Uncovering time-varying drivers of agricultural output in Türkiye: A panel ARDL and Kalman Filter Analysis
Öz
Purpose: This study analyzes the factors affecting Türkiye's agricultural production performance between 1991 and 2022. By examining the role of key production inputs, it seeks to provide insights into the determinants of agricultural output and contributes to policy discussions on improving agricultural efficiency and sustainability.
Design/Methodology/Approach: The study employs the agricultural output index as the dependent variable, while independent variables include agricultural labor, capital, land, and material indices, along with the import and export rates of agricultural raw materials. Panel ARDL and Kalman filter methods assess short- and long-run dynamics.
Findings: The results indicate that capital and exports positively impact agricultural output, while land use contributes significantly. In contrast, labor's impact diminishes due to mechanization and the adoption of modern technology. Material use has a limited effect, highlighting the importance of cost management. Additionally, agricultural imports negatively influence productivity by increasing external dependence and costs.
Research Limitations/Implications: Potential limitations include data constraints and unobserved structural changes in the agricultural sector that may affect the robustness of the results. Future research could incorporate additional variables related to climate change and technological advancements.
Originality/Value: This study is among the rare analyses examining agricultural inputs' time-varying effects using advanced econometric techniques. It offers valuable insights into the evolving dynamics of Türkiye’s agricultural sector.
Anahtar Kelimeler
Kaynakça
- Barton, G.T. and Cooper, M.R. (1948), “Relation of agricultural production to inputs”, The Review of Economics and Statistics, Vol. 30 No. 2, pp. 117–126.
- Barton, G.T. and Durost, D.D. (1960), “Measuring input changes in agriculture”, Journal of Farm Economics, Vol. 42 No. 5, pp. 1398–1410.
- Coca, O., Creangă, D., Viziteu, Ș., Brumă, I.S. and Ștefan, G. (2023), “Analysis of the determinants of agriculture performance at the European Union level”, Agriculture, Vol. 13 No. 3, p. 616, available at: https://doi.org/10.3390/agriculture13030616.
- Coppola, A., Ianuario, S., Chinnici, G., Di Vita, G., Pappalardo, G. and D'Amico, M. (2018), “Endogenous and exogenous determinants of agricultural productivity: What is the most relevant for the competitiveness of the Italian agricultural systems?”, AGRIS on-line Papers in Economics and Informatics, Vol. 10 No. 2, pp. 33–47, available at: https://doi.org/10.7160/aol.2018.100204.
- Durbin, J. and Koopman, S.J. (2012), Time Series Analysis by State Space Methods, Oxford University Press, Oxford.
- Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press, Princeton, NJ.
- Harvey, A.C. (1990), The Econometric Analysis of Time Series, MIT Press, Cambridge, MA.
- He J, Wei Z, Lei X (2025) Unveiling the digital revolution: Catalyzing total factor productivity in agriculture. PLoS ONE 20(3): e0318333. https://doi.org/10.1371/journal.pone.0318333
- Ketema, A.M. (2020), “Determinants of agricultural output in Ethiopia: ARDL approach to co-integration”, International Journal of Business and Social Research, Vol. 10 No. 3, pp. 1–10, available at: https://doi.org/10.18533/ijbsr.v10i3.1293.
- Muraya, B.W. (2017), “Determinants of agricultural productivity in Kenya”, Unpublished master’s thesis, University of Nairobi.
- Narayan, P.K. and Narayan, S. (2005), “Estimating income and price elasticities of imports for Fiji in a cointegration framework”, Economic Modelling, Vol. 22 No. 3, pp. 423–438, available at: https://doi.org/10.1016/j.econmod.2004.06.004.
- Odhiambo, M.O., Nyangweso, P.M. and Odhiambo, G.M. (2004), “Effects of macroeconomic factors on agricultural performance in Kenya”, International Journal of Agricultural Economics, Vol. 12 No. 3, pp. 197–205.
- Peplinski, J. (2022), “Regional determinants of agricultural production development in Poland”, Annals of the Polish Association of Agricultural and Agribusiness Economists, Vol. 24 No. 1, pp. 225–242.
- Pesaran, M.H., Yongcheol, S. and Richard, J.S. (2001), “Bounds testing approaches to the analysis of level relationships”, Journal of Applied Econometrics, Vol. 16 No. 3, pp. 289–326, available at: https://doi.org/10.1002/jae.616.
- Raza, J. and Siddiqui, W. (2014), “Determinants of agricultural output in Pakistan: A Johansen co-integration approach”, Academic Research International, Vol. 5 No. 4, pp. 30–39.
- Seker, F., Ertugrul, H.M. and Cetin, M. (2015), “The impact of foreign direct investment on environmental quality: A bounds testing and causality analysis for Türkiye”, Renewable and Sustainable Energy Reviews, Vol. 52, pp. 347–356, available at: https://doi.org/10.1016/j.rser.2015.07.118.
