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Parlato MC, Valenti F, Porto SM. GIS-based methodology for tracking the grazing cattle site use. Heliyon 2024; 10:e33166. [PMID: 39035523 PMCID: PMC11259835 DOI: 10.1016/j.heliyon.2024.e33166] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 06/14/2024] [Accepted: 06/14/2024] [Indexed: 07/23/2024] Open
Abstract
Interest in tracking and monitoring animals in livestock farming using wearable sensors has been steadily increasing. The use of these devices is particularly crucial in extensive livestock systems where direct interaction between animals and farmers is infrequent, necessitating strenuous efforts in long-distance herd monitoring. Internet of Things (IoT) technologies offer a promising solution to address the challenges posed by vast distances, enabling real-time and remote animal monitoring. In this study, an experimental trial was conducted using a custom-designed device, located in a Polyvinyl Chloride (PVC) case, specifically tailored to fit onto a collar. This case incorporates an integrated SigFox communication system, i.e., a Low Power Global Positioning System (LP-GPS) omnidirectional system, and a power supply. The trial took place in two grazing areas located in different territorial zones, designated as Case Study I and II. A LP-GPS collar was provided for each selected animal, and the data were recorded at 20-min intervals for Case Study I and 10-min intervals for Case Study II. The acquired data were then imported and analysed using Geographical Information Systems (GIS) software. Information was collected through a purpose-built web application (AppWeb). The objective was to analyze those territorial areas mostly occupied by animals within the two considered grazing areas by developing a GIS-based methodology. Specifically, customized algorithms such as Heatmap and Kernel Density Estimation (KDE) plugins were employed to conduct spatial analyses. The maps obtained through Heatmap plugin, showed the temporal-spatial distribution of animals within their grazing areas. Additionally, the KDE tool was used to classify preferred territorial areas, generating tailored charts for each animal in the sample. The individual Core Areas, determined through KDE evaluation for each animal, were overlaid to provide a comprehensive analysis of the monitored animals.The results achieved applying the GIS-based methodology facilitated the identification of animal positions and could be adopted to provide insights into feeding behavior and soil erosion, thereby aiding in the prevention of environmental issues.
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Affiliation(s)
- Monica C.M. Parlato
- Department of Land, Environment, Agriculture and Forestry, University of Padova, 35020, Legnaro, PD, Italy
| | - Francesca Valenti
- Alma Mater Studiorum - University of Bologna, Department of Agricultural and Food Sciences, viale Giuseppe Fanin 50, 40127, Bologna Italy
| | - Simona M.C. Porto
- University of Catania, Department of Agriculture, Food and Environment, via S. Sofia 100, 95123, Catania, Italy
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Liu J, Bailey DW, Cao H, Son TC, Tobin CT. Development of a Novel Classification Approach for Cow Behavior Analysis Using Tracking Data and Unsupervised Machine Learning Techniques. SENSORS (BASEL, SWITZERLAND) 2024; 24:4067. [PMID: 39000846 PMCID: PMC11243785 DOI: 10.3390/s24134067] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/17/2024] [Revised: 06/17/2024] [Accepted: 06/19/2024] [Indexed: 07/16/2024]
Abstract
Global Positioning Systems (GPSs) can collect tracking data to remotely monitor livestock well-being and pasture use. Supervised machine learning requires behavioral observations of monitored animals to identify changes in behavior, which is labor-intensive. Our goal was to identify animal behaviors automatically without using human observations. We designed a novel framework using unsupervised learning techniques. The framework contains two steps. The first step segments cattle tracking data using state-of-the-art time series segmentation algorithms, and the second step groups segments into clusters and then labels the clusters. To evaluate the applicability of our proposed framework, we utilized GPS tracking data collected from five cows in a 1096 ha rangeland pasture. Cow movement pathways were grouped into six behavior clusters based on velocity (m/min) and distance from water. Again, using velocity, these six clusters were classified into walking, grazing, and resting behaviors. The mean velocity for predicted walking and grazing and resting behavior was 44, 13 and 2 min/min, respectively, which is similar to other research. Predicted diurnal behavior patterns showed two primary grazing bouts during early morning and evening, like in other studies. Our study demonstrates that the proposed two-step framework can use unlabeled GPS tracking data to predict cattle behavior without human observations.
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Affiliation(s)
- Jiefei Liu
- Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
| | - Derek W Bailey
- Department of Animal and Range Sciences, New Mexico State University, Las Cruces, NM 88003, USA
| | - Huiping Cao
- Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
| | - Tran Cao Son
- Department of Computer Science, New Mexico State University, Las Cruces, NM 88003, USA
| | - Colin T Tobin
- Carrington Research Extension Center, North Dakota State University, Carrington, ND 58421, USA
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Hlimi A, El Otmani S, Elame F, Chentouf M, El Halimi R, Chebli Y. Application of Precision Technologies to Characterize Animal Behavior: A Review. Animals (Basel) 2024; 14:416. [PMID: 38338058 PMCID: PMC10854988 DOI: 10.3390/ani14030416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2023] [Revised: 01/24/2024] [Accepted: 01/25/2024] [Indexed: 02/12/2024] Open
Abstract
This study aims to evaluate the state of precision livestock farming (PLF)'s spread, utilization, effectiveness, and evolution over the years. PLF includes a plethora of tools, which can aid in a number of laborious and complex tasks. These tools are often used in the monitoring of different animals, with the objective to increase production and improve animal welfare. The most frequently monitored attributes tend to be behavior, welfare, and social interaction. This study focused on the application of three types of technology: wearable sensors, video observation, and smartphones. For the wearable devices, the focus was on accelerometers and global positioning systems. For the video observation, the study addressed drones and cameras. The animals monitored by these tools were the most common ruminants, which are cattle, sheep, and goats. This review involved 108 articles that were believed to be pertinent. Most of the studied papers were very accurate, for most tools, when utilized appropriate; some showed great benefits and potential.
