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Digital Technologies' Rising Role in the Livestock Sector: A Future Perspective Based on Academic Studies

Yıl 2024, Cilt: 10 Sayı: 1, 90 - 102, 30.06.2024
https://doi.org/10.61513/tead.1269279

Öz

The primary goal of this study is to explain the digital technologies used in livestock farming, along with the socioeconomic and environmental impacts of these technologies. The second goal is to reveal the historical evolution of the subject's studies. These technologies based on Internet of Things have emerged as electronic ear tags, electronic neck collars, electronic pedometers, sensors, and virtual fences. Furthermore, these technologies have been observed to be widely used in poultry, sheep, and pig farms, particularly in dairy farms. The United States, China, England, and Australia were found to be among the countries where the greatest number of scientific studies were undertaken when the "Bibliometric Analysis" approach was used to study the development processes of the research on the topic. Until to 2015, the majority of the research focused on precision livestock and sensors. However, following 2015, the focus shifted to machine learning, animal and human welfare, animal behavior, and deep learning. As a result of the increased use of digital technology in animal husbandry, animal tracking optimizes animal health, welfare, and efficiency while increasing enterprise profitability. In addition, digital technology enable precise and continuous monitoring of the environmental consequences of livestock farming (greenhouse gas emissions, soil, and water pollution). The digitization of livestock farming in rural areas has the potential to contribute to both rural development and the prevention of rural migration. It also enhances food security and supply.

