated modeling approach to simulate perennial grasses and utilize more site-specific data may increase the accuracy and robustness of the framework. Integrating the use of additional precision agriculture data layers including as-applied nutrient applications, as-planted seeding data, gridded soil sampling, elevation data, and remote sensed layers could provide the necessary increase in spatial resolution needed to extend the process-based modeling framework as a more practical decision making resource.

## DATA AVAILABILITY STATEMENT

All datasets generated for this study are included in the article/**Supplementary Material**.

## AUTHOR CONTRIBUTIONS

GM and AV developed research idea, performed modeling study, and drafted manuscript. EH assisted with interpretation of data. SA assisted with model parameterization. ML assisted with modeling study design. All co-authors reviewed and contributed to the final manuscript.

## REFERENCES

## FUNDING

This work was supported by Iowa Nutrient Research Center and the USDA Hatch Project IOW04414 and the C-CHANGE Presidential Interdisciplinary Research Initiative at Iowa State University.

## ACKNOWLEDGMENTS

Acknowledgment is made to the APSIM Initiative which takes responsibility for quality assurance and a structured innovation programme for APSIM's modeling software, which is provided free for research and development use (see www.apsim.info for details). Acknowledgment is made to the land manager for access to the field and assistance with the experiment. We also thank Jennifer Jenson, Emily Marrs, Kati Togliatti, and Patrick Edmonds for assisting with data collection.

## SUPPLEMENTARY MATERIAL

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs. 2019.00108/full#supplementary-material

Conference on Precision Agriculture (Bloomington, MN), 16–19. doi: 10.1023/A:1011481832064

sensing for variable-rate nitrogen corn fertilization. Agron. J. 102, 71–84. doi: 10.2134/agronj2009.0114

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2019 McNunn, Heaton, Archontoulis, Licht and VanLoocke. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# Innovation Uncertainty Impacts the Adoption of Smarter Farming Approaches

Callum R. Eastwood1 \* and Alan Renwick 2

*1 Feed and Farm Systems Group, DairyNZ Ltd., Lincoln, New Zealand, 2 Department of Global Value Chains and Trade, Faculty of Agribusiness and Commerce, Lincoln University, Lincoln, New Zealand*

#### Edited by:

*Matt Bell, University of Nottingham, United Kingdom*

#### Reviewed by:

*David Rose, University of Reading, United Kingdom Yong Liu, Hunan Academy of Agricultural Sciences (CAAS), China*

> \*Correspondence: *Callum R. Eastwood<EMAIL_ADDRESS>
#### Specialty section:

*This article was submitted to Agroecology and Ecosystem Services, a section of the journal Frontiers in Sustainable Food Systems*

> Received: *14 December 2019* Accepted: *24 February 2020* Published: *20 March 2020*

#### Citation:

*Eastwood CR and Renwick A (2020) Innovation Uncertainty Impacts the Adoption of Smarter Farming Approaches. Front. Sustain. Food Syst. 4:24. doi: 10.3389/fsufs.2020.00024* There are increasing opportunities to use smart farming technologies for improved management of farming systems. However, there is limited understanding of how the potential can be translated into effective use in the farming sector. Previous studies have highlighted the role that uncertainty plays in technological innovation systems. In this paper, we present the results of an international survey investigating the impact of innovation uncertainty on adoption of a smart farming technology, automatic milking systems (AMS). The objective of this study was to review adoption of AMS internationally and propose lessons for developing institutional knowledge and effective networks of practice in emerging smart farming innovation systems. We used an online survey of AMS experts globally and received 81 completed survey responses. The main countries represented were Canada, The Netherlands, USA, Denmark, and the UK. Respondents identified a range of adoption trends in their country and some of the reasons behind these adoption profiles were suppression of uptake due to low milk prices, financial markets, and issues with early installations and perceptions of these issues by other farmers. In terms of the impact of uncertainty, technological uncertainty was historically an important issue around the early development of AMS, with decommissioning occurring in some cases due to perceived technology issues. Political uncertainty also impacted adoption, with implications of food safety regulations or rules around herd testing systems. Our study highlighted the potential impact of negative experiences associated with new technologies from farmers who struggle with the adaptation process as such occurrences may act to stall the uptake of smart farming technologies. If public policy organizations are to realize the desired impacts of smart farming technology, there needs to be greater focus on understanding where (and which) technologies can have an actual impact on farm as opposed to technologies that only create greater farmer distrust and uncertainty. Our study highlights that to reduce uncertainty with emerging smart technologies, greater public and private R&D collaboration is required to foster knowledge development and exchange.

Keywords: automatic milking systems, innovation uncertainty, dairy, smart farming, advisors

## INTRODUCTION

There are increasing opportunities to use smart farming technologies for improved management of farming systems (Shepherd et al., 2018). Potential management improvements are related to enhanced collection of data to manage animals, plants, and the wider farming environment (Eastwood et al., 2017a). However, there is limited understanding of how the potential can be translated into widespread adoption in the farming sector, which has been slow to date (Gargiulo et al., 2018). The uptake of smarter farming approaches often represents more than a "plug and play" process for farmers (Jago et al., 2013). Successful use of these new tools depends on aspects of technology fitfor-purpose, on-farm adaptation, learning about data-driven decision-making, and social learning within a farmer's network of practice (Eastwood et al., 2012; Rose et al., 2016; Higgins et al., 2017; Klerkx et al., 2019). To turn the opportunity of smarter farming into a reality on-farm, we need to better understand the wider issues affecting a farmer's investment decision making (Rutten et al., 2018).

Previous studies have highlighted the role that uncertainty plays in the functioning of technological innovation systems. For example, Meijer et al. (2007b) identified the importance of technological, resource, competitive, supplier, consumer, and political uncertainty. The use of farm system-changing smart farming technologies such as automatic milking systems (AMS) [see Rodenburg (2017) for a description of AMS technology] requires not only a reconfiguration of farming practice, but also in the systems that operate around the farmer, for example, knowledge of veterinarians on how to maintain reproductive performance under AMS or structural changes to herd testing protocols (Svennersten-Sjaunja and Pettersson, 2008; Hansen, 2015; Rodenburg, 2017). The success of an innovation system can depend on minimizing the uncertainty around the innovation (Meijer et al., 2007b; Kuehne et al., 2017). Poor or haphazard innovation system reconfiguration can increase the uncertainty that farmers or their advisors have about a technology and impact on its successful uptake and implementation.

