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28 results on '"Departement of Soil and Environment"'

1. Management and spatial resolution effects on yield and water balance at regional scale in crop models

Author: Eric Casellas, Edwin Haas, Frank Ewert, Matthias Kuhnert, Thomas Gaiser, Jacques-Eric Bergez, Xenia Specka, Zhigan Zhao, Jagadeesh Yeluripati, Helene Raynal, Ganga Ram Maharjan, Luca Doro, Enli Wang, Ana Villa, Giacomo Trombi, Holger Hoffmann, Marco Bindi, Julie Constantin, Balász Grosz, Claas Nendel, Kurt Christian Kersebaum, Lutz Weihermüller, Henrik Eckersten, Steffen Klatt, Pier Paolo Roggero, Elisabet Lewan, Marco Moriondo, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Crop Science Group, INRES, University of Bonn, Partenaires INRAE, Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Desertification Research Centre and Department of Agricultural Sciences, University of Sassari, Texas A&M University [College Station], Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Institute of Meteorology and Climate Research – Atmospheric Environmental Research, Karlsruhe Institute of Technology, Leibniz Centre for Agricultural Landscape Research (ZALF), Information and Computational Sciences Group, The James Hutton Insitite, Carigiebuckler, Departement of Soil and Environment, CSIRO Land and Water, ACT, and Institute of Bio- & Geosciences, Agrosphere (IBG-3)
Subjects: 0106 biological sciences, Atmospheric Science, WINTER-WHEAT YIELD, 010504 meteorology & atmospheric sciences, ASSIMILATION, [SDV]Life Sciences [q-bio], Agricultural engineering, 01 natural sciences, Scaling, [SHS]Humanities and Social Sciences, SOWING DATES, CARBON, Crop, Aggregation, Water balance, DATA AGGREGATION, IRRIGATION, Evapotranspiration, ddc:550, [INFO]Computer Science [cs], [MATH]Mathematics [math], Drainage, Spatial analysis, AREA INDEX, 0105 earth and related environmental sciences, 2. Zero hunger, Global and Planetary Change, PRODUCTIVITY, Crop yield, Sowing, Forestry, 15. Life on land, Adaptive management, [SDE]Environmental Sciences, GROWTH, Environmental science, Decision rules, INPUT DATA, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: International audience; Due to the more frequent use of crop models at regional and national scale, the effects of spatial data input resolution have gained increased attention. However, little is known about the influence of variability in crop management on model outputs. A constant and uniform crop management is often considered over the simulated area and period. This study determines the influence of crop management adapted to climatic conditions and input data resolution on regional-scale outputs of crop models. For this purpose, winter wheat and maize were simulated over 30 years with spatially and temporally uniform management or adaptive management for North Rhine-Westphalia ((similar to)34 083 km(2)), Germany. Adaptive management to local climatic conditions was used for 1) sowing date, 2) N fertilization dates, 3) N amounts, and 4) crop cycle length. Therefore, the models were applied with four different management sets for each crop. Input data for climate, soil and management were selected at five resolutions, from 1 x 1 km to 100 x 100 km grid size. Overall, 11 crop models were used to predict regional mean crop yield, actual evapotranspiration, and drainage. Adaptive management had little effect (< 10% difference) on the 30-year mean of the three output variables for most models and did not depend on soil, climate, and management resolution. Nevertheless, the effect was substantial for certain models, up to 31% on yield, 27% on evapotranspiration, and 12% on drainage compared to the uniform management reference. In general, effects were stronger on yield than on evapotranspiration and drainage, which had little sensitivity to changes in management. Scaling effects were generally lower than management effects on yield and evapotranspiration as opposed to drainage. Despite this trend, sensitivity to management and scaling varied greatly among the models. At the annual scale, effects were stronger in certain years, particularly the management effect on yield. These results imply that depending on the model, the representation of management should be carefully chosen, particularly when simulating yields and for predictions on annual scale.
Published: 2019
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2. Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study

Author: Qianjun Gan, Wenjun Ji, Viacheslav I. Adamchuk, Ashraf A. Ismail, Songchao Chen, Zhou Shi, Asim Biswas, Ahmad Mat Su, Department of Bioresource Engineering, Université McGill, Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Unité INFOSOL (ORLEANS INFOSOL), Institut National de la Recherche Agronomique (INRA), Department of Agriculture Technology, Universiti Putra Malaysia, Department of Food Science and Agricultural Chemistry, Institute of Applied Remote Sensing and Information Technology, Zhejiang University, School of Environmental Sciences, Nova Gorica Polytechnic, McGill University = Université McGill [Montréal, Canada], Departement of Soil and Environment, and InfoSol (InfoSol)
Subjects: 2. Zero hunger, Soil map, Multivariate adaptive regression splines, [SDV]Life Sciences [q-bio], Soil organic matter, Soil Science, Soil science, 04 agricultural and veterinary sciences, 15. Life on land, 010501 environmental sciences, Sensor fusion, complex mixtures, 01 natural sciences, Random forest, soil sciences, sciences du sol, Partial least squares regression, Soil water, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Soil horizon, 0105 earth and related environmental sciences
Abstract: In this research, proximal soil sensor data fusion was defined as a multifaceted process which integrates geospatially correlated data, or information, from multiple proximal soil sensors to accurately characterize the spatial complexity of soils. This has capability of providing improved understanding of soil heterogeneity for potential applications associated with crop production and natural resource management. To assess the potential of data fusion for the purpose of improving thematic soil mapping over the single sensor approach, data from multiple proximal soil sensors were combined to develop and validate predictive relationships with laboratory-measured soil physical and chemical properties. The work was conducted in an agricultural field with both mineral and organic soils. The integrated data included: topography records obtained using a real-time kinetic (RTK) global navigation satellite system (GNSS) receiver, apparent soil electrical conductivity (ECa) obtained using an electromagnetic induction sensor, and content of several naturally occurring radioisotopes detected using a mobile gamma-ray spectrometer. In addition, the soil profile data were collected using a commercial ruggedized multi-sensor platform carrying a visible and near-infrared (vis-NIR) optical sensor and a galvanic contact soil ECa sensor. The measurements were carried out at predefined field locations covering the entire study area identified from sensor measured a priori information on field elevation, ECa and gamma-ray count. The information was used to predict: soil organic matter (SOM), pH, lime buffer capacity (LBC), as well as concentration of phosphorus (P), potassium (K), calcium (Ca), magnesium (Mg), and aluminum (Al). Partial least squares regressions (PLSRs) were used to predict soil properties from individual sensors and different sensor combinations (sensor data fusion). By integrating the data from all of the proximal soil sensors, SOM, pH, LBC, Ca, Mg, and Al were predicted simultaneously with R2 > 0.5 (RPD > 1.50). Improved predictions were observed for most soil properties based on sensor data fusion than those based on individual sensors. After choosing the optimal sensor combination for each soil property, the predictive capability was compared using different data mining algorithms, including support vector machines (SVM), random forest (RF), multivariate adaptive regression splines (MARS), and regression trees (CART). Improved predictions for SOM, Ca, Mg, and Al were observed using SVM over PLSR. The predictive capability was followed by RF and MARS, with CART. Predictions of pH and LBC were only feasible using MARS and PLSR, respectively. In this field, it was not possible to predict extractable P and K using all tested sensor combinations or algorithms. With large variability in SOM, the field presents a special situation and thus, the result could be specific to the study site. Further research includes an extended number of experimental sites covering different geographic areas around Eastern Canada.
Published: 2019
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3. Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

Author: Wenjun Ji, Shuo Li, Gan-Lin Zhang, Dongyun Xu, Zhengli Shi, Songchao Chen, W. Ma, InfoSol (InfoSol), Institut National de la Recherche Agronomique (INRA), Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Ministry of Agriculture, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, and Chinese Academy of Sciences [Changchun Branch] (CAS)
Subjects: Calibration (statistics), Soil texture, [SDV]Life Sciences [q-bio], Soil organic matter, Soil Science, Soil classification, Soil science, 04 agricultural and veterinary sciences, 010501 environmental sciences, 15. Life on land, 01 natural sciences, Support vector machine, Unified Soil Classification System, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Soil horizon, ComputingMilieux_MISCELLANEOUS, 0105 earth and related environmental sciences, Mathematics, USDA soil taxonomy
Abstract: Visible‐near infrared (vis–NIR) spectroscopy can reveal various soil properties and facilitate soil classification. However, few studies have attempted to classify vertical soil profiles that contain several genetic horizons. Here, we propose the ‘multiple objectives mixed support vector classification’ (MOM–SVC) method to classify soil profiles. A total of 130 soil profiles were collected from genetic horizons in Zhejiang Province, China. After laboratory analysis, soil profiles were classified according to the Chinese Soil Taxonomy system. Vis–NIR spectra were recorded from each genetic horizon of each soil profile and were then pre‐processed. We performed the MOM–SVC method as follows: (i) created a support vector machine (SVM) model (one‐versus‐one approach) using spectral data from all soil horizons in calibration profiles, (ii) applied the SVM model on each horizon of the profile to be predicted, (iii) extracted ‘votes’ from each horizon and mixed (or summarized) them into the votes of each profile to be predicted and (iv) classified each profile by the majority‐voting method. We also investigated whether the additional input of auxiliary soil information (e.g. moist soil colour, soil organic matter and soil texture), which could be measured easily or be well predicted by vis–NIR spectroscopy, could improve the accuracy of soil classification when combined with it. Independent validation results showed that the MOM–SVC method performed better at the soil order level than at the suborder level. Adding auxiliary soil information to the classification model improved the overall accuracy of classification at the soil order level. The proposed MOM–SVC method provides a fast objective diagnostic of soil classes for use in soil surveys and can help to update soil databases when a more objective soil classification system is developed. HIGHLIGHTS: The MOM–SVC method can be used to classify soil profiles objectively with a variety of soil horizons. Stratified random sampling was used to quantify prediction uncertainty in classification MOM–SVC can predict soil orders with greater accuracy than suborders. Adding auxiliary soil information into the classification model improved prediction accuracy.
Published: 2018
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4. Winter warming effects on tundra shrub performance are species-specific and dependent on spring conditions

