scholarly journals Partitioning soil organic carbon into its centennially stable and active fractions with statistical models based on Rock-Eval® thermal analysis (PARTY<sub>SOC</sub>v2.0 and PARTY<sub>SOC</sub>v2.0<sub>EU</sub>)

2021 ◽  
Author(s):  
Lauric Cécillon ◽  
François Baudin ◽  
Claire Chenu ◽  
Bent T. Christensen ◽  
Uwe Franko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Root Mean Square Error ◽  
Mean Square ◽  
Machine Learning Model ◽  
Rock Eval ◽  
Soc Fractions

Abstract. Partitioning soil organic carbon (SOC) into two kinetically different fractions that are centennially stable or active is key information for an improved monitoring of soil health and for a more accurate modelling of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measureable using Rock-Eval® thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval® thermal analysis data of soil samples from long-term experiments where the size of the centennially stable and active SOC fractions can be estimated, and (2) apply this model on the Rock-Eval® data of unknown soils, to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of the machine-learning model published by Cécillon et al. [Biogeosciences, 15, 2835–2849, 2018, https://doi.org/10.5194/bg-15-2835-2018], and built upon this strategy. The second version of this statistical model, which we propose to name PARTYSOC, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTYSOCv2.0EU) predicts the proportion of the centennially stable SOC fraction with a conservative root-mean-square error of 0.15 (relative root-mean-square error of 0.27) in a wide range of agricultural topsoils from Northwestern Europe. We plan future expansions of the PARTYSOC global model using additional reference soils developed under diverse pedoclimates and ecosystems, and we already recommend the application of PARTYSOCv2.0EU in European agricultural topsoils to provide accurate information on SOC kinetic pools partitioning that may improve the simulations of simple models of SOC dynamics.

scholarly journals Partitioning soil organic carbon into its centennially stable and active fractions with machine-learning models based on Rock-Eval® thermal analysis (PARTY<sub>SOC</sub>v2.0 and PARTY<sub>SOC</sub>v2.0<sub>EU</sub>)

2021 ◽  
Vol 14 (6) ◽  
pp. 3879-3898
Author(s):  
Lauric Cécillon ◽  
François Baudin ◽  
Claire Chenu ◽  
Bent T. Christensen ◽  
Uwe Franko ◽  
...  
Keyword(s):  
Machine Learning ◽  
Thermal Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Root Mean Square Error ◽  
Mean Square ◽  
Machine Learning Model ◽  
Rock Eval ◽  
Soc Fractions

Abstract. Partitioning soil organic carbon (SOC) into two kinetically different fractions that are stable or active on a century scale is key for an improved monitoring of soil health and for more accurate models of the carbon cycle. However, all existing SOC fractionation methods isolate SOC fractions that are mixtures of centennially stable and active SOC. If the stable SOC fraction cannot be isolated, it has specific chemical and thermal characteristics that are quickly (ca. 1 h per sample) measurable using Rock-Eval® thermal analysis. An alternative would thus be to (1) train a machine-learning model on the Rock-Eval® thermal analysis data for soil samples from long-term experiments for which the size of the centennially stable and active SOC fractions can be estimated and (2) apply this model to the Rock-Eval® data for unknown soils to partition SOC into its centennially stable and active fractions. Here, we significantly extend the validity range of a previously published machine-learning model (Cécillon et al., 2018) that is built upon this strategy. The second version of this model, which we propose to name PARTYSOC, uses six European long-term agricultural sites including a bare fallow treatment and one South American vegetation change (C4 to C3 plants) site as reference sites. The European version of the model (PARTYSOCv2.0EU) predicts the proportion of the centennially stable SOC fraction with a root mean square error of 0.15 (relative root mean square error of 0.27) at six independent validation sites. More specifically, our results show that PARTYSOCv2.0EU reliably partitions SOC kinetic fractions at its northwestern European validation sites on Cambisols and Luvisols, which are the two dominant soil groups in this region. We plan future developments of the PARTYSOC global model using additional reference soils developed under diverse pedoclimates and ecosystems to further expand its domain of application while reducing its prediction error.

scholarly journals A robust initialization method for accurate soil organic carbon simulations

2021 ◽  
Author(s):  
Eva Kanari ◽  
Lauric Cécillon ◽  
François Baudin ◽  
Hugues Clivot ◽  
Fabien Ferchaud ◽  
...  
Keyword(s):  
Machine Learning ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Co2 Concentration ◽  
Monitoring Networks ◽  
Machine Learning Model ◽  
Soc Stock ◽  
Rock Eval ◽  
New Generation

