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

Soil organic carbon (SOC) plays a major role in the global carbon budget. It can act as a source or a sink of atmospheric carbon, thereby possibly influencing the course of climate change. Improving the tools that model the spatial distributions of SOC stocks at national scales is a priority, both for monitoring changes in SOC and as an input for global carbon cycles studies. In this paper, we compare and evaluate two recent and promising modelling approaches. First, we considered several increasingly complex boosted regression trees (BRT), a convenient and efficient multiple regression model from the statistical learning field. Further, we considered a robust geostatistical approach coupled to the BRT models. Testing the different approaches was performed on the dataset from the French Soil Monitoring Network, with a consistent cross-validation procedure. We showed that when a limited number of predictors were included in the BRT model, the standalone BRT predictions were significantly improved by robust geostatistical modelling of the residuals. However, when data for several SOC drivers were included, the standalone BRT model predictions were not significantly improved by geostatistical modelling. Therefore, in this latter situation, the BRT predictions might be considered adequate without the need for geostatistical modelling, provided that i) care is exercised in model fitting and validating, and ii) the dataset does not allow for modelling of local spatial autocorrelations, as is the case for many national systematic sampling schemes

Hybrid (bolted/bonded)joints applied to aeronautic parts: analytical two-dimensional model of a single-lap joint

The mechanical behavior of hybrid (bolted/bonded) joints is investigated. The joints under study are balanced single-lap joints, and an elastic behavior of the materials is assumed. A fully parametric analytical two-dimensional model, based on the Finite Element Method, is presented. A special Finite Element ("Bonded Beams" element) is computed in order to simulate the bonded adherends. The simulation of fasteners is examined through experimental and numerical approaches. Good agreement was found between the experimental and numerical results

Spatial prediction of soil texture in Region Centre (France) from summary data

International audienceLand use and agricultural practices, in interaction with soil texture, influence soil fauna and soil microbial community, water holding capacity, and C and nutrient cycling among other agroecosystem properties. Detailed knowledge on the spatial distribution of soil texture can improve land-use planning and crop management. Our objective was to predict soil texture in agricultural land for the Region Centre (France), combining regression models and area-to-point kriging. The French soil-test database (BDAT) is largely populated with topsoil analysis requested by farmers mainly interested in soil fertility. To protect the anonymity of the farms, their coordinates are unknown and texture is aggregated by municipality. The nature of the data requires novel disaggregation techniques (i.e., area-to-point kriging) to develop high-resolution maps on point support. We applied an additive log-ratio transformation (alr-transform) on texture data to remove the closure effect and achieve normality. Average values of environmental covariates by municipality were used to fit predictive models with multiple linear regression, Cubist, and boosted regression trees (BRT). Data from 104 plots from the systematic soil quality monitoring network (RMQS) were used for independent validation. Only BRT models provided better predictions (clay-alr R2 = 0.54, sand-alr R2 = 0.76) than reference BDAT texture values averaged by commune (clay-alr R2 = 0.33, sand-alr R2 = 0.64). In a second step, BRT predictions were used as auxiliary variables for area-to-point kriging following the summary statistics approach developed by Orton et al. (2012). To deal with the dependence between clay- and sand-alr transforms we applied a linear model of coregionalization. This approach allowed to include the relationships between soil forming factors and soil texture, and to account for the uncertainty on areal means in the area-to-point kriging step. We are currently testing whether incorporating remote sensing data (e.g., Landsat 8) in the regression models further improves soil texture predictions despite the loss of information when averaging by municipality. The combination of regression and area-to-point kriging is a promising method to produce high-resolution maps from soil-test data missing the exact coordinates

A method for modeling the effects of climate and land use changes on erosion and sustainability of soil in a Mediterranean watershed (Languedoc, France)

Global climate and land use changes could strongly affect soil erosion and the capability of soils to sustain agriculture and in turn impact regional or global food security. The objective of our study was to develop a method to assess soil sustainability to erosion under changes in land use and climate. The method was applied in a typical mixed Mediterranean landscape in a wine-growing watershed (75 km(2)) within the Languedoc region (La Peyne, France) for two periods: a first period with the current climate and land use and a second period with the climate and land use scenarios at the end of the twenty-first century. The Intergovernmental Panel on Climate Change A1B future rainfall scenarios from the Mete France General circulation model was coupled with four contrasting land use change scenarios that were designed using a spatially-explicit land use change model. Mean annual erosion rate was estimated with an expert-based soil erosion model. Soil life expectancy was assessed using soil depth. Soil erosion rate and soil life expectancy were combined into a sustainability index. The median simulated soil erosion rate for the current period was 3.5 t/ha/year and the soil life expectancy was 273 years, showing a low sustainability of soils. For the future period with the same land use distribution, the median simulated soil erosion rate was 4.2 t/ha/year and the soil life expectancy was 249 years. The results show that soil erosion rate and soil life expectancy are more sensitive to changes in land use than to changes in precipitation. Among the scenarios tested, institution of a mandatory grass cover in vineyards seems to be an efficient means of significantly improving soil sustainability, both in terms of decreased soil erosion rates and increased soil life expectancies

