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Animals
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Measuring the Sustainability of Precision Livestock Farming
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Measuring the Sustainability of Precision Livestock Farming

A special issue of Animals (ISSN 2076-2615).

Deadline for manuscript submissions: closed (15 February 2021) | Viewed by 11548

Special Issue Editors

Prof. Dr. Marcella Guarino

E-Mail Website
Guest Editor
Department of Environmental Science and Policy, Università degli Studi di Milano, 20133 Milan, Italy
Interests: animal production; dairy management; farm animal welfare
Special Issues, Collections and Topics in MDPI journals
Dr. Daniela Lovarelli

E-Mail Website
Guest Editor
Department of Environmental Science and Policy, University of Milan, 20133 Milan, Italy
Interests: precision livestock farming; environmental sustainability; emissions
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As is widely known, technology has been changing our capabilities in almost all production sectors. For livestock activities, continuously monitoring reared animals and alerting farmers early on animals’ welfare and health status is becoming more and more important in terms of making livestock farming affordable and socially acceptable.

These issues considered from the perspective of sustainable productions are also important, because livestock farming is required to be efficient, sustainable, and animal-friendly, which means it must guarantee animal welfare, a good health status, and an efficient production level.

In this context, the role of researchers is fundamental, as we are responsible for the dissemination of results that have a direct effect on society and on their comprehension of this production sector. 

In this Special Issue, we are inviting contributions in which precision livestock farming is dealt as a sustainable solution on the environmental, economic, and social points of view. In particular, we consider sustainable those solutions that make the production system more efficient, giving the same weight to the quality and quantity of livestock products. In other words, improving air quality in barns, manure and slurry management techniques, and health and welfare living conditions of animals and of farmers and workers. 

Prof. Marcella Guarino
Dr. Daniela Lovarelli
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

  • precision livestock farming
  • livestock emissions
  • technology
  • sensors
  • monitoring tools
  • LCA
  • LCC
  • SLCA

Published Papers (3 papers)

