Research Article
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A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey

Year 2020, Volume: 60 Issue: 1, 24 - 31, 13.08.2020
https://doi.org/10.46897/lahaed.719095

Abstract

A number of reasons such as the increase in the world population, changes in the climate due to global warming and pandemic diseases affecting many regions have brought the importance of vegetative and animal production to the agenda, which is necessary for the healthy and balanced nutrition of the societies. The livestock sector along with all its sub-branches positively contributes to the economic development of societies with employment provided in many sectors from production to consumption. Due to the global changes occurring for many years, researchers and policy makers have carried out studies on sustainable agriculture and livestock policies at the national and international level of food supply. In the literature, a limited number of forecasting studies on animal production have been carried out. The aim of our study is to develop a comparative forecasting approach and determine the best forecasting methods and models for each type of red meat (i.e. goat, seep, buffalo carcass, and cattle and calf carcass). Accordingly, we used ARIMA, exponential smoothing and STLF forecasting methods. Quarterly data between 2010 and 2018 published by Turkish Statistical Institute were used. The results of the study showed that comparing more than one forecasting method rather than using a single method in estimating the amount of red meat production will produce more reliable and accurate results.

References

  • Er S, Özçelik A, (2016): The Examination of Economic Structure of Cattle Fattening Farms in Ankara Province by Factor Analysis. Yuzuncu Yıl University Journal of Agricultural Sciences, 26(1):17-25.
  • TIGEM, (2016): Livestock Sector Report, Republic of Turkey General Directorate of Agricultural Enterprises, https://www.tigem.gov.tr/, Accessed on 15.04.2020.
  • FAO, (2016): Food and Agricultural Organization. http://www.fao.org/faostat/, Accessed on 18.04.2019.
  • Ministry of Agriculture and Forestry, (2019): The final declaration of the 3rd Agricultural Forest Council, https://www.tarimorman.gov.tr/Haber/4207/3-Tarim-Orman-Surasi-Sonuc-Bildirgesi, Accessed on 15.04.2020.
  • TUIK, (2006): Turkish Statistical Institute. http://www.tuik.gov.tr/, Accessed on 14.03.2020.
  • Nardone A, Ronchi B, Lacetera N, Ranieri MS, Bernabucci U. (2010): Effects of climate changes on animal production and sustainability of livestock systems. Livestock Science, 130, 57-69.
  • Yavuz F, Zulauf CR, (2004): Introducing a New Approach to Estimating Red Meat Production in Turkey. Turkish Journal of Veterinary and Animal Sciences, 28(2004), 641-648.
  • 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), 432-439.
  • Tutkun M, (2017): The Red Meat Production in Turkey. Scientific Papers Series D Animal Science, 60.
  • Alhas Eroğlu N, Bozoğlu M, Kılıç Topuz B, Başer U, (2019): Forecasting the amount of beef production in Turkey. The Journal of Agricultural Economics Researches, 5(2), 101-107.
  • Karkacıer O, (2000): An Analysis of Import Demand for Dairy Products in Turkey. Turkish Journal of Agriculture and Forestry, 24(2000), 421-427.
  • Lohano HD, Soomro FM, (2006): Unit Root Test and Forecast of Milk Production in Pakistan. International Journal of Dairy Science, 1(1), 63-69.
  • Kaygısız F, Sezgin FH, (2017): Forecasting goat milk production in Turkey using artificial neural networks and Boz-Jenkins Model. Animal Review, 4(3), 45-52.
  • Doğan N, Kızıloğlu S, Bilgiç A, (2018): Türkiye’de Organik Süte Yönelik Potansiyel Talebin Tahminlenmesi. KSU Journal of Agriculture and Nature, 21, 35-43.
  • Akın AC, Arıkan MS, Çevrimli MB, Tekindal MA, (2020): Assessment of the Effect of Beef and Mutton Meat Prices on Chicken Meat Prices in Turkey Using Different Regression Models and the Decision Tree Algorithm. Kafkas Universitesi Veteriner Fakultesi Dergisi, 26(1), 47-52.
  • Hyndman RJ, Athanasopoulos G, (2014): Forecasting Principles and Practice. Otexts.
  • DeLurgio SA, (1998): Forecasting Principles and Applications. McGraw – Hill.
  • Hyndman RJ, Khandakar Y, (2008): Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27.
  • Makridakis S, Wheelwright SC, Hyndman RJ, (1998): Forecasting Methods and Applications. John Wiley & Sons, New York, USA.
  • Hyndman RJ, Koehler AB, Ord JK, Snyder RD, (2008): Forecasting with Exponential Smoothing: The State Space Approach. Springer, Berlin, Germany.
  • Hyndman RJ, (2018): Package ‘forecast’. https://cran.r‐project.org/web/packages/forecast/forecast.pdf, Accessed on 26.05.2017.
  • Ordu M, Demir E, Tofallis C, (2019): A comprehensive modelling framework to forecast the demand for all hospital services. The International Journal of Health Planning and Management, 34(2), e1257–e1271.