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Affiliation(s)
- Abdellah Hlimi
- Regional Center of Agricultural Research of Tangier, National Institute of Agricultural Research, Avenue Ennasr, BP 415 Rabat Principale, Rabat 10090, Morocco
- Laboratory of Mathematics and Applications, Faculty of Science and Technology, Abdelmalek Essaâdi University, Tangier 90000, Morocco
| | - Samira El Otmani
- Regional Center of Agricultural Research of Tangier, National Institute of Agricultural Research, Avenue Ennasr, BP 415 Rabat Principale, Rabat 10090, Morocco
| | - Fouad Elame
- Regional Center of Agricultural Research of Agadir, National Institute of Agricultural Research, Avenue Ennasr, BP 415 Rabat Principale, Rabat 10090, Morocco
| | - Mouad Chentouf
- Regional Center of Agricultural Research of Tangier, National Institute of Agricultural Research, Avenue Ennasr, BP 415 Rabat Principale, Rabat 10090, Morocco
| | - Rachid El Halimi
- Laboratory of Mathematics and Applications, Faculty of Science and Technology, Abdelmalek Essaâdi University, Tangier 90000, Morocco
| | - Youssef Chebli
- Regional Center of Agricultural Research of Tangier, National Institute of Agricultural Research, Avenue Ennasr, BP 415 Rabat Principale, Rabat 10090, Morocco
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Bretas IL, Dubeux JCB, Cruz PJR, Queiroz LMD, Ruiz-Moreno M, Knight C, Flynn S, Ingram S, Pereira Neto JD, Oduor KT, Loures DRS, Novo SF, Trumpp KR, Acuña JP, Bernardini MA. Monitoring the Effect of Weed Encroachment on Cattle Behavior in Grazing Systems Using GPS Tracking Collars. Animals (Basel) 2023; 13:3353. [PMID: 37958108 PMCID: PMC10649354 DOI: 10.3390/ani13213353] [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: 09/21/2023] [Revised: 10/23/2023] [Accepted: 10/26/2023] [Indexed: 11/15/2023] Open
Abstract
Weed encroachment on grasslands can negatively affect herbage allowance and animal behavior, impacting livestock production. We used low-cost GPS collars fitted to twenty-four Angus crossbred steers to evaluate the effects of different levels of weed encroachment on animal activities and spatial distribution. The experiment was established with a randomized complete block design, with three treatments and four blocks. The treatments were paddocks free of weeds (weed-free), paddocks with weeds established in alternated strips (weed-strips), and paddocks with weeds spread throughout the entire area (weed-infested). Animals in weed-infested paddocks had reduced resting time and increased grazing time, distance traveled, and rate of travel (p < 0.05) compared to animals in weed-free paddocks. The spatial distribution of the animals was consistently greater in weed-free paddocks than in weed-strips or weed-infested areas. The effects of weed encroachment on animal activities were minimized after weed senescence at the end of the growing season. Pasture weed encroachment affected cattle behavior and their spatial distribution across the pasture, potentially impacting animal welfare. Further long-term studies are encouraged to evaluate the impacts of weed encroachment on animal performance and to quantify the effects of behavioral changes on animal energy balance.
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Affiliation(s)
- Igor L. Bretas
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Jose C. B. Dubeux
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Priscila J. R. Cruz
- Range Cattle Research and Education Center, University of Florida, Ona, FL 33865, USA;
| | - Luana M. D. Queiroz
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Martin Ruiz-Moreno
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Colt Knight
- University of Maine Cooperative Extension, Orono, ME 04469, USA;
| | - Scott Flynn
- Corteva Agriscience, Lee’s Summit, MO 64015, USA; (S.F.); (S.I.)
| | - Sam Ingram
- Corteva Agriscience, Lee’s Summit, MO 64015, USA; (S.F.); (S.I.)
| | | | - Kenneth T. Oduor
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Daniele R. S. Loures
- Departament of Animal Science, Universidade Federal do Recôncavo da Bahia, Cruz das Almas 44430-622, BA, Brazil;
| | - Sabina F. Novo
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Kevin R. Trumpp
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Javier P. Acuña
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
| | - Marilia A. Bernardini
- North Florida Research and Education Center, University of Florida, Marianna, FL 32446, USA (L.M.D.Q.); (M.R.-M.); (K.T.O.); (S.F.N.); (K.R.T.); (J.P.A.); (M.A.B.)
