Quantifying soil organic carbon fractions by infrared-spectroscopy

Zimmermann, Michael; Leifeld, Jens; Fuhrer, Jürg

doi:10.1016/j.soilbio.2006.07.010

Cited by 160 publications

(106 citation statements)

References 48 publications

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“…When expressed as the proportion of OC increase per unit C respired, the proportion of OC found in the intra-aggregate fractions was 6.8 times greater after adding lowquality biochar than high-quality green manure averaged across the degradation gradient (P = 0.042; n = 3). Thus, we see that the OC remaining in the light fraction after adding biochar will be incorporated into the intra-aggregate and organo-mineral fractions that are typically interpreted as the more stable soil fractions (Six et al 2002;Zimmermann et al 2007). Therefore, low-quality biochar not only remained unprocessed but could be stabilised to a greater degree in soil in the long term.…”

Section: Effects Of Added Om Quality On the Quantity And Quality Of Smentioning

confidence: 87%

Stability and stabilisation of biochar and green manure in soil with different organic carbon contents

2010

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Abstract. Due to its recalcitrance against microbial degradation, biochar is very stable in soil compared to other organic matter additions, making its application to soils a suitable approach for the build-up of soil organic carbon (SOC). The net effects of such biochar addition also depend on its interactions with existing organic matter in soils. A study was established to investigate how the status of pre-existing soil organic matter influences biochar stabilisation in soil in comparison to labile organic additions. Carbon loss was greater in the C-rich sites (C content 58.0 g C/kg) than C-poor soils (C content 21.0-24.0 g C/kg), regardless of the quality of the applied organic resource. Biochar-applied, C-rich soil showed greater C losses, by >0.5 kg/m 2 .year, than biochar-applied C-poor soil, whereas the difference was only 0.1 kg/m 2 .year with Tithonia diversifolia green manure. Biochar application reduced the rate of CO 2 -C loss by 27%, and T. diversifolia increased CO 2 -C losses by 22% in the C-poor soils. With biochar application, a greater proportion of C (6.8 times) was found in the intraaggregate fraction per unit C respired than with green manure, indicating a more efficient stabilisation in addition to the chemical recalcitrance of biochar. In SOC-poor soils, biochar application enriched aromatic-C, carboxyl-C, and traces of ketones and esters mainly in unprotected organic matter and within aggregates, as determined by Fourier-transform infrared spectroscopy. In contrast, additions of T. diversifolia biomass enriched conjugated carbonyl-C such as ketones and quinones, as well as CH deformations of aliphatic-C mainly in the intra-aggregate fraction. The data indicate that not only the stability but also the stabilisation of biochar exceeds that of a labile organic matter addition such as green manure.

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Section: Effects Of Added Om Quality On the Quantity And Quality Of Smentioning

confidence: 87%

Stability and stabilisation of biochar and green manure in soil with different organic carbon contents

2010

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“…There are recent advances in reflectance methods to achieve this (Couteaux et al, 2003). Zimmermann et al (2007) related fractions of grassland soil carbon that were chemically distinguished to the carbon pools used in the Roth-C model. These pools then could be classified quite well by analysing reflectance spectra.…”

