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Frizzarin M, Miglior F, Berry DP, Gormley IC, Baes CF. Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows. J Dairy Sci 2023; 106:9115-9124. [PMID: 37641249 DOI: 10.3168/jds.2023-23290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Accepted: 07/02/2023] [Indexed: 08/31/2023]
Abstract
Directly measuring individual cow energy balance is not trivial. Other traits such as body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used worldwide to estimate cow body reserves, but the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. The ΔBCS records were merged with milk MIR spectra recorded on the same week. The dataset comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec, Canada. Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from (1) MIR spectra only, (2) DIM only, or (3) MIR spectra and DIM together. The ΔBCS data in both the first 120 and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40 × 10-3 BCS units in the 120-d dataset and of 3.63 × 10-3 BCS units in the 305-d dataset. A 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81 × 10-3 BCS units and 1.51 × 10-3 BCS units, respectively, using the 120-d and 305-d dataset). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the dataset and of the prediction model used, combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with the inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation reduced by up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data were more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.
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Affiliation(s)
- M Frizzarin
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland; Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, P61 P302, Co. Cork, Ireland
| | - F Miglior
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada; Lactanet Canada, Guelph, ON, N1K 1E5, Canada
| | - D P Berry
- Teagasc, Animal & Grassland Research and Innovation Centre, Moorepark, Fermoy, P61 P302, Co. Cork, Ireland
| | - I C Gormley
- School of Mathematics and Statistics, University College Dublin, Dublin, D04 V1W8, Ireland
| | - C F Baes
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada; Vetsuisse Faculty, Institute of Genetics, University of Bern, Bern, 3002, Switzerland.
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Voogt AM, Schrijver RS, Temürhan M, Bongers JH, Sijm DTHM. Opportunities for Regulatory Authorities to Assess Animal-Based Measures at the Slaughterhouse Using Sensor Technology and Artificial Intelligence: A Review. Animals (Basel) 2023; 13:3028. [PMID: 37835634 PMCID: PMC10571985 DOI: 10.3390/ani13193028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/19/2023] [Accepted: 09/20/2023] [Indexed: 10/15/2023] Open
Abstract
Animal-based measures (ABMs) are the preferred way to assess animal welfare. However, manual scoring of ABMs is very time-consuming during the meat inspection. Automatic scoring by using sensor technology and artificial intelligence (AI) may bring a solution. Based on review papers an overview was made of ABMs recorded at the slaughterhouse for poultry, pigs and cattle and applications of sensor technology to measure the identified ABMs. Also, relevant legislation and work instructions of the Dutch Regulatory Authority (RA) were scanned on applied ABMs. Applications of sensor technology in a research setting, on farm or at the slaughterhouse were reported for 10 of the 37 ABMs identified for poultry, 4 of 32 for cattle and 13 of 41 for pigs. Several applications are related to aspects of meat inspection. However, by European law meat inspection must be performed by an official veterinarian, although there are exceptions for the post mortem inspection of poultry. The examples in this study show that there are opportunities for using sensor technology by the RA to support the inspection and to give more insight into animal welfare risks. The lack of external validation for multiple commercially available systems is a point of attention.
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Affiliation(s)
- Annika M. Voogt
- Office for Risk Assessment & Research (BuRO), Netherlands Food and Consumer Product Safety Authority (NVWA), P.O. Box 43006, 3540 AA Utrecht, The Netherlands
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Curti PDF, Selli A, Pinto DL, Merlos-Ruiz A, Balieiro JCDC, Ventura RV. Applications of livestock monitoring devices and machine learning algorithms in animal production and reproduction: an overview. Anim Reprod 2023; 20:e20230077. [PMID: 37700909 PMCID: PMC10494883 DOI: 10.1590/1984-3143-ar2023-0077] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2023] [Accepted: 07/10/2023] [Indexed: 09/14/2023] Open
Abstract
Some sectors of animal production and reproduction have shown great technological advances due to the development of research areas such as Precision Livestock Farming (PLF). PLF is an innovative approach that allows animals to be monitored, through the adoption of cutting-edge technologies that continuously collect real-time data by combining the use of sensors with advanced algorithms to provide decision tools for farmers. Artificial Intelligence (AI) is a field that merges computer science and large datasets to create expert systems that are able to generate predictions and classifications similarly to human intelligence. In a simplified manner, Machine Learning (ML) is a branch of AI, and can be considered as a broader field that encompasses Deep Learning (DL, a Neural Network formed by at least three layers), generating a hierarchy of subsets formed by AI, ML and DL, respectively. Both ML and DL provide innovative methods for analyzing data, especially beneficial for large datasets commonly found in livestock-related activities. These approaches enable the extraction of valuable insights to address issues related to behavior, health, reproduction, production, and the environment, facilitating informed decision-making. In order to create the referred technologies, studies generally go through five steps involving data processing: acquisition, transferring, storage, analysis and delivery of results. Although the data collection and analysis steps are usually thoroughly reported by the scientific community, a good execution of each step is essential to achieve good and credible results, which impacts the degree of acceptance of the proposed technologies in real life practical circumstances. In this context, the present work aims to describe an overview of the current implementations of ML/DL in livestock reproduction and production, as well to identify potential challenges and critical points in each of the five steps mentioned, which can affect results and application of AI techniques by farmers in practical situations.
