Hasil untuk "Dairying"

R. Weng Zheng, J. Velez, N. Rodriguez et al.

ABSTRACT: The efficiency of automatic milking systems depends on the continuous flow of cows, which may be affected by specific cow conducts, including milking unit preference. Moreover, providing resources that favor the expression of natural cow behaviors during the milking process may result in improved animal welfare. The objective of this study was to analyze the selection behavior for automatic milking units, comparing preference consistency across the lactation of 3 genetic groups (Holstein [HO], Jersey [JE], and Holstein × Jersey [HJ]) in a multibreed organic dairy farm using a batch milking system. To expand the main objective, a secondary aim was to examine the relationship between milking unit selection behavior and premilking waiting time (WT). The study included data from 1,762,461 milking events in 1,355, 1,876, and 475 HO, JE, and HJ cows, respectively, from May 2023 to September 2024 in a commercial organic grass-fed dairy in Texas in the United States. Cows were moved to the waiting area of the milk center twice per day, where they could select their milking visits among 22 automatic milking units. Cow preferences were analyzed at 3 levels: automatic milking unit (n = 22), barn location (east, n = 11; west), and robotic arm configuration (left, n = 11; right). Milking visit information was collected from the management software to establish the frequency of specific milking unit usage per cow during the study period. Subsequently, the frequencies for selection of the top choice for 1, 3, and 5 automatic milking units, the top choice for barn location, and the top choice for robotic arm configuration were calculated for each cow. Preference consistency scores (PCS) were calculated as a ratio between the excess frequencies of the first choice over the base frequency of any option other than the first choice. The PCS calculations considered the frequency of access to each automatic milking unit, barn location, and robotic arm configuration in 30-d periods until 305 DIM. Premilking waiting times were calculated for each cow as the difference between the entry time into the waiting area of the milk center, indicated by a pedometer affixed in the rear leg of each cow, and the start of the corresponding milking, as indicated by the milking unit in use. Least squares means for PCS were calculated within parity category by genetic group for each 30-d period and compared using repeated-measures ANOVA. Subsequently, PCS and WT values were categorized into quartiles, and LSM for PCS and WT were calculated for each of the PCS by WT combinations within each parity category. As indicated by the chi-squared test of independence and the ANOVA, the frequency of selection for the top choice and the PCS were different from those expected by random selection of automatic milking unit, barn location, and robotic arm configuration, signifying different levels of selection behavior in the study cows. Overall, HO cows evidenced the greatest PCS values, indicating more consistent behaviors in milking unit preference. Additionally, the analysis of the association between PCS and WT indicated that greater PCS were associated with shorter WT. The resulting PCS suggest variable degrees of consistency in the selection behavior for automatic milking units in dairy cows. The selection behaviors identified in this study may have implications for cow welfare, as well as for improving cow traffic and system efficiency.

Jianli Li, Qian Wang, Chunle Tian et al.

ABSTRACT: Co-fermentation using functional lactic acid bacteria strains provides a promising approach to improve the quality and health benefits of fermented dairy products. This study examined the effects of the commercial starter PYS-010 (containing Streptococcus salivarius ssp. thermophilus and Lactobacillus delbrueckii ssp. bulgaricus) and stabilizer on the physicochemical properties and metabonomics of brown fermented milk prepared using Lactobacillus helveticus H11, and the changes of metabolic flavor during storage. The combination of L. helveticus H11 and PYS-010 significantly shortened fermentation time, improved viscosity, water-holding capacity, and texture, and showed peak angiotensin-converting enzyme inhibitory activity on d 7 and 14 of storage. Nontargeted metabolomics revealed that the addition of PYS-010 significantly changed the metabolism of amino acids and fatty acids fermented by a single L. helveticus H11 strain. Further dynamic monitoring of the group fermented using L. helveticus H11 with 0.5% stabilizer and 0.003% PYS-010 during storage revealed that W5S, W2W, sourness, and saltiness sensor responses could serve as indicators for storage duration. On d 21, the contents of arachidonic acid, butyric acid, and l-malic acid increased significantly. This study provides a comprehensive view on the fermentation characteristics and metabolic changes of L. helveticus H11 during co-fermentation with PYS-010.

