Search Results (77)

Accurate body weight monitoring is essential for efficient sheep production, yet conventional weighing methods are labor-intensive and require frequent animal handling. Computer vision provides a promising non-invasive alternative; however, many published studies rely on validation strategies that may overestimate predictive performance because repeated observations from the same animals are simultaneously included in training and testing datasets. This study developed and evaluated an integrated analytical framework for image-based body weight estimation in ewes that combines standardized image processing, robust frame-level quality control, longitudinal statistical modeling, and strict leakage-controlled validation. A longitudinal dataset comprising 20 Dorper ewes monitored over six sampling periods was acquired using top-view depth imaging synchronized with body weight measurements under semi-arid conditions. The analytical framework integrated three complementary modules: longitudinal mixed-effects modeling, early prediction of final body weight, and contemporaneous body weight estimation. Predictive analyses were evaluated using nested leave-one-animal-out cross-validation, in which all preprocessing, correlation filtering, feature selection, model optimization, and model selection were performed exclusively within the training animals of each outer fold. The longitudinal mixed-effects model accurately characterized individual growth trajectories (conditional R² = 0.98). Initial body weight remained the strongest predictor of final body weight (R² = 0.770), whereas image-derived morphometric descriptors alone showed limited predictive performance under strict animal-level validation (R² = −0.899 to −0.031). Combining baseline body weight with selected morphometric descriptors produced modest but biologically informative improvements, achieving a maximum R² of 0.839. Contemporaneous body weight estimation achieved moderate predictive performance (maximum R² = 0.332) and revealed temporal changes in the importance of morphometric descriptors throughout growth. Overall, the proposed framework provides a reproducible methodology for evaluating image-derived phenotypes under rigorous animal-level validation, contributing to the development of more robust, interpretable, and biologically grounded computer vision systems for Precision Livestock Farming. Full article

Extensive livestock farming is characteristic of the landscape of the Mediterranean regions of Southern Iberian Peninsula. These production systems based on dryland pastures provide a wide range of services and contribute to maintaining environmental balance when compared with intensive agricultural or other livestock production systems. The Portuguese Montado is a habitat of Community importance and is protected under the European Natura 2000 network. This makes any research aimed at preserving, maintaining, or restoring this ecosystem particularly important to ensure its sustainable management. The main objectives of this study were to evaluate: (i) the impact of dolomitic limestone application on soil pH; (ii) the relation between multiple soil parameters; (iii) the impact of grazing preferences and livestock stocking rates on topsoil compaction; (iv) temporal and spatial sheep grazing patterns and sward productivity, quality and floristic composition throughout the growing vegetative season. This study was carried out during the vegetative cycle of 2023/2024 on a 4-ha pasture field located at Mitra farm (Southern Portugal). The experimental design included four treatments resulting from the combination of limestone application (with and without) and stocking rate (traditional: 7 sheep ha⁻¹; high: 18 sheep ha⁻¹). Sheep grazing preferences, soil compaction and fertility, sward productivity, quality and floristic composition were monitored at 48 sampling areas. The results confirm that improving soil pH through the application of dolomitic limestone is an effective, although slow and gradual process. When combined with grazing management through increased stocking rates, several important outcomes were observed: (i) preferential grazing areas did not exhibit significant differences in soil trampling; (ii) higher stocking rates resulted in less selective grazing; (iii) soil amendment and higher livestock stocking rates contributed to greater pasture crude protein content; and (iv) the influence of pasture quality on grazing preferences depended on the phase of the pasture-grazing cycle. Overall, these findings are promising indicators of sustainability for extensive animal production in Mediterranean dryland silvopastoral systems. However, with regard to the sward, as this study only monitored a single growing season, will need to be validated in future studies. Full article