Kaynakça

  • Abafe, Ejovi Akpojevwe, Yonas T. Bahta, and Henry Jordaan. 2022. ‘Exploring Biblioshiny for Historical Assessment of Global Research on Sustainable Use of Water in Agriculture’. Sustainability 14(17). doi: 10.3390/su141710651.
  • Abdullahi, Umar Sani, Mopa Nyabam, Kingsley Orisekeh, S. Umar, B. Sani, E. David, and A. Umoru. 2020. ‘Exploiting IoT and LoRaWAN Technologies For Effective Livestock Monitoring In Nigeria’.
  • Ambrosin, Moreno, Arman Anzanpour, Mauro Conti, Tooska Dargahi, Sanaz Rahimi Moosavi, Amir M. Rahmani, and Pasi Liljeberg. 2016. ‘On the Feasibility of Attribute-Based Encryption on Internet of Things Devices’. IEEE Micro 36(6):25–35. doi: 10.1109/MM.2016.101.
  • Anonim. 2022a. ‘Elektronik Künye - Hayvan Takip Sistemi’. Retrieved 2 December 2022 (http://teta.com.tr/elektronik-kunye-hayvan-takip-sistemi-1).
  • Anonim. 2022b. ‘Trade Statistics for International Business Development’. Retrieved 8 March 2023 (https://www.trademap.org/Product_SelProductCountry.aspx?nvpm=1%7c842%7c%7c%7c%7c01%7c%7c%7c4%7c1%7c1%7c2%7c1%7c1%7c1%7c1%7c1%7c1).
  • Aquilani, C., A. Confessore, R. Bozzi, F. Sirtori, and C. Pugliese. 2022. ‘Review: Precision Livestock Farming Technologies in Pasture-Based Livestock Systems’. Animal 16(1):100429. doi: 10.1016/J.ANIMAL.2021.100429.
  • Aria, Massimo, and Corrado Cuccurullo. 2022. Science Mapping Analysis with Bibliometrix R-Package: An Example.
  • Ayalew, W., J. M. King, E. Bruns, and B. Rischkowsky. 2003. ‘Economic Evaluation of Smallholder Subsistence Livestock Production: Lessons from an Ethiopian Goat Development Program.’ Ecological Economics 45(3):473–85. doi: 10.1016/S0921-8009(03)00098-3.
  • Banhazi, T. M., H. Lehr, J. L. Black, H. Crabtree, P. Schofield, M. Tscharke, and D. Berckmans. 2012. ‘Precision Livestock Farming: An International Review of Scientific and Commercial Aspects †’. doi: 10.3965/j.ijabe.20120503.001.
  • Bello, Rotimi-Williams, and Moradeyo Oluwatomilola Motunrayo. 2019. Monitoring Cattle Grazing Behavior and Intrusion Using Global Positioning System and Virtual Fencing Precision Livestock Farming View Project Moradeyo Oluwatomilola Motunrayo.
  • Berckmans, Daniel, and Marcella Guarino. 2008. ‘Preface’. Computers and Electronics in Agriculture 64(1):1. doi: https://doi.org/10.1016/j.compag.2008.05.006.
  • Bugge, Markus M., Teis Hansen, and Antje Klitkou. 2016. ‘What Is the Bioeconomy? A Review of the Literature’. Sustainability (Switzerland) 8(7).
  • Çelik, Mustafa Y., and Mehmet S. Tanışık. 2015. Küçükbaş Hayvancılıkta Sürü Yönetimi ve ‘Sürü Yönetimi Elemanı Benim’ Projesi. Vol. 3.
  • Conti, Mauro, Ali Dehghantanha, Katrin Franke, and Steve Watson. 2018. ‘Internet of Things Security and Forensics: Challenges and Opportunities’. Future Generation Computer Systems 78:544–46. doi: 10.1016/J.FUTURE.2017.07.060.
  • Cropin. 2022. ‘Precision Agriculture: How Is It Different from Smart Farming?’ Retrieved 14 November 2022 (https://www.cropin.com/blogs/smart-farming-vs-precision-farming-systems).
  • Dlodlo, Nomusa, and Josephat Kalezhi. 2015. ‘The Internet of Things in Agriculture for Sustainable Rural Development’. Pp. 13–18 in Proceedings of 2015 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2015. Institute of Electrical and Electronics Engineers Inc.
  • FAO. 2022. ‘Value of Agricultural Production’. Retrieved 8 March 2023 (https://www.fao.org/faostat/en/#data/QV).
  • Jachowski, D. S., R. Slotow, and J. J. Millspaugh. 2014. ‘Good Virtual Fences Make Good Neighbors: Opportunities for Conservation’. Animal Conservation 17(3):187–96. doi: https://doi.org/10.1111/acv.12082.
  • Ku, Linly. 2022. ‘New Agriculture Technology in Modern Farming’. Retrieved 29 November 2022 (https://www.plugandplaytechcenter.com/resources/new-agriculture-technology-modern-farming/).
  • Li, Guoming, Yanbo Huang, Zhiqian Chen, Gary D. Chesser, Joseph L. Purswell, John Linhoss, and Yang Zhao. 2021. ‘Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review’. Sensors 21(4):1492. doi: 10.3390/s21041492.
  • Lomax, Sabrina, Patricia Colusso, and Cameron E. F. Clark. 2019. ‘Does Virtual Fencing Work for Grazing Dairy Cattle?’ Animals 9(7). doi: 10.3390/ani9070429.