Within this context, the objective of this current study was to understand drivers for adoption of AMS internationally and propose lessons for developing institutional knowledge and effective networks of practice in emerging smart farming innovation systems. In this paper, we present the results of an international survey investigating the impact of innovation uncertainty within AMS support networks across different institutional environments. First, we outline the conceptual framework based around innovation uncertainty, and then we present the methods and results of the survey and discuss them in relation to the conceptual framework. The novel contribution of this paper is 2-fold: first, it adds to knowledge of the specific factors influencing farmer adoption of smarter farming technologies such as AMS, and second, it adds to the limited literature that empirically explores the role of various factors of uncertainty in technological innovation systems.

## CONCEPTUAL FRAMEWORK

Adoption of agricultural technologies has been extensively studied and perspectives vary from a diffusion of innovations perspective (Rogers, 1962) to the more holistic concepts of agricultural innovation systems (Klerkx et al., 2010). The agricultural innovation systems (AIS) approach considers the role of institutional change within agricultural innovation and potential benefits from different ways of organizing within such systems (Morriss et al., 2006; Klerkx et al., 2010). Successful agricultural innovations depend on factors such as technology development, institutional change, supply chain reorganization, market development, and creating societal acceptance (Klerkx et al., 2010). The AIS concept has value as an analytical framework to "improve everyday innovation capacity" (Spielman and von Grebmer, 2006). One feature of AIS is the role of uncertainty in the uptake and use of technologies. Meijer et al. (2007b) identify six forms of uncertainty that might occur: technological, resource, competitive, supplier, consumer, and political. Individuals (including farmers or service providers) within AIS may have little ability to influence the uncertainty existing around an innovation. Uncertainty within innovation systems can potentially reduce the uptake of a technology, affect its integration into the farm system or industry, and can prevent some actors from engaging in the innovation system (Meijer et al., 2007b).

While the sources of uncertainty cited by Meijer et al. (2007b) focus on the formation of innovation projects, and in particular the impact on entrepreneurial action, the framework could be applied to the actions of farmers and advisors in respect to new system-changing innovations such as AMS. An ongoing process of AMS innovation could therefore be viewed as dependent on not just the technology or its developers, but also the farmers, distributors, milk companies, researchers, consultants, and regulatory agencies that also operate in the AMS space. Through the AIS approach, the actors involved in an innovation system can be identified, along with possibilities for different ways of organizing the actors. Innovation systems analysis can highlight the development of agency in actors and the reduction of uncertainty in the environment in which the actors operate.

Uncertainty surrounding a smart farming innovation can occur during the early development phase, including uncertainty related to available support and finance, and around best practice when using the technology. While innovation developments are rarely associated with low uncertainty, too much uncertainty can cause stagnation in respect to the ongoing innovation process, or lead to "failure" of an innovation (Kuehne et al., 2017). Meijer et al. (2007b) describes a framework for analyzing "perceived uncertainty" in the early stage of an innovation (**Figure 1**). Few empirical studies have applied the innovation uncertainty framework to case studies (Meijer et al., 2007a; Roper and Tapinos, 2016); therefore, the novelty of our study is in relation to both the empirical survey of uncertainty factors and explaining longitudinal adoption trends.

## ADOPTION OF AMS IN DIFFERENT DAIRY FARMING COUNTRIES

AMS involve the milking of dairy cows without human labor and are based on robotics and sensor technology. Since the first commercial AMS units were installed on a dairy farm in The Netherlands in 1992, there has been a range of adoption rates across different dairy farming countries. No single organization maintains statistics of the milking installations across different countries, and the information is held tightly by AMS retailers; however, some publications have provided data on installations over the last two decades (de Koning, 2010; Barkema et al., 2015; Tse et al., 2017). By 2015, there was up to 25,000 dairy farms using AMS worldwide, with the technology most popular in The Netherlands and Scandinavia (Rodenburg, 2017).

In the current paper, we focus on the "box-type" AMS rather than the robotic rotary systems that are also commercially available. In **Table 1**, we present data drawn from several sources to highlight the AMS adoption trends in dairy-producing countries where there were sufficient data from 2002 to 2018. Through to 2018, Iceland and Sweden had the greatest percentage of farms using AMS, at around 30% of farms, followed by another cluster of countries between 20 and 25% including Denmark, The Netherlands, Norway, Belgium, and Switzerland (Hogenkamp, 2018; Sigurdsson et al., 2019; Vik et al., 2019). Less data are published for other dairy countries; however, Canada (7% of farms) has seen a steady increase in installations (Tse et al., 2017). Limited data are available for the UK and USA; however, it is estimated that around 7% of farms in the UK (Hogenkamp, 2018) and 3% of farms in USA were using AMS by 2018 (Reed, 2018). There are few farms using AMS in Australia or NZ (<1% of farms). Interestingly, the data show that, in recent years, the percentage of farms using AMS in Denmark has peaked and is now declining, in part due to increasing farm sizes making other milking parlors more cost-effective (Sigurdsson et al., 2019).

## METHODS

An online survey was designed to capture processes around AMS uptake, through three phases of the adoption process: (1) when farmers are initially thinking of investing in AMS, TABLE 1 | Automatic milking system adoption rates from 2002 to 2018 in several dairy producing countries (% of total farms in each country, rounded to nearest 0.5%).

*Sources: Barkema et al. (2015), Hansen (2015), Tse et al. (2017), CDIC (2019), and Vik et al. (2019).*

(2) when farmers have made the decision to invest, and (3) after they have installed and are using the AMS. The survey was conducted online via the SurveyMonkeyTM platform. Closed questions were primarily used, with the number of open-ended questions limited to minimize survey length (Bryman, 2001). There were 116 questions, including demographic questions and questions on the role of the respondents and their organization in AMS development and extension, respondents' experience with AMS adoption, and patterns of AMS adoption in their country. In the current paper, we focus on a subset relevant to innovation uncertainty. The questions were developed based on AMSrelated studies that had been published prior to survey design (Meskens et al., 2001; Shephard, 2004; Svennersten-Sjaunja and Pettersson, 2008; de Koning, 2010; Khanal et al., 2010). Below, we discuss the drivers for selecting AMS-related questions for each of the innovation uncertainty factors. The survey respondent answers to the questions are then listed in **Table 2**.

