16 results on '"Nicolas P. A. Saby"'
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2. Management of soil pH promotes nitrous oxide reduction and thus mitigates soil emissions of this greenhouse gas
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Catherine Hénault, Hocine Bourennane, Adeline Ayzac, Céline Ratié, Nicolas P. A. Saby, Jean-Pierre Cohan, Thomas Eglin, and Cécile Le Gall
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Medicine ,Science - Abstract
Abstract While concerns about human-induced effects on the Earth’s climate have mainly concentrated on carbon dioxide (CO2) and methane (CH4), reducing anthropogenic nitrous oxide (N2O) flux, mainly of agricultural origin, also represents an opportunity for substantial mitigation. To develop a solution that induces neither the transfer of nitrogen pollution nor decreases agricultural production, we specifically investigated the last step of the denitrification pathway, the N2O reduction path, in soils. We first observed that this path is mainly driven by soil pH and is progressively inhibited when pH is lower than 6.8. During field experiments, we observed that liming acidic soils to neutrality made N2O reduction more efficient and decreased soil N2O emissions. As we estimated acidic fertilized soils to represent 37% [27–50%] of French soils, we calculated that liming could potentially decrease France’s total N2O emissions by 15.7% [8.3–21.2%]. Nevertheless, due to the different possible other impacts of liming, we currently recommend that the deployment of this solution to mitigate N2O emission should be based on local studies that take into account agronomic, environmental and economic aspects.
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- 2019
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3. Modeling of Soil Functions for Assessing Soil Quality: Soil Biodiversity and Habitat Provisioning
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Jeroen P. van Leeuwen, Rachel E. Creamer, Daniel Cluzeau, Marko Debeljak, Fabio Gatti, Christian B. Henriksen, Vladimir Kuzmanovski, Cristina Menta, Guénola Pérès, Calypso Picaud, Nicolas P. A. Saby, Aneta Trajanov, Isabelle Trinsoutrot-Gattin, Giovanna Visioli, and Michiel Rutgers
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ecosystem service ,soil function ,soil biodiversity ,land management ,qualitative modeling ,Europe ,Environmental sciences ,GE1-350 - Abstract
Soil biodiversity and habitat provisioning is one of the soil functions that agricultural land provides to society. This paper describes assessment of the soil biodiversity function (SB function) as a proof of concept to be used in a decision support tool for agricultural land management. The SB function is defined as “the multitude of soil organisms and processes, interacting in an ecosystem, providing society with a rich biodiversity source and contributing to a habitat for aboveground organisms.” So far, no single measure provides the full overview of the soil biodiversity and how a soil supports a habitat for a biodiverse ecosystem. We have assembled a set of attributes for a proxy-indicator system, based on four “integrated attributes”: (1) soil nutrient status, (2) soil biological status, (3) soil structure, and (4) soil hydrological status. These attributes provide information to be used in a model for assessing the capacity of a soil to supply the SB function. A multi-criteria decision model was developed which comprises of 34 attributes providing information to quantify the four integrated attributes and subsequently assess the SB function for grassland and for cropland separately. The model predictions (in terms of low—moderate—high soil biodiversity status) were compared with expert judgements for a collection of 137 grassland soils in the Netherlands and 52 French soils, 29 grasslands, and 23 croplands. For both datasets, the results show that the proposed model predictions were statistically significantly correlated with the expert judgements. A sensitivity analysis indicated that the soil nutrient status, defined by attributes such as pH and organic carbon content, was the most important integrated attribute in the assessment of the SB function. Further progress in the assessment of the SB function is needed. This can be achieved by better information regarding land use and farm management. In this way we may make a valuable step in our attempts to optimize the multiple soil functions in agricultural landscapes, and hence the multifaceted role of soils to deliver a bundle of ecosystem services for farmers and citizens, and support land management and policy toward a more sustainable society.
