Soil texture prediction in tropical soils: A portable X-ray fluorescence spectrometry approach

Geoderma 362 (2020) 114136

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Soil texture prediction in tropical soils: A portable X-ray fluorescence spectrometry approach

T

⁎

Sérgio Henrique Godinho Silvaa, David C. Weindorfb, , Leandro Campos Pintoa, Wilson Missina Fariaa, Fausto Weimar Acerbi Juniorc, Lucas Rezende Gomidec, José Márcio de Melloc, Alceu Linares de Pádua Juniord, Igor Alexandre de Souzad, Anita Fernanda dos Santos Teixeiraa, Luiz Roberto Guimarães Guilhermea, Nilton Curia a

Department of Soil Science, Federal University of Lavras, Lavras, MG, Brazil Department of Plant and Soil Science, Texas Tech University, Lubbock, TX, USA Department of Forest Sciences, Federal University of Lavras, Lavras, Minas Gerais, Brazil d Instituto de Ciências Agrárias, Federal University of Jequitinhonha and Mucuri Valleys, Unaí, Minas Gerais, Brazil b c

A R T I C LE I N FO

A B S T R A C T

Handling Editor: Alex McBratney

Soil texture is an important feature in soil characterization, although its laboratory determination is costly and time-consuming. As an alternative, this study aimed at predicting soil texture from portable X-ray fluorescence (pXRF) spectrometry data in Brazilian soils. 1565 soil samples (503 from superficial and 1062 from subsuperficial horizons) were analyzed in the laboratory for soil texture and scanned with the pXRF. Elemental contents determined by pXRF were correlated with soil texture and used to calibrate regression models through the generalized linear model (GLM), support vector machine (SVM), and random forest (RF) algorithm. Models were created with 70% of the data using three datasets: i) only superficial horizon data; ii) only subsuperficial horizon data; and iii) data from both horizons. Validation was performed with 30% of the data. Clay content was positively correlated with Fe (0.79) and Al2O3 (0.41) reflecting the great residual concentration of Fe- and Aloxides in this fraction. This same fraction correlated negatively with SiO2 (-0.75), while the sand fraction correlated positively with SiO2 corresponding to quartz dominance in the sand fraction of Brazilian soils. For the separated superficial and subsuperficial horizon datasets, SVM promoted the best predictions of clay (R2 0.83; RMSE = 7.04%) and sand contents (R2 0.87; RMSE = 9.11%), while RF provided the best results for silt (R2 0.60; RMSE = 6.33%). When combining both datasets, RF was better for sand prediction (R2 0.73; RMSE = 5.79%), while SVM promoted better predictions for silt (R2 0.72; RMSE = 5.77%) and clay (R2 0.84; RMSE = 7.08%). Elemental contents obtained by pXRF are capable of accurately predicting soil texture for a great variety of Brazilian soils.

Keywords: Proximal sensors Soil particle size Prediction models Brazilian soils

1. Introduction With the increase in world demand for food, there is a growing need for worldwide soil information, indispensable to enhance soil productivity. Among many countries, Brazil has stood out as a leader in productivity in tropical agriculture. Recent estimates indicate that Brazilian agriculture will account for up to 40% of total food production needs by 2050, when global population is expected to reach 9.2 billion people (Lopes and Guilherme, 2016). In this sense, detailed knowledge of soils will be essential for increasing crop yields.

Besides the correct management of soil fertility, soil texture is also essential to support decision making. It contributes to planning agricultural activities, facilitating appropriate soil management (Hillel, 1980), and determining the suitability crops for each area with deference to climate and other ancillary factors. Soil texture largely influences the degree of cohesion and adhesion between soil particles, affecting the rate of water infiltration and retention, aeration, and nutrient availability (White, 2013) among others. Moreover, soil texture data supports the rational and efficient application of soil amendments to optimize soil fertility (Lopes and Guilherme, 2016). It is noteworthy

Abbreviations: CV, coefficient of variation; GLM, generalized linear model; MAE, mean absolute error; pXRF, portable X-ray fluorescence (pXRF); R2, coefficient of determination; RF, Random Forest; RMSE, root mean square error; SVM, support vector machine (SVM) ⁎ Corresponding author. E-mail address: [email protected] (D.C. Weindorf). https://doi.org/10.1016/j.geoderma.2019.114136 Received 21 January 2019; Received in revised form 16 September 2019; Accepted 11 December 2019 0016-7061/ © 2019 Elsevier B.V. All rights reserved.

