dataesr/scientific-paragraphs-categorization huggingface dataset:
EDGAR is using data from FAOSTAT thus, for the majority of countries (figures found at DOI: link as described in Appendix D), we observe similar estimates between these two sources (e. g. France, Italy, Poland). A reason for discrepancies may be attributed to the different way the data sources allocate their emissions to sub-370 activities (Table  3 ). For example, CAPRI N2OSYNsynthetic fertilizer application -does not have a correspondent in GAINS activities. The leaching, ammonia and atmospheric deposition N2O emissions in CAPRI do not have a clear correspondent sub-activity in UNFCCC, while in FAOSTAT those N2O emissions are reported under other categories: manure left on pasture and manure applied to soils. For N2O emissions, uncertainties are mostly in the range of 100 % or more. The countries reporting the 375 highest N2O uncertainties are Buglaria, Denmark, Estonia and Cyprus, which, for manure management and agricultural soils, count as much as 200 % to 300 %.
Grasslands tend to be concentrated in regions with less favorable conditions for growing crops or where forests have been cut down. In 2015 just above one fifth of the EU28's (21 %) was covered by grassland. Some of these are found in northern Europe (for example, most of Finland and Sweden), while others are in the far south, for example, the south of Spain. Ireland was the only EU Member State with more than half of its land area as grassland in 2015 (56 %) of the total area. At the other end of the scale, grassland 545 covered less than 6 % of the land in Finland and Sweden (EUROSTAT: https://ec. europa. eu/eurostat/statisticsexplained/index. php/Land_cover_statistics). From Figure  11  we see that in the EU28 croplands and grasslands are CO2 sources to the atmosphere in the UNFCCC and FAOSTAT databases.
For other cattle (3. A. 4), only three countries (Romania, France and Portugal) are using higher tiers, with all the others combining different methods. CH4 and N2O emissions from manure management (3. B. 1 and 3. B. 2) it is even more mixed, with Germany, Denmark, Finland, France, Croatia and Romania using exclusively https://doi. org/10. 5194/essd-2019-199 Open   , 2018b). All these differences in calculating emissions produce evidently higher uncertainties in the results. Access Earth System Science Data Discussions Preprint. For the UNFCCC, throughout the variability of the analyzed national GHG inventories, it turned out that N2O emissions from manure management and direct and indirect emissions together with CH4 emissions from rice 700 cultivation have the largest uncertainties. When we aggregated UNFCCC uncertainties at country level (using the methodology described in Appendix C), we also noticed the fact that not all countries report sub-sectoral uncertainties (e.
Conclusions 780 There are many independent estimates of GHG emissions, but adequate understanding of their differences (either qualitatively or quantitatively) is lacking. For CH4 and N2O the main differences between countries reports and models are the use of tiers and methodologies (for both emissions and uncertainty calculation). Countries reporting to UNFCCC use an inconsistent mix of tiers depending on the animal type and activity following the approach described 785 by the 2006 IPCC Guidelines, while models run with more accurate data being able to disaggregate better the activities. One detected similarity between all sources is the use of EFs, as almost all sources make use of the IPCC defaults. AD is as well somehow shared, coming mostly from the MS, FAOSTAT, Eurostat or UNFCCC, with the flow between these four sources not totally understood.
At EU28 level, countries are generally doing well in reporting their total GHG emissions but there is large 790 room for improvement mainly when looking at differences between UNFCCC Tier use and models (e. g. for CH4 from agriculture 10-20 % difference). We stress the need of looking as well on the LULUCF CO2 estimates, where a quantification of differences between net emission estimates (inventories, models etc. ) caused by inconsistencies in methodology and/or Tier application in the EU28 is not yet available. More data is needed to account for and reduce these differences. Narrowing down the analysis to sensitive parameters (e. g. AD) which may trigger the differences 795 (e. g. Appendix A, Table  A1 ) also requires more information on uncertainties. As previously discussed, it is of great importance to better distinguish between direct and indirect effects on land use emissions especially for the purpose of reconciling land-related emissions from global datasets and NGHGI.
