Individual Cow Milk Research Articles

Supplementation of isoacids to lactating dairy cows fed low or high forage diets: Effects on performance, digestibility, and milk fatty acid profile

Our objective was to determine the effects of isoacids (ISO) on the lactation performance, digestibility, and milk fatty acid (FA) profile of Holstein cows fed 2 forage NDF levels (FL). The study was 10-wk long (including 2-wk for covariate) utilizing a randomized complete block design. Sixty-four mid-lactating Holstein cows [662 ± 71 kg BW, 119 ± 51 DIM, 2 ± 0.9 parity] were blocked by parity, DIM, and prior milk yield (MY) for multiparous cows or genetic merit for primiparous cows, and randomly assigned to 1 of the 4 diets (n = 16). Diets were arranged as a 2 × 2 factorial, with 2 FL containing 21 (HF) and 17% forage NDF (LF) without (WIA) or with ISO supplementation (IA, 7.85 mmol/kg DM and 3.44 mmol/kg DM for isobutyrate and 2-methylbutyrate, respectively). Diets were balanced for similar NEL (1.58 Mcal/kg DM), CP (16.0%) and total NDF (27.2%). Feed intake and MY were recorded daily. Nutrient digestibility for each cow was determined using indigestible NDF as a marker, and fecal samples were collected at 8-time points (4 h intervals between samples). Individual cow milk samples composited over a 10-wk period were analyzed using gas chromatography for FA profile. The statistical model included FL, ISO, and FL × ISO as fixed effects and block as a random effect (lme4 in R). The ISO did not affect DMI (P = 0.13), while LF had greater DMI than HF diets (27.8 vs. 26.0 kg/d; P < 0.01). However, ISO increased MY (34.7 vs. 37.2 kg/d; P < 0.01) and ECM (41.9 vs. 39.0 kg/d; P < 0.01) by 7% in HF but not in the LF diet, suggesting FL × ISO interaction (P = 0.04). Interestingly, ISO increased ADG (0.4 kg/d) but decreased MUN by 9% only in LF diet as indicated by FL × ISO interaction (P < 0.01). Additionally, ISO increased DM, OM, NDF, and CP digestibility by 10-24% in HF (P < 0.01), but not in LF (FL × ISO; P > 0.05). As expected, ISO increased milk odd chain FA profiles in the IA groups irrespective of FL, e.g., the IA had greater C15:0 (1.87 vs. 1.54 g/100g FA; P = 0.03) and a tendency to be greater C17:0 levels (0.86 vs. 0.76 g/100g FA; P = 0.05) compared with WIA groups. Overall, ISO improved MY and nutrient digestibility in the HF whereas it increased ADG and decreased MUN in LF diet. Additionally, ISO increased milk odd chain FA (C15:0 and C17:0) regardless of FL.

Effect of sward species diversity combined with a reduction in nitrogen fertilizer on the performances of spring-calving grazing dairy cows

The objective of this study was to evaluate the impact of sward diversification combined with a reduction of chemical nitrogen (N) fertilizer on the performance of spring calving grazing dairy cows within a farm systems experiment. Three farmlets were created; a monoculture of perennial ryegrass (Lolium perenne L.; PRG) fertilized with 250 kg N/ha (PRG-250N), a PRG - white clover (Trifolium repens; WC) sward fertilized with 125 kg N/ha (PRGWC-125N) and a multispecies sward (MSS) comprising of grasses, legumes and herbs also fertilized with 125 kg N/ha (MSS-125N). Each farmlet had its own herd of dairy cows on a total area of 18.7 ha divided into 20 paddocks. Each herd was comprised of pure Holstein-Friesian (HF) and HF Jersey crossbred (JFX) animals and randomly assigned through the 3 treatments. For 3 years (2021 to 2023), the performances of both swards (grass yield, botanical composition, nutritive value) and grazing animals (milk production and composition, body weight (BW) and body condition score (BCS)) were recorded. There were no significant differences in pasture production or sward nutritive value between sward systems and grazing season length was also similar (264 d). On average over the 3 years, PRGWC-125N contained 150 g/kg DM of legumes and the MSS-125N contained 160 g/kg DM legumes, 130 g/kg DM plantain and 40 g/kg DM chicory. Both individual cow milk and fat plus protein (Milk solids; MS) yield were lower for PRG-250N (5,018 and 452 kg, respectively), intermediate for PRGWC-125N (5,139 and 463 kg, respectively) and highest for MSS-125N (5,297 and 476 kg, respectively) while milk and MS production per hectare from grazing were similar during the study period (11,523 and 1,016 kg/ha, respectively). Breed also had a significant effect with the JFX having lower milk yield but higher fat and protein concentration compared with HF. This resulted in higher MS production per kg of BW for the JFX compared with HF (0.96 and 0.87 kg MS/kg BW, respectively). The results of this study highlight the possibility for more diverse pastures to reduce chemical N fertilizer input requirements and maintain pasture productivity while increasing animal performance within pasture-based spring calving systems.

