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Each soil bacterial taxon exhibits a specific wide scale distribution explained by particular environmental filters., Over the last two decades, a considerable effort has been made to decipher the biogeography of soil microbial communities as a whole, from small to broad scales. In contrast, few studies have focused on the taxonomic groups constituting these communities; thus, our knowledge of their ecological attributes and the drivers determining their composition and distribution is limited. We applied a pyrosequencing approach targeting 16S ribosomal RNA (rRNA) genes in soil DNA to a set of 2173 soil samples from France to reach a comprehensive understanding of the spatial distribution of bacteria and archaea and to identify the ecological processes and environmental drivers involved. Taxonomic assignment of the soil 16S rRNA sequences indicated the presence of 32 bacterial phyla or subphyla and 3 archaeal phyla. Twenty of these 35 phyla were cosmopolitan and abundant, with heterogeneous spatial distributions structured in patches ranging from a 43- to 260-km radius. The hierarchy of the main environmental drivers of phyla distribution was soil pH > land management > soil texture > soil nutrients > climate. At a lower taxonomic level, 47 dominant genera belonging to 12 phyla aggregated 62.1% of the sequences. We also showed that the phylum-level distribution can be determined largely by the distribution of the dominant genus or, alternatively, reflect the combined distribution of all of the phylum members. Together, our study demonstrated that soil bacteria and archaea present highly diverse biogeographical patterns on a nationwide scale and that studies based on intensive and systematic sampling on a wide spatial scale provide a promising contribution for elucidating soil biodiversity determinism.

There are many factors that influence ecosystem scale carbon, nitrogen and greenhouse gas dynamics, including the inherent heterogeneity of soils and vegetation, anthropogenic management interventions, and biotic and abiotic disturbance events. It is important therefore, to document the characteristics of the soils and vegetation and to accurately report all management activities, and disturbance events to aid the interpretation of collected data, and to determine whether the ecosystem either amplifies or mitigates climate change. This paper outlines the importance of assessing both the spatial and temporal variability of soils and vegetation and to report all management events, the import or export of C or N from the ecosystem, and the occurrence of biotic/abiotic disturbances at ecosystem stations of the Integrated Carbon Observation System, a pan-European research infrastructure.

Though soil bulk density (BD) is important for crop growth and land management, information about BD is often missing in soil database due to the fact that the determination of BD is time consuming and labor intensive. In order to fill this gap, pedotransfer functions (PTFs) have been developed for predicting bulk density during last decades. To avoid non-valid extrapolation, the validity domain of PTFs should be investigated. In this study, a PTFs for bulk density was built using GBM model on data from Region Centre and we tested its validity on data from mainland France except Region Centre. Standardized Euclidean distance was applied to distinguish dissimilar soil samples from Region Centre data and other regions data. The established PTFs were not suitable for predicting these dissimilar samples. In order to make a balance between prediction accuracy and the number of samples can be predicted with our PTFs, different cutoff limits of Standardized Euclidean distance was compared. When optimized distance cutoff limit was at 97%, compared to result without excluding dissimilar samples, RMSPE value on test data decreased from 0.179 to 0.163 g cm − 3 . An additional sampling strategy based on these dissimilar samples can improve the predictive ability of PTFs. We also suggest that the purposive sampling strategy or the systematic and dense sampling strategy will do large contribution in robust modelling of PTFs.

Soil resources provide many important ecosystem goods and services. However, they are at risk from a variety of threats operating over a broad range of scales. Political awareness that soil is threatened by increasing pressures has been rising for several years (European Commission 2006). Indeed, the demand for soil information is increasing continuously (Richer de Forges and Arrouays 2010). Although rates of soil degradation are often slow and only detectable over long timescales, they are often irreversible. Therefore, monitoring soil quality and condition is essential in order to detect adverse changes in their status at an early stage.

International audience; Increasing soil organic carbon (SOC) stocks via land management has been proposed as a temporary climate change mitigation measure. An upper limit of soil stable SOC storage, which refers to the concepts of SOC saturation has been proposed. Using systematic grid sampling of topsoil in mainland France and an equation that predicts this SOC saturation, we derived estimates of the SOC sequestration potential density (SOCspd) for various land uses. First, using French database and data from the literature we estimated the proportions of the SOC stored in the fine fraction (SOCfine) in total SOC for grassland (69%), cropland (85%) and forest (66%). Then, SOCspd was calculated as the difference between the theoretical SOC saturation value and SOCfine. The SOCspd stocks for French topsoil were estimated at about 1.1 Pg, in which cropland, forest and grassland accounted for 66%, 17% and 17%, respectively. Varying the proportions of SOCfine in the calculations by assuming a possible range of 0.8-0.9 for cropland and 0.6-0.9 for grassland and forest soils led to variations of total SOCspd stocks of about 0.1 Pg for each land use. Most importantly, we demonstrate that the distribution of SOCspd in forest soils is exactly centred at zero, which suggests that on average, forest topsoils are saturated in SOCfine, and thus Hassink’s equation provides a valid estimate for the SOC sequestration potential for French topsoil.

International audience; In this work we aimed at developing a conceptual framework in which we improve our understanding of the controlling factors for soil organic carbon (SOC) over vast areas at different depths. We postulated that variability in SOC levels may be better explained by modeling SOC within soil-landscape systems (SLSs). The study was performed in mainland France, and explanatory SOC models were developed for the sampled topsoil (0–30 cm) and subsoil (>30 cm), using both directed and undirected data-mining techniques. With this study we demonstrated that there is a shift in controlling factors both in space and depth which were mainly related to (1) typical SLS characteristics and (2) human-induced changes to SLSs. The controlling factors in relation to depth alter from predominantly biotic to more abiotic with increasing depth. Especially, water availability, soil texture, and physical protection control deeper stored SOC. In SLSs with similar SOC levels, different combinations of physical protection, the input of organic matter, and climatic conditions largely determined the SOC level. The SLS approach provided the means to partition the data into data sets that were having homogenous conditions with respect to this combination of controlling factors. This information may provide important information on the carbon storage and sequestration potential of a soil.

