Abstract The occupation of sandy soils in Brazil has occurred with poor adoption of management technology, which has caused its degradation. The recovery of organic matter, particularly when growing annual plants and cover crops, with mulch residue allows the maintenance of the productive potential of these soils. Thus, the shoot dry mass production (SDMP) of winter cover crops was evaluated in an Ultisol under no-tillage system and in rotation with soybean and maize using 0, 2, 4 and 8 t ha-1 year-1 of poultry litter, mineral fertilizer exclusively and 2 t ha-1 year-1 of poultry litter associated with a half-dose of mineral fertilizer. The experimental design included randomized blocks with four replicates. The use of poultry litter increased the SDMP of the cover crops; the production was relatively high when high doses of poultry litter were applied; on the other hand, the yield of SDMP and grain was limited by the exclusive use of mineral fertilizer. Organic matter and nutrient status improvement by cover crops were one of the reasons for the increase in soybean and maize grain yields.

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.

Abstract The objective of this work was to evaluate the use of near-infrared spectroscopy (NIRS) to predict P, C, and N forms in soils developed from basalt, using multivariate models and soil samples with different granulometries. Samples from Oxisols and Alfisols from croplands, pasture, and native vegetation areas were collected at the 0.0-0.20 and 0.60-0.80-m soil layers, and analyzed for contents of available P, remaining P, organic C, total C, and total N. Spectra in the region between 1,100 and 2,500 nm were obtained on samples with granulometry smaller than 2.0 and 0.2 mm. Multivariate prediction models were constructed and validated from the results obtained by the reference methods and spectra. Sample screenings through 0.2 mm mesh improved the relationship between wavelengths in NIRS and the contents of organic C, total C, and total N in the soil, which allowed better calibration and prediction models for these attributes. Irrespectively of particle sizes, the models did not show appropriate robustness, accuracy, and precision for extractable P and remaining P.

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.

O objetivo deste trabalho foi identificar atributos de solo importantes para a estimativa do índice de estabilidade de agregados. Para a identificação dos atributos, utilizou-se a regressão múltipla "stepwise" em dados coletados de Latossolo Vermelho eutroférrico, cultivado em sistema de plantio direto. Para a obtenção dos modelos de estimativa do índice de estabilidade de agregados (IeaE), foram analisadas dez variáveis independentes: argila total, densidade do solo, resistência à penetração, matéria orgânica, pH em água, capacidade efetiva de troca de cátions, água disponível, volume total de poros, macroporosidade e umidade gravimétrica. Os atributos argila total (Arg), densidade do solo (Ds), resistência à penetração (RP), matéria orgânica (MO) e pH são os mais importantes para a estimativa do IeaE, e a função de pedotrânsferência que melhor descreve esse índice é: IeaE = 27,80 + 0,04Arg + 27,97Ds + 12,35RP + 0,45MO - 9,72pH (R² = 0,89).

The objective of this work was to identify the most important soil attributes to estimate the aggregate stability index. To identify these attributes, multiple stepwise regression was used on data collected from an Eutrorthox cultivated under a no-tillage system. To obtain the prediction models for the aggregate stability index (IeaE), ten independent variables were evaluated: total clay, bulk density, penetration resistance, organic matter, pH in water, effective cation exchange capacity, available water, total porosity, macroporosity, and gravimetric moisture. The attributes total clay (Arg), bulk density (Ds), penetration resistance (RP), organic matter (MO), and pH are the most important for the estimation of the IeaE, and the pedotransfer function that best describes this index is: IeaE = 27.80 + 0.04Arg + 27.97Ds + 12.35RP + 0.45MO - 9.72pH (R² = 0.89).

A resistência mecânica do solo é um importante atributo que altera o crescimento e alongamento das raízes e o movimento da água no solo. Considerando que a semeadura direta tende a aumentar o teor de matéria orgânica do solo, o objetivo deste trabalho foi analisar o ajuste da curva de resistência do solo à penetração considerando a umidade, a densidade e o teor de argila e de matéria orgânica, em um Latossolo Vermelho eutroférrico com 665, 221 e 114 g kg-1 de argila, silte e areia, respectivamente, sob culturas anuais em plantio direto, localizado em Londrina, Paraná. A resistência à penetração foi avaliada de forma aleatória e de maneira ininterrupta entre os meses de maio de 2008 e fevereiro de 2011, mediante o uso do penetrômetro de impacto, num total de 960 repetições. No momento da determinação, foram coletadas amostras entre 0 e 20 cm de profundidade, para determinação da umidade gravimétrica (G), da densidade do solo (D) e do teor de matéria orgânica (M). A curva de resistência do solo à penetração (RP) do solo foi ajustada por dois modelos não lineares (RP = a Db Gc e RP' = a Db Gc Md), em que a, b, c e d são coeficientes de ajuste do modelo. O modelo com inclusão da M apresentou coeficiente de determinação de 91 %, ao passo que o modelo sem inclusão desta variável apresentou coeficiente de determinação de 82 %.

Soil penetration resistance is an important property that affects root growth and elongation and water movement in the soil. Since no-till systems tend to increase organic matter in the soil, the purpose of this study was to evaluate the efficiency with which soil penetration resistance is estimated using a proposed model based on moisture content, density and organic matter content in an Oxisol containing 665, 221 and 114 g kg-1 of clay, silt and sand respectively under annual no-till cropping, located in Londrina, Paraná State, Brazil. Penetration resistance was evaluated at random locations continually from May 2008 to February 2011, using an impact penetrometer to obtain a total of 960 replications. For the measurements, soil was sampled at depths of 0 to 20 cm to determine gravimetric moisture (G), bulk density (D) and organic matter content (M). The penetration resistance curve (PR) was adjusted using two non-linear models (PR = a Db Gc and PR' = a Db Gc Md), where a, b, c and d are coefficients of the adjusted model. It was found that the model that included M was the most efficient for estimating PR, explaining 91 % of PR variability, compared to 82 % of the other model.