Individual Goats Research Articles

Development of a dynamic model for prediction of energy in milk protein, lactose, fat, and enteric methane emissions in goats based on energy balance and indirect calorimetry studies.

Feed costs are overwhelmingly the largest expense for dairy producers. Thus, improving milk production efficiency (milk fat and protein are the main incomes for farmers) is of great economic importance in the dairy industry. The main objective of this study was to develop a dynamic energy partitioning model to describe and quantify how dietary energy from carbohydrate, protein, and fat is transferred to milk (protein, lactose, and fat) in dairy goats. In addition, due to increasing worldwide concerns regarding livestock contribution to global warming, methane (CH4) emission was quantified. For modeling purposes, 158 individual goat observations were used and randomly split into 2/3 for model development and 1/3 for internal evaluation. For external evaluation, 20 different energy balance studies from the literature (77 observations) were evaluated. The Root Mean Square Prediction Error (RMSPE) was 13.2% for loss of energy in CH4, 16.8% for energy in fat, 19.4% for energy in protein, and 22.3 energy in lactose. Mean bias was around zero for all variables and the slope bias was zero for milk energy in lactose, close to 1% for milk fat (1.01%), and around 3% and 10% for protein and CH4, respectively. Random bias was greater than 85% for energy in CH4 and milk energy components indicating non-systematic errors and that the equation in the model fitted the data properly. Analyses of residuals appeared to be randomly distributed around zero. Slopes of regression lines for residuals vs. predicted were positive for milk fat energy, zero for lactose, and negative for milk energy in protein and CH4. This model suggested for use with mixed diets and by-products to obtain balanced macronutrient supply, methane emissions, and milk performance during mid lactation could be an interesting tool to help farmers simulate scenarios that increase milk fat and protein, evaluate CH4 emissions, without the costs of running animal trials.

Integrating remote international experience and community engagement into course-based animal behavior research.

Human-centered, active-learning approaches can help students develop core competencies in biology and other STEM fields, including the ability to conduct research, use quantitative reasoning, communicate across disciplinary boundaries, and connect science education to pressing social and environmental challenges. Promising approaches for incorporating active learning into biology courses include the use of course-based research, community engagement, and international experiences. Disruption to higher education due to the COVID-19 pandemic made each of these approaches more challenging or impossible to execute. Here, we describe a scalable course-based undergraduate research experience (CURE) for an animal behavior course that integrates research and community engagement in a remote international experience. Students in courses at two U.S. universities worked with community partners to analyze the behavior of African goats grazing near informal settlements in Western Cape, South Africa. Partners established a relationship with goat herders, and then created 2-min videos of individual goats that differed in criteria (goat sex and time of day) specified by students. Students worked in small groups to choose dependent variables, and then compared goat behavior across criteria using a factorial design. In postcourse surveys, students from both universities indicated overall enthusiasm for the experience. In general, students indicated that the laboratory provided them with "somewhat more" of a research-based experience compared with biology laboratories they had taken of similar length, and "somewhat more" to "much more" of a community-engagement and international experience. Educational benefits were complemented by the fact that international educational partners facing economic hardship due to the pandemic received payment for services. Future iterations of the CURE can focus on goat behavior differences across ecological conditions to help herders increase production in the face of continued environmental and social challenges. More generally, applying the structure of this CURE could facilitate mutually beneficial collaborations with residents of under-resourced areas around the world.