Abstract. Changes in soil organic carbon (SOC) stocks are a major source of uncertainty for the evolution of atmospheric CO2 concentration during the 21st century. They are usually simulated by models dividing SOC into conceptual pools with contrasted turnover times. The lack of reliable methods to initialize these models, by correctly distributing soil carbon amongst their kinetic pools, strongly limits the accuracy of their simulations. Here, we demonstrate that PARTYsoc, a machine-learning model based on Rock-Eval® thermal analysis optimally partitions the active and stable SOC pools of AMG, a simple and well validated SOC dynamics model, accounting for effects of soil management history. Furthermore, we found that initializing the SOC pool sizes of AMG using machine-learning strongly improves its accuracy when reproducing the observed SOC dynamics in nine independent French long-term agricultural experiments. Our results indicate that multi-compartmental models of SOC dynamics combined with a robust initialization can simulate observed SOC stock changes with excellent precision. We recommend exploring their potential before a new generation of models of greater complexity becomes operational. The approach proposed here can be easily implemented on soil monitoring networks, paving the way towards precise predictions of SOC stock changes over the next decades.

Predicting bi-decadal soil organic carbon mineralization with Rock-Eval® thermal analysis

2020 ◽  
Author(s):  
Pierre Barré ◽  
Laure Soucémarianadin ◽  
Baudin François ◽  
Chenu Claire ◽  
Bent Christensen ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Regression Models ◽  
Soil Samples ◽  
Organic C ◽  
Significant Regression ◽  
Rock Eval ◽  
Soc Mineralization

&lt;p&gt;The organic carbon reservoir of soils is a key component of climate change, calling for an accurate knowledge of the residence time of soil organic carbon (SOC). Existing proxies of the labile SOC pool such as particulate organic carbon or basal respiration tests are time consuming and unable to consistently predict SOC mineralization over years to decades. Similarly, models of SOC dynamics often yield unrealistic values of the size of SOC kinetic pools. Rock-Eval&amp;#174; 6 (RE6) thermal analysis of bulk soil samples has recently been shown to provide useful and cost-effective information regarding the long-term in-situ decomposition of SOC. The objective of this study was to design a method based on RE6 indicators to assess for a given soil, the proportion of SOC that will be mineralized in the coming 20 years.&lt;/p&gt;&lt;p&gt;To do so, we needed samples ready to be analyzed using RE6 with a known proportion of SOC mineralized in 20 years. We used archived soil samples from 4 long-term bare fallows and 8 C&lt;sub&gt;3&lt;/sub&gt;/C&lt;sub&gt;4&lt;/sub&gt; chronosequences. For each sample, the value of bi-decadal SOC mineralization was obtained from the observed SOC dynamics of its long-term bare fallow plot or the calculated C&lt;sub&gt;3&lt;/sub&gt;-derived SOC decline following the conversion to C&lt;sub&gt;4&lt;/sub&gt; plants. Those values ranged from 0.3 to 14.3 gC&amp;#183;kg&lt;sup&gt;&amp;#8722;1&lt;/sup&gt; (concentration data), representing 8.6 to 52.6% of total SOC (proportion data). All samples were analyzed using RE6 and simple linear regression models were used to predict bi-decadal SOC loss (concentration and proportion data) from 4 RE6 parameters: 1) HI (the amount of hydrogen-rich effluents formed during the pyrolysis phase of RE6; mgCH.g&lt;sup&gt;-1&lt;/sup&gt; SOC), 2) OI&lt;sub&gt;RE6&lt;/sub&gt; (the O recovered as CO and CO&lt;sub&gt;2&lt;/sub&gt; during the pyrolysis phase of RE6; mgO&lt;sub&gt;2&lt;/sub&gt;.g&lt;sup&gt;-1&lt;/sup&gt; SOC), 3) PC/SOC (the amount of organic C evolved during the pyrolysis phase of RE6; % of total SOC) and 4) T50 CO&lt;sub&gt;2&lt;/sub&gt; oxidation (the temperature at which 50% of the residual organic C was oxidized to CO&lt;sub&gt;2&lt;/sub&gt; during the RE6 oxidation phase; &amp;#176;C).&lt;/p&gt;&lt;p&gt;The RE6 HI parameter yielded the best predictions of bi-decadal SOC mineralization, for both concentration and proportion data. PC/SOC and T50 CO&lt;sub&gt;2&lt;/sub&gt; oxidation parameters also yielded significant regression models. The OI&lt;sub&gt;RE6&lt;/sub&gt; parameter was not a good predictor of bi-decadal SOC loss, with non-significant regression models. The results showed that SOC chemical composition (HI is a proxy for SOC H/C ratio), and to a lesser degree SOC thermal stability, are related to bi-decadal SOC dynamics. The RE6 thermal analysis method can therefore provide a quantitative and accurate estimate of SOC biogeochemical stability.&lt;/p&gt;

scholarly journals Estimating Soil Organic Carbon Under Different Land-Use Types in Australia’s Northern Grains Region Using Mid-Infrared Spectroscopy

