The objectives of this review paper are to present udder defences, including teat of the udder, mammary epithelial cells, leucocytes, immunoglobulins, complement system and chemical antibacterial agents, to describe cooperation and interactions between them and to elaborate on potentials regarding their significance in mammary immunisation strategies. The teat of the udder provides initial protection to the mammary gland. The mammary epithelial cells synthesise antibacterial proteins and the leucocytes produce various inflammation mediators (cytokines or chemokines), phagocytose bacteria and recognise antigenic structures. In the mammary gland, four immunoglobulins (IgG1, IgG2, IgM and IgA) have important roles against bacterial pathogens. The complement system is a collection of proteins, participating in the inflammatory process through various pathways. Other components contributing to humoral mammary defence include lactoferrin, lysozyme and the lactoperoxidase/myeloperoxidase systems, as well as oligosaccharides, gangliosides, reactive oxygen species, acute phase proteins (e.g., haptoglobin and serum amyloid A), ribonucleases and a wide range of antimicrobial peptides. Management practices, genetic variations and nutrition can influence mammary defences and should be taken into account in the formulation of prevention strategies against ovine mastitis.

The objective of this work was to develop, use and present a detailed questionnaire for the evaluation of health management in dairy small ruminants; it includes 442 questions organised in seven sections: general, infrastructure, animals, production characteristics, health management, nutrition, human resources. Consistency of replies was evaluated in 27 farmers, interviewed twice. Inconsistent replies were given by all farmers to 30 different questions (Cronbach’s coefficient alpha: 0.987). Then, interviews were performed in 444 farms around Greece. Mean duration of an interview was 63.6 min. Clarifications were requested by 273 farmers to 22 different questions (maximum per farmer: 8). The experience of the investigator, the primary language of farmers and asking clarifications by the farmers affected the duration of the interview. The questionnaire can be used for research work in the field, to record details in the farms under study. In accord with the needs of a particular study, it can be modified, by adding more specific questions or omitting others deemed of less importance. Moreover, it can also be used for routine monitoring purposes, as a useful means to record and maintain details of farms during clinical work. To the best of our knowledge, the questionnaire is the most extensive and detailed one available internationally for dairy small ruminants.

Objectives were to investigate somatic cell counts (SCC) and total bacterial counts (TBC) in the raw bulk-tank milk of sheep flocks in Greece, to study factors potentially influencing increased SCC and TBC in the bulk-tank milk of sheep and to evaluate possible associations of SCC and TBC with milk content. Throughout Greece, 325 dairy sheep flocks were visited for collection of milk sampling for somatic cell counting, microbiological examination and composition measurement. Geometric mean SCC were 0.488 × 10⁶ cells mL⁻¹; geometric mean TBC were 398 × 10³ cfu mL⁻¹; 228 staphylococcal isolates were recovered form 206 flocks (63.4%). Multivariable analyses revealed annual incidence risk of clinical mastitis, age of the farmer and month into lactation period (among 53 variables) to be significant for SCC > 1.0 × 10⁶ cells mL⁻¹ and month into lactation period at sampling and availability of mechanical ventilators (among 58 variables) to be significant for TBC > 1500 × 10³ cfu mL⁻¹. Negative correlation of SCC with fat, total protein and lactose and positive correlation of SCC with added water were found. With SCC > 1.0 × 10⁶ cells mL⁻¹, significant reduction of protein content (2%) was observed, whilst in flocks with SCC > 1.5 × 10⁶ cells mL⁻¹, significantly lower annual milk production per ewe (42.9%) was recorded.