800 It is also important to recognize that just because independent inventories agree well for a sector, does not necessarily mean that the estimate is better in the sense that it is closer to real emissions. The reason for agreement across inventories may simply be that the different inventories used the same methodology and data sources. In recent years there has been increased attention to the quantitative differences between land-based CO2 emissions, with a much better understanding between inventories and estimates from the scientific community. However, there remain 805 gaps in our understanding of differences between FAOSTAT and UNFCCC and between different DGVMs and bookkeeping models. One explanation can be linked to the fact that models use different methods to estimate emissions/removal then countries use in reporting to UNFCCC.
The current atmospheric GHG network is coordinated by the Integrated Carbon Observation System (ICOS) infrastructure at the European level. Within the future UNFCCC reporting framework, we argue that countries should 810 use, whenever possible, global inversions to provide additional constraints for the verification and reconciliation purposes. Within the VERIFY project framework, we will use in a following study, inversions based on better, higher resolution, transport models to assimilate the precise ICOS GHG concentration data complemented by satellite retrievals of column CO2, CH4 and N2O concentrations. The main challenge for the inversion community remains the separation of natural and anthropogenic part of the total emission column. For the moment, global inverse models are 815 widely used to estimate emissions of CH4 and N2O at global/continental scale, using mainly high-accuracy surface measurements at remote stations (e.
Access Earth System Science Data Discussions Preprint. The Revised UNFCCC Reporting Guidelines for Greenhouse Gas Inventories of Parties in Annex I to the Convention (UNFCCC, 2013), here after UNFCC Reporting Guidelines, define what and how to report GHG emission by source and removals by sinks in order to comply with requirements. However, these requirements only commit 915 developed country parties listed in the Annex I of the UNFCCC. This is explained by the fact that in the 1990s, when the Convention's and the Kyoto Protocol's reporting system was developed and adopted, there was a clear division of the regional distribution of GHG emissions. In industrialized countries, most GHG emissions were released, while in developing and emerging countries, emissions were low  (Berger et al. , 2016).
EDGAR The Emissions Database for Global Atmospheric Research (EDGAR) with versions EDGARv4. The assumptions introduced by, e. g. Bond et al, (2004) ,  Bergamaschi et al, (2015 ), Olivier et al. , (2002)  hold: -Uncertainties of different source categories are uncorrelated; -Subsectors for CH4 and N2O are fully correlated, thus the uncertainty of the sum is the sum of the uncertainties;.
ensemble and bookkeeping models(BLUE and H&N). In this section we compare these global model results with data from FAOSTAT and UNFCCC. There is significant uncertainty in both the underlying datasets of land use changes, the coverage of different land use change practices, and the calculation of carbon fluxes (see below). In 570 addition, marked differences in definitions must also be considered to compare independent estimates. Bookkeeping models give net emissions from land use change including immediate emissions during land conversion, legacy emissions from slash and soil carbon after land use change, regrowth of secondary forest after abandonment, and emissions from harvested wood products when they decay. DGVMs estimate net land use emission as the difference between a run with and a run without land use change, and their estimate includes the loss of atmospheric sink capacity, 575 https://doi.
Data availability 1145 All raw data files reported in this work which were used for calculations and figures are available for public download at http://doi. org/10. 5281/zenodo. 3460311 (Petrescu et al. , 2019). The data we submitted is reachable with one click (without the need for entering login and password), and a second click to download the data, consistent with the two-click access principle for data published in ESSD  (Carlson and Oda, 2018). The data and the DOI number is subject to future updates and it refers only to this version of the manuscript.
The similarities between the two models lie in the fact that both EFISCEN https://doi. org/10. 5194/essd-2019-199 Discussions Preprint. Discussion started: 4 November 2019 c Author(s) 2019. CC BY 4. 0 License. and CBM models use national forest inventory (NFI) data as main source of input to describe the current structure and composition of European forest. numbers include af/reforestation, while the others datasets not, resulting in an even bigger sink if af/reforestation is removed. Since the UNFCCC GHGI uncertainty of CO2 estimates for FL at EU28 level, computed with the 95 % 500 confidence interval method (IPCC, 2006) is 19. 6 % (the uncertainty increases to 25-50 % when analyzed at country level (EU NIR 2014)), and given the fact that both CBM and EFISCEN use different methodologies to estimate emissions/removals.
