Determining the maximum temperature reached in soil during burning is important when evaluating fire intensity. Forest conversion is an ongoing process in the Amazon ecosystem. It is of utmost ...importance to predict fire effects on soil properties and avoid damaging environmental systems. Spectroscopic methods combined with multivariate statistics may provide chemical and mineralogical information from soil. This study aims at predicting the maximum temperature reached in Oxisols from an Amazonian region in Brazil. Slash-and-burn and pasture samples collected after fire events and unburned forest soil samples submitted to heating (250-800 °C) were investigated. Energy-dispersive X-ray fluorescence (EDXRF) was used to acquire chemical data. The experimental procedure is rapid and requires minimal sample preparation and no hazardous chemical reagents. The EDXRF data, combined with partial least squares regression, were applied in controlled-heated samples from five different sites of forest, pasture, and slash-and-burn areas. Estimated temperatures for burned samples ranged from 317 to 609 °C. Considering the instrumental advantages and the achieved results, the use of EDXRF combined with multivariate analysis is a feasible alternative technology to evaluate fire effects in soil.
The objective of this research was to evaluate the interaction of landfill leachate of urban solid waste in clayey (CL) and sandy soils (SL) in order to determine physical and chemical parameters ...that can be used as indicators of soil contamination when there are faults in the landfill waterproofing. In the diffusion tests, compacted soil samples were placed in contact with leachate (methanogenic phase). The temporal analysis (200 days) considered the parameters pH, electrical conductivity (EC), alkalinity, nitrogen series, chemical oxygen demand (COD), solids and color for the leachate and pH, ΔpH, EC, total nitrogen (TN), chemical elements, and cation exchange capacity (CEC) for the soils. Correlation analysis and principal component analysis (PCA) were performed to results. It was observed that the studied soils have potential to attenuate chemicals present in the leachate; this indicates the possibility of using them as base in landfills. Correlation analysis and PCA carried out to CL showed that in a process of CL monitoring the pH would be the key parameter to indicate contamination of this soil, due to the high correlation of this parameter with the others analyzed. For the SL, the parameters pH, alkalinity, apparent color, and COD (total and filtered) could be used as indicators of contamination. In both soils, monitoring of concentrations of Ca, Mg, K, SB,
V
, and CTC can be used to indicate possible faults in the waterproofing system of the landfill.
ABSTRACT Tropical Savannas cover an area of approximately 1.9 billion hectares around the word and are subject to regular fires every 1 to 4 years. This study aimed to evaluate the influence of ...burning windrow wood from Cerrado (Brazilian Savanna) deforestation on the spatial variability of soil chemical properties, in the field. The data were analysed by using geostatistical methods. The semivariograms for pH(H2O), pH(CaCl2), Ca, Mg and K were calculated according to spherical models, whereas the phosphorus showed a nugget effect. The cross semi-variograms showed correlations between pH(H2O) and pH(CaCl2) with other variables with spatial dependence (exchangeable Ca and Mg and available K). The spatial variability maps for the pH(H2O), pH(CaCl2), Ca, Mg and K concentrations also showed similar patterns of spatial variability, indicating that burning the vegetation after deforestation caused a well-defined spatial arrangement. Even after 20 years of use with agriculture, the spatial distribution of pH(H2O), pH(CaCl2), Ca, Mg and available K was affected by the wood windrow burning that took place during the initial deforestation.
RESUMO As Savanas tropicais cobrem uma área de aproximadamente 1,9 bilhões de hectares ao redor do mundo e estão sujeitas a incêndios regulares a cada 1-4 anos. Objetivou-se avaliar a influência da queima de madeira proveniente do desmatamento do Cerrado na variabilidade espacial das propriedades químicas do solo, em campo. Os dados foram analisados utilizando-se métodos geoestatísticos. Os semivariogramas para pH(H2O), pH(CaCl2), Ca, Mg e K foram calculados de acordo com modelos esféricos, enquanto o fósforo apresentou efeito pepita puro. Os semivariogramas cruzados mostraram correlações entre pH(H2O) e pH(CaCl2) com outras variáveis com dependência espacial (Ca e Mg trocáveis e K disponível). Os mapas de variabilidade das concentrações de pH(H2O), pH(CaCl2), Ca, Mg e K também apresentaram padrões semelhantes de variabilidade espacial, indicando que a queima da vegetação após o desmatamento causou arranjo espacial bem definido. Mesmo após 20 anos de uso com agricultura, a distribuição espacial de pH(H2O), pH(CaCl2), Ca, Mg e K disponível foi afetada pela queima da madeira que ocorreu após enleiramento, durante o desmatamento inicial.
