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Journal articles on the topic 'Soil Organic Carbon model calibration'

Author: Grafiati

Published: 4 June 2025

Last updated: 1 August 2025

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1

Hedley, C. B., I. J. Payton, I. H. Lynn, S. T. Carrick, T. H. Webb, and S. McNeill. "Random sampling of stony and non-stony soils for testing a national soil carbon monitoring system." Soil Research 50, no. 1 (2012): 18. http://dx.doi.org/10.1071/sr11171.

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The New Zealand Soil Carbon Monitoring System (Soil CMS) was designed, and has been used, to account for soil organic carbon change under land-use change, during New Zealand’s first Commitment Period (2008–2012) to the Kyoto Protocol. The efficacy of the Soil CMS model has been tested for assessing soil organic carbon stocks in a selected climate–land-use–soil grouping (cell). The cell selected for this test represents an area of 709 683 ha and contains soils with a high-activity clay mineralogy promoting long-term stabilisation of organic matter, and is under low-producing grassland in a dry

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Jafarov, Elchin E., Hélène Genet, Velimir V. Vesselinov, et al. "Estimation of above- and below-ground ecosystem parameters for DVM-DOS-TEM v0.7.0 using MADS v1.7.3." Geoscientific Model Development 18, no. 12 (2025): 3857–75. https://doi.org/10.5194/gmd-18-3857-2025.

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Abstract. The permafrost region contains a significant portion of the world's soil organic carbon, and its thawing, driven by accelerated Arctic warming, could lead to substantial release of greenhouse gases, potentially disrupting the global climate system. Accurate predictions of carbon cycling in permafrost ecosystems hinge on the robust calibration of model parameters. However, manually calibrating numerous parameters in complex process-based models is labor-intensive and is complicated further by equifinality – the presence of multiple parameter sets that can equally fit the observed data

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3

Mäkelä, Jarmo, Laura Arppe, Hannu Fritze, et al. "Implementation and initial calibration of carbon-13 soil organic matter decomposition in the Yasso model." Biogeosciences 19, no. 17 (2022): 4305–13. http://dx.doi.org/10.5194/bg-19-4305-2022.

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Abstract. Soils account for the largest share of carbon found in terrestrial ecosystems, and their status is of considerable interest for the global carbon cycle budget and atmospheric carbon concentration. The decomposition of soil organic matter depends on environmental conditions and human activities, which raises the question of how permanent are these carbon storages under changing climate. One way to get insight into carbon decomposition processes is to analyse different carbon isotope concentrations in soil organic matter. In this paper we introduce a carbon-13-isotope-specific soil org

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Nielsen, Claudia Kalla, and Anton Gårde Thomsen. "Local Calibration of TDR Measurements for Determining Water and Organic Carbon Contents of Peaty Soils." Soil Systems 7, no. 1 (2023): 10. http://dx.doi.org/10.3390/soilsystems7010010.

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Time domain reflectometry (TDR) measurements of the volumetric water content (θ) of soils are based on the dielectric permittivity (ε), relating ε to θ, using an empirical calibration function. Accurate determination of θ for peaty soils is vital but complicated by the complexity of organic soils and the lack of a general calibration model. Site-specific calibration models were developed to determine θ from TDR measurements for a heterogenous peatland across gradients of peat decomposition and organic carbon (OC) content; derived by soil organic matter conversion. The possibility of predicting

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Viskari, Toni, Janne Pusa, Istem Fer, Anna Repo, Julius Vira, and Jari Liski. "Calibrating the soil organic carbon model Yasso20 with multiple datasets." Geoscientific Model Development 15, no. 4 (2022): 1735–52. http://dx.doi.org/10.5194/gmd-15-1735-2022.

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Abstract. Soil organic carbon (SOC) models are important tools for assessing global SOC distributions and how carbon stocks are affected by climate change. Their performances, however, are affected by data and methods used to calibrate them. Here we study how a new version of the Yasso SOC model, here named Yasso20, performs if calibrated individually or with multiple datasets and how the chosen calibration method affects the parameter estimation. We also compare Yasso20 to the previous version of the Yasso model. We found that when calibrated with multiple datasets, the model showed a better

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Robinson, Nathan, and Kurt Benke. "Analysis of Uncertainty in the Depth Profile of Soil Organic Carbon." Environments 10, no. 2 (2023): 29. http://dx.doi.org/10.3390/environments10020029.

