ABSTRACT Bulk density (BD) is a soil physical property used as a soil quality indicator and variations in this measurement influence soil water content and carbon stock estimates. This study aims to compile a database of samples of bulk density, textural fractions, and organic carbon values, as well as evaluate the accuracy of published pedotransfer functions (PTF) that predict bulk density, and propose a hierarchical PTF to predict the bulk density of Brazilian Soils. The performance of eleven PTFs and the newly proposed PTFs were evaluated and compared using the root mean square error (RMSE) and coefficient of determination (R2) based on a testing soil database collected from the literature. We noticed a slight improvement in accuracy when organic carbon and coarse and fine sand fractions were included as predictors alongside silt and clay. The best results with existing PTFs were obtained by PTF-A in Tomasella and Hodnett (1998) (RMSE = 0.20 g cm–3) and PTF-F in Benites et al. (2007) (RMSE = 0.17 g cm–3). Our proposed PTFs use textural fractions and organic carbon as predictors in a hierarchical form. The proposed PTF-4, which uses fine sand, coarse sand, clay, and organic carbon, presented the lowest value for RMSE (0.14 g cm–3) for BD prediction.

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 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 Modelos digital de elevação (MDE) usados no mapeamento digital de solo (MDS) são comumente selecionados com base em medidas e indicadores (critérios de qualidade) que refletem como um determinado MDE representa a superfície da paisagem. A hipótese é que, quanto mais acurado for um DEM, mais acurada serão as predições do MDS. O objetivo deste estudo foi avaliar diferentes critérios para identificar o MDE que fornece as predições mais acuradas do MDS. Um conjunto de 10 critérios foi utilizado para avaliar a qualidade de nove MDEs construídos com diferentes fontes de dados, rotinas de processamento e três resoluções (5, 20 e 30 m). Modelos de regressão logística multinomial foram calibrados usando 157 observações de solo e atributos de terreno derivados de cada MDE. As predições de classe de solo foram validadas usando validação cruzada leave-one-out. Os resultados mostraram que, para cada resolução, os critérios de qualidade são úteis para identificar o MDE que representa mais precisamente a superfície da paisagem. No entanto, para todas as três resoluções, o MDE mais acurado não produziu as predições mais acuradas do MDS. Com os MDEs de resolução de 20 m, as predições do MDS foram cinco pp menos acuradas ao usar o MDE mais acurado. A resolução de 5 m foi o MDE mais acurado e resultou em predições com 44% de acurácia; este valor foi igual ao obtido com duas resoluções mais grossas, MDEs com menor acurácia. Assim, a identificação do melhor MDE para o MDS exige a avaliação da acurácia das predições do MDS usando alguma forma de validação externa, porque não necessariamente o MDE mais acurado produzirá as melhores predições do MDS.

ABSTRACT Digital elevation models (DEM) used in digital soil mapping (DSM) are commonly selected based on measures and indicators (quality criteria) that are thought to reflect how well a given DEM represents the terrain surface. The hypothesis is that the more accurate a DEM, the more accurate will be the DSM predictions. The objective of this study was to assess different criteria to identify the DEM that delivers the most accurate DSM predictions. A set of 10 criteria were used to evaluate the quality of nine DEMs constructed with different data sources, processing routines and three resolutions (5, 20, and 30 m). Multinomial logistic regression models were calibrated using 157 soil observations and terrain attributes derived from each DEM. Soil class predictions were validated using leave-one-out cross-validation. Results showed that, for each resolution, the quality criteria are useful to identify the DEM that more accurately represents the terrain surface. However, for all three resolutions, the most accurate DEM did not produce the most accurate DSM predictions. With the 20-m resolution DEMs, DSM predictions were five percentage points less accurate when using the more accurate DEM. The 5-m resolution was the most accurate DEM overall and resulted in DSM predictions with 44% accuracy; this value was equal to that obtained with two coarser resolution, lower accuracy DEMs. Thus, identifying the truly best DEM for DSM requires assessment of the accuracy of DSM predictions using some form of external validation, because not necessarily the most accurate DEM will produce the best DSM predictions.

