Soil organic carbon storage by shaded and unshaded coffee systems and its implications for climate change mitigation in China

2021 ◽  
pp. 1-8
Author(s):  
Ziwei Xiao ◽  
Xuehui Bai ◽  
Mingzhu Zhao ◽  
Kai Luo ◽  
Hua Zhou ◽  
...  
Keyword(s):  
Climate Change ◽  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Carbon Storage ◽  
Soil Ph ◽  
Soil Bulk Density ◽  
Soil N ◽  
And Storage

Abstract Shaded coffee systems can mitigate climate change by fixation of atmospheric carbon dioxide (CO2) in soil. Understanding soil organic carbon (SOC) storage and the factors influencing SOC in coffee plantations are necessary for the development of sound land management practices to prevent land degradation and minimize SOC losses. This study was conducted in the main coffee-growing regions of Yunnan; SOC concentrations and storage of shaded and unshaded coffee systems were assessed in the top 40 cm of soil. Relationships between SOC concentration and factors affecting SOC were analysed using multiple linear regression based on the forward and backward stepwise regression method. Factors analysed were soil bulk density (ρb), soil pH, total nitrogen of soil (N), mean annual temperature (MAT), mean annual moisture (MAM), mean annual precipitation (MAP) and elevations (E). Akaike's information criterion (AIC), coefficient of determination (R2), root mean square error (RMSE) and residual sum of squares (RSS) were used to describe the accuracy of multiple linear regression models. Results showed that mean SOC concentration and storage decreased significantly with depth under unshaded coffee systems. Mean SOC concentration and storage were higher in shaded than unshaded coffee systems at 20–40 cm depth. The correlations between SOC concentration and ρb, pH and N were significant. Evidence from the multiple linear regression model showed that soil bulk density (ρb), soil pH, total nitrogen of soil (N) and climatic variables had the greatest impact on soil carbon storage in the coffee system.

scholarly journals Soil greenhouse gas fluxes from different tree species on Taihang Mountain, North China

2014 ◽  
Vol 11 (6) ◽  
pp. 1649-1666 ◽  
Author(s):  
X. P. Liu ◽  
W. J. Zhang ◽  
C. S. Hu ◽  
X. G. Tang
Keyword(s):  
Soil Moisture ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Temperature ◽  
Soil Ph ◽  
Tree Species ◽  
Soil Bulk Density ◽  
Total N ◽  
Gas Fluxes ◽  
Greenhouse Gas Fluxes

Abstract. The objectives of this study were to investigate seasonal variation of greenhouse gas fluxes from soils on sites dominated by plantation (Robinia pseudoacacia, Punica granatum, and Ziziphus jujube) and natural regenerated forests (Vitex negundo var. heterophylla, Leptodermis oblonga, and Bothriochloa ischcemum), and to identify how tree species, litter exclusion, and soil properties (soil temperature, soil moisture, soil organic carbon, total N, soil bulk density, and soil pH) explained the temporal and spatial variation in soil greenhouse gas fluxes. Fluxes of greenhouse gases were measured using static chamber and gas chromatography techniques. Six static chambers were randomly installed in each tree species. Three chambers were randomly designated to measure the impacts of surface litter exclusion, and the remaining three were used as a control. Field measurements were conducted biweekly from May 2010 to April 2012. Soil CO2 emissions from all tree species were significantly affected by soil temperature, soil moisture, and their interaction. Driven by the seasonality of temperature and precipitation, soil CO2 emissions demonstrated a clear seasonal pattern, with fluxes significantly higher during the rainy season than during the dry season. Soil CH4 and N2O fluxes were not significantly correlated with soil temperature, soil moisture, or their interaction, and no significant seasonal differences were detected. Soil organic carbon and total N were significantly positively correlated with CO2 and N2O fluxes. Soil bulk density was significantly negatively correlated with CO2 and N2O fluxes. Soil pH was not correlated with CO2 and N2O emissions. Soil CH4 fluxes did not display pronounced dependency on soil organic carbon, total N, soil bulk density, and soil pH. Removal of surface litter significantly decreased in CO2 emissions and CH4 uptakes. Soils in six tree species acted as sinks for atmospheric CH4. With the exception of Ziziphus jujube, soils in all tree species acted as sinks for atmospheric N2O. Tree species had a significant effect on CO2 and N2O releases but not on CH4 uptake. The lower net global warming potential in natural regenerated vegetation suggested that natural regenerated vegetation were more desirable plant species in reducing global warming.