Donglin Wu, Lei Zhang, Zhanhe Zhang et al.

ABSTRACT: Sodium butyrate (SB) is a common feed additive used in calf nutrition to support early growth and gastrointestinal health; however, its long-term programming effects remain poorly characterized. This study examined the dose-dependent effects of preweaning SB supplementation in milk on long-term growth, metabolic profiles, and gastrointestinal microbiota in dairy cattle. Eighty Holstein calves were assigned to one of 4 treatments beginning at 2 to 4 d of age: milk supplemented with 0 (CON), 4.4 (LSB), 8.8 (MSB), or 17.6 (HSB) g/d of SB. The same animals were evaluated later as heifers at 15 mo of age for performance, metabolic parameters, and microbial communities. Ruminal fluid, fecal, and plasma samples were collected from 8 animals per group and analyzed via 16S rRNA sequencing (V3–V4 regions) and liquid chromatography-tandem MS–based metabolomics. The HSB group showed a significant reduction in withers height compared with CON, although no significant differences were detected in BW, heart girth, or reproductive measures. Metabolomic and biochemical profiling indicated disrupted sterol metabolism and signs of hepatic stress in HSB heifers, reflected by increased alanine aminotransferase and total bilirubin, alongside decreased total cholesterol and creatine. Ruminal microbiota in the HSB group exhibited reduced diversity, richness, and evenness, accompanied by a decline in beneficial bacteria such as Rikenellaceae_RC9_gut_group. Predicted microbial function indicated inhibited steroid biosynthesis in the rumen. In contrast, the intestinal microbiota composition remained largely unchanged, though steroid degradation function was suppressed. Correlation and network analyses linked these changes, suggesting that early high-dose SB disrupts ruminal microbial ecology, resulting in lasting impairments in host metabolic health and growth. Key biomarkers included Rikenellaceae_RC9_gut_group, steroid biosynthesis, and plasma creatine. Collectively, these results indicate that milk-supplemented high-dose SB in early life leads to long-term inhibitory effects on growth and metabolic homeostasis in dairy heifers, largely mediated through rumen microbiota-driven alterations in sterol metabolism.

Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby et al.

Ruminal acidosis is a prevalent metabolic disorder in dairy cattle causing significant economic losses and animal welfare concerns. Current diagnostic methods rely on invasive pH measurement, limiting scalability for continuous monitoring. We present FUME (Fused Unified Multi-gas Emission Network), the first deep learning approach for rumen acidosis detection from dual-gas optical imaging under in vitro conditions. Our method leverages complementary carbon dioxide (CO2) and methane (CH4) emission patterns captured by infrared cameras to classify rumen health into Healthy, Transitional, and Acidotic states. FUME employs a lightweight dual-stream architecture with weight-shared encoders, modality-specific self-attention, and channel attention fusion, jointly optimizing gas plume segmentation and classification of dairy cattle health. We introduce the first dual-gas OGI dataset comprising 8,967 annotated frames across six pH levels with pixel-level segmentation masks. Experiments demonstrate that FUME achieves 80.99% mIoU and 98.82% classification accuracy while using only 1.28M parameters and 1.97G MACs--outperforming state-of-the-art methods in segmentation quality with 10x lower computational cost. Ablation studies reveal that CO2 provides the primary discriminative signal and dual-task learning is essential for optimal performance. Our work establishes the feasibility of gas emission-based livestock health monitoring, paving the way for practical, in vitro acidosis detection systems. Codes are available at https://github.com/taminulislam/fume.

