Hayvancılıkta Modern Teknolojiler ve Gelecek Beklentileri
References
Madan, M.L., 2005. Animal biotechnology: applications and economic implications in developing countries. Revue Scientifique Et Technique-Office International Des Epizooties, 24(1):127.
Gale, H.F., 2005. Commercialization of food consumption in rural China. Economic Research Report, (8).
Akın S, Kara A., 2019. Factors affecting the farmers’decision on artificial insemination: a case study of Diyarbakir province, Turkey. Appl Ecol Environ Res, 17: 1389-1399.
Food and Agriculture Organization of the United Nations (FAO)., 2020. World Livestock: Transforming the livestock sector through the sustainable development goals; c2020. Retrieved from http://www.fao.org/documents/card/en/c/ca8632en Erişim tarihi 09.09.2024
Ali, W., Ali, M., Ahmad, M., Dilawar, S., Firdous, A., Afzal, A., 2020. Application of modern techniques in animal production sector for human and animal welfare. Turkish Journal of Agriculture - Food Science and Technology, 8(2):457-463. doi:10.24925/turjaf.v8i2.457-463.3159
Dumas, A., Dijkstra, J., France, J.. 2008. Mathematical modelling in animal nutrition: a centenary review. J. Agric. Sci., 146:123-142.
Butler, W.R., 2000. Nutritional interactions with reproductive performance in dairy cattle. Anim. Reprod. Sci., 60(61): 449-457.
Göncü, S., Güngör, C., 2018. The innovative techniques in animal husbandry. Ani. Husbandry and Nut.: 1.
Thibier, M., 2005. The zootechnical applications of biotechnology in animal reproduction: current methods and perspectives. Reprod Nutr Dev., 45: 235-242.
Verma, O.P., Kumar, R., Kumar, A., Chand, S., 2012. Assisted reproductive techniques in farm animal - from artificial ınsemination to nanobiotechnology. Vet. World., 5(5):301-310. doi: 10.5455/vetworld.2012.301-310
Holtz, W., 2005. Recent developments in assisted reproduction in goats. Small Ruminant Research, 60(1-2): 95-110.
Widayati, D.T., 2012. Embryo transfer as an assisted reproductive technology in farm animals. World Acad. Sci. Eng. Technol, 6 (2012): 10-21.
Mapletoft, R.J., 2018. History and perspectives on bovine embryo transfer. Animal Reproduction (AR), 10(3):168-173.
Yousuf, M., Yusuf, A., Mohammed, I., 2024. Review on current animal breeding and genetic technologies to ıncrease production and productivity of cattle. Global Journal of Animal Scientific Research, 12(1):19-36.
Moore, K., Thatcher, W.W., 2006. Major advances associated with reproduction in dairy cattle. Journal of Dairy Science, 89(4): 1254-1266.
Khare, V., Khare, A., 2017. Modern approach in animal breeding by use of advanced molecular genetic techniques. International Journal of Livestock Research, 7(5):1-22. doi:10.5455/ijlr.20170404010154
Lamb, C.,2015. What are the long-term impacts of estrus synchronization and artificial insemination? Web site. http://nwdistrict. ifas.ufl.edu/phag/2015/10/09/what-are-the-long-term impacts- of-estrus-synchronization-and-artificial-insemination/. Erişim tarihi 09.09.2024
Holm, D.E. Thompson, P.N., Irons, P.C., 2008. The economic effects of an estrus synchronization protocol using prostaglandin in beef heifers. Theriogenology, 70(9):1507-1515.
Vikrama, C.P., Balaji, N.S., 2002. Use of assisted reproductive technologies for livestock development. Veterinary World, 3(5): 238-240.
Kahi, A.K., Rewe, T.O., 2008. Biotechnology in livestock production: Overview of possibilities for Africa. African Journal on Biotechnology, 7(25):4984-4991.
Neethirajan, S., Reimert, I., Kemp, B., 2021. Measuring farm animal emotions-sensor-based approaches. Sensors, 21(2):553. doi:10.3390/s21020553
Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., Zhang, X., 2021. Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. J. Clean. Prod. 312: 127712.
