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Journal articles on the topic 'Soil organic matter modeling'

Author: Grafiati

Published: 4 June 2021

Last updated: 13 February 2022

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1

Wang, Yang, Ronald Amundson, and Susan Trumbore. "Radiocarbon Dating of Soil Organic Matter." Quaternary Research 45, no. 3 (1996): 282–88. http://dx.doi.org/10.1006/qres.1996.0029.

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AbstractRadiocarbon ages of soil organic matter are evaluated with a model which incorporates the dynamics of the 14C content of soil organic matter. Measured 14C ages of soil organic matter or its fractions are always younger than the true ages of soils due to continuous input of organic matter into soils. Differences in soil C dynamics due to climate or soil depth will result in significantly different 14C signatures of soil organic matter for soils of the same age. As a result, the deviation of the measured 14C age from the true age of soil formation could differ significantly among differe

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2

Sierra, C. A., M. Müller, and S. E. Trumbore. "Modeling radiocarbon dynamics in soils: SoilR version 1.1." Geoscientific Model Development 7, no. 5 (2014): 1919–31. http://dx.doi.org/10.5194/gmd-7-1919-2014.

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Abstract. Radiocarbon is an important tracer of the global carbon cycle that helps to understand carbon dynamics in soils. It is useful to estimate rates of organic matter cycling as well as the mean residence or transit time of carbon in soils. We included a set of functions to model the fate of radiocarbon in soil organic matter within the SoilR package for the R environment for computing. Here we present the main system equations and functions to calculate the transfer and release of radiocarbon from different soil organic matter pools. Similarly, we present functions to calculate the mean

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Sierra, C. A., M. Müller, and S. E. Trumbore. "Modeling radiocarbon dynamics in soils: SoilR version 1.1." Geoscientific Model Development Discussions 7, no. 3 (2014): 3161–92. http://dx.doi.org/10.5194/gmdd-7-3161-2014.

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Abstract. Radiocarbon is an important tracer of the global carbon cycle that helps to understand carbon dynamics in soils. It is useful to estimate rates of organic matter cycling as well as the mean residence or transit time of carbon in soils. We included a set of functions to model the fate of radiocarbon in soil organic matter within the SoilR package for the R environment for computing. Here we present the main system equations and functions to calculate the transfer and release of radiocarbon from different soil organic matter pools. Similarly, we present functions to calculate the mean

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4

Sierra, C. A., M. Müller, and S. E. Trumbore. "Models of soil organic matter decomposition: the SoilR package, version 1.0." Geoscientific Model Development 5, no. 4 (2012): 1045–60. http://dx.doi.org/10.5194/gmd-5-1045-2012.

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Abstract. Soil organic matter decomposition is a very important process within the Earth system because it controls the rates of mineralization of carbon and other biogeochemical elements, determining their flux to the atmosphere and the hydrosphere. SoilR is a modeling framework that contains a library of functions and tools for modeling soil organic matter decomposition under the R environment for computing. It implements a variety of model structures and tools to represent carbon storage and release from soil organic matter. In SoilR, organic matter decomposition is represented as a linear

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Sierra, C. A., M. Müller, and S. E. Trumbore. "Models of soil organic matter decomposition: the SOILR package, version 1.0." Geoscientific Model Development Discussions 5, no. 2 (2012): 993–1039. http://dx.doi.org/10.5194/gmdd-5-993-2012.

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Abstract. Organic matter decomposition is a very important process within the Earth System because it controls the rates of mineralization of carbon and other biogeochemical elements, determining their flux to the atmosphere and the hydrosphere. SOILR is a modeling framework that contains a library of functions and tools for modeling soil organic matter decomposition under the R environment for computing. It implements a variety of model structures and tools to represent carbon storage and release from soil organic matter. In SOILR organic matter decomposition is represented as a linear system

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Prays, Nadia, Peter Dominik, Anja Sänger, and Uwe Franko. "Biogas residue parameterization for soil organic matter modeling." PLOS ONE 13, no. 10 (2018): e0204121. http://dx.doi.org/10.1371/journal.pone.0204121.

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7

Verberne, E. L. J., J. Hassink, P. de Willigen, J. J. R. Groot, and J. A. van Veen. "Modelling organic matter dynamics in different soils." Netherlands Journal of Agricultural Science 38, no. 3A (1990): 221–38. http://dx.doi.org/10.18174/njas.v38i3a.16585.

