Determination of Efficiency and Factors Affecting Efficiency in Maize Production in Konya Province (Cumra District)

Year 2023, Volume: 10 Issue: 4, 1079 - 1087, 13.10.2023

Aysun Yener , Gürhan Özaydın

https://doi.org/10.30910/turkjans.1109856

Abstract

The study aims to determine the efficiency of input use and to analyze the factors affecting technical efficiency in farms producing maize. Maize is among the most cultivated cereals in the world. Konya, on the other hand, ranks first in Turkey with a 10% share in maize production. The research area of Cumra district, which constitutes 15.76% of the maize production in Konya province, has been selected according to the purposive sampling method. In the study, the sample volume was determined as 77, with a 95% confidence interval and a 5% margin of error, according to the stratified sampling method. In the study, linear regression analysis was carried out to determine the factors affecting the technical efficiency of maize producers. According to the results of the research, gross production value (USD), total land size (ha), and age were found to be statistically significant at the 5% significance level. Variable costs and education were statistically significant at the 10% significance level. The DEA method, which is a non-parametric method, was used to determine the technical efficiency and scale efficiency of farms under the assumption of technical efficiency, VRS, and CRS. Farms should be informed about the optimum use of inputs. In addition, a farmer training program to be organized on this subject should be given to the farmers.

Keywords

DEA, Konya, maize production, regression

Konya İli (Çumra İlçesi) Mısır Üretiminde Etkinlik Analizi ve Etkinliğe Etki Eden Faktörlerin Belirlenmesi

Abstract

Çalışmanın amacı, mısır üretimi yapan işletmelerin girdi kullanım etkinliğinin belirlenmesi ve teknik etkinliğe etki eden faktörlerin tespit edilmesidir. Mısır dünyada en fazla tarımı yapılan tahıllar arasındadır. Konya ili ise mısır üretiminde %10’luk bir pay ile ilk sırada yer almaktadır. Araştırma alanı, Konya ili mısır üretiminin %15.76’sını oluşturan Çumra ilçesi gayeli örnekleme yöntemine göre seçilmiştir. Araştırmada örnek hacmi, tabakalı örnekleme yöntemine göre, %95 güven aralığı, %5 hata payı ile 77 olarak belirlenmiştir. Çalışmada mısır üretimi yapan işletmelerin teknik etkinliklerini etkileyen faktörleri belirlemek için doğrusal regresyon analizi yapılmıştır. Araştırma sonuçlarına göre gayri safi üretim değeri (USD), toplam arazi büyüklüğü (ha) ve yaş %5 önem düzeyinde istatistiksel olarak anlamlı bulunmuştur. Değişen masraflar ve eğitim, %10 anlamlılık düzeyinde istatistiksel olarak anlamlıydı. Teknik etkinlik, VRS ve CRS varsayımı altında çiftliklerin teknik etkinliği ve ölçek etkinliğinin belirlenmesinde parametrik olmayan bir yöntem olan VZA yöntemi kullanılmıştır. Çiftlikler girdilerin optimum kullanımı konusunda bilgilendirilmelidir. Ayrıca bu konuda düzenlenecek bir çiftçi eğitim programı çiftçilere verilmelidir.

