Dairy farmers with larger herd sizes adopt more precision dairy technologies

An increase in the average herd size on Australian dairy farms has also increased the labor and animal management pressure on farmers, thus potentially encouraging the adoption of precision technologies for enhanced management control. A survey was undertaken in 2015 in Australia to identify the relationship between herd size, current precision technology adoption, and perception of the future of precision technologies. Additionally, differences between farmers and service providers in relation to perception of future precision technology adoption were also investigated. Responses from 199 dairy farmers, and 102 service providers, were collected between May and August 2015 via an anonymous Internet-based questionnaire. Of the 199 dairy farmer responses, 10.4% corresponded to farms that had fewer than 150 cows, 37.7% had 151 to 300 cows, 35.5% had 301 to 500 cows; 6.0% had 501 to 700 cows, and 10.4% had more than 701 cows. The results showed that farmers with more than 500 cows adopted between 2 and 5 times more specific precision technologies, such as automatic cup removers, automatic milk plant wash systems, electronic cow identification systems and herd management software, when compared with smaller farms. Only minor differences were detected in perception of the future of precision technologies between either herd size or farmers and service providers. In particular, service providers expected a higher adoption of automatic milking and walk over weighing systems than farmers. Currently, the adoption of precision technology has mostly been of the type that reduces labor needs; however, respondents indicated that by 2025 adoption of data capturing technology for monitoring farm system parameters would be increased.

Validation of a Commercial Automated Body Condition Scoring System on a Commercial Dairy Farm

Simple Summary

The evaluation and implementation of an automated body condition scoring technology for dairy cattle. Body condition scoring in cattle is an effective tool to assess body reserves of individual animals. On-farm body condition scoring requires training and time to appropriately evaluate the animals. The aim of this study was to evaluate the implementation of an automated body condition scoring technology compared to conventional manual scoring. We found that the automated body condition scoring technology was highly correlated with manual scoring. The system was accurate for a body condition scoring (BCS) between 3.0 and 3.75, with a lower error rate compared to the standard detection threshold of 0.25 for manual scoring. However, the system was found to be in a different range of scores and was inaccurate at determining under- and over-conditioned cattle compared to manual scoring.

Abstract

Body condition scoring (BCS) is the management practice of assessing body reserves of individual animals by visual or tactile estimation of subcutaneous fat and muscle. Both high and low BCS can negatively impact milk production, disease, and reproduction. Visual or tactile estimation of subcutaneous fat reserves in dairy cattle relies on their body shape or thickness of fat layers and muscle on key areas of the body. Although manual BCS has proven beneficial, consistent qualitative scoring can be difficult to implement. The desirable BCS range for dairy cows varies within lactation and should be monitored at multiple time points throughout lactation for the most impact, a practice that can be hard to implement. However, a commercial automatic BCS camera is currently available for dairy cattle (DeLaval Body Condition Scoring, BCS DeLaval International AB, Tumba, Sweden). The objective of this study was to validate the implementation of an automated BCS system in a commercial setting and compare agreement of the automated body condition scores with conventional manual scoring. The study was conducted on a commercial farm in Indiana, USA, in April 2017. Three trained staff members scored 343 cows manually using a 1 to 5 BCS scale, with 0.25 increments. Pearson’s correlations (0.85, scorer 1 vs. 2; 0.87, scorer 2 vs. 3; and 0.86, scorer 1 vs. 3) and Cohen’s Kappa coefficients (0.62, scorer 1 vs. 2; 0.66, scorer 2 vs. 3; and 0.66, scorer 1 vs. 3) were calculated to assess interobserver reliability, with the correlations being 0.85, 0.87, and 0.86. The automated camera BCS scores were compared with the averaged manual scores. The mean BCS were 3.39 ± 0.32 and 3.27 ± 0.27 (mean ± SD) for manual and automatic camera scores, respectively. We found that the automated body condition scoring technology was strongly correlated with the manual scores, with a correlation of 0.78. The automated BCS camera system accuracy was equivalent to manual scoring, with a mean error of −0.1 BCS and within the acceptable manual error threshold of 0.25 BCS between BCS (3.00 to 3.75) but was less accurate for cows with high (>3.75) or low (<3.00) BCS scores compared to manual scorers. A Bland–Altman plot was constructed which demonstrated a bias in the high and low automated BCS scoring. The initial findings show that the BCS camera system provides accurate BCS between 3.00 to 3.75 but tends to be inaccurate at determining the magnitude of low and high BCS scores. However, the results are promising, as an automated system may encourage more producers to adopt BCS into their practices to detect early signs of BCS change for individual cattle. Future algorithm and software development is likely to increase the accuracy in automated BCS scoring.

