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Journal of Dairy Science, Volume 104, pp 11317-11332; https://doi.org/10.3168/jds.2020-19097
Current sensor systems are used to detect cows with clinical mastitis. Although, the systems perform well enough to not negatively affect the adoption of automatic milking systems, the performance is far from perfect. An important advantage of sensor systems is the availability of multiple measurements per day. By clearly defining the need for detection of subclinical mastitis (SCM) and clinical mastitis (CM) from the farmers' management perspective, detection and management of SCM and CM may be improved. Sensor systems may also be used for other aspects of mastitis management. In this paper we have defined 4 mastitis situations that could be managed with the support of sensor systems. Because of differences in the associated management and the epidemiology of these specific mastitis situations, the required demands for performance of the sensor systems do differ. The 4 defined mastitis situations with the requirements of performance are the following: (1) Cows with severe CM needing immediate attention. Sensor systems should have a very high sensitivity (>95% and preferably close to 100%) and specificity (>99%) within a narrow time window (maximum 12 h) to ensure that close to all cows with true cases of severe CM are detected quickly. Although never studied, it is expected that because of the effects of severe CM, such a high detection performance is feasible. (2) Cows with mastitis that do not need immediate attention. Although these cows have a risk of progressing into severe CM or chronic mastitis, they should get the chance to cure spontaneously under close monitoring. Sensor alerts should have a reasonable sensitivity (>80%) and a high specificity (>99.5%). The time window may be around 7 d. (3) Cows needing attention at drying off. For selective dry cow treatment, the absence or presence of an intramammary infection at dry-off needs to be known. To avoid both false-positive and false-negative alerts, sensitivity and specificity can be equally high (>95%). (4) Herd-level udder health. By combining sensor readings from all cows in the herd, novel herd-level key performance indicators can be developed to monitor udder health status and development over time and raise alerts at significant deviances from predefined thresholds; sensitivity should be reasonably high, >80%, and because of the costs for further analysis of false-positive alerts, the specificity should be >99%. The development and validation of sensor-based algorithms specifically for these 4 mastitis situations will encourage situation-specific farmer interventions and operational udder health management.
Journal of Dairy Science, Volume 104, pp 10566-10575; https://doi.org/10.3168/jds.2021-20388
Journal of Dairy Science, Volume 104, pp 10678-10698; https://doi.org/10.3168/jds.2020-20111
Journal of Dairy Science, Volume 104, pp 11018-11034; https://doi.org/10.3168/jds.2020-19839
Studies performed on individual research farms have reported that dairy cattle developing sole hemorrhages or sole ulcers in peak to mid lactation spent more time standing during the weeks around calving. The aim of this prospective observational longitudinal study was to evaluate whether this relationship is evident in commercial dairy herds. A convenience sample of 8 herds were visited every other week, and animals without previous severe horn lesions and deemed sound at 4 to 8 wk before calving were enrolled. Standing behavior was measured with data loggers attached to a rear leg, and standing time and duration of the longest standing bout were determined for each cow. Standing behavior was summarized into 3 periods: before (d -14 to -2), around (d -1 to 1), and after (d 2 to 14) calving. Average daily standing time and average daily longest standing bout were determined for each cow and period. Average daily standing time was normally distributed, with a mean ± standard deviation of 12.1 ± 1.6, 14.4 ± 2.2, and 13.8 ± 1.7 h/d for the 3 periods, respectively. Average daily longest standing bout was right skewed with a median of 3.6 h/d [interquartile range (IQR): 3.0 to 4.3; range: 1.7 to 12.1], 3.9 h/d (IQR: 3.1 to 4.8; range: 1.3 to 11.5), and 3.7 (IQR: 3.2 to 4.4; range: 1.5 to 11.7) h/d before, around, and after calving, respectively. Hoof trimming was performed 8 to 12 wk postpartum; hoof lesion data were summarized per cow, and the most serious injury of each type of lesion was noted. Sole hemorrhages or sole ulcers were found in 25 of 256 cows. Mixed-effect logistic regression models with herd as random effect were used to analyze the risk of developing sole hemorrhages and sole ulcers, using animals without hoof lesions as reference category. Separate models were fitted for the 2 standing behaviors, and for the periods before, around, and after calving. Change in standing behavior from before to after calving was also analyzed. Body condition score at calving, body condition score loss in early lactation, milk yield, parity, and days in milk at trimming were included as covariates. In this study, no evidence for an association was found between sole hemorrhages and sole ulcers and standing behavior before or around calving. Longer standing time and longer standing bouts after calving were associated with increased odds of developing sole hemorrhages and sole ulcers, as was an increase in standing bout duration from before to after calving. Animals with sole horn or white line lesions had higher unconditional sample odds of becoming lame (odds ratio = 2.5) and severely lame (odds ratio = 11.7) after calving, compared with animals with no registered lesions at trimming. Multiparous animals had higher lameness incidence, both before and after calving. Avoiding practices that exacerbate increases in standing time and standing bout duration in early lactation may reduce the incidence of sole hemorrhages and sole ulcers.