  • Madzingira, Oscar. 2018. ‘Animal Welfare Considerations in Food-Producing Animals’. in Animal Welfare. InTech.
  • McSweeney, Diarmuid, Bernadette O’Brien, Neil E. Coughlan, Alexis Férard, Stepan Ivanov, Paddy Halton, and Christina Umstatter. 2020. ‘Virtual Fencing without Visual Cues: Design, Difficulties of Implementation, and Associated Dairy Cow Behaviour’. Computers and Electronics in Agriculture 176:105613. doi: 10.1016/J.COMPAG.2020.105613.
  • Milian, Eduardo Z., Mauro de M. Spinola, and Marly M. de Carvalho. 2019. ‘Fintechs: A Literature Review and Research Agenda’. Electronic Commerce Research and Applications 34:100833. doi: 10.1016/J.ELERAP.2019.100833.
  • Narendra, V. G., and K. S. Hareesh. 2010. ‘Prospects of Computer Vision Automated Grading and Sorting Systems in Agricultural and Food Products for Quality Evaluation’. International Journal of Computer Applications 1(4):1–12. doi: 10.5120/111-226.
  • Odintsov Vaintrub, M., H. Levit, M. Chincarini, I. Fusaro, M. Giammarco, and G. Vignola. 2021. ‘Review: Precision Livestock Farming, Automats and New Technologies: Possible Applications in Extensive Dairy Sheep Farming’. Animal 15(3).
  • Özcan, Tuğba. 2022. ‘Nesnelerin İnterneti Özellikli Sensörlerin Akıllı Atık Yönetimine Katkısı’. Yapı Bilgi Modelleme 4(1).
  • Pretto, Andrea, Gianpaolo Savio, Flaviana Gottardo, Francesca Uccheddu, and Gianmaria Concheri. 2022. ‘A Novel Low-Cost Visual Ear Tag Based Identification System for Precision Beef Cattle Livestock Farming’. Information Processing in Agriculture. doi: 10.1016/j.inpa.2022.10.003.
  • RCoreTeam. 2022. ‘R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing’. Retrieved 7 November 2022 (https://www.R-project.org/).
  • Rojas-Sánchez, Mario A., Pedro R. Palos-Sánchez, and José A. Folgado-Fernández. 2022. ‘Systematic Literature Review and Bibliometric Analysis on Virtual Reality and Education’. Education and Information Technologies. doi: 10.1007/s10639-022-11167-5.
  • Sarkar, Apurbo, Hongyu Wang, Airin Rahman, Waqar Hussain Memon, and Lu Qian. 2022. ‘A Bibliometric Analysis of Sustainable Agriculture: Based on the Web of Science (WOS) Platform’. Environmental Science and Pollution Research 29(26):38928–49.
  • Sektörel Araştırma ve Strateji Geliştirme Dairesi Başkanlığı. 2022. ‘Akıllı Tarım’. Retrieved 2 December 2022 (https://www.btk.gov.tr/uploads/pages/arastirma-raporlari/akilli-tarim.pdf).
  • Shu, Hang, Wensheng Wang, Leifeng Guo, and Jérôme Bindelle. 2021. ‘Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review’. Animals 11(4):980. doi: 10.3390/ani11040980. da Silva, José Graziano. 2012. Feeding the World Sustainably.
  • Soylu, Tuncay. 2012. ‘Kablosuz Algılayıcı Ağların Uygulama Alanları ve Bir Algılayıcı Düğüm Tasarımı’. T.C. Trakya Üniversitesi Fen Bilimleri Enstitüsü, Edirne.
  • Stampa, Ekaterina, Katrin Zander, and Ulrich Hamm. 2020. ‘Insights into German Consumers’ Perceptions of Virtual Fencing in Grassland-Based Beef and Dairy Systems: Recommendations for Communication’. Animals 10(12):1–18. doi: 10.3390/ani10122267.
  • Tangorra, Francesco M., Aldo Calcante, Stefano Nava, Gabriele Marchesi, and Massimo Lazzari. 2013. ‘Design and Testing of a GPS/GSM Collar Prototype to Combat Cattle Rustling’. Journal of Agricultural Engineering 44(2):71–76. doi: 10.4081/jae.2013.e10.
  • Thornton, Philip K. 2010. ‘Livestock Production: Recent Trends, Future Prospects’. Philosophical Transactions of the Royal Society B: Biological Sciences 365(1554):2853–67.
  • UN. 2022. World Population Prospects 2022: Summary of Results. New York.
  • Wathes, C. M., H. H. Kristensen, J. M. Aerts, and D. Berckmans. 2008. ‘Is Precision Livestock Farming an Engineer’s Daydream or Nightmare, an Animal’s Friend or Foe, and a Farmer’s Panacea or Pitfall?’ Computers and Electronics in Agriculture 64(1):2–10. doi: 10.1016/j.compag.2008.05.005.
  • Weinstein, Ben G. 2018. ‘A Computer Vision for Animal Ecology’. Journal of Animal Ecology 87(3):533–45. doi: 10.1111/1365-2656.12780.
  • Yüceer, Sema Ezgi, and Sibel Tan. 2022. ‘Tarım Politikaları Literatürünün Bibliyometrik Analiz Yöntemiyle İncelenmesi’. Tarım Ekonomisi Araştırmaları Dergisi 8(2):156–69.

