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Dergiler / Endüstri Mühendisliği / 2026 / Cilt: 36 Sayı: 2

OECD ÜLKELERİNİN TARIMSAL TİCARET PERFORMANSININ DEĞERLENDİRİLMESİ: DİNAMİK BİR MODEL ÖNERİSİ

EVALUATION OF OECD COUNTRIES' AGRICULTURAL TRADE PERFORMANCE: PROPOSAL OF A DYNAMIC MODEL

Melis Almula Karadayı(ISTANBUL MEDIPOL UNIVERSITY)0000-0002-6959-9168
Umut Aydın(İSTANBUL TİCARET ÜNİVERSİTESİ)0000-0003-4802-8793
Füsun Ülengin(SABANCI UNIVERSITY)0000-0003-1738-9756
Burç Ülengin(ISTANBUL TECHNICAL UNIVERSITY)0000-0001-5276-8861
Sayfa
236–270
Yayın Tarihi
2025-08-31
DOI
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

The Categorical-DEA values, as well as maximum reservoir of water, reservoir minimum water area (% of total land area), Logistics Performance Index, and Red List Index, are used as independent variables in order to reveal the primary reasons behind these eigenvector centrality values and the actions that should be taken to improve them are specified.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Throughout history, the agricultural sector has played an important role in the economies of both developed and developing countries and has contributed to employment, GDP, international commerce, and, most importantly, rural development.
📊
Temel Bulgular & Veri:Within the scope of the study, the performance of the OECD nations on agricultural product trade is analyzed using the proposed dynamic model.
💡
Sonuç & Katkı:The Categorical-DEA values, as well as maximum reservoir of water, reservoir minimum water area (% of total land area), Logistics Performance Index, and Red List Index, are used as independent variables in order to reveal the primary reasons behind these eigenvector centrality values and the actions that should be taken to improve them are specified.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Agricultural Products · Trade · Efficiency

Özet

Throughout history, the agricultural sector has played an important role in the economies of both developed and developing countries and has contributed to employment, GDP, international commerce, and, most importantly, rural development. Within the scope of the study, the performance of the OECD nations on agricultural product trade is analyzed using the proposed dynamic model. Categorical Data Envelopment Analysis (DEA)-Malmquist Total Factor Productivity Index (TFPI) approaches are employed to examine in detail the changes that occurred in the OECD members' agricultural trade performance in efficiency, technology, and total factor productivity over time.Additionally, Dynamic Social Network Analysis is conducted to reveal the eigenvector centrality of the countries investigated in the same period for both import and export-related trade to reveal which countries have the highest trade connection with the highest trade volume to which countries. Finally, regression analysis takes those eigenvector centrality measures as dependent variables. The Categorical-DEA values, as well as maximum reservoir of water, reservoir minimum water area (% of total land area), Logistics Performance Index, and Red List Index, are used as independent variables in order to reveal the primary reasons behind these eigenvector centrality values and the actions that should be taken to improve them are specified.

Abstract

Throughout history, the agricultural sector has played an important role in the economies of both developed and developing countries and has contributed to employment, GDP, international commerce, and, most importantly, rural development. Within the scope of the study, the performance of the OECD nations on agricultural product trade is analyzed using the proposed dynamic model. Categorical Data Envelopment Analysis (DEA)-Malmquist Total Factor Productivity Index (TFPI) approaches are employed to examine in detail the changes that occurred in the OECD members' agricultural trade performance in efficiency, technology, and total factor productivity over time.Additionally, Dynamic Social Network Analysis is conducted to reveal the eigenvector centrality of the countries investigated in the same period for both import and export-related trade to reveal which countries have the highest trade connection with the highest trade volume to which countries. Finally, regression analysis takes those eigenvector centrality measures as dependent variables. The Categorical-DEA values, as well as maximum reservoir of water, reservoir minimum water area (% of total land area), Logistics Performance Index, and Red List Index, are used as independent variables in order to reveal the primary reasons behind these eigenvector centrality values and the actions that should be taken to improve them are specified.