Proceedings ◽  
2020 ◽  
Vol 36 (1) ◽  
pp. 141
Author(s):  
Alwyn Williams ◽  
Ryan Farquharson ◽  
David Lawrence ◽  
Jeff Baldock ◽  
Mike Bell
Keyword(s):  
Land Use ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Native Vegetation ◽  
Land Use Type ◽  
Short Term ◽  
Mid Infrared ◽  
Land Use Types ◽  
Soc Fractions

Land-use type is known to affect levels of soil organic carbon (SOC). However, the degree to which SOC is affected by land-use type over the short—(<10-years) and long—(≥10-years) term remains relatively uncertain. Moreover, there is limited data on the distribution of SOC across particulate (POC), humus (HOC) and resistant (ROC) fractions, and the responses of these fractions to land-use. Using mid-infrared spectroscopy (MIR) coupled with partial least squares regression (PLSR) algorithms generated from the Australian Soil Carbon Research Program (SCaRP), soil organic carbon (TOC, POC, HOC and ROC) was estimated across 280 paired samples across Australia’s Northern Grains Regions. Our analysis covered five land-use types: remnant native vegetation, long-term pasture (≥10-years), short-term pasture (<10-years), short-term cropping (<10-years) and long-term cropping (≥10-years). All land-use types except long-term pasture generated significant declines across all SOC fractions compared with native vegetation. Long-term cropping resulted in the greatest declines, with an average decrease of 6.25 g TOC/kg soil relative to native vegetation. Long-term cropping also reduced POC (−0.71 g/kg) and HOC (−3.19 g/kg) below that of short-term cropping. In addition, the ROC fraction responded to land-use type, with native vegetation and long-term pasture maintaining greater ROC compared with other land-use types. The results demonstrate substantial reductions across all SOC fractions with long-term cropping. The ability of long-term pastures to maintain levels of SOC similar to that of native vegetation indicates the importance of limiting soil disturbance and maintaining more continuous living plant cover within cropping systems.

Influences of repeated application of organic waste products on soil organic carbon content and stability assessed using Rock-Eval 6® thermal analysis

2020 ◽  
Author(s):  
Amicie Delahaie ◽  
Pierre Barré ◽  
Lauric Cécillon ◽  
François Baudin ◽  
Camille Resseguier ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Organic Matter ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Organic Waste ◽  
Waste Products ◽  
Clear Effect ◽  
Wide Range ◽  
Rock Eval ◽  
The Impact

