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Soil inorganic carbon (SIC) contributes to up to half of the terrestrial C stock and is especially significant in arid and semi-arid environments, yet has not been explored as much as soil organic carbon (SOC). SIC plays an important role in agriculture, CO2sequestration and emission and climate regulation. To address this, a comprehensive review is presented on the digital mapping of soil inorganic carbon including a discussion of SIC vertical variation, its controlling factors, and sequestration/emission capability. We surveyed SIC distribution and mapping efforts in Australia, South Africa, Chile, the Mediterranean basin, Iran, China, France, and the United States. We found that current detailed spatial information on SIC distribution and stock is relatively scarce and digital soil mapping (DSM) efforts to address this are modest. Furthermore, we do not have a complete soil C model that explicitly accounts for all sources and sinks of soil carbon. This review showed that many aspects of SIC in DSM and soil C studies have been so far ignored and that SIC has a crucial role in climate regulation. This review provides some insights into the importance and unknown aspects of SIC.

•Simple pedotransfer functions based on commonly-measured soil properties yielded accurate estimates of aggregate stability.•Approaches based on commonly-measured soil properties and infrared spectroscopy had similar results.•Model interpretation revealed well-known relationships of aggregate stability with basic soil properties and land use.•It is possible to develop cheap and cost-efficient methods to estimate soil aggregate stability.

Contamination of the environment by pesticide residues is a growing concern given their widespread presence in the environment and their effects on ecosystems. Only a few studies have addressed the occurrence of pesticides in soils, and their results highlighted the need for further research on the persistence and risks induced by those substances. We monitored 111 pesticide residues (48 fungicides, 36 herbicides, 25 insecticides and/or acaricides, and two safeners) in 47 soils sampled across France under various land uses (arable lands, vineyards, orchards, forests, grasslands, and brownfields). Pesticides were found in 98% of the sites (46 of the 47 sampled), including untreated areas such as organic fields, forests, grasslands, and brownfields, with up to 33 different substances detected in one sample, mostly fungicides and herbicides. The concentrations of herbicides were the highest in soils with glyphosate, and its transformation product, AMPA, contributed 70% of the cumulative herbicides. Risk assessment underlined a moderate to high risk for earthworms in arable soils mostly attributed to insecticides and/or acaricides. Finally, the comparison with pesticide application by farmers underlines the presence of some residues long after their supposed 90% degradation and at concentrations higher than predicted environmental concentrations, leading to questions their real persistence in soils.

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.

En 1983, Mazur et Wiles ont démontré un théorème de comparaison reliant la fonctionL-p-adique de Kubota–Léopoldt à la série caractéristique associé à la tour des courbes d'Igusa de niveaupn(ppremier impair). Je généralise ce résultat au cas de la tour des courbes d'Igusa de niveauNpn, oùNest un entier plus grand que 5 premier àp, avec des hypothèses peu restrictives sur le caractère de la fonctionL-p-adique et par uneméthode nouvelle. Pour cela, on définit un idéal d'Eisenstein dans l'algèbre de Hecke ordinaire de Hida qui est relié à la fonctionL-p-adique. L'essentiel du travail est ensuite de bien comprendre les fonctorialités de Picard et d'Albanese sur les jacobiennes des courbes d'Igusa ainsi que l'action du Frobénius. Un résultat de Tilouine sur la structure d'un sous-groupep-divisible de la jacobienne de la courbe modulaire permet finalement d'obtenir le résultat.