Suchergebnisse

Funding Information: This modeling study was a joint effort of the Models4Pastures project within the framework of FACCE-JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE-JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc. Mobility fellowship (SNSF funded project: P1EZP2_172121). Lutz Merbold further acknowledges the support received for CGIAR Fund Council, Australia (ACIAR), Irish Aid, the European Union, the Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as well as for the CGIAR Research Program on Livestock. The NZ contributors acknowledge funding from the New Zealand Government Ministry of Primary Industries to support the aims of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) research program). The UK partners acknowledge funding by DEFRA and the RCUK projects: N-Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli, Camilla Dibari, and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from CN-MIP project funded by the French National Research Agency (ANR-13-JFAC-0001) and from ADEME (no. 12-60-C0023). Open access funding enabled and organized by Projekt DEAL Funding Information: This modeling study was a joint effort of the Models4Pastures project within the framework of FACCE‐JPI. Lutz Merbold and Kathrin Fuchs acknowledge funding received for the Swiss contribution to Models4Pastures (FACCE‐JPI project, SNSF funded contract: 40FA40_154245/1) and for the Doc. Mobility fellowship (SNSF funded project: P1EZP2_172121). Lutz Merbold further acknowledges the support received for CGIAR Fund Council, Australia (ACIAR), Irish Aid, the European Union, the Netherlands, New Zealand, Switzerland, UK, USAID, and Thailand for funding to the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) as well as for the CGIAR Research Program on Livestock. The NZ contributors acknowledge funding from the New Zealand Government Ministry of Primary Industries to support the aims of the Livestock Research Group of the Global Research Alliance on Agricultural Greenhouse Gases and from AgResearch's Strategic Science Investment Fund (the Forages for Reduced Nitrate Leaching (FRNL) research program). The UK partners acknowledge funding by DEFRA and the RCUK projects: N‐Circle (BB/N013484/1), UGRASS (NE/M016900/1), and GREENHOUSE (NE/K002589/1). R.M. Rees and C.F.E. Topp also received funding from the Scottish Government Strategic Research Programme. Lorenzo Brilli, Camilla Dibari, and Marco Bindi received funding from the Italian Ministry of Agricultural Food and Forestry Policies (MiPAAF). The FR partners acknowledge funding from CN‐MIP project funded by the French National Research Agency (ANR‐13‐JFAC‐0001) and from ADEME (no. 12‐60‐C0023). Open access funding enabled and organized by Projekt DEAL Publisher Copyright: ©2020. The Authors. Open access funding enabled and organized by Projekt DEAL ; Peer reviewed ; Publisher PDF

The toolbox for imaging molecules is well-equipped today. Some techniques visualize the geometrical structure, others the electron density or electron orbitals. Molecules are many-body systems for which the correlation between the constituents is decisive and the spatial and the momentum distribution of one electron depends on those of the other electrons and the nuclei. Such correlations have escaped direct observation by imaging techniques so far. Here, we implement an imaging scheme which visualizes correlations between electrons by coincident detection of the reaction fragments after high energy photofragmentation. With this technique, we examine the H2two-electron wave function in which electron-electron correlation beyond the mean-field level is prominent. We visualize the dependence of the wave function on the internuclear distance. High energy photoelectrons are shown to be a powerful tool for molecular imaging. Our study paves the way for future time resolved correlation imaging at FELs and laser based X-ray sources ; This work was funded by the Deutsche Forschungsgemeinschaft, the BMBF, the European Research Council under the European Union Seventh Framework Programme (FP7/2007-2013)/ERC grant agreement 290853 XCHEM, the MINECO projects FIS2013-42002-R and FIS2016-77889-R, and the European COST Action XLIC CM1204. All calculations were performed at the CCC-UAM and Mare Nostrum Supercomputer Centers. We are grateful to the staff of PETRA III for excellent support during the beam time. K.M. and M.M. would like to thank the DFG for support via SFB925/A3. A.K. and V.S. thank the Wilhelm und Else Heraeus-Foundation for support. J.L. would like to thank the DFG for support. S.K. acknowledges support from the European Cluster of Advanced Laser Light Sources (EUCALL) project which has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 654220. T.W. was supported by the U.S. Department of Energy Basic Energy Sciences under Contract No. DE-AC02-05CH11231. A.P. acknowledges a Ramón y Cajal contract from the Ministerio de Economa y Competitividad

