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Determination of soil properties helps in the correct management of soil fertility. The portable X-ray fluorescence spectrometer (pXRF) has been recently adopted to determine total chemical element contents in soils, allowing soil property inferences. However, these studies are still scarce in Brazil and other countries. The objectives of this work were to predict soil properties using pXRF data, comparing stepwise multiple linear regression (SMLR) and random forest (RF) methods, as well as mapping and validating soil properties. 120 soil samples were collected at three depths and submitted to laboratory analyses. pXRF was used in the samples and total element contents were determined. From pXRF data, SMLR and RF were used to predict soil laboratory results, reflecting soil properties, and the models were validated. The best method was used to spatialize soil properties. Using SMLR, models had high values of R² (≥0.8), however the highest accuracy was obtained in RF modeling. Exchangeable Ca, Al, Mg, potential and effective cation exchange capacity, soil organic matter, pH, and base saturation had adequate adjustment and accurate predictions with RF. Eight out of the 10 soil properties predicted by RF using pXRF data had CaO as the most important variable helping predictions, followed by P 2 O 5 , Zn and Cr. Maps generated using RF from pXRF data had high accuracy for six soil properties, reaching R 2 up to 0.83. pXRF in association with RF can be used to predict soil properties with high accuracy at low cost and time, besides providing variables aiding digital soil mapping.Index terms: Soil analyses; spatial prediction; proximal sensor. RESUMOA determinação de atributos do solo auxilia no correto manejo da sua fertilidade. O equipamento portátil de fluorescência de raios-X (pXRF) foi recentemente adotado para determinar o teor total de elementos químicos em solos, permitindo inferências sobre atributos do solo. No entanto, esses estudos ainda são escassos no Brasil e em outros países. Os objetivos deste trabalho foram prever atributos do solo a partir de dados do pXRF, comparando-se os métodos de regressão linear múltipla stepwise (SMLR) e de random forest (RF), além de mapear e validar atributos do solo. 120 amostras de solo foram coletadas em três profundidades e submetidas a análises laboratoriais. Utilizou-se o pXRF para leitura das amostras e determinou-se o teor total de elementos. A partir dos dados do pXRF, foram utilizadas SMLR e RF para predizer resultados laboratoriais, que refletem atributos do solo, e os modelos foram validados. O melhor método foi utilizado para espacializar os atributos do solo. Utilizando SMLR, os modelos apresentaram valores elevados de R² (≥0,8), porém maior acurácia foi obtida na modelagem com RF. A capacidade de troca de cátions potencial e efetiva, matéria orgânica do solo, pH, saturação por bases e teores trocáveis de Ca, Al e Mg apresentaram ajustes adequados e predições acuradas com RF. Dos dez atributos do solo preditos por RF a partir de dados do pXRF, sete apr...

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Portable X-ray fluorescence (pXRF) spectrometry has been increasingly adopted for varying studies worldwide. This work aimed at characterizing effects of soil management on the content of chemical elements detected by pXRF in managed and unmanaged areas of Inceptisols, and evaluating the potential of using pXRF data to generate prediction models for soil fertility attributes, evaluating the effect of land uses on such models. Samples were collected in A, B, and C horizons of soils under native forest, native Cerrado, coffee crops with 1 and 5 years of implantation and eucalyptus. Soil fertility attributes were determined through laboratory analyses, whereas, elemental contents were obtained through pXRF analysis. PXRF data were used for modeling (regressions) and validation of soil fertility attributes and necessity of lime (NL) application, with or without distinction between managed and unmanaged areas. Management practices on coffee crops increased the levels of Sr, CaO, P2O5, Cu, and Zn. CaO content was efficient for prediction of exchangeable Ca2+ contents (R2 = 0.91), pH (R2 = 0.88), base saturation (R2 = 0.89) in managed areas. General models presented adequate results to predict exchangeable Ca2+ (R2 = 0.92), pH (R2 = 0.85), and base saturation (R2 = 0.90). Models for unmanaged areas were less effective. PXRF detected modifications in elemental contents caused by management practices and provided reliable predictions of soil fertility attributes.