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A mathematical model was developed to describe carbon (C) and nitrogen (N) cycling in different soil types, e.g. clay and sandy soils. Transformation rates were described by first-order kinetics. Soil organic matter is divided into four fractions (including microbial biomass pool) and three fractions of residues. The fraction of active soil organic matter was assumed to be affected by the extent of physical protection within the soil, as was the soil microbial biomass. The extent of protection influenced the steady state level of the model, and, hence, the mineralization rates. The mineralizat

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8

Braakhekke, M. C., T. Wutzler, C. Beer, et al. "Modeling the vertical soil organic matter profile using Bayesian parameter estimation." Biogeosciences 10, no. 1 (2013): 399–420. http://dx.doi.org/10.5194/bg-10-399-2013.

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Abstract. The vertical distribution of soil organic matter (SOM) in the profile may constitute an important factor for soil carbon cycling. However, the formation of the SOM profile is currently poorly understood due to equifinality, caused by the entanglement of several processes: input from roots, mixing due to bioturbation, and organic matter leaching. In this study we quantified the contribution of these three processes using Bayesian parameter estimation for the mechanistic SOM profile model SOMPROF. Based on organic carbon measurements, 13 parameters related to decomposition and transpor

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9

Polyakov, V., and R. Lal. "Modeling soil organic matter dynamics as affected by soil water erosion." Environment International 30, no. 4 (2004): 547–56. http://dx.doi.org/10.1016/j.envint.2003.10.011.

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10

Braakhekke, M. C., T. Wutzler, C. Beer, et al. "Modeling the vertical soil organic matter profile using Bayesian parameter estimation." Biogeosciences Discussions 9, no. 8 (2012): 11239–92. http://dx.doi.org/10.5194/bgd-9-11239-2012.

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Abstract. The vertical distribution of soil organic matter (SOM) in the profile may constitute a significant factor for soil carbon cycling. However, the formation of the SOM profile is currently poorly understood due to equifinality, caused by the entanglement of several processes: input from roots, mixing due to bioturbation, and organic matter leaching. In this study we quantified the contribution of these three processes using Bayesian parameter estimation for the mechanistic SOM profile model SOMPROF. Based on organic carbon measurements, 13 parameters related to decomposition and transpo

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11

Smith, P., and P. D. Falloon. "Modelling refractory soil organic matter." Biology and Fertility of Soils 30, no. 5-6 (2000): 388–98. http://dx.doi.org/10.1007/s003740050019.

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12

Plante, A. F., Y. Feng, and W. B. McGill. "A modeling approach to quantifying soil macroaggregate dynamics." Canadian Journal of Soil Science 82, no. 2 (2002): 181–90. http://dx.doi.org/10.4141/s01-024.

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While several researchers have suggested that soil aggregate turnover is a significant control on organic matter dynamics, the quantification of soil aggregate dynamics has yet to be achieved. Quantification of soil aggregate turnover is essential to testing any hypothesis concerning the relationship between aggregate turnover and organic matter dynamics. The goal of the current work was to propose a modeling approach to the quantification of soil macroaggregate dynamics. The approach taken was to define model compartments representing water-stable soil aggregate size fractions and describing

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13

Elhaes, Hanan, Abdullah Al-Hossain, Osama Sayed, and Medhat Ibrahim. "Modeling the Effect of Salinity on Soil Organic Matter." Quantum Matter 4, no. 2 (2015): 94–97. http://dx.doi.org/10.1166/qm.2015.1175.

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14

Schaumann, G. E., and S. Thiele-Bruhn. "Molecular modeling of soil organic matter: Squaring the circle?" Geoderma 166, no. 1 (2011): 1–14. http://dx.doi.org/10.1016/j.geoderma.2011.04.024.

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15

Zhang, Hui Juan, Shou Chen Ma, Wen Kai Liu, He Bing Zhang, and Song He Yuan. "Three-Dimensional Spatial Simulation and Distribution Characteristics of Soil Organic Matter in Coal Mining Subsidence Area." Materials Science Forum 980 (March 2020): 437–48. http://dx.doi.org/10.4028/www.scientific.net/msf.980.437.

