S. B. Calderano

Taxonomia de solos desenvolvidos sobre depósitos sedimentares da formação Solimões no estado do Acre

Os solos do Estado do Acre na maioria sao formados sobre material de origem com grande influencia da orogenese Andina, com elevados teores de Ca2+, Mg2+ e Al3+ concomitantemente, associado as combinacoes diferenciadas dos valores da CTC, V e m. O objetivo deste trabalho foi caracterizar e classificar os solos de uma topossequencia sobre material sedimentar da Formacao Solimoes, no municipio de Feijo, Acre. Foram abertas trincheiras em tres pontos de uma topossequencia: terco superior (P1), terco medio (P2) e terco inferior (P3). Os solos foram analisados quanto a morfologia, granulometria (areia, silte e argila), complexo sortivo (Ca2+, Mg2+, Na+, K+ e Al3+), acidez potencial (H+Al), P assimilavel, pH (agua e KCl), superficie especifica, ataque sulfurico (oxidos de Fe, Al, Ti e Si), mineralogia (fracoes areia, silte e argila). Os solos foram classificados segundo o Sistema Brasileiro de Classificacao de Solos (SiBCS, 2006) e apresentada uma proposta de classificacao considerando as peculiaridades do solos da regiao. Os solos tem baixo grau de desenvolvimento pedogenetico, com minerais da fracao argila de alta atividade, alem da presenca de minerais primarios, como feldspatos e plagioclasios, nas fracoes areia e silte. Os solos foram classificados segundo o SiBCS atual como Argissolo Vermelho Alitico plintico (P1), Argissolo Acinzentado Distrofico plintico (P2) e Cambissolo Haplico Ta Eutrofico tipico (P3).

Artificial neural networks applied for soil class prediction in mountainous landscape of the Serra do Mar

Soil information is needed for managing the agricultural environment. The aim of this study was to apply artificial neural networks (ANNs) for the prediction of soil classes using orbital remote sensing products, terrain attributes derived from a digital elevation model and local geology information as data sources. This approach to digital soil mapping was evaluated in an area with a high degree of lithologic diversity in the Serra do Mar. The neural network simulator used in this study was JavaNNS and the backpropagation learning algorithm. For soil class prediction, different combinations of the selected discriminant variables were tested: elevation, declivity, aspect, curvature, curvature plan, curvature profile, topographic index, solar radiation, LS topographic factor, local geology information, and clay mineral indices, iron oxides and the normalized difference vegetation index (NDVI) derived from an image of a Landsat-7 Enhanced Thematic Mapper Plus (ETM+) sensor. With the tested sets, best results were obtained when all discriminant variables were associated with geological information (overall accuracy 93.2 - 95.6 %, Kappa index 0.924 - 0.951, for set 13). Excluding the variable profile curvature (set 12), overall accuracy ranged from 93.9 to 95.4 % and the Kappa index from 0.932 to 0.948. The maps based on the neural network classifier were consistent and similar to conventional soil maps drawn for the study area, although with more spatial details. The results show the potential of ANNs for soil class prediction in mountainous areas with lithological diversity.

Quantification of aluminium in soil of the Solimões Formation, Acre State, Brazil

The variety of soils in the State of Acre is wide and their chemical profiles are still not fully understood. The nature of the material of origin of these soils is indicated by the high aluminium (Al) content, commonly associated with high calcium (Ca) and magnesium (Mg) contents. The study objective was to use different methods to quantify Al in soils from toposequences formed from material of a sedimentary nature originating from the Solimoes Formation, in Acre, Brazil. Trenches were opened at three distinct points in the landscape: shoulder, backslope and footslope positions. Soil samples were collected for physical, chemical, mineralogical analyses. The Al content was quantified using different methods. High Al contents were found in most of these horizons, associated with high Ca and Mg levels, representing the predominant cations in the sum of exchangeable bases. The mineralogy indicates that the soils are still in a low weathering phase, with the presence of significant quantities of 2:1 minerals. Similar Al contents were determined by the methods of NaOH titration, xylenol orange spectrometry and inductively coupled plasma optical emission spectrometry. However, no consistent data were obtained by the pyrocatechol violet method. Extraction with KCl overestimated the exchangeable Al content due to its ability to extract the non-exchangeable Al present in the smectite interlayers. It was observed that high Al contents are related to the instability of the hydroxyl-Al smectite interlayers.