Mastitis challenges in Serbian dairy farming: a study on somatic cell counts and pathogen distribution

Mastitis is pressing concern for dairy herds due to its economic impact and potential health risks. Somatic cell counts (SCC), reflecting udder health, plays a crucial role in mastitis diagnosis. Current research explores the distribution of SCC and its correlation with various mastitis-causing pathogens in dairy farms in Serbia. The study analyzed 194 individual cow milk samples and microbiological testing was conducted under aseptic conditions to isolate and identify mastitis pathogens. The microscopic reference method was employed for assessing SCC in the milk samples. Among mastitis-associated isolates, bacteria were present in 28.87 %, yeast in 12.38 %, while in 5.15 % of milk samples, both bacteria and yeast were present. The relationship between SCC in various sample types (negative, bacteria-positive, yeast-positive, and samples with both bacteria and yeast) was noted. Importantly, samples with both bacteria and yeast presence had the highest SCC. While SCC is a valuable tool for monitoring udder health and the effectiveness of mastitis control programs, its response to specific pathogens is complex and doesn’t allow differentiation between pathogen types easily. This research highlights the challenges in distinguishing pathogen types based solely on SCC.

Produtividade vs. área

The objective of this study was to analyze the development dynamic of the Uruguayan dairy farms in the last decade integrating country, experimental and commercial databases. A large commercial dairy farm database was segmented into four groups according to productivity and growth rate (PGR) during the fiscal years 2015/2016 to 2021/2022. Productivity growth rate was calculated as: PGR = (P2122/P1516)1/6-1, where: P2122 = productivity fiscal year 2021/22, and P1516 = productivity fiscal year 2015/2016. The PGR groups were defined as: negative (NPGR; PGR &lt; 0% per year), low (LPGR; 0 ≤ PGR &lt; 3%), medium (MPGR; 3 ≤ PGR ≤ 6%) and high productivity growth rate (HPGR; PGR ≥ 6%). A mixed model was used to evaluate productivity slope heterogeneity with fiscal year as a continuous variable, PGR group as categorical and their interaction. Farms that were able to increase productivity (M and H PGR) had higher mean productivity, pasture DMI and margin over feed cost vs. the less dynamic systems (N and L PGR). Larger changes in productivity (+64 and +27% for H and M PGR, respectively) were likely primarily due to changes in stocking rate (+20 to 30%) and in individual cow milk production (+10 to 20%). Production systems that increased productivity relied on increasing stocking rate and individual milk production based on more home-grown forage consumption. However, higher PGR was linked to lower initial values of productivity, which suggests decreasing returns as the dairy farms reached higher milk yields and forage DMI.

Comparison of 3 mathematical models to estimate lactation performance in dairy cows

Milk yield dynamics and production performance reflect how dairy cows cope with their environment. To optimize farm management, time series of individual cow milk yield have been studied in the context of precision livestock farming, and many mathematical models have been proposed to translate raw data into useful information for the stakeholders of the dairy chain. To gain better insights on the topic, this study aimed at comparing 3 recent methods that allow one to estimate individual cow potential lactation performance, using daily data recorded by the automatic milking systems of 14 dairy farms (7 Holstein, 7 Italian Simmental) from Belgium, the Netherlands, and Italy. An iterative Wood model (IW), a perturbed lactation model (PLM), and a quantile regression (QR) were compared in terms of estimated total unperturbed (i.e., expected) milk production and estimated total milk loss (relative to unperturbed yield). The IW and PLM can also be used to identify perturbations of the lactation curve and were thus compared in this regard. The outcome of this study may help a given end-user in choosing the most appropriate method according to their specific requirements. If there is a specific interest in the post-peak lactation phase, IW can be the best option. If one wants to accurately describe the perturbations of the lactation curve, PLM can be the most suitable method. If there is need for a fast and easy approach on a very large dataset, QR can be the choice. Finally, as an example of application, PLM was used to analyze the effect of cow parity, calving season, and breed on their estimated lactation performance.