10 pages; International audience; A better understanding of the links between dissolved organic matter and biogeochemical processes in soil could help in evaluating global soil dynamics. To assess the effects of land cover and parental material on soil biogeochemistry, we studied 120 soil samples collected from various ecosystems in Burgundy, France. The potential solubility and aromaticity of dissolved organic matter was characterised by pressurised hot-water extraction of organic carbon (PH-WEOC). Soil physico-chemical characteristics (pH, texture, soil carbon and nitrogen) were measured, as was the δ13C signature both in soils and in PH-WEOC. We also determined bacterial and fungal abundance and the genetic structure of bacterial communities. Our results show that the potential solubility of soil organic carbon is correlated to carbon and clay content in the soil. The aromaticity of PH-WEOC and its δ13C signature reflect differences in the decomposition pathways of soil organic matter and in the production of water-extractable organic compounds, in relation to land cover. The genetic structure of bacterial communities is related to soil texture and pH, and to PH-WEOC, revealing that water-extractable organic matter is closely related to the dynamics of bacterial communities. This comprehensive study, at the regional scale, thus provides better definition of the relationships between water-extractable organic matter and soil biogeochemical properties.

International audience; Soils act as a sink or a source of atmospheric carbon, and great efforts are made to monitor soil organic carbon stocks, but soil inorganic carbon (SIC) stocks are not measured by many national- and continental-scale soil monitoring networks. Topsoil (0-30cm) SIC concentrations were determined for >2000 sites on a regular 16-km grid as part of the French, Reseau de Mesures de la Qualite des Sols (RMQS). We used design-based statistical methods to calculate unbiased estimates of the mean SIC concentration and total stocks across France. Model-based methods were used to determine the uncertainty of these estimates and to map the spatial distribution of these quantities. Observations of inorganic carbon were highly skewed and did not conform to standard statistical models. Data were normalized using a nonparametric transformation. The estimates and predictions of inorganic carbon are baselines against which the results of future phases of the network can be compared. We found that the total topsoil inorganic carbon stocks in France amount to 1070 +/- 61 Tg, ca. one-third of the corresponding organic carbon stocks. Spatial distribution of SIC was strongly linked to the underlying geology. We tested the reliability of estimating SIC concentrations and stocks from the French Soil Test Database, which contains the results of 280000 soil analyses requested by farmers between 1990 and 2004. A biased estimate of soil inorganic carbon concentrations resulted, presumably because soil samples were selected according to concerns of farmers rather than by a statistical design.

L’Observatoire pérenne de l’environnement (OPE) de l’ANDRA a mis en œuvre depuis 2007 un réseau d’inventaires et d’observations à long terme des différents milieux de l’environnement sur un territoire de 240 km2 dans les départements de la Meuse et de la Haute-Marne. Un réseau de suivi et d’observation de la qualité des sols a ainsi été mis en place selon un maillage systématique en suivant un protocole d’échantillonnage et d’analyse de type « RMQS » conduit à l’échelle du paysage sur une grille de 1,5 km x 1,5 km. Des prélèvements d’échantillons composites ont été effectués lors de 4 campagnes successives entre 2009 et 2012 sur un total de 127 sites, dont 57 disposaient en plus d’une fosse pédologique. Les propriétés physico-chimiques (texture, carbone, pH, calcaire, azote, CEC, phosphore assimilable, cations échangeables et éléments majeurs totaux) et microbiologiques (structure des communautés microbiennes) ont été analysées dans le but d’établir un état de référence des sols de la zone d’observation. Ces observations ont servi de base à une analyse statistique afin de comprendre la distribution spatiale de la diversité pédologique et des micro-organismes telluriques à l’échelle d’un paysage, et les facteurs qui la pilotent. Pour cela, nous avons eu recours aux techniques de cartographie numérique. Nous avons ainsi couplé des analyses en composantes principales (ACP) sous contraintes spatiales avec des outils de régression et de géostatistique (krigeage universel). Les ACP ont été réalisées sur trois matrices différentes regroupant respectivement les données des principales propriétés pédologiques, les communautés bactériennes et les communautés de champignons toutes deux caractérisées par les empreintes moléculaires issues des analyses de type ARISA. Nous avons ensuite tenté d’expliquer les trois premiers axes de chacune des ACP en les mettant en relation avec des covariables environnementales dont la couverture spatiale est exhaustive sur la zone d’étude. Deux algorithmes ont été testés : les arbres de régressions boostés implémentés dans la librairie « gbm » et dans la librairie Cubist du logiciel de statistique R. Les covariables environnementales retenues sont : (1) les dérivées morphométriques issues du MNA à la résolution de 25 mètres (15 variables), (2) l’occupation du sol, (3) le fond géologique (carte géologique à 1/50 000) et (4) les unités cartographiques du sol issues de la carte pédologique à 1/50 000. Enfin, les résidus des modèles de régression ont été interpolés par des techniques géostatiques. L’ensemble de la procédure a été validée par validation croisée. Malgré une relative homogénéité, les propriétés physico-chimiques des sols se distribuent selon la nature géologique et pédologique de la zone ainsi que selon la géomorphologie du paysage (MNA et ses dérivées) et l’occupation du sol. La structure génétique des communautés bactériennes et fongiques présente des structures spatiales moyennement marquées à l’échelle du paysage. Les communautés bactériennes s’organisent selon la morphologie du relief (rugosité, orientation des pentes), les unités cartographiques des sols et les structures hydrologiques (distance au plus proche court d’eau). La distribution des communautés fongiques est impactée par la topographie, et les structures hydrographiques. La performance des 6 modèles construits mesurée par le R² varie de 0,13 à 0,94. Nos résultats confirment qu’il est possible d’identifier et de hiérarchiser les filtres environnementaux qui pilotent les diversités physico-chimiques et biologiques du sol à l’échelle d’un paysage.

