Browsing by Author "Yesilova, A."

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Comparing Covariance Structures Using Different Optimization Techniques in Glmm on Some Sexual Behaviors of Male Lambs
(Pakistan Agricultural Scientists Forum, 2013) Ser, G.; Yesilova, A.; Yilmaz, A.
This study is concerned with use of generalized linear mixed models (GLMM) to analyse the repeated measurements based on count data obtained from the sexual behaviors of male lambs. A combination of different optimization techniques and covariance structures were applied to four constructed models. These models were defined in terms of random effect specifications. Therefore, residuals was assumed to be random (Model A), intercept assumed to be random effect (Model B), time (slope) assumed to be random effect (Model C) and both intercept and time assumed to be random effects (Model D). Five different techniques quasi-newton (QUANEW), newton-raphson (NEWRAP), trust region (TRUREG), newton-raphson ridge (NRRIDG) and double-dogleg (DBLDOG) optimization techniques were used for analyzing these models. Three different covariance structures compound symmetry (CS), unstructured (UN) and first-order autoregressive (AR(1)) were used. In conclusion, based on likelihood criteria, the Model A with CS structure outperformed other models for the repeated measurement data of sexual behavior characteristics.
Comparison of Different Count Models for Investigation of Some Environmental Factors Affecting Stillbirth in Holsteins
(Agricultural Research Communication Centre, 2022) Gevrekci, Y.; Guneri, O. I.; Takma, C.; Yesilova, A.
Background: The objective of this study is comparing different count data models for stillbirth data. In modeling this type of data, Poisson regression or alternative models can be preferred. Methods: The poisson, negative binomial, zero-inflated poisson, zero-inflated negative binomial, poisson-logit hurdle and negative binomial-logit hurdle regressions were compared and used to examine the effects of the gender, parity and herd-year-season independent variables on stillbirth. Furthermore, the Log-Likelihood statistics, Akaike Information Criteria, Bayesian Information Criteria and rootogram graphs were used as comparison criteria for performance of the models. According to these criteria, Negative Binomial-Logit Hurdle Regression model was chosen as the best model. Result: The parameter estimates obtained by Negative Binomial-Logit Hurdle Regression model in relation to the effects of the gender, parity and herd-year-season independent variables on stillbirth were found to be significant (p<0.01). It was found that while stillbirth incidence was higher in males than females, it was found to decrease as the parity increased. As a result, the Negative Binomial Logit Hurdle model was found the best model for stillbirth count data with overdispersion.
The Effects of Secondary Treatment on Nodulation of Alfalfa (Medicago Sativa L.) and Nutrients Supply in Domestic Wastewater
(Aloki Applied Ecological Research and Forensic inst Ltd, 2022) Arvas, O.; Yagan, F.; Yesilova, A.
The use of domestic wastewater in irrigation is becoming widespread. The aim of this study was to determine the effect of Secondary Treated wastewater (STW) irrigation on alfalfa nodulation, agronomic parameters, exchangeable cations and heavy metals in soil and plant. Three levels of STW diluted with distilled water and distilled water were applied for 6 months with three replications in randomized plots in laboratory. Negative binomial regression was applied to model the overdispersion. Diluted STW reduced the total and active nodule number by at least 79.5% and 91.2%, respectively. Diluted STW decreased total biomass of alfalfa compared to distilled water. STW increased the EC and Mg ratios in the soil and the Na ratio in the soil, root and shoot. However, it did not affect the K and Ca ratios in the soil, root and shoot. Co, Ni, As and Pb uptake were inhibited in roots, however elements such as Fe, Cu, Mo, Mn, Al, Cr and Cd accumulated in high concentration in the root. Increasing Zn concentration in shoot may be attributed physiological selectiveness of the alfalfa. Consequently, the use of STW cannot be recommended in alfalfa irrigation as it could reduce the biomass and prevent nodulation.
Modeling the Mite Counts Having Overdispersion and Excess Values of Zero Using Zero-Inflated Generalized Poisson Regression
(Agricultural Research Communication Centre, 2016) Yesilova, A.; Kaki, B.
The aim of this study was to apply for zero-inflated generalized Poisson regression in the modelling of mite counts that include excess values of zero and overdispersion. The results of zigp(μi,-i,ωi, as mean regression, overdispersion and zero-inflated regression, were determined in three stages. It was obtained that 33.33% (120 observations) of the total numbers of mite taken as a dependent variable to model had zero values. The over dispersion parameter range was detected to be quite high. It was determined that zero-inflated data and overdispersion had an important effect on mite counts (P< 0.01). The effects of region, month, year, varieties, temperature and humidity were found to be statistically significant on mite counts (P< 0.01). The number of eggs found in harmful mites (Aculus schlechtendali) in the Starking variety was relatively higher than in the Golden variety. The results displayed that the differences among regions and varieties regarding the number of eggs found in harmful mites were statistically significant (P<0.01). © 2016 Indian J. Agric. Res.