151
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Rutten C, Steeneveld W, Oude Lansink A, Hogeveen H. Delaying investments in sensor technology: The rationality of dairy farmers' investment decisions illustrated within the framework of real options theory. J Dairy Sci 2018; 101:7650-7660. [DOI: 10.3168/jds.2017-13358] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2017] [Accepted: 03/25/2018] [Indexed: 11/19/2022]
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152
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Miglior F, Fleming A, Malchiodi F, Brito LF, Martin P, Baes CF. A 100-Year Review: Identification and genetic selection of economically important traits in dairy cattle. J Dairy Sci 2018; 100:10251-10271. [PMID: 29153164 DOI: 10.3168/jds.2017-12968] [Citation(s) in RCA: 214] [Impact Index Per Article: 35.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Accepted: 07/09/2017] [Indexed: 01/14/2023]
Abstract
Over the past 100 yr, the range of traits considered for genetic selection in dairy cattle populations has progressed to meet the demands of both industry and society. At the turn of the 20th century, dairy farmers were interested in increasing milk production; however, a systematic strategy for selection was not available. Organized milk performance recording took shape, followed quickly by conformation scoring. Methodological advances in both genetic theory and statistics around the middle of the century, together with technological innovations in computing, paved the way for powerful multitrait analyses. As more sophisticated analytical techniques for traits were developed and incorporated into selection programs, production began to increase rapidly, and the wheels of genetic progress began to turn. By the end of the century, the focus of selection had moved away from being purely production oriented toward a more balanced breeding goal. This shift occurred partly due to increasing health and fertility issues and partly due to societal pressure and welfare concerns. Traits encompassing longevity, fertility, calving, health, and workability have now been integrated into selection indices. Current research focuses on fitness, health, welfare, milk quality, and environmental sustainability, underlying the concentrated emphasis on a more comprehensive breeding goal. In the future, on-farm sensors, data loggers, precision measurement techniques, and other technological aids will provide even more data for use in selection, and the difficulty will lie not in measuring phenotypes but rather in choosing which traits to select for.
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Affiliation(s)
- Filippo Miglior
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada; Canadian Dairy Network, Guelph, Ontario, N1K 1E5, Canada.
| | - Allison Fleming
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Francesca Malchiodi
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Luiz F Brito
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Pauline Martin
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
| | - Christine F Baes
- Centre for Genetic Improvement of Livestock, Department of Animal Biosciences, University of Guelph, Guelph, Ontario, N1G 2W1, Canada
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153
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Schlageter-Tello A, Van Hertem T, Bokkers EA, Viazzi S, Bahr C, Lokhorst K. Performance of human observers and an automatic 3-dimensional computer-vision-based locomotion scoring method to detect lameness and hoof lesions in dairy cows. J Dairy Sci 2018; 101:6322-6335. [DOI: 10.3168/jds.2017-13768] [Citation(s) in RCA: 38] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 02/24/2018] [Indexed: 11/19/2022]
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154
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Hojo T, Sakatani M, Takenouchi N. Efficiency of a pedometer device for detecting estrus in standing heat and silent heat in Japanese Black cattle. Anim Sci J 2018; 89:1067-1072. [DOI: 10.1111/asj.13023] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Accepted: 01/09/2018] [Indexed: 01/05/2023]
Affiliation(s)
- Takuo Hojo
- Livestock and Grassland Research Division; Kyushu Okinawa Agricultural Research Center; National Agriculture and Food Research Organization (NARO); Kumamoto Japan
| | - Miki Sakatani
- Livestock and Grassland Research Division; Kyushu Okinawa Agricultural Research Center; National Agriculture and Food Research Organization (NARO); Kumamoto Japan
| | - Naoki Takenouchi
- Livestock and Grassland Research Division; Kyushu Okinawa Agricultural Research Center; National Agriculture and Food Research Organization (NARO); Kumamoto Japan
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155
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King M, LeBlanc S, Pajor E, Wright T, DeVries T. Behavior and productivity of cows milked in automated systems before diagnosis of health disorders in early lactation. J Dairy Sci 2018; 101:4343-4356. [DOI: 10.3168/jds.2017-13686] [Citation(s) in RCA: 29] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2017] [Accepted: 12/21/2017] [Indexed: 01/01/2023]
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156
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Rojo-Gimeno C, Fievez V, Wauters E. The economic value of information provided by milk biomarkers under different scenarios: Case-study of an ex-ante analysis of fat-to-protein ratio and fatty acid profile to detect subacute ruminal acidosis in dairy cows. Livest Sci 2018. [DOI: 10.1016/j.livsci.2018.02.001] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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157
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Song X, Bokkers E, van der Tol P, Groot Koerkamp P, van Mourik S. Automated body weight prediction of dairy cows using 3-dimensional vision. J Dairy Sci 2018; 101:4448-4459. [DOI: 10.3168/jds.2017-13094] [Citation(s) in RCA: 72] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2017] [Accepted: 01/04/2018] [Indexed: 11/19/2022]
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158
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Beauchemin KA. Invited review: Current perspectives on eating and rumination activity in dairy cows. J Dairy Sci 2018; 101:4762-4784. [PMID: 29627250 DOI: 10.3168/jds.2017-13706] [Citation(s) in RCA: 178] [Impact Index Per Article: 29.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Accepted: 01/11/2018] [Indexed: 11/19/2022]
Abstract
Many early studies laid the foundation for our understanding of the mechanics of chewing, the physiological role of chewing for the cow, and how chewing behavior is affected by dietary characteristics. However, the dairy cow has changed significantly over the past decades, as have the types of diets fed and the production systems used. The plethora of literature published in recent years provides new insights on eating and ruminating activity of dairy cows. Lactating dairy cows spend about 4.5 h/d eating (range: 2.4-8.5 h/d) and 7 h/d ruminating (range: 2.5-10.5 h/d), with a maximum total chewing time of 16 h/d. Chewing time is affected by many factors, most importantly whether access to feed is restricted, intake of neutral detergent fiber from forages, and mean particle size of the diet. Feed restriction and long particles (≥19 mm) have a greater effect on eating time, whereas intake of forage neutral detergent fiber and medium particles (4-19 mm) affects rumination time. It is well entrenched in the literature that promoting chewing increases salivary secretion of dairy cows, which helps reduce the risk of acidosis. However, the net effect of a change in chewing time on rumen buffing is likely rather small; therefore, acidosis prevention strategies need to be broad. Damage to plant tissues during mastication creates sites that provide access to fungi, adhesion of bacteria, and formation of biofilms that progressively degrade carbohydrates. Rumination and eating are the main ways in which feed is reduced in particle size. Contractions of the rumen increase during eating and ruminating activity and help move small particles to the escapable pool and into the omasum. Use of recently developed low-cost sensors that monitor chewing activity of dairy cows in commercial facilities can provide information that is helpful in management decisions, especially when combined with other criteria. Although accuracy and precision can be somewhat variable depending on sensor and conditions of use, relative changes in cow behavior, such as a marked decrease in rumination time of a cow or sustained low rumination time compared with a contemporary group of cows, can be used to help detect estrus, parturition, and some illnesses. This review provides a comprehensive understanding of the dietary, animal, and management factors that affect eating and ruminating behavior in dairy cows and presents an overview of the physiological importance of chewing with emphasis on recent developments and practical implications for feeding and managing the modern housed dairy cow.
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Affiliation(s)
- K A Beauchemin
- Agriculture and Agri-Food Canada, Lethbridge, Alberta, Canada T1J 4B1.
