Analysis of the Effects of Agricultural Credits and Subsidies on Crop Production Efficiency: Fractional-Frequency Fourier ARDL Bounds Testing

Yıl 2024, Cilt: 8 Sayı: 1, 213 - 227, 27.06.2024

Ömer Keskin

https://doi.org/10.33399/biibfad.1452129

Öz

This paper aims to analyze the effects of agricultural credits and subsidies on crop production efficiency in Türkiye. Firstly, the stationary of the variables are tested by applying Fractional-Frequency Fourier ADF unit root test, using three different annual time series consisting of 33 observations belonging to the period 1990-2022. The findings of the unit root test reveals that the dependent variable is I (1), and the independent variables are I (0). Subsequently, Fractional-Frequency Fourier ARDL bounds testing was conducted to determine whether there is a cointegration relationship between these variables. The findings of the bounds testing reveals a positive relationship in both the long and short-term, from the agricultural credit and subsidy variables to the crop production efficiency variable. A 1% increase in the agricultural credit balance and the source size of subsidy causes an increase in the crop production efficiency by 0.054% and 0.062%, respectively, in the long-term and 0.07% and 0.08%, respectively, in the short-term. On the other hand, short-term deviations from the long-term equilibrium that occur in the short-term disappear by 1.25% after 1 period. Based on the findings, it can be clearly stated that the agricultural credit balance and the source size of subsidy should be increased in order to get higher efficiency from a unit of arable land in crop production.

Anahtar Kelimeler

Agricultural credit, subsidy, crop production efficiency, time series analysis

Tarımsal Kredilerin ve Desteklerin Bitkisel Üretim Verimliliğine Etkilerinin Analizi: Kesirli-Frekanslı Fourier ARDL Sınır Testi

Öz

Bu çalışma, Türkiye örnekleminde tarımsal kredilerin ve desteklerin bitkisel üretim verimliliğine etkilerini analiz etmeyi amaçlamaktadır. 1990-2022 dönemine ait olup 33 gözlemden oluşan 3 farklı yıllık zaman serisinin kullanıldığı çalışmada ilk olarak, değişken durağanlıkları, Kesirli-Frekanslı Fourier ADF birim kök testi uygulanarak sınanmıştır. Test sonucunda bağımlı değişken I (1), bağımsız değişkenler ise I (0) çıkmıştır. Daha sonra, Kesirli-Frekanslı Fourier ARDL sınır testi uygulanarak değişkenler arasında bir eşbütünleşme ilişkisinin var olup olmadığına bakılmıştır. Test sonucuna göre değişkenler arasında hem uzun hem kısa dönemde pozitif bir ilişki bulunmaktadır. Tarımsal kredi bakiyesinde ve devlet desteğinin kaynak büyüklüğünde yaşanan %1’lik yükseliş, bitkisel üretim verimliliğini uzun dönemde sırasıyla %0.054 ve %0.062 yükseltirken kısa dönemde ise sırasıyla %0.07 ve %0.08 kadar yükseltmektedir. Diğer taraftan kısa dönemde oluşabilecek uzun dönemli dengeden sapmalar, 1 dönem sonra yaklaşık %1.25 oranında düzelmektedir. Bulgular doğrultusunda bitkisel üretimde bir birim araziden daha yüksek verim almak için tarımsal kredilerin bakiyesinin ve devlet desteklerinin kaynak büyüklüğünün artırılması gerektiği açıkça söylenebilir.

Anahtar Kelimeler

Tarımsal kredi, devlet desteği, bitkisel üretim verimliliği, zaman serisi analizi

