Understanding the adoption of precision agriculture technologies in Australia

Even though precision agriculture (PA) technologies are available to farmers in Australia, the adoption rate in agricultural industries is lower than desired by government and industry leaders, and the potential business productivity and profitability improvements, as well as environmental benefits, from PA technologies adoption are not fully realised. In particular, the potential for PA technologies to generate significant international competitiveness gains as well as reductions in environmental impacts caused by agricultural activities is yet to be fully understood. To date, there is a paucity of literature examining the multifaceted interactions between different components/determinants in the adoption process of PA technologies in Australia. Whilst there are various adoption tools, and this thesis examines many of them, one comprehensive theoretical model, the model of determinants of diffusion, dissemination and implementation of innovations (MDDDII), offers a strong conceptual framework to explore multiple components/determinants that are likely to influence PA technologies adoption. This research utilised MDDDII, which consists of nine components and several determinants to explore the presence and interactions of the model’s components within the PA technologies adoption literature. Through examining the presence or absence of the components/determinants of MDDDII in PA technologies adoption literature, and the case studies and the reports, and the strategic plans of the RDCs (the Australian grey literature), this thesis offers insights into why poor adoption rates are being experienced in Australia . First, a systematic review of 58 PA technologies adoption literature was conducted, second, thematic analysis was carried out to identify the themes of 43 Australian grey literature, and third, the themes were then examined through the lens of MDDDII. After conducting the systematic review of PA technologies adoption literature, it was found that PA technologies adoption literature covered five components of MDDDII: the innovation, communication and influence, outer context, adopter, system antecedents for innovation and linkage. Thus, none of the publications covered the other four components of MDDDII: system antecedents for innovation, system readiness for innovation, assimilation and implementation process. Likewise, thematic analysis found that the Australian grey literature included only four components of MDDDII: the innovation, communication and influence, outer context, and adopter. Thus, in the case of the grey literature, five components of MDDDII were absent: system antecedents for innovation, system readiness for innovation, linkage, assimilation, and implementation process. Analysis of both the PA technologies adoption and the Australian grey literature on PA technologies adoption revealed that only a few studies encompassed a broad range of components/determinants recognised in the broader PA technologies adoption literature, and none covered all components of MDDDII. As a result, it was concluded that the relationship between poor adoption rates of PA technologies in Australian agricultural industry and the systematic absence of several components/determinants of MDDDII within academic and the Australian grey literature needs further investigation.