🔖

Bu Makaleye Atıf Yap

Karadayı, M. A., Aydın, U., Ülengin, F., Ülengin, B. (2025). OECD ÜLKELERİNİN TARIMSAL TİCARET PERFORMANSININ DEĞERLENDİRİLMESİ: DİNAMİK BİR MODEL ÖNERİSİ. Endüstri Mühendisliği, Cilt: 36 Sayı: 2, 236–270. https://ajindex.com/articles/1142363

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Kaynakça (61)

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  2. Adler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research, 202(1), 273–284. Doi: https://doi.org/10.1016/j.ejor.2009.03.050
  3. Armagan, G., Ozden, A., & Bekcioglu, S. (2010). Efficiency and total factor productivity of crop production at NUTS1 level in Turkey: Malmquist index approach. Quality & Quantity, 44(3), 573–581. Doi: https://doi.org/10.1007/s11135-008-9216-y
  4. Aydın, U., Karadayı, M. A., Ülengin, F., & Ülengin, K. B. (2021). Enhanced performance assessment of airlines with integrated balanced scorecard, network-based superefficiency DEA and PCA methods. In Y. I. Topcu, Ö. Özaydın, Ö. Kabak, & Ş. Önsel Ekici (Eds.), Multiple criteria decision making. MCDM 2019. Contributions to management science (pp. 151–164). Springer. Doi: https://doi.org/10.1007/978-3-030-52406-7_9
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  6. Boakye, A., Lee, H., Annor, F., Dadzie, S. K., & Salifu, M. (2024). Data envelopment analysis (DEA) to estimate technical and scale efficiencies of smallholder pineapple farmers in Ghana. International Journal of Agricultural Economics, 9(2), 45–56. Doi: https://doi.org/10.11648/j.ijae.20240902.11
  7. Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414. Doi: https://doi.org/10.2307/1913388
  8. Chen, P.-C., Yu, M.-M., Chang, C.-C., & Hsu, S.-H. (2008). Total factor productivity growth in China’s agricultural sector. China Economic Review, 19(4), 580–593. Doi: https://doi.org/10.1016/j.chieco.2008.07.001
  9. Coelli, T. J. (1996). Measurement of total factor productivity growth and biases in technological change in Western Australian agriculture. Journal of Applied Econometrics, 11(1), 77–91. Doi: https://doi.org/10.1002/(SICI)1099-1255(199601)11:1
  10. Coelli, T. J., & Rao, D. S. P. (2005). Total factor productivity growth in agriculture: A Malmquist index analysis of 93 countries, 1980–2000. Agricultural Economics, 32(s1), 115–134. Doi: https://doi.org/10.1111/j.0169-5150.2004.00018.x
  11. Djoumessi, Y. F. (2022). New trend of agricultural productivity growth in sub-Saharan Africa. Scientific African, 18, e01410. Doi: https://doi.org/10.1016/j.sciaf.2022.e01410
  12. Dong, H. (2022). The impact of trade facilitation on the networks of value-added trade—Based on social network analysis. Emerging Markets Finance and Trade, 58(8), 2290–2299. Doi: https://doi.org/10.1080/ 1540496X.2021.1974393
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  14. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. Doi: https://doi.org/10.1098/rsta.2015.0202
  15. Karaman, S., & Özalp, A. (2017). Türkiye tarım sektörü bölgesel toplam faktör verimliliğinin Malmquist endeks ile belirlenmesi. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 34(1), 209–217. Doi: https://doi.org/10.13002/jafag4200
  16. Kouriati, A., Tafidou, A., Lialia, E., Prentzas, A., Moulogianni, C., Dimitriadou, E., & Bournaris, T. (2023). The impact of data envelopment analysis on effective management of inputs: The case of farms located in the Regional Unit of Pieria. Agronomy, 13(8), 2109. Doi: https://doi.org/10.3390/agronomy13082109
  17. Kyriacou, A. P., Muinelo-Gallo, L., & Roca-Sagalés, O. (2019). The efficiency of transport infrastructure investment and the role of government quality: An empirical analysis. Transport Policy, 74, 93–102. Doi: https://doi.org/10.1016/j.tranpol.2018.11.008
  18. Liu, D., Liu, J. C., Huang, H., & Sun, K. (2019). Analysis of the international polysilicon trade network. Resources, Conservation and Recycling, 142, 122–130. Doi: https://doi.org/10.1016/j.resconrec.2018.11.025
  19. Lovrić, M., Da Re, R., Vidale, E., Pettenella, D., & Mavsar, R. (2018). Social network analysis as a tool for the analysis of international trade of wood and non-wood forest products. Forest Policy and Economics, 86, 45–66. Doi: https://doi.org/10.1016/j.forpol.2017.10.006
  20. Luttermann, S., Kotzab, H., & Halaszovich, T. (2020). The impact of logistics performance on exports, imports and foreign direct investment. World Review of Intermodal Transportation Research, 9(1), 27–46. Doi: https://doi.org/10.1504/WRITR.2020.106444
