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Animal Activity in Farms

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 42976

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Special Issue Editors

Dr. María Dolores Fernández Rodríguez

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Guest Editor

Department of Agroforestry Engineering, University of Santiago de Compostela, 27002 Lugo, Spain
Interests: sustainable animal production; smart farming; environmental and animal variables modeling and control; smart farming; agriculture monitoring; animal behavior; agriculture emissions
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Manuel Ramiro Rodríguez Rodríguez

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Guest Editor

Department of Agroforestry Engineering, University of Santiago de Compostela, 27002 Lugo, Spain
Interests: environmental and animal variables modeling and control; sustainability and energy efficiency; smart farming; agriculture monitoring; precision farming; livestock management; animal behavior; agriculture emissions
Special Issues, Collections and Topics in MDPI journals

Dr. Joaquim Orlando Lima Cerqueira

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Guest Editor

1. Escola Superior Agrária do Instituto Politécnico de Viana do Castelo, 4990-706 Ponte de Lima, Portugal
2. Veterinary and Animal Research Centre, UTAD, Quinta de Prados, Apartado 1013, 5000-801 Vila Real, Portugal
Interests: agricultural and biological sciences; animal welfare

Special Issue Information

The study of animal activity, related to animal welfare and its measurement, modeling and prediction is a subject of great importance. The proliferation of sensors to determine the behaviour of animals has provided objective measurements that allow replacing the observations made directly and subjectively by farmers on production animals. Thus, the use of PIDs, sound level meters, GPS, images, the temperature–humidity index, gas concentrations etc., or of physiological variables such as body temperature or cortisol in the saliva provides highly valuable data for characterizing animal activity, which can be completed with qualitative behaviour assessment. The consumption of water and the intake and weight of the animals are related to the activity and greatly condition the performance of the livestock farms. Based on these data, modeling using new machine learning techniques can provide vital information for improving animal welfare. The application of the models will allow reducing stress, carrying out the early treatment of diseases, optimizing food distribution and controlling the interior climate, as well as improving productivity, constituting great support for livestock farmers.

The aim of this Special Issue is to present recent research and reviews on animal activity in farm animals. Papers related to animal activity measurement, modelling and prediction and their relationship with welfare, the environment, productivity or health and even livestock farm management methods based on animal activity will be welcome.

Dr. María Dolores Fernández Rodríguez
Dr. Manuel Ramiro Rodríguez Rodríguez
Dr. Joaquim Orlando Lima Cerqueira
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

farm animals
animal activity
animal welfare
model
prediction
machine learning
farm environment
farm optimization
activity measurement
appropriate behavior

Published Papers (10 papers)

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Research

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10 pages, 895 KiB

Open AccessArticle

Validation of an Accelerometer Sensor-Based Collar for Monitoring Grazing and Rumination Behaviours in Grazing Dairy Cows

by Muhammad Wasim Iqbal, Ina Draganova, Patrick C. H. Morel and Stephen T. Morris

Animals 2021, 11(9), 2724; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11092724 - 17 Sep 2021

Cited by 10 | Viewed by 3215

Abstract

This study evaluated the accuracy of a sensor-based device (AfiCollar) to automatically monitor and record grazing and rumination behaviours of grazing dairy cows on a real-time basis. Multiparous spring-calved dairy cows (n = 48) wearing the AfiCollar were selected for the visual observation of their grazing and rumination behaviours. The total observation period was 36 days, divided into four recording periods performed at different times of the year, using 12 cows in each period. Each recording period consisted of nine daily observation sessions (three days a week for three consecutive weeks). A continuous behaviour monitoring protocol was followed to visually observe four cows at a time for each daily observation session, from 9:00 a.m. to 5:00 p.m. Overall, 144 observations were collected and the data were presented as behaviour activity per daily observation session. The behaviours visually observed were also recorded through an automated AfiCollar device on a real-time basis over the observation period. Automatic recordings and visual observations were compared with each other using Pearson’s correlation coefficient (r), Concordance correlation coefficient (CCC), and linear regression. Compared to visual observation (VO), AfiCollar (AC) showed slightly higher (10%) grazing time and lower (4%) rumination time. AC results and VO results had strong associations with each other for grazing time (r = 0.91, CCC = 0.71) and rumination time (r = 0.89, CCC = 0.80). Regression analysis showed a significant linear relationship between AC and VO for grazing time (R² = 0.83, p < 0.05) and rumination time (R² = 0.78, p < 0.05). The relative prediction error (RPE) values for grazing time and rumination time were 0.17 and 0.40, respectively. Overall, the results indicated that AfiCollar is a reliable device to accurately monitor and record grazing and rumination behaviours of grazing dairy cows, although, some minor improvements can be made in algorithm calibrations to further improve its accuracy. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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21 pages, 6947 KiB

