9 research outputs found

    'One size fits all'? Ð The relationship between the value of genetic traits and the farm system

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    The wide use of artificial insemination by dairy farmers has facilitated the development of a multi-billion dollar international market in animal genetics. In the major western dairy producing nations, each country has developed a single index to rank bulls, based on the value of traits they are expected to pass on to their offspring. One of the assumptions behind these indexes is that there is a positive linear relationship between profit (and welfare) with increases in a particular trait, regardless of the farm system. In this paper, it is shown, with examples, that the assumption of linearity is false. More importantly, it is shown that for a combination of reasons, including risk aversion, constraints and other issues, the optimal direction of genetic improvement for New Zealand dairy farmers on an individual and industry level could be quite different. Alternatives to the Òone size fits allÓ index are described.

    Quantitative analysis of behaviour of grazing dairy cows

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    This research thesis describes the quantitative analysis of behaviour of grazing dairy cows in terms of sward height (SH) in combination with the length of the grazing session (grazing duration, GD), the time of allocation of fresh pasture and the type of carbohydrate supplement offered. A review of the literature (Chapter 2) identified that there was limited information on the combined affects of SH and GD on behaviour, herbage dry matter intake (DMI) and intake rate (IR) of dairy cows grazing sub-tropical pastures and how these interact to influence sward structure. Also, there was limited information on how SH x GD, time of allocation of fresh pasture and type of carbohydrate supplement offered affects the temporal patterns of behaviour and the subsequent time-dependent probabilities. In this current study, 2 levels of SH (10 and 13cm) and 5 levels of GD (1, 2, 4, 8 and 15h) were used to quantify the effects of SH and GD on dairy cow grazing behaviour, IR and herbage DMI. Sward height significantly (P70% of their total herbage DMI within the first 4h GD. Quantification of the sward profiles after each SH x GD combination showed that dairy cows grazing kikuyu using the management described in this current study did not graze at random. ... The results from this current thesis highlight the factors that either encourage or discourage grazing by dairy cows and should also help to improve decision tools used for pasture rotation, supplementary feeding and stocking density

    Choosing the best forage species for a dairy farm: The whole-farm approach

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    Although a handful of forage species such as perennial ryegrass are predominant, there are a wide range of forage species that can be grown in sub tropical and temperate regions in Australia as dairy pastures. These species have differing seasonal yields, nutrient quality and water use efficiency characteristics, as demonstrated in a large study evaluating 30 species University of Sydney in New South Wales, Australia. Some species can be grazed, while others require mechanical harvesting that incurs a further cost. Previous comparisons of species that relied on yields of dry matter per unit of some input (typically land or water) cannot simultaneously take into account the season in which forage is produced, or other factors related to the costs of production and delivery to the cows. To effectively compare the profitability of individual species, or combinations of species, requires the use of a whole-farm model. Linear programming was used to find the most profitable mix of forage species for an irrigated dairy farm in an irrigation region of New South Wales, Australia. It was concluded that a typical farmer facing the prevailing milk and purchased feed prices with average milk production per cow would find a mix of species including large proportions of perennial ryegrass (Lolium perenne) and prairie grass (Bromus willdenowii) was most profitable. The result was robust to changes in seasonal milk pricing and moving from year round to seasonal calving patterns

    An Economic Evaluation of the FutureDairy Complementary Forage Rotation System – Using Whole Farm Budgeting