L'agriculture française contribue pour près d'un cinquième aux émissions de gaz à effet de serre (GES). En même temps, elle représente un potentiel de stockage du carbone. L'ADEME et les ministères chargés de l'Agriculture et de l'Environnement ont sollicité l'INRA pour réaliser une étude de l'agriculture française pour déterminer et analyser une dizaine d'actions portant sur des pratiques agricoles et susceptibles de favoriser le stockage de carbone par l'agriculture ou de réduire ses émissions de GES.

L'agriculture française contribue pour près d'un cinquième aux émissions de gaz à effet de serre (GES). En même temps, elle représente un potentiel de stockage du carbone. L'ADEME et les ministères chargés de l'Agriculture et de l'Environnement ont sollicité l'INRA pour réaliser une étude de l'agriculture française pour déterminer et analyser une dizaine d'actions portant sur des pratiques agricoles et susceptibles de favoriser le stockage de carbone par l'agriculture ou de réduire ses émissions de GES.

International audience ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871501. ARTIFICIAL INTELLIGENCE AT THE EDGE. NEUROMORPHIC COMPUTING ONN Architecture for 5 neurons neurons (oscillators) Synapse Array with 2D MoS 2 memristors MIT VO 2 oscillators 2D MoS 2 memristor Materials Modeling (DFT) Density of States (DOS) Mobility Effective Mass Semiclassical Transport Modeling (TCAD) Compact Modeling (SPICE) Devices Characteristics Compact Models

International audience ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871501. ARTIFICIAL INTELLIGENCE AT THE EDGE. NEUROMORPHIC COMPUTING ONN Architecture for 5 neurons neurons (oscillators) Synapse Array with 2D MoS 2 memristors MIT VO 2 oscillators 2D MoS 2 memristor Materials Modeling (DFT) Density of States (DOS) Mobility Effective Mass Semiclassical Transport Modeling (TCAD) Compact Modeling (SPICE) Devices Characteristics Compact Models

International audience ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871501. ARTIFICIAL INTELLIGENCE AT THE EDGE. NEUROMORPHIC COMPUTING ONN Architecture for 5 neurons neurons (oscillators) Synapse Array with 2D MoS 2 memristors MIT VO 2 oscillators 2D MoS 2 memristor Materials Modeling (DFT) Density of States (DOS) Mobility Effective Mass Semiclassical Transport Modeling (TCAD) Compact Modeling (SPICE) Devices Characteristics Compact Models

International audience ; This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 871501. ARTIFICIAL INTELLIGENCE AT THE EDGE. NEUROMORPHIC COMPUTING ONN Architecture for 5 neurons neurons (oscillators) Synapse Array with 2D MoS 2 memristors MIT VO 2 oscillators 2D MoS 2 memristor Materials Modeling (DFT) Density of States (DOS) Mobility Effective Mass Semiclassical Transport Modeling (TCAD) Compact Modeling (SPICE) Devices Characteristics Compact Models

Depuis le milieu des années 2000, l'élevage fait l'objet de vifs débats en raison de ses impacts sur le climat et l'environnement, accentués par la hausse de la consommation mondiale en viandes et produits laitiers. Les impacts et services issus des élevages sont ici étudiés à l'échelle de l'Europe, en examinant leurs effets sur les marchés, l'emploi et le travail, la consommation d'intrants, l'environnement et le climat, ainsi que les enjeux sociaux et culturels associés à l'élevage. Puis les interactions entre ces volets ou " bouquets de services " sont analysées simultanément. Ces bouquets sont déclinés dans une typologie et cartographiés selon six classes de territoires d'élevage européens à partir de deux critères : la densité en animaux et la part de prairies permanentes dans le paysage agricole. Cet ouvrage reprend les enseignements d'une expertise scientifique collective conduite par 26 experts de disciplines scientifiques complémentaires et coordonnée par l'Inra, réalisée à la demande conjointe des ministères en charge de l'Environnement et de l'Agriculture, et de l'Ademe.

