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Abstract Soil base saturation percentage (BSP) plays an important role in the assessment of soil taxonomic classification and soil fertility. Conventionally, soil BSP measurement methods are fraught with many drawbacks such as being laborious, time consumptive, destructive to the samples, and can lead to the underestimation of true cation exchange capacity (CEC). Recently, proximal sensors such as portable X-ray fluorescence (PXRF) spectrometry have proven to be effective for rapid physicochemical analysis of soils. In this study, we proposed and examined a PXRF-based method to predict BSP using 300 soil samples from the active agricultural lands in six states across the USA; Colorado, California, Minnesota, Nebraska, Oklahoma, and Texas. An Olympus Vanta series PXRF analyzer was employed to measure Mg, Ca, and K for BSP prediction. Results were validated using four different multivariate models [generalized additive model (GAM), multiple linear regression (MLR), random forest (RF), regression tree (RT)] via R 3.5.1. Predictive model performance was assessed via root mean squared error (RMSE), coefficient of determination (R2), residual prediction deviation (RPD), the ratio of performance to interquartile (RPIQ) range, and bias. While predicting BSP from PXRF quantified elements, models exhibited R2, RMSE (%), and RPDs as follows: GAM = 0.58, 9.0, 1.6; MLR = 0.45, 10.4, 1.4; RF = 0.62, 8.7, 1.6; RT = 0.68, 7.9, 1.8, respectively. Soil cation exchange capacity was also predicted using a similar approach, with similar and moderate predictive performance; GAM produced R2, RMSE (cmol c kg−1), and RPD of 0.69, 5.6, 1.8, respectively, relative to laboratory data. This study showed that the PXRF elements can be used to predict BSP with fair accuracy for the range of agricultural soils examined. As such, further study and enhancement of the approach outlined herein on a wider array of soils is warranted. Highlights • Soil base saturation percentage was predicted via PXRF. • Na was negligible in agricultural soils. • Regression tree was optimal for BSP prediction. [ABSTRACT FROM AUTHOR]

Compost salinity is an ongoing concern for compost producers, especially with certain feedstocks and in arid or semiarid regions. Current testing protocols call for sampling and testing ex-situ via 1:5 (w/v) slurries via electrical conductance. For this research an alternate approach has been proposed, the use of portable X-ray fluorescence (PXRF) spectrometry. Adapting methods developed for soil and water salinity analysis via PXRF, elemental data was used as a proxy for the prediction of compost salinity. In total, 74 compost samples were scanned with PXRF followed by traditional laboratory analysis. Results indicated a strong correlation between the datasets (R 2 0.80; RMSE 1.04 dS m −1 ), similar to findings for soil and water salinity. Furthermore, using the same elemental dataset, compost pH was reasonably predicted (R 2 0.63; RMSE 0.35). PXRF has the benefit of being able to be conducted in-situ or in the laboratory. And, multiple chemical parameters of interest can potentially be predicted from the same dataset. In conclusion, PXRF shows promise for rapid, in-situ salinity determination of composted products. [ABSTRACT FROM AUTHOR]

• SOC of 371 soil samples was predicted via Nix, PXRF-Rb, and texture. • Combining Nix color parameters and texture showed the best prediction of SOC. • Using PXRF-Rb as a proxy for clay could not produce satisfactory SOC prediction. • Color variables b* and a* extracted by Nix appeared influential in predicting SOC. The optimum ecosystem functioning is reliant on soil organic carbon (SOC) content which is traditionally measured in the laboratory via a cumbersome wet-chemistry method. This preliminary research tested whether a combination of Nix color sensor and portable X-ray fluorescence (PXRF) spectrometer data with soil texture data can improve soil SOC prediction accuracy relative to using them independently. A total of 371 samples representing diverse soil texture and SOC content were collected from three different ecoregions of eastern India: coastal saline zone, red and lateritic zone, and Gangetic alluvial zone. All dried, ground, and sieved samples were scanned via Nix and PXRF and random forest (RF) regression was used to predict soil SOC with different combinations of data. Soils were grouped into nine textural classes while soil SOC content exhibited substantial variability (0.08–2.26%). Comparing soil SOC with texture (sand + silt + clay), satisfactory prediction accuracy was observed (validation R2 = 0.70). Combining Nix extracted color parameters with texture substantially improved the model performance, producing the validation determination coefficient of 0.81. In contrast, PXRF-Rb, as a proxy of soil clay content was unable to achieve satisfactory prediction performance (R2 = 0.24), indicating the heterogeneity in soil mineralogical composition. The RF variable importance plot using Nix alone identified redness (a*) and yellowness to blueness (b*) as influential predictors, manifesting the impact of red color from Fe and Al-oxides and their significant negative correlation with soil SOC (r = −0.62 and −0.57 for a* and b*, respectively). These color parameters were again identified by the RF variable importance plot of (Texture + Nix)-model, implying that the SOC prediction improvement may be linked with the Nix sensor's capability of extracting useful information in the visible range. Summarily, a combination of Nix color variables and texture data was adept at predicting soil SOC in lieu of traditional laboratory analysis. The robustness of the (Texture + Nix)-based SOC prediction model can be augmented by incorporating more soil samples representing all 12 soil textural classes and variable SOC content, showing all possible soil colors in the pedogenic environment. [ABSTRACT FROM AUTHOR]

