ABSTRACT: In Southeast Brazil, the change of grape harvest from wet summer to dry winter through double-pruning management has improved the quality of wines, currently denominated winter wines. In order to better understand the influences of soil, macroclimate, and vineyard management in winter wines, we investigated seven vineyards in the states of Minas Gerais (Três Corações – TC, Três Pontas – TP, Cordislândia – COR, São Sebastião do Paraíso – SSP and Andradas – AND) and São Paulo (Itobi – ITO and Espirito Santo do Pinhal – PIN) during three consecutive growing seasons. The vineyards are located in warm temperate zones and grouped in four soil types: Acrudox in TC, AND and SSP, Hapludox in TP; Hapludult in AND and PIN; Eutrudept in ITO. The high clay content (> 35 %) observed in all soil types, associated to low evapotranspiration demand, avoided the occurrence of severe water stress, as observed by the high values of leaf and stem water potential, stomatal conductance, photosynthesis, and transpiration. Differences in vigor were more related to vineyard management and did not affect grape composition. Among vineyards, parameters for berry quality from ITO, such as sugar and acidity, were more associated to high soil sand content and winter temperature. No significant differences were found in anthocyanins and total phenols of berries among vineyards, suggesting that the high thermal range and low precipitation during autumn-winter, historically observed in all municipalities, seemed to be the main factor for improvement of phenolic compounds.

ABSTRACT: Different uses of soil legacy data such as training dataset as well as the selection of soil environmental covariables could drive the accuracy of machine learning techniques. Thus, this study evaluated the ability of the Random Forest algorithm to predict soil classes from different training datasets and extrapolate such information to a similar area. The following training datasets were extracted from legacy data: a) point data composed of 53 soil samples; b) 30 m buffer around the soil samples, and soil map polygons excluding: c) 20 m; and d) 30 m from the boundaries of polygons. These four datasets were submitted to principal component analysis (PCA) to reduce multidimensionality. Each dataset derived a new one. Different combinations of predictor variables were tested. A total of 52 models were evaluated by means of error of models, prediction uncertainty and external validation for overall accuracy and Kappa index. The best result was obtained by reducing the number of predictors with the PCA along with information from the buffer around the points. Although Random Forest has been considered a robust spatial predictor model, it was clear it is sensitive to different strategies of selecting training dataset. Effort was necessary to find the best training dataset for achieving a suitable level of accuracy of spatial prediction. To identify a specific dataset seems to be better than using a great number of variables or a large volume of training data. The efforts made allowed for the accurate acquisition of a mapped area 15.5 times larger than the reference area.

RESUMO A capacidade de uso da terra é um dos sistemas de classificação técnico-interpretativa mais difundidos, no entanto, adaptações regionais podem ser necessárias porque diferentes atributos podem afetá-la. Para essas adaptações, o mapa de solos de Minas Gerais foi usado como ponto de partida para este estudo. Os critérios para definição da capacidade de uso do solo foram adaptados aos níveis de manejo com pequena (nível A) e média (nível B) aplicação de capital e tecnologia moderna (nível C). O objetivo do presente estudo foi mapear a capacidade de uso das terras do estado de Minas Gerais, Brasil, segundo os critérios adaptados a diferentes níveis de manejo e medir a acurácia dos mapas resultantes. O sistema de capacidade de uso é amplamente usado pelo INCRA em avaliações de imóveis rurais. O critério de erosão foi substituído pelo critério de erodibilidade. O tratamento das informações foi feito num sistema de informações geográficas. Para a validação, perfis de solos de levantamentos pedológicos regionais foram amostrados, classificados, e sua capacidade de uso da terra foi comparada com a capacidade de uso da terra mostrada no mapa de acordo com os diferentes níveis de manejo. Apesar da escala menos detalhada do mapa de solos, os mapas de capacidade de uso das terras apresentaram acurácia adequada: 73% (nível de manejo A), 71% (B) e 50% (C). Portanto, sua aplicação é possível em fases iniciais de estudos de planejamento regional, nas quais o nível de detalhes requerido é menor (por exemplo, em zoneamentos ecológico-econômicos). Análises mais detalhadas ainda dependem de levantamentos a campo, conforme preconizado pelo sistema de capacidade de uso das terras.