Automatic weaning based on individual solid feed intake: Effects on behavior and performance of dairy calves

Calves are typically weaned from milk to solids once they reach a predetermined age or when they are consuming a predetermined intake of solids. The first aim of this study was to compare feeding behavior and performance of calves weaned based on age versus starter intake. The latter method can result in considerable variation in the age at which calves are weaned, so a secondary aim was to compare calves that weaned early or late when weaned based on starter intake. In experiment 1, dairy calves were randomly assigned to be either (1) weaned by age at d 70 (n = 16), or (2) weaned by intake, where calves were weaned based on starter intake (n = 16). All calves were fed using an automatic milk feeder and offered 12 L/d of milk until 30 d of age. On d 31, all calves had their milk rations reduced. Calves weaned by age were reduced to 6 L/d of milk over 5 d and received 6 L/d milk from d 35 until d 63, when milk was reduced over 7 d until complete weaning at d 70. For calves weaned by intake, the milk ration was reduced on d 31 to 75% of that calf's previous milk intake (3-d average) and further reduced by 25% when the calf met each of 3 targets for starter intake: 225, 675, and 1,300 g/d. Calves that failed to reach the final target by d 63 (failed-intake group; n = 6) were weaned over 7 d to complete weaning at d 70. Ten calves met all 3 starter intake targets (successful-intake group). In experiment 2, all calves were assigned to the weaned-by-intake treatment (n = 48). The weaning strategy was identical to that described for experiment 1, but calves were permitted up to d 84 to reach the final starter intake target. Forty-three calves met all 3 targets and were retrospectively divided into early-weaning (weaned before d 63; n = 31) and late-weaning (weaned on or after d 63; n = 12) categories. In both experiments, the weaning period was considered from the time of initial milk reduction at d 31 until complete weaning at d 70 (weaned by age) or when consuming 1,300 g/d (weaned by intake). Postweaning growth was monitored from weaning until final weight in the calf-rearing period at d 98 (experiment 1) and d 105 (experiment 2). Final weight in the grower period was measured at d 134 (experiment 1) and d 145 (experiment 2). In experiment 1, successful-intake calves (vs. calves weaned by age) consumed 125.3 ± 16.4 L less milk and 41.3 ± 9.3 kg more starter over the experimental period, engaged in more unrewarded visits to the milk feeder during weaning (11.1 ± 1.5 vs. 5.0 ± 1.3 visits/d), and achieved similar weights at the end of the grower period (188.2 ± 6.6 vs. 195.2 ± 5.7 kg). In experiment 2, calves that weaned by intake early (vs. late) consumed 93.3 ± 26.0 L less milk and 57.2 ± 12.2 kg more starter, engaged in a similar number of unrewarded visits during weaning (7.0 ± 0.6 vs. 7.6 ± 1.0 visits/d), had greater average daily gain during weaning (1.08 ± 0.02 vs. 0.94 ± 0.03 kg/d), and achieved greater final weights at the end of the grower period (203.2 ± 2.9 vs. 192.6 ± 4.2 kg). These results indicate that calves weaned based on starter intake can achieve similar weights to those weaned by age, despite consuming less milk. However, some calves will fail to meet starter intake targets unless given sufficient time to do so. Variation in preweaning feed intake provides an opportunity for individualized management of calves.

Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases

Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.

Combination of Sensor Data and Health Monitoring for Early Detection of Subclinical Ketosis in Dairy Cows

Subclinical ketosis is a metabolic disease in early lactation. It contributes to economic losses because of reduced milk yield and may promote the development of secondary diseases. Thus, an early detection seems desirable as it enables the farmer to initiate countermeasures. To support early detection, we examine different types of data recordings and use them to build a flexible algorithm that predicts the occurence of subclinical ketosis. This approach shows promising results and can be seen as a step toward automatic health monitoring in farm animals.

Monitoring bovine tick fever on a dairy farm: An economic proposal for rational use of medications

In this study, we evaluated the monitoring of tick fever (TF) in a Brazilian dairy farm in the Minas Gerais state, Brazil, from July 10 to August 4, 2018. We aimed to identify diagnostic and treatment flaws in the protocol adopted by the farm, and to establish a novel and accurate TF monitoring protocol based on precision dairy farming and rational use of antimicrobials and antiparasitic drugs, while evaluating the economic benefits of the proposed strategy. We monitored TF in 395 heifer calves aged between 3 and 14 mo. According to the farm's standard protocol, all calves with an increase of 0.5°C in rectal temperature compared with the previous week's measurement were treated for Anaplasma spp. and Babesia spp. Blood smears were collected from the tail tip of the treated calves. During the last week of the study, we prepared blood smears of all calves regardless of treatment indication. Economic analysis was performed. The results indicated that at least 56.86% (261/459) of the calves did not require treatment for TF, whereas only 23.09% (106/459) had treatment indications. Negative blood smears (45.97%; 211/459) indicated the possibility of calves being affected by another disease or a condition that was not being adequately treated or those not necessarily sick. These results demonstrate the excessive use of medications, representing a direct economic loss, in addition to potentially favoring the occurrence of resistance to antimicrobials. In contrast, 9.42% (26/276) of calves had no treatment indication based on rectal temperature but had treatment indications based on blood smears. Only 5.73% (42/735) of blood smears had co-infection with hemopathogens, and none had triple co-infection. Therefore, we proposed the monitoring of TF using rectal temperature and microscopic analysis. If implemented, this strategy would result in a direct annual savings of approximately $22,638.96 (77.99%) related to medication for the treatment of TF. Therefore, implementing the proposed protocol would be cheaper than treatment based only on rectal temperatures. The currently implemented TF protocols overestimate the occurrence of TF, resulting in overtreatment. Thus, implementing a TF monitoring protocol based on a microscopy tool is justified, with benefits including rational use of medication, potential to generate savings, and reduced morbidity and mortality rates, in addition to enabling other diagnoses.

Technical note: Calving prediction in dairy cattle based on continuous measurements of ventral tail base skin temperature using supervised machine learning