Sheep are a major livestock species in China, yet pneumonia-related mortality poses a significant obstacle to intensive farming. In this study, 115 sheep lung samples were collected and classified into different pneumonia severity groups based on lung lesion scoring. Subsequently, this study employed 16S rRNA sequencing to systematically investigate the structure and diversity of the pulmonary microbiota in sheep, including alpha diversity, beta diversity, and LEfSe analyses. Metagenomic techniques were also applied to analyze the abundance of metabolic pathways, exploring the associations between functional gene differences and pneumonia severity, as well as putative antibiotic resistance genes, virulence factors, and the species contributions of functional genes in severe pneumonia cases. Microbial richness and diversity were significantly higher in the severe pneumonia group than in the healthy/mild lesion group (p < 0.05). While the dominant microbial structures were similar across the groups, notable differences were observed in the abundance of respiratory disease-associated genera, with Pasteurella, Mannheimia, Mycoplasma, Bibersteinia, and Moraxella identified as significantly enriched in severe cases. Moreover, several genera originating from the gut and oral cavity were also associated with pneumonia, suggesting a potential gut–lung axis. Carbohydrate metabolism was the most prevalent pathway in all groups, whereas amino acid metabolism was significantly enriched in the severe pneumonia group. Putative antibiotic resistance genes were differentially enriched; the severe pneumonia group showed significant enrichment of genes conferring resistance to aminoglycosides, tetracyclines, and polymyxins. Virulence factor analysis identified nutritional/metabolic factors and adhesion as the predominant virulence mechanisms. Species contribution analysis further revealed that Mannheimia, Mycoplasma, Pasteurella, and Moraxella were the predominant species associated with functional gene enrichment. In conclusion, the current study reveals associations between changes in the pulmonary microbiota structure and function and the severity of pneumonia in sheep, aiming to provide a foundation for future hypothesis-driven research on the role of the pulmonary microbiota in pneumonia progression. Full article

Reliable individual identification is essential for precision livestock farming. To reduce the performance disparity among coat-color groups in open-set sheep face recognition, this study proposes a color-adaptive recognition method. A coat color classifier routes images to dedicated recognition models, each equipped with a tailored preprocessing pipeline. The method introduces a Retinex-Guided Gradient Perception Convolution (RGPConv), which embeds Retinex physical priors into the convolution operator and generates spatially adaptive modulation maps from the illumination component to dynamically control gradient enhancement intensity, enabling illumination-aware adaptive gradient feature extraction. A Multi-scale Channel Attention Fusion (MCAF) mechanism is further designed to integrate multi-level semantic information. Experiments on the LSFW dataset show that the proposed method achieves an open-set recognition accuracy of 96.08%, outperforming the baseline Li-SheepFaceNet by 1.50 percentage points. Compared with the corresponding routing-only subset baselines, accuracy improves by 2.41 and 2.00 percentage points for black-faced and white-faced sheep, respectively. On the cross-scene RealSheepFace and cross-species GoatFace datasets, accuracy improves by 4.26 and 2.00 percentage points, suggesting potential cross-domain transferability. Full article

Peste des petits ruminants (PPR) is a highly contagious transboundary disease that affects small ruminants and impacts livestock production and trade. This study investigated the seroprevalence and associated risk factors of PPR in sheep, goats, camels, and wild ruminants in Bahrain. A total of 1240 sheep, 1224 goats, 100 camels, and 38 wild ruminants were tested using competitive ELISA. The individual seroprevalence rates were 26% in sheep and 25.5% in goats, with flock/herd-level prevalences of 22.7% and 29.6%, respectively. No antibodies were detected in camels or wild ruminants. The highest seroprevalence was observed in the Northern governorate. The identified risk factors included geographic location, age (<12 months for goats), sex (male for goats), and health status (weak animals). At the flock/herd level, large flock size and semi-intensive farming increased the likelihood of seropositivity. In addition, a 2023–2024 surveillance campaign tested 1044 young, locally born lambs and kids across all governorates. All animals were found to be negative for PPR according to a competitive enzyme-linked immunosorbent assay (cELISA) and a reverse transcription polymerase chain reaction (RT-PCR) test, confirming the absence of antibodies and active virus circulation in the population. These findings support the classification of Bahrain at Progressive Control Pathway for PPR (PCP-PPR) Level 3 status and emphasize the importance of continued surveillance and regional cooperation to mitigate the spread of diseases. Full article