&lt;p&gt;The term Organic Waste Products (OWPs) encompasses a wide range of byproducts such as manure, sewage sludge or green waste compost. The use of OWPs impacts soil quality and functioning, agricultural yields, carbon (C) sequestration, biogeochemical cycles of nutrients like nitrogen (N) or phosphorus, and organic matter (OM) dynamics. These impacts likely depend on the considered OWP.&lt;/p&gt;&lt;p&gt;Taking advantage of 3 mid to long-term experimental trials (6 to 20 years) located in the Northern part of France (Paris region; Brittany; Alsace), we investigated the impact of 16 different OWPs on C content and stability. To do so, surface soil samples from replicated plots amended with the different OWPs used either alone or in addition with mineral N fertilization and appropriated control treatments were analyzed using Rock-Eval 6&amp;#174; thermal analyses. Samples taken up at the onset of the experiment and after 6, 18 and 20 years for the 3 sites respectively were analyzed. It resulted in the analyses of 248 different samples whose Rock-Eval 6&amp;#174; (RE6) signature can be used as a proxy for soil organic carbon (SOC) biogeochemical stability. In particular, we determined 2 RE6 parameters that were related to SOC biogeochemical stability in previous studies (e.g. Barr&amp;#233; et al., 2016): HI (the amount of hydrogen-rich effluents formed during the pyrolysis phase of RE6; mgCH.g&lt;sup&gt;-1&lt;/sup&gt; SOC), and T50 CO&lt;sub&gt;2&lt;/sub&gt; oxidation (the temperature at which 50% of the residual organic C was oxidized to CO&lt;sub&gt;2&lt;/sub&gt; during the RE6 oxidation phase; &amp;#176;C). We also computed the amount of centennially stable SOC from RE6 parameters using the model developed in C&amp;#233;cillon et al. (2018). &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;Our results showed that no clear effect of OWPs addition can be established for the youngest site (6 years). On the contrary, OWPs amendments had a clear effect on SOC quantity and quality at the sites having experienced 18 and 20 years of OWPs addition. For these sites, OWPs amendments increased SOC content, decreased SOC thermal stability (T50 CO&lt;sub&gt;2&lt;/sub&gt; oxidation) and increased the Rock-Eval 6&amp;#174; Hydrogen Index (HI) compared to control plots. OWPs amendments tended to increase slightly the amount of centennially stable SOC at the sites having experienced 20 years of repeated OWPs application. Our results suggest that if OWPs addition does increase SOC content, at least in the long run, the majority of this additional SOC is labile and may be quickly lost if OWPs additions are stopped.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;References:&lt;/p&gt;&lt;p&gt;Barr&amp;#233; P., Plante A.F., C&amp;#233;cillon L., Lutfalla S., Baudin F., Bernard S., Christensen B.T., Eglin T., Fernandez J.M., Houot S., K&amp;#228;tterer T., Le Guillou C., Macdonald A., van Oort F. &amp; Chenu C. (2016) The energetic and chemical signatures of persistent soil organic matter. Biogeochemistry, 130: 1-12.&lt;/p&gt;&lt;p&gt;C&amp;#233;cillon L., Baudin F., Chenu C., Houot S., Jolivet R., K&amp;#228;tterer T., Lutfalla S., Macdonald A.J., van Oort F., Plante A.F., Savignac F., Souc&amp;#233;marianadin L.N. &amp; Barr&amp;#233; P. (2018) A model based on Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon pool in temperate soils. Biogeosciences, 15, 2835-2849.&lt;/p&gt;

scholarly journals Effects of long-term fertilisation on soil organic carbon sequestration after a 34-year rice-wheat rotation in Taihu Lake Basin

2021 ◽  
Vol 67 (No. 1) ◽  
pp. 1-7
Author(s):  
Fang He ◽  
Lin-lin Shi ◽  
Jing-cheng Tian ◽  
Li-juan Mei
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Cropping System ◽  
Lake Basin ◽  
Long Term Effects ◽  
Mineral N ◽  
Organic Fertiliser ◽  
Sequestration Rate ◽  
Soc Fractions

To evaluate the long-term effects of fertilisation on soil organic carbon (SOC) sequestration in rice-wheat cropping ecosystems, SOC dynamics, stocks and fractionation were determined. The treatments included no fertiliser, mineral N and P, mineral N, P and K, organic fertiliser (OF), OF plus NP and OF plus NPK. The results showed that the average carbon inputs that derived from crop stubble, root residues and organic fertilisers were between 1.47 and 4.33 t/ha/year over the past 34 years. The average SOC stocks measured in the samples collected in 2011–2013 ranged from 31.20 to 38.52 t/ha. The range of the SOC sequestration rate was 0.11–0.40 t/ha/year with a SOC sequestration efficiency of 6.3%. Overall, organic fertilisation significantly promoted C-input, SOC and the sequestration rate compared to mineral fertilisation. The "active pool" (very labile and labile fractions) and "passive pool" (less labile and recalcitrant fractions) accounted for about 71.0% and 29.0% of the SOC fractions, respectively. Significant positive relationships between C-inputs and SOC fractions indicated that SOC was not saturated in this typical rice-wheat cropping system, and fertilisation, especially organic amendment, is an effective SOC strategy sequestration.  

scholarly journals A model based on Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon pool in temperate soils

2018 ◽  
Vol 15 (9) ◽  
pp. 2835-2849 ◽  
Author(s):  
Lauric Cécillon ◽  
François Baudin ◽  
Claire Chenu ◽  
Sabine Houot ◽  
Romain Jolivet ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Organic Carbon ◽  
Predictive Performance ◽  
Soil Samples ◽  
Temperate Soils ◽  
Organic Carbon Pool ◽  
Validation Set ◽  
Bare Fallow ◽  
Rock Eval

