ABSTRACT Soils classified as Alisols are very frequent in the sedimentary agricultural areas of southern Brazil. The presence of red mottles with morphology similar to plinthite and saprolite residue is very common in these soils, and its identification can be considered a difficult task, both in the field and in the laboratory. The incorrect identification of these redoximorphic features can affect soils’ taxonomic and technical classification. We aimed to compare morphological, physical, chemical and mineralogical data to identify reddish mottles, possibly plinthites or saprolite residues, that occur in soils with high textural contrast in southern Brazil. Four soil profiles classified as Argissolos Bruno-Acinzentados (Alisols) were sampled. Matrix and mottles samples from the horizon Bt, CB, C and Cr were separated and subjected to morphological, granulometric, total sand fractionation, chemical extractions of iron and potassium and mineralogical features. Peds from each horizon were submitted to the submersion test in water for 2 and 8 hours and to 5 wetting and drying cycles. The mineralogy indicated the low degree of alteration of the samples, with abundant presence of 2:1 minerals and feldspars, even in the clay fraction. The saprolite resisted in the water submersion tests, making it difficult to interpret the results for the correct identification between plinthites and saprolite fragments. The morphological field data associated with the results of the tests of submersion in water, the cycle of wetting and drying, the dissolution of K and mineralogy, indicate the saprolithic nature of the mottles in all horizons and profiles. The submersion test in water for 2 and 8 hours was not efficient for the plinthite/saprolite identification. The cycle of wetting and drying tests allowed the identification of saprolite.

RESUMO O objetivo deste estudo foi estimar o potencial de perda de solo por meio da equação universal de perda de solo e identificar os fatores que controlam o processo erosivo em uma bacia hidrográfica de encosta. O fator erosividade da chuva (R) foi calculado por meio de dados normais de precipitação, obtendo-se, assim, índices mensais de erosão. O fator erodibilidade do solo (K) foi obtido a partir de uma amostragem de solo realizada por meio de uma malha de 340 pontos, na qual os valores foram interpolados pelo método da krigagem ordinária. O fator topográfico (LS) foi gerado a partir do Modelo Digital de Elevação (MDE), já os fatores uso e manejo e práticas conservacionistas (CP) foram obtidos por meio de observações de campo e valores tabelados. As maiores taxas de perdas abrangem 27% da área e se concentram em locais de maiores declividades nos quais predominam solos rasos, como Neossolos Litólicos e Neossolos Regolíticos. O fator LS determinou a magnitude do processo erosivo e o fator CP apresentou a maior relação com o controle das perdas de solo. Os resultados encontrados mostram a importância do fator cobertura do solo, em que medidas de manutenção da vegetação e práticas conservacionistas devem ser adotadas e consideradas pelos gestores ambientais em regiões de encosta com predomínio de solos rasos associados a relevo declivoso.

ABSTRACT The objective of this study was to estimate the soil loss potential through the universal soil loss equation and to identify the factors that control the erosive process in a hillside watershed. Rainfall erosivity factor (R) was calculated through normal precipitation data, thus obtaining monthly erosion indexes. The soil erodibility factor (K) was obtained from a soil sampling in an irregular grid of 340 points, in which the values were interpolated by the ordinary kriging method. The topographic factor (LS) was generated from the Digital Elevation Model (MDE) and the use and management and conservationist practices factors (CP) through field observations and tabulated values. The highest loss rates cover around 27% of the area, and are concentrated in places with higher slopes where shallow soils such as Litolics Neosols and Regolithic Neosols predominate. The LS factor determined the magnitude of the erosive process and the CP factor showed the highest relation with the soil loss control. The results found show the importance of the cover-management factor, in which measures of vegetation maintenance and conservation practices should be adopted and considered by the environmental managers in hillside regions with predominance of shallow soils associated to slope relief.

