Amazon landforms and soils in relation to biological diversity

Thirteen main landform units are distinguished for the whole of the forested Amazon region, each with its specific soil pattern and vegetation structure. These landform-soil-vegetation units are delineated on a small-scale map and illustrated by a schematic cross-section. Floristic diversity of the gamma type is to be highest on the steepland-and-valley complexes of the Andean fringe, on the crystalline shield uplands, on the inselberg complexes, and on the eutric variant of the western sedimentary plains. Endemism is expected to be highest on the sandy plains, and parts of the table lands and inselberg complexes. Speciation, linked to the concept of forest refuge areas, is likely to be highest on the sandstone table lands, on the stretches of Amazon planalto, and in the areas of relict valleys, in view of the prolonged geomorphological stability of these units.

Magnetic susceptibility to identify landscape segments on a detailed scale in the region of Jaboticabal, São Paulo, Brazil

The agricultural potential is generally assessed and managed based on a one-dimensional vision of the soil profile, however, the increased appreciation of sustainable production has stimulated studies on faster and more accurate evaluation techniques and methods of the agricultural potential on detailed scales. The objective of this study was to investigate the possibility of using soil magnetic susceptibility for the identification of landscape segments on a detailed scale in the region of Jaboticabal, São Paulo State. The studied area has two slope curvatures: linear and concave, subdivided into three landscape segments: upper slope (US, concave), middle slope (MS, linear) and lower slope (LS, linear). In each of these segments, 20 points were randomly sampled from a database with 207 samples forming a regular grid installed in each landscape segment. The soil physical and chemical properties, CO2 emissions (FCO2) and magnetic susceptibility (MS) of the samples were evaluated represented by: magnetic susceptibility of air-dried fine earth (MS ADFE), magnetic susceptibility of the total sand fraction (MS TS) and magnetic susceptibility of the clay fraction (MS Cl) in the 0.00 - 0.15 m layer. The principal component analysis showed that MS is an important property that can be used to identify landscape segments, because the correlation of this property within the first principal component was high. The hierarchical cluster analysis method identified two groups based on the variables selected by principal component analysis; of the six selected variables, three were related to magnetic susceptibility. The landscape segments were differentiated similarly by the principal component analysis and by the cluster analysis using only the properties with higher discriminatory power. The cluster analysis of MS ADFE, MS TS and MS Cl allowed the formation of three groups that agree with the segment division established in the field. The grouping by cluster analysis indicated MS as a tool that could facilitate the identification of landscape segments and enable the mapping of more homogeneous areas at similar locations.

Soil carbon and nitrogen stocks in traditional agricultural and agroforestry systems in the semiarid region of Brazil

In the semiarid region of Brazil, inadequate management of cropping systems and low plant biomass production can contribute to reduce soil carbon (C) and nitrogen (N) stocks; therefore, management systems that preserve C and N must be adopted. This study aimed to evaluate the changes in soil C and N stocks that were promoted by agroforestry (agrosilvopastoral and silvopastoral) and traditional agricultural systems (slash-and-burn clearing and cultivation for two and three years) and to compare these systems with the natural Caatinga vegetation after 13 years of cultivation. The experiment was carried out on a typical Ortic Chromic Luvisol in the municipality of Sobral, Ceará, Brazil. Soil samples were collected (layers 0-6, 6-12, 12-20, 20-40 and 40-60 cm) with four replications. The plain, convex and concave landforms in each study situation were analyzed, and the total organic C, total N and densities of the soil samples were assessed. The silvopastoral system promoted the greatest long-term reductions in C and N stocks, while the agrosilvopastoral system promoted the smallest losses and therefore represents a sustainable alternative for soil C and N sequestration in these semiarid conditions. The traditional agricultural system produced reductions of 58.87 and 9.57 Mg ha-1 in the organic C and total N stocks, respectively, which suggests that this system is inadequate for these semiarid conditions. The organic C stocks were largest in the concave landform in the agrosilvopastoral system and in the plain landform in the silvopastoral system, while the total N values were highest in the concave landform in the native, agrosilvopastoral and silvopastoral systems.

SPATIAL UNCERTAINTY OF NUTRIENT LOSS BY EROSION IN SUGARCANE HARVESTING SCENARIOS

The assessment of spatial uncertainty in the prediction of nutrient losses by erosion associated with landscape models is an important tool for soil conservation planning. The purpose of this study was to evaluate the spatial and local uncertainty in predicting depletion rates of soil nutrients (P, K, Ca, and Mg) by soil erosion from green and burnt sugarcane harvesting scenarios, using sequential Gaussian simulation (SGS). A regular grid with equidistant intervals of 50 m (626 points) was established in the 200-ha study area, in Tabapuã, São Paulo, Brazil. The rate of soil depletion (SD) was calculated from the relation between the nutrient concentration in the sediments and the chemical properties in the original soil for all grid points. The data were subjected to descriptive statistical and geostatistical analysis. The mean SD rate for all nutrients was higher in the slash-and-burn than the green cane harvest scenario (Student’s t-test, p<0.05). In both scenarios, nutrient loss followed the order: Ca>Mg>K>P. The SD rate was highest in areas with greater slope. Lower uncertainties were associated to the areas with higher SD and steeper slopes. Spatial uncertainties were highest for areas of transition between concave and convex landforms.