Seasonal effects on multiparous dairy cow behavior in early lactation

Controlled studies have shown that heat stress abatement positively influences health, productivity, behavior, and reproductive performance of dairy cows during all stages of the lactation cycle. Based on previous findings, the present study focused on a better understanding of how seasonal changes affect the behavior of multiparous lactating dairy cows kept in typical free-stall housing with the objective to aid in the management of lactating cows exposed to variable environmental conditions. Automated monitoring devices (Nedap, the Netherlands) were used to assess behavioral activity of mature Holstein dairy cows during the "hot season" (HS; n = 19; July, August, and September) and the "cool season" (CS; n = 15; December, January, and February) under normal management conditions. Cows received a leg tag to measure daily lying time, and number of steps and standing bouts, and a neck tag to measure eating and rumination time. All cows were housed in sand-bedded freestall barns equipped with cooling systems (soakers and fans). Behavior, milk production and milk components were recorded for the first 9 wk of lactation after calving. Average temperature-humidity index (THI) was 78.2 ± 0.4 (± standard error) in the HS and 54.4 ± 0.2 in the CS. Fat-corrected milk yield was greater in the CS compared with HS during the first 5 wk of lactation. Milk protein percentage was lower in CS during the first 2 wk of lactation. In contrast with HS, milk fat percentage was greater in the CS. Compared with CS, overall, during HS cows spent less time eating, lying down, and tended to spend less time ruminating. In addition, exposure to high THI resulted in increases in standing bouts, and overall standing time in HS relative to CS. No differences in number of steps were observed between HS and CS. In summary, exposure to high THI during lactation seems to negatively affect the behavior and consequently the daily time budget of lactating Holstein cows, even under housing conditions with active cooling. A better understanding on how different seasons affect the daily time budget of lactating dairy cows may contribute to the development of more effective management strategies to decrease the negative effects of heat exposure.

Supplementing a Bacillus-based direct-fed microbial improves feed efficiency in lactating dairy cows

This experiment was conducted to evaluate the effects of feeding a Bacillus-based direct-fed microbial (DFM) on performance and nutrient digestibility of lactating dairy cows. Seventy-six lactating (42 ± 6 days in milk [DIM]) Holstein-Friesian primiparous and multiparous cows were enrolled to a 16-wk experiment. Cows were blocked by lactation number and DIM and within blocks, assigned to 1 of the 2 treatments: 1) basal partial-mixed ration (PMR) without DFM addition (n = 38; CON) or 2) basal PMR with the addition of 3 g/head/d of a DFM containing B. licheniformis 809 and B. subtilis 810 (n = 38; BOVACILLUS, Chr. Hansen A/S, Hørsholm, Denmark; DFM). The DFM was mixed in a protein-based pellet, whereas the CON group was fed the same pellet without DFM (0.6 kg/cow/d). The PMR contained (dry matter [DM] basis) 50% of forage and 48% of a concentrate feed based on corn meal, soybean meal, wheat meal, wheat middlings, and a mineral-vitamin premix, with the remaining part of the diet being represented by the pellet used as a carrier for the treatments (CON and DFM). Dry matter intake (DMI), milk yield, and production efficiency were recorded daily, whereas milk protein and fat concentrations were recorded using electronic milk meters. An additional milk sample was collected every second week of the study for milk composition. On week 15 of the study, fecal samples were collected from each cow for apparent nutrient digestibility calculation. All data were analyzed using the MIXED procedure of SAS (version 9.4; SAS Inst. Inc., Cary, NC). No treatment effects were observed on cow final body weight, daily DMI, milk yield, energy-corrected milk (ECM), ECM efficiency, milk composition (yield or content), and somatic cell count (SCC) (P ≥ 0.12). However, cows fed DFM had a greater feed and N efficiency (P ≤ 0.03) compared to cows fed CON. Moreover, DM digestibility tended to be greater for DFM-fed cows when compared to CON (P = 0.10), whereas no further nutrient digestibility differences were observed (P ≥ 0.24). In summary, supplementing a DFM containing Bacillus licheniformis and B. subtilis benefited feed efficiency of lactating dairy cows fed a PMR, while also tending to improve the digestibility of DM.

