This cross-cultural study aimed to determine the main factors behind potential changes in eating habits by analyzing changes in the patterns of beef consumption currently observed in Brazil, Spain, and Turkey. To achieve this aim, 412 regular beef consumers from Brazil, 407 from Spain, and 424 from Turkey answered a self-administered questionnaire. The study surveyed the effects of economic factors, switching from beef to other sources of protein, aspects of credence, health-related concerns, the influence of lifestyle on beef consumption patterns, and purchasing decision factors. The most important factors that changed consumer behavior and resulted in a decrease in consumption, mostly among Brazilian and Turkish consumers, were the economics and accessibility of the products. Beef was replaced by other alternative sources of protein that were likewise derived from animals. The consumers whose purchasing intentions were most significantly influenced by credence factors (e.g., indiscriminate use of agricultural products, substandard animal welfare requirements, among others) were Brazilian and Turkish and, to a lesser degree, Spanish consumers. Lifestyle factors (e.g., consumption of out-of-home meals, available time to cook, among others) were demonstrated to alter consumption patterns and therefore must be carefully considered by the industry, taking into account cultural differences and consumer needs. The population under investigation considered that eating beef had no impact on their health.

The objective of this study was to compare predictive performances of four machine learning (ML) models: Support Vector Machines with Radial Basis Function Kernel (SVMR), Classification and Regression Trees (CART), Random Forest (RF) and Model Average Neural Networks (MANN) to predict live weight from morphological measurements of Norduz sheep (n=93). Seven morphological measurements; chest girth (CG), chest width (CW), chest depth (CD), height at withers (HW), body length (BL), heigth at rump (HR) and rump width (RW) were used to predict live weigth (LW) of Norduz sheep. All morphological measurements were positively correlated to LW. Live weight had the highest correlation with CG and the lowest correlation with HR. Initially, highly correlated predictors were removed from the data set. The remaining predictors were then subjected to variable selection procedures using the Boruta algorithm. The results of Boruta confirmed the importance of the four predictors HW, BL, CW, and CD. However, HR confirmed to be unimportant was excluded from the dataset. The ML models were trained on selected predictors. The results showed that the prediction performance validated using the test dataset indicated that RF had the lowest values of Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and Mean Absolute Percent Error (MAPE). The permutation-based variable importance scores indicate that CW and CD were the most important variables in predicting LW. The actual LW had the highest significant positive correlations with the values predicted by SVMR and RF, and followed by ANN and CART models respectively. There were no differences between the means of actual and predicted LWs by machine learning models. The fact that the models generalized well on the testing data sets indicates that machine learning algorithms have valid predictive patterns and are effective methods in LW weight of Norduz sheep. Considering runtime of the models, although the CART model had the lowest computational cost, it had the worst performance. The MANN algorithm, on the other hand, required a longer runtime to process the same dataset.

This study aims to comparatively determine the consumer perception of food products marketed under ecologically friendly concepts (organic food, good agriculture, and natural production) and food sold directly by farmers, conventional food, and farmer cooperative branded food. For this purpose, a face-to-face survey was conducted with 171 identified consumers. R program was used to perform all of the analyses. Machine learning methods such as Logistic Regression (LR), Correspondence Analysis (CA), and Principal Component Analysis (PCA) are used for determining consumer perception from obtained data. Descriptive statistics results showed that 51.5 percent of those polled were male and 48.5 percent were female. It found that the mean age of the consumers was joined to the survey was 36.4. According to the LR findings, consumer socioeconomic characteristics have a considerable impact on the purchase of various foods (such as organic labeled foods, good agricultural practices foods, producer cooperative branded foods, etc.). It has been discovered as the result of the PCA, people perceived organic branded food and good agricultural practices foods, which are healthier, more flavorful, and more trustworthy than other food. however, it has been discovered that they believe the costs of these types of food are expensive and that they are difficult to obtain. On the other hand, they perceive the pricing of farmer cooperative branded foods and food sold directly by the farmer to be lower. Furthermore, it was observed in CA findings that there was a correlation between these various food groups and purchase locations. While products sold directly by farmers are mostly purchased from public markets, they prefer grocery stores and supermarkets when purchasing foods with good agricultural practices and natural labeled (from the markets). When seen from this perspective, it is possible to conclude that ecologically friendly food and other food products are regarded differently by customers based on product characteristics. The use of marketing techniques that create a positive perspective in terms of affordability and accessibility and the development of policies and production techniques that boost consumers' current perceptions of these items are considered will promote the consumption of these products.