158 research outputs found

    PRECISION DAIRY FARMING TECHNOLOGY SOLUTIONS FOR DETECTING DAIRY COW DISEASE TO IMPROVE DAIRY COW WELL-BEING

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    Dairy cow health is multifactorial and complex. High producing dairy cows have been described as metabolic athletes, but metabolic and infectious diseases around calving affect many cows. These diseases have drastic negative effects on dairy cow well-being, milk production, and dairy farm economics. Early disease detection could potentially improve disease management, treatment, and future prevention techniques. The first objective of this research was to evaluate the use of activity, lying behavior, reticulorumen temperature, and rumination time determined by precision dairy farming technologies to detect transition cow diseases including hypocalcemia, ketosis, and metritis. The second objective was to evaluate the ability of activity, body weight, feeding behavior, lying behavior, milking order, milk yield and components, reticulorumen temperature, and rumination time determined by precision dairy farming technologies to predict clinical mastitis cases. The last objective of this research was to evaluate the precision dairy farming technologies used in Objective 3 to predict subclinical cases

    LSTM Models to Support the Selective Antibiotic Treatment Strategy of Dairy Cows in the Dry Period

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    Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceUdder inflammation, known as mastitis, is the most significant disease of dairy cows worldwide, invoking substantial economic losses. The current common strategy to reduce this problem is the prophylactic administration of antibiotics treatment of cows during their dry period. Paradoxically, the indiscriminate use of antibiotics in animals and humans has been the leading cause of antimicrobial resistance, a concern in several public health organizations. In light of these assumptions, at the beginning of 2022, the European Union made it illegal to routinely administer antibiotics on farms, with Regulation 2019/6 of 11 December 2018. Considering this new scenario, the objective of this study was to produce a model that supports the decisions of veterinarians when administering antibiotics in the dry period of dairy cows. Deep learning models were used, namely LSTM layers that operate with dynamic features from milk recordings and a dense layer that uses static features. Two approaches were chosen to deal with this problem. The first is based on a binary classification model that considers the occurrence of mastitis within 60 days after calving. The second approach was a multiclass classification model based on veterinary expert judgment. In each approach, three models were implemented, a Vanilla LSTM, a Stacked LSTM, and a Stacked LSTM with a dense layer working in parallel. The best performances from binary and multiclass approaches were 65% and 84% accuracy, respectively. It was possible to conclude that the models of the multiclass classification approach had better performance than the other classification. The capture of long- and short-term dependencies in the LSTM models, especially with the combination of static features, obtained promising results, which will undoubtedly contribute to producing a machine learning system with a prompt and affordable response, allowing for a reduction in the administration of antibiotics in dairy cows to the strictly necessary

