ABSTRACT Soils with high organic carbon content, such as those with “humic A horizon”, occur in different regions of Brazil. This study aimed to determine the quantitative and qualitative characteristics of organic matter regarding the humic A horizon under different land uses in Bom Jardim, in Rio de Janeiro State, Brazil. Samples from forest, pasture, Eucalyptus plantation, coffee cultivation, olericulture, and also passion fruit soils were sampled and analyzed. In October 2011, undisturbed samples from humic A horizons, were collected for soil density analysis. In addition, disturbed samples were collected for the determination of organic carbon (Corg) content, total carbon, humic substance fractions contents and natural abundance of 13Carbon (δ13C). Low Corg contents were observed in areas under olericulture, pasture and passion fruit cultivation. The fulvic acid fraction was higher than other fractions, regardless of land use. The small variability in δ13C indicated maintenance of original Corg and suggest high resistance of Soil Organic Matter in humic A horizons.

ABSTRACT Notwithstanding the importance of soil surveys, advances in digital soil mapping have mainly focused on mapping soil attributes or properties rather than developing digital maps of soil units or soil classes. The purpose of this research was to develop digital soil unit maps based on primary soil data collection in areas without previously collected soil information. The covariate variability, the random effect across the data subset and the map outputs were the focuses of this study. We used five datasets with four models (Random Forest - RF, Gradient Boosted Machine - GBM, C5.0, and multinomial log-linear model - MLR). The covariates were grouped into five datasets, where four were grouped by Region Of Interest per Class (ROIC) and one was not grouped by ROIC. To evaluate the random effect to split the dataset, we ran each model 50 times and observed the overall accuracy (OA) and kappa index, and uncertainty, majority and variety maps. The OA of Dataset01 to 04 was lower than to Dataset05 accuracy. However, map outputs of RF and GBM for Dataset01 and Dataset05 had the same majority prediction. It seems that RF and GBM produce consistent results in map outputs according to this methodology and pedologist expertise. To evaluate the uncertainty and the consistency of soil unit prediction, we used the majority maps process. Random Forest, similar to GBM, presented the best results. The increase in the number of covariates was not a guarantee of improvement in the OA or in the quality of the map output. Geographic position and distance raster did not improve the map output according to expert evaluation. Because the variance between the ROICs, when the training and validation datasets were split based on it, the subsets are quite different in relation to the covariates, and this is the reason for the worse results of this model, comparing with the Dataset05. On the other hand, when considering one complete dataset not based on ROICs, the variance of training and validation subsets is lower and produced more accurate parameters of quality.

ABSTRACT: The evaluation of soils in representative landscapes constitutes an opportunity to evaluate spatial distribution, discuss formation processes, and apply this knowledge to land use and management. In this sense, from the perspective of an environmentally diversified region, the aim of the present study is to evaluate the occurrence and understand the formation of soils in different geomorphic surfaces of a landscape from a mountain region in the state of Rio de Janeiro. The study was developed in the Pito Aceso microbasin in the municipality of Bom Jardim, composed of narrow valleys and a rugged mountain domain, with elevation between 640 and 1,270 m. In a representative landscape, the geomorphic surfaces were obtained from the slope segments and flow lines. On the geomorphic surfaces, soil profiles were described by their morphological properties, collected, and analyzed to describe the chemical and physical properties of each horizon. Geomorphological aspects and possible variations of the parent material directly affected pedogenesis and led to distinct soil classes in the landscape. Variation in the geomorphic surfaces directs the processes for soil formation under current conditions, as well as the preservation of polygenetic soils. Soils of lower development and with greater participation of the exchangeable cations were identified at the summit (talus deposit) (Neossolo Litólico and Cambissolo Húmico) and toeslope (colluvial-alluvial) (Neossolo Flúvico), whereas more developed soils with lower nutrient content occur in the concave (Argissolos Vermelho and Amarelo) and convex (Latossolo Amarelo) backslope, except for the Argissolo Vermelho-Amarelo in the shoulder, which had high exchangeable cations contents.

As propriedades dos solos têm grande impacto sobre aspectos econômicos e ambientais da produção agropecuária. As relações quantitativas entre as propriedades dos solos e os fatores que condicionam sua variabilidade são a base do mapeamento digital de solos. Os modelos preditivos de propriedades dos solos avaliados neste trabalho são os estatísticos (Regressão Linear Múltipla-RLM) e geoestatísticos (krigagem ordinária e cokrigagem). Este estudo foi desenvolvido para o município de Bom Jardim, RJ, e usou um banco de dados de solos com 208 pontos amostrais. Foram avaliados modelos preditivos para as frações areia, silte e argila, pH em água e carbono orgânico para seis profundidades, de acordo com as especificações do consórcio de mapeamento digital de solos em nível global (GlobalSoilMap). Utilizaram-se covariáveis preditoras contínuas e categóricas, estas últimas para avaliar suas contribuições ao modelo. Apenas as covariáveis ambientais elevação, aspecto, índice de potência de fluxo (SPI), índice de umidade (SWI), índice de vegetação por diferença normalizada (NDVI) e relação entre bandas b3/b2 apresentaram correlação significativa com as propriedades do solo. Os modelos preditivos tiveram em média coeficiente de determinação de 0,21. Os modelos preditivos que apresentaram os melhores resultados foram os geoestatísticos, com o maior coeficiente de determinação 0,43 associado à propriedade areia entre 60 e 100 cm de profundidade. A utilização de conjunto de dados de solos esparsos para mapeamento digital de propriedades de solos pode explicar apenas uma parte da variação espacial dessas propriedades. Os resultados podem estar relacionados à densidade de amostragem, à quantidade e qualidade das covariáveis ambientais usadas e aos modelos preditivos utilizados.

