Special Issue "Dairy Cattle Health Management"
Deadline for manuscript submissions: 31 May 2022 | Viewed by 4286
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
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.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Animals is an international peer-reviewed open access semimonthly journal published by MDPI.
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.
- dairy cattle
- practical application
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.