EA BIOME INRA AGROSUP UB; International audience

International audience

EA SPE BIOME GENOSOL UB Agrosup EA SPE BIOME GENOSOL UB AgrosupEASPEBIOMEGENOSOL UB Agrosup; New generation sequencing evidenced that soil houses a huge microbial biodiversity. Based on these observations, it is now possible to better describe the spatial distribution of soil microbial communities, to study the ecological processes involved (environmental selection, dispersal limitations), and to compute the data to define the ecological attributes of soil microbial taxa. These huge datasets also offer the opportunity to study how soil microbial populations are interrelated. With this study, we revealed the first soil bacterial "social" network across French national territory based on 2,200 soils sampled in the French Soil Quality Monitoring Network. To characterize how bacterial taxa are related to one-another, the bacterial community composition was characterized using 454-pyrosequencing based on 16S rRNA genes directly amplified from soil DNA and bacterial taxa were identified based on SILVA database. Then, a co-occurrence network analysis was computed to visualize and quantify the network structure and complexity according to the combination of environmental parameters (soil, climate, land use). The results demonstrate that the soil bacterial network is highly complex at the scale of France with more positive relationships than negative. Comparing environmental conditions, the network was more complex in forest soil than in cropland or grassland when those of vineyards/orchards are drastically less complex. Beyond the global network structure, the relationships of each specific taxon changed across the land-use systems, implying both switch of partner, modify the kind or the number of relationships. Understanding these microbial networks represents a cornerstone of soil microbial ecology to go further in investigating ecological processes shaping soil microbial communities and to predict the impact of land-use on soil microbial diversity, and functioning.

EA SPE BIOME GENOSOL UB Agrosup EA SPE BIOME GENOSOL UB AgrosupEASPEBIOMEGENOSOL UB Agrosup; New generation sequencing evidenced that soil houses a huge microbial biodiversity. Based on these observations, it is now possible to better describe the spatial distribution of soil microbial communities, to study the ecological processes lying behind (environmental selection, dispersal limitations), and to mine the data to define the ecological attributes of soil microbial taxa. These huge datasets also offer the opportunity to study how soil microbial populations are interrelated. The objective of this study was to design the first bacterial "social" network in french soils to characterize how bacterial taxa are related to one-another. To do so, the bacterial community composition was characterized for the 2,200 soils of the French Soil Quality Monitoring Network using 454-pyrosequencing based on 16S rRNA genes directly amplified from soil DNA and bacterial taxa were identified based on SILVA database. Then, the bacterial social network was designed by means of a network analysis. First results at the phylum level demonstrate that the soil bacterial social network is highly complex with different relationships between bacterial phyla (positive/negative, strong/weak). This complexity is determined both by biotic relationships between bacterial phyla and by environmental conditions. Indeed, the soil bacterial social networks in different ecosystem types (forests, grasslands, croplands and vineyards) did not have the same complexity. Understanding these relationships represents a cornerstone of soil microbial ecology to go further in understanding ecological processes shaping soil microbial communities; and to identify land-use practices preserving soil bacterial social network complexity.

International audience; Digital soilmapping has largely expanded across the globe since the early 2000s. Numerous national, continental and global digital soil mapping products have been produced. In this paper, we reviewsomemajor developments and highlight areas where progress is needed, including refinements in uncertainty assessments and increasing soil data collection. There are several useful top-down approaches, but we sense that digital soil mapping should be conducted at a regional or local level to be consistent with its use and application, and to ensure efficient soil data collection, and involvement of end users. Despite the numerous mapping activities, a concerted action is needed for enhanced investment in soil mapping, including capacity building and training the next generation of soil surveyors who are grounded in pedology as well as digital soil mapping.

GlobalSoilMap is an initiative of the Digital Soil Mapping Working Groups of the International Union of Soil Sciences (IUSS, digitalsoilmapping.org. Available at http://digitalsoilmapping.org/. Accessed 22 Oct 2015). It aims to meet the needs of the modelling community, farmers, land managers, policy developers and decision-makers, by creating a fine resolution (100 × 100 m grid) quantitative digital soil map of the world, using state-of-the-art and emerging technologies such as remote sensing, data mining and spatial databases. The data will be stored in a freely available distributed system with a set of standards for Web services. The approach has three components: digital soil mapping, recommendations for soil management and providing service to end users (Sanchez et al. Science 325, 2009). The project originated in 2006 as an effort to address the unmet need for quantitative answers to questions about soil-related issues such as soil carbon sequestration, the impact of soil carbon on biomass production and the change in soil status over time. To address such questions requires information about stores and fluxes of water, carbon, nutrients and solutes, in other words, functional properties of soils. The most significant stocks and flows are water including run-off, leaching, waterlogging and water available to plants, nutrients, carbon, solutes and acidification. Access to information about soil properties reduces risks in decision-making, but in order to understand and manage these risks, estimates of uncertainties in soil properties are required. Therefore, all quantitative data in the GlobalSoilMap will have an associated uncertainty. The project is facilitated by the synthesis of pedology, which focuses on soil processes, and pedometrics, which focuses on quantitative analyses.

Even at low concentrations, the presence of arsenic and mercury in soils can lead to ecological and health impacts. The recent European-wide LUCAS Topsoil Survey found that the arsenic concentration of a large proportion of French soils exceeded a threshold which indicated that further investigation was required. A much smaller proportion of soils exceeded the corresponding threshold for mercury but the impacts of mining and industrial activities on mercury concentrations are not well understood. We use samples from the French national soil monitoring network (RMQS: Réseau de Mesures de la Qualité des Sols) to explore the variation of topsoil arsenic and mercury concentrations across mainland France at a finer spatial resolution than was reported by LUCAS Topsoil. We use geostatistical methods to map the expected concentrations of these elements in the topsoil and the probabilities that the legislative thresholds are exceeded. We find that, with the exception of some areas where the geogenic concentrations and soil adsorption capacities are very low, arsenic concentrations are generally larger than the threshold which indicates that further assessment of the area is required. The lower of two other guideline values indicating risks to ecology or health is exceeded in fewer than 5% of RMQS samples. These exceedances occur in localised hot-spots primarily associated with mining and mineralization. The probabilities of mercury concentrations exceeding the further assessment threshold value are everywhere less than 0.01 and none of the RMQS samples exceed either of the ecological and health risk thresholds. However, there are some regions with elevated concentrations which can be related to volcanic material, natural mineralizations and industrial contamination. These regions are more diffuse than the hot-spots of arsenic reflecting the greater volatility of mercury and therefore the greater ease with which it can be transported and redeposited. The maps provide a baseline against which future phases of the RMQS can be compared and highlight regions where the threat of soil contamination and its impacts should be more closely monitored.