2007. (the manual). Total sensitivity is caused by esp. young forest growth, width of volume classes, age of felling and few more. Scenario uncertainty comes on top of this when projecting in future. FAOSTAT The FAOSTAT emissions database is The FAOSTAT emissions database is N/A computed following Tier 1 IPCC 2006 computed following Tier 1 IPCC 2006 Guidelines for National GHG Inventories Guidelines for National GHG Inventories (http://www. ipcc- (http://www. ipcc- nggip. iges. or. jp/public/2006gl/index. html). nggip. iges. or. jp/public/2006gl/index. html). DGVMs Can be considered as Tier 3 although the Can be considered as Tier 3. Cover only Model specific (TRENDYv6) models have never been used for any LCC emissions for CO2 reporting Bookkeeping Simple assumptions about C-stock Transient change in C-stocks following a There is no models (H&N densities (per biome or per biome/country) given transition (time dependent EF after uncertainty estimate and BLUE) based on literature an land use transition) per model.
Supplementary Information: https://iopscience. iop. org/1748-November 2019 c Author(s) 2019. CC BY 4. 0 License. Open Access Earth System Science Data EDGAR Emission Database for Global Atmospheric Research NDCs -Nationally Determined Contributions EEA European Environmental Agency NEP Net Ecosystem production 1200 EF Emission factor NFI National forest inventory EFISCEN European Forest Information SCENario Model NGHGI National Greenhous Gas Inventory 1170 ESA CCI European Space Agency Climate Change Initiative NIRs National Inventory Reports ETS Emissions Trading System NPP Net Primary Production EU28 European Union NUTS2 Nomenclature of territorial units for statistics 1205 EUROSTAT European Statistical Office PA Paris Agreement FADN Farm Accountancy Data Network PBL Planbureau voor de Leefomgeving (Netherlands Environmental Assessment Agency) 1175 FAOSTAT Food and Agriculture Organization of the United Nations OECD The Organisation for Economic Co-operation and Development FL Forest Land TACCC Transparency, Accuracy, Completeness, Comparability, Consistency 9326/12/9/094013/media/ERL_12_9_094013_suppdata.
notatum AGO proteins (PnAGOs) were aligned with O. sativa Japonica and A. thaliana AGO proteins and a phylogenetic tree was inferred after careful visual inspection and post-processing multiple alignments for masking gaps. As shown in Fig. 1 , PnA-GOs distributed into the three main classes of plant AGOs. Homologs of AGO involved in meiosis (MEL1) and in reproductive cell fate specification (AGO4, AGO9) were identified. Similarly, we next identified P. notatum transcript homologs to most of the genes encoding proteins involved in key phases of PTGS and RdDM  [44]  including: Pol IV-dependent small RNA biogenesis, Pol V-dependent de novo DNA methylation, chromatin alterations and DNA methylation (n = 67 hits) (for details see Additional file 1, section 1). Note that, despite using less stringent criteria for filtering Blastx results, we identified no homolog for RDR2, a key member of RNA biogenesis in the RdDM pathway.
Furthermore, differential expression analysis of the detected orthologs between sexual and apomictic florets in the reference transcriptome showed significant variation for four transcripts out of all identified, including: decreased expression for a transcript predicted to encode for OsAGO1D (isotig18136; FDR = 0. 00091 and FC > 3. 24) and increased expression for transcripts predicted to encode Os HISTONE DEACETYLASE 705 (isotig09390 and isotig09392; FDR < 1e-03; FC > 30) and OsDCL1 (iso-tig27521; FDR = 0. 0048, FC > 1. 8) in the apomictic library respect to the sexual one (Additional file 1, section 2). In conclusion, our analysis strongly suggests that small RNA-directed pathways are likely functional in P. notatum floral tissues and show differential modulation in apomictic and sexual pathways.
In particular, this reference was assembled using 454/Roche FLX+ sequences. It contains 48,842 isogroups (genes), out of which 18% had at least two isotigs (alleles) and was validated for ~100 sequences previously characterized. Based on these results, this reference likely represents a comprehensive resource that reflects transcripts variation and occurrence. Therefore, we assume that its use greatly helped to contend the constraints resulting from the polyploid and highly heterozygous nature of Q4117 and C4-4x during mapping and subsequent sRNA representation comparisons. Moreover, since both genotypes used in the analysis are tetraploid, the polyploidy status should not interfere with results. Only 21-23% of the reads could be mapped using the Bowtie aligner, indicating that a large fraction of the sRNAs derived from sequences with low expression in flowers or corresponded to non-silencing small RNAs (Table  2 ).