Comparison of Sensor-Based and Audible Detection of Milking Liner Slips during Machine Milking of Dairy Cows.

On-farm milk flow meter technology facilitates real-time assessment of individual cow milking observations and could be used to detect milking liner slips during machine milking of dairy cows. Here, we compared the accuracy of on-farm milk flow meters for detecting milking liner slips with that of audible detection and that of a portable vacuum recording system. Compared to audible detection methods, the on-farm milk flow meter facilitated the detection of milking liner slips with moderate accuracy. Using the vacuum recording system as the gold standard, the milk flow meter system failed to detect most of the liner slips, leading to poor agreement between the two devices. We conclude that the on-farm milk flow meter system tested here compared well with audible detection; however, when vacuum recordings were considered, we found significant levels of under-detection. Taken together, dairy operators may use the on-farm milk flow meter system to inform adjustments of the milking machine settings and monitor milking routine performance. However, the system is not suitable for monitoring short-duration vacuum fluctuations. Future research is warranted to optimize the sensor-based detection of milking liner slips.

Milk as an indicator of dietary imbalance.

Milk provides a readily available diagnostic fluid collected daily or more frequently on an individual animal or herd basis. Milk, as an aggregated sample in bulk tank milk (BTM) represents the status of a herd instead of a single animal. In this review, we examine the potential for milk to predict risks to efficient production, reproductive success, and health on the individual cow and herd level. For many conditions related to disorders of metabolism including hyperlipdaemia and ketonaemia, improved individual cow milk testing may allow a temporally useful detection of metabolic disorder that can target intervention. However, the extension of these tests to the BTM is made more difficult by the tight temporal clustering of disorder to early lactation and the consequent mixing of cows at even moderately different stages of lactation. Integrating herd recording demographic information with Fourier-transformed mid-infrared spectra (FT-MIR) can provide tests that are useful to identify cows with metabolic disorders. The interpretation of BTM urea and protein content provides useful indications of herd nutrition. These may provide indicators that encourage further investigations of nutritional influences on herd fertility but are unlikely to provide strong diagnostic value. The fat-to-protein ratio has a high specificity, but poor sensitivity for detection of fibre insufficiency and acidosis on an individual cow basis. Selenium, zinc, β-carotene, and vitamin E status of the herd can be determined using BTM. There appears to be increasing potential for the use of milk as a diagnostic fluid as more in-parlour tests become available for individual cows. However, the BTM appears to have under-utilised potential for herd monitoring.

Predicting methane emissions of individual grazing dairy cows from spectral analyses of their milk samples

