Data on Mapping 444 Dairy Small Ruminant Farms during a Countrywide Investigation Performed in Greece

The Use of Machine Learning to Predict Prevalence of Subclinical Mastitis in Dairy Sheep Farms

The objective of the study was to develop a computational model with which predictions regarding the level of prevalence of mastitis in dairy sheep farms could be performed. Data for the construction of the model were obtained from a large Greece-wide field study with 111 farms. Unsupervised learning methodology was applied for clustering data into two clusters based on 18 variables (17 independent variables related to health management practices applied in farms, climatological data at the locations of the farms, and the level of prevalence of subclinical mastitis as the target value). The K-means tool showed the highest significance for the classification of farms into two clusters for the construction of the computational model: median (interquartile range) prevalence of subclinical mastitis among farms was 20.0% (interquartile range: 15.8%) and 30.0% (16.0%) (p = 0.002). Supervised learning tools were subsequently used to predict the level of prevalence of the infection: decision trees, k-NN, neural networks, and Support vector machines. For each of these, combinations of hyperparameters were employed; 83 models were produced, and 4150 assessments were made in total. A computational model obtained by means of Support vector machines (kernel: 'linear', regularization parameter C = 3) was selected. Thereafter, the model was assessed through the results of the prevalence of subclinical mastitis in 373 records from sheep flocks unrelated to the ones employed for the selection of the model; the model was used for evaluation of the correct classification of the data in each of 373 sets, each of which included a test (prediction) subset with one record that referred to the farm under assessment. The median prevalence of the infection in farms classified by the model in each of the two categories was 10.4% (5.5%) and 36.3% (9.7%) (p < 0.0001). The overall accuracy of the model for the results presented by the K-means tool was 94.1%; for the estimation of the level of prevalence (<25.0%/≥25.0%) in the farms, it was 96.3%. The findings of this study indicate that machine learning algorithms can be usefully employed in predicting the level of subclinical mastitis in dairy sheep farms. This can facilitate setting up appropriate health management measures for interventions in the farms.

Associations of Socio-Demographic Characteristics of Dairy Goat Farmers in Greece with Biosecurity-Related Practices in the Farms

Given the significance of goat farming in Greece, our study aimed to explore biosecurity conditions on goat farms and refers to an investigation performed on 119 farms countrywide in Greece. The objective of the present work was to evaluate and report on potential associations between the socio-demographic characteristics of goat farmers and practices that play a role in biosecurity on farms. Data were obtained during a large cross-sectional study performed across Greece in 119 goat herds. The socio-demographic characteristics of farmers, as well as 16 variables related to biosecurity, were recorded; these were practices or events that could potentially enhance or compromise biosecurity on farms. A score based on all the biosecurity-related variables was devised by assigning a '1' or '-1' mark for each variable recorded on a farm that could enhance or compromise, respectively, biosecurity. Among variables potentially enhancing biosecurity on farms, the application of disinfections on the farm and the maintenance of isolation for sick animals were practiced more frequently; among variables potentially compromising biosecurity on farms, the presence of hunters in the area around the farm and grazing practices for animals were recorded more frequently. The median overall score for biosecurity-related practices for all farms in the study was 1 (interquartile range: 2.5; minimum: -4; maximum: 5). Among socio-demographic characteristics, the presence of working staff on a farm was associated with five individual biosecurity-related practices and the length of farming experience with three individual biosecurity-related practices. In the multivariable analysis, female farmers (p = 0.007) and the presence of working staff on the farm (p = 0.025) emerged as the two significant socio-demographic characteristics with an association with the overall biosecurity level on farms. This knowledge can be useful when developing biosecurity programs on goat farms. Recognition of locally applied farm-level practices enhancing biosecurity could form a basis for farmers to apply more rigorous and effective relevant plans.

A Combined Approach for the Characterization of Small Ruminant Lentivirus Strains Circulating in the Islands and Mainland of Greece

Small ruminant lentiviruses are a group of viruses infecting goat and sheep worldwide. These viruses exhibit an extraordinary degree of genetic and antigenic variability that severely influence in vivo and in vitro features, as well as diagnostic test results. Small ruminant farming is the most important animal farming business in Greece, with a high impact on the Greek primary economy. Although SRLV infection and its impact on animal production are well established in the country, little is known about the circulating SRLV strains and their prevalence. The aim of this study was to characterize SRLVs circulating in Greece with a combined serological and molecular approach, using the bulk milk matrix collected from 60 farms in different municipalities. This study allowed us to estimate a seroprevalence of around 52% at the herd level. The B1, B2 and A3 subtypes and a novel A viral cluster were identified. Moreover, the amplicon sequencing method allowed us to identify more than one viral subtype in a sample. These results again confirm the high variability of these viruses and highlight the importance of the constant monitoring of viral evolution, in particular in antigens of diagnostic interest.

