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

Ruminal acidosis and its definition: A critical review

Ruminal acidosis occurs as a continuum of disorders, stemming from ruminal dysbiosis and disorders of metabolism, of varying severity. The condition has a marked temporal dynamic expression resulting in cases expressing quite different rumen concentrations of VFA, lactic acid, ammonia, and rumen pH over time. Clinical ruminal acidosis is an important condition of cattle and subclinical ruminal acidosis (SRA) is very prevalent in many dairy populations with estimates between 10 to 26% of cows in early lactation. Estimates of the duration of a case suggest the lactational incidence of the condition may be as high as 500 cases per 100 cows in the first 100 d of lactation. Historical confusion about the etiology and pathogenesis of ruminal acidosis led to definitions that are not fit for purpose as acidic ruminal conditions solely characterized by ruminal pH determination at a single point fail to reflect the complexity of the condition. Use of a model, based on integrated ruminal measures including VFA, ammonia, lactic acid, and pH, for evaluating ruminal acidosis is fit for purpose, as indicated by meeting postulates for assessing metabolic disease, but requires a method to simplify application in the field. While it is likely that this model, that we have termed the Bramley Acidosis Model (BAM), will be refined, the critical value in the model is that it demonstrates that ruminal acidosis is much more than ruminal pH. Disease, milk yield and milk composition are more associated with the BAM than rumen pH alone. Two single VFA, propionate and valerate are sensitive and specific for SRA, especially when compared with rumen pH. Even with the use of such a model, astute evaluations of the condition whether in experimental or field circumstances will be aided by ancillary measures that can be used in parallel or in series to enhance diagnosis and interpretation. Sensing methods including rumination detection, behavior, milk analysis, and passive analysis of rumen function have the potential to improve the detection of SRA; however, these may advance more rapidly if SRA is defined more broadly than by ruminal pH alone.

Farm-level risk factors associated with increased milk β-hydroxybutyrate and hyperketolactia prevalence on farms with automated milking systems

The objectives of this study were to determine the farm-level hyperketolactia (HKL) prevalence, as diagnosed from milk β-hydroxybutyrate (BHB) concentration, on dairy farms milking with an automatic milking system (AMS) and to describe the farm-level housing, management, and nutritional risk factors associated with increased farm-average milk BHB and the within-herd HKL prevalence in the first 45 DIM. Canadian AMS farms (n = 162; eastern Canada n = 8, Quebec n = 23, Ontario n = 75, western Canada n = 55) were visited once between April to September 2019 to record housing and herd management practices. The first test milk data for each cow under 45 DIM were collected, along with the final test of the previous lactations for all multiparous cows, from April 1, 2019 to September 30, 2020. The first test milk BHB was then used to classify each individual cow as having HKL (milk BHB ≥ 0.15 mmol/L) at the time of testing. Milk fat and protein content, milk BHB, and HKL prevalence were summarized by farm and lactation group (all, primiparous, and multiparous). During this same time period, formulated diets for dry and lactating cows, including ingredients and nutrient composition, and AMS milking data were collected. Data from the AMS were used to determine milking behaviors and milk production of each herd during the first 45 DIM. Multivariable regression models were used to associate herd-level housing, feeding management practices, and formulated nutrient composition with first test milk BHB concentrations and within-herd HKL levels separately for primiparous and multiparous cows. The within-herd HKL prevalence for all cows was 21.8%, with primiparous cows having a lower mean prevalence (12.2 ± 9.2%) than multiparous cows (26.6 ± 11.3%). Milk BHB concentration (0.095 ± 0.018 mmol/L) and HKL prevalence for primiparous cows were positively associated with formulated prepartum DMI and forage content of the dry cow diet while being negatively associated with formulated postpartum DMI, the major ingredient in the concentrate supplemented through the AMS, and the PMR-to-AMS concentrate ratio. However, multiparous cows' milk BHB concentration (0.12 ± 0.023 mmol/L) and HKL prevalence were positively associated with the length of the previous lactation, milk BHB at dry off, prepartum diet nonfiber carbohydrate content, and the major forage fed on farm, while tending to be negatively associated with feed bunk space during lactation. This is the first study to determine the farm-level risk factors associated with herd-level prevalence of HKL in AMS dairy herds, thus helping optimize management and guide diet formulation to promote the reduction of HKL prevalence.