Analyzing the spatial distribution of PCB concentrations in soils using below-quantification limit data

Polychlorinated biphenyls (PCBs) are highly toxic environmental pollutants that can accumulate in soils. We consider the problem of explaining and mapping the spatial distribution of PCBs using a spatial data set of 105 PCB-187 measurements from a region in the north of France. A large proportion of our data (35%) fell below a quantification limit (QL), meaning that their concentrations could not be determined to a sufficient degree of precision. Where a measurement fell below this QL, the inequality information was all that we were presented with. In this work, we demonstrate a full geostatistical analysis-bringing together the various components, including model selection, cross-validation, and mapping using censored data to represent the uncertainty that results from below-QL observations. We implement a Monte Carlo maximum likelihood approach to estimate the geostatistical model parameters. To select the best set of explanatory variables for explaining and mapping the spatial distribution of PCB-187 concentrations, we apply the Akaike Information Criterion (AIC). The AIC provides a trade-off between the goodness-of-fit of a model and its complexity (i.e., the number of covariates). We then use the best set of explanatory variables to help interpolate the measurements via a Bayesian approach, and produce maps of the predictions. We calculate predictions of the probability of exceeding a concentration threshold, above which the land could be considered as contaminated. The work demonstrates some differences between approaches based on censored data and on imputed data (in which the below-QL data are replaced by a value of half of the QL). Cross-validation results demonstrate better predictions based on the censored data approach, and we should therefore have confidence in the information provided by predictions from this method

Carbon content and stocks in the O horizons of French forest soils

International audienceWe propose to estimate the Organic Carbon (OC) stocks for the O soil layers, at the national scale. First, the determinants of the OC content variability in soil O-horizons were examined, then the OC content was mapped at the national scale and this map was used to estimate the OC stocks for French forest soils. Three soil datasets were used. Two were national datasets provided by national soil survey programs for soils knowledge and inventory and soil quality survey (IGCS and RMQS). One was provided by the Level I of the European program BioSoil. Various covariates were used to identify the determinants of OC stocks in O-horizons: the soil descriptions provided by the pre-mentioned datasets, with collection of biological, chemical and physical properties of O-horizons and soils, plus a description of surrounding landscapes and climate properties. For OC content mapping in O-horizons, spatially exhaustive covariates were used, such as a digital elevation model (SRTM DEM, 90-m grid) and its derived attributes, the French soils map (1:1,000,000), climatic and land use data. Predictive models of OC stocks in the O-horizons were created using Generalized Boosted Regression Models. Estimation of uncertainties has been afterwards modeled using fuzzy k-means clustering. The final OC stocks in O soil layers were estimated to 15 TgC

Mapping black carbon content in topsoils of central France

International audienceBlack Carbon (BC) is an important carbon pool due to its relative stability in soil. Thus, it is essential to determine the amount of BC in soil to have a better understanding of the global carbon cycle. The spatial distribution of BC was determined in the central region of France in relation to the main controlling factors. BC was measured for topsoil at 158 sites in the French soil monitoring network on a regular 16 × 16-km grid. A linear mixed model (LMM) which included fixed effects (linear relationships between BC content and covariates) and spatially correlated random effects was used for mapping BC to aid explanation. Covariates were selected from a set of factors linked to the BC cycle using the Akaike Information Criterion (AIC). The results show high variability in BC content with a minimum of 0.9%, a maximum of 32% and an average of 5.3% for total organic carbon. The fine-earth fraction and clay content gave the best statistical explanation for the spatial distribution of BC. Data on these covariates were not available in total for the whole study area, and therefore we reselected covariates using the fine-earth amount and density of fires from burning crop residues

BDAT : Base de Données des Analyses de Terre

Année de la première version : 1997Documents associés disponibles : Spécifications, Architecture et conception, Documentation utilisateur – GuideInterface utilisateur : ligne de commande, interface We