O objetivo deste trabalho foi testar metodologias de mapeamento digital de solos (MDS) e avaliar a possibilidade de extrapolação de mapas entre áreas fisiograficamente semelhantes. A área de ...referência para o treinamento do modelo localizou-se no Município de Sentinela do Sul, RS, e a extrapolação foi feita para o Município Cerro Grande do Sul, RS. Desenvolveram-se pelo MDS modelos com o uso de variáveis ambientais, como preditoras, e as classes de solos - obtidas de um levantamento convencional na escala 1:50.000 - como variáveis dependentes. Testou-se o uso combinado de dois modelos de árvore de decisão (AD), treinados em duas paisagens com diferentes classes de drenagem. Para Sentinela do Sul, a concordância dos mapas preditos com os produzidos pelo levantamento convencional foi avaliada por matrizes de erro. Como a importância dos erros de mapeamento é variável, criou-se uma matriz ponderada, para atribuir diferentes importâncias aos erros específicos de mapeamento entre as distintas unidades de mapeamento. A acurácia do mapa de Cerro Grande do Sul foi avaliada pela verdade de campo. A extrapolação dos mapas gera resultados satisfatórios, com acurácia maior do que 75%. O uso de modelos com duas AD separadas por paisagens homogêneas gera mapas extrapolados com maior acurácia, avaliada pela verdade de campo.
This study aimed to generate prediction models to estimate the chemical composition of fresh and dry Brachiaria brizantha cv. Piatã grass using near infrared spectroscopy (NIRS). Chemical analyses of ...249 samples were performed to determine oven-dried sample (ODS), dry matter (DM), crude protein (CP), neutral detergent fibre (NDF), acid detergent fibre (ADF), acid detergent lignin (ADL), cellulose (CEL) and total digestible nutrients (TDN). The samples were scanned in an NIRS spectrometer and different percentages were used to compose and develop the models (100% fresh; 100% dry; 25% fresh:75% dry; 50% fresh:50% dry and 75% fresh:25% dry). The purpose of these mixed models is to know if it is possible to obtain reliable predictions from fresh samples in a database that contains dry samples. The calibration models were developed using modified partial least squares (MPLS) and evaluated by statistical parameters, including coefficient of determination (R²) and residual predictive deviation (RPD). The model with 100% dry samples obtained the best results in R² and RPD validations, for CP (0.94; 3.98), NDF (0.92; 3,49) and TDN (0.90; 3.12). The 100% fresh samples produced the best R² results in ODS (0.83), CP (0.85), ADF (0.84) and ADL (0.83). A screening model was validated to predict the characteristics and components of the fresh samples. The model using 100% dry grass was suitable for predicting all the variables, except ODS, DM and CEL.
Highlights
Prediction models can be used for assessment of fresh forage, allowing producers to make quicker decisions, thereby saving time and money.
Determining the nutritional composition of a pasture is necessary to evaluate its quality, aiming to meet the requirements of feeding animals and guarantee good performance. The quality of the forage ...biomass produced depends directly on the fertility and type of soil, environmental conditions, and management. In this scenario, the aim was to evaluate the chemical composition, in vitro digestibility of dry matter, and macro- and micronutrient contents of Brachiaria brizantha cv. Marandu cultivated in soils developed from basalt and sandstone in the state of Paraná. A total of 120 forage samples were collected in a soil developed from basalt and 116 in soil developed from sandstone. Forage harvesting occurred in autumn and spring for basalt soil (season effect), and in spring (soil effect) for sandstone soil. Soil samples (0–20 cm) were collected in the spring season, at the same forage collection sites, to obtain a greater sample representativity regarding climate–soil–plant relationships. Forage samples were separated in leaf blade and stem + sheath, and analyses of dry matter (DM), mineral matter (MM), crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), lignin, cellulose, hemicellulose, in vitro dry matter digestibility (IVDMD), total digestible nutrients (TDN), nitrogen, and macro- and micronutrient contents were performed. In soil samples, the contents of P, K, Ca, Mg, Mn, and Zn were analyzed. There were season and soil-type effects on the chemical composition of the cultivar studied. The highest levels of protein, NDF, ADF, lignin, and cellulose were obtained in autumn. In samples from soil developed from basalt, higher levels of hemicellulose, IVDMD, and TDN were obtained in the spring. K, P, Mn, and Zn presented levels within the range recommended for the studied forage, but the values of Ca and Mg in autumn and N in the three studied periods remained below the level considered adequate. Soil nutrient contents were influenced (P < 0.05) by the soil source material, and the highest levels of K, Ca, Mg, Mn, and Zn were observed in soil developed from basalt. Forage cultivated in spring in the soil area developed from basalt, was nutritionally superior in relation to forage cultivated in the soil developed from sandstone.
