The objective of the paper was to investigate the main factors determining the yield of fat in goat milk in the dairy goat population of Lithuania. The research was carried out on a total of 1,079 dairy goats (4 breeds: Czech White Shorthaired (n = 610), Saanen (n = 364), Lithuanian native (n = 94) and Anglo-Nubian (11)) in 7 dairy farms. The average milk yield during lactation was 797.42 (±53.3) kg. The average milk fat content was 4.14% (±0.4). The research of the goat milk fat content demonstrated that the indicators investigated in different herds varied. The highest milk fat content in percent was determined among AngloNubian -5.2%. The milk fat content of all breeds of goats increased with increasing the parity up to the 4-6th parity, respectively, and started decreasing then, after reaching the peak. While assessing the quantity of milk fat during the stage of lactation, the highest milk fat content was determined during the first months (4.5-4.7%) and at the end of lactation (4.5%). The research performed by us showed that such factors as breed, parity, stage of lactation and herd had an impact on the quantity of goat milk fat.

Background: Several single nucleotide polymorphisms had been detected in goats and for the researchers it is important to reveal candidate genes with substantial effects on the traits of economic importance. The aim of this study was to investigate the association between LPL, STAT5 and AGPAT6 gene polymorphisms and milk traits of goats. Methods: We investigated about 204 goats, four different breeds (Czech White Shorthaired, Saanen, Anglo Nubian, Alpine) and two crossbreeds (Saanen and Alpine, Saanen and Anglo Nubian). The milk samples were analysed using spectrophotometers LactoScope 550 and LactoScope FTIR (Delta Instruments, the Netherlands). The somatic cell count (SCC) in milk was determined by flow cytometry method using the Somascope CA-3A4 (Delta Instruments, the Netherlands). Variations of the goat AGPAT6, STAT5, LPL genes were detected by PCR-RFLP method. Result: Analysis of AGPAT6 gene revealed that goats with GC genotype had higher milk yield, fat content and lactose content; goats with CG genotype had higher protein content. STAT5 gene analysis showed that goats with CT genotype had higher milk yield; goats with CC genotype had higher fat content and lactose content; goats with TT genotype had higher protein and urea content and also SCC. According to LPL gene analysis, goats with CC genotype had higher milk yield while goats with GG genotype had higher fat and protein content. The highest milk yield was estimated in Saanen and Alpine crossbreed goats while the highest milk fat and protein content was estimated in Anglo Nubian goats.

The main goal of this study was to evaluate the relation of different SCC levels in goat milk with goat milk yield, milk composition, FA, and AA profiles. Whereas the investigated herd was composed of Alpine and Saanen goats, the influence of breed on milk parameters and milk yield was also assessed. The research was carried out in 2022 at a Lithuanian dairy goat farm with 135 goats (Saanen = 66 and Alpine = 69) without evidence of clinical mastitis. The current research revealed a relationship between SCC with goat milk yield and composition. Goats with a high SCC had significantly lower milk yield (p < 0.001), lower content of lactose (p < 0.01), fat (p < 0.001) and higher protein content (p < 0.05) in their milk. The increase in most AA was significantly associated with increased SCC. The higher quantity of Asp, Glu, Ala, Met, His, Lys, Arg, EAA, NEAA, and TAA (compared with the low SCC group) (p < 0.05–0.01), Leu, Tyr, and BCAA (compared with the low and medium SCC group) were found in the milk of the high SCC group (p < 0.05–0.01). The distribution of the main FA groups was also related to SCC and showed a significant decrease in SCFA (p < 0.01–0.001) and an increase in LCFA, PUFA, and BCFA in the high SCC group (p < 0.05). All individual AA and their groups (EAA, NEAA, TAA, BCAA) were significantly lower in the milk of the Saanen goat breed (p < 0.001). The most individual FA ranged between goat breeds, while the total amount of SFA, UFA, and MUFA wasn’t affected by breed (p > 0.05). The research revealed a statistically significant relationship between SCC, AA, and FA, suggesting that these traits may be used as a biomarker in the goat selection process.

The aim of this study was to estimate the relation between milking traits and somatic cell count with electrical conductivity of goat milk during different milking phases. The research was carried out in the herd of Czech White Shorthaired and Saanen goat breeds (n=323) with the help of the electronic milk flow meter LactoCorder®. The milk yield, milking duration, milking flow rate and electrical conductivity of milk in the different phases of milking showed the significant mean differences between the breeds. Almost all (except electrical conductivity during the initial time) investigated indicators of electrical conductivity had a significant positive correlation with SCC. The bimodality of milk flow was determined in 9.69 % of goats and associated with milk yield decrease and SCC increase (P&lt;0.05). The results confirm that the milk flow curve data is a good tool to control milking traits of goats, to predict the prevalence of mastitis and, thus, to improve the health of the udder of goats.

The aim of the work was to perform a research of phenotypic correlations between milk production and composition traits in Lithuanian dairy cattle breeds according to the data of all controlled cows (n = 1,489,264) during the period 2003-2012. The analysis showed strong positive, statistically significant correlations in all analyzed breeds between milk kg and fat kg correlations ranged from r = 0.915 in Lithuanian White-backed to r = 0.948 (p < 0.001) in Lithuanian Ash-grey controlled cows, and between milk kg and protein kg correlations ranged from r = 0.969 in Lithuanian White-backed to r = 0.984 (p < 0.001) in Lithuanian Red, and between fat kg and protein kg correlations ranged from r = 0.928 in Lithuanian Black-and-White to r = 0.949 (p < 0.001) in Lithuanian Ash-grey controlled cows. Low positive, statistically significant correlations in all analyzed breeds were estimated between fat and protein percentages. Correlations ranged from r = 0.256 in Lithuanian Black-and-White to r = 0.353 (p < 0.001) in Lithuanian Ashgrey controlled cows. Also low positive, statistically significant correlations in all analyzed breeds were estimated between milk kg and protein percentages, r = 0.020 (p < 0.01) in Lithuanian Red and r = 0.042 (p < 0.001) in Lithuanian Black-and-White controlled cows.