With the rapid development of digital soil mapping it is not unusual to find several maps for the same soil property in an area of interest. We applied two standard methods of model averaging for combining two regional maps and a European map of topsoil texture in agricultural land for the Region Centre (France). The two methods for model ensemble were the Granger-Ramanathan (G-R) and the Bates-Granger (B-G). A calibration dataset was used for fitting the coefficients of the G-R model, and for calculating a global variance: prediction error ratio which was then used to re-scale the weights of the B-G model. The prediction performance of the three primary maps and the two ensemble maps was compared with an independent validation dataset consisting on 100 observations from the French soil monitoring network. The prediction accuracy of the ensemble models improved only for clay in comparison to the primary maps (∆ R 2 = 0.02–0.06, ∆ RMSE = − 1.56–− 4.97 g kg − 1 ). Overall, the G-R models obtained smaller RMSE and greater bias than B-G, and G-R estimated better the prediction uncertainty. The dissimilarities between the methods for estimating the prediction variance and non-optimal estimated uncertainties were important limitations for the B-G models despite applying a global correction factor for the prediction variances. The results suggested that both the calibration and validation datasets should represent the patterns of spatial variation and range of values of the soil property for the prediction space. Nonetheless, model ensemble methods proved to be useful for merging maps with different types of datasets, spatial coverage, and methodological approaches.

International audience; The ‘4 per mille Soils for Food Security and Climate’ was launched at the COP21 with an aspiration to increase global soil organic matter stocks by 4 per 1000 (or 0.4 %) per year as a compensation for the global emissions of greenhouse gases by anthropogenic sources. This paper surveyed the soil organic carbon (SOC) stock estimates and sequestration potentials from 20 regions in the world (New Zealand, Chile, South Africa, Australia, Tanzania, Indonesia, Kenya, Nigeria, India, China Taiwan, South Korea, China Mainland, United States of America, France, Canada, Belgium, England & Wales, Ireland, Scotland, and Russia). We asked whether the 4 per mille initiative is feasible for the region. The outcomes highlight region specific efforts and scopes for soil carbon sequestration. Reported soil C sequestration rates globally show that under best management practices, 4 per mille or even higher sequestration rates can be accomplished. High C sequestration rates (up to 10 per mille) can be achieved for soils with low initial SOC stock (topsoil less than 30 t C ha−1), and at the first twenty years after implementation of best management practices. In addition, areas which have reached equilibrium will not be able to further increase their sequestration. We found that most studies on SOC sequestration only consider topsoil (up to 0.3 m depth), as it is considered to be most affected by management techniques. The 4 per mille number was based on a blanket calculation of the whole global soil profile C stock, however the potential to increase SOC is mostly on managed agricultural lands. If we consider 4 per mille in the top 1m of global agricultural soils, SOC sequestration is between 2-3 Gt C year−1, which effectively offset 20–35% of global anthropogenic greenhouse gas emissions. As a strategy for climate change mitigation, soil carbon sequestration buys time over the next ten to twenty years while other effective sequestration and low carbon technologies become viable. The challenge for cropping farmers is to find disruptive technologies that will further improve soil condition and deliver increased soil carbon. Progress in 4 per mille requires collaboration and communication between scientists, farmers, policy makers, and marketeers.

Version 20180322; International audience; Soil Samples Collection and Preparation

EA SPE GENOSOL ECOLDUR IPM CT3 EJ3 BDD Sol Infosols Autres projets nationaux; International audience; The evaluation of the taxa-area relationship (TAR) with molecular fingerprinting data demonstrated the spatial structuration of soil microorganisms and provided insights into the processes shaping their diversity. The increasing use of massive sequencing technologies in biodiversity investigations has now raised the question of the advantages of such technologies over the fingerprinting approach for elucidation of the determinism of soil microbial community assembly in broad-scale biogeographic studies. Our objectives in this study were to compare DNA fingerprinting and meta-barcoding approaches for evaluating soil bacterial TAR and the determinism of soil bacterial community assembly on a broad scale. This comparison was performed on 392 soil samples from four French geographic regions with different levels of environmental heterogeneity. Both molecular approaches demonstrated a TAR with a significant slope but, because of its more sensitive description of soil bacterial community richness, meta-barcoding provided significantly higher and more accurate estimates of turnover rates. Both approaches were useful in evidencing the processes shaping bacterial diversity variations on a broad scale. When different taxonomic resolutions were considered for meta-barcoding data, they significantly influenced the estimation of turnover rates but not the relative importance of each component process. Altogether, DNA meta-barcoding provides a more accurate evaluation of the TAR and may lead to re-examination of the processes shaping soil bacterial community assembly. This should provide new insights into soil microbial ecology in the context of sustainable use of soil resources.

Reconnaissance soil maps at 1:250,000 scale are the most detailed source of soil information for large parts of France. For many environmental applications, however, the level of detail and accuracy of these maps is insufficient. Funds are lacking to refine and update these maps by traditional soil survey. In this study we investigated the merit of digital soil mapping to refine and improve the 1:250,000 reconnaissance soil map of a 1580 km2 area in Haute-Normandie, France. The soil map was produced in 1988 and distinguishes nine soil class units. The approach taken was to predict soil class from a large number of environmental covariates using regression techniques. The covariates used include DEM derivatives, geology and land cover maps. Because very few soil point observations were available within the area, we calibrated the regression model by sampling the soil map on a grid. We calibrated three models: classification tree (CT), multinomial logistic regression (MLR) and random forests (RF), and used these models to predict the nine soil classes across the study area. The new and original maps were validated with field data from 123 locations selected with a stratified simple random sampling design. For MLR, the estimate of the overall purity was 65.9%, while that of the reconnaissance map was 55.5%. The difference between the purity estimates of these maps was statistically significant (p = 0.014). The significant improvement over the existing soil map is remarkable because the regression model was calibrated with the existing soil map and uses no additional soil observations.