Samantha M. Lawler, Michele T. Bannister, Laura E. Revell

The commercial space industry is launching more satellites into Low Earth Orbit every year. Aotearoa New Zealand (NZ) has a thriving dairy and cattle industry. Unfortunately, these industries could come into (high speed) cow-llision, as the rapid launch rate and short operational lifetimes of satellites in megaconstellations like Starlink result in a high reentry rate at NZ's latitudes. This could intersect with NZ's famously large population of livestock. We predict this will be an udder disaster for any cows that are hit, as they are squishy and moo-ve much more slowly than space debris. Using a global bovine density dataset, previously published satellite casualty probability code, and a complete lack of funding to do this calculation carefully enough for submission to a peer-reviewed journal, we calculate a $\simeq 0.3-1% chance of a cow-sualty in NZ from reentering Starlink Gen2 debris over the next 5 years.

Jiaxin Xu, Taha Majid Mahmood Sheikh, Muhammad Shafiq et al.

ABSTRACT: Cow milk protein allergy (CMPA) is a significant health concern characterized by adverse immune reactions to cow milk proteins. Biomarkers for the accurate diagnosis and prognosis of CMPA are lacking. This study analyzed the clinical features of CMPA, and 16S RNA sequencing was used to investigate potential biomarkers through fecal microbiota profiling. Children with CMPA exhibit a range of clinical symptoms, including gastrointestinal (83% of patients), skin (53% of patients), and respiratory manifestations (26% of patients), highlighting the complexity of this condition. Laboratory analysis revealed significant differences in red cell distribution width and inflammatory markers between the CMPA and control groups, suggesting immune activation and inflammatory responses in CMPA. Microbial diversity analysis revealed higher specific diversity indices in the CMPA group compared with those in control group, with significant differences at the genus and species levels. Bacteroides were more abundant in the CMPA group, whereas Bifidobacterium, Ruminococcus, Faecalibacterium, and Parabacteroides were less abundant. The control group exhibited a balanced microbial profile, with a predominant presence of Bifidobacterium bifidum and Akkermansia muciniphila. The significant abundance of Bifidobacterium in the control group (23.19% vs. 9.89% in CMPA) was associated with improved growth metrics such as height and weight, suggesting its potential as a probiotic to prevent CMPA and enhance gut health. Correlation analysis linked specific microbial taxa such as Coprococcus and Bifidobacterium to clinical parameters such as family allergy history, weight, and height, providing insights into CMPA pathogenesis. Significant differences in bacterial abundance suggested diagnostic potential, with a panel of 6 bacteria achieving high predictive accuracy (area under curve = 0.8708). This study emphasizes the complex relationship between the gut microbiota and CMPA, offering valuable insights into disease mechanisms and diagnostic strategies.

Hidemasa Motoshima, Ikumi Fujioka, Kenji Uchida

ABSTRACT: Dominant species common to the kefir grains from 7 different origins were analyzed using culture-dependent methods using agar media that were able to grow and discriminate Lactobacillus kefiranofaciens ssp. kefiranofaciens. Identification of all isolates was performed on the basis of MALDI-TOF MS analysis and colony morphology. Lactic acid bacteria were present at 9 log cfu·g−1 in all kefir grains. Lactobacillus kefiranofaciens was the predominant species (∼87%), followed by Lentilactobacillus kefiri (∼12%) in all grains. Within Lb. kefiranofaciens, Lb. kefiranofaciens ssp. kefirgranum (flocculent and nonflocculent strains) was most abundant (∼82%) and Lb. kefiranofaciens ssp. kefiranofaciens (kefiran producer) was present at ∼18%. Lentilactobacillus parakefiri, Leuconostoc mesenteroides, and Lactococcus lactis were also present in some grains as minor species. Yeasts were present at 6 to 7 log cfu·g−1 and were highly diverse; only Kazachstania exigua was found in all grains. We attempted to reconstruct kefir grains using strains of the following major and minor common species: Lb. kefiranofaciens ssp. kefiranofaciens, Lb. kefiranofaciens ssp. kefirgranum, Len. kefiri, Len. parakefiri, Leu. mesenteroides, Lc. lactis, and K. exigua. We obtained reconstructed kefir grains that could grow logarithmically to some extent in milk. They were elastic, but each grain was smaller than a natural kefir grain. At least a combination of Lb. kefiranofaciens ssp. kefiranofaciens, Lb. kefiranofaciens ssp. kefirgranum, and Len. kefiri or Len. parakefiri could initiate grain formation.