Journal of Dairy Science, Volume 104, pp 10896-10904; https://doi.org/10.3168/jds.2021-20332
Dairy bull fertility is traditionally evaluated using semen production and quality traits; however, these attributes explain only part of the differences observed in fertility among bulls. Alternatively, bull fertility can be directly evaluated using cow field data. The main objective of this study was to investigate bull fertility in the Italian Brown Swiss dairy cattle population using confirmed pregnancy records. The data set included a total of 397,926 breeding records from 1,228 bulls and 129,858 lactating cows between first and fifth lactation from 2000 to 2019. We first evaluated cow pregnancy success, including factors related to the bull under evaluation, such as bull age, bull inbreeding, and AI organization, and factors associated with the cow that receives the dose of semen, including herd-year-season, cow age, parity, and milk yield. We then estimated sire conception rate using only factors related to the bull. Model predictive ability was evaluated using 10-fold cross-validation with 10 replicates. Interestingly, our analyses revealed that there is a substantial variation in conception rate among Brown Swiss bulls, with more than 20% conception rate difference between high-fertility and low-fertility bulls. We also showed that the prediction of bull fertility is feasible as our cross-validation analyses achieved predictive correlations equal to 0.30 for sire conception rate. Improving reproduction performance is one of the major challenges of the dairy industry worldwide, and for this, it is essential to have accurate predictions of service sire fertility. This study represents the foundation for the development of novel tools that will allow dairy producers, breeders, and artificial insemination companies to make enhanced management and selection decisions on Brown Swiss male fertility.
Journal of Dairy Science, Volume 104, pp 10950-10969; https://doi.org/10.3168/jds.2020-20086
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20748
JDS Communications; https://doi.org/10.3168/jdsc.2021-0123
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20618
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20460
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20523
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20588
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20726
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20906
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20446
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20437
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20590
Journal of Dairy Science, Volume 104; https://doi.org/10.3168/jds.2021-104-10-11335
Journal of Dairy Science, Volume 104; https://doi.org/10.3168/jds.2021-104-10-11334
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20401
Journal of Dairy Science, Volume 104, pp 10828-10840; https://doi.org/10.3168/jds.2021-20419
There is an increasing recognition throughout the world that many of the feeding problems of dairy herds are linked to the presence of aerobically deteriorated parts on a silo face, causing farmers to pose questions on what amount of silage should be removed daily to feed their animals. Since an adequate feed-out rate helps to prevent silage spoilage, a simple tool is needed to manage the aerobic deterioration of corn silages during feed-out. The aims of this study were to develop an unloading rate index, which we have called the mass feed-out rate (MFR), expressed in kilograms of fresh matter silage unloaded daily per square meter of silo face, to better predict the aerobic deterioration of silage and to offer management solutions to help prevent spoilage, through a survey on 97 commercial dairy farms in Italy and Brazil. Silages were sampled and analyzed for their main microbial, fermentative, and nutritional characteristics, whereas silage temperatures were measured in the core and peripheral areas of the silo working face. Moreover, a detailed questionnaire on silo management and silage utilization was administered to the farmers during each farm visit. The size and silage density of the silos presented a wide variability in the 2 countries, thus indicating that different management practices were adopted during corn harvesting, silo filling, and silage compaction. The differences between pH and temperature in the peripheral areas and in the core of the silage (dpH and dT, respectively) were tested as a single indicator to identify any aerobic deteriorated areas on the silo face, associated with the yeast and mold counts. Both indicators correctly identified aerobic deterioration in 86.6% and 93.8% of the studied silos, respectively. The lactic acid and ethanol increased as the MFR increased, whereas the starch, dT, and the yeast and mold counts decreased with increasing MFR. A daily removal rate of over 250 kg of silage/m2 markedly reduced the risk of spoilage in corn silages at a farm level in both temperate and tropical environments. The new MFR index can substitute for the commonly used linear feed-out rate as it includes the silage density and can be obtained from 1 single recording.
