Using activity and milk yield as predictors of fresh cow disorders

Predictive modelling of deviation from expected milk yield in transition cows on automatic milking systems

The transition period is a pivotal time in the production cycle of the dairy cow. It is estimated that between 30% and 50% of all cows experience metabolic or infectious disease during this time. One of the most common and economically consequential effects of disease during the transition period is a reduction in early lactation milk production. This has led to the utilisation of deviation from expected milk yield in early lactation as a proxy measure for transition health. However, to date, this analysis has been used exclusively for the retrospective assessment of transition cow health. Statistical models capable of predicting deviations from expected milk yield may allow producers to proactively manage animals predicted to suffer negative deviations in early lactation milk production. The objective of this retrospective cohort study was first, to explore the accuracy with which cow-level production and behaviour data collected on automatic milking systems (AMS) from 1-3 days in milk (DIM) can predict deviation from expected 30-day cumulative milk yield in multiparous cows. And second, to assess the accuracy with which predicted yield deviations can classify cows into groups which may facilitate improved transition management. Production, rumination, and physical activity data from 31 commercial AMS were accessed. A 3-step analytical procedure was then conducted. In Step 1, expected cumulative yield for 1-30 DIM for each individual cow-lactation was calculated using a mixed effect linear model. In Step 2, 30-Day Yield Deviation (YD) was calculated as the difference between observed and expected cumulative yield. Lactations were then assigned to one of three groups based on their YD, RED Group (0% YD). In Step 3, yield, rumination, and physical activity data from days 1-3 in lactation were used to predict YD using machine learning models. Following external validation, YD was predicted across the test data set with a mean absolute error of 9%. Categorisation of animals suffering large negative deviations (RED group) was achieved with a specificity of 99%, sensitivity of 35%, and balanced accuracy of 67%. Our results suggest that milk yield, rumination and physical activity patterns expressed by dairy cows from 1-3 DIM have utility in the prediction of deviation from expected 30-day cumulative yield. However, these predictions currently lack the sensitivity required to classify cows reliably and completely into groups which may facilitate improved transition cow management.

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.

Combination of milk variables and on-farm data as an improved diagnostic tool for metabolic status evaluation in dairy cattle during the transition period

Milk composition, particularly milk fatty acids, has been extensively studied as an indicator of the metabolic status of dairy cows during early lactation. In addition to milk biomarkers, on-farm sensor data also hold potential in providing insights into the metabolic health status of cows. While numerous studies have explored the collection of a wide range of sensor data from cows, the combination of milk biomarkers and on-farm sensor data remains relatively underexplored. Therefore, this study aims to identify associations between metabolic blood variables, milk variables, and various on-farm sensor data. Second, it seeks to examine the supplementary or substitutive potential of these data sources. Therefore, data from 85 lactations on metabolic status and on-farm data were collected during 3 wk before calving up to 5 wk after calving. Blood samples were taken on d 3, 6, 9, and 21 after calving for determination of β-hydroxybutyrate (BHB), nonesterified fatty acids (NEFA), glucose, insulin-like growth factor-1 (IGF-1), insulin, and fructosamine. Milk samples were taken during the first 3 wk in lactation and analyzed by mid-infrared for fat, protein, lactose, urea, milk fatty acids, and BHB. Walking activity, feed intake, and body condition score (BCS) were monitored throughout the study. Linear mixed effect models were used to study the association between blood variables and (1) milk variables (i.e., milk models); (2) on-farm data (i.e., on-farm models) consisting of activity and dry matter intake analyzed during the dry period ([D]) and lactation ([L]) and BCS only analyzed during the dry period ([D]); and (3) the combination of both. In addition, to assess whether milk variables can clarify unexplained variation from the on-farm model and vice versa, Pearson marginal residuals from the milk and on-farm models were extracted and related to the on-farm and milk variables, respectively. The milk models had higher coefficient of determination (R2) than the on-farm models, except for IGF-1 and fructosamine. The highest marginal R2 values were found for BHB, glucose, and NEFA (0.508, 0.427, and 0.303 vs. 0.468, 0.358, and 0.225 for the milk models and on-farm models, respectively). Combining milk and on-farm data particularly increased R2 values of models assessing blood BHB, glucose, and NEFA concentrations with the fixed effects of the milk and on-farm variables mutually having marginal R2 values of 0.608, 0.566, and 0.327, respectively. Milk C18:1 was confirmed as an important milk variable in all models, but particularly for blood NEFA prediction. On-farm data were considerably more capable of describing the IGF-1 concentration than milk data (marginal R2 of 0.192 vs. 0.086), mainly due to dry matter intake before calving. The BCS [D] was the most important on-farm variable in relation to blood BHB and NEFA and could explain additional variation in blood BHB concentration compared with models solely based on milk variables. This study has shown that on-farm data combined with milk data can provide additional information concerning the metabolic health status of dairy cows. On-farm data are of interest to be further studied in predictive modeling, particularly because early warning predictions using milk data are highly challenging or even missing.

Effects of targeted clinical examination based on alerts from automated health monitoring systems on herd health and performance of lactating dairy cows

Our objectives were to compare the proportion of lactating dairy cows diagnosed with health disorders (HD) and herd performance when using a health monitoring program designed to rely primarily but not exclusively on alerts from automated health monitoring (AHM) systems or a health monitoring program based primarily on systematic clinical examinations, milk yield monitoring, and visual observation of cows. In a clinical trial, at ∼30 d before expected parturition, nulliparous and parous Holstein cows, stratified by parity and days in gestation, were randomly assigned to the high-intensity clinical monitoring (HIC-M; n = 625) or automated monitoring (AUT-M; n = 624) treatment. Cows were fitted with a neck-attached rumination and physical activity monitoring tag, and individual daily milk yield data were collected from parlor milk meters. For cows in HIC-M, clinical examination was conducted daily until 10 d in milk (DIM) and then in response to milk yield reduction alerts or visual observation of clinical signs of HD over the course of 21 DIM. For cows in AUT-M, clinical examination until 21 DIM was because of health index (HI) score alerts and reduced milk yield alerts. The HI score alerts used were generated based on the manufacturer's settings for the system for the last 2-h period before cows were selected for examination. Visual observation of clinical signs of HD was used for identifying cows potentially missed by automated alerts. Binomial and quantitative data were analyzed by logistic regression and ANOVA with repeated measures, respectively. The percentage of cows diagnosed with at least 1 HD during the experimental treatments risk period tended to be greater and the incidence rate ratio of HD diagnosed was greater in the HIC-M than in the AUT-M treatment. We found no difference between treatments for cows that exited the herd up to 60 or 150 DIM, but more cows tended to exit the herd from 61 to 150 DIM in the HIC-M than in the AUT-M treatment. No differences were detectable between treatments in daily or total milk yield to 21 DIM or in weekly mean milk yield and total milk yield to 150 DIM. More cows were inseminated in estrus for first service if in the HIC-M treatment and had no HD diagnosed than if in the HIC-M treatment but with HD diagnosed, or in the AUT-M treatment and had no HD diagnosed. Cows in the AUT-M treatment with HD diagnosed did not differ from other groups. No differences between treatments were observed in pregnancies per artificial insemination or pregnancy loss for first service. Despite a reduction in the risk of diagnosis of HD, no evidence indicated that a health monitoring program that relied on AHM system alerts to select cows for clinical examination reduced herd performance compared with a more intensive program that included systematic clinical examinations of all cows for the first 10 DIM, reduced milk yield alerts, and visual observation. However, to obtain the same herd performance as with the HIC-M treatment, the AUT-M treatment required use of visual observation. In conclusion, a health monitoring program designed to rely primarily on targeted clinical examination based on alerts from automated health monitoring systems might be a feasible alternative to programs that rely more on clinical examination, provided that visual observation is used to identify cows not detected by automated alerts.