A.N. Homan, G. Ziegler, K.E. Kaylegian et al.

ABSTRACT: Palmitic and stearic acid are commonly fed to dairy cows but limited data are available on their effects on thermal properties of milk fat, especially when fed at different levels. Recently, some consumers voiced concerns about butter being harder at room temperature and questioned if it was caused by palmitic acid supplementation of dairy cows. Our hypothesis was that increasing palmitic acid intake would linearly increase palmitic acid in milk fat and, therefore, increase the solid fat content (SFC) of milk fat at 20°C, whereas increasing stearic acid intake would increase both stearic and oleic acid in milk fat and not change the SFC of milk fat at 20°C. A total of 12 second-lactation Holstein cows (106 ± 31 DIM) were arranged in a replicated 3 × 3 Latin square design with a dose escalation design within period and ≥10 d washouts between periods. Treatments included a no-supplement fat control (CON), a fatty acid (FA) supplement high in palmitic acid (PA; 88% palmitic and 8% oleic), and a FA supplement high in stearic acid (SA; 81% stearic and 10% oleic). The FA supplements were fed at increasing doses every 4 d, targeting 150, 300, 500, and 750 g/d. Milk samples were collected on d 3 and 4 of each dose, composited, and milk fat extracted from the fat cake by centrifugation. The FA profile of whole milk was analyzed using GC, and the melting properties of milk fat were characterized by differential scanning calorimetry. Data were analyzed by ANOVA with preplanned contrasts testing CON versus PA and CON versus SA at each dose level. Increasing PA progressively increased palmitic acid in milk fat, whereas increasing SA progressively increased both stearic and oleic acid. At 750 g/d, PA increased palmitic and palmitoleic acid in milk fat 5.7 and 0.25 percentage units compared with CON, whereas SA increased stearic and oleic acid in milk fat by 2.4 units and 3.0 units compared with CON. The SFC of milk fat at 20°C was linearly increased by PA but was decreased by SA. At the 750 g/d dose, PA increased SFC by 5.3 percentage units, whereas SA decreased it 3.2 percentage units compared with CON. In conclusion, increasing palmitic acid intake increases the SFC of milk fat at room temperature, whereas increasing stearic acid modestly decreases it, likely due to differences in the rates of desaturation of these FA by the stearoyl-CoA desaturase enzyme.

Helena Russello, Rik van der Tol, Eldert J. van Henten et al.

This study presents a lameness detection approach that combines pose estimation and Bidirectional Long-Short-Term Memory (BLSTM) neural networks. Combining pose-estimation and BLSTMs classifier offers the following advantages: markerless pose-estimation, elimination of manual feature engineering by learning temporal motion features from the keypoint trajectories, and working with short sequences and small training datasets. Motion sequences of nine keypoints (located on the cows' hooves, head and back) were extracted from videos of walking cows with the T-LEAP pose estimation model. The trajectories of the keypoints were then used as an input to a BLSTM classifier that was trained to perform binary lameness classification. Our method significantly outperformed an established method that relied on manually-designed locomotion features: our best architecture achieved a classification accuracy of 85%, against 80% accuracy for the feature-based approach. Furthermore, we showed that our BLSTM classifier could detect lameness with as little as one second of video data.

Rahul Jana, Shubham Dixit, Mrityunjay Sharma et al.

