Abstract

ALEXANDRE, Carlos  and  SILVA, José R. Marques da. Terrain morphology influence on soil characteristics in agricultural fields of Alentejo: application of geomorphometric classifications. Rev. de Ciências Agrárias [online]. 2009, vol.32, n.1, pp.407-422. ISSN 0871-018X.

This paper applies a Digital Elevation Model (DEM) to produce and compare different classifications of land morphology in respect to its potential to differentiate soil thickness and soil texture. Land morphology classifications are based on field observation (UTobs and PEobs-L) or on geomorphometric variables calculated on a DEM (UTx, UTx-L and PEx-L). They are tested in a field area located 80 km at East of Évora, near Terena, Alandroal, in a parcel cultivated with maize, irrigated by centre pivot since 1994, with an undulated morphology where slopes gradient vary between 1% and 28%. The study area was surveyed using a methodology based on the global positioning system, and a DEM was produced to calculate some local and regional geomorphometric parameters for the entire area. Soil sampling was made using a mechanical cylindrical probe with 87 mm of diameter and 120 cm of depth, and was concentrated in two sites (A and B) following a relatively regular scheme. Soil thickness and texture of the 0-20 cm layer for the 203 soil samples are used to evaluate the degree of homogeneity and differentiation of the land units (UT), defined by the morphologic classifications. None of the tested classifications is able to differentiate meaningful soil groups based only on texture (0-20 cm). Results are better for soil thickness, especially for observed maximum soil depth (PMX) and soil depth to R layer (PR), for which the qualitative sequence of classifications are as follow: UTobs-L > PEobs-L > PEx-L. Classifications based on field observation give the best results but the classifications based on geomorphometric variables of regional nature (PEx-L) are better than those based ones on local variables (UTx and UTx-L). For soil thickness, the elimination of UT < 100 m² improved the results for all the classifications.

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