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This study was carried out to determine the productive responses of 10 chickpeas (Cicer arietinum L.) genotypes to different places and years. Hasanbey, Aksu, Seckin, Damla 89, Gulumser, Cagatay, Sezenbey, Inci, Gokce and Uzunlu 99 chickpea genotypes were used as plant material. This research was conducted in Yozgat, Kirikkale and Kirsehir Provinces of Turkey in 2014, 2015 and 2016. The experimental design was a randomized block with 4 replicates. Environmental variance, variation coefficient, ecovalance, stability variance, superiority measure, regression coefficient, deviation from regression and coefficient of determination methods were used for stability calculations. Aksu genotype had the highest stability level, whereas Seckin, Damla 89 and Uzunlu 99 chickpea genotypes also successfully grown with respect to stability parameters. Cagatay chickpea genotype showed the highest yield potential, if grown in ideal environmental conditions. To conclude, the ideal yield would be obtained in the event that the requirements of if the genotypes are fulfilled by desired environmental conditions. [ABSTRACT FROM AUTHOR]

Multivariate statistical methods are successfully used in many areas. Cluster Analysis and Principal Component Analysis methods are the most used of these methods. In this study, water quality data prepared for Seyfe Lake were evaluated. For this purpose, principal components and cluster analysis techniques were used. The 26 selected parameters were gathered at 11 of the points and evaluated. The cluster analysis was obtained from three different groups. These sampling points have different physicochemical characteristics and the pollution levels. According to the results of Principal Component Analysis, the six factors explained 92.72% of the total variance. The first factor 29.52%, the second factor 17.89%, the third factor 16.75%, the fourth 13.65%, the fifth 9.73%, and the sixth 5.17% of the cumulative variances explained respectively. Results reveal that sulfate, nitrate, total phosphorus, hardness, electrical conductivity, total dissolved solid, magnesium, potassium, sodium, calcium, biochemical oxygen demand, chemical oxygen demand, aluminum, iron, chromium, and lead were the most important parameters used to evaluate changes in water quality of the lake. [ABSTRACT FROM AUTHOR]