The factors contributing to better workplaces for farmers on pasture-based dairy farms

Herd size expansion, combined with the reduced availability of people to work on farms, has led to an increased focus on techniques that can improve dairy farm social sustainability. Effective work organization is one such entity, which could influence farm social sustainability; focusing on having a productive, flexible and standardized farm workload. The objective of this study was to examine the factors that contribute to better workplaces for the farmer using a survey of representative pasture-based dairy farms in Ireland. Potential contributing factors to better workplaces for farmers were identified, namely; farm and farmer characteristics, working day structure, farmer attitudes, farm facilities, labor efficient practices and human resource management practices. A survey was completed by 313 Irish dairy farmers between 20 November and 3 January 2019 to capture relevant information. One proxy indicator was selected to represent each of productivity, flexibility and standardization within the workplace, and each of the 313 farms were categorized into quartiles based on their ranking for these 3 indicators (1 = most effective quartile to 4 = least effective quartile). The average farmer that completed the survey was 51 years old, milked 125 cows, reported to work 69.6 h/ week, take 10.3 d of holidays/ year and had a finish time of 19:52 in spring. The quartile of farms with the most effective farmer workplace reported reduced hours worked per week (58.6 v 82.6 h per week), more holiday days (16.6 v 5.1 d) and weekends off (8.3 v 2.4) per year, and earlier finish times (18:41 v 21:14 in spring) compared with the least effective quartile. Similarly, the most effective farms reported better facilities, and greater implementation of labor efficient and human resource management practices compared with the least effective farms. The most effective quartile for farmer workplace effectiveness were more positive about the industry's potential to offer an effective work-life balance, would be more likely to encourage young people to pursue careers in dairy, and had more positive attitudes toward attracting and retaining workers compared with the least effective quartile. The study highlighted the range of factors contributing to more effective workplaces for farmers, indicating scope for improvement on many farms, and challenges across all farms when compared with other industries in the case of some indicators (e.g., time-off). The results can support the continued extension of concepts regarding work organization to assist farms in alleviating social sustainability challenges; highlighting the differentiating factors between the most and least effective farmer workplaces.

Survey of farm, parlour and milking management, parlour technologies, SCC control strategies and farmer demographics on Irish dairy farms

BACKGROUND

This cross-sectional study describes a survey designed to fill knowledge gaps regarding farm management practices, parlour management practices and implemented technologies, milking management practices, somatic cell count (SCC) control strategies, farmer demographics and attitudes around SCC management on a sample of Irish dairy farms.

RESULTS

We categorized 376 complete responses by herd size quartile and calving pattern. The average respondent herd was 131 cows with most (82.2%) operating a seasonal calving system. The median monthly bulk tank somatic cell count for seasonal calving systems was 137,000 cells/ml (range 20,000 - 1,269,000 cells/ml), 170,000 cells/ml for split-calving systems (range 46,000 - 644,000 cells/ml) and 186,000 cells/ml for 'other' herds (range 20,000 - 664,000 cells/ml). The most common parlour types were swing-over herringbones (59.1%) and herringbones with recording jars (22.2%). The average number of units across herringbone parlours was 15, 49 in rotary parlours and two boxes on automatic milking system (AMS) farms. The most common parlour technologies were in-parlour feeding systems (84.5%), automatic washers on the bulk tank (72.8%), automatic cluster removers (57.9%), and entrance or exit gates controlled from the parlour pit (52.2%). Veterinary professionals, farming colleagues and processor milk quality advisors were the most commonly utilised sources of advice for SCC management (by 76.9%, 50.0% and 39.2% of respondents respectively).

CONCLUSIONS

In this study, we successfully utilised a national survey to quantify farm management practices, parlour management practices and technology adoption levels, milking management practices, SCC control strategies and farmer demographics on 376 dairy farms in the Republic of Ireland. Rotary and AMS parlours had the most parlour technologies of any parlour type. Technology add-ons were generally less prevalent on farms with smaller herds. Despite finding areas for improvement with regard to frequency of liner changes, glove-wearing practices and engagement with bacteriology of milk samples, we also found evidence of high levels of documentation of mastitis treatments and high use of post-milking teat disinfection. We discovered that Irish dairy farmers are relatively content in their careers but face pressures regarding changes to the legislation around prudent antimicrobial use in their herds.

A qualitative survey approach to investigating beef and dairy veterinarians' needs in relation to technologies on farms

Globally, farmers are being increasingly encouraged to use technologies. Consequently, veterinarians often use farm data and technologies to provide farmers with advice. Yet very few studies have sought to understand veterinarians' perceptions of data and technologies on farms. The aim of this study was to understand veterinarians' experiences and opinions on data and technology on beef and dairy farms. An online qualitative survey was conducted with a convenience sample of 36 and 24 veterinarians from the United Kingdom and Ireland, respectively. The data were analysed using reflexive thematic analysis to generate four themes: (1) Improving veterinary advice through data; (2) Ensuring stock person skills are retained; (3) Longevity of technology; and (4) Solving social problems on farms. We show that technologies and data can make veterinarians feel more confident in the advice they give to farmers. However, the quality and quantity of data collected on cattle farms were highly variable. Furthermore, veterinarians were concerned that farmers can become over-reliant on technologies by not using their stockperson skills. As herd sizes increase, technologies can help to improve working conditions on farms with multiple employees of various skillsets. Veterinarians would like innovations that can help them to demonstrate their competence, influence farmers' behaviour, and ensure sustainability of the beef and dairy industries.