    ON-FARM UTILIZATION OF PRECISION DAIRY MONITORING: USEFULNESS, ACCURACY, AND AFFORDABILITY

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    Precision dairy monitoring is used to supplement or replace human observation of dairy cattle. This study examined the value dairy producers placed on disease alerts generated from a precision dairy monitoring technology. A secondary objective was calculating the accuracy of technology-generated disease alerts compared against observed disease events. A final objective was determining the economic viability of investing in a precision dairy monitoring technology for detecting estrus and diseases. A year-long observational study was conducted on four Kentucky dairy farms. All lactating dairy cows were equipped with a neck and leg tri-axial accelerometer. Technologies measured eating time, lying time, standing time, walking time, and activity (steps) in 15-min sections throughout the day. A decrease of ≥ 30% or more from a cow’s 10-d moving behavioral mean created an alert. Alerts were assessed by dairy producers for usefulness and by the author for accuracy. Finally, raw information was analyzed with three machine-learning techniques: random forest, least discriminate analyses, and principal component neural networks. Through generalized linear mixed modeling analyses, dairy producers were found to utilize the alert list when ≤ 20 alerts occurred, when alerts occurred in cows’ ≤ 60 d in lactation, and when alerts occurred during the week. The longer the system was in place, the less likely producers were to utilize alerts. This is likely because the alerts were not for a specific disease, but rather informed the dairy producer an issue might have occurred. The longer dairy producers were exposed to a technology, producers more easily decided which alerts were worth their attention. Sensitivity, specificity, accuracy, and balanced accuracy were calculated for disease alerts that occurred and disease events that were reported. Sensitivity ranged from 12 to 48%, specificity from 91 to 96%, accuracy from 90 to 96%, and balanced accuracy from 50 to 59%. The high number of false positives correspond with the lack of usefulness producers reported. Machine learning techniques improved sensitivity (66 to 86%) and balanced accuracy (48 to 85%). Specificity (24 to 89%) and accuracy (70 to 86%) decreased with the machine learning techniques, but overall detection performance was improved. Precision dairy monitoring technologies have potential to detect behavior changes linked to disease events. A partial budget was created based on the reproduction, production, and early lactation removal rate of an average cow in a herd. The cow results were expanded to a 1,000 cow herd for sensitivity analyses. Four analyses were run including increased milk production from early disease detection, increased estrus detection rate, decreased early lactation removal from early disease detection, and all changes in combination. Economic profitability was determined through net present value with a value ≥ $0 indicating a profitable investment. Each sensitivity analysis was conducted 10,000, with different numbers for key inputs randomly selected from a previously defined distribution. If either milk production or estrus detection were improved, net present value was ≥ 0 in 76 and 85% of the iterations. However, reduced early lactation removal never resulted in a value ≥ 0. Investing in precision dairy technology resulting in improved estrus detection rate and early disease detection was a positive economic decision in most iterations

    AUTOMATED BODY CONDITION SCORING: PROGRESSION ACROSS LACTATION AND ITS ASSOCIATION WITH DISEASE AND REPRODUCTION IN DAIRY CATTLE

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    Body condition scoring is a technique used to noninvasively assess fat reserves. It provides an objective estimate to describe the current and past nutritional status of the dairy cow and has been associated with increased disease risk and breeding success. Traditionally body condition scores are taken manually by visual appraisal on a 1 to 5 scale, in one-quarter increments. However, recent studies have shown the potential of automating the body condition scoring of cows using images. The first objective was to estimate the likelihood of disease development and breeding success, using odds ratios, associated with body condition score scored automatically at various points in lactation. The second objective of our research was to use a commercially available automated body condition scoring camera system to monitor body condition across the lactation period to evaluate differences between stratified parameters and to develop an equation to predict the dynamics of the body condition score. We found that poor body condition score at different times during the transition period are associated with increased disease occurrence and lower reproductive success. Automated body condition scoring (ABCS) curve during lactation was influenced by many factors, such as parity, ABCS at time of calving, disease occurrence, and milk production

    Energy balance and metabolic status of dairy cows : a study using metabolomics, proteomics and machine learning approaches

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    In early lactation, dairy cows typically experience a negative energy balance (NEB) due to the high energy requirement for milk yield and low energy intake from feed. Negative energy balance has been related to metabolic disorders, compromised health and fertility, and reduced productive lifespan. Estimation of the energy balance and metabolic status is not easy on the farm. Therefore, the aims of this thesis were to estimate energy balance and metabolic status of dairy cows using metabolomics and machine learning techniques, and to investigate the metabolic pathways related to energy metabolism of dairy cows in early lactation using metabolomics and proteomics techniques. In this thesis, on-farm cow data collected from two earlier studies (study I, 168 cows and study II, 127 cows) were used to estimate energy balance and metabolic status of cows with machine learning approach. In addition, milk and blood samples obtained from study II were analysed with metabolomics and proteomics approaches. To estimate energy balance of dairy cows, estimated performance of reduced models with either milk metabolites, or milk production traits or both ranged from 0.53 to 0.78 (adjusted R-square).Milk metabolites important in explaing negative energy balance in cows where glycine, choline and carnitine. . To estimate metabolic status of dairy cows using on-farm cow data, random forest, support vector machine, and partial least square discriminant analysis performed better than other machine learning algorithms. Based on the metabolomics results, plasma and milk metabolites altered during NEB of dairy cows in early lactation reflected the metabolism in the body or the mammary gland of dairy cows. Metabolic processes in the mammary gland during NEB were related to leakage of cell content due to mammary cell apoptosis and, to synthesis of nucleic acids and cell membrane phospholipids, protein glycosylation and an increase in one-carbon metabolic processes. The processes are related to cell renewal and proliferation. Since NEB is highly related to milk production this seem logical. Blood metabolites related to energy balance were mainly reflecting energy metabolism (mobilization of body fat, skeleton muscle, bone) increased blood flow and gluconeogenesis. Better understanding of the metabolic pathways through a metabolomics and proteomics approach does not only provide biomarkers for pathways under stress during NEB but may also allow for targeted dietary interventions when glucose and rumen protected choline are interesting candidates. In conclusion, the energy balance of dairy cows can be estimated by milk metabolites based on metabolomics study, and metabolic status can be estimated by machine learning algorithms using on-farm cow data. Moreover, energy balance of dairy cows in early lactation was related with milk and plasma metabolites which revealed metabolic pathways that allow more targeted intervention strategies

