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Milking efficiency of swingover herringbone parlours in pasture-based dairy systems

Published online by Cambridge University Press:  04 September 2013

J Paul Edwards ,
Bernadette O'Brien ,
Nicolas Lopez-Villalobos  and
Jenny G Jago
J Paul Edwards*
Affiliation:
DairyNZ, Private Bag 3221, Hamilton 3240, New Zealand Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand
Bernadette O'Brien
Affiliation:
Teagasc Animal and Grassland Research and Innovation Centre, Moorepark, Fermoy, Co. Cork, Ireland
Nicolas Lopez-Villalobos
Affiliation:
Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11222, Palmerston North 4442, New Zealand
Jenny G Jago
Affiliation:
DairyNZ, Private Bag 3221, Hamilton 3240, New Zealand
*
*For correspondence; e-mail: [email protected]
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Abstract

The objective of this study was to collect and analyse milking data from a sample of commercial farms with swingover herringbone parlours to evaluate milking efficiency over a range of parlour sizes (12–32 milking units). Data were collected from 19 farms around the Republic of Ireland equipped with electronic milk metres and herd management software that recorded data at individual milking sessions. The herd management software on each farm was programmed to record similar data for each milking plant type. Variables recorded included cow identification, milking date, identification time, cluster-attachment time, cluster/unit number, milk yield, milking duration, and average milk flow rate. Calculations were performed to identify efficiency benchmarks such as cow throughput (cows milked per h), milk harvesting efficiency (kg of milk harvested per h) and operator efficiency (cows milked per operator per h). Additionally, the work routine was investigated and used to explain differences in the benchmark values. Data were analysed using a linear mixed model that included the fixed effects of season-session (e.g. spring-AM), parlour size and their interaction, and the random effect of farm. Additionally, a mathematical model was developed to illustrate the potential efficiency gains that could be achieved by implementing a maximum milking time (i.e. removing the clusters at a pre-set time regardless of whether the cow had finished milking or not). Cow throughput and milk harvesting efficiency increased with increasing parlour size (12 to 32 units), with throughput ranging from 42 to 129 cows/h and milk harvesting efficiency from 497 to 1430 kg/h (1–2 operators). Greater throughput in larger parlours was associated with a decrease in operator idle time. Operator efficiency was variable across farms and probably dependent on milking routines in use. Both of these require consideration when sizing parlours so high levels of operator efficiency as well as cow throughput can be achieved simultaneously. The mathematical model indicated that application of a maximum milking time within the milking process could improve cow throughput (66% increase in an 18-unit parlour when truncating the milking time of 20% of cows). This could allow current herd milking durations to be maintained as herd size increases.

Keywords

Milking efficiencyparlourdairyovermilking
Type
Research Article
Information
Journal of Dairy Research , Volume 80 , Issue 4 , November 2013 , pp. 467 - 474
Copyright
Copyright © Proprietors of Journal of Dairy Research 2013 