## Technological Uncertainty

Uncertainty around the characteristics of an innovation, related infrastructural implications, the level of adaptation required, and the impact on future options are all aspects of technological uncertainty (Meijer et al., 2007b; Klerkx et al., 2010; Tomy and Pardede, 2018). Relevant factors to AMS could be the support available to farmers when making investment decisions, the

TABLE 2 | Survey questions asked in relation to the six forms of uncertainty within innovation systems [adapted from Meijer et al. (2007b)].

degree of technological lock-in (including the ability to expand herd sizes), and the specific challenges of adapting farming systems to AMS.

## Resource Uncertainty

This factor focuses on the availability of resources, such as human, financial, and material, and also encompasses organization of the process of innovation (Meijer et al., 2007b). The uncertainty around forecasting resources and capital required for the innovation involves factors such as availability of knowledge and skills, required R&D expenditure, and potential revenue streams (Tomy and Pardede, 2018). In an AMS context, resource uncertainty could relate to the ability to get finance for the AMS investment, uncertainty around milk price and its impact on viability, along with other factors such as uncertain pricing of secondhand AMS technology and how to revert to previous milking methods.

## Competitive Uncertainty

The behavior of competitors in the innovation system can affect its success (Meijer et al., 2007b). Factors behind this uncertainty can include level of market share, the impact of leading competitors, and the type of competition in the market (Tomy and Pardede, 2018). This relates to the competition between retailers of AMS technology (i.e., is there sufficient competition in a market dominated by two main players?) and how each competitor might refer to each other's product. We assessed this by asking questions around the availability of independent advice on technology, the adequacy of advice provided by technology retailers, and what industry support was available to farmers.

## Supplier Uncertainty

This source of uncertainty relates to perceptions around the reliability of the supplier (Meijer et al., 2007b). In respect to AMS, we asked questions around the availability of AMS technology (was there sufficient supply to match demand), the access that farmers had to back-up service for both technical and learning support, and whether farmers knew where to access advice about farming with AMS.

## Consumer Uncertainty

Consumer uncertainty concerns the preferences consumers might have for an innovation, the characteristics of consumers, and the development of demand for the technology (Meijer et al., 2007b). It also includes factors associated with knowledge of consumer acceptance of the technology, and the potential changes in demographics of the target population and therefore potential market size (Tomy and Pardede, 2018). These features are more applicable to entrepreneurs looking to work with consumers (farmers) rather than the consumers themselves. Therefore, for this factor, we asked industry-related questions such as uncertainty around future patterns of AMS adoption, the nature of technological development, whether farmer ability to succeed was included in the design and sale process, and the fit of AMS with farmer typologies. This is one aspect of the framework that potentially has less applicability when taking the farmer perspective in an AMS innovation system.

## Political Uncertainty

The policy environment can have a major impact on the innovation process, for example, the interpretation of policy, existence of regulations, and uncertainty regarding government and policy changes (Meijer et al., 2007b). It also includes the potential impact of government support for the innovation, the impact of exchange rates, and taxation that may relate to the innovation (Tomy and Pardede, 2018). In respect to AMS, this may include the implications of milk quality and food safety regulations, general political and community support for AMS, the awareness of regulations, and the existence of incentives.

The survey design incorporated the framework derived from Meijer et al. (2007b) and Klerkx et al. (2010) to assess the impact of uncertainty in the AMS innovation system. For each of the uncertainty factors, a series of statements were developed by the project team (**Table 2**), and participants were asked to indicate to what extent they agreed or disagreed with each statement, based on a five-point Likert scale from 1 = strongly disagree to 5 = strongly agree.

A pilot of the survey was run with five experienced AMS researchers from New Zealand, The Netherlands, USA, England, and Australia. Feedback from the pilot group was incorporated into the final survey design. Participants in the full survey were chosen to represent a range of those in the network of practice of AMS farmers internationally including: AMS researchers, technology developers, and sales/support representatives. Actors in the research and service/support sectors were targeted primarily due to language differences across the countries surveyed. The project team decided that people from these sectors were more likely to engage and complete the extensive English-based survey. The survey was therefore designed for these actors to use their knowledge of both farmer experiences, and innovation system-wide issues, to answer the questions posed.

Contacts were sourced initially through researcher networks, and then a snowball method was used (Bryman, 2001). The study was approved by the University of Melbourne Human Research Ethics Committee (HREC), and a plain language statement outlining the project aims, funders, use of data, and key contacts was provided on the opening survey page. If participants consented to participate, they were invited to click "next" to enter the survey.

The survey weblink was sent out twice to those on the contact list. Results were exported as a.csv file and imported into Microsoft ExcelTM Data were reviewed for quality and completeness and any erroneous responses were removed. The data were analyzed for interactions using multivariate statistics, but no strong associations were found; therefore, we focused the analysis on counts and summary statistics.

## RESULTS

In this section we outline the key results, beginning with an overview of the survey participant demographics, their experience and opinions related to AMS, and finally the results of questions related to the uncertainty factors.

## Survey Participants

There were 84 survey responses of which three were removed due to incomplete responses; therefore, 81 responses were used in the analysis. The major countries represented were Canada (24), The Netherlands (14), USA (10), Denmark (7), and the UK/Ireland (6). Other countries represented were Germany (4), Sweden (3), Israel (2), Norway (2), and Switzerland (2). There was one representative from each of Finland, France, Ireland, New Zealand, India, Iceland, and Japan. The respondents were primarily male (89%) and 63% were aged 35–54.

There was a range among respondents when it came to their day-to-day experience with AMS farmers with 27 stating it was "a major part of my job," 27 said it was "often part of my job," and 26 a "small part of my job." There was a similar mix in respect to years of experience that respondents had with AMS farms and farmers with 27 having over 10 years' experience, 27 had 5–10 years' experience, and 27 had <5 years' experience. There were 36 respondents from AMS retailers and 45 respondents not from AMS retailers (36 from public or industry funded research and advisory organizations, and 9 from privately funded advisors or consultants). In answering the survey questions, the respondents drew from experience that ranged from working with 1 farm to 1,000 farms. Most respondents interacted with between 20 and 100 farmers. The responses for each country, grouped by role (retailer vs. non-retailer) and experience, are shown in **Table 3**.

Of non-retailer respondents who were in a publicly funded research/advisory position, or identified as consultants, most were aligned with research. Many also provided general farm management advice to farmers and to a lesser extent helped farmers before and after AMS installation. Providing technical support was generally a small part of their role.