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- 2019
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4. Development of an Agricultural Primary Productivity Decision Support Model: A Case Study in France
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Taru Sandén, Aneta Trajanov, Heide Spiegel, Vladimir Kuzmanovski, Nicolas P. A. Saby, Calypso Picaud, Christian Bugge Henriksen, and Marko Debeljak
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decision support model ,data mining ,expert knowledge ,yield ,soil functions ,agricultural decision-making ,Environmental sciences ,GE1-350 - Abstract
Agricultural soils provide society with several functions, one of which is primary productivity. This function is defined as the capacity of a soil to supply nutrients and water and to produce plant biomass for human use, providing food, feed, fiber, and fuel. For farmers, the productivity function delivers an economic basis and is a prerequisite for agricultural sustainability. Our study was designed to develop an agricultural primary productivity decision support model. To obtain a highly accurate decision support model that helps farmers and advisors to assess and manage the provision of the primary productivity soil function on their agricultural fields, we addressed the following specific objectives: (i) to construct a qualitative decision support model to assess the primary productivity soil function at the agricultural field level; (ii) to carry out verification, calibration, and sensitivity analysis of this model; and (iii) to validate the model based on empirical data. The result is a hierarchical qualitative model consisting of 25 input attributes describing soil properties, environmental conditions, cropping specifications, and management practices on each respective field. An extensive dataset from France containing data from 399 sites was used to calibrate and validate the model. The large amount of data enabled data mining to support model calibration. The accuracy of the decision support model prior to calibration supported by data mining was ~40%. The data mining approach improved the accuracy to 77%. The proposed methodology of combining decision modeling and data mining proved to be an important step forward. This iterative approach yielded an accurate, reliable, and useful decision support model for the assessment of the primary productivity soil function at the field level. This can assist farmers and advisors in selecting the most appropriate crop management practices. Embedding this decision support model in a set of complementary models for four adjacent soil functions, as endeavored in the H2020 LANDMARK project, will help take the integrated sustainability of arable cropping systems to a new level.
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- 2019
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5. Monitoring Grassland Management Effects on Soil Organic Carbon—A Matter of Scale
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Alexandra Crème, Cornelia Rumpel, Sparkle L. Malone, Nicolas P. A. Saby, Emmanuelle Vaudour, Marie-Laure Decau, and Abad Chabbi
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temporary (ley) grassland ,carbon sequestration ,landscape ,plot ,N-fertilization ,grazing ,Agriculture - Abstract
Introduction of temporary grasslands into cropping cycles could be a sustainable management practice leading to increased soil organic carbon (SOC) to contribute to climate change adaption and mitigation. To investigate the impact of temporary grassland management practices on SOC storage of croplands, we used a spatially resolved sampling approach combined with geostatistical analyses across an agricultural experiment. The experiment included blocks (0.4- to 3-ha blocks) of continuous grassland, continuous cropping and temporary grasslands with different durations and N-fertilizations on a 23-ha site in western France. We measured changes in SOC storage over this 9-year experiment on loamy soil and investigated physicochemical soil parameters. In the soil profiles (0–90 cm), SOC stocks ranged from 82.7 to 98.5 t ha−1 in 2005 and from 81.3 to 103.9 t ha−1 in 2014. On 0.4-ha blocks, the continuous grassland increased SOC in the soil profile with highest gains in the first 30 cm, while losses were recorded under continuous cropping. Where temporary grasslands were introduced into cropping cycles, SOC stocks were maintained. These observations were only partly confirmed when changing the scale of observation to 3-ha blocks. At the 3-ha scale, most grassland treatments exhibited both gains and losses of SOC, which could be partly related to soil physicochemical properties. Overall, our data suggest that both management practices and soil characteristics determine if carbon will accumulate in SOC pools. For detailed understanding of SOC changes, a combination of measurements at different scales is necessary.