There is a paucity of information regarding staphylococcal populations on teatcups of milking parlours in sheep and goat farms. The objectives were to describe the populations of staphylococci on teatcups in milking parlours in sheep or goat farms in two field investigations throughout Greece and to potentially associate the findings with the use of anti-staphylococcal mastitis vaccinations in the farms visited during the two investigations. In a cross-sectional (255 sheep and 66 goat farms across Greece) and a longitudinal (12 sheep farms, four samplings, throughout lactation) study, swab samples were collected from 1418 teatcups (upper and lower part) for staphylococcal recovery, identification and assessment of biofilm-formation. A total of 328 contaminated teatcups (23.1%) were found in 105 sheep (41.2%) and 35 goat (53.0%) farms. Staphylococci were more frequently recovered from the upper than the lower part of teatcups: 269 versus 139 teatcups, respectively. After identification, 253 staphylococcal isolates were found: Staphylococcus aureus, Staphylococcus equorum, Staphylococcus lentus, and Staphylococcus capitis predominated. Of these isolates, 87.4% were biofilm-forming. The proportion of contaminated teatcups was smaller in farms where vaccination against anti-staphylococcal mastitis in general or vaccination specifically against mastitis caused specifically by biofilm-forming staphylococcal strains was applied, 19.7% or 10.9%, respectively, versus 25.5% in farms without vaccination. In the longitudinal study, contaminated teatcups were identified in 28 (58.3%) sampling occasions, with staphylococci being recovered more frequently from their upper part. The same species as in the cross-sectional study predominated. Of these isolates, 61.9% were biofilm-forming. In farms where vaccination against mastitis caused specifically by biofilm-forming staphylococcal strains was applied, the proportion of contaminated teatcups was smaller: 20.4% versus 48.3% in farms without vaccination. There were no differences in proportions of contaminated teatcups between sampling occasions. In conclusion, the great majority of staphylococci recovered from teatcups of milking parlours in sheep and goat farms included biofilm-forming isolates. Reduced staphylococcal isolation was noted in farms where anti-staphylococcal vaccination was performed; this was possibly the effect of reduced excretion of staphylococci in the milk of vaccinated animals.

The specific objective of the present study was to develop computational models, by means of which predictions could be performed regarding the quality of the bulk-tank milk in dairy sheep and goat farms. Our hypothesis was that use of specific variables related to the health management applied in the farm can facilitate the development of predictions regarding values related to milk quality, specifically for fat content, protein content, fat and protein content combined, somatic cell counts, and total bacterial counts. Bulk-tank milk from 325 sheep and 119 goat farms was collected and evaluated by established techniques for analysis of fat and protein content, for somatic cell counting, and for total bacterial counting. Subsequently, computational models were constructed for the prediction of five target values: (a) fat content, (b) protein content, (c) fat and protein, (d) somatic cell counts, and (e) total bacterial counts, through the use of 21 independent variables related to factors prevalent in the farm. Five machine learning tools were employed: decision trees (18 different models evaluated), random forests (16 models), XGBoost (240 models), k-nearest neighbours (72 models), and neural networks (576 models) (in total, 9220 evaluations were performed). Tools found with the lowest mean absolute percentage error (MAPE) between the five tools used to test predictions for each target value were selected. In sheep farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 3.95%); for the prediction of fat and protein content combined, neural networks was selected (6.00%); and for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (6.55%); no tool provided useful predictions for fat content and for total bacterial counts. In goat farms, for the prediction of protein content, k-nearest neighbours was selected (MAPE: 6.17%); for the prediction of somatic cell counts, random forests and k-nearest neighbours were selected (4.93% and 5.00%); and for the prediction of total bacterial counts, neural networks was selected (8.33%); no tool provided useful prediction models for fat content and for fat and protein content combined. The results of the study will be of interest to farmers, as well as to professionals; the findings will also be useful to dairy processing factories. That way, it will be possible to obtain a distance-aware, rapid, quantitative estimation of the milk output from sheep and goat farms with sufficient data attributes. It will thus become easier to monitor and improve milk quality at the farm level as part of the dairy production chain. Moreover, the findings can support the setup of relevant and appropriate measures and interventions in dairy sheep and goat farms.

The aim of this study was the economic comparison of two equivalent sheep farms with different reproductive management systems. Financial data were selected from a farm that applied artificial insemination (AI) and from one that applied natural mating (NM). The main objective of the analysis was to estimate the cost of each farm’s products and then to calculate their economic indicators. The AI farm had higher production costs, as a result of higher labor and fixed capital costs. On the other hand, the invested capital for the equipment and buildings of the NM farm was lower. Furthermore, the invested livestock capital based on the genetic value of the animals was higher in the AI farm. The AI farm produced milk, replacement ewe lambs and replacement ram lambs as its primary products, whereas the NM farm produced only milk as its primary product. The production costs for milk were 0.08 EUR/kg lower in the AI farm compared with the NM farm. The AI farm had a higher gross revenue and net and gross profit, resulting from the higher genetic value of the AI farm’s livestock. As indicated, the breeding and sale of genetically improved animals can increase the financial results of a farm and offer alternative sources of income. In conclusion, AI results in more sustainable and economically efficient sheep farming. In this regard, training for farmers and governmental economic support could promote AI application. Finally, the fortification of farmer group initiatives that facilitate the trade of dairy sheep products can accelerate AI utilization in dairy sheep farms in Greece.