GENETIC VARIATION AND PHYLOGENETIC RELATIONSHIPS BASED ON TESTIS DETERMINING REGION-Y (SRY) GENE IN THE LAKOR GOAT BREED

Lakor goat is an Indonesian indigenous goat breeds that successfully survive and is adaptive in the ecology of Lakor island in Southwest Maluku with high temperatures and curah limited rainfall. Genetic variations in Y-chromosome enable researchers to identify paternal lineages, which are informative for introgressions and migrations. This study aims to characterize genetic variation and phylogenetic relationships of Lakor goats based on male-specific region markers, i.e., sex-determining region-Y (SRY) gene. The genomes of 84 follicle samples of male goats (bucks) collected from Lakor island, were analyzed. The amplified product for SRY was 850 bp in encoding region (exon-1). Genetic variation and paternal phylogenetic relationship were established following sequencing using MEGA version X.0 software. The results of Y-chromosome alignments of the entire sequences identified two polymorphic nucleotides between individual goats within the population. Two polymorphism nucleotides, i.e. site 362 (A-G), and found in LK3, LK7, LK11, and LK13 samples, while site 410 (C-T) was found in LK3, LK7, LK11, LK13, and LK24 samples. The two mutations are categorized as substitution (transition type) and this is in the exon-1 area. This study concluded that genetic variation is very low in the Lakor goat breed and phylogenetic analysis grouping is a single clade or monophyletic group.

Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach.

Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

Evaluation of lipidomic change in goat sperm after cryopreservation.

The current study aimed to detect the relationship between the spermatozoa cryotolerance and the post-thawed sperm lipidome. Ejaculates from 20 goats, and performed a uniform frozen-thawed procedure in this study. According to the total motility of thawed sperm of goats, semen samples were classified into HF group (High Freezers, n = 8) with >60% total motility and LF group (Low Freezers, n = 8) with < 45% total motility. The lipidomic analysis based on UHPLC-MS/MS was utilized to investigate the relationship between sperm cryotolerance and their lipid metabolites expression. The results showed that the cryotolerance of sperm from different individual goats were in great variation. The total motility of post-thawed sperm in HF group (60.93 ± 2.43%) is significantly higher than that in LF group (34.04 ± 3.41%, P < 0.01). And the post-thawed sperm in HF group exhibited significantly higher plasma membrane (59.06 ± 2.34%) and acrosome integrity (62.93 ± 1.15%) than that in LF group (34.06 ± 4.85%, 44.92 ± 2.19% respectively, P < 0.01). The total of 29 lipid subclasses and 1,133 lipid molecules in the post-thawed goat sperm were identified by lipidomics analysis. The lipid content of thawed sperm in HF group was higher than that in LF group, the lipid profile in HF group was significantly separated from LF group, which indicated that the difference in lipid composition and lipid metabolism mode of sperm between the two groups was existed, especially the expression of phosphatidylcholine and triglyceride molecules. In conclusion, the cryotolerance of sperm from different individual goats were in great variation. Sperm with high cryotolerance may be able to uptake more lipids during cryopreservation. The increase in phosphatidylcholine and triglyceride content of thawed. Sperm may relate to more active lipid anabolic processes.

Characterisation and Identification of Individual Intact Goat Muscle Samples (Capra sp.) Using a Portable Near-Infrared Spectrometer and Chemometrics.

Adulterated, poor-quality, and unsafe foods, including meat, are still major issues for both the food industry and consumers, which have driven efforts to find alternative technologies to detect these challenges. This study evaluated the use of a portable near-infrared (NIR) instrument, combined with chemometrics, to identify and classify individual-intact fresh goat muscle samples. Fresh goat carcasses (n = 35; 19 to 21.7 Kg LW) from different animals (age, breeds, sex) were used and separated into different commercial cuts. Thus, the longissimus thoracis et lumborum, biceps femoris, semimembranosus, semitendinosus, supraspinatus, and infraspinatus muscles were removed and scanned (900–1600 nm) using a portable NIR instrument. Differences in the NIR spectra of the muscles were observed at wavelengths of around 976 nm, 1180 nm, and 1430 nm, associated with water and fat content (e.g., intramuscular fat). The classification of individual muscle samples was achieved by linear discriminant analysis (LDA) with acceptable accuracies (68–94%) using the second-derivative NIR spectra. The results indicated that NIR spectroscopy could be used to identify individual goat muscles.

Comparison of florfenicol depletion in dairy goat milk using ultra-performance liquid chromatography with tandem mass spectrometry and a commercial on-farm test.

