Öz
Cattle breeding is of critical importance in meeting the animal protein needs of the increasing population due to its significant contribution to meat and milk production, which are the main animal protein sources. In addition, cattle breeding has important potential for both the agricultural economy and the general economy in terms of the production and export of value-added agricultural goods and processed products, especially for countries with a large number of cattle. In order to maximize these and similar benefits, to evaluate the structural problems in the livestock sector and to implement effective policies to increase the cattle population to optimum levels, it is of great importance to make data-based decisions and therefore produce sufficient and necessary data. Achieving this will be possible not only with existing data but also by making forward projections with solid scientific methods and estimating the necessary data to plan for the future now. The purpose of this research is to estimate the number of cattle for the next ten years by comparing the results of the artificial neural networks (ANN) and autoregressive integrated moving average (ARIMA) models, using Türkiye's cattle number at the beginning of the year for the years 1930-2024. According to the research results, ARIMA had a greater ability to forecast than ANN. Box-Jenkins method was used in the ARIMA estimations. The ARIMA (1,1,0) model was determined to be the most appropriate model for the data, and it was estimated that the number of cattle at the beginning of the year will increase in the next ten years, reaching 17313762 head in 2025 and 17317161 head in 2033, representing a 5.5% increase in the ten-year period.
Anahtar Kelimeler
Kaynakça
- Akdi, Y. (2010). Zaman serileri analizi:(birim kökler ve kointegrasyon) (in Turkish). Gazi Kitabevi. Akgül, S. & Yıldız, Ş. (2016). Red Meat Production Forecast and Policy Recommendations in Line with 2023 Targets in Turkey. European Journal of Multidisciplinary Studies, 1(2), 431-438.
- Alhas Eroğlu, N., Bozoğlu, M., Kılıç Topuz, B., & Başer U. (2019). Forecasting the amount of beef production in Turkey. Tarım Ekonomisi Araştırmaları Dergisi, 5(2), 101-107. Retrieved from https://dergipark.org.tr/en/pub/tead/issue/50910/664731.
- Aral, Y., Altın, O., Şahin, TS., & Gökdai, A. (2020). Assessment of the cattle fattening from structural and economic perspectives in Turkey. Journal of The Turkish Veterinary Medical Society, 91(2): 182-192, 2020, DOI: 10.33188/vetheder.672270.
- Box, GE., Jenkins, GM., Reinsel, GC. & Ljung, GM. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
- Cenan, N., & Gürcan, İS. (2011). Forward projection of the number of farm animals of Turkey: ARIMA modeling. Journal of The Turkish Veterinary Medical Society, 82(1), 35-42.
- Çelik, Ş., & Köleoğlu, N. (2022). Trend analysis and artificial neural networks: An application in agriculture Journal of Awareness, 7(1), 39-46.
- Çiçek, H., & Doğan, İ. (2018). Developments in live cattle and beef import and the analysis of producer prices with trend models in Turkey. Kocatepe Vet J. (2018) 11(1): 1-10.
- Dalgıç, A., Sarıca, D., & Demircan, V. (2023). Prediction of beef production in Turkey with ARIMA (Box-Jenkins) Model. Journal of the Faculty of Agriculture, 18(1), 5-12.
- Doğan, H.G., & Kan, M. (2021). Cattle Presence Development Trend and Projections in D-8 Countries. Turkish Journal of Agricultural Engineering Research (TURKAGER), 2(1): 34-46. https://doi.org/10.46592/turkager.2021.v02i01.003.
- Duke University (2023). ARIMA models for time series forecasting. https://people.duke.edu/~rnau/411arim.htm#arima110.
- FAO (2024). Food and agriculture projections to 2050, https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en/. [04 October 2024].
- Faostat (2023). Crops and livestock products, https://www.fao.org/faostat/en/#data/QCL. [30 January 2024].
- Hyndman, RJ. (2023). New in forecast 4.0. https://robjhyndman.com/hyndsight/forecast4/ [15 February 2024].
- Hyndman, RJ., & Athanasopoulos, G. (2021). Forecasting: Principles and practice, OTexts, Melbourne. OTexts.com/fpp3.
- Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for stock price prediction. In E3S Web of Conferences (Vol. 218, p. 01026). EDP Sciences.
- Novanda, R., Sumartono, E., Asriani, P., Yuliarti, E., Sukiyono, K., Priyono, B., Irnad, Reswita, Melli, S., & Octalia, V. (2018). A comparison of various forecasting techniques for coffee prices. Journal of Physics: Conference Series, 1114(1). doi:10.1088/1742-6596/1114/1/012119.
