Mehmet Topal

Determination of Best Fitted Regression Model for Estimation of Body Weight in Awassi Sheep

Abstract Topal, M., Yildiz, N., Esenbuğa, N., Aksakal, V., Macit, M. and Özdemir, M. 2003. Determination of best fitted regression model for estimation of body weight in Awassi sheep. J. Appl. Anim. Res., 23: 201–208. In this research, linear regression models were improved for estimation of body weights using various body measurements from Awassi sheep. Simple and multiple regression models were formed when body weight (BW) was dependent variable and body length (BL), heart depth (HD), height at wither (HW), height at shoulder (HS), shin girth (SG), chest width (CW), heart girth (HG), pump width (PW) were independent variables. The best-fitted regression model for estimation of body weight was determined by using mean square error (MSEp), R2 p and Cp statistics. The model including body length, heart depth, heart girth and pump width was the best fitted model (R2 P=0.851, MSEP =8.803, Cp=5.972) for estimation of body weight in Awassi sheep in this study.

Comparison of Nonlinear Growth Curve Models in Broiler Chickens

Abstract Topal, M. and Bolukbasi, Ş.C. 2008. Comparison of nonlinear growth curve models in broiler chickens. J. Appl. Anim. Res., 34: 149–152. This study was conducted to estimate the growth curves of broiler chickens using weekly body weight data from lto42d of age. The Logistic, Gompertz, Von Bertalanffy, Morgan-Mercer-Flodin (MMF) and Weibull growth curve models were compared to the best fit to the broiler age- weight data. MMF and Weibull were determined to be the best fitted models and Logistic was found the worst fitted for female and male broiler chickens. It is suggested that MMF, Weibull and Gompertz models can be useful for explaining broiler chickens growth performance.

Determination of Genetic Distance Between East Anatolian Red, Brown Swiss, Holstein and Simmental Breeds of Cattle Using Polymorphic Systems

Abstract Dogru, U., Ozdemir, M., Soysal, M.I. and Topal, M. 2008. Determination of genetic distance between East Anatolian Red, Brown Swiss, Holstein and Simmental breeds of cattle using polymorphic systems. J. Appl. Anim. Res., 33: 145–148. Genetic distances were determined according to Nei, Cavalli-Sforza and Reynolds methods between the Brown Swiss (173), Simmental (80), Holstein (18) and East Anatolian Red (51) cattle breeds using milk protein (αs1) and blood protein types (Hb and Tf). The distribution of phenotypic frequencies in all the four breeds for all systems except αs1-Cn was significant (P<0.01) using chi-square test. Phylogenetic trees based on the polymorphic systems of the populations obtained by three different methods of Nei, Cavalli-Sforza and Reynolds were found to be similar. According to the phylogenetic trees Brown Swiss and Simmental cattle breeds fall in one group and dairy (Holstein) and Native (East Anatolian Red) breeds in different groups.

GENOTİP × ÇEVRE ETKİLEŞİMİNİN BELİRLENMESİNDE KULLANILAN PARAMETRİK KARARLILIK ANALİZ YÖNTEMLERİ ARASINDAKİ İLİŞKİNİN ARAŞTIRILMASI

Bitki ve hayvan islahinda Genotip × Cevre etkilesimi oldukca onemli bir konudur. Genotip × Cevre etkilesiminin tespitinde kullanilan parametrik ve parametrik olmayan yontemler genotiplerin her bir cevredeki verim degerlerine ve bunlarin ranklarina dayanmaktadir. Kararlilik yontemleri ile genotiplerin cevrelerden etkilesimleri bireysel olarak tespit edilmektedir. Bu arastirmada normal ve kesikli uniform dagilisa gore turetilen verilerde etkilesimi tespit etmek icin ilk once varyans analizi uygulandi daha sonra parametrik yontemlerden Cevresel Varyans, Shukla’nin Kararlilik Varyansi, Varyasyon Katsayisi, Ecovalence, Lin ve Binns’in Pi yontemi, Finlay ve Wilkinson’un regresyon katsayisi, Perkins ve Jinks’in regresyon katsayisi ve Eberhart ve Russel yontemi uygulandi. Yontemlerin normal ve kesikli uniform dagilis gosteren verilere uygulanmasinda yontemler arasinda iliski yonunden dagilislar arasinda bir fark olmadigi gozlendi. Her iki dagilista varyans analizine gore Genotip × Cevre etkilesiminin onemsiz oldugu durumda parametrik kararlilik yontemlerine gore elde edilen katsayi degerleri onemsiz bulunurken Genotip × Cevre etkilesiminin onemli oldugu durumlarda kararlilik katsayi degerleri arasindaki fark onemli bulunmustur. Normal ve kesikli uniform dagilis gosteren verilerde parametrik yontemlerde en yuksek korelasyonlar CV, VK, EV, SSV ve ERSKT yontemleri arasinda gorulurken bu yontemlerin FWbi ve PJbi yontemleriyle olan iliskisi dusuk bulunmustur. Ayrica en yuksek korelasyonlar SSV ile EV, CV ile VK ve FWbi ile PJbi yontemleri arasinda bulundu. Genotip × Cevre etkilesiminin tespitinde aralarinda korelasyonun yuksek oldugu yontemlerden herhangi biri kullanildiginda benzer sonuclarin alinabilecegi gozlenmistir. Sonuc olarak Genotip × Cevre etkilesim degerini genotiplere bireysel olarak parcalayan EV ve SSV yontemlerinden birisinin veya bu yontemlerle yuksek sira korelasyona sahip yontemlerden herhangi biri tercih edilebilir. Genotype Environment interaction is the most important issue for animal and plant breeding. Parametric and nonparametric methods used in determination of Genotype × Environment interaction are based on the yield values of genotypes and their ranks in each environment. Genotype × Environment interactions are individually established by stability methods. At the beginning, variance analysis was applied to determine the interactions related to data simulated according to normal and discrete uniform distribution, and then environmental variance, Shukla’s stability variance, variation coefficient, ecovalance, Lin and Bins’ Pi method, Finlay and Wilkinson’s regression coefficient, Perkins and Jinks’ regression coefficient and Eberhart and Russell’s deviation parameter from parametric methods were applied to present data. Differences among the methods applied for data displayed normal and discrete uniform distribution were not significant. When the interactions obtained according to variance analysis in both normal and discreate uniform distribution were insignificant, coefficient values from parametric stability estimation methods were also found insignificant. In addition, when interaction was determined to be significant, difference among the coefficient values was significant. While the highest correlation in parametric methods for normal and discrete uniform distributions was established among CV, VK, EV, SSV and ERSKT, the correlation of these methods with FWbi and PJbi methods were found to be very low. Also, the highest correlations were determined between SSV and EV, CV and VK, FWbi and PJbi methods. It was observed that similar results may be obtained when any method showing high correlation with other methods was used to determine the Genotype × Environment interaction. It was concluded that one of EV and SSV methods individually partitioning Genotype × Environment interaction values to genotype or any method showing high rank correlation with these methods is preferable to other methods.