The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.
Automated milk feeders (AMF) allow farmers to raise ...calves in groups while generating individual records on milk consumption, drinking speed, and frequency of visits. Calves raised in groups benefit from social interaction, which facilitates learning and adapting to novelty. However, calves in large groups (>12 calves/feeder) experience a higher risk of disease transmission and competition than those housed individually or in smaller groups. Therefore, if group size, grouping strategy, and disease detection are not optimal, the health and performance of calves can be compromised. The objectives of this narrative literature review, from publications available as of February 2023, are to (1) describe the use of AMF in group housing systems for calves and the associated feeding behavior variables they automatically collect, (2) linking feeding behavior collected from AMF to disease risk in calves, (3) describe research on social behavior in AMF systems, and (4) introduce social networks as a promising tool for the study of social behavior and disease transmission in group-housed AMF-fed calves. Existing research suggests that feeding behavior measures from AMF can assist in detecting bovine respiratory disease and enteric disease, which are common causes of morbidity and mortality for preweaning dairy heifers. Automated milk feeder records show reduced milk intake, drinking speed, or frequency of visits when calves are sick. However, discrepancies exist among published research about the sensitivity of feeding behavior measures as indicators of sickness, likely due to differences in feeding plans and disease-detection protocols. Therefore, considering the influence of milk allowance, group density, and individual variation on the analysis of AMF data is essential to derive meaningful information used to inform management decisions. Research using dynamic social networks derived from precision data show potential for the use of social network analysis to understand disease transmission and the effect of disease on social behavior of group-housed calves.
Before weaning, dairy calves are at high risk for illness, especially respiratory and digestive diseases, which reduces average daily gain, age at first calving, and first-lactation milk production. ...Although these illnesses are commonly treated with antibiotics, efforts are being made to reduce antibiotic use, due to concerns about antibiotic-resistant bacteria. The objective was to evaluate the effects of Saccharomyces cerevisiae fermentation products (SCFP) on the immune status of calves, following a lipopolysaccharide (LPS) challenge administered just before weaning. Thirty Holstein bull calves were blocked based on initial body weight and then assigned to 1 of 2 study treatments. The control group (CON) was fed a 24% crude protein:17% fat milk replacer (MR) and calf starter with no SCFP added. The SCFP treatment was fed the same 24% crude protein:17% fat MR with 1 g/d of SmartCare (Diamond V) and calf starter with 0.8% NutriTek (Diamond V). SmartCare and NutriTek are both produced from anaerobic fermentation of S. cerevisiae. Calves were offered 2.84 L (12.5% solids) of MR twice daily at 0630 and 1630 h through d 51; from d 52 to 56, calves were fed MR once daily at 0630 h; and calves were weaned on d 57. Calves also received ad libitum access to a texturized calf starter and water. On d 50, a subset of calves (n = 20, 10 calves per treatment) were enrolled in an LPS challenge. At −1.5, −0.5, 0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 5, 6, 7, 8, and 24 h relative to dosing with LPS, 20 mL of blood was collected, and rectal temperature and respiration rate were measured for each calf. Blood serum samples were analyzed for interleukin 6, TNF-α (tumor necrosis factor-α), interferon-gamma, haptoglobin, serum amyloid-A, fibrinogen, nonesterified fatty acid, cortisol, and glucose. This study observed increased concentrations of TNF-α at 1 h and 1.5 h and glucose at 0.5 h after dosing with LPS in SCFP calves compared with CON. Calves supplemented with SCFP also had an increase in respiration rate 0.5 h after dosing with LPS and reduced feed intake the day of the challenge compared with CON calves. These results suggest that dairy calves supplemented with SCFP exhibit an increased acute immune response, as observed by increased TNF-α, glucose, and respiration rate immediately after dosing with LPS, compared with CON calves.
