-Light soils occupy 8% of the Brazilian territory and are especially expressive in the new and last agricultural frontier in Brazil: the Matopiba region -in the states of Maranhão, Tocantins, Piauí, and Bahia -, where they represent 20% of the area. These soils fit into the textural classes of sand and loamy sand or sandy loam, down to 0.75-m soil depth or deeper, and they are mainly represented by Neossolos Quartzarênicos (Quartzipsamments) and, partly, by Latossolos (Oxisols) and Argissolos (Ultisols). The understanding of soil functioning depends on the establishment of distinguishing criteria for: organic matter dynamics; content and mineralogy of the clay fraction; coarse sand and total sand contents, in relation to those of fine sand; mean diameter of the sand fraction; and water retention capacity. These criteria can contribute for the zoning and for the conservation and fertility management of light soils, as well as for the estimation of their agricultural potential. Integrated production systems, such as crop-livestock and crop-livestock-forestry integration, besides no-tillage with crop rotation, mixed forestry planting with legumes, and the use of green manure and cover crops are relevant for the proper management of these soils. The objective of this review was to characterize light soils and to highlight the main challenges regarding their agricultural potential and their conservation and fertility managements, in face of the expansion and consolidation of the new Brazilian agricultural frontier.
A database with 431 soil profiles of Rio de Janeiro State was used in a research project entitled "Quantifying the magnitude, spatial distribution and organic carbon in soils of Rio de Janeiro State, using quantitative modeling, GIS and database technologies" (Projeto Carbono_RJ, sponsored by FAPERJ-Carlos Chagas Filho Foundation for Research Support in Rio de Janeiro State). These soil data were collected for other purposes and there were only limited soil bulk density data (103), which is needed for estimating soil organic carbon (SOC) stocks. Pedotransfer functions (PTFs) were estimated to be used in the modeling of organic soil carbon of topsoil (0-10 cm), using the scorpan model. The following environmental correlates were used as predictor variables: satellite data (Landsat ETM +), lithology and soil maps, and a DEM and its derivatives. This dataset represents the best organized soil dataset in Brazil and is working as an educational trial for Digital Soil Mapping using a variety of methods for predicting soil classes and their properties. Multilinear analysis and regression-kriging were used to perform the modeling. Seven different models were built and compared through statistical methods. The main difference between the models was the set of predictor variables used to perform them. In general, all models performed well to predict the SOC stock. Nevertheless, model 6 was considered the best one since it presented the smallest AIC and RMSE as it used existing soil information (polygon soil maps) as a predictor variable, in addition to the variables used in the other models. The results obtained with this model were used to map topsoil carbon stock at a spatial resolution of 90 m.
RESUMOAvaliou-se a aptidão para reflorestamento das terras das partes não edificadas da vertente norte do maciço da Tijuca, sub-bacias dos canais do Mangue e do Cunha, com o intuito de subsidiar ações do Programa Mutirão Reflorestamento da Secretaria Municipal de Meio Ambiente do Rio de Janeiro. A avaliação da aptidão das terras estimou graus de limitação dos parâmetros: deficiência de nutrientes, deficiência de água, susceptibilidade à erosão e impedimentos ao manejo. Estes graus de limitação foram estimados para os componentes das unidades de mapeamento de solos, considerando as informações de solos e paisagens do mapeamento existente. Para a digitalização e organização das informações geradas, foram utilizados sistemas de informações geográficas. As seguintes classes de aptidão para reflorestamento foram determinadas: 11,2 % de Regular, 81,5 % de Restrita e 7,3 % de Inapta. A declividade representa o fator limitante de maior importância para as terras da classe de aptidão restrita, seguida da presença de rochosidade.Termos de indexação: avaliação de terras, uso da terra, geoprocessamento.
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