Special Issue "Dairy Cattle Health Management"

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Cattle".

Deadline for manuscript submissions: 31 May 2022 | Viewed by 4286

Special Issue Editor

Dr. Kiro Petrovski
E-Mail Website
Guest Editor
School of Animal and Veterinary Science, The University of Adelaide, Roseworthy, SA 5371, Australia
Interests: cattle health; herd health; mastitis; antimicrobial susceptibility; colostrum; predictability of health events using precision technologies

Special Issue Information

Dear Colleagues,

Dairy (herd) health and welfare are well-known causes of loss of profit and the image of the industry. It is also an important cause of involuntary culling. Animal scientists, veterinarians, and farmers have an obligation to do what is possible to prevent or alleviate health problems as soon as possible. The management of health problems on dairy farms can be carried out using many approaches. In the past, herd health approaches were traditionally discussed as an art. We are now aware that they can be taught. For an easy educational and practical approach, all health problems should be dealt with in a similar way. Therefore, the approach recommended for this Special Issue of Animals is the 3M (measure, manage, monitor) method.

The first step in management of health of dairy cattle is to measure how much of a particular health problem is present (measure). Management strategies can be then implemented (manage). The success of implemented strategies and the prevalence of the health problem should be thereafter under a regular surveillance (monitor). This will make it possible to set triggers and timely interventions should the health problem reoccur. Indeed, depending on the health problem, measure and monitor indicators are not always the same.

Narrative and systematic reviews, as well as original research manuscripts that address aspects of the management of health on dairy farms using the 3M (measure, manage, monitor) approach are invited for this Special Issue. Topics can include the role of population management in herd health, colostrum and life-long productivity and fertility, cattle health related to the control of infectious and parasitic disorders, hoof management, infrastructure, nutrition, reproductive management, udder management, and how staff training may affect health on dairy farms. Additional topics may include biosecurity, heat stress, judicious use of antimicrobials, and the use of precision technologies in monitoring health. Manuscripts should encourage evidence-based approaches to assist with decision-making in practice. The overall objective of the manuscripts should be what is known, and what needs to be investigated in the herd-level approaches using the 3M system to improve animal health and welfare on dairy farms.

Dr. Kiro Petrovski
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • dairy cattle
  • health
  • management
  • measure
  • manage
  • monitor
  • practical application

Published Papers (6 papers)

