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Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling

Year 2021, , 46 - 54, 23.12.2021
https://doi.org/10.46897/livestockstudies.610202

Abstract

In this study, by Non-metric multidimensional scaling (NMDS) which is one of the multivariate statistical analysis methods, configuration of 36 OECD countries in two dimensional space was examined and similarities/dissimilarities between these countries were determined for the variables regarding with livestock data. As variables, "number of horses, pigs, donkeys, turkeys, mules, geese, goats, sheep, buffaloes, ducks, cattle and chickens" of OECD countries obtained from the website of FAO was used. Euclidean distance was used as distance measures. According to the results of NMDS; USA, Germany, France, England, Canada and Poland had the highest positive effect on livestock. However, Israel, Iceland, Lithuania and Luxembourg were different from other countries with the lowest effects. Similarly, while buffalo was the lowest effective one; pigs, cattle, chicken and turkey were found the highest effective species on the livestock sector. As a result, it was suggested that NMDS can be used as an effective method in the analysis of multivariate data in agriculture and livestock with simple graphical representation and interpretation of the results.

References

  • Ağgün, F. (2011). Investigating of Multidimensonal Scaling Analysis and an Application [Unpublished Master Thesis]. University of Yüzüncü Yıl.
  • Akdamar, E. (2019). Investigation of The OECD Countries by Using Some Labor Market Indicators with Cluster Analysis and Multi-Dimensional Scaling Analysis. Journal of Academic Researches and Studies, 11(20), 50-65.
  • Akın, H. B., & Eren, Ö. (2012). OECD Countries with Education Indicators Comparative Analysis of Cluster Analysis and Multi-Dimensional Scaling Analysis. Öneri Journal, 10(37), 175-181.
  • Alp, S., & Gündoğdu, C. E. (2007). Ceza Mahkemelerinin Dava Sayılarının Görev Ayırımı ve Coğrafi Bölgelere Göre Dağılımlarının Monte Carlo Simülasyonu Kullanılarak Tahmin Edilmesi. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26, 55-68.
  • Alpar, R. (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, Ankara.
  • Basalaj, W. (2001). Proximity visualisation of abstract data. University of Cambridge, United Kingdom.
  • Beyhan Acar, A. (2013). Comparison of Turkey and The Other OECD Countries in Terms of Labour Markets’ Main Indicators Using Multi-Dimensional Scale Analysis. İ.Ü. İşletme Fakültesi İşletme İktisadı Enstitüsü Yönetim Dergisi, 24(75), 121-144.
  • Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling Theory and Applications. Springer, New York.
  • Borg, I., Groenen, P. J. F., & Mair, P. (2018). Applied Multidimensional Scaling and Unfolding. Springer, Rotterdam.
  • Boz, C., Sur, H., & Söyük, S. (2016). The Similarities and Differences Analysis of OECD Countries in Terms of Health System Indicators. ACU Sağlık Bilimleri Dergisi, 3,154-164.
  • Cox, T. F., & Cox, M. A. A. (2001). Multidimensional Scaling. Chapman & Hall/CRC, New York.
  • Çankaya, S., Kayaalp, G. T., & Önder, H. (2003). Use of Multidimensional Scaling Analysis in Animal Science. The Second International Biometric Society Confererence of The Eastern Mediterranean Region, Antalya, Turkey, 12-15 Ocak 2003, 75-80.
  • Çelik, Ş. (2015). Classification of Provinces in Turkey in terms of Livestock Using Multidimensional Scaling Analysis. Erciyes University Journal of Institute of Science and Technology, 31(4), 159-164.
  • Ding, C. S. (2018). Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research. Springer, Cham.
  • Ersöz, F. (2008). Analysis of health levels and expenditures of Turkey and OECD countries. İstatistikçiler Dergisi, 2, 95-104.
  • Everitt, B., & Dunn, G. (2001). Applied Multivariate Data Analysis. John Wiley & Sons, Chichester.
  • FAO. (2020). Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat /en/#data /QA (2020, February 5).
  • Giguère, G. (2006). Collecting and analyzing data in multidimensional scaling experiments: A guide for psychologists using SPSS. Tutorials in Quantitative Methods for Psychology, 2(1), 26-37.
  • Güler, D. (2021). Investigation of the Sericulture in Turkey by Multidimensional Scaling and Cluster Analyses. KSU J. Agric Nat, 24 (1), 212-220.
  • Gündüz, S. (2011). Investigation of Effects on Multidimensional Scaling Algorithms of Distance Functions and Applications [Unpublished Master Thesis]. Çukurova University.
  • Härdle, W. K., & Simar, L. (2015). Applied Multivariate Statistical Analysis. Springer, Heidelberg.
  • IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
  • Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Prentice Hall, New Jersey.
  • Kandemir, Ç., Adanacıoğlu, H., Taşkın, T., & Koşum N. (2019). Comparison with Multidimensional Scale Analysis by Regions of Live Sheep and Mutton Prices in Turkey. Journal of Tekirdag Agricultural Faculty, 16(2), 315-327.
  • Kruskal, J. B. (1964). Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis. Psychometrica, 29(1), 1-27.
  • Kruskal, J. B., & Wish, M. (1978). Multidimensional Scaling. Sage Publications, California.
  • Mackay, D. B., & Zinnes, J. L. (1986). A Probabilistic Model for the Multidimensional Scaling of Proximity and Preference Data. Marketing Science, 5, 325-344.
  • Mead, A. (1992). Review of the Development of Multidimensional Scaling Methods. The Statistician, 41, 27–39
  • Özdamar, K. (2010). Paket Programlar ile İstatistiksel Veri Analizi-2 (Çok Değişkenli Analizler). Kaan Kitapevi, Eskişehir.
  • Seber, G. A. F. (2004). Multivariate Observations. John Wiley & Sons, New Jerse.
  • Takane, Y., Young, F.W., & De Leeuw, J. (1977). Nonmetric Individual Differences Multidimensional Scaling: An Alternating Least Squares Method with Optimal Scaling Features. Psychometrıka, 42(1), 7-67.
  • Tatlıdil, H. (1992). Uygulamalı Çok Değişkenli İstatistiksel Analiz. Akademi Matbaası, Ankara.
Year 2021, , 46 - 54, 23.12.2021
https://doi.org/10.46897/livestockstudies.610202

