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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]

• Genesis of an apparently homogenous and 4.5 m deep Oxisol profile was investigated. • Proximal sensors data brought to light variations within and between horizons. • Very low fertility even in C horizon was found. • X-ray diffraction correlated well with Vis-NIR data of sand, silt and clay fractions. • Maps of elemental contents determined by pXRF showed high variation in the profile. This study combined proximal sensors and soil analyses for the assessment of a 4.5-m deep Oxisol representative of southeastern Brazil. The soil was derived from gneiss. Soil samples were collected in a 20 × 20 cm grid design across the profile wall, analyzed for texture and chemical properties, and scanned by visible near infrared diffuse reflectance spectroscopy (Vis-NIR DRS), portable X-ray fluorescence spectrometry (pXRF), and X-ray diffraction (XRD). Total elemental contents by pXRF analyses were mapped to provide 2D elemental content maps. The elemental contents revealed alternating patterns in the C horizon attributed to the mafic and felsic bands of the gneiss. The extremely low exchangeable/available nutrient contents in the C horizon are remarkable. Profile maps showed Si accumulation downwards and relative Fe and Al accumulation in the upper horizons indicating desilication and intense weathering. Vis-NIR DRS spectra differentiated horizons and detected mineralogy traits. Vis-NIR DRS data correlated well with XRD data, strengthening its potential for assessing soil mineralogy. Proximal sensors can detect variations of soil properties in apparently homogeneous soil profiles, and improve studies of soil genesis. [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]

Oxisols are generally deep, weathered-leached soils that present morphologically homogeneous profiles. Despite their apparent homogeneity, different parent materials and pedogenetic processes grant these soils diverse chemical and mineralogical attributes. Proximal sensors might help to detect the variability of soil attributes along such old and deep profiles. The objective of this study was to investigate the physical, chemical and mineralogical variability of Oxisol profiles originated from different parent materials with aid of visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS) and portable X-ray fluorescence (pXRF) spectrometry, in order to evaluate if these proximal sensors can complement the analysis of these deep and weathered tropical soils. Two Oxisol profiles were selected: Anionic Acrudox (AA) and Typic Hapludox (TH). One hundred samples were collected in each profile down to 2 m of depth, following a grid of 10 × 10 cm, for pXRF, Vis-NIR and XRD analyses. Vis-NIR DRS spectra provided key mineralogical information in agreement with X-ray diffraction results, showing differential occurrence of kaolinite, hematite, maghemite and gibbsite/kaolinite ratio in these profiles. PXRF detected high chemical variability across the profiles and allowed to map the spatial chemical distribution of both soils. Proximal sensors provided an inexpensive and efficient way to help complement the identification of meaningful variability across Oxisol profiles that remained even after the long formation process of these very weathered tropical soils. • Very weathered and apparently homogeneous Oxisols were detailed investigated. • Anionic Acrudox (AA) and Typic Hapludox (TH) were analyzed via proximal sensing. • Proximal sensors detected detailed variations across horizons of the studied profiles. • AA showed higher contents of metals, while TH presented more Si and Al. • TH presented greater variation of properties between horizons than AA. [ABSTRACT FROM AUTHOR]

Proximal sensors have been increasingly used for a variety of purposes worldwide, with great interest on portable X-ray fluorescence (pXRF) spectrometry applications in Soil Science. pXRF provides rapid, inexpensive, non-destructive, and environmentally-friendly elemental analysis in soils, rocks, sediments, plant tissues, water, and other matrices. While temperate regions concentrate the largest number of studies involving pXRF for soil characterization, tropical regions have recently demonstrated increasing interests in--and acceptance of--pXRF applications for soil-related studies due to the wide range of purposes in which this equipment can be utilized. However, the lack of a standard methodology for tropical soil analysis via pXRF coupled with the recognized variation of results according to the procedures used during analyses underlie the need for routine protocols for improving pXRF analyses in tropical environments. Besides establishing such a standard methodology, this review elucidates the main differences between soils from tropical and temperate regions that are key in pXRF research, presents state-of-the-art achievements in pXRF analysis, and envisions applications of pXRF in tropical soils. [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]

