Automatic Milking System Research Articles

Is heat stress a growing problem for dairy cattle husbandry in the temperate regions? A case study of Baden-Württemberg in Germany.

Heat stress with measurable effects in dairy cattle is a growing concern in temperate regions. Heat stress in temperate regions differs between environments with different geophysical characteristics. Microclimates specific to each environment were found to greatly impact at what level heat stress occurs and will occur in the future. The landlocked state of Baden-Württemberg, Germany, provides several different environments, hence, a good case-study. Temperature-Humidity Index (THI) from 17 weather stations for the years 2003 to 2022 was calculated and milking yields from 22 farms for the years 2017 to 2022 were collected. The occurrences and evolving patterns of heat stress were analyzed with the use of a THI, and the effect of heat stress on milk yield was analyzed based on milking records from Automated Milking Systems. Daily average THI was calculated using hourly readings of relative humidity and ambient temperature, disregarding solar radiation and wind, as all animals were permanently stabled. Based on studies conducted in Baden-Württemberg and neighboring regions, cited ahead in the section of THI, THI = 60 was the threshold for heat stress occurrence. Findings show that the heat stress period varied between stations from 64 to 120 d with THI ≥ 60 in a year. This aligns with yearly and summer averages, also steadily increasing from May to September. The length of the heat stress period was found to increase 1 extra day every year. Extreme weather events such as heat waves did not increase the heat stress period of that year in length but increased the average THI. Milk yield was found to be significantly (α = 0.05) different between counties grouped into different zones according to heat stress severity and rate of increase in daily average THI. Future attempts at managing heat stress on dairy cattle farms in the temperate regions should account for microclimate, as geographical proximity does not mean that the increase in heat stress severity will be the same in the 2 neighboring areas.

Effects of pellet starch levels in automatic milking systems on rumen fermentation, plasma metabolites, and milk production in mid-lactation cows.

The aim of this study was to evaluate whether the starch levels in pellets fed to cows in automatic milking systems (AMS) affect subacute ruminal acidosis (SARA) occurrence and metabolite parameters. Twenty-four lactating cows (124.4 ± 49.9 days in milk) were studied in a crossover design with two periods of 21 days each and two treatment groups-a control group fed AMS pellets containing 30.0% of starch dry matter (DM) and an experimental group fed AMS pellets containing 23.5% of starch DM. All cows received the same partial mixed ration (PMR). The 1-hr mean ruminal pH in both groups decreased over 4 hr after feeding on PMR but recovered by the next morning. The ruminal pH was unaffected by either treatment, and both groups developed SARA. The groups had no significant differences in the concentrations of ruminal volatile fatty acids, lipopolysaccharides, plasma acute-phase proteins, other metabolites, and hormones. The milk yield and composition were not different in both groups. Feeding low-starch pellets in the AMS did not contribute to the risk of SARA occurrence in cows and had no additive effects on rumen fermentation, plasma metabolites, or milk production.

Comparative Analysis of Milking and Behavior Characteristics of Multiparous and Primiparous Cows in Robotic Systems.

Robotic milking systems are successful innovations in the development of dairy cattle. The objective of this study was to analyse the milking characteristics and behavior of dairy cows of different calving orders in "milk first" robotic milking systems. The data were collected from a commercial herd located in the Midwest region of Minas Gerais (Brazil), which uses an automatic milking system (AMS TM, DeLaval). Were analysed 26,574 observations of 235 Holstein cows were available. Data were evaluated by multivariate analysis of variance and the Tukey test. - Tthe characteristics milk flow and milking efficiency were more favourable for multiparous cows (p <0.01), while the time in the stall was more favourable for primiparous females (p <0.01). The values of handling time were better in the primiparous cows (p <0.01). Primiparous cows had higher amounts of kick-off (p <0.001), and multiparous cows had higher incomplete milkings (p <0.001). The number of incomplete milkings showed a higher ratio in terms of reduction in milk production in 26.6% in primiparous cows and 26.7% in multiparous cows (p <0.01). Regarding the behavioral characteristics, primiparous cows had higher amounts of kickbacks, while multiparous cows had greater quantities of incomplete milkings.

Exploring positive welfare measures: preliminary findings from a prototype protocol in loose housing dairy cattle farms.