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The soil organic carbon (SOC) depth profile provides information for many applications, including monitoring climate change, carbon sequestration, reforestation, and land erosion. Models of the SOC profile support data interpolation, trend analysis, and carbon mapping, and can be used in larger pedometric models in support of carbon farming. Model errors may be due to statistical variability in discrete data and the limited sample size available for model calibration. Uncertainties in the model can arise from a process of iterative parameter adjustment and can be estimated by gradient-based me

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Skjemstad, J. O., L. R. Spouncer, B. Cowie, and R. S. Swift. "Calibration of the Rothamsted organic carbon turnover model (RothC ver. 26.3), using measurable soil organic carbon pools." Soil Research 42, no. 1 (2004): 79. http://dx.doi.org/10.1071/sr03013.

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A fractionation scheme that provided the measurement of a labile pool (particulate organic carbon), a charcoal-carbon pool, and a humic pool by difference was tested as a means of initialising the Rothamsted organic carbon turnover model version 26.3. Equating these 3 fractions with the resistant plant material, inert organic matter, and humic pools of the model, respectively, gave good agreement between measured and modelled data for 2 long-term rotation trials in Australia using a soil depth of 30 cm. At one location, Brigalow Research Station in Queensland, there were 3 distinct soil types,

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Yu, Y. Y., P. A. Finke, H. B. Wu, and Z. T. Guo. "Sensitivity analysis and calibration of a soil carbon model (SoilGen2) in two contrasting loess forest soils." Geoscientific Model Development 6, no. 1 (2013): 29–44. http://dx.doi.org/10.5194/gmd-6-29-2013.

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Abstract. To accurately estimate past terrestrial carbon pools is the key to understanding the global carbon cycle and its relationship with the climate system. SoilGen2 is a useful tool to obtain aspects of soil properties (including carbon content) by simulating soil formation processes; thus it offers an opportunity for both past soil carbon pool reconstruction and future carbon pool prediction. In order to apply it to various environmental conditions, parameters related to carbon cycle process in SoilGen2 are calibrated based on six soil pedons from two typical loess deposition regions (Be

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Yu, Y. Y., P. A. Finke, H. B. Wu, and Z. T. Guo. "Sensitivity analysis and calibration of a soil carbon model (SoilGen2) in two contrasting loess forest soils." Geoscientific Model Development Discussions 5, no. 3 (2012): 1817–49. http://dx.doi.org/10.5194/gmdd-5-1817-2012.

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Abstract. To accurately estimate past terrestrial carbon pools is the key to understand the global carbon cycle and its relationship with the climate system. SoilGen2 is a useful tool to obtain aspects of soil properties (including carbon content) by simulating soil formation processes; thus it offers an opportunity for past soil carbon pool reconstruction. In order to apply it to various environmental conditions, parameters related to carbon cycle process in SoilGen2 are calibrated based on 6 soil pedons from two typical loess deposition regions (Belgium and China). Sensitivity analysis using

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Guy, Amanda L., Steven D. Siciliano, and Eric G. Lamb. "Spiking regional vis-NIR calibration models with local samples to predict soil organic carbon in two High Arctic polar deserts using a vis-NIR probe." Canadian Journal of Soil Science 95, no. 3 (2015): 237–49. http://dx.doi.org/10.4141/cjss-2015-004.

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Guy, A. L., Siciliano, S. D. and Lamb, E. G. 2015. Spiking regional vis-NIR calibration models with local samples to predict soil organic carbon in two High Arctic polar deserts using a vis-NIR probe. Can. J. Soil Sci. 95: 237–249. In situ visible and near-infrared (vis-NIR) spectroscopy is a potential solution to the logistic constraints limiting the accuracy and spatial resolution of soil organic carbon (SOC) estimates for Arctic regions. The objective of our study was to develop a calibration model based on field-condition soils for in situ applications to predict SOC in High Arctic polar d

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Shaffer, G., S. Malskǽr Olsen, and J. O. P. Pedersen. "Presentation, calibration and validation of the low-order, DCESS Earth System Model." Geoscientific Model Development Discussions 1, no. 1 (2008): 39–124. http://dx.doi.org/10.5194/gmdd-1-39-2008.

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Abstract. A new, low-order Earth system model is described, calibrated and tested against Earth system data. The model features modules for the atmosphere, ocean, ocean sediment, land biosphere and lithosphere and has been designed to simulate global change on time scales of years to millions of years. The atmosphere module considers radiation balance, meridional transport of heat and water vapor between low-mid latitude and high latitude zones, heat and gas exchange with the ocean and sea ice and snow cover. Gases considered are carbon dioxide and methane for all three carbon isotopes, nitrou

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Luo, Z., E. Wang, H. Zheng, J. A. Baldock, O. J. Sun, and Q. Shao. "Convergent modeling of past soil organic carbon stocks but divergent projections." Biogeosciences Discussions 12, no. 5 (2015): 4245–72. http://dx.doi.org/10.5194/bgd-12-4245-2015.

