Hayvancılık İşletmelerinde Teknoloji Kullanımı ve Ekonomik Verimlilik

Yıl 2023, Sayı: 377, 26 - 32, 30.06.2023

Mustafa Gezici , Engin Ünay , Kerim Üstün , Muhammed İkbal Coşkun

https://doi.org/10.33724/zm.1281613

Öz

Artan Dünya nüfusunun beslenmesi için dengeli ve sürdürülebilir gıdaya erişiminin sağlanması konusunda bilim insanları, üreticiler, tedarikçiler ve piyasalara yön veren politika yapıcıları zaman ve bütçe harcamaktadır. Küresel insan nüfusunun 2050 yılına kadar 9 milyara ulaşacağı tahmin edilmektedir (Alexandratos ve Bruinsma, 2012). Birleşmiş Milletler Gıda ve Tarım Örgütü (FAO) nun 2009 raporuna göre, artan insan nüfusuna ayak uydurabilmek için küresel gıda üretiminin %70 oranında artması gerektiğini belirtmiştir. Benzer şekilde, et ve diğer hayvansal gıda ürünlerine yönelik küresel talep giderek artmaktadır. Ayrıca, gelişmekte olan ülkelerdeki ekonomik koşullar iyileştikçe, gıda tercihinde hayvansal proteine doğru kayma ve talebin daha da artması beklenmektedir (Thompson, 2015). Ölçek ekonomileri, çiftçileri faaliyetlerini genişletmeye ve büyütmeye zorlayarak daha yüksek çıktı sağlar. Sonuç olarak, daha az sayıda çiftçi tarafından işlenen, daha fazla sayıda hayvanın bakıldığı çiftliklerin ortaya çıkması beklenmektedir. Ayrıca, özellikle sanayileşmiş ülkelerde çiftçilerin ortalama yaşı artmaktadır (ABD ve Avrupa'da ortalama 58, Japonya'da 63) (Morrone vd., 2022). Bu faktörler göz önüne alındığında (artan çiftlik ölçeği ve yetiştirilen hayvan sayısı), çiftçilerin geçmişte bel bağladıkları gözlem kapasitesi ve uygulamalı deneyim artık etkin günlük sürü yönetimi sağlamak için yeterli değildir (Frost vd., 2003; Parsons vd., 2007).

Anahtar Kelimeler

Ekonomik verimlilik, hayvancılık teknoloji kullanımı, akıllı çiftlik

Technology Use and Economic Efficiency in Livestock Enterprises

Öz

Scientists, producers, suppliers and policy makers who direct the markets spend time and budget to ensure that the growing world population has access to balanced and sustainable food to feed. The global human population is estimated to reach 9 billion by 2050 (Alexandratos and Bruinsma, 2012). According to the 2009 report of the United Nations Food and Agriculture Organization (FAO), it was stated that global food production should increase by 70% in order to keep up with the increasing human population. Similarly, the global demand for meat and other animal food products is increasing. In addition, as the economic conditions in developing countries improve, it is expected that the food preference will shift towards animal protein and the demand will increase further (Thompson, 2015). Economies of scale force farmers to expand and grow their operations, resulting in higher output. As a result, it is expected that farms will emerge where more animals are kept, while fewer farmers are processed. In addition, the average age of farmers is increasing, especially in industrialized countries (Avarage 58 years old in USA and EU region, 63 in Japan) (Morrone et al., 2022). Given these factors (increasing farm scale and number of animals raised), the observation capacity and hands-on experience that farmers have relied on in the past are no longer sufficient to provide effective daily herd management (Frost et al., 2003; Parsons et al., 2007).