Application of Optical Quality Control Technologies in the Dairy Industry: An Overview

Sustainable development of the agricultural industry, in particular, the production of milk and feed for farm animals, requires accurate, fast, and non-invasive diagnostic tools. Currently, there is a rapid development of a number of analytical methods and approaches that meet these requirements. Infrared spectrometry in the near and mid-IR range is especially widespread. Progress has been made not only in the physical methods of carrying out measurements, but significant advances have also been achieved in the development of mathematical processing of the received signals. This review is devoted to the comparison of modern methods and devices used to control the quality of milk and feed for farm animals.

Genomewide Association Analyses of Lactation Persistency and Milk Production Traits in Holstein Cattle Based on Imputed Whole-Genome Sequence Data

Lactation persistency and milk production are among the most economically important traits in the dairy industry. In this study, we explored the association of over 6.1 million imputed whole-genome sequence variants with lactation persistency (LP), milk yield (MILK), fat yield (FAT), fat percentage (FAT%), protein yield (PROT), and protein percentage (PROT%) in North American Holstein cattle. We identified 49, 3991, 2607, 4459, 805, and 5519 SNPs significantly associated with LP, MILK, FAT, FAT%, PROT, and PROT%, respectively. Various known associations were confirmed while several novel candidate genes were also revealed, including ARHGAP35, NPAS1, TMEM160, ZC3H4, SAE1, ZMIZ1, PPIF, LDB2, ABI3, SERPINB6, and SERPINB9 for LP; NIM1K, ZNF131, GABRG1, GABRA2, DCHS1, and SPIDR for MILK; NR6A1, OLFML2A, EXT2, POLD1, GOT1, and ETV6 for FAT; DPP6, LRRC26, and the KCN gene family for FAT%; CDC14A, RTCA, HSTN, and ODAM for PROT; and HERC3, HERC5, LALBA, CCL28, and NEURL1 for PROT%. Most of these genes are involved in relevant gene ontology (GO) terms such as fatty acid homeostasis, transporter regulator activity, response to progesterone and estradiol, response to steroid hormones, and lactation. The significant genomic regions found contribute to a better understanding of the molecular mechanisms related to LP and milk production in North American Holstein cattle.

Evaluation of MEMS NIR Spectrometers for On-Farm Analysis of Raw Milk Composition

Today, measurement of raw milk quality and composition relies on Fourier transform infrared spectroscopy to monitor and improve dairy production and cow health. However, these laboratory analyzers are bulky, expensive and can only be used by experts. Moreover, the sample logistics and data transfer delay the information on product quality, and the measures taken to optimize the care and feeding of the cattle render them less suitable for real-time monitoring. An on-farm spectrometer with compact size and affordable cost could bring a solution for this discrepancy. This paper evaluates the performance of microelectromechanical system (MEMS)-based near-infrared (NIR) spectrometers as on-farm milk analyzers. These spectrometers use Fabry–Pérot interferometers for wavelength tuning, giving them the advantage of very compact size and affordable price. This study discusses the ability of MEMS spectrometers to reach the accuracy limits set by the International Committee for Animal Recording (ICAR) for at-line analyzers of the milk content regarding fat, protein and lactose. According to the achieved results, the transmission measurements with the NIRONE 2.5 spectrometer perform best, with an acceptable root mean squared error of prediction (RMSEP = 0.21% w/w) for the measurement of milk fat and excellent performance (RMSEP ≤ 0.11% w/w) for protein and lactose. In addition, the transmission measurements using the NIRONE 2.0 module give similar results for fat and lactose (RMSEP of 0.21 and 0.10% w/w respectively), while the prediction of protein is slightly deteriorated (RMSEP = 0.15% w/w). These results show that the MEMS spectrometers can reach sufficient prediction accuracy compared to ICAR standard values for at-line and in-line fat, protein and lactose prediction.