Forecasting sensor-derived animal-state indicators can provide forward-looking information for precision sheep farming, but sheep responses are shaped by their barn environment, previous state, and routine operations. We developed Graph-Enhanced Contextual Long-Range Forecasting for Sheep Farming (GCL-Sheep), a management-context-aware model for multi-step forecasting of active duration, rumination duration, feeding duration, intense exercise duration, and body temperature in Hu sheep. The study used monitoring records from 115 Hu sheep in two farms and three barns, covering monitoring campaigns from March 2024 to December 2025. After domain screening and preprocessing, the data were organized into five farm–season–barn domains, containing approximately 325,000 usable hourly records and 304,000 supervised samples. Barn environmental records, individual physiological/behavioral measurements, and management-operation data were aligned to hourly sequences. GCL-Sheep combines Cross-Variable Graph Construction, hierarchical management-context prefixes, and long-context temporal modeling. For the representative in-domain active-duration forecasting task at the 12 h horizon ($H=12$), GCL-Sheep reduced the mean absolute error and root-mean-square error by 20.0% and 19.2%, respectively, compared with the second-best baseline, and improved the coefficient of determination by 0.079. In Leave-One-Domain-Out evaluation for active-duration forecasting at $H=12$, it achieved an average coefficient of determination of 0.792, and few-shot target-domain fine-tuning further improved accuracy. A 96 h historical window achieved the best balance between accuracy and temporal coverage. These results indicate promising retrospective multi-step forecasting performance and suggest that sensor-based animal-state forecasting may provide decision-support information for inspection scheduling and environmental management in sheep farms; however, welfare-threshold-based early-warning and intervention effects still require prospective field validation. Full article

In modern intensive mutton sheep farming, the high cost and limited supply of conventional feed resources necessitate the exploration of sustainable alternatives. Cotton stalks and sugar beet pulp, abundant agricultural by-products in China, have potential as ruminant feed after proper fermentation treatment, yet their systematic application in sheep production remains underinvestigated. This study evaluated the effects of replacing whole-plant corn microsilage with mixed fermented feed (cotton stalks and sugar beet pulp, 1:1 dry matter ratio) on Suffolk rams (n = 84, 4 months old). Animals were randomly assigned to four groups: control (CK, 0% replacement), MS30 (30% replacement), MS60 (60% replacement), and MS90 (90% replacement). After a 15-day adaptation, the 120-day feeding trial assessed growth performance, slaughter characteristics, meat quality, muscle metabolomics (LC-MS), and jejunal microbiota (16S rRNA sequencing). The MS60 group significantly outperformed the CK group in final body weight, carcass weight, and net weight gain (p < 0.01), slaughter rate (p < 0.05), and meat tenderness (p < 0.05). Fatty acid composition was optimized, with lower SFAs (p < 0.01) and higher MUFAs (p < 0.01). Metabolomic analysis revealed 206 differentially abundant metabolites, with significant enrichment in linoleic acid metabolism, unsaturated fatty acid biosynthesis, and primary bile acid synthesis pathways. The MS60 group exhibited significantly altered jejunal microbiota structure (p < 0.05), including increased Patescibacteria abundance (p < 0.05) and decreased Bifidobacterium (p < 0.001). Replacing 60% of whole-plant corn microsilage with cotton stalk–beet pulp mixed microsilage effectively improved production performance, meat quality, and fatty acid profiles in Suffolk rams, while modulating muscle metabolism and intestinal microbiota structure. These findings provide a practical strategy for sustainable sheep farming utilizing regional agricultural by-products. Full article

