Synthesis of proximal sensing, terrain analysis, and parent material information for available micronutrient prediction in tropical soils

Pelegrino, Marcelo Henrique Procópio; Weindorf, David C.; Silva, Sérgio Henrique Godinho; Menezes, Michele Duarte de; Pôggere, Giovana Clarice; Guilherme, Luiz Roberto Guimarães; Curi, Nilton

doi:10.1007/s11119-018-9608-z

Cited by 18 publications

(10 citation statements)

References 61 publications

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“…PXRF can be used directly in the field or in the laboratory to detect total elemental content of the Periodic Table from Mg to U in soil samples and other materials (McGladdery et al, 2018;Pearson et al, 2017;Ribeiro et al, 2017;Weindorf;Bakr;Zhu, 2014). Some studies have shown good results for predicting soil chemical properties, such as pH (Sharma et al, 2014) and cation exchange capacity (Sharma et al, 2015), but few have attempted to predict available nutrient contents from pXRF analyses (Silva et al, 2017;Pelegrino et al, 2018). However, several factors can affect soil elemental contents, such as soil parent material, the degree of weathering-leaching, and management systems (Araujo et al, 2014;Curi;Franzmeier, 1987;Lopes;Guilherme, 2016;Resende et al, 2014;Schaetzl;Anderson, 2005).…”

Section: Introductionmentioning

confidence: 99%

Portable X-ray fluorescence (pXRF) spectrometry applied to the prediction of chemical attributes in Inceptisols under different land uses

et al. 2018

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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.

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Section: Introductionmentioning

confidence: 99%

Portable X-ray fluorescence (pXRF) spectrometry applied to the prediction of chemical attributes in Inceptisols under different land uses

et al. 2018

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“…Some of those relationships were not found for soils derived from phyllite (Silva et al, 2018a). Finally, the strong correlations found herein indicate that the pXRF data can be used as basis for the development of prediction models for quantifying soil physicochemical properties, as already investigated in temperate regions (Sharma et al, 2014;Zhu et al, 2011;Rawal et al, 2019), and with initial efforts in tropical regions such as Brazil (Pelegrino et al, 2018;Teixeira et al, 2018).…”

Section: Correlations Between Elemental Contents By Pxrf and Propertimentioning

confidence: 65%

“…Singh and Gilkes (1992) suggested a strong retention of Cu in soil oxide minerals reducing its leaching. Cu is a micronutrient for plants and its availability was successfully predicted via pXRF and parent material data in Brazil (Pelegrino et al, 2018). Also, Cu has been reported as an isomorphic substitute of Fe in Fe oxides (Lu et al, 2009).…”

Section: Resultsmentioning

confidence: 99%

“…Thus, this equipment allows for fast, low cost, environmentally friendly, and in‐field identification and quantification of several elements from the periodic table (Weindorf et al, 2014; Stockmann et al, 2016; Ribeiro et al, 2017). It has proven useful for investigations related to soil genesis and horizon variability (Stockmann et al, 2016; Weindorf et al, 2012a, 2012b; Silva et al, 2018a, Sharma et al, 2014, 2015; Zhu et al, 2011), parent material spatial variability (Mancini et al, 2019), soil property prediction and mapping (Silva et al, 2017; Duda et al, 2017; Pelegrino et al, 2018), and for other pedological and environmental studies (Rouillon and Taylor, 2016; Silva et al, 2018b; Suh et al, 2016).…”

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confidence: 99%

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Tropical Soil Toposequence Characterization via pXRF Spectrometry