Data on the enteric methane emissions of individual cows are useful not just in assisting management decisions and calculating herd inventories but also as inputs for animal genetic evaluations. Data generation for many animal characteristics, including enteric methane emissions, can be expensive and time consuming, so being able to extract as much information as possible from available samples or data sources is worthy of investigation. The objective of the present study was to attempt to predict individual cow methane emissions from the information contained within milk samples, specifically the spectrum of light transmittance across different wavelengths of the mid-infrared (MIR) region of the electromagnetic spectrum. A total of 93,888 individual spot measures of methane (i.e., individual samples of an animal's breath when using the GreenFeed technology) from 384 lactations on 277 grazing dairy cows were collapsed into weekly averages expressed as grams per day; each weekly average coincided with a MIR spectral analysis of a morning or evening individual cow milk sample. Associations between the spectra and enteric methane measures were performed separately using partial least squares regression or neural networks with different tuning parameters evaluated. Several alternative definitions of the enteric methane phenotype (i.e., average enteric methane in the 6 d preceding or 6 d following taking the milk sample or the average of the 6 d before and after the milk sample, all of which also included the enteric methane emitted on the day of milk sampling), the candidate model features (e.g., milk yield, milk composition, and milk MIR) as well as validation strategy (i.e., cross-validation or leave-one-experimental treatment-out) were evaluated. Irrespective of the validation method, the prediction accuracy was best when the average of the milk MIR from the morning and evening milk sample was used and the prediction model was developed using neural networks; concurrently including milk yield and days in milk in the prediction model generated superior predictions relative to just the spectral information alone. Furthermore, prediction accuracy was best when the enteric methane phenotype was the average of at least 20 methane spot measures across a 6-d period flanking each side of the milk sample with associated spectral data. Based on the strategy that achieved the best accuracy of prediction, the correlation between the actual and predicted daily methane emissions when based on 4-fold cross-validation varied per validation stratum from 0.68 to 0.75; the corresponding range when validated on each of the 8 different experimental treatments focusing on alternative pasture grazing systems represented in the dataset varied from 0.55 to 0.71. The root mean square error of prediction across the 4-folds of cross-validation was 37.46 g/d, whereas the root mean square error averaged across all folds of leave-one-treatment-out was 37.50 g/d. Results suggest that even with the likely measurement errors contained within the MIR spectrum and gold standard enteric methane phenotype, enteric methane can be reasonably well predicted from the infrared spectrum of milk samples. What is yet to be established, however, is whether (a) genetic variation exists in this predicted enteric methane phenotype and (b) selection on estimates of genetic merit for this phenotype translate to actual phenotypic differences in enteric methane emissions.

The impact of automated, constant incomplete milking on energy balance, udder health, and subsequent performance in early lactation of dairy cows

Incomplete milking (IM) is one way of mitigating the negative energy balance (NEB) that is characteristic for early lactation and may increase the risk for disease. Our objectives were to test the effects of IM in early lactation on energy balance (EB), metabolic status, udder health, and subsequent performance. To facilitate the practical application, an automated system was used to remove the milking clusters once a predefined amount of milk is withdrawn. Forty-six Holstein cows were equally allocated to either the treatment (TRT, starting on 8 d in milk) or the control group (CON; conventional cluster removal at milk flow rate <0.3 kg/min). Milk removal in the TRT group was limited to the individual cow's milk yield 1 d before IM started and held constant for 14 d. Thereafter, all cows were conventionally milked and records related to EB, performance, and udder health were continued up to 15 wk of lactation. During the 14 d of IM, on average 11.1% less milk was obtained from the TRT cows than from the CON cows. Thereafter, milk yield increased in the TRT group, eliminating the group difference throughout the remaining observation period until wk 15 of lactation. The TRT cows tended to have less dry matter intake and also water intake than the CON cows. The extent of the NEB and the circulating concentrations of fatty acids, β-hydroxybutyrate, insulin-like growth factor-1, and leptin mostly did not differ between the groups. The IM did not affect body condition. Udder health was maintained over the entire observation period in all cows. Our results demonstrate the applicability of the automated cluster removal for limiting milk withdrawal to a defined amount in early lactation. However, it remains to be determined whether the absent effect on energy metabolism was due to the relatively stable energy status of the cows or to the relatively mild IM setting used herein.

Predicting dairy herd resilience on farms with conventional milking systems.

This research paper addresses the problem that, thus far, there is no method available to predict herd resilience for farms that do not use automated milking systems (AMS). Recently, a methodology was developed to estimate both individual cow as well as herd resilience using daily milk yield observations at individual cow level from farms with AMS. This AMS-based method, however, is not suitable on farms that use conventional milking systems (CMS) where such individual cow milk yield observations are lacking. Therefore, this research aimed at predicting herd resilience using herd performance data that is commonly available on CMS farms. To do so, data consisting of 585 Dutch AMS farms where herd resilience estimates using the AMS-based method were available was examined. To predict herd resilience with herd performance data, only those data that are also commonly available on CMS farms were used in a 5-fold cross validation Random Forest model. These herd resilience estimates were subsequently compared with the AMS-based herd resilience estimates. Results showed that it is possible to predict with a 69.9% probability whether a herd performs with above or below average herd resilience using only variables available on CMS farms. Especially, the proportion of cows with an indication of rumen acidosis, proportion of cows with an elevated somatic cell count and the fluctuation in herd size over the years are good predictors of herd resilience. Since herd management decisions appear to affect herd resilience, a lower predicted herd resilience could be taken as a general indication that tactical or strategic management changes could be taken to improve the herd resilience.