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Abstract. Soil carbon models are important tool to understand soil carbon balance and project carbon stocks in terrestrial ecosystems, particularly under global change. The initialization and/or parameterization of soil carbon models can vary among studies even when the same model and dataset are used, causing potential uncertainties in projections. Although a few studies have assessed such uncertainties, it is yet unclear what these uncertainties are correlated with and how they change across varying environmental and management conditions. Here, applying a process-based biogeochemical model

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Bartholomeus, Harm, Gabriela Schaepman-Strub, Daan Blok, Roman Sofronov, and Sergey Udaltsov. "Spectral Estimation of Soil Properties in Siberian Tundra Soils and Relations with Plant Species Composition." Applied and Environmental Soil Science 2012 (2012): 1–13. http://dx.doi.org/10.1155/2012/241535.

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Predicted global warming will be most pronounced in the Arctic and will severely affect permafrost environments. Due to its large spatial extent and large stocks of soil organic carbon, changes to organic matter decomposition rates and associated carbon fluxes in Arctic permafrost soils will significantly impact the global carbon cycle. We explore the potential of soil spectroscopy to estimate soil carbon properties and investigate the relation between soil properties and vegetation composition. Soil samples are collected in Siberia, and vegetation descriptions are made at each sample point. F

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14

Xie, H. T., X. M. Yang, C. F. Drury, J. Y. Yang, and X. D. Zhang. "Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada." Canadian Journal of Soil Science 91, no. 1 (2011): 53–63. http://dx.doi.org/10.4141/cjss10029.

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Xie, H. T., Yang, X. M., Drury, C. F., Yang, J. Y. and Zhang, X. D. 2011. Predicting soil organic carbon and total nitrogen using mid- and near-infrared spectra for Brookston clay loam soil in Southwestern Ontario, Canada. Can. J. Soil Sci. 91: 53–63. Mid-infrared (MIR) and near-infrared (NIR) spectroscopy of soils have been tested to estimate soil organic carbon (SOC) and total N (TN) concentrations at local, regional and national scales. However, these methods have rarely been used to assess SOC and TN concentrations of the same soil under different management practices. The objective of thi

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Zhang, Yao, Jocelyn M. Lavallee, Andy D. Robertson, et al. "Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model." Biogeosciences 18, no. 10 (2021): 3147–71. http://dx.doi.org/10.5194/bg-18-3147-2021.

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Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant gro

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16

Tonitto, Christina, and Ali Volkan Bilgili. "Combustion and Spectral Methods for Quantifying Carbon and Nitrogen Concentrations in Pacific Northwest Douglas-Fir Forest Soils." Journal of Agricultural Science 8, no. 6 (2016): 8. http://dx.doi.org/10.5539/jas.v8n6p8.

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<p>Traditional combustion methods for assessing soil carbon (C) and nitrogen (N) stocks are time consuming and expensive; visible and near-infrared (VNIR) methods offer a quick and inexpensive alternative for establishing soil C and N concentrations. We compared combustion and spectral methods for quantifying soil carbon and nitrogen concentrations. We sampled organic and mineral soil horizons in managed and old-growth Douglas-fir (<em>Pseudotsuga menziesii</em>) forests in western Oregon. We applied combustion methods to determine total soil carbon and nitrogen concentration

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17

Brunmayr, Alexander S., Frank Hagedorn, Margaux Moreno Duborgel, Luisa I. Minich, and Heather D. Graven. "Radiocarbon analysis reveals underestimation of soil organic carbon persistence in new-generation soil models." Geoscientific Model Development 17, no. 15 (2024): 5961–85. http://dx.doi.org/10.5194/gmd-17-5961-2024.

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Abstract. Reflecting recent advances in our understanding of soil organic carbon (SOC) turnover and persistence, a new generation of models increasingly makes the distinction between the more labile soil particulate organic matter (POM) and the more persistent mineral-associated organic matter (MAOM). Unlike the typically poorly defined conceptual pools of traditional SOC models, the POM and MAOM soil fractions can be directly measured for their carbon content and isotopic composition, allowing for fraction-specific data assimilation. However, the new-generation model predictions of POM and MA

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18

Shaffer, G., S. Malskær Olsen, and J. O. Pepke Pedersen. "Presentation, calibration and validation of the low-order, DCESS Earth System Model (Version 1)." Geoscientific Model Development 1, no. 1 (2008): 17–51. http://dx.doi.org/10.5194/gmd-1-17-2008.

