Sondergaard E.,University of Aarhus |
Applied Animal Behaviour Science | Year: 2010
The natural behaviour of horses in response to danger is to take flight, and consequently human handlers can be injured. Reducing the flight response and general reactivity of horses is therefore likely to reduce the incidence of injuries to handlers. In this experiment we investigated the effect of handling foals in the first 2 days after birth on their subsequent response to handling, humans and novelty, and the foal-mare relationship. Standardbred foals were assigned to one of two groups, handled (H) (N = 22, 12 colts, 10 fillies) and control (C) (N = 22, 11 colts, 11 fillies). Handling took place 3 times/day on days 1 and 2 after birth for 10 min/session. Individual foals were gently restrained and stroked all over their body using bare hands and then a plastic bag and each leg was lifted once. C foals received no handling. C and H foals did not differ in their reaction to freeze branding at a mean age of 14 days. The approach and leave behaviour of mare-foal pairs were observed at pasture during week 5 to evaluate their relationship. Mares of H foals were less active in keeping the pair together than mares of C foals (GLM: F1,33 = 6.81; P < 0.05). At 6 weeks of age all colts were introduced to an arena, together with their mare, and their reaction to a novel object and an unknown human were tested. Treatment did not affect heart rate of foals or in mares. C foals initiated more suckling bouts than H when no human was present (Wilcoxon: Z = 2.44, N = 22, P < 0.05) indicating that they responded differently to the novel arena than H foals. However, there was no difference between H and C foals in their exploratory behaviour in the arena. When a human was present in the arena, H foals had a shorter flight distance than C foals (Z = -1.98, N = 22, P < 0.05) and tended to move further away from the mare (Z = -1.80, N = 22, P = 0.07). Handling of foals in the first 2 days after birth appeared to affect the foal-mare relationship and alter their perception of humans at a later age but did not alter their response to novelty or to handling. The effects of early handling of foals on the foal-mare relationship require further investigation. © 2010 Elsevier B.V. All rights reserved.
Vogeler I.,Agresearch Ltd. |
Beukes P.,DairyNZ |
Burggraaf V.,Agresearch Ltd.
Agricultural Systems | Year: 2013
Dairy farms are under pressure to increase productivity while reducing environmental impacts. We used the DairyNZ Whole Farm Model (WFM) and APSIM to evaluate the effect of mitigation strategies within an efficient farm (EF) in the Waikato region, NZ, on these targets. Mitigation strategies compared with the baseline farm (BF) included the use of fewer more efficient cows, low nitrogen (N) feed supplements, loafing pads, less N fertiliser and nitrification inhibitor (DCD). To encompass climate affects three different years with average, high and low annual rainfall were modelled. The WFM predicted number of urinations and urinary N loads deposited during individual grazing events were used as an input for APSIM to simulate N leaching from urine patches, as well as from non-urinated areas. Results were aggregated to obtain total N leached on a paddock and farm scale. For all 3. years, farm averaged N leaching was lower, by 20-55%, in the EF compared with the BF farm. DCD reduced leaching in two of the 3. years by 12% and 15%. N leaching was lowest for N deposited in the wet year and highest for the dry year. Milk production was consistently greater for the EF compared to the BF, with an increase in milksolids (MS)/ha ranging from 8% in the wet, to 17% in the dry year. © 2012 Elsevier Ltd.
Eastwood C.R.,University of Melbourne |
Chapman D.F.,Lincoln University at Christchurch |
Agricultural Systems | Year: 2012
The on-farm use of commercial decision support systems (DSSs) presents learning and adaptation challenges for farmers and their social learning networks. A study of six Australian dairy farms installing new precision dairy farming technology was undertaken to develop an in-depth picture of the issues occurring at the interface where precision farming data and decision-making meet. A qualitative exploratory case study method was used, with farmers each interviewed up to five times from pre-installation until 2. years of use. A three-phase learning trajectory was observed amongst farmers involving early learning, consolidation, and advanced use. Farmers exhibited experiential learning but also learned via interaction with a network of on- and off-farm contacts forming a network of practice around the new users. This precision dairy farming network of practice formed a vital method of exchanging knowledge on how to best use technology and data in farming systems, with DSSs acting as a boundary object for learning. Externalisation of tacit knowledge into an explicit form suitable for DSSs was a major focus of this social learning. Co-construction of DSS knowledge in the emerging network was impeded by the absence of potentially important agents, in addition to the incomplete links between existing agents such as technology retailers and farmers. A technological innovation systems perspective is used to propose an improved framework to make greater use of translators and intermediaries. It is aimed at improving links amongst the community to more effectively aid farmers in creating new knowledge in agricultural DSS use. © 2012 Elsevier Ltd.