The welfare of ill and injured feedlot cattle: a review of the literature and implications for managing feedlot hospital and chronic pens

By definition, ill and injured animals are on the negative valence of animal welfare. For beef cattle kept in feedlot settings, advances in cattle health management have resulted in a greater understanding and prevention of illness and injury. However, the management of cattle once they become ill and injured is an understudied area, and there are gaps in knowledge that could inform evidence-based decision-making and strengthen welfare for this population. The aim of this review is to provide a comprehensive overview of the acquired knowledge regarding ill and injured feedlot cattle welfare, focusing on existing knowledge gaps and implications for hospital and chronic pen management and welfare assurance. Ill and injured feedlot cattle consist of acutely impaired animals with short-term health conditions that resolve with treatment and chronically impaired animals with long-term health conditions that may be difficult to treat. A literature search identified 110 articles that mentioned welfare and ill and injured feedlot cattle, but the population of interest in most of these articles was healthy cattle, not ill and injured cattle. Articles about managing ill and injured cattle in specialized hospital (n = 12) or chronic (n = 2) pens were even more sparse. Results from this literature search will be used to outline the understanding of acutely and chronically ill and injured feedlot cattle, including common dispositions and welfare considerations, behavior during convalescence, and strategies for identifying and managing ill and injured cattle. Finally, by working through specific ailments common in commercial feedlot environments, we illustrate how the Five Domains Model can be used to explore feelings and experiences and subsequent welfare state of individual ill or injured feedlot cattle. Using this approach and our knowledge of current industry practices, we identify relevant animal-based outcomes and critical research questions to strengthen knowledge in this area. A better understanding of this overlooked topic will inform future research and the development of evidence-based guidelines to help producers care for this vulnerable population.

Associations among post-partum rumen fill and motility, subclinical ketosis and fertility in Holstein dairy cows

This prospective observational study aimed to investigate the association of rumen fill and motility in post-partum Holstein cows with their future reproductive performance and subclinical ketosis (SCK). The study population consisted of two independent data sets: the first (DS1) included 237 cows from 6 herds and the second one (DS2) 709 cows from 9 herds. Rumen Fill Score (RFS) was transformed into a 3 level-trait, representing very low, low and adequate dry matter intake, respectively. A binary Rumen Contraction Score (RCS) was defined as: 0: <2 contractions/2 min, impaired rumen motility and 1: ≥2 contractions/2 min, normal rumen motility. A combined binary trait based on RFS and RCS (RFCS) was also established, representing unsatisfactory and satisfactory rumen function. Three SCK traits were defined, based on 3 different thresholds, SCK_I: BHB≥1,000 mmol/L, SCK_II: BHB≥1,100 mmol/L and SCK_III: BHB≥1,200 mmol/L. Scores were assessed and blood samples collected on day 7 (DS1) or day 8 (DS2), postpartum. Kaplan-Meier survival analysis, multivariable Cox proportional hazards models and Generalized Linear Mixed Models were performed to evaluate the association of rumen and SCK traits with reproduction. Herd, parity, calving season and several postparturient diseases were also included as potential explanatory variables. Mean days from calving to pregnancy after the 1st artificial insemination (AI) and from calving to pregnancy (all AIs) were shorter for levels of rumen traits representing adequate DMI and normal rumen motility; in most cases these differences were statistically significant in both datasets. Cows with adequate DMI and normal rumen motility (only in DS2) had greater hazard (hazard ratio [HR] = 1.84 and 1.61, for RFS and RFCS, respectively) and odds (odds ratio [OR] = 2.49 and 1.98, for RFS and RFCS, respectively) for pregnancy at 1st AI. Assessment of the association of examined rumen traits with hazard and odds for pregnancy at all AIs yielded statistically significant results in both datasets. For RFS, RCS and RFCS, HRs ranged from 1.57 to 3.31 and ORs from 1.95 to 4.83. No statistically significant associations with hazard and odds for pregnancy at 1st or all AIs were detected, for any of the 3 SCK traits, in either dataset. Overall, the combined RFCS trait constantly identified more than twice the number of cows with future reproductive problems than a positive SCK blood test.