l-Glutamine supplementation improves gastrointestinal and immune function in dairy calves during controlled immune and stress challenges. However, it is unknown whether supplementing milk replacer ...(MR) with l-glutamine improves preweaning dairy calf health and welfare under production conditions. Therefore, the study objective was to evaluate the effects of supplementing MR with l-glutamine on gastrointestinal permeability, immune function, growth performance, postabsorptive metabolic biomarkers, and physiological stress response in preweaning dairy calves. In 3 repetitions, Holstein heifer calves (n = 30; 1.5 ± 0.5 d old; 37.1 ± 0.86 kg body weight) were blocked by serum total protein, body weight, and age, and provided MR (3.8 L/calf per d; 24% CP, 17% fat, 12.5% solids) supplemented with l-glutamine (GLN; 10g/kg MR powder; n = 5 calves/repetition) or nonsupplemented (NSMR; n = 5 calves/repetition). Calves were individually housed with ad libitum starter grain and water access until weaning (56.4 ± 0.5 d old). At 1 and 6 wk of age, urinary catheters were placed, and calves were orally dosed with 1 L of chromium (Cr)-EDTA. Urine samples were collected over a 24-h period for Cr output analysis as an in vivo biomarker of gastrointestinal permeability. Blood was collected on study d 1, 5, 7, 14, 21, 42, and 56 to measure white blood cell counts, cortisol, insulin, glucose, nonesterified fatty acids, serum amyloid A, haptoglobin, and neutrophil: lymphocytes. Two study intervals were used in the statistical analyses, representing greater (P1; wk 1-3) and reduced (P2; wk 4-8) enteric disease susceptibility. Data were analyzed using PROC GLIMMIX in SAS 9.4 (SAS Institute Inc.) with calf as the experimental unit. Overall, total urinary Cr output was reduced in GLN versus NSMR calves. Total Cr output was reduced at 1 wk of age in GLN versus NSMR calves, but no differences were detected at 6 wk of age. Neutrophil:lymphocyte was decreased both overall and during P2 in GLN versus NSMR calves, and neutrophil counts tended to be reduced in GLN versus NSMR calves during P2. No MR treatment differences were detected for average daily feed intake, average daily gain, body measurements, postabsorptive metabolic biomarkers, disease scores, and therapeutic treatments between GLN and NSMR calves. In summary, l-glutamine supplementation reduced gastrointestinal permeability and biomarkers of physiological stress in preweaning Holstein heifer calves.
Video analytic system for detecting cow structure Liu, He; Reibman, Amy R.; Boerman, Jacquelyn P.
Computers and electronics in agriculture,
November 2020, 2020-11-00, 20201101, Letnik:
178
Journal Article
Recenzirano
Odprti dostop
•This paper introduces a video-analytic system which automatically detects the cow structure from captured video sequences.•We combine deep learning with domain knowledge about cows to develop a cow ...structure detection system that operates on videos captured from a practical dairy farm.•All the videos are captured and processed without interfering with the daily work on the dairy farm.•This system can detect and track multiple cow objects at the same time with videos captured on a farm during normal operation.
In animal agriculture, animal health directly influences productivity. For dairy cows, many health conditions can be evaluated by trained observers based on visual appearance and movement. However, to manually evaluate every cow in a commercial farm is expensive and impractical. This study introduces a video-analytic system which automatically detects the cow structure from captured video sequences. A side-view cow structural model is designed to describe the spatial positions of the joints (keypoints) of the cow, and we develop a system using deep learning to automatically extract the structural model from videos. The proposed system can detect multiple cows in the same frame and provides robust performance for the body region under practical challenges like obstacles (fences) and poor illumination. Compared to other object detection methods, this system provides better detection results and successfully isolates the body keypoints of each cow even when the cows are close to each other.
Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from ...automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 range: 0.020, 0.110, DMC = 0.460 range: 0.050, 0.680, DSDD = 0.180 range: 0.010, 0.340, DS = 0.19 range: 0.070, 0.430, DNRV = 0.120 range: 0.030, 0.450) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of −0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.
The number of dairy farms adopting automatic milking systems (AMS) has considerably increased around the world aiming to reduce labor costs, improve cow welfare, increase overall performance, and ...generate a large amount of daily data, including production, behavior, health, and milk quality records. In this context, this study aimed to (1) estimate genomic-based variance components for milkability traits derived from AMS in North American Holstein cattle based on random regression models; and (2) derive and estimate genetic parameters for novel behavioral indicators based on AMS-derived data. A total of 1,752,713 daily records collected using 36 milking robot stations and 70,958 test-day records from 4,118 genotyped Holstein cows were used in this study. A total of 57,600 SNP remained after quality control. The daily-measured traits evaluated were milk yield (MY, kg), somatic cell score (SCS, score unit), milk electrical conductivity (EC, mS), milking efficiency (ME, kg/min), average milk flow rate (FR, kg/min), maximum milk flow rate (FRM, kg/min), milking time (MT, min), milking failures (MFAIL), and milking refusals (MREF). Variance components and genetic parameters for MY, SCS, ME, FR, FRM, MT, and EC were estimated using the AIREMLF90 software under a random regression model fitting a third-order Legendre orthogonal polynomial. A threshold Bayesian model using the THRGIBBS1F90 software was used for genetically evaluating MFAIL and MREF. The daily heritability estimates across days in milk (DIM) ranged from 0.07 to 0.28 for MY, 0.02 to 0.08 for SCS, 0.38 to 0.49 for EC, 0.45 to 0.56 for ME, 0.43 to 0.52 for FR, 0.47 to 0.58 for FRM, and 0.22 to 0.28 for MT. The estimates of heritability (± SD) for MFAIL and MREF were 0.02 ± 0.01 and 0.09 ± 0.01, respectively. Slight differences in the genetic correlations were observed across DIM for each trait. Strong and positive genetic correlations were observed among ME, FR, and FRM, with estimates ranging from 0.94 to 0.99. Also, moderate to high and negative genetic correlations (ranging from −0.48 to −0.86) were observed between MT and other traits such as SCS, ME, FR, and FRM. The genetic correlation (± SD) between MFAIL and MREF was 0.25 ± 0.02, indicating that both traits are influenced by different sets of genes. High and negative genetic correlations were observed between MFAIL and FR (−0.58 ± 0.02) and MFAIL and FRM (−0.56 ± 0.02), indicating that cows with more MFAIL are those with lower FR. The use of random regression models is a useful alternative for genetically evaluating AMS-derived traits measured throughout the lactation. All the milkability traits evaluated in this study are heritable and have demonstrated selective potential, suggesting that their use in dairy cattle breeding programs can improve dairy production efficiency in AMS.