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 analysis methods provide cost-effective information on SOC thermal stability that has been shown to be qualitatively related to SOC biogeochemical stability. The objective of this work was to build the first quantitative model of the size of the centennially persistent SOC pool based on thermal analysis. We used a unique set of 118 archived soil samples from four agronomic experiments in northwestern Europe with long-term bare fallow and non-bare fallow treatments (e.g., manure amendment, cropland and grassland) as a sample set for which estimating the size of the centennially persistent SOC pool is relatively straightforward. At each experimental site, we estimated the average concentration of centennially persistent SOC and its uncertainty by applying a Bayesian curve-fitting method to the observed declining SOC concentration over the duration of the long-term bare fallow treatment. Overall, the estimated concentrations of centennially persistent SOC ranged from 5 to 11 g C kg−1 of soil (lowest and highest boundaries of four 95 % confidence intervals). Then, by dividing the site-specific concentrations of persistent SOC by the total SOC concentration, we could estimate the proportion of centennially persistent SOC in the 118 archived soil samples and the associated uncertainty. The proportion of centennially persistent SOC ranged from 0.14 (standard deviation of 0.01) to 1 (standard deviation of 0.15). Samples were subjected to thermal analysis by Rock-Eval 6 that generated a series of 30 parameters reflecting their SOC thermal stability and bulk chemistry. We trained a nonparametric machine-learning algorithm (random forests multivariate regression model) to predict the proportion of centennially persistent SOC in new soils using Rock-Eval 6 thermal parameters as predictors. We evaluated the model predictive performance with two different strategies. We first used a calibration set (n = 88) and a validation set (n = 30) with soils from all sites. Second, to test the sensitivity of the model to pedoclimate, we built a calibration set with soil samples from three out of the four sites (n = 84). The multivariate regression model accurately predicted the proportion of centennially persistent SOC in the validation set composed of soils from all sites (R2 = 0.92, RMSEP = 0.07, n = 30). The uncertainty of the model predictions was quantified by a Monte Carlo approach that produced conservative 95 % prediction intervals across the validation set. The predictive performance of the model decreased when predicting the proportion of centennially persistent SOC in soils from one fully independent site with a different pedoclimate, yet the mean error of prediction only slightly increased (R2 = 0.53, RMSEP = 0.10, n = 34). This model based on Rock-Eval 6 thermal analysis can thus be used to predict the proportion of centennially persistent SOC with known uncertainty in new soil samples from different pedoclimates, at least for sites that have similar Rock-Eval 6 thermal characteristics to those included in the calibration set. Our study reinforces the evidence that there is a link between the thermal and biogeochemical stability of soil organic matter and demonstrates that Rock-Eval 6 thermal analysis can be used to quantify the size of the centennially persistent organic carbon pool in temperate soils.

scholarly journals A model based on Rock-Eval thermal analysis to quantify the size of the centennially persistent organic carbon pool in temperate soils

2018 ◽  
Author(s):  
Lauric Cécillon ◽  
François Baudin ◽  
Claire Chenu ◽  
Sabine Houot ◽  
Romain Jolivet ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Organic Carbon ◽  
Soil Samples ◽  
Temperate Soils ◽  
Organic Carbon Pool ◽  
Validation Set ◽  
Bare Fallow ◽  
Rock Eval ◽  
Sample Set

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 analysis methods provide cost-effective information on SOC thermal stability that has been shown to be qualitatively related to SOC biogeochemical stability. The objective of this work was to build the first quantitative thermal analysis based model of the size of the centennially persistent SOC pool. We used a unique set of soil samples from four agronomic experiments in Northwestern Europe with long-term bare fallow and non-bare fallow treatments (e.g. manure amendment, cropland and grassland), as a sample set for which estimating the size of the centennially persistent SOC pool is relatively straightforward. At each experimental site, we estimated the average concentration of centennially persistent SOC and its uncertainty by applying a Bayesian curve fitting method on the observed declining SOC concentration over the duration of the long-term bare fallow treatment. Overall, the estimated concentrations of centennially persistent SOC ranged from 5 to 11 gC.kg−1 soil (lowest and highest boundaries of four 95 % confidence intervals). Then, by dividing site-specific concentrations of persistent SOC by the total SOC concentration of 118 archived soil samples from long-term bare fallow and non-bare fallow treatments, we could estimate the proportion of centennially persistent SOC in the samples and the associated uncertainty. The proportion of centennially persistent SOC ranged from 0.14 (standard deviation of 0.01) to 1 (standard deviation of 0.15). Samples were subjected to thermal analysis by Rock-Eval 6 that generated a series of 30 parameters reflecting their SOC thermal stability and bulk chemistry. The sample set was split into a calibration set (n = 88) and a validation set (n = 30). We trained a non-parametric machine learning algorithm (random forests multivariate regression model) that accurately predicted the size of the centennially persistent SOC pool using Rock-Eval 6 thermal parameters as predictors in the calibration set (pseudo-R² = 0.91, RMSEC = 0.06) and the validation set (R² = 0.91, RMSEP = 0.07). The uncertainty of the predictions obtained using the multivariate regression model was quantified by a Monte Carlo approach that produced conservative 95% prediction intervals across the 30 samples of the validation set. This model based on Rock-Eval 6 thermal analysis can thus be used to predict the proportion of centennially persistent SOC with known uncertainty in new soil samples from similar pedoclimates. Our study strengthens the evidence for a link between the thermal and biogeochemical stability of soil organic matter, and demonstrates that Rock-Eval 6 thermal analysis can be used to quantify the size of the centennially persistent organic carbon pool in temperate soils.