The Impact on Cow Performance and Feed Efficiency When Individual Cow Milk Composition and Energy Intake Are Accounted for When Allocating Concentrates

The objective of this three-treatment, 12-week study (involving 69 dairy cows) was to test three methods of concentrate allocation on milk production efficiency. All treatments were offered a basal mixed ration of grass silage and concentrates, with additional concentrates offered to individual cows based on either milk yield alone (Control), milk energy output (Precision 1) or energy intake and milk energy output (Precision 2). Concentrate requirements were calculated and adjusted weekly. Control cows had lower concentrate dry matter intake (DMI; p = 0.040) and milk protein content (p = 0.003) but yield of milk and energy-corrected milk (ECM), energy balance, bodyweight and condition score were unaffected by treatment. Efficiency measures such as ECM/DMI and ECM/metabolizable energy intake were also unaffected by treatment. Less concentrates were used per kg ECM yield in the Control compared to the Precision treatments (p &lt; 0.001). In conclusion, accounting for individual cow milk composition or milk composition combined with individual cow energy intake did not improve production efficiency compared to an approach based on individual cow milk yield only.

Longitudinal Study for the Detection and Quantification of Campylobacter spp. in Dairy Cows during Milking and in the Dairy Farm Environment.

Campylobacteriosis outbreaks have repeatedly been associated with the consumption of raw milk. This study aimed to explore the variation in the prevalence and concentration of Campylobacter spp. in cows' milk and feces, the farm environment and on the teat skin over an entire year at a small German dairy farm. Bi-weekly samples were collected from the environment (boot socks), teats, raw milk, milk filters, milking clusters and feces collected from the recta of dairy cows. Samples were analyzed for Campylobacter spp., E. coli, the total aerobic plate count and for Pseudomonas spp. The prevalence of Campylobacter spp. was found to be the highest in feces (77.1%), completely absent in milking equipment and low in raw milk (0.4%). The mean concentration of Campylobacter spp. was 2.43 log10 colony-forming units (CFU)/g in feces and 1.26 log10 CFU/teat swab. Only a single milk filter at the end of the milk pipeline and one individual cow's raw milk sample were positive on the same day, with a concentration of 2.74 log10 CFU/filter and 2.37 log10 CFU/mL for the raw milk. On the same day, nine teat swab samples tested positive for Campylobacter spp. This study highlights the persistence of Campylobacter spp. for at least one year in the intestine of individual cows and within the general farm environment and demonstrates that fecal cross-contamination of the teats can occur even when the contamination of raw milk is a rare event.

Characterizing ruminal acidosis risk: A multiherd, multicountry study

A multicenter observational study was conducted on early lactation Holstein cows (n = 261) from 32 herds from 3 regions (Australia, AU; California, CA; and Canada, CAN) to characterize their risk of acidosis into 3 groups (high, medium, or low) using a discriminant analysis model previously developed. Diets ranged from pasture supplemented with concentrates to total mixed ration (nonfiber carbohydrates = 17 to 47 and neutral detergent fiber = 27 to 58% of dry matter). Rumen fluid samples were collected <3 h after feeding and analyzed for pH, and ammonia, d- and l-lactate, and volatile fatty acid (VFA) concentrations. Eigenvectors were produced using cluster and discriminant analysis from a combination of rumen pH, and ammonia, d-lactate, and individual VFA concentrations and were used to calculate the probability of the risk of ruminal acidosis based on proximity to the centroid of 3 clusters. Bacterial 16S ribosomal DNA sequence data were analyzed to characterize bacteria. Individual cow milk volume, fat, protein, and somatic cell count values were obtained from the closest herd test to the rumen sampling date (median = 1 d before rumen sampling). Mixed model analyses were performed on the markers of rumen fermentation, production characteristics, and the probability of acidosis. A total of 26.1% of the cows were classified as high risk for acidosis, 26.8% as medium risk, and 47.1% as low risk. Acidosis risk differed among regions with AU (37.2%) and CA (39.2%) having similar prevalence of high-risk cows and CAN only 5.2%. The high-risk group had rumen phyla, fermentation, and production characteristics consistent with a model of acidosis that reflected a rapid rate of carbohydrate fermentation. Namely, acetate to propionate ratio (1.98 ± 0.11), concentrations of valerate (2.93 ± 0.14 mM), milk fat to protein ratio (1.11 ± 0.047), and a positive association with abundance of phylum Firmicutes. The medium-risk group contains cows that may be inappetant or that had not eaten recently or were in recovery from acidosis. The low-risk group may represent cattle that are well fed with a stable rumen and a slower rumen fermentation of carbohydrates. The high risk for acidosis group had lower diversity of bacteria than the other groups, whereas CAN had a greater diversity than AU and CA. Rumen fermentation profile, abundance of ruminal bacterial phyla, and production characteristics of early lactation dairy cattle from 3 regions were successfully categorized in 3 different acidosis risk states, with characteristics differing between acidosis risk groups. The prevalence of acidosis risk also differed between regions.

