A 100-Year Review: A century of change in temperate grazing dairy systems

peer-reviewedFrom 1917 to 2017, dairy grazing systems have evolved from uncontrolled grazing of unimproved pastures by dual-purpose dairy-beef breeds to an intensive system with a high output per unit of land from a fit-for-purpose cow. The end of World War I signaled significant government investments in agricultural research institutes around the world, which coincided with technological breakthroughs in milk harvesting and a recognition that important traits in both plants and animals could be improved upon relatively rapidly through genetic selection. Uptake of milk recording and herd testing increased rapidly through the 1920s, as did the recognition that pastures that were rested in between grazing events yielded more in a year than those continuously grazed. This, and the invention and refinement of the electric fence, led to the development of “controlled” rotational grazing. This, in itself, facilitated greater stocking rates and a 5 to 10% increase in milk output per hectare but, perhaps more importantly, it allowed a more efficient use of nitrogen fertilizer, further increasing milk output/land area by 20%. Farmer inventions led to the development of the herringbone and rotary milking parlors, which, along with the “unshortable” electric fence and technological breakthroughs in sperm dilution rates, allowed further dairy farm expansion. Simple but effective technological breakthroughs in reproduction ensured that cows were identified in estrus early (a key factor in maintaining the seasonality of milk production) and enabled researchers to quantify the anestrus problem in grazing herds. Genetic improvement of pasture species has lagged its bovine counterpart, but recent developments in multi-trait indices as well as investment in genetic technologies should significantly increase potential milk production per hectare. Decades of research on the use of feeds other than pasture (i.e., supplementary feeds) have provided consistent milk production responses when the reduction in pasture intake associated with the provision of supplementary feed (i.e., substitution rate) is accounted for. A unique feature of grazing systems research over the last 70 yr has been the use of multi-year farm systems experimentation. These studies have allowed the evaluation of strategic changes to a component of the system on all the interacting features of the system. This technique has allowed excellent component research to be “systemized” and is an essential part of the development of the intensive grazing production system that exists today. Future challenges include the provision of skilled labor or specifically designed automation to optimize farm management and both environmental sustainability and animal welfare concerns, particularly relating to the concentration of nitrogen in each urine patch and the associated risk of nitrate leaching, as well as concerns regarding exposure of animals to harsh climatic conditions. These combined challenges could affect farmers' “social license” to farm in the future

Comparative evaluation of a new lactation curve model for pasture-based Holstein-Friesian dairy cows

Fourteen lactation models were fitted to average and individual cow lactation data from pasture-based dairy systems in the Australian states of Victoria and Tasmania. The models included a new "log-quadratic" model, and a major objective was to evaluate and compare the performance of this model with the other models. Nine empirical and 5 mechanistic models were first fitted to average test-day milk yield of Holstein-Friesian dairy cows using the nonlinear procedure in SAS. Two additional semiparametric models were fitted using a linear model in ASReml. To investigate the influence of days to first test-day and the number of test-days, 5 of the best-fitting models were then fitted to individual cow lactation data. Model goodness of fit was evaluated using criteria such as the residual mean square, the distribution of residuals, the correlation between actual and predicted values, and the Wald-Wolfowitz runs test. Goodness of fit was similar in all but one of the models in terms of fitting average lactation but they differed in their ability to predict individual lactations. In particular, the widely used incomplete gamma model most displayed this failing. The new log-quadratic model was robust in fitting average and individual lactations, and was less affected by sampled data and more parsimonious in having only 3 parameters, each of which lends itself to biological interpretation

Genetic and environmental factors influencing milk, protein and fat yields of pasture-based dairy cows in Tasmania

