scholarly journals Quantification of soil organic carbon at regional scale: Benefits of fusing vis-NIR and MIR diffuse reflectance data are greater for in situ than for laboratory-based modelling approaches

Geoderma ◽  
2022 ◽  
Vol 405 ◽  
pp. 115426
Author(s):  
Michael Vohland ◽  
Bernard Ludwig ◽  
Michael Seidel ◽  
Christopher Hutengs
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Diffuse Reflectance ◽  
Regional Scale ◽  
Reflectance Data ◽  
Modelling Approaches

scholarly journals Regional simulation of soil organic carbon dynamics for dry farmland in Northeast China using the CENTURY model

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0245040
Author(s):  
Feng Zhang ◽  
Shihang Wang ◽  
Mingsong Zhao ◽  
Falv Qin ◽  
Xiaoyu Liu
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Content ◽  
Regional Scale ◽  
Organic Carbon Content ◽  
Century Model ◽  
Carbon Sequestration Potential ◽  
Soil Organic Carbon Content ◽  
Sequestration Potential ◽  
Temporal And Spatial

Soil organic carbon content has a significant impact on soil fertility and grain yield, making it an important factor affecting agricultural production and food security. Dry farmland, the main type of cropland in China, has a lower soil organic carbon content than that of paddy soil, and it may have a significant carbon sequestration potential. Therefore, in this study we applied the CENTURY model to explore the temporal and spatial changes of soil organic carbon (SOC) in Jilin Province from 1985 to 2015. Dry farmland soil polygons were extracted from soil and land use layers (at the 1:1,000,000 scale). Spatial overlay analysis was also used to extract 1282 soil polygons from dry farmland. Modelled results for SOC dynamics in the dry farmland, in conjunction with those from the Yushu field-validation site, indicated a good level of performance. From 1985 to 2015, soil organic carbon density (SOCD) of dry farmland decreased from 34.36 Mg C ha−1 to 33.50 Mg C ha−1 in general, having a rate of deterioration of 0.03 Mg C ha−1 per year. Also, SOC loss was 4.89 Tg from dry farmland soils in the province, with a deterioration rate of 0.16 Tg C per year. 35.96% of the dry farmland its SOCD increased but 64.04% of the area released carbon. Moreover, SOC dynamics recorded significant differences between different soil groups. The method of coupling the CENTURY model with a detailed soil database can simulate temporal and spatial variations of SOC at a regional scale, and it can be used as a precise simulation method for dry farmland SOC dynamics.

scholarly journals On the rebound: soil organic carbon stocks can bounce back to near forest levels when agroforests replace agriculture in southern India

2015 ◽  
Vol 2 (2) ◽  
pp. 871-902 ◽  
Author(s):  
H. C. Hombegowda ◽  
O. van Straaten ◽  
M. Köhler ◽  
D. Hölscher
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Tree Species ◽  
Regional Scale ◽  
Clay Fraction ◽  
Carbon Stocks ◽  
Southern India ◽  
Reference Plot ◽  
Soc Stock ◽  
Soc Stocks

Abstract. Tropical agroforestry has an enormous potential to sequester carbon while simultaneously producing agricultural yields and tree products. The amount of soil organic carbon (SOC) sequestered is however influenced by the type of the agroforestry system established, the soil and climatic conditions and management. In this regional scale study, we utilized a chronosequence approach to investigate how SOC stocks changed when the original forests are converted to agriculture, and then subsequently to four different agroforestry systems (AFSs): homegarden, coffee, coconut and mango. In total we established 224 plots in 56 plot clusters across four climate zones in southern India. Each plot cluster consisted of four plots: a natural forest reference plot, an agriculture reference and two of the same AFS types of two ages (30–60 years and > 60 years). The conversion of forest to agriculture resulted in a large loss the original SOC stock (50–61 %) in the top meter of soil depending on the climate zone. The establishment of homegarden and coffee AFSs on agriculture land caused SOC stocks to rebound to near forest levels, while in mango and coconut AFSs the SOC stock increased only slightly above the agriculture stock. The most important variable regulating SOC stocks and its changes was tree basal area, possibly indicative of organic matter inputs. Furthermore, climatic variables such as temperature and precipitation, and soil variables such as clay fraction and soil pH were likewise all important regulators of SOC and SOC stock changes. Lastly, we found a strong correlation between tree species diversity in homegarden and coffee AFSs and SOC stocks, highlighting possibilities to increase carbon stocks by proper tree species assemblies.

scholarly journals Prediction of Soil Organic Carbon for Ethiopian Highlands Using Soil Spectroscopy

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Tadele Amare ◽  
Christian Hergarten ◽  
Hans Hurni ◽  
Bettina Wolfgramm ◽  
Birru Yitaferu ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Diffuse Reflectance ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Coefficient Of Determination ◽  
Calibration Models ◽  
The Stability

Soil spectroscopy was applied for predicting soil organic carbon (SOC) in the highlands of Ethiopia. Soil samples were acquired from Ethiopia’s National Soil Testing Centre and direct field sampling. The reflectance of samples was measured using a FieldSpec 3 diffuse reflectance spectrometer. Outliers and sample relation were evaluated using principal component analysis (PCA) and models were developed through partial least square regression (PLSR). For nine watersheds sampled, 20% of the samples were set aside to test prediction and 80% were used to develop calibration models. Depending on the number of samples per watershed, cross validation or independent validation were used. The stability of models was evaluated using coefficient of determination (R2), root mean square error (RMSE), and the ratio performance deviation (RPD). The R2 (%), RMSE (%), and RPD, respectively, for validation were Anjeni (88, 0.44, 3.05), Bale (86, 0.52, 2.7), Basketo (89, 0.57, 3.0), Benishangul (91, 0.30, 3.4), Kersa (82, 0.44, 2.4), Kola tembien (75, 0.44, 1.9), Maybar (84. 0.57, 2.5), Megech (85, 0.15, 2.6), and Wondo Genet (86, 0.52, 2.7) indicating that the models were stable. Models performed better for areas with high SOC values than areas with lower SOC values. Overall, soil spectroscopy performance ranged from very good to good.

