Open legacy soil survey data in Brazil: geospatial data quality and how to improve it

Spatial soil data applications require sound geospatial data including coordinates and a coordinate reference system. However, when it comes to legacy soil data we frequently find them to be missing or incorrect. This paper assesses the quality of the geospatial data of legacy soil observations in Brazil, and evaluates geospatial data sources (survey reports, maps, spatial data infrastructures, web mapping services) and expert knowledge as a means to fix inconsistencies. The analyses included several consistency checks performed on 6,195 observations from the Brazilian Soil Information System. The positional accuracy of geospatial data sources was estimated so as to obtain an indication of the quality for fixing inconsistencies. The coordinates of 20 soil observations, estimated using the web mapping service, were validated with the true coordinates measured in the field. Overall, inconsistencies of different types and magnitudes were found in half of the observations, causing mild to severe misplacements. The involuntary substitution of symbols and numeric characters with similar appearance when recording geospatial data was the most common typing mistake. Among the geospatial data sources, the web mapping service was the most useful, due to operational advantages and lower positional error (~6 m). However, the quality of the description of the observation location controls the accuracy of estimated coordinates. Thus, the error of coordinates estimated using the web mapping service ranged between 30 and 1000 m. This is equivalent to coordinates measured from arc-seconds to arc-minutes, respectively. Under this scenario, the feedback from soil survey experts is crucial to improving the quality of geospatial data

EROSÃO EM ÁREAS DE ENCOSTA COM SOLOS FRÁGEIS E SUA RELAÇÃO COM A COBERTURA DO SOLO

A literatura sobre o processo erosivo no Brasil ainda é escassa, sobretudo em áreas de encosta com topografia complexa. Considerando que solos de áreas de encosta apresentam maior fragilidade frente ao processo erosivo, o uso da terra nessas áreas tem efeito significativo na redução das perdas de solo e água? Pensando nisso, o presente trabalho teve como objetivo avaliar as perdas de solo e água em diferentes usos da terra e classes de solos em áreas de encosta. Foram selecionadas três áreas (A1, A2 e A3) na região do Rebordo do Planalto do Estado do Rio Grande do Sul. Áreas 1 e 2 com Neossolo Litólico e A2 com Argissolo Bruno-Acinzentado. Foram instaladas duas parcelas com superfície útil de 0,5 m² e declividade entre 19 a 21% nos usos da terra floresta nativa (FN), campo nativo (CN) e lavoura (LA). As chuvas erosivas foram avaliadas durante 12 meses. As maiores perdas de solo e água foram observadas no uso LA, seguido dos usos CA e FN. As perdas de solo e água na LA foram: A2 112,55 Mg ha-1 e 333,73 mm; A1 13,36 Mg ha-1 e 422,30 mm; A3 79,71 Mg ha-1 e 481,04 mm. As maiores perdas de solo ocorreram nos meses de abril, agosto e outubro. A cobertura do solo teve efeito significativo na redução das perdas de solo e água em áreas de encosta com solos frágeis

Armazenabilidade de maçã 'SCS417 Monalisa' conforme maturação na colheita, tratamento com 1-metilciclopropeno e atmosfera de armazenagem

