Special Issue "New technologies to improve the sustainability of livestock farming (precision livestock farming)"

A special issue of Animals (ISSN 2076-2615). This special issue belongs to the section "Animal System and Management".

Deadline for manuscript submissions: closed (31 March 2019)

Special Issue Editors

Guest Editor
Prof. Daniel Berckmans

KU Leuven, M3-BIORES Catholic University Leuven 3000 Leuven, Belgium
E-Mail
Interests: real-time monitoring of individual living organisms; monitor individual humans and animals (livestock, pets)
Guest Editor
Prof. Marcella Guarino

Department of Environmental Science and Policy, Universita degli Studi di Milano, Milan, Italy
Website | E-Mail
Interests: Animal Production; Dairy Management; Farm Animal Welfare

Special Issue Information

Dear Colleagues,

The size of animal production worldwide and the number of animals reared for food production is growing, today about 65 billion chickens, 1.5 billion pigs, 1 billion goat and sheep and around 330 million cattle and buffaloes are reared for animal products annually. This incredible global animal production is the result of the dietary evolution of people, who are consuming more animal products as their income increases. The livestock sector is an important user of natural resources and has a significant influence on human health, air quality, global climate, soil quality, biodiversity and water quality, by altering the biogeochemical cycles of nitrogen, phosphorus and carbon, giving rise to environmental concerns. Due to the link between animal health and the antimicrobial resistance of humans, we need to guard animal health while the worldwide demand for animal products is increasing by 70% in 2050. Livestock farming should be oriented towards more sustainable systems and more efficiency: more animal products with less feed input We believe that greater animal welfare is crucial to reaching greater efficiency, today the metabolic energy going into animal discomfort should go in animal products. The application of precision livestock farming could be a good approach to reach the health and environmental sustainability of livestock farming.

PLF technologies offer a tool to farmers through continuous automated monitoring and control of environmental, physiological and behavioural variables, helping ensuring livestock health, performance and well-being in relation to their environment. Ensuring an animal’s good health and welfare is a necessary condition to obtaining good productive and reproductive performance, lowering the environmental impact per unit of animal product.

Original manuscripts that address any aspects of new PLF technologies or evaluations of PLF systems to improve animal health and welfare and thus the sustainability of livestock farming are invited for publication in this Special Issue.

Prof. Daniel Berckmans
Prof. Guarino Marcella
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 papers will be 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 monthly 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 1000 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

  • Animal health
  • Animal welfare
  • Animal production
  • Sustainability
  • GHG
  • Livestock
  • Precision livestock farming
  • Environment
  • IoT
  • sensors

Published Papers (7 papers)