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- 2020
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6. Pesticide Residues in French Soils: Occurrence, Risks, and Persistence
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Claire Froger, Claudy Jolivet, Hélène Budzinski, Manon Pierdet, Giovanni Caria, Nicolas P. A. Saby, Dominique Arrouays, Antonio Bispo, Info&Sols (Info&Sols), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Earth and Planetary Observation Centre (EPOC), University of Stirling, Interactions Sol Plante Atmosphère (UMR ISPA), Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Analyses des Sols (LAS), Laboratoire Avancé de Spectroscopie pour les Intéractions la Réactivité et l'Environnement - UMR 8516 (LASIRE), and Institut de Chimie du CNRS (INC)-Université de Lille-Centre National de la Recherche Scientifique (CNRS)
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[SDE]Environmental Sciences ,Environmental Chemistry ,General Chemistry - Abstract
International audience
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- 2023
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7. Shapley values reveal the drivers of soil organic carbon stock prediction
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Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin
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Soil Science - Abstract
Insights into the controlling factors of soil organic carbon (SOC) stock variation are necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning have become popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stock variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and used a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stock prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how does the covariate importance vary locally from one location to another and between carbon-landscape zones? Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariate contribution that yielded the prediction – in one case climate variables contributed positively, whereas in the second case climate variables contributed negatively – and this effect was mitigated by land use. We demonstrate that Shapley values are a methodological development that yield useful insights into the importance of factors controlling SOC stock variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.
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- 2023
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8. Satellite data integration for soil clay content modelling at a national scale.
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Thomas Loiseau, Songchao Chen, Vera L. Mulder, Mercedes Román Dobarco, Anne C. Richer-de-Forges, Sébastien Lehmann, Hocine Bourennane, Nicolas P. A. Saby, Manuel P. Martin, Emmanuelle Vaudour, Cécile Gomez, Philippe Lagacherie, and Dominique Arrouays
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- 2019
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9. Supplementary material to 'Shapley values reveal the drivers of soil organic carbon stocks prediction'
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Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin
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- 2022
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10. Shapley values reveal the drivers of soil organic carbon stocks prediction
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Alexandre M. J.-C. Wadoux, Nicolas P. A. Saby, and Manuel P. Martin
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Insights into the controlling factors of soil organic carbon (SOC) stocks variation is necessary both for our scientific understanding of the terrestrial carbon balance and to support policies that intend to promote carbon storage in soils to mitigate climate change. In recent years, complex statistical and algorithmic tools from the field of machine learning became popular for modelling and mapping SOC stocks over large areas. In this paper, we report on the development of a statistical method for interpreting complex models, which we implemented for the study of SOC stocks variation. We fitted a random forest machine learning model with 2206 measurements of SOC stocks for the 0–50 cm depth interval from mainland France and using a set of environmental covariates as explanatory variables. We introduce Shapley values, a method from coalitional game theory, and use them to understand how environmental factors influence SOC stocks prediction: what is the functional form of the association in the model between SOC stocks and environmental covariates, and how the covariate importance varies locally from one location to another and between carbon-landscape zones. Results were validated both in light of the existing and well-described soil processes mediating soil carbon storage and with regards to previous studies in the same area. We found that vegetation and topography were overall the most important drivers of SOC stock variation in mainland France but that the set of most important covariates varied greatly among locations and carbon-landscape zones. In two spatial locations with equivalent SOC stocks, there was nearly an opposite pattern in the individual covariates contribution that yielded the prediction: in one case climate variables contributed positively whereas in the second case climate variables contributed negatively, and that this effect was mitigated by landuse. This shows that SOC stock variation is complex and should be interpreted at multiple levels. We demonstrate that Shapley values are a methodological development that yielded useful insights into the importance of factors controlling SOC stocks variation in space. This may provide valuable information to understand whether complex empirical models are predicting a property of interest for the right reasons and to formulate hypotheses on the mechanisms driving the carbon sequestration potential of a soil.