The objective of this study was to provide an update on milk production performance, heritability, genetic and phenotypic correlations among production traits that are valuable for management, breeding and selection decisions in pasture-based dairy systems. The study utilised a total of 106 990 lactation records of Holstein–Friesian (FF), Jersey (JJ) and their crossbreds (HF) from 428 Tasmanian dairy herds collected between 2000 and 2005. The data were analysed using the least-squares approach with a general linear model and restricted maximum likelihood approach with a linear animal model. Results indicated highly significant (P < 0.01) effects of breed, herd size, cow's parity, season and year of calving on milk, protein and fat yields. Average milk and protein yields per cow per lactation were highest in the FF breed (5212 L and 171 kg, respectively) and lowest in the JJ breed (3713 L and 143 kg, respectively). FF cows also produced 13.5 kg more milk fat than JJ and HF cows. Furthermore, milk, fat and protein yields were highest for cows calving during spring and lowest for autumn-calving cows. It was also evident that cows in very large herds (>1110 cows/herd) out-produced those in smaller herds. Heritability was highest for milk yield and lowest for somatic cell count ranging from 0.28 to 0.41. Genetic and phenotypic correlations between milk, fat and protein yields ranged from 0.41 to 0.85, and 0.66 to 0.92, respectively. However, genetic and phenotypic correlations between the log of somatic cell count and the production traits ranged from 0.03 to 0.09 and –0.03 to –0.05. We conclude that breed, herd size, parity, season and year of calving were among the main factors correlated with the productivity of dairy cows in Tasmania and adjustments for these factors would be mandatory for any unbiased comparison of lactation performance within and between pasture-based dairy production systems. The practical application of this information would be valuable to dairy farmers for decisions related to breeding, selection and management of their herds

Medials For Meshing And More

INTRODUCTION The goal of an automated FE modelling system is to accept a general problem definition as input and to return results of prescribed accuracy. A general problem definition will include the geometric model of the component to be analysed as well as all the required attributes such as loading, restraints and material properties. Automatic, adaptive mesh generation is an essential prerequisite for generating analysis results of prescribed accuracy for a given computational domain. However for many problems, the geometric design model is too complex a domain to analyse in a realistic timeframe. The purpose here is to argue that the medial axis transform of a geometric domain is a powerful tool for recognising features which are significant in the derivation of appropriate analysis models from design geometry. THE MEDIAL AXIS TRANSFORM The medial axis of a 2D region is the locus of the centre of an inscribed disc of maximal diameter as it rolls around t

Response of perennial ryegrass (Lolium perenne) to renovation in Australian dairy pastures

This study reports on the effect of oversowing perennial ryegrass (Lolium perenne L.) into a degraded perennial ryegrass and white clover (Trifolium repens L.) pasture to extend its productive life using various intensities of seedbed preparation. Sites in New South Wales (NSW), Western Australia (WA), South Australia (SA) and Tasmania (Tas.) were chosen by a local group of farmers as being degraded and in need of renovation. Control (nil renovation) and medium (mulch and graze, spray with glyphosphate and sow) renovation treatments were common to all sites whereas minimum (mulch and graze, and sow) and full seedbed (graze and spray with glyphosphate and then full seedbed preparation) renovation were imposed only at some sites. Plots varied in area from 0.14 to 0.50 ha, and were renovated then sown in March or April 2000 and subsequently grazed by dairy cows. Pasture utilisation was estimated from pre- and post-grazing pasture mass assessed by a rising plate pasture meter. Utilised herbage mass of the renovated treatments was significantly higher than control plots in period 1 (planting to August) and 2 (first spring) at the NSW site only. There was no difference among treatments in period 3 (first summer) at any site, and only at the WA and NSW sites in period 4 (March to July 2001) was there a response to renovation. As a result, renovation at the NSW site only significantly increased ryegrass utilisation over the whole experimental period. Ryegrass plant density was higher at the NSW, WA (excluding minimum renovation) and Tas. (excluding full renovation) sites 6 months after renovation but this was only sustained for 12 months for the minimum and medium treatments at the NSW and Tas. sites, respectively, presumably due to reduced competition from naturalised C4 summer grasses [kikuyu (Pennisetum clandestinum) and paspalum (Paspalum dilatatum)] in NSW At the NSW, WA and SA sites, the original ryegrass plant density was low (<35 plants/m2) compared with the Tas. site where density was around 185/m2. The response to renovating a degraded perennial ryegrass pasture varied between sites in Australia. Positive responses were generally small and were most consistent where renovation removed competing C4 summer grasses