Effects of Different Land Use Changes and Spatial Variation in Rainfall on Soil Properties and Soil Carbon Storage in Western Rajasthan, India

In order to predict the potential of soils to store carbon in response to land use or climate changes, we measured the fluxes and distribution of residence times of C in French cultivated soils. We used the natural abundances in 13C and 14C to measure this distribution in long-term experiments of maize cultivation in France. 75% of the topsoil carbon had a mean residence time of 40 yr. Coarse particle-size fractions contained most of the younger carbon. A compartment of stable C was estimated using radiocarbon dating. Belowground plant material inputs stored as much as C as aboveground inputs. The effect of temperature on soil carbon mineralization affected only rate constants, with a Q10 = 3.1 constant in the range 1–25 °C. The data were summerized in a simple simulation model, which predicted a nil or low effect of climatic change on soil carbon storage in the next 50 yr. In France, land use changes will have more influence than atmospheric changes on C storage. Key words: France, greenhouse gases, mineralization, model, soil carbon, storage, temperature

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Prediction and digital mapping of soil carbon storage in the Lower Namoi Valley

Soil Research ◽

10.1071/sr05136 ◽

2006 ◽

Vol 44 (3) ◽

pp. 233 ◽

Cited By ~ 125

Author(s):

Budiman Minasny ◽

Alex. B. McBratney ◽

M. L. Mendonça-Santos ◽

I. O. A. Odeh ◽

Brice Guyon

Keyword(s):

Land Use ◽

Organic Carbon ◽

Soil Carbon ◽

Carbon Storage ◽

Environmental Data ◽

Pedotransfer Functions ◽

Soil Carbon Storage ◽

Depth Function ◽

Terrain Attributes ◽

Organic Carbon Storage

Estimation and mapping carbon storage in the soil is currently an important topic; thus, the knowledge of the distribution of carbon content with depth is essential. This paper examines the use of a negative exponential profile depth function to describe the soil carbon data at different depths, and its integral to represent the carbon storage. A novel method is then proposed for mapping the soil carbon storage in the Lower Namoi Valley, NSW. This involves deriving pedotransfer functions to predict soil organic carbon and bulk density, fitting the exponential depth function to the carbon profile data, deriving a neural network model to predict parameters of the exponential function from environmental data, and mapping the organic carbon storage. The exponential depth function is shown to fit the soil carbon data adequately, and the parameters also reflect the influence of soil order. The parameters of the exponential depth function were predicted from land use, radiometric K, and terrain attributes. Using the estimated parameters we map the carbon storage of the area from surface to a depth of 1 m. The organic carbon storage map shows the high influence of land use on the predicted storage. Values of 15–22 kg/m2 were predicted for the forested area and 2–6 kg/m2 in the cultivated area in the plains.

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Modelling soil carbon stocks in semi-natural and agro-ecosystems – quantifying national scale impacts of the Anthropocene

10.5194/egusphere-egu2020-18355 ◽

2020 ◽

Author(s):

Victoria Janes-Bassett ◽

Jessica Davies ◽

Richard Bassett ◽

Dmitry Yumashev ◽

Ed Rowe ◽

...

Keyword(s):

Land Use ◽

Nutrient Cycling ◽

Soil Carbon ◽

Carbon Storage ◽

Management Practices ◽

Carbon Stocks ◽

Soil Carbon Storage ◽

National Scale ◽

Soil Carbon Stocks

Throughout the Anthropocene, the conversion of land to agriculture and atmospheric deposition of nitrogen have resulted in significant changes to biogeochemical cycling, including soil carbon stocks. Quantifying these changes is complex due to a number of influential factors (including climate, land use management, soil type) and their interactions. As the largest terrestrial store of carbon, soils are a key component in climate regulation. In addition, soil carbon storage contributes to numerous ecosystem services including food provision. It is therefore imperative that we understand changes to soil carbon stocks, and provide effective strategies for their future management.Modelling soil systems provides a means to estimate changes to soil carbon stocks. Due to linkages between the carbon cycle and other major nutrient cycles (notably nitrogen and phosphorus which often limit the productivity of ecosystems), models of integrated nutrient cycling are required to understand the response of the carbon cycle to global pressures. Simulating the impacts of land use changes requires capacity to model both semi-natural and intensive agricultural systems.In this study, we have developed an integrated carbon-nitrogen-phosphorus model of semi-natural systems to include representation of both arable and grassland systems, and a range of agricultural management practices. The model is applicable to large spatial scales, as it uses readily available input data and does not require site-specific calibration. &#160;After being validated both spatially and temporally using data from long-term experimental sites across Northern-Europe, the model was applied at a national scale throughout the United Kingdom to assess the impacts of land use change and management practices during the last two centuries. Results indicate a decrease in soil carbon in areas of agricultural expansion, yet in areas of semi-natural land use, atmospheric deposition of nitrogen has resulted in increased net primary productivity and subsequently soil carbon. The results demonstrate anthropogenic impacts on long-term nutrient cycling and soil carbon storage, and the importance of integrated nutrient cycling within models.

