dataesr/scientific-paragraphs-categorization huggingface dataset:
Similar results are reported in a study by  Oulehle et al. (2010)  who estimated total deposition of around 10 kg S ha À1 a À1 in the Carpathian Mountains in Ukraine and Romania in 2008. The correlation between the model and grid-average throughfall measurements is quite good with r ¼ 0. 734, n ¼ 195, but the model generally overestimates total deposition relative to the ICP Forest throughfall measurements (Fig. 3. 18b). The throughfall data have been compared with the high resolution EMEP regional model  (Simpson et al. , 2006)  which shows better agreement than the HTAP ensemble-mean model results shown here. As well, the EMEP coarse resolution hemispheric model in our HTAP model ensemble compared better with the ICP throughfall data than the HTAP ensemble-mean (data not shown). An analysis suggests that the better comparability of the EMEP models is due to higher estimates of wet deposition in Europe by the EMEP model relative to the HTAP ensemble-mean.
Further, the authors showed that the uncertainties in the inferential dry deposition estimates could be reduced by on-site recording of vegetation parameters like leaf area index and canopy height. To validate and improve these models, it is necessary to compare the results to direct long-term micrometeorological flux measurements over different vegetation types, but few of these datasets are available  (Flechard et al. , 2011). Data from a national study on the German/Czech border yielded an average inferential dry deposition of 6. 1 kg N ha À1 a À1 for the period 2002e2004  (Zimmermann et al. , 2006). This is consistent with the modeling results in Fig. 4. 7a, showing a value of 4e10 kg N ha À1 a À1. These results indicate the importance of dry deposition in large parts of Europe and illustrate the challenge in estimating dry deposition.
e. , 17. 1 cm in the first period and 44. 0 cm in the second. Although not included in our database because of the urban nature of the site, N wet deposition of 21. 09 kg N ha À1 a À1 was measured at Serpong, Indonesia. It is mentioned here to show that N wet deposition in areas with large anthropogenic impacts is more than an order of magnitude higher than in areas such as Tanah Rata or Bukit Kototabang, which are largely free from anthropogenic influences. Measured and modeled N wet deposition (Fig. 4. 5c) are generally in good agreement, with measured deposition at Tanah Rata and Bukit Kototabang-closely matching with the model-predicted range of 2e4 kg N ha À1 a À1. The modeled wet deposition is 1e 2 kg N ha À1 a À1 both for Wagga Wagga and Coffs Harbour. The flux for Wagga Wagga is in this range and that at Coffs Harbour is only slightly higher at 3.
Since phosphorus is not among the constituents routinely reported by aerosol monitoring programs, measurements are not widespread nor generally of long duration. Drawing on observations from the limited land-based measurements and oceanic cruises, they produced a map showing the global distribution of airborne phosphorus concentrations. Complementing these observations was a map produced from a chemical transport and deposition model that simulated the global phosphorus distribution in the year 2000. As input for this model, atmospheric phosphorus emissions were derived from ancillary models and from estimates of phosphorus from surficial dust, combustion, sea salt, volcanoes, and primary biogenic sources. Very little data are available on how phosphorus varies with aerosol size, adding to the uncertainty of the estimated deposition.
For instance, in the western tropical North Atlantic (w5e12 N) significant differences appear between the datasets of  Chen (2004 , as given in Mahowald et al. , 2008)  and  Baker et al. (2010) , while these two datasets are in much better agreement further to the north off North Africa (w25e 35 N). The differences between the datasets in the western Atlantic likely reflect the strong seasonality in Saharan dust transport. Much of the Chen (  2004 ) dataset was acquired during boreal winter and summer periods of high dust transport (e. g. Basart et al. , 2009) , while the  Baker et al. (2010)  data in this region were obtained under autumn low dust transport conditions. As a whole, the  Baker et al. (2010)  dataset gives the impression of rather uniform DIP concentrations across the Atlantic basin, but as a longterm representation this is probably misleading.
All of these data were obtained during periods of weak mineral dust transport, so that it does not show the strong spatial heterogeneity associated with dust-derived phosphorus transport. Where cruise data were acquired during different seasons in a given region (e. g. , the western tropical North Atlantic and central Pacific) strong variations in aerosol DIP concentrations were observed. Baker et al. (2010)  classified their aerosol samples by source region. Median DIP concentrations varied from w0. 4 ng m À3 for samples that had spent at least 5 days over the South Atlantic Ocean to w1. 6 ng m À3 for samples that contained Saharan dust. They also reported that DIP concentrations of samples originating in southern Africa were slightly higher in samples influenced by biomass burning (median 1.
1, we used modeled gridded annual average deposition velocities from  Mahowald et al. (2008)  to calculate the fluxes shown in  Figs. 8. 6 and 8. 7. The size distribution of the phosphorus aerosols was assumed to be the same as that of mineral dust. This assumption may not be valid over tropical forests where the concentrations of large primary biogenic particles are likely to be high and where dust concentrations are relatively low. We emphasize that the uncertainty in deposition velocities and the resultant dry deposition is considerable. In order to maximize the information derived from the dataset available, we represent the calculated dry deposition of phosphorus in units of mg P ha À1 day À1 in Fig. 8. 6 and 8. 7. This recognizes that the daily aerosol sampling conducted during ship-based studies is unlikely to be representative of annual average conditions.
