The study’s main goal was to compare several data mining and machine learning algorithms to estimate body weight based on body measurements at a different share of Polish Merino in the genotype of crossbreds (share of Suffolk and Polish Merino genotypes). The study estimated the capabilities of CART, support vector regression and random forest regression algorithms. To compare the estimation performances of the evaluated algorithms and determine the best model for estimating body weight, various body measurements and sex and birth type characteristics were assessed. Data from 344 sheep were used to estimate the body weights. The root means square error, standard deviation ratio, Pearson’s correlation coefficient, mean absolute percentage error, coefficient of determination and Akaike’s information criterion were used to assess the algorithms. A random forest regression algorithm may help breeders obtain a unique Polish Merino Suffolk cross population that would increase meat production.

The purpose of this paper was to utilize the decision trees technique to determine the factors responsible for high monthly milk yield in Polish Holstein‐Friesian cows from 27 herds equipped with milking robots. The applied statistical method—the decision tree technique—showed that the most important factors responsible for monthly milk yield of dairy cows using robots were, in descending order of importance: milking frequency, lactation number, month of milking, and type of lying stall. At the same time, it has been ascertained that the highest monthly milk yield (47.24 kg) can be expected from multiparous cows kept in barns with a deep bedding that were milked more frequently than three times per day. On the other hand, the lowest milk production (13.56 kg) was observed among dairy cows milked less frequently than two times a day, with an average number of milked quarters lower than 3.97. The application of the decision trees technique allows a breeder to select appropriate levels of environmental factors and parameters that will help to ensure maximized milk production.

In barns equipped with an automatic milking system, the profitability of production depends primarily on the milking efficiency of a cow (ME; kg/min) defined as cow milk yield per minute of box time. This study was carried out on 1823 Polish Holstein–Friesian cows milked by the automatic milking system (AMS) in 20 herds. Selected milking parameters recorded by the AMS were analyzed in the research. The aim of the study was to forecast ME using two statistical techniques (analysis of variance and decision trees). The results of the analysis of variance showed that the average ME was 1.67 kg/min. ME was associated with: year of AMS operation (being the highest in the first year), number of cows per robot (the highest in robots with 61–75 cows), lactation number (highest for multiparas), season of calving (the highest in spring), age at first calving (>36 months), days in milk (151–250 days) and finally, rear quarter to total milk yield ratio (the highest between 51% and 55%). The decision tree predicted that the highest ME (2.01 kg/min) corresponded with cows that produced more than 45 kg of milk per day, were milked less than four times/day, had a short teatcup attachment time (<7.65 s) and were milked in robots that had an occupancy lower than 56 cows.

The aim of the present study was to evaluate the genotype and allele frequencies of 24 polymorphisms in casein alpha S1 (CSN1S1), casein alpha S2 (CSN1S2), beta-casein (CSN2), kappa-casein (CSN3), and progestagen-associated endometrial protein (PAEP) genes. The study included 1900 Polish Black and White Holstein-Friesian dairy cows that were subjected to genotyping via microarrays. A total of 24 SNPs (Single Nucleotide Polymorphisms) within tested genes were investigated. Two CSN1S1 SNPs were monomorphic, while allele CSN1S1_3*G in CSN1S1_3 SNP dominated with a frequency of 99.39%. Out of seven CSN2 SNPs, four were polymorphic; however, only for CSN2_3 all three genotypes were detected. Only three out of nine SNPs within CSN3 were monomorphic. Three PAEP SNPs were also found to be polymorphic with heterozygotes being most frequent. Hardy–Weinberg equilibrium (HWE) was observed for eight variants. It was shown that only CSN3_6 was not in HWE. The fact that many of investigated SNPs were monomorphic may suggest that in the past the reproduction program favored one of these genotypes. SNPs that are included in commercially available microarrays should be monitored in relation to changes in their frequencies. If a SNP has turned monomorphic, maybe it should be considered for removal from the microarray.

The aim of this study was to classify placental characteristics of Akkaraman, Morkaraman, Karayaka, Awassi, Malya, and Bafra sheep breeds using the hierarchical clustering method. In total, 240 individual data records were used as experimental material. Placental characteristics such as total cotyledon surface area, small and large cotyledon length, small cotyledon depth, etc. were used as explanatory variables to classify the breeds’ characteristics. Hierarchical clustering was used with the nearest neighbour method with Euclidean distance in order to classify the sheep breeds’ variations. As a result, six breeds were separated into three clusters: the first cluster consisted of Bafra, Karayaka, and Awassi breeds; the second consisted of Akkaraman and Malya breeds; and the third cluster included only the Morkaraman breed. Bafra and Karayaka were pointed as the nearest breeds, with a similarity of 98.7% in terms of placental characteristics. The similarity rate of the Akkaraman and Malya breeds was at a level of 97.5%, whereas it was 96.8% for Bafra, Karayaka, and Awassi breeds. The similarity of Akkaraman, Karayaka, Awassi, Malya, and Bafra sheep breeds was estimated as 95.7%. The overall similarity was found to be at a level of 93.2% among sheep breeds. The outcomes of the study might be useful as a selection tool for reproductivity and can be used to select the breed to be reared.