Penry J.F.,Camperdown Veterinary Center |
Brightling P.B.,Harris Park Group |
Dyson R.S.,Dairy Focus |
Paine M.S.,Dairy NZ
New Zealand Veterinary Journal | Year: 2011
A new veterinary service to promote ongoing, incremental improvements in the risk management of mastitis and milk quality was developed between 2005 and 2008. This was designed to enhance the relationship between the farmer and advisor, as an extension of the Countdown Downunder programme, Australia's national mastitis and milk quality programme. This service was co-developed between the Countdown Downunder programme team and a core development group of veterinarians involved with trialling the service, and farmers and social researchers. The service, known as Countdown MAX, involved advisory input at the planning stage, a written risk management plan, multiple engagements between the farm team and advisor for tracking and re-planning, and a service fee. Risk management resources (modules) were developed to be employed at the drying-off and calving periods, and during lactation. During the development and implementation phase eight veterinary practices conducted Countdown MAX consultations on 55 farms. Eighty-eight Countdown MAX modules were delivered in total, with 55% of farms completing more than one module but only 38% of modules reviewed successfully. A social research project examined the implementation of the Countdown MAX service in participating veterinary practices during the development phase. Findings of the project were that the successful uptake of a new mastitis service into a veterinary practice was enhanced through uptake by practice owners of the concept, the formation of a written practice plan, adequate communication and explanation of the new service to all staff, logistical support for the service within the practice, and transfer of mastitis expertise within the practice.
Watkins N.L.,University of Waikato |
Schipper L.A.,University of Waikato |
Sparling G.P.,University of Waikato |
Thorrold B.,Dairy NZ |
Balks M.,University of Waikato
New Zealand Journal of Agricultural Research | Year: 2013
The effectiveness of multiple small doses of the nitrification inhibitor dicyandiamide (DCD) to decrease denitrification under warm moist conditions was tested in a 1-year field trial on a grazed dairy pasture. DCD was applied approximately every 4 weeks as an aqueous spray onto ten replicate plots 3 days after rotational grazing by dairy cows. Each application was at the rate of 3 kg DCD ha-1, with a total annual application of 33 kg ha-1. Denitrification was assessed 5 days after each DCD application using the acetylene block method. At the end of the trial, the rate of degradation of DCD under summer conditions was measured. DCD significantly decreased the mean annual nitrate concentration by about 17%. Denitrification and denitrification enzyme activity were highly variable and no significant effect of DCD in decreasing denitrification was detected. In the summer month of December, DCD degraded rapidly with an estimated half-life of 5 ± 3 days (mean and standard deviation). Copyright © 2013 The Royal Society of New Zealand.
Rutledge S.,University of Waikato |
Mudge P.L.,University of Waikato |
Mudge P.L.,Landcare Research |
Wallace D.F.,University of Waikato |
And 5 more authors.
Agriculture, Ecosystems and Environment | Year: 2014
It is well known that frequent cultivation of cropped soils leads to increased soil respiration and loss of soil carbon (C). In contrast, little is known about the impact of occasional cultivation of permanent grasslands on soil C and CO2 dynamics. Occasional cultivation of pastures is common if a pasture is part of an arable-ley rotation, or as part of pasture renewal.Here we report on the CO2 balance following three cultivation events of temperate permanent pasture in New Zealand. For two experiments, one during a drought during late summer/autumn 2008 and one under moist soil conditions in spring 2008, CO2 losses following cultivation were measured using the closed chamber technique. During the spring 2008 experiment, two soils with different clay mineralogy and drainage were studied. During a third cultivation event in autumn 2010 CO2 exchange was measured using eddy covariance.Measured short-term respiratory losses following cultivation across the three experiments ranged from 151 to 329gCm-2 over 39 to 43 days. Rates of CO2 loss measured during non-drought conditions were generally higher than those previously reported from studies in Europe and North America, presumably because of generally high soil temperatures, non-limiting moisture conditions and high organic carbon availability at our study site. The 'net impact of cultivation' (taking into account both direct respiratory losses of CO2 and the lack of photosynthetic carbon input following cultivation) across the three experiments ranged between 77 and 406gCm-2 over 39-43 days. Both direct CO2 respiratory losses and the net impact of cultivation appeared highly dependent on soil moisture status, with lowest losses measured during a severe drought and highest losses measured in spring when ample moisture was present. Rates of respiratory CO2 losses did not decrease over the duration of our experiments (39-43 days).Our results suggest that when aiming to reduce C losses resulting from cultivation of permanent grassland, it is preferable to cultivate when conditions for soil microbial activity and photosynthesis are sub-optimal; for our study site this meant in autumn instead of spring because of lower soil moisture availability. We also recommend minimising the duration of the period between spraying the old sward and establishment of the new sward or crop. © 2013 Elsevier B.V.