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2.2. Laboratory analyses

that such decisions are even more valuable when texture information is interpreted together with soil mineralogy (Zhu et al., 2011). Commonly, soil texture analysis requires laboratory preparation of the samples and is costly, time-consuming, and includes chemical reagents that can generate environmental impacts (Izzo, 2000). To overcome these constraints, new methodologies that are less costly, more rapid in providing the results, and that mitigate chemical impacts have been increasingly sought. One such approach is that of using proximal sensors such as portable X-ray fluorescence (pXRF) spectrometry. This equipment is able to identify and quantify several elements present in the analyzed material (Ribeiro et al., 2017; Weindorf et al., 2014). Furthermore, pXRF is low-cost, non-destructive to samples, provides rapid/robust information, and requires minimum sample preparation (Zhu et al., 2011). Data from pXRF have been used as predictor variables in models estimating several soil properties (Duda et al., 2017; Pelegrino et al., 2018; Sharma et al., 2015; Silva et al., 2017; Teixeira et al., 2018), including soil texture (Wang et al., 2013; Zhu et al., 2011). Moreover, pXRF has been successfully applied in several other soil-related studies (Mancini et al., 2019; Ribeiro et al., 2018; Sharma et al., 2014; Silva et al., 2016, 2018a, 2018b; Stockmann et al., 2016; Terra et al., 2014; Wang et al., 2013; Weindorf et al., 2012; Wu et al., 2016). In tropical conditions, soil texture, soil mineralogy, and soil chemical composition drastically differ from those of soils from temperate regions, where pXRF has been successfully used to predict soil texture (Zhu et al., 2011; Wang et al., 2013). No works in tropical developing countries have tried to predict soil texture from pXRF solely using multiple soil data to date. If possible, such results will be valuable considering that such countries generally have limited detailed information and scarce financial funds for soil characterization which limit effective management decisions. As such, the adaptation and testing of temperate soil texture predictive models based upon pXRF data for tropical, highly leached soils appears timely. The objectives of this study were to: i) characterize the soil textural data for 1565 samples of Brazilian soils; ii) correlate soil texture with elemental data obtained from pXRF; and iii) develop prediction models for soil texture based on pXRF results in tropical soils. We hypothesize that sand, silt, and clay contents will be accurately predicted from pXRF data for Brazilian soils, with support of robust algorithms, independently of soil parent material, soil class, or horizon type.

Soil samples were air-dried, disaggregated to pass a 2-mm sieve, and analyzed in the laboratory for soil texture determination via the pippete method per Gee and Bauder (1986). A portion of each sample was separated for analysis with a pXRF model S1 Titan LE (Bruker Nano Analytics, Kennewick, WA, USA) per Weindorf and Chakraborty (2016). These analyses were performed in triplicate with a dwell time of 60 s using the Trace (dual soil) mode and associated Geochem software (software included in the pXRF and used to process and deliver the results of elemental contents after a sample is analyzed). The elements used for development of the prediction models were: Al, Ca, Cl, Cu, Fe, K, Mn, Nb, Ni, P, Pb, Rb, Si, Sr, Ti, V, Y, Zn, and Zr. Prior to scanning the samples, two National Institute of Standards and Technology (NIST) certified reference soils (2710a and 2711a) and a material certified by the pXRF manufacturer (check sample) were analyzed by the equipment for assessing the accuracy of pXRF elemental quantification. The recovery values [100x (obtained content/ certified content)] for 2710a, 2711a, and check sample are shown in Table 1. 2.3. Statistical analysis Pearson correlation was performed between soil texture and elemental contents obtained by pXRF for both the superficial and subsuperficial horizons. For this purpose, a correlogram was created in R software (R Core Team, 2018), through the package corrplot (Wei et al., 2017). Prior to generating the prediction models, all pXRF data were normalized and scaled according to the default scale function in R software. This function is given by: standardized elemental content = (x – mean(x))/std(x), where x is the original elemental content, mean (x) is the mean of the values for each element, and std (x) is the standard deviation of the values for each element. For the development of the regression models for soil texture prediction, three different machine learning algorithms were used: stepwise generalized linear model (GLM) contained in the “STATS” R package; support vector machine (SVM) contained in the “e1071” R package (Hornik et al., 2015); and random forest (RF) algorithm contained in the “randomforest” R package (Liaw and Wiener, 2015). The GLM was built with standard R library “STATS” after the application of the variance inflation factor (VIF) to avoid multicollinearity. In order to reduce skewness of silt data from superficial and subsuperficial data when separated, a square root transformation was applied before fitting the models. Similarly, for the combined superficial and subsuperficial data, cube root transformation was applied. In total, 2 and 7 outliers were removed from the modeling datasets for the subsuperficial horizon (n = 743) and superficial and subsuperficial horizons combined (n = 1095), respectively. These outliers are equivalent to only 0.27% and 0.64% of those datasets, respectively. In order to train the SVM, an error parameter C and kernel parameter sigma (γ) were set as the default. The RF analysis was performed with the following parameters established: number of trees of the model (ntrees) = 1000, number of variables used in each tree (mtry) = 6, correspondent to one third of the number of predictors (Liaw and Wiener, 2002). For each soil texture variable (sand, silt, and clay), prediction models using GLM, SVM, and RF were created for varying datasets: i) only superficial horizon data; ii) only subsuperficial horizon data; and iii) superficial and subsuperficial horizons data together. This aimed to evaluate whether soil horizon depth (surface vs. subsoil) influences soil texture predictions. Also, it allows for investigation of the possibility of creating a general model that would be adequate for samples from any unknown soil horizon, i.e., a model that could be used in cases when horizon information is not available. Models were developed using 70% of the total data of each dataset