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Research

Article
Effects of Parity and Stage of Lactation on Trend and Variability of Metabolic Markers in Dairy Cows
Animals 2022, 12(8), 1008; https://doi.org/10.3390/ani12081008 - 13 Apr 2022
Viewed by 382
Abstract
Metabolic monitoring is a tool that is helpful with the increasing requirements regarding feeding and health management in dairy herds. This study aimed at describing the trend and variability of different biochemical parameters in blood and urine in relation to the stages of [...] Read more.
Metabolic monitoring is a tool that is helpful with the increasing requirements regarding feeding and health management in dairy herds. This study aimed at describing the trend and variability of different biochemical parameters in blood and urine in relation to the stages of lactation and parity, in a retrospective analysis of laboratory data from clinically healthy German Holstein cows. The results were derived from metabolic monitoring in Thuringia (Germany), during 2009–2019. A total of 361,584 measured values, of 13 different metabolic variables, were assigned to parity (primiparous and multiparous) and stage of lactation (10 classes from −30 to 300 days in milk). The Kruskal–Wallis test was applied for the evaluation of differences regarding parity or the stage of lactation. Non-esterified fatty acids, beta hydroxybutyrate, and the activity of aspartate aminotransferase in serum were clearly affected by parity and lactation. Serum concentrations of cholesterol, bilirubin, and phosphorus, as well as the serum activity of glutamate dehydrogenase, were affected by the stage of lactation, while parity impacted urea concentration. The serum activity of creatine kinase, serum concentrations of calcium, and urine concentrations of net acid base excretion, potassium, and sodium were not affected by parity or lactation. In conclusion, specific reference limits, with respect to parity and the stage of lactation, are necessary. Full article
(This article belongs to the Special Issue Dairy Cattle Health Management)
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Article
Separable Confident Transductive Learning for Dairy Cows Teat-End Condition Classification
Animals 2022, 12(7), 886; https://doi.org/10.3390/ani12070886 - 31 Mar 2022
Viewed by 369
Abstract
Teat-end health assessments are crucial to maintain milk quality and dairy cow health. One approach to automate teat-end health assessments is by using a convolutional neural network to classify the magnitude of teat-end alterations based on digital images. This approach has been demonstrated [...] Read more.
Teat-end health assessments are crucial to maintain milk quality and dairy cow health. One approach to automate teat-end health assessments is by using a convolutional neural network to classify the magnitude of teat-end alterations based on digital images. This approach has been demonstrated as feasible with GoogLeNet but there remains a number of challenges, such as low performance and comparing performance with different ImageNet models. In this paper, we present a separable confident transductive learning (SCTL) model to improve the performance of teat-end image classification. First, we propose a separation loss to ameliorate the inter-class dispersion. Second, we generate high confident pseudo labels to optimize the network. We further employ transductive learning to narrow the gap between training and test datasets with categorical maximum mean discrepancy loss. Experimental results demonstrate that the proposed SCTL model consistently achieves higher accuracy across all seventeen different ImageNet models when compared with retraining of original approaches. Full article
(This article belongs to the Special Issue Dairy Cattle Health Management)
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Article
Evaluating Alternatives to Locomotion Scoring for Detecting Lameness in Pasture-Based Dairy Cattle in New Zealand: In-Parlour Scoring
Animals 2022, 12(6), 703; https://doi.org/10.3390/ani12060703 - 11 Mar 2022
Viewed by 598
Abstract
Earlier detection followed by efficient treatment can reduce the impact of lameness. Currently, locomotion scoring (LS) is the most widely used method of early detection but has significant limitations in pasture-based cattle and is not commonly used routinely in New Zealand. Scoring in [...] Read more.
Earlier detection followed by efficient treatment can reduce the impact of lameness. Currently, locomotion scoring (LS) is the most widely used method of early detection but has significant limitations in pasture-based cattle and is not commonly used routinely in New Zealand. Scoring in the milking parlour may be more achievable, so this study compared an in-parlour scoring (IPS) technique with LS in pasture-based dairy cows. For nine months on two dairy farms, whole herd LS (4-point 0–3 scale) was followed 24 h later by IPS, with cows being milked. Observed for shifting weight, abnormal weight distribution, swollen heel or hock joint, and overgrown hoof. Every third cow was scored. Sensitivity and specificity of individual IPS indicators and one or more, two or more or three positive indicators for detecting cows with locomotion scores ≥ 2 were calculated. Using a threshold of two or more positive indicators were optimal (sensitivity > 92% and specificity > 98%). Utilising the IPS indicators, a decision tree machine learning procedure classified cows with locomotion score class ≥2 with a true positive rate of 75% and a false positive rate of 0.2%. IPS has the potential to be an alternative to LS on pasture-based dairy farms. Full article
(This article belongs to the Special Issue Dairy Cattle Health Management)
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Article
The Processes of Nutrition and Metabolism Affecting the Biosynthesis of Milk Components and Vitality of Cows with High- and Low-Fat Milk
Animals 2022, 12(5), 604; https://doi.org/10.3390/ani12050604 - 28 Feb 2022
Viewed by 514
Abstract
In order to clarify the mechanism of the depression of milk fat formation and preserve the health of animals, the aim of the research was to study the characteristics of rumen digestion, energy metabolism, and milk composition in high-producing dairy cows with high [...] Read more.
In order to clarify the mechanism of the depression of milk fat formation and preserve the health of animals, the aim of the research was to study the characteristics of rumen digestion, energy metabolism, and milk composition in high-producing dairy cows with high and low levels of milk fat that are fed the same diet. Two groups of cows with normal milk fat content (3.94 ± 0.12; n = 10) and low milk fat content (2.95 ± 0.14, n = 10) contained in the same diet were identified. Gas exchange (O2 uptake and CO2 output) was studied in cows and blood samples, rumen contents (pH, NH3-N), and VFA and milk (fat, protein, and fatty acid composition) were collected and analyzed. It was determined that cows with low fat milk are more efficient at using the metabolized energy of their diets due to the tendency to have a decrease in the proportion of heat production (by 6.2 MJ; p = 0.055) and an earlier start of a positive energy balance. At the same time, the fat content in milk did not depend on the level of hormones in the blood or on the formation of acetate in the rumen. An analysis of the duration of the productive use of cows on this farm (n = 650) showed that the number of lactations was inversely correlated with the level of fat in milk (r = −0.68; p < 0.05, n = 1300). These results indicate the advantages of cows that can reduce the fat content of their milk in the first months of lactation. Full article