Laser Fluorescence and Extinction Methods for Measuring the Flow and Composition of Milk in a Milking Machine

Automation of milking systems is linked to accurate measurement of fluctuations in milk flow during milking. To assess the fluctuations of the milk flow, the formation and movement of milk portions in the milking machine-milk pipeline system was studied. By considering the movement of a milk plug along the milk pipeline, a hydraulic model of the formation of a critical volume of milk in the milking machine manifold was compiled. In practice, the most expedient way of determining milk flow parameters may be to measure the laser fluorescent and extinction responses of moving air-milk mixture. We have implemented a new laser sensing method for measuring the flow rate and composition of milk on the basis of counting the optical response pulses received from moving dispersed components by a CCD array or a randomized fiber optic bundle. Using the developed laser sensors, the theoretical model of milk flow was tested.

Dry Period Heat Stress Impacts Mammary Protein Metabolism in the Subsequent Lactation

Simple Summary

Heat stress during the dry period of dairy cows reduces milk yield in the following lactation. Factors such as altered mammary metabolism could impact yields and alter milk composition, including milk protein. We sought to determine if exposure to dry period heat stress would influence mammary milk protein metabolism during the subsequent lactation. Objectives were to first determine the impact of dry period heat stress on milk protein yields and secondly characterize the amino acid and protein profiles in the mammary tissue, milk, and blood to elucidate potential carry-over impact of dry period heat stress on systems that participate directly in milk protein metabolism (i.e., mTOR). We found that heat stress during the dry period reduces milk yield, protein content, and protein yield in the subsequent lactation. The plasma amino acid profile and mammary amino acid transporters are altered in dry period heat-stressed cows, and mammary mTOR signaling proteins are differentially expressed as well. It appears that dry period heat stress impacts mammary metabolism with consequences on milk yield and protein content. The continuous production of high-quality and -quantity milk is vital as a sustainable source of protein in the face of rising global temperatures.

Abstract

Dry period heat stress impairs subsequent milk production, but its impact on milk protein content and yield is inconsistent. We hypothesize that dairy cow exposure to dry period heat stress will reduce milk protein synthesis in the next lactation, potentially through modified amino acid (AA) transport and compromised mTOR signaling in the mammary gland. Cows were enrolled into heat-stressed (dry-HT, n = 12) or cooled (dry-CL, n = 12) treatments for a 46-day dry period then cooled after calving. Milk yield and composition and dry matter intake were recorded, and milk, blood, and mammary tissue samples were collected at 14, 42, and 84 days in milk (DIM) to determine free AA concentrations, milk protein fractions, and mammary AA transporter and mTOR pathway gene and protein expression. Dry matter intake did not significantly differ between treatments pre- or postpartum. Compared with dry-CL cows, milk yield was decreased (32.3 vs. 37.7 ± 1.6 kg/day) and milk protein yield and content were reduced in dry-HT cows by 0.18 kg/day and 0.1%. Further, dry-HT cows had higher plasma concentrations of glutamic acid, phenylalanine, and taurine. Gene expression of key AA transporters was upregulated at 14 and 42 DIM in dry-HT cows. Despite minor changes in mTOR pathway gene expression, the protein 4E-BP1 was upregulated in dry-HT cows at 42 DIM whereas Akt and p70 S6K1 were downregulated. These results indicate major mammary metabolic adaptations during lactation after prior exposure to dry period heat stress.

