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Many factors can influence the results obtained by portable X-ray fluorescence analysis (pXRF). The effect of soil organic matter on pXRF results is not satisfactory understood. Thus, we conducted this study to verify the effect of organic matter removal on oxide determination by pXRF in Oxisols. To obtain soil material with different organic matter contents and maintaining the same elemental composition from soil minerals, six contrasting Oxisols were heated in a muffle furnace for 30 min at the following temperatures (°C): 100; 200; 300; 400; 500 and 600. After heating, the soil samples were scanned using a pXRF Bruker® S1 Titan LE model (Dual Soil mode) for 60 s and the contents of SiO2, Al2O3, Fe2O3, TiO2, P2O5, and MnO were recorded. The soil organic matter presence underestimated the pXRF results for lightest oxides (Si and Al) compared to heaviest oxides (Fe, Ti, and Mn). These oxides are important for tropical soils classification and for many soil-related studies and pXRF technology has been a useful tool for soil chemical characterization. Our findings contribute to more suitable use of pXRF highlighting the possible effect of organic matter. [ABSTRACT FROM AUTHOR]

In the soil aggregate stability analysis using ultrasound, the absorbed energy by soil–water suspension is expressed in J mL−1or J g−1. However, the same energy (J) can be obtained from different combinations of sonication time and power. We evaluated the effect of four different amplitudes (12, 18, 24, and 30 µm) by varying the sonication time in order to obtain the same total ultrasonic energy applied. Aggregates 4–8 mm size from a Brazilian Acrudox were sonicated using a sonicator probe-type. After each sonication, the soil aggregates were wet-sieved and obtained the oven-dried mass of aggregates (8–2 mm; 2–1 mm; 1–0.5 mm; 0.5–0.25 mm; and <0.25 mm). Different responses on soil aggregate breakdown were observed by varying the amplitude, although the amount of applied energy was the same. This work reinforces the need of description of all experimental conditions for suitable comparison of different results. [ABSTRACT FROM AUTHOR]

The color of plant leaves can be assessed qualitatively by color charts or after processing of digital images. This pilot study employed a novel pocket-sized sensor to obtain the color of plant leaves. In order to assess its performance, a color-dependent parameter (SPAD index) was used as the dependent variable, since there is a strong correlation between SPAD index and greenness of plant leaves. A total of 1,872 fresh and intact leaves from 13 crops were analyzed using a SPAD-502 meter and scanned using the Nix™ Pro color sensor. The color was assessed via RGB and CIELab systems. The full dataset was divided into calibration (70% of data) and validation (30% of data). For each crop and color pattern, multiple linear regression (MLR) analysis and multivariate modeling [least absolute shrinkage and selection operator (LASSO), and elastic net (ENET) regression] were employed and compared. The obtained MLR equations and multivariate models were then tested using the validation dataset based on r, R2, root mean squared error (RMSE), and mean absolute error (MAE). In both RGB and CIELab color systems, the Nix™ Pro color sensor was able to differentiate crops, and the SPAD indices were successfully predicted, mainly for mango, quinoa, peach, pear, and rice crops. Validation results indicated that ENET performed best in most crops (e.g., coffee, corn, mango, pear, rice, and soy) and very close to MLR in bean, grape, peach, and quinoa. The correlation between SPAD and greenness is crop-dependent. Overall, the Nix™ Pro color sensor was a fast, sensible and an easy way to obtain leaf color directly in the field, constituting a reliable alternative to digital camera imagery and associated image processing. • For the first time, the Nix™ Pro color sensor has been used to obtain the color of plant leaves. • The color parameters of plant leaves can be easily, accurate, and instantaneously obtained using the Nix™ Pro color sensor. • A color-dependent parameter (SPAD index) was satisfactory predicted based on Nix color parameters. • Nix provided to be a powerful in-field sensor to assess the nutritional and physiological status of plants in future works. [ABSTRACT FROM AUTHOR]

The variability of mineralogical and chemical characteristics of soils reflects their parent material, the different weathering degrees and soil formation processes. Through the association of portable X-ray fluorescence (pXRF) spectrometry and X-ray diffraction (XRD), this work aimed to evaluate the concentration of major and minor elements and their relationship with the mineralogy of the sand, silt and clay fractions in a catena of soils derived from quartzite with different weathering degrees in Southern Minas Gerais State, Brazil. Five soil profiles were described and sampled: Lithic Ustorthent, Typic Ustorthent, Typic Dystrustept, Xanthic Haplustox, and Typic Ustifolist. Sand, silt and clay fractions were separated and analyzed with pXRF and XRD for characterization of elemental contents and mineralogy, respectively. For all soils, Si content was higher in the sand fraction, corresponding to quartz, confirmed by XRD, a predominant mineral in quartzite and in the sand fraction of tropical soils. Al content was always higher in the clay fraction, reflecting the presence of kaolinite, gibbsite and mica. Quartz, mica and kaolinite were the dominant minerals in the silt fraction. Si in the sand fraction showed a strong and negative correlation with K and Ca, while Al in the sand fraction showed high and positive correlations with Ca and Fe. Fe, K, Ca and Al were released from the most weathered soils, while Si remained almost constant due to its large presence in quartz. The pXRF in tandem with XRD provided in-depth clarifications on the chemical and mineralogical differentiation of the sand, silt and clay fractions of soils along weathering. • A sequence of quartzite-derived soils was investigated via pXRF and DRX. • Elemental contents and mineralogy of the textural fractions were correlated. • Detailed chemical and mineralogical differences along weathering were detected. • Investigations of processes driving soil variation were improved with pXRF and DRX. [ABSTRACT FROM AUTHOR]