Abstract
ABSTRACTWe present a 1:100,000 scale soil texture map of Paragominas county (Pará, Brazil), covering 19,330 km2. The method allows rapid production of a soil texture map of a large area where the strength of a duricrust controls the relief. It is based on an easily accessible explanatory variable, topography, which is represented using a Digital Elevation Model. The method makes it possible to map the spatial distribution of the texture of the topsoil layer. Modelling was complemented by field observations to identify the laws governing the spatial organisation of soil textures. The spatial variability of the elevation above sea-level of the duricrust was obtained by Kriging. The error rate of the resulting map is 26%, and the observations of the four soil texture units were respectively 78%, 90%, 41% and 60% accurately located.
Highlights
Soil fertility is an important driver explaining such land use dynamics as deforestation and crop expansion
According to the geological relationships reported in Section 2.1.1., Belterra clay should be above the Paleogene duricrust, and have a thickness of up to 15 m, mottled clay should be beneath the duricrust and have a thickness ranging from 10 to 30 m, and the loamy sand should be below the mottled clay
The modelled surface elevation asl was very close to the elevation of the duricrust at the observation points. 77% of the observed elevations of the Belterra clay were above the interpolation surface, which is in agreement with the regional profile in which the Belterra clay overlies the Paleogene duricrust; the median is +13 m (Figure 5)
Summary
Soil fertility is an important driver explaining such land use dynamics as deforestation and crop expansion. Soil fertility in the humid tropics depends strongly on their texture (Quesada et al, 2009), because they determine erosion and compaction, cation exchange capacity and water holding capacity. For example in eastern Amazonia, as the cation exchange capacity is better in clayey soils than in sandy soils, the clayey soils are more suitable for agriculture and have been preferred areas for soybean and maize expansion since the 2000s (Piketty et al, 2015). Effective time saving approaches are needed to map soil properties. It is necessary to find methodologies that rapidly and effectively capture information about the spatial variability of soils and reduce the need for intensive and expensive sampling (Mora-Vallejo, Claessens, Stoorvogel, & Heuvelink, 2008)
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