scholarly journals Chemical characterization and infrared spectroscopy of soil organic matter from two southern brazilian soils

2003 ◽  
Vol 27 (1) ◽  
pp. 29-39 ◽  
Author(s):  
D. P. Dick ◽  
J. H. Z. Santos ◽  
E. M. Ferranti
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Chemical Characterization ◽  
Fulvic Acids ◽  
Alkaline Treatment ◽  
Hydrophobicity Index ◽  
Elemental Analyses ◽  
Brazilian Soils

Soil organic matter from the surface horizon of two Brazilian soils (a Latosol and a Chernosol), in bulk samples (in situ SOM) and in HF-treated samples (SOM), was characterized by elemental analyses, diffuse reflectance (DRIFT) and transmission Fourier transform infrared spectroscopy (T-FTIR). Humic acids (HA), fulvic acids (FA) and humin (HU) isolated from the SOM were characterized additionally by ultraviolet-visible spectroscopy (UV-VIS). After sample oxidation and alkaline treatment, the DRIFT technique proved to be more informative for the detection of "in situ SOM" and of residual organic matter than T-FTIR. The higher hydrophobicity index (HI) and H/C ratio obtained in the Chernosol samples indicate a stronger aliphatic character of the organic matter in this soil than the Latosol. In the latter, a pronounced HI decrease was observed after the removal of humic substances (HS). The weaker aliphatic character, the higher O/C ratio, and the T-FTIR spectrum obtained for the HU fraction in the Latosol suggest the occurrence of surface coordination of carboxylate ions. The Chernosol HU fraction was also oxygenated to a relatively high extent, but presented a stronger hydrophobic character in comparison with the Latosol HU. These differences in the chemical and functional group composition suggest a higher organic matter protection in the Latosol. After the HF treatment, decreases in the FA proportion and the A350/A550 ratio were observed. A possible loss of FA and condensation of organic molecules due to the highly acid medium should not be neglected.

scholarly journals In situ near-infrared spectroscopy for soil organic matter prediction in paddy soil, Pasak watershed, Thailand

2018 ◽  
Vol 64 (No. 2) ◽  
pp. 70-75 ◽  
Author(s):  
Romsonthi Chutipong ◽  
Tawornpruek Saowanuch ◽  
Watana Sumitra
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Soil Properties ◽  
Soil Quality ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Partial Least Square

Soil organic matter (SOM) is a major index of soil quality assessment because it is one of the key soil properties controlling nutrient budgets in agricultural production systems. The aim of the in situ near-infrared spectroscopy (NIRS) for SOM prediction in paddy area is evaluation of the potential of SOM and prediction of other soil properties. There are keys for soil fertility and soil quality assessments. A spectral reflectance of 130 soil samples was collected by field spectroradiometer in a region of near-infrared. Spectral reflectance collections were processed by the first derivative transformation with the Savitsky-Golay algorithms. Partial least square regression method was used to develop a calibration model between soil properties and spectral reflectance, which was used for prediction and validation processes. Finally, the results of this study demonstrate that NIRS is an effective method that can be used to predict SOM (R<sup>2</sup> = 0.73, RPD (ratio of performance to deviation) = 1.82) and total nitrogen (R<sup>2</sup> = 0.72, RPD = 1.78). Therefore, NIRS is a potential tool for soil properties predictions. The use of these techniques will facilitate the implementation of soil management with a decreasing cost and time of soil study in a large scale. However, further works are necessary to develop more accurate soil properties prediction and to apply this method to other areas.

scholarly journals Characterization of Soil Organic Matter Individual Fractions (Fulvic Acids, Humic Acids, and Humins) by Spectroscopic and Electrochemical Techniques in Agricultural Soils

Agronomy ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 1067
Author(s):  
Aleksandra Ukalska-Jaruga ◽  
Romualda Bejger ◽  
Guillaume Debaene ◽  
Bożena Smreczak
Keyword(s):  
Molecular Weight ◽  
Organic Matter ◽  
Soil Organic Matter ◽  
Humic Substances ◽  
Humic Acids ◽  
Agricultural Soils ◽  
Fulvic Acids ◽  
Sorption Properties ◽  
Aliphatic Chains

