Using lactation curves as a tool for breeding, nutrition and health management decisions in pasture-based dairy systems

Milk yield and reproductive efficiency are crucial to profitable dairying. Although, genetic improvement in the last few decades has led to substantial increases in milk yield/cow, fertility and reproductive health have declined (Dematawewa and Berger, 1998). In a pasture-based system, a 365 day calving interval is crucial for optimum profit. Hence the need to increase milk yield by improving persistence of lactation rather than peak lactation which puts increased stress on the cows at the time when they should be rebreeding. Peak milk yield, persistency and lactation length are the key components of the lactation profile. Dairy cows with high peak yields are more prone to metabolic and physiological disorders (Terkeli et al 1999). Although estimated breeding values (EBV) in dairy cows in Australia incorporates indices of economic value, such as survival and milking speed, the impact of the current breeding approach and management on the shape of the lactation profile is not clear. Mathematical functions such as those previously used to describe a series of milk test day records (Wood, 1967, Wilmink, 1987), have the advantage of minimizing random variation while simultaneously summarising the lactation profile into biologically interpretable parameters

Comparative effects of ASI and APR sire breeding values on the lactation profile of pasture-based Holstein-Friesian cows

Estimated Breeding Values (EBVs) of bulls are useful indicators of the genetic transmission ability of an animal of desirable traits to their progeny. Lactation profile differs between different merit cows but for pasture-based production systems, the impact of emerging EBV evaluation methods remains largely unpublished. In this study, Wood’s incomplete gamma model (Y(t) = atbe−ct ) was utilised to compare the effects of the Australian Selection Index (ASI) and Australian Profit Ranking (APR) EBVs on the shape of the lactation profile of first-parity, pasture-based Holstein-Friesian cows. Initial yield and the rate of increase to peak were significantly influenced by EBV choice, although peak yield was not. It was concluded that Wood’s incomplete gamma function adequately modelled the lactation profile of pasture-based cows explaining over 90% of the observed variation irrespective of using ASI or APR sire breeding values

Modelling the effect of stocking rate on the lactation profiles of grazing Holstein-Friesian dairy cows using cubic splines

The primary purpose of modeling lactation is to predict the dairy cow’s average daily milk yield with minimal error, after adjusting for various environmental factors. While empirical and mechanistic models have been commonly utilised to model the lactation profile of dairy cows, random regression procedures of Legendre polynomials and cubic splines are increasingly being used. The objectives of this study were to compare the lactation profiles and performance of dairy cows on dryland versus irrigated pastures at different stocking rates with or without grain supplementation using cubic splines model. Cubic splines adequately modelled the bi-weekly milk yield data with low residuals and uncorrelated coefficients attributable to the great flexibility of the model. Without supplementation, mean milk yield did not differ, but was slightly higher in cows grazing at 2.5-3.5 cows/ha stocking rate (SR) compared to cows stocked below at 2.0 c/ha and above at 4.0 c/ha (Figure 1). Irrespective of SR, cows on irrigated pasture had higher peaks except those stocked at 4.0 c/ha. Pasture allocation significantly (p<0.05) increased the rise to peak milk yield in cows stocked at lower stocking rates (2.4-2.5 c/ha) compared to those on 2.8-3.5 c/ha but the latter were more persistent and had higher predicted total milk yields. The results demonstrated the accuracy of cubic splines in modeling lactation and that higher stocking rates can improve the efficiency of pasture utilisation when coupled with adequate grain supplementation

Genetic and phenotypic factors inuencing milk, protein and fat yields of dairy cows in Tasmania, Australia