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Research

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Open AccessArticle
A Survey of Italian Dairy Farmers’ Propensity for Precision Livestock Farming Tools
Animals 2019, 9(5), 202; https://doi.org/10.3390/ani9050202
Received: 20 February 2019 / Revised: 19 April 2019 / Accepted: 24 April 2019 / Published: 28 April 2019
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Abstract
A targeted survey was designed with the aim of describing the diffusion of precision livestock farming (PLF) tools in one of the most intensive dairy farming provinces in Italy. Technicians at the Provincial Breeder Association of Cremona interviewed 490 dairy farmers and obtained [...] Read more.
A targeted survey was designed with the aim of describing the diffusion of precision livestock farming (PLF) tools in one of the most intensive dairy farming provinces in Italy. Technicians at the Provincial Breeder Association of Cremona interviewed 490 dairy farmers and obtained data regarding the role and age of the respondents; the land owned by the farmers; their herd sizes (HS, lactating plus dry cows; small HS < 101, medium HS 101–200, large HS > 200 cows/herd); their average 305 day milk yield (low MY < 9501, medium MY 9501–10,500, high MY > 10,500 kg/head); the cow to employed worker ratio (low CW < 33, medium CW 33–47, high CW > 47 cows/worker); the use of PLF tools to monitor production, reproduction, and health; and the criteria and motivations for investing in PLF tools. The use of automated MY recording and estrus detection systems was primarily associated with HS (more present in larger farms), followed by MY (more present in more productive farms), and then CW (more present with a high cow: worker ratio). Concern about the time required to manage data was the most common subjective issue identified as negatively affecting the purchase of these tools. The future of PLF use in this region will depend upon the availability of an effective selection of tools on the market. Full article
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Open AccessArticle
Feasibility Study: Improving Floor Cleanliness by Using a Robot Scraper in Group-Housed Pregnant Sows and Their Reactions on the New Device
Animals 2019, 9(4), 185; https://doi.org/10.3390/ani9040185
Received: 27 February 2019 / Revised: 7 April 2019 / Accepted: 13 April 2019 / Published: 22 April 2019
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Abstract
Successful pig farming needs the best conditions of cleanliness in the housings. The present study examined for the first time whether a robot scraper usually applied in dairy farming is usable in sow housings for cleaning the slatted floors and improving hygiene and [...] Read more.
Successful pig farming needs the best conditions of cleanliness in the housings. The present study examined for the first time whether a robot scraper usually applied in dairy farming is usable in sow housings for cleaning the slatted floors and improving hygiene and thus animal welfare. For evaluating the suitability of the robot scraper with regard to the cleaning performance (polluted surface area and occluded slots), the whole housing area was divided into score-squares, which were individually scored at defined intervals. Selected excrement quantities removed by the robot were weighed. In order to assess the animals’ interactions with the robot scraper, their behaviour towards the device was observed. Although the faeces of pigs had a firmer consistency than bovine excrement, excrement quantities of up to 1.4 kg m−2 were almost completely removed. Even 6 h after the cleaning its effect was still visible. Dry-cleaning led faster to nonslip surfaces for the sows than wet-cleaning. Within half an hour of observation, up to 8.2 of 120 sows were occupied with the robot scraper, but without harming it. The use of robot scrapers in pig housings is recommended, although slight technical modifications should be made to the robot scraper. Full article
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Open AccessArticle
Effects of Feeding Frequency on the Lying Behavior of Dairy Cows in a Loose Housing with Automatic Feeding and Milking System
Animals 2019, 9(4), 121; https://doi.org/10.3390/ani9040121
Received: 10 December 2018 / Revised: 24 March 2019 / Accepted: 24 March 2019 / Published: 28 March 2019
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Abstract
Management systems in modern dairy farms is an important issue in relation to animal comfort and welfare. The objective of this study was to determine the effect of feed delivery frequency on the behavior patterns, visits to an automatic milking system (AMS) and [...] Read more.
Management systems in modern dairy farms is an important issue in relation to animal comfort and welfare. The objective of this study was to determine the effect of feed delivery frequency on the behavior patterns, visits to an automatic milking system (AMS) and on milk production of lactating dairy cows. The study was conducted on a commercial dairy farm with automatic feeding and milking systems. Feeding treatments consisted of two different frequencies, high feed delivery frequency (11 deliveries per day) and low feed delivery frequency (six deliveries per day). Lying behavior of 20 dairy cows was electronically monitored. The results obtained showed that 11 deliveries per day feed delivery frequency decreases the number of long-duration lying bouts, which may indicate that a very high feeding frequency disturbs the cows during their resting periods and thus influences both animal comfort and milk production. High feeding frequency may disturb the duration of lying bouts and alter the pattern of lying behavior throughout the day, affecting mainly the lying time during the 60 min before and following the provision of fresh feed. Delivering feed at a low frequency allow cows to distribute more evenly their lying time over the course of the day and improve their utilization of an AMS. Full article
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Open AccessArticle
Temporary Exclusion of Cattle from a Riparian Zone Using Virtual Fencing Technology
Animals 2019, 9(1), 5; https://doi.org/10.3390/ani9010005
Received: 2 November 2018 / Revised: 17 December 2018 / Accepted: 20 December 2018 / Published: 22 December 2018
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Abstract
Grazing cattle can both negatively and positively impact riparian zones, dependent on controlled grazing management. Virtual fencing technology, using collar devices that operate via GPS can provide audio cues and electrical stimuli to temporarily exclude cattle from specified areas as desired. An early [...] Read more.
Grazing cattle can both negatively and positively impact riparian zones, dependent on controlled grazing management. Virtual fencing technology, using collar devices that operate via GPS can provide audio cues and electrical stimuli to temporarily exclude cattle from specified areas as desired. An early experimental prototype automated virtual fencing system was tested in excluding ten cattle from a riparian zone in Australia. Animals were given free access to an 11.33-hectare area for three weeks, excluded from river access by a virtual fence for ten days (2.86-hectare inclusion zone), followed by free access again for six days. Animals were almost exclusively contained by the virtual fence. All animals received audio cues and electrical stimuli with daily fence interactions, but there was high individual variation with some animals first approaching the fence more often than others. Overall, there was an approximately 25% probability that animals would receive an electrical stimulus following an audio cue. Individual associative learning may have been socially-facilitated by the group’s behaviour. Following fence deactivation, all animals re-entered the previously excluded area. Further research with more groups and longer periods of exclusion using updated collar devices would determine the scope of virtual fencing technology for cattle grazing control. Full article
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Review