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Affiliation(s)
- Paula de Freitas Curti
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Alana Selli
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Diógenes Lodi Pinto
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Alexandre Merlos-Ruiz
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Julio Cesar de Carvalho Balieiro
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
| | - Ricardo Vieira Ventura
- Departamento de Nutrição e Produção Animal, Faculdade de Medicina Veterinária e Zootecnia, Universidade de São Paulo, Pirassununga, SP, Brasil
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Lei Y, Xiang Y, Zhu Y, Guan Y, Zhang Y, Yang X, Yao X, Li T, Xie M, Mu J, Ni Q. Development of a Slow Loris Computer Vision Detection Model. Animals (Basel) 2022; 12:ani12121553. [PMID: 35739889 PMCID: PMC9219483 DOI: 10.3390/ani12121553] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Revised: 06/10/2022] [Accepted: 06/13/2022] [Indexed: 11/24/2022] Open
Abstract
Simple Summary Slow lorises are nocturnal primates native to south-east Asia. All the species of slow loris have been listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES). It is difficult to artificially detect the slow loris due to its nocturnal habit and venomous bite. This article investigates the feasibility of computer vision for slow loris detection and proposes an improved YOLOv5 algorithm that contributes to formulating an available model for behavior recognition of this endangered taxon. Abstract The slow loris (Genus Nycticebus) is a group of small, nocturnal and venomous primates with a distinctive locomotion mode. The detection of slow loris plays an important role in the subsequent individual identification and behavioral recognition and thus contributes to formulating targeted conservation strategies, particularly in reintroduction and post-release monitoring. However, fewer studies have been conducted on efficient and accurate detection methods of this endangered taxa. The traditional methods to detect the slow loris involve long-term observation or watching surveillance video repeatedly, which would involve manpower and be time consuming. Because humans cannot maintain a high degree of attention for a long time, they are also prone to making missed detections or false detections. Due to these observational challenges, using computer vision to detect slow loris presence and activity is desirable. This article establishes a novel target detection dataset based on monitoring videos of captive Bengal slow loris (N. bengalensis) from the wildlife rescue centers in Xishuangbanna and Pu’er, Yunnan, China. The dataset is used to test two improvement schemes based on the YOLOv5 network: (1) YOLOv5-CBAM + TC, the attention mechanism and deconvolution are introduced; (2) YOLOv5-SD, the small object detection layer is added. The results demonstrate that the YOLOv5-CBAM + TC effectively improves the detection effect. At the cost of increasing the model size by 0.6 MB, the precision rate, the recall rate and the mean average precision (mAP) are increased by 2.9%, 3.7% and 3.5%, respectively. The YOLOv5-CBAM + TC model can be used as an effective method to detect individual slow loris in a captive environment, which helps to realize slow loris face and posture recognition based on computer vision.
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Affiliation(s)
- Yujie Lei
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China;
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
- Sichuan Key Laboratory of Agricultural Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
| | - Ying Xiang
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
- Sichuan Key Laboratory of Agricultural Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
| | - Yuhui Zhu
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
| | - Yan Guan
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
- Sichuan Key Laboratory of Agricultural Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
| | - Yu Zhang
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
| | - Xiao Yang
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
| | - Xiaoli Yao
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
| | - Tingxuan Li
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
| | - Meng Xie
- College of Life Science, Sichuan Agricultural University, Ya’an 625014, China;
| | - Jiong Mu
- College of Information Engineering, Sichuan Agricultural University, Ya’an 625014, China; (Y.X.); (Y.Z.); (Y.G.); (Y.Z.); (X.Y.); (X.Y.); (T.L.)
- Sichuan Key Laboratory of Agricultural Information Engineering, Sichuan Agricultural University, Ya’an 625014, China
- Correspondence: (J.M.); (Q.N.); Tel.: +86-133-4060-8699 (J.M.); +86-135-5158-1150 (Q.N.)
| | - Qingyong Ni
- Key Laboratory of Livestock and Poultry Multi-Omics, Ministry of Agriculture and Rural Affairs, College of Animal Science and Technology, Sichuan Agricultural University, Chengdu 611130, China;
- Farm Animal Genetic Resources Exploration and Innovation Key Laboratory of Sichuan Province, Sichuan Agricultural University, Chengdu 611130, China
- Correspondence: (J.M.); (Q.N.); Tel.: +86-133-4060-8699 (J.M.); +86-135-5158-1150 (Q.N.)