Author: Frida Keuper, Eveline J. Krab, Ann Milbau, Jonas Roennefarth, Ellen Dorrepaal, Jonatan Klaminder, Juergen Kreyling, Marina Becher, Kobayashi Makoto, Gesche Blume-Werry, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Umeå University, Universität Greifswald - University of Greifswald, Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Hokkaido University [Sapporo, Japan], Research Institute for Nature and Forest (INBO), VR [621-2011-5444], Formas [214-2011-788], and Wallenberg Academy Fellowship [2012.0152]
Subjects: 0106 biological sciences, Betula nana, 010504 meteorology & atmospheric sciences, [SDV]Life Sciences [q-bio], ved/biology.organism_classification_rank.species, plant phenology, Plant Science, 010603 evolutionary biology, 01 natural sciences, Shrub, Nutrient, Spring (hydrology), snowmelt timing, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology, Vaccinium vitis-idaea, Empetrum nigrum, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, spring climate, geography, geography.geographical_feature_category, Ecology, biology, ved/biology, Cryoturbation, cryoturbation, snow cover, food and beverages, 15. Life on land, biology.organism_classification, eye diseases, Tundra, shrubs, winter climate change, 13. Climate action, [SDE]Environmental Sciences, Frost, Environmental science, sense organs, geographic locations
Abstract: International audience; Climate change-driven increases in winter temperatures positively affect conditions for shrub growth in arctic tundra by decreasing plant frost damage and stimulation of nutrient availability. However, the extent to which shrubs may benefit from these conditions may be strongly dependent on the following spring climate. Species-specific differences in phenology and spring frost sensitivity likely affect shrub growth responses to warming. Additionally, effects of changes in winter and spring climate may differ over small spatial scales, as shrub growth may be dependent on natural variation in snow cover, shrub density and cryoturbation. We investigated the effects of winter warming and altered spring climate on growing-season performance of three common and widespread shrub species in cryoturbated non-sorted circle arctic tundra. By insulating sparsely vegetated non-sorted circles and parts of the surrounding heath with additional snow or gardening fleeces, we created two climate change scenarios: snow addition increased soil temperatures in autumn and winter and delayed snowmelt timing without increasing spring temperatures, whereas fleeces increased soil temperature similarly in autumn and winter, but created warmer spring conditions without altering snowmelt timing. Winter warming affected shrub performance, but the direction and magnitude were species-specific and dependent on spring conditions. Spring warming advanced, and later snowmelt delayed canopy green-up. The fleece treatment did not affect shoot growth and biomass in any shrub species despite decreasing leaf frost damage in Empetrum nigrum. Snow addition decreased frost damage and stimulated growth of Vaccinium vitis-idaea by c. 50%, while decreasing Betula nana growth (p < .1). All of these effects were consistent the mostly barren circles and surrounding heath. Synthesis. In cryoturbated arctic tundra, growth of Vaccinium vitis-idaea may substantially increase when a thicker snow cover delays snowmelt, whereas in longer term, warmer winters and springs may favour E. nigrum instead. This may affect shrub community composition and cover, with potentially far-reaching effects on arctic ecosystem functioning via its effects on cryoturbation, carbon cycling and trophic cascading. Our results highlight the importance of disentangling effects of winter and spring climate change timing and nature, as spring conditions are a crucial factor in determining the impact of winter warming on plant performance.
Published: 2017
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5. Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

Author: Xenia Specka, Luca Doro, Frank Ewert, Kurt Christian Kersebaum, Holger Hoffmann, Heidi Webber, Julie Constantin, Elsa Coucheney, Claas Nendel, Ana Villa, Marco Bindi, Elisabet Lewan, Amit Kumar Srivastava, Helene Raynal, Mathias Kuhnert, Thomas Gaiser, Lutz Weihermüller, Ganga Ram Maharjan, Balázs Grosz, Tommaso Stella, Giacomo Trombi, Marco Moriondo, Rene Dechow, Crop Science Group, INRES, University of Bonn, Partenaires INRAE, Agrosphere Institute (IBG-3), Forschungszentrum Jülich GmbH, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Department of Agri-Food Production and Environmental Science (DISPAA), University of Florence, Desertification Research Centre, University of Sassari, Texas A&M University [College Station], Leibniz Centre for Agricultural Landscape Research (ZALF), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), and Institut National de la Recherche Agronomique (INRA)
Subjects: 0106 biological sciences, Data resolution, PARAMETERIZATION, IMPACT, Climate, [SDV]Life Sciences [q-bio], WHEAT, Soil Science, Library science, Regional yield, FINGERPRINTS, Plant Science, 01 natural sciences, MODEL USE, Modelling, VALIDATION, [SHS]Humanities and Social Sciences, German, CARBON, Soil, Resource (project management), Political science, [INFO]Computer Science [cs], [MATH]Mathematics [math], 2. Zero hunger, CALIBRATION, business.industry, 04 agricultural and veterinary sciences, 15. Life on land, Natural resource, language.human_language, Scale, Agronomy, 13. Climate action, Agriculture, Research council, [SDE]Environmental Sciences, 040103 agronomy & agriculture, language, 0401 agriculture, forestry, and fisheries, GROWTH, Christian ministry, ddc:640, business, Agronomy and Crop Science, German science, MONICA, 010606 plant biology & botany
Abstract: International audience; Soil-crop models are used to simulate ecological processes from the field to the regional scale. Main inputs are soil and climate data in order to simulate model response variables such as crop yield. We investigate the effect of changing the resolution of input data on simulated crop yields at a regional scale using up to ten dynamic crop models. For these models we compared the effects of spatial input data aggregation for wheat and maize yield of two regions with contrasting climate conditions (1) Tuscany (Italy, Mediterranean climate) and (2) North Rhine Westphalia (NRW, Germany, temperate climate). Soil and climate data of 1 km resolution were aggregated to resolutions of 10, 25, 50, and 100 km by selecting the dominant soil class (and corresponding soil properties) and by arithmetic averaging, respectively. Differences in yield simulated at coarser resolutions from the yields simulated at 1 km resolution were calculated to quantify the effect of the aggregation of the input data (soil and climate data) on simulation results. The mean yield difference (bias) at the regional level was positive due to the upscaling of productive dominant soil(s) to coarser resolution. In both regions and for both crops, aggregation effects (i.e. errors in simulation of crop yields at coarser spatial resolution) due to the combined aggregation of soil and climate input data increased with decreasing resolution, whereby the aggregation error for Tuscany was larger than for North Rhine Westphalia (NRW). The average absolute percentage yield differences between grid cell yields at the coarsest resolution (100 km) compared to the finest resolution (1 km) were by about 20-30% for Tuscany and less than 15 and 20% for NRW for winter wheat and silage maize, respectively. In the Mediterranean area, the prediction errors of the simulated yields could reach up to 60% when looking at individual crop model simulations. Additionally, aggregating soil data caused larger aggregation errors in both regions than aggregating climate data. Those results suggest that a higher spatial resolution of climate and especially of soil input data are necessary in Mediterranean areas than in temperate humid regions of central Europe in order to predict reliable regional yield estimations with crop models. For generalization of these outcomes, further investigations in other sub humid or semi-arid regions will be necessary.
Published: 2019
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6. Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

Author: Elisabet Lewan, Eckart Priesack, Edmar Teixeira, Davide Cammarano, Balázs Grosz, Julie Constantin, Elsa Coucheney, Senthold Asseng, Jagadeesh Yeluripati, Pier Paolo Roggero, Giacomo Trombi, Henrik Eckersten, Stefan Siebert, Specka Xenia, Reimund P. Rötter, Frank Ewert, Thomas Gaiser, Marco Bindi, Marco Moriondo, Matthias Kuhnert, Luca Doro, Christian Klein, Fulu Tao, Gang Zhao, Helene Raynal, Ralf Kiese, Kurt Christian Kersebaum, Claas Nendel, Belay T. Kassie, Christian Biernath, Holger Hoffmann, Edwin Haas, Florian Heinlein, Daniel Wallach, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, The James Hutton Institute, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Institute of landscape systems analysis, INSTITUTE OF BIOLOGICAL AND ENVIRONMENTAL SCIENCES, University of Aberdeen, Environmental Impacts Group, Natural resources institute Finland, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Systems Modelling Team (Sustainable Production Group), Plant & Food Research, Johann Heinrich von Thünen Institut, Università degli Studi di Sassari, Dipartimento di Agraria, University of Sassari, Karlsruhe Institute of Technology (KIT), University of Agricultural Sciences (UAS), Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF), Consiglio Nazionale delle Ricerche (CNR), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Institute of Biochemical Plant Pathology (BIOP), German Research Center for Environmental Health - Helmholtz Center München (GmbH), German Federal Ministry of Food and Agriculture (BMEL) through the Federal Office for Agriculture and Food (BLE) [2851ERA01J], Joint Programme Initiative FACCE, MACSUR Knowledge Hub, German Federal Ministry of Education and Research (BMBF) through the SPACES project 'Living Landscapes Limpopo', WASCAL (West African Science Service Center on Climate Change and Adapted Land Use) project, NOAA RISA grant, FACCE MACSUR project by the Finnish Ministry of Agriculture and Forestry (MMM), Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning [220-2007-1218], strategic funding 'Soil-Water-Landscape' from the faculty of Natural Resources and Agricultural Sciences (Swedish University of Agricultural Sciences), ACCAF INRA meta-program, and Università degli Studi di Sassari = University of Sassari [Sassari] (UNISS)
Subjects: Environmental Engineering, 010504 meteorology & atmospheric sciences, Simple random sampling, [SDV]Life Sciences [q-bio], Stratified random sampling, 01 natural sciences, [SHS]Humanities and Social Sciences, Environment variable, Ecosystem model, Statistics, [INFO]Computer Science [cs], Crop model, [MATH]Mathematics [math], 0105 earth and related environmental sciences, Mathematics, 2. Zero hunger, Ecological Modeling, Crop yield, fungi, food and beverages, Sampling (statistics), Model comparison, 04 agricultural and veterinary sciences, 15. Life on land, Simple random sample, Stratified sampling, Sample size determination, [SDE]Environmental Sciences, Soil water, Up-scaling, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Clustering, Crop Model, Model Comparison, Precision Gain, Simple Random Sampling, Stratified Random Sampling, Precision gain, Software
Abstract: We compared the precision of simple random sampling (SimRS) and seven types of stratified random sampling (StrRS) schemes in estimating regional mean of water-limited yields for two crops (winter wheat and silage maize) that were simulated by fourteen crop models. We found that the precision gains of StrRS varied considerably across stratification methods and crop models. Precision gains for compact geographical stratification were positive, stable and consistent across crop models. Stratification with soil water holding capacity had very high precision gains for twelve models, but resulted in negative gains for two models. Increasing the sample size monotonously decreased the sampling errors for all the sampling schemes. We conclude that compact geographical stratification can modestly but consistently improve the precision in estimating regional mean yields. Using the most influential environmental variable for stratification can notably improve the sampling precision, especially when the sensitivity behavior of a crop model is known. We compare eight sampling schemes for estimating regional mean crop yield.Precision of eight schemes is compared across fourteen crop models.Compact geographical stratification can always improve the precision.Stratification with soil had very high gains of precision for twelve crop models.Our findings can improve the precision of site-based regional crop modeling.
Published: 2016
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7. Using SWAT-LUD Model to Estimate the Influence of Water Exchange and Shallow Aquifer Denitrification on Water and Nitrate Flux