Choudhary, K.K., Kavya, K.M., Jerome, A., Sharma, R.K., 2016. Advances in reproductive biotechnologies. Veterinary World, 9(4):388.
Leaky, R., Caron. P., Craufurd, P., Martin, A., McDonald, A., 2009. Impacts of AKST (Agricultural Knowledge Science and Technology) on development and sustainability goals. Agriculture at a crossroads (eds , McIntyre B. D., Herren H. R., Wakhungu J.& Watson R. T.), pp. 145–253. Washington, DC: Island Press.
Lewin H.A., 2009. It's a bull's market. Science, 323:478-479.
Hayes, B.J., Bowman, P.J., Chamberlain, A.J., Goddard, M.E., 2009. Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92:433-443.
Thornton, P., 2010. Livestock production: recent trends, future prospects. Phil. Trans. R. Soc. B., 365:2853-2867. doi:10.1098/rstb.2010.0134
Morrone, S., Dimauro, C., Gambella, F., Cappai, M.G., 2022. Industry 4.0 and Precision Livestock Farming (PLF): An up to date overview across animal productions.Sensors, 22(12): 4319. doi:10.3390/s22124319
Hayes, B.J., Lewin, HA, Goddard ME., 2013. The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics, 29(4):206-14. doi: 10.1016/j.tig.2012.11.009
Satyanarayana, S.D.V., Risheen, G.D., 2023. Current trends and innovations in livestock production: A critical review. International Journal of Veterinary Sciences and Animal Husbandry; 8(3): 22-24.
Hing, S., Foster, S., Evans, D., 2021. Animal welfare risks in live cattle export from Australia to China by sea. Animals, 11(10):2862. doi.org/10.3390/ani11102862
Katainen, A., Norring, M., Manninen, E., Laine, J., Orava, T., Kuoppala, K., Saloniemi, H., 2005. Competitive behaviour of dairy cows at a concentrate self-feeder. Acta Agric. Scand. Sect. A Anim. Sci., 55:98-105.
Weigele, H.C., Gygax, L., Steiner, A., Wechsler, B., Burla, J.B., 2018. Moderate lameness leads to marked behavioral changes in dairy cows. J. Dairy Sci., 101:2370-2382.
Matore, Z., 2023. Drivers and ındicators of dairy animal welfare in large-scale dairies. Trop. Anim. Health Prod., 55(1): 43. doi: 10.1007/s11250-022-03440-z
Martins, B.M., Mendes, A.L.C., Silva, L.F., Moreira, T.R., Costa, J.H.C., Rotta, P.P., Chizzotti, M.L., Marcondes, M.I., 2020. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livest. Sci., 236, 104054. doi:10.1016/j.livsci.2020.104054
Grant, R.J., Ferraretto, L.F., 2018. Silage Review: Silage Feeding Management: Silage Characteristics and Dairy Cow Feeding Behavior. J. Dairy Sci., 101:4111-4121.
Tassinari, P., Bovo, M., Benni, S., Franzoni, S., Poggi, M., Mammi, L.M.E., Mattoccia, S., Di Stefano, L., Bonora, F., Barbaresi, A., vd., 2021. A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Comput. Electron. Agric., 182: 106030.
Stygar, A.H., Gómez, Y., Berteselli, G.V., Dalla Costa, E., Canali, E., Niemi, J.K., Llonch, P., Pastell, M., 2021. A systematic review on commercially available and validated sensor technologies for welfare assessment of dairy cattle. Front. Vet. Sci., 8:634338. doi: 10.3389/fvets.2021.634338
Neethirajan, S., 2023. Artificial ıntelligence and sensor technologies in dairy livestock export: charting a digital transformation. Sensors, 23- 7045. doi:10.3390/ s23167045
Jegadeesan, S., Venkatesan, G.P., 2016. Smart cow health monitoring, farm environmental monitoring and control system using wireless sensor networks. Int J Adv Engg Tec., 7(1): 334-339.