ABSTRACT Land use capability is one of the most widespread technical-interpretative classification systems, however, regional adaptations may be necessary because different attributes may affect it. For these adaptations, the Minas Gerais soil map was used as the starting point for this study. The criteria to define the land use capability were adapted to management levels with small (level A) and medium (level B) application of capital and modern technology (level C). The aim of the present study was to map land use capability for Minas Gerais state, Brazil, following the criteria adapted to different levels of management and measure the accuracy of the resulting maps. The system of land use capability is widely used by INCRA in evaluations of rural properties. Erosion criterion was replaced by erodibility. The information was handled in a geographic information system. For validation, soil profiles from regional pedological surveys were sampled, classified, and its land use capability was compared to the land use capability shown on the map according to the different management levels. In spite of the small scale of the soil map, the maps of land use capability exhibited adequate accuracy: 73% (management level A), 71% (B), and 50% (C). Therefore, it can be applied in initial phases of regional planning studies, in which the level of details required is reduced (for example, in ecological-economic zoning). More detailed analyses still depend on detailed field surveys, as advocated by the system of land use capability.

ABSTRACT: The estimation of soil physical and chemical properties at non-sampled areas is valuable information for land management, sustainability and water yield. This work aimed to model and map soil physical-chemical properties by means of knowledge-based digital soil mapping approach as a study case in two watersheds representative of different physiographical regions in Brazil. Two watersheds with contrasting soil-landscape features were studied regarding the spatial modeling and prediction of physical and chemical properties. Since the method uses only one value of soil property for each soil type, the way of choosing typical values as well the role of land use as a covariate in the prediction were tested. Mean prediction error (MPE) and root mean square prediction error (RMSPE) were used to assess the accuracy of the prediction methods. The knowledge-based digital soil mapping by means of fuzzy logics is an accurate option for spatial prediction of soil properties considering: 1) lesser intense sampling scheme; 2) scarce financial resources for intensive sampling in Brazil; 3) adequacy to properties with non-linearity distribution, such as saturated hydraulic conductivity. Land use seems to influence spatial distribution of soil properties thus, it was applied in the soil modeling and prediction. The way of choosing typical values for each condition varied not only according to the prediction method, but also with the nature of spatial distribution of each soil property.

ABSTRACT Computer models have been used to assess soil organic carbon (SOC) stock change. Commonly, models require to determine soil bulk density (Db), a variable that is often lacking in soil data bases. To partly overcome this problem, pedotransfer functions (PTFs) are developed to estimate Db from other easily available soil properties. However, only a few studies have determined the accuracy of these functions and quantified their effects on the final quality of the spatial variability maps. In this context, the objectives of this study were: i) to develop one PTF to estimate Db in soils of the Brazilian Central Amazon region; ii) to compare the performance of PTFs generated with three other models generally used to estimate Db in soils of the Amazon region; and iii) to quantify the effect of applying these PTFs on the spatial variability maps of SOC stock. Using data from 96 soil profiles in the Urucu river basin in Brazil, a multiple linear regression model was generated to estimate Db using SOC, pH, sum of basic cations, aluminum (Al+3), and clay content. This model outperformed the three other PTFs published in the literature. The average estimation error of SOC stock using our model was 0.03 Mg C ha−1, which is markedly lower than the other PTFs (1.06 and 1.23 Mg C ha−1, or 15 % and 17 %, respectively). Thus, the application of a non-validated PTF to estimate Db can introduce an error that is large enough to skew the significant difference in soil carbon stock change.

RESUMO A determinação de atributos do solo auxilia no correto manejo da sua fertilidade. O equipamento portátil de fluorescência de raios-X (pXRF) foi recentemente adotado para determinar o teor total de elementos químicos em solos, permitindo inferências sobre atributos do solo. No entanto, esses estudos ainda são escassos no Brasil e em outros países. Os objetivos deste trabalho foram prever atributos do solo a partir de dados do pXRF, comparando-se os métodos de regressão linear múltipla stepwise (SMLR) e de random forest (RF), além de mapear e validar atributos do solo. 120 amostras de solo foram coletadas em três profundidades e submetidas a análises laboratoriais. Utilizou-se o pXRF para leitura das amostras e determinou-se o teor total de elementos. A partir dos dados do pXRF, foram utilizadas SMLR e RF para predizer resultados laboratoriais, que refletem atributos do solo, e os modelos foram validados. O melhor método foi utilizado para espacializar os atributos do solo. Utilizando SMLR, os modelos apresentaram valores elevados de R² (≥0,8), porém maior acurácia foi obtida na modelagem com RF. A capacidade de troca de cátions potencial e efetiva, matéria orgânica do solo, pH, saturação por bases e teores trocáveis de Ca, Al e Mg apresentaram ajustes adequados e predições acuradas com RF. Dos dez atributos do solo preditos por RF a partir de dados do pXRF, sete apresentavam CaO como a variável mais importante para auxiliar as predições, seguido por P2O5, Zn e Cr. Os mapas gerados a partir de dados do pXRF usando RF apresentaram adequados valores de R² para seis atributos do solo, atingindo R2 de até 0,83. O pXRF em associação com RF pode ser usado para prever atributos do solo com elevada acurácia, com rapidez e a baixo custo, além de proporcionar variáveis que auxiliam o mapeamento digital de solos.