The Australian State of Tasmania enjoys a cool, temperate climate that remains the backbone of its pasture-based dairy production system. In this study, 330,366 lactation records from 428 Tasmanian dairy herds collected between 2000 and 2005 were analysed. The objective was to determine the inuence of genetic and non-genetic factors on milk, protein and fat yields of pasture-based dairy cows. The data were statistically subjected to analyses of variance using general linear mixed model procedures with repeated measures. State-wide average milk yield per lactation over a standard 305-day lactation length was 5200.7 ± 1239.7 litres (ranging from 1107 to 13256 litres), while fat and protein yields averaged 205.5 ± 47.0 kg (ranging from 53 to 385 kg) and 166.2 ± 41.5 kg (ranging from 47 to 297 kg), respectively. Highly signicant (P<0.001) effects on milk, protein and fat yields attributable to variation in herd size, cow’s parity, breed, season and year of calving were detected. Milk yield increased linearly with increase in parity (means of 3482.4, 4019.5, 4615.4, 4826.1 and 5018.8 litres per lactation for parities 1, 2, 3, 4 and >4, respectively). Milk, fat and protein yields were highest in cows calving during the spring season (4769.8 litres, 215.2kg and 168 kg respectively), Holstein-Friesian genotypes produced the most milk (5211 litres), protein (171 kg) and fat (210kg) yields per lactation. Herd sizes of more than 1110 cows produced the most milk, fat and protein. Productivity per cow increased with calving year except in 2003 when total milk yield was lower than in 2002. We conclude that herd size, breed, parity, season and year of calving were among the main factors driving production 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

Predictive characteristics of lactation models for pasture-based Holstein-Friesian dairy cows

Mathematical functions to describe a series of milk test day records have the advantage of minimizing random variation, while simultaneously summarizing the lactation profile. Five empirical functions and two mechanistic models were used to model herd and individual milk yield profiles of multiparous Holstein-Friesian cows on 113, 290 milk yield records (8438 lactations) collected from 1994-2005. The models tested were the incomplete gamma (IG), a modified gamma (MG), an exponential (EXP), a polynomial regression (PR), a mixed log (ML), the bi-compartmental (BC), and Dijkstra (DJ) functions, the latter two being mechanistic models. Each model was fitted using the non-linear (NLIN) function of SAS. Model accuracy was evaluated based on residual mean square (RMS), the magnitude and distribution of residuals, and the correlation between the observed and predicted values. All the models, except MG, did equally well in portraying the lactation profile. Parameter estimates were significant (P<0.05), with large serial correlations indicating biased predictions, especially during mid-lactation. Correlations of residuals and observed herd average lactations ranged between -0.13 (MG) to 0.19 (IG), while that between observed and predicted was between 0.76 and 0.99 for the same models. Lactation curves of individual cow milk yields were more varied, exhibited the tendency for a second peak which were not accurately modeled. Mechanistic models performed best with herd data, the PR model fitted overall best, while the MG model fitted the profile least accurately in this study

Mapping quantitative trait loci (QTL) in sheep. II. Meta-assembly and identification of novel QTL for milk production traits in sheep

An (Awassi × Merino) × Merino backcross family of 172 ewes was used to map quantitative trait loci (QTL) for different milk production traits on a framework map of 200 loci across all autosomes. From five previously proposed mathematical models describing lactation curves, the Wood model was considered the most appropriate due to its simplicity and its ability to determine ovine lactation curve characteristics. Derived milk traits for milk, fat, protein and lactose yield, as well as percentage composition and somatic cell score were used for single and two-QTL approaches using maximum likelihood estimation and regression analysis. A total of 15 significant (P < 0.01) and additional 25 suggestive (P < 0.05) QTL were detected across both single QTL methods and all traits. In preparation of a meta-analysis, all QTL results were compared with a meta-assembly of QTL for milk production traits in dairy ewes from various public domain sources and can be found on the ReproGen ovine gbrowser http://crcidp.vetsci.usyd.edu.au/cgi-bin/gbrowse/oaries_genome/. Many of the QTL for milk production traits have been reported on chromosomes 1, 3, 6, 16 and 20. Those on chromosomes 3 and 20 are in strong agreement with the results reported here. In addition, novel QTL were found on chromosomes 7, 8, 9, 14, 22 and 24. In a cross-species comparison, we extended the meta-assembly by comparing QTL regions of sheep and cattle, which provided strong evidence for synteny conservation of QTL regions for milk, fat, protein and somatic cell score data between cattle and sheep

Government Policy on Library Development in Oyo State Primary Schools

No Abstrac