Grazing affects metabolic pattern of individual cow milk.

Effective traceability tools able to characterize milk from pasture are important to safeguard low-input farming systems, niche dairy products, and local traditions. The aims of the present study were to investigate the ability of proton nuclear magnetic resonance (1H NMR) spectroscopy to discriminate between milk produced from cows before and after the beginning of the grazing season, and to assess the effects of grazing on milk metabolites. The research trial involved a single alpine holding with 72 lactating cows. Individual milks were repeatedly sampled from the same animals before (i.e., d -3 and -1) and after (i.e., d 2, 3, 7, 10, and 14) the onset of the grazing period. One-dimensional 1H NMR spectra of milk extracts were collected through a Bruker spectrometer. Random forest discriminant analysis was applied to 1H NMR spectra to predict the period of collection for each sample. Data concerning the relative abundance of milk metabolites were analyzed through a linear mixed model, which included the fixed effects of period of sampling, cow breed, stage of lactation, and parity, and the random effect of cow nested within breed. The random forest model exhibited great accuracy (93.1%) in discriminating between samples collected on d -3, -1, 2, and 3 and those collected on d 7, 10, and 14. Univariate analysis performed on the 40 detected metabolites highlighted that milk samples from pasture had lower levels of 14 compounds (with fumarate being the most depressed metabolite) and greater levels of 15 compounds (with methanol and hippurate being the most elevated metabolites). Results indicate that milk 1H NMR spectra are promising to identify milk produced in different conditions. Also, our study highlights that grazing is associated with significant changes of milk metabolic profile, suggesting the potential use of several metabolites as indicators of farm management.

Association of housing and management practices with milk yield, milk composition, and fatty acid profile, predicted using Fourier transform mid-infrared spectroscopy, in farms with automated milking systems

Milk fatty acid (FA) profile can be divided into (1) de novo (C4-C14) that are synthesized in the mammary gland; (2) preformed (≥C18) that are absorbed from blood and originate from mobilized adipose tissues or dietary fat; and (3) mixed (C16), which have both origins. Our objectives were to describe the FA profile, as predicted using Fourier transform mid-infrared spectroscopy, of bulk tank milk from automated milking system (AMS) farms and to assess the association of management and housing factors with the bulk tank milk composition and FA profile of those AMS farms. The data used were collected from 124 commercial Canadian Holstein dairy farms with AMS, located in the provinces of Ontario (n = 68) and Quebec (n = 56). The farms were visited once from April to September 2019, and information were collected on barn design and herd management practices. Information regarding individual cow milk yield (kg/d), days in milk, parity, and the number of milking cows were automatically collected by the AMS units on each farm. These data were extracted for the entire period that the bulk tank milk samples were monitored, from April 2019 to April 2020 in Quebec and from August 2019 to May 2020 in Ontario. Across herds, milk yield averaged (mean ± standard error) 35.9 ± 0.4 kg/d, with 3.97 ± 0.01% fat and 3.09 ± 0.01% protein, whereas FA profile averaged 26.2 ± 0.1, 33.1 ± 0.1, and 40.7 ± 0.2 g/100 g of FA for de novo, mixed, and preformed, respectively. The FA yield averaged 0.34 ± 0.01, 0.44 ± 0.01, and 0.54 ± 0.01 kg/d for de novo, mixed, and preformed, respectively. Multivariable regression models were used to associate herd-level housing factors and management practices with milk production, composition, and FA profile. Milk yield was positively associated with using a robot feed pusher (+2.1 kg/d) and the use of deep bedding (+2.6 kg/d). The use of a robot feed pusher, deep bedding, and greater stall raking frequency were positively associated with greater yield (kg/d) of de novo, mixed, preformed, and de novo + mixed FA. Use of deep bedding was negatively associated with concentration of fat, de novo FA, mixed FA, and de novo + mixed FA, expressed in grams per 100 g (%) of milk. A wider lying alley width (≥305 cm) was associated with a greater concentration (g/100 g of milk) of de novo and de novo + mixed FA. Greater frequency of partial mixed ration delivery (>2×/d vs. 1 and 2×/d) was positively associated with a greater proportion (g/100 g of FA) of de novo, mixed, and de novo + mixed FA and negatively associated with the proportion of preformed FA. Overall, these associations indicated that bulk tank FA profile can be used as a tool to monitor and adjust management and housing in AMS farms.