Kaynakça

• Amponsah, L., Hoggar, G. K., & Yeboah, S. A. (2015). Climate change and agriculture: modelling the impact of carbon dioxide emission on cereal yield in Ghana. Erişim adresi https://mpra.ub.uni-muenchen.de/68051/1/MPRA_paper_68051.pdf (Erişim tarihi: 04.03.2024).
• Assouto, A. B., & Houngbeme, D. J. L. (2023). Access to credit and agricultural productivity: evidence from maize producers in Benin. Cogent Economics and Finance, 11(1), 1-22. https://doi.org/10.1080/23322039.2023.2196856
• Attiaoui, I., & Boufateh, T. (2019). Impacts of climate change on cereal farming in Tunisia: a panel ardl–pmg approach. Environmental Science and Pollution Research, 26(13), 13334-13345. https://doi.org/10.1007/s11356-019-04867-y
• Ben-Ari, T., Adrian, J., Klein, T., Calanca, P., Van der Velde, M., & Makowski, D. (2016). Identifying indicators for extreme wheat and maize yield losses. Agricultural and Forest Meteorology, 220, 130-140. https://doi.org/10.1016/j.agrformet.2016.01.009
• Boansi, D. (2017). Effect of climatic and non-climatic factors on cassava yields in Togo: agricultural policy implications. Climate, 5(2), 1-21. https://doi.org/10.3390/cli5020028
• Bozoklu, Ş., Yılancı, V., & Görüş, M. Ş. (2020). Persistence in per capita energy consumption: a fractional integration approach with a Fourier function. Energy Economics, 91, 1-12. https://doi.org/10.1016/j.eneco.2020.104926
• Chandio, A. A., Öztürk, İ., Akram, W., Ahmad, F., & Mirani, A. A. (2020). Empirical analysis of climate change factors affecting cereal yield: evidence from Turkey. Environmental Science and Pollution Research, 27, 11944-11957.
• Diamoutene, A. K., & Jatoe, J. B. D. (2020). Access to credit and maize productivity in Mali. Agricultural Finance Review, 81(3), 458-477. https://doi.org/10.1108/AFR-05-2020-0066
• Doğan, H. G., & Karakaş, G. (2018). The effect of climatic factors on wheat yield in Turkey: a panel dols approach. Fresenius Environmental Bulletin, 27(6), 4162-4168.
• Dubey, S. K., & Sharma, D. (2018). Assessment of climate change impact on yield of major crops in the Banas River Basin, India. Science of the Total Environment, 635, 10-19. https://doi.org/10.1016/j.scitotenv.2018.03.343
• Enders, W., & Lee, J. (2012). The flexible Fourier form and Dickey-Fuller type unit root tests. Economics Letters, 117(1), 196-199. https://doi.org/10.1016/j.econlet.2012.04.081
• Eruygur, H. O., & Özokcu, S. (2016). Impacts of climate change on wheat yield in Turkey: a heterogeneous panel study. Ekonomik Yaklaşım, 27(101), 219-255. https://doi.org/10.5455/ey.35944
• FAO. (2020). The agriculture orientation index for government expenditures. Erişim adresi https://unstats.un.org/sdgs/metadata/files/metadata-02-0a-01.pdf (Erişim tarihi: 01.03.2024).
• FAO. (2022a). Government expenditure. Investment. Erişim adresi https://www.fao.org/faostat/en/#data/IG (Erişim tarihi: 01.03.2024).
• FAO. (2022b). Indicator 2.a.1-the agriculture orientation index for government expenditures. SDG Indicators. Erişim adresi https://www.fao.org/sustainable-development-goals-data-portal/data/indicators/2a1---agriculture-orientation-index-for-government-expenditures/en#:~:text=Key%20results,2015%20to%200.45%20in%202021 (Erişim tarihi: 01.03.2024).
• FAO. (2023). Credit to agriculture. Investment. Erişim adresi https://www.fao.org/faostat/en/#data/IC (Erişim tarihi: 01.03.2024).
• Keskin, Ö. (2024). Analysis of the impact of agricultural credits on agricultural mechanization in Türkiye. Mustafa Kemal University Journal of Agricultural Sciences, 29(1), 158-167. https://doi.org/10.37908/mkutbd.1386236
• Koondhar, M. A., Udemba, E. N., Cheng, Y., Khan, Z. A., Koondhar, M. A., Batool, M., & Kong, R. (2021). Asymmetric causality among carbon emission from agriculture, energy consumption, fertilizer, and cereal food production-a nonlinear analysis for Pakistan. Sustainable Energy Technologies and Assessments, 45, 1-11. https://doi.org/10.1016/j.seta.2021.101099
• Köprücü, Y., & Acaroğlu, H. (2023). How cereal yield is influenced by eco-environmental factors? ardl and spectral causality analysis for Turkey. Cleaner Environmental Systems, 10, 1-16. https://doi.org/10.1016/j.cesys.2023.100128
• Li, C., Sha, Z., Sun, X., & Jiao, Y. (2022). The effectiveness assessment of agricultural subsidy policies on food security: evidence from China’s poverty-stricken villages. International Journal of Environmental Research and Public Health, 19(21), 1-17. https://doi.org/10.3390/ijerph192113797
