Soil organic carbon enhancement in diverse temperate riparian buffer systems in comparison with adjacent agricultural soils

2021 ◽  
Author(s):  
Enoch Ofosu ◽  
Amir Bazrgar ◽  
Brent Coleman ◽  
Bill Deen ◽  
Andrew Gordon ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Riparian Buffer

scholarly journals Soil organic carbon in northern Spain (Galicia, Asturias, Cantabria and País Vasco)

2015 ◽  
Vol 5 ◽  
Author(s):  
Elías Luis Calvo ◽  
Francisco Casás Sabarís ◽  
Juan Manuel Galiñanes Costa ◽  
Natividad Matilla Mosquera ◽  
Felipe Macías Vázquez ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Forest Soils ◽  
Agricultural Soils ◽  
Calcareous Soils ◽  
Mean Annual Precipitation ◽  
Moisture Regime ◽  
Organic Carbon Content ◽  
Northern Spain ◽  
Pais Vasco

The soil organic carbon content was analyzed in more than 7 000 soil samples under different land uses, climates and lithologies from northern Spain (Galicia, Asturias, Cantábria y País Vasco). GIS maps (1:50 000) were made of the % SOC and SOC stocks. The % SOC varies according to land use (higher in forest and scrub soils and lower in agricultural soils) and climate, and there is a highly significant correlation between SOC content and mean annual precipitation. There are significant differences between the soils of Galicia/Western Asturias (GA<sub>w</sub>) and those of the rest of the study area (Central and Eastern Asturias, Cantabria and País Vasco) (A<sub>ce</sub>CV), although these are neighbouring regions. In forest and/or scrub soils with a <em>udic</em> soil moisture regime, in GA<sub>w</sub>, the SOC is usually &gt; 7% and the average stocks 260 t ha<sup> -1</sup> (0-30 cm), and &gt;340 t ha<sup>-1</sup> (0-50 cm) in soils with thick organic matter rich horizons (&gt; 40 cm); these values greatly exceed the average contents observed in forest soils from temperate zones. Under similar conditions of vegetation and climate in soils of A<sub>ce</sub>CV the SOC average is 3% and the mean stocks 90-100 t ha<sup>-1</sup> (0-30 cm). The <em>andic</em> character of acid forest soils in GA<sub>w</sub> and the formation of C-Al,Fe complexes are pointed out as the SOC stabilization mechanism, in contrast to the neutral and calcareous soils that predominate in A<sub>ce</sub>CV, where the main species of OC are easily biodegradable.

scholarly journals Organic matter mineralisation in contrasting agricultural soils amended with sewage sludge .

2014 ◽  
Vol 4 ◽  
Author(s):  
Jose Navarro Pedreño ◽  
Ignacio Gómez Lucas ◽  
Jose Martín Soriano Disla
Keyword(s):  
Organic Matter ◽  
Single Dose ◽  
Sewage Sludge ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Dry Weight ◽  
Soil C ◽  
Multiple Sensor ◽  
Sludge Application

The mineralisation of organic matter (OM) when sewage sludge was used as amendment in 70 contrasting agricultural soils from Spain was analysed. Soils received a single dose of sewage sludge (equivalent to 50t dry weight ha<sup>-1</sup>) and the O<sub>2</sub> consumption was continuously monitored for 30 days using a multiple sensor respirometer in a laboratory experiment. The cumulative O<sub>2</sub> consumption and rates after 8 and 30 days of incubation (O<sub>2 cum</sub> 8d, 30d and O<sub>2 rate</sub> 8d, 30d), the respiratory quotient (RQ), the maximum O<sub>2</sub> rates over the incubation period (O<sub>2 max</sub>) and time from the beginning of the incubation when O<sub>2 max</sub> occurred (T<sub>max</sub>), were determined in both amended and non-amended soils. Sewage sludge application resulted in increased values for O<sub>2 max</sub>, O<sub>2 rate</sub> 8d, and O<sub>2 cum</sub> 30d. Differences were minor for T<sub>max</sub>, RQ 8d and O<sub>2 rate</sub> 30d. A considerable amount of the initial OM applied was mineralised during the first 8 days. Organic matter decomposition (as expressed by O<sub>2 cum</sub> 30d) was favoured in soils with high values of pH, carbonates, soil organic carbon and low values of amorphous Mn. Soils with these characteristics may potentially lose soil C after sewage sludge application.