Dijital Teknolojilerin Hayvancılık Sektöründe Yükselen Rolü: Akademik Çalışmaların Işığında Geleceğe Bakış

Yıl 2024, Cilt: 10 Sayı: 1, 90 - 102, 30.06.2024
https://doi.org/10.61513/tead.1269279

Öz

Bu çalışmanın birinci amacı hayvancılıkta kullanılan dijital teknolojileri açıklamak, bu teknolojilerin sosyo-ekonomik ve çevresel etkilerini ortaya koymaktır. İkinci amaç ise, konu ile ilgili yapılmış çalışmaların tarihsel evrimini ortaya koymaktır. Nesnelerin interneti temelli olan bu teknolojilerin elektronik kulak küpeleri, elektronik boyun tasması, elektronik adım ölçerler, sensörler ve sanal çitler olarak ortaya çıktığı görülmüştür. Dahası, bu teknolojilerin özellikle, süt üretim çiftlikleri başta olmak üzere kümes hayvancılığı, küçükbaş ve domuz çiftliklerinde yaygın olarak kullanıldığı görülmüştür. Öte yandan, “Bibliyometrik Analiz” yönteminden faydalanarak konu ile ilgili yapılmış çalışmaların gelişim süreçleri incelendiğinde ise Amerika Birleşik Devletleri, Çin, İngiltere ve Avustralya en çok bilimsel çalışmaların yapıldığı ülkelerin başında yer aldığı görülmüştür. Çalışmalarda, 2015 yılına kadar hassas hayvancılık, sensörler gibi konular ağırlıklı ele alınan konular iken, 2015 yılı sonrasında ise, çalışmalar makine öğrenmesi, hayvan ve insan refahı, hayvan davranışları ve derin öğrenme konularına evrildiği görülmüştür. Sonuç olarak, hayvancılıkta dijital teknoloji kullanımının artması ile hayvan takibi, hayvan sağlığı, refahı ve verim unsurlarında optimizasyon sağlar iken işletmelerin karlılığını arttırmaktadır. Dahası, dijital teknolojiler ile hayvancılıktan kaynaklı çevresel etkilerinin (Sera gazı emisyonları, toprak ve su kirliliği) sürekli olarak takip edilebilmektedir. Kırsal alanlarda hayvancılık faaliyetlerinin dijitalleşmesiyle hem kırsal alanların gelişmesinde hem de kırsal alanlardaki göçlerin önlenmesinde fayda sağlanabilir. Ayrıca, gıda arzı ve güvenliğinin arttıracağı düşünülmektedir.

