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Abstract Parent material (PM) type is crucial for understanding the distribution of soils across the landscape. However, such information is not available at a detailed scale in Brazil. Thus, portable X-ray fluorescence (pXRF) spectrometry can aid in PM characterization by measuring elemental concentrations. This work focused on mapping soil PM (specifically variations of phyllite) using pXRF data and evaluating which soil horizon (A, B, or C) provides optimal PM identification in the Brazilian Cerrado. A total of 120 soil samples were collected from A, B, and C horizons across the study area as well as associated PMs; all were subjected to pXRF analysis. Artificial neural network, support vector machine, and random forest were used to model and predict PMs through pXRF data to the entire area. The nine maps (3 soil horizons data × 3 algorithms) generated for PM prediction were validated through overall accuracy, Kappa coefficient, producer's, and user's accuracy. The most accurate PM maps were obtained by using C horizon information (overall accuracy of 0.87 and Kappa coefficient of 0.79) via support vector machine algorithm. Land use dramatically influenced the results. In sum, pXRF data can be successfully used to predict soil PMs by robust algorithms. Specifically, V, Ni, Sr, and Pb were optimal for predicting PM regardless of land use. Highlights • Soil parent material can be accurately predicted via pXRF analysis of soils. • First time detecting parent material variability in area with unique parent rock. • Land use affects PM predictions, with less effect when using soil C horizon data. • V, Ni, Sr, and Pb produced good PM predictions due to their high stability in soils. • SVM and RF algorithms outperformed ANN in most predictions. [ABSTRACT FROM AUTHOR]

• Does combining proximal sensor always bring synergy for predicting soil texture? • Vis-NIR, NixPro TM , and pXRF data were tested for soil texture predictions. • PXRF provided key information to create accurate prediction models. • Smoothed Vis-NIR data preprocessing and dry NixPro TM condition positively influenced the results. • Soil texture and textural classes were accurately predicted by at least one approach. Soil texture is a primary variable influencing many soil chemical-physical-biological processes, providing important information for decision-making regarding sustainable soil management. The standard traditional methods for determining soil texture, however, are performed manually and are time-consuming, costly, and generate chemical wastes. As an alternative, portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared spectroscopy (Vis-NIR) have been increasingly used worldwide to predict soil attributes. Other sensors (e.g., NixPro TM color sensor) are also promising, but less evaluated to date. Thus, investigations towards proximal sensor data fusion for prediction of soil textural separates (clay, silt, and total, coarse, and fine sand contents) and soil textural classes (loam, loamy sand, etc) in tropical soils are rare. Therefore, this study aimed to evaluate proximal sensor data for predicting soil particle size fractions and soil textural classes (both Family particle size classes and USDA soil texture triangle) via random forest algorithm in tropical regions. A total of 464 soil samples were collected from A (n = 208) and B (n = 256) horizons in Brazil. Soil samples were submitted to laboratory analyses for soil texture and proximal sensor (pXRF, Vis-NIR, and NixPro TM) scanning. Samples were randomly split into 70% for modeling and 30% for validation. The best approach varied according to the predicted attribute; however, pXRF data were key information for soil texture prediction accuracy. The best results delivered highly accurate predictions via the aforementioned proximal sensors for rapid assessment of soil texture (total sand R2 = 0.84, RMSE = 7.60%; silt 0.83, 6.11%; clay 0.90, 5.64%; coarse sand 0.87, 6.30%; fine sand 0.82, 5.27%). Categorical prediction accuracy for soil textural classes (Family particle size classes, overall accuracy = 0.97, Kappa index = 0.95; USDA soil texture triangle, 0.83, 0.73) was enhanced when the predictions were made by soil order sub-datasets. Smoothed Vis-NIR preprocessing and dry NixPro TM color data positively influenced the results. The results reported here represent alternatives for reducing costs and time needed for evaluating soil texture, supporting agronomic and environmental strategies in Brazilian conditions. Further works should extend the results of this study to temperate regions to corroborate the conclusions presented herein regarding the fusion of these three proximal sensors. [ABSTRACT FROM AUTHOR]