Following the increasing interest about the development of indicators of positive welfare and affective state in farm animals, the aim of this research is to present some preliminary results on the application of a prototype protocol based exclusively on positive welfare measures and to suggest potential benefits that can promote positive welfare. The protocol was applied in 20 loose housing dairy cattle farms (6 on deep litter with straw, 14 in cubicles) and included only indicators of positive welfare and emotional states: feeding and resting synchronization, rumination during resting, comfortable lying postures, no visible eye white, relaxed ear postures, percentage of cow contacts with humans in the Avoidance Distance test. Potential benefits in terms of housing, feeding and management were then related to these variables (Mann-Whitney U test). Qualitative Behavior Assessment (QBA) was also carried out and analyzed by Principal Component Analysis to explore the effect of factors that were not evenly distributed in our sample (number of feed distributions, access to pasture, presence of paddock or environmental enrichments, automatic milking systems). When hay was included in the diet, higher feeding synchronization (93.7 ± 1.6 vs. 52.2 ± 4.7%; p < 0.01), percentage of cows with relaxed ear postures (35.8 ± 5.4 vs. 15.5 ± 2.1%; p < 0.01) and percentage of cows with no visible eye white (55.9 ± 17.0 vs. 36.6 ± 4.1%; n.s.) were recorded. A higher level of feeding synchronization was observed also when the feeding places/cow ratio was > 1 (72.1 ± 9.9 vs. 53.8 ± 5.8%), although differences were not significant (p = 0.14). Deep litter had a more positive effect than cubicles on comfort at resting, with a significantly higher percentage of ruminating cows (65.8 ± 10.2 vs. 34.2 ± 3.7%; p < 0.01), a higher percentage of cows with no visible eye white (55.6 ± 9.9 vs. 33.1 ± 3.7%; p < 0.05) and a higher percentage of cows in a more comfortable posture, with stretched legs (14.3 ± 5.1 vs. 5.6 ± 1.6%; p = 0.09). QBA highlighted the most positive emotional state in the only farm that allowed access to pasture. This study represents a first attempt to apply a protocol for on-farm welfare evaluation based exclusively on the use of positive welfare indicators and provides suggestions on possible benefits (e.g., deep litter, feeding places/cow ratio > 1, hay in the diet and access to pasture) to enhance dairy cattle welfare.

Planned behavior, social networks, and perceived risks: Understanding farmers' behavior toward precision dairy technologies

Precision dairy tools (PDT) can provide timely information on individual cow's physiological and behavioral parameters, which can lead to more efficient management of the dairy farm. Although the economic rationale behind the adoption of PDT has been extensively discussed in the literature, the socio-psychological aspects related to the adoption of these technologies have received far less attention. Therefore, this paper proposes a socio-psychological model that builds upon the theory of planned behavior and develops hypotheses regarding cognitive constructs, their interaction with the farmers' perceived risks and social networks, and their overall influence on adoption. These hypotheses are tested using a generalized structural equation model for (a) the adoption of automatic milking systems (AMS) on the farms and (b) the PDT that are usually adopted with the AMS. Results show that adoption of these technologies is affected directly by intention, and the effects of subjective norms, perceived control, and attitudes on adoption are mediated through intention. A unit increase in perceived control score is associated with an increase in marginal probability of adoption of AMS and PDT by 0.05 and 0.19, respectively. Subjective norms are associated with an increase in marginal probability of adoption of AMS and PDT by 0.009 and 0.05, respectively. These results suggest that perceived control exerts a stronger influence on adoption of AMS and PDT, particularly compared with their subjective norms. Technology-related social networks are associated with an increase in marginal probability of adoption of AMS and PDT by 0.026 and 0.10, respectively. Perceived risks related to AMS and PDT negatively affect probability of adoption by 0.042 and 0.16, respectively, by having negative effects on attitudes, perceived self-confidence, and intentions. These results imply that integrating farmers within knowledge-sharing networks, minimizing perceived risks associated with these technologies, and enhancing farmers' confidence in their ability to use these technologies can significantly enhance uptake.

Dose effect of Actisaf Sc 47 yeast probiotic (Saccharomyces cerevisiae) supplementation on production, reproduction, and negative energy balance in early lactation dairy cows.