Abstract

References

  • Ağgün, F. (2011). Investigating of Multidimensonal Scaling Analysis and an Application [Unpublished Master Thesis]. University of Yüzüncü Yıl.
  • Akdamar, E. (2019). Investigation of The OECD Countries by Using Some Labor Market Indicators with Cluster Analysis and Multi-Dimensional Scaling Analysis. Journal of Academic Researches and Studies, 11(20), 50-65.
  • Akın, H. B., & Eren, Ö. (2012). OECD Countries with Education Indicators Comparative Analysis of Cluster Analysis and Multi-Dimensional Scaling Analysis. Öneri Journal, 10(37), 175-181.
  • Alp, S., & Gündoğdu, C. E. (2007). Ceza Mahkemelerinin Dava Sayılarının Görev Ayırımı ve Coğrafi Bölgelere Göre Dağılımlarının Monte Carlo Simülasyonu Kullanılarak Tahmin Edilmesi. Uludağ Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 26, 55-68.
  • Alpar, R. (2011). Uygulamalı Çok Değişkenli İstatistiksel Yöntemler. Detay Yayıncılık, Ankara.
  • Basalaj, W. (2001). Proximity visualisation of abstract data. University of Cambridge, United Kingdom.
  • Beyhan Acar, A. (2013). Comparison of Turkey and The Other OECD Countries in Terms of Labour Markets’ Main Indicators Using Multi-Dimensional Scale Analysis. İ.Ü. İşletme Fakültesi İşletme İktisadı Enstitüsü Yönetim Dergisi, 24(75), 121-144.
  • Borg, I., & Groenen, P. J. F. (2005). Modern Multidimensional Scaling Theory and Applications. Springer, New York.
  • Borg, I., Groenen, P. J. F., & Mair, P. (2018). Applied Multidimensional Scaling and Unfolding. Springer, Rotterdam.
  • Boz, C., Sur, H., & Söyük, S. (2016). The Similarities and Differences Analysis of OECD Countries in Terms of Health System Indicators. ACU Sağlık Bilimleri Dergisi, 3,154-164.
  • Cox, T. F., & Cox, M. A. A. (2001). Multidimensional Scaling. Chapman & Hall/CRC, New York.
  • Çankaya, S., Kayaalp, G. T., & Önder, H. (2003). Use of Multidimensional Scaling Analysis in Animal Science. The Second International Biometric Society Confererence of The Eastern Mediterranean Region, Antalya, Turkey, 12-15 Ocak 2003, 75-80.
  • Çelik, Ş. (2015). Classification of Provinces in Turkey in terms of Livestock Using Multidimensional Scaling Analysis. Erciyes University Journal of Institute of Science and Technology, 31(4), 159-164.
  • Ding, C. S. (2018). Fundamentals of Applied Multidimensional Scaling for Educational and Psychological Research. Springer, Cham.
  • Ersöz, F. (2008). Analysis of health levels and expenditures of Turkey and OECD countries. İstatistikçiler Dergisi, 2, 95-104.
  • Everitt, B., & Dunn, G. (2001). Applied Multivariate Data Analysis. John Wiley & Sons, Chichester.
  • FAO. (2020). Food and Agriculture Organization of the United Nations. http://www.fao.org/faostat /en/#data /QA (2020, February 5).
  • Giguère, G. (2006). Collecting and analyzing data in multidimensional scaling experiments: A guide for psychologists using SPSS. Tutorials in Quantitative Methods for Psychology, 2(1), 26-37.
  • Güler, D. (2021). Investigation of the Sericulture in Turkey by Multidimensional Scaling and Cluster Analyses. KSU J. Agric Nat, 24 (1), 212-220.
  • Gündüz, S. (2011). Investigation of Effects on Multidimensional Scaling Algorithms of Distance Functions and Applications [Unpublished Master Thesis]. Çukurova University.
  • Härdle, W. K., & Simar, L. (2015). Applied Multivariate Statistical Analysis. Springer, Heidelberg.
  • IBM Corp. Released 2012. IBM SPSS Statistics for Windows, Version 21.0. Armonk, NY: IBM Corp.
  • Johnson, R. A., & Wichern, D. W. (2007). Applied Multivariate Statistical Analysis. Prentice Hall, New Jersey.
  • Kandemir, Ç., Adanacıoğlu, H., Taşkın, T., & Koşum N. (2019). Comparison with Multidimensional Scale Analysis by Regions of Live Sheep and Mutton Prices in Turkey. Journal of Tekirdag Agricultural Faculty, 16(2), 315-327.
  • Kruskal, J. B. (1964). Multidimensional Scaling by Optimizing Goodness of Fit to a Nonmetric Hypothesis. Psychometrica, 29(1), 1-27.
  • Kruskal, J. B., & Wish, M. (1978). Multidimensional Scaling. Sage Publications, California.
  • Mackay, D. B., & Zinnes, J. L. (1986). A Probabilistic Model for the Multidimensional Scaling of Proximity and Preference Data. Marketing Science, 5, 325-344.
  • Mead, A. (1992). Review of the Development of Multidimensional Scaling Methods. The Statistician, 41, 27–39
  • Özdamar, K. (2010). Paket Programlar ile İstatistiksel Veri Analizi-2 (Çok Değişkenli Analizler). Kaan Kitapevi, Eskişehir.
  • Seber, G. A. F. (2004). Multivariate Observations. John Wiley & Sons, New Jerse.
  • Takane, Y., Young, F.W., & De Leeuw, J. (1977). Nonmetric Individual Differences Multidimensional Scaling: An Alternating Least Squares Method with Optimal Scaling Features. Psychometrıka, 42(1), 7-67.
  • Tatlıdil, H. (1992). Uygulamalı Çok Değişkenli İstatistiksel Analiz. Akademi Matbaası, Ankara.
There are 32 citations in total.