scholarly journals Carbon storage along altitudes in agrisilviculture system- Case study of Devprayag Block, Tehri District, Uttarakhand, India

2020 ◽  
Vol 7 (9) ◽  
pp. 29-38
Author(s):  
K.K. Vikrant ◽  
D.S. Chauhan ◽  
R.H. Rizvi
Keyword(s):  
Climate Change ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Carbon Storage ◽  
Carbon Stock ◽  
Total Carbon ◽  
Total Biomass ◽  
Biomass Carbon ◽  
Total Carbon Stock ◽  
Crop Biomass

Climate change is one of the impending problems that have affected the productivity of agroecosystems which calls for urgent action. Carbon sequestration through agroforestry along altitude in mountainous regions is one of the options to contribute to global climate change mitigation. Three altitudes viz. lower (286-1200m), middle (1200-2000m), and upper (2000-2800m) have been selected in Tehri district. Ten Quadrates (10m × 10 m) were randomly selected from each altitude in agrisilviculture system. At every sampling point, one composite soil sample was taken at 30 cm soil depth for soil organic carbon analysis. For the purpose of woody biomass, Non destructive method and for crop biomass assessment destructive method was employed. Finally, aboveground biomass (AGB), belowground biomass carbon (BGB), Total tree Biomass (TTB), Crop biomass (CB), Total Biomass (TB), Total biomass carbon (TBC), soil organic carbon (SOC), and total carbon stock (TC) status were estimated and variables were compared using one-way analysis of variance (ANOVA).The result indicated that AGB, BGB, TTB, CB , TB, TBC, SOC, and TC varied significantly (p < 0.05) across the altitudes. Results showed that total carbon stock followed the order upper altitude ˃ middle altitudes ˃ lower altitude. The upper altitude (2000-2800 m) AGB, BGB,TTB, TBC,SOC, and TC stock was estimated as 2.11 Mg ha-1 , 0.52 Mg ha-1, 2.63 Mg ha-1, 2.633 Mg ha-1, 1.18 Mg ha-1 , 26.53 Mg ha-1, 38.48 Mg ha-1 respectively, and significantly higher than the other altitudes. It was concluded that agrisilviculture system hold a high potential for carbon storage at temperate zones. Quercus lucotrichophora, Grewia oppositifolia and Melia azadirach contributed maximum carbon storage which may greatly contribute to the climate resilient green economy strategy and their conservation should be promoted.

scholarly journals COMPARISON OF THREE MODELS FOR PREDICTING THE SPATIAL DISTRIBUTION OF SOIL ORGANIC CARBON IN BOALEMO REGENCY, SULAWESI

2016 ◽  
Vol 18 (1) ◽  
pp. 42 ◽  
Author(s):  
Eloise Mason ◽  
Yiyi Sulaeman
Keyword(s):  
Linear Regression ◽  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Multiple Linear Regression ◽  
Regression Tree ◽  
Digital Soil Mapping ◽  
Conditional Inference ◽  
Conditional Inference Tree ◽  
Soil Landscape ◽  
Inference Tree

<p><em>Information on the spatial distribution of soil organic carbon content is required for sustainable land management. But, creating this map is time consuming and costly. Digital soil mapping methodology make use legacy soil data to create provisional soil organic carbon map. This map helps soil surveyors in allocating next soil observation. This study aimed: (i) to develop predictive statistical soil organic carbon models for Sulawesi, and (ii) to evaluate the best model between the three obtained models. Boalemo Regeny in Gorontalo Province (Sulawesi) was selected as studying area due to abundant legacy soil data. The study covered dataset preparation, model development, and model comparison. Dataset of soil organic carbon at 6 different depths as target was established from 176 soil profiles and 7 terrain parameters were selected as predictors. Soil-landscape models for each soil depth were created using regression tree, conditional inference tree, and multiple linear regression technique.  Result showed that model performance differed among 3 modelling techniques and soil depths. The tree models were better than the multiple linear regression model as they have the lowest RMSE index. The best model in the mountanious area seems to be the regression tree model, whereas in the plains it may be the conditional inference tree. In creating provisional map, several model should be developed and the median of predicted value is used as provisional map.</em></p><p><em> </em></p><p><em>Keywords: Digital soil mapping, multiple linear regression, regression tree, soil-landscape model, soil organic carbon map</em></p>

scholarly journals Changes in Storage and the Stratification Ratio of Soil Organic Carbon under Different Vegetation Types in Northeastern China