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Abstract. A new, low-order Earth System Model is described, calibrated and tested against Earth system data. The model features modules for the atmosphere, ocean, ocean sediment, land biosphere and lithosphere and has been designed to simulate global change on time scales of years to millions of years. The atmosphere module considers radiation balance, meridional transport of heat and water vapor between low-mid latitude and high latitude zones, heat and gas exchange with the ocean and sea ice and snow cover. Gases considered are carbon dioxide and methane for all three carbon isotopes, nitrou

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19

Broeg, Tom, Michael Blaschek, Steffen Seitz, Ruhollah Taghizadeh-Mehrjardi, Simone Zepp, and Thomas Scholten. "Transferability of Covariates to Predict Soil Organic Carbon in Cropland Soils." Remote Sensing 15, no. 4 (2023): 876. http://dx.doi.org/10.3390/rs15040876.

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Precise knowledge about the soil organic carbon (SOC) content in cropland soils is one requirement to design and execute effective climate and food policies. In digital soil mapping (DSM), machine learning algorithms are used to predict soil properties from covariates derived from traditional soil mapping, digital elevation models, land use, and Earth observation (EO). However, such DSM models are trained for a specific dataset and region and have so far only allowed limited general statements to be made that would enable the models to be transferred to different regions. In this study, we tes

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Bangelesa, Freddy, Elhadi Adam, Jasper Knight, Inos Dhau, Marubini Ramudzuli, and Thabiso M. Mokotjomela. "Predicting Soil Organic Carbon Content Using Hyperspectral Remote Sensing in a Degraded Mountain Landscape in Lesotho." Applied and Environmental Soil Science 2020 (April 13, 2020): 1–11. http://dx.doi.org/10.1155/2020/2158573.

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Soil organic carbon constitutes an important indicator of soil fertility. The purpose of this study was to predict soil organic carbon content in the mountainous terrain of eastern Lesotho, southern Africa, which is an area of high endemic biodiversity as well as an area extensively used for small-scale agriculture. An integrated field and laboratory approach was undertaken, through measurements of reflectance spectra of soil using an Analytical Spectral Device (ASD) FieldSpec® 4 optical sensor. Soil spectra were collected on the land surface under field conditions and then on soil in the labo

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Karunaratne, S. B., T. F. A. Bishop, J. S. Lessels, J. A. Baldock, and I. O. A. Odeh. "A space–time observation system for soil organic carbon." Soil Research 53, no. 6 (2015): 647. http://dx.doi.org/10.1071/sr14178.

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In this paper, we present a framework for a space–time observation system for soil organic carbon (STOS-SOC). We propose that the RothC model be embedded within the STOS-SOC, which is driven by satellite-derived inputs and readily available geospatial inputs, such as digital soil maps. In particular, advances in remote sensing have enabled the development of satellite products that represent key inputs into soil carbon models, examples being evapotranspiration and biomass inputs to soil, which characterise space–time variations in management and land use. Starting from an initial calibrated ba

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Loria, Nancy, Rattan Lal, and Ranveer Chandra. "Handheld In Situ Methods for Soil Organic Carbon Assessment." Sustainability 16, no. 13 (2024): 5592. http://dx.doi.org/10.3390/su16135592.

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Soil organic carbon (SOC) assessment is crucial for evaluating soil health and supporting carbon sequestration efforts. Traditional methods like wet digestion and dry combustion are time-consuming and labor-intensive, necessitating the development of non-destructive, cost-efficient, and real-time in situ measurements. This review focuses on handheld in situ methodologies for SOC estimation, underscoring their practicality and reasonable accuracy. Spectroscopic techniques, like visible and near-infrared, mid-infrared, laser-induced breakdown spectroscopy, and inelastic neutron scattering each o

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Sakhaee, Ali, Anika Gebauer, Mareike Ließ, and Axel Don. "Spatial prediction of organic carbon in German agricultural topsoil using machine learning algorithms." SOIL 8, no. 2 (2022): 587–604. http://dx.doi.org/10.5194/soil-8-587-2022.

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Abstract. As the largest terrestrial carbon pool, soil organic carbon (SOC) has the potential to influence and mitigate climate change; thus, SOC monitoring is of high importance in the frameworks of various international treaties. Therefore, high-resolution SOC maps are required. Machine learning (ML) offers new opportunities to develop these maps due to its ability to data mine large datasets. The aim of this study was to apply three algorithms commonly used in digital soil mapping – random forest (RF), boosted regression trees (BRT), and support vector machine for regression (SVR) – on the

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Van de Broek, Marijn, Gerard Govers, Marion Schrumpf, and Johan Six. "A microbially driven and depth-explicit soil organic carbon model constrained by carbon isotopes to reduce parameter equifinality." Biogeosciences 22, no. 5 (2025): 1427–46. https://doi.org/10.5194/bg-22-1427-2025.