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Open AccessReview
Precision Livestock Farming in Swine Welfare: A Review for Swine Practitioners
Animals 2019, 9(4), 133; https://doi.org/10.3390/ani9040133
Received: 7 March 2019 / Revised: 22 March 2019 / Accepted: 25 March 2019 / Published: 31 March 2019
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Abstract
The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, [...] Read more.
The burgeoning research and applications of technological advances are launching the development of precision livestock farming. Through sensors (cameras, microphones and accelerometers), images, sounds and movements are combined with algorithms to non-invasively monitor animals to detect their welfare and predict productivity. In turn, this remote monitoring of livestock can provide quantitative and early alerts to situations of poor welfare requiring the stockperson’s attention. While swine practitioners’ skills include translation of pig data entry into pig health and well-being indices, many do not yet have enough familiarity to advise their clients on the adoption of precision livestock farming practices. This review, intended for swine veterinarians and specialists, (1) includes an introduction to algorithms and machine learning, (2) summarizes current literature on relevant sensors and sensor network systems, and drawing from industry pig welfare audit criteria, (3) explains how these applications can be used to improve swine welfare and meet current pork production stakeholder expectations. Swine practitioners, by virtue of their animal and client advocacy roles, interpretation of benchmarking data, and stewardship in regulatory and traceability programs, can play a broader role as advisors in the transfer of precision livestock farming technology, and its implications to their clients. Full article
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Open AccessReview
Review of Sensor Technologies in Animal Breeding: Phenotyping Behaviors of Laying Hens to Select Against Feather Pecking
Animals 2019, 9(3), 108; https://doi.org/10.3390/ani9030108
Received: 28 February 2019 / Revised: 15 March 2019 / Accepted: 18 March 2019 / Published: 22 March 2019
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Abstract
Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to [...] Read more.
Damaging behaviors, like feather pecking (FP), have large economic and welfare consequences in the commercial laying hen industry. Selective breeding can be used to obtain animals that are less likely to perform damaging behavior on their pen-mates. However, with the growing tendency to keep birds in large groups, identifying specific birds that are performing or receiving FP is difficult. With current developments in sensor technologies, it may now be possible to identify laying hens in large groups that show less FP behavior and select them for breeding. We propose using a combination of sensor technology and genomic methods to identify feather peckers and victims in groups. In this review, we will describe the use of “-omics” approaches to understand FP and give an overview of sensor technologies that can be used for animal monitoring, such as ultra-wideband, radio frequency identification, and computer vision. We will then discuss the identification of indicator traits from both sensor technologies and genomics approaches that can be used to select animals for breeding against damaging behavior. Full article
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Other

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Open AccessTechnical Note
Validation of a Commercial Automated Body Condition Scoring System on a Commercial Dairy Farm
Animals 2019, 9(6), 287; https://doi.org/10.3390/ani9060287 (registering DOI)
Received: 24 March 2019 / Revised: 16 May 2019 / Accepted: 24 May 2019 / Published: 29 May 2019
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Abstract
Body condition scoring (BCS) is the management practice of assessing body reserves of individual animals by visual or tactile estimation of subcutaneous fat and muscle. Both high and low BCS can negatively impact milk production, disease, and reproduction. Visual or tactile estimation of [...] Read more.
Body condition scoring (BCS) is the management practice of assessing body reserves of individual animals by visual or tactile estimation of subcutaneous fat and muscle. Both high and low BCS can negatively impact milk production, disease, and reproduction. Visual or tactile estimation of subcutaneous fat reserves in dairy cattle relies on their body shape or thickness of fat layers and muscle on key areas of the body. Although manual BCS has proven beneficial, consistent qualitative scoring can be difficult to implement. The desirable BCS range for dairy cows varies within lactation and should be monitored at multiple time points throughout lactation for the most impact, a practice that can be hard to implement. However, a commercial automatic BCS camera is currently available for dairy cattle (DeLaval Body Condition Scoring, BCS DeLaval International AB, Tumba, Sweden). The objective of this study was to validate the implementation of an automated BCS system in a commercial setting and compare agreement of the automated body condition scores with conventional manual scoring. The study was conducted on a commercial farm in Indiana, USA, in April 2017. Three trained staff members scored 343 cows manually using a 1 to 5 BCS scale, with 0.25 increments. Pearson’s correlations (0.85, scorer 1 vs. 2; 0.87, scorer 2 vs. 3; and 0.86, scorer 1 vs. 3) and Cohen’s Kappa coefficients (0.62, scorer 1 vs. 2; 0.66, scorer 2 vs. 3; and 0.66, scorer 1 vs. 3) were calculated to assess interobserver reliability, with the correlations being 0.85, 0.87, and 0.86. The automated camera BCS scores were compared with the averaged manual scores. The mean BCS were 3.39 ± 0.32 and 3.27 ± 0.27 (mean ± SD) for manual and automatic camera scores, respectively. We found that the automated body condition scoring technology was strongly correlated with the manual scores, with a correlation of 0.78. The automated BCS camera system accuracy was equivalent to manual scoring, with a mean error of −0.1 BCS and within the acceptable manual error threshold of 0.25 BCS between BCS (3.00 to 3.75) but was less accurate for cows with high (>3.75) or low (<3.00) BCS scores compared to manual scorers. A Bland–Altman plot was constructed which demonstrated a bias in the high and low automated BCS scoring. The initial findings show that the BCS camera system provides accurate BCS between 3.00 to 3.75 but tends to be inaccurate at determining the magnitude of low and high BCS scores. However, the results are promising, as an automated system may encourage more producers to adopt BCS into their practices to detect early signs of BCS change for individual cattle. Future algorithm and software development is likely to increase the accuracy in automated BCS scoring. Full article
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