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Rajput AS, Mishra B, Rajawat D, Bhakat M. Early prediction of oestrus for herd fertility management in cattle and buffaloes - a review. Reprod Domest Anim 2024; 59:e14597. [PMID: 38798195 DOI: 10.1111/rda.14597] [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: 02/26/2024] [Revised: 04/25/2024] [Accepted: 05/06/2024] [Indexed: 05/29/2024]
Abstract
Oestrus is defined as a period when a female animal exhibits characteristic sexual behaviour in the presence of a mature male. Oestrous manifestation in dairy animals is due to the oestrogen (E2) effect on the central nervous system (CNS). It is a critical issue to be considered on a priority basis. Inefficient oestrous detection reduces the fertility status of the herd. The primary and most reliable indicator of oestrus is standing to be mounted by a bull or another female herd mate, signalling receptivity and the pre-ovulatory state in dairy cattle. Oestrous detection is primarily a management challenge requiring skill and vigilance. To improve the efficiency of oestrous detection in dairy cattle, visual observation is one of the best methods if done three times a day; however, heat detection aids, if combined, give better results. However, techniques like using teaser bulls, tail painting, chin ball markers, ultrasound (USG) examination, hormonal analysis and examination of cervicovaginal mucus (CVM) improve oestrous detection efficiency. Moreover, the changes in production systems have reduced the expression of oestrous behaviour among cows, due to higher oestrogen (E2) metabolism. Therefore, automated systems, such as pedometers, accelerometers and acoustic sensors like infrared thermography (IRT) and image processing, have significantly enhanced reproductive performance by facilitating oestrous detection and optimizing insemination schedules. From this review, we would conclude that oestrous detection alone contributes considerably to the reproductive status of the herd; therefore, applying different methods of oestrous detection reduces the incidence of missed oestrus and improves the fertility status of the herd.
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Affiliation(s)
- Atul Singh Rajput
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, India
| | - Babita Mishra
- Livestock Production and Management Section, ICAR-Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, India
| | - Divya Rajawat
- Division of Animal Genetics, ICAR-Indian Veterinary Research Institute, Izatnagar, Uttar Pradesh, India
| | - Mukesh Bhakat
- APR Division, ICAR-CIRG, Mathura, Uttar Pradesh, India
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Szelényi Z, Lipthay I, Sánta A, Lénárt L, Répási A, Szenci O. Pregnancy evaluation with a point-of-care pregnancy test in dairy cattle. Theriogenology 2024; 214:201-205. [PMID: 37897849 DOI: 10.1016/j.theriogenology.2023.10.014] [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: 08/13/2023] [Revised: 10/02/2023] [Accepted: 10/12/2023] [Indexed: 10/30/2023]
Abstract
Primiparous and multiparous dairy cattle were evaluated for pregnancy using both transrectal ultrasonography and a point-of-care pregnancy test (Alertys OnFarm Test), which measures pregnancy-associated glycoproteins through lateral diffusion, between Days 28-34 of pregnancy results were compared. A total of 637 animals were included in this study. Pregnancy was confirmed via manual palpation between Days 57-64. Data on parity, calving, and time of artificial insemination (AI) were also collected and evaluated. Overall the accuracy of the lateral diffusion test was 93.1% with 98.9% sensitivity, 88.7 % specificity, 86.8 % positive predictive value, and 99.1% negative predictive value. In heifers, the Alertys OnFarm Test had 100% sensitivity and 81.6% specificity. In contrast, the test had a sensitivity and specificity of 98.5 and 89.5%, respectively for multiparous cows. The pregnancy loss between early diagnosis and confirmation increased with parity. Heifers suffered losses as low as 2.6%, whereas animals in the third parity had significantly more losses (17.9%). Season also affected losses with spring pregnancy losses being considerably higher than autumn losses. The veterinary workload was also evaluated. Using the combined method of pregnancy testing, animals were first tested with the lateral diffusion test; then, the test-negative animals were scanned again, and the number of scanned animals was reduced. The results ranged between 3274% on farms. We concluded that veterinary workload could be reduced by using the point-of-care test. However, farm-level differences may have affected the results of the present study.
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Affiliation(s)
- Zoltán Szelényi
- University of Veterinary Medicine, Department for Obstetrics and Farm Animal Clinic, H-1078, István u. 2., Budapest, Hungary.
