Precision agriculture technologies have been available for adoption and utilization at the farm level for several decades. Some technologies have been readily adopted while others were adopted more slowly. An analysis of 621 Kansas Farm Management Association (KFMA) farmer members provided insights regarding adoption, upgrading, and abandonment of technology. The likelihood that farms adopt specific technology given that other technology had been adopted are reported. The lag, in years, between a technology being commercially available and being adopted were evaluated using duration analysis. Results indicate some technologies were more readily adopted than others. Results are useful to farmers considering adoption, retailers in targeting farmers likely to adopt, and manufacturers in supply chain management.

The number of smart farming technologies available on the market is growing rapidly. Recent surveys show that despite extensive research efforts and media coverage, adoption of smart farming technologies is still lower than expected in Germany. Media analysis, a multi stakeholder workshop, and the Adoption and Diffusion Outcome Prediction Tool (ADOPT) (Kuehne et al. 2017) were applied to analyze the underlying adoption barriers that explain the low to moderate adoption levels of smart farming technologies. Results of the media analysis show that incompatibility (between different software and/or hardware products), lack of decision algorithms, profitability, inconvenience issues, data protection, and data sovereignty are the most important adoption constraints. While low profitability seems to have declining importance over time, the lack of decision algorithms and inconvenience issues remain important. Despite expectations, incompatibility is gaining importance over time; and both data protection and data sovereignty are relatively new aspects in the discussion. These findings were largely confirmed by participants in the workshop conducted. ADOPT was applied to examine the use of sensor technology for site-specific nitrogen management. Based on available information on adoption rates of these technologies we see ADOPT as a valuable tool for predicting peak adoption levels and the time to peak adoption. Scenario analyses with ADOPT show that increasing the ease and convenience of use of nitrogen sensor technologies can significantly increase adoption levels of such environmentally friendly technologies. To conclude, our results provide useful information for industry and policy makers to increase adoption levels of environmentally friendly smart farming technologies.    

The concept of precision farming (PF) was formulated about 40 years ago and the scientific knowledge for some applications of PF in The Netherlands has been available for almost 20 years. Also, in many cases equipment is available to implement PF in practice. In spite of all this PF uptake is still limited. An important reason for the limited uptake of PF is in the challenges that must be overcome to let data flow from sensors to data storage, to combine data sources and process them into recommendations, and to send the recommendations to agricultural implements. These challenges are technical, legal, and ethical in nature. In this paper we describe how Akkerweb is used in The Netherlands to address the above challenges.

Akkerweb (www.akkerweb.eu) is a web-based platform with the following functions. It provides access to external data sources such as weather, parcel boundaries, satellite, and farm management data in commercial Farm Management Information Systems. It stores geo-referenced data, including soil maps and drone imagery. It allows combining data sources and processing of data, through modules (“apps”) that are not unlike the apps on a smartphone. It generates prescription maps and other recommendations. Prescription maps can be downloaded to tractor terminals and used to implement the recommendation. Finally, the use of apps can be subject to a fee, i.e. commercial use of the platform is possible.

Currently, approx. 30 apps are available on Akkerweb. We highlight three apps for potato growers. The Potato Haulm Killing app gives a recommendation for desiccant use to terminate a potato crop and leads to an average reduction in herbicide use of 38% and an increase in gross margin of 30 € ha-1. A recent development is the use of drone imagery (0.25 m) instead of the earlier satellite imagery (10 m). The late blight app makes a recommendation when to apply fungicide to control Phytophthora infestans. Recently, in response to growers’ requests, a dynamic crop growth model was added to this app to better describe re-mobilization of reserves and leaf growth immediately following the end of a drought period. Finally, the N sidedress app has recently been modified to work with drone imagery in addition to tractor-based reflectance sensors.

A number of factors has contributed to the success of Akkerweb. It has been developed and is owned by a consortium consisting of Agrifirm, a farmers’ cooperative, and Wageningen University & Research. Growers were involved from the start and have a sense of ownership. This has led to trust that data stored in Akkerweb will only be used in ways that benefit the farmers. The involvement of Wageningen UR guarantees that Akkerweb recommendations are both science-based and effective. For the future, big data analysis of Akkerweb data will lead to new knowledge that is of benefit to growers; it is expected that growers will consent to their data being used because their direct involvement in Akkerweb guarantees that they contribute to setting the research agenda and remain in control of their data.  

Precision agriculture and soil conservation have the potential to increase crop yield and economic return while reducing environmental impacts. Landform, spatial variability of soil processes, and temporal trends may affect crop N response and should be considered for precision agriculture. The objective of this research was to evaluate the viability of precision agriculture in improving N use efficiency and profitability at the farm and watershed level in western Canada. Two studies are described at different scales, 1) within and between producer fields, 2) and the watershed scale. In the field scale study, research was conducted with producer’s equipment in Alberta, Saskatchewan and Manitoba during 2014, 2015, 2016, and 2017. Seven fields were seeded to canola in 2014. Randomized fertilizer treatments (0, 50, 100 and 150% of soil test recommendations for N) with 4 replicates were located in low, average and high producing zones based on analysis of 3 to 5 years of yield maps. Preliminary results for 2014 show that variable management of N fertilizer had positive effects in some fields, but the results were not consistent. In the watershed scale study, producer survey data from the South Tobacco Creek (STC) watershed in Manitoba were assessed. A productivity index based on 11-year (2006-2016) moving average yield data for canola and wheat and climate and soil variables was used to delineate five management zones at the watershed scale. ArcGIS and Limdep (NLOGIT 4.0) Econometric Software was used to analyze the data. Preliminary results for the watershed scale study indicate that both spatial (zone) and temporal (time) variability had effects on crop productivity, but temporal variability had the greater effect. Soil conservation also had positive effects on increased wheat and canola yields and net revenues. The results of this study identified the potential for spatial management of N fertilizer in the context of temporal variability due to extremes in precipitation and temperature.

Precision nitrogen recommendations for wheat are often based on inherent assumptions and goals regarding yield, grain protein and nitrogen use efficiency. Assessing whether or not expected wheat performance goals, including N use efficiencies, are actually achieved, however, needs further development and application. Furthermore, farmer capacity to evaluate site-specific wheat performance (e.g. yield, protein, nitrogen use efficiency) in the field is becoming technologically and logistically feasible. This development can empower farmers to assess their management practices and promote farm level innovation. We present field-based research that links N use efficiency, precision technologies and wheat performance goals suitable for use by farmers. The developed framework is applicable to research and farm production activities that evaluate wheat performance.

Even with more than a decade long adoption of the precision agriculture (PA) technologies in the United States, its impact on farm profitability is still not clear. This paper uses farm level data from Kansas Farm Management Association (KFMA) to conduct the ex-post evaluation of PA technologies on farm profitability in Kansas. The analysis of the data using propensity score matching method indicates that there is on an average $60,000 difference in net returns of the farm with at least one PA technology and the net returns of the farm without any PA technology. The results also indicate approximately a linear increase in return with adoption of more PA technology. The conclusion from this paper is specific to the state of Kansas and with availability of national level data, analysis can be extended to draw a more generalized conclusion.