CherryChèvre: A fine-grained dataset for goat detection in natural environments

We introduce a new dataset for goat detection that contains 6160 annotated images captured under varying environmental conditions. The dataset is intended for developing machine learning algorithms for goat detection, with applications in precision agriculture, animal welfare, behaviour analysis, and animal husbandry. The annotations were performed by expert in computer vision, ensuring high accuracy and consistency. The dataset is publicly available and can be used as a benchmark for evaluating existing algorithms. This dataset advances research in computer vision for agriculture.

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.

Behavioral Fingerprinting: Acceleration Sensors for Identifying Changes in Livestock Health

During disease or toxin challenges, the behavioral activities of grazing animals alter in response to adverse situations, potentially providing an indicator of their welfare status. Behavioral changes such as feeding behavior, rumination and physical behavior as well as expressive behavior, can serve as indicators of animal health and welfare. Sometimes behavioral changes are subtle and occur gradually, often missed by infrequent visual monitoring until the condition becomes acute. There is growing popularity in the use of sensors for monitoring animal health. Acceleration sensors have been designed to attach to ears, jaws, noses, collars and legs to detect the behavioral changes of cattle and sheep. So far, some automated acceleration sensors with high accuracies have been found to have the capacity to remotely monitor the behavioral patterns of cattle and sheep. These acceleration sensors have the potential to identify behavioral patterns of farm animals for monitoring changes in behavior which can indicate a deterioration in health. Here, we review the current automated accelerometer systems and the evidence they can detect behavioral patterns of animals for the application of potential directions and future solutions for automatically monitoring and the early detection of health concerns in grazing animals.

Industry 4.0 and Precision Livestock Farming (PLF): An up to Date Overview across Animal Productions

Precision livestock farming (PLF) has spread to various countries worldwide since its inception in 2003, though it has yet to be widely adopted. Additionally, the advent of Industry 4.0 and the Internet of Things (IoT) have enabled a continued advancement and development of PLF. This modern technological approach to animal farming and production encompasses ethical, economic and logistical aspects. The aim of this review is to provide an overview of PLF and Industry 4.0, to identify current applications of this rather novel approach in different farming systems for food producing animals, and to present up to date knowledge on the subject. Current scientific literature regarding the spread and application of PLF and IoT shows how efficient farm animal management systems are destined to become. Everyday farming practices (feeding and production performance) coupled with continuous and real-time monitoring of animal parameters can have significant impacts on welfare and health assessment, which are current themes of public interest. In the context of feeding a rising global population, the agri-food industry and industry 4.0 technologies may represent key features for successful and sustainable development.

How Far Are We From Data-Driven and Animal-Based Welfare Assessment? A Critical Analysis of European Quality Schemes