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Underground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the 3D spatial distribution of soil organic matter in coal arable land is crucial for agricultural production and environmental management. However, little research has been done on the three-dimensional modeling of soil organic matter. In this study, 3D kriging interpolation method and 3D stochastic simulation method were used to develop the 3D model of soil organic matter , and the root-mean-square error (RMSE) and mean error (ME) were used as evaluation indexes to compare the simulati

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16

Pan, Tao, Zhen Tao Wu, and Jie Mei Chen. "Near-Infrared Spectroscopic Measurement of Organic Matter in Soil." Advanced Materials Research 499 (April 2012): 414–18. http://dx.doi.org/10.4028/www.scientific.net/amr.499.414.

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Near-infrared (NIR) spectroscopy was successfully applied to chemical free and rapid determination of the organic matter in soil, and moving window partial least square (MWPLS) combining with Savitzky-Golay (SG) smoothing was used to the selection of NIR waveband. Thirty-five samples were randomly selected from all 97 collected soil samples as the validation set. The remaining 62 samples were divided into similar modeling calibration set (37 samples) and modeling prediction set (25 samples) based on partial least square cross-validation predictive bias (PLSPB). The selected waveband was 1896 n

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17

Calvet, Jean-Christophe, Noureddine Fritz, Christine Berne, Bruno Piguet, William Maurel, and Catherine Meurey. "Deriving pedotransfer functions for soil quartz fraction in southern France from reverse modeling." SOIL 2, no. 4 (2016): 615–29. http://dx.doi.org/10.5194/soil-2-615-2016.

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Abstract. The quartz fraction in soils is a key parameter of soil thermal conductivity models. Because it is difficult to measure the quartz fraction in soils, this information is usually unavailable. This source of uncertainty impacts the simulation of sensible heat flux, evapotranspiration and land surface temperature in numerical simulations of the Earth system. Improving the estimation of soil quartz fraction is needed for practical applications in meteorology, hydrology and climate modeling. This paper investigates the use of long time series of routine ground observations made in weather

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18

Bruun, S., G. I. Ågren, B. T. Christensen, and L. S. Jensen. "Measuring and modeling continuous quality distributions of soil organic matter." Biogeosciences 7, no. 1 (2010): 27–41. http://dx.doi.org/10.5194/bg-7-27-2010.

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Abstract. An understanding of the dynamics of soil organic matter (SOM) is important for our ability to develop management practices that preserve soil quality and sequester carbon. Most SOM decomposition models represent the heterogeneity of organic matter by a few discrete compartments with different turnover rates, while other models employ a continuous quality distribution. To make the multi-compartment models more mechanistic in nature, it has been argued that the compartments should be related to soil fractions actually occurring and having a functional role in the soil. In this paper, w

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19

Paustian, Keith, William J. Parton, and Jan Persson. "Modeling Soil Organic Matter in Organic-Amended and Nitrogen-Fertilized Long-Term Plots." Soil Science Society of America Journal 56, no. 2 (1992): 476–88. http://dx.doi.org/10.2136/sssaj1992.03615995005600020023x.

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20

Sohi, Saran P., Nathalie Mahieu, Jonathan R. M. Arah, David S. Powlson, Beáta Madari, and John L. Gaunt. "A Procedure for Isolating Soil Organic Matter Fractions Suitable for Modeling." Soil Science Society of America Journal 65, no. 4 (2001): 1121–28. http://dx.doi.org/10.2136/sssaj2001.6541121x.

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21

Viaud, Valérie, Denis A. Angers, and Christian Walter. "Toward Landscape-Scale Modeling of Soil Organic Matter Dynamics in Agroecosystems." Soil Science Society of America Journal 74, no. 6 (2010): 1847–60. http://dx.doi.org/10.2136/sssaj2009.0412.

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22

Laub, Moritz, Rana Shahbaz Ali, Michael Scott Demyan, et al. "Modeling temperature sensitivity of soil organic matter decomposition: Splitting the pools." Soil Biology and Biochemistry 153 (February 2021): 108108. http://dx.doi.org/10.1016/j.soilbio.2020.108108.

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23

Schaumann, G. E., and S. Thiele-Bruhn. "Reprint of: Molecular modeling of soil organic matter: Squaring the circle?" Geoderma 169 (December 2011): 55–68. http://dx.doi.org/10.1016/j.geoderma.2011.11.001.