Author: Jeffrey G. Arnold, Raghavan Srinivasan, José Miguel Sánchez Pérez, Youen Grusson, Sabine Sauvage, Léonard Bernard-Jannin, Xiaoling Sun, Laboratoire Ecologie Fonctionnelle et Environnement (ECOLAB), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Observatoire Midi-Pyrénées (OMP), Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Météo France-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Centre National de la Recherche Scientifique (CNRS), School of Environmental Science and Engineering, Southern University of Science and Technology of China (SUSTech), State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University [China], Institut des Sciences de la Terre d'Orléans - UMR7327 (ISTO), Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire de Paris, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM), Biogéosystèmes Continentaux - UMR7327, Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Centre National de la Recherche Scientifique (CNRS)-Université d'Orléans (UO)-Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Chaire de Recherche EDS en Previsions et Actions Hydrologiques, Faculté de médecine de l'Université Laval [Québec] (ULaval), Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), USDA-ARS : Agricultural Research Service, Texas A&M University [College Station], ANR-11-CEPL-0008,ADAPT'EAU,Adaptation aux variations des régimes hydrologiques (crues-étiages) dans l'Environnement Fluvio-Estuarien de la Garonne-Gironde. Potentialités, mise à l'épreuve et gouvernance d'Options d'Adaptation(2011), Laboratoire Ecologie Fonctionnelle et Environnement (LEFE), Institut Ecologie et Environnement (INEE), Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales [Toulouse] (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France -Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT), Southern University of Science and Technology (SUSTech), Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université d'Orléans (UO)-Centre National de la Recherche Scientifique (CNRS)-Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)-Observatoire des Sciences de l'Univers en région Centre (OSUC), and Université Laval [Québec] (ULaval)-Université Laval [Québec] (ULaval)
Subjects: lcsh:Hydraulic engineering, Denitrification, Floodplain, Soil and Water Assessment Tool, 0208 environmental biotechnology, Geography, Planning and Development, Aquifer, 02 engineering and technology, Aquatic Science, Biochemistry, floodplain aquifer, chemistry.chemical_compound, lcsh:Water supply for domestic and industrial purposes, Garonne River, Nitrate, lcsh:TC1-978, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment, Water Science and Technology, SWAT-LUD model, water exchange, denitrification, Hydrology, lcsh:TD201-500, geography, geography.geographical_feature_category, Discharge, 15. Life on land, 6. Clean water, 020801 environmental engineering, chemistry, [SDU]Sciences of the Universe [physics], Environmental science, Surface water, Groundwater
Abstract: Numerous studies have pointed out the importance of groundwater and surface water interaction (SW–GW) in a river system. However; those functions have rarely been considered in large scale hydrological models. The SWAT-LUD model has been developed based on the Soil and Water Assessment Tool (SWAT) model; and it integrates a new type of subbasin; which is called subbasin-LU (SL); to represent the floodplain area. New modules representing SW–GW exchanges and shallow aquifer denitrification are developed in the SWAT-LUD model. In this study; the SWAT-LUD model was applied to the middle floodplain area of the Garonne catchment in France. The results showed that the SWAT-LUD model could represent the SW–GW exchange and shallow aquifer denitrification appropriately. An annual 44.1 × 107 m3 of water flowed into the river from the study area; but the annual exchanged water volume was 6.4 × 107 m3; which represented just 1% of the river discharge. A total of 384 tons of N-NO3− (0.023 t·ha−1) was consumed by denitrification in the floodplain shallow aquifer annually. The nitrate concentration (N-NO3−) decrease in the channel was 0.12 mg·L−1; but in the shallow aquifer it reached 11.40 mg·L−1; 8.05 mg·L−1; and 5.41 mg·L−1 in LU1; LU2; and LU3; respectively. Our study reveals that; in the Garonne floodplain; denitrification plays a significant role in the attenuation of nitrate associated with groundwater; but the impacts of denitrification on nitrate associated with river water is much less significant.
Published: 2018
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8. Modelling pesticides leaching in cropping systems: Effect of uncertainties in climate, agricultural practices, soil and pesticide properties

Author: François Brun, Benoit Gabrielle, Julien Moeys, Enrique Barriuso, Sabine Karen Djidemi Lammoglia, Thibaud Quemar, Laure Mamy, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Université Paris-Saclay, AGroécologie, Innovations, teRritoires (AGIR), Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Recherche Agronomique (INRA), Swedish Chemicals Agency, Swedish University of Agricultural Sciences (SLU), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Université Paris Saclay (COmUE), Institut National de la Recherche Agronomique (INRA), Instituts techniques agricoles (ACTA), Departement of Soil and Environment, and Swedish Chemical agency
Subjects: H01 - Protection des végétaux - Considérations générales, Pesticide leaching, Environmental Engineering, P40 - Météorologie et climatologie, F08 - Systèmes et modes de culture, [SDV]Life Sciences [q-bio], 010501 environmental sciences, 01 natural sciences, [SHS]Humanities and Social Sciences, Leaching (agriculture), STICS-MACRO, ComputingMilieux_MISCELLANEOUS, Latin hypercube sampling, 0105 earth and related environmental sciences, Meta-model, 2. Zero hunger, business.industry, Ecological Modeling, 04 agricultural and veterinary sciences, 15. Life on land, Pesticide, 6. Clean water, 13. Climate action, Agriculture, [SDE]Environmental Sciences, 040103 agronomy & agriculture, Pesticide degradation, 0401 agriculture, forestry, and fisheries, Environmental science, Uncertainty analysis, Spatial variability, Water resource management, business, Cropping, Software, Groundwater
Abstract: Modelling of pesticide leaching is paramount to managing the environmental risks associated with the chemical protection of crops, but it involves large uncertainties in relation to climate, agricultural practices, soil and pesticide properties. We used Latin Hypercube Sampling to estimate the contribution of these input factors with the STICS-MACRO model in the context of a 400 km2 catchment in France, and two herbicides applied to maize: bentazone and S-metolachlor. For both herbicides, the most influential input factors on modelling of pesticide leaching were the inter-annual variability of climate, the pesticide adsorption coefficient and the soil boundary hydraulic conductivity, followed by the pesticide degradation half-life and the rainfall spatial variability. This work helps to identify the factors requiring greater accuracy to ensure better pesticide risk assessment and to improve environmental management and decision-making processes by quantifying the probability and reliability of prediction of pesticide concentrations in groundwater with STICS-MACRO.
Published: 2018
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9. A comparison of soil tests for available phosphorus in long-term field experiments in Europe

Author: Nawara, S., Van Dael, T., Merckx, R., Amery, F., Elsen, A., Odeurs, W., Vandendriessche, H., McGrath, S., Roisin, C., Jouany, Claire, Pellerin, Sylvain, Denoroy, Pascal, Eichler-Löbermann, B., Börjesson, G., Goos, P., Akkermans, W., Smolders, E., Université Catholique de Louvain = Catholic University of Louvain (UCL), Research Institute for Agricultural, Fisheries and Food (ILVO), Soil Service of Belgium, Partenaires INRAE, Rothamsted Research, Walloon Center of Agricultural Research, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), University of Rostock, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), and University of Antwerp (UA)
Subjects: [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences, Biology, [SHS]Humanities and Social Sciences
Abstract: International audience; Most soil tests for available phosphorus (P) perform rather poorly in predicting crop response. This study was set up to compare different established soil tests in their capacity to predict crop response across contrasting types of soil. Soil samples from long-term field experiments, the oldest >100 years old, were collected in five European countries. The total number of soil samples (n = 218), which differed in cropping and P treatment, and originated from 11 different soil types, were analysed with five tests: ammonium oxalate (Ox), ammonium lactate (AL), Olsen P, 0.01 m CaCl2 and the diffusive gradient in thin film (DGT). The first three tests denote available P quantity (Q), whereas the last two indicate P intensity (I) of the soil solution. All five tests were positively related to the crop yield data (n = 317). The Q-tests generally outperformed I-tests when evaluated with goodness of fit in Mitscherlich models, but critical P values of the I-tests varied the least among different types of soil. No test was clearly superior to the others, except for the oxalate extraction, which was generally poor. The combination of Q- and I-tests performed slightly better for predicting crop yield than any single soil P test. This Q + I analysis explains why recent successes with I-tests (e.g. DGT) were found for soils with larger P sorption than for those in the present study. This systematic evaluation of soil tests using a unique compilation of established field trials provides critical soil P values that are valid across Europe. Highlights We compared soil P tests for predicting crop response across contrasting soil types. No test was clearly superior to the others except for the oxalate extraction, which was generally poor. This study suggests that intensity tests do not perform markedly better than quantity tests. The evaluation of soil P tests on this unique dataset provided critical soil P values across Europe.
Published: 2017
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10. The implication of input data aggregation on up-scaling soil organic carbon changes

Author: Gang Zhao, Elisabet Lewan, Sren Gebbert, Claas Nendel, Zhigan Zhao, Thomas Gaiser, Edmar Teixeira, Edwin Haas, Elsa Coucheney, Kurt Christian Kersebaum, Reimund Rtter, Giacomo Trombi, Frank Ewert, Enli Wang, Jagadeesh Yeluripati, Davide Cammarano, Marco Moriondo, Stefan Siebert, Matthias Kuhnert, Julie Constantin, Luca Doro, Daniel Wallach, Xenia Specka, Marco Bindi, Belay T. Kassie, Holger Hoffmann, Fulu Tao, Helene Raynal, Henrik Eckersten, Balzs Grosz, Rene Dechow, Senthold Asseng, Pier Paolo Roggero, Lutz Weihermller, Thünen Institute of Climate Smart Agriculture, Crop Science Group, Rheinische Friedrich-Wilhelms-Universität Bonn, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Department of Crop Production Ecology, Institute of Landscape Systems Analysis, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Biological and Environmental Sciences, School of Biological Sciences, University of Aberdeen, The James Hutton Institute, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Systems Modeling Team (Sustainable Production Group), New Zealand Institute for Plant and Food Research Limited, Department of Agri-food Production and Environmental Sciences, University of Florence (UNIFI), Istituto di Biometeorologia [Firenze] (IBIMET), Consiglio Nazionale delle Ricerche (CNR), Desertification Research Centre, University of Sassari, Desertification Research Centre - Department of Agricultural Sciences, CSIRO, Climate Impacts Group, Department of Crop Sciences, Georg-August-Universität Göttingen, Agricultural & Biological Engineering Department, University of Florida [Gainesville], Institute of Bio- and Geosciences Agrosphere (IBG-3), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Departement of Soil and Environment, Plant & Food Research, Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Georg-August-University [Göttingen], Department of Agricultural and Biological Engineering [Gainesville] (UF|ABE), Institute of Food and Agricultural Sciences [Gainesville] (UF|IFAS), and University of Florida [Gainesville] (UF)-University of Florida [Gainesville] (UF)
Subjects: [SDV.SA]Life Sciences [q-bio]/Agricultural sciences, Biogeochemical cycle, Environmental Engineering, 010504 meteorology & atmospheric sciences, data aggregation, biogeochemical model, up-scaling error, Ecological Modeling, Up scaling, Soil science, 04 agricultural and veterinary sciences, Soil carbon, Common method, 15. Life on land, 01 natural sciences, Data availability, Data aggregator, soil organic carbon, North west, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Limit (mathematics), Software, 0105 earth and related environmental sciences
Abstract: In up-scaling studies, model input data aggregation is a common method to cope with deficient data availability and limit the computational effort. We analyzed model errors due to soil data aggregation for modeled SOC trends. For a region in North West Germany, gridded soil data of spatial resolutions between 1km and 100km has been derived by majority selection. This data was used to simulate changes in SOC for a period of 30 years by 7 biogeochemical models. Soil data aggregation strongly affected modeled SOC trends. Prediction errors of simulated SOC changes decreased with increasing spatial resolution of model output. Output data aggregation only marginally reduced differences of model outputs between models indicating that errors caused by deficient model structure are likely to persist even if requirements on the spatial resolution of model outputs are low. Analysis of soil data aggregation on model errors of up-scaled SOC trends.Determination of factors controlling aggregation effects (AE) on modeled SOC trends.Comparison of variability between 7 biogeochemical models and AE.Development of ex ante methods to approximate AE for SOC simulation studies.
Published: 2017
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11. Microbial community structure and activity in trace element-contaminated soils phytomanaged by Gentle Remediation Options (GRO)