    Assessment of associations between transition diseases and reproductive performance of dairy cows using survival analysis and decision tree algorithms

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    This study aimed to evaluate the associations between transition cow conditions and diseases TD with fertility in Holstein cows, and to compare analytic methods for doing so. Kaplan-Meier, Cox proportional hazard, and decision tree models were used to analyze the associations of TD with the pregnancy risk at 120 and 210 DIM from a 1-year cohort with 1946 calvings from one farm. The association between TD and fertility was evaluated as follows: 1 cows with TD whether complicated with another TD or not TD-all, versus healthy cows, and 2 cows with uncomplicated TD TD-single, versus cows with multiple TD TD+; complicated cases, versus healthy cows. The occurrence of twins, milk fever, retained placenta, metritis, ketosis, displaced abomasum, and clinical mastitis were recorded. Using Kaplan-Meier models, in primiparous cows the 120 DIM pregnancy risk was 62% (95% CI: 57-67 %) for healthy animals. This was not significantly different for TD-single (58%; 95% CI: 51-66 %) but was reduced for TD+ (45%; 95% CI: 33-60 %). Among healthy primiparous cows, 80% (95% CI: 75-84 %) were pregnant by 210 DIM, but pregnancy risk at that time was reduced for primiparous cows with TD-single (72%; 95% CI: 65-79 %) and TD+ (62%; 95% CI: 49-75 %). In healthy multiparous cows, the 120 DIM pregnancy risk was 53% (95% CI: 49-56 %), which was reduced for TD-single (36%; 95% CI: 31-42 %) and TD+ (30%; 95% CI: 24-38 %). The 210 DIM pregnancy risk for healthy multiparous cows was 70% (95% CI: 67-72 %), being higher than the 210 DIM pregnancy risk for multiparous cows with TD-single (47%; 95% CI: 42-53 %) or TD+ (46%; 95% CI: 38-54 %). Cows with TD-all presented similar pregnancy risk estimates as for TD+. Cox proportional hazards regressions provided similar magnitudes of effects as the Kaplan-Meier estimates. Survival analysis and decision tree models identified parity as the most influential variable affecting fertility. Both modeling techniques concurred that TD + had a greater effect than TD-single on the probability of pregnancy at 120 and 210 DIM. Decision trees for individual TD identified that displaced abomasum affected fertility at 120 DIM in primiparous while metritis was the most influential TD at 120 and 210 DIM for multiparous cows. The data were too sparse to assess multiple interactions in multivariable Cox proportional hazard models for individual TD. Machine learning helped to explore interactions between individual TD to study their hierarchical effect on fertility, identifying conditional relationships that merit further investigation

    Assessment of feeding, ruminating and locomotion behaviors in dairy cows around calving - a retrospective clinical study to early detect spontaneous disease appearance.