Monitoring cattle health and optimizing yield are key challenges faced by dairy farmers due to difficulties in tracking all animals on the farm. This work aims to showcase modern data-driven farming practices based on explainable machine learning(ML) methods that explain the activity and behaviour of dairy cattle (cows). Continuous data collection of 3-axis accelerometer sensors and usage of robust ML methodologies and algorithms, provide farmers and researchers with actionable information on cattle activity, allowing farmers to make informed decisions and incorporate sustainable practices. This study utilizes Bluetooth-based Internet of Things (IoT) devices and 4G networks for seamless data transmission, immediate analysis, inference generation, and explains the models performance with explainability frameworks. Special emphasis is put on the pre-processing of the accelerometers time series data, including the extraction of statistical characteristics, signal processing techniques, and lag-based features using the sliding window technique. Various hyperparameter-optimized ML models are evaluated across varying window lengths for activity classification. The k-nearest neighbour Classifier achieved the best performance, with AUC of mean 0.98 and standard deviation of 0.0026 on the training set and 0.99 on testing set). In order to ensure transparency, Explainable AI based frameworks such as SHAP is used to interpret feature importance that can be understood and used by practitioners. A detailed comparison of the important features, along with the stability analysis of selected features, supports development of explainable and practical ML models for sustainable livestock management.

Anh-Duy Pham, Olivier Basole Kashongwe, Martin Atzmueller et al.

Balancing performance and interpretability in multivariate time series classification is a significant challenge due to data complexity and high dimensionality. This paper introduces PHeatPruner, a method integrating persistent homology and sheaf theory to address these challenges. Persistent homology facilitates the pruning of up to 45% of the applied variables while maintaining or enhancing the accuracy of models such as Random Forest, CatBoost, XGBoost, and LightGBM, all without depending on posterior probabilities or supervised optimization algorithms. Concurrently, sheaf theory contributes explanatory vectors that provide deeper insights into the data's structural nuances. The approach was validated using the UEA Archive and a mastitis detection dataset for dairy cows. The results demonstrate that PHeatPruner effectively preserves model accuracy. Furthermore, our results highlight PHeatPruner's key features, i.e. simplifying complex data and offering actionable insights without increasing processing time or complexity. This method bridges the gap between complexity reduction and interpretability, suggesting promising applications in various fields.

Surya Jayakumar, Kieran Sullivan, John McLaughlin et al.

This study introduces a novel data-driven framework and the first-ever county-scale application of Spatio-Temporal Graph Neural Networks (STGNN) to forecast composite sustainability indices from herd-level operational records. The methodology employs a novel, end-to-end pipeline utilizing a Variational Autoencoder (VAE) to augment Irish Cattle Breeding Federation (ICBF) datasets, preserving joint distributions while mitigating sparsity. A first-ever pillar-based scoring formulation is derived via Principal Component Analysis, identifying Reproductive Efficiency, Genetic Management, Herd Health, and Herd Management, to construct weighted composite indices. These indices are modelled using a novel STGNN architecture that explicitly encodes geographic dependencies and non-linear temporal dynamics to generate multi-year forecasts for 2026-2030.

Waseem Shaukat, Ellen de Jong, Kayley D. McCubbin et al.