Within the European Union, there is no harmonization of farm animal welfare quality schemes for meat and dairy products. Instead, there are several industry-driven initiatives and voluntary schemes that seek to provide information on animal welfare for attentive consumers. This study had two aims. First, we quantified how selected industry-wide quality schemes cover the welfare of pigs and dairy cattle on farms by comparing the evaluation criteria selected by schemes with the animal-, resource- and management-based measures defined in the Welfare Quality protocol (WQ®). Second, we identified how these quality schemes use the data generated along the value chain (sensors, breeding, production, and health recordings) for animal welfare assessments. A total of 12 quality schemes, paying attention to animal welfare but not necessarily limited to welfare, were selected for the analysis. The schemes originated from eight European countries: Finland, Sweden, Denmark, Ireland, the Netherlands, Germany, Austria, and Spain. Among the studied quality schemes, we have identified 19 standards for certification: nine for dairy and 10 for pig production. Most of the analyzed standards were comprehensive in welfare assessment. In total, 15 out of 19 standards corresponded to WQ® in more than 70%. However, this high correspondence was obtained when allowing for different information sources (environment instead of animal) than defined in WQ®. Compared to WQ®, the investigated schemes were lagging in terms of the number of measures evaluated based on the animals, with only five standards, out of 19, using predominantly animal-based measures. The quality schemes mostly applied resource-based instead of animal-based measures while assessing good health and appropriate behavior. The utilization of data generated along the value chain by the quality schemes remains insignificant as only one quality scheme allowed the direct application of sensor technologies for providing information on animal welfare. Nevertheless, several schemes used data from farm recording systems, mostly on animal health. The quality schemes rely mostly on resource-based indicators taken during inspection visits, which reduce the relevance of the welfare assessment. Our results suggest that the quality schemes could be enhanced in terms of data collection by the broader utilization of data generated along the value chain.

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” (&lt;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.

Brief Research Report: How Do Claw Disorders Affect Activity, Body Weight, and Milk Yield of Multiparous Holstein Dairy Cows?

Claw disorders are among the most relevant health problems in dairy herds. Despite being often not clearly visible and not easily detectable for farmers, they may appear as peculiar cow behavioral and performance patterns. This retrospective study aimed to assess cow's behavior and production variations associated with claw disorders. The study involved 54 lactating Italian Holstein cows reared on the same dairy farm. A veterinarian performed the routine hoof trimming every 6 months, diagnosing specific claw disorders. Multiparous cows with no disorders at the first trimming were selected and monitored for the two following trimming sessions. Data coming from the automatic milking system and neck collars and related to the 15 days before a given cow was diagnosed with claw problems during trimming were further collected. These data were compared with those recorded for the same animal over the 15 days preceding the previous trimming in which no claw disorders were observed. Compared to when they had no disorders, the cows affected by claw disorders had a lower daily activity (405 vs. 429 ± 27.7 units/day, p < 0.001), showing a constant decrease in the last 10 days before the trimming, a lower milk yield (26.5 vs. 28.4 ± 1.57 kg/day, p = 0.03), and only a decreasing trend of rumination time. These patterns of activity, milk yield, and rumination characterizing cows affected by claw disorders should promote the development of specific algorithms that would enable early detection of lameness thanks to the deviations of these parameters that are sensitive to cow claw health.

Changes in social and feeding behaviors, activity, and salivary serum amyloid A in cows with subclinical mastitis

The aim of this study was to identify detailed changes in behavior, and in salivary serum amyloid A (SAA), associated with subclinical mastitis. This included standard sickness behaviors, such as decreased activity, feeding and drinking (here labeled "core maintenance" behaviors), and less well-studied social, grooming, and exploratory behaviors (here labeled "luxury" behaviors). Luxury behaviors are biologically predicted to change at lower levels of mastitis infection and are, therefore, particularly relevant to detecting subclinical mastitis. Salivary serum amyloid A is a physiological marker of systemic inflammation, with levels in milk and serum already known to increase during subclinical mastitis. We investigated whether the same was true for SAA in cow saliva. Data were collected for 17 matched pairs of commercial barn-housed Holstein-Friesian cows. Each pair comprised a cow with subclinical mastitis (SCM) and a healthy control (CTRL), identified using somatic cell count (SCC; SCM: SCC >200 × 1,000 cells/mL; CTRL: SCC <100 × 1,000 cells/mL). SCM cows were selected for study ad hoc, at which point they were paired with a CTRL cow, based upon parity and calving date; consequently, the full data set was accrued over several months. Data were collected for each pair over 3 d: SCC (d 1), behavior (d 2), salivary SAA (d 3). All behaviors performed by the focal cows over a single 24-h period were coded retrospectively from video footage, and differences between the SCM and CTRL groups were investigated using the main data set and a subset of data corresponding to the hour immediately following morning food delivery. Saliva was collected using cotton swabs and analyzed for SAA using commercially available ELISA kits. We report, for the first time, that an increase in salivary SAA occurs during subclinical mastitis; SAA was higher in SCM cows and demonstrated a positive (weak) correlation with SCC. The behavioral comparisons revealed that SCM cows displayed reductions in activity (behavioral transitions and distance moved), social exploration, social reactivity (here: likelihood to be displaced following receipt of agonism), performance of social grooming and head butts, and the receipt of agonistic noncontact challenges. In addition, SCM cows received more head swipes, and spent a greater proportion of time lying with their head on their flank than CTRL cows. The SCM cows also displayed an altered feeding pattern; they spent a greater proportion of feeding time in direct contact with 2 conspecifics, and a lower proportion of feeding time at self-locking feed barriers, than CTRL cows. Behavioral measures were found to correlate, albeit loosely, with serum SAA in a direction consistent with predictions for sickness behavior. These included positive correlations with lying duration and the receipt of all agonistic behavior, and negative correlations with feeding, drinking, the performance of all social and all agonistic behavior, and social reactivity. We conclude that changes in salivary SAA, social behavior, and activity offer potential in the detection of subclinical mastitis and recommend further investigation to substantiate and refine our findings.