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    A complementary forage rotation (CFR) aims to achieve high levels of home-grown forage to complement high performance dairy pastures. An economic evaluation of the CFR technology is undertaken by combining biophysical modelling with preliminary results from farm trials conducted at the University of Sydney’s Corstorphine Dairy. This data is applied to steady state whole farm budgets to compare four alternate or progressive scenarios that might be considered by farmers looking at the potential to increase farm productivity through their feeding system beyond a base farm scenario. A base scenario of a relatively well managed dairy farm in NSW, with 140 ha of milking area, stocked at 2.4 cows/ha, utilises about 12 t DM/ha/year under irrigation and produces more than 16,000 L/ha/year from 6,900 L/cow, achieves 0.9 per cent return on assets. A system with improved pasture management over the base scenario, utilising 15.6 t DM/ha/year and 1.3 t DM/cow/lactation of concentrates to achieve 6,900 L/cow obtains 3.4 per cent return on assets. A production system where pasture and supplement (concentrates) are emphasised achieves 6 per cent return on assets (3.7 cows/ha, 9,000L/cow and 2.3 t DM/cow/lactation concentrates). In comparison the CFS system obtains a return on total assets of 8 to 12 per cent, based upon actual or targeted (best case) forage yield results, respectively. The CFS-based farm business becomes relatively more profitable when scenarios with increased cost of fertiliser, water and especially grain are examined. A production system incorporating the complementary forage rotation (CFS) has the potential to be profitable. However, these analyses are based upon a steady state situation, after the implementation of the systems on farm, and implementation costs associated with adopting the technology on individual farms should be considered

    Improving the Production and Persistence of Temperate Pasture Species in Subtropical Dairy Regions of Australia

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    In subtropical dairy regions of Australia, temperate pasture species are sown to overcome a shortage of forage in the cooler months (late autumn to mid-spring), due to a decline in growth and quality of tropical grasses. Ryegrass ('Lolium') species are the most widely sown temperate grasses, and with appropriate management, are capable of high yields of quality forage under the subtropical climate. Perennial ryegrass ('Lolium perenne' L.)/white clover ('Trifolium repens' L.) pastures are less costly, and provide a higher quality forage with more even dry matter (DM) production throughout the year, than annual ('L. temulentum' L. or 'L. rigidum' Gaudin) or biennial (L. multiflorum L.) ryegrass pastures. However, under current management, perennial ryegrass pastures have not persisted beyond 2 years in the subtropics, and this lack of persistence is associated with severe stress conditions over summer. Previous studies have indicated that perennial ryegrass survival over simmer could be substantially improved, and incursion of tropical grasses minimised, by appropriate defoliation management. The studies reported in this thesis aimed to confirm the importance of defoliation interval under grazing, and to determine the mechanism by which defoliation affects the survival of perennial ryegrass and white clover. The initial strategy was to determine the critical time of defoliation on subsequent plant survival over summer. The observed association between ryegrass survival and tiller and root growth, and water soluble carbohydrate (WSC) reserves, was then studied further in the glasshouse, in order to determine the mechanism of action. Inducing plants to become dormant over summer, or to regenerate from seed the following autumn, was then evaluated as an alternative to managing plants to survive summer. Lastly, the effect of defoliation on the white clover component of mixed perennial ryegrass/white clover pasture was studied

    Predicting pasture biomass using a statistical model and machine learning algorithm implemented with remotely sensed imagery

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    Accurate daily estimates of pasture biomass can improve the profitability of pasture-based dairy system by optimising input of feed supplements and pasture utilisation. However, obtaining accurate pasture mass estimates is a laborious and time-consuming task. The aim of this study was to test the performance of an integrated method combining remote sensing imagery acquired with a multispectral camera mounted on an unmanned aerial vehicle (UAV), statistical models (generalised additive model, GAM) and machine learning algorithms (random forest, RF) implemented with publicly available data to predict future pasture biomass loads. This study showed that using observations of pasture growth along with environmental and pasture management variables enabled both models, GAM and RF to predict the pre-grazing pasture biomass production at field scale with an average error below 20%. If predictive variables (i.e. post-grazing pasture biomass) were excluded, model performance was reduced, generating errors up to 40%. The post-grazing biomass information at high spatial resolution (<1 m) acquired with the UAV-multispectral camera system was used as predictive variable for future pasture biomass. With the inclusion of the spatially explicit post-grazing biomass variable both models accurately predicted the pre-grazing pasture biomass with an error of 27.7% and 22.9% for RF and GAM, respectively. However, the GAM model performed better than RF in reproducing the spatial variability of pre-grazing pasture biomass. This study demonstrates the capability of statistical and machine learning models implemented with UAV or manually obtained pasture information along with publicly available data to accurately predict future pasture biomass at field and farm scale.</p