National and sub-national assessments of soil organic carbon stocks and changes: The GEFSOC modelling system

Milne, E.; Al‐Adamat, Rida; Batjes, N.H.; Bernoux, Martial; Bhattacharyya, T.; Cerri, Carlos C.; Cerri, Carlos Eduardo Pellegrino; Coleman, Kevin; Easter, Mark; Falloon, Pete; Feller, Christian; Gicheru, Patrick; Kamoni, P.; Killian, K.; Pal, Dilip Kumar; Paustian, Keith; Powlson, D. S.; Rawajfih, Z.; Sessay, Mohamed F.; Williams, Stephen; Wokabi, S.M.

doi:10.1016/j.agee.2007.01.002

Cited by 90 publications

(42 citation statements)

References 42 publications

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“…A Convenção-Quadro das Nações Unidas sobre Mudança do Clima (UNFCCC), criada na Conferência das Nações Unidas sobre Meio Ambiente e Desenvolvimento ocorrida no Rio de Janeiro em 1992, aborda o comprometimento dos países de prover inventários nacionais de emissão de gases do efeito estufa e, para o setor agropecuário, inventários de estoque de carbono (Milne et al, 2007).…”

Section: Introductionunclassified

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Carvalho

Filho

Chagas

et al. 2016

Pesq. agropec. bras.

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Resumo -O objetivo deste trabalho foi o desenvolvimento de modelos com diferentes conjuntos de dados, para estimar a densidade de solos de regiões tropicais montanhosas, a partir de atributos de solos comumente encontrados nas análises de perfis de solos descritos nos levantamentos regionais. O conjunto total de dados compõe-se de 163 amostras e foi dividido em seis grupamentos, dos quais três com 73 amostras, com o máximo de 32 covariáveis, e três com 163 amostras, com o máximo de 18 covariáveis. Testaram-se modelos de regressão linear múltipla (RLM) e randomForest (RF). A menor incerteza entre os modelos foi alcançada pelo RLM2, com R 2 de 0,56, 13 covariáveis e 73 amostras. Nos grupamentos com 163 amostras, os melhores modelos foram os RF, com R 2 médio de 0,48. A raiz quadrada da média do erro ao quadrado variou entre 0,09 e 0,14. As covariáveis mais importantes no modelo RF foram: carbono orgânico, hidrogênio, areia fina e grossa, saturação por bases e capacidade de troca catiônica. Pelo método "stepwise regression", as variáveis mais importantes foram: a relação silte/argila; areia grossa e fina; carbono orgânico; saturação por bases; e potássio.Termos para indexação: estoque de carbono, funções de pedotransferência, modelos dirigidos pelos dados, stepwise. Multiple linear regression and Random Forest model to estimate soil bulk density in mountainous regionsAbstract -The objective of this work was the development of models with different sets of data for estimating soil bulk density in tropical mountainous regions, from soil attributes commonly found in the analyses of soil profiles described in regional surveys. The complete dataset is composed of 163 samples and it was divided into six groups, of which three groups have 73 samples and the maximum of 32 covariables, and three have 163 samples and the maximum of 18 covariables. The linear regression (RLM) and randomForest (RF) models were tested. The lowest uncertainty between the models was achieved by RLM2, with R 2 of 0.56, 13 covariables, and 73 samples. Considering the groups with 163 samples, the best models were the RFs with mean R 2 of 0.48. The root mean squared error ranged between 0.09 and 0.14. The most important covariables in the RF model were: organic carbon, hydrogen, fine and coarse sand, base saturation, and cation exchange capacity. By the stepwise backward regression, the main covariables were: silt and clay relation; fine and coarse sand; organic carbon; base saturation; and potassium.

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Section: Introductionunclassified

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Carvalho

Filho

Chagas

et al. 2016

Pesq. agropec. bras.

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“…Although most regional studies in agricultural soils report a SOC loss, some studies have also registered a gain in SOC (7) or both losses and gains depending on the land use (6). To the contrary, model-based inventories that build on detailed land use and management observations for change attribution typically lack a national network of SOC stock observations (8,9). Hence, satisfactory explanations of historic SOC changes at the regional scale have not yet been given (3).…”

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confidence: 99%

Agricultural management explains historic changes in regional soil carbon stocks

Wesemael

Paustian

Meersmans

et al. 2010

Proc. Natl. Acad. Sci. U.S.A.

