Use of Innovative Technologies in Sustainable Livestock Production

Öz

Despite the increase in the world population and food needs day by day, there are serious pressures on food production. In order for these pressures to be alleviated and production to continue in a stable manner, it is necessary to make maximum use of the innovative technologies brought by the age. At this point, it is vital that animal husbandry, which has a value and importance as an important protein source in the healthy nutrition of the society, has a sustainable structure. In this study, previous studies on innovative technologies that have the capacity to contribute to sustainable livestock have been examined. According to the findings obtained from the literature review; it is understood that the use of innovative technology contributes to sustainable animal husbandry in different ways; it still has many opportunities as it is significantly open to improvement, but small family businesses with limited capital accumulation face critical obstacles in catching these contributions and opportunities. Therefore, it is important for governments to implement incentive and support policies specifically designed for the use of innovative technologies in livestock production. In addition, farmer training and extension programs need to be implemented to educate farmers on the use of innovative technologies and to improve technology literacy in order to expand the use of innovative technologies.

Anahtar Kelimeler

• Innovative technology
• Livestock
• Sustainability

Sürdürülebilir Hayvancılıkta Yenilikçi Teknolojilerin Kullanımı

Öz

Dünya nüfusunun ve gıda ihtiyacının günden güne artmasına karşılık, gıda üretiminin üzerinde ciddi baskılar bulunmaktadır. Bu baskıların hafifletilip üretimin istikrarlı bir şekilde devam edebilmesi için, çağın getirdiği yenilikçi teknolojilerden azami ölçüde yararlanılması gerekmektedir. Toplumun sağlıklı beslenmesinde önemli bir protein kaynağı olarak değere ve öneme sahip olan hayvancılığın, sürdürülebilir bir yapıya kavuşması bu noktada hayatidir. Bu çalışmada, sürdürülebilir hayvancılığa katkı sağlama kapasitesi olan yenilikçi teknolojiler hakkında yapılan araştırmalar incelenmiştir. Yapılan literatür incelemesinden elde edilen bulgulara göre; yenilikçi teknoloji kullanımının sürdürülebilir hayvancılığa farklı yönlerden katkı sağladığı, hâlâ önemli derecede gelişime açık olduğu için birçok fırsatı barındırdığı, ancak sermaye birikimi kısıtlı olan küçük aile işletmelerinin bu katkı ve fırsatları yakalama konusunda kritik engellerle karşı karşıya kaldıkları anlaşılmaktadır. Bu nedenle hükümetlerin hayvansal üretimde yenilikçi teknolojilerin kullanımı için spesifik olarak tasarlanmış teşvik ve destekleme politikalarını hayata geçirmesi önem arz etmektedir. Ayrıca çiftçilerin yenilikçi teknolojilerin kullanımı konusunda eğitilmesi ve yenilikçi teknolojilerin kullanımının yaygınlaşması amacıyla teknoloji okur yazarlığının geliştirmesi için çiftçi eğitim ve yayım programlarının uygulanması gerekmektedir.

Anahtar Kelimeler

• Yenilikçi teknoloji
• Hayvancılık
• Sürdürülebilirlik

Kaynakça

1. Ahmed, S. R., Nagy, E., & Neethirajan, S. (2017). Self – assembled star – shaped chiroplasmonic gold nanoparticles for an ultrasensitive chiro – immunosensor for viruses. RSC Advances, 7(65), 40849-40857. 10.1039/C7RA07175B.
2. Alary, V., Corniaux, C., & Gautier, D. (2011). Livestock’s contribution to poverty alleviation: how to measure it? World Development, 39(9), 1638-1648.
3. Alders, R. B., Campbell, A., Costa, R., Guèye, E. F., Hoque, E. A., Perezgrovas-Garza, R., Rota, A., & Wingett, K. (2021). Livestock across the world: diverse animal species with complex roles in human societies and ecosystem services. Animal Frontiers, 11(5), 20-29.
4. Benjamin, M., & Yik, S. (2019). Precision livestock farming in swine welfare: a review for swine practitioners. Animals, 9(4), 1-21. https://doi.org/10.3390/ani9040133.
5. Berckmans, D. (2006). Automatic online monitoring of animals by precision livestock farming. Livestock Production and Society, 287, 27-30.
6. Berckmans, D., Hemeryck, M., & Berckmans, D. (2015). Animal sound talks! In real–time sound analysis for health monitoring in livestock. (pp. 1-8)
7. BTİK (2020). Akıllı Tarım. Sektörel Araştırma ve Strateji Geliştirme Daire Başkanlığı.
8. Capper, J. L. (2011). The environmental impact of beef production in the United States: 1977 compared with 2007. Journal of Animal Sciences, 89, 4249-4261.

