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Context. Studies comparing the elemental contents obtained via portable X-ray fluorescence (pXRF) spectrometry under different scanning conditions and traditional time-consuming concentrated acid-digestion-based methods are rare in tropical soils. Aims. (1) To compare the contents of Al, Ca, Cu, Fe, K, Mn, Zn, Cr, Ni, Ti, V and Zr obtained by pXRF with the results of the USEPA 3051a method; and (2) to evaluate the impacts of different conditions on the pXRF results. Methods. Soil samples were analysed with pXRF under five conditions: field (F), post-field (PF), air-dried fine earth (ADFE, Keywords: chemical attributes modelling, environmentally-friendly method, geochemistry, ICP atomic emission spectroscopy, linear regression, pedology, proximal sensors, USEPA 3051a., Introduction Soil is a fundamental, slowly-renewable natural resource for many human activities, sustaining life on the planet and aiding the economic development of nations (Adhikari and Hartemink 2016). Soil characterisation [...]

Portable X-ray fluorescence (pXRF) spectrometry has been successfully used for soil attribute prediction. However, recent studies have shown that accurate predictions may vary according to soil type and environmental conditions, motivating investigations in different biomes. Hence, this work attempted to accurately predict soil pH, sum of bases (SB), cation exchange capacity (CEC) at pH 7.0 and base saturation (BS) using pXRF-obtained data with high variability and robust prediction models in the Brazilian Coastal Plains biome. A total of 285 soil samples were collected to generate prediction models for A (n = 123), B (n = 162) and A+B (n = 285) horizons through stepwise multiple linear regression, support vector machine with linear kernel (SVM) and random forest. Data were divided into calibration (75%) and validation (25%) sets. Accuracy of the predictions was assessed by coefficient of determination ([R.sup.2]), root mean square error (RMSE), mean absolute error (MAE) and residual prediction deviation (RPD). The A+B horizons dataset had optimal performance, especially for SB predictions using SVM, achieving [R.sup.2] = 0.82, RMSE = 1.02 [cmol.sub.c] [dm.sup.-3] MAE =1.17 [cmol.sub.c] [dm.sup.-3] and RPD = 2.33. The most important predictor variable was Ca. Predictions using pXRF data were accurate especially for SB. Limitations of the predictions caused by soil classes and environmental conditions should be further investigated in other regions. Additional keywords: modelling, soil analysis, soil fertility, tropical soils. Received 9 May 2020, accepted 24 July 2020, published online 19 August 2020, Introduction The determination of soil chemical attributes is fundamental for agricultural management, pedological and geochemical mapping and for management of the environment (Zhang and Hartemink 2020). During past decades, soil [...]

The aim of this study was to evaluate the spatial and morphological configuration of the pore space in 3D images of Latosols with different mineralogy under a conservation tillage system in a coffee crop area. The visualisation and quantification of pore size distribution by data mining and spatial variability by semi-variogams were investigated in 3D images with 60-µm spatial resolution generated by X-ray CT scan (EVS/GE MS8x-130) in soil core samples collected at different depths of a kaolinitic Red-Yellow Latosol (RYL) and a gibbsitic Red Latosol (RL) from Brazil. Greater spatial variability occurred in the horizontal direction of the 3D image, a novel finding in this area of research. The pores detected were different between the Latosols studied, mainly at 0.20-0.34 m depth. The largest number (>4000) and volume (± 30 [mm.sup.3]) of pores was found in the RL. The soil classes differed in 3D pore characteristics, and this aspect may be important in the characterisation of causes of pore variability. Sphericity was similar for both soils, with greater emphasis on pore classes with a diameter 15%). Additional keywords: Brazil, CT scan, coffee crop, geostatistics, regression models, soil pore configuration., Introduction Soils present a heterogeneous structure made up of the arrangement of solid and organic particles with a formation of porous space (Klein and Libardi 2002) distributed according to a [...]

In the Brazilian Cerrado Biome, Latosols (Oxisols) predominate on gently sloping areas and are highly suitable for the development of mechanised agriculture. However, inappropriate management may lead to reduced crop yields and environmental degradation. We examined the relationships between compression behaviour and texture of Latosols to support decisions on the sustainable use and management of agricultural land. We collected soil samples in 10 Latosols with variable textures under native vegetation and determined the oxide content, bulk and particle density, total porosity, water retention curves, and preconsolidation pressure at different water potentials to develop preconsolidation pressure models. Our results showed that soil texture influenced soil physical properties, that is, an increase in the clay content enhanced the total porosity and water retention and decreased bulk density. Consequently, the susceptibility of these soils to compaction increased with an increase in the clay content. Considering the characteristics of the machinery used in intensive Brazilian agriculture, the compaction risk of these soils was considered high, despite the differences in resistance of the soils. In clayey and very clayey soils, some additional compaction is almost inevitable, even when the water content is low. Thus, caution is required in the evaluation of structural changes after mechanised field operations., Introduction Incorporated agricultural production, which is based on the intensive use of machinery, inputs, and improved crop varieties, is relatively new to Brazil. The Cerrado Biome contains >2 million [km.sup.2] [...]