Soil Cleanup by In-Situ Aeration. XXII. Impact of Natural Soil Organic Matter on Cleanup Rates

1995 ◽  
Vol 30 (5) ◽  
pp. 659-682 ◽  
Author(s):  
César Gómez-Lahoz ◽  
José M. Rodriguez-Maroto ◽  
David J. Wilson
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Natural Soil ◽  
Soil Cleanup

scholarly journals The Soil Organic Matter in Connection with Soil Properties and Soil Inputs

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 779
Author(s):  
Václav Voltr ◽  
Ladislav Menšík ◽  
Lukáš Hlisnikovský ◽  
Martin Hruška ◽  
Eduard Pokorný ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Soil Texture ◽  
Management Practices ◽  
Soil Depth ◽  
Operating Conditions ◽  
Hot Water ◽  
Natural Soil ◽  
Soil Productivity ◽  
Linear Regression Models

The content of organic matter in the soil, its labile (hot water extractable carbon–HWEC) and stable (soil organic carbon–SOC) form is a fundamental factor affecting soil productivity and health. The current research in soil organic matter (SOM) is focused on individual fragmented approaches and comprehensive evaluation of HWEC and SOC changes. The present state of the soil together with soil’s management practices are usually monitoring today but there has not been any common model for both that has been published. Our approach should help to assess the changes in HWEC and SOC content depending on the physico-chemical properties and soil´s management practices (e.g., digestate application, livestock and mineral fertilisers, post-harvest residues, etc.). The one- and multidimensional linear regressions were used. Data were obtained from the various soil´s climatic conditions (68 localities) of the Czech Republic. The Czech farms in operating conditions were observed during the period 2008–2018. The obtained results of ll monitored experimental sites showed increasing in the SOC content, while the HWEC content has decreased. Furthermore, a decline in pH and soil´s saturation was documented by regression modelling. Mainly digestate application was responsible for this negative consequence across all soils in studied climatic regions. The multivariate linear regression models (MLR) also showed that HWEC content is significantly affected by natural soil fertility (soil type), phosphorus content (−30%), digestate application (+29%), saturation of the soil sorption complex (SEBCT, 21%) and the dose of total nitrogen (N) applied into the soil (−20%). Here we report that the labile forms (HWEC) are affected by the application of digestate (15%), the soil saturation (37%), the application of mineral potassium (−7%), soil pH (−14%) and the overall condition of the soil (−27%). The stable components (SOM) are affected by the content of HWEC (17%), soil texture 0.01–0.001mm (10%), and input of organic matter and nutrients from animal production (10%). Results also showed that the mineral fertilization has a negative effect (−14%), together with the soil depth (−11%), and the soil texture 0.25–2 mm (−21%) on SOM. Using modern statistical procedures (MRLs) it was confirmed that SOM plays an important role in maintaining resp. improving soil physical, biochemical and biological properties, which is particularly important to ensure the productivity of agroecosystems (soil quality and health) and to future food security.

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

Soil Organic Matter and Phosphate Sorption on Natural and Synthetic Fe Oxides under in Situ Conditions

2019 ◽  
Vol 53 (22) ◽  
pp. 13081-13087 ◽  
Author(s):  
Kristof Dorau ◽  
Lydia Pohl ◽  
Christopher Just ◽  
Carmen Höschen ◽  
Kristian Ufer ◽  
...  
Keyword(s):  
Organic Matter ◽  
Soil Organic Matter ◽  
Phosphate Sorption ◽  
Fe Oxides ◽  
In Situ Conditions ◽  
Natural And Synthetic

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.

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