Abstract

To evaluate the performance of different sampling arrangements, this work aimed to study, in a field of Eucalyptus camaldulensis, the variability and spatial dependence of some attributes of plant and soil (LATOSSOLO VERMELHO Distrófico [Typic hapludox]). To collect the soil (water content [WC] and soil mechanical penetration resistance [PR]) and plant data (tree height [TH] and circumference at breast height [CBH]), a sampling arrangement containing 122 points was used in an area of 1.98 ha and three other arrangements, with 84, 48 and 42 points. Except for the CBH, the reduction of points did not expressively affect the parameters of the classical statistics of all other parameters evaluated. Spatially, for TH and WC, the reduction of collection points did not significantly affect the geostatistical parameters, allowing estimating mapping similar to the initial mapping with high correlation coefficients. The WC at 0.10-0.20 m layer could be estimated from 48 samples of the WC at 0-0.10 m layer and 48 samples in the layer of 0.10-0.20 m with a correlation coefficient (r) equal to 0.94 when compared to initial kriging from an arrangement with 122 sample points.

Highlights

  • It is currently common sense in the agrarian sciences to consider that the spatial variability of soil attributes is virtually unquestionable

  • The study of spatial variability, which is given by the existence of spatial dependence in a sample field, is prioritized as object of study in geoestatistics, which represents a group of mathematical procedures applicable when the data are georeferenced (Yamamoto & Landim, 2013)

  • On the other hand, according to Souza et al (2014) one of the factors limiting the application of geoestatistics is the amount of data necessary for the sampling process to be representative in a way to capture the real spatial variability of a given attribute

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Summary

Introduction

It is currently common sense in the agrarian sciences to consider that the spatial variability of soil attributes is virtually unquestionable. The study of spatial variability, which is given by the existence of spatial dependence in a sample field, is prioritized as object of study in geoestatistics, which represents a group of mathematical procedures applicable when the data are georeferenced (Yamamoto & Landim, 2013). The use of this methodology of analysis has been consolidated for some time in the agrarian sciences and has been increasingly applied in the study of the spatial variability of soil attributes Due to the high costs of sampling and attribute analysis, there is some difficulty in harmonizing geostatistical rigor with both economic and operational feasibility for the spatial characterization of soil variability on a commercial scale

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