The X-ray fluorescence (XRF) is an analytical technique for determination of elemental composition of different materials. In soils, the XRF has many pedological, environmental and agronomic applications, mainly after the emergence of portable equipments (pXRF). This technique has been recently adopted and successfully used for soil characterization worldwide, but very rare works have been carried out in soils of developing countries. The soil characterization includes the complete elemental composition determination (nutrients, trace and rare-earth elements) and allows estimating some soil physical and chemical properties. In Brazil, this technique is still incipient, mainly the use of pXRF, however, it can greatly contribute to soil characterization in-field or in-lab conditions and also replacing methods of soil analyses considered non-environmentally friendly. This review summarizes the XRF technique including principles and the main applications of pXRF in soils highlighting its potential for tropical Soil Science.Index terms: Soil analyses; soil morphometrics; soil characterization. RESUMOFluorescência de raios-X (FRX) é uma técnica analítica para determinação da composição elementar de diferentes materiais. Em solos, a FRX apresenta muitas aplicações pedológicas, ambientais e agronômicas, principalmente após a emergência de equipamentos portáteis (pXRF). Essa técnica tem sido utilizada com sucessso no mundo todo para caracterização do solo, entretanto, são raros os trabalhos em solos de países em desenvolvimento. A caracterização do solo inclui a determinação completa da composição elementar (nutrientes, elementos-traço e terras-raras) e permite a estimativa de atributos químicos e físicos do solo. No Brasil, a FRX é ainda incipiente, principalmente o uso do pXRF, entretanto, essa técnica pode contribuir grandemente para a caracterização do solo no campo, em condições laboratoriais e, também, substituindo alguns métodos de análise do solo considerados não prejudicial ao ambiente. Esta revisão sumariza a técnica de FRX incluindo princípios e as principais aplicações do pXRF, destacando seu potencial de uso na Ciência do Solo tropical. Termos para indexação:Análise do solo; morfometria do solo; caracterização do solo.

Solum depth and its spatial distribution play an important role in different types of environmental studies. Several approaches have been used for fitting quantitative relationships between soil properties and their environment in order to predict them spatially. This work aimed to present the steps required for solum depth spatial prediction from knowledge-based digital soil mapping, comparing the prediction to the conventional soil mapping approach through field validation, in a watershed located at Mantiqueira Range region, in the state of Minas Gerais, Brazil. Conventional soil mapping had aerial photo-interpretation as a basis. The knowledgebased digital soil mapping applied fuzzy logic and similarity vectors in an expert system. The knowledge-based digital soil mapping approach showed the advantages over the conventional soil mapping approach by applying the field expert-knowledge in order to enhance the quality of final results, predicting solum depth with suited accuracy in a continuous way, making the soillandscape relationship explicit.

Digital terrain models (DTM) have been used in soil mapping worldwide. When using such models, improved predictions are often attained with the input of extra variables provided by the use of proximal sensors, such as magnetometers and portable X-ray fluorescence scanners (pXRF). This work aimed to evaluate the efficiency of such tools for mapping soil classes and properties in tropical conditions. Soils were classified and sampled at 39 locations in a regular-grid design with a 200-m distance between samples. A pXRF and a magnetometer were used in all samples, and DTM values were obtained for every sampling site. Through visual analysis, boxplots were used to identify the best variables for distinguishing soil classes, which were further mapped using fuzzy logic. The map was then validated in the field. An ordinary least square regression model was used to predict sand and clay contents using DTM, pXRF and the magnetometer as predicting variables. Variables obtained with pXRF showed a greater ability for predicting soil classes (overall accuracy of 78% and 0.67 kappa index), as well as for estimating sand and clay contents than those acquired with DTM and the magnetometer. This study showed that pXRF offers additional variables that are key for mapping soils and predicting soil properties at a detailed scale. This would not be possible using only DTM or magnetic susceptibility.