Open AccessArticle

Computational Fluid Dynamics Analysis of Alternative Ventilation Schemes in Cage-Free Poultry Housing

by Long Chen, Eileen E. Fabian-Wheeler, John M. Cimbala, Dan Hofstetter and Paul Patterson

Animals 2021, 11(8), 2352; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11082352 - 09 Aug 2021

Cited by 11 | Viewed by 3980 | Correction

Abstract

This work investigated alternative ventilation schemes to help define a proper ventilation system design in cage-free hen houses with the goal of assuring bird welfare through comfortable conditions. Computational fluid dynamics (CFD) modeling was employed to simulate indoor and outdoor airflows to quantify the effectiveness of ventilation systems in maintaining suitable and uniform living conditions at the hen level. Four three-dimensional CFD models were developed based on a full-scale floor-raised layer house, corresponding to ventilation schemes of the standard top-wall inlet, sidewall exhaust, and three alternatives: mid-wall inlet, ceiling exhaust; mid-wall inlet, ridge exhaust; and mid-wall inlet, attic exhaust with potential for pre-treatment of exhaust air. In a sophisticated and powerful achievement of the analysis, 2365 birds were individually modeled with simplified bird-shapes to represent a realistic number, body heat, and airflow obstruction of hens housed. The simulated ventilation rate for the layer house models was 1.9–2.0 m³/s (4100 ft³/min) in the desired range for cold weather (0 °C). Simulation results and subsequent analyses demonstrated that these alternative models had the capacity to create satisfactory comfortable temperature and air velocity at the hen level. A full-scale CFD model with individual hen models presented robustness in evaluating bird welfare conditions. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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11 pages, 992 KiB

Open AccessArticle

Dairy Cows Activity under Heat Stress: A Case Study in Spain

by Adrián Ramón-Moragues, Patricia Carulla, Carlos Mínguez, Arantxa Villagrá and Fernando Estellés

Animals 2021, 11(8), 2305; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11082305 - 04 Aug 2021

Cited by 19 | Viewed by 5007

Abstract

Heat stress plays a role in livestock production in warm climates. Heat stress conditions impair animal welfare and compromise the productive and reproductive performance of dairy cattle. Under heat stress conditions, dairy cattle modify their behavior. Thus, the assessment of behavior alterations can be an indicator of environmental or physiological anomalies. Moreover, precision livestock farming allows for the individual and constant monitoring of animal behavior, arising as a tool to assess animal welfare. The purpose of this study was to evaluate the effect of heat stress on the behavior of dairy cows using activity sensors. The study was carried out in Tinajeros (Albacete, Spain) during the summer of 2020. Activity sensors were installed in 40 cows registering 6 different behaviors. Environmental conditions (temperature and humidity) were also monitored. Hourly data was calculated for both animal behavior and environmental conditions. Temperature and Heat Index (THI) was calculated for each hour. The accumulated THI during the previous 24 h period was determined for each hour, and the hours were statistically classified in quartiles according to the accumulated THI. Two groups were defined as Q4 for no stress and Q1 for heat stress. The results showed that animal behavior was altered under heat stress conditions. Increasing THI produces an increase in general activity, changes in feeding patterns and a decrease in rumination and resting behaviors, which is detrimental to animal welfare. Daily behavioral patterns were also affected. Under heat stress conditions, a reduction in resting behavior during the warmest hours and in rumination during the night was observed. In conclusion, heat stress affected all behaviors recorded as well as the daily patterns of the cows. Precision livestock farming sensors and the modelling of daily patterns were useful tools for monitoring animal behavior and detecting changes due to heat stress. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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18 pages, 7193 KiB

