No abstract

The primary purpose of this study was to determine factors influencing consumer preferences for UHT milk consumption in Erzurum province. The primary data used in this research was derived from Palandoken, Yakutiye and Aziziye districts of Erzurum province in 2010. The factor analysis was used to find out the factors affecting consumer preferences for UHT milk and to reduce these factors. As for the segmentation of consumers and bringing out the profile of each segment, cluster analysis was used. According to the results, 95.00% of households consumed UHT milk. 18 factors that are affecting the consumption of UHT milk were reduced to five main factors with factor analysis. The factor scores which determined with factor analysis were divided into three clusters by cluster analysis. UHT milk for consumers entering the first cluster has because of homogenous and packaging as well as intrinsic and extrinsic properties for advertising and price advantage is preferred. UHT milk for consumers entering the second cluster has ease of preparation and transportation, and confidential properties are preferred by reason. On the contrary, consumers entering the third cluster prefer to UHT milk for a good diet product.

The aim of this investigation was to determine risk factors affecting production of beekeeping farms in Igdir province of Turkey and to develop strategies in coping with these risks. Research was based on data collected through a questionnaire applied to 85 beekeeping farms registered to Igdir Beekeepers' Union according to exact counting method. Factor analysis was applied to collected data to identify risk factors and risk management strategies. Factor analysis was conducted under principle component extraction method and VARIMAX rotation. A stepwise regression analysis was used to reveal the relationship between each of four strategy factors and eight risk factors. As risks in procuring labor occur, farmers are more likely to adopt modern agricultural techniques and risk management strategies, such as registering to a cooperative, product insurance, contract farming, and cooperating with public bodies. Unfavorable security conditions and lack of proper bookkeeping in farms are more likely to lead to adoption of careful production and investment planning. As enterprise conditions get better or external conditions get worse, protecting the investment through disease-prevention and better marketing through getting more market information becomes important. Thus, thirteen applicable strategies are determined in the study. As a result, the approach developed in this research could be suggested for beekeepers in selecting necessary strategies against possible risk factors defined here for sustainable honey production and more income.

This study was conducted to compare predictive performances of different data-mining algorithms for determining factors influencing the average daily milk yield at dairy cattle enterprises of Ardahan province, located in the Eastern Anatolia region of Turkey. The algorithms employed in the present study were Classification and Regression Tree (CART), Chi-Square Automatic Interaction Detector (CHAID), Exhaustive Chi-Square Automatic Interaction Detector (Exhaustive CHAID), Multivariate Adaptive Regression Splines (MARS), and Multilayer Perceptron (MLP). The MARS algorithm outperformed the other algorithms in the study. Visual results of CART revealed that the culture-breed cows with a lactation length greater than 237.500 days had the highest milk yield (10.64 kg/day). Culture-breed cows calving earlier than the 4th month gave the highest yield of approximately 10 kg/day in the regression tree of CHAID. The Exhaustive CHAID results were almost the same as the structure of the CHAID. The use of MARS may provide an opportunity to detect factors affecting milk production (breed, feed supply, type of milking, mastitis control, cow year group, and lactation length) and their interactions. Moreover, the MARS algorithm may be useful in making an accurate decision about increasing milk yield per cow.