In this study, we developed a calving prediction model based on continuous measurements of ventral tail base skin temperature (ST) with supervised machine learning and evaluated the predictive ability of the model in 2 dairy farms with distinct cattle management practices. The ST data were collected at 2- or 10-min intervals from 105 and 33 pregnant cattle (mean ± standard deviation: 2.2 ± 1.8 parities) reared in farms A (freestall barn, in a temperate climate) and B (tiestall barn, in a subarctic climate), respectively. After extracting maximum hourly ST, the change in values was expressed as residual ST (rST = actual hourly ST - mean ST for the same hour on the previous 3 d) and analyzed. In both farms, rST decreased in a biphasic manner before calving. Briefly, an ambient temperature-independent gradual decrease occurred from around 36 to 16 h before calving, and an ambient temperature-dependent sharp decrease occurred from around 6 h before until calving. To make a universal calving prediction model, training data were prepared from pregnant cattle under different ambient temperatures (10 data sets were randomly selected from each of the 3 ambient temperature groups: <15°C, ≥15°C to <25°C, and ≥25°C in farm A). An hourly calving prediction model was then constructed with the training data by support vector machine based on 15 features extracted from sensing data (indicative of pre-calving rST changes) and 1 feature from non-sensor-based data (days to expected calving date). When the prediction model was applied to the data that were not part of the training process, calving within the next 24 h was predicted with sensitivities and precisions of 85.3% and 71.9% in farm A (n = 75), and 81.8% and 67.5% in farm B (n = 33), respectively. No differences were observed in means and variances of intervals from the calving alerts to actual calving between farms (12.7 ± 5.8 and 13.0 ± 5.6 h in farms A and B, respectively). Above all, a calving prediction model based on continuous measurement of ST with supervised machine learning has the potential to achieve effective calving prediction, irrespective of the rearing condition in dairy cattle.

Change in Rumination Behavior Parameters around Calving in Cows with Subclinical Ketosis Diagnosed during 30 Days after Calving

We hypothesized that cows with SCK (blood BHB over >1.2 mmol/L) diagnosed within the first 30 days of calving can be predicted by changes in rumination and activity behavioral parameters in the period before calving and indeed subsequently. A total of 45 cows were randomly selected from 60 dry cows from at least 40 days before calving. All the cows were fitted with RuniWatch sensors monitoring both intake behaviors (faceband) and general movement and activity behavior (pedometer) (RWS-ITIN + HOCH, Switzerland). Following an adaptation period of 10 days, rumination, eating, and activity parameters were monitored for 30 days before calving and 30 days after calving. Considering the design of the study, we divided the data of cows into three stages for statistical evaluation: (1) the last thirty days before calving (from day -30 to -1 of the study); (2) day of calving; and (3) the first thirty days after calving (from day 1 to 30 of the study). We found that before calving, those cows with a higher risk of having SCK diagnosed after calving had lower rumination time, eating time, drinking gulps, bolus, chews per min, chews per bolus, downtime, maximal temperature, and activity change. On the calving day, in cows with higher risk of SCK after calving, we found lower rumination time, eating time, chews per min, chews per bolus, uptime, downtime, minimal temperature, other chews, eating chews, drinking time, drinking gulps, activity, average temperature, maximal temperature, activity change, rumination chews, and eating chews. After calving in cows with SCK, we found lower rumination time, eating time 1, eating time 2, bolus, chews per bolus, uptime, downtime, minimal temperature, maximal temperature, rumination chews, and eating chews. Moreover, after calving we found higher drinking gulps, drinking time, activity, activity change, average temperature, other chews, and eating chews in cows with SCK. From a practical point of view, we recommend that by tracking changes in rumination and activity behavior parameters registered with RuniWatch sensors (such as rumination time, eating time, drinking time, drinking gulps, bolus, chews per minute, chews per bolus, downtime, maximal temperature, and activity change) before, during, and after calving, we can identify cows with a higher risk of SCK in the herd.

Dynamic Changes in Progesterone Concentration in Cows' Milk Determined by the At-Line Milk Analysis System Herd NavigatorTM

Simple Summary

According to the literature, the at-line progesterone monitoring system Herd Navigator^TM (Lattec I/S, Hillerød, Denmark) was used in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). It works automatically and provides real-time physiological information about lactating dairy cows. For making farm-management decisions, it is not only a novel tool for scientific research, but also a mechanism for improving productivity, food safety, animal well-being, the environment, and the public perception of the dairy industry. It has been hypothesized that the progesterone concentration determined by the at-line milk analysis system and changes in its dynamics correlate with the parity, reproductive status, and milk yield of cows. The aim of the current study was to evaluate relative at-line milk progesterone (mP4) dynamic changes, according to the parity and status of reproduction, and to estimate the relationship with productivity in dairy cows. Frequent automated mP4 sampling can help identify characteristics of mP4 dynamic changes associated with successful pregnancies, pregnancy losses, and potential differences in mP4 dynamics among parity groups, which have not been studied previously.