Patterns of Fourier-transform infrared estimated milk constituents in early lactation Holstein cows on a single New York State dairy

Cows undergo immense physiological stress to produce milk during early lactation. Monitoring early lactation milk through Fourier-transform infrared (FTIR) spectroscopy might offer an understanding of which cows transition successfully. Daily patterns of milk constituents in early lactation have yet to be reported continuously, and the study objective was to initially describe these patterns for cows of varying parity groups from 3 through 10 d postpartum, piloted on a single dairy. We enrolled 1,024 Holstein cows from a commercial dairy farm in Cayuga County, New York, in an observational study, with a total of 306 parity 1 cows, 274 parity 2 cows, and 444 parity ≥3 cows. Cows were sampled once daily, Monday through Friday, via proportional milk samplers, and milk was stored at 4°C until analysis using FTIR. Estimated constituents included anhydrous lactose, true protein, and fat (g/100 g of milk); relative % (rel%) of total fatty acids (FA) and concentration (g/100 g of milk) of de novo, mixed, and preformed FA; individual fatty acids C16:0, C18:0, and C18:1 cis-9 (g/100 g of milk); milk urea nitrogen (MUN; mg/100 g of milk); and milk acetone (mACE), milk β-hydroxybutyrate (mBHB), and milk-predicted blood nonesterified fatty acids (mpbNEFA) (all expressed in mmol/L). Differences between parity groups were assessed using repeated-measures ANOVA. Milk yield per milking differed over time between 3 and 10 DIM and averaged 8.7, 13.3, and 13.3 kg for parity 1, 2, and ≥3 cows, respectively. Parity differences were found for % anhydrous lactose, % fat, and preformed FA (g/100 g of milk). Parity differed across DIM for % true protein, de novo FA (rel% and g/100 g of milk), mixed FA (rel% and g/100 g of milk), preformed FA rel%, C16:0, C18:0, C18:1 cis-9, MUN, mACE, mBHB, and mpbNEFA. Parity 1 cows had less true protein and greater fat percentages than parity 2 and ≥3 cows (% true protein: 3.52, 3.76, 3.81; % fat: 5.55, 4.69, 4.95, for parity 1, 2, ≥3, respectively). De novo and mixed FA rel% were reduced and preformed FA rel% were increased in primiparous compared with parity 2 and ≥3 cows. The increase in preformed FA rel% in primiparous cows agreed with milk markers of energy deficit, such that mpbNEFA, mBHB, and mACE were greatest in parity 1 cows followed by parity ≥3 cows, with parity 2 cows having the lowest concentrations. When measuring milk constituents with FTIR, these results suggest it is critical to account for parity for the majority of estimated milk constituents. We acknowledge the limitation that this study was conducted on a single farm; however, if FTIR technology is to be used as a method of identifying cows maladapted to lactation, understanding variations in early lactation milk constituents is a crucial first step in the practical adoption of this technology.

Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle

With all the sensor data currently generated at high frequency in dairy farms, there is potential for earlier diagnosis of postpartum diseases compared with traditional monitoring methodologies. Our objectives were 1) to compare the impact of sensor data pre-processing on classifier performance by using multiple time windows before a given metritis event, while considering other cow-level factors and farm-scheduled activities; 2) to compare the performance of random forest (RF), k-nearest neighbors (k-NN), and support vector machine (SVM) classifiers at different decision thresholds using different number of past observations (time-lags) for the detection of behavioral patterns associated with changes in metritis scores; and 3) to compare classifier performance between each one of the five behaviors registered every hour by an ear-tag 3-axis accelerometer (CowManager, Agis Autimatisering, Harmelen, Netherlands). A total of 239 metritis events were created by comparing metritis scores between two consecutive clinical evaluations from cows that were retrospectively selected from a dataset containing sensor data and health information during the first 21 days postpartum from June 2014 to May 2017. Hourly sensor data classified by the accelerometer as either ruminating, eating, not active (including both standing or lying), and two different levels of activity (active and high activity) behaviors corresponding to the 3 days before each metritis event were aggregated every 24-, 12-, 6-, and 3-hour time windows. Multiple time-lags were also used to determine the optimal number of past observations needed for optimal classification. Similarly, different decision thresholds were compared in terms of model performance. Depending on the classifier, algorithm hyperparameters were optimized using grid search (RF, k-NN, SVM) and random search (RF). All behaviors changed throughout the study period and showed distinct daily patterns. From the three algorithms, RF had the highest F₁ score followed by k-NN and SVM. Furthermore, sensor data aggregated every 6- or 12-h time windows had the best model performance at multiple time-lags. We concluded that the data from the first 3 days post-partum should be discarded when studying metritis, and either one of the five behaviors measured with CowManager could be used when predicting metritis when sensor data were aggregated every 6- or 12-hour time windows, and using time-lags corresponding to 2-3 days before a given event, depending on the time window used. This study shows how to maximize sensor data in their potential for disease prediction, enhancing the performance of algorithms used in machine learning.

Sensitivity and Specificity for the Detection of Clinical Mastitis by Automatic Milking Systems in Bavarian Dairy Herds

In automatic milking systems (AMSs), the detection of clinical mastitis (CM) and the subsequent separation of abnormal milk should be reliably performed by commercial AMSs. Therefore, the objectives of this cross-sectional study were (1) to determine the sensitivity (SN) and specificity (SP) of CM detection of AMS by the four most common manufacturers in Bavarian dairy farms, and (2) to identify routinely collected cow data (AMS and monthly test day data of the regional Dairy Herd Improvement Association (DHIA)) that could improve the SN and SP of clinical mastitis detection. Bavarian dairy farms with AMS from the manufacturers DeLaval, GEA Farm Technologies, Lely, and Lemmer-Fullwood were recruited with the aim of sampling at least 40 cows with clinical mastitis per AMS manufacturer in addition to clinically healthy ones. During a single farm visit, cow-level milking information was first electronically extracted from each AMS and then all lactating cows examined for their udder health status in the barn. Clinical mastitis was defined as at least the presence of visibly abnormal milk. In addition, available DHIA test results from the previous six months were collected. None of the manufacturers provided a definition for clinical mastitis (i.e., visually abnormal milk), therefore, the SN and SP of AMS warning lists for udder health were assessed for each manufacturer individually, based on the clinical evaluation results. Generalized linear mixed models (GLMMs) with herd as random effect were used to determine the potential influence of routinely recorded parameters on SN and SP. A total of 7411 cows on 114 farms were assessed; of these, 7096 cows could be matched to AMS data and were included in the analysis. The prevalence of clinical mastitis was 3.4% (239 cows). When considering the 95% confidence interval (95% CI), all but one manufacturer achieved the minimum SN limit of >80%: DeLaval (SN: 61.4% (95% CI: 49.0%−72.8%)), GEA (75.9% (62.4%−86.5%)), Lely (78.2% (67.4%−86.8%)), and Lemmer-Fullwood (67.6% (50.2%−82.0%)). However, none of the evaluated AMSs achieved the minimum SP limit of 99%: DeLaval (SP: 89.3% (95% CI: 87.7%−90.7%)), GEA (79.2% (77.1%−81.2%)), Lely (86.2% (84.6%−87.7%)), and Lemmer-Fullwood (92.2% (90.8%−93.5%)). All AMS manufacturers’ robots showed an association of SP with cow classification based on somatic cell count (SCC) measurement from the last two DHIA test results: cows that were above the threshold of 100,000 cells/mL for subclinical mastitis on both test days had lower chances of being classified as healthy by the AMS compared to cows that were below the threshold. In conclusion, the detection of clinical mastitis cases was satisfactory across AMS manufacturers. However, the low SP will lead to unnecessarily discarded milk and increased workload to assess potentially false-positive mastitis cases. Based on the results of our study, farmers must evaluate all available data (test day data, AMS data, and daily assessment of their cows in the barn) to make decisions about individual cows and to ultimately ensure animal welfare, food quality, and the economic viability of their farm.