García-Rodríguez, L., Pastor, J.M., Piles, M., Guzmán, J.L., 2021. Data-driven management and digital tools in livestock farming: A review. Animals, 11(5):1471.
Neethirajan, S., 2020. Transforming the adaptation physiology of farm animals through sensors. Animals, 10(9):1512. doi:10.3390/ani10091512
Lee, M., Seo, S., 2021. Wearable wireless biosensor technology for monitoring cattle: A review. Animals, 11(10): 2779. doi: 10.3390/ani11102779
Llonch, P., Mainau, E., Ipharraguerre, I.R., Bargo, F., Tedó, G., Blanch, M., Manteca, X., 2018. Chicken or the egg: the reciprocal association between feeding behavior and animal welfare and their ımpact on productivity in dairy cows. Front. Vet. Sci., 5(5):305. doi: 10.3389/fvets.2018.00305
Tzanidakis, C., Tzamaloukas, O., Simitzis, P., Panagakis, P., 2023. Precision livestock farming applications (PLF) for grazing animals. Agriculture, 13:288.
Alshehri, M., 2023. Blockchain-assisted Internet of Things framework in smart livestock farming. Internet Things, 22-100739. doi:10.1016/j.iot.2023.100739
Džermeikaite, K., Baceninaite, D., Antanaitis, R., 2023. Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13-780.
Berckmans, D., Vranken, E., Van Hertem, T., 2021. Precision livestock farming technologies for welfare management in intensive livestock systems. Animals, 11(4):1127.
Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., vd., 2013. Tackling climate change through livestock: A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO); c2013.
Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., Skarin, A., 2021. Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals, 11(3):829. doi:10.3390/ani11030829
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J., 2017. Big data in smart farming-A review. Agric. Syst., 153: 69-80.
Amiri-Zarandi, M., Dara, R.A. Duncan, E., Fraser, E.D., 2022. Big data privacy in smart farming: A review. Sustainability, 14(15):9120. https://doi.org/10.3390/su14159120
Faverjon, C., Bernstein, A., Grütter, R., Nathues, C., Nathues, H., Sarasua, C., Sterchi, M., Vargas, M.E. Berezowski, J., 2019. A transdisciplinary approach supporting the implementation of a Big Data project in livestock production: An example from the Swiss pig production industry. Front. Vet. Sci., 6-215.
Dawkins, M.S., 2021. Does smart farming improve or damage animal welfare? Technology and what animals want. Front. Anim. Sci., 2-736536.
Goedde, L., Katz, J., Ménard, A., Revellat, J.,2020. Agriculture’s connected future: How technology can yield new growth; McKinsey and Company: Atlanta, GA, USA, 2020.
Koltes, J.E., Cole, J.B., Clemmens, R., Dilger, R.N., Kramer, L.M., Lunney, J.K., McCue, M.E., McKay, S.D., Mateescu, R.G., Murdoch, B.M., vd., 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front. Genet. 2019, 10-1197. doi: 10.3389/fgene.2019.01197
Mancuso, D., Castagnolo, G., Porto, S.M., 2023. Cow behavioural activities in extensive farms: challenges of adopting automatic monitoring systems. Sensors, 23(8):3828. doi:10.3390/s23083828
Hou, S., Cheng, X., Shi, L., Zhang, S., 2020. Study on individual behavior of dairy cows based on activity data and clustering. In Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence, Shanghai, China, 17-19 October 2020; pp. 210-216.
Gale, H.F., 2005. Commercialization of food consumption in rural China. Economic Research Report, (8).
Akın S, Kara A., 2019. Factors affecting the farmers’decision on artificial insemination: a case study of Diyarbakir province, Turkey. Appl Ecol Environ Res, 17: 1389-1399.