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- 2022
11. Supplementary material to 'Spatial variations, origins, and risk assessments of polycyclic aromatic hydrocarbons in French soils'
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Claire Froger, Nicolas P. A. Saby, Claudy C. Jolivet, Line Boulonne, Giovanni Caria, Xavier Freulon, Chantal de Fouquet, Hélène Roussel, Franck Marot, and Antonio Bispo
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- 2021
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12. Spatial variations, origins, and risk assessments of polycyclic aromatic hydrocarbons in French soils
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Claire Froger, Nicolas P. A. Saby, Claudy C. Jolivet, Line Boulonne, Giovanni Caria, Xavier Freulon, Chantal de Fouquet, Hélène Roussel, Franck Marot, Antonio Bispo, InfoSol (InfoSol), Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Laboratoire d'Analyses des Sols (LAS), MINES ParisTech - École nationale supérieure des mines de Paris, Université Paris sciences et lettres (PSL), Centre de Géosciences (GEOSCIENCES), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL), and Agence de l'Environnement et de la Maîtrise de l'Energie (ADEME)
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13. Climate action ,[SDE]Environmental Sciences ,[SDV.SA.SDS]Life Sciences [q-bio]/Agricultural sciences/Soil study ,[SDU.STU.AG]Sciences of the Universe [physics]/Earth Sciences/Applied geology - Abstract
Polycyclic aromatic hydrocarbons (PAHs) are persistent organic pollutants produced by anthropogenic activities that contaminate all environmental spheres, including soils. This study focused on PAHs measured in 2154 soils in France, covering the entire territory based on a regular sampling grid. The quantified concentrations in the Σ15 PAHs ranged from 5.1 to 31 200 µg kg−1, with a median value of 32.6 µg kg−1, and PAHs were detected in 70 % of the soil samples. The map of Σ15 PAH concentrations revealed strong spatial variations in soil contamination throughout France, with larger concentrations in soils of industrial regions and near major cities. PAH molecular diagnostic ratios support the historical origin of PAHs in the northern part of France being linked to the significant emissions of PAHs in Europe during the industrial period of 1850–1950, in particular with the contribution of coal and/or biomass combustion and iron–steel production. A health risk assessment conducted for the residential population resulted in a median value of 1.07 × 10−8 in total lifetime cancer risk, with only 20 sites above the limit of 10−6 and one above the limit of 10−5 adopted by the French government. These results reveal the need to conduct large-scale studies on soil contamination to determine the fate of PAHs and evaluate the risks induced by soil pollution at a country-level scale.
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- 2021
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13. Biogeography of soil microbial communities: a review and a description of the ongoing french national initiative
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Lionel Ranjard, Samuel Dequiedt, Claudy Jolivet, Nicolas P. A. Saby, Jean Thioulouse, Jérome Harmand, Patrice Loisel, Alain Rapaport, Saliou Fall, Pascal Simonet, Richard Joffre, Nicolas Chemidlin-Prévost Bouré, Pierre-Alain Maron, Christophe Mougel, Manuel P. Martin, Benoît Toutain, Dominique Arrouays, Philippe Lemanceau, Microbiologie du Sol et de l'Environnement (MSE), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), Ecologie quantitative et évolutive des communautés, Département écologie évolutive [LBBE], Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS)-Laboratoire de Biométrie et Biologie Evolutive - UMR 5558 (LBBE), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-VetAgro Sup - Institut national d'enseignement supérieur et de recherche en alimentation, santé animale, sciences agronomiques et de l'environnement (VAS)-Centre National de la Recherche Scientifique (CNRS), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie (MISTEA), Institut National de la Recherche Agronomique (INRA)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Laboratoire de Biotechnologie de l'Environnement [Narbonne] (LBE), Water Resource Modeling (MERE), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de la Recherche Agronomique (INRA), Ampère (AMPERE), École Centrale de Lyon (ECL), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre d’Ecologie Fonctionnelle et Evolutive (CEFE), Université Paul-Valéry - Montpellier 3 (UPVM)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École Pratique des Hautes Études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Eric Lichtfouse, Marjolaine HAMELIN, Mireille NAVARRETE, Philippe Debaeke, Unité INFOSOL (ORLEANS INFOSOL), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA), Ampère, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS), Université Paul-Valéry - Montpellier 3 (UM3)-Institut National de la Recherche Agronomique (INRA)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-École pratique des hautes études (EPHE)-Université de Montpellier (UM)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD [France-Sud])-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut National de la Recherche Agronomique (INRA), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Centre National de la Recherche Scientifique (CNRS)-Université de Montpellier (UM)-École pratique des hautes études (EPHE), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Centre international d'études supérieures en sciences agronomiques (Montpellier SupAgro)-Institut National de la Recherche Agronomique (INRA)-Université Paul-Valéry - Montpellier 3 (UPVM)-Institut national d’études supérieures agronomiques de Montpellier (Montpellier SupAgro), Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut national d'enseignement supérieur pour l'agriculture, l'alimentation et l'environnement (Institut Agro)-Institut de Recherche pour le Développement (IRD [France-Sud]), Microbiologie du Sol et de l'Environnement ( MSE ), Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ), Unité INFOSOL ( ORLEANS INFOSOL ), Institut National de la Recherche Agronomique ( INRA ), Laboratoire de Biométrie et Biologie Evolutive ( LBBE ), Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Université de Lyon-Institut National de Recherche en Informatique et en Automatique ( Inria ) -Centre National de la Recherche Scientifique ( CNRS ), Mathématiques, Informatique et STatistique pour l'Environnement et l'Agronomie ( MISTEA ), Institut National de la Recherche Agronomique ( INRA ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Laboratoire de Biotechnologie de l'Environnement [Narbonne] ( LBE ), Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro ), Water Resource Modeling ( MERE ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de la Recherche Agronomique ( INRA ), École Centrale de Lyon ( ECL ), Université de Lyon-Université de Lyon-Université Claude Bernard Lyon 1 ( UCBL ), Université de Lyon-Institut National des Sciences Appliquées de Lyon ( INSA Lyon ), Université de Lyon-Institut National des Sciences Appliquées ( INSA ) -Institut National des Sciences Appliquées ( INSA ) -Centre National de la Recherche Scientifique ( CNRS ), Centre d’Ecologie Fonctionnelle et Evolutive ( CEFE ), and Université Paul-Valéry - Montpellier 3 ( UM3 ) -Centre international d'études supérieures en sciences agronomiques ( Montpellier SupAgro ) -École pratique des hautes études ( EPHE ) -Institut national de la recherche agronomique [Montpellier] ( INRA Montpellier ) -Université de Montpellier ( UM ) -Centre National de la Recherche Scientifique ( CNRS ) -Institut de Recherche pour le Développement ( IRD [France-Sud] ) -Institut national d’études supérieures agronomiques de Montpellier ( Montpellier SupAgro )
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2. Zero hunger ,0303 health sciences ,[ SDE.BE ] Environmental Sciences/Biodiversity and Ecology ,[SPI.NRJ]Engineering Sciences [physics]/Electric power ,microbial communities ,04 agricultural and veterinary sciences ,15. Life on land ,bacterial communities ,microbial ecology ,diversity ,Europe ,03 medical and health sciences ,soil biogeography ,[ SPI.NRJ ] Engineering Sciences [physics]/Electric power ,13. Climate action ,soil survey ,040103 agronomy & agriculture ,0401 agriculture, forestry, and fisheries ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,environment ,030304 developmental biology - Abstract
International audience; Microbial biogeography is the study of the distribution of microbial diversity on large scales of space and time. This science aims at understanding biodiversity regulation and its link with ecosystem biological functioning, goods and services such as maintenance of productivity, of soil and atmospheric quality, and of soil health. Although the initial concept dates from the early 20th century (Beijerinck (1913) De infusies en de ontdekking der backterien, in: Jaarboek van de Knoniklijke Akademie van Wetenschappen, Muller, Amsterdam), only recently have an increasing number of studies have investigated the biogeographical patterns of soil microbial diversity. A such delay is due to the constraints of the microbial models, the need to develop relevant molecular and bioinformatic tools to assess microbial diversity, and the non-availability of an adequate sampling strategy. Consequently, the conclusions from microbial ecology studies have rarely been generally applicable and even the fundamental power-laws differ because the taxa-area relationship and the influence of global and distal parameters on the spatial distribution of microbial communities have not been examined. In this article we define and discuss the scientific, technical and operational limits and outcomes resulting from soil microbial biogeography together with the technical and logistical feasibility. The main results are that microbial communities are not stochastically distributed on a wide scale and that biogeographical patterns are more influenced by local parameters such as soil type and land use than by distal ones, e. g. climate and geomorphology, contrary to plants and animals. We then present the European soil biological survey network, focusing on the French national initiative and the "ECOMIC-RMQS" project. The objective of the ECOMIC-RMQS project is to characterise the density and diversity of bacterial communities in all soils in the RMQS library in order to assess, for the first time, not only microbial biogeography across the whole of France but also the impact of land use on soil biodiversity (Reseau de Mesures de la Qualite des Sols = French Soil Quality Monitoring Network, 2200 soils covering all the French territory with a systematic grid of sampling). The scientific, technical and logistical outputs are examined with a view to the future prospects needed to develop this scientific domain and its applications in sustainable land use.