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Abstract. Over the past years, microbially driven models have been developed to improve simulations of soil organic carbon (SOC) and have been put forward as an improvement to assess the fate of SOC stocks under environmental change. While these models include a better mechanistic representation of SOC cycling compared to cascading-reservoir-based approaches, the complexity of these models implies that data on SOC stocks are insufficient to constrain the additional model parameters. In this study, we constructed a novel depth-explicit SOC model (SOILcarb – Simulation of Organic carbon and its

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Jiří, Zbíral, Čižmár David, Malý Stanislav, and Obdržálková Elena. "Determination of glomalin in agriculture and forest soils by near-infrared spectroscopy." Plant, Soil and Environment 63, No. 5 (2017): 226–30. http://dx.doi.org/10.17221/181/2017-pse.

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Determining and characterizing soil organic matter (SOM) cheaply and reliably can help to support decisions concerning sustainable land management and climate policy. Glomalin was recommended as one of possible indicators of SOM quality. Extracting glomalin from and determining it in soils using classical chemical methods is too complicated and therefore near-infrared spectroscopy (NIRS) was studied as a method of choice for the determination of glomalin. Representative sets of 84 different soil samples from arable land and grasslands and 75 forest soils were used to develop NIRS calibration m

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Helfenstein, Anatol, Philipp Baumann, Raphael Viscarra Rossel, Andreas Gubler, Stefan Oechslin, and Johan Six. "Quantifying soil carbon in temperate peatlands using a mid-IR soil spectral library." SOIL 7, no. 1 (2021): 193–215. http://dx.doi.org/10.5194/soil-7-193-2021.

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Abstract. Traditional laboratory methods for acquiring soil information remain important for assessing key soil properties, soil functions and ecosystem services over space and time. Infrared spectroscopic modeling can link and massively scale up these methods for many soil characteristics in a cost-effective and timely manner. In Switzerland, only 10 % to 15 % of agricultural soils have been mapped sufficiently to serve spatial decision support systems, presenting an urgent need for rapid quantitative soil characterization. The current Swiss soil spectral library (SSL; n = 4374) in the mid-in

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Kumar, Amit, Pravash Chandra Moharana, Roomesh Kumar Jena, et al. "Digital Mapping of Soil Organic Carbon Using Machine Learning Algorithms in the Upper Brahmaputra Valley of Northeastern India." Land 12, no. 10 (2023): 1841. http://dx.doi.org/10.3390/land12101841.

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Soil Organic Carbon (SOC) is a crucial indicator of ecosystem health and soil quality. Machine learning (ML) models that predict soil quality based on environmental parameters are becoming more prevalent. However, studies have yet to examine how well each ML technique performs when predicting and mapping SOC, particularly at high spatial resolutions. Model predictors include topographic variables generated from SRTM DEM; vegetation and soil indices derived from Landsat satellite images predict SOC for the Lakhimpur district of the upper Brahmaputra Valley of Assam, India. Four ML models, Rando

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Gurung, Ram B., Stephen M. Ogle, F. Jay Breidt, Stephen A. Williams, and William J. Parton. "Bayesian calibration of the DayCent ecosystem model to simulate soil organic carbon dynamics and reduce model uncertainty." Geoderma 376 (October 2020): 114529. http://dx.doi.org/10.1016/j.geoderma.2020.114529.

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Semella, Sebastian, Christopher Hutengs, Michael Seidel, et al. "Accuracy and Reproducibility of Laboratory Diffuse Reflectance Measurements with Portable VNIR and MIR Spectrometers for Predictive Soil Organic Carbon Modeling." Sensors 22, no. 7 (2022): 2749. http://dx.doi.org/10.3390/s22072749.

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Soil spectroscopy in the visible-to-near infrared (VNIR) and mid-infrared (MIR) is a cost-effective method to determine the soil organic carbon content (SOC) based on predictive spectral models calibrated to analytical-determined SOC reference data. The degree to which uncertainty in reference data and spectral measurements contributes to the estimated accuracy of VNIR and MIR predictions, however, is rarely addressed and remains unclear, in particular for current handheld MIR spectrometers. We thus evaluated the reproducibility of both the spectral reflectance measurements with portable VNIR

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Shaukat, Muhammad, Aaron Kinyu Hoshide, Sher Muhammad, Irshad Ahmad Arshad, Muhammad Mushtaq, and Daniel Carneiro de Abreu. "Predicting Soil Carbon Sequestration and Harvestable C-Biomass of Rice and Wheat by DNDC Model." Crops 3, no. 3 (2023): 220–40. http://dx.doi.org/10.3390/crops3030021.