## Survey Responses

## How Respondents Perceived Their Role in the AMS Innovation System

The AMS retailer representatives who completed the survey primarily described their role as helping farmers learn to use AMS, along with providing technical support. A smaller part of their jobs in general was actively selling AMS or installing the equipment. They indicated that the organization they worked for had a wider role from installing equipment, providing presale and after-sales support. In terms of their AMS skill base, the company representatives indicated they learned slightly more through practical experience and interacting with farmers than via specific training. They were generally happy with their skill levels but felt they could learn more about farm management. The roles of non-retailers were less focused on technical support, and more on delivering AMS-related research and development knowledge, providing farmers with support prior to AMS investment, and providing general farm management advice. Few non-retailers (22%) had been specifically trained in supporting farmers using AMS, compared to 77% of retailers. Around half of non-retailers (49%) agreed that they were happy with their skills and knowledge related to AMS, compared to 71% of retailers. Both cohorts agreed they learned through interacting with AMS farmers (non-retailers 86% and retailers 91%). Additionally, they also agreed that they needed to learn more about farm management associated with AMS (nonretailers 67% and retailers 71%).

Respondents were asked about their opinions of the impact and future role of AMS in the dairy sector (**Table 4**). Most (87%) agreed that AMS required farmers to make significant changes to their farm systems, and responses were relatively consistent between retailers and non-retailers. Respondents also agreed (80%) that AMS represented the biggest transformation in dairy farming in the last 50 years. When asked if AMS would become the dominant milk harvesting method, most agreed (69%), and more retailers strongly agreed (53% compared with 30%).

### Perceptions on AMS Investment, Past, and Future

The most significant reasons for past AMS investment by farmers were identified as reducing total farm labor, reducing hours spent milking, more family time, and to reduce physical work. Improving milk quality, increasing production, and sustaining the farm business were not seen as overly important. Main reasons for farmers not investing were identified by respondents to be the cost of technology and to a lesser extent issues around herd expansion, difficulty obtaining finance, fit with farm system, and experiences of other farmers. Less of an issue were farmer perceptions of management issues during transition and availability of advice for farmers during the investment decision.

Respondents noted some decommissioning of AMS farms, although most respondents classified it as a "rare" occurrence. Countries where more than 10 decommissioned AMS farms had been observed included Denmark, The Netherlands, and the UK but this needs to be viewed in the context of the total number of AMS units in these countries and their position as sites of early adoption in the 1990s before the technology was mature. Reasons for decommissioning suggested by respondents included economic factors leading to bankruptcy (farm or company), initial lack of knowledge about using AMS and availability of support, initial technology issues (mostly in the 1990s), need for large herd flexibility, and lack of fit with farmers (incompatible expectations or skills). Specific comments of respondents highlighted the range in adoption trajectories in the first 10–15 years of AMS use (i.e., up to 2010). Below, we outline comments of respondents from The Netherlands (a mature market) and Canada (an emerging market) in relation to the adoption trajectories.

In The Netherlands, a mature AMS market at the time of surveying, respondents identified that in the early installations, there were technical problems and skepticism among farmers that automated milking was possible. Factors that led to greater uptake were more trust in the technology, a period of higher milk prices, the ability to have increased work flexibility, and more understanding among farmers of how to run AMS-based farm systems. There were up to five different AMS suppliers in The Netherlands. Comments made by some respondents were as follows:

TABLE 4 | Respondent opinions regarding the impact of AMS on dairy farming. Results are presented as percentages, with retailer, non-retailer in brackets, respectively.

TABLE 5 | Respondent opinions regarding the future adoption of AMS from 2011 to 2015, presented as percentages.

"At the moment approximately 1 out of 2 new [milking] machines is an AMS, quite popular among the family farms. Larger farms (>200 cows) often decide to have a rotary parlor or large rapid exit side by side or herringbone parlors." (Netherlands, non-retailer)

"In the first 5–10 years, AMS was bought mainly by early adopters, sometimes farmers who were very interested in the technology. In the recent decade this changed to farmers interested in optimizing individual cow management by using this technology." (Netherlands, non-retailer)

In Canada, an emerging AMS market at the time of surveying, respondents noted a pattern of some installations, then a "tapering off " or some deinstallation, followed by a more rapid increase around the time of the survey. This was for a variety of reasons including after-sales service quality, poor understanding of the farm systems (in particular feeding) changes required, and farm economic issues. There were three different AMS suppliers in Canada. Comments made by some respondents were as follows:

"In the beginning we did not understand robotic milking correctly in Canada. We had to learn how to be successful. Robots were pulled out and this slowed the sales process for about three years. Robot knowledge then became better and the units themselves continued to progress. We now know that robotics work. We also know that it only works with some farmers and we have to be very careful who we sell to." (Canada, retailer)

"The first robots were pulled out because we did not understand robotic milking fully. A learning curve had to happen and feeding strategies in Canada needed to be implemented to make sure the robots ran successfully" (Canada, retailer)

We also asked questions about predictions for future adoption and the predicted adoption trajectories are presented in **Table 5**. Across all respondents, 62% of respondents thought AMS uptake in their country would increase over the next 5 years, and another 23% thought it would rapidly increase. There was more confidence in the level of increased AMS adoption among retailers in comparison to non-retailers, with 35 and 14% expecting a rapid increase, respectively. Expectations of a rapid increase were also greater in the emerging markets of Canada and USA, when compared with the more mature markets of Denmark and The Netherlands. Increased labor costs were seen as a major future driver, along with increased confidence in AMS. Possible factors holding back AMS use was incompatibility with increased herd size and fluctuations in milk price or the farm economic environment.

Comments of respondents from The Netherlands (mature market) indicated that future adoption would be driven by higher farm labor costs, a desire to maintain family farming, and well-being factors. One limitation to adoption identified in The Netherlands was farm expansion, with herds of over 200 cows seen as a point where farmers considered other milking technologies such as a rotary or herringbone parlor. Canadian respondents felt that there would be more installations in the future. The reasons behind this were related to generational change on farm (younger farmers would invest in AMS to have more social time), it has become more reliable and trusted among farmers, and a lack of available agricultural workforce. One respondent noted that "With new anti-expansion quota policies several larger dairies are now considering robots as well."

## Role of Innovation Uncertainty on AMS Adoption Trends

Responses to survey questions related to different uncertainty factors are summarized in **Table 6** and displayed in **Figures 2**–**4**. The results are summarized across all respondents, by role, and by country. The countries listed represent those with the most responses in the survey, and show mature AMS markets (The Netherlands, Denmark) and emerging markets (UK/Ireland, USA, Canada). Below, each individual factor is numbered and we refer to them in the text from F1 to F28.

