Determination of Precipitation-Quality Relationship By Different Statistical Methods in Bread Wheat (Triticum aestivum L.)

Year 2021, Volume: 5 Issue: 2, 171 - 183, 28.12.2021

Murat Olgun , Savaş Belen , Yaşar Karaduman , Metin Turan , Zekiye Başçiftçi , Nazife Gözde Ayter Arpacıoğlu

Abstract

In this study, monthly precipitations between 2007-2018 in Eskişehir, Konya, Afyonkarahisar, Uşak and Kütahya provinces were examined and their effects on protein content, zeleny sedimentation (MSDS), thousand seed weight and test weight, constituting of the quality components in wheat, were revealed. Monthly precipitations affecting these quality components were determined by using correlation analysis, principle component analysis (PCA), stepwise regression analysis, path analysis and decision tree analysis. In the research; the effects of precipitations falling in September, October, November, March, April, May and June and total precipitation on the quality components (protein content, zeleny sedimentation (MSDS), thousand seed weight and test weight) in Eskişehir, Konya, Afyonkarahisar, Uşak and Kütahya were determined. It is also aimed to determine effective monthly precipitations on the quality components by determining different analysis programs. As a result; March precipitation, April precipitation, June precipitation, October precipitation and total precipitation were determined as significant precipitations affecting the quality components (protein content, macro sedimentation (MSDS), thousand seed weight and test weight) in bread wheat. Increasing June precipitation, October precipitation and total precipitation affect the quality of bread wheat increase seed weight and test weight, while it causes a relative decrease in protein content and MSDS. Uşak and Kütahya provinces were found to be superior regions for thousand seed weight and test weight, whereas, Eskişehir and Konya provinces were determined as better provinces for zeleny sedimentation (MSDS) and protein content.

Keywords

bread wheat, quality, rainfall, protein content, MSDS, statistical metods, thausant seed weight, test weight