Kaynakça

  • Abafe, Ejovi Akpojevwe, Yonas T. Bahta, and Henry Jordaan. 2022. ‘Exploring Biblioshiny for Historical Assessment of Global Research on Sustainable Use of Water in Agriculture’. Sustainability 14(17). doi: 10.3390/su141710651.
  • Abdullahi, Umar Sani, Mopa Nyabam, Kingsley Orisekeh, S. Umar, B. Sani, E. David, and A. Umoru. 2020. ‘Exploiting IoT and LoRaWAN Technologies For Effective Livestock Monitoring In Nigeria’.
  • Ambrosin, Moreno, Arman Anzanpour, Mauro Conti, Tooska Dargahi, Sanaz Rahimi Moosavi, Amir M. Rahmani, and Pasi Liljeberg. 2016. ‘On the Feasibility of Attribute-Based Encryption on Internet of Things Devices’. IEEE Micro 36(6):25–35. doi: 10.1109/MM.2016.101.
  • Anonim. 2022a. ‘Elektronik Künye - Hayvan Takip Sistemi’. Retrieved 2 December 2022 (http://teta.com.tr/elektronik-kunye-hayvan-takip-sistemi-1).
  • Anonim. 2022b. ‘Trade Statistics for International Business Development’. Retrieved 8 March 2023 (https://www.trademap.org/Product_SelProductCountry.aspx?nvpm=1%7c842%7c%7c%7c%7c01%7c%7c%7c4%7c1%7c1%7c2%7c1%7c1%7c1%7c1%7c1%7c1).
  • Aquilani, C., A. Confessore, R. Bozzi, F. Sirtori, and C. Pugliese. 2022. ‘Review: Precision Livestock Farming Technologies in Pasture-Based Livestock Systems’. Animal 16(1):100429. doi: 10.1016/J.ANIMAL.2021.100429.
  • Aria, Massimo, and Corrado Cuccurullo. 2022. Science Mapping Analysis with Bibliometrix R-Package: An Example.
  • Ayalew, W., J. M. King, E. Bruns, and B. Rischkowsky. 2003. ‘Economic Evaluation of Smallholder Subsistence Livestock Production: Lessons from an Ethiopian Goat Development Program.’ Ecological Economics 45(3):473–85. doi: 10.1016/S0921-8009(03)00098-3.
  • Banhazi, T. M., H. Lehr, J. L. Black, H. Crabtree, P. Schofield, M. Tscharke, and D. Berckmans. 2012. ‘Precision Livestock Farming: An International Review of Scientific and Commercial Aspects †’. doi: 10.3965/j.ijabe.20120503.001.
  • Bello, Rotimi-Williams, and Moradeyo Oluwatomilola Motunrayo. 2019. Monitoring Cattle Grazing Behavior and Intrusion Using Global Positioning System and Virtual Fencing Precision Livestock Farming View Project Moradeyo Oluwatomilola Motunrayo.
  • Berckmans, Daniel, and Marcella Guarino. 2008. ‘Preface’. Computers and Electronics in Agriculture 64(1):1. doi: https://doi.org/10.1016/j.compag.2008.05.006.
  • Bugge, Markus M., Teis Hansen, and Antje Klitkou. 2016. ‘What Is the Bioeconomy? A Review of the Literature’. Sustainability (Switzerland) 8(7).
  • Çelik, Mustafa Y., and Mehmet S. Tanışık. 2015. Küçükbaş Hayvancılıkta Sürü Yönetimi ve ‘Sürü Yönetimi Elemanı Benim’ Projesi. Vol. 3.
  • Conti, Mauro, Ali Dehghantanha, Katrin Franke, and Steve Watson. 2018. ‘Internet of Things Security and Forensics: Challenges and Opportunities’. Future Generation Computer Systems 78:544–46. doi: 10.1016/J.FUTURE.2017.07.060.
  • Cropin. 2022. ‘Precision Agriculture: How Is It Different from Smart Farming?’ Retrieved 14 November 2022 (https://www.cropin.com/blogs/smart-farming-vs-precision-farming-systems).
  • Dlodlo, Nomusa, and Josephat Kalezhi. 2015. ‘The Internet of Things in Agriculture for Sustainable Rural Development’. Pp. 13–18 in Proceedings of 2015 International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2015. Institute of Electrical and Electronics Engineers Inc.
  • FAO. 2022. ‘Value of Agricultural Production’. Retrieved 8 March 2023 (https://www.fao.org/faostat/en/#data/QV).
  • Jachowski, D. S., R. Slotow, and J. J. Millspaugh. 2014. ‘Good Virtual Fences Make Good Neighbors: Opportunities for Conservation’. Animal Conservation 17(3):187–96. doi: https://doi.org/10.1111/acv.12082.
  • Ku, Linly. 2022. ‘New Agriculture Technology in Modern Farming’. Retrieved 29 November 2022 (https://www.plugandplaytechcenter.com/resources/new-agriculture-technology-modern-farming/).
  • Li, Guoming, Yanbo Huang, Zhiqian Chen, Gary D. Chesser, Joseph L. Purswell, John Linhoss, and Yang Zhao. 2021. ‘Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review’. Sensors 21(4):1492. doi: 10.3390/s21041492.
  • Lomax, Sabrina, Patricia Colusso, and Cameron E. F. Clark. 2019. ‘Does Virtual Fencing Work for Grazing Dairy Cattle?’ Animals 9(7). doi: 10.3390/ani9070429.
  • Madzingira, Oscar. 2018. ‘Animal Welfare Considerations in Food-Producing Animals’. in Animal Welfare. InTech.
  • McSweeney, Diarmuid, Bernadette O’Brien, Neil E. Coughlan, Alexis Férard, Stepan Ivanov, Paddy Halton, and Christina Umstatter. 2020. ‘Virtual Fencing without Visual Cues: Design, Difficulties of Implementation, and Associated Dairy Cow Behaviour’. Computers and Electronics in Agriculture 176:105613. doi: 10.1016/J.COMPAG.2020.105613.
