Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry

Teixeira, Anita Fernanda dos Santos; Pelegrino, Marcelo Henrique Procópio; Faria, Wilson Missina; Silva, Sérgio Henrique Godinho; Gonçalves, Mariana Gabriela Marcolino; Júnior, Fausto Weimar Acerbi; Gomide, Lucas Rezende; Júnior, Alceu Linares Pádua; Souza, Igor Alexandre de; Chakraborty, Somsubhra; Weindorf, David C.; Guilherme, Luiz Roberto Guimarães; Curi, Nilton

doi:10.1016/j.geoderma.2019.114132

Cited by 17 publications

(3 citation statements)

References 44 publications

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“…To sum up, the Cubist model employs the stratified linear regression strategy, and, thus, can explore the stratified heterogeneous relationship between the target variable and covariates. Moreover, its regression results are well interpretable [40,60]. Similar to the RF model, the Cubist model employs an ensemble learning strategy, and the final prediction result is equal to the average of the prediction results of all committees.…”

Section: Cubist Modelmentioning

confidence: 99%

“…The Cubist model is also notable because of its high accuracy. Several studies have found that the Cubist model outperforms the RF model in the spatial estimation of soil properties [26,[37][38][39][40]. However, scholars mainly focus on the high prediction accuracy of the Cubist model, ignoring its vital role in revealing stratified heterogeneous relationships, and failing to explore the determinants of SOC in different subregions.…”

Section: Introductionmentioning

confidence: 99%

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Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Chen

Yang

et al. 2022

Remote Sensing

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Accurate mapping of farmland soil organic carbon (SOC) provides valuable information for evaluating soil quality and guiding agricultural management. The integration of natural factors, agricultural activities, and landscape patterns may well fit the high spatial variation of SOC in low-relief farmlands. However, commonly used prediction methods are global models, ignoring the stratified heterogeneous relationship between SOC and environmental variables and failing to reveal the determinants of SOC in different subregions. Using 242 topsoil samples collected from Jianghan Plain, China, this study explored the stratified heterogeneous relationship between SOC and natural factors, agricultural activities, and landscape metrics, determined the dominant factors of SOC in each stratum, and predicted the spatial distribution of SOC using the Cubist model. Ordinary kriging, stepwise linear regression (SLR), and random forest (RF) were used as references. SLR and RF results showed that land use types, multiple cropping index, straw return, and percentage of water bodies are global dominant factors of SOC. Cubist results exhibited that the dominant factors of SOC vary in different cropping systems. Compared with the SOC of paddy fields, the SOC of irrigated land was more affected by irrigation-related factors. The effect of straw return on SOC was diverse under different cropping intensities. The Cubist model outperformed the other models in explaining SOC variation and SOC mapping (fitting R2 = 0.370 and predicted R2 = 0.474). These results highlight the importance of exploring the stratified heterogeneous relationship between SOC and covariates, and this knowledge provides a scientific basis for farmland zoning management. The Cubist model, integrating natural factors, agricultural activities, and landscape metrics, is effective in explaining SOC variation and mapping SOC in low-relief farmlands.

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Section: Cubist Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Chen

Yang

et al. 2022

Remote Sensing

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“…Based on pXRF spectra, prediction models of many soil properties (e.g., pH, texture, soil organic matter, macroand micronutrients) have been established [27][28][29][30][31]. Several pXRF soil studies have been successfully conducted in tropical environments [32,33].…”

Section: Introductionmentioning

confidence: 99%

Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry

Borges

Weindorf

Carvalho

et al. 2020

Sensors

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Foliar analysis is very important for the nutritional management of crops and as a supplemental parameter for soil fertilizer recommendation. The elemental composition of plants is traditionally obtained by laboratory-based methods after acid digestion of ground and sieved leaf samples. This analysis is time-consuming and generates toxic waste. By comparison, portable X-ray fluorescence (pXRF) spectrometry is a promising technology for rapid characterization of plants, eliminating such constraints. This worked aimed to assess the pXRF performance for elemental quantification of leaf samples from important Brazilian crops. For that, 614 samples from 28 plant species were collected across different regions of Brazil. Ground and sieved samples were analyzed after acid digestion (AD), followed by quantification via inductively coupled plasma optical emission spectroscopy (ICP-OES) to determine the concentration of macronutrients (P, K, Ca, Mg, and S) and micronutrients (Fe, Zn, Mn, and Cu). The same plant nutrients were directly analyzed on ground leaf samples via pXRF. Four certified reference materials (CRMs) for plants were used for quality assurance control. Except for Mg, a very strong correlation was observed between pXRF and AD for all plant-nutrients and crops. The relationship between methods was nutrient- and crop-dependent. In particular, eucalyptus displayed optimal correlations for all elements, except for Mg. Opposite to eucalyptus, sugarcane showed the worst correlations for all the evaluated elements, except for S, which had a very strong correlation coefficient. Results demonstrate that for many crops, pXRF can reasonably quantify the concentration of macro- and micronutrients on ground and sieved leaf samples. Undoubtedly, this will contribute to enhance crop management strategies concomitant with increasing food quality and food security.

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Pattern Recognition Methods

Bian

2022

Chemometric Methods in Analytical Spectroscopy Technology

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Tropical soil pH and sorption complex prediction via portable X-ray fluorescence spectrometry

Cited by 17 publications

References 44 publications

Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Mapping Soil Organic Carbon in Low-Relief Farmlands Based on Stratified Heterogeneous Relationship

Foliar Elemental Analysis of Brazilian Crops via Portable X-ray Fluorescence Spectrometry

Pattern Recognition Methods

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