2. Material and methods 2.1. Study area and soil sampling For this study, a total of 1565 soil samples were collected from superficial horizons (A or O; 502 samples) and from subsuperficial horizons (E, B, or C; 1062 samples) from eight Brazilian states (Minas Gerais, São Paulo, Espírito Santo, Rio de Janeiro, Bahia, Pernambuco, Paraná, and Santa Catarina) whose area totals 1,884,796 km2 (Fig. 1). Also, these regions present different tropical climate conditions, mostly featuring hot and humid summers, cold and dry winters. Mean annual temperature and precipitation in those places range from 17 to 27 °C, and 42 to 2500 mm, respectively (Alvares et al., 2013). Moreover, such soils were developed from variable parent materials, including granite, gneiss, gabbro, alluvial and colluvial sediments, shale, sandstone, phyllite, basalt, itabirite, micaschist, amphibolite, chernockite, limestone, and tuffite as identified during field work. Soil classes used in this study were (number of samples): Oxisols (244), Ultisols (359), Entisols (54), Histosols (1), Spodosols (17), Alfisols (2), and Inceptisols (132) per Keys to Soil Taxonomy (Soil Survey Staff, 2014). Additionally, 756 samples came from unclassified soils. Notably, these samples encompass the most common soil classes found in Brazil, equivalent to ~97.31% of the territory (Anjos et al., 2012; IBGE, Embrapa, 2001). 2

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Fig. 1. Map of the Brazilian states and the places where soil samples were collected. Table 1 Recovery values (%) of the portable X-ray fluorescence (pXRF) spectrometer based on the reference materials (RM) 2710a, 2711a, and check sample (CS). RM

Al

Ca

Cl

Cu

Fe

K

Mn

Nb

Ni

P

Pb

Rb

Si

Sr

Ti

V

Y

Zn

Zr

2710a 2711a CS

84 71 92

34 44 01

0 0 0

81 72 91

73 68 87

59 46 88

72 65 83

0 0 0

0 72 91

380 545 0

0 0 0

98 103 0

57 51 91

112 121 0

76 68 0

52 29 0

0 0 0

86 81 0

104 0 0

1

The 0 (zero) values represent no reference value in the certified material or that pXRF was not able to detect the element.