• Mahjoubi, S., & Mkaddem, C. (2022). Impact of climate change on yield production in Algeria: evidence from ardl empirical approach. Erişim adresi https://mpra.ub.uni-muenchen.de/115565/1/MPRA_paper_115565.pdf (Erişim tarihi: 03.03.2024).
• Mbingui, C. (2022). Climate change and agricultural yield in the republic of Congo: an analysis using the ardl approach. Theoretical Economics Letters, 12(6), 1903-1920. https://doi.org/10.4236/tel.[phone]
• Nguyen-Anh, T., Hoang-Duc, C., Tiet, T., Nguyen-Van, P., & To-The, N. (2022). Composite effects of human, natural, and social capitals on sustainable food-crop farming in Sub-Saharan Africa. Food Policy, 113, 1-52. https://doi.org/10.1016/j.foodpol.2022.102284
• OECD (2023). Agricultural support-producer support. Agriculture-Türkiye. Erişim adresi https://data.oecd.org/agrpolicy/agricultural-support.htm (Erişim tarihi: 04.03.2024).
• Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics, 16(3), 289-326.
• Pilevneli, T., Capar, G., & Sánchez-Cerdà, C. (2023). Investigation of climate change impacts on agricultural production in Turkey using volumetric water footprint approach. Sustainable Production and Consumption, 35, 605-623. https://doi.org/10.1016/j.spc.2022.12.013
• Rivera-Acosta, J., & Xiuchuan, X. (2023). The impact of credit on agricultural productivity of Musaceae: evidence from Valle Del Cauca, Colombia. Revista Facultad Nacional de Agronomía Medellín, 76(1), 1-14.
• Sharma, R. K., Dhillon, J., Kumar, P., Bheemanahalli, R., Li, X., Cox, M. S., & Reddy, K. N. (2023). Climate trends and maize production nexus in Mississippi: empirical evidence from ardl modelling. Scientific Reports, 13, 1-16. https://doi.org/10.1038/s41598-023-43528-6
• Shoko, R. R., Belete, A., & Chaminuka, P. (2019). Maize yield sensitivity to climate variability in South Africa: application of the ardl-ecm approach. Journal of Agribusiness and Rural Development, 54(4), 363-371. https://doi.org/10.17306/j.jard.2019.01201
• Sossou, S., Igue, C. B., & Diallo, M. (2020). Impact of climate change on cereal yield and production in the Sahel: case of Burkina Faso. Asian Journal of Agricultural Extension, Economics & Sociology, 37(4), 1-11. https://doi.org/10.9734/ajaees/2019/v37i430288
• Strateji ve Bütçe Başkanlığı. (2023). Vatandaşın bütçe rehberi-2023 yılı bütçesi. Erişim adresi https://www.sbb.gov.tr/wp-content/uploads/2023/03/2023_VatandasinButceRehberi.pdf (Erişim tarihi: 04.03.2024).
• TBB. (2023). Bankalarımız kitabı. İstatistiki Raporlar. Erişim adresi https://www.tbb.org.tr/tr/bankacilik/banka-ve-sektor-bilgileri/istatistiki-raporlar/59 (Erişim tarihi: 04.03.2024).
• TÜİK. (2023). İstatistiksel tablolar-tahıllar ve diğer bitkisel ürünler, sebzeler, meyveler, içecekler ve baharat bitkileri, tarım ve orman alanları. Tarım-Bitkisel Üretim İstatistikleri. Erişim adresi https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=1 (Erişim tarihi: 04.03.2024).
• World Bank. (2022). Agriculture finance & agriculture insurance. Brief. Erişim adresi https://www.worldbank.org/en/topic/financialsector/brief/agriculture-finance (Erişim tarihi: 01.03.2024).
• Xiang, X., & Solaymani, S. (2022). Change in cereal production caused by climate change in Malaysia. Ecological Informatics, 70, 1-10. https://doi.org/10.1016/j.ecoinf.2022.101741
• Xie, H., & Wang, B. (2017). An empirical analysis of the impact of agricultural product price fluctuations on China’s grain yield. Sustainability (Switzerland), 9(6), 1-14. https://doi.org/10.3390/su9060906
• Yadav, I. S., & Rao, M. S. (2022). Agricultural credit and productivity of crops in India: field evidence from small and marginal farmers across social groups. Journal of Agribusiness in Developing and Emerging Economies. https://doi.org/10.1108/JADEE-05-2022-0092
• Yang, T., Chandio, A. A., Zhang, A., & Liu, Y. (2023). Do farm subsidies effectively increase grain production? evidence from major grain-producing regions of China. Foods, 12(7), 1-20. https://doi.org/10.3390/foods12071435
• Yılancı, V., Bozoklu, Ş., & Görüş, M. Ş. (2020). Are BRICS countries pollution havens? evidence from a bootstrap ardl bounds testing approach with a Fourier function. Sustainable Cities and Society, 55, 1-12. https://doi.org/10.1016/j.scs.2020.102035
• Zhai, S., Song, G., Qin, Y., Ye, X., & Lee, J. (2017). Modeling the impacts of climate change and technical progress on the wheat yield in inland China: an autoregressive distributed lag approach. PLoS ONE, 12(9), 1-20. https://doi.org/10.1371/journal.pone.0184474