  • Milian, Eduardo Z., Mauro de M. Spinola, and Marly M. de Carvalho. 2019. ‘Fintechs: A Literature Review and Research Agenda’. Electronic Commerce Research and Applications 34:100833. doi: 10.1016/J.ELERAP.2019.100833.
  • Narendra, V. G., and K. S. Hareesh. 2010. ‘Prospects of Computer Vision Automated Grading and Sorting Systems in Agricultural and Food Products for Quality Evaluation’. International Journal of Computer Applications 1(4):1–12. doi: 10.5120/111-226.
  • Odintsov Vaintrub, M., H. Levit, M. Chincarini, I. Fusaro, M. Giammarco, and G. Vignola. 2021. ‘Review: Precision Livestock Farming, Automats and New Technologies: Possible Applications in Extensive Dairy Sheep Farming’. Animal 15(3).
  • Özcan, Tuğba. 2022. ‘Nesnelerin İnterneti Özellikli Sensörlerin Akıllı Atık Yönetimine Katkısı’. Yapı Bilgi Modelleme 4(1).
  • Pretto, Andrea, Gianpaolo Savio, Flaviana Gottardo, Francesca Uccheddu, and Gianmaria Concheri. 2022. ‘A Novel Low-Cost Visual Ear Tag Based Identification System for Precision Beef Cattle Livestock Farming’. Information Processing in Agriculture. doi: 10.1016/j.inpa.2022.10.003.
  • RCoreTeam. 2022. ‘R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing’. Retrieved 7 November 2022 (https://www.R-project.org/).
  • Rojas-Sánchez, Mario A., Pedro R. Palos-Sánchez, and José A. Folgado-Fernández. 2022. ‘Systematic Literature Review and Bibliometric Analysis on Virtual Reality and Education’. Education and Information Technologies. doi: 10.1007/s10639-022-11167-5.
  • Sarkar, Apurbo, Hongyu Wang, Airin Rahman, Waqar Hussain Memon, and Lu Qian. 2022. ‘A Bibliometric Analysis of Sustainable Agriculture: Based on the Web of Science (WOS) Platform’. Environmental Science and Pollution Research 29(26):38928–49.
  • Sektörel Araştırma ve Strateji Geliştirme Dairesi Başkanlığı. 2022. ‘Akıllı Tarım’. Retrieved 2 December 2022 (https://www.btk.gov.tr/uploads/pages/arastirma-raporlari/akilli-tarim.pdf).
  • Shu, Hang, Wensheng Wang, Leifeng Guo, and Jérôme Bindelle. 2021. ‘Recent Advances on Early Detection of Heat Strain in Dairy Cows Using Animal-Based Indicators: A Review’. Animals 11(4):980. doi: 10.3390/ani11040980. da Silva, José Graziano. 2012. Feeding the World Sustainably.
  • Soylu, Tuncay. 2012. ‘Kablosuz Algılayıcı Ağların Uygulama Alanları ve Bir Algılayıcı Düğüm Tasarımı’. T.C. Trakya Üniversitesi Fen Bilimleri Enstitüsü, Edirne.
  • Stampa, Ekaterina, Katrin Zander, and Ulrich Hamm. 2020. ‘Insights into German Consumers’ Perceptions of Virtual Fencing in Grassland-Based Beef and Dairy Systems: Recommendations for Communication’. Animals 10(12):1–18. doi: 10.3390/ani10122267.
  • Tangorra, Francesco M., Aldo Calcante, Stefano Nava, Gabriele Marchesi, and Massimo Lazzari. 2013. ‘Design and Testing of a GPS/GSM Collar Prototype to Combat Cattle Rustling’. Journal of Agricultural Engineering 44(2):71–76. doi: 10.4081/jae.2013.e10.
  • Thornton, Philip K. 2010. ‘Livestock Production: Recent Trends, Future Prospects’. Philosophical Transactions of the Royal Society B: Biological Sciences 365(1554):2853–67.
  • UN. 2022. World Population Prospects 2022: Summary of Results. New York.
  • Wathes, C. M., H. H. Kristensen, J. M. Aerts, and D. Berckmans. 2008. ‘Is Precision Livestock Farming an Engineer’s Daydream or Nightmare, an Animal’s Friend or Foe, and a Farmer’s Panacea or Pitfall?’ Computers and Electronics in Agriculture 64(1):2–10. doi: 10.1016/j.compag.2008.05.005.
  • Weinstein, Ben G. 2018. ‘A Computer Vision for Animal Ecology’. Journal of Animal Ecology 87(3):533–45. doi: 10.1111/1365-2656.12780.
  • Yüceer, Sema Ezgi, and Sibel Tan. 2022. ‘Tarım Politikaları Literatürünün Bibliyometrik Analiz Yöntemiyle İncelenmesi’. Tarım Ekonomisi Araştırmaları Dergisi 8(2):156–69.
Toplam 41 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Tarım Politikaları
Bölüm Derleme Makale
Yazarlar

Yusuf Çakmakçı 0000-0002-5136-9102

Harun Hurma 0000-0003-1845-3940

Cihan Çakmakçı 0000-0001-6512-9268

Erken Görünüm Tarihi 28 Haziran 2024
Yayımlanma Tarihi 30 Haziran 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 10 Sayı: 1

Kaynak Göster

APA Çakmakçı, Y., Hurma, H., & Çakmakçı, C. (2024). Dijital Teknolojilerin Hayvancılık Sektöründe Yükselen Rolü: Akademik Çalışmaların Işığında Geleceğe Bakış. Tarım Ekonomisi Araştırmaları Dergisi, 10(1), 90-102. https://doi.org/10.61513/tead.1269279