scholarly journals Volatile and intermediate volatility organic compounds in suburban Paris: variability, origin and importance for SOA formation

2014 ◽  
Vol 14 (19) ◽  
pp. 10439-10464 ◽  
Author(s):  
W. Ait-Helal ◽  
A. Borbon ◽  
S. Sauvage ◽  
J. A. de Gouw ◽  
A. Colomb ◽  
...  
Keyword(s):  
Organic Carbon ◽  
Particulate Organic Carbon ◽  
Organic Compounds ◽  
Regional Scale ◽  
Integrated Approach ◽  
Anthropogenic Sources ◽  
Time Resolved ◽  
Times Series

Abstract. Measurements of gaseous and particulate organic carbon were performed during the MEGAPOLI experiments, in July 2009 and January–February 2010, at the SIRTA observatory in suburban Paris. Measurements comprise primary and secondary volatile organic compounds (VOCs), of both anthropogenic and biogenic origins, including C12–C16 n-alkanes of intermediate volatility (IVOCs), suspected to be efficient precursors of secondary organic aerosol (SOA). The time series of gaseous carbon are generally consistent with times series of particulate organic carbon at regional scale, and are clearly affected by meteorology and air mass origin. Concentration levels of anthropogenic VOCs in urban and suburban Paris were surprisingly low (2–963 ppt) compared to other megacities worldwide and to rural continental sites. Urban enhancement ratios of anthropogenic VOC pairs agree well between the urban and suburban Paris sites, showing the regional extent of anthropogenic sources of similar composition. Contrary to other primary anthropogenic VOCs (aromatics and alkanes), IVOCs showed lower concentrations in winter (< 5 ppt) compared to summer (13–27 ppt), which cannot be explained by the gas-particle partitioning theory. Higher concentrations of most oxygenated VOCs in winter (18–5984 ppt) suggest their dominant primary anthropogenic origin. The respective role of primary anthropogenic gaseous compounds in regional SOA formation was investigated by estimating the SOA mass concentration expected from the anthropogenic VOCs and IVOCs (I / VOCs) measured at SIRTA. From an integrated approach based on emission ratios and SOA yields, 38 % of the SOA measured at SIRTA is explained by the measured concentrations of I / VOCs, with a 2% contribution by C12–C16 n-alkane IVOCs. From the results of an alternative time-resolved approach, the average IVOC contribution to SOA formation is estimated to be 7%, which is half of the average contribution of the traditional aromatic compounds (15%). Both approaches, which are based on in situ observations of particular I / VOCs, emphasize the importance of the intermediate volatility compounds in the SOA formation, and support previous results from chamber experiments and modeling studies. They also support the need to make systematic the IVOCs' speciated measurement during field campaigns.

scholarly journals Evaluation of modelling approaches for predicting the spatial distribution of soil organic carbon stocks at the national scale

Geoderma ◽  
2014 ◽  
Vol 223-225 ◽  
pp. 97-107 ◽  
Author(s):  
M.P. Martin ◽  
T.G. Orton ◽  
E. Lacarce ◽  
J. Meersmans ◽  
N.P.A. Saby ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Stocks ◽  
National Scale ◽  
Soil Organic Carbon Stocks ◽  
Modelling Approaches

Potential soil organic carbon stock and its uncertainty under various cropping systems in Australian cropland

Soil Research ◽  
2014 ◽  
Vol 52 (5) ◽  
pp. 463 ◽  
Author(s):  
Zhongkui Luo ◽  
Enli Wang ◽  
Jeff Baldock ◽  
Hongtao Xing
Keyword(s):  
Monte Carlo ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Cropping Systems ◽  
Regional Scale ◽  
Cropping System ◽  
Soil Conditions ◽  
Sequestration Potential ◽  
Soc Stock ◽  
Soc Stocks

The diversity of cropping systems and its variation could lead to great uncertainty in the estimation of soil organic carbon (SOC) stock across time and space. Using the pre-validated Agricultural Production Systems Simulator, we simulated the long-term (1022 years) SOC dynamics in the top 0.3 m of soil at 613 reference sites under 59 representative cropping systems across Australia’s cereal-growing regions. The point simulation results were upscaled to the entire cereal-growing region using a Monte Carlo approach to quantify the spatial pattern of SOC stock and its uncertainty caused by cropping system and environment. The predicted potential SOC stocks at equilibrium state ranged from 10 to 140 t ha–1, with the majority in a range 30–70 t ha–1, averaged across all the representative cropping systems. Cropping system accounted for ~10% of the total variance in predicted SOC stocks. The type of cropping system that determined the carbon input into soil had significant effects on SOC sequestration potential. On average, the potential SOC stock in the top 0.3 m of soil was 30, 50 and 60 t ha–1 under low-, medium- and high-input cropping systems in terms of carbon input, corresponding to –2, 18 and 26 t ha–1 of SOC change. Across the entire region, the Monte Carlo simulations showed that the potential SOC stock was 51 t ha–1, with a 95% confidence interval ranging from 38 to 64 t ha–1 under the identified representative cropping systems. Overall, predicted SOC stock could increase by 0.99 Pg in Australian cropland under the identified representative cropping systems with optimal management. Uncertainty varied depending on cropping system, climate and soil conditions. Detailed information on cropping system and soil and climate characteristics is needed to obtain reliable estimates of potential SOC stock at regional scale, particularly in cooler and/or wetter regions.

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