Turgay Şengül

Türkiye’de Organik Bal Üretiminin Yıllara Göre Değişiminin Regresyon Analizi ile İncelenmesi Üzerine Bir Çalışma

Bu calismada, Turkiye’de 2004-2016 yillari arasindaki organik bal uretimdeki degisimler regresyon analizi ile incelenmistir. Regresyon analizinde, lineer, kuadratik, kubik, logaritmik ve ters regresyon modelleri karsilastirmali olarak incelenmistir. Bu modeller ile elde edilen R2 degerleri sirasiyla; 0,16; 0,62; 0,70; 0,37; 0,52, R ^2 degerleri 0,08; 0,54; 0.60; 0.31; 0.48 ve hata kareler ortalamasi (HKO) degerleri 48743,01; 24376,61; 21228,61; 36580,48; 27563,47 olarak bulunmustur. Parametre tahminleri anlamli bulunan, R ^2 degeri en yuksek ve HKO degeri en dusuk olan kuadratik regresyon modeli en uygun model olmustur. Bu regresyon modeline gore, 2017 ve 2018 yillarinda organik bal uretim miktarinin sirasiyla; 693 ve 891 ton olacagi tahmin edilmistir. Ayrica, ayni donem icinde organik olmayan bal uretiminin regresyon analizi de yapilmis ve lineer regresyon modeli en uygun model olarak belirlenmistir. Bu model icin R2=0,77 ve R ^2=0,75 olarak hesaplanmistir. Sonuc olarak, organik ve organik olmayan bal uretim miktarlarinin farkli regresyon modelleri ile tahmin edilebilecegi kanisina varilmistir.

Analysis of Variables Affecting Carcass Weight of White Turkeys by Regression Analysis Based on Factor Analysis Scores and Ridge Regression

In this study, the influence of carcass parts weights (thigh, breast, wing, back weight, gizzard, heart, and feet) on whole carcass weight of white turkeys (Big-6) was analyzed by regression analysis based on ridge regression and factor analysis scores. For this purpose, a total of 30 turkey carcasses of 15 males and 15 females with 17 weeks of age, were used. To determine the carcass weight (CW), thigh weight (TW), breast weight (BRW), wing weight (WW), back weight (BW), gizzard weight (GW), heart weight (HW), and feet weight (FW) were used. In the ridge regression model, since the Variance Inflation Factor (VIF) values of the variables were less than 10, the multicollinearity problem was eliminated. Furthermore, R2=0.988 was obtained in the ridge regression model. Since the eigenvalues of the two variables predicted by factor analysis scores were greater than 1, the model can be explained by two factors. The variance explained by two factors constitutes 88.80% of the total variance. The regression equation was statistically significant (p<0.01). In the regression equation, two factors obtained by using factor analysis scores were independent variables and standardized carcass weight was considered as dependent variable. In the regression model created by factor analysis scores, the Variance Inflation Factor values were 1 and R2=0.966. Both regression models were found to be suitable for predicting carcass weight of turkeys. However, the ridge regression method, which presented higher R2 value, has been shown to better explain the carcass weight.

The General Structure of Turkey Breeding Carried out under Extensive Conditions in the Province of Batman

This research was conducted to reveal the structure of turkey production carried out extensively (based on grazing in the pasture) in Batman province and the districts. In the study a survey was conducted with 190 attendants through proportional sampling and in order to represent the sample population from the villages of Batman province. Questionnaires to be applied to producers included the questions related to producers’ socioeconomic status, technical-structural characteristics of coops, and care and feeding of animals. As a result, it can be pointed out that Batman province has important problems of turkey breeding and the solutions to the problems that the producers face have great importance in terms of increasing profitability.