Jago J.G.,DairyNZ |
Animal | Year: 2011
Dairy herd size is expected to increase in many European countries, given the recent policy changes within the European Union. Managing more cows may have implications for herd performance in the post-quota era. The objective of this study was to characterise spring-calving herds according to size and rate of expansion, and to determine trends in breeding policy, reproduction and production performance, which will inform industry of the likely implications of herd expansion. Performance data from milk recording herds comprising 775 795 lactations from 2555 herds for the years 2004 to 2008 inclusive were available from the Irish Cattle Breeding Federation. Herds were classified into Small (average of 37 cows), Medium (average of 54 cows) and Large (average of 87 cows) and separately into herds that were not expanding (Nil expansion), herds expanding on average by three cows per year (Slow expansion) and herds expanding on average by eight cows per year (Rapid expansion). There was no association between rate of expansion and 305-day fat and protein yield. However, 305-day milk yield decreased and milk protein and fat percentage increased with increasing rate of expansion. There were no associations between herd size and milk production except for protein and fat percentage, which increased with increasing herd size. Average parity number of the cows decreased as rate of expansion increased and tended to decrease as herd size increased. In rapidly expanding herds, cow numbers were increased by purchasing more cattle. The proportion of dairy sires relative to beef sires used in the breeding programme of expanding herds increased and there was more dairy crossbreeding, albeit at a low rate. Similarly, large herds were using more dairy sires and fewer beef sires. Expanding herds and large herds had superior reproductive performance relative to non-expanding and small herds. Animals in expanding herds calved for the first time at a younger age, had a shorter calving interval and were submitted for breeding by artificial insemination at a higher rate. The results give confidence to dairy producers likely to undergo significant expansion post-quota such that, despite managing more cows, production and reproductive performance need not decline. The management skills required to achieve these performance levels need investigation. © 2011 The Animal Consortium.
Doole G.J.,University of Western Australia |
Doole G.J.,University of Waikato |
Agricultural Systems | Year: 2013
Grazing systems constitute the most extensive land use worldwide. However, economic analysis of these systems has mainly involved the use of linear optimisation methods that provide a general description of the complex processes contained therein. This paper describes a nonlinear optimisation model of a New Zealand dairy farm that incorporates a detailed depiction of key biophysical processes present within grazing systems. The capacity of this optimisation model to provide rich insight into the effects of higher stocking rates within grazing systems is demonstrated in an empirical application. In accordance with system trials, this application shows that higher stocking rates on pasture-based New Zealand dairy farms generally increase pre-grazing pasture biomass, decrease post-grazing pasture biomass, increase pasture utilisation, decrease herbage allowance, decrease intake and energy consumption per cow, decrease milk production per cow, increase milk production per ha, and reduce conception rate. Nevertheless, an intermediate stocking rate is optimal, as greater milk production with a higher stocking rate is not sufficient to offset the associated costs. © 2013 Elsevier Ltd.
Animal Production Science | Year: 2012
Within a day, grazing decisions such as 'when' to begin, 'which' frequency and 'how' to distribute grazing events determine ruminants' diurnal grazing pattern. Ruminants can have between three and five daily grazing events. The major grazing events occur in the early morning and late afternoon/early evening; the later grazing event is the longest and most significant in terms of herbage intake. This review first attempts to answer 'why does this happen?' and then to examine evidence for managing this pattern to improve animal production. Due to photosynthesis and transpiration during the day, herbage accumulates DM, sugars and essential fatty acids, which dilute fibre and protein contents and facilitate herbage particle breakdown during ingestion. Diurnal fluctuations in light intensity stimulate circadian release of neuropetides and hormones, providing the cue to start grazing and modulating ingestive-digestive behaviours that interact with the diurnal fluctuation in herbage feeding value. Grazing decisions depend on grazing environments, the current state of the animal, and on past and anticipated states of the animal. The dusk grazing event seems to be an adaptative feeding strategy to maximise daily energy acquisition, providing a steady release of nutrients over night. Hunger deceives ruminants and makes them graze at dawn, when herbage presents the lowest feeding value. Hunger, however, can be used to concentrate and intensify grazing events. Strategic management of these interactions emerges as the tool to alter the frequency, intensity and temporal distribution of diurnal grazing events, and thereby to increase and modulate nutrient supply to and productivity of grazing ruminants. © 2012 CSIRO.