### **Technological uncertainty**

There were four questions related to technological uncertainty. Retailers were more positive when scoring these factors (average 3.9), compared with non-retailers (3.4), with the most divergence regarding whether farmers were well-supported when deciding to invest in AMS. The most agreement between these groups was in relation to farmers understanding the farm system challenges associated with AMS, with both cohorts being less positive for this factor (3.2 and 3.4). In terms of responses from different countries, respondents from The Netherlands and Canada had the most positive responses (4.0 and 3.8, respectively) with a 3.0 average for UK respondents. The UK respondents did not agree that farmers were well-supported in making investment decisions (2.3) or that farmers understood the challenges of AMS (2.8) particularly compared with Dutch responses to these factors, 3.8 and 4.0, respectively.

#### **Competitive uncertainty**

Retailers and non-retailers provided the same average responses to the competitive uncertainty factors (3.2), but there was divergence among the individual factors, for example F6 where retailers agreed (4.5) that farmers can obtain sufficient knowledge about different AMS features, whereas non-retailers were more neutral (3.3). Conversely for F7, retailers (2.0) did not think support was available through industry extension initiatives, while non-retailers were more neutral (2.7). In respect to responses by country, UK respondents again were less positive on average (2.6), with The Netherlands most positive (3.8). The mature markets (The Netherlands 4.3, Denmark 4.3) agreed that farmers could easily obtain independent AMS advice (F5), compared with neutral responses from emerging markets (UK 2.7, USA 2.7, Canada 3.1). There was a similar trend for F7 on industry extension initiatives.

### **Consumer uncertainty**

Retailers were more positive about the two consumer uncertainty factors (F8 and F9) than non-retailers, particularly in relation to whether the capacity for farmers to succeed with AMS was considered in the sale process. Additionally, respondents from countries in mature AMS markets provided overall neutral (3.0) responses while emerging markets showed a higher level of agreement with both factors (USA 4.2, Canada 3.6).

#### **Resource uncertainty**

There were seven factors related to resource uncertainty, and the average response for all respondents was neutral (3.1), with limited difference in the average between non-retailers and retailers. Dutch respondents (3.6) were slightly more positive than respondents from other countries. In terms of the individual factors, retailers (3.9) and non-retailers (2.5) differed most about whether farmers could confidently choose a milk price for their budgets. Overall, there was most disagreement (2.7) that farmers understood about reverting back to conventional milking, with respondents from UK/Ireland providing a rating of 2.3. Dutch respondents agreed most strongly that farmers were confident about getting finance (3.9), could determine depreciation values (4.0), were certain around secondhand AMS markets (4.0), and were aware of staff roles with AMS (4.0). Respondents from Denmark (1.6), UK/Ireland (1.8), and USA (2.0) most strongly disagreed that farmers could be confident on milk prices for their budgeting.

### **Supplier uncertainty**

Retailers agreed more strongly with the factors related to supplier uncertainty, with an average rating of 4.2 compared with 3.4 for non-retailers. There was most divergence about whether farmers were aware of after-sales service by AMS suppliers (retailer 4.5, non-retailer 3.6) and the learning support they will receive from AMS suppliers (retailer 4.0, non-retailer 2.7). UK/Ireland respondents provided the lowest average rating (3.1) to supplier factors and, along with Dutch respondents, gave a low rating for farmer knowledge of learning support they would receive from AMS providers (2.8 UK/Ireland, 2.6 Netherlands). The most agreement for individual factors were from Danish respondents who felt there was a ready supply of AMS units (4.7) and Dutch respondents who agreed strongly that farmers were aware of the technical service they would receive (4.8).

## **Political uncertainty**

The factors associated with political uncertainty were rated the lowest of all the uncertainty areas at an average rating of 2.5. There was little difference between retailers (2.4) and nonretailers (2.6). The lowest rated individual factor was F26, that public sector financial incentives have increased AMS adoption. On average, respondents from most countries disagreed that current regulations acted to make farming with AMS easier (F23), in particular USA (1.7) and UK/Ireland (2.5). Respondents across role and different countries were all neutral as to whether farmers were aware of regulations relating to AMS use (F24). While respondents from Denmark agreed (3.6) that the dairy community was aware of future regulations related to AMS (F28), TABLE 6 | Average responses to questions for the six innovation uncertainty categories, by role and country (scale of 1–5 where 1 = strongly disagree, 5 = strongly agree).

*(Continued)*

#### TABLE 6 | Continued

respondents from all other countries disagreed (2.0–2.5). On average, respondents from USA disagreed the most (2.2) with the factors related to political uncertainty.

## DISCUSSION

In this paper, we aimed to understand the impact of innovation uncertainty adoption of AMS internationally and propose lessons for developing institutional knowledge and effective networks of practice in emerging smart farming innovation systems.

## Major Themes Associated With Predicted AMS Adoption

Survey responses in this current study indicated a range of influences on potential AMS adoption. While historical adoption had been negatively influenced by financial factors such as low milk prices and the 2008 financial crisis, there were examples of technological uncertainty affecting early installations and perceptions of these issues by other farmers. Respondents identified some examples of decommissioning, which created a level of uncertainty in the local farming population about the

suitability of AMS. Farmers in early adopting countries (e.g., The Netherlands and Denmark) had some issues with learning to use AMS successfully, in some cases farmer skills and perceptions did not fit with AMS. The historical adoption factors associated with AMS identified in our study, such as reducing total farm labor, reducing hours spent milking, more family time, and reducing physical work, are supported by several authors (de Koning, 2010; Jacobs and Siegford, 2012; Hansen, 2015; Rodenburg, 2017; Vik et al., 2019).