  21. Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadística, 4(2), 209–242. Doi: https://doi.org/10.1007/BF03006863
  22. Marchetti, D., & Wanke, P. (2017). Brazil’s rail freight transport: Efficiency analysis using two-stage DEA and cluster-driven public policies. Socio-Economic Planning Sciences, 59, 26–42. Doi: https://doi.org/10.1016/j.seps.2016.10.005
  23. Pınarbaşı, A., Aydın, U., Karadayı, M. A., & Tozan, H. (2022). An integrated performance measurement framework for restaurant chains: A case study in Istanbul. Endüstri Mühendisliği, 33(3), 484–499. Doi: https://doi.org/10.46465/endustrimuhendisligi.1087736
  24. Sultana, S., Hossain, M. M., & Haque, M. N. (2023). Estimating the potato farming efficiency: A comparative study between stochastic frontier analysis and data envelopment analysis. PLOS ONE, 18(4), e0284391. Doi: https://doi.org/10.1371/journal.pone.0284391
  25. Suroso, A. I. (2022). The effect of logistics performance index indicators on palm oil and palm-based products export: The case of Indonesia and Malaysia. Economies, 10(10), 261. Doi: https://doi.org/10.3390/economies10100261
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  27. Telleria, R., & Aw-Hassan, A. (2011). Agricultural productivity in the WANA region. The Journal of Comparative Asian Development, 10(1), 157–185. Doi: https://doi.org/10.1080/15339114.2011.578490
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  32. Yu, J. K., & Ma, J. Q. (2020). Social network analysis as a tool for the analysis of the international trade network of aquatic products. Aquaculture International, 28(3), 1195–1211. Doi: https://doi.org/10.1007/s10499-020-00520-5
  33. Adamic, L. A., & Adar, E. (2003). Friends and neighbors on the web. Social Networks, 25(3), 211–230. Doi: https://doi.org/10.1016/S0378-8733(03)00009-1
  34. Adler, N., & Yazhemsky, E. (2010). Improving discrimination in data envelopment analysis: PCA–DEA or variable reduction. European Journal of Operational Research, 202(1), 273–284. Doi: https://doi.org/10.1016/j.ejor.2009.03.050
  35. Armagan, G., Ozden, A., & Bekcioglu, S. (2010). Efficiency and total factor productivity of crop production at NUTS1 level in Turkey: Malmquist index approach. Quality & Quantity, 44(3), 573–581. Doi: https://doi.org/10.1007/s11135-008-9216-y
  36. Aydın, U., Karadayı, M. A., Ülengin, F., & Ülengin, K. B. (2021). Enhanced performance assessment of airlines with integrated balanced scorecard, network-based superefficiency DEA and PCA methods. In Y. I. Topcu, Ö. Özaydın, Ö. Kabak, & Ş. Önsel Ekici (Eds.), Multiple criteria decision making. MCDM 2019. Contributions to management science (pp. 151–164). Springer. Doi: https://doi.org/10.1007/978-3-030-52406-7_9
  37. Banker, R. D., & Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513–521. Doi: https://doi.org/10.1287/opre.34.4.513
  38. Boakye, A., Lee, H., Annor, F., Dadzie, S. K., & Salifu, M. (2024). Data envelopment analysis (DEA) to estimate technical and scale efficiencies of smallholder pineapple farmers in Ghana. International Journal of Agricultural Economics, 9(2), 45–56. Doi: https://doi.org/10.11648/j.ijae.20240902.11
  39. Caves, D. W., Christensen, L. R., & Diewert, W. E. (1982). The economic theory of index numbers and the measurement of input, output, and productivity. Econometrica, 50(6), 1393–1414. Doi: https://doi.org/10.2307/1913388
  40. Chen, P.-C., Yu, M.-M., Chang, C.-C., & Hsu, S.-H. (2008). Total factor productivity growth in China’s agricultural sector. China Economic Review, 19(4), 580–593. Doi: https://doi.org/10.1016/j.chieco.2008.07.001
  41. Coelli, T. J. (1996). Measurement of total factor productivity growth and biases in technological change in Western Australian agriculture. Journal of Applied Econometrics, 11(1), 77–91. Doi: https://doi.org/10.1002/(SICI)1099-1255(199601)11:1
  42. Coelli, T. J., & Rao, D. S. P. (2005). Total factor productivity growth in agriculture: A Malmquist index analysis of 93 countries, 1980–2000. Agricultural Economics, 32(s1), 115–134. Doi: https://doi.org/10.1111/j.0169-5150.2004.00018.x
  43. Djoumessi, Y. F. (2022). New trend of agricultural productivity growth in sub-Saharan Africa. Scientific African, 18, e01410. Doi: https://doi.org/10.1016/j.sciaf.2022.e01410
