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Milking the austrian mature cows


It is well established that milk composition is affected by the breed and genotype of a cow. The present study investigated the relationship between the proportion of exotic genes and milk composition in Tanzanian crossbred dairy cows.

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Milk samples were collected from animals kept under smallholder production systems in Rungwe and Lushoto districts of Tanzania. The milk samples were analyzed for the content of components including fat, protein, casein, lactose, solids-not-fat SNFand the total solids TS through infrared spectroscopy using Milko-Scan FT1 analyzer Foss Electric, Denmark. Hair samples for DNA analysis were collected from individual cows and breed composition determined usingsingle nucleotide polymorphism SNP markers.

Cows were grouped into four genetic classes based on the proportion of exotic genes present: The breed types were defined based on international commercial dairy breeds as follows: Results obtained indicate low variation in milk composition traits between genetic groups and breed types. These results suggest that selection of breed types to be used in smallholder systems need not pay much emphasis on milk quality differences as most admixed animals would have similar milk composition profiles.

However, a larger sample size would be required to quantify any meaningful differences between groups. Development of efficient strategies to optimize milk composition has long been an active area of Milking the austrian mature cows and continues to attract increasing interest for the global dairy industry. Milk component levels and characteristics are important factors that have a significant effect on dairy product quality and yield Murphy et al.

Farmers in many developed countries are currently paid for milk deliveries based on fat and protein levels Bailey et al. As such, the dairy industry must make strategic decisions on optimizing factors that affect milk composition to better meet the ever-changing technological requirements and consumer preferences. In East Africa, milk component pricing based on milk fat, true proteins, and other dairy solids has not been adopted. However, major dairy processors in the region have expressed strong interest in implementing a quality-based pricing system and routinely offer bonus payment depending on other measures of milk quality which include both compositional completeness as well as somatic cell and bacterial counts; Foreman and De Leeuw, This has been largely driven by the demand for high-quality dairy products that meet consumer and export market demand.

Whereas there are three broad options for modifying milk composition: Significant progress has been made in the past to improve the gross composition of milk through selective breeding and nutrition management of cows Jenkins and McGuire, Bovine milk composition is influenced by many factors including breed and genotype Coleman et al.

Previous studies have established the potential to exploit variation of milk composition among breeds to improve milk quality Glantz et al. According to De Marchi et al. Variations in the milk composition among breeds have been widely demonstrated in the literature see review Schwendel et al.

Although it is well Milking the austrian mature cows that there is significant variation in milk quality among cattle breeds, little is known about the variation in milk composition of different dairy crosses with varying admixture levels. The limited studies available have shown that increasing the proportion of exotic genes in a cow leads to decreased milk component levels Haile et al.

In smallholder systems, pedigree records are typically unavailable. The use of single nucleotide Milking the austrian mature cows SNP markers for prediction of breed composition of admixed animals is gaining popularity with the substantial decrease in genotyping costs. The information on breed composition obtained through SNP markers is not Milking the austrian mature cows useful in understanding the variation of milk traits in crossbred animals, but also allows their incorporation into genomic selection programs to improving milk quality traits VanRaden and Cooper, Crossbreeding of local indigenous breeds with exotic cattle has been widely adopted in Tanzania since independence, Milking the austrian mature cows with the aim of increasing the productivity of local breeds.

Often, these breeding practices are carried out indiscriminately resulting in animals with unknown and large variation in breed composition Weerasinghe et al. Therefore, the complex within herd genetic composition and variability in Tanzania provides a unique opportunity to investigate the effect of breed admixture on milk quality traits in a smallholder setting as well as under a wide range of production environments.

Understanding the milk quality profile of crossbred cattle is critical in the planning for the extent to which smallholder farmers, who are the main suppliers of milk in East Africa, can participate and maximize their incomes in the QBPS. The aim of this study was to evaluate the relationship between individual animal exotic gene proportions and associated milk composition profiles.

In addition, the study examines the effect of breed types and other environmental factors on milk components. Animals were handled by experienced animal health professionals to minimize discomfort and injury. The study was undertaken in two districts of Tanzania, namely, Rungwe and Lushoto located in the Southern Highlands and the Usambara Mountains in Tanga, respectively.

Households selected to participate in the study were recruited based on strict entry criteria. They had to have at least two cows, one of which was in milk Milking the austrian mature cows have a crossbred bull in active service. Additionally, unrelated animals were preferred and where possible households with observable breed diversity were sought.

Animal recruitment was purposive within households. To qualify, animals had to be either pregnant heifers or cows in the third trimester of pregnancy or a cow that had calved 3 months prior to recruitment.

Hair samples were collected from the tail switch of the animals, taking care to avoid fecal contamination following the protocol described by the Animal Genetics Laboratory A total of samples were obtained from animals in Rungwe district and animals in Lushoto district. Cows were classified into four genetic groups based on the individual admixture profile and level of exotic dairy genes the whole complement of genetic material derived from international commercial dairy breeds.

The groups were defined as follows: Two explanations informed this definition. Second, due to "Milking the austrian mature cows" need to balance the number of individuals in each genetic group, a hard cutoff point was not considered, e. Additionally, cows were categorized into four breed types according to the level of international commercial dairy breeds as follows: The first breed in the combinations is the dominant breed in terms of proportions of exotic genes present.