Author: Laura Giagnoni, Rafal Galazka, Markus Puschenreiter, Jolien Janssen, Ángeles Prieto-Fernández, Silke Neu, Jurate Kumpiene, J. Eriksson, Giancarlo Renella, Ioannis Dimitriou, Angela Sessitsch, Günter Brader, Wolfgang Friesl-Hanl, Jaco Vangronsveld, P.S. Kidd, Grzegorz Siebielec, Michel Mench, Ingo Müller, M. Touceda-González, Instituto de Investigaciones Agrobiológicas de Galicia (IIAG), Spanish National Research Council (CSIC), Department of Agri-Food Production and Environmental Sciences, University delgi Studi di Firenze, Center for Health & Bioresources, Austrian Institute of Technology (AIT), Waste Science & Technology, Luleå University of Technology (LUT), Department of Crop Production Ecology, Swedish University of Agricultural Sciences (SLU), Departement of Soil and Environment, Institute of Soil Science and Plant Cultivation (IUNG), Centre for Environmental Sciences, Hasselt University (UHasselt), Biodiversité, Gènes & Communautés (BioGeCo), Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB), Saxon State Office for Environment, Agriculture and Geology, Department of Forest and Soil Sciences, and Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU)-Institute of Silviculture
Subjects: Enzyme activity, Microbial community structure, Phytoexclusion, Phytoextraction, Phytostabilization, Bacteria, Betaproteobacteria, Biomass, Denaturing Gradient Gel Electrophoresis, Europe, France, Plants, Soil, Soil Pollutants, Trace Elements, Biodegradation, Environmental, Soil Microbiology, Environmental remediation, Health, Toxicology and Mutagenesis, [SDV]Life Sciences [q-bio], 010501 environmental sciences, Toxicology, 01 natural sciences, complex mixtures, Actinobacteria, Environmental, phytoextraction, Soil functions, phytoexclusion, 0105 earth and related environmental sciences, phytostabilization, 2. Zero hunger, biology, 04 agricultural and veterinary sciences, General Medicine, 15. Life on land, biology.organism_classification, Pollution, 6. Clean water, enzyme activity, Soil conditioner, Phytoremediation, Microbial population biology, 13. Climate action, microbial community structure, Environmental chemistry, Soil water, 040103 agronomy & agriculture, Biodegradation, 0401 agriculture, forestry, and fisheries, Temperature gradient gel electrophoresis
Abstract: Gentle remediation options (GRO) are based on the combined use of plants, associated microorganisms and soil amendments, which can potentially restore soil functions and quality. We studied the effects of three GRO (aided-phytostabilisation, in situ stabilisation and phytoexclusion, and aided-phytoextraction) on the soil microbial biomass and respiration, the activities of hydrolase enzymes involved in the biogeochemical cycles of C, N, P, and S, and bacterial community structure of trace element contaminated soils (TECS) from six field trials across Europe. Community structure was studied using denaturing gradient gel electrophoresis (DGGE) fingerprinting of Bacteria, α - and β - Proteobacteria , Actinobacteria and Streptomycetaceae , and sequencing of DGGE bands characteristic of specific treatments. The number of copies of genes involved in ammonia oxidation and denitrification were determined by qPCR. Phytomanagement increased soil microbial biomass at three sites and respiration at the Biogeco site (France). Enzyme activities were consistently higher in treated soils compared to untreated soils at the Biogeco site. At this site, microbial biomass increased from 696 to 2352 mg ATP kg −1 soil, respiration increased from 7.4 to 40.1 mg C-CO 2 kg −1 soil d −1 , and enzyme activities were 2–11-fold higher in treated soils compared to untreated soil. Phytomanagement induced shifts in the bacterial community structure at both, the total community and functional group levels, and generally increased the number of copies of genes involved in the N cycle ( nirK , nirS , nosZ , and amoA ). The influence of the main soil physico-chemical properties and trace element availability were assessed and eventual site-specific effects elucidated. Overall, our results demonstrate that phytomanagement of TECS influences soil biological activity in the long term.
Published: 2016
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12. Quantifying the pluri-centennial soil organic carbon pool using Rock-Eval pyrolysis

Author: Cécillon, Lauric, Baudin, François, Chenu, Claire, Christensen, B.T., Houot, Sabine, Kätterer, Thomas, Luftalla, Suzanne, Macdonald, Andy, Van Oort, Folkert, F. Plante, Alain, Ecosystèmes montagnards (UR EMGR), Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA), Université Pierre et Marie Curie - Paris 6 (UPMC), Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Faculty of Agricultural Sciences, Department of Agroecology and Environment, Aarhus University [Aarhus], Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), École normale supérieure - Paris (ENS Paris), Université Paris sciences et lettres (PSL), Earth and Environmental Science, University of Pennsylvania [Philadelphia], École normale supérieure - Paris (ENS-PSL), University of Pennsylvania, and Université Pierre et Marie Curie (Paris 6)
Subjects: [SDE.MCG]Environmental Sciences/Global Changes, Milieux et Changements globaux
Abstract: Quantifying the pluri-centennial soil organic carbon pool using Rock-Eval pyrolysis. EGU General Assembly 2016 Conference Abstracts, European Geophysical Union
Published: 2016

13. Immobilization of Cu and As in two contaminated soils with zero-valent iron – Long-term performance and mechanisms

Author: Charlotta Tiberg, Jurate Kumpiene, Aleksandra Marsz, Ingmar Persson, Dan Berggren Kleja, Jon Petter Gustafsson, Michel Mench, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Swedish Geotechnical Institute, Luleå University of Technology (LUT), Royal Institute of Technology in Stockholm (KTH), Biodiversité, Gènes & Communautés (BioGeCo), and Institut National de la Recherche Agronomique (INRA)-Université de Bordeaux (UB)
Subjects: ZVI, Exafs spectroscopy, Environmental remediation, [SDV]Life Sciences [q-bio], Zero-valent iron, Surface complexation model, Remediation, 010501 environmental sciences, 01 natural sciences, Arsenic, Immobilization, Geochemistry and Petrology, remediation, Environmental Chemistry, Contaminated soil, geochemical modeling, surface complexation model, 0105 earth and related environmental sciences, Zerovalent iron, Contaminated soils, zero-valent iron, Chemistry, arsenic, X-ray absorption spectroscopy, Geokemi, 04 agricultural and veterinary sciences, EXAFS spectroscopy, iron (hydr)oxide, Pollution, Soil contamination, Stabilization, stabilization, Iron (hydr)oxide, Environmental Management, Geochemistry, Environmental chemistry, contaminated soil, copper, immobilization, 040103 agronomy & agriculture, Geochemical modeling, 0401 agriculture, forestry, and fisheries, Copper
Abstract: Immobilization of trace elements in contaminated soils by zero-valent iron (ZVI) is a promising remediation method, but questions about its long-term performance remain unanswered. To quantify immobilization and predict possible contaminant remobilization on long timescales detailed knowledge about immobilization mechanisms is needed. This study aimed at assessing the long-term effect of ZVI amendments on dissolved copper and arsenic in contaminated soils, at exploring the immobilization mechanism(s), and at setting up a geochemical model able to estimate dissolved copper and arsenic under different scenarios. Samples from untreated and ZVI-treated plots in two field experiments where ZVI had been added 6 and 15 years ago were investigated by a combination of batch experiments, geochemical modeling and extended X-ray absorption fine structure (EXAFS) spectroscopy. Dissolved copper and arsenic concentrations were described by a multisurface geochemical model with surface complexation reactions, verified by EXAFS. The ZVI remained "reactive" after 6-15 years, i. e. the dissolved concentrations of copper and arsenic were lower in the ZVI-treated than in the untreated soils. There was a shift in copper speciation from organic matter complexes in the untreated soil to surface complexes with iron (hydr)oxides in the ZVI-treated soil. The pH value was important for copper immobilization and ZVI did not have a stabilizing effect if pH was lower than about 6. Immobilization of arsenic was slightly pH-dependent and sensitive to the competition with phosphate. If phosphate was ignored in the modeling, the dissolution of arsenate was greatly underestimated. (C) 2016 Elsevier Ltd. All rights reserved. QC 20160428
Published: 2016
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14. Special issue on clubroot and blackleg diseases of brassicas - Foreword

Author: Geoffrey Richard Dixon, Marie-Hélène Balesdent, Dilantha Fernando, Ann-Charlotte Wallenhammar, Elke Diederichsen, Inst Biol Angew Genet, Free University of Berlin (FU), Dept Agr Policy & Dev Berks, University of Reading (UOR), Kilmarnock KA1 5PP, GreenGeneInternational, Rural Econ & Agr Soc, HS Konsult AB, Dept Soil & Environm, Swedish University of Agricultural Sciences (SLU), Department Plant Sci, University of Manitoba [Winnipeg], BIOlogie et GEstion des Risques en agriculture (BIOGER), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Departement of Soil and Environment, Institut National de la Recherche Agronomique (INRA)-AgroParisTech, and Université Paris Saclay (COmUE)
Subjects: 0106 biological sciences, Leptosphaeria maculans, Clubroot, business.industry, Ecology (disciplines), [SDV]Life Sciences [q-bio], Blackleg, 04 agricultural and veterinary sciences, Plant Science, Horticulture, Biology, phoma, colza, medicine.disease, 01 natural sciences, Biotechnology, Plasmodiophora brassicae, [SDE]Environmental Sciences, 040103 agronomy & agriculture, medicine, 0401 agriculture, forestry, and fisheries, business, Agronomy and Crop Science, 010606 plant biology & botany
Abstract: First Online: 23 May 2016; Special issue on clubroot and blackleg diseases of brassicas - Foreword
Published: 2016
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15. Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Author: Frank Ewert, Stephen M. Ogle, Lutz Weihermüller, Holger Hoffmann, Gang Zhao, Edmar Teixeira, Pete Smith, Xenia Specka, Balász Grosz, Thomas Gaiser, Zhigan Zhao, Ralf Kiese, Helene Raynal, Matthias Kuhnert, Kurt Christian Kersebaum, Rene Dechow, Jagadeesh Yeluripati, Elisabet Lewan, Claas Nendel, Enli Wang, Elsa Coucheney, Edwin Haas, Marcel van Oijen, Carmen Sosa, Julie Constantin, Henrik Eckersten, Steffen Klatt, Institute of Biological and Environmental Sciences, (SFIRC), Crop Science Group, INRES, Rheinische Friedrich-Wilhelms-Universität Bonn, Centre for Ecology and Hydrology, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Thünen Institute of Climate-Smart Agriculture, Department of Crop Production Ecology, Institut für Meteorologie und Klimaforschung - Atmosphärische Umweltforschung (IMK-IFU), Karlsruher Institut für Technologie (KIT), Institute of Landscape Systems Analysis, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), Systems Modelling Team (Sustainable Production Group), New Zealand Institute for Plant and Food Research Limited, CSIRO, Institute of Bio- and Geosciences Agrosphere (IBG-3), Colorado State University [Fort Collins] (CSU), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Departement of Soil and Environment, and Plant & Food Research
Subjects: extreme events, 010504 meteorology & atmospheric sciences, data aggregation, [SDV]Life Sciences [q-bio], Soil Science, Growing season, Plant Science, Atmospheric sciences, 01 natural sciences, Ecology and Environment, Extreme weather, Range (statistics), Data and Information, Spatial analysis, climate, 0105 earth and related environmental sciences, 2. Zero hunger, npp, Ecology, Phenology, scaling, Primary production, net primary production, 04 agricultural and veterinary sciences, 15. Life on land, Agriculture and Soil Science, Agronomy, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Monoculture, Scale (map), Agronomy and Crop Science, crop modelling
Abstract: For spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km2), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1–100 km grid cells show changes of 0.4–7.8% and 0.0–4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9–4.2 g C m−2 d−1 and 2.7–6.1 g C m−2 d−1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data aggregation. However, the scale of climate data is more relevant for impacts on annual averages of NPP or if the period is strongly affected or dominated by drought stress. There should be an awareness of the greater uncertainty for the NPP values in these situations if data are not available at high resolution. On the other hand, the results suggest that there is no need to simulate at high resolution for long term regional NPP averages based on the simplified assumptions (soil and management constant in time and space) used in this study.
Published: 2016
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16. Forçage du modèle de simulation du devenir des pesticides MACRO avec le modèle de culture STICS pour évaluer les flux de pesticides dans les systèmes de culture innovants