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Fang C, Zheng H, Yang J, Deng H, Zhang T. Study on Poultry Pose Estimation Based on Multi-Parts Detection. Animals (Basel) 2022; 12:ani12101322. [PMID: 35625168 PMCID: PMC9137532 DOI: 10.3390/ani12101322] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2022] [Revised: 05/15/2022] [Accepted: 05/19/2022] [Indexed: 11/22/2022] Open
Abstract
Simple Summary Poultry farming is an important part of China’s agriculture system. The automatic estimation of poultry posture can help to analyze the movement, behavior, and even health of poultry. In this study, a poultry pose-estimation system was designed, which realized the automatic pose estimation of a single broiler chicken using a multi-part detection method. The experimental results show that this method can obtain better pose-estimation results for a single broiler chicken with respect to precision, recall, and F1 score. The pose-estimation system designed in this study provides a new means to provide help for poultry pose/behavior researchers in the future. Abstract Poultry pose estimation is a prerequisite for evaluating abnormal behavior and disease prediction in poultry. Accurate pose-estimation enables poultry producers to better manage their poultry. Because chickens are group-fed, how to achieve automatic poultry pose recognition has become a problematic point for accurate monitoring in large-scale farms. To this end, based on computer vision technology, this paper uses a deep neural network (DNN) technique to estimate the posture of a single broiler chicken. This method compared the pose detection results with the Single Shot MultiBox Detector (SSD) algorithm, You Only Look Once (YOLOV3) algorithm, RetinaNet algorithm, and Faster_R-CNN algorithm. Preliminary tests show that the method proposed in this paper achieves a 0.0128 standard deviation of precision and 0.9218 ± 0.0048 of confidence (95%) and a 0.0266 standard deviation of recall and 0.8996 ± 0.0099 of confidence (95%). By successfully estimating the pose of broiler chickens, it is possible to facilitate the detection of abnormal behavior of poultry. Furthermore, the method can be further improved to increase the overall success rate of verification.
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Affiliation(s)
- Cheng Fang
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.); (H.Z.); (J.Y.); (H.D.)
| | - Haikun Zheng
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.); (H.Z.); (J.Y.); (H.D.)
| | - Jikang Yang
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.); (H.Z.); (J.Y.); (H.D.)
| | - Hongfeng Deng
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.); (H.Z.); (J.Y.); (H.D.)
| | - Tiemin Zhang
- College of Engineering, South China Agricultural University, 483 Wushan Road, Guangzhou 510642, China; (C.F.); (H.Z.); (J.Y.); (H.D.)
- National Engineering Research Center for Breeding Swine Industry, Guangzhou 510642, China
- Guangdong Laboratory for Lingnan Modern Agriculture, Guangzhou 510642, China
- Correspondence:
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Shine P, Murphy MD. Over 20 Years of Machine Learning Applications on Dairy Farms: A Comprehensive Mapping Study. SENSORS (BASEL, SWITZERLAND) 2021; 22:52. [PMID: 35009593 PMCID: PMC8747441 DOI: 10.3390/s22010052] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 12/17/2021] [Accepted: 12/19/2021] [Indexed: 05/06/2023]
Abstract
Machine learning applications are becoming more ubiquitous in dairy farming decision support applications in areas such as feeding, animal husbandry, healthcare, animal behavior, milking and resource management. Thus, the objective of this mapping study was to collate and assess studies published in journals and conference proceedings between 1999 and 2021, which applied machine learning algorithms to dairy farming-related problems to identify trends in the geographical origins of data, as well as the algorithms, features and evaluation metrics and methods used. This mapping study was carried out in line with PRISMA guidelines, with six pre-defined research questions (RQ) and a broad and unbiased search strategy that explored five databases. In total, 129 publications passed the pre-defined selection criteria, from which relevant data required to answer each RQ were extracted and analyzed. This study found that Europe (43% of studies) produced the largest number of publications (RQ1), while the largest number of articles were published in the Computers and Electronics in Agriculture journal (21%) (RQ2). The largest number of studies addressed problems related to the physiology and health of dairy cows (32%) (RQ3), while the most frequently employed feature data were derived from sensors (48%) (RQ4). The largest number of studies employed tree-based algorithms (54%) (RQ5), while RMSE (56%) (regression) and accuracy (77%) (classification) were the most frequently employed metrics used, and hold-out cross-validation (39%) was the most frequently employed evaluation method (RQ6). Since 2018, there has been more than a sevenfold increase in the number of studies that focused on the physiology and health of dairy cows, compared to almost a threefold increase in the overall number of publications, suggesting an increased focus on this subdomain. In addition, a fivefold increase in the number of publications that employed neural network algorithms was identified since 2018, in comparison to a threefold increase in the use of both tree-based algorithms and statistical regression algorithms, suggesting an increasing utilization of neural network-based algorithms.