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159
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Alawneh JI, Henning J, Olchowy TWJ. Functionality and Interfaces of a Herd Health Decision Support System for Practising Dairy Cattle Veterinarians in New Zealand. Front Vet Sci 2018. [PMID: 29527531 PMCID: PMC5829518 DOI: 10.3389/fvets.2018.00021] [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] [Indexed: 11/30/2022] Open
Abstract
Decision-making processes to assess and improve the health of dairy herds are often unstructured due to the complexity of interactions that exist between the health and productivity of the herd, for which there are no ready to hand solutions. Decisions made in the face of these complex herd health problems are often based on the experience and perceptions of what might be a quick or the easiest solution. To shift from this unstructured process to semistructured decision-making requires a more holistic understanding of potential health problems and access to herd productivity information and to analytical methods suitable for examining and evaluating such data. Technological advances in agriculture have made the development of such information technology systems both possible and relatively accessible to decision makers working with dairy herds (e.g., veterinarians). The timely access and appropriate analysis of herd productivity data provides the herd health advisor with the opportunity to track and benchmark the performance of dairy herds. Thus, a decision support system (DSS) will use best available evidence to guide the allocation of resources to specific, most promising herd health interventions. This article presents an example of a DSS-based on collection of data and algorithm of analysis.
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Affiliation(s)
- John I Alawneh
- School of Veterinary Science, University of Queensland, Gatton, QLD, Australia
| | - Joerg Henning
- School of Veterinary Science, University of Queensland, Gatton, QLD, Australia
| | - Timothy W J Olchowy
- School of Veterinary Science, University of Queensland, Gatton, QLD, Australia
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160
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Gargiulo JI, Eastwood CR, Garcia SC, Lyons NA. Dairy farmers with larger herd sizes adopt more precision dairy technologies. J Dairy Sci 2018. [PMID: 29525319 DOI: 10.3168/jds.2017-13324] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/19/2022]
Abstract
An increase in the average herd size on Australian dairy farms has also increased the labor and animal management pressure on farmers, thus potentially encouraging the adoption of precision technologies for enhanced management control. A survey was undertaken in 2015 in Australia to identify the relationship between herd size, current precision technology adoption, and perception of the future of precision technologies. Additionally, differences between farmers and service providers in relation to perception of future precision technology adoption were also investigated. Responses from 199 dairy farmers, and 102 service providers, were collected between May and August 2015 via an anonymous Internet-based questionnaire. Of the 199 dairy farmer responses, 10.4% corresponded to farms that had fewer than 150 cows, 37.7% had 151 to 300 cows, 35.5% had 301 to 500 cows; 6.0% had 501 to 700 cows, and 10.4% had more than 701 cows. The results showed that farmers with more than 500 cows adopted between 2 and 5 times more specific precision technologies, such as automatic cup removers, automatic milk plant wash systems, electronic cow identification systems and herd management software, when compared with smaller farms. Only minor differences were detected in perception of the future of precision technologies between either herd size or farmers and service providers. In particular, service providers expected a higher adoption of automatic milking and walk over weighing systems than farmers. Currently, the adoption of precision technology has mostly been of the type that reduces labor needs; however, respondents indicated that by 2025 adoption of data capturing technology for monitoring farm system parameters would be increased.
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Affiliation(s)
- J I Gargiulo
- Mastellone Hnos, General Rodriguez BA 1748, Argentina
| | | | - S C Garcia
- Dairy Science Group, School of Life and Environmental Sciences, Faculty of Science, The University of Sydney, Camden NSW 2570, Australia
| | - N A Lyons
- Intensive Livestock Industries, NSW Department of Primary Industries, Elizabeth Macarthur Agricultural Institute, Menangle NSW 2568, Australia.
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161
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Weigele H, Gygax L, Steiner A, Wechsler B, Burla JB. Moderate lameness leads to marked behavioral changes in dairy cows. J Dairy Sci 2018; 101:2370-2382. [DOI: 10.3168/jds.2017-13120] [Citation(s) in RCA: 59] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2017] [Accepted: 11/08/2017] [Indexed: 11/19/2022]
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162
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Richeson JT, Lawrence TE, White BJ. Using advanced technologies to quantify beef cattle behavior. Transl Anim Sci 2018; 2:223-229. [PMID: 32704706 PMCID: PMC7200524 DOI: 10.1093/tas/txy004] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 02/15/2018] [Indexed: 12/03/2022] Open
Abstract
For decades, we have relied upon visual observation of animal behavior to define clinical disease, assist in breeding selection, and predict growth performance. Limitations of visual monitoring of cattle behavior include training of personnel, subjectivity, and brevity. In addition, extensive time and labor is required to visually monitor behavior in large numbers of animals, and the prey instinct of cattle to disguise abnormal behaviors in the presence of a human evaluator is problematic. More recently, cattle behavior has been quantified objectively and continuously using advanced technologies to assess animal welfare, indicate lameness or disease, and detect estrus in both production and research settings. The current review will summarize three methodologies for quantification of cattle behavior with focus on U.S. beef production systems; 1) three-axis accelerometers that quantify physical behavior, 2) systems that document feeding and watering behavior via radio frequency, and 3) triangulation or global positioning systems to determine location and movement of cattle within a pen or pasture. Furthermore, advances in Wi-Fi and radio frequency technology have allowed many of these systems to operate remotely and in real-time and efforts are underway to develop commercial applications that may allow early detection of respiratory or other cattle diseases in the production environment. Current challenges with commercial application of technology for early disease detection include establishment of an appropriate algorithm to ensure maximum sensitivity and specificity, reliable and repeatable data collection in harsh environments, cost:benefit, and integration with traditional methodology for clinical diagnosis. Advanced technologies have also allowed cattle researchers to determine temporal variance in behavior or variability between experimental treatments. However, these data sets are typically very large and challenges exist regarding statistical analysis and reporting.
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Affiliation(s)
- John T Richeson
- Department of Agricultural Sciences, West Texas A&M University, Canyon, TX
| | - Ty E Lawrence
- Department of Agricultural Sciences, West Texas A&M University, Canyon, TX
| | - Brad J White
- Department of Clinical Sciences, Kansas State University, Manhattan, KS
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163
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Abstract
Helminth infections have large negative impacts on production efficiency in ruminant farming systems worldwide, and their effective management is essential if livestock production is to increase to meet future human needs for dietary protein. The control of helminths relies heavily on routine use of chemotherapeutics, but this approach is unsustainable as resistance to anthelmintic drugs is widespread and increasing. At the same time, infection patterns are being altered by changes in climate, land-use and farming practices. Future farms will need to adopt more efficient, robust and sustainable control methods, integrating ongoing scientific advances. Here, we present a vision of helminth control in farmed ruminants by 2030, bringing to bear progress in: (1) diagnostic tools, (2) innovative control approaches based on vaccines and selective breeding, (3) anthelmintics, by sustainable use of existing products and potentially new compounds, and (4) rational integration of future control practices. In this review, we identify the technical advances that we believe will place new tools in the hands of animal health decision makers in 2030, to enhance their options for control and allow them to achieve a more integrated and sustainable approach to helminth control in support of animal welfare and production.
