EVALUATION OF HOLSTEIN-FRIESIAN DAIRY SIRES FOR CONFORMATION OF THEIR DAUGHTERS

1978 ◽  
Vol 58 (3) ◽  
pp. 409-417 ◽  
Author(s):  
L. R. SCHAEFFER ◽  
E. B. BURNSIDE ◽  
M. S. HUNT
Keyword(s):  
Milk Production ◽  
Data Sets ◽  
Genetic Trend ◽  
Holstein Friesian ◽  
Type Traits

A model is described for evaluating dairy sires for the conformation of their daughters accounting for age at classification, year, classification of the dam, genetic trend, and sire effects. Results of the application of this model to two data sets are presented. Estimates of genetic trend for type traits indicated at least twice as much progress is being made for type as for milk production.

Relationships among udder type traits and milk yield of Iranian Holstein-Friesian cattle

2003 ◽  
Vol 2003 ◽  
pp. 141-141
Author(s):  
M. R. Sanjabi ◽  
M. G. Govindaiah ◽  
M. M. Moeini
Keyword(s):  
Milk Production ◽  
Milk Yield ◽  
Genetic Correlations ◽  
Holstein Friesian ◽  
Type Traits ◽  
High Production ◽  
Friesian Cattle

Correlation among type traits and with milk production has been investigated by Brotherstone (1994) and Misztal et al (1992). One of the primary reasons for collecting and utilizing information on type traits is to aid breeders in selecting profitable functional cows for high production and suitable herd life. The objectives of this study were to estimate phenotypic and genetic correlations among milk production and with udder traits.

Dairy herd life in relation to linear type traits and production 2. Genetic analyses for pedigree and non-pedigree cows

1991 ◽  
Vol 53 (3) ◽  
pp. 289-297 ◽  
Author(s):  
S. Brotherstone ◽  
W. G. Hill
Keyword(s):  
Dairy Herd ◽  
Positive Association ◽  
Data Sets ◽  
Significant Positive Association ◽  
Linear Type ◽  
The United Kingdom ◽  
Holstein Friesian ◽  
Type Traits ◽  
Genetic Regression ◽  
Milk Marketing

AbstractThe relation between linear type traits and survival to complete lactations 2, 3 and 4 of pedigree and non-pedigree Holstein-Friesian dairy cattle in the United Kingdom was analysed. Regressions of survival on sire transmitting abilities for type, i.e. estimates of genetic regression, were obtained for different data sets, either pedigree or non-pedigree offspring of sires whose progeny had linear type assessments made by either the Holstein Friesian Society or the Milk Marketing Board, thereby spanning different segments of the cattle population. Results were not completely consistent either over lactations or over data sets. In general, however, there was a significant positive association between survival and angularity, fore udder attachment and udder depth scores, and a negative association with chest width, rump width, and teat length. Regressions on yield were positive, while those on fat and protein content were usually negative. Survival in the herd can be predicted, although not with high accuracy, using type traits.

scholarly journals Random Forest Regression models for Lactation and Successful Insemination in Holstein Friesian cows

2020 ◽  
Author(s):  
Lillian Oluoch ◽  
László Stachó ◽  
László Viharos ◽  
Andor Viharos ◽  
Edit Mikó
Keyword(s):  
Random Forest ◽  
Milk Production ◽  
Production Control ◽  
Large Data ◽  
Data Sets ◽  
Random Forest Regression ◽  
Data Set ◽  
Explanatory Variables ◽  
Holstein Friesian ◽  
Reliable Model

AbstractTo overcome well-known difficulties in establishing reliable models based on large data sets, the Random Forest Regression (RFR) method is applied to study economical breeding and milk production of dairy cows. As for the features of RFR, there are several positive experiences in various areas of applications supporting that with RFR one can achieve reliable model predictions for industrial production of any product providing a useful base for decisions. In this study, a data set of a period of ten years including about eighty thousand cows was analysed by means of RFR. Ranking of production control parameters is obtained, the most important explanatory variables are found by computing the variances of the target variable on the sets created during the training phases of the RFR. Predictions are made for the milk production and the conception of the calves with high accuracy on given data and simulations are used to investigate prediction accuracy. This paper is primarily concerned with the mathematical aspects of a forthcoming work focused on the agricultural viewpoints. As for future mathematical research plans, the results will be compared with models based on factor analysis and linear regression.