Fig. 2. Soil texture of the samples, together and separated per soil class, used in this study for Brazilian soils.

assessed by comparing the predicted with the observed values of sand, silt, and clay contents through the coefficient of determination (R2), mean absolute error (MAE) (Eq. (1)), and root mean square error (RMSE) (Eq. (2)) using the remaining 30% of samples of each dataset. The models with the greatest R2, and the lowest RMSE and MAE values were considered the best ones for prediction of soil texture using pXRF data.

separated using the createDataPartition function provided by the CARET library in the R software (Kuhn et al., 2018). This procedure ensures that even if the selection is done randomly, the maximum and minimum values of each variable will be allocated to the training set. Since each variable has its own training/validation dataset, a total of 27 models were generated (three datasets × three algorithms × three soil properties), i.e. 9 models per soil property (sand, silt, and clay). The quality of the soil texture predictions by the models was

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3. Results

Table 2 Descriptive statistics of soil texture for the samples collected in different Brazilian states (n = 1565). Soil horizon

Surficial

Subsurficial

4

Particle size fraction

Min1

Clay (%) Silt (%) Sand (%) Clay (%) Silt (%) Sand (%)

2 0 0 1 0 1

Max2

81 65 96 85 64 96

Mean

34 17 49 43 16 42

Median

35 16 45 44 14 39

SD3

17 13 26 17 10 21

3.1. Data overview

CV (%)4

The analyzed samples showed a wide range of soil textural classes (Fig. 2). Most of the Brazilian soils analyzed (occupying ~97.31% of the territory) were rich in sand or clay. None of the analyzed soils were classified as silty, owing to the parent materials and weathering degree. The range of clay, silt, and sand contents varied, respectively, from 1 to 85%, from 0 to 65%, and from 0 to 96%, considering both superficial and subsuperficial horizons (Table 2). However, the boxplots (Fig. 3) show the distribution of the samples per horizon according to their texture. The mean and median values of the clay, silt, and sand contents (Table 2) for both horizons indicate the trending scenarios for soil texture in such tropical soils. Compared to the superficial horizon, there was a slight increase in the clay content and decrease in the sand content in the subsurficial horizon. The high coefficient of variation (CV) for all the soil particle size fractions in both horizons indicates the texture varies considerably in Brazilian soils. It occurs even though these soils have been subjected to weathering for a very long time. Regarding the development of regression models for predicting soil texture, the high variability may contribute to the generation of more reliable models that can be useful for different soil and environmental conditions.

51 76 54 40 66 50

1 Min: minimum value; 2Max: maximum value; 3SD – standard deviation; CV – coefficient of variation.

3.2. Correlations between elemental contents obtained by pXRF and soil texture Fig. 3. Boxplots of soil texture in superficial horizons (A and O) and subsuperficial horizons (E, B, or C) of Brazilian soils.

MAE =

1 n

Fig. 4 presents the correlation between laboratory-determined soil texture and elemental contents obtained by pXRF in the superficial and subsuperficial horizons. Clay content was positively correlated with Fe (0.79 in superficial and 0.64 in subsuperficial horizons), and Al2O3 contents (0.41 in superficial and 0.26 in subsuperficial horizons). In contrast, negative correlations were found between clay and SiO2 contents (−0.75 in superficial and −0.72 in subsuperficial horizons). The sand content correlated positively with SiO2 (0.73 in superficial and 0.63 in subsuperficial horizons). In a smaller intensity, sand content also correlated positively with Zr (0.40 in superficial horizon and 0.46 in subsuperficial horizons). Negative correlations were found between sand contents and most other elements. The silt fraction presented a positive correlation with most elements

n

∑ |(Xobs − X mod el )| i=1

(1)

n

RMSE =

∑i = 1 (Xobs − X mod el )2 n

(2)

where Xobs represents observed values in laboratory analysis and Xmodel represents estimated values from pXRF in n observation points.

Fig. 4. Correlogram between elemental contents determined by portable X-Ray fluorescence (pXRF) spectrometry and soil texture determined in laboratory for surficial and subsuperficial soil horizons of Brazilian soils. Positive correlations are displayed in blue and negative correlations, in red color. Color intensity and the size of the circle are proportional to the correlation coefficients. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

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0.52

0.67 Sand

0.62 Sand

Silt

0.49 Silt

evaluated, mainly K2O, Rb, and Zn in both soil horizons. However, no strong correlation was found between this fraction and the elements evaluated.