The study evaluated the dose effect of dietary supplementation with yeast probiotic Saccharomyces cerevisiae (CNCM I-4407, 1010 CFU/g, Actisaf Sc 47; Phileo by Lesaffre, France) on production, energy metabolism, and reproduction in lactating dairy cows. About 117 multiparous Holstein cows from 3 to 60 d in milk held in a barn with an automatic milking system were enrolled in a randomized complete block design and blocked according to calving day, parity, and previous milk yield. The cows were assigned to a basal diet (15% CP, 22% starch) plus either 5g (Y5 group, n = 39), 10g (Y10 group, n = 39), or 0g (CON, n = 39) of yeast probiotic, presented on top of concentrate fed in the robot. Milk yield and body weight were recorded daily, milk composition, and somatic cell count (SSC) every 2wk, and body condition score (BCS) was estimated at days -14, 14, and 40 post-calving. Data were analyzed using a linear mixed model. The Y10 group showed an increased average daily yield of energy-corrected milk (ECM) over CON (+3.5kg, P < 0.05) and Y5 (+0.8kg). There were no significant differences between the groups in milk fat, milk protein, milk SCC linear score, milk urea, blood beta-hydroxy-butyric acid levels, and BCS. Body weight loss from 3 to 90 d in milk was numerically lower (13.8kg) in Y5 than in CON (25.3kg), and the success rate from the first insemination was the highest in YP5 and YP10 groups (39%) than in Control (26%). The yeast probiotic supplementation to early lactation high-producing dairy cows showed a clear effect of the high dose (10g) on ECM milk production, although the lower dose (5g) showed only numerical ECM production increase, both doses displayed better use of energy from the diet than the control and suggest a better resource efficiency.

Evaluating environmental effects of adopting automatic milking systems on Norwegian dairy farms

Abstract We present a novel procedure based on eco-efficiency for assessing farm-level effects of technology adoption while considering secondary effects. Secondary effects are defined as structural and behavioural adaptations to technology that may impact environmental, social or economic outcomes. We apply the procedure to automatic milking systems (AMS) in Norway and find that AMS induces secondary effects, most strongly by decreasing labour per cow and increasing herd sizes. For estimating effects of AMS we employ a novel causal machine learning approach. AMS induce heterogenous effects on eco-efficiency, negatively associated with herd expansion and labour per cow.

Impact of parity on cow stress, behavior, and production at a farm with guided traffic automatic milking system

The widespread adoption of advanced technology, like automatic milking systems in the United States, allows cows to establish individual milking, feeding, and resting schedules, setting them apart from others. However, it is unknown how cow parity affects cow behavior, stress, and system efficiency. We hypothesized primiparous (PR) cows would spend more time in and around the milking robot (AMS), receive greater agonistic interactions, and show elevated physiological stress behaviors compared to multiparous (MU) cows. The study aimed to evaluate the impact of cow parity on behavior and welfare near and inside the AMS. Twenty-four lactating-Holstein dairy cows [12 primiparous (3.0 ± 0.2 yr) and 12 multiparous (6.1 ± 1.9 yr)] housed in the same pen at a guided traffic AMS facility were marked and observed for 6-consecutive days (91 to 102 of lactation). Study cows were identified by specific colored-paint markings, their milk yield and visits, their behavior, Heart Rate-Variability (HRV), and activity were recorded inside the commitment pen (CP) and the AMS. Statistical calculations were performed using JMP Pro 16.1.0, and P ≤ 0.05 was considered significant. Multiparous cows produced more daily milk yield than PP cows (47.30 Vs 33.79 kg), while parity showed no effect on daily milking frequency or milk yield per visit. Primiparous cows spent significantly more time inside the CP than MP cows (68.87 vs 24.38 m), while MP cows approached the AMS entry more often than the PR cows (4.83 vs 2.03), MU cows displaced other cows more inside the CP than PR cows (6.90 vs 2.59). PR cows showed lower HRV (RMSSD: 9.23 vs 17.58 ms) and (SDRR: 19.58 vs 33.64 ms) values than MU cows, whereas MU cows showed a lower Low-Frequency to High-Frequency Ratio (4.39 vs 8.65) than PR cows inside the CP. MU cows spent more time (m) lying (698.06), particularly at night (396.57), than PP cows (556.96, 286.68), while MP cows exhibited more prolonged total lying bouts than PP cows (93.06 vs 71.32 m). Overall, cow parity influenced behavior, activity, and stress indicators of primiparous more than multiparous cows and impacted the overall efficiency and success of the AMS

Comparative performance analysis between two different generations of an automatic milking system

The adoption of Automatic Milking Systems (AMS) in Europe and Italy has been increasing gradually in recent years, driven by the advantages they offer over traditional milking methods. AMS reduces the need for manual labor, increases milk production, standardizes teat cleaning and disinfection, and promotes animal welfare. The adoption of milking robots also allows for continuous monitoring of milk parameters, animal health status, and production performance. In a recent study, the authors analyzed the improvements achieved by a buffalo farm in Southern Italy that switched from an older model (Classic ) to a newer generation model (VMS 300) of milking robot, showing an increase in production quantity and quality.