Keywords

Konya, Mısır, VZA, GSÜD, regresyon analizi

References

• Adnan, N., Nordin, S.M., Bahruddin, M.A. and Tareq, A.H. A. 2019. State-of-the-art review on facilitating sustainable agriculture through green fertilizer technology adoption: Assessing farmers behavior. Trends in Food Science & Technology, 86, 439-452.
• Banker, R.D., Charnes, A. and Cooper, W.W. 1984. Some models for estimating technical and scale inefficiencies in data envelopment analysis. Management science, 30, 1078-1092.
• Below, T.B. 2012. Mutabazi K.D., Kirschke D., Franke C., Sieber S., Siebert R., Tscherning K. Can farmers’ adaptation to climate change be explained by socio-economic household-level variables? Global Environmental Change, 22, 223-235.
• Charnes, A., Cooper, W.W. and Rhodes, E. 1978. Measuring the efficiency of decision making units. European journal of operational research, 2, 429-444. 1978.
• Chauhan, N.S., Mohapatra, P.K. and Pandey, K.P. 2006. Improving energy productivity in paddy production through benchmarking—An application of data envelopment analysis. Energy conversion and Management, 47, 1063-1085.
• Coelli, T., Rahman, S. and Thirtle, C. 2002. Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: a non‐parametric approach. Journal of Agricultural Economics, 53, 607-626.
• Cooper, W.W., Seiford, L.M. and Tone, K. 2007. Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software. In Secondary Data envelopment analysis: a comprehensive text with models, applications, references and DEA-solver software.
• Danso-Abbeam, G., Ehiakpor, D.S. and Aidoo, R. 2018. Agricultural extension and its effects on farm productivity and income: insight from Northern Ghana. Agriculture & Food Security, 7, 1-10.
• Doğan, K. and Külekçi, M. 2020. Iğdır İli Silajlık Mısır Üretiminde Etkinliğin ve Etkinliğe Etki Eden Faktörlerin Belirlenmesi. Journal of the Institute of Science and Technology, 10, 1338-1349.
• FAO. 2017. FAO Statistical Databases. web (Food and Agriculture Organization of the United Nations): FAOSTAT.
• FAO 2021. https://www.fao.org/faostat/en/ (Accessed of date, 11.12.2021).
• Farrel, J. 1957. The measurement of productive efficiency. Journal of the Royal Statistical Society. Series A, General 125. Part, 252. 1957.
• Fusuo, Z., Xinping, C. and Vitousek, P. 2013. Chinese agriculture: An experiment for the world. Nature, 497, 33-35.
• Gujarati, D.N. and Porter, D.C. 2009. Basic econometrics (international edition). New York: McGraw-Hills Inc.
• Güneş, T. and Arıkan, R. 1985. Agricultural Economics Statistics. Ankara University Publication, 924.
• Hajihassaniasl, S. 2019. Efficiency, Analysis in the Agricultural Sector in Iran: The Case of West Azerbaijan Sunflower Producers. International Journal of Management, Accounting and Economics, 6, 389-399. 2019.
• Harniati, H. and Anwarudin, O. 2018. The interest and action of young agricultural entrepreneur on agribusiness in Cianjur Regency, West Java. Jurnal Penyuluhan, 14.
• Hašková, S. 2017. Holistic assessment and ethical disputation on a new trend in solid biofuels. Science and engineering ethics, 23, 509-519.
• Kaur, R. and Bhaskar, T. 2020. Potential of castor plant (Ricinus communis) for production of biofuels, chemicals, and value-added products. In Waste biorefinery, 269-310. Elsevier. Knickel, K., Tisenkopfs, T. and Peter S. 2009. Innovation processes in agriculture and rural development. Results of a cross-national analysis of the situation in seven countries, research gaps and recommendations. In-Sight project report. 2009.
• Läpple, D., Renwick, A., Cullinan J. and Thorne, F. 2016. What drives innovation in the agricultural sector? A spatial analysis of knowledge spillovers. Land use policy, 56, 238-250.
• Läpple, D., Renwick, A. and Thorne, F. 2015. Measuring and understanding the drivers of agricultural innovation: Evidence from Ireland. Food policy, 51, 1-8. 2015.
• Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J. and Garnier, J. 2014. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environmental Research Letters, 9, 105011.
• Manal, M.S. 2018. Expected Economic Effects of Applying a Proposed Class Map for Maize Crop in Egypt. Egypt. J. Agri. Eco, 28, 269-304. 2018.
• Maroušek, J., Kolář, L., Vochozka, M., Stehel, V. and Maroušková, A. 2017. Novel method for cultivating beetroot reduces nitrate content. Journal of Cleaner Production, 168, 60-62.
• OECD. 2013. Agricultural Innovation Systems: A Framework for Analyzing the Role of the Government. Paris. web (Organisation for Economic Co-operation and Development): OECD Publishing.
• Oğuz, C. and Yener, A. 2018. Productivity analysis of dairy cattle farms in Turkey: case study of Konya Province. Custos e Agronegocio, 14, 298-319.
• Oğuz, C., Öğür A.Y. and Ayhan, A. 2019. Input Use Efficiency in Sunflower Production; A Case Study of Konya Province (Karatay District). Turkish Journal of Agriculture-Food Science and Technology, 7, 2012-2017.
• Oğuz, C. and Yener, A. 2019. The use of energy in milk production; a case study from Konya province of Turkey. Energy, 183, 142-148.
• Ögür, A.Y., Kaygusuz M., Bilik Ş. and Alp, M. 2021. The Effect of Climate Change and The Information Sources Used In The Enterprises Producing Sunflowerseed. In Secondary. Agrosym 2021 329-335.
• Parlakay, O. and Çimrin, T. 2021. Determination of technical efficiency in broiler production using Data Envelopment Analysis method: a case study of Hatay Province in Turkey. CUSTOS E Agronegocio Online, 17, 239-250.
• Ren, C., Liu, S., Van Grinsven, H., Reis, S., Jin, S., Liu, H.and Gu, B. 2019. The impact of farm size on agricultural sustainability. Journal of Cleaner Production, 220, 357-367.
• Smetanová, A., Dotterweich, M., Diehl, D. and Ulrich, U. 2013. Dotterweich N.F. Influence of biochar and terra preta substrates on wettability and erodibility of soils. Zeitschrift für Geomorphologie, Supplementary Issues, 57, 111-134.
• Spielman, D.J. and Birner, R. 2008. How innovative is your agriculture? Using innovation indicators and benchmarks to strengthen national agricultural innovation systems. World Bank Washington, DC.
• Tey, Y.S. and Brindal, M. 2012. Factors influencing the adoption of precision agricultural technologies: a review for policy implications. Precision agriculture, 13, 713-730.
• Thiombiano, B.A. 2017. Maize and Livestock Production Efficiencies and Their Drivers in Heterogeneous Smallholder Systems in Southwestern Burkina Faso. 2017.
• TURKSTAT. 2021. https://data.tuik.gov.tr/Kategori/GetKategori?p=tarim-111&dil=1 (Accessed of date, 11.12.2021).
• Tümer, E.İ., Ağır, H.B. and Aydoğan, İ. 2020. Evaluating technical efficiency of hair goat farms in Turkey: the case of Mersin Province. Tropical Animal Health and Production, 52, 3707-3712.
• Yamane, T. 1967. Elementary Sampling Theory. In Secondary Elementary Sampling.
• Yener, A. 2017. Konya ilinde süt sığırcılığı yapan aile işletmelerinde yeniliklerin benimsenmesi ve yayılmasına etki eden faktörler. Selçuk Üniversitesi Fen Bilimleri Enstitüsü.