Soil properties have an enormous impact on economic and environmental aspects of agricultural production. Quantitative relationships between soil properties and the factors that influence their variability are the basis of digital soil mapping. The predictive models of soil properties evaluated in this work are statistical (multiple linear regression-MLR) and geostatistical (ordinary kriging and co-kriging). The study was conducted in the municipality of Bom Jardim, RJ, using a soil database with 208 sampling points. Predictive models were evaluated for sand, silt and clay fractions, pH in water and organic carbon at six depths according to the specifications of the consortium of digital soil mapping at the global level (GlobalSoilMap). Continuous covariates and categorical predictors were used and their contributions to the model assessed. Only the environmental covariates elevation, aspect, stream power index (SPI), soil wetness index (SWI), normalized difference vegetation index (NDVI), and b3/b2 band ratio were significantly correlated with soil properties. The predictive models had a mean coefficient of determination of 0.21. Best results were obtained with the geostatistical predictive models, where the highest coefficient of determination 0.43 was associated with sand properties between 60 to 100 cm deep. The use of a sparse data set of soil properties for digital mapping can explain only part of the spatial variation of these properties. The results may be related to the sampling density and the quantity and quality of the environmental covariates and predictive models used.

O presente estudo teve como objetivo identificar as relações existentes entre atributos do terreno, material de origem e os solos em uma área no Noroeste do Estado do Rio de Janeiro. Os atributos do terreno: elevação, declividade, aspecto, plano de curvatura, radiação solar e índice CTI, e as litologias dominantes na área tiveram influência direta na distribuição dos solos da área, com os solos derivados de migmatitos apresentando mudança textural abrupta, diferentemente dos solos originados dos granulitos. A elevação e a declividade ajudaram a explicar a distribuição dos Neossolos Litólicos e Cambissolos na área, enquanto a declividade e o índice CTI explicam a ocorrência dos Argissolos Amarelos, Vermelho-Amarelos e dos Gleissolos em relevo suavizado. Diferenças na profundidade são condicionadas pela curvatura do terreno, com áreas côncavas apresentando solos menos profundos (saprolíticos) do que nas áreas convexas. Os Argissolos derivados dos granulitos, que ocorrem em relevo forte ondulado ou montanhoso, em encostas voltadas para norte, com maior incidência de radiação solar, apresentam coloração vermelha e são eutróficos, enquanto os Argissolos das encostas voltadas para sul (< radiação solar) são vermelho-amarelados, distróficos e com horizonte B latossólico em profundidade.

The present study aimed to identify the relationships between land attributes, source material and soils in a Northwest Rio de Janeiro State area. Land attributes as: elevation, slope, aspect, plan curvature, solar radiation and compound topographic index (CTI), and the dominant lithology in the area had a strict influence on the soil distribution, which that ones derived from migmatites showing abrupt textural change, unlike soils originating from the granulites. Elevation and slope attributes helped out to explain the distribution of Leptosols and Cambisols in the area, while slope and CTI attributes explain the occurrence of Yelow and Red Yelow Acrisols and Gleysols on low relief. Distincts depths are constrained by the curvature of the terrain, with concave areas presenting shallower soils (saprolitic) than in convex areas. The Acrisols derived from granulites, which occur in hilly conditions, on slopes facing north and highest incidence of solar radiation, show red hue and are eutrophic, while the Acrisols on slopes facing south (< solar radiation) are yellowish-red, dystrophic and show ferralic horizon at depth.

O estudo teve o objetivo de produzir informações físico-bióticas dos elementos componentes da paisagem, visando fornecer subsídios para o planejamento geoambiental de paisagens rurais montanhosas na serra dos Órgãos, ocupadas com agricultura familiar. Com suporte de geotecnologias, o trabalho combinou técnicas de mapeamento digital, produtos de sensoriamento remoto, sistema de informação geográfica (SIG) e modelo digital de elevação (MDE), buscando otimizar os mapeamentos temáticos no campo e facilitar a geração de mapas derivados e interpretações úteis ao planejamento da área. Os procedimentos utilizados envolveram a geração de bancos de dados digitais no SIG e a geração de dados básicos no campo, através de mapeamentos temáticos do meio físico, que foram associadas com informações socioeconômicas, de pluviosidade e de áreas protegidas por legislação específica ou reservas existentes, constituindo assim, um conjunto de informações agro-socioambientais em meio digital. Os resultados produzidos permitiram discriminar e caracterizar aspectos físicos e ecológicos dos elementos componentes da paisagem, contribuindo para a melhor compreensão dos recursos ambientais com suas potencialidades e limitações, além de contribuir para planejamento geoambiental e elaboração do plano de manejo conservacionista da área.

This research aims to produce biophysical information of the landscape components, with the objective to supply a background to the environmental planning of the rural areas in the mountainous region of Serra dos Orgaos, under family agriculture. With support of geographical information system, this research has combined digital mapping techniques based on remote sensing with digital elevation model (DEM), aiming to optimize the field thematic maps and to facilitate the generation of maps and useful interpretations, in order to use them for planning purposes. The procedures used in this research took into consideration the creation of a database based on digital data from the GIS, and the generation of basic data obtained in the field, through environmental thematic maps associated with socio-economic information, rainfall data and protected areas, based on legislation, creating therefore, a set of agro-socio-environmental information in a digital system. The results allow us to discriminate and to characterize the physical and ecological landscape elements, contributing to a better understanding of the environmental resources and their potentialities and limitations, besides to contribute to the environmental planning and to create a conservationist managing plan to the research area.