TYPE TRAITS OF AYRSHIRE COWS IN DIFFERENT REGIONAL POPULATIONS AND THEIR RELATIONSHIP WITH MILK PRODUCTION

2019 ◽  
pp. 17-23
Author(s):  
E. Smotrova ◽  
◽  
N. Abramova ◽  
V. Berezina ◽  
E. Krysova ◽  
...  
Keyword(s):  
Milk Production ◽  
Type Traits

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

Comparison of milk production, mastitis, lameness and days to conception of Brown Swiss, Holstein and Holstein cross Brown Swiss dairy cattle

2006 ◽  
Vol 2006 ◽  
pp. 86-86
Author(s):  
G Wellwood ◽  
J K Margerison
Keyword(s):  
Dairy Cattle ◽  
Somatic Cell ◽  
Milk Production ◽  
Milk Yield ◽  
Complex Disease ◽  
Economic Loss ◽  
Brown Swiss ◽  
Somatic Cell Counts ◽  
Cell Counts ◽  
Holstein Friesian

Mastitis is a complex disease causing inflammation of the udder, which has been estimated to cost the dairy farmer between £40-£117/cow per year (Stott et al., 2002). Economic loss occurs as a result of discarded milk, reduced milk yield and milk quality, increased vet costs and an increase in replacement costs. The objective of this study was to examine the effect of breed on the incidence of mastitis and somatic cell counts and milk production capabilities of Holstein Friesian, Brown Swiss and Brown Swiss crossbred cows.

Detection and Classification of Anomalies in Large Data Sets on the Basis of Information Granules

2021 ◽  
pp. 1-1
Author(s):  
Adam Kiersztyn ◽  
Pawe Karczmarek ◽  
Krystyna Kiersztyn ◽  
Witold Pedrycz
Keyword(s):  
Large Data ◽  
Large Data Sets ◽  
Data Sets ◽  
Information Granules

scholarly journals Software Benchmark—Classification Tree Algorithms for Cell Atlases Annotation Using Single-Cell RNA-Sequencing Data

2021 ◽  
Vol 12 (2) ◽  
pp. 317-334
Author(s):  
Omar Alaqeeli ◽  
Li Xing ◽  
Xuekui Zhang
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Classification Tree ◽  
Area Under The Curve ◽  
Data Sets ◽  
Sequencing Data ◽  
Single Cell Rna Sequencing ◽  
Tree Algorithms ◽  
R Packages

Classification tree is a widely used machine learning method. It has multiple implementations as R packages; rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision; evtree is the best in Recall, F1-score and AUC; C5.0 prefers more complex trees; tree is consistently much faster than others, although its complexity is often higher than others.

Association of the Leu127 variant of the bovine growth hormone (bGH) gene with increased yield of milk, fat, and protein in Australian Holstein-Friesians

2000 ◽  
Vol 51 (4) ◽  
pp. 515 ◽  
Author(s):  
M. R. Shariflou ◽  
C. Moran ◽  
F. W. Nicholas
Keyword(s):  
Growth Hormone ◽  
Milk Production ◽  
Milk Fat ◽  
Bovine Growth Hormone ◽  
Data Sets ◽  
Production Traits ◽  
Milk Production Traits ◽  
Maximum Benefit ◽  
Pcr Products ◽  
Trait Locus

The occurrence of the Leu127/Val127 variants of the bovine growth hormone (bGH) gene and their effect on milk production traits was investigated in Australian Holstein-Friesian cattle. Animals were genotyped for the Leu127/Val127 variants, with RFLP methodology, using PCR and AluI digestion of PCR products (AluI-RFLP). Alleles Leu127 and Val127 occurred with frequencies of 82% and 18%, respectively. The quantitative effect of this polymorphic site on milk-production traits was estimated from lactation data and test-day data. Results from the 2 data sets consistently showed that the Leu127 allele is associated with higher production of milk, fat, and protein and is dominant to Val127. The average effects of the gene substitution are 95 L for milk yield, 7 kg for fat yield, and 3 kg for protein yield per lactation. This locus may be directly responsible for quantitative variation or it may be a marker for a closely linked quantitative trait locus (QTL) for milk-production traits in Australian dairy cattle. In either case, it will be useful as an aid to selection for improvement of milk production traits. As the Leu127 allele is dominant, selection of AI sires homozygous for the Leu127 allele (Leu127/Leu127) will result in maximum benefit without the need for genotyping cows.

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