A machine learning framework to predict the next month's daily milk yield, milk composition and milking frequency for cows in a robotic dairy farm

Robotic milking systems (RMS) are increasingly utilised by modern livestock farmers because they can reduce labour costs, and they have the potential to collect data that will improve animal welfare and animal productivity through better monitoring. Sensors and devices installed in RMS enable farmers to routinely collect data on environment conditions, individual animal's behaviours, health, productivity, and milk quality. This dataset can be used to train artificial intelligence algorithms to predict trends in these variables. This study developed a machine learning framework using 5 years' behaviour, heath and productivity data from 80 cows in a robotic dairy farm. Here we demonstrate the development of a framework to automatically train models with up-to-date farm data and predict daily milk yield, composition (fat and protein content) and frequency of individual cow milking during the subsequent 28 days. A time series cross-validation was applied to simulate the application of this framework under commercial conditions and to evaluate the performance. A high accuracy of prediction (R2 > 0.90 and overall accuracy > 80%) was achieved with the models created by this framework. The practical potential of using such frameworks to enhance the management efficiency and animal welfare in robotic dairy farms is discussed.

Phosphorylation and glycosylation isoforms of bovine κ-casein variant E in homozygous Swedish Red cow milk detected by liquid chromatography-electrospray ionization mass spectrometry

Variations in the phosphorylation and glycosylation patterns of the common κ-casein (CN) variants A and B have been explored, whereas studies on variant E heterogeneity are scarce. This study reports for the first time the detailed phosphorylation and glycosylation pattern of the κ-CN variant E in comparison with variants A and B. Individual cow milk samples representing κ-CN genotype EE (n = 12) were obtained from Swedish Red cows, and the natural posttranslational modifications of its κ-CN were identified and quantified by liquid chromatography-electrospray mass spectrometry. In total, 12 unique isoform masses of κ-CN variant E were identified. In comparison, AA and BB milk consisted of 14 and 17 unique isoform masses, respectively. The most abundant κ-CN E isoform detected in the EE milk was the monophosphorylated, unglycosylated [1P 0G, ∼70%; where P indicates phosphorylation from single to triple phosphorylation (1-3P), and G indicates glycosylation from single to triple glycosylation (1-3G)] form, followed by diphosphorylated, unglycosylated (2P 0G, ∼12%) form, resembling known patterns from variants A and B. However, a clear distinction was the presence of the rare triphosphorylated, nonglycosylated (3P 0G, ∼0.05%) κ-CN isoform in the EE milk. All isoforms detected in variant E were phosphorylated, giving a phosphorylation degree of 100%. This is comparable with the phosphorylation degree of variants A and B, being also almost 100%, though with very small amounts of nonphosphorylated, glycosylated isoforms detected. The glycosylation degree of variant E was found to be around 17%, a bit higher than observed for variant B (around 14%), and higher than variant A (around 7%). Among glycosylation, the glycan e was the most common type identified for all 3 variants, followed by c/d (straight and branched chain trisaccharides, respectively), and b. In contrast to κ-CN variants A and B, no glycan of type a was found in variant E. Taken together, this study shows that the posttranslational modification pattern of variant E resembles that of known variants to a large extent, but with subtle differences.

