Search

Quartz is one of the most common minerals in soils, mostly present in sand and silt fractions. Although quartz is basically formed of SiO2, other elements can be easily detected and assessed nowadays using a portable X-ray fluorescence (pXRF) spectrometer. Our study aims to evaluate the chemical composition of different quartz varieties, identifying their main elements, and relating them to soil attributes. Six quartz varieties (hyaline, amethyst, milky, rose, smoky, and ferriferous) were analyzed via pXRF and 13 oxides/trace elements were identified and used for quartz discrimination (Al2O3, CaO, P2O5, SiO2, Cl, Cr, Fe, K2O, Mn, Rb, S, Ti, and V). Hyaline quartz was characterized by the highest SiO2 and the lowest contents of other elements. Al2O3 was the second-highest compound present in all varieties of quartz, reaching 21,547 mg kg−1 in the smoky variety. S, P2O5, Cl, SiO2, and K2O were the main components determined by Random Forest algorithm for discriminating quartz varieties. Some elements detected may serve as a reserve of nutrients to plants to be released in soils along weathering, depending on quartz particle size, soil texture, leaching, and associated attributes. pXRF enhanced the information on chemical characterization of quartz varieties, without the generation of chemical pollutants. [ABSTRACT FROM AUTHOR]

Several materials have been characterized using proximal sensors, but still incipient efforts have been driven to plant tissues. Eucalyptus spp. cultivation in Brazil covers approximately 7.47 million hectares, requiring faster methods to assess plant nutritional status. This study applies portable X-ray fluorescence (pXRF) spectrometry to (i) distinguish Eucalyptus clones using pre-processed pXRF data; and (ii) predict the contents of eleven nutrients in the leaves of Eucalyptus (B, Ca, Cu, Fe, K, Mg, Mn, N, P, S, and Zn) aiming to accelerate the diagnosis of nutrient deficiency. Nine hundred and twenty samples of Eucalyptus leaves were collected, oven-dried, ground, and analyzed using acid-digestion (conventional method) and using pXRF. Six machine learning algorithms were trained with 70% of pXRF data to model conventional results and the remaining 30% were used to validate the models using root mean square error (RMSE) and coefficient of determination (R2). The principal component analysis clearly distinguished developmental stages based on pXRF data. Nine nutrients were accurately predicted, including N (not detected using pXRF spectrometry). Results for B and Mg were less satisfactory. This method can substantially accelerate decision-making and reduce costs for Eucalyptus foliar analysis, constituting an ecofriendly approach which should be tested for other crops. [ABSTRACT FROM AUTHOR]

Farms use large quantities of fertilizers from many sources, making quality control a challenging task, as the traditional wet-chemistry analyses are expensive, time consuming and not environmentally-friendly. As an alternative, this work proposes the use of portable X-ray fluorescence (pXRF) spectrometry and machine learning algorithms for rapid and low-cost estimation of macro and micronutrient contents in mineral and organic fertilizers. Four machine learning algorithms were tested. Whole (i.e., as delivered by the manufacturer) (CP) and ground (AQ) samples (429 in total) were analyzed to test the effect of fertilizer granulometry in prediction performance. Model validation indicated highly accurate predictions of macro (N: R2 = 0.92; P: 0.97; K: 0.99; Ca: 0.94, Mg: 0.98; S: 0.96) and micronutrients (B: 0.99; Cu: 0.99; Fe: 0.98; Mn: 0.91; Zn: 0.94) for both organic and mineral fertilizers. RPD values ranged from 2.31 to 9.23 for AQ samples, and Random Forest and Cubist Regression were the algorithms with the best performances. Even samples analyzed as they were received from the manufacturer (i.e., no grinding) provided accurate predictions, which accelerate the confirmation of nutrient contents contained in fertilizers. Results demonstrated the potential of pXRF data coupled with machine learning algorithms to assess nutrient composition in both mineral and organic fertilizers with high accuracy, allowing for clean, fast and accurate quality control. Sensor-driven quality assessment of fertilizers improves soil and plant health, crop management efficiency and food security with a reduced environmental footprint. [Display omitted] • Quality control of organic and mineral fertilizers can be accurately done via pXRF. • Sample preprocessing did not affect results significantly. • Random Forest and Cubist Regression achieved the best prediction performances. • Nutrient content assessment in fertilizers is possible via a green method. [ABSTRACT FROM AUTHOR]