The Intergovernmental Technical Panel on Soils has completed the first State of the World’s Soil Resources Report. Globally soil erosion was identified as the gravest threat, leading to deteriorating water quality in developed regions and to lowering of crop yields in many developing regions. We need to increase nitrogen and phosphorus fertilizer use in infertile tropical and semi-tropical soils - the regions where the most food insecurity among us are found - while reducing global use of these products overall. Stores of soil organic carbon are critical in the global carbon balance, and national governments must set specific targets to stabilize or ideally increase soil organic carbon stores. Finally the quality of soil information available for policy formulation must be improved - the regional assessments in the State of the World’s Soil Resources Report frequently base their evaluations on studies from the 1990s based on observations made in the 1980s or earlier. Fil: Montanarella, Luca. European Commission Joint Research Centre; Fil: Pennock, Daniel Jon. University of Saskatchewan; Canadá Fil: McKenzie, Neil. Csiro Agriculture Flagship, Brisbane; Australia Fil: Badraoui, Mohamed. Institut National de la Recherche Agronomique, Morocco; Fil: Chude, Victor. National Programme For Food Security; Nigeria Fil: Baptista, Isaurinda. Instituto Nacional de Investigação e Desenvolvimento Agrário; Cabo Verde Fil: Mamo, Tekalign. Ministry of Agriculture; Etiopía Fil: Yemefack, Martin. International Institute Of Tropical Agriculture Yaounde; Fil: Aulakh, Mikha Singh. Banda University Of Agriculture And Technology; India Fil: Yagi, Kazuyuki. Institute For Agro-environmental Sciences, Naro; Fil: Hong, Suk Young. Rural Development Administration; Fil: Vijarnsorn, Pisoot. Chaipattana Foundation; Fil: Zhang, Gan-Lin. Chinese Academy of Sciences; República de China Fil: Arrouays, Dominique. Institut National de la Recherche Agronomique. InfoSol Unit; Francia Fil: Black, Helaina. The James Hutton Institute; Reino Unido Fil: Krasilnikov, Pavel. Lomonosov Moscow State University; Fil: Sobocká, Jaroslava. National Agricultural And Food Centre; Fil: Alegre, Julio. Fil: Henriquez, Carlos Roberto. Universidad de Costa Rica; Costa Rica Fil: Mendonça-Santos, Maria de Lourdes. Ministerio da Agricultura Pecuaria e Abastecimento de Brasil. Empresa Brasileira de Pesquisa Agropecuaria; Brasil Fil: Taboada, Miguel Angel. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Instituto Nacional de Tecnología Agropecuaria. Centro de Investigación de Recursos Naturales. Instituto de Suelos; Argentina Fil: Espinosa-Victoria, David. Colegio de Postgraduados; México Fil: AlShankiti, Abdullah. International Center For Biosaline Agriculture; Fil: AlaviPanah, Sayed Kazem. University Of Tehran; Irán Fil: Mustafa Elsheikh, Elsiddig Ahmed El. Khartoum University; Sudán Fil: Hempel, Jon. United States Department of Agriculture; Estados Unidos Fil: Arbestain, Marta Camps. Institute Of Agriculture And Environment; Fil: Nachtergaele, Freddy. Food And Agriculture Organization Of The United Nations; Fil: Vargas, Ronald. Food And Agriculture Organization Of The United Nations

Many studies have highlighted significant interactions between soil C reservoir dynamics and global climate and environmental change. However, in order to estimate the future soil organic carbon sequestration potential and related ecosystem services well, more spatially detailed predictions are needed. The present study made detailed predictions of future spatial evolution (at 250 m resolution) of topsoil SOC driven by climate change and land use change for France up to the year 2100 by taking interactions between climate, land use and soil type into account. We conclude that climate change will have a much bigger influence on future SOC losses in mid-latitude mineral soils than land use change dynamics. Hence, reducing CO2 emissions will be crucial to prevent further loss of carbon from our soils. ispartof: Scientific Reports vol:6 issue:1 ispartof: location:England status: published

EA SPE BIOME GENOSOLEASPEBIOMEGENOSOL; Biogeographical patterns of soil bacterial diversity at the scale of France. 16. International Symposium on Microbial Ecology - ISME16

Chapter 54; absent

Preservation and sustainable use of soil biological communities represent major challenges in the current agroecological context. However, to identify the agricultural practices/systems that match with these challenges, innovative tools have to be developed to establish a diagnosis of the biological status of the soil. Here, we have developed a statistical polynomial model to predict the molecular biomass of the soil microbial community according to the soil physicochemical properties. For this, we used a dataset of soil molecular microbial biomass estimates and pedoclimatic properties derived from analyses of samples collected in the context of the “French monitoring soil quality network = Reseau de Mesures de la qualite des Sols” (RMQS). This sampling network has provided 2115 soil samples covering the range of variability of soil type and land use at the scale of France. The best model obtained from the data showed that soil organic carbon content, clay content, altitude, and pH were the best explanatory variables of soil microbial biomass while other variables such as longitude, latitude and annual temperature were negligeable. Based on these variables, the multilinear model developed allowed very accurate prediction of the soil microbial biomass, with an excellent adjusted coefficient of determination R adj 2 of 0.6772 (P R adj 2 , the model was further validated by results from cross validation and sensitivity analyses. The model provides a reference value for microbial biomass for a given pedoclimatic condition, which can then be compared with the corresponding measured data to provide for the first time a robust diagnosis of soil quality. Application of the model to a set of soil samples obtained at the scale of an agricultural landscape is presented and discussed, showing the suitability of the model to diagnose of the impact of particular agricultural practices such as tillage and catch crops in field conditions, at least over the French nation.