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Several biogeochemical models have been applied to understand the potential effects of management practices on soil organic carbon (SOC) sequestration, crop growth, and yield. In this study, the denitrification and decomposition (DNDC) model was used to simulate soil SOC dynamics and harvested C-biomass in rice–wheat rotation under organic/inorganic fertilization with conventional tillage (CT) and reduced tillage (RT). Before calibration, DNDC underpredicted harvestable grain C-biomass of rice where percent difference (PD) varied from 29.22% to 42.14%, and over-simulated grain C-biomass of whe

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McNeill, Stephen J. E., Nancy Golubiewski, and James Barringer. "Development and calibration of a soil carbon inventory model for New Zealand." Soil Research 52, no. 8 (2014): 789. http://dx.doi.org/10.1071/sr14020.

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A soil organic carbon (SOC) and SOC change model for New Zealand is developed for use in national SOC inventory reporting. The foundation for the model is a generalised least-squares regression, based on explanatory variables of land use, soil–climate class, and erosivity. The SOC change model is based on the assumption that changes in SOC over a decadal timescale are usually restricted to transitions in land use. Improvements to the model are then considered that are intended to reduce the uncertainty of SOC changes through reduction of the standard error of the land-use effects. Stochastic g

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Guenet, Bertrand, Fernando Esteban Moyano, Philippe Peylin, Philippe Ciais, and Ivan A. Janssens. "Towards a representation of priming on soil carbon decomposition in the global land biosphere model ORCHIDEE (version 1.9.5.2)." Geoscientific Model Development 9, no. 2 (2016): 841–55. http://dx.doi.org/10.5194/gmd-9-841-2016.

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Abstract. Priming of soil carbon decomposition encompasses different processes through which the decomposition of native (already present) soil organic matter is amplified through the addition of new organic matter, with new inputs typically being more labile than the native soil organic matter. Evidence for priming comes from laboratory and field experiments, but to date there is no estimate of its impact at global scale and under the current anthropogenic perturbation of the carbon cycle. Current soil carbon decomposition models do not include priming mechanisms, thereby introducing uncertai

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Xu, Haoyu, Tao Zhang, Yiqi Luo, Xin Huang, and Wei Xue. "Parameter calibration in global soil carbon models using surrogate-based optimization." Geoscientific Model Development 11, no. 7 (2018): 3027–44. http://dx.doi.org/10.5194/gmd-11-3027-2018.

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Abstract. Soil organic carbon (SOC) has a significant effect on carbon emissions and climate change. However, the current SOC prediction accuracy of most models is very low. Most evaluation studies indicate that the prediction error mainly comes from parameter uncertainties, which can be improved by parameter calibration. Data assimilation techniques have been successfully employed for the parameter calibration of SOC models. However, data assimilation algorithms, such as the sampling-based Bayesian Markov chain Monte Carlo (MCMC), generally have high computation costs and are not appropriate

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Zhu, Changda, Yuchen Wei, Fubin Zhu, et al. "Digital Mapping of Soil Organic Carbon Based on Machine Learning and Regression Kriging." Sensors 22, no. 22 (2022): 8997. http://dx.doi.org/10.3390/s22228997.

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In the last two decades, machine learning (ML) methods have been widely used in digital soil mapping (DSM), but the regression kriging (RK) model which combines the advantages of the ML and kriging methods has rarely been used in DSM. In addition, due to the limitation of a single-model structure, many ML methods have poor prediction accuracy in undulating terrain areas. In this study, we collected the SOC content of 115 soil samples in a hilly farming area with continuous undulating terrain. According to the theory of soil-forming factors in pedogenesis, we selected 10 topographic indices, 7

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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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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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Laub, Moritz, Michael Scott Demyan, Yvonne Funkuin Nkwain, et al. "DRIFTS band areas as measured pool size proxy to reduce parameter uncertainty in soil organic matter models." Biogeosciences 17, no. 6 (2020): 1393–413. http://dx.doi.org/10.5194/bg-17-1393-2020.

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Abstract. Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the a

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Attila, Tóth József, Döbröntey Réka, Szegi Tamás, Michéli Erika, and Csorba Ádám. "Fourier-transzformációs közép-infravörös spektroszkópia alapú szervesanyag-tartalom becslés tábla szintű reprezentativitás-vizsgálata kemometriai módszerekkel." Agrokémia és Talajtan 70, no. 1 (2021): 65–82. http://dx.doi.org/10.1556/0088.2021.00076.

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Szervesszén térképezést segítő módszertani kutatásként vizsgáltuk egy szántóföldi művelés alatt álló terület, 3 mélységből származó mintáinak MIR reflektanciáját, illetve szervesszén tartalmát (Walkley-Black). Ezt követően a spektroszkópia mérések eredményeit használtuk a talaj szervesszén-mennyiségének (TOC %) becslésére. Tettük ezt 3 mintakijelölési módszer (Kennard-Stone Sampling - KSS, K-means Sampling - KMS, Latin Hypercube Sampling - LHS) bevonásával, az így kijelölt kalibrációs mintákkal a PLSR modell segítségével becslést végeztünk az adathalmaz további értékeire. Annak érdekében, hogy

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Guenet, B., F. E. Moyano, P. Peylin, P. Ciais, and I. A. Janssens. "Towards a representation of priming on soil carbon decomposition in the global land biosphere model ORCHIDEE (version 1.9.5.2)." Geoscientific Model Development Discussions 8, no. 10 (2015): 9193–227. http://dx.doi.org/10.5194/gmdd-8-9193-2015.

