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Animals
Special Issues
Improving Milk Quality through Farm Management and Technology
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Improving Milk Quality through Farm Management and Technology

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

Deadline for manuscript submissions: closed (31 May 2021) | Viewed by 18901

Special Issue Editors

Dr. Maddalena Zucali

E-Mail Website
Guest Editor
Department of Agricultural and Environmental Sciences—Production, Landscape, Agroenergy, University of Milan, Milan, Italy
Interests: dairy science; dairy animal science; cattle milk; milk quality; dairy management; precision livestock farming
Prof. Anna Sandrucci

E-Mail Website
Guest Editor
Department of Agricultural and Environmental Sciences—Production, Landscape, Agroenergy, University of Milan, Milan, Italy
Interests: dairy science; cattle milk; milk quality; dairy management; precision livestock farming; animal welfare

Special Issue Information

Dear Colleagues,

The concept of milk quality is changing rapidly and expanding to include aspects that a few years ago were not considered of primary importance. In recent years, more and more attention has been paid to technological quality, hygiene and safety aspects, and their relationships. The improvement of hygiene conditions in dairy farms led to a decrease in milk bacterial count, which is a desirable result. However, biodiversity of microorganisms can suffer and technological problems can occur. The prevention of contamination (e.g., by pathogens, mycotoxins, antibiotics) is a subject of special attention and increasingly regulated. Moreover, the concept of food quality, especially that of animal origin, is currently further expanding to include aspects related to animal welfare and environmental sustainability.

The spread and the effective use of technology and sensors in dairy farms can allow farms not only to improve the productive and reproductive efficiency of the herds but also to obtain a better milk quality, especially from a hygienic-sanitary and technological point of view.  At the same time, technology-supported management can contribute to safeguard animal health and welfare and to reduce environmental impact. Technology in dairy farms can thereby positively influence the quality of milk and dairy products in the broadest sense.

Dr. Maddalena Zucali
Prof. Anna Sandrucci
Guest Editors

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 2400 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

  • milk quality
  • food safety
  • livestock management
  • milking
  • feeding
  • sensors
  • technology
  • Precision Livestock Farming
  • udder health
  • animal welfare

Published Papers (4 papers)