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164
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Kerslake J, Amer P, O'Neill P, Wong S, Roche J, Phyn C. Economic costs of recorded reasons for cow mortality and culling in a pasture-based dairy industry. J Dairy Sci 2018; 101:1795-1803. [DOI: 10.3168/jds.2017-13124] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2017] [Accepted: 10/18/2017] [Indexed: 11/19/2022]
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165
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Nørstebø H, Rachah A, Dalen G, Rønningen O, Whist AC, Reksen O. Milk-flow data collected routinely in an automatic milking system: an alternative to milking-time testing in the management of teat-end condition? Acta Vet Scand 2018; 60:2. [PMID: 29325588 PMCID: PMC5765711 DOI: 10.1186/s13028-018-0356-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2017] [Accepted: 01/02/2018] [Indexed: 12/05/2022] Open
Abstract
Background Having a poor teat-end condition is associated with increased mastitis risk, hence avoiding milking machine settings that have a negative effect on teat-end condition is important for successful dairy production. Milking-time testing (MTT) can be used in the evaluation of vacuum conditions during milking, but the method is less suited for herds using automatic milking systems (AMS) and relationships with teat end condition is poorly described. This study aimed to increase knowledge on interpretation of MTT in AMS and to assess whether milk-flow data obtained routinely by an AMS can be useful for the management of teat-end health. A cross-sectional study, including 251 teats of 79 Norwegian Red cows milked by AMS was performed in the research herd of the Norwegian University of Life Sciences. The following MTT variables were obtained at teat level: Average vacuum level in the short milk tube during main milking (MTVAC), average vacuum in the mouthpiece chamber during main milking and overmilking, teat compression intensity (COMPR) and overmilking time. Average and peak milk flow rates were obtained at quarter level from the AMS software. Teat-end callosity thickness and roughness was registered, and teat dimensions; length, and width at apex and base, were measured. Interrelationships among variables obtained by MTT, quarter milk flow variables, and teat dimensions were described. Associations between these variables and teat-end callosity thickness and roughness, were investigated. Results Principal component analysis showed clusters of strongly related variables. There was a strong negative relationship between MTVAC and average milk flow rate. The variables MTVAC, COMPR and average and peak milk flow rate were associated with both thickness and roughness of the callosity ring. Conclusions Quarter milk flow rate obtained directly from the AMS software was useful in assessing associations between milking machine function and teat-end condition; low average milk flow rates were associated with a higher likelihood of the teat having a thickened or roughened teat-end callosity ring. Since information on milk flow rate is readily available from the herd management system, this information might be used when evaluating causes for impaired teat-end condition in AMS.
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166
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Crowe MA, Hostens M, Opsomer G. Reproductive management in dairy cows - the future. Ir Vet J 2018; 71:1. [PMID: 29321918 PMCID: PMC5759237 DOI: 10.1186/s13620-017-0112-y] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2017] [Accepted: 12/12/2017] [Indexed: 12/19/2022] Open
Abstract
Background Drivers of change in dairy herd health management include the significant increase in herd/farm size, quota removal (within Europe) and the increase in technologies to aid in dairy cow reproductive management. Main body There are a number of key areas for improving fertility management these include: i) handling of substantial volumes of data, ii) genetic selection (including improved phenotypes for use in breeding programmes), iii) nutritional management (including transition cow management), iv) control of infectious disease, v) reproductive management (and automated systems to improve reproductive management), vi) ovulation / oestrous synchronisation, vii) rapid diagnostics of reproductive status, and viii) management of male fertility. This review covers the current status and future outlook of many of these key factors that contribute to dairy cow herd health and reproductive performance. Conclusions In addition to improvements in genetic trends for fertility, numerous other future developments are likely in the near future. These include: i) development of new and novel fertility phenotypes that may be measurable in milk; ii) specific fertility genomic markers; iii) earlier and rapid pregnancy detection; iv) increased use of activity monitors; v) improved breeding protocols; vi) automated inline sensors for relevant phenotypes that become more affordable for farmers; and vii) capturing and mining multiple sources of “Big Data” available to dairy farmers. These should facilitate improved performance, health and fertility of dairy cows in the future.
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Affiliation(s)
- Mark A Crowe
- UCD School of Veterinary Medicine, University College Dublin, Belfield, Dublin 4 Ireland
| | - Miel Hostens
- Faculty of Veterinary Medicine, University of Ghent, Salisburylaan 133, 9820 Merelbeke, Belgium
| | - Geert Opsomer
- Faculty of Veterinary Medicine, University of Ghent, Salisburylaan 133, 9820 Merelbeke, Belgium
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167
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168
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Validation of a pedometer algorithm as a tool for evaluation of locomotor behaviour in dairy Mediterranean buffalo. J DAIRY RES 2017; 84:391-394. [DOI: 10.1017/s0022029917000668] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
This research communication validates an algorithm to monitor natural occurrence of locomotor behaviours in dairy Mediterranean buffalo based on the output of a 3-dimensional accelerometer (RumiWatch®, pedometer). Several characteristics of the locomotor behaviour were detected with a very high (up-right, lying and standing time) or high degree of correlation (walking time and number of strides) and a low mean difference with the video recording. The proportion of correctly detected events exceeded 99 % for the following variables: stand up and lie down events, as well as number of lying, standing or walking bouts. The mean relative measurement error was less than 10 % for the variables: lying, standing, up-right times and number of strides as compared with gold standard. This new algorithm may represent the base for a future early and real-time disease warning system aiming to gain higher health standard in these ruminants.
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169
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Charlier J, Thamsborg SM, Bartley DJ, Skuce PJ, Kenyon F, Geurden T, Hoste H, Williams AR, Sotiraki S, Höglund J, Chartier C, Geldhof P, van Dijk J, Rinaldi L, Morgan ER, von Samson-Himmelstjerna G, Vercruysse J, Claerebout E. Mind the gaps in research on the control of gastrointestinal nematodes of farmed ruminants and pigs. Transbound Emerg Dis 2017; 65 Suppl 1:217-234. [PMID: 29124904 DOI: 10.1111/tbed.12707] [Citation(s) in RCA: 56] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2017] [Indexed: 12/31/2022]
Abstract
Gastrointestinal (GI) nematode control has an important role to play in increasing livestock production from a limited natural resource base and to improve animal health and welfare. In this synthetic review, we identify key research priorities for GI nematode control in farmed ruminants and pigs, to support the development of roadmaps and strategic research agendas by governments, industry and policymakers. These priorities were derived from the DISCONTOOLS gap analysis for nematodes and follow-up discussions within the recently formed Livestock Helminth Research Alliance (LiHRA). In the face of ongoing spread of anthelmintic resistance (AR), we are increasingly faced with a failure of existing control methods against GI nematodes. Effective vaccines against GI nematodes are generally not available, and anthelmintic treatment will therefore remain a cornerstone for their effective control. At the same time, consumers and producers are increasingly concerned with environmental issues associated with chemical parasite control. To address current challenges in GI nematode control, it is crucial to deepen our insights into diverse aspects of epidemiology, AR, host immune mechanisms and the socio-psychological aspects of nematode control. This will enhance the development, and subsequent uptake, of the new diagnostics, vaccines, pharma-/nutraceuticals, control methods and decision support tools required to respond to the spread of AR and the shifting epidemiology of GI nematodes in response to climatic, land-use and farm husbandry changes. More emphasis needs to be placed on the upfront evaluation of the economic value of these innovations as well as the socio-psychological aspects to prioritize research and facilitate uptake of innovations in practice. Finally, targeted regulatory guidance is needed to create an innovation-supportive environment for industries and to accelerate the access to market of new control tools.