ABSTRACT: Endemic infectious diseases remain a major challenge for dairy producers worldwide. For effective disease control programs, up-to-date prevalence estimates are of utmost importance. The objective of this study was to estimate the herd-level prevalence of bovine leukemia virus (BLV), Salmonella enterica ssp. enterica serovar Dublin (Salmonella Dublin), and Neospora caninum in dairy herds in Alberta, Canada, using a serial cross-sectional study design. Bulk tank milk samples from all Alberta dairy farms were collected 4 times, in December 2021 (n = 489), April 2022 (n = 487), July 2022 (n = 487), and October 2022 (n = 480), and tested for antibodies against BLV, Salmonella Dublin, and N. caninum using ELISA. Herd-level apparent prevalence was calculated as positive herds divided by total tested herds at each time point. A mixed-effect modified Poisson regression model was employed to assess the association of prevalence with region, herd size, herd type, and type of milking system. Apparent prevalence of BLV was 89.4%, 88.7%, 86.9%, and 86.9% in December, April, July, and October, respectively, whereas for Salmonella Dublin apparent prevalence was 11.2%, 6.6%, 8.6%, and 8.5%, and for N. caninum apparent prevalence was 18.2%, 7.4%, 7.8%, and 15.0%. For BLV, Salmonella Dublin, and N. caninum, a total of 91.7%, 15.6%, and 28.1% of herds, respectively, were positive at least once, whereas 82.5%, 3.6%, and 3.0% of herds were ELISA positive at all 4 times. Compared with the north region, central Alberta had a high prevalence (prevalence ratio [PR] = 1.13) of BLV antibody-positive herds, whereas south Alberta had a high prevalence (PR = 2.56) of herds positive for Salmonella Dublin antibodies. Furthermore, central (PR = 0.52) and south regions (PR = 0.46) had low prevalence of N. caninum-positive herds compared with the north. Hutterite colony herds were more frequently BLV positive (PR = 1.13) but less frequently N. caninum-positive (PR = 0.47). Large herds (>7,200 L/d milk delivered ∼>250 cows) were 1.1 times more often BLV positive, whereas small herds (≤3,600 L/d milk delivered ∼≤125 cows) were 3.2 times more often N. caninum positive. For Salmonella Dublin, Hutterite colony herds were less frequently (PR = 0.07) positive than non-colony herds only in medium and large strata but not in small stratum. Moreover, larger herds were more frequently (PR = 2.20) Salmonella Dublin-positive than smaller herds only in non-colony stratum but not in colony stratum. Moreover, N. caninum prevalence was 1.6 times higher on farms with conventional milking systems compared with farms with an automated milking system. These results provide up-to-date information of the prevalence of these infections that will inform investigations of within-herd prevalence of these infections and help in devising evidence-based disease control strategies.

Oshana Dissanayake, Sarah E. McPherson, Joseph Allyndree et al.

Getting new insights on pre-weaned calf behavioral adaptation to routine challenges (transport, group relocation, etc.) and diseases (respiratory diseases, diarrhea, etc.) is a promising way to improve calf welfare in dairy farms. A classic approach to automatically monitoring behavior is to equip animals with accelerometers attached to neck collars and to develop machine learning models from accelerometer time-series. However, to be used for model development, data must be equipped with labels. Obtaining these labels requires annotating behaviors from direct observation or videos, a time-consuming and labor-intensive process. To address this challenge, we propose the ActBeCalf (Accelerometer Time-Series for Calf Behaviour classification) dataset: 30 pre-weaned dairy calves (Holstein Friesian and Jersey) were equipped with a 3D-accelerometer sensor attached to a neck-collar from one week of birth for 13 weeks. The calves were simultaneously filmed with a camera in each pen. At the end of the trial, behaviors were manually annotated from the videos using the Behavioral Observation Research Interactive Software (BORIS) by 3 observers using an ethogram with 23 behaviors. ActBeCalf contains 27.4 hours of accelerometer data aligned adequately with calf behaviors. The dataset includes the main behaviors, like lying, standing, walking, and running, and less prominent behaviors, such as sniffing, social interaction, and grooming. Finally, ActBeCalf was used for behavior classification with machine learning models: (i)two classes of behaviors, [active and inactive; model 1] and (ii)four classes of behaviors [running, lying, drinking milk, and 'other' class; model 2] to demonstrate its reliability. We got a balanced accuracy of 92% [model1] and 84% [model2]. ActBeCalf is a comprehensive and ready-to-use dataset for classifying pre-weaned calf behaviour from the acceleration time series.

Belinda Neo, Noel Nannup, Dale Tilbrook et al.