Food and Agriculture Organization of the United Nations (FAO)., 2020. World Livestock: Transforming the livestock sector through the sustainable development goals; c2020. Retrieved from http://www.fao.org/documents/card/en/c/ca8632en Erişim tarihi 09.09.2024
Ali, W., Ali, M., Ahmad, M., Dilawar, S., Firdous, A., Afzal, A., 2020. Application of modern techniques in animal production sector for human and animal welfare. Turkish Journal of Agriculture - Food Science and Technology, 8(2):457-463. doi:10.24925/turjaf.v8i2.457-463.3159
Dumas, A., Dijkstra, J., France, J.. 2008. Mathematical modelling in animal nutrition: a centenary review. J. Agric. Sci., 146:123-142.
Butler, W.R., 2000. Nutritional interactions with reproductive performance in dairy cattle. Anim. Reprod. Sci., 60(61): 449-457.
Göncü, S., Güngör, C., 2018. The innovative techniques in animal husbandry. Ani. Husbandry and Nut.: 1.
Thibier, M., 2005. The zootechnical applications of biotechnology in animal reproduction: current methods and perspectives. Reprod Nutr Dev., 45: 235-242.
Verma, O.P., Kumar, R., Kumar, A., Chand, S., 2012. Assisted reproductive techniques in farm animal - from artificial ınsemination to nanobiotechnology. Vet. World., 5(5):301-310. doi: 10.5455/vetworld.2012.301-310
Holtz, W., 2005. Recent developments in assisted reproduction in goats. Small Ruminant Research, 60(1-2): 95-110.
Widayati, D.T., 2012. Embryo transfer as an assisted reproductive technology in farm animals. World Acad. Sci. Eng. Technol, 6 (2012): 10-21.
Mapletoft, R.J., 2018. History and perspectives on bovine embryo transfer. Animal Reproduction (AR), 10(3):168-173.
Yousuf, M., Yusuf, A., Mohammed, I., 2024. Review on current animal breeding and genetic technologies to ıncrease production and productivity of cattle. Global Journal of Animal Scientific Research, 12(1):19-36.
Moore, K., Thatcher, W.W., 2006. Major advances associated with reproduction in dairy cattle. Journal of Dairy Science, 89(4): 1254-1266.
Khare, V., Khare, A., 2017. Modern approach in animal breeding by use of advanced molecular genetic techniques. International Journal of Livestock Research, 7(5):1-22. doi:10.5455/ijlr.20170404010154
Lamb, C.,2015. What are the long-term impacts of estrus synchronization and artificial insemination? Web site. http://nwdistrict. ifas.ufl.edu/phag/2015/10/09/what-are-the-long-term impacts- of-estrus-synchronization-and-artificial-insemination/. Erişim tarihi 09.09.2024
Holm, D.E. Thompson, P.N., Irons, P.C., 2008. The economic effects of an estrus synchronization protocol using prostaglandin in beef heifers. Theriogenology, 70(9):1507-1515.
Vikrama, C.P., Balaji, N.S., 2002. Use of assisted reproductive technologies for livestock development. Veterinary World, 3(5): 238-240.
Kahi, A.K., Rewe, T.O., 2008. Biotechnology in livestock production: Overview of possibilities for Africa. African Journal on Biotechnology, 7(25):4984-4991.
Neethirajan, S., Reimert, I., Kemp, B., 2021. Measuring farm animal emotions-sensor-based approaches. Sensors, 21(2):553. doi:10.3390/s21020553
Zhang, M., Wang, X., Feng, H., Huang, Q., Xiao, X., Zhang, X., 2021. Wearable Internet of Things enabled precision livestock farming in smart farms: A review of technical solutions for precise perception, biocompatibility, and sustainability monitoring. J. Clean. Prod. 312: 127712.
Choudhary, K.K., Kavya, K.M., Jerome, A., Sharma, R.K., 2016. Advances in reproductive biotechnologies. Veterinary World, 9(4):388.
Leaky, R., Caron. P., Craufurd, P., Martin, A., McDonald, A., 2009. Impacts of AKST (Agricultural Knowledge Science and Technology) on development and sustainability goals. Agriculture at a crossroads (eds , McIntyre B. D., Herren H. R., Wakhungu J.& Watson R. T.), pp. 145–253. Washington, DC: Island Press.