Both genetic group and breed types were assigned to each cow using the admixture methodology. The clusters used in this study were obtained from classification done as part of the larger AgriTT Agricultural Technology Transfer project manuscript in preparation. Next, factor analysis was performed and five broad factors that can be used to describe smallholder farmers in the study sites were derived: These extracted factors were subjected to cluster analysis.

The analysis revealed four distinct production clusters. The main factors that determined the production environment groupings were: Given that the herd sizes were very small Milking the austrian mature cows farmers had only two qualifying animals in the analysisthese production clusters served as the contemporary group used in the association analysis.

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Approximately 10 ml of raw milk was collected in the months of June and December from each of cows in both Rungwe and Lushoto districts. A larger sample size could not be obtained given that milk yields in the target households are often low and farmers would not agree to larger samples being drawn. Sampling was done once per animal for either morning or evening milk. Transportation from the field labs to ILRI was done with the samples placed under dry ice.

Information regarding parity, the age of the cow, and season of calving for each cow was also collected. Other variables related to production system including farm characteristics, feeding practices, as well as general health management practices were recorded and used to determine production Milking the austrian mature cows. Since the cows in the study sites were managed differently, cluster analysis was necessary to group animals into homogeneous clusters in order to minimize the confounding effect of production management on milk component traits.

The number of milk samples available for the present study from each cluster was 57, 90, 37, and 25 for cluster 1, 2, 3, and 4, respectively. Only one milk sample was available for each cow.

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The Milko-Scan FT1 analyzer requires a minimum of 26 ml of milk for duplicate analysis of each sample. However, since the total milk sample volume obtainable was low 8—12 mlsamples had to be diluted to obtain the optimum volume suitable for analysis. Consequently, and before analysis, two dilution procedures were undertaken based on the exact volume of each milk sample. Samples with 10 ml volume were diluted to To obtain regression models for predicting the actual milk composition for the Milking the austrian mature cows study samples, 50 ml fresh milk samples from 15 individual cows were collected from the University of Nairobi farm.

The milk samples were collected purposely from crossbred cows to Milking the austrian mature cows comparable with the study cows with respect to genetic composition. The cows at the University of Nairobi farm are managed semi-intensively and were milked twice a day.

Three sets of "Milking the austrian mature cows" [undiluted milk, dilution 1 After checking for normality and presence of outliers for each of the analyzed milk trait fat, protein, casein, lactose, and SNFtwo prediction models were obtained by regressing milk composition estimates for the undiluted milk samples on the diluted samples using the REG procedure of SAS version 9.

Before analysis, the values obtained for fat percentage were log transformed to base 10 to correct for non-uniform variance and skewness.

All the other milk components protein, casein, lactose, SNF, and TS did not show any obvious deviation from normality or non-constancy of variance, and hence they were not log transformed. Actual milk component content of the study cows was determined as predicted values using the defined models for the respective dilutions. To find out the relationship between breed type and genetic group on predicted milk composition traits, data were analyzed using the MIXED procedure in SAS version 9.

Milking the austrian mature cows effects included in the model were the genetic group, breed type, the age of the cow at the time of milk sample collectionthe month of sampling, and production cluster membership of cows cluster.

Component trait averages for each genetic group and the breed type were obtained by fitting two separate statistical models, Model 1 and Model 2 for breed types and genetic group, respectively. Although farmers provided parity information for study cows, this information was mainly based on guesses and estimates since most farmers purchase cows that are already in production and have calved several times before.

As such, parity information was deemed unreliable and was excluded from the analysis. Overall, the differences between means were small for all traits, within breed types, genetic groups, and production clusters.

Milk samples were diluted in order to obtain the volume required by the infrared spectrometer to quantify the content of the milk components. Regression equations were then used to determine the predicted component content of the undiluted milk samples. The parameter estimates for all the milk traits were slightly lower for dilution 2 Fat content exhibited the largest CV; 2.

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Coefficient of determination R 2root-mean-square error RMSEand coefficient of variation CV of the prediction models for the milk Milking the austrian mature cows derived from the University of Nairobi dairy cattle used as a training population.

Of all the milk traits, fat content and lactose had the largest Milk total protein and casein displayed a relatively moderate and similar CV with mean content ranging from 2. Means and the coefficients of variation of the predicted milk traits for the study samples Tanzanian milk data. A fixed model was used to determine the relationships between milk component content and a set of fixed effects. The fixed effects included in the model were breed type, dairyness proportion of exotic genesage of the cow, production cluster, and month of sampling.

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Least square means and standard errors for milk component traits for the age of the cow. The total protein content was higher 3.

Least square means and standard errors for milk component traits for each genetic group. For lactose and casein, the trend is not clear. Least square means of milk fat, protein, casein, and lactose for each level of exotic genes. The RG breed type consisting of crossbreeds of Jersey, Guernsey, Holstein, and Norwegian Red breed had the highest average fat content 4.

Treatment with antibiotics is less frequent for dairy cows. The use of Pilot study on antibiotic treatments for dairy cows in Austria. Bovine milk composition is influenced by many factors including breed . purchase mature cows already in Milking the austrian mature cows, with no accompanying. records improvement of efficiency in Austrian dairy cattle. In. efficient cows were found to have a higher milk yield, lower BW mature animals.

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