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    The study aims to verify the usefulness of new intervals-based algorithms for clinical interpretation of animal behavior in dairy cows around calving period. Thirteen activities associated with feeding-ruminating-locomotion-behaviors of 42 adult Holstein-Friesian cows were continuously monitored for the week (wk) -2, wk -1 and wk +1 relative to calving (overall 30'340 min/animal). Soon after, animals were retrospectively assigned to group-S (at least one spontaneous diseases; n = 24) and group-H (healthy; n = 18). The average activities performed by the groups, recorded by RumiWatch® halter and pedometer, were compared at the different weekly intervals. The average activities on the day of clinical diagnosis (dd0), as well as one (dd-1) and two days before (dd-2) were also assessed. Differences of dd0 vs. dd-1 (ΔD1), dd0 vs. wk -1 (ΔD2), and wk +1 vs. wk -1 (Δweeks) were calculated. Variables showing significant differences between the groups were used for a univariate logistic regression, a receiver operating characteristic analysis, and a multivariate logistic regression model. At wk +1 and dd0, eating- and ruminating-time, eating- and ruminate-chews and ruminating boluses were significantly lower in group-S as compared to group-H, while other activity time was higher. For ΔD2 and Δweeks, the differences of eating- and ruminating-time, as well as of eating-and ruminate-chews were significantly lower in group-S as compared to group-H. Concerning the locomotion behaviors, the lying time was significantly higher in group-S vs. group-H at wk +1 and dd-2. The number of strides was significantly lower in group-S compared to group-H at wk +1. The model including eating-chews, ruminate-chews and other activity time reached the highest accuracy in detecting sick cows in wk +1 (area under the curve: 81%; sensitivity: 73.7%; specificity: 82.4%). Some of the new algorithms for the clinical interpretation of cow behaviour as described in this study may contribute to monitoring animals' health around calving

    Behavioral fingerprinting: Acceleration sensors for identifying changes in livestock health

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    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

    The serum proteome and metabolome of dairy cows clustered for body fat content from a large dairy herd