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Affiliation(s)
- J Charlier
- Kreavet, Kruibeke, Belgium.,Avia-GIS, Zoersel, Belgium
| | - S M Thamsborg
- Department of Veterinary Disease Biology, University of Copenhagen, Frederiksberg C, Denmark
| | | | - P J Skuce
- Moredun Research Institute, Edinburgh, UK
| | - F Kenyon
- Moredun Research Institute, Edinburgh, UK
| | | | - H Hoste
- UMR IHAP 1225, INRA, ENVT, Université de Toulouse, Toulouse, France
| | - A R Williams
- Department of Veterinary Disease Biology, University of Copenhagen, Frederiksberg C, Denmark
| | - S Sotiraki
- VetResInst, HAO-DEMETER, Thessaloniki, Greece
| | - J Höglund
- BVF, Section for Parasitology, Swedish University of Agricultural Sciences, Uppsala, Sweden
| | | | - P Geldhof
- Laboratory of Parasitology, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - J van Dijk
- Institute of Infection and Global Health, University of Liverpool, Neston, Cheshire, UK
| | - L Rinaldi
- Department of Veterinary Medicine and Animal Productions, University of Naples Federico II, Napoli, Italy
| | - E R Morgan
- Institute for Global Food Security, Queen's University Belfast, Belfast, UK.,School of Veterinary Science, University of Bristol, North Somerset, UK
| | | | - J Vercruysse
- Laboratory of Parasitology, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
| | - E Claerebout
- Laboratory of Parasitology, Faculty of Veterinary Medicine, Ghent University, Merelbeke, Belgium
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170
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Van De Gucht T, Saeys W, Van Meensel J, Van Nuffel A, Vangeyte J, Lauwers L. Farm-specific economic value of automatic lameness detection systems in dairy cattle: From concepts to operational simulations. J Dairy Sci 2017; 101:637-648. [PMID: 29102143 DOI: 10.3168/jds.2017-12867] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Accepted: 09/01/2017] [Indexed: 11/19/2022]
Abstract
Although prototypes of automatic lameness detection systems for dairy cattle exist, information about their economic value is lacking. In this paper, a conceptual and operational framework for simulating the farm-specific economic value of automatic lameness detection systems was developed and tested on 4 system types: walkover pressure plates, walkover pressure mats, camera systems, and accelerometers. The conceptual framework maps essential factors that determine economic value (e.g., lameness prevalence, incidence and duration, lameness costs, detection performance, and their relationships). The operational simulation model links treatment costs and avoided losses with detection results and farm-specific information, such as herd size and lameness status. Results show that detection performance, herd size, discount rate, and system lifespan have a large influence on economic value. In addition, lameness prevalence influences the economic value, stressing the importance of an adequate prior estimation of the on-farm prevalence. The simulations provide first estimates for the upper limits for purchase prices of automatic detection systems. The framework allowed for identification of knowledge gaps obstructing more accurate economic value estimation. These include insights in cost reductions due to early detection and treatment, and links between specific lameness causes and their related losses. Because this model provides insight in the trade-offs between automatic detection systems' performance and investment price, it is a valuable tool to guide future research and developments.
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Affiliation(s)
- Tim Van De Gucht
- Technology and Food Sciences Unit, Institute for Agricultural and Fisheries Research, Burg. van Gansberghelaan 115, 9820 Merelbeke, Belgium; Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
| | - Wouter Saeys
- Department of Biosystems, Katholieke Universiteit Leuven, Kasteelpark Arenberg 30, 3001 Leuven, Belgium
| | - Jef Van Meensel
- Social Sciences Unit, Institute for Agricultural and Fisheries Research, Burg. van Gansberghelaan 115, 9820 Merelbeke, Belgium
| | - Annelies Van Nuffel
- Technology and Food Sciences Unit, Institute for Agricultural and Fisheries Research, Burg. van Gansberghelaan 115, 9820 Merelbeke, Belgium.
| | - Jurgen Vangeyte
- Technology and Food Sciences Unit, Institute for Agricultural and Fisheries Research, Burg. van Gansberghelaan 115, 9820 Merelbeke, Belgium
| | - Ludwig Lauwers
- Social Sciences Unit, Institute for Agricultural and Fisheries Research, Burg. van Gansberghelaan 115, 9820 Merelbeke, Belgium; Ghent University, Department of Agricultural Economics, Coupure Links 653, 9000 Ghent, Belgium
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171
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Mathematical characterization of the milk progesterone profile as a leg up to individualized monitoring of reproduction status in dairy cows. Theriogenology 2017; 103:44-51. [DOI: 10.1016/j.theriogenology.2017.07.040] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2017] [Revised: 07/17/2017] [Accepted: 07/27/2017] [Indexed: 11/19/2022]
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172
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Van De Gucht T, Van Weyenberg S, Van Nuffel A, Lauwers L, Vangeyte J, Saeys W. Supporting the Development and Adoption of Automatic Lameness Detection Systems in Dairy Cattle: Effect of System Cost and Performance on Potential Market Shares. Animals (Basel) 2017; 7:ani7100077. [PMID: 28991188 PMCID: PMC5664036 DOI: 10.3390/ani7100077] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2017] [Revised: 09/19/2017] [Accepted: 09/28/2017] [Indexed: 11/23/2022] Open
Abstract
Simple Summary Most prototypes of systems to automatically detect lameness in dairy cattle are still not available on the market. Estimating their potential adoption rate could support developers in defining development goals towards commercially viable and well-adopted systems. We simulated the potential market shares of such prototypes to assess the effect of altering the system cost and detection performance on the potential adoption rate. We found that system cost and lameness detection performance indeed substantially influence the potential adoption rate. In order for farmers to prefer automatic detection over current visual detection, the usefulness that farmers attach to a system with specific characteristics should be higher than that of visual detection. As such, we concluded that low system costs and high detection performances are required before automatic lameness detection systems become applicable in practice. Abstract Most automatic lameness detection system prototypes have not yet been commercialized, and are hence not yet adopted in practice. Therefore, the objective of this study was to simulate the effect of detection performance (percentage missed lame cows and percentage false alarms) and system cost on the potential market share of three automatic lameness detection systems relative to visual detection: a system attached to the cow, a walkover system, and a camera system. Simulations were done using a utility model derived from survey responses obtained from dairy farmers in Flanders, Belgium. Overall, systems attached to the cow had the largest market potential, but were still not competitive with visual detection. Increasing the detection performance or lowering the system cost led to higher market shares for automatic systems at the expense of visual detection. The willingness to pay for extra performance was €2.57 per % less missed lame cows, €1.65 per % less false alerts, and €12.7 for lame leg indication, respectively. The presented results could be exploited by system designers to determine the effect of adjustments to the technology on a system’s potential adoption rate.
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Affiliation(s)
- Tim Van De Gucht
- Institute for Agricultural and Fisheries Research-ILVO, Technology and Food Sciences Unit, Burg, van Gansberghelaan 115, 9820 Merelbeke, Belgium.
- KU Leuven Department of Biosystems, MeBioS, Kasteelpark Arenberg 30 Box 2456, 3001 Leuven, Belgium.
| | - Stephanie Van Weyenberg
- Institute for Agricultural and Fisheries Research-ILVO, Technology and Food Sciences Unit, Burg, van Gansberghelaan 115, 9820 Merelbeke, Belgium.
| | - Annelies Van Nuffel
- Institute for Agricultural and Fisheries Research-ILVO, Technology and Food Sciences Unit, Burg, van Gansberghelaan 115, 9820 Merelbeke, Belgium.
| | - Ludwig Lauwers
- Institute for Agricultural and Fisheries Research-ILVO, Social Sciences Unit, Burg, van Gansberghelaan 115, 9820 Merelbeke, Belgium.
- Department of Agricultural Economics, Faculty of Bio-Engineering, Ghent University, Coupure Links 653, 9000 Gent, Belgium.
| | - Jürgen Vangeyte
- Institute for Agricultural and Fisheries Research-ILVO, Technology and Food Sciences Unit, Burg, van Gansberghelaan 115, 9820 Merelbeke, Belgium.
| | - Wouter Saeys
- KU Leuven Department of Biosystems, MeBioS, Kasteelpark Arenberg 30 Box 2456, 3001 Leuven, Belgium.