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García García MJ, Maroto Molina F, Pérez Marín CC, Pérez Marín DC. Potential for automatic detection of calving in beef cows grazing on rangelands from Global Navigate Satellite System collar data. Animal 2023; 17:100901. [PMID: 37480757 DOI: 10.1016/j.animal.2023.100901] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2022] [Revised: 06/20/2023] [Accepted: 06/22/2023] [Indexed: 07/24/2023] Open
Abstract
Dystocia is one of the main causes of calf death around calving. In addition, peripartum deaths may occur due to other factors, such as weather or predators, especially in the case of grazing animals. Precision Livestock Farming (PLF) tools aimed at the automatic detection of calving may be useful for farmers, allowing cow assistance in case of dystocia or checking the condition of the cow-calf pair after calving. Such PLF systems are commercially available for dairy cows, but these tools are not suitable for rangelands, mainly due to power and connectivity constraints. Thus, since most commercial PLF tools for rangelands are based on Global Navigate Satellite System (GNSS) technology, the objective of this study was to design and evaluate several indicators built from data gathered with GNSS collars to characterise their potential for the detection of calving on rangelands. Location data from 57 cows, 42 of which calved during the study, were curated and analysed following a standardised procedure. Several indicators were calculated using two different strategies. The first approach consisted of having indicators that could be computed using the data of a single GNSS collar (cow indicators). The second strategy involved the use of data from several animals (herd indicators), which requires more animals to be monitored, but may allow the characterisation of social behaviour. Several indicators, such as the length of the daily trajectory or the sinuosity of cow path, showed significant differences between the pre- and postpartum periods, but no clear differences between calving day and previous days. Herd indicators, such as the distance to herd centroid or to the nearest peer were superior in terms of the detection of calving day, as cows showed isolation behaviour from 24 hours before calving. Relative indicators, i.e., the value of cow or herd indicators for the calving cow in relation to the average value of the same indicators for its herdmates, provided additional information on cow behaviour. For instance, according to the relative indicator for the change in daily trajectory, pregnant cows had a differential exploratory behaviour up to 14 days before calving. In conclusion, data from commercial GNSS collars proved to be useful for the computation of several indicators related to the occurrence of calving on rangelands. Some of those indicators showed changes from baseline values on the day before calving, which could serve to predict the onset of parturition.
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Affiliation(s)
- M J García García
- Department of Animal Production, School of Agricultural and Forestry Engineering, University of Cordoba, Campus de Rabanales, Madrid-Cadiz Rd. km 396, 14071 Cordoba, Spain
| | - F Maroto Molina
- Department of Animal Production, School of Agricultural and Forestry Engineering, University of Cordoba, Campus de Rabanales, Madrid-Cadiz Rd. km 396, 14071 Cordoba, Spain.
| | - C C Pérez Marín
- Department of Animal Medicine and Surgery, Faculty of Veterinary Medicine, University of Cordoba, Campus de Rabanales, Madrid-Cadiz Rd. km 396, 14071 Cordoba, Spain
| | - D C Pérez Marín
- Department of Animal Production, School of Agricultural and Forestry Engineering, University of Cordoba, Campus de Rabanales, Madrid-Cadiz Rd. km 396, 14071 Cordoba, Spain
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Mancuso D, Castagnolo G, Porto SMC. Cow Behavioural Activities in Extensive Farms: Challenges of Adopting Automatic Monitoring Systems. SENSORS (BASEL, SWITZERLAND) 2023; 23:3828. [PMID: 37112171 PMCID: PMC10143811 DOI: 10.3390/s23083828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/19/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 06/19/2023]
Abstract
Animal welfare is becoming an increasingly important requirement in the livestock sector to improve, and therefore raise, the quality and healthiness of food production. By monitoring the behaviour of the animals, such as feeding, rumination, walking, and lying, it is possible to understand their physical and psychological status. Precision Livestock Farming (PLF) tools offer a good solution to assist the farmer in managing the herd, overcoming the limits of human control, and to react early in the case of animal health issues. The purpose of this review is to highlight a key concern that occurs in the design and validation of IoT-based systems created for monitoring grazing cows in extensive agricultural systems, since they have many more, and more complicated, problems than indoor farms. In this context, the most common concerns are related to the battery life of the devices, the sampling frequency to be used for data collection, the need for adequate service connection coverage and transmission range, the computational site, and the performance of the algorithm embedded in IoT-systems in terms of computational cost.
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Affiliation(s)
- Dominga Mancuso
- Department of Agriculture, Food and Environment (Di3A), Building and Land Engineering Section, University of Catania, Via S. Sofia 100, 95123 Catania, Italy; (D.M.); (S.M.C.P.)
| | - Giulia Castagnolo
- Department of Electrical, Electronic and Computer Engineering (DIEEI), University of Catania, Viale A. Doria 6, 95125 Catania, Italy
| | - Simona M. C. Porto
- Department of Agriculture, Food and Environment (Di3A), Building and Land Engineering Section, University of Catania, Via S. Sofia 100, 95123 Catania, Italy; (D.M.); (S.M.C.P.)
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