The objective of this paper was to investigate the molecular characterization of soil organic matter fractions (humic substances (HS): fulvic acids-FAs, humic acids-HAs, and humins-HNs), which are the most reactive soil components. A wide spectrum of spectroscopic (UV–VIS and VIS–nearIR), as well as electrochemical (zeta potential, particle size diameter, and polydispersity index), methods were applied to find the relevant differences in the behavior, formation, composition, and sorption properties of HS fractions derived from various soils. Soil material (n = 30) used for the study were sampled from the surface layer (0–30 cm) of agricultural soils. FAs and HAs were isolated by sequential extraction in alkaline and acidic solutions, according to the International Humic Substances Society method, while HNs was determined in the soil residue (after FAs and HAs extraction) by mineral fraction digestion using a 0.1M HCL/0.3M HF mixture and DMSO. Our study showed that significant differences in the molecular structures of FAs, Has, and HNs occurred. Optical analysis confirmed the lower molecular weight of FAs with high amount of lignin-like compounds and the higher weighted aliphatic–aromatic structure of HAs. The HNs were characterized by a very pronounced and strong condensed structure associated with the highest molecular weight. HAs and HNs molecules exhibited an abundance of acidic, phenolic, and amine functional groups at the aromatic ring and aliphatic chains, while FAs mainly showed the presence of methyl, methylene, ethenyl, and carboxyl reactive groups. HS was characterized by high polydispersity related with their structure. FAs were characterized by ellipsoidal shape as being associated to the long aliphatic chains, while HAs and HNs revealed a smaller particle diameter and a more spherical shape caused by the higher intermolecular forcing between the particles. The observed trends directly indicate that individual HS fractions differ in behavior, formation, composition, and sorption properties, which reflects their binding potential to other molecules depending on soil properties resulting from their type. The determined properties of individual HS fractions are presented as averaged characteristics over the examined soils with different physico-chemical properties.

scholarly journals Predicting the decomposability of arctic tundra soil organic matter with mid infrared spectroscopy

2019 ◽  
Vol 129 ◽  
pp. 1-12 ◽  
Author(s):  
Roser Matamala ◽  
Julie D. Jastrow ◽  
Francisco J. Calderón ◽  
Chao Liang ◽  
Zhaosheng Fan ◽  
...  
Keyword(s):  
Organic Matter ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Arctic Tundra ◽  
Tundra Soil ◽  
Mid Infrared ◽  
Mid Infrared Spectroscopy ◽  
Arctic Tundra Soil

In situ photo-polymerization of soil organic matter by heterogeneous nano-TiO2 and biomimetic metal-porphyrin catalysts

2016 ◽  
Vol 52 (4) ◽  
pp. 585-593 ◽  
Author(s):  
Assunta Nuzzo ◽  
Elisa Madonna ◽  
Pierluigi Mazzei ◽  
Riccardo Spaccini ◽  
Alessandro Piccolo
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Metal Porphyrin ◽  
Nano Tio2 ◽  
Photo Polymerization

Effective carbon sequestration in Italian agricultural soils by in situ polymerization of soil organic matter under biomimetic photocatalysis

2018 ◽  
Vol 29 (3) ◽  
pp. 485-494 ◽  
Author(s):  
Alessandro Piccolo ◽  
Riccardo Spaccini ◽  
Vincenza Cozzolino ◽  
Assunta Nuzzo ◽  
Marios Drosos ◽  
...  
Keyword(s):  
Organic Matter ◽  
Carbon Sequestration ◽  
Soil Organic Matter ◽  
In Situ Polymerization ◽  
Agricultural Soils

Quantifying in situ and modeling net nitrogen mineralization from soil organic matter in arable cropping systems

2017 ◽  
Vol 111 ◽  
pp. 44-59 ◽  
Author(s):  
Hugues Clivot ◽  
Bruno Mary ◽  
Matthieu Valé ◽  
Jean-Pierre Cohan ◽  
Luc Champolivier ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Nitrogen Mineralization ◽  
Cropping Systems ◽  
Net Nitrogen Mineralization ◽  
Quantifying In

scholarly journals Glomalin – an interesting protein part of the soil organic matter