Florfenicol is a broad-spectrum antibiotic commonly prescribed in an extra-label manner for treating meat and dairy goats. Scientific data in support of a milk withdrawal interval recommendation is limited to plasma pharmacokinetic data and minimal milk residue data that is limited to cattle. Therefore, a rapid residue detection test (RRDT) could be a useful resource to determine if milk samples are free of drug residues and acceptable for sale. This study compared a commercially available RRDT (Charm® FLT strips) to detect florfenicol residues in fresh milk samples from healthy adult dairy breed goats treated with florfenicol (40 mg/kg subcutaneously twice 4 days apart) with quantitative analysis of florfenicol concentrations using ultra-performance liquid chromatography with tandem mass spectrometry (UPLC-MS/MS). In addition, storage claims for testing bovine milk using the RRDT were assessed using stored goat milk samples. Milk samples were collected every 12 h for a minimum of 26 days. Commercial RRDT strips remained positive in individual goats ranging from 528 to 792 h (22–33 days) after the second dose, whereas, UPLC-MS/MS indicated the last detectable florfenicol concentration in milk samples ranged from 504 to 720 h (21–30 days) after the second dose. Results from stored milk samples from treated goats indicate that samples can be stored for up to 5 days in the refrigerator and 60 days in the freezer after milking prior to being tested with a low risk of false-negative test results due to drug degradation. Elevated somatic cell counts and bacterial colony were noted in some of the milk samples in this study, but further study is required to understand the impact of these quality factors on RRDT results.

The Gymnesic Islands at the crossroads: Livestock body size as marker of social interactions during the first millennium BC

This study presents an analysis of 7575 individual sheep, goat, cattle and pig measurements from 22 sites from Mallorca and Menorca dating to different phases throughout the 1st millennium BC. Two main episodes of size change have been identified, although some variations in the speed and timing by species are noted. The earliest evidence for size change in cattle and sheep occurs between the 6–5th century BC and may be connected to the increase of trade exchanges and social interactions between Gymnesic Islands and the colonial area of influence of the Punics. This process of livestock size increasing probably occurred as a combined consequence of agricultural innovations, the increasing commercialisation of animal farming, and as the meat demands required by the expanding population. Subsequent size increases in livestock occurred as the process of integration continued to develop, reaching its peak at the end of the millennium with the Roman colonisation of the islands.

The Effect of Alpha s1 Genotype on Some Physiological and Chemical Milk Characteristics in Garganica Goat

In the present study individual milk samples were collected from 80 Garganica goats and the population was genotyped for CSN1S1. Seven different alleles were observed with the A allele found as predominant, followed by the F and B allele, and 14 different genotypes were observed. A proteomic approach was used to classify the individual milk on the basis of protein abundance: four levels of αs1-casein (CN) expression were identified as high (HAS-1), intermediate (IAS-1), low (LAS-1), and null (NAS-1). The present paper aimed to investigate the influence of the level of expression of αs1-casein fraction in milk on proteolytic activities, chemical composition, and renneting properties of individual Garganica goat milk. Results from the present study evidenced that the level of expression of αs1-casein fraction in Garganica goat milk affected chemical composition, curd firmness, and indigenous proteolytic enzymes. In particular, fat, protein, and casein content were higher in HAS-1 and IAS-1 compared to LAS-1 and NAS-1. The SCC decreased passing from the HAS-1 to the NAS-1 and the curd firmness displayed the highest value in HAS-1. The activity of plasmin (PL) was higher in HAS-1 and IAS-1 groups compared to the LAS-1 and NAS-1; whereas, PL plasminogen-derived (PG) activity was the highest in NAS-1 group. On the contrary, the activity of elastase was the highest in LAS-1, intermediate in the IAS-1 group, and the lowest in HAS-1 and NAS-1 groups. Present data demonstrated that the level of synthesis of αs1-CN reflects the efficiency of the mammary gland in the activation of the cellular mechanisms and influences the proteolytic activity in milk, especially the PL-PG system.