Open AccessArticle

Application of YOLOv4 for Detection and Motion Monitoring of Red Foxes

by Anne K. Schütz, Verena Schöler , E. Tobias Krause , Mareike Fischer , Thomas Müller , Conrad M. Freuling, Franz J. Conraths , Mario Stanke, Timo Homeier-Bachmann and Hartmut H. K. Lentz

Animals 2021, 11(6), 1723; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11061723 - 09 Jun 2021

Cited by 18 | Viewed by 5504

Abstract

Animal activity is an indicator for its welfare and manual observation is time and cost intensive. To this end, automatic detection and monitoring of live captive animals is of major importance for assessing animal activity, and, thereby, allowing for early recognition of changes indicative for diseases and animal welfare issues. We demonstrate that machine learning methods can provide a gap-less monitoring of red foxes in an experimental lab-setting, including a classification into activity patterns. Therefore, bounding boxes are used to measure fox movements, and, thus, the activity level of the animals. We use computer vision, being a non-invasive method for the automatic monitoring of foxes. More specifically, we train the existing algorithm ‘you only look once’ version 4 (YOLOv4) to detect foxes, and the trained classifier is applied to video data of an experiment involving foxes. As we show, computer evaluation outperforms other evaluation methods. Application of automatic detection of foxes can be used for detecting different movement patterns. These, in turn, can be used for animal behavioral analysis and, thus, animal welfare monitoring. Once established for a specific animal species, such systems could be used for animal monitoring in real-time under experimental conditions, or other areas of animal husbandry. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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11 pages, 2240 KiB

Open AccessArticle

Long-Term Measurement of Piglet Activity Using Passive Infrared Detectors

by Roberto Besteiro, Tamara Arango, Juan Ortega, María D. Fernández and Manuel R. Rodríguez

Animals 2021, 11(6), 1607; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11061607 - 29 May 2021

Cited by 6 | Viewed by 3819

Abstract

Measuring animal activity is useful for monitoring animal welfare in real time. In this regard, passive infrared detectors have been used in recent years to quantify piglet activity because of their robustness and ease of use. This study was conducted on a commercial farm in Northwest Spain during six complete breeding cycles. The hourly average activity of weaned piglets with a body mass of 6–20 kg was recorded and further analyzed by using a multiplicative decomposition of the series followed by a wavelet analysis. Finally, the real series were compared to the theoretical models of activity. Results showed a high level of movement immediately after weaning and a sustained level of activity throughout the cycles. The daily behavior of the piglets followed a clear circadian pattern with several peaks of activity. No differences in behavior were observed between spring–summer cycles and autumn–winter cycles. Single-peak models achieved the best predictive results. In addition, the installed sensors were found to underestimate mild activity. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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10 pages, 886 KiB

Open AccessArticle

Comparison of the Productivity of Primiparous Sows Housed in Individual Stalls and Group Housing Systems

by Yejin Min, Yohan Choi, Joeun Kim, Doowan Kim, Yongdae Jeong, Younghwa Kim, Minho Song and Hyunjung Jung

Animals 2020, 10(11), 1940; https://0-doi-org.brum.beds.ac.uk/10.3390/ani10111940 - 22 Oct 2020

Cited by 5 | Viewed by 2372

Abstract

This study was conducted to provide commercial pig farms with information about group housing systems for sows in accordance with the amendment of the prohibition law for individual stalls for sows in South Korea. Therefore, this experiment was performed to compare the effects of individual stalls (IS) and group housing systems (GS) on the productivity of sows to investigate the feasibility of replacing individual stalls with group housing systems in commercial sow units. Forty primiparous sows (Landrace × Yorkshire; 210.67 ± 2.22 kg average initial body weight) were randomly assigned to four treatments with restricted feeding after 8 weeks from artificial insemination. The four treatments were (i) individual stalls (IS; housed in pen stalls), (ii) short stalls (SS; sows housed in pens with non-gated feeding stalls), (iii) free access stalls (FAS; a non-competitive housing system), and (iv) electronic sow feeders (ESF; used with radio frequency identification technology to allow individual sow management without individual confinement). All sows were transferred to farrowing crates at 110 days of gestation. There were no differences in sow productive performance, reproductive performance, and colostrum composition between IS and GS and among GS. The considered GS did not negatively affect any productivity parameters of primiparous sows compared with IS; the GS could replace IS in commercial sow units. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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19 pages, 2015 KiB