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173
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Silper B, Madureira A, Polsky L, Soriano S, Sica A, Vasconcelos J, Cerri R. Daily lying behavior of lactating Holstein cows during an estrus synchronization protocol and its associations with fertility. J Dairy Sci 2017; 100:8484-8495. [DOI: 10.3168/jds.2016-12160] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Accepted: 06/19/2017] [Indexed: 11/19/2022]
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174
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Saint-Dizier M, Chastant-Maillard S. Potential of connected devices to optimize cattle reproduction. Theriogenology 2017; 112:53-62. [PMID: 28987825 DOI: 10.1016/j.theriogenology.2017.09.033] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2017] [Revised: 09/19/2017] [Accepted: 09/25/2017] [Indexed: 01/17/2023]
Abstract
Estrus and calving are two major events of reproduction that benefit from connected devices because of their crucial importance in herd economics and the amount of time required for their detection. The objectives of this review are to: 1) provide an update on performances reached by sensor systems to detect estrus and calving time; 2) discuss current economic issues related to connected devices for the management of cattle reproduction; 3) propose perspectives for these devices. The main physiological parameters monitored separately or in combination by connected devices are the cow activity, body temperature and rumination or eating behavior. The combination of several indicators in one sensor may maximize the performances of estrus and calving detection. An effort remains to be made for the prediction of calvings that will require human assistance (dystocia). The main reasons to invest in connected devices are to optimize herd reproductive performances and reduce labor on farm. The economic benefit was evaluated for estrus detection and depends on the initial herd performances, herd size, labor cost and price of the equipment. Major issues associated with the use of automated sensor systems are the weight of financial investment, the lack of economic analysis and limited skills of the users to manage associated technologies. In the near future, connected devices may allow a precise phenotyping of reproductive and health traits on animals and could help to improve animal welfare and public perception of animal production.
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Affiliation(s)
- Marie Saint-Dizier
- Université François Rabelais de Tours, INRA, UMR 85 Physiologie de la Reproduction et des Comportements, Centre INRA Val-de-Loire, Nouzilly, France.
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175
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Swartz TH, Findlay AN, Petersson-Wolfe CS. Short communication: Automated detection of behavioral changes from respiratory disease in pre-weaned calves. J Dairy Sci 2017; 100:9273-9278. [PMID: 28918146 DOI: 10.3168/jds.2016-12280] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2016] [Accepted: 06/22/2017] [Indexed: 11/19/2022]
Abstract
Group housing of calves can pose a challenge in identifying respiratory disease; therefore, it is necessary to develop tools that can identify these disease events. In this experiment, pre-weaned calves (n = 30) were housed in groups with an automatic calf feeder and were fitted with an accelerometer. Step activity, lying behaviors, and feeding behaviors were recorded to determine the effect of respiratory disease. All calves were health scored twice daily, and calves with respiratory scores ≥5 were diagnosed with respiratory disease (n = 10). Each diseased calf was match paired with a healthy control based on the date of disease diagnosis, breed, and age. Control calves were determined to be healthy if they had respiratory scores ≤4, as well as fecal, navel, and joint scores of 0 or 1. Diseased calves were less active before, on the day of, and after respiratory disease diagnosis. Furthermore, diseased calves had reduced lying frequencies starting 2 d before diagnosis, as well as after diagnosis. Last, diseased calves consumed less milk on the day of diagnosis when compared with healthy controls. Step activity, lying bouts, and milk intake may prove to be a useful tool in identifying respiratory disease under practical farming, but this requires further research.
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Affiliation(s)
- T H Swartz
- Department of Dairy Science, Virginia Tech, Blacksburg 24061
| | - A N Findlay
- Department of Biological Sciences, Virginia Tech, Blacksburg 24061
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176
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Nasirahmadi A, Edwards SA, Sturm B. Implementation of machine vision for detecting behaviour of cattle and pigs. Livest Sci 2017. [DOI: 10.1016/j.livsci.2017.05.014] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
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177
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Ambriz-Vilchis V, Jessop N, Fawcett R, Webster M, Shaw D, Walker N, Macrae A. Effect of yeast supplementation on performance, rumination time, and rumen pH of dairy cows in commercial farm environments. J Dairy Sci 2017; 100:5449-5461. [DOI: 10.3168/jds.2016-12346] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 03/28/2017] [Indexed: 11/19/2022]
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178
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Van De Gucht T, Saeys W, Van Nuffel A, Pluym L, Piccart K, Lauwers L, Vangeyte J, Van Weyenberg S. Farmers' preferences for automatic lameness-detection systems in dairy cattle. J Dairy Sci 2017; 100:5746-5757. [DOI: 10.3168/jds.2016-12285] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Accepted: 03/30/2017] [Indexed: 11/19/2022]
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179
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Borchers MR, Chang YM, Proudfoot KL, Wadsworth BA, Stone AE, Bewley JM. Machine-learning-based calving prediction from activity, lying, and ruminating behaviors in dairy cattle. J Dairy Sci 2017; 100:5664-5674. [PMID: 28501398 DOI: 10.3168/jds.2016-11526] [Citation(s) in RCA: 89] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Accepted: 03/26/2017] [Indexed: 11/19/2022]
Abstract
The objective of this study was to use automated activity, lying, and rumination monitors to characterize prepartum behavior and predict calving in dairy cattle. Data were collected from 20 primiparous and 33 multiparous Holstein dairy cattle from September 2011 to May 2013 at the University of Kentucky Coldstream Dairy. The HR Tag (SCR Engineers Ltd., Netanya, Israel) automatically collected neck activity and rumination data in 2-h increments. The IceQube (IceRobotics Ltd., South Queensferry, United Kingdom) automatically collected number of steps, lying time, standing time, number of transitions from standing to lying (lying bouts), and total motion, summed in 15-min increments. IceQube data were summed in 2-h increments to match HR Tag data. All behavioral data were collected for 14 d before the predicted calving date. Retrospective data analysis was performed using mixed linear models to examine behavioral changes by day in the 14 d before calving. Bihourly behavioral differences from baseline values over the 14 d before calving were also evaluated using mixed linear models. Changes in daily rumination time, total motion, lying time, and lying bouts occurred in the 14 d before calving. In the bihourly analysis, extreme values for all behaviors occurred in the final 24 h, indicating that the monitored behaviors may be useful in calving prediction. To determine whether technologies were useful at predicting calving, random forest, linear discriminant analysis, and neural network machine-learning techniques were constructed and implemented using R version 3.1.0 (R Foundation for Statistical Computing, Vienna, Austria). These methods were used on variables from each technology and all combined variables from both technologies. A neural network analysis that combined variables from both technologies at the daily level yielded 100.0% sensitivity and 86.8% specificity. A neural network analysis that combined variables from both technologies in bihourly increments was used to identify 2-h periods in the 8 h before calving with 82.8% sensitivity and 80.4% specificity. Changes in behavior and machine-learning alerts indicate that commercially marketed behavioral monitors may have calving prediction potential.
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Affiliation(s)
- M R Borchers
- Department of Animal and Food Sciences, University of Kentucky, Lexington 40546
| | - Y M Chang
- Research Support Office, Royal Veterinary College, University of London, NW1 0TU, United Kingdom
| | - K L Proudfoot
- Department of Veterinary Preventive Medicine, The Ohio State University, Columbus 43210
| | - B A Wadsworth
- Department of Animal and Food Sciences, University of Kentucky, Lexington 40546
| | - A E Stone
- Department of Animal and Food Sciences, University of Kentucky, Lexington 40546
| | - J M Bewley
- Department of Animal and Food Sciences, University of Kentucky, Lexington 40546.