(This article belongs to the Special Issue Dairy Cattle Health Management)
Article
The Use of Multilayer Perceptron Artificial Neural Networks to Detect Dairy Cows at Risk of Ketosis
Animals 2022, 12(3), 332; https://doi.org/10.3390/ani12030332 - 29 Jan 2022
Viewed by 708
Abstract
Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to [...] Read more.
Subclinical ketosis is one of the most dominant metabolic disorders in dairy herds during lactation. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as animal welfare reasons. Recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. ANNs offer a modeling method that enables the mapping of highly complex functional relationships. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows, using feedforward multilayer perceptron (MLP) artificial neural networks. The results were verified based on the estimated sensitivity and specificity of selected network models, an optimum cut-off point was identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC). The study demonstrated that BHB, ACE and lactose (LAC) levels, as well as the fat-to-protein ratio in milk, were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, variables such as BHB and ACE levels in milk were of particular relevance, with a sensitivity and specificity of 0.84 and 0.61, respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool. Full article
(This article belongs to the Special Issue Dairy Cattle Health Management)
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Article
Evaluating Alternatives to Locomotion Scoring for Lameness Detection in Pasture-Based Dairy Cows in New Zealand: Infra-Red Thermography
Animals 2021, 11(12), 3473; https://doi.org/10.3390/ani11123473 - 06 Dec 2021
Cited by 2 | Viewed by 787
Abstract
Lameness in cattle is a complex condition with huge impacts on welfare, and its detection is challenging for the dairy industry. The present study aimed to evaluate the association between foot skin temperature (FST) measured using infrared thermography (IRT) and locomotion scoring (LS) [...] Read more.
Lameness in cattle is a complex condition with huge impacts on welfare, and its detection is challenging for the dairy industry. The present study aimed to evaluate the association between foot skin temperature (FST) measured using infrared thermography (IRT) and locomotion scoring (LS) in dairy cattle kept at pasture. Data were collected from a 940-cow dairy farm in New Zealand. Cows were observed at two consecutive afternoon milkings where LS was undertaken at the first milking (4-point scale (0–3), DairyNZ). The next day, cows were thermally imaged from the plantar aspect of the hind feet using a handheld T650sc forward-looking infrared camera (IRT). The association between FST and locomotion score was analysed using a generalised linear model with an identity link function and robust estimators. ROC curves were performed to determine optimal threshold temperature cut-off values by maximising sensitivity and specificity for detecting locomotion score ≥ 2. There was a linear association between individual locomotion scores and FST. For mean temperature (MT), each one-unit locomotion score increase was associated with a 0.944 °C rise in MT. Using MT at a cut-off point of 34.5 °C produced a sensitivity of 80.0% and a specificity of 92.4% for identifying cows with a locomotion score ≥ 2 (lame). Thus, IRT has a substantial potential to be used on-farm for lameness detection. However, automation of the process will likely be necessary for IRT to be used without interfering with farm operations. Full article
(This article belongs to the Special Issue Dairy Cattle Health Management)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: The use of multilayer perceptron artificial neural networks to detect dairy cows at risk of ketosis
Authors: Edyta A. Bauer1; Wojciech Jagusiak2
Affiliation: 1Department of Animal Reproduction, Anatomy and Genomics, 2Department of Genetics, Animal Breeding and Ethology University of Agriculture in Krakow, al. Mickiewicza 21, 31-120 Kraków, Poland
Abstract: Ketosis is a serious metabolic disease in high-yield dairy cows, observed to affect all productive herds across the world. Ketosis is attributable to negative energy balance due to deficiency of energy in the postpartum period or to excessive fatness of animals. Cows suffering from ketosis experience elevated ketone body levels in blood and milk, including β-hydroxybutyric acid (BHB), acetone (ACE) and acetoacetic acid. Ketosis reduces milk yield and has an adverse impact on cows’ reproductive potential and their resistance to diseases. Ketosis causes serious financial losses to dairy cattle breeders and milk producers due to the costs of diagnosis and management as well as impairs the reproductive performance, increases cow culling rates and can even increase cow mortality rates. The recent years have seen a growing interest in the use of artificial neural networks (ANNs) in various fields of science. Neural networks can be used whenever there are problems with prediction, classification or control. ANNs offer a modeling method that enables mapping highly complex functional relationships. They are characterized by high operating speed when solving problems, which is attributable to parallel calculations, as well as high accuracy in predicting various phenomena and processes when there is no clear causal correlation or there are no rules that allow establishing a logical cause-and-effect relationship. The purpose of this study was to determine the relationship between milk composition and blood BHB levels associated with subclinical ketosis in dairy cows using feedforward multilayer perceptron (MLP) artificial neural networks. The results of using MLP networks were verified based on estimated sensitivity and specificity of selected network models. For each network model, an optimum cut-off point was also identified for the receiver operating characteristic (ROC) curve and the area under the ROC curve (AUC) was determined. The study results demonstrated that β-hydroxybutyric acid (BHB), acetone (ACE) and lactose (LAC) levels as well as fat-to-protein ratio in milk were important input variables in the network training process. For the identification of cows at risk of subclinical ketosis, of particular relevance were variables such as BHB and ACE levels in milk, and the related sensitivity and specificity were 0.84 and 0.61 respectively. It was found that the back propagation algorithm offers opportunities to integrate artificial intelligence and dairy cattle welfare within a computerized decision support tool.

