Search Results (605)

Cow efficiency is vitally important to beef sustainability, and computer simulation models may be useful tools to identify characteristics of the most efficient cow genotypes for a given production environment. The objective of this analysis was to determine whether the Beef Cattle Systems Model could replicate empirical research demonstrating a genotype–nutritional environment interaction for efficiency of feed conversion to calves weaned. Combinations of cow genotypes for lactation potential (8, 10, and 12 kg/d at peak milk) and growth potential (450, 505, and 650 kg mature weight) were simulated across four dry matter intake levels (58, 76, 93, and 111 g/kg BW^0.75). At lower dry matter intakes, cows had lesser body condition scores and weight and longer postpartum intervals, but dry matter intake had minimal influence on pregnancy percentage or calf-weaning weight. These trends match empirical research except for pregnancy percentage, where decreasing dry matter intake had a dramatic effect on pregnancy percentage in high-milking, high-growth-potential genotypes. Efficiency of feed conversion was greatest at low dry matter intake for the model simulation with no evidence of a genotype–dry matter intake interaction, which is in contrast to empirical research demonstrating a genotype–dry matter intake interaction. In conclusion, standard nutrition equations do not replicate the genotype–nutritional environment interaction observed in empirical research studies. Full article

Automatic Milking Systems (AMSs) enable the continuous recording of production, milkability, behavioral, and physiological traits, offering new opportunities for genetic evaluation in dairy cattle. This study aimed to estimate variance components and genetic parameters for milk yield-related traits, milking efficiency traits, rumination time (RT), and daily average milk temperature (MTEMP) using AMS-derived data from 1252 Holstein cows. 65,475 weekly records from a single commercial herd were analyzed using repeatability animal models fitted by restricted maximum likelihood. Heritability estimates were moderate to high for milking time (MT) (0.31), milking speed (MS) (0.38), RT (0.30), and MTEMP (0.28), whereas behavioral traits such as number of milking (NoM) (0.26) and number of refused (NoREF) (0.11) showed lower but meaningful heritabilities. Repeatability was highest for MT and MS (0.77 and 0.79), indicating consistent milking performance across repeated records. MTEMP demonstrated clear seasonal variation, increasing in warmer periods and decreasing during colder months, indicating sensitivity to environmental conditions. Genetic correlations among traits revealed both favorable and unfavorable associations; however, several estimates were associated with relatively large standard errors and should therefore be interpreted with caution. The inclusion of MTEMP as a proxy physiological trait derived from AMS data showed measurable genetic variation, although its biological interpretation requires careful consideration. Overall, the results suggest that AMS-derived phenotypes may contribute useful information for genetic studies of functional traits, but the single-herd structure, limited pedigree depth, and data aggregation procedures restrict the generalizability of the findings. Further multi-herd and genomics-based studies are required to validate these results and assess their applicability in breeding programs. Full article

Weaning age is a critical management decision in beef cattle production, influencing herd productivity, financial outcomes, and overall system sustainability. Commonly practiced in South African beef systems, is where calves are weaned at 6–9 months (conventional weaning), while early weaning (EW) at approximately 90 days remains underutilized. This study presents a farm case study and preliminary financial assessment of EW and CW using a farm calculation model incorporating revenue, weaning costs, supplementation, and labor. Data from 152 Bonsmara cow–calf pairs were analyzed. CW calves achieved higher weaning weights (237 kg) and average daily gains (992 g/day) than EW calves (210 kg; 889 g/day), generating greater revenue (R630,420 vs. R558,600). The Pearson Chi-square test showed an association between weaning system and dam reproductive performance, with EW cows achieving a 94% pregnancy rate compared to 84% under CW. Although CW produced higher short-term gross margins (R6446 per system vs. R3068 for EW), sensitivity analyses indicated that EW becomes financially competitive when price premiums are applied. Simulations showed that an EW price range of R34–R40/kg could yield higher returns despite lower weights. These findings demonstrate that EW, when supported by structured price incentives, can enhance reproductive efficiency and contribute to more sustainable and financially resilient beef production systems in South Africa. Full article

