dataesr/scientific-paragraphs-categorization huggingface dataset:
(2005 ) et Herrero et al. (2013). Toutefois, lorsque l'on agrège ces quantités et qu'on les rapporte aux surfaces classées comme « pâtures et prairies permanente » dans la base de données FAOStat, on s'aperçoit que la productivité en herbe révélée de ces surfaces est très hétérogène dans l'espace. Outre le fait que cette productivité est très mal connue à une échelle globale, ceci suggère que la catégorie « pâtures et prairies permanentes » de FAOStat recouvre des types de pâture très différentes d'une région à l'autre : des zones désertiques jusqu'aux prairies les plus fertiles. Il y a là également une source de biais et d'incertitude dans les données initiales.
(2005) et Herrero et al. (2013). L'aggrégation des rations animales par système et par espèce, issues de ces travaux ne concorde pas toujours avec les quan-tités correspondantes fournies par 1400 1200 la base de données FAOStat. Et il est nécessaire de procéder à différents types d'ajustements et de rééchelon- nements pour harmoniser les diffé- rentes sources. Métropolisation_Ultrap Métropolisation_Animp Régionalisation_A Régionalisation_B Régimes sains_C Régimes sains_D Communautés_AE Communautés_EFF.
Remerciements Les recherches chez les caprins menant au développement des traitements lumineux sans mélatonine et des protocoles d'IA sous la marque communautaire FLOCK-REPROD® ont été cofinancées par l' Union Européenne  ( FP7 -Grant agreement n° 243520) et par la région Centre-Val de Loire  ( APR IR 2016 , projet MALEFIC). Un grand merci aux consortiums de ces deux projets.
We present the model structure, concepts, limitations, and evaluation at the scale of the Rhine catchment for the period 1850-2005 CE (Common Era). Model results are validated against independent estimates of gross and net soil and C erosion rates and the spatial variability of SOC stocks from high-resolution modeling studies and observational datasets. We show that despite local differences, the resulting soil and C erosion rates, as well as SOC stocks from CE-DYNAM, are comparable to high-resolution estimates and observations at subbasin level. We find that soil erosion mobilized around 66 ± 28 Tg (10 12 g) of C under changing climate and land use over the non-Alpine region of the Rhine catchment over the entire period, assuming that the erosion loop of the C cycle was nearly steady state by 1850.
(Transport from one grid cell to another can only follow the connectivity of floodplains. ) It should be noted that global soil databases do not identify floodplain soil as a separate soil class, although national soil databases might. Because we aim to present a carbon erosion model that should also be applicable for other similar catchments, we followed a two-step methodology to derive floodplains in the Rhine catchment. For this purpose, we used hydrological parameters and existing data on hillslopes and valleys. First, grid cells were identified that consisted entirely of floodplains. For this, we used the gridded global dataset of soil at 5 arcmin resolution, with intact regolith and sedimentary deposit thicknesses by  Pelletier et al. (2016)  (Table  1 ), and we identified lowlands and hillslopes based on soil thickness and depth to bedrock.
The lowlands were classified as grid cells that contain only floodplains and no hillslopes. Second, we calculated the floodplain area fraction (A fl ) of a grid cell i, which has both hillslopes and floodplains as a function of stream length and width based on the methodology developed by  Hoffmann et al. (2007)  for the Rhine: A fl (i) = L stream (i) × W stream (i). (2) Here, L stream is the stream length derived from the Hy-droSHEDS database  (Lehner and Grill, 2013)  (Table  1 ). W stream (i) = a × A b upstream (i) (3) Here, A upstream is the upstream catchment area, a is equal to 60. 8, and b is equal to 0. 3. The parameters a and b have been derived using the scaling behavior of floodplain width as estimated from measurements on the Rhine  (Hoffmann et al. , 2007). The sediment deposition on hillslopes (D hs ) and in floodplains (D fl ) is calculated as a function of the gross soil removal rates (E) according to  Naipal et al.
The rate by which sediment and SOC leave the floodplain of a grid cell to go to the floodplain of an adjacent grid cell is determined by the sediment residence time. The sediment residence time (τ ) is a function of the upstream contributing area (Flowacc): τ (i) = e Flowacc(i)-aτ bτ. ( 6 ) The study by  Hoffmann et al. (2008)  showed that the majority of floodplain sediments have a residence time that ranges between 0 and 2000 years, with a median of 50 years. The constants a τ and b τ are chosen in such a way that basin τ varies between the 5th and 95th percentiles of those observations, with a median for the whole catchment of 50 years. These constants are uniform for the whole basin, and need to be calibrated based on local data of sediment ages before CE-DYNAM can be applied to other catchments.