New Technology Tools and Life Cycle Analysis (LCA) Applied to a Sustainable Livestock Production

Agriculture 4.0, a combination of mechanical innovation and information and communication technologies (ICT) using precision farming, omics technologies and advanced waste treatment techniques, can be used to enhance the biological potential of animal and crop productions and reduce livestock gaseous emissions. In addition to animal proteins being excellent nutritional ingredients for the human diet, there is a growing concern regarding the amount of energy spent converting vegetable crops into animal protein and the relevant environmental impacts. Using the value chain analysis derived from the neoclassic production theory extended to industrial processing and the market, the hypothesis to be tested concerns the sustainability and convenience of different protein sources. The methodology implies the use of life cycle analysis (LCA) to evaluate the efficiency of different livestock diet ingredients. The use of feeding products depend upon various factors, including cost reduction, consumer acceptance, incumbent industry response, civil society support, policy consensus, lower depletion of natural resources, improved sustainable agri-food supply chain and LCA. EU policy makers should be aware of these changes in livestock and market chains and act proactively to encourage the use of alternative animal proteins.

A Systematic Review on Commercially Available and Validated Sensor Technologies for Welfare Assessment of Dairy Cattle

In order to base welfare assessment of dairy cattle on real-time measurement, integration of valid and reliable precision livestock farming (PLF) technologies is needed. The aim of this study was to provide a systematic overview of externally validated and commercially available PLF technologies, which could be used for sensor-based welfare assessment in dairy cattle. Following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, a systematic literature review was conducted to identify externally validated sensor technologies. Out of 1,111 publications initially extracted from databases, only 42 studies describing 30 tools (including prototypes) met requirements for external validation. Moreover, through market search, 129 different retailed technologies with application for animal-based welfare assessment were identified. In total, only 18 currently retailed sensors have been externally validated (14%). The highest validation rate was found for systems based on accelerometers (30% of tools available on the market have validation records), while the lower rates were obtained for cameras (10%), load cells (8%), miscellaneous milk sensors (8%), and boluses (7%). Validated traits concerned animal activity, feeding and drinking behavior, physical condition, and health of animals. The majority of tools were validated on adult cows. Non-active behavior (lying and standing) and rumination were the most often validated for the high performance. Regarding active behavior (e.g., walking), lower performance of tools was reported. Also, tools used for physical condition (e.g., body condition scoring) and health evaluation (e.g., mastitis detection) were classified in lower performance group. The precision and accuracy of feeding and drinking assessment varied depending on measured trait and used sensor. Regarding relevance for animal-based welfare assessment, several validated technologies had application for good health (e.g., milk quality sensors) and good feeding (e.g., load cells, accelerometers). Accelerometers-based systems have also practical relevance to assess good housing. However, currently available PLF technologies have low potential to assess appropriate behavior of dairy cows. To increase actors' trust toward the PLF technology and prompt sensor-based welfare assessment, validation studies, especially in commercial herds, are needed. Future research should concentrate on developing and validating PLF technologies dedicated to the assessment of appropriate behavior and tools dedicated to monitoring the health and welfare in calves and heifers.

Identifying gram-negative and gram-positive clinical mastitis using daily milk component and behavioral sensor data

Opportunities exist for automated animal health monitoring and early detection of diseases such as mastitis with greater on-farm adoption of precision technologies. Our objective was to evaluate time series changes in individual milk component or behavioral variables for all clinical mastitis (CM) cases (ACM), for CM caused by gram-negative (GN) or gram-positive (GP) pathogens, or CM cases in which no pathogen was isolated (NPI). We developed algorithms using a combination of milk and activity parameters for predicting each of these infection types. Milk and activity data were collated for the 14 d preceding a CM event (n = 170) and for controls (n = 166) matched for breed, parity, and days in milk. Explanatory variables in the univariate and multiple regression models were the slope change in milk (milk yield, conductivity, somatic cell count, lactose percentage, protein percentage, and fat percentage) and activity parameters (steps, lying time, lying bout duration, and number of lying bouts) over 7 d. Slopes were estimated using linear regression between d -7 and -5, d -7 and -4, d -7 and -3, d -7 and -2, and d -7 and -1 relative to CM detection for all parameters. Univariate analyses determined significant slope ranges for explanatory variables against the 4 responses: ACM, GN, GP, and NPI. Next, all slope ranges were offered into the multivariate models for the same 4 responses using 3 baselines: d -10, -7, and -3 relative to CM detection. In the univariate analysis, no explanatory variables were significant indicators of ACM, whereas at least 1 parameter was significant for each of GN, GP, and NPI models. Superior sensitivity (Se) and specificity (Sp) estimates were observed for the best GP (Se = 82%, Sp = 87%) and NPI (Se = 80%, Sp = 94%) multiple regression models compared with the best ACM (Se = 73%, Sp = 75%) and GN (Se = 71%, Sp = 74%) models. Sensitivity for the GN model was greater at the baseline closest to the day of CM detection (d -3), whereas the opposite was observed for the GP and NPI model as Se was maximized at the d -10 baseline. Based on this screening of relationships, milk and activity sensor data could be used in CM detection systems.

