Öncü tarım ekonomilerinde tarımsal verimlilik perspektifi: Ticaret kapasitesi, büyüme, sermaye ve nüfus dinamikleri

Abstract

Amaç: Bu araştırma, 1992-2023 döneminde öncü tarım ekonomisi ülkelerinde dış ticaret kapasitesi, ekonomik büyüme, sermaye ve nüfus dinamiklerinin tarımsal verimlilik üzerindeki etkisine odaklanmaktadır. Amaç bağlamında ihracatın ithalatı karşılama oranı, dış ticaret yoğunluğu, büyüme oranı, sabit sermaye birikimi, toplam nüfus, kırsal nüfus oranı ve tarımsal verimlilik arasındaki olası bağlantılar Çin, Hindistan, ABD, Avrupa Birliği-27, Brezilya, Endonezya, Türkiye, Rusya, Japonya ve Meksika örnekleminde irdelenmektedir.

Tasarım/Metodoloji/Yaklaşım: Tarımsal verimliliğin itici faktörlerini belirleyebilmek amacıyla panel zaman serisi metodolojisi takip edilmiştir. Değişkenler arasındaki uzun dönemli denge ilişkisi belirlendikten sonra Moment Kantil Regresyon Yöntemi (MMQR) ve Panel Düzeltilmiş Standart Hatalar (PCSEs) tahmincileri aracılığıyla parametre tahminleri elde edilmiştir. Ayrıca nedensellik analizi ile değişkenler arasındaki ilişkilerin yönü belirlenmiştir.

Bulgular: MMQR ve PCSEs tahmincilerinden ulaşılan ortak bulgular; dış ticaret yoğunluğu, büyüme ve sermayenin tarımsal verimliliği artırdığını kanıtlamıştır. Aynı zamanda farklı kantil değerlerinde ihracatın ithalatı karşılama oranı ve toplam nüfusun verimlilik üzerinde olumsuz, buna karşılık kırsal nüfusun olumlu etkiler yarattığı raporlanmıştır. Nedensellik analizi bulguları ise amaç değişkeni dikkate alındığında dış ticaret yoğunluğu ve toplam nüfus değişkenleri ile tarımsal verimlilik arasında çift yönlü geri besleme etkili bağlantıları doğrulamıştır.

Özgünlük/Değer: Küresel tarımsal üretimin yaklaşık %75’ini oluşturan örneklem kümesindeki ülkelerde bu araştırmada kullanılan değişkenlerin tarımsal verimlilik üzerindeki bütünleşik etkilerini değerlendiren ampirik bir araştırmaya rastlanılmamış olması çalışmanın özgün değerine atfedilmektedir.

Keywords

• Dış ticaret kapasitesi
• moment kantil regresyon yöntemi
• nüfus dinamikleri
• sermaye birikimi
• tarımsal üretim değeri

Agricultural productivity perspective in leading agricultural economies: Trade capacity, growth, capital and population dynamics

Abstract

Purpose: This research focuses on the impact of foreign trade capacity, economic growth, capital, and population dynamics on agricultural productivity in leading agricultural economy countries during the period 1992-2023. Within this context, the potential links between export-to-import ratio, foreign trade intensity, growth rate, fixed capital accumulation, total population, rural population ratio, and agricultural productivity are examined using samples from China, India, USA, European Union-27, Brazil, Indonesia, Türkiye, Russia, Japan, and Mexico.

Design/Methodology/Approach: Panel time series methodology was used to identify the driving factors of agricultural productivity. After determining the long-term equilibrium relationship between the variables, parameter estimates were obtained using Moment Quantile Regression (MMQR) and Panel-Corrected Standard Errors (PCSEs) estimators. In addition, the direction of the relationships between the variables was determined through causality analysis.

Findings: The common findings from the MMQR and PCSEs estimators demonstrated that trade intensity, growth, and capital increase agricultural productivity. Simultaneously, it was reported that the export-to-import ratio and total population had negative effects on productivity at different quantile values, while rural population had a positive effect. The causality analysis findings confirmed that, when the objective variable is considered, the variables of foreign trade intensity and total population have a bidirectional feedback loop with agricultural productivity.

Originality/Value: The fact that no empirical research evaluating the integrated effects of the variables used in this study on agricultural productivity was found in the countries in the sample group, which accounts for approximately 75% of global agricultural production, is attributed to the originality of this study.