Effective nitrogen management in dairy cow diets is essential for optimising milk production and minimising environmental nitrogen emissions. This study develops a simplified model to estimate nitrogen excretion in dairy farms, distinguishing excretion by animal category (lactating cows, heifers, calves) and organic matrix (faeces, urine), with nitrogen intake as a key input. A comprehensive literature review guided the selection of equations for estimating nitrogen excretion based on dietary nitrogen content, dry matter intake and milk yield. The model was specifically calibrated for Holstein dairy herd in the Po Valley (Italy) context using data collected from ten Lombardy dairy farms over 30 months, focusing on diet composition and nitrogen excretion via faeces, urine, and milk. Validation against established the literature and the Nitrates Directive (91/676/EEC) excretion factors demonstrated the model’s alignment in estimating nitrogen excretion. Within this context, the proposed framework may support nitrogen management at farm level by providing a practical, descriptive tool to explore nitrogen flows and to identify potential areas for improving nutrient efficiency and reducing environmental impacts. Full article

Fetal loss, the spontaneous termination of pregnancy between day 42 and 260 of gestation, is poorly understood. Impacts of fetal loss include loss of production, increased health risk, and economic loss. The aims of this study were to identify loci associated with fetal loss in Holstein heifers and primiparous cows to facilitate the selection of reproductively efficient cattle and identify the genetic causes of fetal loss. A genome-wide association analysis (GWAA) compared 5714 heifers that calved at term (controls) to 416 heifers that experienced fetal loss (cases), and for primiparous cows, 2519 controls were compared to 273 cases. The efficient mixed-model association eXpedited approach in the SNP and Variation Suite (v 9.1) statistical software was used with additive, dominant, and recessive inheritance models for the GWAA. In heifers, 16 loci were associated (FDR < 0.05) with fetal loss in the recessive model. In primiparous cows, there were 44 loci associated (FDR < 0.05) with fetal loss in the recessive model. No loci associated with fetal loss were shared between cows and heifers or in the additive and dominant models. These results improve the characterization of genetic factors contributing to fetal loss in Holstein heifers and primiparous cows and provide targets for genomic selection. Full article

A key objective of the dairy industry is to balance genetic progress with reproductive efficiency. Ovum pick-up followed by in vitro embryo production (OPU-IVP) is a pivotal technology for accelerating genetic gain. However, the relationship between follicle size and oocyte developmental competence in high-producing dairy cows under hormonal stimulation remains to be fully elucidated. This study systematically evaluated the effects of follicle diameter ovum pick-up on OPU-IVP outcomes and the underlying follicular fluid (FF) microenvironment. A total of 109 high-yielding Holstein cows were subjected to ovarian stimulation and OPU. Follicles were categorized as small (2.0–5.9 mm), medium (6.0–9.9 mm), or large (10.0–20.0 mm). Oocyte recovery, quality, and developmental competence were assessed. FF was analyzed for hormonal profiles, including anti-Müllerian hormone (AMH), estradiol (E2), follicle-stimulating hormone (FSH), and progesterone (PROG); oxidative stress markers, including malondialdehyde (MDA), glutathione peroxidase (GPx), superoxide dismutase (SOD), and total antioxidant capacity (T-AOC); and untargeted metabolomics (n = 10 per group). Consistently, oocytes from medium follicles exhibited superior developmental competence, achieving the highest maturation (89.93%), cleavage (72.19%), and blastocyst rates (41.88%). In contrast, large follicles had a low recovery rate (32.64%), a high proportion of degenerated oocytes (32.00%), and reduced embryonic efficiency. Metabolomic profiling revealed distinct microenvironmental differences, with medium follicles enriched in pathways like pyruvate metabolism and arachidonic acid metabolism indicating an optimal metabolic state. Hormonally, AMH decreased while E2 and PROG increased with follicle size. Large follicles exhibited significantly elevated MDA levels, indicating oxidative stress, without a concurrent rise in antioxidant capacity. In conclusion, while small follicles provide an abundant source of morphologically good oocytes, medium follicles (6.0–9.9 mm) represent a distinct “window of competence” for OPU-IVP, characterized by a follicular microenvironment most conducive to embryo production. Excessive reliance on large follicle aspiration should be avoided due to signs of over-maturity and oxidative damage. These findings provide a physiological basis for optimizing OPU strategies to enhance IVP efficiency in high-producing dairy cows. Full article