Journal of Dairy Science, Volume 104, pp 10970-10978; https://doi.org/10.3168/jds.2021-20382
Journal of Dairy Science, Volume 104, pp 10753-10779; https://doi.org/10.3168/jds.2020-20055
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20801
JDS Communications; https://doi.org/10.3168/jdsc.2021-0094
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20465
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20193
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20541
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20613
JDS Communications; https://doi.org/10.3168/jds.2021-0118
JDS Communications; https://doi.org/10.3168/jdsc.2021-0149
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20743
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20923
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20535
Journal of Dairy Science, Volume 104, pp 11306-11316; https://doi.org/10.3168/jds.2021-20544
Trans 10,cis-12 conjugated linoleic acid (t10,c12 CLA) is well recognized as a key CLA isomer responsible for the reduction in milk fat synthesis that leads to milk fat depression in dairy cows. Sterol regulatory element binding protein-1 (SREBP1) is a key transcription factor in bovine mammary gland coordinating transcription of the genes for fatty acid synthesis. SREBP1 activation requires the removal of insulin-induced gene-1 (Insig1) that serves as a repressor of SREBP1 in the endoplasmic reticulum (ER). We hypothesized that t10,c12 CLA reduced SREBP1 activation by delaying Insig1 degradation. In the present study, we used undifferentiated bovine mammary epithelial cells (MAC-T cells) and treated them with t10,c12 CLA for 6 h. We found that SREBP1 protein expression declined over 56% when cells were treated with 60 µM or greater concentration of t10,c12 CLA. Such inhibitory effects were also observed in the mRNA expression of SREBP1-regulated genes including SREBP1, fatty acid synthetase, stearoyl-CoA desaturase, and Insig1. Compared with no CLA group, 60 µM or higher concentration of t10,c12 CLA increased Insig1 protein expression over 2-fold in cells transfected with FLAG-tagged Insig1. This stimulatory effect was not specific to t10,c12 CLA but also other polyunsaturated fatty acids including cis-9,trans-11 CLA and linoleic acid. Oleic acid had no effect on Insig1 protein expression, whereas palmitic acid decreased Insig1 protein expression. Further investigation revealed that increased abundance of FLAG-Insig1 with t10,c12 CLA was due to the inhibition of the proteasomal degradation of Insig1. The t10,c12 CLA delayed the Insig1 decay when protein synthesis was blocked. Immunoprecipitation also confirmed that the interaction between ubiquitin-like domain-containing protein 8 and Insig1, the key step of removing Insig1 from ER and freeing SREBP1 for proteolytic processing, was inhibited by t10,c12 CLA, but not palmitic acid. These findings suggested that t10,c12 CLA played a role in regulating SREBP1 activation by reducing proteasomal degradation of Insig1. We concluded that stabilized Insig1 retained SREBP1 in the ER from activation, thus reducing lipogenic gene transcription.
Journal of Dairy Science, Volume 104, pp 11082-11090; https://doi.org/10.3168/jds.2020-19995
Journal of Dairy Science, Volume 104, pp 10528-10539; https://doi.org/10.3168/jds.2021-20352
Journal of Dairy Science, Volume 104, pp 10449-10461; https://doi.org/10.3168/jds.2021-20311
Sensor technologies for mastitis detection have resulted in the collection and availability of a large amount of data. As a result, scientific publications reporting mastitis detection research have become less driven by approaches based on biological assumptions and more by data-driven modeling. Most of these approaches try to predict mastitis events from (combinations of) raw sensor data to which a wide variety of methods are applied originating from machine learning and classical statistical approaches. However, an even wider variety in terminologies is used by researchers for methods that are similar in nature. This makes it difficult for readers from other disciplines to understand the specific methods that are used and how these differ from each other. The aim of this paper was to provide a framework (filtering, transformation, and classification) for describing the different methods applied in sensor data-based clinical mastitis detection research and use this framework to review and categorize the approaches and underlying methods described in the scientific literature on mastitis detection. We identified 40 scientific publications between 1992 and 2020 that applied methods to detect clinical mastitis from sensor data. Based on these publications, we developed and used the framework and categorized these scientific publications into the 2 data processing techniques of filtering and transformation. These data processing techniques make raw data more amendable to be used for the third step in our framework, that of classification, which is used to distinguish between healthy and nonhealthy (mastitis) cows. Most publications (n = 34) used filtering or transformation, or a combination of these 2, for data processing before classification, whereas the remaining publications (n = 6) classified the observations directly from raw data. Concerning classification, applying a simple threshold was the most used method (n = 19 publications). Our work identified that within approaches several different methods and terminologies for similar methods were used. Not all publications provided a clear description of the method used, and therefore it seemed that different methods were used between publications, whereas in fact just a different terminology was used, or the other way around. This paper is intended to serve as a reference for people from various research disciplines who need to collaborate and communicate efficiently about the topic of sensor-based mastitis detection and the methods used in this context. The framework used in this paper can support future research to correctly classify approaches and methods, which can improve the understanding of scientific publication. We encourage future research on sensor-based animal disease detection, including that of mastitis detection, to use a more coherent terminology for methods, and clearly state which technique (e.g., filtering) and approach (e.g., moving average) are used. This paper, therefore, can serve as a starting point and further stimulates the interdisciplinary cooperation in sensor-based mastitis research.