The effect of activity on some milking parameters in holstein cows

The study was conducted on 41-second lactation Holstein cows of German origin. The shelter type is a semi-open field type and the research period is 12 months. The activities in the first 100 days of lactation per day were 451.4 ± 133.5, and in the second 100 days, it was determined at 420.78 ± 118.0. The activities are divided into 3 parts within 24 hours (at night, during the day between two milkings, and in the evening). While there was no statistical difference between days 100 and 200 of lactation, the lowest activity was recorded at night and the highest activity during the day. The conductance, milk flow, and milking duration of the milk were within the normal range in the first 100 and 200 days and no statistical difference between them could be determined. Mean daily milk yield was 28.28 ± 3.86 kg for the first 100 days and 25.15 ± 3.61 kg for the following 100 days, and the difference was found to be significant (P

The Early Prediction of Common Disorders in Dairy Cows Monitored by Automatic Systems with Machine Learning Algorithms

We use multidimensional data from automated monitoring systems and milking systems to predict disorders of dairy cows by employing eight machine learning algorithms. The data included the season, days in milking, parity, age at the time of disorders, milk yield (kg/day), activity (unitless), six variables related to rumination time, and two variables related to the electrical conductivity of milk. We analyze 131 sick cows and 149 healthy cows with identical lactation days and parity; all data are collected on the same day, which corresponds to the diagnosis day for disordered cows. For disordered cows, each variable, except the ratio of rumination time from daytime to nighttime, displays a decreasing/increasing trend from d-7 or d-3 to d0 and/or d-1, with the d0, d-1, or d-2 values reaching the minimum or maximum. The test data sensitivity for three algorithms exceeded 80%, and the accuracies of the eight algorithms ranged from 65.08% to 84.21%. The area under the curve (AUC) of the three algorithms was >80%. Overall, Rpart best predicts the disorders with an accuracy, precision, and AUC of 81.58%, 92.86%, and 0.908, respectively. The machine learning algorithms may be an appropriate and powerful decision support and monitoring tool to detect herds with common health disorders.

Detecting Ultra- and Circadian Activity Rhythms of Dairy Cows in Automatic Milking Systems Using the Degree of Functional Coupling—A Pilot Study

Ultra- and circadian activity rhythms of animals can provide important insights into animal welfare. The consistency of behavioral patterns is characteristic of healthy organisms, while changes in the regularity of behavioral rhythms may indicate health and stress-related challenges. This pilot study aimed to examine whether dairy cows in free-stall barns with an automatic milking system (AMS) and free cow traffic can develop ultra- and circadian activity rhythms. On 4 dairy farms, pedometers recorded the activity of 10 cows each over 28 days. Based on time series calculation, the Degree of Functional Coupling (DFC) was used to determine the cows' activity rhythms. The DFC identified significant rhythmic patterns in sliding 7-day periods and indicated the percentage of activity (0–100%) that was synchronized with the 24-h day-night rhythm. As light is the main factor influencing the sleep-wake cycle of organisms, light intensity was recorded in the AMS, at the feed alley and in the barn of each farm. In addition, feeding and milking management were considered as part of the environmental context. Saliva samples of each cow were taken every 3 h for 1 day to determine the melatonin concentration. The DFC approach was successfully used to detect activity rhythms of dairy cows in commercial housing systems. However, large inter- and intra-individual variations were observed. Due to a high frequency of 0 and 100%, a median split was used to dichotomize into “low” (<72.34%) and “high” (≥72.34%) DFC. Forty percent of the sliding 7-day periods corresponded to a low DFC and 50% to a high DFC. No DFC could be calculated for 10% of the periods, as the cows' activity was not synchronized to 24 h. A generalized linear mixed-effects model revealed that the DFC levels were positively associated with a longer milking interval and a higher amount of daytime activity and negatively associated with higher number of lactations. The DFC is a novel approach to animal behavior monitoring. Due to its automation capability, it represents a promising tool in its further development for the purpose of longitudinal monitoring of animal welfare.

Development of resilience indicator traits based on daily step count data for dairy cattle breeding

Background

Resilient animals are minimally affected by disturbances, such as diseases and heat stress, and quickly recover. Daily activity data can potentially indicate resilience, because resilient animals likely keep variations due to disturbances that threat animal homeostasis at a low magnitude. We used daily step count of cows to define resilience indicators based on theory, exploratory analysis and literature, and then investigated if they can be used to genetically improve resilience by estimating heritability and repeatability, and genetic associations with other resilience-related traits, i.e. health traits, longevity, fertility, and body condition score (BCS).

Results

Two groups of resilience indicators were defined: indicators describing (1) mean step count at different lactation stages for individual cows, and (2) fluctuations in step count from individual step count curves. Heritability estimates were highest for resilience indicators describing mean step count, from 0.22 for the 2-week period pre-partum to 0.45 for the whole lactation. High mean step count was consistently, but weakly, genetically correlated with good health, fertility, and longevity, and high BCS. Heritability estimates of resilience indicators describing fluctuations ranged from 0.01 for number of step count drops to 0.15 for the mean of negative residuals from individual curves. Genetic correlations with health traits, longevity, fertility, and BCS were mostly weak, but were moderate and favorable for autocorrelation of residuals (− 0.33 to − 0.44) and number of step count drops (− 0.44 to − 0.56) with hoof health, fertility, and BCS. Resilience indicators describing variability of residuals and mean of negative residuals showed strong genetic correlations with mean step count (0.86 to 0.95, absolute), which suggests that adjustment for step count level is needed. After adjustment, ‘mean of negative residuals’ was highly genetically correlated with hoof health, fertility, and BCS.

Conclusions

Mean step count, autocorrelation and mean of negative residuals showed most potential as resilience indicators based on resilience theory, heritability, and genetic associations with health, fertility, and body condition score. Other resilience indicators were heritable, but had unfavorable genetic correlations with several health traits. This study is an important first step in the exploration of the use of activity data to breed more resilient livestock.

Adoption of Precision Technologies by Brazilian Dairy Farms: The Farmer's Perception

The use of precision farming technologies, such as milking robots, automated calf feeders, wearable sensors, and others, has significantly increased in dairy operations over the last few years. The growing interest in farming technologies to reduce labor, maximize productivity, and increase profitability is becoming noticeable in several countries, including Brazil. Information regarding technology adoption, perception, and effectiveness in dairy farms could shed light on challenges that need to be addressed by scientific research and extension programs. The objective of this study was to characterize Brazilian dairy farms based on technology usage. Factors such as willingness to invest in precision technologies, adoption of sensor systems, farmer profile, farm characteristics, and production indexes were investigated in 378 dairy farms located in Brazil. A survey with 22 questions was developed and distributed via Google Forms from July 2018 to July 2020. The farms were then classified into seven clusters: (1) top yield farms; (2) medium-high yield, medium-tech; (3) medium yield and top high-tech; (4) medium yield and medium-tech; (5) young medium-low yield and low-tech; (6) elderly medium-low yield and low-tech; and (7) low-tech grazing. The most frequent technologies adopted by producers were milk meters systems (31.7%), milking parlor smart gate (14.5%), sensor systems to detect mastitis (8.4%), cow activity meter (7.1%), and body temperature (7.9%). Based on a scale containing numerical values (1-5), producers indicated "available technical support" (mean; σ²) (4.55; 0.80) as the most important decision criterion involved in adopting technology, followed by "return on investment-ROI" (4.48; 0.80), "user-friendliness" (4.39; 0.88), "upfront investment cost" (4.36; 0.81), and "compatibility with farm management software" (4.2; 1.02). The most important factors precluding investment in precision dairy technologies were the need for investment in other sectors of the farm (36%), the uncertainty of ROI (24%), and lack of integration with other farm systems and software (11%). Farmers indicated that the most useful technologies were automatic milk meters systems (mean; σ²) (4.05; 1.66), sensor systems for mastitis detection (4.00; 1.57), automatic feeding systems (3.50; 2.05), cow activity meter (3.45; 1.95), and in-line milk analyzers (3.45; 1.95). Overall, the concerns related to data integration, ROI, and user-friendliness of technologies are similar to those of dairy farms located in other countries. Increasing available technical support for sensing technology can have a positive impact on technology adoption.

