Linking thermogravimetric data with soil organic carbon fractions

Tokarski, David; Wiesmeier, Martin; Weissmannová, Helena Doležalová; Kalbitz, Karsten; Demyan, Michael Scott; Kučerík, Jiří; Siewert, Christian

doi:10.1016/j.geoderma.2019.114124

Cited by 19 publications

(8 citation statements)

References 37 publications

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“…Second issue is related to the soil sampling. The grass land soils can be considered as very stable and protected against physical perturbation (Jensen et al, 2019), and the correlations between TML's and soil properties are always stronger (Tokarski et al, 2020). However, although the grassland soils were sampled at the same time as arable soils, the sampling was carried out under permanent vegetation.…”

Section: The Verification Of Resultsmentioning

confidence: 99%

“…A number of works confirmed significant correlations between TML and various soil properties. In particular, TML correlations were observed for SOC, total nitrogen (TN) and clay contents (Siewert, 2004(Siewert, , 2001, soil carbonates ( (Siewert, 2004(Siewert, , 2001, amount and rate of CO2 evolution measured during basal soil respiration under laboratory conditions (Kučerík et al, 2013;Siewert et al, 2012), total SOM content (Kučerík et al, 2018) and soil organic matter fractions of vary biological turnover rates (Tokarski et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Previously, we have shown that the LT M L 200−300 and LT M L 300−450 correlate with active (sum of dissolved and particulate organic matter) and intermediate (pools SOC in the sand fraction and in stable aggregates + SOC attached to silt and clay particles). The LT M L 450−550 showed a weak correlation with a passive pool as measured by chemical oxidation(Tokarski et al, 2020). It can be observed that the higher correlation coefficients of microbial parameters were observed mostly in LT M L 200−300 for grassland soils, but arable soils showed in some https://doi.org/10.5194/soil-2021-109 Preprint.…”

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Microbial soil characteristics of grassland and arable soils linked to thermogravimetry data: correlations, use and limits

Doležalová-Weissmannová

Malý

Brtnický

et al. 2021

Preprint

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Abstract. Thermogravimetry (TG) is a simple method that enables rapid analysis of soil properties such as the content of total organic C, nitrogen, clay and C fractions with different stability. However, the possible link between TG data and microbiological soil properties has not been systematically tested yet and limits TG application for soil and soil organic matter assessment. This work aimed to search and to validate relationships of thermal mass losses (TML) to total C and N contents, microbial biomass C and N, basal and substrate-induced respiration, extractable organic carbon content, anaerobic ammoniﬁcation, urease activity, short-term nitriﬁcation activity, speciﬁc growth rate, and time to reach the maximum respiration rate for two sample sets of arable and grassland soils. Analyses of the training soil set revealed signiﬁcant correlations of TML with basic soil properties such as carbon and nitrogen content with distinguishing linear regression parameters and temperatures of correlating mass losses for arable and grassland soils. In a second stage the equations of signiﬁcant correlations were used for validation with an independent second sample set. This conﬁrmed applicability of developed equations for prediction of microbiological properties mainly for arable soils. For grassland soils was the applicability lower, which was explained as the inﬂuence of rhizosphere processes. Nevertheless, the application of TG can facilitate the understanding of changes in soil caused by microorganism’s activity and the different regression equations between TG and soil parameters reﬂect changes in proportions between soil components caused by land use management.

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Section: The Verification Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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confidence: 99%

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Microbial soil characteristics of grassland and arable soils linked to thermogravimetry data: correlations, use and limits

Doležalová-Weissmannová

Malý

Brtnický

et al. 2021

Preprint

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“…In general, the variance explained by the mixed effect models was not similar, but varied between SOC and its labile fractions HWEC and MBC. It became clear that SOC and the labile fractions HWEC and MBC are not fully correlated but quantitatively quite distinct SOM pools with different annual dynamics (Wander, 2004;Tokarski et al, 2020). Not last, the faster turnover of the labile fractions is one of the reasons for the lower explained variability by the different models.…”

Section: Discussionmentioning

confidence: 99%

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Ortner

2021

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“…In general, the variance explained by the mixed-effect models was not sim-ilar but varied between SOC and its labile fractions HWEC and MBC. It became clear that SOC and the labile fractions HWEC and MBC are not fully correlated but are quantitatively quite distinct SOM pools with different dynamics (Wander, 2004;Tokarski et al, 2020). Not least, it is suggested that the faster turnover of the labile fractions is one of the reasons for the lower explained variability by the different models.…”

Section: Distinct Model Parameters For the Estimation Of Soc Hwec And...mentioning

confidence: 99%

Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics

Ortner

Seidel

Semella

et al. 2022

SOIL

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Abstract. Soil organic matter (SOM) is an indispensable component of terrestrial ecosystems. Soil organic carbon (SOC) dynamics are influenced by a number of well-known abiotic factors such as clay content, soil pH, or pedogenic oxides. These parameters interact with each other and vary in their influence on SOC depending on local conditions. To investigate the latter, the dependence of SOC accumulation on parameters and parameter combinations was statistically assessed that vary on a local scale depending on parent material, soil texture class, and land use. To this end, topsoils were sampled from arable and grassland sites in south-western Germany in four regions with different soil parent material. Principal component analysis (PCA) revealed a distinct clustering of data according to parent material and soil texture that varied largely between the local sampling regions, while land use explained PCA results only to a small extent. The PCA clusters were differentiated into total clusters that contain the entire dataset or major proportions of it and local clusters representing only a smaller part of the dataset. All clusters were analysed for the relationships between SOC concentrations (SOC %) and mineral-phase parameters in order to assess specific parameter combinations explaining SOC and its labile fractions hot water-extractable C (HWEC) and microbial biomass C (MBC). Analyses were focused on soil parameters that are known as possible predictors for the occurrence and stabilization of SOC (e.g. fine silt plus clay and pedogenic oxides). Regarding the total clusters, we found significant relationships, by bivariate models, between SOC, its labile fractions HWEC and MBC, and the applied predictors. However, partly low explained variances indicated the limited suitability of bivariate models. Hence, mixed-effect models were used to identify specific parameter combinations that significantly explain SOC and its labile fractions of the different clusters. Comparing measured and mixed-effect-model-predicted SOC values revealed acceptable to very good regression coefficients (R2=0.41–0.91) and low to acceptable root mean square error (RMSE = 0.20 %–0.42 %). Thereby, the predictors and predictor combinations clearly differed between models obtained for the whole dataset and the different cluster groups. At a local scale, site-specific combinations of parameters explained the variability of organic carbon notably better, while the application of total models to local clusters resulted in less explained variance and a higher RMSE. Independently of that, the explained variance by marginal fixed effects decreased in the order SOC > HWEC > MBC, showing that labile fractions depend less on soil properties but presumably more on processes such as organic carbon input and turnover in soil.

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Linking thermogravimetric data with soil organic carbon fractions

Cited by 19 publications

References 37 publications

Microbial soil characteristics of grassland and arable soils linked to thermogravimetry data: correlations, use and limits

Microbial soil characteristics of grassland and arable soils linked to thermogravimetry data: correlations, use and limits

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Content of soil organic carbon and labile fractions depend on local combinations of mineral-phase characteristics

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