Lewin H.A., 2009. It's a bull's market. Science, 323:478-479.
Hayes, B.J., Bowman, P.J., Chamberlain, A.J., Goddard, M.E., 2009. Genomic selection in dairy cattle: progress and challenges. J. Dairy Sci. 92:433-443.
Thornton, P., 2010. Livestock production: recent trends, future prospects. Phil. Trans. R. Soc. B., 365:2853-2867. doi:10.1098/rstb.2010.0134
Morrone, S., Dimauro, C., Gambella, F., Cappai, M.G., 2022. Industry 4.0 and Precision Livestock Farming (PLF): An up to date overview across animal productions.Sensors, 22(12): 4319. doi:10.3390/s22124319
Hayes, B.J., Lewin, HA, Goddard ME., 2013. The future of livestock breeding: Genomic selection for efficiency, reduced emissions intensity, and adaptation. Trends in Genetics, 29(4):206-14. doi: 10.1016/j.tig.2012.11.009
Satyanarayana, S.D.V., Risheen, G.D., 2023. Current trends and innovations in livestock production: A critical review. International Journal of Veterinary Sciences and Animal Husbandry; 8(3): 22-24.
Hing, S., Foster, S., Evans, D., 2021. Animal welfare risks in live cattle export from Australia to China by sea. Animals, 11(10):2862. doi.org/10.3390/ani11102862
Katainen, A., Norring, M., Manninen, E., Laine, J., Orava, T., Kuoppala, K., Saloniemi, H., 2005. Competitive behaviour of dairy cows at a concentrate self-feeder. Acta Agric. Scand. Sect. A Anim. Sci., 55:98-105.
Weigele, H.C., Gygax, L., Steiner, A., Wechsler, B., Burla, J.B., 2018. Moderate lameness leads to marked behavioral changes in dairy cows. J. Dairy Sci., 101:2370-2382.
Matore, Z., 2023. Drivers and ındicators of dairy animal welfare in large-scale dairies. Trop. Anim. Health Prod., 55(1): 43. doi: 10.1007/s11250-022-03440-z
Martins, B.M., Mendes, A.L.C., Silva, L.F., Moreira, T.R., Costa, J.H.C., Rotta, P.P., Chizzotti, M.L., Marcondes, M.I., 2020. Estimating body weight, body condition score, and type traits in dairy cows using three dimensional cameras and manual body measurements. Livest. Sci., 236, 104054. doi:10.1016/j.livsci.2020.104054
Grant, R.J., Ferraretto, L.F., 2018. Silage Review: Silage Feeding Management: Silage Characteristics and Dairy Cow Feeding Behavior. J. Dairy Sci., 101:4111-4121.
Tassinari, P., Bovo, M., Benni, S., Franzoni, S., Poggi, M., Mammi, L.M.E., Mattoccia, S., Di Stefano, L., Bonora, F., Barbaresi, A., vd., 2021. A computer vision approach based on deep learning for the detection of dairy cows in free stall barn. Comput. Electron. Agric., 182: 106030.
Stygar, A.H., Gómez, Y., Berteselli, G.V., Dalla Costa, E., Canali, E., Niemi, J.K., Llonch, P., Pastell, M., 2021. A systematic review on commercially available and validated sensor technologies for welfare assessment of dairy cattle. Front. Vet. Sci., 8:634338. doi: 10.3389/fvets.2021.634338
Neethirajan, S., 2023. Artificial ıntelligence and sensor technologies in dairy livestock export: charting a digital transformation. Sensors, 23- 7045. doi:10.3390/ s23167045
Jegadeesan, S., Venkatesan, G.P., 2016. Smart cow health monitoring, farm environmental monitoring and control system using wireless sensor networks. Int J Adv Engg Tec., 7(1): 334-339.
García-Rodríguez, L., Pastor, J.M., Piles, M., Guzmán, J.L., 2021. Data-driven management and digital tools in livestock farming: A review. Animals, 11(5):1471.