Predictive formulas for different measures of cheese yield using milk composition from individual goat samples

The objective of this study was to develop formulas based on milk composition of individual goat samples for predicting cheese yield (%CY) traits (fresh curd, milk solids, and water retained in the curd). The specific aims were to assess and quantify (1) the contribution of major milk components (fat, protein, and casein) and udder health indicators (lactose, somatic cell count, pH, and bacterial count) on %CY traits (fresh curd, milk solids, and water retained in the curd); (2) the cheese-making method; and (3) goat breed effects on prediction accuracy of the %CY formulas. The %CY traits were analyzed in duplicate from 600 goats, using an individual laboratory cheese-making procedure (9-MilCA method; 9 mL of milk per observation) for a total of 1,200 observations. Goats were reared in 36 herds and belonged to 6 breeds (Saanen, Murciano-Granadina, Camosciata delle Alpi, Maltese, Sarda, and Sarda Primitiva). Fresh %CY (%CYCURD), total solids (%CYSOLIDS), and water retained (%CYWATER) in the curd were used as response variables. Single and multiple linear regression models were tested via different combinations of standard milk components (fat, protein, casein) and indirect udder health indicators (UHI; lactose, somatic cell count, pH, and bacterial count). The 2 %CY observations within animal were averaged, and a cross-validation (CrV) scheme was adopted, in which 80% of observations were randomly assigned to the calibration (CAL) set and 20% to the validation (VAL) set. The procedure was repeated 10 times to account for sampling variability. Further, the model presenting the best prediction accuracy in CrV (i.e., comprehensive formula) was used in a secondary analysis to assess the accuracy of the %CY predictive formulas as part of the laboratory cheese-making procedure (within-animal validation, WAV), in which the first %CY observation within animal was assigned to CAL, and the second to the VAL set. Finally, a stratified CrV (SCrV) was adopted to assess the %CY traits prediction accuracy across goat breeds, again using the best model, in which 5 breeds were included in CAL and the remaining one in the VAL set. Fitting statistics of the formulas were assessed by coefficient of determination of validation (R2VAL) and the root mean square error of validation (RMSEVAL). In CrV, the formula with the best prediction accuracy for all %CY traits included fat, casein, and UHI (R2VAL = 0.65, 0.96, and 0.23 for %CYCURD, %CYSOLIDS, and %CYWATER, respectively). The WAV procedure showed R2VAL higher than those obtained in CrV, evidencing a low effect of the 9-MilCA method and, indirectly, its high repeatability. In the SCrV, large differences for %CYCURD and %CYWATER among breeds evidenced that the breed is a fundamental factor to consider in %CY predictive formulas. These results may be useful to monitor milk composition and quantify the influence of milk traits in the composite selection indices of specific breeds, and for the direct genetic improvement of cheese production.

Individual identification of cashmere goats via method of fusion of multiple optimization

AbstractFacial recognition technology and related research have matured over time, but research in the field of individual animal recognition is still very limited. Therefore, this article focuses on the identification of cashmere goats with similar characteristics. First, the single shot multibox detector network was used to process the dataset. Next, transfer learning was applied to learn the characteristics of the goats, as well as the loss function is composed of Triplet Loss and Label Smoothing CrossEntropy Loss function. The result of Label Smoothing CrossEntropy Loss function is fused by multiple different branches, which is convenient for classification. We added a small number of images of 24 different breeds of sheep to each cashmere goat dataset with different ID to promote the distance between training individuals, and then used the trained model to find the number of goats with the lowest recognition accuracy. The Cycle‐Consistent Adversarial Network (Cycle‐GAN) learned the goat dataset with a high error rate in individual identification. Unlike previous studies using the Cycle‐GAN, we took the novel approach of using this network to learn and combine the features seen in photos of cashmere goats. Since the learned features were all observed in the same goats, this method achieved better results in learning the features of the goats. Finally, we found that recognition can be performed on this data with an accuracy of 93.75%. These results suggest that identification based on deep learning has a high accuracy rate, as well as great value in identifying individual cashmere goats.