In-Line Detection of Clinical Mastitis by Identifying Clots in Milk Using Images and a Neural Network Approach.

Automated milking systems (AMSs) already incorporate a variety of milk monitoring and sensing equipment, but the sensitivity, specificity, and positive predictive value of clinical mastitis (CM) detection remain low. A typical symptom of CM is the presence of clots in the milk during fore-stripping. The objective of this study was the development and evaluation of a deep learning model with image recognition capabilities, specifically a convolutional neural network (NN), capable of detecting such clots on pictures of the milk filter socks of the milking system, after the phase in which the first streams of milk have been discarded. In total, 696 pictures were taken with clots and 586 pictures without. These were randomly divided into 60/20/20 training, validation, and testing datasets, respectively, for the training and validation of the NN. A convolutional NN with residual connections was trained, and the hyperparameters were optimized based on the validation dataset using a genetic algorithm. The integrated gradients were calculated to explain the interpretation of the NN. The accuracy of the NN on the testing dataset was 100%. The integrated gradients showed that the NN identified the clots. Further field validation through integration into AMS is necessary, but the proposed deep learning method is very promising for the inline detection of CM on AMS farms.

The effects of dietary iodine content, milking system, and farming practices on milk iodine concentration and quality traits

Various management practices can influence milk quality traits in dairy cattle. As an example, an increasing investment in automatic milking system to substitute milking parlors has been observed in the last 2 decades in dairy farms which could have affected certain bulk milk quality traits. What is more, milking practices can also affect certain milk parameters; as an example, teat disinfectants containing I are used in commercial farms where pre- or postdipping is performed, leading to presence of some I in the bulk milk. However, this trace mineral is also supplied in cows' diet to fulfill their nutritional requirements, partly contributing to the milk I final concentration. Therefore, the aim of this study was to evaluate the sources of variation of milk I along with other traditional milk quality traits. A total of 91 dairy farms in northeastern Italy were enrolled in the study. In each farm, diet and bulk milk samples were collected on the same day for chemical analysis. Concentration of I, in particular, was determined in both milk and feed with gold standard. Pearson correlations were calculated among the traits available for milk and diet, and a general linear model was used to test significance of fixed effects (feeding system, milking system, farming system, herd size, herd stage of lactation, and sampling month) on milk quality traits including the I concentration. In the case of milk I, diet I and presence of I-based predipping and postdipping teat disinfect application were also tested as fixed effects. Results showed a positive linear correlation between milk and diet I content (correlation coefficient [r] = 0.78). Although milk I was also positively correlated with lactose content (r = 0.25), dietary I was not correlated with other milk traits. Milk I content was significantly affected by dietary I, I-based predipping teat disinfectant application, and herd composition. Compared with conventional farms, organic farms showed lower protein content and greater somatic cell score (SCS) but similar milk I. Milking system significantly affected only lactose content and SCS of milk. Sampling month was only significant for milk urea nitrogen and herd composition, feeding system, herd size, and herd average days in milk did not modify milk gross composition and SCS. In conclusion, dietary supply of I is the main factor affecting milk I concentration and findings suggest that I level in milk can be naturally improved in dairy cows by modulating the I content in the diet administered. However, further research is needed to evaluate the effect of I-based sanitizers on milk I.