Abstract

The aim of the current instant study was to evaluate relative at-line milk progesterone dynamic changes according to parity and status of reproduction and to estimate the relationship with productivity in dairy cows by at-line milk analysis system Herd Navigator^TM. According to the progesterone assay, experimental animals were divided into three periods: postpartum, after insemination, and pregnancy. In the first stage of the postpartum period, progesterone levels in milk were monitored every 5 days. This period of reproductive cycle recovery was followed for 30 days (days 0–29). The second stage of the postpartum period (30–65 days) lasted until cows were inseminated. In the period (0–45 days) after cow insemination, progesterone levels were distributed according to whether or not cows became pregnant. For milk progesterone detection, the fully automated real-time progesterone analyzer Herd Navigator^TM (Lattec I/S, Hillerød, Denmark) was used in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). We found that an at-line progesterone concentration is related to different parities, reproductive statuses, and milk yield of cows: the 12.88% higher concentration of progesterone in milk was evaluated in primiparous cows. The average milk yield in non-pregnant primiparous cows was 4.64% higher, and in non-pregnant multiparous cows 6.87% higher than in pregnant cows. Pregnancy success in cows can be predicted 11–15 days after insemination, when a significant increase in progesterone is observed in the group of pregnant cows.

Relating Lying Behavior With Climate, Body Condition Score, and Milk Production in Dairy Cows

Attention on animal behavior and welfare has been increasing. Scientific knowledge about the effect of behavior and welfare on animals' production augmented and made clear the need of improving their living conditions. Among the variables to monitor in dairy cattle farming, lying time represents a signal for health and welfare status as well as for milk production. The aim of this study is to identify the relationship among the lying behavior of dairy cows and milk production, body condition score (BCS), weather variables, and the temperature–humidity index (THI) in the barn from a dairy farm located in Northern Italy. One-year data were collected on this farm with sensors that allowed monitoring of the environmental conditions in the barn and the activity of primiparous lactating cows. Principal components analysis (PCA), factor analysis (FA), generalized linear model select (GLMSelect), and logistic analysis (LA) were carried out to get the relationships among variables. Among the main results, it emerges that the effect of weather parameters is quite restrained, except for THI > 70, which negatively affects the lying time. In addition, the most productive cows are found to lie down more than the less productive ones, and the parameters of milk production, lying time, and BCS are found to be linked by a similar trend.

Genetic parameters for calf feeding traits derived from automated milk feeding machines and number of bovine respiratory disease treatments in North American Holstein calves