Performance of Locally Adopted Goats in Sundarbazar Municipality of Lamjung District, Nepal

Goat (Capra hircus) is a small ruminant with hollow horn that is found throughout the world. A survey (N = 60) was carried out to evaluate the performance of locally adopted goats under farmers’ management practices in Sundarbazar municipality of Lamjung district between February and August, 2017. The study focused on two breeds, Khari and Jamunaparias well as their crossbred. Individual goats’ primary data, collected using a convenient sampling technique and pretested questionnaires with open and close ended questions, were analyzed using SPSS 20 and MS Excel 2010. Results revealed significant effects of a Doe’s breed on age at first estrus (p &lt; 0.001), age at first kidding (p &lt; 0.001), gestation length (p &lt; 0.001), postpartum estrus (p &lt; 0.001), and kidding interval (p &lt; 0.05). Age at first estrus and age at first kidding were lowest (252.3 days and 403.9 days, respectively) for Khari. Highly significant lowest kidding interval (214.7 days) and significantly shortest gestation length (148.2 days) were found for Khari. No significant effect of parity was observed on age at first estrus, age at first kidding, kidding interval, and postpartum estrus, but a significant effect (p &lt; 0.05) was observed on gestation length with lowest values in 4th to 6th parity (149.5 days). Doe’s breed showed a significant effect on litter size at birth (p &lt; 0.05). However, no significant effect of the breed was found on litter birth weight and litter size at weaning (p &lt; 0.05). The results demonstrated Khari as the desirable breed for the locality.

Insertion/deletion variants within the IGF2BP2 gene identified in reported genome-wide selective sweep analysis reveal a correlation with goat litter size.

Insulin-like growth factor 2 mRNA-binding protein 2 (IGF2BP2, also called IMP2) plays an essential role in the development and maturation of germ cells and embryos and is a candidate gene for goat litter size, based on a previous genome-wide selective sweep analysis. In this study, the mRNA expression level of IGF2BP2 was found to be significantly higher in a single-lamb group than in a multi-lamb group. Insertions/deletions (indels) within the goat IGF2BP2 gene, including P4-Ins-13bp and P5-Del-12bp, were verified in 918 Shaanbei White Cashmere (SBWC) female goats. The minor allelic frequencies (MAFs) of P4-Ins-13bp and P5-Del-12bp loci were 0.349 and 0.295, respectively. Analysis using the Chi-square (χ2) test showed that the genotype (χ2=14.479, P=0.006) distribution of P4-Ins-13bp was significantly different between the single-lamb and multi-lamb groups. Correlation analysis demonstrated that P4-Ins-13bp was significantly associated with goat litter size (P=0.022), and individual goats with the homozygous deletion/deletion (DD) genotype produced more litters than other goats. Therefore, considered as a potential molecular marker significantly related to lambing traits, the P4-Ins-13bp mutation of the goat IGF2BP2 gene can be used in goat breeding with practical molecular marker-assisted selection (MAS) to optimize female reproduction and improve economic efficiency in the goat industry.

Distribution of somatic cell count and udder pathogens in Norwegian dairy goats

Compared with dairy cows, goat somatic cell count (SCC) is higher and probably more affected by physiological factors such as parity, stage of lactation, and season. Thus, SCC is believed to be a less precise indicator of intramammary infections in dairy goats, and no consensus exists on SCC thresholds for considering goats as infected. The Norwegian Goat Recording System maintains individual goat production records and results from microbiological analyses of milk samples. In this retrospective observational study, we used recordings over a 10-yr period (2010 to 2020) to describe the association between individual goat SCC and noninfectious factors, as well as intramammary infections. The median SCC in the 1,000,802 milk recordings included in the study was 440,000 cells/mL, and the mode was 70,000 cells/mL. Somatic cell count increased with parity, days in milk, estrus, pasture season, and intramammary infections. The effect of parity and stage of lactation was significantly higher in infected compared with uninfected goats. Staphylococci dominated as causes of intramammary infections, with Staphylococcus aureus as the udder pathogen associated with highest SCC. The most prevalent non-aureus staphylococci were Staphylococcus warneri, Staphylococcus epidermidis, and Staphylococcus caprae. This study provides guidelines for interpretation of goat SCC at different parities and stages of lactations under Norwegian management conditions. We revealed a considerable variation in SCC associated with physiological factors, indicating that the cutoff for identifying infected goats should be a dynamic threshold adjusted for parity, stage of lactation, and season.

