Beekeeping, performed in many parts of the world, has a very large place in the world trade market with bee products such as wax, bee venom, propolis and royal jelly, especially honey production. However, honey bee diseases are quite common and restricted the production of bee products. One of the most important of these diseases, Nosema, is caused by spores in intestinal epithelium cells of the honeybee. Nosema apis and Nosema ceranae are the factors of this disease and also common in our country. These two species can be distinguished from each other by molecular diagnostic methods. In this study, materials collected from 152 apiaries located in 13 districts of Muğla province and 62 water sources close to these apiaries. The spores were counted using Thoma lame under light microscope. DNA isolation was carried out from spore positive samples. 218MITOC FOR-REV and 321APIS FOR-REV primers were used to figure out the N. apis and N. ceranae species. After DNA sequence analysis of the obtained amplifications, it was determined that all samples formed 3 haplotypes according to studied sequences for the first time. In Muğla region, the presence of only N. ceranae as a disease agent was determined and the prevalence of the disease was detected at a rate of 71.53±6.02%. Moreover, blast analysis showed that the N. ceranae sequence detected high similarity (94-100 %) with the previously reported in Lebanon, France, Morocco and Thailand samples.

The Caucasian honeybee, Apis mellifera caucasica Pollmann, 1889 (Hymenoptera: Apidae), is one of the most productive bee subspecies. This subspecies, which has special importance for Türkiye, has been taken under protection in two isolated regions (Artvin and Ardahan) since 2000. To date, no study has been conducted on whether genetic diversity has decreased in these protected Caucasian honeybee colonies. Therefore in 2022, worker bees were collected from 100 different colonies in 15 different locations in these two regions and their genetic variations were examined using 30 microsatellite loci. The average number of alleles per locus was 13.57, and the loci had a high level of information content according to the PIC (0.7) value. FIS (0.96) and FST (0.01) values showed low genetic diversity and high inbreeding in populations. Genetic variations were calculated as 0.77% among populations, 99.23% among individuals in populations, and 0% among all individuals. Also, populations deviated from the Hardy-Weinberg equilibrium (p<0.001). Significant bottleneck evidence was found for Artvin in the analysis results using the two-phase mutation model. These results provide important information that can be used as a guide for Caucasian bee breeding strategies and conservation programs.

Introduction The Japanese quail which has been used as a model animal for a long time has become an improtant poultry species in commercial production. Consumption of quail meat has increased significantly, especially in South America and Southern

Türkiye, with its rich flora diversity, holds a significant share in global honey production. However, honey bee populations, essential for agricultural ecosystems, face multifaceted threats such as climate change, habitat degradation, diseases, parasites, and exposure to pesticides. Alongside the increasing global food demand driven by population growth, there is a pressing need for a substantial increase in honey production. In this context, advances in machine learning algorithms offer tools to predict future food needs and production levels. The objective of this work is to develop a predictive model using machine learning techniques to predict Türkiye's honey output in the next years. To achieve this goal, a range of machine learning algorithms including K-Nearest Neighbor, Random Forest, Linear Regression, and Gaussian Naive Bayes were employed. Following investigations, Linear Regression emerged as the most effective method for predicting honey production levels (R2= 0.97).

The rapid increase in the global population and evolving dietary habits have significantly heightened the demand for high-quality protein sources. Beef, as a vital protein source, plays a crucial role in meeting this growing demand. This study aims to develop and evaluate a machine-learning model to predict beef production using meteorological, agricultural, and economic data. To achieve this, three different machine learning algorithms—Linear Regression, Random Forest, and k-Nearest Neighbors—were employed. The results indicate that the Random Forest algorithm outperformed the other methods in terms of R² and error metrics, demonstrating superior predictive accuracy. The study highlights the potential of machine learning techniques in predicting beef production, offering valuable insights for stakeholders involved in strategic decision-making to meet nutritional needs. As the global demand for protein continues to rise, the importance of such predictive models becomes increasingly significant, emphasizing the distinct advantages that machine learning approaches provide in this context.