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References

Clarke, T, Cuthbertson, EM, Greenall, RK, Hannah, MC, Jongman, E & Shoesmith, D 2004 Milking regimes to shorten milking duration. Journal of Dairy Research 71 419–426. doi: 10.1017/s0022029904000421Google Scholar
Clarke, T, Cuthbertson, EM, Greenall, RK, Hannah, MC & Shoesmith, D 2008 Incomplete milking has no detectable effect on somatic cell count but increased cell count appears to increase strip yield. Australian Journal of Experimental Agriculture 48 1161–1167. doi: 10.1071/EA07259Google Scholar
Cuthbert, S 2008 DairyNZ Milking Practices and Technology use Survey. Hamilton, New Zealand: LICGoogle Scholar
DairyNZ & LIC 2011 New Zealand dairy statistics 2010–11. http://www.dairynz.co.nz/file/fileid/39959Google Scholar
DairyNZ MilkSmart 2013a Applying MaxT info sheet. http://www.milksmart.co.nz/CDF_Resources/Download/Index/79097Google Scholar
DairyNZ MilkSmart 2013b Pitstep animation. http://www.milksmart.co.nz/FlashFiles/DairyNZMilksmart/PitSteps.swfGoogle 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. doi: 10.1071/AN12071Google Scholar
Edwards, JP, Jago, JG & Lopez-Villalobos, N 2013a Large rotary dairies achieve high cow throughput but are not more labour efficient than medium sized rotaries. Animal Production Science 53 573–579. doi: 10.1071/AN12312Google Scholar
Edwards, JP, Jago, JG & Lopez-Villalobos, N 2013b Milking efficiency can be improved by increasing automatic cluster remover thresholds to grazing dairy cows without applying pre-milking stimulation. Journal of Dairy Science 96 3766–3773. doi: 10.3168/jds.2012-6394Google Scholar
Edwards, JP, O'Brien, B, Lopez-Villalobos, N & Jago, JG 2013c Overmilking causes deterioration in teat-end condition of dairy cows in late lactation. Journal of Dairy Research 80 344–348Google Scholar
Edwards, JP, Jago, JG & Lopez-Villalobos, N 2013d Short-term application of pre-stimulation and increased automatic cluster remover threshold affect milking characteristics of grazing dairy cows in late lactation. Journal of Dairy Science 96 1886–1893. doi: 10.3168/jds.2012-6191Google Scholar
EU 2004 Annex III, Section IX, Chapter I, Part II, Subpart B, Point 1(b) of Regulation (EC) No 853/2004. Official Journal of the European Commission L 226 22–82Google Scholar
Gleeson, DE, O'Callaghan, EJ & Rath, MV 2003 The effects of genotype, milking time and teat-end vacuum pattern on the severity of teat-end hyperkeratosis. Irish Journal of Agricultural and Food Research 42 195–203Google Scholar
ICBF 2011 ICBF Dairy Cattle Statistics. Bandon, Co. Cork: Irish Cattle Breeding Federation Society LimitedGoogle Scholar
Jago, JG, Burke, JK & Williamson, JH 2010a Effect of automatic cluster remover settings on production, udder health, and milking duration. Journal of Dairy Science 93 2541–2549. doi: 10.3168/jds.2009-2949Google Scholar
Jago, JG, McGowan, JE & Williamson, JH 2010b Effect of setting a maximum milking time, from peak lactation, on production, milking time and udder health. New Zealand Veterinary Journal 58 246–252. doi: 10.1080/00480169.2010.69298Google Scholar
Kelly, PT 2009 A study of the somatic cell count (SCC) of Irish milk from herd management and environmental perspectives. PhD Thesis. Dublin, Ireland: National University of IrelandGoogle Scholar
Neijenhuis, F, Barkema, HW, Hogeveen, H & Noordhuizen, J 2001 Relationship between teat-end callosity and occurrence of clinical mastitis. Journal of Dairy Science 84 2664–2672. doi: 10.3168/jds.S0022-0302(01)74720-0Google Scholar
O'Brien, B, Jago, JG, Edwards, JP, Lopez-Villalobos, N & McCoy, F 2012 Milking parlour size, pre-milking routine and stage of lactation affect efficiency of milking in single-operator herringbone parlours. Journal of Dairy Research 79 216–223. doi: 10.1017/S0022029912000088Google Scholar
O'Donnell, S, Horan, B, Butler, AM & Shalloo, L 2011 A survey of the factors affecting the future intentions of Irish dairy farmers. Journal of Agricultural Science 149 647–654. doi: 10.1017/S0021859611000037Google Scholar
O'Donovan, K, O'Brien, B, Ruane, DJ, Kinsella, J & Gleeson, D 2008 Labour input on Irish dairy farms and the effect of scale and seasonality. Journal of Farm Management 13 327–342Google Scholar
Taylor, G, van der Sande, L & Douglas, R 2009 Smarter not Harder: Improving Labour Productivity in the Primary Sector. Hamilton, New Zealand: DairyNZGoogle Scholar
Thiel, CC & Dodd, FH 1979 Machine Milking. Technical Bulletin No. 1. Reading, England: NIRDGoogle Scholar