First Sequencing of Caprine Mdr1 (Abcb1) mRNA Due to Suspected Neurological Adverse Drug Reaction in a Thuringian Goat Following Extra-Label Use of Doramectin.

The multidrug resistance gene MDR1 encodes for an efflux transporter called P-glycoprotein (P-gp). In the canine Mdr1 gene, a nonsense mutation was identified in certain dog breeds causing increased drug sensitivity to various P-gp substrates such as antiparasitic macrocyclic lactones. Symptoms of neurologic toxicity include ataxia, depression, salivation, tremor, apparent blindness, and mydriasis. In the current report, a Thuringian goat developed similar neurological signs after treatment with doramectin, a compound from the macrocyclic lactone class. Therefore, Mdr1 might be defective in this individual goat. For diagnostic purposes, sequencing of the complete mRNA transcript coding for caprine Mdr1 was performed to investigate a potential missense mutation. The Mdr1 transcripts of two related goats without drug sensitivity were also sequenced to allow differential diagnosis and were compared to the suspected drug-sensitive goat. The only position where the Mdr1 sequence from the suspected drug-sensitive goat differed was in the 3′-untranslated region, being a heterozygous single nucleotide polymorphism c.3875C>A. It can be suspected that this variant affects the expression level, stability, or translation efficiency of the Mdr1 mRNA transcript and therefore might be associated with the suspected drug sensitivity. To clarify this, further studies are needed, particularly investigating the Mdr1 mRNA and protein expression levels from brain material of affected goats. In conclusion, Mdr1 variants may exist not only in dogs, but also in individual goats. The current report provides the first Mdr1 mRNA transcript sequence of a goat and therefore represents the basis for more detailed Mdr1 sequence and expression analyses.

Further Insights Into Caprine Arthritis Encephalitis (CAE): The Current Status of Seroprevalence Among Small Ruminants in Two Selected States of Peninsular Malaysia.

Caprine arthritis-encephalitis virus (CAEV) is a member of the genus lentivirus causing caprine arthritis-encephalitis (CAE), a chronic inflammatory condition affecting the lungs, joints, udder and central nervous system of small ruminants such as sheep and goats. CAE is distributed worldwide and is recognised as a significant cause of morbidity and decreased milk production in dairy goats. Earlier studies highlighted the clinicopathological features and supplied preliminary serological evidence for the existence of CAE among selected goat herds in Malaysia. Therefore, this study aims to provide further insights into the seroprevalence and contributing factors of CAE among sheep and goat herds in two states of Peninsular Malaysia. The blood samples and biodata were randomly collected from a total of 262 individual sheep (40) and goat (222) in seven smallholder farms. Blood sera were tested for specific anti-CAEV antibodies using Qayee-Bio CAEV sandwich-ELISA test kits according to standard procedures. Our results of the study revealed 21.4% (95% CI: 15.8–28.6) apparent and 20.6% (95% CI: 14.5–27.8) true seroprevalence with significant differences (p < 0.05) in seroconversion rates between the states, farms, production systems and breeds of small ruminants. The prevalence of CAE in the Malaysian Peninsular is a potential threat to the small ruminant industry and developing agricultural economy. Further studies are required to determine the genetic characteristics, distribution and risk factors of CAEV for effective prevention and control in Malaysia.