In our study, we examined the predicted AMS adoption and the potential reasons for this among a group of experts. Almost all respondents predicted increased AMS adoption, with almost a quarter predicting a rapid increase. Understandably, this expectation of a rapid increase was higher among those selling AMS technology. Analyzed by country, respondents expected a slower adoption rate in Denmark and The Netherlands, when compared with Canada and USA. The data presented in **Table 1** mostly agree with these predictions. Danish AMS installations have not increased from 2010 to 2018, and in recent years have actually decreased. However, installations in The Netherlands and Sweden doubled from 2010 to 2018, and there have been dramatic increases in countries such as Germany (650%), Canada (475%), and Norway (250%)—albeit off a relatively low 2010 base. Of the emerging markets in our survey, USA still shows a low level of adoption, with an estimated 3% of farms. This could be primarily due to the farm system types and sizes employed there, and relatively low labor costs. The number of large farms has previously been highlighted as a barrier to AMS adoption in USA by Jacobs and Siegford (2012), and increased farm size is also having an impact in countries such as Denmark and Norway (Sigurdsson et al., 2019; Vik et al., 2019). However, the information presented in our survey adds weight to the need for alternative automated milking approaches for larger farm systems, such as robotic rotaries and stand-alone robotic cup attachment systems that work in rotary parlors. We further explore the potential reasons for the different adoption trajectories in section The Impact of Uncertainty on AMS Adoption below.

## The Impact of Uncertainty on AMS Adoption

Respondents were most positive toward factors associated with technological and supplier uncertainty. This indicates that at the time of the survey, the AMS technology was relatively mature and reliable, but in some countries, the knowledge associated with AMS use in different farm systems was not so developed. In this study, respondents felt that farmers were well-supported when making AMS investment decisions but were not always certain of the implications AMS had on farm systems challenges such as expanding their herd size, or reverting back to a conventional milking system (Hansen, 2015). The survey results therefore provide insights into the development and potential future adoption of AMS, including the need for greater certainty around issues of technological lock-in (where farmers face difficulties reversing technology investment decisions) and the forms of after-sales support required (for example, managing the farm systems changes related to AMS use).

There was most potential uncertainty around political factors, with ratings lowest among USA respondents. The political environment can have a large impact on innovations such as AMS through even apparently minor regulations or policies. One example highlighted in comments by respondents was food safety regulations in Europe requiring a person to be present at milking, which was not feasible under the 24 h milking cycle of AMS. Altering these regulations can take considerable effort and can act to discourage farmers. Other institutional arrangements can also be affected, for example, herd test protocols that require two milk samples at 12 h intervals have had impacts on the ability of AMS farmers to participate in herd improvement schemes (Eastwood et al., 2017b). Political factors have also been highlighted as driving industry structural change that can impact smart farming adoption such as AMS (Vik et al., 2019).

Respondents perceived a lack of awareness among farmers as to future regulations that may have an impact on AMS use and felt that farmers were moderately aware of the current regulations that are related to AMS use. There was a strong perception that current regulations did not act to make farming with AMS easier. There was also a perception that financial incentives at an industry level had not played a role in the uptake of AMS. In terms of the role of public and industry good organizations, respondents identified a lack of industrylevel extension programs related to AMS use but perceptions were mixed as to whether there was a role for the dairy industry or public organizations in the learning support space. Smart farming technology is dominated by commercial interests, which has been shown to have implications for private and public R&D roles in terms of supporting adoption (Eastwood et al., 2017b; Klerkx et al., 2019). This tension was also highlighted by respondents in factors related to competitive uncertainty, with an indication that sourcing independent advice on AMS technology was difficult for farmers in the emerging markets of Canada, USA, and the UK. Farmer uncertainty about adopting AMS and the lack of service providers for technical support were also found to be an issue in USA by Jacobs and Siegford (2012).

Responses related to supplier uncertainty highlighted differences between retailers and non-retailers. Retailers were much more positive that farmers were receiving good technical and learning support. The ratings showed that while farmers could be certain about the extent of technical support they will receive, they may be less aware of the learning support available from their retailer. This retailer focus on technical support is common in the smart farming domain (Eastwood et al., 2016). However, this can be compensated by farmers having access to AMS farming system advice from other agents in the innovation system, but these skills take time to build. For example, a network of farm system advisors took two decades to develop in The Netherlands, primarily because a certain AMS market size was needed to make it worthwhile for advisory firms to upskill (Eastwood et al., 2017b).

When investing in smart farming technology, farmers need a clear value proposition and business case, which in turn requires more transparent sharing of financials and robot performance by farmers and retailers (Rojo-Gimeno et al., 2019). In terms of our factors related to resource uncertainty, respondents perceived relative certainty for farmers accessing finance for AMS, but that ascertaining the depreciation value of the technology was more difficult. This was more of an issue in the emerging markets, as the mature markets had more knowledge of AMS performance, and potentially more experience with use of secondhand AMS units. Additionally, in some countries, the uncertainty around future milk prices could impair the ability to create robust investment cases for smart technologies. It is interesting to note that AMS retailers indicated much more confidence in predicting milk prices than non-retailers, potentially indicating undue optimism in the sales process.

## Usefulness of the Perceived Uncertainty Framework

The framework of Meijer et al. (2007b) provided a good lens with which to look at the AMS issues internationally. While most of the factors of uncertainty were relevant to the "farmer as entrepreneur" perspective adopted in our analysis, some of the factors relate better to other actors in the network. For example, consumer uncertainty relates more to technology providers and their uncertainty of the farmers' needs or to consultants and their uncertainty whether there is a business case for them to become involved. Use of the framework highlighted clear differences between technology retailers in specific aspects, and therefore, the framework could be used with other smart farming technologies to assess where potential issues occur between retailer and nonretail actors.

Difficulties we encountered with an empirical investigation using the framework factors involved first determining robust factors that related to each area of uncertainty. While we developed these within the research team, and tested them in a pilot, they would benefit from further refinement subsequent to this study. Additionally, with such survey methods in a niche research area, achieving sufficient responses is difficult. In our study, we concentrated on results from countries with the most respondents, but further empirical studies using this framework would benefit from greater targeting of respondents.

## Implications for Minimizing Uncertainty Related to Smart Technologies in Agriculture

This current study indicates some lessons for the configuration of smart farming innovation systems. In the case of AMS, the dominant forms of uncertainty uncovered across all respondents were in the resource and political domains, a finding supported by another study of uncertainty in technological start-ups (Tomy and Pardede, 2018). In particular, we identify a need for further discussion regarding the role of private providers of advice to farmers, and the related role of public or industry good AMS support programs. Our study indicates that development of commercial roles for consultants in providing advice to AMS farmers took some time to occur in the established markets in Europe, a finding supported by Eastwood et al. (2017b). In relation to smart farming technologies generally, the potential role of farm advisors in reducing innovation uncertainty has been highlighted in other studies (Ayre et al., 2019; Eastwood et al., 2019). There exists a significant opportunity for farm advisors to support farmers, so they get the most from their technology investments, requiring more focus from public R&D in the smart farming domain.

