The use of different sampling grids in determining the variability of soil physical attributes of Oxisol
This study aimed to analyze the influence of different sampling grids in determining the spatial variability of physical attributes of Oxisol. It was used to study an area of approximately 90 hectares, where soil was sampled at depth from 0 to 0.20 meters, using a grid of 2 points per hectare (G1)....
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | Brasil |
| Institución: | Universidade Federal de Viçosa (UFV) |
| Repositorio: | LOCUS Repositório Institucional da UFV |
| Idioma: | inglés |
| OAI Identifier: | oai:locus.ufv.br:123456789/23371 |
| Acceso en línea: | https://dialnet.unirioja.es/servlet/articulo?codigo=5022044 http://www.locus.ufv.br/handle/123456789/23371 |
| Access Level: | acceso abierto |
| Palabra clave: | Precision agriculture Geostatistics Spatial variability Soil Agricultura de precisão Geoestatística Solo Variabilidade espacial |
| Sumario: | This study aimed to analyze the influence of different sampling grids in determining the spatial variability of physical attributes of Oxisol. It was used to study an area of approximately 90 hectares, where soil was sampled at depth from 0 to 0.20 meters, using a grid of 2 points per hectare (G1). Each soil sample was composed of four subsamples and obtained using a soil sampler Dutch type. The samples were sent to the laboratory to granulometric analysis. From the initial grid, the area was divided into sampling cells of 2.9 (G2) and 4.7 hectares (G3), and assigned a coordinate value representative of the center of each cell. Classical statistical and geostatistical methods were used to characterize the data and to model the spatial dependence. Spatial dependence was detected for all physical variables of the soil, regardless of the sampling grid used. The utilization of sampling grid of 1 point for each 2.9 hectares, and the sampling cell characterized by 12 subsamples, showed itself capable of detecting the spatial variability of the physical attributes of the soil, guaranteeing reliability in the estimates, even reducing the quantity of points when compared to the densest grid. |
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