ABSTRACT Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P2O5, Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.

RESUMO Modelos de terreno de rios são usados para análise de mudanças geomorfológicas e para simulações hidrológicas. Estes modelos são interpolados a partir de pontos batimétricos. A batimetria fluvial é geralmente conduzida através de seções transversais, o que pode acarretar em uma malha amostral esparsa. Métodos híbridos de krigagem, como krigagem por regressão (KR) e co-krigagem (CK), empregam a correlação com preditores auxiliares, além da auto-correlação entre variáveis, na predição da variável resposta. Neste estudo, sugere-se que a distância ortogonal de um ponto até a linha de centro do talvegue de um rio pode ser usada como covariável para KR e CK. Considerando-se que a variabilidade da cota do leito do rio é abrupta transversalmente a direção do fluxo, espera-se que quanto maior a distância euclidiana de um ponto até o talvegue, maior será sua elevação. O objetivo deste estudo foi avaliar o uso da covariável proposta em métodos híbridos de krigagem para a predição espacial da topografia do leito de rios. Para tanto, foi realizada uma validação externa, em que seções transversais foram usadas para interpolação e dados levantados entre as seções consistiram na amostra de teste. Os resultados da KR e CK foram comparados aos da krigagem ordinária. A KR apresentou a menor REQM. No mapa resultante da KR, o talvegue foi preservado nas lacunas não amostradas entre as seções, enquanto os demais métodos subestimaram a profundidade do talvegue nestes espaços. Assim, conclui-se que a KR pode melhorar a predição espacial de dados batimétricos fluviais.

ABSTRACT Terrain models that represent riverbed topography are used for analyzing geomorphologic changes, calculating water storage capacity, and making hydrologic simulations. These models are generated by interpolating bathymetry points. River bathymetry is usually surveyed through cross-sections, which may lead to a sparse sampling pattern. Hybrid kriging methods, such as regression kriging (RK) and co-kriging (CK) employ the correlation with auxiliary predictors, as well as inter-variable correlation, to improve the predictions of the target variable. In this study, we use the orthogonal distance of a (x, y) point to the river centerline as a covariate for RK and CK. Given that riverbed elevation variability is abrupt transversely to the flow direction, it is expected that the greater the Euclidean distance of a point to the thalweg, the greater the bed elevation will be. The aim of this study was to evaluate if the use of the proposed covariate improves the spatial prediction of riverbed topography. In order to asses such premise, we perform an external validation. Transversal cross-sections are used to make the spatial predictions, and the point data surveyed between sections are used for testing. We compare the results from CK and RK to the ones obtained from ordinary kriging (OK). The validation indicates that RK yields the lowest RMSE among the interpolators. RK predictions represent the thalweg between cross-sections, whereas the other methods under-predict the river thalweg depth. Therefore, we conclude that RK provides a simple approach for enhancing the quality of the spatial prediction from sparse bathymetry data.

ABSTRACT Bulk density (Bd) can easily be predicted from other data using pedotransfer functions (PTF). The present study developed two PTFs (PTF1 and PTF2) for Bd prediction in Brazilian organic soils and horizons and compared their performance with nine previously published equations. Samples of 280 organic soil horizons used to develop PTFs and containing at least 80 g kg-1 total carbon content (TOC) were obtained from different regions of Brazil. The multiple linear stepwise regression technique was applied to validate all the equations using an independent data set. Data were transformed using Box-Cox to meet the assumptions of the regression models. For validation of PTF1 and PTF2, the coefficient of determination (R2) was 0.47 and 0.37, mean error -0.04 and 0.10, and root mean square error 0.22 and 0.26, respectively. The best performance was obtained for the PTF1, PTF2, Hollis, and Honeysett equations. The PTF1 equation is recommended when clay content data are available, but considering that they are scarce for organic soils, the PTF2, Hollis, and Honeysett equations are the most suitable because they use TOC as a predictor variable. Considering the particular characteristics of organic soils and the environmental context in which they are formed, the equations developed showed good accuracy in predicting Bd compared with already existing equations.

