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Context: Social aspects of livestock farming systems, including farm workforce and how work is organised, have received less research attention than the biophysical and technological aspects. This constrains understanding of social challenges to the sustainability of livestock systems, such as farm labour shortages, farmers' overwork, the undesirability of farming careers, workforce and skills changes linked to new technologies, and the connections of all these to farm performance and profitability. Aims: We introduce and test the applicability and utility in the Australian context of a method developed in France for assessing work organisation: The Work Assessment Method (WAM). The WAM goes beyond standard labour productivity metrics, such as total labour cost or livestock units per worker, to examine different types of work (routine, seasonal), who does the work (owner–managers, employees, contractors) and how these change seasonally. A measure of social sustainability ('calculated time available') is a key feature. Methods: We conducted the first Australian trial of the WAM on two Victorian dairy farms. Through facilitated discussions between participating farmers and the research team, we evaluated the utility of the method, and identified requirements to adapt it for larger, pasture-based dairy systems. Key results: The WAM was applied successfully on the pilot farms, despite differences in farm systems between France and Australia. The method characterised in detail the sources of overwork for each farm, enabling discussion of how the social sustainability and overall performance of the farms could be improved. The participating farmers reported that the method provided insights that were not available from current financial and physical analyses. Conclusions: The WAM shows potential for supporting farmers, advisors and researchers in work aimed at improving farm social sustainability and profitability, but requires adaptation to suit Australian conditions. A larger exploratory study applying the WAM on additional dairy farms, and on mixed farms, is suggested. Implications: This study provides a strong foundation for further research to develop the WAM as a useful research and advisory tool for Australian livestock production systems. Farmers, advisors and researchers need better methods for analysing farm work, to address challenges affecting the social sustainability and profitability of livestock farming. We report results from the first Australian trial of a method developed in France for assessing farm work organisation: The Work Assessment Method. Participating farmers reported that the Work Assessment Method provided insights not available from standard physical and financial analyses and the study provides a foundation for research to further develop the Work Assessment Method for Australian livestock production systems. [ABSTRACT FROM AUTHOR]

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. • 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. [ABSTRACT FROM AUTHOR]