National versus global modelling the 3D distribution of soil organic carbon in mainland France

Mulder, Vera Leatitia; Lacoste, Marine; Richer-de-Forges, Anne C.; Martín, Manuel; Arrouays, Dominique

doi:10.1016/j.geoderma.2015.08.035

Cited by 141 publications

(75 citation statements)

References 31 publications

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“…The uncertainty in the simulation of P TOT was high in North America and western Russia (coefficient of variation of the simulations performed with drivers varying within their range of uncertaintỹ 60%) and low in China and Europe (~20%; Fig. GlobalSoil-Map (Mulder et al, 2016), SoilGrid 1 km (Hengl et al, 2014)) could help to increase our confidence in simulated soil P maps. A similar spatial pattern was found for P ILAB despite higher uncertainty in North America, Africa and Argentina (Fig.…”

Section: Distribution Of P Tot and P Ilab At The Global Scale And Assmentioning

confidence: 99%

“…The increasing resolution of soil data sets (e.g. GlobalSoil-Map (Mulder et al, 2016), SoilGrid 1 km (Hengl et al, 2014)) could help to increase our confidence in simulated soil P maps. Despite the large uncertainty in the distribution of P TOT and P ILAB , we were able to robustly decipher the relative contribution of the drivers of global soil P distribution (see following and small error-bars in Fig.…”

Section: Distribution Of P Tot and P Ilab At The Global Scale And Assmentioning

confidence: 99%

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Phosphorus in agricultural soils: drivers of its distribution at the global scale

Ringeval

Augusto

Monod

et al. 2017

Global Change Biology

133

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Phosphorus (P) availability in soils limits crop yields in many regions of the World, while excess of soil P triggers aquatic eutrophication in other regions. Numerous processes drive the global spatial distribution of P in agricultural soils, but their relative roles remain unclear. Here, we combined several global data sets describing these drivers with a soil P dynamics model to simulate the distribution of P in agricultural soils and to assess the contributions of the different drivers at the global scale. We analysed both the labile inorganic P (P ), a proxy of the pool involved in plant nutrition and the total soil P (P ). We found that the soil biogeochemical background corresponding to P inherited from natural soils at the conversion to agriculture (BIOG) and farming practices (FARM) were the main drivers of the spatial variability in cropland soil P content but that their contribution varied between P vs. P . When the spatial variability was computed between grid cells at half-degree resolution, we found that almost all of the P spatial variability could be explained by BIOG, while BIOG and FARM explained 38% and 63% of P spatial variability, respectively. Our work also showed that the driver contribution was sensitive to the spatial scale characterizing the variability (grid cell vs. continent) and to the region of interest (global vs. tropics for instance). In particular, the heterogeneity of farming practices between continents was large enough to make FARM contribute to the variability in P at that scale. We thus demonstrated how the different drivers were combined to explain the global distribution of agricultural soil P. Our study is also a promising approach to investigate the potential effect of P as a limiting factor for agroecosystems at the global scale.

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Section: Distribution Of P Tot and P Ilab At The Global Scale And Assmentioning

confidence: 99%

Section: Distribution Of P Tot and P Ilab At The Global Scale And Assmentioning

confidence: 99%

Phosphorus in agricultural soils: drivers of its distribution at the global scale

Ringeval

Augusto

Monod

et al. 2017

Global Change Biology

133

View full text Add to dashboard Cite

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“…Many previous studies have used legacy soil data (Aitkenhead and Coull, 2016;Minasny et al, 2013;Mulder et al, 2016 …”