The average numbers of mapped small RNA reads on apomictic and sexual libraries were equivalent (216,543. 66 ± 53,362. 90 and 207,796. 00 ± 18,421. 28, respectively; t = 0. 831, paired samples) and clustering analysis revealed high similarities between replicates (Fig. 2 b ). Out of 67,617 reference transcripts (assembled into 48,769 genes), 12,942 (19. 14%) were targeted by small RNA sequences (n ≥ 5. 0 considering all libraries) and thus selected for further analyses. The number of reads mapped in this set of transcripts for each library is provided (Additional file 3, section 1) together with the list of the top 1000 transcripts showing the highest read coverage (Additional file 3, section 2). Within the most represented targets, clustering revealed differences in the number of mapped  reads occurring in both directions (i.
e. a significant higher number of sRNA reads in the apomictic or the sexual libraries) (Fig. 2 c ). Many genes involved in reproduction have been identified in the Paspalum reference transcriptome. Next, we focused in determining to which extent small-RNA target genes belong to this particular group. To achieve this, we first categorized O. sativa orthologs of annotated small RNA target genes (4560 unique genes) using the plant structure development stage ontologies provided Plant Ontology database  [45]. This revealed numerous genes with ontologies associated with male and female reproduction (e. g. androecium development stage, gametophyte development stage, inflorescence development stage, mature plant embryo stage, plant embryo development stage, pollen mother cell meiosis, reproductive shoot system development stage, reproductive shoot system development stage, and sporophyte reproductive stage) (Additional file 3, section 3).
Next, edgeR was used to compare the number of sRNA reads between sexual and apomictic libraries that map over all Paspalum floral transcripts. Counts and edgeR scores are detailed in Additional file 3, section 5, and pictured in Fig. 2 d. Using an FDR cut-off value of 0. 05, we identified 1525 transcripts showing significant differences. These transcripts split evenly into two groups, including those that match sRNAs overrepresented in apomictic samples (753) or in sexual samples (772). However, if we consider only those transcripts displaying larger differences (> four-fold change), a higher number match sRNAs overrepresented in apomictic samples compared to sexual ones (480 vs 384; p = 0. 0014, Pearson's chi-squared test). Interestingly, among all differential transcripts, we detected several homologs of O.
italic and a minimum in rice (Table  3 ), most of them producing DicerCall scores (i. e. mature small-RNAs lengths) inconsistent with Dicer endoribonuclease activity (Fig. 3 ). Interestingly, ShortStack analysis revealed no evidence for the occurrence of phased small RNAs (PhaseScore < 10 for 21 and 24-nt reads) across the four references used. Computational analysis for hairpin configuration detected 8, 35, 42 and 42 putative miRNA precursors over P. notatum, O. sativa, S. italica and S. bicolor references, respectively (Table  3 ). Candidate miRNA genes mainly generated mature miRNA sequences of 21-22 nt (Fig. 3 ). The corresponding transcripts of P. notatum and the genomic locations in reference genomes of all precursors, as well as, the length and the sequence of the major mature miRNAs detected are shown in Additional file 4, section 1.
Prediction of miRNA target genes and putative miRNAdependent pathways for installing apomixis With the aim of identifying putative miRNAs targets, we computationally predicted binding sites for the 40 mature miRNAs reported in Table  4  in the Paspalum transcriptome reference using TargetFinder. Using a cut off value of 4. 0, 376 hits were identified representing 283 different transcripts, out of which 224 were unique hits (i. e. target of a single miRNA), while 40, 11, 5, and 3 were found 2, 3, 4 and 5 times, respectively (Additional file 7, section 1). Most multiple-hits transcripts (n = 48) were targeted by miRNAs from a single family, but a few ones were targeted by members of two families (miRNA156/miR529; miR159/miR319; miR160/miR166; miR396/Pn_miR1, and; Pn_miR1/Pn_miR2). We identified targets for all predicted miRNA families but miR158.