Title: The value of ‘cow signs’ in the assessment of the quality of nu-trition on dairy farms
Authors: Kiro Risto Petrovski 1,*; Paul Cusack 2; Jakob Malmo 3; Peter Cockcroft 4
Affiliation: 1. Davies Livestock Research Centre, The University of Adelaide, School of Animal and Veterinary Sciences, Roseworthy Campus, Roseworthy, SA, 5371, Australia; 2 Australian Livestock Production Services, Cowra, NSW, 2794, Australia; 3 MAFFRA Veterinary Centre, Maffra, VIC, 3860, Australia; 4 University of Surrey, School of Veterinary Medicine, Guilford, Surrey, GU2 7AL, UK;
Abstract: The aim of this review is to provide dairy farm advisors, consultants, nutritionists, practitioners and their dairy farmer clients with an additional toolkit that can be used in the assessment of the quality of their dairy cattle nutrition. Cow signs are behavioral, physiological and management param-eters that can be observed and measured. Cow signs are detected by examining and observing the animal. Other physiological parameters such as fecal scoring, rumen fill and body condition scoring are also included in ‘Cow signs’. The assessment should be both qualitative and quantitative, for example is the animal lame and what is the severity of lameness. The ‘diagnosis’ of a problem should be based on establishing a farm profile of ‘cow signs’ and other relevant information. Information gathered through assessment of cow signs should be used as an advisory tool to assist and improve decision-making. Cow signs can be used as part of an investigation and or farm audit.

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