To derive a more realistic NPP for wheat and barley in the Rhine catchment, we used the long-term crop yield data obtained from a dataset on 120 000 yield observations over the 20th century in northeast French departments (NUTS3 administrative division)  (Schauberger et al. , 2018). According to the yield data assembled by  Schauberger et al. (2018) , yields in northeast France (covering part of the Rhine catchment) for these crops increased fourfold during the last century. Note that crop residues like straw constituted a larger fraction of the total biomass in 1850 than in 2005, but those residues were likely collected and used for animal feed and housing fuel. We did not account for this harvest of residue in the simulation of SOC.
The land use change bookkeeping model The land use change bookkeeping scheme includes the yearly changes in forest, grassland, and cropland areas in each grid cell as reconstructed by  Peng et al. (2017)  (Table  1 ). Peng et al. (2017)  derived historical changes in PFT fractions based on the LUHv2 land use dataset  (Hurtt et al. , 2011) , historical forest area data from  Houghton (2003) , and the presentday forest area from ESA CCI satellite land cover (European Space Agency, 2014). By using different transition rules and independent forest data to constrain the changes in crop and urban PFTs, they derived the most suitable historical PFT maps. When land use change takes place, the litter and SOC pools of all shrinking PFTs are summed and allocated proportionally to the expanding PFTs, maintaining the mass balance.
Validation methods and data We performed a detailed model validation of the sediment and the C parts of the model according to the following steps: (1) validation of soil erosion rates using observational and high-resolution model estimates for Germany and Europe, (2) validation of C erosion rates using high-resolution model estimates for Europe from  Lugato et al. (2018) , (3) validation of the spatial variability of hillslope and floodplain C storage using observational results from  Hoffmann et al. (2013a) , and (4) validation of SOC stocks using observational data from a global soil database and a European land use survey. The validation of the soil erosion module has been done before in the studies by  Naipal et al. (2015 Naipal et al. ( , 2016)). However, we do it again in this study due to different input datasets.
In addition, the validation includes soil erosion data from new global soil erosion studies such as  Borrelli et al. (2018)  and  Panagos et al. (2015). For the validation of gross soil erosion rates, we used the high-resolution model estimates of  Panagos et al. (2015) , who applied the RUSLE2015 model at a 100 m resolution at European scale for the year 2010. Similarly to the Adj. RUSLE, RUSLE2015 is also derived from the original RUSLE model. However, in contrast to our model, RUSLE2015 does include the erosion factors L and P. Furthermore, our model uses more coarsely resolved input datasets (Table  1 ), for which the equations for the R and S factors have been modified. Thus, even though both Adj. RUSLE and RUSLE2015 are derived from the same erosion model, the differences between the models are large, which justifies our model comparison.
The extensive validation of the Adj. RUSLE model in this study and previous studies  (Naipal et al. , 2015 (Naipal et al. , , 2016 (Naipal et al. , , 2018) )  shows that despite its coarse resolution, it is applicable at large spatial scales. Furthermore, we used independent high-resolution erosion estimates from the study by  Cerdan et al. (2010) , available at a 1 km resolution at European scale, which were based on an extensive database of measured erosion rates under natural rainfall in Europe. For the comparison, we aggregated the high-resolution model results of both datasets to the resolution of CE-DYNAM. We also used the potential soil erosion map of the Federal Institute for Geosciences and Natural Resources of Germany  (Bug et al. , 2014)  for comparison. This map presents the yearly-average soil erosion rates at a 250 m resolution on agricultural land derived from a USLE-based approach (Universal Soil Loss Equation), with some modifications to the erosion factors and input data.
