Examinando por Materia "Apicultura"

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Apis mellifera bee: importance, challenges, and its impact on agriculture in Peru
(Frontiers Media S.A., 2026-07-29) Ruiz Llontop, Deysi Yenny; Paucar, Ysai; Bardales, William; Saucedo Uriarte, José Américo
Bees are fundamental to the health of both natural ecosystems and agricultural systems, providing essential pollination services that support biodiversity and agriculture. In Peru, they play an important role in pollinating major agricultural crops such as coffee, blueberries, avocados, citrus fruits, and cacao, among others; at the same time, they help preserve biodiversity. However, bee populations in the country face significant threats, including habitat loss driven by deforestation and agricultural expansion, the spread of parasitic pests like Varroa destructor, pesticide exposure, and the effects of climate change... This review synthesizes recent research on the state of bee populations and apiculture in Peru, drawing on a comprehensive dataset of 89 studies published between 1914 and 2024. It examines the key challenges facing Peruvian A. mellifera bee, including the impact of environmental stressors, the sustainability of beekeeping practices, and the regional distribution of research efforts. Additionally, the review explores the importance of grassland ecosystems as critical habitats for pollinators and their role in supporting agricultural productivity. The review identified significant knowledge gaps in Peru, including limited geographic coverage, insufficient understanding of pollination ecology and ecosystem services, and a lack of studies quantifying the economic value of pollination for agriculture. By identifying research gaps and proposing future directions, this article seeks to advocate for the adoption of sustainable agricultural practices in Peru to safeguard A. mellifera.
Geographic information system applications in bee research
(MDPI, 2026-05-29) Rojas Briceño, Nilton B.; Silva López, Jhonsy O.; Guzman Valqui, Betty Karina; Ix Balam, Manuel A.; Ramos Tejeda, José L.; Oliva Cruz, Manuel; Veneros, Jaris; García, Ligia
Bees play crucial ecological, economic, and environmental roles, and research on them increasingly includes a spatial dimension. Geographic Information Systems (GISs) enable the acquisition, storage, analysis, management, and visualization of spatial data. However, GIS applications in bee research have expanded while remaining dispersed across topics, tools, taxa, and methodological approaches. This study provides a comprehensive and updated review of GIS applications in bee research by integrating bibliometric analysis with a structured synthesis of GIS purposes and techniques. A total of 228 publications were analyzed to assess publication trends, co-authorship patterns, keyword themes, study areas, taxonomic coverage, GIS application themes, and methodological tools. GIS was used to select suitable apiary sites, map floral resources, analyze bee behavior, assess diseases and pests, monitor bee products, evaluate urban and landscape contexts, and predict climate change effects. The main GIS-related approaches included multicriteria decision analysis (MCDA), remote sensing, species distribution models (SDMs), spatial interpolation, WebGIS platforms, and emerging machine-learning applications. The review also identified underrepresented taxa, especially wild bees, stingless bees, and other Apis species. Future advances should integrate MCDA with data-driven models, improve floral-resource mapping with remote sensing, and strengthen reproducibility through standardized spatial data and workflows.
Predictive modeling of honey yield in rural apiaries: insight from Chachapoyas, Amazonas, Peru
(MDPI, 2025-11-18) Briceño Mendoza, Yander Mavila; Saucedo Uriarte, José Américo; Quiñones Huatangari, Lenin; Gaslac Gomez, Jhoyd B.; Quispe Ccasa, Hurley Abel; Cayo Colca, I.S.
Honey production is influenced by multiple factors, including climatic conditions, hive management practices, and harvest scheduling. This study evaluated the predictive capacity of statistical modeling techniques using data mining algorithms (MARS, CHAID, CART, and Exhaustive) and artificial neural network algorithms (Multilayer Perceptron, MLP) to estimate honey yields in apiaries located in northeastern Peru. A structured survey was conducted with sixty-nine beekeepers across nineteen districts in the Chachapoyas province. Variables included beekeeper experience, instruction, hive count, visit frequency, harvest frequency, additional income-generating activities, and geographic location. Descriptive statistics, non-parametric tests, Spearman correlations, and exploratory factor analysis were applied to identify latent structures. A linear mixed-effects model was used to assess the combined influence of predictors on honey production, with district included as a random effect. Results indicated that hive number, beekeeping experience, harvest frequency, and exclusive engagement in apiculture were statistically associated with increased honey yields. The model explained a substantial proportion of variance, supporting the integration of technical and socio-demographic variables in production forecasting. These findings demonstrate the utility of predictive modeling for informing hive management strategies and improving the operational efficiency of small-scale beekeeping systems in Andean regions.