Is big data for big farming or for everyone? Perceptions in the Australian grains industry

Abstract

Continued population growth and land intensification put increasing pressure on agricultural production and point to a need for a 'step change' in agriculture to meet the demand. Advances in digital technology-often encapsulated in the term 'big data'-are increasingly assumed to be the way this challenge will be met. For this to be achieved, it is necessary to understand the ways that farmers and other industry stakeholders perceive big data and how big data might change the industry. It is also necessary to address emerging moral and ethical questions about access, cost, scale and support, which will determine whether farms will be able to be 'big data enabled'. We conducted a discourse analysis of 26 interviews with stakeholders in the grains industry in Australia. Two main discourses were identified: (1) big data as a technology that will significantly benefit a few larger farms or businesses-Big Data is for Big Farming-and conversely (2) big data as a way for every farmer to benefit-Big Data is for Everyone. We relate these findings and the literature on adoption of technology and social studies in agriculture to the potential of farmers to embrace big data, from basic concerns about network infrastructure through to more complex issues of data collection and storage. The study highlights that there are key questions and issues that need to be addressed in further development of digital technology and big data in agriculture, specifically around trust, equity, distribution of benefits and access. This is the first study of big data in agriculture that takes a discourse analysis approach and thus interrogates the status quo and the prevailing norms and values driving decisions with impacts on both farmers and wider society.

Understanding the values behind farmer perceptions of trees on farms to increase adoption of agroforestry in Australia

“…The interviews were transcribed, entered in an NVIVO database (to facilitate analysis) and examined using a constructivist grounded theory approach (Charmaz 2006) and a discourse analysis methodology (Fleming et al 2018). These qualitative methods fall under the umbrella of content analysis, based on the common technique of 'coding' (Bazeley and Jacson 2013).…”

“…This reflective starting point is the critical contribution of this paper, but it is just the first step. Once the values underlying behaviour are made visible, it becomes possible to consciously assess them and alter them if desired (Fleming et al 2018). This is an area we emphasise as needing prioritisation in establishing future programs.…”

“…Understanding of motivations (and barriers) for tree planting are well documented (Pannell et al 2006;Schirmer and Bull 2014;Torabi et al 2016). What is lacking, in the relevant sociological literature, is sufficient analysis of the social world views and discourses that create farmer perceptions and behaviour (Fleming et al 2018;Fleming and Vanclay 2010). This paper aims to understand farmer perspectives around trees on farms and to identify mechanisms to support farmers to establish agroforestry systems that add value to farming enterprises.…”

“…Identifying and evaluating these key factors should inform strategies to better communicate and to better integrate trees on farms into farmers' rationales and reasons for farming. The focus, for this paper, is primarily on values, but key language, rules, norms, assumptions and information sources are also identified to examine how values might be leveraged into changing behaviour (Fleming et al 2018).…”

Edge Computing-Enabled Wireless Sensor Networks for Multiple Data Collection Tasks in Smart Agriculture

“…At present, precision agriculture and smart agriculture have attracted wide attentions in academia and industry, which is seen as a new means for achieving food growth [1][2][3]. As data is the foundation of agricultural artificial intelligence, big data analysis, and other applications, different data are the most important driving force for realizing smart agriculture [4][5][6][7]. Therefore, data acquisition and recording are the first steps towards scientific research and applications of smart agriculture.…”