Determination of The Effect of Zeolite and Organic Acid Mixture Supplementation on Performance and Egg Quality in the Layer Diet Using Grey Relational Analysis Method

Year 2022, Volume: 5 Issue: 1, 16 - 20, 01.01.2022

Aycan Mutlu Yağanoğlu Mehmet Topal

https://doi.org/10.47115/bsagriculture.998526

Abstract

Grey system theory is a method used when the assumptions of homogeneity of variances in the application of parametric statistical methods of analysis, the assumption that the distribution of data to be applied is appropriate to the normal distribution and that it is, for example, of sufficient size. Grey relational analysis is one of the subtitles of the grey system theory and it is a method of grading, classification and decision making in the data set using the Grey Relation Coefficient (GIA). In this study, the effects of addition of zeolite, organic acid and zeolite-organic acid added to the rations of twenty-four Lohmann LSL type white-laying hens at 28 weeks of age on the quality and performance of eggs were examined. Grey relational analysis was applied to egg quality and performance criteria. It was found that the highest quality eggs were added with organic acid (P) additions to the chicken rations (P6, P3, P1), and the K1 in the control group and the (Z + P) 1 sample in which the zeolite and organic acid additives were applied together were also found in the quality egg group. In terms of quality, middle class egg samples were determined to be zeolite additive group and low quality egg group was found to be control and zeolite + organic acid added group.

Keywords

Grey relational analysis, Performance, Egg quality, Zeolit, Organic acid

References

• Deng JJ. 1989. Indroduction to Grey System Theory. J Grey Syst, 1(1): 1-24.
• Henderson CR. 1984. Application of linear models in animal breeding. Henderson CR. University of Guelph Press, Guelph: Ontorio, Canada, 1st ed., pp. 461.
• Kumlu S. 1999. Hayvan ıslahı. Türkiye Damızlık Sığır Yetiştiricileri Merkez Birliği Yayınları, Ankara, Türkiye 1. baskı, ss. 296.
• Kuo Y, Yang T, Huang GW. 2008. The use of grey relational analysis in solving multiple attribute decision-making problems. Comput Indust Engin, 55: 80-93.
• Mao PL, Liu J, Wang C Hu. 2010. A study on gray relational analysis of many factor weights in tobacco leaves classification. Adv Mater Res, 139-141: 1728-1731.
• Memmedoca N, Keskin İ. 2009. Hayvancılıkta bulanık mantık uygulamaları. Selçuk Üniv Tarım Gıda Bilim Derg, 23(47): 89-95.
• Morag I, Edan Y, Maltz E. 2001. An individual feed allocation decision support system for the dairy farm. J Agric Eng Res, 79(2): 167-176.
• Patterson HD, Thompson R. 1971. Recovery of interblock information when block sizes are unequal. Biometrica, 58: 545-554.
• Tosun N. 2006. Determination of optimum parameters for multi-performance characteristics in drilling by using grey relational analysis. Inter J Advan Manufact Technol, 28: 450–455.
• Üstünışık NZ. 2007. Türkiye'deki iller ve bölgeler bazında sosyo-ekonomik gelişmişlik sıralaması araştırması: Gri ilişkisel analiz yöntemi ve uygulaması. Yüksek Lisans Tezi, Gazi Üniversitesi, Fen Bilimleri Enstitüsü, Ankara, Türkiye, pp. 257.
• Wade KM, Lacroix R, Strasser M. 1998. Fuzzy logic membership values as a ranking tool for breeding purposes in dairy cattle. In: Proceedings of the 6th World Congress on Genetics Applied to Livestock Production, January 11-16, Armidale, Australia, 27: 433-436.
• Wu CH. 2007. On the application of grey relational analysis and RIDIT analysis to likert scale surveys. Inter Math Forum, 2(14): 675-687.
• Zhu CJ, Hao ZC. 2009. Application of grey relation analysis in evaluation of water quality. Environ Sci Inform Applicat Technol, (1): 255–257.