A avaliação da qualidade do solo depende do uso de indicadores e índices construídos com as propriedades dele. Entretanto, essas propriedades podem variar entre tipos de uso do solo, bem como interferir nas propriedades do solo usadas para construir os índices. Objetivou-se com este estudo determinar a capacidade de propriedades intrínsecas do solo (teor de argila e óxido de ferro) para explicar as propriedades variáveis (soma de bases, acidez potencial, carbono orgânico, porosidade e densidade) em diferentes usos do solo (floresta nativa, plantio direto e agricultura convencional) em pequenas propriedades familiares no sul do Brasil. Os resultados evidenciaram que as cinco propriedades do solo estudadas podem ser incluídas em avaliações de qualidade e não são influenciadas pelo teor de argila e óxido de ferro. Sugeriu-se que, para solos pouco intemperizados, ricos em filossilicatos do tipo 1:1 e 2:1, se o teor de argila estiver em um intervalo de aproximadamente 200 g kg-1 e o de óxido de ferro for menor que aproximadamente 15 g kg-1, o uso do solo é o maior fator para controlar as propriedades físico-químicas da camada superficial do solo.

The assessment of soil quality is based on indicators and indices derived from soil properties. However, intrinsic soil properties may interfere with other soil properties that vary under different land uses and are used to calculate the indices. The aim of this study was to assess the extent to which intrinsic soil properties (clay and iron oxide contents) explain variable soil properties (sum of bases, potential acidity, organic carbon, total porosity, and bulk density) under different land uses (native forest, no-tillage and conventional agriculture) on small family farms in Southern Brazil. The results showed that the five properties evaluated can be included in soil quality assessments and are not influenced by the clay and iron oxide contents. It was concluded that for little weathered 1:1 and 2:1 phyllosilicate rich-soils, if the difference between the maximum and the minimum clay content under the different land uses is less than about 200 g kg-1 and the iron oxide content less than about 15 g kg-1, the physico-chemical soil properties in the surface layer are determined mostly by the land use.

É possível construir modelos preditivos (MPs) da distribuição do tamanho de partículas do solo (DTP) em uma região que possua geologia complexa e uma superfície geomórfica jovem e instável? O principal objetivo deste trabalho foi responder a essa questão. Um conjunto de 339 amostras de solo de uma pequena bacia hidrográfica de encosta do sul do Brasil foi usado para construir MPs da DTP na camada superficial do solo. Modelos de regressão linear múltiplos foram construídos com atributos de terreno (elevação, declividade, área de captação, índice de convergência, índice de umidade topográfica). Os MPs explicaram mais da metade da variância dos dados. Esse desempenho é semelhante (se não melhor) ao da abordagem tradicional de mapeamento de solos. Para algumas frações de tamanho, o desempenho dos MPs pode chegar a 70 %. As maiores incertezas ocorrem nas áreas de maior complexidade geológica. Assim, melhorias significativas nas predições somente poderão ser alcançadas se dados geológicos acurados forem disponibilizados. Enquanto isso, MPs construídos a partir de atributos de terreno são eficientes em estimar a DTP de solos de regiões com geologia complexa.

Is it possible to build predictive models (PMs) of soil particle-size distribution (psd) in a region with complex geology and a young and unstable land-surface? The main objective of this study was to answer this question. A set of 339 soil samples from a small slope catchment in Southern Brazil was used to build PMs of psd in the surface soil layer. Multiple linear regression models were constructed using terrain attributes (elevation, slope, catchment area, convergence index, and topographic wetness index). The PMs explained more than half of the data variance. This performance is similar to (or even better than) that of the conventional soil mapping approach. For some size fractions, the PM performance can reach 70 %. Largest uncertainties were observed in geologically more complex areas. Therefore, significant improvements in the predictions can only be achieved if accurate geological data is made available. Meanwhile, PMs built on terrain attributes are efficient in predicting the particle-size distribution (psd) of soils in regions of complex geology.