Selected Parameters Affecting the Electricity Consumption of Automatic Milking Systems

Abstract The paper presented is aimed at the determination of electricity consumption at selected dairy farms during milking employing automatic milking systems (AMS). The research was focused on the determination of electricity consumption during the performance of essential activities that are related to the AMS utilization, i.e., electricity consumption by a single AMS unit per day, by a single assembly compressor per day, by a single AMS unit and a single assembly compressor per day, per milking, and per 1 litre of milk. Measurements were conducted at eight dairy farms in Central Europe that house dairy cows of the black-spotted Holstein cattle breed. The cattle groups assessed comprised 95 to 105 animals subjected to milking over the entire observed period. On the basis of the measurements conducted, it was observed that the average amount of energy consumed per 1 litre of milked milk was 0.0232 kWh over the entire observed period. Regarding the essential activities, the results obtained indicate that the energy consumption throughout the year is relatively balanced, despite the fact that it is affected by a wide range of factors, including chiefly the herd management, as well as overall organization of entire farm, milk yield, health status of cattle, current climatic conditions, fodder quality, etc.

Development of prediction model for body weight and energy balance indicators from milk traits in lactating dairy cows based on deep neural networks

To develop a body weight (BW) prediction model using milk production traits and present a useful indicator for energy balance (EB) evaluation in dairy cows. Data were collected from 30 Holstein cows using an automatic milking system. BW prediction models were developed using multiple linear regression (MLR), local regression (LOESS), and deep neural networks (DNN). Milk production traits readily available on commercial dairy farms, such as energy-corrected milk (ECM), fat-to-protein ratio, days in milk (DIM), and parity, were used as input variables for BW prediction. The EB was evaluated as the difference between energy intake and energy demand. The DNN model showed the greatest predictive accuracy for BW compared with the LOESS and MLR models. The BW predicted using the DNN model was used to calculate the energy demand. Our results revealed that the day on which the EB status transitioned from negative to positive differed among cows. The cows were assigned to one of the three EB index groups. EB index 1 indicated that the day of EB transition was within DIM ≤ 70. The EB indexes 2 and 3 were 70 < DIM ≤ 140 and 140 < DIM ≤ 305, respectively. EB index 3 had the lowest EB, which is the slowest to transition from a negative to a positive energy balance compared with EB indexes 1 and 2. The highest ECM and feed efficiency were observed for EB index 3. The calving interval was the shortest for EB index 1. EB of individual cows during lactation can be estimated and monitored with moderately high accuracy using EB indexes.

Association between management practices and estimated mastitis incidence and milk losses on robotic dairy farms.

This study aims to describe the relation between farm-level management factors and estimated farm-level mastitis incidence and milk loss traits (MIMLT) at dairy farms with automated milking systems. In this observational study, 43 commercial dairy farms in Belgium and the Netherlands were included and 148 'management and udder health related variables' were obtained during a farm visit through a farm audit and survey. The MIMLT were estimated from milk yield data. Quarter-level milk yield perturbations that were caused by presumable mastitis cases (PMC) were selected based on quarter-level milk yield and electrical conductivity. On average, 57.6±5.4% of the identified milk yield perturbations complied with our criteria. From these PMC, 3 farm-level MIMLT were calculated over a one-year period around the farm visit date: (1) the 'average number of PMC per cow per year', (2) the 'absolute milk loss per cow per day', calculated as the farm-level sum of all milk losses during PMC in one year, divided by the average number of lactating cows and the number of days, and (3) the 'relative milk loss', calculated as the farm-level sum of milk losses during PMC in one year, divided by the estimated total production in the absence of PMC. The 'average number of PMC per cow per year' was on average 1.81±0.47. The PMC caused an average milk loss of 0.77±0.26kg per lactating cow per day, which corresponded to an average production loss of 2.38±0.82% of the expected production in the absence of PMC. We performed a principal component regression (PCR) analysis to link the 3 MIMLT to the 'management and udder health related variables', whilst reducing the multicollinearity and the number of dimensions. The first principal component was mainly related to 'milking system brand, maintenance and settings'. The second component mainly linked to average productivity and somatic cell counts, whereas the third component mainly contained variables linked with mastitis management, treatment, and biosecurity. The 3 PCR models had R² ranging from 0.46 (for absolute milk loss per cow per day) to 0.57 (for relative milk loss). For all models, the second PC had the largest effect size. This analysis raises awareness of the impact of management factors on a factual basis and provides handles to take management actions to improve udder health.