scholarly journals Soil organic carbon stocks are systematically overestimated by misuse of the parameters bulk density and stone content

2016 ◽  
Author(s):  
Christopher Poeplau ◽  
Cora Vos ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Bulk Density ◽  
Agricultural Soils ◽  
Soil Layer ◽  
Future Studies ◽  
Soil Organic Carbon Stocks ◽  
Soc Stock ◽  
The Mean ◽  
Soc Stocks

Abstract. Estimation of soil organic carbon (SOC) stocks requires estimates of the carbon content, bulk density, stone content and depth of a respective soil layer. However, different application of these parameters could introduce a considerable bias. Here, we explain why three out of four frequently applied methods overestimate SOC stocks. In stone rich soils (> 30 Vol. %), SOC stocks could be overestimated by more than 100 %, as revealed by using German Agricultural Soil Inventory data. Due to relatively low stone content, the mean systematic overestimation for German agricultural soils was 2.1–10.1 % for three different commonly used equations. The equation ensemble as re-formulated here might help to unify SOC stock determination and avoid overestimation in future studies.

scholarly journals Application of RothC model to predict soil organic carbon stock on agricultural soils of Slovakia

2010 ◽  
Vol 5 (No. 1) ◽  
pp. 1-9 ◽  
Author(s):  
G. Barančíková ◽  
J. Halás ◽  
M. Gutteková ◽  
J. Makovníková ◽  
M. Nováková ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stock ◽  
Agricultural Land ◽  
Agricultural Soils ◽  
Plant Residues ◽  
Soil Organic Carbon Stock ◽  
Rothc Model ◽  
Soc Stock ◽  
Organic Carbon Stock

Soil organic matter (SOM) takes part in many environmental functions and, depending on the conditions, it can be a source or a sink of the greenhouse gases. Presently, the changes in soil organic carbon (SOC) stock can arise because of the climatic changes or changes in the land use and land management. A promising method in the estimation of SOC changes is modelling, one of the most used models for the prediction of changes in soil organic carbon stock on agricultural land being the RothC model. Because of its simplicity and availability of the input data, RothC was used for testing the efficiency to predict the development of SOC stock during 35-year period on agricultural land of Slovakia. The received data show an increase of SOC stock during the first (20 years) phase and no significant changes in the course of the second part of modelling. The increase of SOC stock in the first phase can be explained by a high carbon input of plant residues and manure and a lower temperature in comparison with the second modelling part.

Soil Organic Carbon Prediction at National Scale (Germany)

2020 ◽  
Author(s):  
Ali Sakhaee ◽  
Anika Gebauer ◽  
Mareike Ließ ◽  
Axel Don
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Agricultural Soils ◽  
Model Performance ◽  
Spatial Prediction ◽  
Spatial Modelling ◽  
Agricultural Landscapes ◽  
Boosted Regression Trees ◽  
Environmental Drivers ◽  
National Scale

&lt;p&gt;Soil Organic Carbon (SOC) plays a crucial role in agricultural ecosystems. However, its abundance is spatially variable at different scales. In recent years, machine learning (ML) algorithms have become an important tool in the spatial prediction of SOC at regional to continental scales. Particularly in agricultural landscapes, the prediction of SOC is a challenging task.&lt;/p&gt;&lt;p&gt;In this study, our aim is to evaluate the capability of two ML algorithms (Random Forest and Boosted Regression Trees) for topsoil (0 to 30 cm) SOC prediction in soils under agricultural use at national scale for Germany. In order to build the models, 50 environmental covariates representing topography, climate factors, land use as well as soil properties were selected. The SOC data we used was from the German Agricultural Soil inventory (2947 sampling points). A nested 5-fold cross-validation was used for model tuning and evaluation. Hyperparameter tuning for both ML algorithms was done by differential evolution optimization.&amp;#160;&lt;/p&gt;&lt;p&gt;This approach allows exploring an extensive set of field data in combination with state of the art pedometric tools. With a strict validation scheme, the geospatial-model performance was assessed. Current results indicate that the spatial SOC variation is to a minor extent predictable with the considered covariate data (&lt;30% explained variance). This may partly be explained by a non-steady state of SOC content in agricultural soils with environmental drivers. We discuss the challenges of geo-spatial modelling and the value of ML algorithms in pedometrics.&lt;/p&gt;

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