Investigating the Use of Dry Matter Intake and Energy Balance Prepartum as Predictors of Digestive Disorders Postpartum

One objective was to evaluate the association of dry matter intake as a percentage of body weight (DMI%BW) and energy balance (EB) prepartum and postpartum, and energy-corrected milk (ECM) postpatum with digestive disorders postpartum. For this, ANOVA was used, and DMI%BW, EB, and ECM were the outcome variables, and left displaced abomasum (LDA), indigestion, and other digestive disorders (ODDZ) were the explanatory variables. The main objective was to evaluate prepartum DMI%BW and EB as predictors of digestive disorders. For this, logistic regression was used, and LDA, indigestion, and ODDZ were the outcome variables and DMI%BW and EB were the explanatory variables. Data from 689 cows from 11 experiments were compiled. Left displaced abomasum was not associated with prepartum DMI%BW or EB. Postpartum data were normalized to the day of the event (day 0). Cows that developed LDA had lesser postpartum DMI%BW on days −24, −23, −12, −7 to 0 and from days 1 to 8, 10 to 12, and 14 and 16, lesser postpartum EB from days −7 to −5, −3 to 0, and 12, and lesser postpartum energy-corrected milk on days −19, −2, −1, 0, 7, 9, 10, 15, and 17 relative to diagnosis than cows without LDA. Cows that developed indigestion had lesser prepartum DMI%BW and EB than cows without indigestion, and lesser postpartum DMI%BW on days −24, −1, 0, 1, and 2, and greater DMI%BW on day 26, lesser ECM on days −24, −2, −1, 0, 1, and 2 relative to diagnosis. Postpartum EB was not associated with indigestion postpartum. Cows that developed ODDZ had lesser prepartum DMI%BW on day −8 and from days −5 to −2, lesser prepartum EB on day −8 and from days −5 to −2, and lesser postpartum DMI%BW than cows without ODDZ. Each 0.1 percentage point decrease in the average DMI%BW and each Mcal decrease in the average EB in the last 3 days prepartum increased the odds of having indigestion by 9% each. Cutoffs for DMI%BW and EB during the last 3 days prepartum to predict indigestion were established and were ≤1.3%/day and ≤0.68 Mcal/day, respectively. In summary, measures of prepartum DMI%BW and EB were associated with indigestion and ODDZ postpartum and were predictors of indigestion postpartum, although the effect sizes were small.

Sensor and Video: Two Complementary Approaches for Evaluation of Dairy Cow Behavior after Calving Sensor Attachment

Studies evaluating calving sensors provided evidence that attaching the sensor to the tail may lead to changes in the cows' behavior. Two different calving sensors were attached to 18 cows, all of which were equipped with a rumen bolus to record their activity. Two methodological approaches were applied to detect potential behavioral changes: analysis of homogeneity of variance in cow activity (5 days pre-sensor and 24 h post-sensor) and analysis of video-recorded behavior (12 h pre- and post-sensor, respectively) in a subgroup. The average results across the sample showed no significant changes in the variability of activity and no statistically significant mean differences in most visually analyzed behaviors, namely walking, eating, drinking, social interaction, tail raising, rubbing the tail, and the number of standing and lying bouts after calving sensor attachment. In addition to considering mean values across all cows, individual cow investigations revealed an increased number of time slots showing a significant increase in the variability of activity and an increased frequency of tail raising and rubbing the tail on objects after calving sensor attachment in some cows, which should be investigated in more detail on a larger scale.

Changes of Plasma Analytes Reflecting Metabolic Adaptation to the Different Stages of the Lactation Cycle in Healthy Multiparous Holstein Dairy Cows Raised in High-Welfare Conditions

Simple Summary

This study investigates the changes occurring in plasma analytes of healthy multiparous Holstein dairy cows during the dry, the postpartum, the early and the late lactation phases. A welfare assessment at the herd level and a retrospective subclinical diseases screening were used as blocking factors for the selection of reference individuals. Thus, this study provides measurements of the physiological variations affecting plasma analytes concentrations during the pivotal stages of the lactation cycle in a healthy, high welfare-raised subset of reference individuals and suggest an explanation for the underlying processes involved. Finally, we propose reference intervals for plasma analytes in the stages investigated.

Abstract

Here, we tested the changes occurring in several plasma analytes during different stages of the lactation cycle of high welfare raised multiparous Holstein cows, and provided reference intervals (RI) for plasma analytes concentrations. Eleven high-welfare farms (HWF) located in Northern Italy were selected and their herds used to recruit 361 clinically healthy cows undergoing the dry (from −30 to −10 days from real calving; DFC), the postpartum (from 3 to 7 DFC), the early lactation (from 28 to 45 DFC) and the late lactation phases (from 160 to 305 DFC). Cows affected by subclinical diseases (SCD) were retrospectively excluded, and a subset of 285 cows was selected. Data of plasma analytes underwent ANOVA testing using physiological phases as predictors. The individual effect of each phase was assessed using a pairwise t-test assuming p ≤ 0.05 as a significance limit. A bootstrap approach was used to define the reference interval (RI) for each blood analyte within physiological phases having a pairwise t-test p ≤ 0.05. The concentration of nonesterified fatty acids, albumin, cholesterol, retinol, paraoxonase and tocopherol changed throughout all the physiological phases, whereas the concentration of K, alkaline phosphatase and thiol groups remained stable. Triglycerides, Zn, and ferric ion reducing antioxidant power in the dry phase and BHB, Ca, myeloperoxidase, haptoglobin, reactive oxygen metabolites and advanced oxidation of protein product in postpartum differed compared with other physiological phases. During the dry phase, Packed cell volume, Cl, and urea concentrations were similar to during the postpartum phase. Similarly, Na, γ-glutamyl transferase and β-carotene concentrations were similar to during the early lactation phase; fructosamine and bilirubin concentrations were similar to during the late lactation phase. During the postpartum phase, fructosamine and P concentrations were similar to during the early lactation phase, and the aspartate transaminase concentration was similar to during the late lactation phase. During the early lactation phase, Mg, creatinine, total protein, globulin and ceruloplasmin concentrations were similar to during the postpartum phase, while the urea concentration was similar to during the late lactation phase. All these plasma analytes differed among the other phases. This study identifies physiological trends affecting plasma analytes concentrations during the different stages of the lactation cycle and provides a guideline for the duration and magnitude of their changes when animals are healthy and raised in optimal welfare conditions.

Role of insulin, insulin sensitivity, and abomasal functions monitors in evaluation of the therapeutic regimen in ketotic dairy cattle using combination therapy with referring to milk yield rates

Background:

Ketosis is one of the most critical metabolic disorders that occur in dairy cows after parturition due to negative energy balance around calving.

Aim:

The study evaluated a specific therapeutic regimen of ketosis in Holstein dairy cattle by using the combination therapy including hormones, corticosteroids, propylene glycol, and vitamin B12 as well as the use of milk yield rates, insulin, insulin sensitivity, and abomasal functions monitors as diagnostic biomarkers for the recovery of ketotic cows either pre-therapy (0 days) or post-therapy (7 and 14 days).

Methods:

This study was conducted on ketotic cattle (n = 20) belonged to different dairy farms in Cairo and Giza governorates, Egypt. The diseased cows were undergoing clinical and biochemical investigations for the estimation of serum insulin. Quantitative Insulin Sensitivity Check Index (RQUICKI) and abomasal functions monitor mainly serum levels of gastrin, pepsinogen, and chloride.

Results:

The milk production rates, cost: benefit analysis ratio, and benefit of the dairy farm in ketotic animals were significantly increased post-treatment. An improvement of insulin sensitivity was stated as serum insulin, and RQUICKI were remarkably increased in post-therapeutic ketotic cows. Monitors of the abomasal function revealed abomasal functions improvement through the significant elevation of blood gastrin and a substantial reduction in serum pepsinogen due to treatment.

Conclusion:

The study revealed high efficacy of the applied therapeutic strategy regime. It led to a high recovery rate and a very low relapse rate for ketosis. An improvement in milk yield rates, insulin sensitivity, and abomasal function monitors was reported. Hypoinsulinaemia was still reported, however, serum insulin was improved.

