Application of geostatistical methods to identify the spatial dependence in the data analysis of a fan systematic design experiment / Aplicação de métodos geoestatísticos para identificação de dependência espacial na análise de dados de um experimento em delineamento sistemático tipo "leque"

AUTOR(ES)
DATA DE PUBLICAÇÃO

2005

RESUMO

Systematic designs are utilized in many areas, such as: forestry, horticulture, soils, etc. In forestry, the systematic designs are frequently used for preliminary studies and they aim at evaluating the largest number of possible spacings. However, there are some limitations on their use. The first limitation is the systematic design (non-randomized) of plants, which does not allow the use of conventional analyses. The second is the high sensitivity to lost values. When a plant is lost, the neighboring plant spacings are altered, so these values cannot be added to the data collection, and a great sum of information is excluded from the analyses. This study aimed at applying geostatistical methods to identify the spatial dependence in the data analysis of a fan systematic design experiment, taking into account: the exclusion of neighboring plant data to the lost values and the information regarding the occurrence of lost parcels as well as the time of their occurrence. The plant solid volume data utilized in this study were taken from a fan systematic design Eucalyptus dunnii spacing study. The data utilized were referent to the sixth year, commercial age for cutting of the specie, with the following procedures exclusion of the data from a neighboring plant next to a dead tree (Model I); the information of tree mortality as covariable in the model (Model II); and the time of occurrence of tree mortality, besides the tree mortality covariable (Model III). The semivariogram parameters were estimated by the maximum likelihood method, and the model selection was done by the utilization of the Akaike s Information Criterion (AIC). It was possible to conclude from the result analyses that there is a weak spatial dependence, which does not justify neither taking it into account nor the utilization of a geostatistical model. The correlation function that showed the best performance was the Matérn, with kappa=2 for the three models considered. By the comparison of these three models and the utilization of the Akaike s Information Criterion, the most suitable model was Model II, as it showed lower AIC value.

ASSUNTO(S)

cutting (plants) spatial statistical espaçamento verossimilhança geoestatística estatística espacial espaçamento - spacing corte (plantas) estatística aplicada applied statistical likehood geostatistical eucalipto eucalyptus

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