Open AccessArticle

“HerdGPS-Preprocessor”—A Tool to Preprocess Herd Animal GPS Data; Applied to Evaluate Contact Structures in Loose-Housing Horses

by Jennifer Salau, Frederik Hildebrandt, Irena Czycholl and Joachim Krieter

Animals 2020, 10(10), 1932; https://0-doi-org.brum.beds.ac.uk/10.3390/ani10101932 - 21 Oct 2020

Cited by 6 | Viewed by 2638

Abstract

Sensors delivering information on the position of farm animals have been widely used in precision livestock farming. Global Positioning System (GPS) sensors are already known from applications in military, private and commercial environments, and their application in animal science is increasing. However, as trade-offs between sensor cost, battery life and sensor weight have to be made, GPS based studies scheduling long data collection periods and including a high number of animals, have to deal with problems like high hardware costs and data disruption during recharging of sensors. Furthermore, human–animal interaction due to sensor changing at the end of battery life interferes with the animal behaviour under analysis. The present study thus proposes a setting to deal with these challenges and offers the software tool “HerdGPS-Preprocessor”, because collecting position data from multiple animals nonstop for several weeks produces a high amount of raw data which needs to be sorted, preprocessed and provided in a suitable format per animal and day. The software tool “HerdGPS-Preprocessor” additionally outputs contact lists to enable a straight analysis of animal contacts. The software tool was exemplarily deployed for one month of daily and continuous GPS data of 40 horses in a loose-housing boarding facility in northern Germany. Contact lists were used to generate separate networks for every hour, which are then analysed with regard to the network parameter density, diameter and clique structure. Differences depending on the day and the day time could be observed. More dense networks with more and larger cliques were determined in the hours prior to the opening of additional pasture. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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Review

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17 pages, 381 KiB

Open AccessReview

Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition

by Severiano R. Silva, José P. Araujo, Cristina Guedes, Flávio Silva, Mariana Almeida and Joaquim L. Cerqueira

Animals 2021, 11(8), 2253; https://0-doi-org.brum.beds.ac.uk/10.3390/ani11082253 - 30 Jul 2021

Cited by 39 | Viewed by 6659 | Correction

Abstract

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

14 pages, 596 KiB

Open AccessReview

Social Network Analysis in Farm Animals: Sensor-Based Approaches

by Suresh Neethirajan and Bas Kemp

Animals 2021, 11(2), 434; https://doi.org/10.3390/ani11020434 - 08 Feb 2021

Cited by 8 | Viewed by 6355

Abstract

Natural social systems within animal groups are an essential aspect of agricultural optimization and livestock management strategy. Assessing elements of animal behaviour under domesticated conditions in comparison to natural behaviours found in wild settings has the potential to address issues of animal welfare effectively, such as focusing on reproduction and production success. This review discusses and evaluates to what extent social network analysis (SNA) can be incorporated with sensor-based data collection methods, and what impact the results may have concerning welfare assessment and future farm management processes. The effectiveness and critical features of automated sensor-based technologies deployed in farms include tools for measuring animal social group interactions and the monitoring and recording of farm animal behaviour using SNA. Comparative analyses between the quality of sensor-collected data and traditional observational methods provide an enhanced understanding of the behavioural dynamics of farm animals. The effectiveness of sensor-based approaches in data collection for farm animal behaviour measurement offers unique opportunities for social network research. Sensor-enabled data in livestock SNA addresses the biological aspects of animal behaviour via remote real-time data collection, and the results both directly and indirectly influence welfare assessments, and farm management processes. Finally, we conclude with potential implications of SNA on modern animal farming for improvement of animal welfare. Full article

(This article belongs to the Special Issue Animal Activity in Farms)

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