A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey

Year 2020, Volume: 60 Issue: 1, 24 - 31, 13.08.2020
https://doi.org/10.46897/lahaed.719095

Abstract

Dünya nüfusundaki artış, küresel ısınmaya bağlı iklimde meydana gelen değişmeler ve birçok bölgeyi etkileyen pandemik hastalıklar gibi birtakım nedenler toplumların sağlıklı ve dengeli beslenmesi için gerekli olan gıda temini noktasında bitkisel ve hayvansal üretimin önemini gündeme getirmiştir. Hayvancılık sektörü bütün alt dalları ile birlikte üretimden tüketime birçok sektörde sağladığı istihdam ile toplumların ekonomik olarak gelişmelerine pozitif katkılar sunmaktadır. Uzun yıllardır meydana gelen küresel çaptaki değişmelerden dolayı araştırmacılar ve politika yapıcılar ulusal ve uluslararası düzeyde gıda temini noktasında sürdürülebilir tarım ve hayvancılık politikaları ile ilgili çalışmalar yapmıştır. Literatürde ise hayvancılık ve hayvansal üretim ile ilgili sınırlı sayıda tahmin çalışmaları yürütülmüştür. Çalışmamızın amacı, karşılaştırmalı bir tahmin yaklaşımı geliştirmek ve Türkiye'deki her bir kırmızı et türü için (keçi, koyun, manda ve sığır) en iyi tahmin sonucunu veren tahmin metotlarını ve modellerini belirlemektir. Bu doğrultuda, ARIMA, üstel yumuşatma ve STLF yöntemleri kullanılmıştır. Türkiye İstatistik Kurumu tarafından yayımlanan 2010-2018 yılları arasındaki çeyrek yıllık veriler kullanılmıştır. Çalışmanın sonuçları, kırmızı et üretim miktarının tahminlerinde tek bir tahmin yöntemini kullanılmak yerine birden fazla yöntemin karşılaştırılmasının daha güvenilir ve doğru sonuçlar vereceğini göstermiştir.