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24

Andjelkovic, Darko, Tatjana Andjelkovic, Ruzica Nikolic, et al. "Leaching of chromium from chromium contaminated soil: Speciation study and geochemical modeling." Journal of the Serbian Chemical Society 77, no. 1 (2012): 119–29. http://dx.doi.org/10.2298/jsc101216154a.

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Distribution of chromium between soil and leachate was monitored. A natural process of percolating rainwater through the soil was simulated in the laboratory conditions and studied with column leaching extraction. Migration of chromium in the soil is conditioned by the level of chromium soil contamination, the soil organic matter content, and rainwater acidity. Chromium (III) and chromium(VI) were determined by spectrophotometric method with diphenilcarbazide in acidic media. Comparing the results of chromium speciation in leachate obtained by experimental model systems and geochemical modelli

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25

Liang, Yuzhen, Yang Ding, Pei Wang, Guining Lu, Zhi Dang, and Zhenqing Shi. "Modeling Sorptive Fractionation of Organic Matter at the Mineral-Water Interface." Soil Science Society of America Journal 83, no. 1 (2019): 107–17. http://dx.doi.org/10.2136/sssaj2018.07.0275.

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26

Vaezi, A. R., H. A. Bahrami, S. H. R. Sadeghi, and M. H. Mahdian. "Modeling relationship between runoff and soil properties in dry-farming lands, NW Iran." Hydrology and Earth System Sciences Discussions 7, no. 2 (2010): 2577–607. http://dx.doi.org/10.5194/hessd-7-2577-2010.

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Abstract. The process of transformation of rainfall into runoff over a catchment is very complex and exhibits both temporal and spatial variability. However, in a semi-arid area this variability is mainly controlled by the physical and chemical properties of the soil surface. Developing an accurate and easily-used model that can appropriately determine the runoff generation value is of strong demand. In this study a simple, an empirically based model developed to explore effect of soil properties on runoff generation. Thirty six dry-farming lands under follow conditions in a semi-arid agricult

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27

Mandal, Umesh K. "SPECTRAL COLOR INDICES BASED GEOSPATIAL MODELING OF SOIL ORGANIC MATTER IN CHITWAN DISTRICT, NEPAL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 43–48. http://dx.doi.org/10.5194/isprsarchives-xli-b2-43-2016.

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Space Technology provides a resourceful-cost effective means to assess soil nutrients essential for soil management plan. Soil organic matter (SOM) is one of valuable controlling productivity of crops by providing nutrient in farming systems. Geospatial modeling of soil organic matter is essential if there is unavailability of soil test laboratories and its strong spatial correlation. In the present analysis, soil organic matter is modeled from satellite image derived spectral color indices. Brightness Index (BI), Coloration Index (CI), Hue Index (HI), Redness Index (RI) and Saturation Index (

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Mandal, Umesh K. "SPECTRAL COLOR INDICES BASED GEOSPATIAL MODELING OF SOIL ORGANIC MATTER IN CHITWAN DISTRICT, NEPAL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 43–48. http://dx.doi.org/10.5194/isprs-archives-xli-b2-43-2016.

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Space Technology provides a resourceful-cost effective means to assess soil nutrients essential for soil management plan. Soil organic matter (SOM) is one of valuable controlling productivity of crops by providing nutrient in farming systems. Geospatial modeling of soil organic matter is essential if there is unavailability of soil test laboratories and its strong spatial correlation. In the present analysis, soil organic matter is modeled from satellite image derived spectral color indices. Brightness Index (BI), Coloration Index (CI), Hue Index (HI), Redness Index (RI) and Saturation Index (

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29

Liang, B. C., E. G. Gregorich, and A. F. MacKenzie. "MODELING THE EFFECTS OF INORGANIC AND ORGANIC AMENDMENTS ON ORGANIC MATTER IN A QUEBEC SOIL." Soil Science 161, no. 2 (1996): 109–14. http://dx.doi.org/10.1097/00010694-199602000-00005.