Section: The Transient Correction -An Approach To Account For Former mentioning

confidence: 99%

Soils apart from equilibrium – consequences for soil carbon balance modelling

2007

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Abstract. Many projections of the soil carbon sink or source are based on kinetically defined carbon pool models. Parameters of these models are often determined in a way that the steady state of the model matches observed carbon stocks. The underlying simplifying assumption is that observed carbon stocks are near equilibrium. This assumption is challenged by observations of very old soils that do still accumulate carbon. In this modelling study we explored the consequences of the case where soils are apart from equilibrium. Calculation of equilibrium states of soils that are currently accumulating small amounts of carbon were performed using the Yasso model. It was found that already very small current accumulation rates cause big changes in theoretical equilibrium stocks, which can virtually approach infinity. We conclude that soils that have been disturbed several centuries ago are not in equilibrium but in a transient state because of the slowly ongoing accumulation of the slowest pool. A first consequence is that model calibrations to current carbon stocks that assume equilibrium state, overestimate the decay rate of the slowest pool. A second consequence is that spinup runs (simulations until equilibrium) overestimate stocks of recently disturbed sites. In order to account for these consequences, we propose a transient correction. This correction prescribes a lower decay rate of the slowest pool and accounts for disturbances in the past by decreasing the spinup-run predicted stocks to match an independent estimate of current soil carbon stocks. Application of this transient correction at a Central European beech forest site with a typical disturbance history resulted in an additional carbon fixation of 5.7±1.5 tC/ha within 100 years. Carbon storage capacity of disturbed forest soils is potentially much higher than currently assumed. Simulations that do not adequately account for the transient state of soil carbon stocks neglect a considerable amount of current carbon accumulation.

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“…The comparison of different data mining algorithms for prediction of soil properties from the spectral reflectance data showed regression performance via Support Vector Machine(0.92%, RMSE) followed respectively by the partial least square regression (0.96%, RMSE) and the Stochastic gradient boosting (1.02%, RMSE) [16]. One of the advantages of PLSR compared to other chemometric methods like principal component analysis is the possibility to interpret the first few latent variables (LV), because they show the correlations between the property values and the spectral features [22]. The PLSR enables to understand and describe the often complex relationship between two types of variables X and Y [23]; X often composed of several variables, is called explanatory variables and Y represents the response variable [24].…”

Section: The Plsr Modelmentioning

confidence: 99%

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

Loum¹,

Diack²,

Ndour³

et al. 2016

OJSS

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Spectroscopy plays a major role in the access of the analytical parameters of the soil. It tends to substitute the conventional laboratory analysis because hyperspectral data were least expensive and easier to obtain. The objective of this study was to evaluate the effect of the continuum removal (CR) in the validation of the accurate prediction model of the soil properties with Vis-NIR spectroscopy data. Few studies using Vis-NIR reflectance spectroscopy have well focused the calculation of the CR method; its effect in the calibration of the accurate models was also not well emphasized. In this study, we used the remote sensing software ENVI 4.7 to compute the CR function where the value of the continuum for each sample and for each spectral wavelength was obtained by dividing the reflectance values of the full spectrum (FS) with those of the continuum curve (CC). The partial least square regression (PLSR) model was applied in the spectral data from the soil of the Senegal Sahelian region. It was calibrated with both data from the full spectrum (FS) and those obtained after the application of the continuum removal. With the application of the CR, ultraviolet wavelengths (350 -429 nm) and those of near infrared (2491 -2500 nm) were removed from the explanatory variables of PLSR model. With the FS, all wavelengths between 350 and 2500 nm were taken into account in predicting soil properties. Our findings show a positive effect of the application of CR in the estimation of soil organic carbon. In calibration, the R2 increased up to 10% with the continuum removal in the model of 12 components (CP). In terms of validation, it's the 15-component model which is the most accurate with the same range in calibration between the FS and the CR. The lowest RMSE ranged from 0.04 with the FS to 0.03 with the application of the CR in calibration and validation. These results show that the interest of this study as soil organic carbon is recognized as a key indicator of fertility of the soil 136in Sahelian-African regions. For future studies, it's important to apply the model of neural networks to better evaluate the effect of continuum removal in predicting soil properties from the spectral data and other methods of preprocessing like the multiplicative scatter correction (msc).

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Quantifying soil organic carbon fractions by infrared-spectroscopy

Abstract: Sanabria quienes, en su calidad de director y codirectora de tesis, me brindaron siempre su total apoyo y confianza para desarrollar este trabajo.

Cited by 160 publications

References 48 publications

Stability and stabilisation of biochar and green manure in soil with different organic carbon contents

Stability and stabilisation of biochar and green manure in soil with different organic carbon contents

Soils apart from equilibrium – consequences for soil carbon balance modelling

Effect of the Continuum Removal in Predicting Soil Organic Carbon with Near Infrared Spectroscopy (NIRS) in the Senegal Sahelian Soils

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