Trabalhos têm apontado demandas em relação ao conhecimento da variação pedológica de Argissolos. Essa variação pode ser analisada pela estatística multivariada - também responsável pela classificação numérica dos dados. A comparação entre a classificação numérica e a classificação hierárquica pode contribuir para a evolução do Sistema Brasileiro de Classificação de Solos (SiBCS). Os objetivos deste trabalho foram analisar a variação pedológica de Argissolos derivados de materiais sedimentares na Depressão Central do Rio Grande do Sul e validar a estrutura da ordem dos Argissolos do SiBCS por meio da interpretação da classificação numérica. Foram considerados 42 perfis de Argissolos derivados da Formação Santa Maria e da Formação Caturrita, na região central do Rio Grande do Sul. O estudo foi realizado por meio da análise das componentes principais e da análise de agrupamento. Foram determinadas cinco componentes principais, com poder de explicação de 84,3 % da variância dos dados, representadas de um a cinco pelas variáveis: teor de areia, matiz (cor), saturação por bases, atividade da argila e teor de C orgânico total. A análise de agrupamento separou os perfis de Argissolos em três grupos principais. O grupo A apresentou perfis com drenagem deficiente, derivados das três rochas consideradas neste trabalho. O grupo B, derivado exclusivamente dos arenitos das Formações Santa Maria e Caturrita, mostrou perfis bem drenados, porém com menor teor de argila, maior gradiente textural e menor capacidade de troca de cátions potencial em relação ao grupo C. Este grupo apresentou perfis bem drenados, alíticos e com teores de argila superiores em relação aos demais grupos, derivados, predominantemente, do lamito da Formação Santa Maria. A classificação numérica validou a estrutura do SiBCS para a ordem dos Argissolos. Contudo, a análise de agrupamento evidenciou aspectos morfológicos importantes e ainda não considerados pelo SiBCS em nível categórico elevado (grande grupo), como a variação na espessura e textura do horizonte A ou A + E, o teor de argila no horizonte B e o gradiente textural nos perfis de Argissolos.

There is lack of research on Argissolos (Ultisols and Alfisols) in relation to theunderstanding of the pedologic variations. Variations can be analyzed by multivariate statisticsand the data numerically classified. The comparison between the numeric classification andthe hierarchical classification can contribute to the evolution of the Brazilian Soil ClassificationSystem (SiBCS). The aims of this research were to analyze the pedologic variation of Argissolosderived from sedimentary materials in the central region of Rio Grande do Sul and to validatethe organization of the order of Argissolos in the SiBCS based on the interpretation of thenumeric classification. Forty-two profiles of Argissolos in the central area of Rio Grande doSul were considered, derived from the Formation Santa Maria and Caturrita. The study wasbased on principal component analysis and cluster analysis. The principal component analysisindicated five components that explained 84.3 % of the data variance. These componentsare represented, from one to five, by the following variables: sand content, hue (color), basesaturation, clay activity and total organic C content. The cluster analysis separated the Argissoloprofiles in three main groups. The drainage of the profiles in group A was deficient. Theprofiles in group B were well-drained, but contained less clay and had a higher texture gradientand lower cation exchange capacity than group C with well-drained profiles, high Al contentand higher clay content than the other groups. The numeric classification validated the structureof the SiBCS for the order of Argissolos. However, the cluster analysis detected importantmorphologic aspects that are not considered by the SiBCS at a high categorical level (largegroup), e.g., variation in thickness and texture of the A or A + E horizon, clay content in the Bhorizon and the textural gradient in the Argissolo profiles.

Inúmeros trabalhos abordam a elaboração de estratégias amostrais e a aplicação de ferramentas (geo)estatísticas no estudo de atributos do solo. Entretanto, são escassos os trabalhos envolvendo a aplicação desta abordagem no monitoramento de solos construídos sobre aterros encerrados de resíduos sólidos urbanos. Este estudo mostra que a densidade amostral necessária para tornar possível o uso da geoestatística em tais casos, elevaria os custos operacionais. A melhor alternativa é a utilização dos métodos de estatística multivariada (análise de componentes principais e de agrupamento) para definição de zonas homogêneas de manejo. Os atributos que melhor explicam a estrutura da variabilidade do solo construído são o teor de areia (ou argila), a saturação por bases e o pH, todos relacionados com a contaminação do solo com chorume e o adequado desenvolvimento da vegetação.

Several studies address the development of sampling strategies and implementation of (geo)statistical tools in the study of soil properties. However, there is a lack of studies in the application of such approach to monitor soil covers in closed landfill sites of urban solid waste. This study shows that the sampling density needed to make possible the use of geostatistics in such cases would raise operational costs. The best alternative is the use of multivariate statistics methods (principal components and cluster analysis) to define homogeneous management zones. The soil attributes that best explain the structure of soil variability are sand (or clay) content, base saturation and pH, all related with soil contamination by leachate and with the proper development of vegetation.