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Research

18 pages, 4566 KiB  
Article
Random Forest Modelling of Milk Yield of Dairy Cows under Heat Stress Conditions
by Marco Bovo, Miki Agrusti, Stefano Benni, Daniele Torreggiani and Patrizia Tassinari
Animals 2021, 11(5), 1305; https://doi.org/10.3390/ani11051305 - 30 Apr 2021
Cited by 27 | Viewed by 3029
Abstract
Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems [...] Read more.
Precision Livestock Farming (PLF) relies on several technological approaches to acquire, in the most efficient way, precise and real-time data concerning production and welfare of individual animals. In this regard, in the dairy sector, PLF devices are being increasingly adopted, automatic milking systems (AMSs) are becoming increasingly widespread, and monitoring systems for animals and environmental conditions are becoming common tools in herd management. As a consequence, a great amount of daily recorded data concerning individual animals are available for the farmers and they could be used effectively for the calibration of numerical models to be used for the prediction of future animal production trends. On the other hand, the machine learning approaches in PLF are nowadays considered an extremely promising solution in the research field of livestock farms and the application of these techniques in the dairy cattle farming would increase sustainability and efficiency of the sector. The study aims to define, train, and test a model developed through machine learning techniques, adopting a Random Forest algorithm, having the main goal to assess the trend in daily milk yield of a single cow in relation to environmental conditions. The model has been calibrated and tested on the data collected on 91 lactating cows of a dairy farm, located in northern Italy, and equipped with an AMS and thermo-hygrometric sensors during the years 2016–2017. In the statistical model, having seven predictor features, the daily milk yield is evaluated as a function of the position of the day in the lactation curve and the indoor barn conditions expressed in terms of daily average of the temperature-humidity index (THI) in the same day and its value in each of the five previous days. In this way, extreme hot conditions inducing heat stress effects can be considered in the yield predictions by the model. The average relative prediction error of the milk yield of each cow is about 18% of daily production, and only 2% of the total milk production. Full article
(This article belongs to the Special Issue Measuring the Sustainability of Precision Livestock Farming)
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18 pages, 288 KiB  
Article
Smart Products in Livestock Farming—An Empirical Study on the Attitudes of German Farmers
by Sirkka Schukat and Heinke Heise
Animals 2021, 11(4), 1055; https://doi.org/10.3390/ani11041055 - 08 Apr 2021
Cited by 14 | Viewed by 3660
Abstract
In recent years, the fourth industrial revolution has found its way into agriculture. Under the term smart farming, various so-called smart products are offered that may positively influence both the daily work of farmers and animal welfare. These smart products can collect data [...] Read more.
In recent years, the fourth industrial revolution has found its way into agriculture. Under the term smart farming, various so-called smart products are offered that may positively influence both the daily work of farmers and animal welfare. These smart products can collect data from the farm, extract important information, and in some cases even make decisions independently. Particularly in Germany, where intensive livestock farming is criticized by society, such smart products could make a significant contribution to improving animal welfare. However, an important prerequisite is the acceptance of the users, who are usually the livestock farmers themselves. So far, there is little knowledge about farmers’ attitudes towards smart products in livestock production. In this study, a factor analysis and a cluster analysis are conducted to evaluate the attitudes of German livestock farmers towards smart products. Based on the analysis of an online questionnaire in which German livestock farmers (n = 422) participated, four clusters could be derived. The main distinguishing characteristics of the clusters are the influence of the social environment, the expected effort for implementation, the general trust in smart products, and the technology readiness of the farms. As a result, this study provides valuable insights for technology providers of smart products for livestock farming as well as for policy makers. Full article
(This article belongs to the Special Issue Measuring the Sustainability of Precision Livestock Farming)
14 pages, 2101 KiB  
Article
Contactless Video-Based Heart Rate Monitoring of a Resting and an Anesthetized Pig
by Meiqing Wang, Ali Youssef, Mona Larsen, Jean-Loup Rault, Daniel Berckmans, Jeremy N. Marchant-Forde, Joerg Hartung, André Bleich, Mingzhou Lu and Tomas Norton
Animals 2021, 11(2), 442; https://doi.org/10.3390/ani11020442 - 08 Feb 2021
Cited by 10 | Viewed by 3830
Abstract
Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in [...] Read more.
Heart rate (HR) is a vital bio-signal that is relatively easy to monitor with contact sensors and is related to a living organism’s state of health, stress and well-being. The objective of this study was to develop an algorithm to extract HR (in beats per minute) of an anesthetized and a resting pig from raw video data as a first step towards continuous monitoring of health and welfare of pigs. Data were obtained from two experiments, wherein the pigs were video recorded whilst wearing an electrocardiography (ECG) monitoring system as gold standard (GS). In order to develop the algorithm, this study used a bandpass filter to remove noise. Then, a short-time Fourier transform (STFT) method was tested by evaluating different window sizes and window functions to accurately identify the HR. The resulting algorithm was first tested on videos of an anesthetized pig that maintained a relatively constant HR. The GS HR measurements for the anesthetized pig had a mean value of 71.76 bpm and standard deviation (SD) of 3.57 bpm. The developed algorithm had 2.33 bpm in mean absolute error (MAE), 3.09 bpm in root mean square error (RMSE) and 67% in HR estimation error below 3.5 bpm (PE3.5). The sensitivity of the algorithm was then tested on the video of a non-anaesthetized resting pig, as an animal in this state has more fluctuations in HR than an anaesthetized pig, while motion artefacts are still minimized due to resting. The GS HR measurements for the resting pig had a mean value of 161.43 bpm and SD of 10.11 bpm. The video-extracted HR showed a performance of 4.69 bpm in MAE, 6.43 bpm in RMSE and 57% in PE3.5. The results showed that HR monitoring using only the green channel of the video signal was better than using three color channels, which reduces computing complexity. By comparing different regions of interest (ROI), the region around the abdomen was found physiologically better than the face and front leg parts. In summary, the developed algorithm based on video data has potential to be used for contactless HR measurement and may be applied on resting pigs for real-time monitoring of their health and welfare status, which is of significant interest for veterinarians and farmers. Full article
(This article belongs to the Special Issue Measuring the Sustainability of Precision Livestock Farming)
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