Prepartum behavior changes in dry Holstein cows at risk of postpartum diseases

The objective of this study was to identify changes in prepartum behavior associated with the incidence of postpartum diseases in dairy cows. Multiparous Holstein cows (n = 489) were monitored with accelerometers for 3 wk prepartum. Accelerometers measured steps, time at the feed bunk, frequency of meals, lying bouts, and lying time. Postpartum health was monitored from 0 to 30 d in milk and cases of metritis, mastitis, retained placenta, displaced abomasum (DA), ketosis, and hypocalcemia were recorded. A multivariate linear mixed model was used to assess differences in behavior between diseased and not diagnosed diseased cows. A multivariate logistic regression was used to predict the occurrence of diseases. Predictors were selected using a manual backward stepwise selection process of variables until all remaining predictors had a P < 0.10. Models were submitted to a leave-one-out cross-validation process, and sensitivity, specificity, false discovery rate, and false omission rate were calculated. On average, over the 3-wk prepartum period, cows not diagnosed diseased (n = 345) took 1,613 ± 38 steps, spent 181 ± 7.1 min at the feed bunk, had 8.3 ± 0.17 meals, had 9.8 ± 0.32 lying bouts, and spent 742 ± 11.3 min lying per day. Behavior of diseased cows (n = 144) did not differ from those not diagnosed diseased. However, differences for specific diseases were observed, being significant in the week prepartum. When considering changes in behavior for only the week before calving, cows with metritis had more lying bouts (+21%), cows with DA had fewer meals (-24%) and tended to take fewer steps (-18%), and cows with ketosis had fewer meals (-22%) and spent less time at the feed bunk (-40%). Prediction models with the best outcomes were found for DA and ketosis using data of the prepartum week only. The model for DA included time at the feed bunk. Cross-validation resulted in a 80% sensitivity, 58.1% specificity, 59.2% accuracy, 91.2% false discovery rate, and 1.7% false omission rate. The model for ketosis included time at the feed bunk and number of meals. Cross-validation resulted in 64.3% sensitivity, 59.3% specificity, 59.5% accuracy, 93.0% false discovery rate, and 2.8% false omission rate. Prepartum behavior of cows affected with metritis, DA, and ketosis was different from that of cows not diagnosed with diseases. Prediction equations were able to classify cows at high or low risk of ketosis and DA and can be used in taking management decisions, but the high false discovery rates requires further refinement.

Precision dairy farming: Opportunities and challenges for India

Effective management of a dairy farm has to focus on individual animal apart from group or herd management since 'smallest production unit in the dairy is the individual animal’. In this context, precision dairy farming (PDF) aims to manage the basic production unit in order to exploit its maximal production capacity. PDF is the use of information and technology based farm management system to measure physiological, behavioural and production indicators of individual animals to improve management strategies, profitability and farm performance. PDF applications are finding their way on dairy farms, although there seem to be differences in the uptake of PDF applications between dairy systems. The authors have attempted to identify different PDF tools utilized across the globe and have highlighted the status of adoption in Indian scenario by highlighting about few farms/organizations involved in its utilization and uptake over the years. In this direction, the authors have also focused on several benefits and challenges faced by developing countries including India since the benefits are often not immediately apparent and they require more management expertise along with an investment of time and money to realize. In addition, the adoption rate depends on various factors like farmer education, farm size, perceptions of risk, ownership of a non-farm business etc. Addressing these issues is very essential for the uptake of technologies and hence, an effort has been made to propose strategies for adoption and operationalization of PDF in India and other developing countries where the similar scenario exists. The study also highlights that PDF in many developing countries including India is in its infancy, but there are tremendous opportunities for improvements in individual animal and herd management in dairy farms. The progressive farmers or the farmers’ groups, with guidance from the public and private sectors, and professional associations, can adopt it on a limited scale as the technology shows potential for raising yields and economic returns on fields with significant variability, and for minimizing environmental degradation. Additional research needs to be undertaken to examine the adoption process for not only successful adoption of technology, but also to solve the issues associated with the technology adoption. Further, right extension approaches and advisory services for the farmers interested in PDF needs to be undertaken for its effective application under different socio-economic and ecological conditions.

Relation of Subclinical Ketosis of Dairy Cows with Locomotion Behaviour and Ambient Temperature

Simple Summary

The use of innovative tools and the registration of new biomarkers can help with identification of certain diseases in fresh dairy cows earlier and more accurately, thus improving the quality of treatment and reducing the losses incurred. One of the most often diagnosed diseases of postpartum cows is subclinical ketosis. According to our knowledge there exists limited information about how subclinical ketosis is related to locomotion behaviour (walking activity, feeding time with head position down, feeding time with head position up, change between activities) and average, minimal and maximal ambient temperature. We hypothesized that continuous maximal monitoring of cow locomotion behaviour (in combination with measuring the ambient temperature) could identify cows with subclinical ketosis. In addition, we hoped that changes of the above-mentioned parameters prior to clear clinical signs of subclinical ketosis would aid in earlier detection of the disease.

Abstract

Rumination time, chewing time and drinking time are indicators that can be assessed in case of cow disease. In this research, two groups of cows were formed: cows with subclinical ketosis (SCK; n = 10) and healthy cows (HG; n = 10). Behaviour such as walking activity, feeding time with head position up, feeding time with head position down, change of activity and average, minimal and maximal ambient temperature of cows were recorded by the RumiWatch noseband system (RWS; RumiWatch System, Itin+Hoch GmbH, Liestal, Switzerland). The RWS comprises a noseband halter with a built-in pressure sensor and a liquid-filled pressure tube. Data from each studied cow were recorded for 420 h. According to the results of our study, it was determined that cows diagnosed with subclinical ketosis showed a tendency to change their activity more frequently. Our data indicates that minimal and maximal ambient temperatures are related with SCK.

Associations between lying behavior and activity and hypocalcemia in grazing dairy cows during the transition period

Hypocalcemia is a common metabolic disorder of transition dairy cows that is considered a gateway disease, increasing the risk of other health disorders and reducing cow performance. Clinical milk fever is associated with long periods of recumbency, and it is plausible that cows experiencing non-paretic hypocalcemia may spend more time lying; hence, lying behavior and activity measures may be useful in identifying at-risk cows. The objective of this study was to describe associations among blood calcium (Ca) status at calving and lying behavior and activity measures during the transition period in grazing dairy cows. Blood was sampled on the day of calving (d 0), and d 1, 2, 3, and 4 postcalving, and analyzed for total plasma Ca concentration. Twenty-four multiparous Holstein-Friesian and Holstein-Friesian × Jersey grazing dairy cows were classified, retrospectively, as clinically hypocalcemic (CLIN; blood Ca ≤ 1.4 mmol/L at 1 or more consecutive samplings within 48 h postcalving, but without parturient paresis). These cows were pair-matched (using milk production potential from their estimated breeding value for milk protein, mean body weight at wk -5 and -6 precalving, and, where possible, parity) with 24 cows classified as subclinically hypocalcemic (SUB; blood Ca > 1.4 and < 2.0 mmol/L at 2 consecutive samplings within 48 h postcalving), and 24 cows classified as normocalcemic (NORM; blood Ca ≥ 2.0 mmol/L at 3 consecutive samplings within 72 h postcalving). Lying behavior and activity were monitored using triaxial accelerometers from -21 to +35 d relative to calving. Data were summarized to calculate daily lying time (h/d), daily number of lying bouts (LB; no./d), mean LB duration (min/bout), and the number of steps taken (steps/d). On d 0, the CLIN group were less active and spent approximately 2.6 h longer lying than the SUB and NORM groups, particularly between 0200 and 1400 h. On d 0, the NORM group had fewer LB (16.3/d) than the SUB and CLIN groups (18.2 and 19.2/d, respectively). These differences in behavior were no longer detected 2 d postcalving, and no further differences were observed. The day before calving, the CLIN group spent 1.4 h longer lying down than did the SUB and NORM groups. Further, the relative change in steps from a precalving baseline period (d -14 to -7) until d 0 was positively, linearly associated with blood Ca concentration within 24 h postcalving. Future work should consider daily and temporal changes in behavior in individual cows to determine the potential for these measures to allow early detection of hypocalcemia.