ABSTRACT: Diffuse reflectance spectroscopy (DRS) has the potential to predict soil organic carbon (SOC). However, it is still little used as a matter of routine in soil laboratories in Brazil. The objective of this study was to make evaluations as to whether SOC predicted by spectral techniques can replace measurement by routine chemical methods with no loss in quality and be applied in the recommendation of nitrogen fertilizer as well as identifying the best prediction strategies to use. A data set containing 2,471 samples from six soil spectral libraries (SSL) was used to develop spectroscopic models for SOC content prediction, including consideration of sample stratification and preprocessing techniques. The SOC was quantified through the analytical-chemical methods of wet combustion with determination by titration, designated as the reference method (REM), and colorimeter, designated as the routine method (ROM in an independent data set). SOC contents predicted by the spectral analysis method (SAM) were compared to the REM and ROM results, converted to soil organic matter (SOM) and used for N recommendations. The best estimate for SOM content using the SAM was achieved through stratification of the SSL and application of the standard normal variate (SNV) preprocessing. The SOC predicted by spectral techniques proved capable of replacing the SOC measured by routine chemical methods with no loss of quality and supported by an appropriate nitrogen fertilizer recommendation, provided the models met the conditions and possessed the characteristics of the samples to be analyzed.

ABSTRACT The method used to sample the runoff collected from soil erosion plots can be a significant source of error. In this study, we performed a laboratory experiment to evaluate the efficiency of the runoff sampling method most commonly used in Brazil. It is based on the manual homogenization and sampling of the collected runoff. Using soil material with 583 g kg–1 of sand and 89 g kg–1 of clay, the manual sampling method was tested for its ability to produce representative samples of artificial suspensions with a concentration of 2, 10 and 50 g L–1 of total solids. An underestimation of 30 % or more of the concentration of total solids was observed, with a variation of the same magnitude (CV between 20 and 45 %). We then developed a prototype sample splitter to replace the manual sampling method and tested it using the same artificial suspensions. The splitter was efficient in producing samples representative of the artificial suspensions, even without altering the particle size distribution of the total solids. Both absolute percentage errors (|< 5 %|) and the variation between five replicates (CV < 3 %) were small. The problems with the manual method are due to the inefficient homogenization that facilitates the differential sedimentation of particles of different sizes. If these problems are also found in other areas, then the prototype that we developed is a reasonable alternative.

ABSTRACT Soil physical properties can be changed after vehicle traffic, especially by heavy military tracked vehicles. The Santa Maria military Instruction Field, where the experiment was carried out, is currently the most used area for armored tracked vehicles training in Brazil, with the M113 BR being the main equipment. The aim of this study was to determine the effect of the straight line and pivoted traffic intensity of the M113 BR on the physical properties and advancement of the compaction state of an Abruptic Alisol (Argissolo Vermelho-Amarelo Ta Distrófico abrúptico) with military vehicle traffic history. The study was conducted in a completely randomized design, independently evaluating the effect of straight and pivoting traffic in three soil layers (0.00-0.04, 0.10-0.14, and 0.20-0.24 m). For the straight-line traffic, traffic intensities (TI) with one (TI1), two (TI2), and five (TI5) passes on the same trail were evaluated. For the pivoting traffic, TI with one (TI1P) and two (TI2P) pivots was evaluated. Both studies had two non-traffic treatments (NT). The soil bulk density, total porosity, macroporosity, microporosity, saturated soil hydraulic conductivity, soil penetration resistance, soil bearing capacity, preconsolidation pressure, and gravimetric water content were analyzed in this study. In the straight-line traffic, cases with no significant differences in TP, Ma, Mi, and Ks prevailed. The highest TP, smallest Bd and largest Mi were observed in the 0.10-0.14 m layer. In the pivoting traffic, one pass was sufficient to increase Bd and decrease TP, Ma, Mi, and Ks in the 0.00-0.04 m layer and the increase from one to two pivotings had a significant difference only in Mi in this same layer. Both types of traffic intensity did not affect the PR in any layer. The Abruptic Alisol, pre-compacted from long-term military training, supported the loads applied by the M113 BR when additional traffic occurred in straight line mode, but not when in pivoting mode. The preconsolidation pressure parameter was not appropriate to assess the ability of the soil to support loads applied by pivoting traffic.

