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

Getting the most out of advanced farm management technologies: roles of technology suppliers and dairy industry organisations in supporting precision dairy farmers

C. R. Eastwood A B C , J. G. Jago B , J. P. Edwards B and J. K. Burke B
+ Author Affiliations
- Author Affiliations

A Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Vic. 3010, Australia.

B DairyNZ, Private Bag 3221, Hamilton 3240, New Zealand.

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

Animal Production Science 56(10) 1752-1760 https://doi.org/10.1071/AN141015
Submitted: 19 December 2014  Accepted: 9 April 2015   Published: 2 July 2015

Abstract

The use of advanced management technologies is increasing in pasture-based dairy systems, an evolution that has been termed precision dairying. This change has been driven, at least in part, by a continual increase in the scale of dairy farming and an associated drive for efficiencies, and technological advances in the area of sensors and automated devices for animal and resource management. In this paper, a survey of New Zealand precision dairy farmers is presented, highlighting lessons from farmers for technology developers and industry. Respondents indicated that they invested in technologies such as electronic identification, milk meters, automated cup removers, and automated drafting for labour saving and to make herd management easier. Most were positive about their investments, with perceived benefits from saved time during milking, decreased farm workforce requirement, and increased farm profitability. Farmers also felt there was unused functionality in their herd management systems and that they could benefit from increased support and training to get more from their technology. Technology suppliers need to refocus on after-sales service and tailor their support programs to stages of learning development, while creating a value proposition for farmers to pay for such services. Dairy industry organisations need to take the lead in building awareness of the opportunities such technologies offer, while facilitating access to independent information about technology capability and investment.

Additional keywords: adoption of technology, dairy, decision making, farmer co-learning, precision farming.


References

Alawneh JI, Stevenson MA, Williamson NB, Lopez-Villalobos N, Otley T (2011) Automatic recording of daily walkover liveweight of dairy cattle at pasture in the first 100 days in milk. Journal of Dairy Science 94, 4431–4440.
Automatic recording of daily walkover liveweight of dairy cattle at pasture in the first 100 days in milk.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtV2gsLnE&md5=75b72da8a9cf7afbf7ea4c59da58d6efCAS | 21854916PubMed |

Alvarez J, Nuthall P (2006) Adoption of computer based information systems: the case of dairy farmers in Canterbury, NZ, and Florida, Uruguay. Computers and Electronics in Agriculture 50, 48–60.
Adoption of computer based information systems: the case of dairy farmers in Canterbury, NZ, and Florida, Uruguay.Crossref | GoogleScholarGoogle Scholar |

Anon. (2014) New Zealand Farm Data Code of Practice: for organisations involved in collecting, storing, and sharing primary production data in New Zealand. Available at http://www.farmdatacode.org.nz/wp-content/uploads/2014/06/farm-data-code-of-practice-final.pdf [Verified 24 November 2014]

Bewley J (2013) Exciting dairy breakthroughs: science fiction or precision dairy farming? In ‘Proceedings of the precision dairy conference and expo, Rochester, Minneapolis, USA, 26–27 June, 2013’. (Ed. M Endres) (University of Minnesota: Rochester, MN) Available at http://precisiondairy.umn.edu/prod/groups/cfans/@pub/@cfans/@ansci/documents/asset/cfans_asset_463117.pdf [Verified 18 September 2014]

Bewley JM, Boehlje MD, Gray AW, Hogeveen H, Kenyon SJ, Eicher SD, Schutz MM (2010) Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation. Agricultural Finance Review 70, 126–150.
Assessing the potential value for an automated dairy cattle body condition scoring system through stochastic simulation.Crossref | GoogleScholarGoogle Scholar |

Clark DA, Caradus JR, Monaghan RM, Sharp P, Thorrold BS (2007) Issues and options for future dairy farming in New Zealand. New Zealand Journal of Agricultural Research 50, 203–221.
Issues and options for future dairy farming in New Zealand.Crossref | GoogleScholarGoogle Scholar |

Clason DL, Dormody TJ (1994) Analyzing data measured by individual Likert-type items. Journal of Agricultural Education 35, 31–35.
Analyzing data measured by individual Likert-type items.Crossref | GoogleScholarGoogle Scholar |

Dela Rue BT, Kamphuis C, Burke CR, Jago JG (2014) Using activity-based monitoring systems to detect dairy cows in oestrus: a field evaluation. New Zealand Veterinary Journal 62, 57–62.
Using activity-based monitoring systems to detect dairy cows in oestrus: a field evaluation.Crossref | GoogleScholarGoogle Scholar | 1:STN:280:DC%2BC2c%2FpsFCmtg%3D%3D&md5=e5b6c5004d0f21888417810a565ce8bfCAS | 24156478PubMed |

Eastwood C (2013) Precision dairy in Australia – lessons for end users, technology developers, and industry organisations. In ‘Proceedings of the precision dairy conference and expo, Rochester, Minneapolis, USA, 26–27 June 2013’. pp. 85–86. (Ed. M Endres) (University of Minnesota: Rochester, MN) Available at http://precisiondairy.umn.edu/prod/groups/cfans/@pub/@cfans/@ansci/documents/asset/cfans_asset_463117.pdf [Verified 18 September 2014]

Eastwood C, Kenny S (2009) Art or science? Heuristic versus data driven grazing management on dairy farms. Extension Farming Systems Journal 5, 95–102.