- Özbek, FŞ. (2017). Forecasting Agricultural Commodity Prices in Turkey Using Artificial Neural Network, 62nd ISI World Statistics Congress, 18-23 August 2019, Kuala Lumpur, Malaysia.
- Özbek, FŞ. (2023). Impact of anomalies on food prices in Türkiye. JAPS: Journal of Animal & Plant Sciences, 33(2), 453-461.
- Putri, R.T., Sukiyono, K., & Sumartono, E. (2019). Estimation of Indonesian beef price forecasting model. Agritropica: Journal of Agricultural Science. 2 (1): 46-56. DOI: https://doi.org/10.31186/J.Agritropica.2.1.46-56
- Qaddoura, R., M. Al-Zoubi, A., Faris, H., & Almomani, I. (2021). A multi-layer classification approach for intrusion detection in iot networks based on deep learning. Sensors, 21(9), 2987.
- TurkStat (2023). Indicators of 100 Years. Turkish Statistical Institute Publication. ISBN 978-625-8368-49-9. TurkStat (2024a). Animal Production Statistics, 2023. https://data.tuik.gov.tr/Bulten/Index?p=Animal-Production-Statistics-2023-49681 [11 January 2024].
- TurkStat (2024b). Population and Demography Statistical Tables. https://data.tuik.gov.tr/Kategori/GetKategori?p=nufus-ve-demografi-109&dil=1 [19 January 2024].
- Wang, X., & Meng, M. (2012). A hybrid neural network and ARIMA model for energy consumption forcasting. J. Comput., 7(5), 1184-1190.
- Yıldız, MY., & Atış, E. (2019). Forecasting of organic dried fig export prices of Turkey Using ARMA Method. Turkish Journal of Agricultural Economics, 25(2), 141-147.
Öz
Sığır yetiştiriciliği, temel hayvansal protein kaynakları olan et ve süt üretimine yaptığı önemli katkı nedeniyle artan nüfusun hayvansal protein ihtiyacının karşılanmasında kritik öneme sahiptir. Ayrıca sığır yetiştiriciliği, özellikle sığır sayısı fazla olan ülkeler için katma değerli tarımsal mal ve işlenmiş ürün üretimi ve ihracatı açısından hem tarım ekonomisi hem de genel ekonomi için önemli bir potansiyele sahiptir. Bu ve benzeri faydaların en üst düzeye çıkarılması, hayvancılık sektöründeki yapısal sorunların değerlendirilmesi ve sığır varlığının optimum seviyelere çıkarılmasına yönelik etkin politikaların uygulanabilmesi için veriye dayalı kararlar alınması ve dolayısıyla yeterli ve gerekli verinin üretilmesi büyük önem taşımaktadır. Bunu başarmak sadece mevcut verilerle değil, güçlü bilimsel yöntemlerle ileriye dönük projeksiyonlar yapmak ve geleceği şimdiden planlamak için gerekli verileri tahmin etmekle mümkün olacaktır. Bu araştırmanın amacı, 1930-2024 yılları için Türkiye'nin yılbaşındaki sığır sayısını kullanarak yapay sinir ağları (YSA) ve otoregresif bütünleşik hareketli ortalama (ARIMA) modellerinin sonuçlarını karşılaştırarak gelecek 10 yıl için sığır sayısını tahmin etmektir. Araştırma sonuçlarına göre ARIMA, YSA'ya göre daha yüksek tahmin yeteneğine sahip bulunmuştur. ARIMA tahminlerinde Box-Jenkins yöntemi kullanılmıştır. ARIMA (1,1,0) modelinin veriler için en uygun model olduğu belirlenmiş ve yılbaşındaki sığır sayısının önümüzdeki 10 yıl içinde artarak 2025 yılında (2024 yılı sonunda) 17313762 başa, 2033 yılında ise 17317161 başa ulaşacağı tahmin edilmiştir.
Anahtar Kelimeler
Kaynakça
- Akdi, Y. (2010). Zaman serileri analizi:(birim kökler ve kointegrasyon) (in Turkish). Gazi Kitabevi. Akgül, S. & Yıldız, Ş. (2016). Red Meat Production Forecast and Policy Recommendations in Line with 2023 Targets in Turkey. European Journal of Multidisciplinary Studies, 1(2), 431-438.
- Alhas Eroğlu, N., Bozoğlu, M., Kılıç Topuz, B., & Başer U. (2019). Forecasting the amount of beef production in Turkey. Tarım Ekonomisi Araştırmaları Dergisi, 5(2), 101-107. Retrieved from https://dergipark.org.tr/en/pub/tead/issue/50910/664731.
- Aral, Y., Altın, O., Şahin, TS., & Gökdai, A. (2020). Assessment of the cattle fattening from structural and economic perspectives in Turkey. Journal of The Turkish Veterinary Medical Society, 91(2): 182-192, 2020, DOI: 10.33188/vetheder.672270.
