“Caracterización de los sistemas de producción de ovinos de pelo en el suroeste del departamento de Matagalpa 2010”

Con el objetivo de caracterizar los sistemas de producción de ovinos de pelo en el territorio suroeste del departamento de Matagalpa 2010. (Sébaco, Ciudad Darío, San Isidro y Matagalpa). Se aplicó una encuesta a 103 productores que poseen ovinos de pelo, la muestra se definió aleatoriamente utilizando la ecuación planteada por Scheaffer (1987), se utilizó la técnica de muestreo de bola de nieve, planteada por Frey et al (2000). Esta investigación permitió conocer las debilidades y oportunidades en los sistemas de explotación de esta especie promisoria para la zona seca del país. Con los resultados obtenidos de las encuestas se procedió ha elaborar una base de datos en el programa SPSS versión 11.5 en español. Encontrando un predominio del sexo femenino como titulares de las explotaciones ovinas, 58.3% cursó educación primaria, el 98% de las explotaciones cuentan con raza pelibuey, el 100% de las explotaciones realizan destete y monta de forma natural, una media de mortalidad de corderos de 1, alimentan a las ovejas con potrero sin división (81.6%), se suministra pasto de corte, pastoreo, leguminosas y se suplementa sal común 49.5%, aplican vacunas contra ántrax y pierna negra (63.1%), desparasitaciones internas y externas (66%), ambos con una frecuencia de 2 veces al año, en el manejo productivo no se lleva control en la actividad ovina (100%), los equipo e instalaciones son rústicas, los corrales ovinos el son elaborados con alambre y/o madera, techado con plástico y/o zinc (49.51%), en cuanto a asistencia técnica el 58.3% ha recibid

Thresholds for the kernel density obtained using the head/tail break and the corresponding similarity values for the kernel density approach.

Thresholds for the kernel density obtained using the head/tail break and the corresponding similarity values for the kernel density approach.</p

Maximum similarity values of using different approaches for the delineation of built-up areas with road network data.

Maximum similarity values of using different approaches for the delineation of built-up areas with road network data.</p

Empirical approach to threshold determination for the delineation of built-up areas with road network data

<div><p>Various approaches have been proposed to address the delineation of built-up areas for a wide range of applications. Recently developed approaches are based on the increasing availability of road network data. However, most approaches have employed one or more parameters to divide built-up from non-built-up areas. Very few studies have discussed how to determine appropriate thresholds for such parameters. This study employed an empirical approach for threshold determination, and validated that the approach is applicable for the delineation of built-up areas using road network data. A series of experiments were designed to investigate the most-appropriate thresholds (determined using a similarity measure) for multiple parameters of three existing approaches (street blocks, grid-based, and kernel density) with regard to different administrative regions and cities/towns. The results show that in most cases, the most-appropriate thresholds or ranges for different subdivisions are either identical or overlap—thus validating the use of the most-appropriate thresholds to delineate built-up areas for one or multiple small subdivisions and, by inference, for a much larger region.</p></div

Empirical approach to threshold determination for the delineation of built-up areas with road network data - Fig 10

Most-appropriate thresholds for the West Midlands region of England and its eight counties using (a) the approach based on street blocks, (b) the grid-based approach, and (c) the kernel density approach.</p

Empirical approach to threshold determination for the delineation of built-up areas with road network data - Fig 7

Most-appropriate thresholds for the nine administrative districts of the North Island, using the kernel density approach with different bandwidths: (a) 0.3 km, (b) 0.5 km, (c) 0.7 km, and (d) 0.9 km. For each bandwidth, the most-appropriate threshold range is highlighted with two vertical dotted lines.</p

Empirical approach to threshold determination for the delineation of built-up areas with road network data - Fig 2

<p>Three approaches for the delineation of built-up areas: (a) a schematic road network, (b) the approach based on street blocks, (c) the grid-based approach, and (d) the kernel density approach.</p

Heterochromatin evolution on the <i>D</i>. <i>busckii</i> neo-Y.

<p>Shown is the normalized log2 enrichment level of H3K9me2 (<b>A-C</b>) or H3K9me3 (<b>D-F</b>) over genes on different chromosomes. <b>A</b>. Enrichment level of H3K9me2 at silent neo-Y linked genes (in blue) is significantly higher than that of the neo-X (in orange, Wilcoxon test significance level, <i>P</i><0.001:***), chrX (red) and autosomes (green). <b>B-C.</b> ‘Metagene’ profiles for H3K9me2 enrichment. Metagene profiles scale all genes of the same chromosome into the same number of bins for calculating average enrichment frequency along the gene body (Methods and Materials). We divide genes into actively transcribed (<b>B.</b>) and silent (<b>C</b>.) genes based on the gene expression levels of neo-Y alleles. We also include the up- and down- stream 1.5kb flanking regions. <b>D</b>. Enrichment level of H3K9me3. <b>E-F.</b> Metagene profiles for H3K9me3 enrichment at active (<b>E</b>.) and silent (<b>F</b>.) genes.</p

Maximum similarity values for different cell sizes obtained from the nine administrative districts of the North Island.

<p>Maximum similarity values for different cell sizes obtained from the nine administrative districts of the North Island.</p

Evaluation of the thresholds for two parameters (cell density and kernel density) by using both the similarity measure (<i>M</i>_<i>1</i> and the integrated measure (<i>M</i>_<i>2</i>, for the case study of Lower & Upper Hutt City.

<p>Evaluation of the thresholds for two parameters (cell density and kernel density) by using both the similarity measure (<i>M</i>_<i>1</i> and the integrated measure (<i>M</i>_<i>2</i>, for the case study of Lower & Upper Hutt City.</p