ABSTRACT The use of new technologies, the development of new software, and the advances in the machines ability to process data have brought a new perspective to soil science and especially to pedology, with the advent of digital soil mapping (DSM). To meet the demand for soil surveys in Brazil, it will be necessary to popularize the techniques used in DSM. To identify and map the soil to generate maps of land use capability, we proposed a theoretical and practical course focused on the training in DSM for professionals involved in the management of land resources. The methodology was divided into five modules: I. Introduction to pedology, soil-landscape relationship, soil survey and soil classification (theory); II. Identification of soils in the field and study of the soil-landscape relationship (practice); III. Digital soil mapping and geographic information system (theory) and obtaining environmental covariates (practice); IV. Statistical learners and quality measures of spatial predictions (theory) and spatial pseudo-sampling (practice); V. Database organization, calibration, and validation of predictive models (practice). Results such as the average level of confidence of the participants in the soil classification, as well as the number of pseudo-sampling classified by the participants, chosen statistical apprentice, environmental covariables used, and overall accuracy, were influenced by the participants level of knowledge regarding DSM soils and techniques. The structure, focus, and time of each module should be based on the participants needs. It is suggested that a survey should be carried out to consider the level of knowledge in relation to the topics addressed in DSM before the preparation and execution of the course. The contribution of individual experiences showed the importance of multidisciplinarity in the teaching-learning process because it is a technique that involves soil knowledge, statistics, and mathematics applied to geoinformation science to understand soil variability in the landscape. The practical classes were fundamental, enabling an approximation of the content studied with the participants’ reality and consolidation of the acquired knowledge. In general, the course was well evaluated by the participants regarding the contents covered and practical field training and laboratory geoprocessing, who reported that the practical classes were fundamental for the appropriation of knowledge in DSM. This course could be a model for the PronaSolos, which tend to have heterogeneous groups of participants, being necessary to plan specific protocols to tend the particularity.

Resumo: O objetivo deste trabalho foi avaliar o uso da seleção de covariáveis por conhecimento especializado no desempenho de modelos de predição de classes de solos em uma paisagem complexa, para identificar o melhor modelo preditivo para o mapeamento digital de solos na região Sul do Brasil. Um total de 164 pontos foram amostrados em campo, com uso do hipercubo latino condicionado, tendo-se considerado as covariáveis elevação, declividade e aspecto. A partir do modelo digital de elevação, extraíram-se as covariáveis ambientais que compuseram três conjuntos, formados por: 21 covariáveis, covariáveis após exclusão das multicolineares e covariáveis escolhidas por conhecimento especializado. A predição foi realizada com os seguintes modelos: árvore de decisão, floresta aleatória, regressão logística múltipla e máquina de vetor de suporte. A acurácia dos modelos foi avaliada pelo índice kappa (K), pela acurácia geral (AG) e pela acurácia da classe. Os modelos de previsão foram sensíveis à amostragem desproporcional de classes de solo. O melhor mapa predito obteve AG de 71% e K de 0,59. O uso do conjunto de covariáveis escolhido pelo conhecimento especializado melhora o desempenho do modelo em prever as classes de solo em uma paisagem complexa, e floresta aleatória é o melhor modelo para previsão espacial das classes de solo.

Abstract: The objective of this work was to evaluate the use of covariate selection by expert knowledge on the performance of soil class predictive models in a complex landscape, in order to identify the best predictive model for digital soil mapping in the Southern region of Brazil. A total of 164 points were sampled in the field using the conditioned Latin hypercube, considering the covariates elevation, slope, and aspect. From the digital elevation model, environmental covariates were extracted, composing three sets, made up of: 21 covariates, covariates after the exclusion of the multicollinear ones, and covariates chosen by expert knowledge. Prediction was performed with the following models: decision tree, random forest, multiple logistic regression, and support vector machine. The accuracy of the models was evaluated by the kappa index (K), general accuracy (GA), and class accuracy. The prediction models were sensitive to the disproportionate sampling of soil classes. The best predicted map achieved a GA of 71% and K of 0.59. The use of the covariate set chosen by expert knowledge improves model performance in predicting soil classes in a complex landscape, and random forest is the best model for the spatial prediction of soil classes.