Sensor technology to support herd health monitoring: Using rumination duration and activity measures as unspecific variables for the early detection of dairy cows with health deviations

A significant number of lactating dairy cows are affected by health disorders in the early postpartum period. Precision dairy farming technologies have tremendous potential to support farmers in detecting disordered cows before clinical manifestation of a disease. The objective of this study was to evaluate if activity and rumination measures obtained by a commercial 3D-accelerometer system, i.e. "lying", "high active", "inactive", and "rumination" times, can be used for early identification of cows with health deviations before the clinical manifestation of disease. A total of 312 Holstein cows equipped with an ear attached accelerometer (Smartbow GmbH, Weibern, Austria) were monitored and analyzed from 14 days prior to parturition to eight days in milk (DIM). Animals were checked daily for clinical disorders from zero to eight DIM using standard operating procedures and by blood β-hydroxybutyrate measurements at three, five, and eight DIM. Cows that presented no symptoms of health problems and with BHB concentrations <1.2 mmol/L in the first eight DIM were classified as healthy (n = 156) and used as the reference in this study. Cows with disorders were allocated in groups with one disorder (n = 65) and >1 disorders (n = 91). "Rumination" durations per day were already shorter five days before the clinical diagnosis (D0) in diseased cows (401.9 ± 147.4 min/day) compared with healthy controls (434.6 ± 140.3 min/day). "Rumination" time decreased before the diagnosis, with a nadir at Day -1 for healthy cows and cows with >1 disorder (392.0 ± 147.9 vs. 313.4 ± 162.6 min/day). Cows with one disorder reached a nadir on Day -3 (388.8 ± 158.6 min/day). Similarly, the "high active" time started to become shorter three days before the clinical diagnosis in diseased cows compared to healthy cows (164.1 ± 119.1 vs. 200.3 ± 111.5 min/day). The times cows spent "inactive" were significantly longer three days before clinical diagnosis in diseased cows compared to healthy cows (421.7 ± 168.3 vs. 362.8 ± 117.6 min/day). "Lying" time started to become significantly longer one day before the diagnosis of disorders in disordered cows compared to healthy cows (691.8 ± 183.3 vs. 627.3 ± 158.0 min/day). On average, these results indicated a strong disturbance of physiological parameters before the clinical onset of disease. In summary, it was possible to show differences between disordered and healthy cows based on activity and "rumination" data recorded by a 3D-accelerometer.

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

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

Associations between motion activity, ketosis risk and estrus behavior in dairy cattle

Ketosis (acetonaemia) is a metabolic disorder that occurs in cattle when energy demand exceeds energy intake and results in a negative energy balance. The course of the disease often starts with a subclinical phase, so early detection is crucial for decisive strategies. The aim of this study was to determine whether daily motion activity could be used as an indicator of subclinical ketosis in early lactation and to evaluatethe effect of subclinical ketosis on activity at estrus. The study was carried out on a 75-cow dairy farm over 6 months. Data were collected from 48 cows between day 0 and day 70 post-partum. Beta-Hydroxybutyrate (BHB) concentrations were evaluated in milk samples using rapid on-site ketosis tests. A test was considered positive at a concentration of >100 μmol/l. The animals were divided into two groups: group 'Healthy' (H) and group 'Ketosis' (K). Once the on-site test was positive, the cows were assigned to group K. Progesterone concentrations were evaluated in milk by photometric detection of the colour reaction of a competitive, heterologous enzyme immunoassay (EIA). Each drop from ≥0.3 ng/ml to <0.3 ng/ml with a subsequent increase to ≥0.3 ng/ml was considered estrus. Daily milk yield, concentrate intake and motion activity were recorded from a computerized dairy management system with the associated software (DairyPlan C21). Animals in group K had lower average daily activity levels than animals in group H. In this study, statistically significant reduced motion activity in animals in group K was observed on days 6-12 post-partum (P < 0.001, χ² test) compared with the herd mean daily motion activity. Furthermore, a significant association could be found between motion activity and group affiliation (logistic regression models). The sensitivity of the detection of cows at risk for ketosis was 81.8 %, and the specificity was 65.4 %, retrospectively determined by their activity behaviour. The mean motion activity on the day of estrus was significantly (P < 0.05) lower in animals in group K than in those in group H. This method may help to establish a future early warning system for the risk of ketosis in dairy cows. Thus, cows at risk may be identified for further targeted diagnostics and for selective treatment procedures. This study confirms the already reported lasting effect of subclinical ketosis on reproductive efficiency.

On-farm use of disease alerts generated by precision dairy technology

Wearable precision dairy monitoring (PDM) technologies currently used to detect estrus may provide insight into disease detection. However, the incorporation of PDM into farm management and its perceived usefulness for dairy producers have not been explored. As the targeted end users of these products, information is needed on how producers use generated disease alerts as well as barriers to adoption and usefulness. The objective of this research was to assess the perceived usefulness producers attributed to alerts from a daily generated alert list designed to identify sick or injured cows or cows that experienced a major management change. Data from 1,171 cows on 4 commercial farms in Kentucky were collected from October 2015 to October 2016. Each cow was equipped with 2 PDM technologies: a leg tag (measuring activity in steps/d and lying time in h/d) and a neck collar (measuring eating time in h/d). Alerts were generated based on an individual cow's decrease of ≥30% in activity, lying, and eating time compared with each cow's 10-d moving mean. Producers sorted alerts into 3 overall categories: (1) the cow alert was perceived to be true and the cow was visually checked, (2) the cow alert was perceived to be true, but the cow was not visually checked, and (3) the cow alert behavior change was doubted by the producer and the cow was not visually checked. Further subdivisions were also provided to explain the choice for an overall category. Over the year, 24,012 cow alerts were generated (eating time, n = 9,543; lying time, n = 9,777; activity, n = 1,590; or a combination of behaviors, n = 3,102). Only 8% of the alerts were doubted by the producer. Although 55% of alerts were perceived to be true, producers visually assessed cows based on only 21% of the alerts with a large variation between farms (2 to 45% of the alerts visually assessed). Producers were more likely to completely ignore alerts over time. Producers were more likely to perceive cow alerts to be true and visually check cows when ≤20 alerts occurred per day, cows were fresh or in early lactation, alerts occurred during the work week, or when cow alerts were for eating time, activity, or a combination of multiple behaviors. Behavioral disease alerts must be improved and correspond to an actionable change for producers to use them. Incorporating herd management software, creating and managing alerts by lactation stage, and focusing on behaviors producers already find useful could improve future alert utilization.

Technical note: An evaluation of technology-recorded rumination and feeding behaviors in dairy heifers

Precision dairy monitoring technologies have become increasingly popular for recording rumination and feeding behaviors in dairy cattle. The objective of this study was to validate the rumination and feeding time functions of the CowManager SensOor (Agis, Harmelen, the Netherlands) against visual observation in dairy heifers. The study took place over a 44-d period beginning June 1, 2016. Holstein heifers equipped with CowManager SensOor tags attached according to manufacturer specifications (n = 49) were split into 2 groups based on age, diet, and housing type. Group 1 heifers (n = 24) were calves (mean ± SD) 2.0 ± 2.7 mo in age, fed hay and calf starter, and housed on a straw-bedded pack. Group 2 heifers (n = 25) were 17.0 ± 1.3 mo in age, fed a TMR, confirmed pregnant, and housed in freestalls. Visual observation shifts occurred at 1500, 1700, 1900, and 2100 h. Each heifer was observed for 2 hour-long periods, with both observation periods occurring on the same day. Visual observations were collected using a synchronized watch, and "start" and "stop" times were recorded for each rumination and feeding event. For correlations, data from CowManager SensOor tags and observations were averaged, so a single 1-h observation was provided per animal, reducing the potential for confounding repeated measures being collected for each animal. Concordance correlations (CCC; epiR package; R Foundation for Statistical Computing, Vienna, Austria) and Pearson correlations (r; CORR procedure; SAS Institute Inc., Cary, NC) were used to calculate association between visual observations and technology-recorded behaviors. Visually observed rumination time was correlated with the CowManager SensOor (r = 0.63, CCC = 0.55). Visually observed feeding time was also correlated with the CowManager SensOor (r = 0.88, CCC = 0.72). The difference between technology-recorded data and visual observation was treated as the dependent variable in a mixed linear model (MIXED procedure of SAS). Time of day, age in months, and group were treated as fixed effects. Individual heifers were treated as random and repeated effects. The effects of time of day, age, and group on rumination and feeding times were not significant. The CowManager SensOor was more effective at recording feeding behavior than rumination behavior in dairy heifers. The CowManager SensOor can be used to provide relatively accurate measures of feeding time in heifers, but its rumination time function should be used with caution.

