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Analytical and theoretical studies were conducted in working cattle facilities in order to identify infectious, parasitic, and nervous diseases in large horned cattle. Our analytical study was based on the analysis of available scientific research papers. The theoretical research was based on processing the measurement results with existing hardware and software. Both environmental and physiological parameters were obtained from five farms for at least 30 days. The studied cows were divided into two groups. One group consisted of 37 dairy cows of the Holstein breed aged 2–3 years having no clinical signs of disease. All cows in this group were fed the same diet, kept in the same conditions, and had the same lactation period (from 3 to 5 months). Their average weight was 517 (±2.03) kg. For inclusion into the second group, we selected 23 dairy cows with parameters similar to those of the cows in the first group but with some clinical signs of diseases such as encephalomyelitis, infectious enteritis, and hypodermatosis. The data obtained from the animals in the first group were considered as the parameters’ standardized boundary values for the estimation of a cow’s conditions, i.e., as the norm (the setpoint). As for the data obtained for the second group, they were considered to be deviations from the threshold values of the parameters (deviations from the setpoint, which required a pre-planned action). The analysis was carried out using the program code implemented in the software package “Matlab R2019b”. We analyzed the correlations between the cows’ rumen temperature and pH, their locomotive activity, and environmental parameters such as air temperature and relative humidity in the cowsheds. We then constructed graphs of inter-correlating functions. As a result of the study, for the first time, algorithms were compiled enabling the detection of infectious, parasitic, and nervous diseases.

The process of milking healthy cows and those with mastitis is analyzed in terms of such parameters as average onetime milk yield, milk flow rate, milking duration to assess their diff erences and the need to use alternative milking methods. (Research purpose) To study the impact of mastitis on the milk yield of cows and develop recommendations for milking diseased animals. (Materials and methods) Three groups of animals were formed according to the mastitis test results: the first group included healthy cows, the second one those with subclinical mastitis, and the third one included the cows with the clinical form of the disease. The data were collected by control milkings and the milk flow rate results were recorded every 15 seconds. (Results and discussion) The results revealed significant differences for the tree groups, both in the rate of milk flow (1.90; 0.89 and 0.49 kilograms per minute, respectively) and the duration of milking (281; 375 and 294 seconds, respectively). (Conclusions) The longest milking duration is detected in the case of subclinical mastitis, (375 seconds on average). There is a shift in the peak of the milk flow rate from the second minute of milking to the third in comparison with healthy animals. In the case of clinical mastitis, the milking duration (295 seconds) proves to be less than the subclinical one, while there is practically no peak in the rate of milk flow. It is confirmed that there is a necessity to shift the intensive milking mode by 30-45 seconds for an animal with subclinical mastitis, as well as to use a gentle milking mode for cows with clinical mastitis.

This study aims to determine the relationship between indicators of the motor activity, pH factor, rumen and rectal temperature within 10 days after calving and to analyze the possibility of using the studied parameters as prognostic signs for diagnosing sub-acute rumen acidosis (SARA). The measurements were taken using bolus with sensors designed to monitor cow health. The motor activity, pH factor and ruminal temperature of 10 cows were measured during 10 days at a measurement interval of every 60 seconds. Next, the researchers calculated the average values of the obtained readings, which were divided into 2 groups according to a measurement interval of every 12 hours. Rectal temperature was measured using a veterinary thermometer every 12 hours (at 8 a.m. and at 8 p.m.). As a result, 200 measurements were obtained. Descriptive sampling statistics were calculated using the SPSS Statistics program. An increase in motor activity reduces the pH level of the rumen environment. Lowering the pH factor of the rumen environment leads to an increase in ruminal temperature. There is a positive statistically significant correlation between ruminal and rectal temperature. The nosology of SARA can be predicted by measuring the motor activity and rectal temperature of dairy cows.

Monitoring the temperature and pH of the contents of the rumen can be useful for assessing the health status and detecting physiological heat in cows. The purpose of the research was to develop methods and means of livestock control in cattle breeding to manage the physiological state of the herd based on mathematical models for detecting the heat, upcoming calving, initial signs of diseases, monitoring the level of feeding and water intake. The work was carried out under the production conditions of farms using existing hardware and software. The measurements were carried out using a non-invasive control method using special sensors-boluses designed to monitor the health of cows. Boluses were placed orally in the rumen of the cows under research. As a result of the research, algorithms and mathematical models were compiled to identify the heat, upcoming calving, diseases, as well as monitoring the level of feeding and water intake. The source data was imported from a standard file format compatible with other applications (csv table). Correlations between the temperature and pH of the rumen, as well as the motor activity of cows were also analyzed. As illustrations, graphs of the main indicators of vital activity, as well as graphs of mutually correlative functions and an illustration of the working console of the program have been given. Table of the results of the program for each cow, average values and standard deviation have been given. A mathematical model is a set of algorithms and calculation results. In order to implement it, a program code was created in the Matlab R2019b software package. This mathematical model can be used to process and interpret data placed in the animal’s rumen of measuring elements (boluses).