L'initiative "4 pour mille : les sols pour la sécurité alimentaire et le climat" propose d'augmenter chaque année d'un quatre millième le stock de carbone présent dans tous les sols du monde. À la demande de l'Ademe et du ministère de l'Agriculture et de l'Alimentation, l'Inra (devenu aujourd'hui INRAE) a conduit une étude, centrée sur la France métropolitaine, visant à estimer le potentiel de stockage de carbone des sols agricoles et forestiers et, in fine, à mesurer la contribution potentielle de ce levier à l'objectif de réduction des émissions nettes de gaz à effet de serre. Diverses pratiques candidates (cultures intermédiaires, apport de nouvelles ressources organiques, gestion des prairies, agroforesterie…) ont été évaluées. Les résultats obtenus ont montré une forte variabilité du stockage additionnel de carbone. L'étude a également permis d'estimer le coût supplémentaire, pour les agriculteurs, de mise en oeuvre de ces pratiques de stockage, puis une allocation de l'effort de stockage entre les pratiques et les régions a été effectuée. Ces données permettront aux différents acteurs concernés de faire les meilleurs choix pour stocker davantage de carbone dans les sols. Cet ouvrage s'adresse aux décideurs chargés de l'élaboration des politiques publiques climatiques dans le domaine agricole, aux responsables territoriaux, aux aménageurs, aux ingénieurs et techniciens, aux agriculteurs et à l'ensemble des citoyens intéressés par la problématique de l'agriculture et du changement climatique.

Questions. Compensatory dynamics are described as one of the main mechanisms that increase community stability, e.g., where decreases of some species on a year‐to‐year basis are offset by an increase in others. Deviations from perfect synchrony between species (asynchrony) have therefore been advocated as an important mechanism underlying biodiversity effects on stability. However, it is unclear to what extent existing measures of synchrony actually capture the signal of year‐to‐year species fluctuations in the presence of long‐term directional trends in both species abundance and composition (species directional trends hereafter). Such directional trends may lead to a misinterpretation of indices commonly used to reflect year‐to‐year synchrony. Methods. An approach based on three‐term local quadrat variance (T3) which assesses population variability in a three‐year moving window, was used to overcome species directional trend effects. This "detrending" approach was applied to common indices of synchrony across a worldwide collection of 77 temporal plant community datasets comprising almost 7,800 individual plots sampled for at least six years. Plots included were either maintained under constant "control" conditions over time or were subjected to different management or disturbance treatments. Results. Accounting for directional trends increased the detection of year‐to‐year synchronous patterns in all synchrony indices considered. Specifically, synchrony values increased significantly in ~40% of the datasets with the T3 detrending approach while in ~10% synchrony decreased. For the 38 studies with both control and manipulated conditions, the increase in synchrony values was stronger for longer time series, particularly following experimental manipulation. Conclusions. Species' long‐term directional trends can affect synchrony and stability measures potentially masking the ecological mechanism causing year‐to‐year fluctuations. As such, previous studies on community stability might have overemphasised the role of compensatory dynamics in real‐world ecosystems, and particularly in manipulative conditions, when not considering the possible overriding effects of long‐term directional trends. ; We thank multiple entities for the financial support necessary to obtain the different databases: the U.S. National Science Foundation under grant numbers DEB‐8114302, DEB‐8811884, DEB‐9411972, DEB‐0080382, DEB‐0620652, DEB‐1234162, DEB‐9707477, DEB‐0316402, DEB‐08‐16453, and DEB‐12‐56034, DEB‐0618210, the Nutrient Network (http://www.nutnet.org) experiment from the National Science Foundation Research Coordination Network (NSF‐DEB‐1042132), the New Zealand National Vegetation Survey Databank, the Spanish MINECO (Project CGL2014‐53789‐R), the Madrid Regional Government (Projects REMEDINAL‐3 and REMEDINAL‐TE), the European Research Council Synergy grant 610028 (IMBALANCE‐P), the Institute on the Environment (DG‐0001‐13), the SOERE‐ACBB financed through French National Agency for Research (ANAEE‐F, ANR‐11‐INBS‐0001), the Estonian Research Council (IUT 20‐28, IUT 20‐29), Czech Science Foundation (GAČR 17‐05506S and 19‐28491X), the European Regional Development Fund (Centre of Excellence EcolChange), the German Federal Environmental Foundation (DBU) for a grant to the NABU Hamburg (management experiment Calamagrostis epigejos), and the German Federal Ministry of Education and Research within the framework of the project BIOTA Southern Africa (promotion numbers 01LC0024, 01LC0024A and 01LC0624A2), Task 159 of SASSCAL (promotion number 01LG1201) and the Scottish Government's Rural and Environmental Science and Analytical Services division. Acknowledgement Data owned by NERC© Database Right/Copyright NERC. Further support was provided by the Jornada Basin Long‐Term Ecological Research (LTER) project, Cedar Creek Ecosystem Science Reserve and the University of Minnesota. We also thank the Lawes Agricultural Trust and Rothamsted Research for data from the e‐RA database. The Rothamsted Long‐term Experiments National Capability (LTE‐NCG) is supported by the UK Biotechnology and Biological Sciences Research Council (Grant BBS/E/C/000J0300) and the Lawes Agricultural Trust. ; Peer reviewed