Livestock production significantly contributes to greenhouse gas (GHG) emissions, particularly methane (CH₄) and nitrous oxide (N₂O) originating from enteric fermentation and manure management. This study quantified the GHG emissions and cumulative carbon footprint of four commercial sheep farms (SF1, SF2, SF3, and SF4) in the Bursa region of Türkiye, with flock sizes of 200, 500, 150, and 800 adult Merino sheep (mature ewes and breeding rams), respectively. Using the IPCC Tier 2 methodology, the biogenic carbon footprint was estimated at 15.6 kg CO₂-eq per kg of boneless sheep meat. However, when indirect inputs were included, the cumulative carbon footprint reached 28.8 kg CO₂-eq for ewes and 32.3 kg CO₂-eq for breeding rams. These results indicate that indirect emissions from feed production account for the primary environmental load (49.8%), while on-farm energy-related emissions represent a minor portion (0.3%) of the total impact. The results demonstrate that while enteric fermentation (32.5%) remains a critical biological factor, the environmental burden of the feed supply chain is equally significant in intensive systems. These findings highlight that excluding indirect inputs leads to a substantial underestimation of the climate impact, suggesting that mitigation strategies must integrate nutritional optimization with enteric methane reduction to decarbonize sheep production effectively. Full article

The objective of the present study is to evaluate the effects of lactation stage, age, somatic cell count (SCC), and daily milk yield on plasmin–plasminogen (PL–PG) system activity and physicochemical milk traits in intensively reared Chios and Frizarta ewes. A total of 52 purebred ewes (26 ewes per breed and farm) were randomly selected and prospectively monitored during the 3rd, 5th, and 6th month post-lambing. Daily milk yield and body condition score (BCS) were recorded, and individual milk samples were collected for the assessment of PL–PG activities using enzymatic assays, SCC, electrical conductivity (EC), refractive index (RI), and pH. Correlation analysis and mixed linear regression models were used for the assessment of the effects. Lactation stage significantly affected PL–PG system traits in both breeds, but in opposite direction; plasmin and plasminogen plus plasmin declined toward late lactation in Chios ewes, whereas it increased in Frizarta ewes. Lower SCC was associated with reduced plasmin system activity in Chios ewes, whereas no effect was observed in Frizarta ewes. The plasminogen-to-plasmin ratio remained stable across lactation, breeds, and SCC classes, indicating coordinated regulation of the system. BCS was positively associated with plasmin activity during late lactation, suggesting a stage-dependent metabolic modulation. EC and pH were closely associated with SCC, while RI mainly reflected compositional variation. Our findings underline that, although the PL–PG system is primarily affected by lactation stage and mammary health status in sheep, there are breed-specific regulatory patterns which should be further investigated. Full article

Sheep welfare outcomes vary depending on production systems, breeds, and environmental conditions. This study examined the effects of extensive, semi-extensive, and semi-intensive sheep production systems on animal welfare in Serbia, using the AWIN Welfare Protocol to evaluate 30 farms. Welfare indicators were categorised into resource-based, management-based, and animal-based metrics. The results indicated that there was no significant difference in space allowance among the production systems (p > 0.05). This suggests that the space provided was adequate for semi-intensive farms and suitable for both semi-extensive and extensive farms. However, management practices showed significant variations (p < 0.05), indicating diverse impacts on sheep welfare. No ocular discharge or stereotypic behaviours were observed, while respiratory issues, social withdrawal, and excessive itching were found to have a very low prevalence across all farms. The primary welfare concern identified in the extensive farms was the use of painful mutilations. Semi-extensive and semi-intensive farms had significantly higher rates of tail docking (p < 0.05) and poorer fleece cleanliness. These findings highlight the necessity of addressing the root causes of poor welfare to improve sheep welfare standards. Therefore, achieving sustainable welfare outcomes requires an integrated approach that combines genetic suitability, adequate housing, and effective management practices. Full article