Differences in post-translational modifications of proteins in milk from early and mid-lactation dairy cows as studied using total ion chromatograms from LC-ESI/MS

Liquid chromatography top-down mass-spectrometry combined with an in-house library with 5180 entries of known milk protein masses was used to identify common and rare isoforms of major milk proteins from individual cow's milk samples from early- and mid-lactating (EL and ML, respectively) cows. The milk samples were from 24 Danish Holstein-Friesians, with 12 cows each in EL and ML. A Latin square design with four periods was used to obtain one composite milk sample from each cow per period. The deconvoluted masses were used for identifying common and rare isoforms of the major milk proteins (αS1-CN, αS2-CN, β-CN, κ-CN, α-LA, and β-LG). αS1-CN splice variants were found in all samples, αS- and κ-CN isoforms differed between EL and ML. κ-CN post-translational modifications were found to relate to κ-CN genotype. This approach could identify 66 isoforms of major milk proteins, where 15 were of the αS-CNs and 39 from κ-CN.

Computer vision and weigh scale-based prediction of milk yield and udder traits for individual cows

The measurement of the performance of dairy cow milk production contributes to improve the productivity and profitability of dairy farms. Milk yield is used on-farm for decisions around culling, drying off, heat detection and off-farm for estimation of sire breeding values. Milk yield is measured by herd testing at multiple times through a lactation and by individual milk meters, although the rate of herd testing is decreasing and only a minority of milking systems have milk meters. The goal of this study was to develop a non-contact computer vision and/or walk-over weigh scale technology based on cow 3D images and/or weights before and after milking to determine individual cow milk yield and the lactation milk yield for individual cows. This technology could provide some of the benefits of individual milk meters, although only 1–2 sensors would be required per milking apparatus as opposed to a milk meter per bale on the milking apparatus. An algorithm was developed to estimate the udder volume before milking, udder volume after milking and the difference in udder volume before and after milking. A calibration model based on in-line milk meter measured milk yield from 20 cows was developed to predict milk yield per milking. The model based on udder volume before milking and the difference in udder volume before and after milking had a calibration R2 = 0.92 and RMSE = 0.84 L. The algorithm RMSE was inferior to the in-line milk meter (RMSE = 0.5 L for this trial). This imaging technology provides an inexpensive method to determine the milk yield of individual cows, which can be used for better on-farm management of cows in the herd. The feasibility of determining lactation yield from walk-over weigh scale measurements of each cow before and after each milking was also demonstrated, with algorithm prediction (based on a 2.5% weigh scale error) of lactation milk yield estimated to be similar to that based on two herd tests per lactation. Furthermore, the potential of image-based phenotyping of udder traits such as front/rear teat placement, teat length and teat orientation were demonstrated, with an estimated model accuracy of 94% for front/rear teat placement on a 9-point scale. Average rear teat length was 39 ± 1.5 mm and average front teat length was 49 ± 1.3 mm. This highlights the potential for using computer vision for on-farm prediction of milk performance and udder traits from dairy cows.

Differential in vitro digestion rates in gastric phase of bovine milk with different κ-casein phenotypes

Casein (CN) micelles will coagulate in the stomach after ingestion, which is similar to the cheesemaking process. Although genetic variants of bovine proteins, especially κ-CN, have been confirmed to influence the coagulation properties of the CN micelle, its influence on milk digestibility has not been revealed yet. This study aimed to investigate how genetic variants, glycosylation degree of κ-CN, and CN micelle size influence digestion rates during in vitro gastrointestinal digestion. Three milk pools, representing κ-CN phenotypes of either AA, BB, or AB composition were prepared from milk of individual Danish Holstein cows representing these different genotypes. In vitro digestion of the 3 milk pools, AA, BB, or AB, was investigated by sodium dodecyl sulfate-PAGE, liquid chromatography-mass spectrometry, and degree of hydrolysis. The results showed that κ-CN AA milk had faster digestion rate in the gastric phase compared with BB and AB milks, whereas only small differences were apparent in the intestinal digestion phase. The results further documented that the milk pools representing κ-CN phenotypes BB and AB had comparable overall glycosylation degrees (50.9% and 50.0%, respectively) and higher than that of the AA milk pool (46.9%). Further, the AA milk pool was associated with larger CN micelles. These differences in CN micelle sizes and glycosylation degrees can be part of underlying explanations for the differential in vitro digestion rates observed between the AA, BB, and AB κ-CN milk pools.