Author: Yemadje, Sabine Karen, Moeys, Julien, Justes, Eric, Barriuso, Enrique, Ubertosi, Marjorie, Munier-Jolain, Nicolas, Mamy, Laure, Ecologie fonctionnelle et écotoxicologie des agroécosystèmes (ECOSYS), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Agroécologie [Dijon], Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, and Simulateur mulTIdisciplinaire pour les Cultures Standard (STICS).
Subjects: innovative cropping systems, [SDV]Life Sciences [q-bio], modélisation, systèmes de culture innovants, pesticides, pratiques agricoles, MACRO, STICS, modelling, agricultural practices, [SHS]Humanities and Social Sciences, [SDE]Environmental Sciences, [SDV.BV]Life Sciences [q-bio]/Vegetal Biology
Abstract: National audience; Le défi du plan Ecophyto consiste à diminuer d’une manière significative le recours aux produits phytosanitaires, tout en continuant à assurer un niveau de production élevé en quantité et en qualité. Seule une combinaison de différentes techniques culturales peut remplacer la protection chimique et cette combinaison implique l’introduction de nouveaux systèmes de culture. Cependant, un très grand nombre de systèmes peut être conçu et il est impossible de réaliser des expérimentations in situ pour étudier la durabilité de chacun de ces systèmes. Il est donc nécessaire de développer des outils pour évaluer les impacts sur l’environnement (liés à l’usage des pesticides) des systèmes de culture. Parmi les outils existants, la modélisation numérique permet la description des flux et des concentrations en pesticides dans les différents compartiments de l’environnement. Les modèles (modèles « pesticides ») permettent d’intégrer les effets induits par la variabilité des contextes agro-pédo-climatiques, cependant les pratiques agricoles y sont à l’heure actuelle représentées de manière incomplète. L’objectif de ce travail est donc de forcer un modèle « pesticides », MACRO (Larsbo et Jarvis, 2003), avec un modèle capable de représenter l’évolution des couverts végétaux sous différentes pratiques agricoles (rotation, associations de culture, gestion des résidus, travail du sol…) et sous différentes conditions pédoclimatiques, le modèle STICS (Brisson et al., 1998).
Published: 2015

17. Uncertainties in scaling-up crop models for large-area climate change impact assessments

Author: Frank Ewert, Lenny G. J. van Bussel, Gang Zhao, Holger Hoffmann, Thomas Gaiser, Xenia Specka, Claas Nendel, Kurt-Christian Kersebaum, Carmen Sosa, Elisabet Lewan, Jagadeesh Yeluripati, Matthias Kuhnert, Fulu Tao, Reimund Rötter, Julie Constantin, Helene Raynal, Daniel Wallach, Edmar Teixeira, Balasz Grosz, Michaela Bach, Luca Doro, Pier Paolo Roggero, Zhigan Zhao, Enli Wang, Ralf Kiese, Edwin Haas, Henrik Eckersten, Giacomo Trombi, Marco Bindi, Christian Klein, Christian Biernath, Florian Heinlein, Eckart Priesack, Davide Cammarano, Senthold Asseng, Joshua Elliott, Michael Glotter, Bruno Basso, Guillermo A. Baigorria, Consuelo C. Romero, Marco Moriondo, Institute of Crop Science and Resource Conservation, Division of Plant Nutrition-University of Bonn, Wageningen University and Research Center (WUR), Instutute of Landscape Biogeochemistry, Leibniz-Zentrum für Agrarlandschaftsforschung = Leibniz Centre for Agricultural Landscape Research (ZALF), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Biological and Environmental Sciences, University of Stirling, Agrifood Research Finland, UMR : AGroécologie, Innovations, TeRritoires, Ecole Nationale Supérieure Agronomique de Toulouse, Unité de Mathématiques et Informatique Appliquées de Toulouse (MIAT INRA), Institut National de la Recherche Agronomique (INRA), University of Canterbury, Thünen Institute of Climate Smart Agriculture, University of Sassari, CSIRO, Karlsruhe Institute of Technology (KIT), Dipartimento di Scienze delle Produzioni Agroalimentari e dell’Ambiente (DISPAA ), Helmholtz-Zentrum München (HZM), University of Florida [Gainesville], University of Chicago, Michigan State University [East Lansing], Michigan State University System, University of Nebraska [Lincoln], University of Nebraska System, Institute of Food Sciences of National Research Council (IFS - CNR), University of Bonn-Division of Plant Nutrition, Institute of Crop Science and Resource Conservation [Bonn] (INRES), Rheinische Friedrich-Wilhelms-Universität Bonn, Wageningen University and Research [Wageningen] (WUR), Departement of Soil and Environment, AGroécologie, Innovations, teRritoires (AGIR), Institut National de la Recherche Agronomique (INRA)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, University of Canterbury [Christchurch], and University of Florida [Gainesville] (UF)
Subjects: 010504 meteorology & atmospheric sciences, Operations research, [SDV]Life Sciences [q-bio], Climate change, 01 natural sciences, Life Science, 0105 earth and related environmental sciences, 2. Zero hunger, Sustainable development, Food security, business.industry, Impact assessment, Environmental resource management, 04 agricultural and veterinary sciences, 15. Life on land, Geography, Environmental Systems Analysis, 13. Climate action, Agriculture, Obstacle, Milieusysteemanalyse, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Economic model, Climate model, business
Abstract: Problems related to food security and sustainable development are complex (Ericksenet al., 2009) and require consideration of biophysical, economic, political, and social factors, as well as their interactions, at the level of farms, regions, nations, and globally. While the solution to such societal problems may be largely political, there is a growing recognition of the need for science to provide sound information to decision-makers (Meinke et al., 2009). Achieving this, particularly in light of largely uncertain future climate and socio-economic changes, will necessitate integrated assessment approaches and appropriate integrated assessment modeling (IAM) tools to perform them. Recent (Ewertet al., 2009; van Ittersumet al., 2008) and ongoing (Rosenzweiget al., 2013) studies have tried to advance the integrated use of biophysical and economic models to represent better the complex interactions in agricultural systems that largely determine food supply and sustainable resource use. Nonetheless, the challenges for model integration across disciplines are substantial and range from methodological and technical details to an often still-weak conceptual basis on which to ground model integration (Ewertet al., 2009; Janssenet al., 2011). New generations of integrated assessment models based on well-understood, general relationships that are applicable to different agricultural systems across the world are still to be developed. Initial efforts are underway towards this advancement (Nelsonet al., 2014; Rosenzweiget al., 2013). Together with economic and climate models, crop models constitute an essential model group in IAM for large-area cropping systems climate change impact assessments. However, in addition to challenges associated with model integration, inadequate representation of many crops and crop management systems, as well as a lack of data for model initialization and calibration, limit the integration of crop models with climate and economic models (Ewertet al., 2014). A particular obstacle is the mismatch between the temporal and spatial scale of input/output variables required and delivered by the various models in the IAM model chain. Crop models are typically developed, tested, and calibrated for field-scale application (Booteet al., 2013; see also Part 1, Chapter 4 in this volume) and short time-series limited to one or few seasons. Although crop models are increasingly used for larger areas and longer time-periods (Bondeauet al., 2007; Deryng et al., 2011; Elliottet al., 2014) rigorous evaluation of such applications is pending. Among the different sources of uncertainty related to climate and soil data, model parameters, and structure, the uncertainty from methods used to scale-up crop models has received little attention, though recent evaluations indicate that upscaling of crop models for climate change impact assessment and the resulting errors and uncertainties deserve attention in order to advance crop modeling for climate change assessment (Ewertet al., 2014; R¨ otteret al., 2011). This reality is now reflected in the scientific agendas of new international research projects and programs such as the Agricultural Model Intercomparison and Improvement Project (AgMIP; Rosenzweiget al., 2013) and MACSUR (MACSUR, 2014). In this chapter, progress in evaluation of scaling methods with their related uncertainties is reviewed. Specific emphasis is on examining the results of systematic studies recently established in AgMIP and MACSUR. Main features of the respective simulation studies are presented together with preliminary results. Insights from these studies are summarized and conclusions for further work are drawn.
Published: 2015
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18. Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France

Author: Elsa Coucheney, Nicolas Beaudoin, Françoise Ruget, Bruno Mary, Eric Justes, Julie Constantin, Kasaina Sitraka Andrianarisoa, Samuel Buis, Dominique Ripoche, Marie Launay, Christine Le Bas, Joël Léonard, Iñaki García de Cortázar-Atauri, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Agroressources et Impacts environnementaux (AgroImpact), Institut National de la Recherche Agronomique (INRA), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Agroclim (AGROCLIM), Agrosystèmes Cultivés et Herbagers (ARCHE), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse (ENSAT), Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université de Toulouse (UT)-Université de Toulouse (UT), Biotechnologie et Gestion des Agents Pathogènes en agriculture (BioGAP), Institut Supérieur d'Agriculture de Lille (ISA), Université catholique de Lille (UCL)-Université catholique de Lille (UCL)-Institut Charles Viollette (ICV) - EA 7394 (ICV), Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille-Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille, InfoSol (InfoSol), UR 1158 Unité de recherche Agronomie Laon-Reims-Mons, Institut National de la Recherche Agronomique (INRA)-Environnement et Agronomie (E.A.)-Unité de recherche Agronomie Laon-Reims-Mons (UA LRM), Department of Soil and Environment, UE Agroclim (UE AGROCLIM), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure Agronomique de Toulouse-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées, Institut Charles Viollette (ICV) - EA 7394 (ICV), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture-Institut Supérieur d'Agriculture de Lille (Groupe ISA), Unité INFOSOL (ORLEANS INFOSOL), Institut National de la Recherche Agronomique (INRA)-École nationale supérieure agronomique de Toulouse [ENSAT]-Institut National Polytechnique (Toulouse) (Toulouse INP), Institut Supérieur d'Agriculture de Lille (ISA)-Institut Charles Viollette (ICV) - EA 7394 (ICV), Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture-Université du Littoral Côte d'Opale (ULCO)-Université de Lille-Institut National de la Recherche Agronomique (INRA)-Université d'Artois (UA)-Institut Supérieur d'Agriculture, and Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille-Université d'Artois (UA)-Institut National de la Recherche Agronomique (INRA)-Université du Littoral Côte d'Opale (ULCO)-Institut Supérieur d'Agriculture-Université de Lille-Institut Supérieur d'Agriculture de Lille (Groupe ISA)
Subjects: Biomass (ecology), Environmental Engineering, Mean squared error, Ecological Modeling, [SDV]Life Sciences [q-bio], Environmental engineering, Scale (descriptive set theory), Soil science, 15. Life on land, chemistry.chemical_compound, Nitrate, chemistry, Soil water, Range (statistics), Environmental science, Scenario testing, Robustness (economics), Software
Abstract: Soil-crop models are increasingly used as predictive tools to assess yield and environmental impacts of agriculture in a growing diversity of contexts. They are however seldom evaluated at a given time over a wide domain of use. We tested here the performances of the STICS model (v8.2.2) with its standard set of parameters over a dataset covering 15 crops and a wide range of agropedoclimatic conditions in France. Model results showed a good overall accuracy, with little bias. Relative RMSE was larger for soil nitrate (49%) than for plant biomass (35%) and nitrogen (33%) and smallest for soil water (10%). Trends induced by contrasted environmental conditions and management practices were well reproduced. Finally, limited dependency of model errors on crops or environments indicated a satisfactory robustness. Such performances make STICS a valuable tool for studying the effects of changes in agro-ecosystems over the domain explored. STICS v8.2.2 soil-crop model was evaluated over a large and varied dataset using its standard set of parameters.Level of accuracy is 10-50% for plant, soil water and nitrate outputs.Model reproduces well trends arising from contrasted agro-environmental conditions.Errors are weakly dependent on the agro-environmental conditions tested.Model accuracy and robustness is considered good for scenario testing and large scale use within the conditions tested here.
Published: 2015
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19. On the need to establish an international soil modelling consortium