- Box, GE., Jenkins, GM., Reinsel, GC. & Ljung, GM. (2015). Time series analysis: Forecasting and control. John Wiley & Sons.
- Cenan, N., & Gürcan, İS. (2011). Forward projection of the number of farm animals of Turkey: ARIMA modeling. Journal of The Turkish Veterinary Medical Society, 82(1), 35-42.
- Çelik, Ş., & Köleoğlu, N. (2022). Trend analysis and artificial neural networks: An application in agriculture Journal of Awareness, 7(1), 39-46.
- Çiçek, H., & Doğan, İ. (2018). Developments in live cattle and beef import and the analysis of producer prices with trend models in Turkey. Kocatepe Vet J. (2018) 11(1): 1-10.
- Dalgıç, A., Sarıca, D., & Demircan, V. (2023). Prediction of beef production in Turkey with ARIMA (Box-Jenkins) Model. Journal of the Faculty of Agriculture, 18(1), 5-12.
- Doğan, H.G., & Kan, M. (2021). Cattle Presence Development Trend and Projections in D-8 Countries. Turkish Journal of Agricultural Engineering Research (TURKAGER), 2(1): 34-46. https://doi.org/10.46592/turkager.2021.v02i01.003.
- Duke University (2023). ARIMA models for time series forecasting. https://people.duke.edu/~rnau/411arim.htm#arima110.
- FAO (2024). Food and agriculture projections to 2050, https://www.fao.org/global-perspectives-studies/food-agriculture-projections-to-2050/en/. [04 October 2024].
- Faostat (2023). Crops and livestock products, https://www.fao.org/faostat/en/#data/QCL. [30 January 2024].
- Hyndman, RJ. (2023). New in forecast 4.0. https://robjhyndman.com/hyndsight/forecast4/ [15 February 2024].
- Hyndman, RJ., & Athanasopoulos, G. (2021). Forecasting: Principles and practice, OTexts, Melbourne. OTexts.com/fpp3.
- Ma, Q. (2020). Comparison of ARIMA, ANN and LSTM for stock price prediction. In E3S Web of Conferences (Vol. 218, p. 01026). EDP Sciences.
- Novanda, R., Sumartono, E., Asriani, P., Yuliarti, E., Sukiyono, K., Priyono, B., Irnad, Reswita, Melli, S., & Octalia, V. (2018). A comparison of various forecasting techniques for coffee prices. Journal of Physics: Conference Series, 1114(1). doi:10.1088/1742-6596/1114/1/012119.
- Özbek, FŞ. (2017). Forecasting Agricultural Commodity Prices in Turkey Using Artificial Neural Network, 62nd ISI World Statistics Congress, 18-23 August 2019, Kuala Lumpur, Malaysia.
- Özbek, FŞ. (2023). Impact of anomalies on food prices in Türkiye. JAPS: Journal of Animal & Plant Sciences, 33(2), 453-461.
- Putri, R.T., Sukiyono, K., & Sumartono, E. (2019). Estimation of Indonesian beef price forecasting model. Agritropica: Journal of Agricultural Science. 2 (1): 46-56. DOI: https://doi.org/10.31186/J.Agritropica.2.1.46-56
- Qaddoura, R., M. Al-Zoubi, A., Faris, H., & Almomani, I. (2021). A multi-layer classification approach for intrusion detection in iot networks based on deep learning. Sensors, 21(9), 2987.
- TurkStat (2023). Indicators of 100 Years. Turkish Statistical Institute Publication. ISBN 978-625-8368-49-9. TurkStat (2024a). Animal Production Statistics, 2023. https://data.tuik.gov.tr/Bulten/Index?p=Animal-Production-Statistics-2023-49681 [11 January 2024].
- TurkStat (2024b). Population and Demography Statistical Tables. https://data.tuik.gov.tr/Kategori/GetKategori?p=nufus-ve-demografi-109&dil=1 [19 January 2024].
- Wang, X., & Meng, M. (2012). A hybrid neural network and ARIMA model for energy consumption forcasting. J. Comput., 7(5), 1184-1190.
- Yıldız, MY., & Atış, E. (2019). Forecasting of organic dried fig export prices of Turkey Using ARMA Method. Turkish Journal of Agricultural Economics, 25(2), 141-147.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Tarım Ekonomisi (Diğer) |
| Bölüm | Araştırma Makalesi |
| Yazarlar | |
| Yayımlanma Tarihi | 16 Nisan 2025 |
| Gönderilme Tarihi | 31 Temmuz 2024 |
| Kabul Tarihi | 6 Mart 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 12 Sayı: 2 |