Precision livestock farming technologies, such as automatic milk feeding machines, have increased the availability of on-farm data collected from dairy operations. We analyzed feeding records from automatic milk feeding machines to evaluate the genetic background of milk feeding traits and bovine respiratory disease (BRD) in North American Holstein calves. Data from 10,076 preweaning female Holstein calves were collected daily over a period of 6 yr (3 yr included per-visit data), and daily milk consumption (DMC), per-visit milk consumption (PVMC), daily sum of drinking duration (DSDD), drinking duration per-visit, daily number of rewarded visits (DNRV), and total number of visits per day were recorded over a 60-d preweaning period. Additional traits were derived from these variables, including total consumption and duration variance (TCV and TDV), feeding interval, drinking speed (DS), and preweaning stayability. A single BRD-related trait was evaluated, which was the number of times a calf was treated for BRD (NTT). The NTT was determined by counting the number of BRD incidences before 60 d of age. All traits were analyzed using single-step genomic BLUP mixed-model equations and fitting either repeatability or random regression models in the BLUPF90+ suite of programs. A total of 10,076 calves with phenotypic records and genotypic information for 57,019 SNP after the quality control were included in the analyses. Feeding traits had low heritability estimates based on repeatability models (0.006 ± 0.0009 to 0.08 ± 0.004). However, total variance traits using an animal model had greater heritabilities of 0.21 ± 0.023 and 0.23 ± 0.024, for TCV and TDV, respectively. The heritability estimates increased with the repeatability model when using only the first 32 d preweaning (e.g., PVMC = 0.040 ± 0.003, DMC = 0.090 ± 0.009, DSDD = 0.100 ± 0.005, DS = 0.150 ± 0.007, DNRV = 0.020 ± 0.002). When fitting random regression models (RRM) using the full dataset (60-d period), greater heritability estimates were obtained (e.g., PVMC = 0.070 [range: 0.020, 0.110], DMC = 0.460 [range: 0.050, 0.680], DSDD = 0.180 [range: 0.010, 0.340], DS = 0.19 [range: 0.070, 0.430], DNRV = 0.120 [range: 0.030, 0.450]) for the majority of the traits, suggesting that RRM capture more genetic variability than the repeatability model with better fit being found for RRM. Moderate negative genetic correlations of -0.59 between DMC and NTT were observed, suggesting that automatic milk feeding machines records have the potential to be used for genetically improving disease resilience in Holstein calves. The results from this study provide key insights of the genetic background of early in-life traits in dairy cattle, which can be used for selecting animals with improved health outcomes and performance.

In-Line Registered Milk Fat-to-Protein Ratio for the Assessment of Metabolic Status in Dairy Cows

This study endeavors to ascertain alterations in the in-line registered milk fat-to-protein ratio as a potential indicator for evaluating the metabolic status of dairy cows. Over the study period, farm visits occurred biweekly on consistent days, during which milk composition (specifically fat and protein) was measured using a BROLIS HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania). Clinical examinations were performed at the same time as the farm visits. Blood was drawn into anticoagulant-free evacuated tubes to measure the activities of GGT and AST and albumin concentrations. NEFA levels were assessed using a wet chemistry analyzer. Using the MediSense and FreeStyle Optium H systems, blood samples from the ear were used to measure the levels of BHBA and glucose in plasma. Daily blood samples were collected for BHBA concentration assessment. All samples were procured during the clinical evaluations. The cows were categorized into distinct groups: subclinical ketosis (SCK; n = 62), exhibiting elevated milk F/P ratios without concurrent clinical signs of other post-calving diseases; subclinical acidosis (SCA; n = 14), characterized by low F/P ratios (<1.2), severe diarrhea, and nondigestive food remnants in feces, while being free of other post-calving ailments; and a healthy group (H; n = 20), comprising cows with no clinical indications of illness and an average milk F/P ratio of 1.2. The milk fat-to-protein ratios were notably higher in SCK cows, averaging 1.66 (±0.29; p < 0.01), compared to SCA cows (0.93 ± 0.1; p < 0.01) and healthy cows (1.22). A 36% increase in milk fat-to-protein ratio was observed in SCK cows, while SCA cows displayed a 23.77% decrease. Significant differences emerged in AST activity, with SCA cows presenting a 26.66% elevation (p < 0.05) compared to healthy cows. Moreover, SCK cows exhibited a 40.38% higher NEFA concentration (p < 0.001). A positive correlation was identified between blood BHBA and NEFA levels (r = 0.321, p < 0.01), as well as a negative association between BHBA and glucose concentrations (r = -0.330, p < 0.01). Notably, AST displayed a robust positive correlation with GGT (r = 0.623, p < 0.01). In light of these findings, this study posits that milk fat-to-protein ratio comparisons could serve as a non-invasive indicator of metabolic health in cows. The connections between milk characteristics and blood biochemical markers of lipolysis and ketogenesis suggest that these markers can be used to check the metabolic status of dairy cows on a regular basis.