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180
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Mandel R, Nicol CJ, Whay HR, Klement E. Short communication: Detection and monitoring of metritis in dairy cows using an automated grooming device. J Dairy Sci 2017; 100:5724-5728. [PMID: 28478012 DOI: 10.3168/jds.2016-12201] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 03/13/2017] [Indexed: 11/19/2022]
Abstract
Metritis, a prevalent disease on dairy farms, is negatively associated with reproduction, milk production, and the welfare of cows. The objective of this study was to evaluate the efficacy of monitoring low-resilience activities (i.e., behaviors that typically decrease when energy resources are limited or when the cost involved in the activity increases; e.g., brush usage) in the early detection of metritis. Data on daily brush usage (i.e., proportion of cows using the brush and the duration of usage) were collected from 28 metritic and 60 control cows 28 d postpartum using an automated monitoring system developed for the purpose of this study. During the first week following partum (before clinical diagnosis), we found no differences in brush usage between sick and control cows. However, 8 to 21 d postpartum (the week of clinical diagnosis and the first week of medical treatment), a lower proportion of metritic cows used the brush compared with control cows (0.49 compared with 0.64, respectively, at brushes installed away from the feed bunk). In addition, the daily duration of brush usage was 50% lower among cows diagnosed with metritis compared with control cows 8 to 28 d postpartum (44 s/d compared with 88 s/d, respectively). The results of this study suggest that on-farm monitoring of low-resilience behaviors, combined with existing systems that monitor core behaviors (e.g., activity and rumination), may serve as an improved method for detecting events that compromise the welfare of animals. The slow recovery of low-resilience behaviors following medical treatment (wk 4) might serve as a particularly useful indicator of progress of recovery from disease.
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Affiliation(s)
- R Mandel
- Koret School of Veterinary Medicine, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University, Rehovot 76100, Israel; Department of Environmental Systems Science, Institute of Agricultural Sciences, ETH Zürich, Zurich 8092, Switzerland.
| | - C J Nicol
- School of Veterinary Science, University of Bristol, BS40 5DU, United Kingdom
| | - H R Whay
- School of Veterinary Science, University of Bristol, BS40 5DU, United Kingdom
| | - E Klement
- Koret School of Veterinary Medicine, Robert H. Smith Faculty of Agriculture, Food and Environment, The Hebrew University, Rehovot 76100, Israel
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181
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Review: Deciphering animal robustness. A synthesis to facilitate its use in livestock breeding and management. Animal 2017; 11:2237-2251. [PMID: 28462770 DOI: 10.1017/s175173111700088x] [Citation(s) in RCA: 86] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
As the environments in which livestock are reared become more variable, animal robustness becomes an increasingly valuable attribute. Consequently, there is increasing focus on managing and breeding for it. However, robustness is a difficult phenotype to properly characterise because it is a complex trait composed of multiple components, including dynamic elements such as the rates of response to, and recovery from, environmental perturbations. In this review, the following definition of robustness is used: the ability, in the face of environmental constraints, to carry on doing the various things that the animal needs to do to favour its future ability to reproduce. The different elements of this definition are discussed to provide a clearer understanding of the components of robustness. The implications for quantifying robustness are that there is no single measure of robustness but rather that it is the combination of multiple and interacting component mechanisms whose relative value is context dependent. This context encompasses both the prevailing environment and the prevailing selection pressure. One key issue for measuring robustness is to be clear on the use to which the robustness measurements will employed. If the purpose is to identify biomarkers that may be useful for molecular phenotyping or genotyping, the measurements should focus on the physiological mechanisms underlying robustness. However, if the purpose of measuring robustness is to quantify the extent to which animals can adapt to limiting conditions then the measurements should focus on the life functions, the trade-offs between them and the animal's capacity to increase resource acquisition. The time-related aspect of robustness also has important implications. Single time-point measurements are of limited value because they do not permit measurement of responses to (and recovery from) environmental perturbations. The exception being single measurements of the accumulated consequence of a good (or bad) adaptive capacity, such as productive longevity and lifetime efficiency. In contrast, repeated measurements over time have a high potential for quantification of the animal's ability to cope with environmental challenges. Thus, we should be able to quantify differences in adaptive capacity from the data that are increasingly becoming available with the deployment of automated monitoring technology on farm. The challenge for future management and breeding will be how to combine various proxy measures to obtain reliable estimates of robustness components in large populations. A key aspect for achieving this is to define phenotypes from consideration of their biological properties and not just from available measures.
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182
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Negussie E, de Haas Y, Dehareng F, Dewhurst R, Dijkstra J, Gengler N, Morgavi D, Soyeurt H, van Gastelen S, Yan T, Biscarini F. Invited review: Large-scale indirect measurements for enteric methane emissions in dairy cattle: A review of proxies and their potential for use in management and breeding decisions. J Dairy Sci 2017; 100:2433-2453. [DOI: 10.3168/jds.2016-12030] [Citation(s) in RCA: 90] [Impact Index Per Article: 12.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2016] [Accepted: 12/07/2016] [Indexed: 01/15/2023]
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183
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Yu GM, Maeda T. Inline Progesterone Monitoring in the Dairy Industry. Trends Biotechnol 2017; 35:579-582. [PMID: 28279486 DOI: 10.1016/j.tibtech.2017.02.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2017] [Revised: 02/09/2017] [Accepted: 02/13/2017] [Indexed: 12/12/2022]
Abstract
An inline progesterone monitoring system that works automatically and provides real-time physiological information about lactating dairy cows for making farm management decisions is not only a novel tool for scientific research but also improves productivity, food safety, animal well-being, the environment, and the public perception of the dairy industry.
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Affiliation(s)
- Guang-Min Yu
- Department of Bioresource Science, Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima 739-8528, Japan
| | - Teruo Maeda
- Department of Bioresource Science, Graduate School of Biosphere Science, Hiroshima University, Higashi-Hiroshima 739-8528, Japan; The Research Center for Animal Science, Hiroshima University, Higashi-Hiroshima 739-8528, Japan.
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184
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Stone A, Jones B, Becker C, Bewley J. Influence of breed, milk yield, and temperature-humidity index on dairy cow lying time, neck activity, reticulorumen temperature, and rumination behavior. J Dairy Sci 2017; 100:2395-2403. [DOI: 10.3168/jds.2016-11607] [Citation(s) in RCA: 41] [Impact Index Per Article: 5.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2016] [Accepted: 10/25/2016] [Indexed: 11/19/2022]
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185
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Rees A, Fischer-Tenhagen C, Heuwieser W. Udder firmness as a possible indicator for clinical mastitis. J Dairy Sci 2017; 100:2170-2183. [DOI: 10.3168/jds.2016-11940] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2016] [Accepted: 11/22/2016] [Indexed: 12/20/2022]
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186
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Association between body energy content in the dry period and post-calving production disease status in dairy cattle. Animal 2017; 11:1590-1598. [PMID: 28196553 PMCID: PMC5561438 DOI: 10.1017/s1751731117000040] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/07/2022] Open
Abstract
The transition from gestation to lactation is marked by significant physiological changes for the individual cow such that disease incidence is highest in early lactation. Around the time of calving, cows rely on mobilisation of body energy reserves to fill the energy deficit created by an increase in nutrient demands at a time of restricted feed intake. It is well established that monitoring of body energy reserves in lactation is an important component of herd health management. However, despite their influence on future health and productivity, monitoring of body energy reserves in the dry period is often sparse. Further, there is increasing concern that current dry off management is inappropriate for modern cattle and may influence future disease risk. This study aimed to identify candidate indicators of early lactation production disease from body energy data collected in the dry period and production data recorded at the time of dry off. Retrospective analysis was performed on 482 cow-lactations collected from a long-term Holstein-Friesian genetic and management systems project, the Langhill herd in Scotland. Cow-lactations were assigned to one of four health groups based on health status in the first 30 days of lactation. These four groups were as follows: healthy, reproductive tract disorders (retained placenta and metritis), subclinical mastitis and metabolic disorders (ketosis, hypocalcaemia, hypomagnesaemia and left displaced abomasum). ANOVA, employing a GLM was used to determine effects for the candidate indicator traits. Cows which were diagnosed with a reproductive tract disorder in the first 30 days of lactation experienced a significantly greater loss in body energy content, body condition score and weight in the preceding dry period than healthy cows. The rate of change in body energy content during the first 15 days of the dry period was −18.26 MJ/day for cows which developed reproductive tract disorder compared with +0.63 MJ/day for healthy cows. Cows diagnosed with subclinical mastitis in the first 30 days of lactation had significantly greater milk yield at dry off in the previous lactation than cows that developed a reproductive tract disorder or metabolic disease in addition to a significantly higher yield to body energy content ratio at dry off than healthy cows. Physiological and production traits recorded in the lactation and dry period preceding a disease event differed between cows which developed different diseases post-calving. Differences in these traits allow the development of new disease indicators for use in models for the prediction of disease risk in the transition period.
