Views of Irish Farmers on Smart Farming Technologies: An Observational Study

Abstract

The primary objective of this research is to find the disparity for slow adoption of Smart Farming Technologies (SFT) in Ireland. The usage of Cloud Computing technology among Irish farmers would help to find out the adoption behaviour barrier and way to enhance from the present system. The research will also help us to indicate the reasons for farmers in adopting and not adopting any technology. The research followed a mixed method where both surveys and interviews were used to collect the data from Irish farmers. A total sample of 32 farmers were selected through snowball sampling with the help of social websites. This study explored the major factors in adopting new technology among Irish farmers. It also helped to find the perception of farmers and ways to improve from the present system. The result shows that Cloud Computing adoption among the young farmers is greater while it is lower among the old farmers in Ireland.

The adoption of drones in German agriculture: a structural equation model

“…The TAM was developed by Davis (1989) and has been used for agricultural research in several research areas including PAT adoption (e.g., Adrian et al, 2005;Das et al, 2019). According to Verma and Sinha (2018), the TAM is the most widely applied model for the intention to adopt a technology.…”

“…Schaak & Musshoff, 2018). Unsurprisingly, literature has also shown that farmers' perceptions and beliefs towards the technology play a major role in decision making with respect to PAT and decision support tools (DST) (e.g., Adrian et al, 2005;Das et al, 2019;Rose et al, 2016). For instance, Rose et al (2016) provided evidence that besides other factors, perceived performance, ease of use and relevance to the user are important factors in the decision process for the uptake and use of DST.…”

Restoring Nature at Lower Food Production Costs

“…Furthermore, Schimmelpfennig (2016) investigates the ways and whether farm managers decide to adopt new technologies highlighting that farm size (determined from total cropland area) is a driver influencing adoption based on the finding that farms adopting agricultural technologies tend to be of larger size than those that do not. Schimmelpfennig's study offers support to relevant approaches that similarly suggest smaller farm sizes have an inverse relationship with technological adoption (Cavallo et al, 2014;Mottaleb et al, 2016;Das V. et al, 2019).…”

Acceptance of artificial intelligence in German agriculture: an application of the technology acceptance model and the theory of planned behavior

“…Results from a survey and choice experiment with 250 Italian farmers show that farm size, age, innovativeness, and farmers' tendency to protect the environment increase the likelihood of adoption, while high investment costs are identified as main barrier for adoption (Blasch et al, 2020). Using a mixed-methods approach with 32 Irish farmers, Das et al (2019) identify financial resources and socio-demographics, such as age, gender, and education as influencing factors on the adoption of smart farming technologies. Toma et al (2018) use a SEM to analyze influencing factors on the uptake of innovative crop technologies among Scottish crop farmers.…”