Efforts to improve efficiency and profitability on dairy farms have renewed focus on how culling practices affect herd sustainability and economic outcomes. This study surveyed decision-makers on 60 high-producing, freestall-housed dairy farms in Wisconsin, with a mean (SD) turnover rate of 36.0 (8.0)%. Using a structured questionnaire, we examined herd management, culling criteria, and motivations. Most farms (93%) used on-farm management systems to guide culling, yet only 48% used designated reports, relying instead on individual cow records. Milk production, infertility, and somatic cell count were the top culling criteria, with high milk yield cited as the most difficult factor in removal decisions. While 54% recorded the most obvious reason for culling, only 7% documented multiple causes. Cull cows were typically transported by third parties; 80% farms sent cows directly to slaughter, while 52% sent them to auction. One-third of farms sold cows for continued dairy use. Euthanasia was performed on 93% of farms, mostly by employees, with minimal veterinary input. The study aimed to investigate producer perspectives on the culling decision-making process on commercial dairy farms. The findings highlight opportunities for improved veterinary involvement and the use of structured herd-level reports to support more strategic culling decisions. Full article

Sward structure and post-grazing heights (SH) significantly influence plant growth and animal intake, crucial for dairy grazing systems. However, these interactions are dynamic and vary with season, resource heterogeneity, and defoliation patterns. Seasonal effects of control (TC), medium (TM), and lax (TL) post-grazing SH of grazed Lolium arundinaceum-based pasture on forage production and utilization, herbage mass, green cover, and chemical composition were tested during autumn-winter and spring seasons and among tall (TP), medium (MP), and short (SP) patches in spring. Thirty-six lactating Holstein cows were randomized evenly to TC, TM, and TL grazing treatments to achieve 6, 9, and 12 cm of post-grazing SH during autumn-winter, and 9, 12, and 15 cm in spring. Forage production was higher on TL than TM and TC, yet utilization was similar across all treatments. The TP relative to MP on SP increased for TL compared to TC and TM. The TP-TC presented higher leaf-density and leaf-proportion, than TP-TL, without modifying leaf canopy distribution of superior-medium horizons among treatments. Grazing management modulated forage production and structural heterogeneity across SH treatments. Critically, monitoring patch-level dynamics—rather than mean height—is essential for optimizing production and harvest efficiency in temperate systems by improving grazing horizon accessibility. Full article

To address the low efficiency and stress-inducing nature of traditional manual weighing for dairy cows, this study proposes a semantically guided 3D reconstruction and body weight estimation method for dairy cows. First, a dual-viewpoint Kinect V2 camera synchronous acquisition system captures top-view and side-view point cloud data from 150 calves and 150 lactating cows. Subsequently, the CSS-PointNet++ network model was designed. Building upon PointNet++, it incorporates Convolutional Block Attention Module (CBAM) and Attention-Weighted Hybrid Pooling Module (AHPM) to achieve precise semantic segmentation of the torso and limbs in the side-view point cloud. Based on this, point cloud registration algorithms were applied to align the dual-view point clouds. Missing parts were mirrored and completed using semantic information to achieve 3D reconstruction. Finally, a body weight estimation model was established based on volume and surface area through surface reconstruction. Experiments demonstrate that CSS-PointNet++ achieves an Overall Accuracy (OA) of 98.35% and a mean Intersection over Union (mIoU) of 95.61% in semantic segmentation tasks, representing improvements of 2.2% and 4.65% over PointNet++, respectively. In the weight estimation phase, the BP neural network (BPNN) delivers optimal performance: For the calf group, the Mean Absolute Error (MAE) was 1.8409 kg, Root Mean Square Error (RMSE) was 2.4895 kg, Mean Relative Error (MRE) was 1.49%, and Coefficient of Determination (R²) was 0.9204; for the lactating cows group, MAE was 12.5784 kg, RMSE was 14.4537 kg, MRE was 1.75%, and R² was 0.8628. This method enables 3D reconstruction and body weight estimation of cows during walking, providing an efficient and precise body weight monitoring solution for precision farming. Full article