The objective of this work was to determine the storability of 'SCS417 Monalisa' apple fruit in response to harvest maturity, 1-methylcyclopropene (1-MCP) treatment, and storage atmospheres. Fruit quality was evaluated after two, four, six, and eight months plus one day or seven days in shelf life at 22°C. The controlled atmosphere (CA) and 1-MCP (1.0 μL L-1) treatments reduce fruit ethylene production and respiration, prevent rapid softening, and inhibit the incidence of scald-like symptoms, flesh browning, cracking, and fungal decay, in comparison with air storage . The combination of 1-MCP and CA provides additive benefits in firmness retention and in the reduction of the incidence of physiological disorders. CA and/or 1-MCP increase the risk of fruit developing wrinkly skin disorder. The loss of flesh firmness and acidity and the development of all physiological disorders and decay are higher in late-harvested fruit. The storage life of 'SCS417 Monalisa' apple is about two months in cold air and from six to eight months in cold CA, considering the time necessary to reach a flesh firmness of 53 N. The limiting factor for the long-term storage of 'SCS417 Monalisa' apple fruit under CA without 1-MCP is the development of physiological disorders and fungal decay.O objetivo deste trabalho foi determinar a armazenabilidade de maçãs 'SCS417 Monalisa' em resposta à maturação na colheita, ao tratamento com 1-metilciclopropeno (1-MCP) e às atmosferas de armazenamento. A qualidade dos frutos foi avaliada após dois, quatro, seis e oito meses mais um dia ou sete dias de vida de prateleira a 22°C. Os tratamentos atmosfera controlada (AC) e 1-MCP (1,0 μL L-1) reduzem a produção de etileno e a respiração dos frutos, previnem o amolecimento rápido da polpa, e inibem a incidência de escaldadura, escurecimento da polpa, rachaduras e podridões fúngicas, em comparação ao armazenamento ao ar. A combinação de 1-MCP e AC proporciona benefícios aditivos na retenção da firmeza da polpa e na redução da incidência de distúrbios fisiológicos. A AC e/ou 1-MCP aumentam o risco de os frutos desenvolverem o distúrbio superfície rugosa. A perda de firmeza e acidez dos frutos e o desenvolvimento de todos os distúrbios fisiológicos e a podridão são maiores em frutos colhidos tardiamente. O potencial de armazenamento das maçãs 'SCS417 Monalisa' é de cerca de dois meses em ar refrigerado e de seis a oito meses em AC refrigerada, considerando o tempo necessário para atingir a firmeza de polpa de 53 N. O fator limitante para o armazenamento a longo prazo da maçã 'SCS417 Monalisa' sob AC sem 1-MCP é o desenvolvimento de distúrbios fisiológicos e podridões fúngicas

Predição de classes de solo em uma paisagem complexa no Sul do Brasil

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.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

Tolerance of cultivars and clonal selections of peach rootstocks to excess aluminum

Forms of aluminum (Al) present in the solution of tropical and subtropical soils can cause toxicity in rootstocks and peach cultivars, impairing growth and productivity. This can be minimized by growing Al-tolerant rootstocks and cultivars. However, this is not sufficiently known, especially because plant breeding programs do not always consider tolerance as a selection variable for genetic materials. The study aimed to (a) select cultivars and clonal selections of Al-tolerant peach rootstocks, (b) identify variables that confer Al tolerance for use in genetic improvement programs, and (c) propose critical levels (NC) and ranges of toxicity (TF) of Al in relation to morphological variables of the root system. The experimental design was completely randomized, comprising a factorial of 13 (cultivars and clonal selections) x 2 (with and without Al) with three replications. Own-rooted ‘BRS Mandinho’ peach seedlings (without rootstock) and grafted seedlings of ‘BRS Mandinho’ on different cultivars and clonal rootstock selections were cultivated in a hydroponic system, composing two levels for the Al factor (absence and presence at 100 mg L−1). The morphological variables of the canopy and root system, Al accumulation in tissues, translocation factor, and the critical level (NC) and toxicity range (TF) of Al in the roots were evaluated. Rootstocks FB-SM-09-43, JB-ESM-09-13, SAS-SAU-09-71, SS-CHI-09-40, ‘Sharpe’ and VEH-GRA-09-55 were tolerant at high Al concentrations. The NC of Al in the roots in relation to the root surface area of peach rootstocks was 1400 mg Al kg−1, and the FT was between 1200 and 1500 mg Al kg−1

Digital soil mapping and its implications in the extrapolation of soil-landscape relationships in detailed scale

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.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

Suscetibilidade magnética na caracterização da variabilidade espacial de atributos do solo em solos subtropicais