9. Capper, J., & Hayes, D. (2012). The Environmental and economic impact of removing growth – enhancing technologies from United States beef production. Journal of Animal Sciences, 90, 3527-3537.
10. Catarinucci, L., Colella, R., Mainetti, L., Mighali, V., Patrono, L., Sergi, I., & Tarricone, L. (2012). An innovative animals tracking system based on passive UHF RFID technology. In SoftCOM 2012, 20th International Conference on Software, Telecommunications and Computer Networks. (pp. 1-7)
11. Chadwick, D., Sommer, S., Thorman, R., Fangueiro, D., Cardenas, L., Amon, B., & Misselbrook, T. (2011). Manure management: implications for greenhouse gas emissions. Animal Feed Science Technology, 166, 514-531.
12. Chand, R., Wang, Y. L., Kelton, D., & Neethirajan, S. (2018). Isothermal DNA amplification with functionalized graphene and nanoparticle assisted electroanalysis for rapid detection of johne’s disease. Sensors and Actuators B: Chemical, 261, 31-37, https://doi.org/10.1016/j.snb.2018.01.140.
13. Chandrud, W., Wisanmongkol, J., & Ketprom U. (2008). RFID for Poultry Traceability System at Animal Checkpoint. In Proceedings of ECTI-CON. (pp. 753-756)
14. Çakır, A., & İşlek, F. (2021). Türkiye’de Organik Tarım ve Agro – Ekolojik Gelişmeler. In Türkiye’nin Akıllı Tarım (Tarım 4.0) Potansiyeli. (pp. 155-174)
15. Delgado, C. (2005). Rising demand for meat and milk in developing countries: implications for grasslands based livestock production. In grassland: global resource. (pp. 29-39).
16. Deloitte (2017). Smart livestock farming, potential of digitalization for global meat supply. No. 11, Discussion Paper, Deloitte.
17. Doğan, H., Çağlar, M. F., Yavuz, M., & Gözel, M. A. (2016). Use of Radio Frequency Identification Systems on Animal Monitoring. Suleyman Demirel University International Journal of Technological Science, 8, 38-53. https://doi.org/10.1002/mmce.21674
18. Domdouzis, K., Kumar, B., & Anumba, C. (2007) Radio Frequency Identification (RFID) Applications: A Brief Introduction. Advanced Engineering Informatics, 21, 350-355.
19. Fallon, R. J. (2001) The development and use of electronic ruminal boluses as a vehicle for bovine identification. Revue Scientifique et Technique Office International, 20(2), 480-490.
20. FAO (2006). World agriculture: towards 2030/2050. interim report, global perspective studies unit.
21. FAO (2010). Food and Agriculture Organization of the United Nations Statistical Databases.
22. FAO (2018). World livestock: transforming the livestock sector through the sustainable development goals. Rome: Food and Agriculture Organization of the United Nations.
23. FAO (2019). The future of livestock in Nigeria. ın: opportunities and challenges in the face of uncertainty: Guidelines. FAO, Rome.
24. Finkenzeller, K. (2003). RFID Handbook: Fundamentals and Applications in Contactless Smart Cards and Identification. John Wiley & Sons, Inc. New York, USA.
25. Groher, T., Heitkämper, K., & Umstätter, C. (2020). Digital technology adoption in livestock production with a special focus on ruminant farming. Animal, 14(11), 2404-2413. https://doi.org/10.1017/S1751731120001391
26. Gwaka, L.T. (2017). Digital technologies and sustainable livestock systems in rural communities. The Electronic Journal of Information Systems in Developing Countries, 81(6), 1-24.
27. Hong-Da, W. (2012). Application of radio frequency ıdentification (RFID) in diary ınformation management. Journal of Northeast Agricultural University, 19, 78-81.
28. Jorquera-Chavez, M., Fuentes, S., Dunshea, F. R., Jongman, E. C., & Warner, R. D. (2019). Computer vision and remote sensing to assess physiological responses of cattle to pre – slaughter stress, and its impact on beef quality: a review. Meat Science, 156, 11-22. https://doi.org/10.1016/j.meatsci.2019.05.007.