Internet of Things (IoT): Sensors Application in Dairy Cattle Farming

The expansion of dairy cattle farms and the increase in herd size have made the control and management of animals more complex, with potentially negative effects on animal welfare, health, productive/reproductive performance and consequently farm income. Precision Livestock Farming (PLF) is based on the use of sensors to monitor individual animals in real time, enabling farmers to manage their herds more efficiently and optimise their performance. The integration of sensors and devices used in PLF with the Internet of Things (IoT) technologies (edge computing, cloud computing, and machine learning) creates a network of connected objects that improve the management of individual animals through data-driven decision-making processes. This paper illustrates the main PLF technologies used in the dairy cattle sector, highlighting how the integration of sensors and devices with IoT addresses the challenges of modern dairy cattle farming, leading to improved farm management.

Ruminating, Eating, and Locomotion Behavior Registered by Innovative Technologies around Calving in Dairy Cows

The hypothesis for this study was that there are correlations between ruminating, eating, and locomotion behavior parameters registered by the RumiWatch sensors (RWS) before and after calving. The aim was to identify correlations between registered indicators, namely, rumination, eating, and locomotion behavior around the calving period. Some 54 multiparous cows were chosen from the entire herd without previous calving or other health problems. The RWS system recorded a variety of parameters such as rumination time, eating time, drinking time, drinking gulps, bolus, chews per minute, chews per bolus, activity up and down time, temp average, temp minimum, temp maximum, activity change, other chews, ruminate chews, and eating chews. The RWS sensors were placed on the cattle one month before expected calving based on service data and removed ten days after calving. Data were registered 10 days before and 10 days after calving. We found that using the RumiWatch system, rumination time was not the predictor of calving outlined in the literature; rather, drinking time, downtime, and rumen chews gave the most clearcut correlation with the calving period. We suggest that using RumiWatch to combine rumination time, eating time, drinking, activity, and down time characteristics from ten days before calving, it would be possible to construct a sensitive calving alarm; however, considerably more data are needed, not least from primiparous cows not examined here.

Alterations in Rumination, Eating, Drinking and Locomotion Behavior in Dairy Cows Affected by Subclinical Ketosis and Subclinical Acidosis

This study delves into the effects of subclinical ketosis (SCK) and subclinical acidosis (SCA) on various parameters related to dairy cow rumination, eating, drinking and locomotion behavior. The research hypothesized that these subclinical metabolic disorders could affect behaviors such as rumination, feeding, and locomotion. A total of 320 dairy cows, with a focus on those in their second or subsequent lactation, producing an average of 12,000 kg/year milk in their previous lactation, were examined. These cows were classified into three groups: those with SCK, those with SCA, and healthy cows. The health status of the cows was determined based on the milk fat-protein ratio, blood beta-hydroxybutyrate, and the results of clinical examinations performed by a veterinarian. The data collected during the study included parameters from the RumiWatch sensors. The results revealed significant differences between the cows affected by SCK and the healthy cows, with reductions observed in the rumination time (17.47%) and various eating and chewing behaviors. These changes indicated that SCK had a substantial impact on the cows' behavior. In the context of SCA, the study found significant reductions in Eating Time 2 (ET2) of 36.84% when compared to the healthy cows. Additionally, Eating Chews 2 (EC2) exhibited a significant reduction in the SCA group, with an average of 312.06 units (±17.93), compared to the healthy group's average of 504.20 units (±18.87). These findings emphasize that SCA influences feeding behaviors and chewing activity, which can have implications for nutrient intake and overall cow health. The study also highlights the considerable impact of SCK on locomotion parameters, as the cows with SCK exhibited a 27.36% reduction in the walking time levels. These cows also displayed reductions in the Walking Time (WT), Other Activity Time (OAT), and Activity Change (AC). In conclusion, this research underscores the critical need for advanced strategies to prevent and manage subclinical metabolic disorders within the dairy farming industry. The study findings have far-reaching implications for enhancing the well-being and performance of dairy cattle. Effective management practices and detection methods are essential to mitigate the impact of SCK and SCA on dairy cow health and productivity, ultimately benefiting the dairy farming sector.