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187
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188
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Norton T, Berckmans D. Developing precision livestock farming tools for precision dairy farming. Anim Front 2017. [DOI: 10.2527/af.2017.0104] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Affiliation(s)
- T. Norton
- M3-BIORES: Measure, Model & Manage Bioresponses, Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium
| | - D. Berckmans
- M3-BIORES: Measure, Model & Manage Bioresponses, Division Animal and Human Health Engineering, Department of Biosystems, KU Leuven, Kasteelpark Arenberg 30, 3001 Heverlee, Belgium
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189
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Non-Invasive Sensor Technology for the Development of a Dairy Cattle Health Monitoring System. COMPUTERS 2016. [DOI: 10.3390/computers5040023] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
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190
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Borchers M, Chang Y, Tsai I, Wadsworth B, Bewley J. A validation of technologies monitoring dairy cow feeding, ruminating, and lying behaviors. J Dairy Sci 2016; 99:7458-7466. [DOI: 10.3168/jds.2015-10843] [Citation(s) in RCA: 126] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2015] [Accepted: 05/20/2016] [Indexed: 11/19/2022]
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191
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Tullo E, Fontana I, Gottardo D, Sloth K, Guarino M. Technical note: Validation of a commercial system for the continuous and automated monitoring of dairy cow activity. J Dairy Sci 2016; 99:7489-7494. [DOI: 10.3168/jds.2016-11014] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2016] [Accepted: 05/20/2016] [Indexed: 11/19/2022]
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192
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Jensen DB, Hogeveen H, De Vries A. Bayesian integration of sensor information and a multivariate dynamic linear model for prediction of dairy cow mastitis. J Dairy Sci 2016; 99:7344-7361. [DOI: 10.3168/jds.2015-10060] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2015] [Accepted: 05/01/2016] [Indexed: 11/19/2022]
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193
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Rutten C, Steeneveld W, Vernooij J, Huijps K, Nielen M, Hogeveen H. A prognostic model to predict the success of artificial insemination in dairy cows based on readily available data. J Dairy Sci 2016; 99:6764-6779. [DOI: 10.3168/jds.2016-10935] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2016] [Accepted: 04/14/2016] [Indexed: 11/19/2022]
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194
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Stangaferro ML, Wijma R, Caixeta LS, Al-Abri MA, Giordano JO. Use of rumination and activity monitoring for the identification of dairy cows with health disorders: Part I. Metabolic and digestive disorders. J Dairy Sci 2016; 99:7395-7410. [PMID: 27372591 DOI: 10.3168/jds.2016-10907] [Citation(s) in RCA: 98] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2016] [Accepted: 05/21/2016] [Indexed: 11/19/2022]
Abstract
The objectives of this study were to evaluate (1) the performance of an automated health-monitoring system (AHMS) to identify cows with metabolic and digestive disorders-including displaced abomasum, ketosis, and indigestion-based on an alert system (health index score, HIS) that combines rumination time and physical activity; (2) the number of days between the first HIS alert and clinical diagnosis (CD) of the disorders by farm personnel; and (3) the daily rumination time, physical activity, and HIS patterns around CD. Holstein cattle (n=1,121; 451 nulliparous and 670 multiparous) were fitted with a neck-mounted electronic rumination and activity monitoring tag (HR Tags, SCR Dairy, Netanya, Israel) from at least -21 to 80 d in milk (DIM). Raw data collected in 2-h periods were summarized per 24 h as daily rumination and activity. A HIS (0 to 100 arbitrary units) was calculated daily for individual cows with an algorithm that used rumination and activity. A positive HIS outcome was defined as a HIS of <86 during at least 1 d from -5 to 2 d after CD. Blood concentrations of nonesterified fatty acids, β-hydroxybutyrate, total calcium, and haptoglobin were determined in a subgroup of cows (n=459) at -11±3, -4±3, 0, 3±1, 7±1, 14±1, and 28±1 DIM. The sensitivity of the HIS was 98% [95% confidence interval (CI): 93, 100] for displaced abomasum (n=41); 91% (95% CI: 83, 99) for ketosis (n=54); 89% (95% CI: 68, 100) for indigestion (n=9); and 93% (95% CI: 89, 98) for all metabolic and digestive disorders combined (n=104). Days (mean and 95% CI) from the first positive HIS <86 and CD were -3 (-3.7, -2.3), -1.6 (-2.3, -1.0), -0.5 (-1.5, 0.5), and -2.1 (-2.5, -1.6) for displaced abomasum, ketosis, indigestion, and all metabolic and digestive disorders, respectively. The patterns of rumination, activity, and HIS for cows flagged by the AHMS were characterized by lower levels than for cows without a health disorder and cows not flagged by the AHMS from -5 to 5 d after CD, depending on the disorder and parameter. Differences between cows without health disorders and those flagged by the AHMS for blood markers of metabolic and health status confirmed the observations of the CD and AHMS alerts. The overall sensitivity and timing of the AHMS alerts for cows with metabolic and digestive disorders indicated that AHMS that combine rumination and activity could be a useful tool for identifying cows with metabolic and digestive disorders.
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Affiliation(s)
- M L Stangaferro
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - R Wijma
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - L S Caixeta
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - M A Al-Abri
- Department of Animal Science, Cornell University, Ithaca, NY 14853
| | - J O Giordano
- Department of Animal Science, Cornell University, Ithaca, NY 14853.
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Pryce JE, Parker Gaddis KL, Koeck A, Bastin C, Abdelsayed M, Gengler N, Miglior F, Heringstad B, Egger-Danner C, Stock KF, Bradley AJ, Cole JB. Invited review: Opportunities for genetic improvement of metabolic diseases. J Dairy Sci 2016; 99:6855-6873. [PMID: 27372587 DOI: 10.3168/jds.2016-10854] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2016] [Accepted: 05/26/2016] [Indexed: 02/01/2023]
Abstract
Metabolic disorders are disturbances to one or more of the metabolic processes in dairy cattle. Dysfunction of any of these processes is associated with the manifestation of metabolic diseases or disorders. In this review, data recording, incidences, genetic parameters, predictors, and status of genetic evaluations were examined for (1) ketosis, (2) displaced abomasum, (3) milk fever, and (4) tetany, as these are the most prevalent metabolic diseases where published genetic parameters are available. The reported incidences of clinical cases of metabolic disorders are generally low (less than 10% of cows are recorded as having a metabolic disease per herd per year or parity/lactation). Heritability estimates are also low and are typically less than 5%. Genetic correlations between metabolic traits are mainly positive, indicating that selection to improve one of these diseases is likely to have a positive effect on the others. Furthermore, there may also be opportunities to select for general disease resistance in terms of metabolic stability. Although there is inconsistency in published genetic correlation estimates between milk yield and metabolic traits, selection for milk yield may be expected to lead to a deterioration in metabolic disorders. Under-recording and difficulty in diagnosing subclinical cases are among the reasons why interest is growing in using easily measurable predictors of metabolic diseases, either recorded on-farm by using sensors and milk tests or off-farm using data collected from routine milk recording. Some countries have already initiated genetic evaluations of metabolic disease traits and currently most of these use clinical observations of disease. However, there are opportunities to use clinical diseases in addition to predictor traits and genomic information to strengthen genetic evaluations for metabolic health in the future.