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    The transition from late pregnancy to early lactation implies metabolic and endocrine changes for accomplishing the adaptation to the rapid increase of milk production. Voluntary feed intake can usually not cover the energy and nutrient requirements in the first weeks of lactation, and dairy cows thus need to mobilize body reserves, mainly from adipose tissue. The extent of this mobilization that can be assessed by recording backfat thickness (BFT), varies between animals but is commonly more pronounced in cows that are over-conditioned at calving. Over-conditioned cows are at greater risk for developing metabolic disorders, such as ketosis, and thus for compromised welfare and performance than cows of normal or lean body condition. Making use of a large dataset including also health records from a herd with 1,709 multiparous Holstein cows, the objectives of this thesis work were (1) to characterize the variation in pre-calving back fat thickness (BFT) and the subsequent BFT loss during early lactation, and to relate it to milk production, health condition, and selected blood variables, (2) to perform an untargeted metabolomics analysis for comparing the metabolome in blood serum of selected subgroups differing in body condition loss, health status and in dietary methionine (Met) supply, and (3), to undertake proteome analyses in other subgroups of animals that were either lean or over-conditioned before calving but were otherwise not differing in health status and Met supply. Animals from which serum samples and BFT records were available both at day 25 ante partum (ap) and day 30 post partum (pp) were selected (n =713) and subjected to K-means cluster analyses. Five clusters were obtained each considering the BFT-ap and the difference between BFT-ap and BFT pp (ΔBFT). The clusters were validated and the serum samples analysed for non-esterified fatty acids (NEFA), ß-hydroxybutyrate (BHB), for two adipokines, i.e., adiponectin and leptin, and for one inflammation marker (Haptoglobin). In confirmation of the literature, cows in the clusters with greater ΔBFT underwent more intense lipolysis and ketogenesis than cows with smaller ΔBFT. Cows categorized as very fat ap had lesser milk yields than other clusters. No differences in the serum metabolome at day 30 pp were detectable in cows with different ΔBFT, health status, and Met supply (n = 184). Even though the subset was further limited to fat versus lean cows (n = 30 in total) that were all healthy and did not receive supplemental Met for the proteome analysis, no differences were observed between the two groups. The findings about the classical variables recorded were largely confirmatory whereas the multivariate results from metabolomics and proteomics could not further extend the current knowledge about the relationship between body condition, fat mobilization, and metabolism. Der Übergang von der späten Gravidität zur frühen Laktation bringt metabolische und endokrine Veränderungen mit sich, um die Anpassung an den schnellen Anstieg der Milchproduktion zu bewältigen. Die freiwillige Futteraufnahme kann den Energie- und Nährstoffbedarf in den ersten Wochen der Laktation in der Regel nicht decken, so dass Milchkühe Körperreserven, v.a. Fett, mobilisieren müssen. Das Ausmaß dieser Mobilisierung, das durch die Erfassung der Rückfettdicke (BFT) beurteilt werden kann, variiert tierindividuell, ist aber bei Kühen, die beim Abkalben überkonditioniert sind, meist stärker ausgeprägt. Überkonditionierte Kühe haben ein höheres Risiko für die Entwicklung von Stoffwechselstörungen wie Ketose und damit für eine Beeinträchtigung des Wohlbefindens und der Leistung als Kühe mit normaler oder magerer Körperkondition. Unter Verwendung eines großen Datensatzes, der auch Gesundheitsdaten aus einer Herde mit 1.709 pluriparen Holstein-Kühen enthielt, waren die Ziele dieser Arbeit: (1) die BFT-Variation vor dem Abkalben (ante partum, ap) und des anschließenden BFT-Verlustes während der frühen Laktation in ihrer Beziehung zu Milchleistung, Gesundheitszustand und ausgewählten Blutvariablen zu charakterisieren, (2) das Metabolom im Blutserum ausgewählter Untergruppen, die sich in Bezug auf den Verlust der Körperkondition, den Gesundheitszustand und die Methionin (Met)-Zufuhr mit der Nahrung unterscheiden, mittels einer untargeted Metabolomics-Analyse zu vergleichen, (3) auch das Proteom in anderen Untergruppen von Tieren, die vor dem Kalben entweder mager oder überkonditioniert waren, sich aber ansonsten nicht in Bezug auf den Gesundheitszustand und die Met-Zufuhr unterschieden, zu untersuchen. Es wurden zunächst Tiere ausgewählt, von denen Serumproben und BFT-Aufzeichnungen sowohl am Tag 25 ap als auch am Tag 30 post partum (pp) verfügbar waren, und einer K-Means-Cluster-Analyse unterzogen. Sowohl für den ap BFT-Wert als auch für die Differenz zwischen BFT-ap und BFT-pp (ΔBFT) wurden jeweils fünf Cluster erhalten. Die Cluster wurden validiert und die Serumproben auf nicht veresterte Fettsäuren (NEFA), ß-Hydroxybutyrat (BHB), auf zwei Adipokine (Adiponectin und Leptin) und auf einen Entzündungsmarker (Haptoglobin) untersucht. Wie in der Literatur beschrieben, durchliefen die Kühe in den Clustern mit größerem ΔBFT eine intensivere Lipolyse und Ketogenese als Kühe mit kleinerem ΔBFT. Kühe, die als sehr fett eingestuft wurden, hatten eine geringere Milchleistung als andere Cluster. Bei Kühen mit unterschiedlichem ΔBFT, Gesundheitszustand und Met-Versorgung (n = 184) waren keine Unterschiede im Serum-Metabolom am Tag 30 pp nachweisbar. Auch die für die Proteom-Analysen vorgenommene Reduzierung der Untergruppen auf fette und magere Kühe (insgesamt n = 30), die alle gesund waren und kein zusätzliches Met erhielten, wurden keine Unterschiede zwischen den beiden Gruppen festgestellt. Die Ergebnisse zu den erfassten klassischen Variablen waren weitgehend bestätigend, während die multivariaten Ergebnisse aus Metabolomics und Proteomics unter den gewählten Bedingungen das derzeitige Verständnis der Beziehungen zwischen Körperkondition, Fettmobilisierung und Stoffwechsel nicht weiter vertiefen konnten
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