The effect of J5 bacterins on clinical, behavioral, and antibody response following an Escherichia coli intramammary challenge in dairy cows at peak lactation

Vaccination against coliform mastitis has become part of mastitis control programs in the past 3 decades, as a means of reducing the severity of clinical mastitis. Our study objective was to evaluate the effect of 2 commercially available vaccines on clinical, behavioral, and antibody response following Escherichia coli intramammary challenge in cows near peak lactation. Cows (n = 12 per group) were vaccinated with vaccine 1 (V1) or vaccine 2 (V2) at dry-off, 21 d pre-calving, and 14 d post-calving. Twelve cows served as unvaccinated controls (CTL). Cows were challenged with E. coli in a rear quarter at approximately 100 d in milk. Milk samples were collected pre- and post-challenge to enumerate E. coli and determine somatic cell count. Serum was collected before each vaccination and at d 0, 1, 2, 3, 6, 30, and 60 relative to challenge, to study antibody response. Milk IgA and tumor necrosis factor-α concentrations were determined in whey. Vaginal temperature, cow activity, and milk yield and components were monitored post-challenge. Bacterial count, somatic cell score, milk yield and component decline, vaginal temperature, activity measures, and antibody and cytokine response were analyzed for treatment differences. The effects of parity, breed, and a repeated measure of time were also tested. Seven cows had to be removed from the study post-challenge for antibiotic treatment (CTL and V1, n = 3 each; V2, n = 1), 2 of which were euthanized (both CTL). Vaccinated cows exhibited fever (vaginal temperature ≥39.4°C) 3 h earlier than CTL cows, but we found no differences between treatments for bacterial count, somatic cell score, or milk yield reduction. Vaccinated cows spent more time lying per rest bout 2 d post-challenge, but total daily lying time was not different from CTL cows during the 7 d post-challenge. The vaccines differed in antibody response: V1 cows had greater serum IgG₁ and IgG₂ post-challenge. A parity effect was also evident: primiparous cows had lower bacterial counts, somatic cell score and a smaller milk yield decline than multiparous cows, but also had lower antibody production. Immunization with either J5 bacterin did not reduce clinical signs of mastitis in cows challenged at 100 d in milk, demonstrating that the effects of J5 vaccination had diminished at peak lactation.

Symposium review: Challenges and opportunities for evaluating and using the genetic potential of dairy cattle in the new era of sensor data from automation

