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In this study, milk yield from culture, cross-bred, and native cattle breeds in 7 geographical regions of Turkey were investigated by multivariate analysis of variance (MANOVA) in 2015. The values of Pillai's Trace, Wilks' Lambda, Hotelling's Trace and Roy's Largest Root statistics used for the MANOVA test were 0.522, 0.546, 0.711 and 0.490, respectively. The corresponding F values for these statistics were found to be 2.422, 2.518, 2.594 and 5.634, respectively. Mean annual milk yield of crossbred, culture, and native cattle breeds reached to be 3776.355, 2692.803 and 1311.513 kg, respectively. Therefore, the results of these statistics are very close to each other. As a result of the MANOVA test, the difference within milk yield between the regions was not significant for culture and native cattle breeds, whereas a significant difference was found in hybrid breed cattle (P < 0.001). As a result of the Bonferroni test, it was found that the milk yield difference in hybrid cattle was due to the difference in yield between Eastern Anatolia-Aegean, Eastern Anatolia-Marmara, Southeast Anatolia-Aegean and Southeast Anatolia-Marmara regions. [ABSTRACT FROM AUTHOR]

Daily concentrations of air pollutants greatly affect air quality as an increase in industry and urbanization deteriorate the environment. For forty cities in Turkey, eleven variables are recorded to investigate the determinants of air pollution presumably by regressing the air pollutants PM10, NOX, and NO2. The temperature, wind, human factors such as population, vehicles, manufacturer, and suchlike are used to create a nonlinear air quality model in Turkey due to the multivariate nature of the data. A comparison of the nonparametric models of the concentration of these pollutants, using multivariate adaptive regression splines (MARS), was obtained to estimate the dependence between air pollutants and various factors. Finally, a model for PM10 concentration shows that climate effects are the most significant variables, whereas the predicted models for NOX and NO2 indicate that human factors, such as the number of manufacturers and the number of vehicles, are significant variables. In conclusion, the predicted models are easy to interpret and have advantages of capacity to produce the contributions of the factors for each pollutant model. It is advisable for researchers to examine and determine the suitability of their data sets using nonlinear models when atypical observations and high correlations exist in the data. [ABSTRACT FROM AUTHOR]

Renewable energy sources have been attracting more and more attention of researchers due to the diminishing and harmful nature of fossil energy sources. Because of the importance of solar energy as a renewable energy source, an accurate determination of significant covariates and their relationships with the amount of global solar radiation reaching the Earth is a critical research problem. There are numerous meteorological and terrestrial covariates that can be used in the analysis of horizontal global solar radiation. Some of these covariates are highly correlated with each other. It is possible to find a large variety of linear or non-linear models to explain the amount of horizontal global solar radiation. However, models that explain the amount of global solar radiation with the smallest set of covariates should be obtained. In this study, use of the robust coplot technique to reduce the number of covariates before going forward with advanced modelling techniques is considered. After reducing the dimensionality of model space, yearly and monthly mean daily horizontal global solar radiation estimation models for Turkey are built by using the genetic programming technique. It is observed that application of robust coplot analysis is helpful for building precise models that explain the amount of global solar radiation with the minimum number of covariates without suffering from outlier observations and the multicollinearity problem. Consequently, over a dataset of Turkey, precise yearly and monthly mean daily global solar radiation estimation models are introduced using the model spaces obtained by robust coplot technique and inferences on the sensitivity of the amount of global solar radiation to covariates and the magnitude and direction of effect of covariates on the global solar radiation are drawn. [ABSTRACT FROM AUTHOR]

The article discusses the homogeneity of the precipitation data in Turkey. It says that outlier trimming and homogeneity checking were both performed on a monthly basis at various lengths using the Standard Normal Homogeneity Test in 267 hydrological stations. It likewise states that outlier values are usually found during dry summer months over the southern part of the country. Moreover, it claims that there are several stations that could either not be tested efficiently or could be classified as inhomogeneous.