Risk prediction model of clinical mastitis in lactating dairy cows based on machine learning algorithms

Mastitis is the most common disease among dairy cows and is known to have negative effects on both animal welfare and the profitability of dairy farms. Early detection of clinical mastitis cases is considered the best option for preventing cows from developing mastitis. In this study, we developed clinical mastitis prediction models that only required inputting common indicators from the automatic milking system. We utilized multidimensional data from the cow mastitis database of Afimilk (China) Agricultural Technology Co., Ltd. to predict mastitis in dairy cows. All data were screened for the period of 0–150 days of lactation. The data included parity, lactation day, period, mean and standard deviation of milk yield, of electrical conductivity, and of lying time, which were taken as input features. The classification of whether cows suffer from clinical mastitis was determined as output. We analyzed 426 cows with clinical mastitis and 2087 healthy cows by using four machine learning algorithms: Decision Tree, Random Forest, Back Propagation neural networks, and Support Vector Machines. In these four algorithms, the accuracy ranged from 94% to 98%, while the running times varied widely from seconds to minutes. The decision tree prediction model achieved an accuracy of 98% and the precision rate for healthy cows was 99%, while for mastitis cows it was 97%. Machine learning algorithms have played an important role in predicting cow mastitis, with the Decision Tree algorithm showing great performance and higher accuracy in our research.

Genomic Selection for Dairy Cattle Behaviour Considering Novel Traits in a Changing Technical Production Environment.

Cow behaviour is a major factor influencing dairy herd profitability and is an indicator of animal welfare and disease. Behaviour is a complex network of behavioural patterns in response to environmental and social stimuli and human handling. Advances in agricultural technology have led to changes in dairy cow husbandry systems worldwide. Increasing herd sizes, less time availability to take care of the animals and modern technology such as automatic milking systems (AMSs) imply limited human-cow interactions. On the other hand, cow behaviour responses to the technical environment (cow-AMS interactions) simultaneously improve production efficiency and welfare and contribute to simplified "cow handling" and reduced labour time. Automatic milking systems generate objective behaviour traits linked to workability, milkability and health, which can be implemented into genomic selection tools. However, there is insufficient understanding of the genetic mechanisms influencing cow learning and social behaviour, in turn affecting herd management, productivity and welfare. Moreover, physiological and molecular biomarkers such as heart rate, neurotransmitters and hormones might be useful indicators and predictors of cow behaviour. This review gives an overview of published behaviour studies in dairy cows in the context of genetics and genomics and discusses possibilities for breeding approaches to achieve desired behaviour in a technical production environment.

Comparison of selected parameters of automated milking in dairy cattle barns equipped with a concentrate feeding system

Automatic milking systems (AMSs) give cows relative freedom to choose the time and frequency of milking throughout the day. Feeding stations also may improve the management of farms. Combining milking robots and feeding stations (FS) may improve milking efficiency and milk yield. Therefore, combining AMS and FS may be beneficial for farmers. The objective of the research was to compare selected automatic milking parameters (daily indices per cow) registered by an AMS in relation to selected features including the presence of concentrate feeding stations. We analysed 931 cows born in 2013–14, in lactations 1–8. In total, we collected data from 357 318 milking days. The following parameters were examined: milking frequency (n/24 h), number of rejected milking (n/24 h), the average number of nipple attempts (n/milking), milking speed (kg/min), time spent in the milking box (s/24 h), milk yield (kg/24 h), milking efficiency (kg/min), rumination time (min/24 h), and concentrate intake (kg) per 100 kg of milk produced. The statistical analysis was conducted using a multi-factor analysis of variance. The analysis confirmed a statistical effect of the concentrate feeding system on most of the investigated traits, except for nipple attempts, box time and rumination time. In cows in barns with an FS, the following parameters were statistically higher compared to cows in non-FS barns: milking frequency (3.04 vs 2.73n/24 h), number of rejected milking (2.24 vs 1.51n/24 h), milking speed (2.98 vs 2.64 kg/min), milk yield (33.48 vs 30.14 kg/24 h), milking efficiency (1.80 vs 1.67 kg/min), and concentrate intake per 100 kg of milk produced (14.67 vs 12.67 kg). The study results indicate that using feeding stations in combination with an AMS can increase milking efficiency, hence the milk output from a milking robot.