Clay = 39.97 + 2.8862Al2O3 −1.8016Cu + 11.4070Fe − 5.3672K2O −1.3672Mn + 3.3522Nb −1.6071Ni −1.8527P2O5 −1.3715Pb + 5.2503Rb −5.1004SiO2 −2.7810Ti + 2.9777Y −2.1826Zr Silt = (2.3658 + 0.0542Al2O3 − 0.0421CaO + 0.0763Cu + 0.1877Fe + 0.2386K2O + 0.0796Nb + 0.0935P2O5 − 0.0355Pb − 0.0588SiO2 − 0.1700Ti + 0.0574Y + 0.0756Zn − 0.1028Zr)3 Sand = 43.76–4.7476Al2O3 −14.5466Fe + 1.9516Mn −4.2626Nb + 1.4258Ni + 1.1388Pb −5.4467Rb + 3.6346SiO2 + 1.4546Sr + 2.0329Ti + 2.8646V −2.9192Y −1.5446Zn + 3.7858Zr

Clay = 42.85 + 3.1589Al2O3 −2.4244Cu + 11.6603Fe −5.8764K2O −1.2105Mn + 3.0023Nb −1.6430Ni −1.5679P2O5 −1.6525Pb + 4.3908Rb −5.4822SiO2 −3.2010Ti + 3.2833Y −2.2200Zr Silt = (3.7461 + 0.0604Al2O3 − 0.0892CaO + 0.0701Cl + 0.2511Cu + 0.3780Fe + 0.5150K2O + 0.2223Nb + 0.1192P2O5 − 0.2672Ti − 0.1469 V + 0.1054Y + 0.1639Zn − 0.2370Zr)2 Sand = 41.90–3.5323Al2O3 + 1.4633CaO −11.6061Fe + 2.5542K2O + 3.6601Mn −4.7536Nb + 0.9969Ni + 2.4142Pb −5.6478Rb + 5.6517SiO2 + 1.4889Sr + 2.6311Ti −2.6411Y −3.5125Zn + 2.8862Zr 0.72

Superficial and subsuperficial horizons Clay 0.77

Clay = 33.98 + 5.0580Al2O3 −1.3379CaO + 8.8752Fe − 4.2558K2O + 2.5423Nb −1.5002Pb + 6.6873Rb − 5.7796SiO2 − 1.7394V + 2.0546Y − 2.7022Zn − 1.5876Zr Silt = (3.8031 + 0.2597Al2O3 + 0.7388Fe + 0.6049K2O + 0.1783Nb + 0.2897P2O5 − 0.1869Pb − 0.3028Ti + 0.1103Y + 0.2795Zn − 0.2969Zr)2 Sand = 48.28–5.7919Al2O3 −14.3495Fe −5.6586Nb + 3.3524Pb −11.2971Rb + 4.0235SiO2 + 2.6339Sr + 3.5022Ti −2.1418Y + 4.2317Zr 0.81 0.61 0.78

Superficial horizon Clay Silt Sand Subsuperficial horizon Clay

GLM equations R2 Particle Size fraction

Table 3 Stepwise generalized linear model (GLM) equations and their coefficient of determination (R2) for prediction of soil texture in superficial and subsuperficial horizons, separately and combined, from portable X-ray fluorescence (pXRF) spectrometry data in Brazil.

S.H.G. Silva, et al.