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Research

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14 pages, 753 KiB  
Article
Association between Udder and Quarter Level Indicators and Milk Somatic Cell Count in Automatic Milking Systems
by Maddalena Zucali, Luciana Bava, Alberto Tamburini, Giulia Gislon and Anna Sandrucci
Animals 2021, 11(12), 3485; https://doi.org/10.3390/ani11123485 - 07 Dec 2021
Cited by 3 | Viewed by 2044
Abstract
Automatic Milking Systems (AMS) record a lot of information, at udder and quarter level, which can be useful for improving the early detection of altered udder health conditions. A total of 752,000 records from 1003 lactating cows milked with two types of AMS [...] Read more.
Automatic Milking Systems (AMS) record a lot of information, at udder and quarter level, which can be useful for improving the early detection of altered udder health conditions. A total of 752,000 records from 1003 lactating cows milked with two types of AMS in four farms were processed with the aim of identifying new indicators, starting from the variables provided by the AMS, useful to predict the risk of high milk somatic cell count (SCC). Considering the temporal pattern, the quarter vs. udder percentage difference in milk electrical conductivity showed an increase in the fourteen days preceding an official milk control higher than 300,000 SCC/mL. Similarly, deviations over time in quarter vs. udder milk yield, average milk flow, and milking time emerged as potential indicators for high SCC. The Logistic Analysis showed that Milk Production Rate (kg/h) and the within-cow within-milking percentage variations of single quarter vs. udder milk electrical conductivity, milk yield, and average milk flow are all risk factors for high milk SCC. The result suggests that these variables, alone or in combination, and their progression over time could be used to improve the early prediction of risk situations for udder health in AMS milked herds. Full article
(This article belongs to the Special Issue Improving Milk Quality through Farm Management and Technology)
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14 pages, 574 KiB  
Article
Unraveling the Relationship between Milk Yield and Quality at the Test Day with Rumination Time Recorded by a PLF Technology
by Rosanna Marino, Francesca Petrera, Marisanna Speroni, Teresa Rutigliano, Andrea Galli and Fabio Abeni
Animals 2021, 11(6), 1583; https://doi.org/10.3390/ani11061583 - 28 May 2021
Cited by 5 | Viewed by 4902
Abstract
The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT [...] Read more.
The study aimed to estimate the components of rumination time (RT) variability recorded by a neck collar sensor and the relationship between RT and milk composition. Milk test day (TD) and RT data were collected from 691 cows in three farms. Daily RT data of each animal were averaged for 3, 7, and 10 days preceding the TD date (RTD). Variance component analysis of RTD, considering the effects of farm, cow, parity, TD date, and lactation phase, showed that a farm, followed by a cow, had major contributions to the total variability. The RT10 variable best performed on TD milk yield and quality records across models by a multi-model inference approach and was adopted to study its relationship with milk traits, by linear mixed models, through a 3-level stratification: low (LRT10 ≤ 8 h/day), medium (8 h/day < MRT10 ≤ 9 h/day), and high (HRT10 > 9 h/day) RT. Cows with HRT10 had greater milk, fat, protein, casein, and lactose daily yield, and lower fat, protein, casein contents, and fat to protein ratio compared to MRT10 and LRT10. Higher percentages of saturated fatty acid and lower unsaturated and monounsaturated fatty acid were found in HRT10, with respect to LRT10 and MRT10 observations. Full article
(This article belongs to the Special Issue Improving Milk Quality through Farm Management and Technology)
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11 pages, 215 KiB  
Article
Impact of Photoperiod Length and Treatment with Exogenous Melatonin during Pregnancy on Chemical Composition of Sheep’s Milk
by Edyta Molik, Michał Błasiak and Henryk Pustkowiak
Animals 2020, 10(10), 1721; https://doi.org/10.3390/ani10101721 - 23 Sep 2020
Cited by 9 | Viewed by 2005
Abstract
The aim of the study was to determine the effect of photoperiod and exogenous melatonin on milk yield and chemical composition of sheep’s milk. Sheep (n = 60) were randomly divided into three groups: lambing in February (Group 1—n = 20), [...] Read more.
The aim of the study was to determine the effect of photoperiod and exogenous melatonin on milk yield and chemical composition of sheep’s milk. Sheep (n = 60) were randomly divided into three groups: lambing in February (Group 1—n = 20), lambing in June (Group 2—n = 20), and lambing in June and treated with subcutaneous melatonin implants (Group 3—n = 20). Milk yield was higher for Group 1 and Group 2 than for Group 3 (p < 0.01). The milk of ewes of Groups 2 and 3 had a significantly (p < 0.01) higher content of dry matter, protein, and fat. Group 3 sheep’s milk contained significantly more (p < 0.01) of SFA (Saturated Fatty Acids). The highest content of MUFA (Monounsaturated Fatty Acids) and PUFA (Polyunsaturated Fatty Acids) was found in the samples collected from Group 1, the lowest was in the milk of Group 3 animals. The highest (p < 0.01) CLA, content was identified in the milk of Group 1, while the lowest was recorded for the milk obtained from sheep treated with exogenous melatonin (Group 3). The experiment carried out has shown that day length and treatment with exogenous melatonin modulate the chemical composition of milk. Full article
(This article belongs to the Special Issue Improving Milk Quality through Farm Management and Technology)

Review

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22 pages, 771 KiB  
Review
Review: Application and Prospective Discussion of Machine Learning for the Management of Dairy Farms
by Marianne Cockburn
Animals 2020, 10(9), 1690; https://doi.org/10.3390/ani10091690 - 18 Sep 2020
Cited by 62 | Viewed by 8496
Abstract
Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data [...] Read more.
Dairy farmers use herd management systems, behavioral sensors, feeding lists, breeding schedules, and health records to document herd characteristics. Consequently, large amounts of dairy data are becoming available. However, a lack of data integration makes it difficult for farmers to analyze the data on their dairy farm, which indicates that these data are currently not being used to their full potential. Hence, multiple issues in dairy farming such as low longevity, poor performance, and health issues remain. We aimed to evaluate whether machine learning (ML) methods can solve some of these existing issues in dairy farming. This review summarizes peer-reviewed ML papers published in the dairy sector between 2015 and 2020. Ultimately, 97 papers from the subdomains of management, physiology, reproduction, behavior analysis, and feeding were considered in this review. The results confirm that ML algorithms have become common tools in most areas of dairy research, particularly to predict data. Despite the quantity of research available, most tested algorithms have not performed sufficiently for a reliable implementation in practice. This may be due to poor training data. The availability of data resources from multiple farms covering longer periods would be useful to improve prediction accuracies. In conclusion, ML is a promising tool in dairy research, which could be used to develop and improve decision support for farmers. As the cow is a multifactorial system, ML algorithms could analyze integrated data sources that describe and ultimately allow managing cows according to all relevant influencing factors. However, both the integration of multiple data sources and the obtainability of public data currently remain challenging. Full article
(This article belongs to the Special Issue Improving Milk Quality through Farm Management and Technology)
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