Behavior recognition and multi-object tracking of dairy cows in free-style cow barn environments play a crucial role in monitoring their health status and serve as an essential means for intelligent scientific farming. This study proposes an efficient embedded algorithm, MTW-BYTE, for dairy cow behavior recognition and multi-object tracking. It addresses challenges in free-style cow barn environments, including the impact of lighting variations and common occlusions on behavior recognition, as well as trajectory interruptions and identity ID switching during multi-object tracking. First, the MTW-YOLO cow behavior recognition model is constructed based on the YOLOv11n object detection algorithm. Replacing parts of the backbone network and neck network with MANet and introducing the Task Dynamic Align Detection Head (TDADH). The CIoU loss function of YOLOv11n is replaced with the WIoU loss. The improved model not only effectively handles variations in lighting conditions but also addresses common occlusion issues in cows, enhancing multi-scale behavior recognition capabilities and improving overall detection performance. The improved MTW-YOLO algorithm improves Precision, Recall, mAP50 and F1 score by 4.5%, 0.1%, 1.6% and 2.2%, respectively, compared to the original YOLOv11n model. Second, the ByteTrack multi-object tracking algorithm is enhanced by designing a dynamic buffer and re-detection mechanism to address cow trajectory interruptions and identity ID switching. The MTW-YOLO algorithm is cascaded with the improved ByteTrack to form the multi-target tracking algorithm MTW-BYTE. Compared with the original multi-target tracking algorithm YOLOv11n-ByteTrack (a combination of YOLOv11n and the original ByteTrack), this algorithm improves HOTA by 1.1%, MOTA by 3.6%, MOTP by 0.2%, and IDF1 by 1.9%, reduces the number of ID changes by 11, and achieves a frame rate of 43.11 FPS, which can meet the requirements of multi-target tracking of dairy cows in free-style cow barn environments. Finally, to verify the model’s applicability in real-world scenarios, the MTW-BYTE algorithm is deployed on an NVIDIA Jetson AGX Orin edge device. Based on real-time monitoring of cow behavior on the edge device, the pure inference time for a single frame is 16.62 ms, achieving an FPS of 29.95, demonstrating efficient and stable real-time behavior detection and tracking. The ability of MTW-BYTE to be deployed on edge devices to identify and continuously track cow behavior in various scenarios provides hardware feasibility verification and algorithmic support for the subsequent deployment of intelligent monitoring systems in free-style cow barn environments. Full article

Digital twin technology is widely promoted as a transformative step for precision livestock farming, yet no fully realized, engineering-grade digital twins are deployed in commercial dairy or poultry systems today. This work establishes the current state of knowledge on dairy and poultry digital twins by synthesizing evidence through systematic database searches, thematic evidence mapping and critical analysis of validation gaps, carbon accounting and adoption barriers. Existing platforms are better described as near-digital-twin systems with partial sensing and modelling, digital-twin-inspired prototypes, simulation frameworks or decision-support tools that are often labelled as twins despite lacking continuous synchronization and closed-loop control. This distinction matters because the empirical foundation supporting many claims remains limited. Three critical gaps emerge: life-cycle carbon impacts of digital infrastructures are rarely quantified even as sustainability benefits are frequently asserted; field-validated improvements in feed efficiency, particularly in poultry feed conversion ratios, are scarce and inconsistent; and systematic reporting of failure rates, downtime and technology abandonment is almost absent, leaving uncertainties about long-term reliability. Adoption barriers persist across technical, economic and social dimensions, including rural connectivity limitations, sensor durability challenges, capital and operating costs, and farmer concerns regarding data rights, transparency and trust. Progress for cows and chickens will require rigorous validation in commercial environments, integration of mechanistic and statistical modelling, open and modular architectures and governance structures that support biological, economic and environmental accountability whilst ensuring that system intelligence is worth its material and energy cost. Full article

Individual animal identification is a cornerstone of animal welfare practices and is of crucial importance for food safety and the protection of humans from zoonotic diseases. It is also a key prerequisite for enabling automated processes in modern dairy farming. With newly emerging technologies, visual animal identification based on machine learning offers a more efficient and non-invasive method with high automation potential, accuracy, and practical applicability. However, a common challenge is the limited variability of training datasets, as images are typically captured in controlled environments with uniform backgrounds and fixed poses. This study investigates the impact of foreground segmentation and background removal on the performance of convolutional neural networks (CNNs) for cow identification. A dataset was created in which training images of dairy cows exhibited low variability in pose and background for each individual, whereas the test dataset introduced significant variation in both pose and environment. Both a fine-tuned CNN backbone and a model trained from scratch were evaluated using images with and without background information. The results demonstrate that although training on segmented foregrounds extracts intrinsic biometric features, background cues carry more information for individual recognition. Full article

