Data issues impacting technology adoption

Smart Farming Technology (SFT) has been recognised as a potential solution to many of the challenges that the agriculture sector is facing. Crucial to the adoption of SFT is understanding farmers’ needs, interests and concerns regarding the adoption and use of such technology. In late 2021, DEMETER conducted a survey to better understand the barriers and drivers to SFT adoption. The research was conducted across November and December 2021. In total 484 responses were received from farmers across 46 different countries. The results clearly indicate that farmers are looking for technical solutions that will provide them with better information to help manage their farm. Furthermore, they are looking for technology that will simplify their work and provide them with a better work-life balance. In addition, farmers want new technologies to help them increase their profitability and therefore deliver a significant return on investment.

On the other hand, cost is the biggest barrier to farmers adopting technology. Over half of the respondents indicated that most of the SFT that exists is too costly. In addition, data privacy and sovereignty concerns are seen as a barrier, with significant differences arising by country. Sustainability arose as a key issue for farmers. According to the results of the survey, the majority of farmers do not currently see clear environmental benefits associated with using SFT. However, they believe that such technologies can improve their environmental impact in the future and help them to cope with climate change, indicating an opportunity for technology providers.

Looking closer at the issue of data, we see that farmers recognise the benefit of the data generated from SFT. Data from sensors, weather stations, animal wearables, etc. provides useful information that can help the farmer to optimise operations. However, farmers are concerned with what happens with this data generated. 23% of respondents from the DEMETER survey were worried that third parties would gain ownership of their private data. In addition, 18% of respondents agreed that they were concerned that the value of their data would not be returned. This highlights the need for technology providers to provide clear and transparent policies on how the data is being managed and used. The results vary by country, for example, in Ireland 50% of those that responded indicated that data ownership and data privacy is a key concern.

Previous studies demonstrate that data issues can lead to an aversion towards SFT. Research from Jayashankar et al. (2018) outlines that the potential misuse of data by technology providers or the sale of data to third parties can lead to scepticism towards the value of SFT implementation.  Equally,  Wiseman et al. (2019) explain that farmers’ trust in SFT is linked to their willingness to share data, with farmers very often unsure of how their data is being managed and used. Farmers need to trust that technology providers will manage their data in a fair, responsible, and competent manner.

Understanding and interpreting the data generated from SFT is also key.  Many farmers struggle with the level of data analysis required, as well as the challenge of integrating the data from several technologies (Nettle et al., 2018). The adoption of SFT is hampered by a lack of data analysis skills from the farmer in terms of using the data generated to create meaningful information that can create a competitive advantage (Saiz-Rubio and Rovira-Más, 2020). Specialised education and training, and support from farm advisors and farm associations in terms of interpreting the data can allay farmers’ concerns.

Pierpaoli et al. (2013) demonstrate how interoperability is a key challenge as whatever SFT the farmer adopts must integrate with the existing technologies on farm.  Not only is this limiting the farmer’s choice, but also limits the range of benefits which can be derived from implementing the technology (Relf-Eckstein, Ballantyne and Phillips, 2019).  For example, Rose et al. (2016) find that many farmers do not adopt decision support systems because they are not compatible or interoperable with the existing technologies implemented on farm. This is further supported by Knierim et al. (2018) who cite compatibility as a barrier to SFT adoption. In their study on the adoption of drones by German farmers, Michels, von Hobe and Musshoff (2020) note that some farmers state software compatibility as a barrier to adoption, although cost was by far the biggest barrier.

It is clear that data use and privacy is still a barrier to the increased adoption of SFT. Legal frameworks that clearly outline data privacy and data sharing agreements between the farmer and the technology provider are central to overcoming these barriers. Farmers need to trust that their data is being managed securely. Furthermore, the use of blockchain can reduce some of the privacy concerns that farmers have. Standardisation and interoperability of data are important to ensure that the data generated from SFT can be integrated, giving the farmer an overall picture of their farm’s performance.


This article was written by Grainne Dilleen, Researcher at Walton Institute. Grainne is undertaking a PhD which is focused on understanding the factors which influence farmers’ intention to adopt smart technologies.


Jayashankar, P., Nilakanta, S., Johnston, W.J., Gill, P. and Burres, R. (2018), “IoT adoption in agriculture: the role of trust, perceived value and risk”, Journal of Business & Industrial Marketing, Vol. 33 No. 6, pp. 804-821.

Knierim, A., Borges, F., Kernecker, M., Kraus, T. and Wurbs, A. ‘What drives adoption of smart farming technologies? Evidence from a cross-country study ‘, 13th European IFSA Symposium. Farming systems: facing uncertainties and enhancing opportunities, Chania, Greece, 1-5 July, 2018: International Farming Systems Association (IFSA) Europe

Michels, M., von Hobe, C.-F. and Musshoff, O. (2020) ‘A trans-theoretical model for the adoption of drones by large-scale German farmers’, Journal of Rural Studies, 75, pp. 80-88.

Nettle, R., Crawford, A. and Brightling, P. (2018), “How private-sector farm advisors change their practices: An Australian case study”, Journal of Rural Studies, Vol. 58, pp. 20-27.

Pierpaoli, E., Carli, G., Pignatti, E. and Canavari, M. (2013) ‘Drivers of Precision Agriculture Technologies Adoption: A Literature Review’, Procedia Technology, 8, pp. 61-69.

Relf-Eckstein, J. E., Ballantyne, A. T. and Phillips, P. W. B. (2019) ‘Farming Reimagined: A case study of autonomous farm equipment and creating an innovation opportunity space for broadacre smart farming’, NJAS – Wageningen Journal of Life Sciences, 90-91.

Rose, D. C., Sutherland, W. J., Parker, C., Lobley, M., Winter, M., Morris, C., Twining, S., Ffoulkes, C., Amano, T. and Dicks, L. V. (2016) ‘Decision support tools for agriculture: Towards effective design and delivery’, Agricultural Systems, 149, pp. 165-174.

Saiz-Rubio, V. and Rovira-Más, F. (2020), “From Smart Farming towards Agriculture 5.0: A Review on Crop Data Management”, Agronomy, Vol. 10 No. 2., pp. 1-21

Wiseman, L., Sanderson, J., Zhang, A. and Jakku, E. (2019), “Farmers and their data: An examination of farmers’ reluctance to share their data through the lens of the laws impacting smart farming”, NJAS – Wageningen Journal of Life Sciences, Vol. 90-91.