RESUMO Mapas de solos existentes (dados legados) associados a técnicas de mapeamento digital são alternativas para obter informações a menores custos, entretanto, alguns testes são necessários para se fazer isso de forma mais eficiente. Este estudo objetivou comparar diferentes métodos para extrair informações de mapas de solos em escala detalhada usando árvores de decisão para mapear classes de solos em duas sub-bacias hidrográficas de Minas Gerais, validar os mapas em campo e usar o melhor método para extrapolar informações para áreas maiores, também validando estes mapas. Mapas de solos detalhados das sub-bacias do ribeirão Vista Bela (VBW) e ribeirão Marcela (MCW) foram utilizados como fonte de informação. Sete métodos para extrair informações de mapas foram comparados: polígono todo, eliminando 20 e 40 m das bordas dos polígonos, e com buffers em torno dos pontos amostrados com raios de 25 m, 50 m, 75 m e 100 m. O algoritmo Classification and Regression Trees (CART) foi utilizado para gerar árvores de decisão e permitir a criação de mapas de solos. A acurácia dos mapas foi avaliada através da acurácia global e índice Kappa. O melhor método foi utilizado para extrapolar informações para áreas maiores e esses mapas foram validados. Os melhores métodos para VCW e MCW foram, respectivamente, eliminando 20 m das bordas dos polígonos e buffer com raio de 25 m ao redor dos pontos. Mapas para áreas maiores foram obtidos usando esses métodos. Remoção de áreas de incerteza de mapas legados de solos contribuem para uma melhor modelagem e predição de classes de solos. A utilização de informações geradas neste trabalho permitiu a extrapolação validada de mapas de solos para regiões do entorno das sub-bacias hidrográficas.

ABSTRACT Existing soil maps (legacy data) associated with digital mapping techniques are alternatives to obtain information at lower costs, however, tests are required to do it more efficiently. This study had as objectives to compare different methods to extract information from detailed scale soil maps using decision trees for mapping soil classes at two watersheds in Minas Gerais, validate these maps in the field and use the best method to extrapolate information to larger areas, also validating these maps of larger areas. Detailed soil maps of Vista Bela creek (VBW) and Marcela creek (MCW) watersheds were used as source of information. Seven methods to extract information from maps were compared: the whole polygon, eliminating 20 and 40 m from the polygon boundaries, and with buffers around the sampled points with radii of 25 m, 50 m, 75 m, and 100 m. The Classification and Regression Trees (CART) algorithm was employed to create decision trees and enable creation of soil maps. Accuracy was assessed through overall accuracy and kappa index. The best method was used to extrapolate information to larger areas and maps were validated. The best methods for VCW and MCW were, respectively, eliminating 20 m from polygon edges and buffer of 25 m of radii from points. Maps for larger areas were obtained using these methods. Removing uncertainty areas from legacy soil maps contribute to better modeling and prediction of soil classes. Information generated in this work allowed for validated extrapolation of soil maps for regions surrounding the watersheds.

ABSTRACT The application of quantitative methods to digital soil and geomorphological mapping is becoming an increasing trend. One of these methods, Geomorphons, was developed to identify the ten most common landforms based on digital elevation models. This study aimed to make a quantitative assessment of the relationships between Geomorphons units, determined at three spatial resolutions and nine radii, and soil types and properties of two watersheds with different soil-landscape relationships in Brazil to help soil surveying and mapping under tropical conditions. The study was conducted at Lavrinha Creek (LCW) and Marcela Creek (MCW) watersheds, located in the state of Minas Gerais, Brazil. Spatial resolutions of 10, 20 and 30 m were the basis for generating Geomorphons at 9 radii of calculation for the watersheds. They were overlapped to detailed soil maps of the watersheds and a chi-square test was carried out to assess their relationship with soil types. Observation points were compared with the most highly correlated Geomorphons to also assess relationships with soil properties. Geomorphons with resolution of 30-m and radii of 20 and 50 cells, respectively for LCW and MCW, were more highly correlated with the variability of soil types, in accordance with the terrain features of these watersheds. The majority of observation points for each soil type was located in the same Geomorphon unit that was dominant when analyzing soil maps. There was less variability in soil properties between Geomorphon units, which was probably due to the highly weathered-leached stage of soils. Geomorphons can help to improve soil maps in tropical conditions when assessing soil variability due to its high correlation with tropical soil types variability.