Birth conditions affect the longevity of Holstein offspring

Studies of dairy cow longevity usually focus on the animal life after first calving, with few studies considering early life conditions and their effects on longevity. The objective was to evaluate the effect of birth conditions routinely collected by Dairy Herd Improvement agencies on offspring longevity measured as length of life and length of productive life. Lactanet provided 712,890 records on offspring born in 5,425 Quebec dairy herds between January 1999 and November 2015 for length of life, and 506,066 records on offspring born in 5,089 Quebec dairy herds between January 1999 and December 2013 for length of productive life. Offspring birth conditions used in this study were calving ease (unassisted, pull, surgery, or malpresentation), calf size (small, medium, or large), and twinning (yes or no). Observations were considered censored if the culling reason was "exported," "sold for dairy production," or "rented out" as well as if the animals were not yet culled at the time of data extraction. If offspring were not yet culled when the data were extracted, the last test-day date was considered the censoring date. Conditional inference survival trees were used in this study to analyze the effect of offspring birth conditions on offspring longevity. The hazard ratio of culling between the groups of offspring identified by the survival trees was estimated using a Cox proportional hazard model with herd-year-season as a frailty term. Five offspring groups were identified with different length of life based on their birth condition. Offspring with the highest length of life [median = 3.61 year; median absolute deviation (MAD) = 1.86] were those classified as large or medium birth size and were also the result of an unassisted calving. Small offspring as a result of a twin birth had the lowest length of life (median = 2.20 year; MAD = 1.69) and were 1.52 times more likely to be culled early in life. Six groups were identified with different length of productive life. Offspring that resulted from an unassisted or surgery calving and classified as large or medium when they were born were in the group with the highest length of productive life (median = 2.03 year; MAD = 1.63). Offspring resulting from a malpresentation or pull in a twin birth were in the group with the lowest length of productive life (median = 1.15 year; MAD = 1.11) and were 1.70 times more likely to be culled early in life. In conclusion, birth conditions of calving ease, calf size, and twinning greatly affected offspring longevity, and such information could be used for early selection of replacement candidates.

Holstein Cattle Face Re-Identification Unifying Global and Part Feature Deep Network with Attention Mechanism

In precision dairy farming, computer vision-based approaches have been widely employed to monitor the cattle conditions (e.g., the physical, physiology, health and welfare). To this end, the accurate and effective identification of individual cow is a prerequisite. In this paper, a deep learning re-identification network model, Global and Part Network (GPN), is proposed to identify individual cow face. The GPN model, with ResNet50 as backbone network to generate a pooling of feature maps, builds three branch modules (Middle branch, Global branch and Part branch) to learn more discriminative and robust feature representation from the maps. Specifically, the Middle branch and the Global branch separately extract the global features of middle dimension and high dimension from the maps, and the Part branch extracts the local features in the unified block, all of which are integrated to act as the feature representation for cow face re-identification. By performing such strategies, the GPN model not only extracts the discriminative global and local features, but also learns the subtle differences among different cow faces. To further improve the performance of the proposed framework, a Global and Part Network with Spatial Transform (GPN-ST) model is also developed to incorporate an attention mechanism module in the Part branch. Additionally, to test the efficiency of the proposed approach, a large-scale cow face dataset is constructed, which contains 130,000 images with 3000 cows under different conditions (e.g., occlusion, change of viewpoints and illumination, blur, and background clutters). The results of various contrast experiments show that the GPN outperforms the representative re-identification methods, and the improved GPN-ST model has a higher accuracy rate (up by 2.8% and 2.2% respectively) in Rank-1 and mAP, compared with the GPN model. In conclusion, using the Global and Part feature deep network with attention mechanism can effectively ameliorate the efficiency of cow face re-identification.