4.6 Article

Spatial patterns of magnetic susceptibility optimized by anisotropic correction in different Alisols in southern Amazonas, Brazil

Journal

PRECISION AGRICULTURE
Volume 23, Issue 2, Pages 419-449

Publisher

SPRINGER
DOI: 10.1007/s11119-021-09843-6

Keywords

Agricultural conversion; Spatial variability; Isotropy; Kriging; Soil magnetism

Funding

  1. Coordination for the Improvement of Higher Education Personnel (CAPES)

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This study analyzed the isotropic and anisotropic patterns of magnetic susceptibility in soil properties in the Amazonas region, and found that anisotropic correction improved model fitting. Spatial dependency of magnetic susceptibility was confirmed, supporting the isotropy hypothesis.
Changes in primary cover for agricultural crops in Amazonas region influence the phenomenon of spatial variability in soil properties. This phenomenon is still studied assuming that the spatial data is isotropic, but does not consider the anisotropic pattern of soil properties. Thus, the aim of this work was to characterize, identify and correct isotropic patterns of magnetic susceptibility (MS) measurements using anisotropic models that actually represent the spatial aspects of the data. Three cultivation areas and one under native forest, classified as Haplic Alisol, were georeferenced and sampled by a mesh system covering 192 samples per area. Texture, X-ray diffraction and frequency-dependent (chi(fd)) and mass-specific (chi(lf) and chi(hf)) magnetic susceptibility analyzes were performed. Then, classical and geostatistical analyzes were applied to the data, assuming isotropy and anisotropy. All chi frequencies were shown to be spatially dependent, satisfying the isotropy hypothesis. Thereby, the application of anisotropic analysis was able to confirm the presence of all types of anisotropy in Alisols. Anisotropic correction provided an improvement in models that fit the directional trends within the areas, and provided a reduction in the nugget effect and an increase in the correlation ranges. Thus, the generated kriging maps improved the patches of zonal trends of greater or lesser chi that stand out at the level of sub-regions. These zones should, therefore, be used as indicators of variability, paying special attention during their management, especially in research related to the delimitation of specific management zones.

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