ABSTRACT This study compared the performance of ordinary kriging (OK) and regression kriging (RK) to predict soil physical-chemical properties in topsoil (0-15 cm). Mean prediction of error and root mean square of prediction error were used to assess the prediction methods. Two watersheds with contrasting soil-landscape features were studied, for which the prediction methods were performed differently. A multiple linear stepwise regression model was performed with RK using digital terrain models (DTMs) and remote sensing images in order to choose the best auxiliary covariates. Different pedogenic factors and land uses control soil property distributions in each watershed, and soil properties often display contrasting scales of variability. Environmental covariables and predictive methods can be useful in one site study, but inappropriate in another one. A better linear correlation was found at Lavrinha Creek Watershed, suggesting a relationship between contemporaneous landforms and soil properties, and RK outperformed OK. In most cases, RK did not outperform OK at the Marcela Creek Watershed due to lack of linear correlation between covariates and soil properties. Since alternatives of simple OK have been sought, other prediction methods should also be tested, considering not only the linear relationships between covariate and soil properties, but also the systematic pattern of soil property distributions over that landscape.

RESUMO A poluição ambiental é um grande problema que tem sido notado em decorrência das alterações que ocorrem no ambiente, afetando os recursos naturais. Em relação ao solo, este oferece grande potencial para a destinação final de resíduos. Nesse sentido, objetivou-se, neste trabalho, propor critérios para avaliar a aptidão de locais para a destinação final de resíduos, de acordo com atributos do solo e do terreno, para o sul do Estado de Minas Gerais, e aplicar esses critérios para definir os locais mais apropriados para a instalação de sistemas de fossas sépticas, seguidas de vala de infiltração, em uma sub-bacia hidrográfica piloto. Foram utilizados dados da literatura e a experiência dos autores para a proposição de critérios mais importantes no tocante à aptidão dos locais para a destinação final de resíduos. O conjunto de atributos considerados foi agrupado em quatro classes de aptidão: Adequada, Regular, Restrita e Inadequada. Os critérios definidos e considerados limitantes foram: profundidade do solo, textura, gradiente textural, estrutura, drenagem natural, infiltração de água, tipo de horizonte superficial, profundidade do lençol freático, profundidade do lençol suspenso, distância do curso hídrico, relevo, pedregosidade, rochosidade e risco de inundação. Destes, foram adotados a profundidade do solo, drenagem natural, profundidade do lençol freático, relevo e distância do curso hídrico para a instalação de fossas sépticas. Da área total da sub-bacia, 5,29% se enquadraram na classe de aptidão Adequada. Os locais de aptidão Regular, Restrita e Inadequada totalizaram respectivamente 19,72%, 41,99% e 33% da área da sub-bacia. Fatores como atributos do solo e do terreno dão subsídios para a definição de locais mais propensos à destinação final de resíduos. Trabalhos futuros nessa linha de pesquisa deveriam envolver o refinamento dessas proposições, uma vez que há raros estudos dessa natureza no Brasil.

ABSTRACT Environmental pollution is a problem that has been noted due to changes in the environment, affecting natural resources. Regarding the soil, it may offer great potential for waste disposal. Thus, this study aims to propose criteria for evaluating local suitability for waste disposal, according to soil and terrain attributes for southern Minas Gerais State, and to apply those criteria to define the most appropriate locations for installation of septic systems in a pilot watershed. Literature and the authors' experience were used to propose the more important criteria regarding the suitability of sites for waste disposal. The set of attributes taken into account was grouped into four suitability classes: Adequate, Regular, Restricted and Inadequate. The defined criteria and considered limiting were: soil depth, texture, textural gradient, structure, natural drainage, water infiltration, type of surface horizon, water table depth, depth of perched water table, distance from water bodies, relief, stoniness, rockiness and risk of flooding. From these, soil depth, natural drainage, water table depth, relief and distance from water bodies were adopted for the installation of septic systems. From the total area of the watershed, 5.29% fit in the Adequate suitability class. The Regular, Restricted, and Inadequate sites accounted for, respectively, 19.72%, 41.99% and 33% of the wathershed. Factors such as soil and terrain attributes provide a basis for defining more appropriate places for waste disposal. Future work should involve the refinement of these propositions, since there are rare studies in this research line in Brazil.

