A Quantitative Approach to Wheat Production in Turkey

Abstract

The scarcity of precipitation, which is one of the biggest problems of recent times, has brought with it a weakening in agricultural production. Looking at Turkey in general, it is seen that the last years have been very dry. Increasing droughts have reduced the farmer's ability to obtain irrigation water, and increasing input costs have made it very difficult to obtain fertilizer and consumables. This situation affects the resources of the state, albeit indirectly. Due to these and similar reasons, the production of wheat has decreased compared to previous years and its cost has increased. In this context, the estimation data for the coming years is that wheat production will decrease. Considering all these, the parameters affecting the wheat production were obtained from the Turkish Statistical Institute , the Ministry of Agriculture and Forestry and the Turkish Grain Board .In line with the information obtained, it was explained by using principal component analysis with which parameters wheat production is more related. At the same time, whether the effect parameters have an indirect effect on wheat production was investigated by correlation analysis. The factors that may cause a decrease in wheat production were evaluated quantitatively.

Keywords

• Principal Component Analysis
• Correlation Analysis
• Parameter Determination

Türkiye’deki Buğday Üretimine Kantitatif Bir Yaklaşım

Öz

Son dönemlerin en büyük sorunlarından biri olan yağışın azlığı beraberinde tarımsal üretimlerde zayıflamaları getirmiştir. Türkiye geneline bakıldığında son yılların çok kurak geçtiği görülmektedir. Kuraklıkların artması çiftçinin sulama suyunu elde etme imkanını azaltmış, artan girdi maliyetleri gübrenin ve sarf malzemelerin temin edebilmesini oldukça zorlaştırmıştır. Bu durum dolaylı yoldan da olsa devletin kaynaklarını da etkilemektedir. Bu ve bunun gibi sebeplerden dolayı buğdayın geçmiş yıllara göre üretimi azalmış ve maliyeti de artmıştır. Bu kapsamda gelecek yıllar için tahmin verileri de buğday üretiminin azalacağı yönündedir. Tüm bunlar göz önüne alınarak, buğdayın üretimini etkileyen parametreler Türkiye İstatistik Kurumu (TÜİK) ,Tarım ve Orman Bakanlığı (TOB) ve Toprak Mahsulleri Ofisi (TMO)’dan elde edilmiştir. Elde edilen bilgiler doğrultusunda buğday üretiminin hangi parametrelerle daha çok ilişkili olduğu temel bileşen analizi kullanılarak açıklanmıştır. Aynı zamanda etki parametrelerinin dolaylı olarak buğday üretimine etkisinin olup olmadığı korelasyon analizi ile araştırılmıştır. Buğday üretiminin azalmasına sebep olabilecek faktörler kantitatif olarak değerlendirilmiştir.

Anahtar Kelimeler

• Temel Bileşen Analizi
• Korelasyon Analizi
• Parametre Belirleme

Kaynakça

1. Chen, M., Luo, Y., Shen, Y., Han, Z., & Cui, Y. (2020). Driving force analysis of irrigation water consumption using principal component regression analysis. Agricultural Water Management, 234, 106089.
2. Li, W., & Huang, Y. (2020). A method for damage detection of a jacket platform under random wave excitations using cross correlation analysis and PCA-based method. Ocean Engineering, 214, 107734.
3. Xu, M., Zhang, Y., Zhao, P., & Liu, C. (2020). Study on aging behavior and prediction of SBS modified asphalt with various contents based on PCA and PLS analysis. Construction and Building Materials, 265, 120732.
4. Wang, J., Shao, W., & Kim, J. (2020). Analysis of the impact of COVID-19 on the correlations between crude oil and agricultural futures. Chaos, Solitons & Fractals, 136, 109896.
5. Zhang, X., Zhang, P., Yuan, X., Li, Y., & Han, L. (2020). Effect of pyrolysis temperature and correlation analysis on the yield and physicochemical properties of crop residue biochar. Bioresource technology, 296, 122318.
6. Utrilla-Vázquez, M., Rodríguez-Campos, J., Avendaño-Arazate, C. H., Gschaedler, A., & Lugo-Cervantes, E. (2020). Analysis of volatile compounds of five varieties of Maya cocoa during fermentation and drying processes by Venn diagram and PCA. Food Research International, 129, 108834.
7. Zhang, X., He, L., Zhang, J., Whiting, M. D., Karkee, M., & Zhang, Q. (2020). Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA). Biosystems Engineering, 193, 247-263.
8. Petković, B., Petković, D., & Kuzman, B. (2020). Adaptive neuro fuzzy predictive models of agricultural biomass standard entropy and chemical exergy based on principal component analysis. Biomass Conversion and Biorefinery, 1-11.

9. Yagmur, B., & Gunes, A. (2021). Evaluation of the Effects of Plant Growth Promoting Rhizobacteria (PGPR) on Yield and Quality Parameters of Tomato Plants in Organic Agriculture by Principal Component Analysis (PCA). Gesunde Pflanzen, 73(2), 219-228.
10. Pearson, K. (1901). LIII. On lines and planes of closest fit to systems of points in space. The London, Edinburgh, and Dublin philosophical magazine and journal of science, 2(11), 559-572.
11. Cui, W., Sun, Z., Ma, H., & Wu, S. (2020). The Correlation Analysis of Atmospheric Model Accuracy Based on the Pearson Correlation Criterion. In IOP Conference Series: Materials Science and Engineering (Vol. 780, No. 3, p. 032045).
12. Weston, J., Elisseeff, A., Schölkopf, B., Tipping, M. (2003) Use of the zero norm with linear models and kernel methods. The Journal of Machine Learning Research, 3: 1439−1461.
13. Deng, L., Pei, J., Ma, J. and Lee, D. L. (2004) A rank sum test method for informative gene discovery. In: Tenth Acm Sigkdd International Conference on Knowledge Discovery & Data Mining. ACM.
14. Crothers, L. M., Schreiber, J. B., Field, J. E., & Kolbert, J. B. (2009). Development and Measurement Through Confirmatory Factor Analysis of the Young Adult Social Behavior Scale (YASB) An Assessment of Relational Aggression in Adolescence and Young Adulthood. Journal of Psychoeducational Assessment, 27(1), 17-28.