A Bird Vulnerability Index (BVI) and Potential Vulnerability Map (PVM) was applied to determine which are the most susceptible bird species to collide with wind towers and the riskiest sites, in a wind power plant south from the city of Rivas. Transects were placed in two areas where the towers would be placed: Grasslands without Trees and Grasslands with Trees. Transects were also made in adjacent habitats such as Lake Nicaragua Coast and Riparian Forest. The BVI was calculated with nine factors (Flight Height, Type of Flight, Wingspan, Weight, Status, Abundance, Reproductive Status, International and National Conservation Status). The total PVM was calculated from all detected species, and average PMV only using species that exceeded the specific BVI median. The risk of habitat collision was calculated by determining that less of 50th percentile is considered to be low risk, and high risk when the percentile is greater than 50. The highest vulnerability index is found in the species: Magnificent Frigatebird (Fregata magnificens), Black Vulture (Coragyps atratus), Turkey Vulture (Cathartes aura), Osprey (Pandion haliaetus), Crested Caracara (Caracara cheriway), Nicaraguan Grackle (Quiscalus nicaraguensis), and Great Heron (Ardea herodias). The riparian forest and grassland with trees are the sites with the highest risk of collision to install wind towers. The BVI and PVM are important tools that allow the identification of potential risks of bird collision with wind towers before their construction. | Se aplicó un Índice de Vulnerabilidad de Aves (IVA) y Mapa de Vulnerabilidad Potencial (MVP) para determinar cuáles son las especies de aves más susceptibles a colisionar con torres eólicas y los sitios con mayor riesgo, en una central eólica al sur de la ciudad de Rivas. Se colocaron transectos en dos zonas donde se colocarían las torres: Pastizales sin ‘Árboles y Pastizales con Árboles. También se realizaron transectos en hábitats adyacentes como Costa del lago de Nicaragua y Bosque Ripario. El IVA se calculó con nueve factores (altura de vuelo, tipo de vuelo, longitud de ala, peso, estatus, abundancia, estado reproductivo, estado de conservación internacional y nacional). Se calculó el MVP total a partir de todas las especies detectadas, y MVP medio solo utilizando las especies que superaron la mediana del IVA específico. El riesgo de colisión por hábitat se calculó determinando que menor al percentil 50 se considera de riesgo bajo, y de riesgo alto cuando el percentil sea mayor que 50. Los valores más altos de vulnerabilidad se encuentran en las especies: Rabihorcado Magno (Fregata magnificens), el Zopilote Negro (Coragyps atratus), el Zopilote Cabecirroja (Cathartes aura), Águila Pescadora (Pandion haliaetus), Caracara Crestado (Caracara cheriway), Zanate Nicaragüense (Quiscalus nicaraguensis), y la Garza Grande (Ardea herodias). El bosque ripario y pastizales con árboles son los sitios con mayor riesgo de colisión para instalar torres eólicas. El IVA y MVP constituyen herramientas importantes que permiten identificar los riesgos potenciales de colisión de aves en centrales eólicas antes de su construcción.

In order to analyze the innovation technologies offered by different actors in the South Pacific of Nicaragua that could be implemented in family farming production systems and contribute to the improvement of their production and productivity, a research was carried out with a Mixed Approach, which integrated quantitative methods and qualitative: univariate statistical analysis, interviews with seven officials from institutions and organizations in the territory; three focus groups. Quantitative data were collected from 380 surveys of producers in southern Nicaragua. The reductive analysis of the qualitative information, provided by the participating actors, was implemented. The main results obtained and conclusions were: the population under study were small agricultural producers, the agricultural area has an average of 2.83 mz. 46% of the producers in the production systems are Leading Producers; 37% are members of Community Seed Bank; 16% are owners of Research and Technological Innovation Farms. 71% of the producers implemented some type of innovation that has been adopted by another producer; These producers affirmed that the innovations are helping to improve the production of their crops. The main technologies offered by the Institutions and other actors in the territory were: three for Basic Grains, four for Major and Minor Livestock, two for IPM Practices, five for OCS and A Implementation, and eight for Agro-socioeconomic Practices. | Con el objetivo de analizar las tecnologías de innovación ofertadas por diferentes actores del Pacifico Sur de Nicaragua que pudieran  implementarse  en sistemas de producción de agricultura familiar y contribuir al mejoramiento de su producción y productividad, se realizó una investigación con Enfoque Mixto, que integró métodos cuantitativos y cualitativos: análisis estadístico univariado, entrevistas a siete funcionarios de  instituciones y organismos del territorio; tres grupos focales. Los datos cuantitativos se recolectaron a partir de 380 encuestas a productores (as) del sur de Nicaragua. Se implementó el análisis reductivo de la información cualitativa, aportada por los actores participantes. Los principales resultados obtenidos fueron: la población en estudio, fueron pequeños productores (as) agropecuarios, el área agrícola presenta un promedio de 2.83 mz. El 46% de los productores (as) en los sistemas de producción, son Productores Protagonistas; el 37% son miembros de Banco Comunitarios de Semilla; un 16% son propietarios de Fincas de Investigación e Innovación Tecnológica. El 71% de los productores (as), implementaron algún tipo de innovación que ha sido adoptada por otro productor (a); estos productores (as) afirmaron que las innovaciones están contribuyendo a mejorar la producción en sus cultivos. Las principales tecnologías ofertadas por las Instituciones y otros actores del territorio fueron: tres de Granos básicos, cuatro de Ganadería mayor y menor, dos de Practicas MIP, cinco de Implementación de OCS y A y ocho de Prácticas agro socioeconómica.