Sensor data from automation are becoming available on an increasingly large scale, and associated research is slowly starting to appear. This new era of sensor data from automation leads to many challenges but also new opportunities for assessing and maximizing the genetic potential of dairy cattle. The first challenge is data quality, because all uses of sensor data require careful data quality validation, potentially using external references. The second issue is data accessibility. Indeed, sensor data generated from automation are often designed to be available on-farm in a given system. However, to make these data useful-for genetic improvement for example-the data must also be made available off-farm. By nature, sensor data often are very complex and diverse; therefore, a data consolidation and integration layer is required. Moreover, the traits we want to select have to be defined precisely when generated from these raw data. This approach is obviously also beneficial to limit the challenge of extremely high data volumes generated by sensors. An additional challenge is that sensors will always be deployed in a context of herd management; therefore, any efforts to make them useful should focus on both breeding and management. However, this challenge also leads to opportunities to use genomic predictions based on these novel data for breeding and management. Access to relevant phenotypes is crucial for every genomic evaluation system. The automatic generation of training data, on both the phenotypic and genomic levels, is a major opportunity to access novel, precise, continuously updated, and relevant data. If the challenges of bidirectional data transfer between farms and external databases can be solved, new opportunities for continuous genomic evaluations integrating genotypes and the most current local phenotypes can be expected to appear. Novel concepts such as federated learning may help to limit exchange of raw data and, therefore, data ownership issues, which is another important element limiting access to sensor data. Accurate genome-guided decision-making and genome-guided management of dairy cattle should be the ultimate way to add value to sensor data from automation. This could also be the major driving force to improve the cost-benefit relationship for sensor-based technologies, which is currently one of the major obstacles for large-scale use of available technologies.

Evaluation of milk components as diagnostic indicators for rumen indigestion in dairy cows

OBJECTIVE To identify milk component alterations that might be useful for detecting cows with rumen indigestion. DESIGN Prospective case-control study. ANIMALS 23 Holstein cows with rumen indigestion (cases) and 33 healthy cohorts (controls) from 1 herd. PROCEDURES Cases were defined as cows between 30 and 300 days postpartum with a > 10% decrease in milk yield for 2 consecutive milkings or > 20% decrease in milk yield from the 10-day rolling mean during any milking, abnormally decreased rumen motility, and no other abnormalities. Each case was matched with 2 healthy cows (controls) on the basis of pen, parity, days postpartum, and mean milk yield. Some cows were controls for multiple cases. All cows underwent a physical examination and collection of a rumen fluid sample for pH measurement at study enrollment. Individual-cow milk yield and milk component data were obtained for the 16 milkings before and after study enrollment. Rumen motility and pH and milk components were compared between cases and controls. RESULTS Rumen motility for cases was decreased from that of controls. Cases had an abrupt increase in milk fat percentage and the milk fat-to-lactose ratio during the 2 milkings immediately before diagnosis of rumen indigestion. Receiver operating characteristic analyses revealed that a 10% increase in the milk fat-to-lactose ratio had the highest combined sensitivity (57%) and specificity (85%) for identifying cows with rumen indigestion. CONCLUSIONS AND CLINICAL RELEVANCE Results indicated that a positive deviation in the milk fat-to-lactose ratio might be useful for identifying cows with rumen indigestion.

Activity and milk compositional changes following experimentally induced Streptococcus uberis bovine mastitis

Milk constituents and physical activity of cows experimentally infected with Streptococcus uberis mastitis were compared with those of uninfected cows. Twelve late-lactation Holsteins cows were paired based on milk production and parity. One cow in each pair was experimentally infected in the right front mammary gland with Strep. uberis. The remaining cow in each pair served as an uninfected control. Real-time analyses of milk constituents provided fat, protein, and lactose percentages at each milking. Pedometers were placed on the left front leg of all cows and activity was measured. Intramammary infections with Strep. uberis reduced milk yield in experimental cows by approximately 1.6kg/d in the first week after challenge compared with control cows. Lactose percentage in milk was reduced on d 3, 4, 5, and 6 after challenge in treatment cows compared with controls. Percentages of fat and protein in milk did not differ between infected and uninfected cows the week after infections were induced. Total steps per day were reduced and minutes resting per day were increased the week after experimental challenge in infected cows compared with control cows. The number of resting bouts did not differ between infected and uninfected cows. Changes in percentage of lactose in milk and animal activity caused by experimentally induced Strep. uberis mastitis were detected by the automated milk analyzer and pedometer systems.