A rapid method for in-situ elemental composition analysis of metal-laden water would be indispensable for studying polluted water. Current analytical lab methods to determine water quality include flame atomic absorption spectrometry (FAAS), atomic absorption spectrophotometry (AAS), electrothermal atomic absorption spectrometry (EAAS), and inductively coupled plasma (ICP) spectroscopy. However only two field methods, colorimetry and absorptiometry, exist for elemental analysis of water. Portable X-ray fluorescence (PXRF) spectrometry is an effective method for elemental analysis of soil, sediment, and other matrices. However, the accuracy of PXRF is known to be affected while scanning moisture-laden soil samples. This study sought to statistically establish PXRF’s predictive ability for various elements in water at different concentrations relative to inductively coupled plasma atomic emission spectroscopy (ICP-AES). A total of 390 metal-laden water samples collected from leaching columns of mine tailings in South Africa were analyzed via PXRF and ICP-AES. The PXRF showed differential effectiveness in elemental quantification. For the collected water samples, the best relationships between ICP and PXRF elemental data were obtained for K and Cu (R 2  = 0.92). However, when scanning ICP calibration solutions with elements in isolation, PXRF results indicated near perfect agreement; Ca, K, Fe, Cu and Pb produced an R 2 of 0.99 while Zn and Mn produced an R 2 of 1.00. The utilization of multiple PXRF (stacked) beams produced stronger correlation to ICP relative to the use of a single beam in isolation. The results of this study demonstrated the PXRF's ability to satisfactorily predict the composition of metal-laden water as reported by ICP for several elements. Additionally this study indicated the need for a “Water Mode” calibration for the PXRF and demonstrates the potential of PXRF for future study of polluted or contaminated waters. [ABSTRACT FROM AUTHOR]

Optimized agronomic production is vital to life and livelihood in Haiti. As such, rapid, low-cost soil characterization may inform specific decision making for improved crop production. Ahead of a larger soil characterization campaign (120,000 points) in Eastern Haiti, this study evaluated the use of two proximal sensors (portable X-ray fluorescence – pXRF; NixPro color sensor) at farms of Association Zanmi Agrikol (AZA) for soil characterization and evaluation of spatial variability of soil properties. Analysis of 166 surface soil samples (0–5 cm) collected in three different areas revealed that similar inter-elemental associations reported in other countries remain applicable in Haiti; weak, yet significant relationships between Al, K, and Fe contents and color parameters (L*, A*, B*) were observed. Considerable variability in soil morphology and chemical properties exist even across short distances, underscoring the need for high density sampling schemes. Soil pH (observed up to 8.4) likely imposes restrictions on the bioavailability of certain micronutrients; soil salinity was low (not agronomically limiting) across the study areas, and reflective of excessive drainage. Soil profile characterization identified one Eutrudept and one Quartzipsamment; both used for agronomic production. The proliferation of skeletal coarse fragments (e.g., cobbles, stones) and sandy matrix with depth emphasize the need for periodic supplemental irrigation and fertilization via either soil amendments (e.g., compost) or commercial fertilizers as water holding capacity and nutrient storage (e.g., clays, organic matter) are likely to be limited. Use of the NixPro color sensor showed differences in topsoil color likely reflective of differential soil organic carbon levels. The proximal sensors applied herein represent their first such use for agronomic management in Haiti and have shown considerable promise for additional study and application. • PXRF was applied for the first time in Haiti for soil characterization. • Inter-elemental relationships were established. • NixPro and PXRF revealed substantial surface soil variability. • Eutrudept and Quartzipsamment soil taxonomies were established. [ABSTRACT FROM AUTHOR]