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Affiliation(s)
- J E Pryce
- Department of Economic Developments, Jobs, Transport and Resources and La Trobe University, Agribio, 5 Ring Road, Bundoora, VIC 3083, Australia.
| | - K L Parker Gaddis
- Department of Animal Sciences, University of Florida, Gainesville 32611
| | - A Koeck
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada
| | - C Bastin
- Agriculture, Bio-engineering and Chemistry Department, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
| | - M Abdelsayed
- Holstein Australia, 24-36 Camberwell Road, Hawthorn East, Victoria, 3122, Australia
| | - N Gengler
- Agriculture, Bio-engineering and Chemistry Department, Gembloux Agro-Bio Tech, University of Liège, B-5030 Gembloux, Belgium
| | - F Miglior
- Centre for Genetic Improvement of Livestock, University of Guelph, Guelph, ON, N1G 2W1, Canada; Canadian Dairy Network, Guelph, ON, N1K 1E5, Canada
| | - B Heringstad
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, PO Box 5003, NO-1432 Ås, Norway
| | - C Egger-Danner
- ZuchtData EDV-Dienstleistungen GmbH, Dresdner Str. 89/19, A-1200 Vienna, Austria
| | - K F Stock
- Vereinigte Informationssysteme Tierhaltung w.V. (vit), Heinrich-Schroeder-Weg 1, D-27283 Verden, Germany
| | - A J Bradley
- University of Nottingham, School of Veterinary Medicine and Science, Sutton Bonington Campus, Sutton Bonington, Leicestershire, LE12 5RD, United Kingdom, and; Quality Milk Management Services Ltd., Cedar Barn, Easton Hill, Easton, Wells, Somerset, BA5 1EY, United Kingdom
| | - J B Cole
- Animal Genomics and Improvement Laboratory, Agricultural Research Service, USDA, Beltsville, MD 20705
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196
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Abstract
Current trends in the global milk market and the recent abolition of milk quotas have accelerated the trend of the European dairy industry towards larger farm sizes and higher-yielding animals. Dairy cows remain in focus, but there is a growing interest in other dairy species, whose milk is often directed to traditional and protected designation of origin and gourmet dairy products. The challenge for dairy farms in general is to achieve the best possible standards of animal health and welfare, together with high lactational performance and minimal environmental impact. For larger farms, this may need to be done with a much lower ratio of husbandry staff to animals. Recent engineering advances and the decreasing cost of electronic technologies has allowed the development of ‘sensing solutions’ that automatically collect data, such as physiological parameters, production measures and behavioural traits. Such data can potentially help the decision making process, enabling early detection of health or wellbeing problems in individual animals and hence the application of appropriate corrective husbandry practices. This review focuses on new knowledge and emerging developments in welfare biomarkers (e.g. stress and metabolic diseases), activity-based welfare assessment (e.g. oestrus and lameness detection) and sensors of temperature and pH (e.g. calving alert and rumen function) and their combination and integration into ‘smart’ husbandry support systems that will ensure optimum wellbeing for dairy animals and thereby maximise farm profitability. Use of novel sensors combined with new technologies for information handling and communication are expected to produce dramatic changes in traditional dairy farming systems.
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197
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Use of Extended Characteristics of Locomotion and Feeding Behavior for Automated Identification of Lame Dairy Cows. PLoS One 2016; 11:e0155796. [PMID: 27187073 PMCID: PMC4871330 DOI: 10.1371/journal.pone.0155796] [Citation(s) in RCA: 54] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2016] [Accepted: 05/04/2016] [Indexed: 11/19/2022] Open
Abstract
This study was carried out to detect differences in locomotion and feeding behavior in lame (group L; n = 41; gait score ≥ 2.5) and non-lame (group C; n = 12; gait score ≤ 2) multiparous Holstein cows in a cross-sectional study design. A model for automatic lameness detection was created, using data from accelerometers attached to the hind limbs and noseband sensors attached to the head. Each cow’s gait was videotaped and scored on a 5-point scale before and after a period of 3 consecutive days of behavioral data recording. The mean value of 3 independent experienced observers was taken as a definite gait score and considered to be the gold standard. For statistical analysis, data from the noseband sensor and one of two accelerometers per cow (randomly selected) of 2 out of 3 randomly selected days was used. For comparison between group L and group C, the T-test, the Aspin-Welch Test and the Wilcoxon Test were used. The sensitivity and specificity for lameness detection was determined with logistic regression and ROC-analysis. Group L compared to group C had significantly lower eating and ruminating time, fewer eating chews, ruminating chews and ruminating boluses, longer lying time and lying bout duration, lower standing time, fewer standing and walking bouts, fewer, slower and shorter strides and a lower walking speed. The model considering the number of standing bouts and walking speed was the best predictor of cows being lame with a sensitivity of 90.2% and specificity of 91.7%. Sensitivity and specificity of the lameness detection model were considered to be very high, even without the use of halter data. It was concluded that under the conditions of the study farm, accelerometer data were suitable for accurately distinguishing between lame and non-lame dairy cows, even in cases of slight lameness with a gait score of 2.5.
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198
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Friggens N, Duvaux-Ponter C, Etienne M, Mary-Huard T, Schmidely P. Characterizing individual differences in animal responses to a nutritional challenge: Toward improved robustness measures. J Dairy Sci 2016; 99:2704-2718. [DOI: 10.3168/jds.2015-10162] [Citation(s) in RCA: 21] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2015] [Accepted: 12/05/2015] [Indexed: 11/19/2022]
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199
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Sakatani M, Takahashi M, Takenouchi N. The efficiency of vaginal temperature measurement for detection of estrus in Japanese Black cows. J Reprod Dev 2016; 62:201-7. [PMID: 26853785 PMCID: PMC4848578 DOI: 10.1262/jrd.2015-095] [Citation(s) in RCA: 25] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/20/2022] Open
Abstract
Recently, weak estrous behavior was assumed to be the cause of a decline in breeding efficiency in cattle.
The present study investigated the effect of measuring the vaginal temperature on the detection of estrus in
Japanese Black cows. First, the effect of hormone administration to cows with a functional corpus luteum on
the vaginal temperature was evaluated by continuous measurement using a temperature data logger. After 24 h of
cloprostenol (PG) treatment, the vaginal temperature was significantly lower than on day 7 after estrus, and
the low values were maintained until the beginning of estrus (P < 0.05). The cows that received PG and
exogenous progesterone (CIDR) did not show a temperature decrease until the CIDR was removed. This finding
suggested that the vaginal temperature change reflected the progesterone concentration. The rate of detection
of natural estrus was lower for a pedometer than for the vaginal temperature (P < 0.05); synchronization of
estrus resulted in a high estrus detection rate regardless of the detection method. In a subsequent
experiment, the effect of vaginal temperature measurement and the use of a pedometer on estrus detection was
evaluated in the cool and hot seasons. The average activities during non-estrus and the activity increase
ratio (estrus/non-estrus) changed according to season (P < 0.01, P < 0.05). However, the average vaginal
temperatures during estrus and non-estrus were not affected by season. The estrus detection rate of the
pedometer was lower in summer and lower than that obtained using the vaginal temperature. These results
indicated that vaginal temperature measurement might be effective for detecting estrus regardless of estrous
behavior.
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Affiliation(s)
- Miki Sakatani
- Livestock and Grassland Research Division, Kyushu Okinawa Agricultural Research Center, National Agriculture and Food Research Organization (NARO), Kumamoto 861-1192, Japan
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Kamphuis C, Dela Rue B, Eastwood C. Field validation of protocols developed to evaluate in-line mastitis detection systems. J Dairy Sci 2016; 99:1619-1631. [DOI: 10.3168/jds.2015-10253] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2015] [Accepted: 10/19/2015] [Indexed: 11/19/2022]
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