A questionnaire-based survey in Spain provides relevant information to improve the control of ovine coccidiosis

Ovine coccidiosis is a widespread intestinal parasitic disease caused by Eimeria spp. Lambs are infected by the ingestion of sporulated oocysts, experiencing diarrhea and low growth rates. Control should be based on measures to reduce infection pressure and stress on the animals as well as on appropriate diagnosis and strategic treatment. To obtain information on how control measures are implemented in the ovine sector in Spain, a questionnaire-based survey was completed in 2022 by 154 veterinarians and 173 farmers working in this sector. Coccidiosis was highlighted as a relevant disease by 34% of the respondents. The period of greatest risk seemed to differ between production systems, being mainly early after weaning (7-15 days after weaning) in meat flocks and feedlots and later (1-2 months after weaning) in dairy flocks. The absence of cleaning and disinfection measures was identified as a risk factor by 51% of the veterinarians, with 22% mentioning overcrowding of animals and 22% indicating that coccidiosis has more incidence in flocks with large number of animals. The use of laboratory diagnosis methods (fecal oocyst count) was unusual in 70 and 84% of the veterinarians and farmers, respectively. Regarding control, dairy flocks usually housed a larger number of animals under intensive conditions, and they implemented more frequently control measures for coccidiosis than meat flocks. Anticoccidial drugs were used in 79% of the flocks, and in 74-82% of them, they were applied based on clinical criteria. Comparing protocols for anticoccidial treatment among different production systems, in meat flocks, anticoccidial drugs were applied more frequently when clinical signs were observed, and coccidiostats were used for less than 28 days compared to dairy flocks. These results highlight the need for improvement in the use of anticoccidial treatments adjusted to the new regulatory framework in the EU, which in turn will rationalize the use of antimicrobial compounds and may help to mitigate the impact of coccidiosis in flocks.

Isolation of Biofilm-Forming Staphylococci from the Bulk-Tank Milk of Small Ruminant Farms in Greece

The objectives of this study were (i) to describe staphylococcal isolates recovered from bulk-tank raw milk collected from sheep and goat farms during a countrywide study performed in Greece, (ii) to study management factors potentially associated with their presence in bulk-tank milk and (iii) to provide evidence regarding their association with the quality of the milk. In total, 312 staphylococcal isolates, recovered from samples of bulk-tank raw milk from 444 small ruminant farms in Greece, were evaluated in this work. The in vitro formation of biofilm by the isolates was tested by combining the findings of (a) culture appearance on Congo Red agar plates and (b) results of a microplate adhesion test. The most frequently identified species was Staphylococcus aureus (75 isolates); other frequently recovered species were S. simulans (44 isolates), S. equorum (34 isolates) and S. haemolyticus (26 isolates); in total, 23 species were identified. In total, 224 (71.8%) isolates were biofilm-forming and were recovered from the bulk-tank milk samples of 148 sheep flocks (45.5%) and 55 goat herds (46.2%). There was evidence of seasonality in the isolation of staphylococci: during spring, mostly biofilm-forming isolates were recovered, whilst during summer, mostly non-biofilm-forming isolates were recovered. Among farms applying machine-milking, the proportion of farms from which biofilm-forming isolates were recovered was higher where water with temperature < 50 °C or ≥90 °C was used to clean the milking parlour. In the multivariable analyses, for farms applying machine-milking, the temperature of the water emerged as the only significant variable (p = 0.024), whilst in farms applying hand-milking, the only tendency that emerged was for the frequency of collection of milk from the farm tank (p = 0.08). In sheep flocks, recovery of biofilm-forming staphylococci from the bulk-tank milk was associated with higher somatic cell counts and higher total bacterial counts in the milk. The study identified abiotic factors related to the presence and isolation of these bacteria, specifically the temperature of water used for the cleaning of the milking parlour (in farms where machine-milking is applied) and the frequency of milk collection from the farm tank. These factors apply after the production of milk, and they could thus be regulated appropriately in order to reduce bacterial load and improve the quality of milk delivered to dairy plants. In sheep farms, an association was also seen between recovery of biofilm-forming staphylococci and high somatic cell counts in milk.