Precision agriculture to study soil chemical properties and the yield of a coffee field
Precision agriculture appears as an important tool in the management of coffee farms where the knowledge of some soil fertility characteristics associated with the coffee production could help in specific application of fertilizers with positive environmental and economic results. So the aim of this...
| Autores: | , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2012 |
| País: | Brasil |
| Institución: | Universidade Federal de Lavras (UFLA) |
| Repositorio: | Coffee Science (Online) |
| Idioma: | portugués |
| OAI Identifier: | oai:coffeescience.ufla.br:article/204 |
| Acceso en línea: | https://coffeescience.ufla.br/index.php/Coffeescience/article/view/204 |
| Access Level: | acceso abierto |
| Palabra clave: | Geostatistic Spatial variability Kriging Semivariogram coffee plant Geoestatística Variabilidade espacial Krigagem Semivariograma cafeeiro |
| Sumario: | Precision agriculture appears as an important tool in the management of coffee farms where the knowledge of some soil fertility characteristics associated with the coffee production could help in specific application of fertilizers with positive environmental and economic results. So the aim of this article was to use precision agriculture and geoestatistics to evaluate the availability of phosphorus, potassium and yield of the coffee plant by evaluating the semivariogram and kriging maps and show that these tools are important for coffee management. This study was conducted on the Brejão farm in Três Pontas, Minas Gerais state, Brazil. As a data base we used soil chemical property data obtained by sampling in a georreferenced location using a quadricycle with a sampler and a GPS, and the yield data was obtained from manual harvest on the georreferenced location. The analysis of these data by using statistics and geostatistics tools allowed to characterize the spatial variability of the phosphorus, potassium, and the coffee yield, and allowed to analyze the relation among these variables. It was possible to observe that spatial dependence exists so it is possible to create maps of spatial distribution of the variables. |
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