ABSTRACT Human activities often alter soils found in urban areas. These alterations affect their environmental functions and potential for use. However, soils from urban areas in Brazil have not been surveyed and identified, which can pose a technical problem for the development of the city. Considering the importance of urban environments and their soils, this study aimed to evaluate the environmental and morphological characteristics and the physical properties of urban soils in Santa Maria, Rio Grande do Sul, Brazil. The purpose was then to organize a protocol for describing urban soils and to identify diagnostic qualifiers to subsidize establishment of an order that allows proper classification of these soils in the Brazilian Soil Classification System. The protocol containing general and environmental notes and morphological notes and qualifiers was organized based on the experience of this study and the relevant literature. The environmental, morphological, and physical analyses carried out supported interpretation of the urban soils of Santa Maria, their classification in the World Reference Base for Soil Resources system, and identification of diagnostic qualifiers proposed for the Brazilian Soil Classification System. The protocol tested in this study was adequate for data collection in the field. Soils had high variation in layer thickness, particle size composition, color, bulk density, saturated hydraulic conductivity, penetration resistance, stoniness, and the presence of artifacts. Considering the establishment of a Brazilian taxonomy for urban soils, the field data led to the proposal for new qualifiers, such as Saprolitic (residual saprolite material), Impervic (sealed/impermeable layer), Multigranic (layers with contrasting textures), Stonic (layers with stoniness), and Saprorockic (layers constituted of transported saprolite), which can be used as diagnostic attributes in the Brazilian Soil Classification System and even contribute to improvement of the order of Technosols in the World Reference Base For Soil Resources system.

ABSTRACT Soil reflectance spectroscopy has become an innovative method for soil property quantification supplying data for studies in soil fertility, soil classification, digital soil mapping, while reducing laboratory time and applying a clean technology. This paper describes the implementation of a Graphical User Interface (GUI) using R named AlradSpectra. It contains several tools to process spectroscopic data and generate models to predict soil properties. The GUI was developed to accomplish tasks such as perform a large range of spectral preprocessing techniques, implement several multivariate calibration methods, generate statistics assessment and graphical output, validate the models using independent dataset, and predict unknown variables using soil spectral data. AlradSpectra has four main modules: Import Data, Spectral Preprocessing, Modeling, and Prediction. The implementation of AlradSpectra is demonstrated by applying visible near-infrared reflectance spectroscopy for soil organic carbon (SOC) prediction. The data contains the value of SOC and Vis-NIR reflectance for 595 soil samples. The prediction statistic assessment of SOC was performed applying all spectral preprocessing and methods. The R 2 considering all models ranged from 0.54 to 0.80. In the partial least squares regression (PLSR) models, the performances were similar to multiple linear regression (MLR) and support vector machines (SVM). The lowest error in the SOC prediction was achieved by PLSR method with standard normal variate (SNV) preprocessing reaching an R 2 of 0.80, the smallest root mean square error (RMSE) of 0.47 %, and ratio of performance to inter-quartile distance (RPIQ) of 3.12. The capacity of performing multiple tasks, being free and open-source, easy to operate, and requiring no initial knowledge of R programming language are features that make AlradSpectra a useful tool to perform different modeling approaches and predict the desired soil variable.

ABSTRACT The study of the relationships between the yield potential of forest stands and the conditions offered for plant development is fundamental for the adequate management of the forest when aiming at sustainable high yields. However, these relations are not clear, especially in commercial forests, on rugged terrain where relationships between the landscape, soil, and plants are more complex. Considering this, we tested the hypothesis that the morphological aspects of the soil conditioned by topography are the main limiting factors for tree development. Our objective was to evaluate the edaphic and topographic influence on the dendrometric variation of Pinus taeda L. of a forest stand in a subtropical climate at high altitude. For that, Spearman’s correlation analysis and canonical correspondence were performed on two data datasets containing pedological, topographic, and dendrometric information of a commercial plantation of Pinus taeda, in the Campo Belo do Sul, Santa Catarina state, Brazil. Soil sampling and characterization was performed in two distinct designs. The first design was based on the morphological description of 11 soil profiles. The second was performed with intensive prospecting of the area, with 102 sampling locations determined through conditioned Latin hypercube sampling. For each sampling point, the height and diameter of the four nearest trees were measured and the terrain attributes were calculated from the digital elevation model. The solum depth, the thickness of the superficial horizon, elevation, and vertical distance to channel network were the main conditioning factors of the dendrometric variation, wherein taller trees were found in deeper soils, with a thicker surface horizon, in lower areas that are vertically closer to the drainage network. Our results showed that the selection of topographic and morphological variables has a significant effect on the tree height and should therefore be used to select homogeneous areas for the development of the species. In addition, we showed the importance of using an intensive sampling survey to understand dendrometric variation.

