The aim of this study was to determine knowledge, attitudes, and behaviors of farmers dealing with animal husbandry in eastern Turkey with regard to antibiotic knowledge, use, and resistance. A face to face questionnaire survey, consisting of five sections with 42 questions in total, was applied to 360 farmers located in the region. The questions in the first and fifth sections were closed-ended while those in other sections were prepared using the Likert scale. It was determined that knowledge of the farmers about antibiotic use, duration, storage, and resistance was well below desired levels. This was particularly remarkable in the participants with a low level of education, living in rural areas, and those at 48 years of age or over. In contrast, younger and highly educated participants living in urban areas were more knowledgeable about antibiotic use and they were well aware of the fact that resistance might pose a great risk for public health. Providing appropriate antibiotic use in animals through systematic training of livestock farmers is crucial in tackling the resistance problem.

Body weight of dogs is crucial trait for breeding, racing and housekeeping. However, variables and factors that correctly estimate this trait are lacking. Here, we applied classification and regression tree (CART) and multivariate adaptive regression splines (MARS) approaches to estimate the most important variables in predicting the body weight of Turkish Tazi dogs. Using various body measurements, the CART algorithm proposed that withers height (WH), abdominal width (AW), rump height (RH) and chest depth (CD) can significant effect the body weight. Quantitatively, it was identified that values of WH > 62.500 cm and RH > 67.500 cm can positively correlated with the highest body weights. On the other hands, MARS model’s finding showed that the dogs which had the values of WH > 51 cm can be expected to have the highest body weights. The calculated model evaluation criteria of CART algorithm was R2=0.6889, Adj. R2=0.6810, r=0.830, SD ratio=0.5549, RMSE=1.1802, RRMSE=6.3838 and ρ=3.4884, respectively, whereas the calculated model evaluation criteria of MARS method were R2=0.9193, Adj. R2=0.8983, r=0.9588, SD ratio=0.2840, RMSE=0.6041, RRMSE=3.2635 and ρ=1.6661. Taken together, the MARS algorithm appeared to be efficient compared to CART algorithm since the MARS algorithm’s goodness-of-fit criteria yielded better results. Using MARS algorithm, the body weight of animals (dogs) can be predicted and exploited in different performances.

Comparing Predictive Performances of Tree-Based Data Mining Algorithms and MARS Algorithm in the Prediction of Live Body Weight from Body Traits in Pakistan Goats

The present study aimed at comparing predictive performance of some data mining algorithms (CART, CHAID, Exhaustive CHAID, MARS, MLP, and RBF) in biometrical data of Mengali rams. To compare the predictive capability of the algorithms, the biometrical data regarding body (body length, withers height, and heart girth) and testicular (testicular length, scrotal length, and scrotal circumference) measurements of Mengali rams in predicting live body weight were evaluated by most goodness of fit criteria. In addition, age was considered as a continuous independent variable. In this context, MARS data mining algorithm was used for the first time to predict body weight in two forms, without (MARS_1) and with interaction (MARS_2) terms. The superiority order in the predictive accuracy of the algorithms was found as CART > CHAID ≈ Exhaustive CHAID > MARS_2 > MARS_1 > RBF > MLP. Moreover, all tested algorithms provided a strong predictive accuracy for estimating body weight. However, MARS is the only algorithm that generated a prediction equation for body weight. Therefore, it is hoped that the available results might present a valuable contribution in terms of predicting body weight and describing the relationship between the body weight and body and testicular measurements in revealing breed standards and the conservation of indigenous gene sources for Mengali sheep breeding. Therefore, it will be possible to perform more profitable and productive sheep production. Use of data mining algorithms is useful for revealing the relationship between body weight and testicular traits in describing breed standards of Mengali sheep.