Individual dairy cow management: achievements, obstacles and prospects

This review deals with the prospects and achievements of individual dairy cow management (IDCM) and the obstacles and difficulties encountered in attempts to successfully apply IDCM into routine dairy management. All aspects of dairy farm management, health, reproduction, nutrition and welfare are discussed in relation to IDCM. In addition, new IDCM R&D goals in these management fields are suggested, with practical steps to achieve them. The development of management technologies is spurred by the availability of off-the-shelf sensors and expanded recording capacity, data storage, and computing capabilities, as well as by demands for sustainable dairy production and improved animal wellbeing at a time of increasing herd size and milk production per cow. Management technologies are sought that would enable the full expression of genetic and physiological potential of each cow in the herd, to achieve the dairy operation's economic goals whilst optimizing the animal's wellbeing. Results and conclusions from the literature, as well as practical experience supported by published and unpublished data are analyzed and discussed. The object of these efforts is to identify knowledge and management routine gaps in the practical dairy operation, in order to point out directions and improvements for successful implementation of IDCM in the dairy cows' health, reproduction, nutrition and wellbeing.

Rumination time around dry-off relative to the development of diseases in early-lactation cows

Monitoring rumination time (RT) around the time of calving is an effective way of identifying cows at risk of disease in early lactation. However, this only allows for the identification of cows a few days before the onset of clinical signs; thus, effective preventive measures cannot be implemented. Recent research has suggested that biomarkers of immune and metabolic function measured at dry-off (DO) can predict higher disease risk in early lactation. Nevertheless, the extent to which RT around DO is associated with early-lactation disease risk remains unexplored. Thus, the objective of this study was to compare RT in the weeks before and after DO between cows that did and did not experience health disorders in early lactation. For this, we conducted an observational retrospective cohort study utilizing the records available from a large commercial dairy herd in which RT is recorded daily using an automated system. Daily RT from -7 to +14 d relative to DO from 2,258 DO cycles and their respective health records in the first 60 d in milk were used. Differences in RT between animals with and without a disease history were tested with the Student t-test with Bonferroni adjustment. Mixed linear regression analyses were performed to assess differences in RT around DO and the association of RT with the occurrence of mastitis, metritis, retained placenta, hyperketonemia, lameness, hypocalcemia, pneumonia, and displaced abomasum. Rumination time decreased abruptly at DO and remained lower for 3 to 4 d compared with the days before DO. On average, cows affected by hyperketonemia and lameness ruminated 9.83 ± 6.40 and 15.00 ± 6.08 min/d less than unaffected cows, respectively. Cows that developed lameness in the first 60 d in milk showed reduced RT from 1 to 3 d following DO compared with cows that were not diagnosed with lameness in early lactation. However, RT around DO was not associated with the occurrence of the other health disorders studied here. Our results demonstrate that DO is a stressful event for dairy cows resulting in decreased RT for several days. Furthermore, the association between RT around DO and some early-lactation diseases suggests that RT could be a useful tool to identify at-risk cows early enough to allow for preventive interventions. Further studies should investigate the diagnostic utility of incorporating RT data early in the dry period in the disease prediction algorithms of rumination sensors.