International audience; The French soil-test database (Base de Données d'Analyses de Terre: BDAT) is populated with analytical results of agricultural topsoil samples requested by farmers for fertilization planning. The coordinates of the farms are unknown due to data confidentiality policies, and the best available georeference is at level of municipality. We compared four approaches for mapping soil texture of agricultural land in Region Centre (France) using BDAT data: 1) a reference approach of mapping the mean of the aggregated data by municipality, 2) a boosted regression tree (BRT) model fitted with the municipality-averaged data, 3) area-to-point cokriging (AToP CK), and 4) a regression kriging version of this (AToP RCK, for which the BRT predictions were used to give the trend). Specifically, parameters for these last two approaches were fitted through the summary statistics approach to AToP kriging, which accounts for the full set of municipality summary statistics data (i.e. the mean, variance and number of measurements from each municipality). We could thus determine whether more complex and statistically-challenging approaches improve our knowledge on the spatial distribution of soil texture compared with maps of data aggregated by municipality. Texture data from 105 sites form the French soil monitoring network (Réseau de Mesures de la Qualité des Sols: RMQS) were used for independent validation. In general, the R2 was greater for sand (average R2 = 0.69) and silt (average R2 = 0.72) than for clay (average R2 = 0.40). The three methods for disaggregating the summary statistics data (BRT, AToP CK, and AToP RCK) showed similar prediction accuracies—although BRT predictions showed the greatest bias—and were better than the BDAT reference approach. AToP RCK was able to give similar prediction accuracy to BRT modelling alone, reduced the bias considerably, and gave a reasonable (although slightly conservative) assessment of prediction uncertainty. The results indicate that geostatistical methods for change of support expand the utility of aggregated data from soil-test databases.

Global Soil Security: Towards More Science-Society Interfaces contains contributions presented at the 2nd Global Soil Security conference, held 5-6 December 2016 in Paris. These chapters focus on how to achieve soil security. This involves scientific, economic, industrial and political engagement to inform soil-users, policy makers and citizens with the objective of implementing appropriate actions. The contributions to this book address the five dimensions of soil security, namely: capability, condition, capital, connectivity and codification.

The objective of this work was to compare three spatially explicit modeling approaches for soil depth (SD) for France (540 K km 2 ), produced using: i) a straight forward digital soil mapping (DSM) approach, based on regression treemodeling ( RTM ), ii) gradient boosting modeling ( GBM ), and iii) multi-resolution kriging ( MrK ) for large datasets. SD was defined from the USDA Soil Survey Manual as the depth (in cm) to a lithic or paralithic contact. SD was determined for 2116 sites from the French Soil Monitoring network (RMQS) and 14,113 sites from French Soil Inventory (IGCS) program. The RMQS dataset had a better spatial coverage and was collected following a standardized procedure. The RMQS dataset was used for calibration; the IGCS dataset was used for validation. SD ranged from 0 to 300 cm for RMQS (mean = 102 cm), and from 3 to 295 cm for IGCS (mean = 93 cm). Exhaustive environmental data were used to characterize the climate, the organisms, the topography and the other known soil forming factors, following the Soil-Landscape paradigm. The final maps were compared using the same strategy; the maps' accuracy and difference were assessed by comparing the SD predictions to observed data (RMQS and IGCS datasets) and to an available soil map (1:1M). The three DSM approaches predicted the SD trend mainly from the covariates derived from the digital elevation model (DEM). Then, the most important covariates were soil properties, climate covariates, and finally land use. The three predictive maps showed similar accuracy, and were consistent with the 1:1M SD map. The four maps presented similar spatial pattern at the country scale, but the RTM and GBM map showed the higher spatial heterogeneity, while the MrK map was smoother. The three maps had poor performance to estimate the shallower and deeper SD values. This issue was discussed and three options were presented to solve it (implementing calibration dataset, addition of kriged residuals, quantile transformation). Finally, this work showed that the choice of the modeling approach should be done considering the users' goals.

Black 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 x 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.

We consider approaches for calculating and mapping statistical predictions of soil organic carbon (SOC), and attendant uncertainty, from data across a region of France. The data were collected from farms across the region. To protect the anonymity of farms that contributed, the locations and values of individual observations were unavailable, and we were only able to use the average value, sample variance, and number of observations from each commune. Communes varied in size up to a maximum of 130?km 2, with a mean of 10?km 2. The uncertainty due to data being commune-wide averageswith sample error varying between communes as a result of variations in their size and the number of samples drawn from within themraises an important methodological issue. We show how a residual maximum likelihood method can be used to estimate covariance parameters on the basis of this form of data and use the empirical best linear unbiased predictor to calculate predictions. Cross-validation shows that by properly representing the commune-wide averaged data, the predictions and attendant uncertainty assessments are more reliable than those from a naive approach based on the summary means only. We compare maps produced using the approaches showing the SOC predictions and the attendant uncertainty. Copyright (c) 2012 John Wiley & Sons, Ltd.

We calculate statistical predictions of changes in soil organic carbon (SOC), and attendant uncertainty from areal data across a region of France. The data consist of measurements of SOC from farms across the region collected in two time periods: 19951999, and 20002004. To protect the anonymity of farms that contributed, the data were summarised by commune; we were only able to use the average value, sample variance and number of observations from each commune. We consider how we can use data of this form to map temporal changes in SOC. We account for the dependence between data from the two surveys through a linear model of coregionalization. Cross-validation shows that by using the linear model of coregionalization to model inter-survey dependence, we obtain better estimates of SOC changes and better uncertainty assessments. We compare maps produced using the approaches showing the estimated SOC changes and probabilities of SOC decrease between the times of the two surveys. Copyright (c) 2012 John Wiley & Sons, Ltd.