  44. Dong, H. (2022). The impact of trade facilitation on the networks of value-added trade—Based on social network analysis. Emerging Markets Finance and Trade, 58(8), 2290–2299. Doi: https://doi.org/10.1080/ 1540496X.2021.1974393
  45. Everitt, B. S., Landau, S., Leese, M., & Stahl, D. (2011). Cluster analysis (5th ed.). Wiley. Hajihassaniasl, S. (2021). Measurement of total factor productivity in Iranian economic sectors: Malmquist index analysis. İzmir İktisat Dergisi, 36(3), 589–600. Doi: https://doi.org/10.24988/ije.202136306
  46. Jolliffe, I. T., & Cadima, J. (2016). Principal component analysis: A review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202. Doi: https://doi.org/10.1098/rsta.2015.0202
  47. Karaman, S., & Özalp, A. (2017). Türkiye tarım sektörü bölgesel toplam faktör verimliliğinin Malmquist endeks ile belirlenmesi. Gaziosmanpaşa Üniversitesi Ziraat Fakültesi Dergisi, 34(1), 209–217. Doi: https://doi.org/10.13002/jafag4200
  48. Kouriati, A., Tafidou, A., Lialia, E., Prentzas, A., Moulogianni, C., Dimitriadou, E., & Bournaris, T. (2023). The impact of data envelopment analysis on effective management of inputs: The case of farms located in the Regional Unit of Pieria. Agronomy, 13(8), 2109. Doi: https://doi.org/10.3390/agronomy13082109
  49. Kyriacou, A. P., Muinelo-Gallo, L., & Roca-Sagalés, O. (2019). The efficiency of transport infrastructure investment and the role of government quality: An empirical analysis. Transport Policy, 74, 93–102. Doi: https://doi.org/10.1016/j.tranpol.2018.11.008
  50. Liu, D., Liu, J. C., Huang, H., & Sun, K. (2019). Analysis of the international polysilicon trade network. Resources, Conservation and Recycling, 142, 122–130. Doi: https://doi.org/10.1016/j.resconrec.2018.11.025
  51. Lovrić, M., Da Re, R., Vidale, E., Pettenella, D., & Mavsar, R. (2018). Social network analysis as a tool for the analysis of international trade of wood and non-wood forest products. Forest Policy and Economics, 86, 45–66. Doi: https://doi.org/10.1016/j.forpol.2017.10.006
  52. Luttermann, S., Kotzab, H., & Halaszovich, T. (2020). The impact of logistics performance on exports, imports and foreign direct investment. World Review of Intermodal Transportation Research, 9(1), 27–46. Doi: https://doi.org/10.1504/WRITR.2020.106444
  53. Malmquist, S. (1953). Index numbers and indifference surfaces. Trabajos de Estadística, 4(2), 209–242. Doi: https://doi.org/10.1007/BF03006863
  54. Marchetti, D., & Wanke, P. (2017). Brazil’s rail freight transport: Efficiency analysis using two-stage DEA and cluster-driven public policies. Socio-Economic Planning Sciences, 59, 26–42. Doi: https://doi.org/10.1016/j.seps.2016.10.005
  55. Pınarbaşı, A., Aydın, U., Karadayı, M. A., & Tozan, H. (2022). An integrated performance measurement framework for restaurant chains: A case study in Istanbul. Endüstri Mühendisliği, 33(3), 484–499. Doi: https://doi.org/10.46465/endustrimuhendisligi.1087736
  56. Sultana, S., Hossain, M. M., & Haque, M. N. (2023). Estimating the potato farming efficiency: A comparative study between stochastic frontier analysis and data envelopment analysis. PLOS ONE, 18(4), e0284391. Doi: https://doi.org/10.1371/journal.pone.0284391
  57. Suroso, A. I. (2022). The effect of logistics performance index indicators on palm oil and palm-based products export: The case of Indonesia and Malaysia. Economies, 10(10), 261. Doi: https://doi.org/10.3390/economies10100261
  58. Telleria, R., & Aw-Hassan, A. (2011). Agricultural productivity in the WANA region. The Journal of Comparative Asian Development, 10(1), 157–185. Doi: https://doi.org/10.1080/15339114.2011.578490
  59. Ward, F. A., & Michelsen, A. (2002). The economic value of water in agriculture: Concepts and policy applications. Water Policy, 4(5), 423–446. Doi: https://doi.org/10.1016/S1366-7017(02)00039-9
  60. Yin, R. R., Guo, Q., Yang, J. N., & Liu, J. G. (2018). Inter-layer similarity-based eigenvector centrality measures for temporal networks. Physica A: Statistical Mechanics and Its Applications, 512, 165–173. Doi: https://doi.org/10.1016/j.physa.2018.08.018
  61. Yu, J. K., & Ma, J. Q. (2020). Social network analysis as a tool for the analysis of the international trade network of aquatic products. Aquaculture International, 28(3), 1195–1211. Doi: https://doi.org/10.1007/s10499-020-00520-5