29. Karaca, S. (2010). Personal Tracking System with RFID. (Master’s Thesis, Maltepe University Graduate School of Natural and Applied Science)
30. Kılıç, U. (2011). Use of Wireless Rumen Sensors in Ruminant Nutrition Research. Asian Journal of Animal Sciences, 5(1), 46-55.
31. Koltes, J. E., Koltes, D. A., Mote, B. E., Tucker, J., & Hubbell, III D. S. (2018). Automated collection of heat stress data in livestock: new Technologies and opportunities. Translational Animal Science, 2(3), 319-323. https://doi.org/10.1093/tas/txy061.
32. 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., & Reuter, R. (2019). A Vision for Vevelopment and Utilization of High Throughput Phenotyping and Big Data Analytics in Livestock. Frontiers in Genetics, 10, 1197.
33. Maddison, A. (2003). The world economy: historical statistics. OECD publishing.
34. Martinez, B., Reaser, J. K., Dehgan, A., Zamft, B., Baisch, D., McCormick, C., Giordano, A. J., Aicher, R., & Selbe, S. (2020). Technology innovation: advancing capacities for the early detection of and rapid response to invasive species. Biol Invasions, 22(1), 75-100. https://doi.org/10.1007/s10530-019-02146-y.
35. McEwen, S. A., & Collignon, P. J. (2018). Antimicrobial resistance: a one health perspective. Microbiology Spectrum, Clinical Microbiology, 6(2), 1-26.
36. Mennecke, B., & Townsend, A. (2005). Radio Frequency Identification Tagging as a Mechanism of Creating a Viable Producer’s Brand in the Cattle Indutsry. MATRIC Research Paper 05-MRP 8.
37. Morota, G., Ventura, R. V., Silva, F. F., Koyama, M., & Fernando, S. C. (2018). Big data analytics and precision animal agriculture symposium: machine learning and data mining advance predictive big data analysis in precision animal agriculture. Journal of Animal Science, 96(4), 1540-1550. https://doi.org/10.1093/jas/sky014.
38. Mungroo, N. A., & Neethirajan, S. (2014). Biosensors for the detection of antibiotics in poultry industry-a review. Biosensors, 4(4), 472-493. https://doi.org/10.3390/bios4040472.
39. Neethirajan, S., & Kemp, B. (2021). Digital livestock farming. Sensing and Bio – Sensing Research, 32, 100408, 1-12.
40. Neumeier, C. J., & Mitloehner, F. M. (2013). Cattle biotechnologies reduce environmental impact and help feed a growing planet. Animal Frontiers, 3(3), 36-41.
41. Ordolff, D. (2001). Introduction of electronics into milking technology. Computers and Electronics in Agriculture, 30, 125-149.
42. Picchi, V. V., Castro, E. F., Marino, F. C., & Ribeiro, S. L. (2019). Increasing the confidence of the Brazilian livestock production chain using blockchain. In Proceedings of the 2019 2nd International Conference on Blockchain Technology and Application. (pp. 93-98)
43. Roberts, C. M. (2006). Radio frequency identification (RFID). Computer&Security, 25, 18-26.
44. Rosegrant, M. W., Cai, X., & Cline, S. A. (2002). Global water outlook to 2025, averting an impending crisis. a 2020 vision for food, agriculture, and the environment initiative. Washington, DC: IFPRI and IWMI. International Water Management Institute.
45. Rosegrant, M. W., McIntyre, B. D., Herren, H. R., Wakhungu, J., & Watson, R. T. (2009). Looking into the future for agriculture and AKST (agricultural knowledge science and technology). In agriculture at a crossroads. (pp. 307-376).
46. Rossing, W. (1999). Animal Identification: Indtroduction and History. Computers and Electronics in Agriculture, 24, 1-4.
47. Schillings, J., Bennett, R., & Rose, D. C. (2021). Exploring the potential of precision livestock farming technologies to help address farm animal welfare. Frontiers in Animal Science, 2, 639678.