Rumination time as an early predictor of metritis and subclinical ketosis in dairy cows at the beginning of lactation: Systematic review-meta-analysis

Daily rumination time (RT; min/d) is recognized as an important tool for assessing the health of dairy cows, which may depend on the disease, lactation stage and individual cows. Using a systematic review-meta-analysis, this study evaluated whether the variation in RT is effective for early detection of metritis and subclinical ketosis (SCK) in dairy cows in the pre and post-partum periods (from three weeks before to three weeks after calving). The research was carried out in four electronic databases - Scopus, Science Direct, Pubmed and Web of Science. The main inclusion criteria were original research; evaluation of RT in dairy cows; and use of RT for early identification of metritis and/or SCK in post-partum dairy cows. A random effect meta-analysis (MA) was conducted for each disease (metritis and SCK) separately, with the RT means of healthy and sick groups, measured in the pre and post-partum. The effect size used was the mean difference (MD).Twenty-two trials from six studies were included in the MA, involving 1494 dairy cows. For metritis, four trials from three studies in the pre-partum period were considered as well as five trials from four studies in the post-partum. For SCK, six trials from four studies pre-partum and seven trials from five studies in the post-partum period were taken into consideration. The heterogeneity between studies for metritis was null (I² = 0%) and low (I² = 5.7 %) in the pre-partum and in the post-partum, respectively. The MD of RT between healthy cows and those with metritis was different in the pre (MD =0.411 min/d; P < 0.001) and in the post-partum (MD =0.279 min/d; P < 0.001). In SCK, heterogeneity was high in the pre (I² = 69 %) and in the post-partum (I² = 58.1 %), and the MD of RT was similar between healthy and sick cows (P> 0.05). In a meta-regression, RT from primiparous cows showed a lower predicted value for MD (0.48 min. d; P < 0.05) compared to multiparous cows, and the increment in each unit of milk production decreased the predicted MD value by 0.08 min. d (P < 0.001). Our MA demonstrates that RT is a good predictor for early detection of metritis in pre and post-partum; however, it is not an adequate predictor for SCK. Further investigations using more frequent blood sampling and the same threshold values for BHB are required to assess the adequacy of rumination time to predict SCK.

Advancements in sensor technology and decision support intelligent tools to assist smart livestock farming

Remote monitoring, modern data collection through sensors, rapid data transfer, and vast data storage through the Internet of Things (IoT) have advanced precision livestock farming (PLF) in the last 20 yr. PLF is relevant to many fields of livestock production, including aerial- and satellite-based measurement of pasture’s forage quantity and quality; body weight and composition and physiological assessments; on-animal devices to monitor location, activity, and behaviors in grazing and foraging environments; early detection of lameness and other diseases; milk yield and composition; reproductive measurements and calving diseases; and feed intake and greenhouse gas emissions, to name just a few. There are many possibilities to improve animal production through PLF, but the combination of PLF and computer modeling is necessary to facilitate on-farm applicability. Concept- or knowledge-driven (mechanistic) models are established on scientific knowledge, and they are based on the conceptualization of hypotheses about variable interrelationships. Artificial intelligence (AI), on the other hand, is a data-driven approach that can manipulate and represent the big data accumulated by sensors and IoT. Still, it cannot explicitly explain the underlying assumptions of the intrinsic relationships in the data core because it lacks the wisdom that confers understanding and principles. The lack of wisdom in AI is because everything revolves around numbers. The associations among the numbers are obtained through the “automatized” learning process of mathematical correlations and covariances, not through “human causation” and abstract conceptualization of physiological or production principles. AI starts with comparative analogies to establish concepts and provides memory for future comparisons. Then, the learning process evolves from seeking wisdom through the systematic use of reasoning. AI is a relatively novel concept in many science fields. It may well be “the missing link” to expedite the transition of the traditional maximizing output mentality to a more mindful purpose of optimizing production efficiency while alleviating resource allocation for production. The integration between concept- and data-driven modeling through parallel hybridization of mechanistic and AI models will yield a hybrid intelligent mechanistic model that, along with data collection through PLF, is paramount to transcend the current status of livestock production in achieving sustainability.