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30

Grechishcheva, N. Yu, N. V. Yaroslavtsev, A. D. Kotelnikova, et al. "Mobilization of Soil Organic Matter by Ultrafresh Water: Modeling and Assessment of the Impact on the Mobility of Heavy Metals." Eurasian Soil Science 54, no. 6 (2021): 843–51. http://dx.doi.org/10.1134/s1064229321060053.

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Abstract The experiments on mobilization of soil organic matter during soil washing with ultrafresh water against the background of salinity pulsing were designed and performed. Unpolluted soil and the soil artificially polluted with copper(II) were used in experiments, namely, clay loamy typical chernozem (Haplic Chernozem) of the Alekhin Central Chernozemic Nature Reserve (Kursk oblast, Russia; 51°34.207 N, 36°05.444 E) and sandy loamy soddy-podzolic soil (Albic Glossic Retisol (Loamic, Cutanic, Ochric)) from the Domodedovo district of Moscow oblast, Russia (55°17.683 N, 37°50.045 E). Soil s

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31

Barančíková, G., J. Halás, M. Gutteková, et al. "Application of RothC model to predict soil organic carbon stock on agricultural soils of Slovakia." Soil and Water Research 5, No. 1 (2010): 1–9. http://dx.doi.org/10.17221/23/2009-swr.

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Soil organic matter (SOM) takes part in many environmental functions and, depending on the conditions, it can be a source or a sink of the greenhouse gases. Presently, the changes in soil organic carbon (SOC) stock can arise because of the climatic changes or changes in the land use and land management. A promising method in the estimation of SOC changes is modelling, one of the most used models for the prediction of changes in soil organic carbon stock on agricultural land being the RothC model. Because of its simplicity and availability of the input data, RothC was used for testing the effic

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Krull, Evelyn S., Jeffrey A. Baldock, and Jan O. Skjemstad. "Importance of mechanisms and processes of the stabilisation of soil organic matter for modelling carbon turnover." Functional Plant Biology 30, no. 2 (2003): 207. http://dx.doi.org/10.1071/fp02085.

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This paper reviews current knowledge of soil organic carbon (SOC) dynamics with respect to physical protection, soil moisture and temperature, and recalcitrant carbon fractions (such as charcoal) in predominantly agricultural soils. These factors are discussed within the framework of current soil organic matter models. The importance of soil structure in the stabilisation of organic residues through physical protection has been documented previously in various studies. In addition, changes in soil structure associated with tillage can significantly affect soil organic matter decomposition rate

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33

Комаров, А. С., and A. S. Komarov. "Modeling of the Soil Organic Matter Dynamics and the Soil Nutrients in Mineral Soil and Forest Floor." Mathematical Biology and Bioinformatics 7, no. 1 (2012): 162–76. http://dx.doi.org/10.17537/2012.7.162.

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34

Ahmed, Ashour A., Oliver Kühn, Saadullah G. Aziz, Rifaat H. Hilal, and Peter Leinweber. "How soil organic matter composition controls hexachlorobenzene–soil-interactions: Adsorption isotherms and quantum chemical modeling." Science of The Total Environment 476-477 (April 2014): 98–106. http://dx.doi.org/10.1016/j.scitotenv.2013.12.096.

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35

Syers, J. K. "Managing soils for long-term productivity." Philosophical Transactions of the Royal Society of London. Series B: Biological Sciences 352, no. 1356 (1997): 1011–21. http://dx.doi.org/10.1098/rstb.1997.0079.

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Meeting the goal of long–term agricultural productivity requires that soil degradation be halted and reversed. Soil fertility decline is a key factor in soil degradation and is probably the major cause of declining crop yields. There is evidence that the contribution of declining soil fertility to soil degradation has been underestimated. Sensitivity to soil degradation is implicit in the assessment of the sustainability of land management practices, with wide recognition of the fact that soils vary in their ability to resist change and recover subsequent to stress. The concept of resilience i

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Mondini, Claudio, Maria Luz Cayuela, Tania Sinicco, Flavio Fornasier, Antonia Galvez, and Miguel Angel Sánchez-Monedero. "Modification of the RothC model to simulate soil C mineralization of exogenous organic matter." Biogeosciences 14, no. 13 (2017): 3253–74. http://dx.doi.org/10.5194/bg-14-3253-2017.

