scholarly journals THE COMPOSITION AND PHYSICOCHEMICAL PROPERTIES OF COLOSTRUM IN BLACK-AND-WHITE POLISH HOLSTEIN-FRIESIAN COWS, MONTBÉLIARDE COWS AND THEIR CROSSBREEDS

2016 ◽  
Vol 15 (2) ◽  
pp. 87-98
Author(s):  
Edyta Wojtas ◽  
◽  
Andrzej Zachwieja ◽  
◽  
Keyword(s):  
Physicochemical Properties ◽  
Black And White ◽  
Holstein Friesian ◽  
Friesian Cows

CATHL2 gene polymorphism in relation to production traits in Holstein-Friesian cows

2019 ◽  
Vol 15 (2) ◽  
pp. 23-30
Author(s):  
Sonia Hiller ◽  
Inga Kowalewska-Łuczak ◽  
Ewa Czerniawska-Piątkowska
Keyword(s):  
Statistical Analysis ◽  
Gene Polymorphism ◽  
Somatic Cell Count ◽  
The Other ◽  
Production Traits ◽  
Black And White ◽  
Potential Association ◽  
Holstein Friesian ◽  
Pcr Rflp ◽  
Friesian Cows

The aim of this study was to identify SNPs mutations in the CATHL2 gene and determine their potential association with dairy performance traits in Polish Black-and-White Holstein-Friesian (phf) cows. Genotypes of individuals were identified by PCR-RFLP. The frequencies of CATHL2/DdeI alleles were C ‒ 0.693 and T ‒ 0.307, and for CATHL2/HhaI polymorphisms, G ‒ 0.763 and C ‒ 0.237. The statistical analysis showed that cows with the CC (CATHL2/DdeI) and CG (CATHL2/HhaI) genotype produced higher milk yield than the other cattle genotypes. In the case of CATHL2/DdeI and CATHL2/HhaI polymorphisms, the highest somatic cell count was found in heterozygous CT and CG cows.

Possible relationship between IGF-1/SnaBI genotypes and milk yield of Holstein-Friesian cows

2019 ◽  
Vol 15 (4) ◽  
pp. 35-41
Author(s):  
Małgorzata WASIELEWSKA ◽  
Iwona SZATKOWSKA
Keyword(s):  
Milk Yield ◽  
Quality Characteristics ◽  
Production Traits ◽  
Performance Parameters ◽  
Milk Traits ◽  
White Variety ◽  
Black And White ◽  
Holstein Friesian ◽  
Milk Performance ◽  
Friesian Cows

The correlation between polymorphisms in the IGF-1 gene and production traits in beef cattle is well known. The effect of insulin-like growth factor on the value of milk traits is not yet adequately understood. The aim of the study was to attempt to describe the effect of IGF-1/SnaBI substitution on selected milk performance parameters of the Black-and-White variety of Holstein-Friesian cows. Three genotypes were identified: CC, CT and TT. The results showed a correlation between IGF-1/SnaBI genotypes and milk yield (highest for CC homozygotes and lowest for CT heterozygotes). No relationship could be established between the genotype and the quality characteristics of milk.

A dependency between PRL gene polymorphism and breeding parameters in Polish Black-and-White Holstein-Friesian cows

2020 ◽  
pp. 1-7
Author(s):  
Jarosław Pytlewski ◽  
Ireneusz Antkowiak ◽  
Ewa Czerniawska-Piątkowska ◽  
Alicja Kowalczyk
Keyword(s):  
Gene Polymorphism ◽  
Breeding Parameters ◽  
Black And White ◽  
Holstein Friesian ◽  
Prl Gene ◽  
Friesian Cows

Shade preferences of lactating Holstein - Friesian cows

1998 ◽  
Vol 38 (1) ◽  
pp. 17 ◽  
Author(s):  
J. B. Gaughan ◽  
P. J. Goodwin ◽  
T. A. Schoorl ◽  
B. A. Young ◽  
M. Imbeah ◽  
...  
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Ambient Temperature ◽  
Coat Colour ◽  
Climatic Conditions ◽  
Black And White ◽  
Sechium Edule ◽  
Holstein Friesian ◽  
Shade Cloth ◽  
Friesian Cows

Summary. Shade-type preferences by Holstein–Friesian cows were investigated under natural climatic conditions. The trial was conducted in south-east Queensland, Australia, over 88 days in summer. Forty-two cows were placed in a dirt-floored yard (zero grazing) provided with different shade types. Shade types provided were a 3 m high galvanised iron roof, Sechium edule (choko) vines on a 3 m high trellis, 70% shade cloth on a 3 m high frame and natural shade trees. The floor area under the shade structures was concrete. An unshaded area (the remainder of the yard) was also provided. Each cow was scored for coat colour based on the proportion of black and white. Number of cows using a particular shade type and their respiration rates were recorded daily at 1300 hours. Ambient temperature, relative humidity, solar radiation and wind speed were also measured. Cows selected the galvanised iron roof most frequently when temperatures rose above 30°C, with no significant differences between the other shade types. At temperatures below 30°C, animals did not seek shade. As ambient temperature, solar radiation and relative humidity rose, respiration rate rose. Cows with a high percentage of black coat preferred shade, while those with a high percentage of white coat did not seek shade.

scholarly journals Analysis of Lifetime Performance and Culling Reasons in Black-and-White Holstein-Friesian Cows Compared with Crossbreds