In order to evaluate the ability of CE-DYNAM to reproduce this scaling behavior for SOC, we selected the grid cells that contained the points of observation of the study by  Hoffmann et al. (2013a)  and performed a regression of the basin area (defined as the upstream contributing area) and the SOC storage for floodplains and hillslopes separately. Comparing the absolute values of the sediment and SOC storages of each grid cell from  Hoffmann et al. (2013a)  was not possible due to the difference in the time period of the studies, where  Hoffmann et al. (2013a)  focused on the entire Holocene, while our study focused only on the period starting from 1850 CE. For the validation of the total SOC stocks, we used the Global Soil Dataset for Earth system modeling (GSDE)  (Shangguan et al. , 2014) , available at a spatial resolution of 1 km, and the Land Use/Land Cover Area Frame Survey (LUCAS)  (Palmieri et al.
(2018) , due to the coarse spatial resolution of our underlying C scheme derived from the OR-CHIDEE LSM. The decreased spread in our simulated values is also a result of the coarse resolution of our model. Accounting for erosion, deposition, and transport of SOC leads to a better representation of the simulated topsoil C stocks per land cover type when compared to SOC stocks of the LUCAS database (Fig. 6 ). The simulated SOC stocks of the top 20 cm of the soil profile fall within the quantile range of the LUCAS SOC stocks for cropland and forest (Fig. 6 ). Although the topsoil SOC stocks for grassland improved, a large uncertainty range remains. Furthermore, we find that in both the erosion and no-erosion simulations the SOC stocks for grassland are higher than for forest. This is also observed in the study by  Wiesmeier et al.
(2012) , where they found considerably higher SOC stocks for grassland with a median of 11. 8 kg C m -2 compared to forest based on the analysis of 1460 soil profiles in southern Germany. Furthermore, the comparison of the simulated total SOC stocks to those of the LUCAS and GSDE databases at subbasin level shows a good model performance with respect to the spatial variability in topsoil SOC stocks (Table  5 ). To validate the spatial variability of floodplain and hillslope SOC stocks separately, we used the scaling relationships found by  Hoffmann et al. (2013a) (Sect. 2. 12). We find a significantly larger exponent for the scaling relationship be- tween the simulated floodplain SOC storage and basin area compared to the simulated hillslope SOC storage when using the grid cells that contain the points of observation corresponding to the study by  Hoffmann et al.
, 2017; Fig. 7b ). This land Figure  6. Comparison of the total SOC stocks per land cover type between the simulation without erosion (red boxes with a "//" pattern), the simulation with erosion (black boxes with a "-" pattern), and the LUCAS data (green boxes without pattern fill). The red horizontal lines are the medians, the dashed vertical lines represent the range between the minimum and maximum, and the black dots are the outliers. cover data also shows that forest increased by 24 % over the period 1910-2005, mostly as a result of grassland-to-forest conversion. Cropland decreased by 6 % over the period 1920 Table  5. This table shows the results of the linear regression between the simulated total SOC stocks (Tg C yr -1 ) and those of the Global Soil Dataset for Earth system modeling (GSDE) and from the LUCAS database.
69 Pg of C for floodplains and 2. 29 Pg of C for hillslopes when these processes are lacking. We also find that floodplains have an overall higher C concentration (12 kg m -2 for a 2 m soil profile) compared to hillslopes (9 kg m -2 for a 2 m soil profile) at the end of the transient period (Fig. 9a ), which is in line with the findings by  Hoffmann et al. (2013a)  and what can be derived from global soil databases. This is a result of higher SOC concentrations in deeper soil layers of floodplains compared to hillslopes (Fig. 9a, b ), as is also shown in the study by  Hoffmann et al. (2013a). To be closer to the observational difference between floodplains and hillslopes, we would need to consider the period before the year 1850, extreme climate events, and a higher plant productivity in floodplains resulting from favorable soil nutrient and hydrological conditions.
Altogether, these results indicate that despite model uncertainties related to the relatively coarse spatial resolution and missing or simplified processes, CE-DYNAM represents an important step forwards to integrating soil erosion processes and sediment dynamics in Earth system models. The next step would be to improve CE-DYNAM with respect to riverine sediment and POC export fluxes and management practices. Code and data availability. The source code of CE-DYNAM is included as a Supplement to this paper. Model data can be accessed from the Zenodo repository under the following link: https://doi. org/10. 5281/zenodo. 2642452 (Naipal et al. , 2019). For the other datasets that are listed in Table  1 , it is encouraged to contact the first authors of the original references. Supplement. The supplement related to this article is available online at: https://doi.