Considering the increasing challenges imposed by climate change and the need to improve animal welfare, breeding more resilient animals capable of better coping with environmental disturbances is of ...paramount importance. In dairy cattle, resilience can be evaluated by measuring the longitudinal occurrences of abnormal daily milk yield throughout lactation. Aiming to estimate genetic parameters for dairy cattle resilience, we collected 5,643,193 daily milk yield records on automatic milking systems (milking robots) and milking parlors across 21,350 lactations 1 to 3 of 11,787 North American Holstein cows. All cows were genotyped with 62,029 SNPs. After determining the best fitting models for each of the 3 lactations, daily milk yield residuals were used to derive 4 resilience indicators: weighted occurrence frequency of yield perturbations (wfPert), accumulated milk losses of yield perturbations (dPert), and log-transformed variance (LnVar) and lag-1 autocorrelation (rauto) of daily yield residuals. The indicator LnVar presented the highest heritability estimates (±standard error), ranging from 0.13 ± 0.01 in lactation 1 to 0.15 ± 0.02 in lactation 2; the other 3 indicators had relatively lower heritabilities across the 3 lactations (0.01–0.06). Based on bivariate analyses of each resilience indicator across lactations, stronger genetic correlations were observed between lactations 2 and 3 (0.88–0.96) than between lactations 1 and 2 or 3 (0.34–0.88) for dPert, LnVar, and rauto. For the pairwise comparisons of different resilience indicators within each lactation, dPert had the strongest genetic correlations with wfPert (0.64) and rauto (0.53) in lactation 1, whereas the correlations in lactations 2 and 3 were more variable and showed relatively high standard errors. The genetic correlation results indicated that different resilience indicators across lactations might capture additional biological mechanisms and should be considered as different traits in genetic evaluations. We also observed favorable genetic correlations of these resilience indicators with longevity and Net Merit index, but further biological validation of these resilience indicators is needed. In conclusion, this study provided genetic parameter estimates for different resilience indicators derived from daily milk yields across the first 3 lactations in Holstein cattle, which will be useful when potentially incorporating these traits in dairy cattle breeding schemes.
The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes.
Identifying genome-enabled methods that provide more ...accurate genomic prediction is crucial when evaluating complex traits such as dairy cow behavior. In this study, we aimed to compare the predictive performance of traditional genomic prediction methods and deep learning algorithms for genomic prediction of milking refusals (MREF) and milking failures (MFAIL) in North American Holstein cows measured by automatic milking systems (milking robots). A total of 1,993,509 daily records from 4,511 genotyped Holstein cows were collected by 36 milking robot stations. After quality control, 57,600 SNPs were available for the analyses. Four genomic prediction methods were considered: Bayesian least absolute shrinkage and selection operator (LASSO), multiple layer perceptron (MLP), convolutional neural network (CNN), and GBLUP. We implemented the first 3 methods using the Keras and TensorFlow libraries in Python (v.3.9) but the GBLUP method was implemented using the BLUPF90+ family programs. The accuracy of genomic prediction (mean square error) for MREF and MFAIL was 0.34 (0.08) and 0.27 (0.08) based on LASSO, 0.36 (0.09) and 0.32 (0.09) for MLP, 0.37 (0.08) and 0.30 (0.09) for CNN, and 0.35 (0.09) and 0.31(0.09) based on GBLUP, respectively. Additionally, we observed a lower reranking of top selected individuals based on the MLP versus CNN methods compared with the other approaches for both MREF and MFAIL. Although the deep learning methods showed slightly higher accuracies than GBLUP, the results may not be sufficient to justify their use over traditional methods due to their higher computational demand and the difficulty of performing genomic prediction for nongenotyped individuals using deep learning procedures. Overall, this study provides insights into the potential feasibility of using deep learning methods to enhance genomic prediction accuracy for behavioral traits in livestock. Further research is needed to determine their practical applicability to large dairy cattle breeding programs.