Lee J.M.,DairyNZ |
Clark A.J.,DairyNZ |
Grass and Forage Science | Year: 2013
Temperate pasture-based dairy farming systems with low input of supplementary feed are vulnerable to changes in climate through alterations in feed supply and nutritive value. Although current systems in New Zealand (NZ) and southeast Australia have been successful in adapting to variable weather conditions, they will need to undergo further changes to continue to profit in the future. This review describes predicted changes in climate in NZ and southeast Australia, likely effects on the feedbase used in the pasture-based dairy industry and the flow-on effect on milk-solids production and profitability. Potential adaptation options that will allow farmers to take advantage of new opportunities and minimize any negative impacts of climate change are also identified. For example, in many regions, annual pasture production is predicted to increase due to carbon dioxide fertilization and warmer temperatures during winter/spring. Production may decline, however, in regions with either reduced rainfall or severe flooding. Should this occur, farmers could strategically use supplementary feed, reduce stocking rates, irrigate or sow alternative plant species with greater drought tolerance. Pasture-based dairy systems have high levels of adaptive capacity, and there are opportunities to continue to improve production efficiencies particularly where rainfall change is small. Further investigation into possible adaptation options is required to determine their impact on milk-solids production and profitability, as well as to identify additional options. © 2013 John Wiley & Sons Ltd.
Jago J.G.,DairyNZ |
Burke J.L.,DairyNZ |
Journal of Dairy Science | Year: 2010
This study evaluated the effect of 4 criteria for determining the end-point of milking on milk yield, milk composition, completeness of milking-out, teat skin condition, somatic cell count (SCC), and the incidence of clinical mastitis (CM) in pasture-based dairy cows milked over 35 wk. The objective was to reduce milking duration without affecting milk production, SCC, or CM. Milking end-point treatments were as follows: cluster removed at a milk flow rate of 0.2 kg/min (ACR200); cluster removed at a milk flow rate of 0.4 kg/min (ACR400); cluster removed at a milk flow rate of 0.2 kg/min or at a maximum cluster attachment time from d 5 of lactation (MaxTEarly); and cluster removed at a milk flow rate of 0.2 kg/min until an average of 63 ± 21 d in milk, then cluster removed at a milk flow rate of 0.2 kg/min or a maximum cluster attachment time (MaxTPeak). Maximum cluster attachment times were set at 7.5. min and 5.4. min for morning and afternoon milkings, respectively. Cows (approximately 94/treatment) were assigned to treatment at calving and milked twice daily at intervals of 9 and 15. h. Milking duration was shorter for ACR400, MaxTEarly, and MaxTPeak compared with ACR200. During wk 1 to 15, milk, protein, and lactose yields were less for MaxTEarly than for ACR400 and MaxTPeak, but not different from ACR200. During wk 16 to 35 and over the entire experiment, total milk, fat, protein, and lactose yields did not differ among treatments. Teat condition did not differ among the 4 treatments. Postmilking strip yield in wk 12 was greatest for MaxTEarly and least for ACR200; at wk 27, however, treatment had no effect on the completeness of milking-out. No differences were observed in either teat condition or the proportion of cows with at least 1 case of CM during the 35 wk. Somatic cell count was low across all treatments, but highest for ACR400. Increasing the automatic cluster remover threshold setting from 0.2 to 0.4 kg/min decreased milking duration without affecting milk production, CM, or teat condition. Combining a cluster removal milk flow threshold setting with a maximum cluster attachment time, when applied from either early lactation or from peak lactation, reduced milking duration without affecting milk production, CM, or SCC. Both strategies have potential to improve milking efficiency in dairy herds in which premilking preparation is minimal. © 2010 American Dairy Science Association.
Kalaugher E.,University of Waikato |
Bornman J.F.,Curtin University Australia |
Clark A.,DairyNZ |
Environmental Modelling and Software | Year: 2013
The development of effective climate change adaptation strategies for complex, adaptive socio-ecological systems such as farming systems, requires an in-depth understanding of both the dynamic nature of the systems themselves and the changing environment in which they operate.To date, adaptation studies in the New Zealand dairy sector have been either bottom-up, qualitative social research with farmers and communities, or top-down, quantitative biophysical modelling. Each of these approaches has clear benefits as well as significant limitations. This review considers concepts and approaches that support the potential for different disciplines to complement each other in developing a more in-depth understanding of farming systems and their adaptive potential. For this purpose, a Mixed Methods Framework is presented, using examples from a pilot study of a New Zealand dairy farm to illustrate the complementarities between the two current approaches.By presenting this methodology in a specific context, the review provides the theoretical basis for a practical way to integrate quantitative and qualitative research for climate change adaptation research. © 2012 Elsevier Ltd.