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Effect of Land Use History and Pattern on Soil Carbon Storage in Arid Region of Central Asia

PLoS ONE ◽

10.1371/journal.pone.0068372 ◽

2013 ◽

Vol 8 (7) ◽

pp. e68372 ◽

Cited By ~ 9

Author(s):

Xiaoyu Li ◽

Yugang Wang ◽

Lijuan Liu ◽

Geping Luo ◽

Yan Li ◽

...

Keyword(s):

Land Use ◽

Soil Carbon ◽

Central Asia ◽

Carbon Storage ◽

Arid Region ◽

Land Use History ◽

Soil Carbon Storage

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Soil carbon storage in mangroves is primarily controlled by soil properties: A study at Dongzhai Bay, China

The Science of The Total Environment ◽

10.1016/j.scitotenv.2017.11.187 ◽

2018 ◽

Vol 619-620 ◽

pp. 1226-1235 ◽

Cited By ~ 11

Author(s):

Yanmei Xiong ◽

Baowen Liao ◽

Ed Proffitt ◽

Wei Guan ◽

Yuxin Sun ◽

...

Keyword(s):

Soil Properties ◽

Soil Carbon ◽

Carbon Storage ◽

Soil Carbon Storage

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Soil carbon storage in plantation forests and pastures: land-use change implications

Tellus B ◽

10.1034/j.1600-0889.1999.00015.x ◽

1999 ◽

Vol 51 (2) ◽

pp. 326-335 ◽

Cited By ~ 48

Author(s):

NEAL A. SCOTT ◽

KEVIN R. TATE ◽

JUSTIN FORD-ROBERTSON ◽

DAVID J. GILTRAP ◽

C. TATTERSALL SMITH

Keyword(s):

Land Use ◽

Land Use Change ◽

Soil Carbon ◽

Carbon Storage ◽

Soil Carbon Storage ◽

Plantation Forests

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Soil Carbon Storage in Silvopasture and Related Land-Use Systems in the Brazilian Cerrado

Journal of Environmental Quality ◽

10.2134/jeq2010.0162 ◽

2011 ◽

Vol 40 (3) ◽

pp. 833-841 ◽

Cited By ~ 37

Author(s):

Rafael G. Tonucci ◽

P. K. Ramachandran Nair ◽

Vimala D. Nair ◽

Rasmo Garcia ◽

Fernando S. Bernardino

Keyword(s):

Land Use ◽

Soil Carbon ◽

Carbon Storage ◽

Brazilian Cerrado ◽

Soil Carbon Storage ◽

Land Use Systems

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PROcess-guided deep learning and DAta-driven modelling (PRODA) uncovers key mechanisms underlying global soil carbon storage

10.5194/ismc2021-10 ◽

2021 ◽

Author(s):

Feng Tao ◽

Yuanyuan Huang ◽

Bruce A. Hungate ◽

Xingjie Lu ◽

Toby D. Hocking ◽

...

Keyword(s):

Deep Learning ◽

Organic Carbon ◽

Spatial Variation ◽

Soil Carbon ◽

Carbon Storage ◽

Data Driven ◽

Soil Carbon Storage ◽

Community Land Model ◽

Global Soil ◽

Primary Driver

Soil carbon storage is a vital ecosystem service. Yet mechanisms that regulate global soil organic carbon (SOC) dynamics remain elusive. Here we explicitly retrieve the spatial patterns of key biogeochemical mechanisms and their regulation pathways on SOC storage using the novel PROcess-guided deep learning and Data-driven modelling (PRODA) approach. PRODA integrates data assimilation, deep learning, big data with 54,073 globally distributed vertical SOC profiles, and the Community Land Model version 5 (CLM5) to best represent and understand global soil carbon cycle. The PRODA-optimised CLM5 can represent 56&#177;2% spatial variation of SOC across the world. Among all the mechanisms we explored in this study, microbial carbon use efficiency (CUE) emerges as the most critical regulator of global SOC storage. Increasing CUE, where more carbon flow is channelled into stabilisation, coincides with decreasing temperature and favours SOC accrual. Global sensitivity analysis further confirms the CUE, surpassing carbon input and decomposition, as the primary driver to SOC storage and its spatial variation. An increase of CUE by 1% from its standing value will lead to an additional 76&#177;3 petagrams global SOC accumulation. We conclude that how efficiently soil microorganisms utilise organic carbon in metabolism is central to global SOC stabilisation. Understanding detailed processes underlying CUE and its environmental dependence will be critical in accurately describing soil carbon dynamics and its feedbacks to climate change.

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