References

  • Er S, Özçelik A, (2016): The Examination of Economic Structure of Cattle Fattening Farms in Ankara Province by Factor Analysis. Yuzuncu Yıl University Journal of Agricultural Sciences, 26(1):17-25.
  • TIGEM, (2016): Livestock Sector Report, Republic of Turkey General Directorate of Agricultural Enterprises, https://www.tigem.gov.tr/, Accessed on 15.04.2020.
  • FAO, (2016): Food and Agricultural Organization. http://www.fao.org/faostat/, Accessed on 18.04.2019.
  • Ministry of Agriculture and Forestry, (2019): The final declaration of the 3rd Agricultural Forest Council, https://www.tarimorman.gov.tr/Haber/4207/3-Tarim-Orman-Surasi-Sonuc-Bildirgesi, Accessed on 15.04.2020.
  • TUIK, (2006): Turkish Statistical Institute. http://www.tuik.gov.tr/, Accessed on 14.03.2020.
  • Nardone A, Ronchi B, Lacetera N, Ranieri MS, Bernabucci U. (2010): Effects of climate changes on animal production and sustainability of livestock systems. Livestock Science, 130, 57-69.
  • Yavuz F, Zulauf CR, (2004): Introducing a New Approach to Estimating Red Meat Production in Turkey. Turkish Journal of Veterinary and Animal Sciences, 28(2004), 641-648.
  • 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), 432-439.
  • Tutkun M, (2017): The Red Meat Production in Turkey. Scientific Papers Series D Animal Science, 60.
  • Alhas Eroğlu N, Bozoğlu M, Kılıç Topuz B, Başer U, (2019): Forecasting the amount of beef production in Turkey. The Journal of Agricultural Economics Researches, 5(2), 101-107.
  • Karkacıer O, (2000): An Analysis of Import Demand for Dairy Products in Turkey. Turkish Journal of Agriculture and Forestry, 24(2000), 421-427.
  • Lohano HD, Soomro FM, (2006): Unit Root Test and Forecast of Milk Production in Pakistan. International Journal of Dairy Science, 1(1), 63-69.
  • Kaygısız F, Sezgin FH, (2017): Forecasting goat milk production in Turkey using artificial neural networks and Boz-Jenkins Model. Animal Review, 4(3), 45-52.
  • Doğan N, Kızıloğlu S, Bilgiç A, (2018): Türkiye’de Organik Süte Yönelik Potansiyel Talebin Tahminlenmesi. KSU Journal of Agriculture and Nature, 21, 35-43.
  • Akın AC, Arıkan MS, Çevrimli MB, Tekindal MA, (2020): Assessment of the Effect of Beef and Mutton Meat Prices on Chicken Meat Prices in Turkey Using Different Regression Models and the Decision Tree Algorithm. Kafkas Universitesi Veteriner Fakultesi Dergisi, 26(1), 47-52.
  • Hyndman RJ, Athanasopoulos G, (2014): Forecasting Principles and Practice. Otexts.
  • DeLurgio SA, (1998): Forecasting Principles and Applications. McGraw – Hill.
  • Hyndman RJ, Khandakar Y, (2008): Automatic Time Series Forecasting: The forecast Package for R. Journal of Statistical Software, 27.
  • Makridakis S, Wheelwright SC, Hyndman RJ, (1998): Forecasting Methods and Applications. John Wiley & Sons, New York, USA.
  • Hyndman RJ, Koehler AB, Ord JK, Snyder RD, (2008): Forecasting with Exponential Smoothing: The State Space Approach. Springer, Berlin, Germany.
  • Hyndman RJ, (2018): Package ‘forecast’. https://cran.r‐project.org/web/packages/forecast/forecast.pdf, Accessed on 26.05.2017.
  • Ordu M, Demir E, Tofallis C, (2019): A comprehensive modelling framework to forecast the demand for all hospital services. The International Journal of Health Planning and Management, 34(2), e1257–e1271.
There are 22 citations in total.

Details

Primary Language English
Subjects Zootechny (Other)
Journal Section Research Article
Authors

Muhammed Ordu 0000-0003-4764-9379

Yusuf Zengin 0000-0003-4639-0940

Publication Date August 13, 2020
Published in Issue Year 2020 Volume: 60 Issue: 1

Cite

APA Ordu, M., & Zengin, Y. (2020). A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi, 60(1), 24-31. https://doi.org/10.46897/lahaed.719095
AMA Ordu M, Zengin Y. A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi. August 2020;60(1):24-31. doi:10.46897/lahaed.719095
Chicago Ordu, Muhammed, and Yusuf Zengin. “A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey”. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi 60, no. 1 (August 2020): 24-31. https://doi.org/10.46897/lahaed.719095.
EndNote Ordu M, Zengin Y (August 1, 2020) A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi 60 1 24–31.
IEEE M. Ordu and Y. Zengin, “A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey”, Lalahan Hayvancılık Araştırma Enstitüsü Dergisi, vol. 60, no. 1, pp. 24–31, 2020, doi: 10.46897/lahaed.719095.
ISNAD Ordu, Muhammed - Zengin, Yusuf. “A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey”. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi 60/1 (August 2020), 24-31. https://doi.org/10.46897/lahaed.719095.
JAMA Ordu M, Zengin Y. A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi. 2020;60:24–31.
MLA Ordu, Muhammed and Yusuf Zengin. “A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey”. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi, vol. 60, no. 1, 2020, pp. 24-31, doi:10.46897/lahaed.719095.
Vancouver Ordu M, Zengin Y. A Comparative Forecasting Approach to Forecast Animal Production: A Case of Turkey. Lalahan Hayvancılık Araştırma Enstitüsü Dergisi. 2020;60(1):24-31.