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

Reading Dickens’s characters: employing psycholinguistic methods to investigate the cognitive reality of patterns in texts

This article reports the findings of an empirical study that uses eye-tracking and follow-up interviews as methods to investigate how participants read body language clusters in novels by Charles Dickens. The study builds on previous corpus stylistic work that has identified patterns of body language presentation as techniques of characterisation in Dickens (Mahlberg, 2013). The article focuses on the reading of ‘clusters’, that is, repeated sequences of words. It is set in a research context that brings together observations from both corpus linguistics and psycholinguistics on the processing of repeated patterns. The results show that the body language clusters are read significantly faster than the overall sample extracts which suggests that the clusters are stored as units in the brain. This finding is complemented by the results of the follow-up questions which indicate that readers do not seem to refer to the clusters when talking about character information, although they are able to refer to clusters when biased prompts are used to elicit information. Beyond the specific results of the study, this article makes a contribution to the development of complementary methods in literary stylistics and it points to directions for further subclassifications of clusters that could not be achieved on the basis of corpus data alone

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

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

Current approaches for mapping and monitoring carbon contents and stocks in French soils

National audienc

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