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Abstract. The development of soil organic C (SOC) models capable of producing accurate predictions for the long-term decomposition of exogenous organic matter (EOM) in soils is important for the effective management of organic amendments. However, reliable C modeling in amended soils requires specific optimization of current C models to take into account the high variability in EOM origin and properties. The aim of this work was to improve the prediction of C mineralization rates in amended soils by modifying the RothC model to encompass a better description of EOM quality. The standard RothC

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de Wit, Helene A., Marek Kotowski, and Jan Mulder. "Modeling Aluminum and Organic Matter Solubility in the Forest Floor Using WHAM." Soil Science Society of America Journal 63, no. 5 (1999): 1141–48. http://dx.doi.org/10.2136/sssaj1999.6351141x.

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Sohi, Saran P., Nathalie Mahieu, David S. Powlson, Beatá Madari, Rienk H. Smittenberg, and John L. Gaunt. "Investigating the Chemical Characteristics of Soil Organic Matter Fractions Suitable for Modeling." Soil Science Society of America Journal 69, no. 4 (2005): 1248–55. http://dx.doi.org/10.2136/sssaj2004.0262.

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Fernandes, Mariele Monique Honorato, Anderson Prates Coelho, Carolina Fernandes, Matheus Flavio da Silva, and Claudia Campos Dela Marta. "Estimation of soil organic matter content by modeling with artificial neural networks." Geoderma 350 (September 2019): 46–51. http://dx.doi.org/10.1016/j.geoderma.2019.04.044.

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40

Shi, Yuanyuan, Junyu Zhao, Xianchong Song, et al. "Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm." PLOS ONE 16, no. 6 (2021): e0253385. http://dx.doi.org/10.1371/journal.pone.0253385.

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Effective soil spectral band selection and modeling methods can improve modeling accuracy. To establish a hyperspectral prediction model of soil organic matter (SOM) content, this study investigated a forested Eucalyptus plantation in Huangmian Forest Farm, Guangxi, China. The Ranger and Lasso algorithms were used to screen spectral bands. Subsequently, models were established using four algorithms: partial least squares regression, random forest (RF), a support vector machine, and an artificial neural network (ANN). The optimal model was then selected. The results showed that the modeling acc

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Shostak, Liubov, Mykhailo Boiko, Olha Stepanchenko, and Olena Kozhushko. "ANALYSIS OF SOIL ORGANIC MATTER TRANSFORMATION DYNAMICS MODELS." Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska 9, no. 3 (2019): 40–45. http://dx.doi.org/10.35784/iapgos.238.

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In this paper we consider the models describing the dynamics of soil organic matter transformation. The results of practical implementation of the ROMUL, CENTURY and ROTHAMSTED models are presented. These models propose theoretical approaches to the modelling of organic remains transformation based on describing physicochemical processes during humus formation with differential relations. Following the analysis, their strengths and weaknesses have been distinguished.

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Gyamfi, C., J. M. Ndambuki, and R. W. Salim. "Spatial Variability Modeling of Soil Erodibility Index in Relation to Some Soil Properties at Field Scale." Environment and Natural Resources Research 6, no. 2 (2016): 16. http://dx.doi.org/10.5539/enrr.v6n2p16.

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<p class="1Body">Soil erosion is a major land degradation issue affecting various facets of human lives. To curtail soil erosion occurrence requires understanding of soil properties and how they influence soil erosion. To this end, the soil erodibility index which gives an indication of the susceptibility of soils to erosion was examined. In particular, we aimed to determine soil erodibility index at field scale and establish relationships that exist between selected soil properties and soil erodibility index. It was hypothesized that for soil erodibility index to vary spatially, then th

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Dumat, Camille, Arnaud Chiquet, Daren Gooddy, et al. "Metal ion geochemistry in smelter impacted soils and soil solutions." Bulletin de la Société Géologique de France 172, no. 5 (2001): 539–48. http://dx.doi.org/10.2113/172.5.539.