2020 ◽  
Vol 15 (No. 2) ◽  
pp. 67-74 ◽  
Author(s):  
Vítězslav Vlček ◽  
Miroslav Pohanka
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Arbuscular Mycorrhiza ◽  
Mycorrhizal Fungi ◽  
Biological Diversity ◽  
Agricultural Practices ◽  
Arbuscular Mycorrhizal ◽  
Fulvic Acids ◽  
Soil Parameters ◽  
Citrate Buffer

The negative effects of the current agricultural practices include erosion, acidification, loss of soil organic matter (dehumification), loss of soil structure, soil contamination by risky elements, reduction of biological diversity and land use for non-agricultural purposes. All these effects are a huge risk to the further development of soil quality from an agronomic point of view and its resilience to projected climate change. Organic matter has a crucial role in it. Relatively significant correlations with the quality or the health of soil parameters and the soil organic matter or some fraction of the soil organic matter have been found. In particular, Ctot, Cox, humic and fulvic acids, the C/N ratio, and glomalin. Our work was focused on glomalin, a glycoprotein produced by the hyphae and spores of arbuscular mycorrhizal fungi (AMF), which we classify as Glomeromycota. Arbuscular mycorrhiza, and its molecular pathways, is not a well understood phenomenon. It appears that many proteins are involved in the arbuscular mycorrhiza from which glomalin is probably one of the most significant. This protein is also responsible for the unique chemical and physical properties of soils and has an ecological and economical relevance in this sense and it is a real product of the mycorrhiza. Glomalin is very resistant to destruction (recalcitrant) and difficult to dissolve in water. Its extraction requires specific conditions: high temperature (121°C) and a citrate buffer with a neutral or alkaline pH. Due to these properties, glomalin (or its fractions) are very stable compounds that protect the soil aggregate surface. In this review, the actual literature has been researched and the importance of glomalin is discussed.  

scholarly journals Deep Learning Application for Predicting Soil Organic Matter Content by VIS-NIR Spectroscopy

2019 ◽  
Vol 2019 ◽  
pp. 1-11 ◽  
Author(s):  
Zhe Xu ◽  
Xiaomin Zhao ◽  
Xi Guo ◽  
Jiaxin Guo
Keyword(s):  
Organic Matter ◽  
Deep Learning ◽  
Infrared Spectroscopy ◽  
Soil Organic Matter ◽  
Near Infrared Spectroscopy ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Spectral Variation ◽  
Full Spectrum ◽  
Reflectance Data

Deep learning is characterized by its strong ability of data feature extraction. This method can provide unique advantages when applying it to visible and near-infrared spectroscopy for predicting soil organic matter (SOM) content in those cases where the SOM content is negatively correlated with the spectral reflectance of soil. This study relied on the SOM content data of 248 red soil samples and their spectral reflectance data of 400–2450 nm in Fengxin County, Jiangxi Province (China) to meet three objectives. First, a multilayer perceptron and two convolutional neural networks (LeNet5 and DenseNet10) were used to predict the SOM content based on spectral variation and variable selection, and the outcomes were compared with that from the traditional back-propagation neural network (BPN). Second, the four methods were applied to full-spectrum modeling to test the difference to selected feature variables. Finally, the potential of direct modeling was evaluated using spectral reflectance data without any spectral variation. The results of prediction accuracy showed that deep learning performed better at predicting the SOM content than did the traditional BPN. Based on full-spectrum data, deep learning was able to obtain more feature information, thus achieving better and more stable results (i.e., similar average accuracy and far lower standard deviation) than those obtained through variable selection. DenseNet achieved the best prediction result, with a coefficient of determination (R2) = 0.892 ± 0.004 and a ratio of performance to deviation (RPD) = 3.053 ± 0.056 in validation. Based on DenseNet, the application of spectral reflectance data (without spectral variation) produced robust results for application-level purposes (validation R2 = 0.853 ± 0.007 and validation RPD = 2.639 ± 0.056). In conclusion, deep learning provides an effective approach to predict the SOM content by visible and near-infrared spectroscopy and DenseNet is a promising method for reducing the amount of data preprocessing.

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