Associations between body condition score, locomotion score, and sensor-based time budgets of dairy cattle during the dry period and early lactation

Lameness, one of the most important disorders in the dairy industry, is related to postpartum diseases and has an effect on dairy cow welfare, leading to changes in cows' daily behavioral variables. This study quantified the effect of lameness on the daily time budget of dairy cows in the transition period. In total, 784 multiparous dairy cows from 8 commercial Dutch dairy farms were visually scored on their locomotion (score of 1-5) and body condition (score of 1-5). Each cow was scored in the early and late dry period as well as in wk 4 and 8 postpartum. Cows with locomotion scores 1 and 2 were grouped together as nonlame, cows with score 3 were considered moderately lame, and cows with scores 4 and 5 were grouped together as severely lame. Cows were equipped with 2 types of sensors that measured behavioral parameters. The leg sensor provided number of steps, number of stand-ups (moving from lying to standing), lying time, number of lying bouts, and lying bout length. The neck sensor provided eating time, number of eating bouts, eating bout length, rumination time, number of rumination bouts, and rumination bout length. Sensor data for each behavioral parameter were averaged between 2 d before and 2 d after locomotion scoring. The percentage of nonlame cows decreased from 63% in the early dry period to 46% at 8 wk in lactation; this decrease was more severe for cows with higher parity. Cows that calved in autumn had the highest odds for lameness. Body condition score loss of >0.75 point in early lactation was associated with lameness in wk 4 postpartum. Moderately lame cows had a reduction of daily eating time of around 20 min, whereas severely lame cows had a reduction of almost 40 min. Similarly, moderately and severely lame dry cows showed a reduction of 200 steps/d, and severely lame cows in lactation showed a reduction of 600 steps/d. Daily lying time increased by 26 min and lying bout length increased by 8 min in severely lame cows compared with nonlame cows. These results indicate a high prevalence of lameness on Dutch dairy farms, with an increase in higher locomotion scores from the dry period into early lactation. Time budgets for multiparous dairy cows differed between the dry period and the lactating period, with a higher locomotion score (increased lameness) having an effect on cows' complete behavioral profile. Body condition score loss in early lactation was associated with poor locomotion postpartum, whereas lameness resulted in less eating time in the dry period and early lactation, creating a harmful cycle.

Alterations in sick dairy cows' daily behavioural patterns

The recent development of dairy production is characterised by increasing herd sizes and therefore increasingly complicated visual observation of cow behaviour, which is traditionally the basis for diagnoses of production diseases. The limitation of the direct visual behavioural observation due to the increasing number of individual cows implies a growing need for an automated detection of changes within behavioural patterns to identify cows that show sickness behaviour. Sensor systems can be used to measure behavioural patterns such as activity, resting, feeding and rumination. Behavioural patterns change with the occurrence of sickness but also interact with external factors. Changes such as prolonged lying duration or shortened feeding duration caused by metabolic disorders or infections, respectively, then serve as a detection tool for sick individuals. The aim of the present review is to outline the impact of production diseases on the daily behavioural patterns of dairy cows by referring to sickness behaviour.

Can rumination time and some blood biochemical parameters be used as biomarkers for the diagnosis of subclinical acidosis and subclinical ketosis?