Neethirajan, S., 2020. Transforming the adaptation physiology of farm animals through sensors. Animals, 10(9):1512. doi:10.3390/ani10091512
Lee, M., Seo, S., 2021. Wearable wireless biosensor technology for monitoring cattle: A review. Animals, 11(10): 2779. doi: 10.3390/ani11102779
Llonch, P., Mainau, E., Ipharraguerre, I.R., Bargo, F., Tedó, G., Blanch, M., Manteca, X., 2018. Chicken or the egg: the reciprocal association between feeding behavior and animal welfare and their ımpact on productivity in dairy cows. Front. Vet. Sci., 5(5):305. doi: 10.3389/fvets.2018.00305
Tzanidakis, C., Tzamaloukas, O., Simitzis, P., Panagakis, P., 2023. Precision livestock farming applications (PLF) for grazing animals. Agriculture, 13:288.
Alshehri, M., 2023. Blockchain-assisted Internet of Things framework in smart livestock farming. Internet Things, 22-100739. doi:10.1016/j.iot.2023.100739
Džermeikaite, K., Baceninaite, D., Antanaitis, R., 2023. Innovations in cattle farming: Application of innovative technologies and sensors in the diagnosis of diseases. Animals, 13-780.
Berckmans, D., Vranken, E., Van Hertem, T., 2021. Precision livestock farming technologies for welfare management in intensive livestock systems. Animals, 11(4):1127.
Gerber, P.J., Steinfeld, H., Henderson, B., Mottet, A., Opio, C., Dijkman, J., vd., 2013. Tackling climate change through livestock: A global assessment of emissions and mitigation opportunities. Food and Agriculture Organization of the United Nations (FAO); c2013.
Herlin, A., Brunberg, E., Hultgren, J., Högberg, N., Rydberg, A., Skarin, A., 2021. Animal welfare implications of digital tools for monitoring and management of cattle and sheep on pasture. Animals, 11(3):829. doi:10.3390/ani11030829
Wolfert, S., Ge, L., Verdouw, C., Bogaardt, M.J., 2017. Big data in smart farming-A review. Agric. Syst., 153: 69-80.
Amiri-Zarandi, M., Dara, R.A. Duncan, E., Fraser, E.D., 2022. Big data privacy in smart farming: A review. Sustainability, 14(15):9120. https://doi.org/10.3390/su14159120
Faverjon, C., Bernstein, A., Grütter, R., Nathues, C., Nathues, H., Sarasua, C., Sterchi, M., Vargas, M.E. Berezowski, J., 2019. A transdisciplinary approach supporting the implementation of a Big Data project in livestock production: An example from the Swiss pig production industry. Front. Vet. Sci., 6-215.
Dawkins, M.S., 2021. Does smart farming improve or damage animal welfare? Technology and what animals want. Front. Anim. Sci., 2-736536.
Goedde, L., Katz, J., Ménard, A., Revellat, J.,2020. Agriculture’s connected future: How technology can yield new growth; McKinsey and Company: Atlanta, GA, USA, 2020.
Koltes, J.E., Cole, J.B., Clemmens, R., Dilger, R.N., Kramer, L.M., Lunney, J.K., McCue, M.E., McKay, S.D., Mateescu, R.G., Murdoch, B.M., vd., 2019. A vision for development and utilization of high-throughput phenotyping and big data analytics in livestock. Front. Genet. 2019, 10-1197. doi: 10.3389/fgene.2019.01197
Mancuso, D., Castagnolo, G., Porto, S.M., 2023. Cow behavioural activities in extensive farms: challenges of adopting automatic monitoring systems. Sensors, 23(8):3828. doi:10.3390/s23083828
Hou, S., Cheng, X., Shi, L., Zhang, S., 2020. Study on individual behavior of dairy cows based on activity data and clustering. In Proceedings of the 2020 2nd International Conference on Robotics, Intelligent Control and Artificial Intelligence, Shanghai, China, 17-19 October 2020; pp. 210-216.
Pages
139-156
Published
January 14, 2025
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