3.3. Modeling and prediction of soil texture from pXRF data Table 3 shows the GLM models. For clay content, the models presented the greatest R2 values in comparison with the other particle size fractions within the same dataset. Models for sand also had high R2 values, although lower than those for clay. Models for silt had the worst performance, with the “best” results achieved in superficial horizons (R2 = 0.61). These results are related to the presence of strong correlations between elements obtained by pXRF and the contents of the particle size fractions. While clay and sand contents had high correlations with some elements (e.g., Fe, Al, and Si), the silt fraction was only moderately correlated with some elements (Fig. 4). Thus, elemental contents obtained by pXRF can be used to model clay and sand contents through GLM, but other predictor variables are needed for better prediction of silt contents using this same algorithm. To assess the quality not only of the GLM models, but also of those generated through SVM and RF, validation of the predictions was performed for superficial and subsuperficial horizons datasets separately and combined for developing the models. Figs. 5 and 6 show the validation results for the different datasets. The SVM was the most efficient model to predict clay and sand contents in the both superficial and subsuperficial horizons, presenting slightly greater R2 and smaller MAE and RMSE values than RF (Fig. 5). For silt content prediction, RF was the most efficient model. GLM was less efficient than SVM and RF for predicting soil texture in samples for superficial and subsuperficial horizons. When the datasets of both horizons were combined, SVM was the most efficient model for predicting clay and silt contents. Although RF delivered the greatest R2 for silt content prediction, SVM promoted the lowest RMSE and MAE values (Fig. 6). RF provided the best results for predictions of sand contents. Considering the predictions for superficial and subsuperficial horizons separately and combined, the best results were obtained by RF and SVM when combining the data. The GLM provided the best predictions for the horizons separately. 4. Discussion 4.1. Soil texture variability The high variability in soil textures evaluated in this study reflects the large area covered by the samples (Fig. 1). Such a large area encompasses variable soil forming factors and processes, resulting in distinct combinations that formed varied soil classes and properties (Resende et al., 2014). Most soils with greater contents of sand were developed from sandstone (Cunha et al., 2005) or formed from sandy sediments of the Quaternary; the latter are common in the Brazilian Coastal Plains region (Silva et al., 2012). The sand fraction in Brazilian soils is mostly dominated by quartz, followed by some micas (mainly muscovite) and other minerals in much smaller proportion such as magnetite, rutile, ilmenite, and zircon (Kämpf et al., 2012). The very high clay contents are mostly related to highly weathered soils (e.g., Oxisols) including those developed from mafic rocks such as gabbro and basalt, other fine textured rocks such as shale, or clayey sediments. Most minerals in the clay fraction of Brazilian soils are kaolinite or Fe- and Al-oxides such as hematite, goethite, and gibbsite. The latter is more commonly found in extremely weathered soils. In smaller proportions, maghemite, illite, hydroxy-Al interlayered vermiculite, and hydroxy-Al interlayered smectite are also found (Brinatti et al., 2010; Kämpf et al., 2012; Schaefer et al., 2008). Although the maximum silt content found was 65%, most Brazilian soils tend to present very low contents of this particle size fraction, 5

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Fig. 5. Performance of the models for clay, silt, and sand contents on the validation datasets for superficial and subsuperficial horizons of Brazilian soils. GLM = Generalized Linear Model; SVM = Support Vector Machine; RF = Random Forest; MAE = Mean Absolute Error; RMSE = Root Mean Square Error.

soil orders. This reflects their small occurrence in Brazil (e.g., Alfisols ~2.81%, Histosols ~0.03%, Mollisols ~0.46% of the Brazilian territory) (IBGE, Embrapa, 2001).

except for some C horizons or O horizons formed from organic material mixed with sediments. Silt represents the maximum instability fraction and most Brazilian soils have undergone long periods of weatheringleaching (Resende et al., 2014). This results in low silt contents even in theoretically less developed soils such as Inceptisols (Silva et al., 2018a). Regarding the distribution of samples per soil class in the textural triangle (Fig. 2), most Oxisols are clayey-textured. This is a consequence of the dominance of parent materials that enable the formation and enrichment of the soil with clay-sized particles after long term weathering such as gneiss, granite, basalt, gabbro, limestone, mica-schist, and sediments. The general rule is: the smaller the quartz content in the parent material, the greater the clay content in the Brazilian soil (Resende et al., 2014). Contrary to Oxisols, which present most samples in only one textural class, most Ultisols have distinct textures considering both the superficial and subsuperficial horizons. For instance, per US Soil Taxonomy (Soil Survey Staff, 2014), the subsuperficial (illuvial) horizon must have an increment in clay content from 3 to 20% depending on the clay content in the superficial (or eluvial) horizon. Such distinct textures between these horizons are intrinsic characteristics of this soil class. Inceptisols contained the greatest silt contents (for Brazilian conditions) among the studied soils, although these contents are not high enough to classify any sample in a silty textural class. Spodosols tend to present high sand contents, most of them being developed from sandy sediments presented in some parts of the Brazilian coast (Silva et al., 2012). Entisols have variable textural classes, ranging from sandy to clayey. Such variability is explained by the diversity of soils included within this soil order, such as Orthents, Psamments, and Aquents. Alfisols and Histosols are less represented in this study, as well as other