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Abstract As part of an extended project to understand the speciation of metal ions in contaminated lands, we conducted a series of chemical extraction experiments on soil and extracted the soil pore water to determine the speciation of Pb, Cd and Cu in a smelter impacted site. The chemical extractions show that soil organic matter and metal oxides (Fe and Mn) control the speciation of Pb, Cu and Cd in the soil. For Pb, these results are in agreement with previous EXAFS data on the same soils. The soil solution speciation is calculated with the help of the NICA-Donnan modelling approach. The mo

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Voltr, Václav, Ladislav Menšík, Lukáš Hlisnikovský, Martin Hruška, Eduard Pokorný, and Lubica Pospíšilová. "The Soil Organic Matter in Connection with Soil Properties and Soil Inputs." Agronomy 11, no. 4 (2021): 779. http://dx.doi.org/10.3390/agronomy11040779.

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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 dependi

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Gustafsson, Jon Petter, Charlotta Tiberg, Abubaker Edkymish, and Dan Berggren Kleja. "Modelling lead(II) sorption to ferrihydrite and soil organic matter." Environmental Chemistry 8, no. 5 (2011): 485. http://dx.doi.org/10.1071/en11025.

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Environmental contextLead(II) is a toxic metal pollutant with many anthropogenic sources. We show that lead(II) is bound more strongly to soil surfaces than previously understood. This knowledge may lead to better models for lead(II) dissolution from the soils, which will improve risk assessments for this metal. AbstractLead(II) adsorption to soil organic matter and iron (hydr)oxides is strong, and may control the geochemical behaviour of this metal. Here, we report the adsorption of Pb2+ (i) to 2-line ferrihydrite, and (ii) to a mor layer. The results showed that ferrihydrite has heterogeneou

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Sobocká, J., J. Balkovič, and M. Lapin. "A CENTURY 5 model using for estimation of soil organic matter behaviour at predicted climate change." Soil and Water Research 2, No. 1 (2008): 25–34. http://dx.doi.org/10.17221/2099-swr.

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The trends of carbon sequestration behaviour have been estimated for the most fertile soil type of Slovakia based on the prognosticated regional climate change scenario. The processes were modelled and simulated by CENTURY 5 model to provide these inputs: predicted information about quantification of carbon and nitrogen fluxes, and primary net of organic matter production. Soil conditions were represented by the soil type calcareous Haplic Chernozem (Danubian lowland), and the climatic scenario was related to the meteorological station Hurbanovo modelled for the period of 2005&minus;2090.

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Kebonye, N. M., P. N. Eze, S. K. Ahado, and K. John. "Structural equation modeling of the interactions between trace elements and soil organic matter in semiarid soils." International Journal of Environmental Science and Technology 17, no. 4 (2020): 2205–14. http://dx.doi.org/10.1007/s13762-019-02610-1.

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Ågren, Göran I., Matthieu Barrandon, Laurent Saint-André, and Julien Sainte-Marie. "Analysis of integro-differential equations modeling the vertical decomposition of soil organic matter." Quarterly of Applied Mathematics 75, no. 1 (2016): 131–53. http://dx.doi.org/10.1090/qam/1438.

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Smagin, Andrey, Nadezhda Sadovnikova, Vyacheslav Vasenev, and Marina Smagina. "Biodegradation of Some Organic Materials in Soils and Soil Constructions: Experiments, Modeling and Prevention." Materials 11, no. 10 (2018): 1889. http://dx.doi.org/10.3390/ma11101889.

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The decomposition of natural and synthetic polymeric materials (peat, humates, biochar, strongly swelling hydrogels and other soil conditioners) in a biologically and chemically active soil environment inevitably leads to a reduced ability to improve the structure, water-retention, absorptive capacity and fertility of artificial soil constructions in urbanized ecosystems and agro landscapes (constructozems). Quantitative assessment of the biodegradation process using field and laboratory incubation experiments, as well as mathematical modeling, showed the possibility of significant (up to 30–5

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Koopal, Luuk K., Willem H. van Riemsdijk, and David G. Kinniburgh. "Humic matter and contaminants. General aspects and modeling metal ion binding." Pure and Applied Chemistry 73, no. 12 (2001): 2005–16. http://dx.doi.org/10.1351/pac200173122005.

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Humic substances are soil and fresh-water components that play an important role in the binding and transport of both organic and inorganic contaminants. Transport of the contaminants due to ground- and fresh-water dynamics is directly related to the risks associated with contaminations. The mobility of soluble humic substances is related to their interaction with soil mineral particles. Some key references for the binding of organic and inorganic contaminants and for the binding of humics to mineral particles are presented. Humic substances also play a role in the analysis of the contaminants

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