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.

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.

This study examined whether there is a relationship between the socio-economic and demographic characteristics of consumers while purchasing food products and the level of environmental awareness and environmental consciousness. In addition, the factors affecting consumers' trust in environmentally friendly food products were examined. The data of the study were obtained from the questionnaires made with 171 people in the district of Süleymanpaşa in Tekirdağ. In the evaluation of the obtained data, frequency distributions, chi-square, and factor analyses were used. In the results of the research, it determined that the level of environmental friendliness of consumers in their general consumption habits differs according to their age, education, having children under the age of 10, monthly income level, monthly food expenditure level. Consumers over the age of 36 were found to be more environmentally sensitive than other consumers. Similarly, consumers with undergraduate and higher education were found to be more environmentally friendly than those who have under undergraduate education. On the other hand, factor analysis was applied to 11 variables that affect the confidence level of consumers for food products produced with environmentally friendly and human health sensitive production methods. 11 variables were grouped under 3 factors as “Legal regulations and promotion efforts”, “Certificates and label information” and “Product awareness”. When evaluated from this point of view, it revealed that certificates and label information are very important as well as legal regulations in increasing the reliability of environmentally friendly food products in the eyes of consumers. However, it is understood that the environmental attitudes of consumers should also be taken into account in consumer-oriented studies in the food products market today.

Bu çalışmanın birinci amacı hayvancılıkta kullanılan dijital teknolojileri açıklamak, bu teknolojilerin sosyo-ekonomik ve çevresel etkilerini ortaya koymaktır. İkinci amaç ise, konu ile ilgili yapılmış çalışmaların tarihsel evrimini ortaya koymaktır. Nesnelerin interneti temelli olan bu teknolojilerin elektronik kulak küpeleri, elektronik boyun tasması, elektronik adım ölçerler, sensörler ve sanal çitler olarak ortaya çıktığı görülmüştür. Dahası, bu teknolojilerin özellikle, süt üretim çiftlikleri başta olmak üzere kümes hayvancılığı, küçükbaş ve domuz çiftliklerinde yaygın olarak kullanıldığı görülmüştür. Öte yandan, “Bibliyometrik Analiz” yönteminden faydalanarak konu ile ilgili yapılmış çalışmaların gelişim süreçleri incelendiğinde ise Amerika Birleşik Devletleri, Çin, İngiltere ve Avustralya en çok bilimsel çalışmaların yapıldığı ülkelerin başında yer aldığı görülmüştür. Çalışmalarda, 2015 yılına kadar hassas hayvancılık, sensörler gibi konular ağırlıklı ele alınan konular iken, 2015 yılı sonrasında ise, çalışmalar makine öğrenmesi, hayvan ve insan refahı, hayvan davranışları ve derin öğrenme konularına evrildiği görülmüştür. Sonuç olarak, hayvancılıkta dijital teknoloji kullanımının artması ile hayvan takibi, hayvan sağlığı, refahı ve verim unsurlarında optimizasyon sağlar iken işletmelerin karlılığını arttırmaktadır. Dahası, dijital teknolojiler ile hayvancılıktan kaynaklı çevresel etkilerinin (Sera gazı emisyonları, toprak ve su kirliliği) sürekli olarak takip edilebilmektedir. Kırsal alanlarda hayvancılık faaliyetlerinin dijitalleşmesiyle hem kırsal alanların gelişmesinde hem de kırsal alanlardaki göçlerin önlenmesinde fayda sağlanabilir. Ayrıca, gıda arzı ve güvenliğinin arttıracağı düşünülmektedir.

Advancements in unmanned aerial vehicle (UAV) technologies have facilitated a novel approach to dairy cattle breed morphological identification. The objective of this study was to employ UAV images, analyzed through deep convolutional neural networks (DCNN), to classify dairy cow breeds. The dataset comprises of 2004 RGB UAV images of dairy cows, including Holstein, Simmental, and Brown-Swiss breeds, obtained from the cattle breeding facility at Van Yüzüncü Yıl University. The images were preprocessed and segmented to contain a single cow each, and subsequently categorized as training (70%), validation (20%), and testing (10%) datasets. To determine the most effective architecture for breed classification, we compared a custom DCNN (C-DCNN) model to well-established pre-trained models including Xception, VGG19, and ResNet50. The C-DCNN demonstrated remarkable performance, achieving precision, recall, accuracy, and F1 scores of 0.98. Among the pre-trained models, Xception demonstrated superior results, with perfect accuracy and an F1 score of 1.00. Conversely, the VGG19 model exhibited a higher level of accuracy; nevertheless, it exhibited lower precision, recall, and F1 scores when evaluated on the test set, compared to the C-DCNN and Xception models. In contrast, ResNet50 displayed the lowest level of performance, with an accuracy of 0.74 and the highest levels of loss. This study demonstrates the potential of integrating DCNN models with UAV technology in precision livestock farming, offering a robust and efficient system for cattle breed classification.