Details

Primary Language English
Subjects Zootechny (Other)
Journal Section 61-2
Authors

Yıldırım Demir This is me

Sıddık Keskin This is me

Publication Date December 23, 2021
Published in Issue Year 2021

Cite

APA Demir, Y., & Keskin, S. (2021). Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling. Livestock Studies, 61(2), 46-54. https://doi.org/10.46897/livestockstudies.610202
AMA Demir Y, Keskin S. Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling. Livestock Studies. December 2021;61(2):46-54. doi:10.46897/livestockstudies.610202
Chicago Demir, Yıldırım, and Sıddık Keskin. “Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling”. Livestock Studies 61, no. 2 (December 2021): 46-54. https://doi.org/10.46897/livestockstudies.610202.
EndNote Demir Y, Keskin S (December 1, 2021) Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling. Livestock Studies 61 2 46–54.
IEEE Y. Demir and S. Keskin, “Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling”, Livestock Studies, vol. 61, no. 2, pp. 46–54, 2021, doi: 10.46897/livestockstudies.610202.
ISNAD Demir, Yıldırım - Keskin, Sıddık. “Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling”. Livestock Studies 61/2 (December 2021), 46-54. https://doi.org/10.46897/livestockstudies.610202.
JAMA Demir Y, Keskin S. Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling. Livestock Studies. 2021;61:46–54.
MLA Demir, Yıldırım and Sıddık Keskin. “Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling”. Livestock Studies, vol. 61, no. 2, 2021, pp. 46-54, doi:10.46897/livestockstudies.610202.
Vancouver Demir Y, Keskin S. Examination of OECD Countries for the Presence of Livestock by Non-Metric Multidimensional Scaling. Livestock Studies. 2021;61(2):46-54.