Following the Fund ão dam failure in Brazil, 60 million m3 of iron-rich tailings were released impacting an extensive area. After this catastrophe, a detailed characterization and monitoring of iron-rich tailings is required for agronomic and environmental purposes. This can be facilitated by using proximal sensors which have been an efficient, fast, and cost-effective tool for eco-friendly analysis of soils and sediments. This work hypothesized that portable X-ray fluorescence (pXRF) spectrometry combined with a pocket-sized (Nix™ Pro) color sensor and benchtop magnetic susceptibilimeter can produce substantial data for fast and clean characterization of iron-rich tailings. The objectives were to differentiate impacted and non-impacted areas (soils and sediments) based on proximal sensors data, and to predict attributes of agronomic and environmental importance. A total of 148 composite samples were collected on totally impacted, partially impacted, and non-impacted areas (natural soils). The samples were analyzed via pXRF to obtain the total elemental composition; via Nix™ Pro color sensor to obtain the red (R), green (G), and blue (B) parameters; and assessed for magnetic susceptibility (MS). The same samples used for analyses via the aforementioned sensors were wet-digested (USEPA 3051a method) followed by ICP-OES quantification of potentially toxic elements. Principal component analysis was performed to differentiate impacted and non-impacted areas. The pXRF data alone or combined with other sensors were used to predict soil agronomic properties and semi-total concentration of potentially toxic elements via random forest regression. For that, samples were randomly separated into modeling (70%) and validation (30%) datasets. The pXRF proved to be an efficient method for rapid and eco-friendly characterization of iron-rich tailings, allowing a clear differentiation of impacted and non-impacted areas. Also, important soil agronomic properties (clay, cation exchange capacity, soil organic carbon, pH and macronutrients availability) and semi-total concentrations of Ba, Pb, Cr, V, Cu, Co, Ni, Mn, Ti, and Li were accurately predicted (based upon the lowest RMSE and highest R2 and RPD values). Sensor data fusion (pXRF + Nix Pro + MS) slightly improved the accuracy of predictions. This work highlights iron-rich tailings from the Fund ão dam failure can be in detail characterized via pXRF ex situ , providing a secure basis for complementary studies in situ aiming at identify contaminated hot spots, digital mapping of soil and properties variability, and embasing pedological, agricultural and environmental purposes. • pXRF data and random forest algorithm accurately predicted iron-rich tailings properties. • Potentially toxic elements (Ba, Pb, Cr, V, Cu, Co, and Ni) were successfully predicted. • Soil agronomic properties were also accurately predicted with high R2 values. • These findings constitute a secure dataset for environmental recomposition planning. [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]

Proximal sensors have proven capable of predicting multiple soil properties under different conditions. However, doubts remain about which sensor is preferable for delivering optimal prediction models and which preprocessing methods produce the most accurate results. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared (Vis-NIR) diffuse reflectance spectroscopy have been widely used, while the NixPro TM color sensor has been explored more recently. This study evaluated the use of pXRF, Vis-NIR, and NixPro TM data to predict soil organic matter content (SOM), pH, base saturation (BS), the sum of bases (SB), cation exchange capacity (CEC) at pH = 7 and effective CEC (eCEC), via each sensor in isolation, and via combined sensors data. Moreover, factors interfering in the prediction models' accuracy (data preprocessing methods, soil horizon, soil class, parent material) were used as auxiliary variables. 604 soil samples were collected in Brazil, encompassing ten soil orders and 19 parent materials. Numerical and categorical prediction models (7,980) were created for six soil properties using a random forest algorithm, totaling 7980 models, delivering almost 24,000 results., Coefficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE), and residual prediction deviation (RPD) were used for validation of numerical predictions., Overall accuracy and kappa coefficient were calcualted for categorical predictions. Although the combination of sensors provided most of the best predictions, pXRF in isolation achieved accuracies close to the three sensors combined. NixPro TM offered superior contributions to SOM and CEC predictions, but pXRF and Vis-NIR were responsible for the best results of most studied variables. On average, by adding pXRF to Vis-NIR data, predictive accuracy improved 32%; while adding Vis-NIR to pXRF data increased accuracy by ca. 6%. Soil-order-specific models improved predictions for Ultisols compared to general models (without soil order distinction), reaching R2 > 0.90. Soil parent material and horizon did not improve models significantly. Categorical predictions improved the accuracy for some properties, reaching an overall accuracy of 100% and kappa index of 1.0 for pH in A horizons of Ultisols via pXRF + Vis-NIR data. Proximal sensor data with no auxiliary variables provided almost all the best results. The fusion of proximal sensors can provide better predictions, but pXRF alone can deliver satisfactory results in most cases for the six soil properties. • Soil pH, SOM, BS, SB, eCEC, CEC can be predicted by pXRF sensor alone. • PXRF provided results similar to the best ones provided by both Vis-NIR and NixPro TM . • Soil horizon, parent material, or class did not significantly improve predictions. • PXRF alone delivers satisfactory results for all the six soil properties evaluated. [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]