This work sought to predict soil microbiological attributes based on soil fertility and texture, elemental contents determined by portable X-ray fluorescence spectrometry, and terrain attribute data with and without addition of season (dry or rainy) and phytophysiognomy as auxiliary predictors. Soil samples were collected in both seasons in four phytophysiognomies. Analyses for prediction of basal soil respiration, microbial biomass carbon, metabolic quotient, and microbial quotient were performed. Terrain attributes, total elemental concentrations obtained by portable X-ray fluorescence spectrometry, soil fertility and texture as well as phytophysiognomy and season were used as predictor variables. Prediction models were created via conditional random forest algorithm and validated with leave-one-out cross-validation through coefficient of determination (R2), root mean square error, mean absolute error and ratio percent deviation. The best results were delivered when phytophysiognomy and season were included as predictors. Metabolic quotient, microbial quotient, microbial biomass carbon and basal soil respiration achieved the best prediction using only soil fertility and texture data (R2 = 0.79, 0.66, 0.65, 0.91, respectively). Predictions of basal soil respiration and metabolic quotient using only terrain data achieved R2 values of 0.91 and 0.73, respectively. Elemental concentrations determined by portable X-ray fluorescence spectrometry reasonably predicted two microbiological attributes. It is possible to adequately predict these four microbiological attributes both locally and spatially through terrain and soil properties data. We encourage further investigations on prediction of these and other microbiological attributes under different environmental conditions and at shorter spatial and temporal scales. • Microbiological attributes were accurately predicted from soil and terrain data. • Phytophysiognomy and season (dry or rainy) data improved predictions. • Soil fertility and texture best predicted microbiological attributes. • Terrain data accurately predicted basal soil respiration and metabolic quotient. • Spatial variability map of microbiological attributes enhanced data visualization. [ABSTRACT FROM AUTHOR]

• Munsell color was accurately predicted by Nix Pro color sensor. • Mean color difference (ΔE* ab) achieved 2.9, indicating the Nix Pro accuracy. • Results achieved overall accuracy and Kappa index of 0.93. • Using a dirty, old Munsell color chart reduced the accuracy of predictions. • Soil color determined by human eye was compared to Nix Pro color predictions. Soil color has historically drawn humans' attention, although its definition is somewhat subjective. It is correlated with several soil attributes and it allows for inferences about several soil aspects. New proximal sensors, such as the Nix™ Pro color sensor, can determine soil color values, but its correlation with the widely used Munsell soil color chart (MSCC) has yet to be investigated. This work aimed to train machine learning models using the Random Forest (RF) algorithm to predict each notation of MSCC chips from data extracted by the Nix™ Pro sensor, test the model's accuracy by evaluating whether it can identify MSCC chips using a brand-new and a dirty MSCC, and compare model predictions with soil color classifications made by the human eye. Additionally, MSCC data obtained via Nix™ was compared to Munsell renotation data to assess the color detection accuracy of the sensor. Prediction models were calibrated by scanning every MSCC chip (437 in total) in triplicate. All validation samples were excluded from model calibration. Accuracy of the predictions of MSCC notation reached overall accuracy and Kappa index values of 0.93 for the brand-new MSCC and of 0.70 for the dirty MSCC. Soil color classification by human eye had little agreement with the predicted MSCC notation, as expected due to the variable conditions affecting soil color conventional determination in the field. Color difference was calculated by the Euclidian distance (ΔE* ab) between three color stimuli in the CIELAB color space. The mean ΔE* ab between Nix™-provided data and renotation data was 2.9, demonstrating high color detection accuracy. The Nix™ Pro color sensor allows for assessment of accurate color data. When applied together with machine learning algorithms, Nix™ Pro provides a reliable determination of soil color classification equivalent to MSCC in an easily reproducible, rapid, inexpensive and non-subjective way. [ABSTRACT FROM AUTHOR]