Eastwood CR, Chapman DF, Paine MS (2012) Networks of practice for co-construction of agricultural decision support systems: case studies of precision dairy farms in Australia. Agricultural Systems 108, 10–18.
Networks of practice for co-construction of agricultural decision support systems: case studies of precision dairy farms in Australia.Crossref | GoogleScholarGoogle Scholar |

Eastwood C, Trotter M, Scott N (2013) Understanding the user: learning from on-farm application of precision farming technologies in the Australian livestock sector. Australian Journal of Multi-Disciplinary Engineering 10, 41–50.
Understanding the user: learning from on-farm application of precision farming technologies in the Australian livestock sector.Crossref | GoogleScholarGoogle Scholar |

Edwards JP, Lopez-Villalobos N, Jago JG (2012) Increasing platform speed and the percentage of cows completing a second rotation improves throughput in rotary dairies. Animal Production Science 52, 969–973.
Increasing platform speed and the percentage of cows completing a second rotation improves throughput in rotary dairies.Crossref | GoogleScholarGoogle Scholar |

Edwards JP, Jago JG, Lopez-Villalobos N (2013) Large rotary dairies achieve high cow throughput but are not more labour efficient than medium sized rotaries. Animal Production Science 53, 573–579.
Large rotary dairies achieve high cow throughput but are not more labour efficient than medium sized rotaries.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 |

García SC, Fulkerson WJ (2005) Opportunities for future Australian dairy systems: a review. Australian Journal of Experimental Agriculture 45, 1041–1055.
Opportunities for future Australian dairy systems: a review.Crossref | GoogleScholarGoogle Scholar |

Hekkert MP, Suurs RAA, Negro SO, Kuhlmann S, Smits REHM (2007) Functions of innovation systems: a new approach for analysing technological change. Technological Forecasting and Social Change 74, 413–432.
Functions of innovation systems: a new approach for analysing technological change.Crossref | GoogleScholarGoogle Scholar |

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

Kilpatrick S, Johns S (1999) Managing farming. How farmers learn. RIRDC report 99/31, Canberra, Australia.

Klerkx L, Jansen J (2010) Building knowledge systems for sustainable agriculture: supporting private advisors to adequately address sustainable farm management in regular service contacts. International Journal of Agricultural Sustainability 8, 148–163.
Building knowledge systems for sustainable agriculture: supporting private advisors to adequately address sustainable farm management in regular service contacts.Crossref | GoogleScholarGoogle Scholar |

Kuehne G, Nicholson C, Robertson M, Llewellyn R, McDonald C (2012) Engaging project proponents in R&D evaluation using bio-economic and socio-economic tools. Agricultural Systems 108, 94–103.
Engaging project proponents in R&D evaluation using bio-economic and socio-economic tools.Crossref | GoogleScholarGoogle Scholar |

Meijer IS, Hekkert MP, Koppenjan JFM (2007) How perceived uncertainties influence transitions; the case of micro-CHP in the Netherlands. Technological Forecasting and Social Change 74, 519–537.
How perceived uncertainties influence transitions; the case of micro-CHP in the Netherlands.Crossref | GoogleScholarGoogle Scholar |

Murphy C, Nettle R, Paine M (2013) The evolving extension environment: implications for dairy scientists. Animal Production Science 53, 917–923.

Tarrant K, Armstrong D (2012) An economic evaluation of automatic cluster removers as a labour saving device for dairy farm businesses. Australian Farm Business Management Journal 9, 43–48.

Watson P (2009) CowTime tracking survey 2009. Report prepared for the Department of Primary Industries Victoria, Melbourne, Vic. Available at http://www.cowtime.com.au/edit/Reports/COWTIME_TRACKING_SURVEY_2009_REPORT_FINAL.PDF [Verified 18 September 2014]

Wenger E (1998) ‘Communities of practice: learning, meaning, and identity.’ (Cambridge University Press: Cambridge, UK)

Yule IJ, Eastwood CR (2011) Challenges and opportunities for precision dairy farming in New Zealand: developing a research agenda to enhance farm management benefits from precision dairy use. Report prepared for DairyNZ, Hamilton, New Zealand.