Magnetic susceptibility (MS) has been used to estimate soil attributes. With the proposal to increase the information of soils in southern Brazil the objectives of this work were: (i) evaluate the correlation of SM with soil attributes in a slope of subtropical basaltic soils; (ii) to characterize the spatial variation structure of MS and the content of sand, clay and COS; and (iii) identify the sample density that captures the spatial variability to assist future work under similar conditions. In a 22 ha area located in Santo Augusto - RS, Brazil, an 87 points sample grid was collected to determine soil attributes. Samples were also collected in five profiles along the slope. The profile data were analyzed by correlation to verify the degree of Pearson correlation of the SM with the attributes of the soil. In the sample grid spatial dependence analyzes were performed to assess the degree of spatial dependence on soil attributes. The MS presented a high correlation with the attributes of clay soil, Fes, Fed and COS. The evaluation of the spatial variation structure showed that the attributes presented a degree of spatial dependence ranging from weak for COS to strong for MS. The spatial variability pattern suggests a sample density of one point every 4 to 12 ha.A Susceptibilidade magnética (SM) tem sido utilizada na estimativa de atributos do solo. Com a proposta de aumentar as informações de solos no sul do Brasil os objetivos deste trabalho foram: (i) avaliar a correlação da SM com atributos do solo em uma vertente de solos basálticos subtropicais; (ii) caracterizar a estrutura da variação espacial da SM e do teor de areia, argila e COS; e (iii) identificar a densidade amostral que capture a variabilidade espacial para auxiliar trabalhos futuros em condições semelhantes. Em uma área de 22 ha, no município de Santo Augusto – RS, Brasil, foi coletada uma malha amostral com 87 pontos para determinação dos atributos do solo. Também foram coletadas amostras em cindo perfis ao longo da vertente. Os dados dos perfis foram analisados pela correlação de Pearson para verificar o grau de correlação da SM com os atributos do solo. Na malha amostral foram realizadas análises de dependência espacial para avaliar grau da dependência espacial dos atributos. A SM apresentou alta correlação com os atributos do solo argila, Fes, Fed e COS. A avaliação da variação espacial mostrou que os atributos apresentaram grau de dependência espacial variando de fraco, para o COS, à forte para a SM. O padrão de variabilidade espacial sugere densidade amostral de um ponto a cada 4 a 12 ha

The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication

Although many Soil Spectral Libraries (SSLs) have been created globally, these libraries still have not been operationalized for end-users. To address this limitation, this study created an online Brazilian Soil Spectral Service (BraSpecS). The system was based on the Brazilian Soil Spectral Library (BSSL) with samples collected in the Visible–Near–Short-wave infrared (vis–NIR–SWIR) and Midinfrared (MIR) ranges. The interactive platform allows users to find spectra, act as custodians of the data, and estimate several soil properties and classification. The system was tested by 500 Brazilian and 65 international users. Users accessed the platform (besbbr.com.br), uploaded their spectra, and received soil organic carbon (SOC) and clay content prediction results via email. The BraSpecS prediction provided good results for Brazilian data, but performed variably for other countries. Prediction for countries outside of Brazil using local spectra (External Country Soil Spectral Libraries, ExCSSL) mostly showed greater performance than BraSpecS. Clay R2 ranged from 0.5 (BraSpecS) to 0.8 (ExCSSL) in vis–NIR–SWIR, but BraSpecS MIR models were more accurate in most situations. The development of external models based on the fusion of local samples with BSSL formed the Global Soil Spectral Library (GSSL). The GSSL models improved soil properties prediction for different countries. Nevertheless, the proposed system needs to be continually updated with new spectra so they can be applied broadly. Accordingly, the online system is dynamic, users can contribute their data and the models will adapt to local information. Our community-driven web platform allows users to predict soil attributes without learning soil spectral modeling, which will invite end-users to utilize this powerful technique