Background: Low vitamin D intake and high prevalence of vitamin D deficiency (serum 25-hydroxyvitamin D concentration < 50 nmol/L) among Aboriginal and Torres Strait Islander peoples highlight a need for public health strategies to improve vitamin D status. As few foods contain naturally occurring vitamin D, fortification strategies may be needed to improve vitamin D intake and status among Aboriginal and Torres Strait Islander peoples. Objective: We aimed to model vitamin D food fortification scenarios among Aboriginal and Torres Strait Islander peoples. Methods: We used nationally representative food consumption data (n=4,109) and vitamin D food composition data to model four food fortification scenarios. The modelling for Scenario 1 included foods and maximum vitamin D concentrations permitted for fortification in Australia: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages, and iv) selected ready-to-eat breakfast cereals. The modelling for Scenarios 2a-c included some vitamin D concentrations higher than permitted in Australia; Scenario 2c included bread, which is not permitted for vitamin D fortification in Australia. Scenario 2a: i) dairy products and alternatives, ii) butter/margarine/oil spreads, iii) formulated beverages. Scenario 2b: as per Scenario 2a plus selected ready-to-eat breakfast cereals. Scenario 2c: as per Scenario 2b plus bread. Results: Vitamin D fortification of a range of staple foods could potentially increase vitamin D intake among Aboriginal and Torres Strait Islander peoples by ~ 3-6 μg/day. Scenario 2c showed the highest potential median vitamin D intake increase to ~ 8 μg/day. Across all modelled scenarios, none of the participants had vitamin D intake above the Australian upper level of intake of 80 μg/day.

J. E. Marquardt, B. Eysel, M. Sadric et al.

Fruit preparations are used in various forms in the food industry. For example, they are used as an ingredient in dairy products such as yogurt with added fruit. The dispersed fruit pieces can be described as soft particles with viscoelastic material behavior. The continuous phase is represented by fluids with complex flow behavior depending on the formulation. Characterization has shown that this can be described by the Herschel-Bulkley model. Since damage to fruit pieces is undesirable in industrial transport processes, the potential for damage to fruit pieces during transport of pipes in cross-sectional constrictions is analyzed. The analysis is performed numerically using the homogenized lattice Boltzmann method and validated by an experiment on industrial fruit preparations at pilot plant scale. The results show a strong dependence of the damage potential on the (local) Metzner-Reed Reynolds number.

Longgang Ma, Hanpeng Luo, Luiz F. Brito et al.

ABSTRACT: The main objectives of this study were to estimate genetic parameters for milk urea nitrogen (MUN) in Holstein cattle and to conduct a single-step (ss)GWAS to identify candidate genes associated with MUN. Phenotypic measurements from 24,435 Holstein cows were collected from March 2013 to July 2019 in 9 dairy farms located in the Beijing area, China. A total of 2,029 cows were genotyped using the Illumina 150K Bovine Bead Chip, containing 121,188 SNP. A single-trait repeatability model was used to evaluate the genetic background of MUN. We found that MUN is a trait with low heritability (0.06 ± 0.004) and repeatability (0.12). Considering similar milk production levels, a lower MUN concentration indicates higher nitrogen digestibility. The genetic correlations between MUN and milk yield, net energy concentration, fat percentage, protein percentage, and lactose percentage were positive and ranged from 0.02 to 0.26. The genetic correlation between MUN and somatic cell score (SCS) was negative (−0.18), indicating that animals with higher MUN levels tend to have lower SCS. Both ssGWAS and pathway enrichment analyses were used to explore the genetic mechanisms underlying MUN. A total of 18 SNP (located on BTA11, BTA12, BTA14, BTA17, and BTA18) were found to be significantly associated with MUN. The genes CFAP77, CAMSAP1, CACNA1B, ADGRB1, FARP1, and INTU are considered to be candidate genes for MUN. These candidate genes are associated with important biological processes such as protein and lipid metabolism and binding to specific proteins. This set of candidate genes, metabolic pathways, and their functions provide a better understanding of the genomic architecture and physiological mechanisms underlying MUN in Holstein cattle.