Tozer K.N.,Agresearch Ltd. |
Minne E.,Dairy NZ |
Cameron C.A.,Agresearch Ltd.
Crop and Pasture Science | Year: 2012
Yellow bristle grass (Setaria pumila) and summer grass (Digitaria sanguinalis) are summer-active annual grass weeds which infest temperate dairy pastures. A study was undertaken over 2 years to compare hand-sown yellow bristle and summer grass establishment, survival, and seed production in pastures grazed by dairy cows and based on (i) tetraploid perennial ryegrass (Lolium perenne), (ii) tetraploid perennial ryegrass and white clover (Trifolium repens), and (iii) tall fescue (Festuca arundinacea) and white clover, to determine which pasture type offered the greatest resistance to these grass weeds. Ingress of grass weeds was similar in all three pasture types. Total dry matter production was similar for all pasture types for the first year and lower in tall fescue+clover than perennial ryegrass pasture in the second year. All pasture types had a similar distribution of microsite types (bare groundcanopy, basal covercanopy) in both years. The annual grass weeds were most prevalent in bare ground+canopy microsites, which were also the most frequent of the four microsite types. In the first year, 5% of microsites were occupied within 2 months of sowing, whereas in the second year, microsite occupation remained 13% for all assessments. In the first year, panicle production of yellow bristle and summer grass was similar (averaging 4.1 panicles plant-1); in the second year, panicle production was greater for summer grass (0.80 v. 0.16paniclesplant-1, respectively). Where present, these annual grass weeds are likely to spread in dryland dairy pastures sown with either perennial ryegrass or tall fescue. Variability in their panicle production between years shows how their impact on pasture performance and consequent need for control measures will also vary from year to year. © CSIRO 2012.
Edwards J.P.,Dairy NZ |
Edwards J.P.,Massey University |
O'Brien B.,Teagasc |
Lopez-Villalobos N.,Massey University |
Jago J.G.,Dairy NZ
Journal of Dairy Research | Year: 2013
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 1430Â 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. © 2013 Proprietors of Journal of Dairy Research.
Ho C.K.M.,Australian Department of Primary Industries and Fisheries |
Newman M.,Dairy NZ |
Dalley D.E.,Canterbury Agriculture and Science Center |
Little S.,IBM |
Wales W.J.,Australian Department of Primary Industries and Fisheries
Animal Production Science | Year: 2013
Changes in the farm operating and policy environments and a need to remain profitable under a cost-price squeeze have contributed to dairy systems in Australia and New Zealand becoming more intensive and complex in recent decades. Farm systems in both countries are now diverse, varying from being based predominantly on pasture with little purchased supplements, to those dependent on high levels of feed supplements and even having zero grazing. Dairy farm performance (defined in this paper as production or technical performance), return (return on assets or profit) and risk (variation in economic performance over time), and intensity of dairy systems was examined using farm survey data, case studies and existing published studies. The level of single technical performance measures, such as milk production, feed conversion efficiency and pasture consumption, prevailing in a business are not guides to the operating profit and return on assets of a business. In addition, when survey data of farm performance was grouped by return on assets, few farms were in the top 25% in successive years. Farms that performed consistently well were characterised by good, but not extreme, technical performance in a range of key areas, which translated to favourable business return (return on asset and profit). The knowledge and skills of farm managers are critical, and means that many different dairy systems can perform well physically and financially and successfully meet farmer goals. The relation between risk and the intensity of dairy systems was also investigated. Options that intensified systems generally resulted in higher net wealth for the farm owner, but almost always at increased risk. The best system for any farmer to operate is one which best meets their multifaceted preferences and goals, regardless of system type. © CSIRO 2013.