According to the past reports, the utility value of monitoring rumination time (RT) around the time at which calving takes place and, in particular, during the first week of lactation, is a way of identifying in a timely fashion those cows that are at a greater level of risk when it comes to developing disease in early lactation. Recent reports have focused on the role of minerals in disease resistance in ruminants, but little is known about the concentrations blood parameters in dairy cows with subclinical acidosis and subclinical clinical ketosis. According this we hypothesised that rumination time and some blood biochemical parameters (including cortisol and lactate) can serve as biomarkers for subclinical acidosis (SARA) and subclinical ketosis (SCK). Accordingly, the aim of the current study was to determinate the impact of subclinical acidosis and ketosis on rumination time and some blood biochemical parameters. For the current study, of a total of 225 fresh dairy cows (between one and sixty days after calving) a general clinical examination produced a selection of 93 cows: ten of these were diagnosed with SARA, thirteen had SCK and seventy were clinical healthy cows. Rumination time (RT), body weight (BW), and milk yield (MY) were registered with the help of Lely Astronaut® A3 milking robots. It was determining the concentrations of blood serum albumin (Alb), total protein levels (TP), glucose (Glu), urea (Urea), calcium (Ca), phosphor (Phos), iron (Fe), alaninaminotranspherase (ALT), aspartataminotranspherase (AST), Gammagliutamyltranspherase (GGT), and creatinine (Cre). RT decreases and blood lactate rates increase in cases of SARA and SKC, while in cases of SARA the total blood protein levels increased and in the SCK group it decreased.A similar trend of differences between the SARA group and the SCK group in terms of healthy cows could be found in changes in blood urea, glucose, Ca, Mg, P, and Fe. Cows in the SCK group showed statistically higher ALB content levels, while the activity of AST and Crea was at a lower level. According to this, rumination time, and some blood biochemical parameters can be used as biomarkers in the diagnosis ofsubclinical acidosis and ketosis. Future studies, however, are needed so that these results can be compared across a greater number of animals.

Consistency of feed efficiency ranking and mechanisms associated with inter-animal variation among growing calves

This study investigated the possible mechanisms for explaining interanimal variation in efficiency of feed utilization in intact male Holstein calves. Additionally, we examined whether the feed efficiency (FE) ranking of calves (n = 26) changed due to age and/or diet quality. Calves were evaluated during three periods (P1, P2, and P3) while fed a high-quality diet (calculated mobilizable energy [ME] of 11.8 MJ/kg DM) during P1 and P3, and a low-quality diet (calculated ME of 7.7 MJ/kg DM) during P2. The study periods were 84, 119, and 127 d, respectively. Initial ages of the calves in P1, P2, and P3 were 7, 11, and 15 mo, respectively, and initial body weight (BW) were 245, 367, and 458 kg, respectively. Individual dry matter intake (DMI), average daily gain (ADG), diet digestibility, and heat production (HP) were measured in all periods. The measured FE indexes were: residual feed intake (RFI), the gain-to-feed ratio (G:F), residual gain (RG), residual gain and intake (RIG), the ratio of HP-to-ME intake (HP/MEI), and residual heat production (RHP). For statistical analysis, animals' performance data in each period, were ranked by RFI, and categorized into high-, medium-, and low-RFI groups (H-RFI, M-RFI, and L-RFI). RFI was not correlated with in vivo digestibility, age, BW, BCS, or ADG in all three periods. The L-RFI group had lowest DMI, MEI, HP, retained energy (RE), and RE/ADG. Chemical analysis of the longissimus dorsi muscle shows that the L-RFI group had a higher percentage of protein and a lower percentage of fat compared to the H-RFI group. We suggested that the main mechanism separating L- from H-RFI calves is the protein-to-fat ratio in the deposited tissues. When efficiency was related to kg/day (DMI and ADG) and not to daily retained energy, the selected efficient L-RFI calves deposited more protein and less fat per daily gain than less efficient H-RFI calves. However, when the significant greater heat increment and maintenance energy requirement of protein compared to fat deposition in tissue were considered, we could not exclude the hypothesis that variation in efficiency is partly explained by efficient energy utilization. The ranking classification of calves to groups according to their RFI efficiency was independent of diet quality and age.

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.