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Abstract. Priming of soil carbon decomposition encompasses different processes through which the decomposition of native (already present) soil organic matter is amplified through the addition of new organic matter, with new inputs typically being more labile than the native soil organic matter. Evidence for priming comes from laboratory and field experiments, but to date there is no estimate of its impact at global scale and under the current anthropogenic perturbation of the carbon cycle. Current soil carbon decomposition models do not include priming mechanisms, thereby introducing uncertai

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Heil, Jannis, Christoph Jörges, and Britta Stumpe. "Fine-Scale Mapping of Soil Organic Matter in Agricultural Soils Using UAVs and Machine Learning." Remote Sensing 14, no. 14 (2022): 3349. http://dx.doi.org/10.3390/rs14143349.

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The fine-scale mapping of soil organic matter (SOM) in croplands is vital for the sustainable management of soil. Traditionally, SOM mapping relies on laboratory methods that are labor-intensive and costly. Recent advances in unmanned aerial vehicles (UAVs) afford new opportunities for rapid and low-cost SOM mapping at the field scale. However, the conversion from UAV measurements to SOM maps requires specific transfer models that still rely on local sampling. This study aimed to develop a method for predicting topsoil SOM at a high resolution on the field scale based on soil color information

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Xu, X., T. Zhang, and Z. Liu. "Calibration model of microbial biomass carbon and nitrogen concentrations in soils using ultraviolet absorbance and soil organic matter." European Journal of Soil Science 59, no. 4 (2008): 630–39. http://dx.doi.org/10.1111/j.1365-2389.2008.01015.x.

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Leone, Antonio, Guido Leone, Natalia Leone, et al. "Capability of Diffuse Reflectance Spectroscopy to Predict Soil Water Retention and Related Soil Properties in an Irrigated Lowland District of Southern Italy." Water 11, no. 8 (2019): 1712. http://dx.doi.org/10.3390/w11081712.

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In this study, we examined the potential of vis-NIR reflectance spectroscopy, coupled with partial least squares regression (PLSR) analysis, for the evaluation and prediction of soil water retention at field capacity (FC) and permanent wilting point (PWP) and related basic soil properties [organic carbon (OC), sand, silt, and clay contents] in an agricultural irrigated land of southern Italy. Soil properties were determined in the laboratory with reference to the Italian Official Methods for Soil Analysis. Vis-NIR reflectance spectra were measured in the laboratory, using a high-resolution spe

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Flores, Omar, Gaby Deckmyn, Jorge Curiel Yuste, et al. "KEYLINK: towards a more integrative soil representation for inclusion in ecosystem scale models—II: model description, implementation and testing." PeerJ 9 (January 15, 2021): e10707. http://dx.doi.org/10.7717/peerj.10707.

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New knowledge on soil structure highlights its importance for hydrology and soil organic matter (SOM) stabilization, which however remains neglected in many wide used models. We present here a new model, KEYLINK, in which soil structure is integrated with the existing concepts on SOM pools, and elements from food web models, that is, those from direct trophic interactions among soil organisms. KEYLINK is, therefore, an attempt to integrate soil functional diversity and food webs in predictions of soil carbon (C) and soil water balances. We present a selection of equations that can be used for

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Fang, Qian, Hanlie Hong, Lulu Zhao, Stephanie Kukolich, Ke Yin, and Chaowen Wang. "Visible and Near-Infrared Reflectance Spectroscopy for Investigating Soil Mineralogy: A Review." Journal of Spectroscopy 2018 (2018): 1–14. http://dx.doi.org/10.1155/2018/3168974.

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Clay minerals are the most reactive and important inorganic components in soils, but soil mineralogy classifies as a minor topic in soil sciences. Revisiting soil mineralogy has been gradually required. Clay minerals in soils are more complex and less well crystallized than those in sedimentary rocks, and thus, they display more complicated X-ray diffraction (XRD) patterns. Traditional characterization methods such as XRD are usually expensive and time-consuming, and they are therefore inappropriate for large datasets, whereas visible and near-infrared reflectance spectroscopy (VNIR) is a quic

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Hounkpatin, Kpade O. L., Johan Stendahl, Mattias Lundblad, and Erik Karltun. "Predicting the spatial distribution of soil organic carbon stock in Swedish forests using a group of covariates and site-specific data." SOIL 7, no. 2 (2021): 377–98. http://dx.doi.org/10.5194/soil-7-377-2021.

