The objectives of this study were (1) to record the major pathogens associated with subclinical mastitis (SCM), (2) to calculate their incidence during the milking period, and (3) to estimate the effect of SCM on daily milk yield (DMY) for goats reared under low-input management schemes. Dairy goats (n=590) of Skopelos and indigenous Greek breeds from 4 herds were randomly selected for the study. The study included monthly monitoring, milk yield recording, and bacteriological analyses of milk of individual goats during the course of 2 successive milking periods. Incidence and cumulative incidence were calculated for SCM cases. Moreover, 2 mixed linear regression models were built to assess the effects of (1) SCM and (2) different pathogens isolated from SCM cases, on DMY. The estimated incidence and cumulative incidence of SCM for the first and the second year of the study were 69.5 and 96.4 new cases of SCM/1,000 goat-months, and 24.1 and 31.7%, respectively. A total of 755 milk samples were subjected to microbiological examination, resulting in 661 positive cultures. Coagulase-negative and coagulase-positive staphylococci were isolated from 50.2 and 34.5% of the positive cultures, respectively. The incidence of infections (new infections per 1,000 goat-months) for the first and the second year of the study were 34 and 53 for coagulase-negative staphylococci, 23 and 28 for coagulase-positive staphylococci, 3 and 5 for Streptococcus/Enterococcus spp., and 5.5 and 9.1 for gram-negative bacteria. Goats with SCM had lower DMY when compared with goats without SCM (ca. 47g/d, corresponding to a 5.7% decrease in DMY). In particular, goats with SCM due to coagulase-positive staphylococci infection produced approximately 80g/d less milk (a reduction of ca. 9.7%) compared with uninfected ones, whereas SCM due to gram-negative bacteria resulted in approximately 15% reduction in DMY. Investigating the epidemiology of SCM and its effects on production traits is critical for the establishment of effective preventive measures against SCM and for the assessment of the sustainability of production in low-input dairy goat herds.

No abstract

No abstract

The objective of the study was to investigate and quantify the effects of subclinical mastitis (SCM) on the gross chemical composition of milk in low-input dairy goat herds. Dairy goats (n=590) of two native Greek breeds from four representative low-input farms were randomly selected and used in the study. Α prospective study was conducted, including monthly monitoring and milk sampling of the same individual goats during the course of two consecutive milking periods. Mixed linear regression models were built to assess how the chemical composition of milk was affected by (1) SCM and (2) the different pathogens isolated from SCM cases. Goats with SCM had lower milk-fat content (MFC), daily milk-fat yield (DMFY), milk-lactose content (MLC) and daily milk-lactose yield (DMLY), and slightly higher milk-protein content (MPC) and daily milk-protein yield (DMPY), compared with goats without SCM. Milk produced by goats with SCM due to coagulase-positive staphylococci and had significantly lower MFC, DMFY, MLC and DMLY, and higher MPC and DMPY, compared with the milk produced by healthy goats. Finally, goats with SCM due to coagulase-negative staphylococci had lower DMFY, MLC and DMLY and higher DMPY compared with the healthy ones.

This Research Communication addresses the hypothesis that fat, protein, lactose and total solids content can be predicted using daily milk yield (DMY), pH, electrical conductivity (MEC) and refractive index (RI) of milk as predictors. It also addresses the possibility of these measurements being used for on-farm benchmarking activities towards selecting the highest yielding animals and flocks regarding milk quality traits (MQT). A total of 308 purebred Frizarta ewes were used for the study. From each individual ewe, a composite milk sample was collected. pH, MEC and RI of milk were measured and the samples were assayed for fat, protein, lactose and total solids content, using an automatic infrared milk analyser. The predictive value of DMY, pH, MEC and RI of milk on its MQT was assessed using multiple linear regression analysis. Significant regression equations were produced for all of the studied traits. RI and MEC were significant and reliable predictors for all studied MQT, whereas DMY was a significant predictor for most MQT with the exception of protein content. pH was a marginally significant predictor for some of the MQTs at the initial development of the equations but proved unreliable after bootstraping. Using these equations a number of ewes varying from 75 (for fat) to 97 (for protein) out of the 100 highest MQT yielders were correctly predicted, whereas, none of the ewes out of the 100 lowest MQT yielders was mispredicted as a high yielder for protein-, lactose- and total solids- content. Three out of 100 lowest fat-yielders were mispredicted as high fat-yielders. Similar equations can be used for benchmarking activities towards selecting the highest protein-, fat-, lactose- and total solids- yielding animals and flocks in cases where laboratories for MQT analyses are not readily available or the cost of chemical analyses is high. The method can be regarded as a handy tool for the dairy industry to readily assess milk quality at the farm level.