Keywords

• Foreign trade capacity
• moment quantile regression method
• population dynamics
• capital accumulation
• agricultural production value

References

1. Adelman, I. (1984), “Beyond export-led growth”, World Development, 12(9), ss. 937-949.
2. Ahmad, J., Wang, Y., Zhang, L., Shah, W.U.H., Yasmeen, R. ve Pathiranage, S.H.K. (2026), “Impact of climate change on agricultural production efficiency in leading agriculture-producing economies: A DEA Malmquist Productivity Index”, Agricultural Water Management, 324, ss. 1-15.
3. Anyanwu, O.S. (2009), “Determinants of aggregate agricultural productivity among food crop farmers in Rivers State, Nigeria”, J. Vocational Education & Technology, 6(1&2), ss. 117-125.
4. Awuna, J.M., Musa, A.U. ve Onah, A.E. (2023), “Effects of gross fixed capital formation and selected international trade policy instruments on agricultural productivity in Nigeria: An autoregressive distributed lag (ARDL) model approach”, Nigerian Journal of Agriculture and Agricultural Technology, 3(2), ss. 1-9.
5. Bergougui, B. ve Zambrano-Monserrate, M. A. (2025), “Assessing the relevance of the Granger non-causality test for energy policymaking: theoretical and empirical insights”, Energy Strategy Reviews, 59, ss. 1-12.
6. Birleşmiş Milletler (2026), “World population projected”, (https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100) (25 Şubat 2026 tarihinde erişildi).
7. Breusch, T.S. ve Pagan, A.R. (1980), “The Lagrange multiplier test and its applications to model specification in econometrics”, The Review of Economic Studies, 47(1), ss. 239-253.
8. Chen, N. ve Hu, H. (2025), “Rural inclusive finance, human capital and agricultural total factor productivity”, Finance Research Letters, 86, ss. 1-9.