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confidence: 99%

Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project

Wijewardane

Wills

et al. 2016

Soil Science Soc of Amer J

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Visible and near infrared reflectance spectroscopy (VisNIr) has been used across a number of spatial scales to predict soil organic carbon (OC) content. The rapid Carbon Assessment Project (raCA) is a nationwide project that collected 144,000+ soil samples from across the conterminous United States for C stock mapping using VisNIr. The objective of this study was to calibrate and validate the VisNIr soil OC and total C (TC) models with ~20,000 samples from raCA. Models were developed with either partial least squares regression (PlSr) or the Artificial Neural Network (ANN) model. Four auxiliary variables-raCA region, land Use land Cover, Master Horizon, and Textural Class-were tested to stratify the dataset and to develop local models. The results showed that OC and TC models calibrated with ANN (R 2 > 0.94; rPD > 4.0) outperformed those of PlSr (R 2 = 0.83; rPD = 2.4). For PlSr, local models developed with all four auxiliary variables exhibited improvement in prediction accuracy, and the improvement was only marginal for ANN models. Master Horizon and Textural Class appeared to be more effective in stratifying samples into homogeneous groups because they gave an overall lower root mean squared error of prediction (rMSE P ) for the validation samples. For the majority of the local Textural Class models, the rMSE P of OC prediction ranged from 0.5 to 1.5%. To maximize the applicability of the raCA spectral library on external soil samples, PlSr local models developed from Master Horizon or Textural Class appeared to be more favorable.Abbreviations: ANN, Artificial Neural Network; IC, inorganic carbon; LULC, land use land cover classes; OC, organic carbon; PLSR, partial least squares regression; RaCA, Rapid Carbon Assessment Project; RPD, ratio of performance to deviation; RPIQ, ratio of performance to interquartile range; TC, total carbon; VisNIR, visible and near infrared reflectance spectroscopy. S oil organic carbon (OC) is a key soil property that plays many critical roles, from agriculture production to biogeochemical cycling to ecosystems functioning (Adhikari and Hartemink, 2016;Chen et al., 2000;Lal, 2004; van Wesemael et al., 2010). The capability of soils to sequester C and therefore regulate atmospheric CO 2 concentration and mitigate climate change is widely recognized and has been an active area of research (Grunwald et al., 2011;Lal, 2004;West and Post, 2002). Up-to-date, baseline soil C stock maps across different scales are a very useful tool for researchers, stakeholders, and policymakers for a wide variety of applications, ranging from best land management practices to natural resource conservation to C auditing (de Gruijter et al., 2016;Minasny et al., 2011).Producing large-scale (national or continental) soil C maps (e.g., for the purpose of C inventory and understanding of soil C sourcing and sinking) is highly challenging. Many previous studies have used legacy soil data (Aitkenhead and Coull, 2016;Minasny et al., 2013;Mulder et al., 2016 Core Ideas• We modeled ~20,000 samples in raCA from...

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“…As a result, there are experiments conducted on almost regular and dense grids, most of which focus on small areas [Lacoste et al, 2014, Taghizadeh-Mehrjardi et al, 2016 and other where the sampling strategy significantly varies across space [Mondal et al, 2016]. The latter studies mainly correspond to regional or even greater scales [Reijneveld et al, 2009, Sreenivas et al, 2016, with only few cases where an optimal sample density is maintained at a national level [Mulder et al, 2016]. The characteristics of environment under study can require the use of different predictors capable of explaining the variability of soil traits, topography and standing biocoenosis, especially (cropped or natural) phytocoenosis, the latter being efficiently explained by remotely sensed (RS) properties [Morellos et al, 2016, Peng et al, 2015.…”

Section: Introductionmentioning

confidence: 99%

Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

et al. 2018

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Soil Organic Carbon (SOC) estimation is crucial to manage both natural and anthropic ecosystems and has recently been put under the magnifying glass after the Paris agreement 2016 due to its relationship with greenhouse gas. Statistical applications have dominated the SOC stock mapping at regional scale so far. However, the community has hardly ever attempted to implement Quantile Regression (QR) to spatially predict the SOC distribution. In this contribution, we test QR to estimate SOC stock (0-30 cm depth) in the agricultural areas of a highly variable semi-arid region (Sicily, Italy, around 25,000 km2) by using topographic and remotely sensed predictors. We also compare the results with those from available SOC stock measurement. The QR models produced robust performances and allowed to recognize dominant effects among the predictors with respect to the considered quantile. This information, currently lacking, suggests that QR can discern predictor influences on SOC stock at specific sub-domains of each predictors. In this work, the predictive map generated at the median shows lower errors than those of the Joint Research Centre and International Soil Reference, and Information Centre benchmarks. The results suggest the use of QR as a comprehensive and effective method to map SOC using legacy data in agro-ecosystems. The R code scripted in this study for QR is included.

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National versus global modelling the 3D distribution of soil organic carbon in mainland France

Cited by 141 publications

References 31 publications

Phosphorus in agricultural soils: drivers of its distribution at the global scale

Phosphorus in agricultural soils: drivers of its distribution at the global scale

Prediction of Soil Carbon in the Conterminous United States: Visible and Near Infrared Reflectance Spectroscopy Analysis of the Rapid Carbon Assessment Project

Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks

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