Although parent material (PM) is one of the five soil formation factors providing key information on soil variability, the complexity of PM distributions and the difficulty of reaching PM in deep soils prevent its detailed assessment. Proximal sensors such as portable X-ray fluorescence (pXRF) spectrometer and magnetic susceptibility (MS) may be helpful in predicting soil PM in a more practical and accessible way. This pilot study aimed to create spatial PM predictive models for three distinct PMs (charnockite, mudstone, and alluvial sediments) of an experimental farm (Brazil) through random forest (RF) algorithm based on soil samples analyzed via pXRF and MS. Soils were sampled in A and B horizons following a regular-grid design covering the whole study area. The RF algorithm was calibrated to predict PMs using samples from the B horizon of soils with known PM. The prediction model was applied to the area for mapping PM across the whole farm. For validation, PM was identified at 15 different sites and compared with the predicted PM shown on the maps via overall accuracy, Kappa coefficient, producer's and user's accuracies. Al, Fe, Si, Ti, and MS proximal sensor data discriminated well among soils derived from charnockite, mudstone, and alluvial sediments. The map built based on B horizon data showed greater accuracy (overall accuracy = 0.93, Kappa coefficient = 0.85, user's accuracy = 0.92, and producer's accuracy = 0.97) than the map built from the model using A horizon samples (0.73, 0.48, 0.48, and 0.58). These results could represent alternative methods for reducing costs and accelerating the assessment of soil PM spatial variability, supporting soil mapping, and optimized agronomic and environmental decision-making. • Proximal sensors were used for soil parent material (PM) prediction. • pXRF and MS differentiated soil types and their respective parent materials. • Different parent materials were predicted using random forest algorithm. • pXRF and MS combined with RF were successful for PM prediction. [ABSTRACT FROM AUTHOR]

Ca and Mg are the most important chemical elements in lime. Properly measuring Ca and Mg contents is essential to assess the quality of lime products. Quality control guarantees the adequate use of lime in industrial processes, in soils, and helps avoiding adulteration. Proximal sensors can aid in this process by determining Ca and Mg contents easily, rapidly and without producing chemical waste. The objective of this study was to evaluate the use an environmentally-friendly method of analyzing the quality of lime. We studied 1) the use of portable X-ray fluorescence (pXRF) to predict concentrations of Ca and Mg in lime, 2) tested if NixPro™ sensor can improve prediction accuracy and 3) tested if sample preparation methods (grinding) affect analyses. 74 samples of lime were analyzed by two different laboratories (lab. 1 = 38, lab. 2 = 36). All samples submitted to pXRF and NixPro™ analyses. Sensor analyses were done in whole (CP) and ground (AQ) samples to test the effect of sample preparation in prediction performance. High correlation was found between Ca and Mg contents measured via pXRF and laboratory analyses. Mg-CP presented the highest correlation coefficient (r = 0.81); Mg-AQ, the lowest (0.57). Predictions presented good performance (R2 > 0.68); Mg had the best results (0.86). Separating models per laboratory showed that some datasets are harder to model, probably due to variability in the source material (limestone). The addition of NixPro™ data contributed to improve prediction accuracy, although slightly. Predictions using CP samples presented the best results, especially for Mg, indicating that grinding is not necessary. This pioneer study demonstrated that fused proximal sensors can be used to rapidly and easily determine contents of Ca and Mg in soil amendments without producing chemical waste. [Display omitted] • Highly accurate predictions of Ca and Mg contents in lime were possible. • Quality control of lime can be done without producing chemical waste. • Grinding samples did not affect results significantly. • PXRF alone was capable of achieving the best prediction performances. • NixPro™ did not contribute significantly to prediction accuracy. [ABSTRACT FROM AUTHOR]