References

• Anwarmalik, M. F. Ashraf, M. Qureshi, A. S. and Ghafoor, A. (2007). Assessment of Genetic Variability, Correlation and Path Analyses for Yield and its Components in Soybean, Pak. J. Bot., 39 (2), p. 405-413
• Armitage P. Berry G. (2002) .Matthews JNS. Statistical Methods in Medical Research (4th edition). Oxford: Blackwell Science.
• Baraki, F. Tsehaye, Y. Abay, F. (2014). AMMI Analysis of Genotype × Environment interaction and stability of sesame genotypes in northern Ethiopia. Asian J. Plant Sci.13, 178.
• Buhlmann, P. and Yu, B. (2003). Boosting with the L2 loss: regression and classification. J. Amer. Statist. Assoc. 98, 324-339.
• Burnett, V. and Clarke S. (2002). Organic farming: Wheat production and marketing. Agriculture Notes. AG1075. ISSN 1329-8062.
• Calderini D.F. Dreccer, M.F. Slafer, G.A. (1997). Consequences of breeding on biomass, radiation interception and radiation-use efficiency in wheat, Field Crops Research, vol. 52 (pg. 271-281).
• Call, H. and Miller, W. (1990). “A Comparison of Approaches and Implementations for Automating decision analysis,” Reliability Engineering and Systems Safety, 30, 115-162.
• Caussınus H. Fekrı M. Hakam S. Ruız-Gazen A. (2003). A monitoring display of multivariate outliers, Computational Statistics and Data Analysis, vol. 44, num. 1–2, p. 237–252.
• Chen, R. B. Ing, C.K. and Lai T.L. (2011). An efficient pathwise variable selection criterion in weakly sparse regression models. Tech. Report, Dept. Statistics, Stanford Univ.
• Curic, D. Karlovic, D. Tusak, D. Petrovic, B. Dugum, J. (2001). Gluten as a Standart of Wheat Flour Quality, Food Tech. Biotechnol., 39(4) : 353-361.
• Dalkani, M. Darvishzadeh, R. and Hassani, A. (2011). Correlation and sequential path analysis in Ajowan (Carum copticum L.), Journal of Medicinal Plants Research, 5 (2), p. 211-216.
• Danielson, M. Ekenberg, L. Larsson, A. (2007). Distribution of expected utility in decision trees. Int. J. Approx. Reason. 46, 387–407.
• Dewey, D.R. Lu, K.H. (1959). A Correlation and pathcoefficient analysis of components of crested wheatgrass seed production. Agronomy Journal, 51: 515-518.
• Efron, B. Hastie, T. Johnstone, I. and Tibshirani, R. (2004). Least angle regression (with discussion). Ann. Statist. 32, 407-499.
• Fasahat, P. Rajabi, A. Mahmoudi, S. Noghabi, M. A. and Rad, J. M. (2015). An overview on the use of stability parameters in plant breeding. Biom. Biostat. Int. J.2, 00043.
• Feldman M. (2001). Origin of cultivated wheat. In: Bonjean AP, Angus WJ, eds. The world wheat book: a history of wheat breeding. Paris, France: Lavoisier Publishing, 3–56.
• Guarda, G., S. Padovan and G. Delogu. (2004). Seed yield, nitrogen-use efficiency and baking quality of old and modern Italian bread-wheat cultivars grown at different nitrogen levels. Europ. J.Agronomy, 21:181-192.
• Jaruchai, W. Monkham, T. Chankaew, S. Suriharn, B. And Sanitchon, J. (2018). Evaluation of stability and yield potential of upland rice genotypes in north and northeast Thailand. J. Integr. Agric.17, 28–36.
• Kashif, M. and Khaliq, I. (2004). Heritability, correlation and path coefficient analysis for some metric traits in wheat, International Journal of Agriculture & Biology, 6, p. 138-142.
• Leibinger, T. and Reiners E. (2001). Demands of sector bodies for organic plant breeding. Language: German. Original title: Anliegen der Oko-Verbande. Zuchtungsforschung, Pflanzenzuchtung und Okologischer Landbau, Quedlinburg, Germany, 22-23 Nov. 2001. Beitrag zur Zuchtungsforschung– Budesanstalt–fur Zuchtungsforschung an Kulturpflanzen, 8 (1): 199-23. (abstract)
• Nie, G. Zhang, L. Liu, Y. Zheng, X. Shi, Y. (2009). Decision analysis of data mining project based on Bayesian risk. Expert Syst. Appl. 36, 4589–4594.
• Pal M. and Mather P.M. (2003). An assessment of the effectiveness of decision tree methods for land cover classification. Remote Sensing of Environment, 86, pp. 554–56.
• Panozzo, J.F. and Eagles H.A. (2000). Cultivar and environmental effects on quality characters in wheat. II. Protein. Aust. J. Agric. Res., 51: 629- 636.
• Peterson, C.J. Graybosch, R.A. Baenziger, P.S. and Grombacher. A.W. (1992). Genotype and Environment Effects on Quality Characteristics of Hard Red Winter Wheat. Crop Sci., 32: 98-103.
• Phadatare, M.M. Nandgaonkar, S.S. (2014). Uncertain data mining using decision tree and bagging technique. Int. J. Comput. Sci. Inf. Technol. 5, 3069–3073.
• Plenet D. and Lemaire G. (2000). Relationships between dynamics of nitrogen uptake and dry matter accumulation in maize crops. Determination of critical N concentration”, Plant Soil. 216: 65–82.
• Poudel, A. Thapa D. B. and Sapkota M. (2017). Assessment of genetic diversity of bread wheat (Triticum aestivum L.) genotypes through cluster and principal component analysis. Int. J. Exp. Res. Rev., 11: 1-9.
• Scheiner, S.M. Mitchell, R.J. and Callahan, H.S. (2000). Using path analysis to measure natural selection, J. Evol. Biol., 13, p. 423-433.
• Sheskin D. (2011). Handbook of Parametric and Nonparametric Statistical Procedures Test, Chapman and Hall/CRC, Fifth Edition, 2011, S.800
• Shewry; P.R. (2009). Wheat. Darwın Revıew, Journal of Experimental Botany, Vol. 60, No. 6, pp. 1537–1553.
• Singh K.B. and Chaudhary B.D. (1977). Biometrical methods in quantitative genetic analysis. Kalyani Publishers New Delhi-India. 304 p
• Slafer, G.A. Calderini, D.F. Miralles, D.J. (1996). Generation of yield components and compensation in wheat: opportunities for further. increasing yield potential. In: Reynolds, M. Ed. , Increasing Yield Potential in Wheat: Breaking the Barriers. CIMMYT Int. Symp.,CIANO, Cd. Obregon, Mexico. CIMMYT, Mexico, D.F., pp. 101–133.
• Smith, J. (1989). “Influence Diagrams for Bayesian Decision Analysis,” European Journal of Operational Research, 40, 363-376.
• Spaldon, S. Samnotra, R. Dolkar, R. Choudhary, D. (2017).Stability analysis and genotype x environment interaction of quality traits in tomato (Solanum lycopersicum L.). Int. J. Curr. Microbiol. App. Sci6, 1506–1515.
• Tibshirani, R. (1996). Regression shrinkage and selection via the Lasso. J. Roy. Statist. Soc. B 58, 267-288.
• Tipples, K.H. Kilborn, R.H. and Preston, K.R. (1981). Bread Wheat Quality Defined. A Dough Height Tracker and its Potential Application to the Study of Dough Characteristics. Cereal Chemistry 58 : 198-201.
• Tiwari, A. K. and Lal, G. (2014). Genotype−environment interaction and stability analysis in tomato (Solanum lycopersicum L.). Indian J. Hill Farming27, 16–18.
• Wang, W. Vinocur, B. Shoseyov, O. and Altman, A. (2004). Role of plant heatshock proteins and molecular chaperones in the abiotic stress response, Trends in Plant Science, 9(5), 244-252.
• Wang, X. Vignjevic, M. Jiang, D. Jacobsen, S. and Wollenweber, B. (2014). Improved Tolerance to Drought Stress After Anthesis due to Priming Before Anthesis in Wheat (Triticum aestivum L.) var. Vinjett. Journal of Experimental Botany, 65(22), 6441-6456.
• Zwingelberg, H. (1961). Beziehungen zwishen gnantzkomasohage halt, Mehlassneghault und mehlfarbe bei weizensorten. Getrede meh. 10:117-119.