Farming smarter with big data: Insights from the case of Australia's national dairy herd milk recording scheme. (May 2020)

• Record Type: Journal Article
• Authors: Newton, Joanna E.; Nettle, Ruth; Pryce, Jennie E.
• Is Part Of: Agricultural systems. Volume 181(2020)
• Publication Date: 2020-05
• Subjects: Farm decision making -- Collaborative governance -- Livestock genomics -- Herd testing; Agricultural systems -- Periodicals; Agriculture -- Environmental aspects -- Periodicals; 338.16
• DOI: 10.1016/j.agsy.2020.102811
• Languages: English
• ISSNs: 0308-521X

Abstract

Abstract: Digitalization and the use of Smart Farming Technologies are considered a major opportunity for the future of agriculture. However, realisation of full benefits is constrained by: (1) farmers' interest in and use of big data to improve farm decision making; (2) issues of data sovereignty and trust between providers and users of data and technology; (3) institutional arrangements associated with the governance of data platforms. This paper examines the case of Australia's dairy herd milk recording system, arguably one of agriculture's first cases of 'big data' use, which collects, analyses and uses farm-level data (milk production, lactation and breeding records) to provide individual cow and herd performance information, used by individual farmers for farm management decisions. The aim of this study was to 1) examine the use of big data to add value to farm decision making; and 2) explore factors and processes, including institutional arrangements, which influence farmer engagement with and use of big data. This paper traces the Australian history of the organisation of dairy herd recording (established in 1912 and digitalized in late 1970s) and then uses findings from a longitudinal study of 7 case study dairy farms, which were incentivised to become involved in herd recording in 2015. Applying a conceptual framework linking path dependency in farm decision making and collaborative governance capacity, we find three new important dimensions of the farm user context influencing farmer demand for big data applications: 1) the transition to a new business stage; 2) the additionality farmers seek from data generated in one component of the farm system to other subsystems, and 3) the use of data in long term or strategic decision making. Further, we identified critical attributes of support services in addressing digital literacy, capacity and capability issues at farm level, including diversity in data presentation formats and facilitation of the on-farm transition process through intermediary herd test organisations. The role of farmers as governance actors, or citizens in the decisions of the trajectory of big data applications, adds to understanding of the nature of collaborative governance arrangements that support farm engagement. Highlights: Farmer interest in data relates to business stage and the decision's importance. Cooperative governance structures facilitate farmer trust and use of big data. Support services reduce the impact of low digital literacy, capacity and capability. Inter-dependence occurs between existing data processes and optimising data use.