News Article | December 1, 2016
This report studies Grasses Silage in Global market, especially in North America, Europe, China, Japan, Southeast Asia and India, focuses on top manufacturers in global market, with production, price, revenue and market share for each manufacturer, covering DairyNZ Hay & Forage Grower Market Segment by Regions, this report splits Global into several key Regions, with production, consumption, revenue, market share and growth rate of Grasses Silage in these regions, from 2011 to 2021 (forecast), like North America Europe China Japan Southeast Asia India Split by product type, with production, revenue, price, market share and growth rate of each type, can be divided into Type I Type II Type III Split by application, this report focuses on consumption, market share and growth rate of Grasses Silage in each application, can be divided into Application 1 Application 2 Application 3 Global Grasses Silage Market Research Report 2016 1 Grasses Silage Market Overview 1.1 Product Overview and Scope of Grasses Silage 1.2 Grasses Silage Segment by Type 1.2.1 Global Production Market Share of Grasses Silage by Type in 2015 1.2.2 Type I 1.2.3 Type II 1.2.4 Type III 1.3 Grasses Silage Segment by Application 1.3.1 Grasses Silage Consumption Market Share by Application in 2015 1.3.2 Application 1 1.3.3 Application 2 1.3.4 Application 3 1.4 Grasses Silage Market by Region 1.4.1 North America Status and Prospect (2011-2021) 1.4.2 Europe Status and Prospect (2011-2021) 1.4.3 China Status and Prospect (2011-2021) 1.4.4 Japan Status and Prospect (2011-2021) 1.4.5 Southeast Asia Status and Prospect (2011-2021) 1.4.6 India Status and Prospect (2011-2021) 1.5 Global Market Size (Value) of Grasses Silage (2011-2021) 2 Global Grasses Silage Market Competition by Manufacturers 2.1 Global Grasses Silage Production and Share by Manufacturers (2015 and 2016) 2.2 Global Grasses Silage Revenue and Share by Manufacturers (2015 and 2016) 2.3 Global Grasses Silage Average Price by Manufacturers (2015 and 2016) 2.4 Manufacturers Grasses Silage Manufacturing Base Distribution, Sales Area and Product Type 2.5 Grasses Silage Market Competitive Situation and Trends 2.5.1 Grasses Silage Market Concentration Rate 2.5.2 Grasses Silage Market Share of Top 3 and Top 5 Manufacturers 2.5.3 Mergers & Acquisitions, Expansion 3 Global Grasses Silage Production, Revenue (Value) by Region (2011-2016) 3.1 Global Grasses Silage Production by Region (2011-2016) 3.2 Global Grasses Silage Production Market Share by Region (2011-2016) 3.3 Global Grasses Silage Revenue (Value) and Market Share by Region (2011-2016) 3.4 Global Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 3.5 North America Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 3.6 Europe Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 3.7 China Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 3.8 Japan Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 3.9 Southeast Asia Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 3.10 India Grasses Silage Production, Revenue, Price and Gross Margin (2011-2016) 4 Global Grasses Silage Supply (Production), Consumption, Export, Import by Regions (2011-2016) 4.1 Global Grasses Silage Consumption by Regions (2011-2016) 4.2 North America Grasses Silage Production, Consumption, Export, Import by Regions (2011-2016) 4.3 Europe Grasses Silage Production, Consumption, Export, Import by Regions (2011-2016) 4.4 China Grasses Silage Production, Consumption, Export, Import by Regions (2011-2016) 4.5 Japan Grasses Silage Production, Consumption, Export, Import by Regions (2011-2016) 4.6 Southeast Asia Grasses Silage Production, Consumption, Export, Import by Regions (2011-2016) 4.7 India Grasses Silage Production, Consumption, Export, Import by Regions (2011-2016) 5 Global Grasses Silage Production, Revenue (Value), Price Trend by Type 5.1 Global Grasses Silage Production and Market Share by Type (2011-2016) 5.2 Global Grasses Silage Revenue and Market Share by Type (2011-2016) 5.3 Global Grasses Silage Price by Type (2011-2016) 5.4 Global Grasses Silage Production Growth by Type (2011-2016) 6 Global Grasses Silage Market Analysis by Application 6.1 Global Grasses Silage Consumption and Market Share by Application (2011-2016) 6.2 Global Grasses Silage Consumption Growth Rate by Application (2011-2016) 6.3 Market Drivers and Opportunities 6.3.1 Potential Applications 6.3.2 Emerging Markets/Countries 7 Global Grasses Silage Manufacturers Profiles/Analysis 7.1 DairyNZ 7.1.1 Company Basic Information, Manufacturing Base and Its Competitors 7.1.2 Grasses Silage Product Type, Application and Specification 18.104.22.168 Type I 22.214.171.124 Type II 7.1.3 DairyNZ Grasses Silage Production, Revenue, Price and Gross Margin (2015 and 2016) 7.1.4 Main Business/Business Overview 7.2 Hay & Forage Grower 7.2.1 Company Basic Information, Manufacturing Base and Its Competitors 7.2.2 Grasses Silage Product Type, Application and Specification 126.96.36.199 Type I 188.8.131.52 Type II 7.2.3 Hay & Forage Grower Grasses Silage Production, Revenue, Price and Gross Margin (2015 and 2016) 7.2.4 Main Business/Business Overview