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- 2011
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14. Mapping and predictive variations of soil bacterial richness across France.
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Sébastien Terrat, Walid Horrigue, Samuel Dequiedt, Nicolas P A Saby, Mélanie Lelièvre, Virginie Nowak, Julie Tripied, Tiffanie Régnier, Claudy Jolivet, Dominique Arrouays, Patrick Wincker, Corinne Cruaud, Battle Karimi, Antonio Bispo, Pierre Alain Maron, Nicolas Chemidlin Prévost-Bouré, and Lionel Ranjard
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Medicine ,Science - Abstract
Although numerous studies have demonstrated the key role of bacterial diversity in soil functions and ecosystem services, little is known about the variations and determinants of such diversity on a nationwide scale. The overall objectives of this study were i) to describe the bacterial taxonomic richness variations across France, ii) to identify the ecological processes (i.e. selection by the environment and dispersal limitation) influencing this distribution, and iii) to develop a statistical predictive model of soil bacterial richness. We used the French Soil Quality Monitoring Network (RMQS), which covers all of France with 2,173 sites. The soil bacterial richness (i.e. OTU number) was determined by pyrosequencing 16S rRNA genes and related to the soil characteristics, climatic conditions, geomorphology, land use and space. Mapping of bacterial richness revealed a heterogeneous spatial distribution, structured into patches of about 111km, where the main drivers were the soil physico-chemical properties (18% of explained variance), the spatial descriptors (5.25%, 1.89% and 1.02% for the fine, medium and coarse scales, respectively), and the land use (1.4%). Based on these drivers, a predictive model was developed, which allows a good prediction of the bacterial richness (R2adj of 0.56) and provides a reference value for a given pedoclimatic condition.
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- 2017
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15. Correction: Mapping and predictive variations of soil bacterial richness across France.
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Sébastien Terrat, Walid Horrigue, Samuel Dequiedt, Nicolas P A Saby, Mélanie Lelièvre, Virginie Nowak, Julie Tripied, Tiffanie Régnier, Claudy Jolivet, Dominique Arrouays, Patrick Wincker, Corinne Cruaud, Battle Karimi, Antonio Bispo, Pierre Alain Maron, Nicolas Chemidlin Prévost-Bouré, and Lionel Ranjard
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Medicine ,Science - Abstract
[This corrects the article DOI: 10.1371/journal.pone.0186766.].
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- 2017
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16. Similar processes but different environmental filters for soil bacterial and fungal community composition turnover on a broad spatial scale.
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Nicolas Chemidlin Prévost-Bouré, Samuel Dequiedt, Jean Thioulouse, Mélanie Lelièvre, Nicolas P A Saby, Claudy Jolivet, Dominique Arrouays, Pierre Plassart, Philippe Lemanceau, and Lionel Ranjard
- Subjects
Medicine ,Science - Abstract
Spatial scaling of microorganisms has been demonstrated over the last decade. However, the processes and environmental filters shaping soil microbial community structure on a broad spatial scale still need to be refined and ranked. Here, we compared bacterial and fungal community composition turnovers through a biogeographical approach on the same soil sampling design at a broad spatial scale (area range: 13300 to 31000 km2): i) to examine their spatial structuring; ii) to investigate the relative importance of environmental selection and spatial autocorrelation in determining their community composition turnover; and iii) to identify and rank the relevant environmental filters and scales involved in their spatial variations. Molecular fingerprinting of soil bacterial and fungal communities was performed on 413 soils from four French regions of contrasting environmental heterogeneity (Landes
- Published
- 2014
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