Considering automatized and robotic milking systems substantially decreasing the contact between producers and the herd, milk analysis is crucial to maintain the quality and safety of all dairy products. These systems naturally also decrease the possibility of health problems and illness identification. Abnormalities in milk can be caused by several factors. Milk quality can be affected by external conditions, such as temperature and contamination in the feedstock; by management practices, such as hygiene, milking frequency, treatment, and feedstuff quality; and by diseases, genetics, or age. Somatic cell count, electric conductivity, and contents of urea, fat, protein, and lactose were reviewed as likely parameters of milk representing its quality with respect to feedback for consumers and breeders. Methods for evaluating milk constituents and parameters are still being developed to provide in-line information. These methods allow the avoidance of enormous economic losses every year caused by milk discard, health treatments, or cow replacements. In addition, individual and in-line milk analysis provides information in terms of nutritional status or lactation period and fertility. The objective of this study is to identify trends and potential methods focusing on in situ and in-line techniques for the analysis of milk parameters during the automatized and robotic milking process. Four methods are described and compared: near-infrared spectroscopy (NIRS) and mid-infrared spectroscopy (MIRS), optical analysis, milk conductivity analysis, and milk leukocyte differential test. The versatility and accessibility of these methods were also evaluated, showing a considerable range of possible related problems.

Infrared thermography (IRT) is a noninvasive and safe method of displaying the temperature map of objects that can be used to detect hoof diseases and lameness to reduce significant financial costs and physically stress animals. A qualitative bibliometric method based on the analysis of publications by the authors themselves using sophisticated tools of scientific databases was applied in this work. This review presents the fundamentals of IRT as well as recent developments in IRT detection in dairy science, including preprocessing, segmentation, and classification of objects in IRT images. In addition, recent studies dealing with the detection of hoof diseases and lameness using IRT are reviewed. As a result of this study, select previous studies are confronted in terms of technical aspects of IRT measurements such as emissivity, distance, temperature range, and reflected air temperature. Subsequently, recommendations for future IRT measurements are discussed.

Determination of ammonia (NH₃) emissions for intensive livestock facilities (pork, poultry) is important from both a regulatory and a research point of view. Buildings housing livestock are a large source of ammonia emissions from the agriculture sector. However, measurements to determine emissions can be time-consuming and costly. Therefore, it is essential to find a suitable methodology for monitoring NH₃. The methodology for determining NH₃ emissions is legislatively unified in terms of sampling methodology, including sampling time (24 h), sampling points (input/output), number of sampling days, and their distribution during the year, and to determine only a general calculation of the annual average NH₃ emissions. For this reason, the researchers chose different approaches for the calculation of NH₃ emissions, and these approaches are not unified. Based on accurate monitoring and created models, the authors proposed a methodology for calculation of NH₃ emissions, which divides the 24 h measurement into time windows (30 min), from which the arithmetic mean and standard deviation are determined, and the total emissions for one year is determined. The chosen time windows for the partial calculation are important from the point of view of reflecting the microclimatic conditions inside the stable and the device limits for sampling the NH₃ concentration and airflow.