160

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Agriculture is considered to be among the economic sectors having the greatest greenhouse gas mitigation potential, largely via soil organic carbon (SOC) sequestration. However, it remains a challenge to accurately quantify SOC stock changes at regional to national scales. SOC stock changes resulting from SOC inventory systems are only available for a few countries and the trends vary widely between studies. Process-based models can provide insight in the drivers of SOC changes, but accurate input data are currently not available at these spatial scales. Here we use measurements from a soil inventory dating from the 1960s and resampled in 2006 covering the major soil types and agricultural regions in Belgium together with region-specific land use and management data and a process-based model. The largest decreases in SOC stocks occurred in poorly drained grassland soils (clays and floodplain soils), consistent with drainage improvements since 1960. Large increases in SOC in well drained grassland soils appear to be a legacy effect of widespread conversion of cropland to grassland before 1960. SOC in cropland increased only in sandy lowland soils, driven by increasing manure additions. Modeled land use and management impacts accounted for more than 70% of the variation in observed SOC changes, and no bias could be demonstrated. There was no significant effect of climate trends since 1960 on observed SOC changes. SOC monitoring networks are being established in many countries. Our results demonstrate that detailed and long-term land management data are crucial to explain the observed SOC changes for such networks.regional inventories | soil organic carbon dynamics modeling | land use history

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“…As an alternative, various models have been developed to predict SOC. There are many models being designed to predict SOC stocks and stock changes at global or regional scales, such as CENTURY (Smith et al 2001), ICBM (Bolinder et al 2006), and the GEFSOC modeling system (Milne et al 2007). As these models were designed to estimate total soil carbon stock changes in large regions, they did not have the capability to estimate relative SOC content distributions at the landscape level at high-resolution.…”

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confidence: 99%

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

Zhao¹,

Yang²,

Benoy³

et al. 2010

Can. J. Soil. Sci.

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network models to produce soil organic carbon content distribution maps across landscapes. Can. J. Soil Sci. 90: 75Á87. Soil organic carbon (SOC) content is an important soil quality indicator that plays an important role in regulating physical, chemical and biological properties of soil. Field assessment of SOC is time consuming and expensive. It is difficult to obtain highresolution SOC distribution maps that are needed for landscape analysis of large areas. An artificial neural network (ANN) model was developed to predict SOC based on parameters derived from digital elevation model (DEM) together with soil properties extracted from widely available coarse resolution soil maps (1:1 000 000 scale). Field estimated SOC content data extracted from high-resolution soil maps (1:10 000 scale) in Black Brook Watershed in northwestern New Brunswick, Canada, were used to calibrate and validate the model. We found that vertical slope position (VSP) was the most important variable that determines distributions of SOC across the landscape. Other variables such as slope steepness, and potential solar radiation (PSR) also had significant influence on SOC distributions. The prediction of the selected two-input-node SOC model (VSP and coarse resolution soil map recorded SOC as inputs) had a correlation coefficient of 0.92 with measured values, and model predicted SOC values had 47.9% of the total points within 90.5% of the measured values and 70.6% within 91% of the measured values. The prediction of the selected four-input-node model (VSP, slope steepness, PSR and coarse resolution SOC values as inputs) had a correlation coefficient of 0.98 with measured values and model predicted SOC values had 75% of the total points within 90.5% of the measured values and 87% within 91% of the measured values. The prediction of the five-input-nodes model with soil drainage as additional input had a correlation coefficient of 0.99 with measured values, and model predicted SOC values had 87% of the total points within 90.5% of the measured values and 98% of the total points within 91% of the measured values. The calibrated SOC prediction model was used to produce a high-resolution SOC map for the Black Brook Watershed and the resulting SOC distribution map is considered to be realistic. Results indicated that DEM-derived hydrological parameters together with widely available coarse resolution soil map data could be used to produce high-resolution SOC maps with the ANN method. For personal use only.

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National and sub-national assessments of soil organic carbon stocks and changes: The GEFSOC modelling system

Cited by 90 publications

References 42 publications

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Regressão linear múltipla e modelo Random Forest para estimar a densidade do solo em áreas montanhosas

Agricultural management explains historic changes in regional soil carbon stocks

Using artificial neural network models to produce soil organic carbon content distribution maps across landscapes

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