PRECISION DAIRY FARMING
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Published:
Jun 15, 2023
Keywords:
Dairy, management, precision, techonology.
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Abstract
Precision Dairy Farming generally refers to excessive use of technologies on individual animals to measure physiological, behavioral, and production indicators to improve management and farm performance. Many Precision Dairy Farming technologies, including Electronic (radio frequency) identification systems and associated management software Automatic recording devices (rumen temperature, pressure, pH) by electronic rumen bolus, Robotic milking systems daily milk yield recording, Automatic body condition scoring milk component monitoring (e.g. fat, protein, and SCC), pedometers, automatic temperature recording devices, milk conductivity indicators, automatic estrus detection monitors, and daily body weight measurements are already being utilized by dairy producers. This review provides preliminary information on the advances in PDF for dairy management.
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References
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Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech., 33 (2014), pp. 189-196
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Edward, J.L., & Tozer, P.R. (2004). Using activity and milk yield as predictors of fresh cow disorders. Journal of Dairy Science. (87):524-531.
Eastwood, C., Chapman, D., & Paine, M. (2012). Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia. Agricultural Systems 108: 10–18.
Eastwood, C., Jago, J., Edwards., & J. and Burke, J. (2015). Getting the most out of advanced farm management technologies: Roles of technology suppliers and dairy industry organisations in supporting precision dairy farmers. Animal Production Science 56: 1752–60.
Ferguson, J.D., Azzaro., & G, Licitra. (2006). Body Condition Assessment Using Digital Images Journal of Dairy Science.89(10): pp.3833-3841
Fournel, S., Rousseau, A.N., & Laberge, B. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming iosyst. Eng., 155 (2017), pp. 96-123
Friggens, N.C., & Chagunda, M.G.G. (2005). Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description. Theriogenology. 64: pp.155-90.
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Holdsworth, R., & J and Markillie, N. A. R. (1982). Evaluation of pedometers for estrus detection in dairy cows. Veterinary Record 111: 16–16.
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Milner, P., Page, K.L., & Hillerton, J.L. (1996). The Effects of Early Antibiotic Treatment Following Diagnosis of Mastitis Detected by a Change in the Electrical Conductivity of Milk. Journal of Dairy Science Vol. 80, No. 5,
Neethirajan, S. (2022). Digital Livestock Farming. Sensing and Bio-Sensing Research
https://www.ft.com/content/2db7e742-7204-11e7-93ff-99f383b09ff9
Northon, T., & Berckmans, D. (2017). Developing precision livestock farming tools for precision dairy farming. Animal Frontiers, Volume 7, Issue 1, January, pp. 18–23
Redden, K.D., Kennedy., A.D, Ingalls, J.R., & Gilson, T.L. 1993. Detection of estrus by radio telemetric monitoring of vaginal and ear skin temperature and pedometer measurements of activity. Journal of Dairy Science. 76: pp.713-21.
Roche, J.R., Friggens, N.C., Kay, J.K., Fisher, M.W., Stafford, K.J.,& Berry, D.P. (2009). Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci. 12: pp.5769-801.
Song, X.,Bokkers, E.A.M., Mourik, S.V., Groot Koerkamp, P.W.G., & Van der Tol, P.P.J. (2019). Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. Journal of Dairy Science. Volume 102,Issue 5, pp.: 3781-4756
Sowell, B. F., Bowman, J. G. P., Branine, M. E.,& Hubbert, M. E. (1998). Radio frequency technology to measure feeding behaviour and health of feedlot steers. Applied Animal Behavioural Science 59: pp.277–84.
Steeneveld, W., Tauer, L.W., Hogeveen, H., & Oude Lansink, A.G.J.M. (2012). Comparing technical efficiency of farms with anvautomatic milking system and a conventional milking system. Journal of Dairy Science. 95: pp.7391-98.
Valenza, A., Giordano, J.O., Lopes, G., Vincenti, L., Amundson, M.C., & Fricke, P.M. (2012). Assessment of an accelerometer system for detection of estrus and treatment with gonadotropin releasing hormone at the time of insemination in lactating dairy cows. Journal of Dairy Science. 95: pp.7115-27.
Vranken, E., &Berckmans, D. (2017). Precision livestock farming for pigs. Anim. Front. (7), pp. 32-37
Zehner, N., Umstaetter,C., Neiderhauser, J.J., & Schick,M. (2017). System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Computers and Electronics in Agriculture 136:31–41
Berckmans, D. (2014). Precision livestock farming technologies for welfare management in intensive livestock systems. Rev. Sci. Tech., 33 (2014), pp. 189-196
Bewley, J. (2010). Precision Dairy Farming: Advanced Analysis Solutions for Future Profitability. In: First North American Conference on Precision Dairy Management, Toranto, Canada during 2–5 March
Caja, G., Andreia, Castro-Costa, A., & Knight, C.H. (2016). Engineering to support wellbeing of dairy animals. Journal of Dairy Research. pp. 83:136-147.