RetIS: Unique Identification System of Goats through Retinal Analysis

Developing an accurate, reliable and tamper-proof unique identification system for identifying individual animals is one of the most important but challenging tasks in intelligent livestock management. Currently, unique identification for animals is done using methods like stenciling, ear tagging, ear notching, branding, etc. which create opportunities for the poachers, middle men and animal thieves to manipulate the tags and transfer animals across international borders, claim false insurance, poach for their meat, bones, etc. illegally. This becomes a huge headache for the livestock owners. In this paper, a novel biometric unique identification system called “RetIS” has been developed based on goat retinal image which is unique, tamper-proof, reliable and accurate. The identification system has been developed based on the origination and distribution pattern of the blood vessels through a circular ring around the optic disc, which remains constant for the entire lifetime of the goat. Goat retinal images have been captured using a handheld fundus camera. The captured images are then segmented, normalized and encoded for the creation of templates, which are eventually stored in a database for matching with query templates based on hamming distance criteria. In matching, it has been observed that if the hamming distance between stored and query template is less than equal to 0.37 (matching >= 63%), it can be confirmed that the stored and query template belongs to the same individual goat providing its unique identification, otherwise the templates belong to two separate individual goats. This model has been tested with more than 200 retinal images obtained from twelve different goats producing 99% accuracy. The performance of this proposed model has been compared with other animal identification technologies and is found to be the most accurate and precise.

Diagnostic accuracy of three commercial immunoenzymatic assays for small ruminant lentivirus infection in goats performed on individual milk samples

Caprine arthritis-encephalitis (CAE) caused by small ruminant lentivirus (SRLV) infection is one of the most widespread and devastating diseases of goats. Serological methods, mainly immunoenzymatic assays (ELISA), are the mainstay of CAE diagnostics. Even though blood is still the most commonly tested material, animal welfare issues and increasing costs of veterinary service prompt the development of serological methods based on milk testing. Several different types of ELISAs for CAE are available on the market. All of them perform well on serum, however their diagnostic accuracy for testing milk has not been so far compared. Therefore, we carried out the study in 5 dairy goat herds in Poland whose previous epidemiological situation regarding CAE was known. Paired serum and milk samples were collected from all adult females (n = 420) and tested with 3 commercial ELISAs – indirect ELISA based on the whole-virus antigen (wELISA), indirect ELISA based on the recombined transmembrane and capsid protein (TM/CA-ELISA), and competitive ELISA based on the surface glycoprotein (SU-ELISA). Milk was tested as lactoserum at dilution of 1/2 in wELISA and TM/CA-ELISA, and undiluted in SU-ELISA. The true status of goats was based on the composite reference standard comprising the results of all three ELISAs done on serum and the true prevalence of SRLV infection in the herd of origin. 243 (57.9 %) goats were classified as truly positive and 177 (42.1 %) goats as truly negative. Diagnostic accuracy was evaluated using the area under the ROC curve (AUROC) as well as sensitivity (Se) and specificity (Sp) for a range of cut-off values. AUROC was 98.8 % (CI 95 %: 97.5 %, 100 %) for wELISA, 97.9 % (CI 95 %: 96.5 %, 99.2 %) for TM/CA-ELISA, and 91.7 % (CI 95 %: 88.9 %, 94.5 %) for SU-ELISA. At the cut-off values recommended by the manufacturers both indirect ELISAs were highly sensitive (89.3 % and 91.4 %, respectively) and highly specific (98.3 % and 95.5 %, respectively), whereas SU-ELISA had only moderate Se (71.2 %) at comparably high Sp (96.6 %). Nevertheless, the optimal cut-off values were lower than those recommended by manufacturers for serum – sample-to-positive control serum ratio (S/P%) of 10 % for wELISA, S/P% of 80 % for TM/CA-ELISA, and percentage inhibition of 23 % for SU-ELISA. Concluding, the study shows that wELISA and TM/CA-ELISA may be interchangeably used for testing individual goat milk samples for SRLV infection. Diagnostic sensitivity and specificity of these ELISAs appear not to be lower on milk than on serum. SU-ELISA is considerably less sensitive on milk samples than indirect ELISAs.