Brazil presents high soil variability, however, the soil magnetic susceptibility (χ) distribution on national scale is yet to be investigated. Thus, soil data (218 samples from the literature and 36 original samples) were utilized to estimate soil low frequency χ (χ lf) across Brazil through machine learning algorithms (Generalized Additive Model Using Splines, Generalized Linear Model, Random Forest and Support Vector Machine) in order to assess the applicability of such methodology on a nationwide scale. Two variable groups were tested: texture and Fe 2 O 3 contents – Group 1; and texture, Fe 2 O 3 , Al 2 O 3 , SiO 2 contents – Group 2. The best performing model was applied to a database containing 3817 and 3973 georeferenced soil samples from superficial and subsuperficial horizons, respectively, obtained from soil surveys conducted by the Brazilian Agricultural Research Corporation. Polynomial Support Vector Machine using Group 2 variables presented the best performance (R2 = 0.91). Variable importance measurements showed that χ lf is chiefly related to Fe 2 O 3 and clay content. The spatial distribution of χ lf on national scale varied from very high (>150 × 10−6 m3 kg−1) to very low (≤5 × 10−6 m3 kg−1), considering the interpretation bands that we propose for Brazilian soils. Extensive areas of magnetic soils were associated with their formation upon mafic rocks or iron-rich formations. Despite few exceptions, the observed tendency shows that χ lf increases parallel to soil red color, drainage and weathering degree, and hence Rhodic Nitisol, Umbric Nitisol, Rhodic Ferralsol and Umbric Ferralsol presented the highest χ lf values. [Display omitted] • Rock type is a key factor to understand the soil magnetic susceptibility (χ lf) distribution. • χ lf increase in red soils with good drainage and higher weathering-leaching degree. • The distribution of χ lf varied largely according to soil classification. • Five value bands were proposed to aid in the interpretation of soil χ lf values. [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]

Phosphorus (P) retention capacity of soils directly affects the management of phosphate fertilization, and has economic and environmental importance. Brazil has expressive agricultural production and high edaphoclimatic variability; still, the detailed spatial distribution of P retention capacity is not known. Thus, machine learning models were created to estimate the P retention capacity of soils at superficial horizons using the variables: clay content, sand content, soil organic matter, pH, base saturation. The four best models were combined to create an ensemble (a combination of models), which was applied in a dataset (5524 samples) that comprises a sample data from the entire Brazilian territory. The ensemble was used to build a map of P retention capacity. Most of the country presented soils with medium P retention (40–60%), and large areas with very high retention capacity were found in the south of Brazil, a subtropical region mostly associated with clayey soils. High P retention was observed for Histosols, probably related to humic-Al(Fe) complexes. Nitisol, Ferralsol, Gleysol and Cambisol classes presented high P retention, especially for clayey soils with qualifiers related to bases/weathering (Dystric) and organic matter (Umbric and Humic). The correlation between Fe oxide minerals and P retention varied with soil color and drainage conditions mainly associated to redoximorphic reactions; the same was not observed for Al oxide minerals. This pioneer study revealed the distribution of P retention capacity across different soil classes in Brazil, which can help to define land use suitability and soil P management strategies that ensure profitable agricultural exploration. [Display omitted] • In 34% of the country, soils had P retention capacity framed as Low and 22% as High. • The Histosol class presenting soils with elevated P retention capacity (>60%). • For mineral soils, the highlighted classes were Nitisol, Ferralsol, Gleysol and Cambisol. • Soil color and drainage conditions had little effect on soil P retention capacity. [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]

• 1975 samples of varied soils were analyzed for fertility and elemental contents. • pXRF data was successfully used to rapidly predict soil fertility properties. • Random Forest outperformed other machine learning algorithms. • Prediction models allowed spatial distribution of soil fertility properties. • Inexpensive and environment-friendly soil fertility prediction was achieved by pXRF. Conventional soil fertility analyses are laborious, costly, time-consuming, and produce hazardous waste. The high demand of these laboratory-based analyses prompted us to investigate an environment-friendly, rapid, and inexpensive methodology for soil fertility assessment. Portable X-ray fluorescence (pXRF) spectrometry allows the determination of total elemental concentration in soils quickly, simply and without hazardous waste production. However, incipient usage of this technology for the prediction of soil fertility properties has been reported for tropical conditions. Soil samples were collected from seven Brazilian states (n = 1975) aiming to use pXRF data to predict contents of available or exchangeable Ca2+, Mg2+, Al3+, K+ and P by testing different algorithms using 70% of the samples for model training, and the remaining 30% for model validation. In addition to point data predictions, the best performing models were applied to data obtained from a farm within the studied regions with a known cropping history to create soil fertility maps and illustrate another applicability of this approach. The attested use of pXRF data and machine learning algorithms stepwise Generalized Linear Model (GLM) and Random Forest (RF) to predict the contents of relevant soil fertility properties exhibited great potential. Validation of the models corroborated that RF resulted in more accurate predictions than GLM. Validation R2 values ranged from 0.59 to 0.82. Maps created were coherent with expected distributions of soil fertility attributes. This environment-friendly methodology may be used for the assessment of soil fertility properties in a wide range of tropical and subtropical soils with minimum waste generation and reduced costs. [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]