A novel approach for anomaly detection in dairy cow gas emission records

Mitigating the considerable contribution of ruminant livestock to global methane emissions, which accounts for 17%, is of vital importance. One significant approach to this is the genetic selection of cows for reduced methane emissions, a strategy that demands large-scale, accurate methane emission measurements from individual cows in commercial farms. The existing ‘sniffer’ technique, which obtains samples of the air near the cow’s nostrils through a tube fixed in the feed bin within an automated milking system (AMS), presents challenges due to the cow’s head movements during the milking recording, creating anomalies in the gas emission data. Moreover, correcting these anomalies directly remains a pressing issue. This study presents a novel data mining approach for detecting anomalous by approximating the cow head position using CO2 time series data collected from cows. The data was collected using sniffer machines on two dairy cattle farms and analyzed using a data-decomposition approach. The performance of the proposed method was evaluated using probabilistic methods such as the Kolmogorov–Smirnov (KS) test and confusion matrix. Results showed that the proposed method was able to accurately detect anomalous peaks in the synthetic CO2 time series, with more than 95% of anomalous peaks being detected. This study demonstrates the potential of the proposed method to improve the accuracy of methane emissions estimates from cows and the understanding of cows’ behavior. To further validate the effectiveness of the proposed method, it was tested on the gas emission dataset recorded by GreenFeed (C-LockTM), which contains various CO2 time series with annotated anomalous points. The validation results demonstrated that the method successfully detected more than 90% of the anomalous points in the time series while marking the peaks containing the anomalous data points. This approach proved to be efficient and accurate, as the peaks represent regions of high anomaly concentration and are more likely to contain true anomalies.

Effect of pre- and postpartum supplementation of a pure glycerol product to dairy cows on feeding behavior, lying behavior, and reticulorumen pH

The objective of this study was to quantify the effects of supplementing a low level of dry glycerol product pre- and postpartum on the feeding behavior, lying behavior, and reticulorumen pH of dairy cows. Multiparous Holstein dairy cows (n = 60) were enrolled in a 2 × 2 factorial design study. Twenty-one days before expected parturition, cows individually received a dry cow diet with (1) 250 g/d glycerol supplementation (GLY; 66% pure glycerol, United States Pharmacopeia grade), or (2) no supplementation (CON). Following parturition, cows were individually assigned to either (1) 250 g/d glycerol product (GLY; 66% pure glycerol), or (2) no supplementation (CON) to their partial mixed ration (PMR) for the first 21 d in milk (DIM). All cows were milked by an automated milking system and offered a target of 5.4 kg/d pellet (23% of target total dry matter intake [DMI]). For both treatment periods, cows were individually assigned to automated feed bins to measure PMR feeding behavior. Rumination time and lying behavior were monitored with electronic sensors for the whole study (-21 to 21 DIM). Reticulorumen pH boluses were administered to a subset of cows (n = 40) where pH was recorded every 10 min from 21 d prepartum to 21 d postpartum. Prepartum, cows fed GLY had fewer, larger meals and spent 20.2% more time feeding than CON while consuming feed at a similar rate. Cows on the CON diet prepartum spent more time lying down in more frequent bouts in the 21 d before calving. Following parturition, cows that received GLY prepartum continued to devote more time to eating, while tending to spend less time ruminating per kilogram of DMI. Cows receiving CON postpartum had larger meals with longer intervals between meals. In the first 21 DIM, cows receiving CON prepartum tended to have shorter, but significantly more frequent, lying bouts than cows fed GLY prepartum. Glycerol supplementation pre- and postpartum resulted in less time spent lying down following parturition. Minimal differences between treatments were observed for pre- and postpartum sorting behavior or reticulorumen pH. Overall, supplementation of glycerol pre- and postpartum altered cow time budgets, with cows spending more time eating pre- and postpartum, less time lying pre- and postpartum, and having fewer, larger meals prepartum when receiving glycerol prepartum, and with cows having slower feeding rates and smaller meals following parturition with postpartum glycerol supplementation.

Cows and Humans as Technology Users: Multispecies Agency and Gender in Automated Milking Systems in Finland.

Automatization has changed the interaction between cows and humans in subtle ways. Automated milking systems (AMS) have been used commercially since the early 1990s, first in the Netherlands, then in Western Europe and North America, before reaching Finland. By 2021, nearly 27 percent of Finnish dairy farms have adopted them. Based on fieldwork at AMS farms and Finnish trade journal items, this article argues that as technology users, both humans and cows are engaged in agential entanglements with milking robots. It highlights challenges that cows and farmers face in learning to use the milking robot, how it impacts their working and living rhythms, and how the technologized work with AMSs is gendered among dairy farmers.