In the daily clinical practice of cattle, the use of locoregional anaesthesia is needed to provide analgesia during standing surgical procedures. It is important to ensure the success of the blockade before starting the surgery. One of the most used techniques is the paravertebral lumbar block. In this pilot study we evaluated the efficacy of thermography in assessing this block. For this matter, 12 cows from our university research and teaching farm, with similar characteristics, were included and in which an ultrasound-guided technique of lumbar paravertebral block (T₁₃/L₁) or (L₁/L₂) with lidocaine was performed. Thermal photographs were taken with a FLIR^® One camera at 0, 15, 30 and 45 min and at the same time a test to evaluate the response to a painful stimulus was performed in each dermatome (T₁₃, L₁, L₂ and L₃). The data was collected in predesigned cards and placed in the Excel programme for further statistical analysis with the R programme. The analysis determined a correlation between the increase in skin temperature of the dermatomes that had been blocked and the increase in skin temperature and the negative response to the painful stimulus test. Although the pilot study has some limitations, this allows us to assess the use of thermography as an efficient method for assessing the success of lumbar paravertebral blockade in cattle. Full article

This study investigated how corn processing and protein degradability affect ruminal fermentation and feeding behavior in lactating Holstein cows. Twenty cows (averaging 162 ± 70 days in lactation, 666 ± 7 kg body weight, and 36.0 ± 7.8 kg/day milk yield) were assigned in a Latin square design with four 21-day periods and four treatments arranged in a 2 × 2 factorial: corn processing [ground corn (GC) vs. steam-flaked corn (SFC)] and crude protein (CP) degradability [high (HCP) vs. low (LCP)]. Ruminal samples were collected at eight time points (0, 2, 4, 6, 8, 10, 12 and 16 h) post-feeding to analyze pH, ammonia, and short-chain fatty acids, while feeding behavior was recorded visually every 5 min for 48 h. Corn processing and protein degradability interacted to influence rumen ammonia nitrogen (p = 0.057), urinary pH, (p = 0.041), nitrogen secretion and efficiency (p = 0.538), and feeding (min/kg DM; p = 0.049) and rumination times (min/kg DM, p = 0.001; min/kg NDF, p = 0.001), reflecting changes in nitrogen metabolism. Steam-flaked corn decreased the acetate/propionate ratio and enhanced propionate production, improving nitrogen retention and reducing urinary N losses, while highly degradable protein increased ruminal NH₃-N and branched-chain VFA concentrations, particularly when combined with ground corn. Additionally, steam flaking reduced feed selectivity and increased rumination efficiency, supporting more effective use of nutrients for milk N secretion and overall nitrogen utilization efficiency in dairy cows. Overall, diets varying in corn processing and protein degradability altered ruminal metabolism, nutrient utilization, feeding behavior, and diet selectivity in lactating cows, highlighting their importance in optimizing dairy cow performance. Full article

The sustainable future of dairy farming will depend on how trade-offs between environmental impact, economic viability, and animal welfare are managed. Dairy production contributes significantly not only to human nutrition but also to greenhouse gas (GHG) emissions, ammonia release, and water pollution. Comprehensive sustainability assessments are essential for addressing these impacts, also in light of evolving regulations like the EU Corporate Sustainability Reporting Directive. However, existing research on sustainable dairy farming and intensification often overlooks trade-offs with other ecological aspects like biodiversity, economic viability, or animal welfare. This study evaluated the sustainability performance of Austrian dairy farms using a tool called NEU.rind, which combines life cycle assessment (LCA) with other indicators. Applied to 170 dairy farms, the tool identified four sustainability clusters across the dimensions of environmental conditions, efficiency, animal health, and sustainability: (1) Alpine farms (high cow longevity, medium-to-high emissions per kg milk), (2) efficient low-input farms (low emissions, high cow longevity), (3) high-output lowland farms (high productivity, lower animal welfare), and (4) input-intensive lowland farms (high emissions, especially per hectare; inefficient use of resources). The analysis revealed fundamental trade-offs between production intensity, environmental impact, and animal welfare, particularly when comparing product-based (per kg milk) versus hectare-based indicators. Key improvement strategies include increasing the use of regional feed and pasture as well as adapting manure management. For policymakers, these findings underline the importance of site-specific sustainability assessments and the need for region-specific incentive schemes that reward both environmental efficiency and animal health performance. In this context, NEU.rind provides farm-specific recommendations with minimal data input, making sustainability assessments practical and feasible. Full article