2018 ◽  
Vol 18 (4) ◽  
pp. 1061-1079
Author(s):  
Krzysztof Adamczyk ◽  
Wojciech Jagusiak ◽  
Joanna Makulska
Keyword(s):  
Dairy Cows ◽  
Herd Size ◽  
Black And White ◽  
Genetic Potential ◽  
Lifetime Performance ◽  
Holstein Friesian ◽  
Mean Lifetime ◽  
History Of ◽  
The Mean ◽  
Friesian Cows

AbstractThe effect of crossbreeding Holstein-Friesian cows with other breeds is usually improved genetic potential of crossbreds in terms of longevity. However, culling decisions, which in practice determine the longevity in dairy cows, are contingent on many environmental and economic factors. Therefore, the aim of this study was to evaluate longevity in relation to culling reasons in Holstein-Friesian cows of the Black-and-White strain (HO) and crossbreds, taking genotype, age at first calving, herd size, culling season, culling reason and milking temperament into consideration. The data analysed concerned 154,256 dairy cows culled in Poland in 2015. It was found that all studied factors significantly affected cow lifetime performance. The mean age at culling in dairy cows of HO strain exceeded 6 years, with mean lifetime energy-corrected milk (LECM) yield of 28,933 kg and mean lifetime energy-corrected milk yield per milking day (DECM) of 20.2 kg. Crossbreds, on the other hand, tended to have shorter lifespans, with mean LECM yield amounting to less than 25,000 kg. Mean LECM yield of cows surviving for the longest period (9.2 years), amounted to 47,771 kg, and reproduction problems were unquestionably the most common (40%) reason for cows’ culling. A suggestion was made to take milking temperament into account in breeding practice, as this trait proves to be closely related to the longevity characteristics of dairy cows. It was also proposed that the culling reasons be subjected to a more comprehensive analysis, considering the “life history” of cows as well as the interactions between different reasons for their removal from the herd.

scholarly journals The Use of Artificial Neural Networks and a General Discriminant Analysis for Predicting Culling Reasons in Holstein-Friesian Cows Based on First-Lactation Performance Records

Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 721
Author(s):  
Krzysztof Adamczyk ◽  
Wilhelm Grzesiak ◽  
Daniel Zaborski
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Discriminant Analysis ◽  
Multilayer Perceptrons ◽  
Correct Classification ◽  
Correct Classification Rate ◽  
Classification Rate ◽  
Holstein Friesian ◽  
Artificial Neural ◽  
Friesian Cows

The aim of the present study was to verify whether artificial neural networks (ANN) may be an effective tool for predicting the culling reasons in cows based on routinely collected first-lactation records. Data on Holstein-Friesian cows culled in Poland between 2017 and 2018 were used in the present study. A general discriminant analysis (GDA) was applied as a reference method for ANN. Considering all predictive performance measures, ANN were the most effective in predicting the culling of cows due to old age (99.76–99.88% of correctly classified cases). In addition, a very high correct classification rate (99.24–99.98%) was obtained for culling the animals due to reproductive problems. It is significant because infertility is one of the conditions that are the most difficult to eliminate in dairy herds. The correct classification rate for individual culling reasons obtained with GDA (0.00–97.63%) was, in general, lower than that for multilayer perceptrons (MLP). The obtained results indicated that, in order to effectively predict the previously mentioned culling reasons, the following first-lactation parameters should be used: calving age, calving difficulty, and the characteristics of the lactation curve based on Wood’s model parameters.

scholarly journals Determination of Body Parts in Holstein Friesian Cows Comparing Neural Networks and k Nearest Neighbour Classification

Animals ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 50
Author(s):  
Jennifer Salau ◽  
Jan Henning Haas ◽  
Wolfgang Junge ◽  
Georg Thaller
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Body Parts ◽  
Nearest Neighbour ◽  
Data Set ◽  
3D Data ◽  
Holstein Friesian ◽  
Knn Classification ◽  
Friesian Cows

Machine learning methods have become increasingly important in animal science, and the success of an automated application using machine learning often depends on the right choice of method for the respective problem and data set. The recognition of objects in 3D data is still a widely studied topic and especially challenging when it comes to the partition of objects into predefined segments. In this study, two machine learning approaches were utilized for the recognition of body parts of dairy cows from 3D point clouds, i.e., sets of data points in space. The low cost off-the-shelf depth sensor Microsoft Kinect V1 has been used in various studies related to dairy cows. The 3D data were gathered from a multi-Kinect recording unit which was designed to record Holstein Friesian cows from both sides in free walking from three different camera positions. For the determination of the body parts head, rump, back, legs and udder, five properties of the pixels in the depth maps (row index, column index, depth value, variance, mean curvature) were used as features in the training data set. For each camera positions, a k nearest neighbour classifier and a neural network were trained and compared afterwards. Both methods showed small Hamming losses (between 0.007 and 0.027 for k nearest neighbour (kNN) classification and between 0.045 and 0.079 for neural networks) and could be considered successful regarding the classification of pixel to body parts. However, the kNN classifier was superior, reaching overall accuracies 0.888 to 0.976 varying with the camera position. Precision and recall values associated with individual body parts ranged from 0.84 to 1 and from 0.83 to 1, respectively. Once trained, kNN classification is at runtime prone to higher costs in terms of computational time and memory compared to the neural networks. The cost vs. accuracy ratio for each methodology needs to be taken into account in the decision of which method should be implemented in the application.

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