9. Dinçer, S. ve Akın, F. (2025), “Tarımsal toplam faktör verimliliği ve ekonomik büyümenin çevre kalitesi üzerindeki etkisi: Tarımda başarılı olan ülkeler örneği”, KSÜ Tarım ve Doğa Dergisi, 28(6), ss. 1531-1544.
10. Dünya Nüfus İncelemesi (2026), “Current world population”, https://worldpopulationreview.com/ (25 Şubat 2026 tarihinde erişildi).
11. Forum Umwelt und Entwicklung (2019), “The Common Agricultural Policy and the Sustainable Development Goals: two trains travelling in opposite directions”, https://forumue.de/the-common-agricultural-policy-and-the-sustainable-development-goals-two-trains-travelling-in-opposite-directions/ (11 Mayıs 2026 tarihinde erişildi).
12. Fwah, F.H., Abu, O. ve Aye, G.C. (2017), “Long-run and short-run effects of foreign trade and foreign direct investment on agricultural productivity in Nigeria”, Asian Journal of Economics, Business and Accounting, 3(3), ss. 1-8,
13. Gingrich, C.D. ve Garber, J.D. (2010), “Trade liberalization’s impact on agriculture in low income countries: A comparison of El Salvador and Costa Rica”, The Journal of Developing Areas, 43(2), ss. 1-17.
14. Hasanov, F.J., Suleymanov, E., Aliyeva, H., Eynalov, H. ve Shannak, S. (2022), “What drives the agricultural growth in Azerbaijan? Insights from autometrics with super saturation”, Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, 70(3), ss. 147-174.
15. Hauka, A. ve Rivža, B. (2015), “Common agricultural policy (CAP) – For increasing EU competitiveness in global market”, Sustainable Rural and Territorial Development, ss. 451-456.
16. Imran, M., Ali, S., Shahwan, Y., Zhang, J. ve Al-Swiety, I.A. (2022), “Analyzing the effects of renewable and nonrenewable energy usage and technological innovation on environmental sustainability: Evidence from QUAD economies”, Sustainability, 14, ss. 1-16.
17. İmrohoroğlu, A., İmrohoroğlu, S. ve Üngör, M. (2012), Agricultural productivity and growth in Turkey, TEPAV Publications No 63. Ankara.
18. Juodis, A., Karavias, Y. ve Sarafidis, V. (2021), “A homogeneous approach to testing for Granger non-causality in heterogeneous panels”, Empirical Economics, 60(1), ss. 93-112.
19. Zehadul Karim, A.H.M. (2013), “Impact of a growing population in agricultural resource management: Exploring the global situation with a micro-level example”, Asian Social Science, 9(15), ss. 14-22.
20. Liu, Z., Zhang, M., Li, Q. ve Zhao, X. (2023), “The impact of green trade barriers on agricultural green total factor productivity: Evidence from China and OECD countries”, Economic Analysis and Policy, 78, ss. 319-331.
21. Ma, Z., Wang, D., Feng, F. ve Zhao, T. (2025), “Moving out of agriculture: How trade liberalization affects agricultural productivity in China”, China Economic Review, 91, ss. 1-24.
22. Malik, N. (2007), “Pakistan agricultural export performance in the light of trade liberalization and economic reforms”, Research in Agricultural & Applied Economics Working Paper Series 2007:4, ss. 1-22.
23. Mandanas, Z.C., Petropoulos, D.P. ve Apostolopoulos, N. (2026), “The impact of CAP investment subsidies on agricultural productivity in Greece: A time-series analysis”, Proceedings, 134, ss. 1-5.
24. Mwesigye, F. ve Matsumoto, T. (2016), “The effect of population pressure and internal migration on land conflicts: Implications for agricultural productivity in Uganda”, World Development, 79, ss. 25-39.
25. Miljkovic, D. ve Shaik, S. (2010), “The impact of trade openness on technical efficiency in U.S. agriculture”, Agribusiness & Applied Economics Report No 660, ss. 1-13.
26. Muzari, W. (2016), “Agricultural productivity and food security in Sub-Saharan Africa”, International Journal of Science and Research, 5(1), ss. 1769-1776.
27. Nica, E., Poliakova, A., Popescu, G. H.., Valaskova, K., Burcea, S. G. ve Constantin, A.-L. D. (2023), “The impact of financial development, health expenditure, CO2 emissions, institutional quality, and energy Mix on life expectancy in Eastern Europe: CS-ARDL and quantile regression approaches”, Heliyon, 9, ss. 1-16.
28. Nkamleu, G.B. (2007), “Productivity growth, technical progress and efficiency change in African agriculture and development”, African Development Review, 16(1), ss. 203-222.
29. Obe, M.E., Olutumise, A.I., Akinrinola, O.O., Omosehin, O.O., Aturamu, O.A. ve Hosu, Y.S. (2024), “Impacts of capital formation and investment sources on total factor productivity: The example of Nigeria’s agriculture”, Diyala Agricultural Sciences Journal, 16(1), ss. 106-171.
30. Pervez, M., Ahmed, Z., Uddin, M.S. ve Rahman, M.M. (2024), “Agricultural output and economic growth nexus: A VECM approach on Bangladesh”, Journal of Agricultural Sciences, 30(4), ss. 644-657.
31. Pesaran, M.H. (2007), “A simple panel unit root test in the presence of cross-section dependence”, Journal of Applied Econometrics, 22, ss. 265-312.
32. Pesaran, M. ve Yamagata, T. (2008), “Testing slope homogeneity in large panels”, Journal of Econometrics, 142(1), ss. 50-93.
33. Ricker-Gilbert, J., Jumbe, C. ve Chamberlin, J. (2014), How does population density influence agricultural intensification and productivity? Evidence from Malawi”, Food Policy, 48, ss. 114-128.
34. Saeed, U.F. (2025), “Achieving SDGs in developing economies: A CS-ARDL analysis of the effects of industrial growth and foreign investment on carbon and ecological footprints”, Journal of Sustainable Finance and Accounting, 8, ss. 1-11.
35. Simionescu, M. ve Plopeanu, A.-P. (2023), “Impact of governance quality on pollution in nuclear energy consuming countries in the European Union”, Energy Reports, 9, ss. 4122-4134.
36. Sahin, G., Aydin, H.I. ve Ozdemir, A. (2025), “The relationship between energy and climate action in the context of sustainable development goals: An empirical analysis of BRICS–T economies”, Sustainability, 17(18), ss. 1-18.
37. Şengül, S. (2025), “Uncovering time-varying drivers of agricultural output in Türkiye: A panel ARDL and Kalman Filter Analysis”, Turkish Journal of Agricultural Economic, 31(1), ss. 119-132.
38. Taş, T. ve Yılmaz, K.Ç. (2025), “Anatomy of agricultural export performance: Sectoral evidence from Türkiye”, Turkish Journal of Agricultural Economic, 31(2), ss. 349-361.
39. Tripathi, A. ve Prasad, A.R. (2008), “Agricultural productivity growth in India”, Journal of Global Economy, 4(4), ss. 322-328.
40. Uçar, M., Ülger, M., Atamer, M.A. ve Alptürker, A. (2025), “Tarımsal verimlilik, ekonomik büyüme ve karbon emisyonu arasındaki ilişki: Türk Cumhuriyetlerinden kanıtlar”, Tarım Ekonomisi Dergisi, 31(2), ss. 389-404.
41. USDA Economic Research Service (2026), “International agricultural productivity - summary findings”, https://www.ers.usda.gov/data-products/international-agricultural-productivity/summary-findings (28 Şubat 2026 tarihinde erişildi).
42. Westerlund, J. (2007), “Testing for error correction in panel data”, Oxford Bulletin of Economics and Statistics, 69(6), ss. 709-748.