48. Steinfeld, H., Gerber, P., Wassenaar, T., Castel, V., Rosales, M., & de Haan, C. (2006). Livestock’s long shadow: environmental issues and options. Food & Agriculture Org.
49. Stoyanov, K., Zhelyazkov, G., & Nikolay, P. (2021). Digitalization of processes in livestock farming: software solutions in the case of Bulgaria. SHS Web of Conferences, 120, 1-6. https://doi.org/10.1051/shsconf/202112002010
50. Stygar, A. H., Gómez, Y., Bertesell, G. V., Costa, E. D., Canall, E., Niemi, J. K., Llonch, P., & Pastell, M. A. (2021). Systematic review on commercially available and validated sensor technologies for welfare assessment for dairy cattle. Frontiers in Veterinary Science, 8, 634338, 1-15.
51. Taneja, M., Byabazaire, J., Jalodia, N., Davy, A., Olariu, C., & AMalone, P. (2020). Machine learning based fog computing assisted data – driven approach for early lameness detection in dairy cattle. Computers and Electronics in Agriculture, 171, 105286.
52. Thornton, P. K. (2010). Livestock production: recent trends, future prospects. Philosophy Transactions of the Royal Society, 365, 2853-2867
53. Trevisi, E., Zecconi, A., Cogrossi, S., Razzuoli, E., Grossi, P., & Amadori, M. (2014).Strategies for reduced antibiotic usage in dairy cattle farms. Research in Veterinary Science, 96, 229-233. https://doi.org/10.1016/j.rvsc.2014.01.001
54. Tuteja, S. K., & Neethirajan, S. (2018). Exploration of two – dimensional bio – functionalized phosphorene nanosheets (black phosphorous) for label free haptoglobin electro – immunosensing applications, Nanotechnology, 29(13), 135101. https://doi.org/10.1088/1361-6528/aaab15.
55. UNFPA (2008). United Nations Population Fund. The state of world population 2007: unleashing the potential of urban growth. 2008. United Nations Population Fund.
56. UNPD (2008). United Nations Population Division. The 2006 Revision and world urbanization prospects: the 2005 revision. Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat, World Population Prospects.
57. Van Vuuren, D. P., McIntyre, B. D., Herren, H. R., Wakhungu, J., & Watson, R. T. (2009) Outlook on agricultural change and its drivers. In agriculture at a crossroads. (pp. 255-305)
58. Varese, E., Buffagni S., & Percivale F. (2008). Application of RFID technology to the agro industrial sector: analysis of some case studies. Journal Commodity Science and Technological Quality, 47, 171-179.
59. Volk, T., & Jansen, D. (2012). Implantable RFID sensor platform to monitor vital functions of small animals controlled by network based software. In Smart SysTech 2012; European Conference on Smart Objects, Systems and Technologies. (pp. 1-6)
60. White, M. (2018). Emerging agricultural technologies: consumer perceptions around emerging agtech. ISBN 978-1-76053-013-6 ISSN 1440-6845 Publication No. 18/048 Project No. PRJ-011141, 97 P, Sydney.
61. Weng, X., & Neethirajan, S. (2018). Immunosensor based on antibody – functionalized MoS 2 for rapid detection of avian coronavirus on cotton thread. IEEE Sensors Journal, 18(11), 4358-4363. https://doi.org/10.1109/JSEN.2018.2829084.
62. Williams, L. R., Moore, S. T., Bishop-Hurley, G. J., & Swain, D. L. (2020). A sensor – based solution to monitor grazing cattle drinking behaviour and water intake. Computers and Electronics in Agriculture, 168, 105141, 1-9. https://doi.org/10.1016/j.compag.2019.105141.
63. Wismans, W. M. (1999). Identification and Registration of Animal in the European Union. Computers and Electronics in Agriculture, 24, 99-108.
64. Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – a review. Agricultural Systems, 153, 69-80, https://doi.org/10.1016/j.agsy.2017.01.023.
65. World Bank (2009) Minding the stock: bringing public policy to bear on livestock sector development. Report no. 44010-GLB.Washington, DC.