Resumo: O objetivo deste trabalho foi testar a extrapolação das relações solo-paisagem de uma área de referência (AR), por meio de mapeamento digital de solos (MDS), para uma carta topográfica (1:50.000), e comparar os resultados aos obtidos em estudos similares anteriormente desenvolvidos no Brasil. O trabalho consistiu no levantamento de solos, com técnicas convencionais de mapeamento de uma AR de 10 km2 (na escala 1:10.000), para mapear uma área fisiograficamente similar de 678 km2 (na escala 1:50.000), tendo-se utilizado o MDS. A técnica de árvore de decisão (AD) foi utilizada para a construção do modelo preditivo de extrapolação, com base nas classes de solos e em oito atributos de terreno da AR. A validação do MDS, com pontos de observação de campo, resultou em 66,1% de exatidão global e 0,36 de índice kappa. Os solos mais representativos da área foram preditos corretamente, enquanto solos menos representativos e de menor ocorrência na paisagem e, consequentemente, com amostragem reduzida, tiveram sua predição comprometida. A proporção da AR, igual a 1,5% da área total, é um fator limitante à formulação das relações solo-paisagem para representar precisamente a área mapeada por MDS.

Abstract: The objective of this work was to test the extrapolation of soil-landscape relationships in a reference area (RA) to a topographic map (scale 1:50,000), using digital soil mapping (DSM), and to compare these results to those obtained in similar studies previously conducted in Brazil. A soil survey in a 10 km2 RA, using conventional mapping techniques (scale 1:10,000), was made in order to map a 678 km2 physiographically similar area (scale 1:50,000) using DSM. The decision tree technique was employed to build a predictive extrapolation model based on soil classes and eight terrain attributes in the RA. The validation of DSM by application of field observation points resulted in a 66.1% global accuracy and in 0.36 kappa index. The most representative soils in the area were correctly predicted, whereas the less representative and less frequent soils in the landscape (and consequently with reduced sampling) had their prediction compromised. The RA proportion, which equals 1.5% of the total area, is a limiting factor in the formulation of soil-landscape relationships to precisely represent the mapped area by DSM.

ABSTRACT The interest in new techniques to support digital soil mapping (DSM) is increasing. Numerous studies pointed out that the measure of magnetic susceptibility (MS) can be extremely useful in the identification of properties related with factors and processes of soil formation, applied to soil mapping. This study addressed the effectiveness of magnetic soil susceptibility to identify and facilitate the distinction of different pedogenic environments of a representative hillslope in the highland Planalto Médio in the state of Rio Grande do Sul (RS), Brazil. In a 350-ha area in the municipality of Santo Augusto, RS, a representative transect was selected, trenches opened for soil characterization and 29 grid points marked at regular distances of 50 m, where soil samples were collected (layers 0.00-0.05, 0.05-0.15, 0.15-0.30, and 0.30-0.60 m) to analyze soil properties. Data from the transect samples were subjected to descriptive statistics. Limits of the pedogenetic environments along the slope were identified by the Split Moving Window (SMW) Boundary Analysis. The combined use of soil magnetic susceptibility and the SMW technique was effective in identifying different pedogenetic environments in the study area.

ABSTRACT Terrain attributes (TAs) derived from digital elevation models (DEMs) are frequently used in digital soil mapping (DSM) as auxiliary covariates in the construction of prediction models. The DEMs and information extracted from it may be limited with regard to the spatial resolution and error magnitude, and can differ in the behavior of terrain features. The objective of this study was to evaluate the quality and limitations of free DEM data and to evaluate a topographic survey (TS) underlying the choice of a more appropriate model, for use in DSMs at a scale of 1:10,000. The study was conducted in an area of 937 ha in the watershed of Lajeado Giruá, in southern Brazil. The DEMs: DEM-TS, DEM-Topographic Map (TM), DEM-ASTER, DEM-SRTM, and DEM-TOPODATA were evaluated with regard to the precision elevation by statistical tests based on field reference points, the root mean square error (RMSE), identification of the number and size of spurious depressions, and the application of the Brazilian Cartographic Accuracy Standards Law (BCASL) to define the scale of each DEM. In addition, the TA derived from each DEM was compared with the TA from DEM-TS, considered to be terrain reality. The results showed that the elevation data of DEM-TS had the best quality (RMSE = 1.93 m), followed by DEM-SRTM (RMSE = 5.95 m), DEM-Topographic Map (RMSE = 8.28 m), DEM-TOPODATA (RMSE = 9.78 m) and DEM-ASTER (RMSE = 15.57 m). The DEM-TS was well-represented at a 1:10,000 scale, while the DEM-Topographic Map and DEM-SRTM fitted 1:50,000, the DEM-TOPODATA 1:50,000 and the DEM-ASTER a 1:100,000 scale. The results of DEM-SRTM and DEM-TOPODATA were closest to terrain reality (DEM-TS) and had the lowest number of spurious depressions and RMSE values for each evaluated attribute, but were inadequate for not fitting detailed scales compatible with small areas. The techniques for the acquisition of elevation data of each DEM and mainly the flat to gently undulating topography were factors that influenced the results. For a DSM at a scale of 1:10,000 in similar areas, the most appropriate model is DEM-TS.