Analysing temporal patterns in plant communities is extremely important to quantify the extent and the consequences of ecological changes, especially considering the current biodiversity crisis. Long-term data collected through the regular sampling of permanent plots represent the most accurate resource to study ecological succession, analyse the stability of a community over time and understand the mechanisms driving vegetation change. We hereby present the LOng-Term Vegetation Sampling (LOTVS) initiative, a global collection of vegetation time-series derived from the regular monitoring of plant species in permanent plots. With 79 data sets from five continents and 7,789 vegetation time-series monitored for at least 6 years and mostly on an annual basis, LOTVS possibly represents the largest collection of temporally fine-grained vegetation time-series derived from permanent plots and made accessible to the research community. As such, it has an outstanding potential to support innovative research in the fields of vegetation science, plant ecology and temporal ecology. ; The authors acknowledge institutional support as follows. Nicola J. Day: Te Apārangi Royal Society of New Zealand (Rutherford Postdoctoral Fellowship). Jiří Danihelka: Czech Science Foundation (project no. 19-28491X) and Czech Academy of Sciences (project no. RVO 67985939). Francesco de Bello: Spanish Plan Nacional de I+D+i (project PGC2018-099027-B-I00). Eric Garnier: La Fage INRA experimental station. Tomáš Herben: GAČR grant 20-02901S. Anke Jentsch: German Federal Ministry of Education and Research (grant 031B0516C - SUSALPS) and Oberfrankenstiftung (grant OFS FP00237). Norbert Juergens: German Federal Ministry of Education and Research (grant 01LG1201N - SASSCAL ABC). Frédérique Louault and Katja Klumpp: AnaEE-France (ANR-11-INBS-0001). Robin J. Pakeman: Strategic Research Programme of the Scottish Government's Rural and Environment Science and Analytical Services Division. Meelis Pärtel: Estonian Research Council (PRG609) and European Regional Development Fund (Centre of Excellence EcolChange). Josep Peñuelas: Spanish Government (grant PID2019-110521GB-I00), Fundación Ramon Areces (grant ELEMENTAL-CLIMATE), Catalan Government (grant SGR 2017-1005), and European Research Council (Synergy grant ERC-SyG-2013-610028, IMBALANCE-P). Ute Schmiedel: German Federal Ministry of Education and Research (Promotion numbers 01LC0024, 01LC0024A, 01LC0624A2, 01LG1201A, 01LG1201N). Hana Skálová: GAČR grant 20-02901S. Karsten Wesche: International Institute Zittau, Technische Universität Dresden. Susan K. Wiser: New Zealand Ministry for Business, Innovation and Employment's Strategic Science Investment Fund. Ben A. Woodcock: NERC and BBSRC (NE/N018125/1 LTS-M ASSIST - Achieving Sustainable Agricultural Systems). Enrique Valencia: Program for attracting and retaining talent of Comunidad de Madrid (no. 2017-T2/AMB-5406) and Community of Madrid and Rey Juan Carlos University (Young Researchers R&D Project. Ref. M2165 – INTRANESTI). Truman P. Young: National Science Foundation (LTREB DEB 19-31224). ; Peer reviewed