Calf mortality is one of important problems of calf rearing in dairy farms worldwide. Besides, several noninfectious factors, such as management around birth, colostrum management, calf housing, feeding system, hygiene and pathogens, play an important role in calf rearing. The aim of the study was to show the most common causes of mortality of calves up to 90 d of their lives. Some data are available concering calf rearing management on small and medium size dairy farm typical for Polish regions. The research was conducted in seven selected herds of Polish Holstein-Friesian cows located in South of Poland. Data on calves mortality covered the period of three years from 2004 to 2007 and were collected using medical documentation and medical inquire in the farms. All evidence was enrolled untill three months of age of calves. There were 1,800 calves tested. The influence of such factors as maintaining system (free stalls barn and stalls barns), feeding systems and herd size on falls of calves was examined. Overall, mortality throughout the three months of study period was diarrhea, which increased the risk of death among calves younger than 90 d of age. Also, respiratory system disorders were the common cause of loss of calves. The calf mortality rate during 90 d in all herds registered in free stall barns was 61% and in stalls barns was only 29%. Effect of pneumonia in free stall barns was 18% and in stall barns was 29%. In all groups, calf mortality rates increased with increasing herd size.
Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as animal welfare reasons. Recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. ANNs offer a modeling method that enables the mapping of highly complex functional relationships. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows, using feedforward multilayer perceptron (MLP) artificial neural networks. The results were verified based on the estimated sensitivity and specificity of selected network models, an optimum cut-off point was identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The study demonstrated that BHB, ACE and lactose (LAC) levels, as well as the fat-to-protein ratio in milk, were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, variables such as BHB and ACE levels in milk were of particular relevance, with a sensitivity and specificity of 0.84 and 0.61, respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool.
The diagnosis of subclinical ketosis in dairy cows based on blood ketone bodies is a challenging and costly procedure. Scientists are searching for tools based on results of milk performance assessment that would allow monitoring the risk of subclinical ketosis. The objective of the study was (1) to design a scoring system that would allow choosing the best machine learning models for the identification of cows-at-risk of subclinical ketosis, (2) to select the best performing models, and (3) to validate them using a testing dataset containing unseen data. The scoring system was developed using two machine learning modeling pipelines, one for regression and one for classification. As part of the system, different feature selections, outlier detection, data scaling and oversampling methods were used. Various linear and non-linear models were fit using training datasets and evaluated on holdout, testing the datasets. For the assessment of suitability of individual models for predicting subclinical ketosis, three β-hydroxybutyrate concentration in blood (bBHB) thresholds were defined: 1.0, 1.2 and 1.4 mmol/L. Considering the thresholds of 1.2 and 1.4, the logistic regression model was found to be the best fitted model, which included independent variables such as fat-to-protein ratio, acetone and β-hydroxybutyrate concentrations in milk, lactose percentage, lactation number and days in milk. In the cross-validation, this model showed an average sensitivity of 0.74 or 0.75 and specificity of 0.76 or 0.78, at the pre-defined bBHB threshold 1.2 or 1.4 mmol/L, respectively. The values of these metrics were also similar in the external validation on the testing dataset (0.72 or 0.74 for sensitivity and 0.80 or 0.81 for specificity). For the bBHB threshold at 1.0 mmol/L, the best classification model was the model based on the SVC (Support Vector Classification) machine learning method, for which the sensitivity in the cross-validation was 0.74 and the specificity was 0.73. These metrics had lower values for the testing dataset (0.57 and 0.72 respectively). Regression models were characterized by poor fitness to data (R2 < 0.4). The study results suggest that the prediction of subclinical ketosis based on data from test-day records using classification methods and machine learning algorithms can be a useful tool for monitoring the incidence of this metabolic disorder in dairy cattle herds.
Dairy cows production plays a significant role in development of Podkarpackie Voivodeship. Progress in this production branch may depend on the dairy production support which is given after relevant types of actions are undertaken. The objective of the research was to determine changes in the level of welfare of production animals, evaluation of practice in dairy cows breeding in the province of Ropczyce and Sędziszów. Breeding documentation and control protocols of the Coordinated Veterinary Inspection Program were applied pursuant to the resolution (1) of the Minister of Agriculture and Rural Development of 28 June 2010 on minimal conditions of maintaining farm animal species. 10 production herds of dairy cows (n-240) and heifers (n=101) which produce ≥ 10200 kilo of milk per lactation were used to achieve this objective. Dairy production was observed during individual visits in farms. Surveys were carried out with farm employees with the use of properly prepared veterinary control protocols of the Coordinated Veterinary Inspection Program. Control results were obtained based on the respondents' opinion from controlling institutions, such as: The Agency for Restructuring and Modernization of Agriculture, Regional Veterinary Office and similar certified bodies.
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