Association between left-displaced abomasum corrected with 2-step laparoscopic abomasopexy and milk production in a commercial dairy farm in Italy

Background

Left displacement of the abomasum (LDA) is a condition of dairy cows that causes huge economic losses. The aim of the study was to evaluate the effect of LDA after on-farm correction by the 2-step laparoscopic abomasopexy on milk production based on 305-d milk yield on a commercial dairy farm in Italy.The study was performed between January 2011 and January 2014 on 58 Holstein Friesian cattle with left displacement of the abomasum in a commercial dairy farm in the farmland of Ozieri, Sardinia (Italy). Each cow underwent a 2-step laparoscopic abomasopexy performed by the same veterinarian. Each case was matched with a control herdmate by age, parity and calving date. Cows with LDA and healthy control cows also had a similar 305-d milk yield in the previous lactation. Data on milk production were collected using a dairy herd management software programme (Afimilk®, Afimilk Ltd., Israel). The 305-d lactation yield was obtained from the sum of daily milk yields for each cow. An unpaired Student’s t-test was used to compare changes in milk production, mean fat and protein percentage of cases and controls before and after surgical procedure.

Results

Data from 4 cows were excluded from the analysis due to post-surgical complications. 54 cases and 54 control cows participated in the study. We found that milk production significantly decreased from a baseline of 12,295 ± 1690 kg to 11,165 ± 1989 kg in the affected lactation. Conversely, a significant increase was observed for mean fat and protein percentage during lactation in case cows.

Conclusions

In the present study cows with left displacement of the abomasum corrected with 2-step laparoscopic abomasopexy produced less milk than their control herdmates. Each case and control pair in the present study came from the same farm in order to eliminate farm to farm differences in management, housing, season, etc. However, this limits the validity of our data to the specific situation described here.

Herd-Level Monitoring and Prevention of Displaced Abomasum in Dairy Cattle

Displaced abomasum (DA) is a postpartum disease that causes significant economic losses in the dairy industry. Abomasal atony and excessive production of gas have been reported as prerequisites for the development of DA. The exact cause of DA is unknown, yet infectious and metabolic disease, diet composition and physical form, cow comfort, and management of dairy cows during the transition period have been associated with the occurrence of this disorder. This review article discusses different factors that lead to the development of DA and strategies for monitoring DA and its comorbidities at the herd level.

Towards practical application of sensors for monitoring animal health; design and validation of a model to detect ketosis

The objective of this study was to design and validate a mathematical model to detect post-calving ketosis. The validation was conducted in four commercial dairy farms in Israel, on a total of 706 multiparous Holstein dairy cows: 203 cows clinically diagnosed with ketosis and 503 healthy cows. A logistic binary regression model was developed, where the dependent variable is categorical (healthy/diseased) and a set of explanatory variables were measured with existing commercial sensors: rumination duration, activity and milk yield of each individual cow. In a first validation step (within-farm), the model was calibrated on the database of each farm separately. Two thirds of the sick cows and an equal number of healthy cows were randomly selected for model validation. The remaining one third of the cows, which did not participate in the model validation, were used for model calibration. In order to overcome the random selection effect, this procedure was repeated 100 times. In a second (between-farms) validation step, the model was calibrated on one farm and validated on another farm. Within-farm accuracy, ranging from 74 to 79%, was higher than between-farm accuracy, ranging from 49 to 72%, in all farms. The within-farm sensitivities ranged from 78 to 90%, and specificities ranged from 71 to 74%. The between-farms sensitivities ranged from 65 to 95%. The developed model can be improved in future research, by employing other variables that can be added; or by exploring other models to achieve greater sensitivity and specificity.

Towards practical application of sensors for monitoring animal health: the effect of post-calving health problems on rumination duration, activity and milk yield

Three sources of sensory data: cow's individual rumination duration, activity and milk yield were evaluated as possible indicators for clinical diagnosis, focusing on post-calving health problems such as ketosis and metritis. Data were collected from a computerised dairy-management system on a commercial dairy farm with Israeli Holstein cows. In the analysis, 300 healthy and 403 sick multiparous cows were studied during the first 3 weeks after calving. A mixed model with repeated measurements was used to compare healthy cows with sick cows. In the period from 5 d before diagnosis and treatment to 2 d after it, rumination duration and activity were lower in the sick cows compared to healthy cows. The milk yield of sick cows was lower than that of the healthy cows during a period lasting from 5 d before until 5 d after the day of diagnosis and treatment. Differences in the milk yield of sick cows compared with healthy cows became greater from 5 to 1 d before diagnosis and treatment. The greatest significant differences occurred 3 d before diagnosis for rumination duration and 1 d before diagnosis for activity and milk yield. These results indicate that a model can be developed to automatically detect post-calving health problems including ketosis and metritis, based on rumination duration, activity and milk yield.

Fresh-cow handling practices and methods for identification of health disorders on 45 dairy farms in California

The aim of the present study was to describe fresh-cow handling practices and techniques used during fresh cow evaluations to identify postpartum health disorders on 45 dairy farms in California ranging from 450 to 9,500 cows. Fresh cow practices were surveyed regarding (a) grouping and housing, (b) scheduling and work organization, (c) screening for health disorders, and (d) physical examination methods. Information was collected based on cow-side observations and responses from fresh cow evaluators. Cows were housed in the fresh cow pen for 3 to 14 (20%), 15 to 30 (49%), or >31 (31%) d in milk. Fresh cow evaluations were performed daily (78%), 6 times a week (11%), 2 to 5 times a week (9%), or were not routinely performed (2%). There was significant correlation between the duration of fresh cow evaluations and the number of cows housed in the fresh pen. Across all farms, the duration of evaluations ranged from 5 to 240 min, with an average of 16 s spent per cow. During fresh cow checks, evaluators always looked for abnormal vaginal discharge, retained fetal membranes, and down cows. Dairies evaluated appetite based on rumen fill (11%), reduction of feed in the feed bunk (20%), rumination sensors (2%), or a combination of these (29%). Milk yield was evaluated based on udder fill at fresh cow checks (40%), milk flow during milking (11%), milk yield records collected by milk meters (2%), or a combination of udder fill and milk meters (5%). Depressed attitude was evaluated on 64% of the dairies. Health-monitoring exams for early detection of metritis were implemented on 42% of the dairies based on rectal examination (13%), rectal temperature (22%), or both (7%). Dairies implementing health-monitoring exams took longer to perform fresh cow evaluations. Physical examination methods such as rectal examination, auscultation, rectal temperature evaluation, and cow-side ketosis tests were used on 76, 67, 38, and 9% of dairies, respectively. Across dairies, we found large variation in signs of health disorders screened and how those signs were evaluated. Fresh cows were primarily evaluated based on nonspecific and subjective observations during screening. Future research efforts should focus on developing and validating scoring systems to more objectively identify health disorders in postpartum cows.

Short communication: Association of lying behavior and subclinical ketosis in transition dairy cows

The objective of this study was to characterize the association of lying behavior and subclinical ketosis (SCK) in transition dairy cows. A total of 339 dairy cows (107 primiparous and 232 multiparous) on 4 commercial dairy farms were monitored for lying behavior and SCK from 14d before calving until 28 d after calving. Lying time, frequency of lying bouts, and average lying bout length were measured using automated data loggers 24h/d. Cows were tested for SCK 1×/wk by taking a blood sample and analyzing for β-hydroxybutyrate; cows with β-hydroxybutyrate ≥1.2mmol/L postpartum were considered to have SCK. Cases of retained placenta, metritis, milk fever, or mastitis during the study period were recorded and cows were categorized into 1 of 4 groups: healthy (HLT) cows had no SCK or any other health problem (n=139); cows treated for at least 1 health issue other than SCK (n=50); SCK (HYK) cows with no other health problems during transition (n=97); or subclinically ketotic plus (HYK+) cows that had SCK and 1 or more other health problems (n=53). Daily lying time was summarized by week and comparisons were made between HLT, HYK, and HYK+, respectively. We found no difference among health categories in lying time, bout frequency, or bout length fromwk -2 towk +4 relative to calving for first-lactation cows. Differences in lying time for multiparous cows were seen inwk +1, when HYK+ cows spent 92±24.0 min/d more time lying down than HLT cows, and duringwk +3 and +4 when HYK cows spent 44±16.7 and 41±18.9 min/d, respectively, more time lying down than HLT cows. Increased odds of HYK+ were found to be associated with higher parity, longer dry period, and greater stall stocking density inwk -1 and longer lying time duringwk +1. When comparing HYK to HLT cows, the same variables were associated with odds of SCK; however, lying time was not retained in the final model. These results suggest that monitoring lying time may contribute to identifying multiparous cows experiencing SCK with another health problem after calving, but may not be useful in the early detection of SCK.

Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders

The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with metabolic and digestive disorders-including displaced abomasum, ketosis, and indigestion-based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of the disorders by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cattle (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. A HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as a HIS of <86 during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 98% [95% confidence interval (CI): 93, 100] for displaced abomasum (n=41); 91% (95% CI: 83, 99) for ketosis (n=54); 89% (95% CI: 68, 100) for indigestion (n=9); and 93% (95% CI: 89, 98) for all metabolic and digestive disorders combined (n=104). Days (mean and 95% CI) from the first positive HIS <86 and CD were -3 (-3.7, -2.3), -1.6 (-2.3, -1.0), -0.5 (-1.5, 0.5), and -2.1 (-2.5, -1.6) for displaced abomasum, ketosis, indigestion, and all metabolic and digestive disorders, respectively. The patterns of rumination, activity, and HIS for cows flagged by the AHMS were characterized by lower levels than for cows without a health disorder and cows not flagged by the AHMS from -5 to 5 d after CD, depending on the disorder and parameter. Differences between cows without health disorders and those flagged by the AHMS for blood markers of metabolic and health status confirmed the observations of the CD and AHMS alerts. The overall sensitivity and timing of the AHMS alerts for cows with metabolic and digestive disorders indicated that AHMS that combine rumination and activity could be a useful tool for identifying cows with metabolic and digestive disorders.

Dairy cows affected by ketosis show alterations in innate immunity and lipid and carbohydrate metabolism during the dry off period and postpartum

The objective of this investigation was to search for alterations in blood variables related to innate immunity and carbohydrate and lipid metabolism during the transition period in cows affected by ketosis. One hundred multiparous Holstein dairy cows were involved in the study. Blood samples were collected at -8, -4, week of disease diagnosis (+1 to +3weeks), and +4weeks relative to parturition from 6 healthy cows (CON) and 6 cows with ketosis and were analyzed for serum variables. Results showed that cows with ketosis had greater concentrations of serum β-hydroxybutyric acid (BHBA), interleukin (IL)-6, tumor necrosis factor (TNF), serum amyloid A (SAA), and lactate in comparison with the CON animals. Serum concentrations of BHBA, IL-6, TNF, and lactate were greater starting at -8 and -4weeks prior to parturition in cows with ketosis vs those of CON group. Cows with ketosis also had lower DMI and milk production vs CON cows. Milk fat also was lower in ketotic cows at diagnosis of disease. Cows affected by ketosis showed an activated innate immunity and altered carbohydrate and lipid metabolism several weeks prior to diagnosis of disease. Serum IL-6 and lactate were the strongest discriminators between ketosis cows and CON ones before the occurrence of ketosis, which might be useful as predictive biomarkers of the disease state.

A decision-tree model to detect post-calving diseases based on rumination, activity, milk yield, BW and voluntary visits to the milking robot

Early detection of post-calving health problems is critical for dairy operations. Separating sick cows from the herd is important, especially in robotic-milking dairy farms, where searching for a sick cow can disturb the other cows' routine. The objectives of this study were to develop and apply a behaviour- and performance-based health-detection model to post-calving cows in a robotic-milking dairy farm, with the aim of detecting sick cows based on available commercial sensors. The study was conducted in an Israeli robotic-milking dairy farm with 250 Israeli-Holstein cows. All cows were equipped with rumination- and neck-activity sensors. Milk yield, visits to the milking robot and BW were recorded in the milking robot. A decision-tree model was developed on a calibration data set (historical data of the 10 months before the study) and was validated on the new data set. The decision model generated a probability of being sick for each cow. The model was applied once a week just before the veterinarian performed the weekly routine post-calving health check. The veterinarian's diagnosis served as a binary reference for the model (healthy-sick). The overall accuracy of the model was 78%, with a specificity of 87% and a sensitivity of 69%, suggesting its practical value.

Movement Behaviour of Traditionally Managed Cattle in the Eastern Province of Zambia Captured Using Two-Dimensional Motion Sensors

Two-dimensional motion sensors use electronic accelerometers to record the lying, standing and walking activity of cattle. Movement behaviour data collected automatically using these sensors over prolonged periods of time could be of use to stakeholders making management and disease control decisions in rural sub-Saharan Africa leading to potential improvements in animal health and production. Motion sensors were used in this study with the aim of monitoring and quantifying the movement behaviour of traditionally managed Angoni cattle in Petauke District in the Eastern Province of Zambia. This study was designed to assess whether motion sensors were suitable for use on traditionally managed cattle in two veterinary camps in Petauke District in the Eastern Province of Zambia. In each veterinary camp, twenty cattle were selected for study. Each animal had a motion sensor placed on its hind leg to continuously measure and record its movement behaviour over a two week period. Analysing the sensor data using principal components analysis (PCA) revealed that the majority of variability in behaviour among studied cattle could be attributed to their behaviour at night and in the morning. The behaviour at night was markedly different between veterinary camps; while differences in the morning appeared to reflect varying behaviour across all animals. The study results validate the use of such motion sensors in the chosen setting and highlight the importance of appropriate data summarisation techniques to adequately describe and compare animal movement behaviours if association to other factors, such as location, breed or health status are to be assessed.

Integrating novel data streams to support biosurveillance in commercial livestock production systems in developed countries: challenges and opportunities

Reducing the burden of emerging and endemic infectious diseases on commercial livestock production systems will require the development of innovative technology platforms that enable information from diverse animal health resources to be collected, analyzed, and communicated in near real-time. In this paper, we review recent initiatives to leverage data routinely observed by farmers, production managers, veterinary practitioners, diagnostic laboratories, regulatory officials, and slaughterhouse inspectors for disease surveillance purposes. The most commonly identified challenges were (1) the lack of standardized systems for recording essential data elements within and between surveillance data streams, (2) the additional time required to collect data elements that are not routinely recorded by participants, (3) the concern over the sharing and use of business sensitive information with regulatory authorities and other data analysts, (4) the difficulty in developing sustainable incentives to maintain long-term program participation, and (5) the limitations in current methods for analyzing and reporting animal health information in a manner that facilitates actionable response. With the significant recent advances in information science, there are many opportunities to develop more sophisticated systems that meet national disease surveillance objectives, while still providing participants with valuable tools and feedback to manage routine animal health concerns.

Clinical ketosis and standing behavior in transition cows

Ketosis is a common disease in dairy cattle, especially in the days after calving, and it is often undiagnosed. The objective of this study was to compare the standing behavior of dairy cows with and without ketosis during the days around calving to determine if changes in this behavior could be useful in the early identification of sick cows. Serum β-hydroxybutyrate (BHBA) was measured in 184 cows on a commercial dairy farm twice weekly from 2 to 21d after calving. Standing behavior was measured from 7d before calving to 21d after calving using data loggers. Retrospectively, 15 cows with clinical ketosis (3 consecutive BHBA samples >1.2mmol/L and at least one sample of BHBA >2.9mmol/L) were matched with 15 nonketotic cows (BHBA <1.2mmol/L). Five periods were defined for the statistical analyses: wk -1 (d -7 to -1), d 0 (day of calving), wk +1 (d 1 to 7), wk +2 (d 8 to 14), and wk +3 (d 15 to 21). The first signs of clinical ketosis occurred 4.5±2.1d after calving. Total daily standing time was longer for clinically ketotic cows compared with nonketotic cows during wk -1 (14.3±0.6 vs. 12.0±0.7h/d) and on d 0 (17.2±0.9 vs. 12.7±0.9h/d) but did not differ during the other periods. Clinically ketotic cows exhibited fewer standing bouts compared with nonketotic cows on d 0 only (14.6±1.9 vs. 20.9±1.8bouts/d). Average standing bout duration was also longer for clinically ketotic cows on d 0 compared with nonketotic cows [71.3min/bout (CI: 59.3 to 85.5) vs. 35.8min/bout (CI: 29.8 to 42.9)] but was not different during the other periods. Differences in standing behavior in the week before and on the day of calving may be useful for the early detection of clinical ketosis in dairy cows.