Silva¹,

Weindorf

Silva³

et al. 2019

Soil Science Soc of Amer J

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Variability of elemental composition in a toposequence of quartzite‐derived soils in a tropical environment was evaluated via portable x‐ray fluorescence (pXRF) spectrometry. The toposequence featured different degrees of weathering and variable soil formation processes. Specifically, this study aimed to: (i) use elemental composition obtained through pXRF as a tool to investigate the degrees of weathering–leaching and pedogenic processes; and (ii) correlate elemental data with physicochemical properties of soils. Four soil profiles (Lithic Ustorthent, Typic Ustorthent, Typic Dystrudept, and Xanthic Hapludox) were described and sampled. Laboratory analysis for soil physicochemical characterization and pXRF were performed. Soils featured high contents of SiO2 due to the predominance of quartz in the rock. However, pXRF analysis revealed that the soils showed a decrease in such content with depth. Potassium contents tended to be greater in the C horizons since they are less weathered and retained greater amounts of K‐bearing minerals, such as muscovite. Al, Fe, Ca, Ti, Cu, and Sr increased their contents with increasing clay content in soils, while Zr generally remained stable. Ba was leached out of soils during weathering. Both CaO and Sr were positively correlated with exchangeable contents of Ca2+ and Mg2+, base saturation, pH, and cation exchange capacity, while inversely correlated to Al3+ saturation. Fe, Al, Ti, Ca, K, Cu, Rb, and Zr accumulated in soil in comparison with the parent material. Variability of elemental contents in soils with differential degrees of weathering–leaching can be easily accessed with pXRF and such data correlate well with several soil physicochemical properties. Core Ideas Toposequence of quartzite‐derived soils variability assessed by pXRF. Soils presented varying weathering degrees, from Entisols to Oxisols. pXRF elemental data correlated well with soil physical and chemical properties. Soil mineralogy and pXRF data elucidated soils variability with weathering. Al, Fe, Ca, Ti, Cu, and Sr increased with increasing clay content.

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“…XRF spectroscopy has been successfully applied in various fields of science that require rapid analytical routines such as agriculture (Freitas et al 2019), soil science (Pelegrino et al 2019), mining (Penido et al 2019, environmental sciences (Muthukalum et al 2020) and chemical (Szczepanik et al, 2015) and archeological studies (Attaelmanan and Mouton 2014).…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Networks To Distinguish Charcoal from Eucalyptus and Native Forests Based on Their Mineral Components

et al. 2020

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Charcoal has been used as a renewable energy source in many countries. However, the indiscriminate use of wood from native forests is detrimental to sustainability. The development of rapid and efficient methodologies for distinguishing charcoal produced from native Forest or Eucalyptus plantations is essential to curb illegal coal transport and trade. The aim of this study was to distinguish charcoal from native and Eucalyptus woods by Artificial Neural Network (ANN) based on their mineral composition. Specimens from native woods (Apuleia sp., Cedrela sp., Aspidosperma sp., Jacaranda sp., Peltogyne sp., Dipteryx sp. and Gochnatia sp.) and from Eucalyptus sp. hybrid woods from commercial forest plantations were pyrolysed at temperatures from 300 to 700°C in order to simulate the actual pyrolysis conditions and species widely used illegally in southeastern Brazil. The composition and proportion of the mineral elements of charcoal were determined by X-ray fluorescence (XRF).ANNs were trained based on the elemental composition of the charcoal specimens to classify the species and origin of the charcoals (native forest and Eucalyptus). ANNs based on mineral element content yielded high percentage of correct classification for charcoal specimens by species (72% accuracy) or origin (97% accuracy) from an independent validation sample set.

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Synthesis of proximal sensing, terrain analysis, and parent material information for available micronutrient prediction in tropical soils

Cited by 18 publications

References 61 publications

Portable X-ray fluorescence (pXRF) spectrometry applied to the prediction of chemical attributes in Inceptisols under different land uses

Portable X-ray fluorescence (pXRF) spectrometry applied to the prediction of chemical attributes in Inceptisols under different land uses

Tropical Soil Toposequence Characterization via pXRF Spectrometry

Artificial Neural Networks To Distinguish Charcoal from Eucalyptus and Native Forests Based on Their Mineral Components

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