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Abstract. The status of the soil organic carbon (SOC) stock at any position in the landscape is subject to a complex interplay of soil state factors operating at different scales and regulating multiple processes resulting either in soils acting as a net sink or net source of carbon. Forest landscapes are characterized by high spatial variability, and key drivers of SOC stock might be specific for sub-areas compared to those influencing the whole landscape. Consequently, separately calibrating models for sub-areas (local models) that collectively cover a target area can result in different pre

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Ahrens, B., M. Reichstein, W. Borken, J. Muhr, S. E. Trumbore та T. Wutzler. "Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints". Biogeosciences 11, № 8 (2014): 2147–68. http://dx.doi.org/10.5194/bg-11-2147-2014.

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Abstract. Soils of temperate forests store significant amounts of organic matter and are considered to be net sinks of atmospheric CO2. Soil organic carbon (SOC) turnover has been studied using the Δ14C values of bulk SOC or different SOC fractions as observational constraints in SOC models. Further, the Δ14C values of CO2 that evolved during the incubation of soil and roots have been widely used together with Δ14C of total soil respiration to partition soil respiration into heterotrophic respiration (HR) and rhizosphere respiration. However, these data have not been used as joint observationa

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Cécillon, Lauric, François Baudin, Claire Chenu, et al. "A model based on Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon pool in temperate soils." Biogeosciences 15, no. 9 (2018): 2835–49. http://dx.doi.org/10.5194/bg-15-2835-2018.

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Abstract. Changes in global soil carbon stocks have considerable potential to influence the course of future climate change. However, a portion of soil organic carbon (SOC) has a very long residence time (> 100 years) and may not contribute significantly to terrestrial greenhouse gas emissions during the next century. The size of this persistent SOC reservoir is presumed to be large. Consequently, it is a key parameter required for the initialization of SOC dynamics in ecosystem and Earth system models, but there is considerable uncertainty in the methods used to quantify it. Thermal analys

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Kania, Mateusz, Dawid Kupka, and Piotr Gruba. "Application of Near-Infrared Spectroscopy to Detect Modification of the Cation Exchange Properties of Soils from European Beech and Silver Fir Forest Stands in Poland." International Journal of Environmental Research and Public Health 20, no. 3 (2023): 2654. http://dx.doi.org/10.3390/ijerph20032654.

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This study investigated changes in the composition of the cation exchange capacity of soil samples caused by the acid leaching of soil cations under laboratory conditions. Furthermore, near-infrared (NIR) spectroscopy was used to evaluate the properties of forest soils. The potential influence of the species composition of stands (beech and fir) was also investigated. Eighty soil samples from the topsoil of plots located in central Poland were analyzed. Soil samples were leached 0 (non-leached), 5, 10, and 15 times and then analyzed to determine the contents of cations (Al3+, Ca2+, K+, and Mg2

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Ahrens, B., M. Reichstein, W. Borken, J. Muhr, S. E. Trumbore та T. Wutzler. "Bayesian calibration of a soil organic carbon model using Δ<sup>14</sup>C measurements of soil organic carbon and heterotrophic respiration as joint constraints". Biogeosciences Discussions 10, № 8 (2013): 13803–54. http://dx.doi.org/10.5194/bgd-10-13803-2013.

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Abstract. Soils of temperate forests store significant amounts of organic matter and are considered to be net sinks of atmospheric CO2. Soil organic carbon (SOC) turnover has been studied using the Δ14C values of bulk SOC or different SOC fractions as observational constraints in SOC models. Further, the Δ14C values of CO2 evolved during the incubation of soil and roots have been widely used together with Δ14C of total soil respiration to partition soil respiration into heterotrophic respiration (HR) and rhizosphere respiration. However, these data have not been used as joint observational con

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Beier, Claus, Henrik Eckersten, and Per Gundersen. "Nitrogen Cycling in a Norway Spruce Plantation in Denmark — A SOILN Model Application Including Organic N Uptake." Scientific World JOURNAL 1 (2001): 394–406. http://dx.doi.org/10.1100/tsw.2001.394.

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A dynamic carbon (C) and nitrogen (N) circulation model, SOILN, was applied and tested on 7�years of control data and 3 years of manipulation data from an experiment involving monthly N addition in a Norway spruce (Picea abies, L. Karst) forest in Denmark. The model includes two pathways for N uptake: (1) as mineral N after mineralisation of organic N, or (2) directly from soil organic matter as amino acids proposed to mimic N uptake by mycorrhiza. The model was parameterised and applied to the data from the control plot both with and without the organic N uptake included. After calibration, t

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