Milk quality has a growing importance for farmers as component-based pricing becomes more widespread. Food quality and precision manufacturing techniques demand consistent milk composition. Udder health, general cow condition, environmental factors, and especially feed composition all influence milk quality. The large volume of routinely collected milk data can be used to build prediction models that estimate valuable constituents from other measured parameters. In this study, casein was chosen as the target variable because of its high economic value. We developed a multiple linear-regression model and a feed-forward neural network model to estimate casein content from twelve commonly recorded milk traits. Evaluated on an independent test set, the regression model achieved R² = 0.86 and RMSE = 0.018%, with mean bias = +0.003% and slope bias = −0.10, whereas the neural network improved performance to R² = 0.924 and RMSE = 0.084%. In silico microgreen inclusion from 0% to 100% of dietary dry matter raised the predicted casein concentration from 2.662% to 3.398%, a relative increase of 27.6%. To extend practical applicability, a simulation module was created to explore how microgreen supplementation might modify milk casein levels, enabling virtual testing of dietary strategies before in vivo trials. Together, the predictive models and the microgreen simulation form a cost-effective, non-invasive decision-support tool that can accelerate diet optimization and improve casein management in precision dairy production.

Precision agriculture brings new artificial intelligence techniques closer to everyday farming. Agriculture historical data is easily available, so using this data to teach a machine-learning model, offers various opportunities to enhance farming efficiency. In our study, we develop a machine learning model to estimate some linear traits of Limousin sires (sore for muscularity, length of the rump, muscularity of breast and muscularity of the width of rump), based on a phenotypic score, using artificial intelligence, in Hungary. Phenotypic scores are usually given by the experts in field. Before scoring, many measurements are made on the animals, which takes time and places a high stress on the cattle. A hands-on prediction application can make the whole process faster, and more comparable, regardless of the expert who created the scoring. We found that after collecting sufficient data from previous observations it is possible to train specifically selected artificial intelligence (AI) algorithms to predict linear traits in Limousin breeding bulls. Machine learning (ML) was used to predict the score values for muscularity, length of the rump, muscularity of the breast and muscularity of the width of the rump for this study. We found no similar experiments for the usage of AI algorithms to predict these variables. The coefficient of determination (R²) of the algorithm, in this study, provided the following range values: (R²=0.77 to 0.86).

Milk quality is an important factor both for the farmers to be able to sell their products and for the milk industry to be able to plan its production based on quantity and quality. Milk quality has a direct link with cow health, more specifically with utter health. One of the most common utter diseases is mastitis. It always captures a lot of interest based on its frequency and cost as a dairy disease which eventually leads to an involuntary and premature culling of milking cows and decreased milk yield. The genetic evaluation of mastitis is very difficult as it is a low heritable trait and categorical in nature [2]. That is why it is necessary to find markers that could predict the occurrence of mastitis. One of the widely used such markers is the somatic cell count (SCC) [9] which is considered to be the most suitable indicator trait for mastitis resistance given its medium to high genetic correlation with mastitis and its greater heritability than mastitis. The SCC is also easy to record in the practice. The selection for lower SCC in milk has a positive effect on the incidence of mastitis. The selection against high SCC also does not deteriorate the immune system of cattle and decreases the risk of infection at the same time. The genetic evaluation [1] of this trait is mostly based on somatic cell score (SCS), a logarithmic transformation of SCC to achieve normality of distribution. In our study, we used the milk database of Holstein cows from 3 different farms. From each farm, we had altogether 8000 samples tested. The samples were analyzed using chemical methods every month for a year. 11 different types of data were recorded from each sample. Our aim was to find the best mixture of recorded data that would predict the value of linearized somatic cell count. After the logarithmic linearization the SCC results were divided into 3 main groups (based on the probability of mastitis). Thus our prediction problem turned into a classification problem. We used machine learning to train our algorithm. We experimented with different types of classification methods and found good results for the prediction of SCC in milk samples. We changed the input variables as not all the 9 measured input variables will be necessary for good prediction results. Our preliminary results show that using machine learning it is possible to build a model that can be used to predict mastitis in dairy cows based on variables generally analyzed during milk quality checking tests.

This paper presents the tradition of insect eating and its rationale on Earth. The development and status of the insect-based food industry is described. Insects have been eaten by mankind since the beginning of history as an easily accessible, nutrient-rich source of protein, and there are still over 2,000 species of edible insects known to be useful in human nutrition. The orders Coleoptera, Lepidoptera and Orthoptera are the most commonly consumed genera. With population growth and increasing demand for food, edible insects may provide an alternative food source. Edible insects have excellent nutritional value, being high in protein, fat, vitamins and minerals. However, there are cultural and psychological barriers to the adaptation of insect consumption. The article highlights the challenges for the insect-based food industry, such as food safety, consumer attitudes and infrastructure development. Their use in feed can increase the nutritional value of animal protein sources. Insects are easy and quick to breed. They can be grown quickly and cost-effectively due to their short life cycle and low-energy feed requirements. Insect production under industrial conditions requires fewer resources and can contribute to climate change mitigation. Insect farming promises to be more environmentally sustainable and efficient than conventional animal husbandry. Further research is needed on the utilisation of by-products and the application of the circular economy model for the sustainable development of insect-based food.

Due to the proven existence of climatic changes, a review on water needs of cattle is doubtlessly important. People increasingly like to be aware of the water footprint of different products. In this study, authors briefly surveyed the role of drinking water as a nutrient of cattle. Based on several international results, factors affecting water consumption of cattle–especially air and water temperatute–were presented. Water quality aspects, including calculation method of water quality index, were also discussed. Estimation (regression exuations) and instrumental measurement possibilities (digital systems) for water consumption of cattle were also presented, as well as several purification methods. As it was concluded, water is inevitably important in health status, welfare and thus, production of cattle. Professional literatures provide several data on the nutritional value of water. However, further comprehensive investigation would be worth to be carried out to collect exact information on water losses of cattle under different conditions. Effect of air and water temperature on water intake is well documented internationally, domestic research under Hungarian climate conditions could be helpful to breeders, as well as the development of a national water quality index. Digitalization can be a great support in collecting accurate data on feed and water intake of cattle under different conditions. Since clear water is not present in an unlimited amount, application and development of different purifying methods and technologies is of great importance, as well as inventing new possibilities for it.

Despite the growing use of k-means clustering in cattle breeding research, its application to weaning performance traits in Limousin young bulls remains limited. This study evaluated the effectiveness of k-means clustering for identifying distinct groups based on age, live weight and morphometric records in a Hungarian Limousin nucleus herd. A total of 146 bulls weaned at an average age of 213 ± 2.03 days and live weight of 263 ± 3.31 kg (mean ± S.E.M.) were analysed. Age, live weight, withers height, rump height, back length, shoulder width, hip width and pin width were evaluated using k-means clustering. The optimal number of clusters was determined using the Elbow method, Average Silhouette Width analysis, the Calinski–Harabasz index, and the Davies–Bouldin index. Three clusters were identified, representing low (213 kg), intermediate (260 kg), and high (309 kg) body size groups. Live weight showed the strongest contribution to clustering (F = 387.3), followed by rump height (F = 85.6) and withers height (F = 82.8). Back length (F = 13.7) and pin width (F = 12.6) contributed the least. The greatest separation was observed between Cluster 1 and Cluster 3 (Euclidean distance = 99.26). Cluster 3 showed the highest within-cluster homogeneity, with no outliers and the lowest dispersion (SD = 10.67). These findings support data-driven phenotypic preselection strategies; however, future research should incorporate additional herds, economically relevant traits, and genomic data to enhance generalisability.