Caracterización biofísica de los sistemas agroforestales en el cantón Pichincha, Provincia de Manabí.

This study was conducted in the Canton Pichincha, Manabi Province, whose goal was to make the biophysical characterization of agroforestry systems for which we established 27 sampling units of 400 m2 at 9 sites in 3 parishes preset San Sebastián, and Pichincha Barraganete using the methodology propo...

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Tác giả chính: Avalos Bravo, Celso Berardo (author)
Tác giả khác: Cedeño Avilés, Wilmer Javier (author)
Định dạng: bachelorThesis
Ngôn ngữ:spa
Được phát hành: 2012
Những chủ đề:
Truy cập trực tuyến:http://repositorio.uteq.edu.ec/handle/43000/2321
Các nhãn: Thêm thẻ
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Tóm tắt:This study was conducted in the Canton Pichincha, Manabi Province, whose goal was to make the biophysical characterization of agroforestry systems for which we established 27 sampling units of 400 m2 at 9 sites in 3 parishes preset San Sebastián, and Pichincha Barraganete using the methodology proposed by Villavicencio and Valdez (2003), who consider concepts of frequency, abundance, density and Importance Value Index for the determination of the horizontal structure. To evaluate the vertical structure of agroforestry parameters were considered absolute and relative position sociological species and strata defined by Finol 1971, considering a stratum classification according to the heights of plants. For the evaluation of diversity, similarity within sampling units were used Shannon index, Simpson and Jaccard, which showed a mean diversity of species within the sites evaluated. Regarding the analysis of the horizontal structure identified a total of 344 individuals, as represented in 37 species and 23 families. Within families were found, the Fabaceae was the most abundant species with 6 Malvaceae and Moraceae followed with 3 species. As regards the analysis of the vertical structure was determined three well-defined layers, in which the predominant lower layer over the layer 158 individuals medium with 99 individuals and 87 individuals with upper layer. The similarity analysis in units of samples established in the study sites, revealed a high similarity between the units 11 to 12, 10 to 5 and between the units 9 to 17. In cluster analysis states that all units are similar above 10%.