- Sharma, H.R. (2023), “Patterns, sources and determinants of agricultural growth in India”, Indian Journal of Agricultural Economics, Vol. 78 No. 1, pp. 26–70.
- Stock, J.H. and Watson, M.W. (1999), “Forecasting inflation”, Journal of Monetary Economics, Vol. 44 No. 2, pp. 293–335.
- Suh, D.H. and Moss, C.B. (2021), “Examining the input and output linkages in agricultural production systems”, Agriculture, Vol. 11 No. 1, p. 54, available at: https://doi.org/10.3390/agriculture11010054.
- USDA Economic Research Service. (2023). Global Agricultural Productivity Report: 2023 GAP Report. United States Department of Agriculture. https://globalagriculturalproductivity.org/wp-content/uploads/2024/01/2023-GAP-Report.pdf.
- Xu, J., Wang, Y., Zhao, X., Etuah, S., Liu, Z., & Zhu, H. (2023). Can agricultural trade improve total factor productivity? Empirical evidence from G20 countries. Frontiers in Sustainable Food Systems, 7, 1100038. https://doi.org/10.3389/fsufs.2023.1100038
- Warsi, A.Z. and Mubarik, M.S. (2015), “Determinants of agricultural production: A cross-country sensitivity analysis”, South Asian Journal of Management Sciences, Vol. 9 No. 2, pp. 32–42, available at: https://doi.org/10.123456/saoms.v9i2.12345.
Türkiye’de tarımsal üretimin zamanla değişen belirleyicileri: Panel ARDL ve Kalman Filtresi bulguları
Öz
Amaç: Bu çalışma, 1991-2022 yılları arasında Türkiye'nin tarımsal üretim performansını etkileyen faktörleri analiz etmeyi amaçlamaktadır. Çalışma, temel üretim girdilerinin rolünü inceleyerek, tarımsal çıktının belirleyicileri hakkında içgörü sağlamayı ve tarımsal verimliliği ve sürdürülebilirliği artırmaya yönelik politika tartışmalarına katkıda bulunmayı amaçlamaktadır.
Tasarım/Metodoloji/Yaklaşım: Çalışmada bağımlı değişken olarak tarımsal hasıla endeksi kullanılırken, bağımsız değişkenler arasında tarımsal işgücü, sermaye, arazi ve malzeme endeksleri ile tarımsal hammadde ithalat ve ihracat oranları yer almaktadır. Kısa ve uzun dönem dinamiklerini değerlendirmek için panel ARDL ve Kalman filtresi yöntemleri kullanılmıştır.
Bulgular: Sonuçlar, sermaye ve ihracatın tarımsal çıktı üzerinde en güçlü pozitif etkiye sahip olduğunu, arazi kullanımının da önemli katkı sağladığını göstermektedir. Buna karşılık, makineleşme ve modern teknolojinin benimsenmesi nedeniyle emeğin etkisi azalmaktadır. Malzeme kullanımı sınırlı bir etkiye sahiptir ve maliyet yönetiminin önemini vurgulamaktadır. Ayrıca, tarımsal ithalat dışa bağımlılığı ve maliyetleri artırarak verimliliği olumsuz etkilemektedir.
Araştırma Sınırlamaları/Etkileri: Potansiyel kısıtlamalar arasında veri kısıtlamaları ve sonuçların sağlamlığını etkileyebilecek tarım sektöründeki gözlenemeyen yapısal değişiklikler yer almaktadır. Gelecekteki araştırmalar iklim değişikliği ve teknolojik ilerlemelerle ilgili ek değişkenler içerebilir.
Özgünlük/Değer: Bu çalışma, tarımsal girdilerin zamanla değişen etkilerini ileri ekonometrik teknikler kullanarak inceleyen nadir analizler arasında yer almakta ve Türkiye'nin tarım sektörünün değişen dinamiklerine ilişkin değerli bilgiler sunmaktadır.
Anahtar Kelimeler
Kaynakça
- Barton, G.T. and Cooper, M.R. (1948), “Relation of agricultural production to inputs”, The Review of Economics and Statistics, Vol. 30 No. 2, pp. 117–126.
- Barton, G.T. and Durost, D.D. (1960), “Measuring input changes in agriculture”, Journal of Farm Economics, Vol. 42 No. 5, pp. 1398–1410.
- Coca, O., Creangă, D., Viziteu, Ș., Brumă, I.S. and Ștefan, G. (2023), “Analysis of the determinants of agriculture performance at the European Union level”, Agriculture, Vol. 13 No. 3, p. 616, available at: https://doi.org/10.3390/agriculture13030616.