Agronomy ◽  
2020 ◽  
Vol 10 (2) ◽  
pp. 290
Author(s):  
Pujia Yu ◽  
Shiwei Liu ◽  
Zhi Ding ◽  
Aichun Zhang ◽  
Xuguang Tang
Keyword(s):  
Organic Carbon ◽  
Soil Organic Carbon ◽  
Soil Depth ◽  
Soil Bulk Density ◽  
Northeastern China ◽  
Leymus Chinensis ◽  
Vegetation Types ◽  
Chloris Virgata ◽  
And Storage ◽  
Pronounced Reduction

The depth distribution of soil organic carbon (SOC) in a soil profile is important to examine the effects of different treatments on SOC sequestration. This study was conducted to determine the effects of different vegetation types on the concentration, storage, and stratification ratio (SR) of SOC in northeastern China. Five vegetation types, Leymus chinensis (LEY), Puccinellia tenuiflora (PUC), Echinochloa phyllopogon (ECH), saline seepweed (SUA), and Chloris virgata Swartz (CHL), were selected as treatments. Soil bulk density and SOC concentration were measured at 0 to 50 cm depth, and SOC storage and four SRs (SR1 [0–10:10–20 cm], SR2 [0–10:20–30 cm], SR3 [0–10:30–40 cm], and SR4 [0–10:40–50 cm]) were calculated under the five vegetation types. Results showed a pronounced reduction in SOC concentration with increasing soil depth. Vegetation types had significant effects on SOC concentration and storage. Under PUC, ECH, SUA, and CHL treatments, SOC concentrations (2.150, 1.068, 4.110, and 2.542 g kg−1, respectively) and storages (15.075, 7.273, 30.024, and 18.078 Mg ha−1, respectively) at 0–50 cm depth were lower than those under the LEY treatment. SR1 values were all < 2, while SR2, SR3, and SR4 values were all > 2 except for SR2 under ECH and SUA treatments. Vegetation types had significant effects on SR3 (p < 0.001) and SR4 (p = 0.040), while no significant differences were found for SR1 and SR2 due to the narrow range, with values of 0.248 and 0.553 for SR1 and SR2, respectively, among the vegetation types. These results indicated that the degraded soils have great potential to sequester organic carbon in northeastern China, and SR3 could be used as an effective index to show the changes in SOC concentration and soil quality in northeastern China.

scholarly journals Dynamics of soil organic carbon in the steppes of Russia and Kazakhstan under past and future climate and land use

2021 ◽  
Vol 21 (3) ◽  
Author(s):  
Susanne Rolinski ◽  
Alexander V. Prishchepov ◽  
Georg Guggenberger ◽  
Norbert Bischoff ◽  
Irina Kurganova ◽  
...  
Keyword(s):  
Climate Change ◽  
Land Use ◽  
Organic Carbon ◽  
Land Use Change ◽  
Soil Organic Carbon ◽  
Arable Land ◽  
Future Climate ◽  
Future Climate Change ◽  
Climate Scenarios ◽  
The Impact

AbstractChanges in land use and climate are the main drivers of change in soil organic matter contents. We investigated the impact of the largest policy-induced land conversion to arable land, the Virgin Lands Campaign (VLC), from 1954 to 1963, of the massive cropland abandonment after 1990 and of climate change on soil organic carbon (SOC) stocks in steppes of Russia and Kazakhstan. We simulated carbon budgets from the pre-VLC period (1900) until 2100 using a dynamic vegetation model to assess the impacts of observed land-use change as well as future climate and land-use change scenarios. The simulations suggest for the entire VLC region (266 million hectares) that the historic cropland expansion resulted in emissions of 1.6⋅ 1015 g (= 1.6 Pg) carbon between 1950 and 1965 compared to 0.6 Pg in a scenario without the expansion. From 1990 to 2100, climate change alone is projected to cause emissions of about 1.8 (± 1.1) Pg carbon. Hypothetical recultivation of the cropland that has been abandoned after the fall of the Soviet Union until 2050 may cause emissions of 3.5 (± 0.9) Pg carbon until 2100, whereas the abandonment of all cropland until 2050 would lead to sequestration of 1.8 (± 1.2) Pg carbon. For the climate scenarios based on SRES (Special Report on Emission Scenarios) emission pathways, SOC declined only moderately for constant land use but substantially with further cropland expansion. The variation of SOC in response to the climate scenarios was smaller than that in response to the land-use scenarios. This suggests that the effects of land-use change on SOC dynamics may become as relevant as those of future climate change in the Eurasian steppes.

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