ABSTRACT Diffuse reflectance spectroscopy (DRS) is a fast and cheap alternative for soil clay, but needs further investigation to assess the scope of application. The purpose of the study was to develop a linear regression model to predict clay content from DRS data, to classify the soils into three textural classes, similar to those defined by a regulation of the Brazilian Ministry of Agriculture, Livestock and Food Supply. The DRS data of 412 soil samples, from the 0.0-0.5 m layer, from different locations in the state of Rio Grande do Sul, Brazil, were measured at wavelengths of 350 to 2,500 nm in the laboratory. The fitting of the linear regression model developed to predict soil clay content from the DRS data was based on a R2 value of 0.74 and 0.75, with a RMSE of 7.82 and 8.51 % for the calibration and validation sets, respectively. Soil texture classification had an overall accuracy of 79.0 % (calibration) and 80.9 % (validation). The heterogeneity of soil samples affected the performance of the prediction models. Future studies should consider a previous classification of soil samples in different groups by soil type, parent material and/or sampling region.

Increasingly intensive use of soil and its disorganized occupancy result in its degradation. There is a lack of more detailed information on soils and on soil surveys. Soil mappings are key for land sustainable management because they provide basic information about this natural resource and its distribution on the landscape. The production of highly detailed soil class and attribute maps, will only be possible using geotechnologies in combination with field data. The objective of this review was to emphasize the importance of soil surveys within a new paradigm, defined as Digital Soil Mapping (DSM). DSM requires: i) the use of data from field sampling and analytical results from laboratories, including existing as well as newly collected data ii) the process of inference and of spacing includes a proposition of mathematical and statistical models based on soil observation, environmental covariates and other scorpan factors iii) results should be presented as soil spatial system format with continuous and/or discrete data, added to the uncertainty of this information, which could be updated whenever new observations become available. The application of DSM will make it possible for soil spatial information, attributes and classes to be generated and be readily available in digital format with its associated uncertainty. Among the challenges for further implementation of several strategies available in DSM, the training of future generations of soil scientists should be a priority.

O uso cada vez mais intensivo do solo e sua ocupação desordenada resultam em sua degradação. Existe uma grande carência de informações mais detalhadas de solos e seus levantamentos. Por apresentarem informações básicas sobre esse recurso natural e sua distribuição na paisagem, os mapeamentos são fundamentais para o manejo sustentável das terras. A produção de mapas de classes e atributos, com maior nível de detalhamento, só será possível com a utilização de geotecnologias combinadas com dados coletados no campo. Assim, o objetivo desta revisão foi ressaltar a importância dos levantamentos de solos dentro de um novo paradigma, definido como Mapeamento Digital de Solos (MDS). A prática de MDS requer que: i) os dados utilizados tenham sua origem em coletas de campo e resultados analíticos de laboratório, incluindo dados legados e novas coletas; ii) o processo de inferência e espacialização inclui a proposição de modelos matemáticos e estatísticos entre as observações do solo, as covariáveis ambientais e os demais fatores scorpan; e, iii) os resultados estejam na forma de um sistema espacial em solos, com dados contínuos e/ou discretos, agregados à incerteza dessas informações, podendo ser atualizados sempre que novas observações estiverem disponíveis. A aplicação do MDS irá permitir que informações espaciais em solos, de atributos e classes, sejam geradas e prontamente disponibilizadas em formatos digitais e com a incerteza associada. Entre os desafios para uma maior aplicação das várias estratégias disponíveis em MDS está a capacitação e formação das futuras gerações de pedólogos.