• Prediction of soil texture via pXRF and Vis-NIR DRS data was evaluated. • Combination of A and B horizons data resulted in predictions with R2 above 0.80. • In general, the best predictions were achieved using pXRF data. • RF algorithm outperformed other algorithms for soil texture prediction. • The best prediction models were obtained with pXRF + RF using B horizon data. Portable X-ray fluorescence (pXRF) spectrometry and visible near-infrared diffuse reflectance spectroscopy (Vis-NIR DRS), used separately or in tandem, have become important techniques for determination and prediction of soil attributes worldwide. However, there is little information available regarding the effectiveness of their combined use in tropical soils. This study aimed to predict soil texture using pXRF and Vis-NIR DRS, evaluating the efficiency of using these proximal sensors separately and in tandem. A total of 315 soil samples were collected from A and B horizons in Brazil. Soil samples were submitted to analyses of texture, pXRF and Vis-NIR DRS. Vis-NIR DRS spectral data pre-processing was evaluated by comparing results delivered by the derivative smoothing methods Savitzky-Golay (WT), Savitzky-Golay with Binning (WB), and data without the pre-processing treatment (WOT). Four algorithms were utilized for predictions: Gaussian Process (Gaussian), Support Vector Machine with linear (SVM-L) and radial (SVM-R) kernels, and Random Forest (RF). In general, models using only pXRF data slightly outperformed models using Vis-NIR DRS (WT, WB, WOT) data alone. Models combining data from both sensors achieved similar results to those obtained by pXRF alone. The best predictions of sand, silt, and clay contents were obtained via pXRF + RF using B horizon data, reaching R2 values of 0.91, 0.81, and 0.83, respectively. Although pXRF alone provided slightly better results, soil texture can be accurately predicted via both pXRF and Vis-NIR DRS data, separately and in tandem. These sensors can contribute to reduce costs and time required for tropical soil texture determination. [ABSTRACT FROM AUTHOR]

• First time pXRF and Vis-NIR data were combined to predict tropical soil types. • Vis-NIR spectral data preprocessing is relevant to increase its predictive efficiency. • SVM-L, SVM-R, C5.0, and RF algorithms provided efficient predictions. • RF model using pXRF and soil B horizon data outperformed other models. • Tropical soil type predictions using only pXRF data provided accurate results. Recently, portable X-ray fluorescence (pXRF) spectrometer and visible near-infrared (Vis-NIR) spectroscopy are increasingly being applied for soil types and attributes prediction, but a few works have used them combined in tropical regions. Thus, this work aimed at analyzing models' performance when predicting soil types at subgroup taxonomic level via pXRF and Vis-NIR separately and together. 315 soil samples were collected in both A and B horizons in three important Brazilian states. Samples undergone laboratorial analyses for soil classification and were submitted to pXRF and Vis-NIR (350–2500 nm) analyses. Vis-NIR spectral data preprocessing was evaluated utilizing Savitzky-Golay (WT) and Savitzky-Golay with Binning (WB) methods. Four classification algorithms were employed in modeling: Support Vector Machine with Linear (SVM-L) and Radial (SVM-R) kernel, C5.0, and Random Forest (RF). Predictions were made using only B horizon and using A + B horizon data. Overall accuracy and Cohen's Kappa index evaluated model quality. Both sensors displayed efficacy in soil types prediction. A + B horizons data combined using pXRF + Vis-NIR via SVM-R (WT and WB) delivered accurate predictions (89.32% overall accuracy and 0.75 Kappa index), but the best predictions were achieved using only B horizon data via pXRF with RF, pXRF + Vis-NIR (WT) with RF, pXRF + Vis-NIR (WB) with C5.0, and pXRF + Vis-NIR (WB) with RF (89.23% overall accuracy and 0.80 Kappa index). For tropical soils, soil subgroup prediction using only B horizon data obtained by pXRF in tandem with RF algorithm may be a viable alternative to assist in soil classification, especially when the acquisition of Vis-NIR is not possible. [ABSTRACT FROM AUTHOR]