One of the most important decisions in dairy cattle production today is the correct choice of culling time for cows. In the culling decision process, the farmer has to take into account a number of factors, the complexity of which makes the decision-making task difficult. A crucial factor is the evolution of reproductive indicators. The aim of the research was to develop a microsimulation method that can be used to easily investigate the impact on profitability of increasing pregnancy rates and when the culling decision is made. In the microsimulation, the stock was examined without changing any other conditions. A microsimulation method has been developed to determine with high accuracy the effect of the pregnancy rate and the increase in culling days on the economic indicators of individual dairy farms. By microsimulation, the effect of changing these two parameters on the expected milk production of cows, the most important economic indicator for cattle farms, was investigated. The other parameters of economic importance were simulated using a cattle farm database. The purpose of microsimulation is to assist in producing certain managerial decisions in order to achieve better profitability and economic efficiency. In summary, the results showed that increasing the pregnancy rate can successfully reduce the length of the calving interval, but the improved pregnancy rate did not show a significant increase in milk production. In order to obtain results that can be used by farms, the authors intend to further develop the model in the future, adapting it to farms and taking into account their specificities.

Egészségügyi álhírkereső eljárások informatikai megoldásai • Computational Tools for Health-Related Fake News Detection

This volume of MPB (Mathematical Programming, Series B) contains 11 high-quality articles in the area of global optimization. It is inspired by the XV Workshop on Global Optimization (HUGO, https://www.inf.u-szeged.hu/hugo/

In Hungary hundreds of thousands of cows produce milk for us. A common disease of them is mastitis that influences their productivity and profitability substantially. The usual practice is to decide on a rule of thumb basis whether the ill cow should be kept or sold. E.g. they are kept till the fifth mastitis case occurs. The present study investigates this problem from a mathematical modelling point of view. The relative amount of the possible lost profit is in the order of magnitude of 10s of percentages, which is quite large, especially regarding the profitability outlooks of the dairy branch. The problem lies in the personal relationship of the farmers to the cows, and in the complexity of the estimation of the uncertain future scenarios. We present a model that is based on collected historical data on the distribution of several model parameters such as the length of the illness, the amount of medicine needed, the number of inseminations required to get into the next lactation cycle etc. The applied methodology is microsimulation (i.e. we simulate all possible events one-by-one) and stochastic optimization. Our typical result is a suggested decision on the basis of the expected value of the profit/loss for the given animal. We report on the first results that confirm our research expectations in terms of improvement of the business decision. The ongoing research will focus on a recommendation system type data mining technology that can utilize the local specialties of the actual dairy farm in question, and to validate the additional advantage involved in it.