Journal of Dairy Science, Volume 104, pp 10654-10668; https://doi.org/10.3168/jds.2021-20299
Correlating volatile compounds with the sensory attributes of whole milk powder (WMP) is fundamental for appreciating the effect of lipid oxidation (LO) on sensory perception. LO compounds can adversely affect the sensory perception of WMP by imparting rancid, metallic, and painty notes. Whole milk powders derived from milk produced by cows maintained on a pasture diet (grass and grass-clover mix) versus a nonpasture diet [total mixed ration (TMR); concentrates and silage] were stored at room temperature 21°C (ambient storage) and 37°C (accelerated storage) and analyzed for volatile compounds and sensory attributes every 2 mo for a total of 6 mo. Thirteen volatile compounds originating from LO were chosen to track the volatile profile of the WMP during storage. Color, composition, total fatty acid, and free fatty acid profiling were also carried out. Significant variations in the concentrations of 14 fatty acids were observed in WMP based on diet. Concentrations of free fatty acids increased in all sample types during storage. Similar trends in sensory attributes were observed with an increase in painty attributes, corresponding to an increase in hexanal. Buttery/toffee attributes were found to be more closely correlated with TMR WMP. Those WMP derived from pasture diets were found to be more susceptible to LO from a volatile perspective, particularly in relation to aldehyde development, which is likely due to increased concentrations of conjugated linoleic acid and α-linolenic acid found in these samples.
Journal of Dairy Science, Volume 104, pp 11291-11305; https://doi.org/10.3168/jds.2021-20527
Journal of Dairy Science, Volume 104, pp 11226-11241; https://doi.org/10.3168/jds.2021-20319
In vitro methods of assessing bull semen quality in artificial insemination (AI) centers are unable to consistently detect individuals of lower fertility, and attempts to reliably predict bull fertility are still ongoing. This highlights the need to identify robust biomarkers that can be readily measured in a practical setting and used to improve current predictions of bull fertility. In this study, we comprehensively analyzed a range of functional, morphological, and intracellular attributes in cryopreserved spermatozoa from a selected cohort of Holstein Friesian AI bulls classified as having either high or low fertility (n = 10 of each fertility phenotype; difference of 11.4% in adjusted pregnancy rate between groups). Here, spermatozoa were assessed for motility and kinematic parameters, morphology, acrosome integrity, plasma membrane lipid packing, viability (or membrane integrity), superoxide production, and DNA integrity. In addition, spermatozoa were used for in vitro fertilization to evaluate their capacity for fertilization and successful embryo development. The information collected from these assessments was then used to phenotypically profile the 2 groups of bulls of divergent fertility status as well as to develop a model to predict bull fertility. According to the results, acrosome integrity and viability were the only sperm attributes that were significantly different between high- and low-fertility bulls. Interestingly, although spermatozoa from low-fertility bulls, on average, had reduced viability and acrosome integrity, this response varied considerably from bull to bull. Principal component analysis revealed a sperm phenotypic profile that represented a high proportion of ejaculates from low-fertility bulls. This was constructed based on the collective influence of several sperm attributes, including the presence of cytoplasmic droplets and superoxide production. Finally, using the combined results as a basis for modeling, we developed a linear model that was able to explain 47% of the variation in bull field fertility in addition to a logistic predictive model that had a 90% chance of distinguishing between fertility groups. Taken together, we conclude that viability and acrosome integrity could serve as fertility biomarkers in the field and, when used alongside other sperm attributes, may be useful in detecting low-fertility bulls. However, the variable nature of low-fertility bulls suggests that additional, in-depth characterization of spermatozoa at a molecular level is required to further understand the etiology of low fertility in dairy bulls.
Journal of Dairy Science, Volume 104; https://doi.org/10.3168/jds.2021-104-10-11333
Journal of Dairy Science, Volume 104, pp 10594-10608; https://doi.org/10.3168/jds.2021-20450
JDS Communications; https://doi.org/10.3168/jdsc.2021-0112
JDS Communications; https://doi.org/10.3168/jdsc.2021-0133
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20674
Journal of Dairy Science; https://doi.org/10.3168/jds.2020-20012
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20875
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20405
Journal of Dairy Science; https://doi.org/10.3168/jds.2021-20685