- Coppola, A., Ianuario, S., Chinnici, G., Di Vita, G., Pappalardo, G. and D'Amico, M. (2018), “Endogenous and exogenous determinants of agricultural productivity: What is the most relevant for the competitiveness of the Italian agricultural systems?”, AGRIS on-line Papers in Economics and Informatics, Vol. 10 No. 2, pp. 33–47, available at: https://doi.org/10.7160/aol.2018.100204.
- Durbin, J. and Koopman, S.J. (2012), Time Series Analysis by State Space Methods, Oxford University Press, Oxford.
- Hamilton, J.D. (1994), Time Series Analysis, Princeton University Press, Princeton, NJ.
- Harvey, A.C. (1990), The Econometric Analysis of Time Series, MIT Press, Cambridge, MA.
- He J, Wei Z, Lei X (2025) Unveiling the digital revolution: Catalyzing total factor productivity in agriculture. PLoS ONE 20(3): e0318333. https://doi.org/10.1371/journal.pone.0318333
- Ketema, A.M. (2020), “Determinants of agricultural output in Ethiopia: ARDL approach to co-integration”, International Journal of Business and Social Research, Vol. 10 No. 3, pp. 1–10, available at: https://doi.org/10.18533/ijbsr.v10i3.1293.
- Muraya, B.W. (2017), “Determinants of agricultural productivity in Kenya”, Unpublished master’s thesis, University of Nairobi.
- Narayan, P.K. and Narayan, S. (2005), “Estimating income and price elasticities of imports for Fiji in a cointegration framework”, Economic Modelling, Vol. 22 No. 3, pp. 423–438, available at: https://doi.org/10.1016/j.econmod.2004.06.004.
- Odhiambo, M.O., Nyangweso, P.M. and Odhiambo, G.M. (2004), “Effects of macroeconomic factors on agricultural performance in Kenya”, International Journal of Agricultural Economics, Vol. 12 No. 3, pp. 197–205.
- Peplinski, J. (2022), “Regional determinants of agricultural production development in Poland”, Annals of the Polish Association of Agricultural and Agribusiness Economists, Vol. 24 No. 1, pp. 225–242.
- Pesaran, M.H., Yongcheol, S. and Richard, J.S. (2001), “Bounds testing approaches to the analysis of level relationships”, Journal of Applied Econometrics, Vol. 16 No. 3, pp. 289–326, available at: https://doi.org/10.1002/jae.616.
- Raza, J. and Siddiqui, W. (2014), “Determinants of agricultural output in Pakistan: A Johansen co-integration approach”, Academic Research International, Vol. 5 No. 4, pp. 30–39.
- Seker, F., Ertugrul, H.M. and Cetin, M. (2015), “The impact of foreign direct investment on environmental quality: A bounds testing and causality analysis for Türkiye”, Renewable and Sustainable Energy Reviews, Vol. 52, pp. 347–356, available at: https://doi.org/10.1016/j.rser.2015.07.118.
- Sharma, H.R. (2023), “Patterns, sources and determinants of agricultural growth in India”, Indian Journal of Agricultural Economics, Vol. 78 No. 1, pp. 26–70.
- Stock, J.H. and Watson, M.W. (1999), “Forecasting inflation”, Journal of Monetary Economics, Vol. 44 No. 2, pp. 293–335.
- Suh, D.H. and Moss, C.B. (2021), “Examining the input and output linkages in agricultural production systems”, Agriculture, Vol. 11 No. 1, p. 54, available at: https://doi.org/10.3390/agriculture11010054.
- USDA Economic Research Service. (2023). Global Agricultural Productivity Report: 2023 GAP Report. United States Department of Agriculture. https://globalagriculturalproductivity.org/wp-content/uploads/2024/01/2023-GAP-Report.pdf.
- Xu, J., Wang, Y., Zhao, X., Etuah, S., Liu, Z., & Zhu, H. (2023). Can agricultural trade improve total factor productivity? Empirical evidence from G20 countries. Frontiers in Sustainable Food Systems, 7, 1100038. https://doi.org/10.3389/fsufs.2023.1100038
- Warsi, A.Z. and Mubarik, M.S. (2015), “Determinants of agricultural production: A cross-country sensitivity analysis”, South Asian Journal of Management Sciences, Vol. 9 No. 2, pp. 32–42, available at: https://doi.org/10.123456/saoms.v9i2.12345.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Sürdürülebilir Tarımsal Kalkınma, Tarım Politikaları, Tarım Ekonomisi (Diğer) |
| Bölüm | Araştıma |
| Yazarlar | |
| Erken Görünüm Tarihi | 30 Haziran 2025 |
| Yayımlanma Tarihi | 30 Haziran 2025 |
| Gönderilme Tarihi | 4 Şubat 2025 |
| Kabul Tarihi | 5 Haziran 2025 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 31 Sayı: 1 |
Kaynak Göster
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