Sampling design for soil micronutrient and sodium in a conilon coffee under Oxisol
Spatial sampling designs used to characterize the spatial variability of soil attributes are crucial for soil science studies in order to reduce the sampling effort and increase representativeness. The purpose of this work was to determine the number of samples required for the determination of the...
| Autores: | , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2015 |
| País: | Brasil |
| Institución: | Universidade do Estado de Santa Catarina (UDESC) |
| Repositorio: | Revista de Ciências Agroveterinárias (Online) |
| Idioma: | portugués |
| OAI Identifier: | oai::article/5704 |
| Acceso en línea: | https://periodicos.udesc.br/index.php/agroveterinaria/article/view/5704 |
| Access Level: | acceso abierto |
| Palabra clave: | Coffea conephora Geostatistics Soil sampling. Geoestatística Amostragem do solo. |
| Sumario: | Spatial sampling designs used to characterize the spatial variability of soil attributes are crucial for soil science studies in order to reduce the sampling effort and increase representativeness. The purpose of this work was to determine the number of samples required for the determination of the Cu, Fe, Mn, Zn and Na in a conilon coffee plantation, as well as to characterize its variability and spatial distribution using classical statistics and geostatistics parameters. The study was carried out in a conilon coffee plantation, in São Mateus, in the state of Espírito Santo, Brazil. The experimental area was 20 x 60 m in a regular grid. Samples were collected at 60 equally spaced points (1.8 x 1 m). All samples were collected at depth of 0-0.20 m in order to evaluate the soil chemical attributes. Using classical statistical parameters, the appropriate number of sampling points for chemical elements was 18. The highest variability was obtained for Cu and the lowest for Fe. The semi-variograms were satisfactorily described by spherical e gaussian models with a strong spatial structure. Knowing the minimum number of samples and the spatial distribution of soil chemical properties can be used to develop sampling strategies that minimize the effort and increase the representativeness. |
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