org/10. 5194/gmd-13-1201-2020-supplement. Author contributions. VN built and implemented the model. YW provided the basic structure for the model and performed simulations with the original ORCHIDEE LSM. All authors contributed to the interpretation of the results and co-wrote the paper. Competing interests. The authors declare that they have no conflict of interest. Acknowledgements. Funding was provided by the Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA, CNRS, and UVSQ. Victoria Naipal, Ronny Lauerwald, and Philippe Ciais acknowledge support from the VERIFY project that received funding from the European Union's Horizon 2020 research and innovation program. Philippe Ciais also acknowledges support from the European Research Council Synergy project SyG-2013-610028 IMBALANCE-P and the ANR CLAND Convergence Institute.
Bertrand Guenet acknowledges support from the project ERANETMED2-72-209 ASSESS. We thank S. Peng for sharing the PFT maps. We also acknowledge the anonymous reviewers for their useful and constructive comments that helped to clarify this paper. Financial support. This research has been supported by the VER-IFY Project (grant no. 776810) Review statement. This paper was edited by Andrew Yool and reviewed by three anonymous referees.
The temperatures of the manifold, the ion source, and the transfer line were set to 150, 250 and 280 ºC, respectively. The analysis was executed with a filament-multiplier delay of 3 minutes and acquisition was performed with a mass/charge (m/z) ratio ranging from 45-550 m/z. Furthermore, to enhance the sensitivity and linearity of the MS detector, the electron multiplier voltage and the total target ion counts (TIC) were adjusted. Therefore, an auto-tuning procedure was accomplished and the TIC values were set to: 65 000 in full scan MS, 25 000 in AMD and 5000 in MS/MS. MS data analysis and control of the system were done by using Varian MS Workstation software (Version 6).
(b) The realization of efficient isolation and storage of the parent ion inside the ion trap relies on the excitation radiofrequency storage level applied (ESL). ESL was calculated by MS toolkit software and the "q" value was set to 0. 3 for all the compounds except for methomyl and methamidophos for which the value was set to 0. 4 as the ESL obtained with a value of 0. 3 was below the acceptable limit, (c) The optimization of collision-induced dissociation (CID) was done by assessing two excitation modes: non-resonant and resonant. Table  1  displays the extraction mode and the two examined transitions (in bold) for each individual pesticide. The one with the highest intensity (underlined) was used for quantification and the second one, for confirmation.
e. surface foliaire par gramme de feuille). Toutes les valeurs des traits ont été extraites de la base de données LEDA  (Kleyer et al. , 2008). L'entropie quadratique de Rao a été calculée avec la fonction FD du package FD de R et a été pondérée par la densité ou la biomasse. Plus l'entropie quadratique de Rao est élevée, plus il existe de différence entre les individus de la communauté sur la base des trois traits cités ci-dessus ; plus l'entropie quadratique de Rao est faible, plus les individus ont la même stratégie (hauteur, masse de graine, surface foliaire spécifique).
Remerciements Les auteurs tiennent à remercier i) tous  les membres du domaine expérimental INRAE d'Epoisses à Bretenière qui ont participé à mettre en oeuvre l'essai au champ (dont Philippe Chamoy, Benjamin Pouilly et Pascal Farcy ), ii) toutes les personnes qui ont participé au travail de terrain (dont  Eric Vieren ,  Dominique Meunier ,  Gilles Louviot ,  Maïwen Abgrall ,  Justine Degenmann  et la team Grenier avec Auxence.
Depuis les années 2000, l'USDA-APHIS procède à l'agrément de tous les bateaux, containers et sondes d'enregistrement utilisés pour importer des produits frais sur le territoire américain (base de données CPHST 8 ). L'autorité phytosanitaire s'assure ainsi que le traitement au froid des produits frais importés est conforme aux conditions décrites par le référentiel national. Bien que l'agrément en question soit un instrument de contrôle des seuls flux entrants (importations), nous avançons l'idée que ces bateaux, containers ou sondes agrées fonctionnent tels des vecteurs de diffusion de la norme états-unienne dans le monde. En ce sens et conformément aux éléments rappelés dans le cadre théorique, s'il est vrai qu'une entreprise de fret maritime a d'autant plus d'intérêt à se conformer à la norme états-unienne, que le nombre de pays importateurs adoptant la même norme augmente, il est aussi vrai que l'augmentation du nombre de sociétés de fret maritime agréées par l'USDA-APHIS, accroît parallèlement la probabilité qu'un pays importateur adopte le standard américain.