Intensive sheep and goat farming in hot-arid regions faces unique welfare challenges that differ substantially from those encountered in cooler climates; however, few practical and validated assessment tools are specifically designed to assess welfare under such extreme conditions. In this study, the term practical refers to field feasibility under routine farm conditions, limited assessment time, and suitability for reliability-based application, rather than comprehensive validation of welfare outcomes. This study aimed to develop and pilot-test a simplified welfare assessment protocol, based on a reduced set of clearly defined, field-applicable indicators supported by explicit operational definitions and standardized scoring criteria, tailored for the United Arab Emirates, with a specific focus on extreme heat and intensive husbandry conditions. Candidate indicators were identified from validated international sources and screened for applicability to arid climates, meat-oriented production, and intensive systems. The refined indicator set was converted into operational scoring sheets and applied by trained undergraduate animal science students as assessors to 100 animals at an intensive research farm. Inter-observer reliability was calculated using Fleiss’ Kappa to evaluate consistency across assessors. Most behavioural and health indicators demonstrated substantial to almost perfect inter-observer agreement (κ-based), while environmental and some tactile indicators, such as body condition and hydration tests, showed moderate reliability. Based on the most reliable indicators, a climate-sensitive Arid-Hot Small Ruminant Welfare Index (ASR-WI) was developed by weighting four welfare domains—Behaviour and Mental State, Environment, Nutrition, and Health. The findings confirm that a simplified welfare assessment protocol can be reliably implemented under intensive hot-arid conditions when clear scoring criteria and structured assessor training are provided. The resulting protocol and index offer a practical foundation for routine welfare monitoring under intensive hot-arid conditions, as well as for policymaking and future longitudinal research. Full article

The Eurasian Griffon Vulture (Gyps fulvus) is the most abundant obligate scavenger in Europe. It depends on wild and domestic carcasses whose availability and location are relatively unpredictable in terms of space and time, but also on predictable sources of anthropogenic origin. In this study, satellite and accelerometer data from 10 adult individuals captured in the Basque Country (N Spain) were analysed with the aims of identifying feeding sites and determining the types of resources used. The annual cycle of the species was subdivided into three phases: pre-laying and incubation (December–March), rearing (April–July) and post-rearing (August–November). Our results showed that 64% of trophic resources were consumed in mountain pastures and on extensive or semi-extensive livestock farms, highlighting the importance of these farming systems for the species in the study area. However, 36% of the resources were exploited in more predictable anthropic environments, such as landfills and supplementary feeding stations and, to a much lesser extent, intensive farms. Individual variability was detected in terms of trophic behaviour. On semi-extensive farms, the most consumed carcasses were sheep (48%) and horses (37%), while on intensive farms, it was pigs (81%). During the pre-laying and incubation phase, feeding events detected in landfills were reduced, with vultures focusing on resources close to the colony. We observed that the population studied differed from other Spanish populations in its greater use of trophic resources from extensive and semi-extensive livestock farms, as expected from their spatial-temporal distribution and local availability. Full article

Precision livestock farming (PLF), by integrating multimodal sensor data, provides opportunities to enhance welfare monitoring and management in small ruminants. This study evaluated whether environmental, physiological, and behavioral measurements—including the temperature–humidity index (THI), carbon dioxide (CO₂) and ammonia (NH₃) concentrations measured at the barn level, body condition score (BCS), rectal and ocular temperatures, GPS-derived locomotion metrics, accelerometry data, and fixed animal traits—can serve as key predictors of welfare and productivity in dairy sheep. Data were collected from 90 ewes: all animals underwent the same repeated welfare assessments, while 30 of them were additionally equipped with GPS–accelerometer sensor collars; environmental conditions were continuously recorded for the entire flock, generating 773 complete multimodal records. All predictive models were developed using data from all 90 ewes; collar-derived behavioral variables were included only for individuals equipped with GPS–accelerometer collars. Nine regression methods (linear regression (LR), partial least square regression (PLSR), elastic net (EN), mixed-effects models, random forest (RF), extreme gradient boosting (XGBoost), support vector regression (SVR), neural networks (multilayer perceptron, MLP), and an ensemble of RF–XGBoost–EN were evaluated using a combination of nested cross-validation (CV) and leave-one-animal-out CV (LOAOCV) to ensure robustness and generalization at the individual animal level. Nonlinear models—particularly RF, XGBoost, SVR, and the ensemble—consistently delivered superior performance across traits. For behavioral (e.g., daily distance movement) and thermal indicators (e.g., medial canthus temperature), the highest predictive capacity (R² ≈ 0.60–0.70) was achieved, while moderate predictive capacity (R² ≈ 0.40–0.50 and ≈0.35–0.45), respectively, was observed for respiratory rate and milk yield, reflecting their multifactorial nature. Feature importance analyses underscored the relevance of THI, CO₂, NH₃, concentrations, and BCS across results. Overall, these findings demonstrate that multimodal sensor fusion can effectively support the prediction of welfare and productivity indicators in intensively reared dairy sheep and emphasize the need for larger and more diverse datasets to further enhance model generalizability and model transferability. Full article