Effect of altering milking interval when milking 3 times in 2 days on milk and component yields in pasture-based dairy systems

Dairy farmers face challenges attracting and retaining staff, partly due to the difficulty meeting the desires of the modern workforce. These include flexible work hours and regular time off. The task of milking fundamentally affects the ability of dairy farmers to meet these desires. Milking contributes to a large proportion of the hours spent working on dairy farms. The number of milkings (milking frequency) and their timing (milking interval) within a day influence the number of hours spent milking and what time in the day they occur. Milking 3 times in 2 d (3-in-2) reduces the amount of time spent milking compared with milking twice a day (TAD), without reducing milk yield as much as milking once a day (OAD). However, long intervals between 3-in-2 milkings can still lead to a long workday if farmers are expected to work between milkings. The objective of this study was to determine the effect of milking interval within a 3-in-2 milking frequency on milk yield and composition at 2 stages of lactation and compare these with OAD and TAD milking. Cows (n = 200) were milked in 5 groups of 40 at 3 intervals of 3-in-2: 8-20-20 h, 10-19-19 h, and 12-18-18 h, along with 24 h (OAD), and 10 and 14 h (TAD), for 6 wk at early lactation (mean 24 d in milk ± 7 d, SD) and again at mid lactation (mean 136 d in milk ± 18 d). Milk yields were recorded at each milking and milk samples collected weekly to determine composition. At both early and mid lactation there were no significant differences in milk, fat, protein, or lactose yields between the three 3-in-2 intervals. Cows milked 3-in-2 produced 8% less milk than cows milked TAD and 14% more than cows milked OAD, with smaller differences observed at mid lactation between TAD and 3-in-2. For a 3-in-2 milking frequency, a shorter milking interval can be implemented on the days when cows are milked twice. This may allow farmers to shorten the working day when using 3-in-2, without compromising milk or component yields.

The impact of work organisation on the work life of people on pasture-based dairy farms

The seasonal workload combined with increased dairy herd sizes and a declining workforce have created social sustainability challenges for pasture-based dairy farms. Effective work organisation can build productive capacity that may have a positive impact on this scenario. Our objective was to develop a framework to characterise and examine the effect of work organisation on the working situations of the people involved in a sample of 55 pasture-based dairy farms in Ireland. We conceptualised that effective work organisation on a dairy farm could be considered as a system that is efficient from a labour input perspective, resulting in a profitable farming system with outcomes of good operator well-being, health and safety, and quality of life. A literature review established efficiency & productivity, flexibility and standardisation as our three characteristics of work organisation. Using data from an existing labour time-use study completed from the 1st February to 30th June 2019, we aimed to test the veracity of these work organisation characteristics in the Irish pasture-based dairy system. Two proxy indicators were selected to represent each of the three work organisation characteristics, and each of the 55 farms were categorised into quartiles based on their ranking for these six indicators (1 = most effective quartile to 4 = least effective quartile). The most and least effective quartiles of farms for work organisation showed similar levels of farm labour input and labour efficiency. Farmers in the most effective work organisation quartile were working 51.2 h/week from February to June compared with 70.0 h/week for farmers in the least effective quartile, which was attributed to later start times, earlier finish times, and more time at non-farm activity. Farms achieving effective work organisation had a labour-efficient system with relatively low farmer working hours. Extension of the work organisation concept to other farms could improve their labour situation and aid in alleviating some of the key quality of life challenges faced by dairy farmers.

The Work Assessment Method shows potential to improve performance and social sustainability on Australian dairy farms

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.

Analysis of adoption trends of in-parlor technologies over a 10-year period for labor saving and data capture on pasture-based dairy farms

The use of precision technology is increasingly seen as an option to improve productivity, animal welfare, resource use efficiency, and workplace features on dairy farms. There is limited research related to longitudinal adoption patterns of precision dairy technologies and reasons for any patterns. The aim of this analysis was to investigate trends in technology adoption regarding both the amount (number of farms with a technology) and intensity (number of technologies per farm) of adoption. Surveys of parlor technology adoption were conducted on New Zealand dairy farms in 2008, 2013, and 2018, with 532, 500, and 500 respondents, respectively. Technologies were grouped into labor-saving (LS, such as automatic cluster removers) or data-capture (DC, such as in-line milk meters) categories. Trends were examined for farms that had only LS, only DC, or LS+DC technologies. Technology adoption increased over time; the likelihood of technology adoption in 2018 (and 2013 in parentheses) increased by 21 (22), 7 (68), and 378% (165) for LS, DC, and LS+DC technology groups, respectively, compared to 2008. Farms with LS+DC technologies also had a greater proportion of LS technologies compared to non-LS+DC farms, although this relationship declined over the 10-yr period. The use of a rotary versus herringbone parlor was estimated to be associated with 356 and 470% increase in the likelihood of adopting LS technologies and LS+DC, respectively, from 2008 to 2018. Regional differences in adoption were also found, with the likelihood of adopting DC and LS+DC technologies found to be 46 and 59% greater, respectively, in the South Island of New Zealand, compared to the base region of Waikato. The results highlight the importance of understanding spatial and temporal farm characteristics when considering future effect and adoption of precision dairy technologies. For example, the analysis indicates the occurrence of 2 trajectories to technology investment on farms, where larger farms are able to take advantage of technology opportunities, but smaller farms may be constrained by factors such as lack of economies of scale, limited capital to invest, and inability to retrofit technology into aging parlor infrastructure.