Register      Login
Animal Production Science Animal Production Science Society
Food, fibre and pharmaceuticals from animals
RESEARCH ARTICLE

Identifying current challenges and research priorities to guide the design of more attractive dairy-farm workplaces in New Zealand

C. R. Eastwood A B , J. Greer A , D. Schmidt A , J. Muir A and K. Sargeant A
+ Author Affiliations
- Author Affiliations

A DairyNZ Ltd, Private Bag 3221, Hamilton 3240, New Zealand.

B Corresponding author. Email: callum.eastwood@dairynz.co.nz

Animal Production Science 60(1) 84-88 https://doi.org/10.1071/AN18568
Submitted: 6 September 2018  Accepted: 30 October 2018   Published: 22 November 2018

Abstract

Globally, dairy farmers face issues with attracting and retaining high-quality staff. In the present study, a qualitative research method was used to explore the current challenges in relation to people on farm, the approaches currently used by farmers to make dairying more attractive and productive for people, and perspectives on the challenges for attracting and retaining people on future farms. Current challenges were in the areas of recruitment, productivity, skills and learning, farm and industry structural issues, and impact of farm profitability on ability to implement new people practices. Participants’ vision of the future dairy workplace was one that is highly dynamic, more open to consumers and the community, and largely data-driven. We suggest that dairy workplace research priorities focus on the design and testing of new systems to provide people with meaningful work and a good lifestyle, without compromising profit. Specific priorities include using new ways of connecting and communicating to create engaged and effective teams, developing flexible farm teams who deeply understand their role in the value chain and the consumer connection, defining the opportunity for technology to make the job easier and more enjoyable, developing farm systems that are safe, innovative, and provide a good career, and helping farming businesses demonstrate their people performance to consumers.

Additional keywords: community perceptions, dairy employee, future farming.


References

Britt JH, Cushman RA, Dechow CD, Dobson H, Humblot P, Hutjens MF, Jones GA, Ruegg PS, Sheldon IM, Stevenson JS (2018) Invited review: learning from the future – a vision for dairy farms and cows in 2067. Journal of Dairy Science 101, 3722–3741.
Invited review: learning from the future – a vision for dairy farms and cows in 2067.Crossref | GoogleScholarGoogle Scholar |

Cardoso CS, Hötzel MJ, Weary DM, Robbins JA, von Keyserlingk MAG (2016) Imagining the ideal dairy farm. Journal of Dairy Science 99, 1663–1671.
Imagining the ideal dairy farm.Crossref | GoogleScholarGoogle Scholar |

Deming J, Gleeson D, O’Dwyer T, Kinsella J, O’Brien B (2018) Measuring labor input on pasture-based dairy farms using a smartphone. Journal of Dairy Science 101, 9527–9543.
Measuring labor input on pasture-based dairy farms using a smartphone.Crossref | GoogleScholarGoogle Scholar |

Eastwood CR, Jago JG, Edwards JP, Burke JK (2016) Getting the most out of advanced farm management technologies: roles of technology suppliers and dairy industry organisations in supporting precision dairy farmers. Animal Production Science 56, 1752–1760.

Eastwood CR, Klerkx L, Ayre M, Dela Rue B (2017a) Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation. Journal of Agricultural & Environmental Ethics
Managing socio-ethical challenges in the development of smart farming: from a fragmented to a comprehensive approach for responsible research and innovation.Crossref | GoogleScholarGoogle Scholar |

Eastwood CR, Dela Rue BT, Gray DI (2017b) Using a ‘network of practice’ approach to match grazing decision-support system design with farmer practice. Animal Production Science 57, 1536–1542.
Using a ‘network of practice’ approach to match grazing decision-support system design with farmer practice.Crossref | GoogleScholarGoogle Scholar |

Edwards JP, Dela Rue BT, Jago JG (2015) Evaluating rates of technology adoption and milking practices on New Zealand dairy farms. Animal Production Science 55, 702–709.
Evaluating rates of technology adoption and milking practices on New Zealand dairy farms.Crossref | GoogleScholarGoogle Scholar |

Gargiulo JI, Eastwood CR, Garcia SC, Lyons NA (2018) Dairy farmers with larger herd sizes adopt more precision dairy technologies. Journal of Dairy Science 101, 5466
Dairy farmers with larger herd sizes adopt more precision dairy technologies.Crossref | GoogleScholarGoogle Scholar |

Jago J, Eastwood CR, Kerrisk K, Yule I (2013) Precision dairy farming in Australasia: adoption, risks and opportunities. Animal Production Science 53, 907–916.

Kolstrup CL (2012) What factors attract and motivate dairy farm employees in their daily work? Work (Reading, Mass.) 41, 5311–5316.

Lobao L, Stofferahn CW (2008) The community effects of industrialized farming: social science research and challenges to corporate farming laws. Agriculture and Human Values 25, 219–240.
The community effects of industrialized farming: social science research and challenges to corporate farming laws.Crossref | GoogleScholarGoogle Scholar |

McKillop J, Heanue K, Kinsella J (2018) Are all young farmers the same? An exploratory analysis of on-farm innovation on dairy and drystock farms in the Republic of Ireland. Journal of Agricultural Education and Extension 24, 137–151.
Are all young farmers the same? An exploratory analysis of on-farm innovation on dairy and drystock farms in the Republic of Ireland.Crossref | GoogleScholarGoogle Scholar |

Nettle R, Crawford A, Brightling P (2018) How private-sector farm advisors change their practices: an Australian case study. Journal of Rural Studies 58, 20–27.
How private-sector farm advisors change their practices: an Australian case study.Crossref | GoogleScholarGoogle Scholar |

Nuthall PL, Old KM (2017) Will future land based food and fibre production be in family or corporate hands? An analysis of farm land ownership and governance considering farmer characteristics as choice drivers. The New Zealand case. Land Use Policy 63, 98–110.
Will future land based food and fibre production be in family or corporate hands? An analysis of farm land ownership and governance considering farmer characteristics as choice drivers. The New Zealand case.Crossref | GoogleScholarGoogle Scholar |

Shadbolt N, Apparao D, Hunter S, Bicknell K, Dooley A (2017) Scenario analysis to determine possible, plausible futures for the New Zealand dairy industry. New Zealand Journal of Agricultural Research 60, 349–361.
Scenario analysis to determine possible, plausible futures for the New Zealand dairy industry.Crossref | GoogleScholarGoogle Scholar |

Strauss A, Corbin J (1990) ‘Basics of qualitative research. Grounded theory procedures and techniques.’ (Sage: Newbury Park, CA)

Taylor G, van der Sande L, Douglas R (2009) ‘Smarter not harder: improving labour productivity in the primary sector.’ (DairyNZ: Hamilton, New Zealand) Available at http://maxa.maf.govt.nz/sff/about-projects/search/05-028/technical-report.pdf [Verified 30 August 2018]

Thornton PK, Whitbread A, Baedeker T, Cairns J, Claessens L, Baethgen W, Bunn C, Friedmann M, Giller KE, Herrero M, Howden M, Kilcline K, Nangia V, Ramirez-Villegas J, Kumar S, West PC, Keating B (2018) A framework for priority-setting in climate smart agriculture research. Agricultural Systems 167, 161–175.
A framework for priority-setting in climate smart agriculture research.Crossref | GoogleScholarGoogle Scholar |

Wolfert S, Ge L, Verdouw C, Bogaardt M-J (2017) Big data in smart farming: a review. Agricultural Systems 153, 69–80.
Big data in smart farming: a review.Crossref | GoogleScholarGoogle Scholar |