Although best known as a major parasite of sheep and goats, the blood-feeding abomasal nematode Haemonchus contortus can also infect cattle and buffaloes under the mixed-grazing Mediterranean conditions prevalent in Greece. The objectives of this study were as follows: (i) to determine the prevalence of H. contortus infections in dairy and beef cattle and buffaloes in Greece through an abattoir survey, (ii) to evaluate potential host- and farm-related risk factors including age, sex, management system, cattle productive orientation, and the co-existence of cattle and buffaloes on the occurrence of haemonchosis, and (iii) to assess the likelihood of detecting homozygous benzimidazole (BZ)-resistant H. contortus in large ruminant populations in relation to these determinants. A total of 213 abomasa (115, 55, and 43 from dairy, beef cattle, and buffaloes, respectively) were examined. A structured questionnaire provided additional animal- and farm-level information. Haemonchus-like helminths were collected and molecularly identified at the species level by amplifying a 321 bp fragment of the internal transcribed spacer 2 region of nuclear DNA. An allele-specific multiplex PCR, targeting codon 200 of the β-tubulin gene, was applied to detect BZ-resistant alleles. The prevalence of H. contortus infection was 21.2% in cattle and 69.8% in buffaloes. In cattle, multivariable analysis revealed that mixed-species farming (i.e., farms where cattle were the primary species and buffaloes were kept in smaller numbers), productive orientation, and slaughter age were significant predictors of increased H. contortus infection. Controversially, none of these factors were significantly associated with infection in buffaloes. Finally, multivariable modelling suggested that resistance patterns varied by host species, being more prevalent in intensively managed, older cattle, yet less common among older buffaloes and in herds where both species coexisted. Full article

This study, carried out as part of a large countrywide investigation into the sheep and goat industries in Greece, focused on respiratory problems of lambs and kids in Greece. The work was performed as part of a wider study performed in farms throughout the country with the participation of farmers, by means of an in-person questionnaire investigation. The specific objectives of the study were (a) the assessment of the presence of respiratory problems in lambs and kids and (b) the identification of variables associated with the presence of these problems in the farms. Data were collected from 325 sheep flocks and 119 goat herds. The annual incidence rate for respiratory problems in lambs was 1.4% (95% confidence intervals: 1.3–1.4%) and that in kids was 1.1% (1.0–1.2%). The annual incidence rate was significantly lower in farms that applied a semi-extensive or extensive management system (1.2% in sheep and 1.0% in goat farms) than in farms that applied an intensive or semi-intensive or extensive (1.5% and 1.3%, respectively) management system. In multivariable analysis, the lack of a barn for lambs, the proximity (<10 km) of the farm to industrial sites, and the experience of farmers emerged as significant predictors in sheep farms, and the proximity to industrial sites and the administration of antibiotics to newborns routinely emerged as significant predictors in goat farms. Sheep (27.4%) and goat (22.7%) farmers considered ‘pneumonia’ as the second most important health problem of lambs and kids. Respiratory problems were more often declared an important problem by farmers in proximity to industrial sites: 21.6% versus 12.5%. Overall, the study contributes information regarding the presence of respiratory problems in lambs and kids in Greece. A notable finding has been the association of proximity to industrial sites with a higher incidence rate of respiratory problems of lambs and kids in the farms. This has similarities to the results of relevant studies on people and potentially reflects that air pollution in the farm environment might be a factor to take into account in health management. One may also postulate that, possibly, data from farms can be employed to indicate potential risk from air pollution for humans, although further and more detailed work will be necessary to draw relevant conclusions. Full article