Edward, J.L., & Tozer, P.R. (2004). Using activity and milk yield as predictors of fresh cow disorders. Journal of Dairy Science. (87):524-531.
Eastwood, C., Chapman, D., & Paine, M. (2012). Networks of practice for co-construction of agricultural decision support systems: Case studies of precision dairy farms in Australia. Agricultural Systems 108: 10–18.
Eastwood, C., Jago, J., Edwards., & J. and Burke, J. (2015). Getting the most out of advanced farm management technologies: Roles of technology suppliers and dairy industry organisations in supporting precision dairy farmers. Animal Production Science 56: 1752–60.
Ferguson, J.D., Azzaro., & G, Licitra. (2006). Body Condition Assessment Using Digital Images Journal of Dairy Science.89(10): pp.3833-3841
Fournel, S., Rousseau, A.N., & Laberge, B. (2017). Rethinking environment control strategy of confined animal housing systems through precision livestock farming iosyst. Eng., 155 (2017), pp. 96-123
Friggens, N.C., & Chagunda, M.G.G. (2005). Prediction of the reproductive status of cattle on the basis of milk progesterone measures: model description. Theriogenology. 64: pp.155-90.
Grummer, R.R. (1995) Impact of changes in organic nutrient metabolism on feeding the transition dairy cow. Journal of Animal Science. 73: pp.2820-2833.
Holdsworth, R., & J and Markillie, N. A. R. (1982). Evaluation of pedometers for estrus detection in dairy cows. Veterinary Record 111: 16–16.
Lehrer, A.R., Lewis, G.S., & Aizinbud, E. (1992). Estrous detection in cattle recent development. J Anim Reprod Sci. 28: pp.355-361.
Lovendahl, P., & Chagunda, M.G.G. (2010). On the use of physical activity monitoring for estrus detection in dairy cows. Journal of Dairy Science. 93: pp.249-59.
Lukas, J.M., Reneau, J.K., Wallace, R., Hawkins, D., & Munoz-Zanzi, C. 2009. A novel method of analyzing daily milk production and electrical conductivity to predict disease onset. Journal of Dairy Science. 92: pp.5964-5976.
Milner, P., Page, K.L., & Hillerton, J.L. (1996). The Effects of Early Antibiotic Treatment Following Diagnosis of Mastitis Detected by a Change in the Electrical Conductivity of Milk. Journal of Dairy Science Vol. 80, No. 5,
Neethirajan, S. (2022). Digital Livestock Farming. Sensing and Bio-Sensing Research
https://www.ft.com/content/2db7e742-7204-11e7-93ff-99f383b09ff9
Northon, T., & Berckmans, D. (2017). Developing precision livestock farming tools for precision dairy farming. Animal Frontiers, Volume 7, Issue 1, January, pp. 18–23
Redden, K.D., Kennedy., A.D, Ingalls, J.R., & Gilson, T.L. 1993. Detection of estrus by radio telemetric monitoring of vaginal and ear skin temperature and pedometer measurements of activity. Journal of Dairy Science. 76: pp.713-21.
Roche, J.R., Friggens, N.C., Kay, J.K., Fisher, M.W., Stafford, K.J.,& Berry, D.P. (2009). Invited review: Body condition score and its association with dairy cow productivity, health, and welfare. J Dairy Sci. 12: pp.5769-801.
Song, X.,Bokkers, E.A.M., Mourik, S.V., Groot Koerkamp, P.W.G., & Van der Tol, P.P.J. (2019). Automated body condition scoring of dairy cows using 3-dimensional feature extraction from multiple body regions. Journal of Dairy Science. Volume 102,Issue 5, pp.: 3781-4756
Sowell, B. F., Bowman, J. G. P., Branine, M. E.,& Hubbert, M. E. (1998). Radio frequency technology to measure feeding behaviour and health of feedlot steers. Applied Animal Behavioural Science 59: pp.277–84.
Steeneveld, W., Tauer, L.W., Hogeveen, H., & Oude Lansink, A.G.J.M. (2012). Comparing technical efficiency of farms with anvautomatic milking system and a conventional milking system. Journal of Dairy Science. 95: pp.7391-98.
Valenza, A., Giordano, J.O., Lopes, G., Vincenti, L., Amundson, M.C., & Fricke, P.M. (2012). Assessment of an accelerometer system for detection of estrus and treatment with gonadotropin releasing hormone at the time of insemination in lactating dairy cows. Journal of Dairy Science. 95: pp.7115-27.
Vranken, E., &Berckmans, D. (2017). Precision livestock farming for pigs. Anim. Front. (7), pp. 32-37
Zehner, N., Umstaetter,C., Neiderhauser, J.J., & Schick,M. (2017). System specification and validation of a noseband pressure sensor for measurement of ruminating and eating behavior in stable-fed cows. Computers and Electronics in Agriculture 136:31–41