Our survey results highlighted a difference in perceptions between technology retailers and other actors in the technological innovation system, particularly around factors related to supplier and consumer uncertainty. The smart farming domain is dominated by private R&D (Eastwood et al., 2017b; Klerkx et al., 2019) and therefore the pressures of being first to market, providing a return on agtech venture capital, and achieving sufficient sales in a niche market can lead to ambitious marketing. This may result in development of smart technologies without a full understanding of market (farmer) needs (i.e., consumer uncertainty) and lack of focus on aftersales service that helps farmers integrate the technology into their farming system context (i.e., supplier uncertainty). It is therefore vital that commercial interests, farm advisors, and public R&D actors foster a collaborative approach to development and support of smart farming technologies (Ayre et al., 2019; Phillips et al., 2019). The need for collaborative approaches is especially the case where technologies are brought together in platforms (e.g., via artificial intelligence) to solve dynamic and complex agricultural problems (Hermans et al., 2019).

We identified an impact of immature AMS technology being marketed to farmers, and AMS technology being sold to farmers who did not have the capability or mindset to adapt their farm systems to suit. This resulted in instances of decommissioning, or reverting to conventional milking technology, and had a subsequent impact on farmer (and advisor) confidence in the technology. This experience highlights an important consideration for smart farming innovation uncertainty. Agricultural NGOs and governments are increasingly viewing smart farming as a tool for improvements in productivity and sustainability of agriculture. However, our study highlighted the potential impact of negative experiences associated with new technologies from farmers who struggle with the adaptation process as such occurrences may act to stall the uptake of smart farming technologies. If public policy organizations are to realize the desired impacts of smart farming technology, there needs to be greater focus on understanding where (and which) technologies can have an actual impact on farm (Shepherd et al., 2018), as opposed to technologies that only create greater farmer distrust and uncertainty (Jakku et al., 2018; Klerkx et al., 2019).

## Limitations of the Approach

The approach used in this paper involved an online survey with a targeted snowball method where domain experts were first identified and then asked to distribute the survey among their networks. The target population (researchers and professionals with knowledge of AMS) was relatively small and dominated by commercial technology and service providers who are often difficult to access in research projects.

This research represented an exploratory approach to use the innovation uncertainty framework to describe major influences on AMS adoption in different countries. The questions were developed by the project team, and tested in a pilot survey, and therefore represent a best design of the appropriate questions. However, the questions could be open to interpretation of individual participants.

## CONCLUSIONS

In this paper, the concept of perceived uncertainty in innovation systems was used to examine the adoption of automated milking systems, a smart farming technology. The major drivers for farmers adopting AMS included reducing total farm labor, hours spent milking, and the amount of physical work, while also having more family time. Adoption was perceived to be negatively impacted by the cost of technology and issues around future herd expansion, difficulty obtaining finance, fit with farm system, and negative experiences of other farmers. This study adds to limited literature focused on empirical analysis of the role of uncertainty in using factors associated with perceived uncertainty; we were able to analyze the AMS innovation system across different countries and institutional contexts and use this to determine implications for smart farming technology adoption. We highlighted perceived impacts of political uncertainty, and the impact of technological uncertainty around not only immature smart farming technologies but also the on-farm adaptation that such technologies can require. We also suggest that to reduce uncertainty with emerging smart technologies, greater effort is required to foster knowledge development and exchange. In emergent markets for smart farming technologies, there is a public or industry-good role in delivering broad knowledge development and capability building programs focused on key actors such as nutritionists, veterinarians, banking finance representatives, and agricultural consultants.

## REFERENCES

## DATA AVAILABILITY STATEMENT

Restrictions apply to the datasets: The datasets for this article are not publicly available to protect the anonymity of respondents. Requests to access the datasets should be directed to Callum R. Eastwood<EMAIL_ADDRESS>
## ETHICS STATEMENT

The studies involving human participants were reviewed and approved by Human Ethics Advisory Group—The University of Melbourne. The participants provided their written informed consent to participate in this study.

## AUTHOR CONTRIBUTIONS

Research method and data collection were undertaken by CE. Data analysis, writing, and revision of the manuscript were undertaken equally by CE and AR.

## FUNDING

This research was conducted as a part of a program funded by Dairy Australia (Melbourne, Australia) through project UM13556 at The University of Melbourne. Writing of the paper was funded by the dairy farmers of New Zealand through DairyNZ Inc. (Hamilton, New Zealand), contract WEN1802 (Systems for People).

## ACKNOWLEDGMENTS

The authors would like to thank those involved in piloting the survey and the AMS retailers, consultants, and researchers who took the time to answer the survey.

studies of precision dairy farms in Australia. Agric. Syst. 108, 10–18. doi: 10.1016/j.agsy.2011.12.005

**Conflict of Interest:** The authors declare that this study received funding from Dairy Australia and DairyNZ Inc. These funders were not involved in the study design, collection, analysis, interpretation of data, the writing of this article, or the decision to submit it for publication.

Copyright © 2020 Eastwood and Renwick. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

# What Are the Implications of Digitalisation for Agricultural Knowledge?

Julie Ingram\* and Damian Maye

Countryside and Community Research Institute, University of Gloucestershire, Gloucester, United Kingdom

In this perspective paper we consider the implications of a digital transformation for agricultural knowledge, a subject which hitherto has received limited attention. We raise critical questions about how digital agriculture will intersect with established modes of knowing and decision-making. We also consider the implications for the wider Agricultural Knowledge and Innovation System (AKIS), specifically the roles and capabilities of those who provide advice to farmers, as well as those responsible for data analytics, and the organizations and institutions that link and support them. We conclude that new data driven processes on farm, as well as the changing AKIS dynamic under digital agriculture, bring new demands, relations and tensions to agricultural decision-making, but also create opportunities to foster new learning by harnessing synergies in the AKIS.