Archived soil samples from the beginning and end of three long-term field trials conducted in central France were analysed for total uranium (U) and thorium (Th) contents to evaluate the effect of 15–30 yr of phosphorus (P) fertilizer treatments on the accumulation of these elements in the topsoil. For comparison, the soil samples were also analysed for total P. Three treatments were compared: no P application (P0), 26 kg P/ha/yr (P26) and 52 kg P/ha/yr (P52). Significant effects of P fertilizer were observed on U content and, to a lesser extent, on Th content as a result of the P52 treatment at two of the field trials. This effect was demonstrated both in the analyses at the end of the field trials [P52–P0: +0.25 and +0.44 mg U/kg soil, +0.58 (not significant) and +1.03 mg Th/kg soil] and when considering the changes in U and Th contents between the beginning and the end of the field trials (end–start: +0.18 and +034 mg U/kg soil, +0.35 and +0.45 mg Th/kg soil). The P fertilizer effect was also supported by the correlations of U and Th with total levels of P in the soil. However, in one of the three trials, no significant accumulation of U or Th because of fertilizer could be seen, suggesting either that less U and Th were applied using a different P fertilizer and/or that soil heterogeneity masked significant effects.

The concept of soil organic C (SOC) saturation suggests that the quantity of stable SOC is limited and determined by the amount of fine particles (clay + fine silt, Clay + fSilt). The difference between the theoretical SOC saturation value and the measured SOC one for the fine fraction corresponds to the soil's saturation deficit and may represent the potential for SOC sequestration in a stable form. We calculate the saturation deficit of French arable soils based on the national soil test database and using the saturation equation. For the whole database (n = 1 454 633), the median saturation deficit was 8.1 gC/kg and this generally increased with the Clay + fSilt content to reach a maximum of 500 g/kg. National mapping of the SOC saturation deficit allowed investigation of spatial variation and controlling factors. Saturated soils were found in localities with specific land use (grassland, meadows) or farming systems (livestock production with high manure production). Smaller deficits occurred at higher altitudes, probably due to the combined effect of cooler temperature and the presence of meadows. Some very sandy soils appeared to be almost saturated, largely due to their very small fine fraction. Soils in the highly cultivated plains in the northern half of the country had a significant saturation deficit. Soils in the southern part of the country had the highest saturation deficit because of the combined effects of climatic factors (low production, high temperature) and land use (vineyards, orchards). Analysis of communal data revealed significant correlations at the national level with Clay + fSilt (r = 0.59), pH (r = 0.44) but also with the proportion of grassland in the cultivated area (r = −0.47). Some areas had apparent oversaturation which may be due to uncertainty associated with the theoretical C saturation equation because of overestimation of the stable soil C fraction. Mapping the C saturation deficit at the national scale demonstrates the influence of climate, soil parameters and land use on the SOC stabilization potential and indicates that a significant proportion of agricultural soils have potential for further SOC storage.

National-scale soil monitoring networks are required to identify where the soil quality is threatened and evaluate the effectiveness of any remediation efforts. In particular, trace elements (TEs) should be monitored to assess human exposure to potentially toxic elements and to ensure that crops take up sufficient quantities of elements that have essential functions in the human body. However the spatial variation of TEs is often highly complex since hot-spots at point sources are superimposed upon more regular patterns produced by diffuse pollution and natural processes. Geostatistical methods used to analyse national-scale soil monitoring networks must be general enough to accommodate such behaviour but simple enough to be computed in a reasonable time for large datasets. We show how a simplified version of a recently developed robust geostatistical algorithm to map the underlying variation of six TEs (Cr, Cu, Pb, Ni, Th and Zn) across France using observations from the French National Soil Monitoring Network (Réseau de Mesures de la Qualité des Sols). Cross-validation results suggest that these TEs cannot be modelled by non-robust methods but that the simplified robust approach is sufficient. Differences between the maps of different elements and the abundance of TEs from different parent materials are evident. Large concentrations of Cr, Ni and Zn occur in soils on Jurassic rocks whereas Pb and Th concentrations are large in soils on crystalline rocks. Volcanic parent material leads to large concentrations of Cr, Cu, Ni and Zn but small concentrations of Pb and Th. Diffuse pollution of certain elements (mainly Pb, and to a lesser extent Zn) is evident in industrial regions in the north and the north-east of France and close to Paris. The pattern of outlying values is indicative of local anthropogenic processes such as industrial pollution in the north of France and close to Paris, and application of Cu on vineyards and of geological anomalies such as large concentrations of some TEs in the south of the Massif Central Mountains. Future phases of the RMQS will describe the spatial and temporal trends of the concentrations of these TEs.

Aim The spatial organization of soil microbial communities on large scales and the identification of environmental factors structuring their distribution have been little investigated. The overall objective of this study was to determine the spatial patterning of microbial biomass in soils over a wide extent and to rank the environmental filters most influencing this distribution.

Soil bulk density (p) is an important physical property, but its measurement is frequently lacking in soil surveys due to the time-consuming nature of making the measurement. As a result pedotransfer functions (PTFs) have been developed to predict p from other more easily available soil properties. These functions are generally derived from regression methods that aim to fit a single model. In this study, we use a technique called Generalized Boosted Regression Modelling (GBM; Ridgeway, 2006) which combines two algorithms: regression trees and boosting. We built two models and compared their predictive performance with published PTFs. All the functions were fitted based on the French forest soil dataset for the European demonstration Biosoil project. The two GBM models were Model G3 which involved the three most frequent quantitative predictors used to estimate soil bulk density (organic carbon, clay and silt), and Model G10, which included ten qualitative and quantitative input variables such as parent material or tree species. Based on the full dataset, Models G3 and G10 gave R 2 values of 0.45 and 0.86, respectively. Model G3 did not significantly outperform the best published model. Even when fitted from an external dataset, it explained only 29% of the variation of ρ with a root mean square error of 0.244 g/cm 3 . In contrast, the more complex Model G10 outperformed the other models during external validation, with a R 2 of 0.67 and a predictive deviation of ± 0.168 g/cm 3 . The variation in forest soil bulk densities was mainly explained by five input variables: organic carbon content, tree species, the coarse fragment content, parent material and sampling depth.