This article addresses the study Estimation of hydric erosion for the current and potential erosion of the soil in the UNI experimental farm using the RUSLE method. The objective was to estimate the hydric erosion for the current and potential erosion of the soil in the Experimental Farm of the UNI. The farm has an area of ​​49.50 blocks, used for agricultural activities. Surface water erosion was estimated using the Revised Universal Soil Loss Equation (RUSLE). To use this model the factors of erosivity, erodability, length and gradient of the slope, coverage and conservation practices were obtained. For this, a series of activities were carried out, including the collection of data from fields such as current land use, topography of the area, daily and monthly rainfall and the respective analyzes to determine the physical and chemical properties of the soil. The theoretical erodibility factor (K) was obtained through four soil properties (Texture, structure, organic matter and permeability), these values ​​were introduced in the Wischmeier (1971) nomogram. The study suggests that the FAE-UNI causes superficial water erosion rates lower than 12 Ton / (ha-year). | En el presente artículo se aborda el estudio Estimación de la erosión hídrica para el uso actual y erosión potencial del suelo en la finca experimental UNI utilizando la Ecuación revisada de la ecuación Universal de Pérdida de Suelo (RUSLE). El objetivo fue estimar la erosión hídrica para el uso actual y erosión potencial del suelo en la Finca Experimental de la UNI. La Finca tiene una extensión de 49.50 manzanas, utilizada para la realización de prácticas y con fines investigativos por parte de los estudiantes de la carrera de ingeniería agrícola. Se estimó la erosión hídrica superficial mediante el uso de la Ecuación Universal de Pérdida de Suelo Revisada (RUSLE). Para utilizar este modelo se obtuvieron los factores de erosividad, erodabilidad, la longitud y el gradiente de la pendiente, cobertura y prácticas de conservación. Para ello se realizaron una serie de actividades entre ellos la recopilación de datos de campos como el uso actual del suelo, topografía de la zona, precipitaciones diarias y mensuales y los respectivos análisis para la determinación de las propiedades físicas y químicas del suelo. El factor de erodabilidad (K) teórico se obtuvo a través de cuatro propiedades del suelo (Textura, estructura, materia orgánica y permeabilidad) dichos valores fueron introducidos en el nomograma de Wischmeier (1971). El estudio sugiere que en la Finca Agrícola Experimental de la Universidad Nacional de Ingeniería (FAE-UNI) se provoca tasas de erosión hídrica superficial menores que 12 Ton/ (ha-año).

Abstract. Methane (CH4) emissions from natural landscapes constitute roughly half of global CH4 contributions to the atmosphere, yet large uncertainties remain in the absolute magnitude and the seasonality of emission quantities and drivers. Eddy covariance (EC) measurements of CH4 flux are ideal for constraining ecosystem-scale CH4 emissions due to quasi-continuous and high-temporal-resolution CH4 flux measurements, coincident carbon dioxide, water, and energy flux measurements, lack of ecosystem disturbance, and increased availability of datasets over the last decade. Here, we (1) describe the newly published dataset, FLUXNET-CH4 Version 1.0, the first open-source global dataset of CH4 EC measurements (available at https://fluxnet.org/data/fluxnet-ch4-community-product/, last access: 7 April 2021). FLUXNET-CH4 includes half-hourly and daily gap-filled and non-gap-filled aggregated CH4 fluxes and meteorological data from 79 sites globally: 42 freshwater wetlands, 6 brackish and saline wetlands, 7 formerly drained ecosystems, 7 rice paddy sites, 2 lakes, and 15 uplands. Then, we (2) evaluate FLUXNET-CH4 representativeness for freshwater wetland coverage globally because the majority of sites in FLUXNET-CH4 Version 1.0 are freshwater wetlands which are a substantial source of total atmospheric CH4 emissions; and (3) we provide the first global estimates of the seasonal variability and seasonality predictors of freshwater wetland CH4 fluxes. Our representativeness analysis suggests that the freshwater wetland sites in the dataset cover global wetland bioclimatic attributes (encompassing energy, moisture, and vegetation-related parameters) in arctic, boreal, and temperate regions but only sparsely cover humid tropical regions. Seasonality metrics of wetland CH4 emissions vary considerably across latitudinal bands. In freshwater wetlands (except those between 20∘ S to 20∘ N) the spring onset of elevated CH4 emissions starts 3 d earlier, and the CH4 emission season lasts 4 d longer, for each degree Celsius increase in mean annual air temperature. On average, the spring onset of increasing CH4 emissions lags behind soil warming by 1 month, with very few sites experiencing increased CH4 emissions prior to the onset of soil warming. In contrast, roughly half of these sites experience the spring onset of rising CH4 emissions prior to the spring increase in gross primary productivity (GPP). The timing of peak summer CH4 emissions does not correlate with the timing for either peak summer temperature or peak GPP. Our results provide seasonality parameters for CH4 modeling and highlight seasonality metrics that cannot be predicted by temperature or GPP (i.e., seasonality of CH4 peak). FLUXNET-CH4 is a powerful new resource for diagnosing and understanding the role of terrestrial ecosystems and climate drivers in the global CH4 cycle, and future additions of sites in tropical ecosystems and site years of data collection will provide added value to this database. All seasonality parameters are available at https://doi.org/10.5281/zenodo.4672601 (Delwiche et al., 2021). Additionally, raw FLUXNET-CH4 data used to extract seasonality parameters can be downloaded from https://fluxnet.org/data/fluxnet-ch4-community-product/ (last access: 7 April 2021), and a complete list of the 79 individual site data DOIs is provided in Table 2 of this paper.