4.2. Correlations between soil texture and elemental contents Positive correlations between clay content and Fe and Al2O3 are due to the mineralogy of most Brazilian soils. The mineralogy consists mostly of kaolinite, gibbsite, hematite, and goethite in different proportions (Kämpf and Curi, 2000). The presence of such minerals depends on the parent material and the weathering-leaching degree to which these soils were submitted (Schaefer et al., 2008; Schwertmann and Taylor, 1989; dos Weber et al., 2005). Fe and Al tend to residually accumulate as oxides in the clay fraction of Brazilian soils as weathering advances. Conversely, Si tends to be removed from the soil after it is released from the structure of silicates with weathering (Kämpf et al., 2012). Zhu et al. (2011) correlated pXRF elemental data and texture in soils from Louisiana and New Mexico, USA, and found positive correlations between the clay contents and K, Cr, Fe, Co, Zn, Rb, and Ba. The soils from these places tend to be much less weathered than the studied Brazilian soils. Silva et al. (2016) found that Fe content obtained from pXRF was among the variables that most influenced models for predicting sand and clay contents in Oxisols developed from gabbro and gneiss, in Minas Gerais State, Brazil. Wang et al. (2013) found that Fe and Rb contents were of particular significance in predicting clay content. The positive correlation between sand and SiO2 contents is a consequence of the great dominance of quartz in this particle size fraction (Resende et al., 2014). However, the Zr positive correlation with sand content is probably due to zircon, which is another mineral very 6

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Fig. 6. Performance of the models for clay, silt, and sand contents on the validation datasets for combined superficial and subsuperficial horizons data of Brazilian soils. GLM = Generalized Linear Model; SVM = Support Vector Machine; RF = Random Forest; MAE = Mean Absolute Error; RMSE = Root Mean Square Error.

According to the results found herein, pXRF data along with robust algorithms can be applied to predict results of texture analysis in tropical regions, independently of soil class, parent material, and weathering degree of soils. This could bring enormous advantages, such as reduced time for obtaining textural results, minimal sample preparation, reduced production of chemical wastes, and financial savings. Finally, the data collected by pXRF is useful not only for soil textural prediction but modeling of other physicochemical parameters concomitantly. In general, the models had satisfactory predictive capacity for sand, silt, and clay contents, with slight superiority offered by SVM for predicting soil texture in diverse Brazilian soils.

resistant to weathering present in Brazilian soils (Marques et al., 2004; Melo et al., 1995). The negative correlations between the other elements and sand content contrast with the findings of other works. In soils from New Mexico (USA), Zhu et al. (2011) found positive correlations between sand contents and Ca, Ti, Sr, Co, Fe, and Mn, and negative with Zr. This indicates the contrasting mineralogy of such soils in comparison with Brazilian soils, which tend to be poor in nutrients and rich in SiO2 in the sand fraction (Blume et al., 2016; Gomes et al., 2004; Melo et al., 2009). Silt content was positively correlated mainly with K2O. Rb, and Zn. One possible explanation is related to the K fixed in interlayers and the occurrence of Rb and Zn as isomorphous substituents for Al in octahedral layers of silt-sized micas (Arfè et al., 2017; Groat et al., 2003). Again, silt content is low in Brazilian soils and there is a possibility of some samples having dispersion problems during soil texture analysis in the laboratory, constituting pseudosilt fractions (Vitorino et al., 2003).

5. Conclusions Correlations between elemental contents obtained by pXRF and soil texture reflected the dominant chemical composition of Brazilian soils as a function of the dominant soil mineralogy: Fe and Al were positively correlated with clay contents, while SiO2 was positively correlated with sand contents. These correlations reflect the dominance of Fe- and Aloxides in the clay fraction and quartz in the sand fraction. The pXRF data was capable of accurately predict clay, silt, and sand contents in datasets composed of very distinct Brazilian soils through GLM, SVM, and RF algorithms for both superficial and subsuperficial soil horizons. SVM and RF algorithm provided the most accurate predictions. These findings will greatly contribute to future soil texture characterization, management, and environmental applications in Brazil, speeding up and reducing the costs for acquiring this information.

4.3. Efficiency of the prediction models Despite the great R2 values of the GLM models (Table 3), their validation demonstrated its inferiority compared to RF and SVM models (Figs. 5 and 6). Regarding the GLM, RF, and SVM methods, the latter produced slightly better results than RF. Some studies have demonstrated the superiority of the SVM in comparison with other algorithms (Heung et al., 2016; Lorenzetti et al., 2015; Taghizadeh-mehrjardi et al., 2015). Wu et al. (2018) evaluated the capability of the SVM, artificial neural networks, and decision tree classifiers based on terrain indicators for identifying the soil texture class in Southwest China. They obtained superior results with the SVM relative to other models. 7

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Declaration of Competing Interest

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