| | - Ildikó Lipthay
- RougeVet Veterinary Practice, H-2351, Fő út 45. Alsónémedi, Hungary
| | - Attila Sánta
- RougeVet Veterinary Practice, H-2351, Fő út 45. Alsónémedi, Hungary
| | - Lea Lénárt
- University of Veterinary Medicine, Department for Obstetrics and Farm Animal Clinic, H-1078, István u. 2., Budapest, Hungary
| | - Atilla Répási
- Pataki Állatorvos Veterinary Practice, H-3950, Dobó F. utca 54. Sárospatak, Hungary
| | - Ottó Szenci
- University of Veterinary Medicine, Department for Obstetrics and Farm Animal Clinic, H-1078, István u. 2., Budapest, Hungary
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Doidge C, Palczynski L, Zhou X, Bearth A, van Schaik G, Kaler J. Exploring the data divide through a social practice lens: A qualitative study of UK cattle farmers. Prev Vet Med 2023; 220:106030. [PMID: 37806078 DOI: 10.1016/j.prevetmed.2023.106030] [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: 01/13/2023] [Revised: 07/31/2023] [Accepted: 09/24/2023] [Indexed: 10/10/2023]
Abstract
Appropriate management decisions are key for sustainable and profitable beef and dairy farming. Data-driven technologies aim to provide information which can improve farmers' decision-making practices. However, data-driven technologies have resulted in the emergence of a "data divide", in which there is a gap between the generation and use of data. Our study aims to further understand the data divide by drawing on social practice theory to recognise the emergence, linkages, and reproduction of youngstock data practices on cattle farms in the UK. Eight focus groups with fifteen beef and nineteen dairy farmers were completed. The topics of discussion included data use, technology use, disease management in youngstock, and future goals for their farm. The transcribed data were analysed using reflexive thematic analysis with a social practice lens. Social practice theory uses practices as the unit of analysis, rather than focusing on individual behaviours. Practices are formed of three elements: meaning (e.g., beliefs), materials (e.g., objects), and competencies (e.g., skills) and are connected in time and space. We conceptualised the data divide as a disconnection of data collection practices and data use and interpretation practices. Consequently, we were able to generate five themes that represent these breaks in connection.Our findings suggest that a data divide exists because of meanings that de-stabilise practices, tensions in farmers' competencies to perform practices, spatial and temporal disconnects, and lack of forms of feedback on data practices. The data preparation practice, where farmers had to merge different data sources or type up handwritten data, had negative meanings attached to it and was therefore sometimes not performed. Farmers tended to associate data and technology practices with larger dairy farms, which could restrict beef and small-scale dairy farms from performing these practices. Some farmers suggested that they lacked the skills to use technologies and struggled to transform their data into meaningful outputs. Data preparation and data use and interpretation practices were often tied to an office space because of the required infrastructure, but farmers preferred to spend time outdoors and with their animals. There appeared to be no normalisation of what data should be collected or what data should be analysed, which made it difficult for farmers to benchmark their progress. Some farmers did not have access to discussion groups or veterinarians who were interested in data and therefore could not get feedback on their data practices.These results suggest that the data divide exists because of three types of disconnect: a disconnect between elements within a practice because of tensions in competencies or negative meanings to perform a practice; a disconnect between practices because of temporal or spatial differences; and a break in the reproduction of practices because of lack of feedback on their practices. Data use on farms can be improved through transformation of practices by ensuring farmers have input in the design of technologies so that they align with their values and competencies.
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Affiliation(s)
- C Doidge
- School of Veterinary Medicine & Science, University of Nottingham, Sutton Bonington LE12 5RD, UK.
| | - L Palczynski
- Innovation for Agriculture, Stoneleigh Park, Warwickshire CV8 2LZ, UK
| | - X Zhou
- Consumer Behaviour, Institute for Environmental Decisions, ETH Zurich, Universitaetstrasse 22, 8092 Zurich, Switzerland
| | - A Bearth
- Consumer Behaviour, Institute for Environmental Decisions, ETH Zurich, Universitaetstrasse 22, 8092 Zurich, Switzerland
| | - G van Schaik
- Department of Population Health Sciences, Faculty of Veterinary Medicine, Utrecht University, Utrecht, the Netherlands; Royal GD, Deventer, the Netherlands
| | - J Kaler
- School of Veterinary Medicine & Science, University of Nottingham, Sutton Bonington LE12 5RD, UK
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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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Innovations in Cattle Farming: Application of Innovative Technologies and Sensors in the Diagnosis of Diseases. Animals (Basel) 2023; 13:ani13050780. [PMID: 36899637 PMCID: PMC10000156 DOI: 10.3390/ani13050780] [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: 01/11/2023] [Revised: 02/19/2023] [Accepted: 02/20/2023] [Indexed: 02/24/2023] Open
Abstract
Precision livestock farming has a crucial function as farming grows in significance. It will help farmers make better decisions, alter their roles and perspectives as farmers and managers, and allow for the tracking and monitoring of product quality and animal welfare as mandated by the government and industry. Farmers can improve productivity, sustainability, and animal care by gaining a deeper understanding of their farm systems as a result of the increased use of data generated by smart farming equipment. Automation and robots in agriculture have the potential to play a significant role in helping society fulfill its future demands for food supply. These technologies have already enabled significant cost reductions in production, as well as reductions in the amount of intensive manual labor, improvements in product quality, and enhancements in environmental management. Wearable sensors can monitor eating, rumination, rumen pH, rumen temperature, body temperature, laying behavior, animal activity, and animal position or placement. Detachable or imprinted biosensors that are adaptable and enable remote data transfer might be highly important in this quickly growing industry. There are already multiple gadgets to evaluate illnesses such as ketosis or mastitis in cattle. The objective evaluation of sensor methods and systems employed on the farm is one of the difficulties presented by the implementation of modern technologies on dairy farms. The availability of sensors and high-precision technology for real-time monitoring of cattle raises the question of how to objectively evaluate the contribution of these technologies to the long-term viability of farms (productivity, health monitoring, welfare evaluation, and environmental effects). This review focuses on biosensing technologies that have the potential to change early illness diagnosis, management, and operations for livestock.
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Lanzoni L, Chincarini M, Giammarco M, Fusaro I, Iannotta M, Podaliri M, Contri A, Gloria A, Vignola G. Changes in the behaviour before normal calving to predict its onset in Mediterranean buffaloes heifers. Appl Anim Behav Sci 2022. [DOI: 10.1016/j.applanim.2022.105721] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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