• pXRF was used to predict five different soil properties in tropical soils. • Three different algorithms were tested and compared: Cubist, RF and SMLR. • Sum of bases, base saturation percentage, and Al sat achieved better results. • CaO was the most influential variable for soil properties prediction. • pXRF can accurately predict fertility properties for varying Brazilian soils. Portable X-ray fluorescence (pXRF) spectrometry delivers results rapidly, at low-cost, and without generating chemical residues. This study aimed to predict soil pH, sum of bases (SB), base saturation percentage (BSP), cation exchange capacity (CEC), and Al saturation (Al sat) of 2017 contrasting Brazilian soil samples through the association of pXRF and three different algorithms [Cubist, Random forest (RF), and stepwise multiple linear regression (SMLR)]. Soil samples were collected from the surface (SURF) and subsurface (SUB) horizons in seven Brazilian states. The prediction models were generated for the SURF and SUB horizons separately and combined (SURF + SUB dataset). Overall, the best predictions were achieved via Cubist followed by RF. For the pH predictions, the model combining SURF and SUB horizons data presented better results. Satisfactory results were achieved for the predictions of SB (validation R2 = 0.86), BSP (validation R2 = 0.81) and Al sat (R2 = 0.76). Moreover, promising results were obtained for predicting pH (R2 = 0.63). Notably, CaO appeared as the most influential variable for soil property prediction models. Overall, pXRF showed great potential for predicting soil fertility properties for diversified tropical soils with low cost, rapidity, and without chemical waste generation. [ABSTRACT FROM AUTHOR]

Portable X-ray fluorescence (pXRF) spectrometry is becoming increasingly popular for predicting soil properties worldwide. However, there are still very few works on this subject under tropical conditions. Therefore, the objectives of this study were to use pXRF data to characterize the Brazilian Coastal Plains (BCP) soils and assess four machine learning algorithms [ordinary least squares regression (OLS), cubist regression (CR), XGBoost (XGB), and random forest (RF)] for prediction of total nitrogen (TN), cation exchange capacity (CEC), and soil organic matter (SOM) using pXRF data. A total of 285 soil samples were collected from the A and B horizons representing Ultisols, Oxisols, Spodosols, and Entisols. The pXRF reported elements helped in the characterization of the BCP soils. In general, the RF model achieved the best performances for TN (R2 = 0.50), CEC (0.75), and SOM (0.56) when A and B horizons were combined, although better results have been reported in the literature for soils from other regions of the world. The results reported here for the BCP soils represent alternatives for reducing costs and time needed for assessing such data, supporting agronomic and environmental strategies. • The elemental data provided by pXRF were used to characterize the BCP soils • Four different prediction algorithms were used to predict SOM, TN, and CEC • In general, RF algorithm outperformed OLS, XGBoost, and CR algorithms [ABSTRACT FROM AUTHOR]

Knowledge about parent material (PM) is crucial to understand the properties of overlying soils. Assessing PM of very deep soils, however, is not easy. Previous studies have predicted PM via proximal sensors and machine learning algorithms, but within small areas and with low soil variety. This study evaluates the efficiency of using portable X-ray fluorescence (pXRF) spectrometry together with machine learning algorithms in a large area populated with a wide variety of soil classes and land uses to build accurate PM distribution maps from soil data. Samples from A and B horizons were collected from 117 sites, totaling 234 samples, along with representative PM samples of the predominant PMs in the region. Elemental contents of PM and soil samples were obtained using pXRF. Random Forest (RF), Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) models built with and without Principal Component Analysis (PCA) were used to predict PMs from A and B horizon samples separately. For validation, PM was identified in 23 different spots and compared with the predicted PM via overall accuracy and Kappa coefficient. Maps built with models excluding PCA had an overall accuracy ranging from 0.87 to 0.96 and kappa coefficient ranging from 0.74 to 0.91. Maps generated with PCA based models reached 100% overall accuracy. Prediction of PM using pXRF with samples from either A or B horizon and machine learning algorithms offers solid results even when applied in large areas with high land use and soil class variability. • Soil parent material can be accurately predicted via pXRF analysis of soils. • Maps from B horizon samples were slightly superior to A horizon samples. • Overall map predictive accuracy was strong. [ABSTRACT FROM AUTHOR]