The number of targets varied greatly across families (from 1 to 78 for miR156) and accessions (from 1 to 19 for Pn_miR2) (Fig. 5 a ). BLASTX against rice reference peptides produced an annotation for 275 (73. 3%) of the 374 transcripts, representing 195 unique genes (Additional file 7, section 1). Target annotations were consistent with previous reports for target-miRNA regulatory modules, as illustrated by the occurrence of homologies to transcription factors of the SQUAMOSA  Within the CC group (73 hits; Fig. 5b ), genes associated with cell parts (39. 9%) and organelles (32. 7%) were the most abundant (Fig. 5 c ). Likewise, within the BP class (103 hits) cellular processes and metabolic processes (37. 5 and 32. 4%, respectively) were also highly represented (Fig. 5 d ). Next, to identify putative major molecular routes differentially modulated by miRNAs during sexual and apomictic developments, we mined the Paspalum Sequences were detected by using four references: Pn: Paspalum transcriptome; Os: Oryza sativa Japonica genome; Sb: Sorghum bicolor genome; Si: Setaria italica genome *Accession number was obtained by performing BLASTn analysis (SSEARCH option) of mature sequences on miRBase  [48]  Fig.
4  Differential expression of predicted miRNA genes. Panels summarize coverage comparative analysis for predicted miRNA clusters ordered by miRNA families and members (x-axis). The references used were indicated on the right side of the panel series. Each dot indicates significance (red: FDR < 0. 05; blue: FDR > 0. 05). The log2 fold change (y-axis) was obtained from the edgeR analysis. miRNA members are indicated by using miRbase  [48]  annotations for conserved mature miRNAs. Magenta-boxed panels display miRNA families with at least one predicted cluster with more than four-fold expression change. Grey boxes: no cluster predicted reference transcriptome for candidate target transcripts already assessed for differential expression between apomixis and sexuality  [24]. Out of 374 predicted target transcripts, 207 could be analyzed (n reads > 10 in at least one sample) and a significant difference (FDR < 0.
RNA samples were then quantified by fluorescence (Qubit 2. 0 Fluorometer, Thermo Fisher Scientific) and RNA integrity was determined using an Agilent Bioanalyzer 2100. Small RNA libraries were prepared following the Illumina TruSeq Small RNA sample preparation guide and 50-bp single-end sequencing was performed using the Illumina MiSeq system by Polo d'Innovazione di Genomica, Genetica e Biologia (Polo GGB), Siena, Italy. Raw reads were trimmed for adapters and low sequence quality using Cutadapt v1. 3  [73] , FastQC v. 0. 11. 5  [74]  and multiQC  [75]. Sequences were finally filtered for 18-26 nt length and for Phred quality score > 25. The sRNA datasets were deposited in the NCBI Sequence Read Archive (SRA) repository under accession number SRP099144.
Paspalum transcripts annotation and AGO proteins phylogenetic analysis We re-annotated the Paspalum transcriptome reference by comparing transcripts with the Oryza sativa Japonica reference peptides (genome assembly IRGPS-1. 0) provided by the Gramene database  [76]. For global annotation, we retained Blastx best alignments with an e-value < 0. 05 and an identity of at least 50%, resulting in 33,071 annotated isogroups/genes (out of 51,168). More stringent criteria were used for searching genes with annotations related to small RNA-directed pathways (e-value <1e -05 , ID > 50%, and alignment length > 200). For AGO proteins phylogenetic analysis, we used the MUSCLE alignment and the Neighbor-joining options of Geneious®10. 0. 9. Phylogenetic tree was inferred after careful visual inspection and postprocessing multiple alignments for masking gaps and using bootstrap values (1000 replicates).
A. thaliana and O. sativa Japonica sequences were recovered from the Gramene database  [76]  and P. notatum protein sequences were predicted from selected transcripts. Small RNA reads alignments and representation over the P. notatum transcriptome Small RNA reads were aligned onto the 454/Roche FLX+ P. notatum reference floral transcriptome generated from Q4117 and C4-4x genotypes (referred to as Paspalum reference transcriptome)  [24]. Mapping was performed using the Bowtie aligner  [77]  with the following parameters: no mismatch allowed (-n 0), 19 high quality bases required to initiate the alignment (-l 19) and reporting the best alignments only ('best' option). Read counting for each reference transcript was performed with the feature counts module of Subread  [78]  using Q = 10 and no strand selection.