The objective of this study was to evaluate the relationships between the spectra in the Vis-NIR range and the soil P concentrations obtained from the PM and Prem extraction methods as well as the ...effects of these relationships on the construction of models predicting P concentration in Oxisols. Soil samples' spectra and their PM and Prem extraction solutions were determined for the Vis-NIR region between 400 and 2500 nm. Mineralogy and/or organic matter content act as primary attributes allowing correlation of these soil phosphorus fractions with the spectra, mainly at wavelengths between 450-550, 900-1100 nm, near 1400 nm and between 2200-2300 nm. However, the regression models generated were not suitable for quantitative phosphate analysis. Solubilization of organic matter and reactions during the PM extraction process hindered correlations between the spectra and these P soil fractions. For Prem,, the presence of Ca in the extractant and preferential adsorption by gibbsite and iron oxides, particularly goethite, obscured correlations with the spectra.
Near-infrared (NIR) spectroscopy is a rapid, non-destructive and accurate technique for analyzing a wide variety of samples, thus, the growing interest of using this technique in soil science. The ...objective of this study was to evaluate the potential of NIR spectroscopy to predict organic carbon (OC), total nitrogen (TN), available phosphorus (P) and available potassium (K) in the soil. NIR spectra from 20 cm
3
of soil samples were acquired on the range of 750 to 2500 nm in diffuse reflectance mode, resolution of 16 cm
−1
and 64 scans. Eight models of calibration/validation were constructed. Calibration and validation models showed that the predictive potential of NIR varied with the specific soil property (OC, TN, P and K) under evaluation and according to the methodology employed in the model construction (cross-validation or test set). Good prediction models were obtained for OC and TN content based on the statistical parameters. Test set methodology was able to predict soil OC, TN, P, and K better than cross-validation methodology.
Resumo O objetivo deste trabalho foi avaliar o emprego da espectroscopia de infravermelho próximo (NIRS) para a predição de formas de P, C e N, em solos desenvolvidos de basalto, com uso de modelos ...multivariados e de amostras com diferentes granulometrias. Amostras de Latossolos e Nitossolos provenientes de áreas de cultivo, pastagem e vegetação nativa foram coletadas nas camadas de 0,0-0,20 e de 0,60-0,80 m, e analisadas quanto aos teores de P disponível, P remanescente, C orgânico, C total e N total. Os espectros na região entre 1.100 e 2.500 nm foram obtidos de amostras com granulometria menor do que 2,0 e 0,2 mm. Modelos multivariados de predição foram construídos e validados a partir dos resultados obtidos pelos métodos de referência e dos espectros. O peneiramento das amostras em malha de 0,2 mm melhorou a relação entre os comprimentos de onda no NIRS e os teores de C orgânico, C total e N total no solo, o que permitiu a obtenção de melhores modelos de calibração e predição para esses atributos. Independentemente do tamanho das partículas, os modelos não apresentam robustez, acurácia e precisão adequadas de predição para o P extraível e o P remanescente.
Vis–NIR spectroscopy is nowadays presented as a possible routine method for soil sample analysis. However, there is still no consensus on which is the best multivariate statistical method to use. We ...propose to use principal component analysis to complete the spectral data treatment. The soil samples came from a pedological cover made up of red–yellow Latosols: 88 samples of 11 soil profiles on four toposequences were collected; clay, organic matter, silica, iron, aluminum and titanium total contents were determined; the contents of goethite, hematite, gibbsite, and kaolinite were calculated. Diffuse reflectance Vis–NIR spectroscopy at wavelengths from 400 to 2400nm combined with principal component analysis (PCA) was sufficiently sensitive to discriminate different Latosols. Wavelengths of 700nm and 2200 to 2300nm were influenced by content ratios of organic matter and iron oxides (700nm), and kaolinite and gibbsite absorption (2200 and 2300nm). The spectral responses were affected not only by the content of these constituents, but also by the composition of the minerals, so that the same class of Latosol may have different or similar spectral responses. The role of microaggregation is discussed.
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