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledge-based digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soil-landscape relationship explicit.

No Brasil, levantamentos de solos em escalas maiores ainda são escassos e necessários para dar apoio mais adequado ao planejamento de atividades relacionadas a solos e ambientes em áreas menores. Em consequência, este trabalho apresenta algumas técnicas de mapeamento digital de solos que permitem a produção mais rápida de levantamentos de solos, além de ajustar a distribuição espacial contínua das propriedades do solo em categorias discretas, de acordo com a complexidade inerente da variabilidade dos mesmos, aumentando a acurácia de informações espaciais. A técnica aqui enfatizada é baseada em sistemas que empregam o conhecimento de um especialista, sob uso de lógica fuzzy e similaridade de vetores. Para isso, é proporcionada a contextualização de cada ferramenta para a predição de classes de solos e suas propriedades, assim como algumas opções de técnicas para a aquisição de conhecimentos. Tais ferramentas têm reduzido a inconsistência e custos associados aos tradicionais procedimentos manuais, utilizando uma relativamente baixa densidade de amostragem. Por outro lado, a técnica baseada no conhecimento de especialistas não é automatizada, e, assim como no método tradicional de levantamentos de solos, o conhecimento das relações solo-paisagem é insubstituível.

In Brazil, soil surveys in more detailed scale are still scarce and necessary to more adequately support the decision makers for planning soil and environment activities in small areas. Hence, this review addresses some digital soil mapping techniques that enable faster production of soil surveys, beyond fitting continuous spatial distribution of soil properties into discrete soil categories, in accordance with the inherent complexity of soil variation, increasing the accuracy of spatial information. The technique focused here is knowledge-based in expert systems, under fuzzy logic and vector of similarity. For that, a contextualization of each tool in the soil types and properties prediction is provided, as well as some options of knowledge extraction techniques. Such tools have reduced the inconsistency and costs associated with the traditional manual processes, relying on a relatively low density of soil samples. On the other hand, knowledge-based technique is not automatic, and just as the traditional soil survey, the knowledge of soil-landscape relationships is irreplaceable.

Um fator muito atuante no processo erosivo e considerado de grande importância é o fator R, denominado erosividade da chuva. Esse fator é um dos componentes da equação universal de perdas de solo. Estudos de variabilidade espacial têm sido realizados almejando definir a distribuição espacial do fator R para determinada região. Objetivou-se com este trabalho calcular a erosividade da chuva (fator R - EI30) e gerar os mapas de espacialização desta. O estudo foi realizado em áreas experimentais de plantios de eucalipto localizadas em nove municípios, no Vale do Rio Doce, região centro-leste do Estado de Minas Gerais. Foram utilizados dados pluviográficos referentes ao período de janeiro de 2005 a dezembro de 2008. A espacialização da erosividade foi feita com base nos princípios da geoestatística, e a interpolação dos dados, por meio da krigagem. Os índices de erosividade anual variaram de 7.970 a 18.646 MJ mm ha-1 h-1 ano-1. O alcance para o índice de erosividade mensal variou de 10 a 177 km, e o anual foi de cerca de 52 km.

A very active and influential parameter in the erosive process is the R factor, called rainfall erosivity. This factor is one of the components of the universal soil loss equation. Studies in spatial variability have been conducted in an attempt to define the spatial distribution of the R factor for a given region. This study aimed to calculate the rainfall erosivity (R factor - EI30) to generate spatial maps. The study was conducted in experimental areas of eucalyptus stands in nine counties of the Rio Doce Valley, Central-Eastern region of Minas Gerais state, Brazil. Rainfall data from January 2005 to December 2008 were used. Erosivity was spatialized according to geostatistical principles and data interpolation using kriging. The annual rainfall erosivity indexes ranged from 7,970 to 18,646 MJ mm ha-1 h-1 year-1 . The reaching distance of the monthly erosivity index varied from 10 to 177 km, and was 52 km for the annual index.