M. Haile-Mariam, M. Khansefid, M. Axford et al.

ABSTRACT: The longevity of dairy cattle has economic, animal welfare, and health implications and is influenced by the frequency of mortality on the farm and sale for slaughter. In this study cows removed from the herd due to death or slaughter during the lactation were coded 1 and cows that were not terminated were coded 0. Genetic parameters for mortality rates (MR) and slaughter rates (SR) were estimated for Holstein (H) and Jersey (J) breeds by applying both linear (LM) and threshold (TM) sire models using about 1.2 million H and 286,000 J cows. Estimated breeding values (EBV) for MR and SR were predicted using animal models to assess the opportunity for selection and genetic trends. Cow termination data, recorded between 1990 and 2020 on a voluntary basis by Australian dairy farmers, were analyzed. Cow MR has increased from below 1% in the 1990s to 4.1% and 3.6% in recent years in H and J cows, respectively. Most dead cows (∼36%) left the herd before 120 d of lactation, while cows that were slaughtered left the herd toward the end of the lactation. Using the LM, heritability (h2) estimates for MR were lower (1%) than those for SR (2%–3.5%). When h2 were estimated using a TM, the estimates for both traits varied between 4% and 20%, suggesting that the difference in incidence level is one of the reasons for the difference in the h2 values between MR and SR. Early test-day milk yield (MY) and 305-d MY (305-d MY) have unfavorable genetic correlations (0.32–0.41) with MR in both breeds. The genetic correlations of calving interval with MR were stronger (0.54–0.68) than with SR (0.28–0.45) suggesting that poor fertility can serve as an early indicator of poor cow health that may lead to increased risk of death. High early test-day somatic cell count is genetically associated with increased likelihood of slaughter (0.24–0.46), but not with increased likelihood of death. In H, 305-d protein yield (PY) had the strongest genetic correlation (−0.34 to −0.40) with SR whereas in J, both 305-d PY and fat yield showed high genetic (−0.64 to −0.70) and moderate environmental (−0.35 to −0.37) correlations with SR. The genetic correlation of removal from the herd due to death and slaughter was negative (−0.3) in J and zero in H. Strong selection for improved fertility and survival and less selection emphasis for MY, has led to an improvement in the genetic trend for cow MR in H and the trend in J has stabilized. Although genetic evaluations for cow MR are feasible, the reliabilities of the EBV are low and the level of cow MR in Australia are relatively low compared with similar countries. Therefore, genetic evaluation for survival based on mortality and slaughter data could be sufficient in the current selection circumstances where breeding objectives are broadly defined. Nevertheless, all Australian farmers should be encouraged to continue recording mortality and slaughter data for monitoring of the trends and for future development of genetic evaluations.

Michele Pazzola, Giorgia Stocco, Alessandro Ferragina et al.

ABSTRACT: The objectives of this study were to explore the use of Fourier-transform infrared (FTIR) spectroscopy on individual sheep milk samples for predicting cheese-making traits, and to test the effect of the farm variability on their prediction accuracy. For each of 121 ewes from 4 farms, a laboratory model cheese was produced, and 3 actual cheese yield traits (fresh cheese, cheese solids, and cheese water) and 4 milk nutrient recovery traits (fat, protein, total solids, and energy) in the curd were measured. Calibration equations were developed using a Bayesian approach with 2 different scenarios: (1) a random cross-validation (80% calibration; 20% validation set), and (2) a leave-one-out validation (3 farms used as calibration, and the remaining one as validation set) to assess the accuracy of prediction of samples from external farms, not included in calibration set. The best performance was obtained for predicting the yield and recovery of total solids, justifying for the practical application of the method at sheep population and dairy industry levels. Performances for the remaining traits were lower, but still useful for the monitoring of the milk processing in the case of fresh curd and recovery of energy. Insufficient accuracies were found for the recovery of protein and fat, highlighting the complex nature of the relationships among the milk nutrients and their recovery in the curd. The leave-one-out validation procedure, as expected, showed lower prediction accuracies, as a result of the characteristics of the farming systems, which were different between calibration and validation sets. In this regard, the inclusion of information related to the farm could help to improve the prediction accuracy of these traits. Overall, a large contribution to the prediction of the cheese-making traits came from the areas known as “water” and “fingerprint” regions. These findings suggest that, according to the traits studied, the inclusion of water regions for the development of the prediction equation models is fundamental to maintain a high prediction accuracy. However, further studies are necessary to better understand the role of specific absorbance peaks and their contribution to the prediction of cheese-making traits, to offer reliable tools applicable along the dairy ovine chain.