Does agricultural practices impact the quantity and the forms of organic carbon stored in cultivated soils of the Senegal groundnut basin? A Rock-Eval approach

2020 ◽  
Author(s):  
Oscar Pascal Malou ◽  
David Sebag ◽  
Patricia Moulin ◽  
Tiphaine Chevallier ◽  
Yacine Badiane Ndour ◽  
...  
Keyword(s):  
Thermal Analysis ◽  
Standard Deviation ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Practices ◽  
Sandy Soils ◽  
Organic Wastes ◽  
Rock Eval ◽  
Cultivated Soils

&lt;p&gt;Soil organic carbon (SOC) is a key element in the functioning of agrosystems. It ensures soil quality and productivity of cultivated systems in the Sahelian region. This study uses Rock-Eval pyrolysis to examine how cultural practices impact SOC quantity and quality of cultivated sandy soils in the Senegal groundnut basin. Such thermal analysis method provides cost-effective information on SOC thermal stability that has been shown to be qualitatively related to SOC biogeochemical stability. Soils were sampled within 2 villages agricultural plots representative of local agricultural systems and for local preserved areas. Total SOC concentrations ranged from 1.8 to 18.5 g.kg&lt;sup&gt;-1&lt;/sup&gt; soil (mean &amp;#177; standard deviation: 5.6 &amp;#177; 0.4 g.kg&lt;sup&gt;-1&lt;/sup&gt; soil) in the surface layer (0-10 cm) and from 1.5 to 11.3 g.kg&lt;sup&gt;-1&lt;/sup&gt; soil (mean &amp;#177; standard deviation: 3.3 &amp;#177; 0.2 g.kg&lt;sup&gt;-1&lt;/sup&gt; soil) in 10-30 cm deep layer. SOC of cultivated soils significantly (p-value &lt; 0.0001) decreased according to treatments in the following order: +organic wastes &gt; +manure &gt; +millet residues &gt; no input. Our results show that the quantity and the quality of SOC are linked to each other and both depend on land-use and agricultural practices, especially the nature of organic inputs. This correlation is very strong in the tree plantation (R&amp;#178; = 0.98) and in the protected shrubby savanna (R&lt;sup&gt;&amp;#178;&lt;/sup&gt; = 0.97). It remains important for cultivated soils receiving organic wastes (R&amp;#178; = 0.82), manure (R&lt;sup&gt;&amp;#178;&lt;/sup&gt; &gt; 0.75), or millet residues (R&lt;sup&gt;2&lt;/sup&gt; = 0.91) but it&amp;#8217;s no more significant in no-input situations. The Rock-Eval based indexes were depicted in a I/R diagram that illustrate the level of SOC stabilization and plotted against comparable results from literature. The Senegalese sandy soils have thermal signatures showing an inversion of the I and the R indexes compared to data from the literature and highlighting SOC stabilization as a function of soil depth. Indeed, the studied soils were characterized by a more abundant refractory pool (A5 which ranged from 7.7 to 21.3 % in 0-10 cm layer and from 12.5 to 24.3 % in 10-30 cm, respectively) compared to other tropical soils. The SOC in these sandy soils while positively affected by organic inputs is dominated by labile forms that mineralize quickly which is excellent for the needs of productivity of these agrosystems but not for mitigation of climate change.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Keywords:&lt;/strong&gt; Soil organic carbon; Organic inputs; Thermal analysis; Agrosystems; West Africa&lt;/p&gt;

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