We studied a database that recorded all requests to the French National Soil Data Centre between April 2008 and November 2009 when a total of 440 requests were made. Our hypothesis is that an analysis of such requests can provide an insight into awareness of soil issues and data needs. These requests covered a wide range of topics. The most important threats were contamination, erosion and soil organic carbon decline. Soil acidification appeared to be no longer a priority concern. We found a lack of awareness of topics such as soil sealing and soil compaction. Gaps in data coverage are stressed and we highlight some data that were inadequate for some users' needs.

Habitat loss and soil organic carbon (SOC) stock variations linked to land-cover change were estimated over two decades in the most densely populated biodiversity hotspot in the world, in order to assess the possible influence of conservation practices on the protection of SOC. For a study area of 88 484km2, 70% of which lie inside theWestern Ghats Biodiversity Hotspot (WGBH), land-cover maps for two dates (1977, 1999) were built from various data sources including remote sensing images and ecological forest maps. SOC stocks were calculated from climatic parameters, altitude, physiography, rock type, soil type and land-cover, with a modelling approach used in predictive learning and based on Multiple Additive Regression Tree. The model was trained on 361 soil profiles data, and applied to estimate SOC stocks from predictor variables using a Geographical Information System (GIS). Comparison of 1977 and 1999 land-cover maps showed 628km2 of dense forests habitat loss (6%), corresponding to an annual deforestation rate of 0.44%. This was found consistent with other studies carried out in other parts of the WGBH, but not with FAO figures showing an increase in forest area. This could be explained by the different forest definitions used, based on ecological classification in the former, and on percentage tree cover in the latter. Unexpectedly, our results showed that despite ongoing deforestation, overall SOC stock was maintained ( _ 0.43 Pg). But a closer examination of spatial differences showed that soil carbon losses in deforested areas were compensated by sequestration elsewhere, mainly in recent plantations and newly irrigated croplands. This suggests that more carbon sequestration in soils could be achieved in the future through appropriate wasteland management. It is also expected that increasing concerns about biodiversity loss will favour more conservation and reinforce the already prevailing protective measures, thus further maintaining C stocks. (Resume d'auteur)

International audience; There is ample evidence that microbial processes can exhibit large variations in activity on a field scale. However, very little is known about the spatial distribution of the microbial communities mediating these processes. Here we used geostatistical modelling to explore spatial patterns of size and activity of the denitrifying community, a functional guild involved in N-cycling, in a grassland field subjected to different cattle grazing regimes. We observed a non-random distribution pattern of the size of the denitrifier community estimated by quantification of the denitrification genes copy numbers with a macro-scale spatial dependence (6–16 m) and mapped the distribution of this functional guild in the field. The spatial patterns of soil properties, which were strongly affected by presence of cattle, imposed significant control on potential denitrification activity, potential N2O production and relative abundance of some denitrification genes but not on the size of the denitrifier community. Absolute abundance of most denitrification genes was not correlated with the distribution patterns of potential denitrification activity or potential N2O production. However, the relative abundance of bacteria possessing the nosZ gene encoding the N2O reductase in the total bacterial community was a strong predictor of the N2O/(N2 + N2O) ratio, which provides evidence for a relationship between bacterial community composition based on the relative abundance of denitrifiers in the total bacterial community and ecosystem processes. More generally, the presented geostatistical approach allows integrated mapping of microbial communities, and hence can facilitate our understanding of relationships between the ecology of microbial communities and microbial processes along environmental gradients.

International audience; Trace elements (TE) occurring naturally in soils derive from the parent materials from which the soils were developed. We propose a feasibility study of the x-ray diffraction determination of TE carriers of a natural origin applied to 28 samples collected from soil 30-50cm deep in the French Soil Quality Monitoring Network from two French provinces. Three main pedogeological families were studied. Results show that mineralogical analysis permits the determination of several potential TE carriers. They also explain the highest concentrations of certain TEs in soils. Such knowledge of carriers is useful for understanding and evaluating the risks of TE transfer in ecosystems. Nevertheless, it is also shown that the use of mineralogical soil analyses for an understanding of the total TE contents can be variable, depending upon which TE or which soil type is considered.

Within the United Nations Framework Convention on Climate Change, articles 3.3 and 3.4 stipulate that some voluntary activities leading to an additional carbon (C) sequestration in soils could be accounted as C sinks in national greenhouse gas inventories. These additional C stocks should be verifiable. In this work, we assess the feasibility of verifying the effects of changes in land use or management practice on soil organic carbon (SOC), by comparing minimum detectable changes in SOC concentration for existing European networks suitable for soil monitoring. Among the tested scenarios, the minimum detectable changes differed considerably among the soil-monitoring networks (SMNs). Considerable effort would be necessary for some member states to reach acceptable levels of minimum detectable change for C sequestration accounting. For SOC, a time interval of about 10 years would enable the detection of some simulated large changes in most European countries. In almost all cases, the minimum detectable change in SOC stocks remains greater than annual greenhouse gases emissions. Therefore, it is unlikely that SMNs could be used for annual national C accounting. However, the importance of organic C in soil functions, and as an indicator of soil condition and trends, underlines the importance of establishing effective national SMNs.

International audience; 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. (C) 2014 Elsevier B.V. All rights reserved.

International audience; Estimation of the water retention capacity of a heterogeneous soil requires knowledge of the hydric properties of each soil phase. Nevertheless, for stony soils, the rock fragments have often been neglected. The objective of this work was then to propose a methodology to improve the calculation of the available water content (AWC) of stony soils at a regional scale. On a 36,200 ha surface area in Beauce located in the Region Centre of France, the AWC was calculated by coupling pedotransfer classes developed for fine earth and rock fragments. When calculating the AWC for the first 120 cm of the soil and considering the rock fragments to be inert, the AWC was underestimated by 15% and showed a high spatial variability. When both the volume and the hydric properties of the rock fragments were ignored, the AWC was underestimated by 20%. This work is then the first proposal to estimate soil water properties at a regional scale by using the water storage capacity of the main part of the stony phase, say from gravels to stones. Results of this study will improve the calculation of water deficit on a regional scale and will aid both in regional water balance modelling and in regional assessment of irrigation needs.