It takes an approximate 2-yr investment to raise a replacement heifer from birth to first calving, and selecting the most productive heifers earlier in life could reduce input costs. Daily milk ...consumption, serum total protein, pneumonia and scours incidences, body size composite, birth weights, and incremental body weights were collected on a commercial dairy farm from October 1, 2015, to January 1, 2019. Holstein calves (n = 5,180) were fed whole pasteurized nonsalable milk with a 30% protein and 5% fat enhancer added at 20 g/L of milk through an automated calf feeding system (feeders = 8) for 60 d on average. Calves were weighed at birth and several other times before calving. Average birth weight of calves was 40.6 ± 4.9 kg (mean ± standard deviation), serum total protein was 6.7 ± 0.63 mg/dL, and cumulative 60-d milk consumption was 508.1 ± 67.3 L with a range of 179.9 to 785.1 L. Daily body weights were predicted for individual animals using a third-order orthogonal polynomial to model body weight curves. The linear and quadratic effects of cumulative 60-d milk consumption, birth weight, feeder, year born, season born, respiratory incidence, scours incidence, and body size composite score were significant when predicting heifer body weight at 400 d (pBW400) of age. There was up to a 263-kg difference in pBW400 between the heaviest and lightest animal. Birth weight had a significant effect on predicted weights up to 400 d, and for every 1-kg increase in birth weight, there was a 2.5-kg increase in pBW400. Quadratic effect of cumulative 60-d milk consumption was significant up to 400 d. We divided 60-d milk consumption into quartiles, and heifers had the highest pBW400 in the third quartile when 60-d consumption was between 507.8 and 552.5 L. Body size composite score showed a 21.5-kg difference in pBW400 between the top and bottom 25th percentile of heifers. Heifers were 4.2 kg lighter at 400 d if treated for respiratory disease 3+ times during the first 60 d of life compared with heifers not treated for respiratory disease. Measurements that can be obtained in the early life of dairy calves continue to influence heifer growth up to 400 d of age.
It is necessary for the dairy industry to reduce calf morbidity and mortality, and the reliance on antibiotics to treat sick calves, to address the growing concern regarding antibiotic resistant ...bacteria. The primary objective of this study was to evaluate the effect that feeding dairy calves medium-chain fatty acids (MCFA) has on growth performance and health, and the secondary objective was to evaluate the effect of MCFA on energy status around weaning and the adaptive immune response following a vaccine challenge. Thirty-three Holstein bull calves (5 ± 1.6 d of age) were randomly assigned to 1 of 2 treatments. Control (CON) calves were fed milk replacer with no C8:0 or C10:0 oil added and MCFA calves were fed milk replacer with 0.5% of a combination of C8:0 or C10:0 oil added. Body weight and average daily gain were measured weekly. Feed efficiency (gain/feed) and the change in body condition score, hip width, hip height, heart girth, and paunch girth were calculated for the duration of the study. Fecal scores were recorded daily and all medical treatments were documented for the duration of the trial. On d 42, 49, and 56 of the study, a serum sample was collected from each calf and used to measure nonesterified fatty acids, β-hydroxybutyric acid, insulin, and glucose concentrations to evaluate energy status around weaning. A subset of 11 calves per treatment were enrolled in a vaccine challenge. At 21 ± 1.9 d of age (mean ± standard deviation) calves were vaccinated intramuscularly with 1 mL of endotoxin-free ovalbumin (OVA) mixed with aluminum hydroxide adjuvant. At 42 d of age (±1.9 d), blood samples were collected and used to analyze OVA-specific IgG1 and IgG2, and calves were vaccinated a second time. At 56 d of age (±1.9 d), blood samples were collected to analyze IgG1 and IgG2 as well as IFN-γ and IL-4 secreted from peripheral blood mononuclear cells (PBMC) treated with OVA or phytohemagglutinin. Data were analyzed as a completely randomized design with repeated measures when applicable. A tendency for greater daily fecal score was observed for MCFA calves compared with CON. At d 42 of the study, nonesterified fatty acid concentrations were greater in CON calves compared with MCFA. At 42 and 56 d of age, anti-OVA IgG1 concentrations for CON and MCFA calves were greater than prevaccination samples. This study suggests that feeding MCFA to calves affects the energy status of calves around weaning and vaccinating dairy calves with ovalbumin combined with an aluminum hydroxide adjuvant is an effective way to evaluate the adaptive immune responses.
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