#### Edited by:

Urs Feller, University of Bern, Switzerland

#### Reviewed by:

Mongi Sghaier, Institut des Régions Arides, Tunisia Aysha Fleming, CSIRO Land and Water, Australia Jon Hellin, International Rice Research Institute, Philippines

> \*Correspondence: Julie Ingram<EMAIL_ADDRESS>
#### Specialty section:

This article was submitted to Agroecology and Ecosystem Services, a section of the journal Frontiers in Sustainable Food Systems

> Received: 19 December 2019 Accepted: 20 April 2020 Published: 21 May 2020

#### Citation:

Ingram J and Maye D (2020) What Are the Implications of Digitalisation for Agricultural Knowledge? Front. Sustain. Food Syst. 4:66. doi: 10.3389/fsufs.2020.00066 Keywords: digital agriculture, agricultural knowledge and innovation system, knowledge, smart farming, farmer, big data

## INTRODUCTION

It is generally agreed that digital agriculture1 will deliver a step change in efficiency, productivity and sustainability at the farm level and across the value chain (Aubert et al., 2012; Wolfert et al., 2017). Sensing systems and associated analytics can provide producers with better information to make more timely decisions with more predictable outcomes, while automating tasks using sensing technologies and machine learning can increase reliability. Rapid developments in the Internet of Things (IoT), cloud computing, robotics and Artificial Intelligence are accelerating the transition to smart farming and the promotion of big data and precision agriculture to improve agri-food sustainability. The expectation is that smart farming approaches will ultimately improve knowledge about an individual enterprise, or via efficient sharing and learning from data from multiple enterprises (Robertson et al., 2018).

However, although this "fourth agricultural revolution" brings the promise of multiple gains, it also brings with it technical, social, economic, ethical and practical questions, with significant implications for how commercial agriculture is structured, practiced and governed. Research to date is only just exploring the full ramifications of this so called "disruptive innovation" in relation to these aspects (Bronson and Knezevic, 2016; Jakku et al., 2016; Carolan, 2018; Klerkx et al., 2019; Rotz et al., 2019). One question that is not being fully addressed, however, is: what are the implications of digitalisation for agricultural knowledge?

1Digital agriculture typically involves both the collection and analysis of data to improve both on-farm and off-farm decision (Leonard et al., 2017), although here we refer to different forms of digitalisation in agricultural production systems.

Digital applications and platforms have the potential to dramatically change the way knowledge is processed, communicated, accessed and utilized. For farmers, digital applications will provide decision-making capabilities that were previously not possible, potentially leading to radical changes in farm management (Sonka, 2014; Wolfert et al., 2017). As smart machines and sensor networks increase on farms and farm data grow in quantity and scope, farming processes will become increasingly data-driven and data-enabled (Wolfert et al., 2017). This raises critical questions about how digital agriculture will require new capabilities, support decision-making and interact with, and potentially disrupt, established modes of knowledge processing.

There are significant implications for the whole Agricultural Knowledge and Innovation System (AKIS)2 , specifically the roles and capabilities of farmers, those who provide advice to farmers, as well as those responsible for data analytics, and the organizations and institutions that link and support them.

These considerations are important if we are to enable digital agriculture to be effectively implemented.

## DIGTIAL AGRICULTURE AND KNOWLEDGE PROCESSES

Our understanding of knowledge in agriculture has evolved from regarding it as a transferable commodity to something more diffuse emerging out of technical and social interactions. This understanding underpins the AKIS concept and the multiple knowledge generation, exchange and utilization processes operating interactively between the heterogenous actors involved (Klerkx et al., 2012). Analysis of the potential impact of digital agriculture on the AKIS to date has tended to follow a supply-orientated narrative, examining, for example: digital services in extension (Steinke et al., 2020), social media usage, digital literacy and access (Bronson and Knezevic, 2016); and adoption of technologies (Pierpaoli et al., 2013; Barnes et al., 2019; Lowenberg-DeBoer and Erickson, 2019). Whilst these perspectives are insightful, we argue that digital agriculture requires us to fundamentally rethink these knowledge processes and to reflect on the consequences of a shift toward data-driven processes.

This perspective piece refers especially to conventional agricultural systems and draws on research primarily from developed countries. In this brief discussion inevitably we have to use shorthand terms for the different AKIS actors: farmers, advisers, researchers, etc. We acknowledge that these groups are not homogeneous and we know that farmers' interactions with, and access to, digital agriculture differs significantly depending on multiple farm, farmer and wider enabling factors (Barnes et al., 2019; Vecchio et al., 2020).

## DIGITAL AGRICULTURE, FARMER KNOWLEDGE, AND DECISION-MAKING

## Decision Support - Analytical Capabilities

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

2The AKIS concept refers to complex arrangements and interactions between actors, knowledge organizations (agricultural research, extension, and education organisations) as well as the informal networks of heterogeneous actors (supply chains, policy makers etc).

3Analytics is the capability available to analyse data (Shepherd et al., 2018)

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 FieldViewTM in USA and Agrivi in UK) to decision support tools (e.g., FieldNET AdvisorTM 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.

## Disruptions To Farmer Knowledge and Decision-Making

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).

## THE CHANGING AKIS DYNAMIC UNDER DIGITAL AGRICULTURE

Farmers draw on multiple sources of knowledge and innovation support services in the AKIS (regulators, supply chain actors, conservation experts, NGOs, policy makers), however, for many, farmer networks and farm advisers remain key. Evidence to date of the impacts of digital agriculture suggest potential shifts in these knowledge relationships.

## Enabling or Disrupting Farmers' Knowledge Networks

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 knowhow, 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).

## Innovation Support Services

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).

## New Entrants and Changing Roles

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.

## CONCLUSION

New data-driven processes on farm as well as the changing AKIS dynamic under digital agriculture brings new demands, relations and tensions. However, there is also great potential to both build on established ways of knowing and to foster new learning by harnessing synergies.

Morakanyane et al. (2017, p. 437) defined digital transformation "as an evolutionary process that leverages digital capabilities and technologies to create value." We would argue that these capabilities are crucial and extend beyond the digital domain per se to the knowledge capabilities of all actors in the AKIS. Enhancing capabilities at every level, from the farm and adviser level, to new technology and software providers and established researchers, will be important if digital technologies are to achieve their full value. Equally facilitating opportunities for combining different analytic approaches and capabilities should be supported. Fostering co-learning and collaboration in

## REFERENCES

implementing new technologies should be an important strand for future development and research.

## AUTHOR CONTRIBUTIONS

JI prepared the main draft. DM contributed material and ideas and edited the text.

## ACKNOWLEDGMENTS

We wish to thank the reviewers for their helpful and constructive comments on a first draft of the paper.

and dairy industry organisations in supporting precision dairy farmers. Anim. Prod. Sci. 56, 1752–1760. doi: 10.1071/AN141015

Digital Agriculture in Australia (Narrabri, NSW: Cotton Research and Development Board).

**Conflict of Interest:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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