Technological Advances to Reduce Apis mellifera Mortality: A Bibliometric Analysis

Honey's Antioxidant and Antimicrobial Properties: A Bibliometric Study

Research attention has been drawn to honey's nutritional status and beneficial properties for human health. This study aimed to provide a bibliometric analysis of honey's antioxidant and antimicrobial properties. The research advancements within this field from 2001 to 2022 were addressed using the Scopus database, R, and VOSviewer. Of the 383 results, articles (273) and reviews (81) were the most common document types, while the annual growth rate of published manuscripts reached 17.5%. The most relevant topics about honey's antimicrobial and antioxidant properties were related to the agricultural and biological sciences, biochemistry, and pharmacology. According to a keyword analysis, the most frequent terms in titles, abstracts, and keywords were honey, antimicrobial, antioxidant, bee, propolis, phenolic compounds, wound, antibacterial, anti-inflammatory, and polyphenols. A trend topic analysis showed that the research agenda mainly encompassed antioxidants, pathogens, and anti-infection and chemical agents. In a co-occurrence analysis, antioxidants, anti-infection agents, and chemistry were connected to honey research. The initial research focus of this domain was primarily on honey's anti-inflammatory and antineoplastic activity, wound healing, and antibacterial agents. The research agenda was enriched in the subsequent years by pathogens, propolis, oxidative stress, and flavonoids. It was possible to pinpoint past trends and ongoing developments and provide a valuable insight into the field of honey research.

Correlation of Climatic Factors with the Weight of an Apis mellifera Beehive

The bee Apis mellifera plays an important role in the balance of the ecosystem. New technologies are used for the evaluation of hives, and to determine the quality of the honey and the productivity of the hive. Climatic factors, management, flowering, and other factors affect the weight of a hive. The objective of this research was to explain the interrelationship between climatic variables and the weight of an Apis mellifera beehive using a vector autoregressive (VAR) model. The adjustment of a VAR model was carried out with seven climatic variables, and hive weight and its lags, by adjusting an equation that represents the studied hive considering all interrelationships. It was proven that the VAR (1) model can effectively capture the interrelationship among variables. The impulse response function and the variance decomposition show that the variable that most influences the hive weight, during the initial period, is the minimum dew point, which represents 5.33% of the variance. Among the variables analyzed, the one that most impacted the hive weight, after 20 days, was the maximum temperature, representing 7.50% of the variance. This study proves that it is possible to apply econometric statistical models to bee data and to relate them to climatic data, contributing significantly to the area of applied and bee statistics.