Risk factors associated with Ctenocephalides felis flea infestation of peri-urban goats: a neglected parasite in an under-appreciated host

Goats are critical in mixed smallholder agricultural systems in lower and middle-income countries, while fleas are important human and animal health concerns around the world. Convenience sampling was used to describe and consider risk factors for flea infestations of peri-urban goats, with the aim of informing the iterative development of animal husbandry and management based control strategies. Seven hundred and ninety-two goats were examined in 228 households across 10 peri-urban communities surrounding Blantyre in southern Malawi. The prevalence of Ctenocephalides felis fleas was 18.3, 37.1 and 100% at the levels of individual goats, households and communities, respectively, highlighting a neglected human and animal health concern. Constant introduction of new livestock coupled to a lack of biosecurity within communities, the ubiquitous presence of dog and cat hosts for C. felis, the frequency and thoroughness of cleaning overnight goat accommodation, and goat age less than 12 months old were identified as risk factors for flea infestation. This focal cross-sectional study highlights the significance of fleas in peri-urban communities and uncovers trends and commonalities that are needed to inform sustainable disease management. The majority of the peri-urban goat keepers were female, had resided in the same community throughout their whole life and had primary level education. Advice on the planned management of fleas in livestock needs to be tailored towards this demographic group. This approach affords an opportunity to promote public health measures to address household flea infestations and zoonotic disease spread.

Breed of goat affects the prediction accuracy of milk coagulation properties using Fourier-transform infrared spectroscopy

The prediction of traditional goat milk coagulation properties (MCP) and curd firmness over time (CFt) parameters via Fourier-transform infrared (FTIR) spectroscopy can be of significant economic interest to the dairy industry and can contribute to the breeding objectives for the genetic improvement of dairy goat breeds. Therefore, the aims of this study were to (1) explore the variability of milk FTIR spectra from 4 goat breeds (Camosciata delle Alpi, Murciano-Granadina, Maltese, and Sarda), and to assess the possible discriminant power of milk FTIR spectra among breeds, (2) assess the viability to predict coagulation traits by using milk FTIR spectra, and (3) quantify the effect of the breed on the prediction accuracy of MCP and CFt parameters. In total, 611 individual goat milk samples were used. Analysis of variance of measured MCP and CFt parameters was carried out using a mixed model including the farm and pendulum as random factors, and breed, parity, and days in milk as fixed factors. Milk spectra for each goat were collected over the spectral range from wavenumber 5,011 to 925 × cm-1. Discriminant analysis of principal components was used to assess the ability of FTIR spectra to identify breed of origin. A Bayesian model was used to calibrate equations for each coagulation trait. The accuracy of the model and the prediction equation was assessed by cross-validation (CRV; 80% training and 20% testing set) and stratified CRV (SCV; 3 breeds in the training set, one breed in the testing set) procedures. Prediction accuracy was assessed by using coefficient of determination of validation (R2VAL), the root mean square error of validation (RMSEVAL), and the ratio performance deviation. Moreover, measured and FTIR predicted traits were compared in the SCV procedure by assessing their least squares means for the breed effect, Pearson correlations, and variance heteroscedasticity. Results showed the feasibility of using FTIR spectra and multivariate analyses to correctly assign milk samples to their breeds of origin. The R2VAL values obtained with the CRV procedure were moderate to high for the majority of coagulation traits, with RMSEVAL and ratio performance deviation values increasing as the coagulation process progresses from rennet addition. Prediction accuracy obtained with the SCV were strongly influenced by the breed, presenting general low values restricting a practical application. In addition, the low Pearson correlation coefficients of Sarda breed for all the traits analyzed, and the heteroscedastic variances of Camosciata delle Alpi, Murciano-Granadina, and Maltese breeds, further indicated that it is fundamental to consider the differences existing among breeds for the prediction of milk coagulation traits.