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Affiliation(s)
| | - Michael D. Murphy
- Department of Process, Energy and Transport Engineering, Munster Technological University, T12 P928 Cork, Ireland;
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Borges Oliveira DA, Ribeiro Pereira LG, Bresolin T, Pontes Ferreira RE, Reboucas Dorea JR. A review of deep learning algorithms for computer vision systems in livestock. Livest Sci 2021. [DOI: 10.1016/j.livsci.2021.104700] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
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Abstract
Individual identification of dairy cows based on computer vision technology shows strong performance and practicality. Accurate identification of each dairy cow is the prerequisite of artificial intelligence technology applied in smart animal husbandry. While the rump of each dairy cow also has lots of important features, so do the back and head, which are also important for individual recognition. In this paper, we propose a non-contact cow rump identification method based on convolutional neural networks. First, the rump image sequences of the cows while feeding were collected. Then, an object detection model was applied to detect the cow rump object in each frame of image. Finally, a fine-tuned convolutional neural network model was trained to identify cow rumps. An image dataset containing 195 different cows was created to validate the proposed method. The method achieved an identification accuracy of 99.76%, which showed a better performance compared to other related methods and a good potential in the actual production environment of cow husbandry, and the model is light enough to be deployed in an edge-computing device.
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Silva SR, Araujo JP, Guedes C, Silva F, Almeida M, Cerqueira JL. Precision Technologies to Address Dairy Cattle Welfare: Focus on Lameness, Mastitis and Body Condition. Animals (Basel) 2021; 11:2253. [PMID: 34438712 PMCID: PMC8388461 DOI: 10.3390/ani11082253] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 07/28/2021] [Accepted: 07/28/2021] [Indexed: 01/28/2023] Open
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.
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Affiliation(s)
- Severiano R. Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - José P. Araujo
- Escola Superior Agrária do Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, Refóios do Lima, 4990-706 Ponte de Lima, Portugal;
- Mountain Research Centre (CIMO), Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, Refóios do Lima, 4990-706 Ponte de Lima, Portugal
| | - Cristina Guedes
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - Flávio Silva
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - Mariana Almeida
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
| | - Joaquim L. Cerqueira
- Veterinary and Animal Research Centre (CECAV), Associate Laboratory of Animal and Veterinary Sciences (AL4AnimalS), University of Trás-os-Montes e Alto Douro, Quinta de Prados, 5000-801 Vila Real, Portugal; (S.R.S.); (C.G.); (F.S.); (M.A.)
- Escola Superior Agrária do Instituto Politécnico de Viana do Castelo, Rua D. Mendo Afonso, 147, Refóios do Lima, 4990-706 Ponte de Lima, Portugal;
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Relation of Automated Body Condition Scoring System and Inline Biomarkers (Milk Yield, β-Hydroxybutyrate, Lactate Dehydrogenase and Progesterone in Milk) with Cow's Pregnancy Success. SENSORS 2021; 21:s21041414. [PMID: 33670528 PMCID: PMC7922414 DOI: 10.3390/s21041414] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/12/2021] [Revised: 02/03/2021] [Accepted: 02/13/2021] [Indexed: 01/10/2023]
Abstract
The aim of the current study was to evaluate the relation of automatically determined body condition score (BCS) and inline biomarkers such as β-hydroxybutyrate (BHB), milk yield (MY), lactate dehydrogenase (LDH), and progesterone (mP4) with the pregnancy success of cows. The cows (n = 281) had 2.1 ± 0.1. lactations on average, were 151.6 ± 0.06 days postpartum, and were once tested with "Easy scan" ultrasound (IMV imaging, Scotland) at 30-35 d post-insemination. According to their reproductive status, cows were grouped into two groups: non-pregnant (n = 194 or 69.0% of cows) and pregnant (n = 87 or 31.0% of cows). Data concerning their BCS, mP4, MY, BHB, and LDH were collected each day from the day of insemination for 7 days. The BCS was collected with body condition score camera (DeLaval Inc., Tumba, Sweden); mP4, MY, BHB, and LDH were collected with the fully automated real-time analyzer Herd Navigator™ (Lattec I/S, Hillerød, Denmark) in combination with a DeLaval milking robot (DeLaval Inc., Tumba, Sweden). Of all the biomarkers, three differences between groups were significant. The body condition score (BCS) of the pregnant cows was higher (+0.49 score), the milk yield (MY) was lower (-4.36 kg), and milk progesterone in pregnant cows was (+6.11 ng/mL) higher compared to the group of non-pregnant cows (p < 0.001). The pregnancy status of the cows was associated with their BCS assessment (p < 0.001). We estimated that cows with BCS > 3.2 were 22 times more likely to have reproductive success than cows with BCS ≤ 3.2.
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