Analyses statistiques Les valeurs moyennes des paramètres étudiés par régime alimentaire testé ont été soumises à une analyse Anova à un facteur. La comparaison multiple des moyennes a été effectuée au seuil de signification de 5 % avec le test de Student-Newman-Keuls si les différences révélées par l'Anova étaient significatives. Tous les calculs ont été effectués avec le logiciel Stata 12  (StataCorp, 2011). ■ RESULTATS.
Remerciements Les auteurs remercient  Prof. P. Legrand  et  Dr D. Catheline   du laboratoire de Biochimie-nutrition humaine d'Agrocampus Ouest de Rennes  pour leurs contributions. Déclaration des contributions des auteurs NDVK, MK et NEA ont participé à la conception et à la planification de l'étude ; SBMC, YDA et NDVK ont recueilli les données et effectué les analyses statistiques ; CEMAK et NDVK ont rédigé et révisé les différentes versions du manuscrit.
2 Pour y contribuer, nous avions choisi d'utiliser les ressources de la cartographie, non pas pour "illustrer" les contributions faites par les membres de l'équipe pluridisciplinaire qui a rédigé, à l'issue de quatre mois de travail, un rapport collectif, mais pour explorer grâce à elle les aspects territoriaux des questions qu'ils ont abordées sous d'autres angles. Ces configurations spatiales (disparités, concentrations, contrastes) étaient déjà contenues dans de longues listes de chiffres -des bases de données sur les questions environnementales, agronomiques des grands organismes internationaux (FAO, OMS), mais peu visibles jusqu'à ce que des cartes les révèlent, sous la forme synoptique qui est l'un de leurs principaux avantages: elles ont donc à la fois une fonction maïeutique -y compris pour leurs auteurs -et de communication -nous l'espérons -pour les lecteurs de ce texte.
RESUMOS O consumo de carne, especialmente de carne bovina, é frequentemente questionado por seus efeitos sobre a saúde humana, particularmente devido à crescente prevalência da obesidade. Para ajudar a ver se são coincidências ou co-incidências, o artigo utiliza recursos de mapeamento, mapas foram produzidos pelas bases de dados FAO e WHO FaoStat, usando o software Cartes et Données (Articque) La consommation de viande, en particulier de viande bovine, est souvent mise en cause pour ses effets sur la santé humaine, notamment en raison de la croissante prévalence de l'obésité à laquelle elle est associée par certains. Pour aider à voir s'il s'agit de coincidences ou de coincidences, l'article utilise les ressources de la cartographie, les cartes ont été produites à partir des base de données FaoStat de la FAO et de l'OMS, en utilisant le logiciel Cartes et Données (Articque) puis un traitement graphique complémentaire sur Adobe Illustrator Consumption of meat, especially beef, is often questioned for its effects on human health, particularly because of the growing prevalence of obesity with which it is associated by some.
Remerciements Nous dédions cet article à Muriel Tichit qui nous a quittés en avril 2019. Directrice de recherche à l' INRA au sein du département Sciences pour l'Action et le Développement , docteure en zootechnie, elle a conduit des travaux pionniers sur le rôle et les services rendus par la biodiversité en agriculture. Muriel, merci pour l'héritage scientifique que tu nous laisses sur cette thématique et qui a ouvert de grandes perspectives de recherche.
Remerciements. Ce travail a été réalisé dans le cadre d'une activité de recherche menée par le Centre de coopération internationale en recherche agronomique pour le développement (CIRAD) avec l'appui du Bureau d'analyses macroéconomiques de l'Institut sénégalais de recherches agricoles (ISRA-BAME), que nous remercions. Nous remercions également le comité de pilotage, constitué du Conseil national de concertation et de coopération des ruraux (CNCR), de l'Initiative prospective agricole et rurale (IPAR) et d'Environnement et développement du Tiers-Monde-protection naturelle (ENDA PRONAT), qui soutient la démarche, ainsi que les experts qui ont co-élaboré les scénarios et qui s'impliquent activement dans la diffusion des résultats  (Camara et al. , 2018). Nous remercions aussi chaleureusement Jérémy Bourgoin pour son soutien et son implication durant l'organisation des ateliers.