Anahtar Kelimeler

Economic efficiency, livestock technology use, smart farm

Kaynakça

• Alexandratos, N., & Bruinsma, J. (2012). World Agriculture towards 2030/2050: The 2012 Revision. FAO, Rome.
• Berckmans, D. (2017). General introduction to precision livestock farming. Animal Frontiers, 7(1), 6-11.
• Crainer, S. (2000). The Management Century: A Critical Review of 20th Century Thought and Practice. Jossey-Bass, San Francisco.
• FAO. (2009). How to Feed the World in 2050, High-Level Expert Forum. Food and Agriculture Organization of the United Nations, 35-35.
• Frost, A. R., Parsons, D. J., Stacey, K. F., Robertson, A. P., Welch, S. K., Filmer, D., & Fothergill, A. (2003). Progress towards the development of an integrated management system for broiler chicken production. Computers and Electronics in Agriculture, 39(3), 227-240.
• Fu, Q., Shen, W., Wei, X., Zhang, Y., Xin, H., Su, Z., & Zhao, C. (2020). Prediction of the diet energy digestion using kernel extreme learning machine: A case study with holstein dry cows. Computers and Electronics in Agriculture, 169, 105231.
• Hansen, Mark F., Smith, L. N., Salter, M. G., Baxter, E. M., Farish, M., & Grieve, B. (2018). Towards on-farm pig face recognition using convolutional neural networks. Computers in Industry, 98, 145-152.
• Hartung, J., Banhazi, T., Vranken, E., & Guarino, M. (2017). European farmers' experiences with precision livestock farming systems. Animal Frontiers, 7(1), 38-44.
• Himesh, S., Rao, E. P., Gouda, K. C., Ramesh, K. V., Rakesh, V., Mohapatra, G. N., ... & Ajilesh, P. (2018). Digital revolution and big data: a new revolution in agriculture. CABI Reviews, (2018), 1-7.
• Hongqian, C., Xin, H., Guanghui, T., Chaoying, M., Xiaodong, D., Taotao, M., & Cheng, W. (2016). Cloud-based data management system for automatic real-time data acquisition from large-scale laying-hen farms. International Journal of Agricultural and Biological Engineering, 9(4), 106-115.
• Kaya, E., Örs, A. (2015). Süt çiftliklerinde hassas tarım teknolojileri,.2. Uluslararası Tarım, Gıda ve Gastronomi Kongresi (2–5 Eylül 2015), Diyarbakır, Türkiye,.
• Kovács, I., & Husti, I. (2018). The role of digitalization in the agricultural 4.0–how to connect the industry 4.0 to agriculture?. Hungarian agricultural engineering, (33), 38-42.
• 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.
• Neethirajan, S. (2020). The role of sensors, big data and machine learning in modern animal farming. Sensing and Bio-Sensing Research, 29, 100367.
• Nikoloski, S., Murphy, P., Kocev, D., Džeroski, S., & Wall, D. P. (2019). Using machine learning to estimate herbage production and nutrient uptake on Irish dairy farms. Journal of Dairy Science, 102(11), 10639-10656.
• Nóbrega, L., Gonçalves, P., Antunes, M., & Corujo, D. (2020). Assessing sheep behavior through low-power microcontrollers in smart agriculture scenarios. Computers and Electronics in Agriculture, 173, 105444.
• Oğuz, C, Bayramoğlu, Z, Ağızan, S, Ağızan, K. Tarım işletmelerinde tarımsal mekanizasyon kullanım düzeyi, Konya İli örneği, Selçuk Tarım ve Gıda Bilimleri Dergisi, 31 (1), 63-72
• Oliveira, J. L., Xin, H., Chai, L., & Millman, S. T. (2019). Effects of litter floor access and inclusion of experienced hens in aviary housing on floor eggs, litter condition, air quality, and hen welfare. Poultry Science, 98(4), 1664-1677.
• Parsons, D. J., Green, D. M., Schofield, C. P., & Whittemore, C. T. (2007). Real-time control of pig growth through an integrated management system. Biosystems Engineering, 96(2), 257-266.
• Saiz-Rubio, V., & Rovira-Más, F. (2020). From smart farming towards agriculture 5.0: A review on crop data management. Agronomy, 10(2), 207.
• Saravanan, K., Saraniya, S. (2017) Cloud IOT based novel livestock monitoring and identification system using UID. Sensor Review, 38(1), 21-33.
• Schimmelpfennig, D. (2016). Farm Profits and Adoption of Precision Agriculture (No. 1477-2016-121190). USDA.
• Taylor, F. W. (2004). Scientific Management. Routledge.
• Thompson, P. B. (2015). From Field To Fork: Food Ethics For Everyone. Oxford University Press, USA.
• Trivelli, L., Apicella, A., Chiarello, F., Rana, R., Fantoni, G., & Tarabella, A. (2019). From precision agriculture to Industry 4.0: Unveiling technological connections in the agrifood sector. British Food Journal, 121(8), 1730-1743.
• VanderWaal, K., Morrison, R. B., Neuhauser, C., Vilalta, C., & Perez, A. M. (2017). Translating big data into smart data for veterinary epidemiology. Frontiers in Veterinary Science, 2017(4), 110.
• Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming–a review. Agricultural Systems, 153, 69-80.
• Yin, Y., Stecke, K. E., & Li, D. (2018). The evolution of production systems from Industry 2.0 through Industry 4.0. International Journal of Production Research, 56(1-2), 848-861.
• Zhang, N., Wang, M., & Wang, N. (2002). Precision agriculture—a worldwide overview. Computers and Electronics in Agriculture, 36(2-3), 113-132.