Author: Vereecken, Harry, Vanderborght, Jan, Schnepf, Andrea, Brüggemann, Nicolas, Amelung, Wulf, Herbst, Michael, Javaux, Mathieu, Van der Zee, Sjoerd E.A.T.M, Or, Dani, Simunek, Jirka, van Genuchten, Martinus Th., Vrugt, Jasper A., Hopmans, Jan W., Young, Michael H., Baveye, Philippe, Pachepsky, Yakov, Vanclooster, Marnik, Hallett, Paul D., Tiktak, Aaldrik, Jacques, Diederik, Vogel, Tomas, Jarvis, Nicholas, Finke, Peter, Jiménez, Juan José, Li, Changsheng S., Ogée, Jérôme, Mollier, Alain, Lafolie, Francois, Cousin, Isabelle, Pot-Genty, Valerie, Maron, Pierre-Alain, Roose, Tiina, Wall, Diana H., Schwen, Andreas, Doussan, Claude, Vogel, Hans-Jörg, Govers, Gerard, Durner, Wolfgang, Priesack, Eckart, Roth, Kurt, Horn, Rainer, Kollet, Stefan, Rinaldo, Andrea, Whitmore, Andy, Goulding, Keith, Parton, William J., Agrosphere Institute, IBG-3, Institute of Bio-geosciences, Forschungszentrum Jülich GmbH, Centre for High-Performance Scientific Computing in Terrestrial Systems, HPSC TerrSys, Earth and Life Institute‐Environmental Sciences, Université Catholique de Louvain ( UCL ), Department Soil Physics and Land Management, Environmental Sciences Group, Wageningen University and Research Centre [Wageningen] ( WUR ), Soil and Terrestrial Environmental Physics, Swiss Federal Institute of Technology in Zürich ( ETH Zürich ), Department of Environmental Sciences [Riverside], University of California [Riverside] ( UCR ), Department of Mechanical Engineering, Federal University of Rio de Janeiro, Department of Earth Sciences [Utrecht], Utrecht University [Utrecht], Department of Civil and Environmental Engineering, University of California [Irvine] ( UCI ), Department of Earth System Science [Irvine] ( ESS ), Institute for Biodiversity and Ecosystem dynamics, University of Amsterdam [Amsterdam] ( UvA ), Department of Land, Air, and Water Resources, College of Agricultural and Environmental Sciences, University of California Davis, Bureau of Economic Geology, Jackson School of Geosciences, University of Texas at Austin [Austin], Soil and Water Laboratory, Department of Civil and Environmental Engineering, Rensselaer Polytechnic Institute ( RPI ), Environmental Microbial and Food Safety Laboratory, Agricultural Research Service, Earth and Life Institute ‐ Environmental Sciences, Institute of Biological and Environmental Sciences, University of Aberdeen, Netherlands Environmental Assessment Agency, Institute for Environment, Health and Safet, Centre d'Etude de l'Energie Nucléaire ( SCK-CEN ), Department of Hydraulics and Hydrology [Praha], Czech Technical University in Prague ( CTU ), Dept. Soil & Environment, Swedish University of Agricultural Sciences ( SLU ), Department of Geology and Soil Science, Ghent University [Belgium] ( UGENT ), Instituto Pirenaico de Ecologia (IPE), Spanish National Research Council ( CSIC ), Institute for the Study of Earth, Oceans, and Space [Durham] ( EOS ), University of New Hampshire ( UNH ), Interactions Sol Plante Atmosphère ( ISPA ), Institut National de la Recherche Agronomique ( INRA ) -Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine ( Bordeaux Sciences Agro ), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes ( EMMAH ), Institut National de la Recherche Agronomique ( INRA ) -Université d'Avignon et des Pays de Vaucluse ( UAPV ), Unité de recherche Science du Sol ( USS ), Institut National de la Recherche Agronomique ( INRA ), Environnement et Grandes Cultures ( EGC ), AgroParisTech-Institut National de la Recherche Agronomique ( INRA ), Agroécologie [Dijon], Institut National de la Recherche Agronomique ( INRA ) -Université de Bourgogne ( UB ) -AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, Bioengineering Sciences Research Group, Faculty of Engineering and Environment, University of Southampton [Southampton], School of Global Environmental Sustainability, Colorado State University [Fort Collins] ( CSU ), Institute of Hydraulics and Rural Water Management, University of Natural Resources and Life Sciences, Department Soil System Science [UFZ Leipzig], Helmholtz Centre for Environmental Research ( UFZ ), Department of Earth and Environmental Sciences, Division of Geography, Katholieke Universiteit Leuven ( KU Leuven ), Institute for Geoecology, Soil Science and Soil Physics, Technische Universität Braunschweig [Braunschweig], Institute of Soil Ecology [Neuherberg] ( IBOE ), Helmholtz-Zentrum München ( HZM ), Institute of Environmental Physics [Heidelberg] ( IUP ), Universität Heidelberg [Heidelberg], Institute for Plant Nutrition and Soil Science, Christian Albrechts University, Dipartimento IMAGE, and International Centre for Hydrology 'Dino Tonini', Universita degli Studi di Padova = University of Padua = Université de Padoue, Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Natural Resource Ecology Laboratory, Université Catholique de Louvain (UCL), Wageningen University and Research Centre [Wageningen] (WUR), University of California [Riverside] (UCR), University of California-University of California, University of California [Irvine] (UCI), Department of Earth System Science [Irvine] (ESS), University of Amsterdam [Amsterdam] (UvA), University of California [Davis] (UC Davis), Rensselaer Polytechnic Institute (RPI), Centre d'Etude de l'Energie Nucléaire (SCK-CEN), Czech Technical University in Prague (CTU), Swedish University of Agricultural Sciences (SLU), Ghent University [Belgium] (UGENT), Spanish National Research Council (CSIC), Institute for the Study of Earth, Oceans, and Space [Durham] (EOS), University of New Hampshire (UNH), Interactions Sol Plante Atmosphère (ISPA), Institut National de la Recherche Agronomique (INRA)-Ecole Nationale Supérieure des Sciences Agronomiques de Bordeaux-Aquitaine (Bordeaux Sciences Agro), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE), Unité de recherche Science du Sol (USS), Institut National de la Recherche Agronomique (INRA), Environnement et Grandes Cultures (EGC), AgroParisTech-Institut National de la Recherche Agronomique (INRA), Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB)-AgroSup Dijon - Institut National Supérieur des Sciences Agronomiques, de l'Alimentation et de l'Environnement, University of Southampton, Colorado State University [Fort Collins] (CSU), Universität für Bodenkultur Wien [Vienne, Autriche] (BOKU), Helmholtz Centre for Environmental Research (UFZ), Department of Earth and Environmental Sciences [Leuven] (EES), Catholic University of Leuven - Katholieke Universiteit Leuven (KU Leuven), Institute of Soil Ecology [Neuherberg] (IBOE), Helmholtz-Zentrum München (HZM), Institute of Environmental Physics [Heidelberg] (IUP), Christian-Albrechts-Universität zu Kiel (CAU), Universita degli Studi di Padova, Natural Resource Ecology Laboratory [Fort Collins] (NREL), Forschungszentrum Jülich GmbH | Centre de recherche de Juliers, Helmholtz-Gemeinschaft = Helmholtz Association-Helmholtz-Gemeinschaft = Helmholtz Association, Université Catholique de Louvain = Catholic University of Louvain (UCL), Wageningen University and Research [Wageningen] (WUR), Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich] (ETH Zürich), Bureau of Economic Geology [Austin] (BEG), Jackson School of Geosciences (JSG), University of Texas at Austin [Austin]-University of Texas at Austin [Austin], USDA-ARS : Agricultural Research Service, Earth and Life Institute - Environmental Sciences (ELIE), Departement of Soil and Environment, Universiteit Gent = Ghent University [Belgium] (UGENT), Consejo Superior de Investigaciones Científicas [Madrid] (CSIC), Interactions Sol Plante Atmosphère (UMR ISPA), Institut National de la Recherche Agronomique (INRA)-AgroParisTech, Helmholtz Zentrum für Umweltforschung = Helmholtz Centre for Environmental Research (UFZ), and Technische Universität Braunschweig = Technical University of Braunschweig [Braunschweig]
Subjects: sol, modélisation des sols, Earth Sciences, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, partenariat de recherche, [ SDU.STU ] Sciences of the Universe [physics]/Earth Sciences, Sciences de la Terre, expert scientifique, soil
Abstract: absent
Published: 2014

20. Organic farming gives no climate change benefit through soil carbon sequestration

Author: Claire Chenu, Thomas Kätterer, David S. Powlson, Denis A. Angers, Jens Leifeld, Jürg Fuhrer, Air Pollution/Climate Group, Agroscope, Agriculture and Agri-Food [Ottawa] (AAFC), Biogéochimie et écologie des milieux continentaux (Bioemco), Centre National de la Recherche Scientifique (CNRS)-AgroParisTech-Université Pierre et Marie Curie - Paris 6 (UPMC)-Institut National de la Recherche Agronomique (INRA)-École normale supérieure - Paris (ENS Paris), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Department of Sustainable Soils and Grassland Systems, Rothamsted Research, Agriculture and Agri-Food (AAFC), École normale supérieure - Paris (ENS-PSL), Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut de Recherche pour le Développement (IRD)-Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS), Departement of Soil and Environment, Biotechnology and Biological Sciences Research Council (BBSRC)-Biotechnology and Biological Sciences Research Council (BBSRC), École normale supérieure - Paris (ENS Paris), and Université Paris sciences et lettres (PSL)-Université Paris sciences et lettres (PSL)-Institut National de la Recherche Agronomique (INRA)-Université Pierre et Marie Curie - Paris 6 (UPMC)-AgroParisTech-Centre National de la Recherche Scientifique (CNRS)
Subjects: Agroecosystem, animal diseases, [SDV]Life Sciences [q-bio], Climate change, 010501 environmental sciences, 01 natural sciences, complex mixtures, Grassland, No-till farming, Soil, agriculture biologique, organic farming, carbone organique du sol, Organic matter, Letters, skin and connective tissue diseases, global change, 0105 earth and related environmental sciences, agriculture, 2. Zero hunger, chemistry.chemical_classification, geography, changement climatique, Organic Agriculture, Multidisciplinary, geography.geographical_feature_category, Agroforestry, food and beverages, 04 agricultural and veterinary sciences, Soil carbon, 15. Life on land, Crop rotation, Carbon, chemistry, Agronomy, 13. Climate action, 040103 agronomy & agriculture, Organic farming, 0401 agriculture, forestry, and fisheries, Environmental science, sense organs, carbone du sol
Abstract: Agricultural management strongly affects soil organic carbon (C), as shown by numerous long-term experiments (1). Practices known to increase soil C include adding organic manures and including grassland in crop rotations. Their effect on C is related to the net primary productivity of the agroecosystem and the fraction of organic matter remaining in the field or returned as residues.
Published: 2013
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21. Carbon and nitrogen pools and fluxes above and below ground in spruce, pine and birch stands in southern Sweden

Author: Karna Hansson, Tryggve Persson, Mats Olsson, Heljä-Sisko Helmisaari, Mats Fröberg, Bengt A. Olsson, Dan Berggren Kleja, Department of Ecology, Swedish University of Agricultural Sciences (SLU), Departement of Soil and Environment, Department of Forest Sciences, University of Alaska [Fairbanks] (UAF), and Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning
Subjects: S1, 010504 meteorology & atmospheric sciences, Nitrogen, [SDV]Life Sciences [q-bio], Management, Monitoring, Policy and Law, 01 natural sciences, Soil, Botany, QD, 0105 earth and related environmental sciences, Nature and Landscape Conservation, Biomass (ecology), biology, Picea abies, QK, Earthworm, Scots pine, Forestry, Pinus sylvestris, 04 agricultural and veterinary sciences, Soil carbon, Vegetation, 15. Life on land, biology.organism_classification, Carbon, Agronomy, Soil water, 040103 agronomy & agriculture, Litter, Betula pendula, 0401 agriculture, forestry, and fisheries, Environmental science
Abstract: We synthesised results on soil carbon (C) and nitrogen (N) fluxes and the accumulation of soil organic C and N under adjacent 50-year-old Norway spruce, Scots pine and silver birch stands growing on similar soils and evaluated the different processes involved. C and N budgets were calculated. Spruce stands had larger stocks of C and N in biomass and soil than birch stands, with pine intermediate. The differences in soil stocks were mainly found in the organic layer, whereas differences in the mineral soil were small. The study showed that there is no simple answer to what is causing the differences in soil C and N stocks, because several processes are interacting. Spruce and pine trees had higher biomass and litter production than birch trees, but total litter inputs showed no significant difference between stands, because the rich ground vegetation under pine and birch contributed with substantial litter inputs, in contrast to the poor ground vegetation under spruce. Decomposition rate (per g of C) was markedly higher under birch than under spruce and pine resulting in lower C and N stocks in the organic layer. This effect was amplified by higher abundance and biomass of earthworms, favoured by higher pH and palatable litter under birch. Earthworm bioturbation probably both increased decomposition rate and damaged the ectomycorrhizal network with negative consequences for the formation of mycorrhizal litter and C storage. In conclusion, the direct effects of spruce, pine and birch litter on C and N pools and fluxes were modified by indirect effects of understorey structure, pH and earthworm responses. (C) 2013 Elsevier B.V. All rights reserved.
Published: 2013
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22. An indicator of pesticide leaching risk to groundwater

Author: Christian Bockstaller, Anna M.L. Lindahl, Laboratoire Agronomie et Environnement - Antenne Colmar (LAE-Colmar ), Laboratoire Agronomie et Environnement (LAE), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL)-Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), Department of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Institut National de la Recherche Agronomique (INRA)-Université de Lorraine (UL), and Departement of Soil and Environment
Subjects: Dependency (UML), Soil texture, Process (engineering), Decision tree, General Decision Sciences, Pesticide leaching, Agricultural engineering, 010501 environmental sciences, 01 natural sciences, Range (statistics), Macro, Ecology, Evolution, Behavior and Systematics, 0105 earth and related environmental sciences, Ecology, pesticide leaching, macro model, Environmental and Society, 04 agricultural and veterinary sciences, data mining, i phy indicator, [SDE.ES]Environmental Sciences/Environmental and Society, 6. Clean water, fuzzy inference system, preferential flow, 13. Climate action, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Environnement et Société, Groundwater
Abstract: The authors regret that there were two errors in the original article published. The first error can be found in equation 6 on page 101 in the Result section. Equation 6 should be: gR(x) = min(cR, x). The second error can be found on page 104 in the Discussion section. This error arose due to inaccurate interpretations of the results by Alleto et al., (2010). The original erroneous sentence states: Field studies (Alletto et al., 2010) have shown that suppression of ploughing can increase leaching by a factor of 60% in average (N = 21, a total of 11 different pesticides reviewed in nine studies). This sentence should instead be changed to: Field studies (Alletto et al., 2010) have shown that suppression of ploughing can increase leaching by a factor of 2.7 in average (N = 28, a total of eight different pesticides reviewed in six studies). Corrigendum, Ecological Indicators, volume 30, http://dx.doi.org/10.1016/j.ecolind.2012.03.014; Since the 90s an increasing number of assessment methods using operational tools like indicators have been proposed for environmental issues linked to pesticides, among them, groundwater contamination by pesticide transfer. To our knowledge none of these indicators address preferential flow, an important process determining pesticide leaching. The objective of this study is twofold: (i) to develop a new groundwater sub indicator for an existing indicator, I-Phy (former Ipest), that explicitly take preferential flow into account, and (ii) to test the possibility of developing an indicator by means of data-mining methods using simulations of a mechanistic model. The groundwater sub indicator developed is in the form of decision trees based on fuzzy inference systems. It was derived through neuro-adaptive learning on data sets from simulations running the process-based MACRO model. Unlike the previous version, the new indicator considers preferential flow, climatic differences and differences in soil texture with depth. Other benefits are less dependency on expert knowledge and the possibility to integrate a broad range of conditions.
Published: 2012
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23. Land-use effects on the distribution of soil organic carbon within particle-size fractions of volcanic soils in the Transmexican Volcanic Belt (Mexico)

Author: Covaleda, S., Gallardo, J.F., Garcia-Oliva, F., Kirchmann, H., Prat, C., Bravo, M., Etchevers, J., CSIC, IRNasa, Centro de Investigaciones en Ecosistemas, Universidad Nacional Autónoma de México (UNAM), Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Laboratoire d'étude des transferts en hydrologie et environnement (LTHE), Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS), Instituto Nacional de Investigaciones Forestales, Agricolas y Pecuarias [Mexico] (INIFAP), Laboratorio de Fertilidad de Suelo, Colegio de Postgraduados (CP), UNAM (SDEI-PTID-02), European Project, Department of Soil Sciences, RIVER, Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Observatoire des Sciences de l'Univers de Grenoble (OSUG), Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA), Campo Experimental Uruapan, I.N.I.F.A.P, I.N.I.F.A.P, Colegio de Postgraduados, Universidad Nacional Autónoma de México = National Autonomous University of Mexico (UNAM), Observatoire des Sciences de l'Univers de Grenoble (OSUG), and Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Université Joseph Fourier - Grenoble 1 (UJF)-Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP )-Institut national des sciences de l'Univers (INSU - CNRS)-Institut national de recherche en sciences et technologies pour l'environnement et l'agriculture (IRSTEA)-Université Savoie Mont Blanc (USMB [Université de Savoie] [Université de Chambéry])-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique de Grenoble (INPG)-Centre National de la Recherche Scientifique (CNRS)
Subjects: Soil organic matter, Andosols, Soil physical fractionation, Thermal analyses, Agriculture, [SDU.STU.GM]Sciences of the Universe [physics]/Earth Sciences/Geomorphology, Forest cover depletion, Andosol
Abstract: 9 p., The aim of this study was to determine the effect of land-use and forest cover depletion on the distribution of soil organic carbon (SOC) within particle-size fractions in a volcanic soil. Emphasis was given to the thermal properties of soils. Six representative sites in Mexico were selected in an area dominated by Andosols: a grassland site, four forested sites with different levels of degradation and an agricultural site. Soils were fractionated using ultrasonic energy until complete dispersion was achieved. The particle-size fractions were coarse sand, fine sand, silt, clay and particulate organic matter from the coarse sand sized fraction (POM-CS) and fine sand (POM-FS). Soil organic carbon decreased by 70% after forest conversion to cropland and long-term cultivation; forest cover loss resulted in a decrease in SOC of up to 60%. The grassland soil contained 45% more SOC than the cropland one. Soil organic carbon was mainly associated with the silt-size fraction; the most sensitive fractions to land-use change and forest cover depletion were POM followed by SOC associated with the silt and clay-sized fractions. Particulate organic matter can be used as an early indicator of SOC loss. The C lost from the clay and silt-sized fractions was thermally labile; therefore, the SOC stored in the more degraded forest soils was more recalcitrant (thermally resistant). Only the transformation of forest to agricultural land produced a similar loss of thermally stable C associated with the silt-sized fraction., The authors gratefully acknowledge the help of two anonymous reviewers, R. Vela´ zquez-Dura´ n for his assistance in fieldwork, H. Ferreira and A. Valencia for their technical assistance. In addition, we thank the European Union for the support of the REVOLSO Project (INCO Program, ICA4- CT-2001-10052), the Spanish Ministry of Education and Science and UNAM (SDEI-PTID-02) that partially financed the Project.
Published: 2011
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24. Spatial patterns of ammonia oxidizing bacteria and archaea at field scale relate to soil ecosystem functioning

Author: Wessen, Ella, Stenberg, Maria, Söderström, Mats, Bru, David, Hellman, Maria, Thomsen, F., Klemedtson, Leif, Philippot, Laurent, Hallin, Sara, Swedish University of Agricultural Sciences (SLU), Departement of Soil and Environment, Microbiologie, Institut National de la Recherche Agronomique (INRA)-Université de Bourgogne (UB), Department of Plant and Environmental Sciences, and University of Gothenburg (GU)
Subjects: ECOSYSTEM, AMMONIA OXIDIZING BACTERIA, CHARACTERZATION SPATIAL PATTERNS, MICROBIAL COMMUNITIES, [SDV]Life Sciences [q-bio], [SDE]Environmental Sciences
Abstract: Characterization of spatial patterns of microbial communities aids in understanding not only the mechanisms driving their distribution, but also the processes shaping their structure. Focusing on spatial patterns of functional microbial communities could facilitate the understanding of the relationships between the ecology of microbial communities, the biogeochemical processes they performed and the corresponding ecosystem functions. For ammonia oxidation there are two known microbial groups that carry out this reaction, the ammonia oxidizing bacteria (AOB) and archaea (AOA), respectively. Because of their role in the cycling of nitrogen, greenhouse gas fluxes and nitrate leaching, understanding the spatial distribution of the AOA and AOB in relation to soil properties is of importance. We explored the spatial distribution of these communities by measuring the activity, abundance and community structure across a 44 ha large farm divided into an organic and an integrated farming system. The spatial patterns were mapped by geostatistical modelling and correlations to nitrate leaching and soil properties were studied. All measured community components, activity, abundance and community structure, for both AOB and AOA exhibited spatial patterns. Soil properties were differently related to the AOB and AOA communities, with clay content, soil organic carbon and total nitrogen correlating positively to the AOB abundance while clay content showed a negative correlation to the AOA abundance. Interestingly, the substrate induced ammonia oxidation rates correlated to the AOB abundance, whereas the amount of leached nitrate had a strong correlation to the AOA abundance at the field site. This indicates that the AOB and AOA may occupy different niches in agroecosystems. Findings in this study are part of a first step in identifying the "meta-habitat" of ammonia oxidizing communities at a scale compatible with management strategies.
Published: 2010

25. A Conceptual Model of Soil Susceptibility to Macropore Flow

Author: Igor G. Dubus, Anna M.L. Lindahl, Nick Jarvis, J. M. Hollis, Julien Moeys, Stefan Reichenberger, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), National Soil Resources Institute, Cranfield University, Institute of Landscape Ecology and Resources Management, Justus-Liebig-Universität Gießen (JLU), and Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)
Subjects: Hydrology, Soil map, Topsoil, Macropore, 0207 environmental engineering, Soil Science, Soil science, 04 agricultural and veterinary sciences, 02 engineering and technology, 15. Life on land, 6. Clean water, Soil series, 13. Climate action, Soil water, Leaching (pedology), 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Soil horizon, 020701 environmental engineering, Groundwater
Abstract: (shrinkage cracks, interaggregate voids, earthworm channels, and root holes) strongly inﬂ u-ence water ﬂ ow in structured soils and therefore patterns of solute displacement. Macropore ﬂ ow increases the risk of leaching of surface-applied contaminants to groundwater, since inﬁ ltrating water can be quickly channeled through only a very small fraction of the total pore volume, bypassing much of the adsorption and degradation capacity of the chemically and biologically reactive topsoil. Th e signiﬁ cance of macropore ﬂ ow has long been recog-nized (e.g., Th omas and Phillips, 1979), and in recent decades this has stimulated a major experimental research eﬀ ort aimed at improving understanding of its causes, controlling factors, and consequences (Jarvis, 2007). Th is research eﬀ ort has gone hand in hand with the development of many models that attempt to synthesize knowledge of the relevant processes in mathematical form (Gerke, 2006). Th ese models can be used as tools in research to test the limits of our understanding and generate new hypoth-eses, and as management tools to support policy development and decision making (Vanclooster et al., 2004).At well-investigated sites, input parameters to macropore ﬂ ow models can be either directly measured or derived by calibra-tion against depth proﬁ les of resident concentrations and solute breakthrough curves to further minimize prediction uncertainty (Larsbo and Jarvis, 2005). Many model users (e.g., agricultural advisors, water managers, and regulatory authorities), however, are also required to make predictions for less well characterized sites or at larger scales, for example, to map and quantify diﬀ use pollution risks at farm, catchment, regional, and even national scales. Models that account for macropore ﬂ ow must then be used predictively, without any direct measurements of input param-eters or site data for calibration, although it can be assumed that “soft” information in the form of soil and topographic maps and land use statistics will generally be available. Soil maps have long been used as a basis for catchment- and regional-scale vulnerabil-ity and risk assessments (Vanclooster et al., 2004). Th is approach to upscaling predictions from local-scale data is based on the assumption that soil mapping units (soil series or associations, characterized by benchmark pedons, i.e., typical sequences of recognized soil horizons in a proﬁ le) are characterized by infor-mation relevant to solute transport and can be considered as macroscopically homogeneous structural units (e.g., Vogel and Roth, 2003; Vereecken et al., 2007). Th is assumption may be
Published: 2009
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26. Site Classification to Predict the Abundance of the Deep-Burrowing Earthworm Lumbricus terrestris L

Author: Igor G. Dubus, Nick Jarvis, Anna M.L. Lindahl, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), and Bureau de Recherches Géologiques et Minières (BRGM) (BRGM)
Subjects: 2. Zero hunger, biology, Water flow, Soil texture, Earthworm, Soil Science, 04 agricultural and veterinary sciences, 010501 environmental sciences, 15. Life on land, biology.organism_classification, 01 natural sciences, Manure, 6. Clean water, Agronomy, 13. Climate action, Soil water, [SDE]Environmental Sciences, 040103 agronomy & agriculture, 0401 agriculture, forestry, and fisheries, Environmental science, Leaching (agriculture), Arable land, Lumbricus terrestris, 0105 earth and related environmental sciences
Abstract: International audience; Channels made by deep-burrowing (anecic) earthworms are known to strongly affect soil water flow and increase the leaching risk of agricultural pollutants. A classification tree that predicts the abundance of the anecic earthworm Lumbricus terrestris L. from readily available survey information (land use, management practices, and soil texture) was derived from literature data (n = 86). The most important factors favoring L. terrestris were perennial land use, no-till arable cropping, organic additions (i.e., manure), and medium-textured soil. The classification scheme correctly predicted earthworm abundance for 71% of the studies in the database. Among other potential applications, the classification tree could be used to identify areas at risk from groundwater pollution in agricultural landscapes and to support catchment- and regional-scale models of contaminant leaching in the vadose zone.
Published: 2009
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27. Field and pulping performances of transgenic trees with altered lignification

Author: Wolfgang Walter Schuch, Lise Jouanin, Wout Boerjan, E. A. Webster, David Hopkins, Karen Holt, Håkan Marstorp, Jean-Charles Leplé, Claire Halpin, Daniel Cornu, Gilles Pilate, Catherine Lapierre, Isabelle Mila, Emma Guiney, Michel Petit-Conil, Brigitte Pollet, Unité de recherche Amélioration, Génétique et Physiologie Forestières (AGPF), Institut National de la Recherche Agronomique (INRA), Syngenta Ltd, Centre Technique du Papier (CTP), Chimie Biologique (UCB), Institut National de la Recherche Agronomique (INRA)-Institut National Agronomique Paris-Grignon (INA P-G), Department of Environmental Science [Stirling], University of Stirling, Departement of Soil and Environment, Swedish University of Agricultural Sciences (SLU), Laboratoire de biologie cellulaire et moléculaire, Flanders Institute for Biotechnology, and University of Dundee
Subjects: 0106 biological sciences, [SDV.BIO]Life Sciences [q-bio]/Biotechnology, Cinnamyl-alcohol dehydrogenase, LIGNIN BIOSYNTHESIS, Gene Expression, Environmental pollution, Lignin, Plant Roots, 01 natural sciences, Applied Microbiology and Biotechnology, CINNAMYL ALCOOL DESHYDROGENASE, chemistry.chemical_compound, Salicaceae, Caffeate O-methyltransferase, CAD, Transgenes, Soil Microbiology, 0303 health sciences, biology, Plants, Genetically Modified, Wood, Genetically modified organism, Populus, Kraft process, Molecular Medicine, France, Soil microbiology, Biotechnology, Paper, Biomedical Engineering, Industrial Waste, Bioengineering, Sensitivity and Specificity, TRANSGENIC POPLAR, 03 medical and health sciences, Botany, Ecosystem, 030304 developmental biology, O-METHYLTRANSFERASE, Reproducibility of Results, Methyltransferases, biology.organism_classification, COMT, United Kingdom, Alcohol Oxidoreductases, Antisense Elements (Genetics), Models, Chemical, chemistry, PEUPLIER, Environmental Pollution, 010606 plant biology & botany
Abstract: International audience; The agronomic and pulping performance of transgenic trees with altered lignin has been evaluated in duplicated, long-term field trials. Poplars expressing cinnamyl alcohol dehydrogenase (CAD) or caffeate/5-hydroxy-ferulate O-methyltransferase (COMT) antisense transgenes were grown for four years at two sites, in France and England. The trees remained healthy throughout the trial. Growth indicators and interactions with insects were normal. No changes in soil microbial communities were detected beneath the transgenic trees. The expected modifications to lignin were maintained in the transgenics over four years, at both sites. Kraft pulping of tree trunks showed that the reduced-CAD lines had improved characteristics, allowing easier delignification, using smaller amounts of chemicals, while yielding more high-quality pulp. This work highlights the potential of engineering wood quality for more environmentally benign papermaking without interfering with tree growth or fitness.
Published: 2002
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28. Simulation of water movement and isoproturon behaviour in a heavy clay soil using the MACRO model

Author: Tim Besien, N.J. Jarvis, R. J. Williams, EGU, Publication, Institute of Hydrology, Departement of Soil and Environment, and Swedish University of Agricultural Sciences (SLU)
Subjects: chemistry.chemical_classification, Hydrology, [SDU.OCEAN]Sciences of the Universe [physics]/Ocean, Atmosphere, business.product_category, [SDU.OCEAN] Sciences of the Universe [physics]/Ocean, Atmosphere, Pesticide application, [SDU.STU]Sciences of the Universe [physics]/Earth Sciences, Hydrograph, Soil science, Pesticide, [SDU.ENVI] Sciences of the Universe [physics]/Continental interfaces, environment, Plough, chemistry, Hydraulic conductivity, Evapotranspiration, [SDU.STU] Sciences of the Universe [physics]/Earth Sciences, Environmental science, Outflow, Organic matter, business, [SDU.ENVI]Sciences of the Universe [physics]/Continental interfaces, environment
Abstract: In this paper, the dual-porosity MACRO model has been used to investigate methods of reducing leaching of isoproturon from a structured heavy clay soil. The MACRO model was applied to a pesticide leaching data-set generated from a plot scale experiment on a heavy clay soil at the Oxford University Farm, Wytham, England. The field drain was found to be the most important outflow from the plot in terms of pesticide removal. Therefore, this modelling exercise concentrated on simulating field drain flow. With calibration of field-saturated and micropore saturated hydraulic conductivity, the drain flow hydrographs were simulated during extended periods of above average rainfall, with both the hydrograph shape and peak flows agreeing well. Over the whole field season, the observed drain flow water budget was well simulated. However, the first and second drain flow events after pesticide application were not simulated satisfactorily. This is believed to be due to a poor simulation of evapotranspiration during a period of low rainfall around the pesticide application day. Apart from an initial rapid drop in the observed isoproturon soil residue, the model simulated isoproturon residues during the 100 days after pesticide application reasonably well. Finally, the calibrated model was used to show that changes in agricultural practice (deep ploughing, creating fine consolidated seed beds and organic matter applications) could potentially reduce pesticide leaching to surface waters by up to 60%.
Published: 1997

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28 results on '"Departement of Soil and Environment"'

1. Management and spatial resolution effects on yield and water balance at regional scale in crop models

2. Simultaneous measurement of multiple soil properties through proximal sensor data fusion: A case study

3. Rapid determination of soil classes in soil profiles using vis–NIR spectroscopy and multiple objectives mixed support vector classification

4. Winter warming effects on tundra shrub performance are species-specific and dependent on spring conditions

5. Effects of input data aggregation on simulated crop yields in temperate and Mediterranean climates

6. Evaluating the precision of eight spatial sampling schemes in estimating regional means of simulated yield for two crops

7. Using SWAT-LUD Model to Estimate the Influence of Water Exchange and Shallow Aquifer Denitrification on Water and Nitrate Flux

8. Modelling pesticides leaching in cropping systems: Effect of uncertainties in climate, agricultural practices, soil and pesticide properties

9. A comparison of soil tests for available phosphorus in long-term field experiments in Europe

10. The implication of input data aggregation on up-scaling soil organic carbon changes

11. Microbial community structure and activity in trace element-contaminated soils phytomanaged by Gentle Remediation Options (GRO)

12. Quantifying the pluri-centennial soil organic carbon pool using Rock-Eval pyrolysis

13. Immobilization of Cu and As in two contaminated soils with zero-valent iron – Long-term performance and mechanisms

14. Special issue on clubroot and blackleg diseases of brassicas - Foreword

15. Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

16. Forçage du modèle de simulation du devenir des pesticides MACRO avec le modèle de culture STICS pour évaluer les flux de pesticides dans les systèmes de culture innovants

17. Uncertainties in scaling-up crop models for large-area climate change impact assessments

18. Accuracy, robustness and behavior of the STICS soil–crop model for plant, water and nitrogen outputs: Evaluation over a wide range of agro-environmental conditions in France

19. On the need to establish an international soil modelling consortium

20. Organic farming gives no climate change benefit through soil carbon sequestration

21. Carbon and nitrogen pools and fluxes above and below ground in spruce, pine and birch stands in southern Sweden

22. An indicator of pesticide leaching risk to groundwater

23. Land-use effects on the distribution of soil organic carbon within particle-size fractions of volcanic soils in the Transmexican Volcanic Belt (Mexico)

24. Spatial patterns of ammonia oxidizing bacteria and archaea at field scale relate to soil ecosystem functioning

25. A Conceptual Model of Soil Susceptibility to Macropore Flow

26. Site Classification to Predict the Abundance of the Deep-Burrowing Earthworm Lumbricus terrestris L

27. Field and pulping performances of transgenic trees with altered lignification

28. Simulation of water movement and isoproturon behaviour in a heavy clay soil using the MACRO model

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28 results on '"Departement of Soil and Environment"'

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