Conclusions The major objective of this assessment was to provide the science and policy communities with the best available information on the composition of precipitation and atmospheric deposition of major compounds globally and regionally. This was accomplished by presenting quality assured measurement-based data from monitoring networks and long-term research studies around the world, complemented by model results generated by 21 Eulerian global models that participated in the Coordinated Model Studies Activities of the Task Force on Hemispheric Transport of Air Pollution (TF HTAP). This approach resulted in the generation of maps that are useful for understanding concentrations and deposition of wet-and dry-deposited substances. The authors assessed the key information necessary to understand the spatial and temporal characteristics of precipitation chemistry and wet and dry deposition.
Recognizing that different research communities would want different products from the paper, the authors aimed to address the needs of many science communities and to provide the basic building blocks to assist other environmental experts in their future research programs. A major product of this assessment is a quality assured, regionally-representative set of global deposition data that is available for downloading from the web page of the World Meteorological Organization World Data Centre for Precipitation Chemistry (WDCPC) at http://wdcpc. org/. This data set was used to create global wet deposition maps for two time periods, 2000e 2002 and 2005e2007, and was complemented by maps of the TF HTAP ensemble-mean modeling results for the year 2001. A subset of the maps generated have been included in the main manuscript; many additional maps and tables appear in the Addendum in this Special Issue.
Acknowledgments The authors gratefully acknowledge the many networks, institutions, agencies and organizations from within and outside the World Meteorological Organization system that contributed precipitation chemistry and wet and dry deposition data to this assessment including: the  World Meteorological Organization Global Atmosphere Watch network and its associated national and regional networks, IDAF (International Global Atmospheric Chemistry/Deposition of Biogeochemically Important Trace Species/Africa ) supported by  INSU/CNRS "Institut National des Sciences de l'Univers/Centre National de Recherche Scientifique" and African universities , the  Acid Deposition Monitoring Network in East Asia (EANET) ,  WMO/GAW Network of the India Meteorological Department , the  Atmospheric Brown Cloud network , the  Integrated Programme on Acidification of Chinese Terrestrial Systems (IMPACTS) , the  Russian Federation Precipitation Chemistry Composition Network, Composition of Asian Deposition network, Taiwan Acid Deposition Network, Australia Regional GAW Precipitation Chemistry Network ,  WMO/GAW Cape Grim Baseline Air Pollution Station , the  UNECE Programmes  and their associated partners:  European Monitoring  and  Evaluation Programme (EMEP)  and the  International Co-operative Programme on Assessment and Monitoring of Air Pollution Effects on Forests (ICP Forests , special thanks to Oliver Granke), the  Norwegian Climate and Pollution agency , the  Swedish Environmental Research Institute , the  U.
Literature search: inclusion criteria and search strategy The literature search strategy and meta-analysis protocols used were based on those previously published by Brandt et al. (27)  , and flow diagrams of the protocols used are shown in Figs. 1  and 2. Relevant publications were identified through an initial search of the literature with Web of Knowledge using the following search terms: (1) organic* or ecologic* or biodynamic*; (2) conventional* or integrated; (3) names of ninety-eight relevant crops and foods (see online supplementary Table  S1  for a full list). Publications in all languages, published in peer-reviewed journals, and reporting data on both desirable and undesirable composition parameters were considered relevant for inclusion in the meta-analyses. The search was restricted to the period between January 1992 (the year when legally binding organic farming regulations were first introduced in the European Union) and December 2011 (the year when the project ended) and provided 17 333 references.
An additional 208 publications (published between 1977 and 2011) were found by (1) studying lists of references or (2) directly contacting the authors of the published papers and reviews identified in the initial literature search. The abstracts of all publications were then examined to determine whether they contained original data obtained by comparing composition parameters in organic and conventional plant foods. This led to the identification of 448 suitable publications. Of these, 105 papers were subsequently rejected, because reading of the full papers indicated that they did not report suitable data sets or contained the same data as other studies. Data sets were deemed suitable if the mean concentrations of at least one mineral, macronutrient, secondary metabolite or NO  2  3 /NO 2 2 or the frequency of occurrence of pesticide residues in organic and conventional crops or crop-based foods were reported.
Only four non-peer-reviewed papers with suitable data sets were identified but subsequently rejected, as the small number minimised any potential bias  (28)  from using peer review as a 'quality' selection criterion. As a result, 343 peer-reviewed publications reporting crop composition data were selected for data extraction, of which 156 references fulfilled the criteria for inclusion in the standard weighted meta-analysis and 343 fulfilled the criteria for inclusion in the standard unweighted meta-analysis. This represents a significantly greater evidence base than the three previous systematic reviews/meta-analyses of comparative crop composition data  (19 -21). All publications included in these previous reviews (including studies published before 1992) were also used in the standard weighted meta-analysis carried out in the present study, except for a small number of papers that were found to report the same data as other publications that had already been included.
Data were extracted from three types of comparative studies: (1) comparisons of matched farms (CF), farm surveys in which samples were collected from organic and conventional farms in the same country or region; (2) basket studies (BS), retail product surveys in which organic and conventional products were collected in retail outlets; (3) controlled field experiments (EX) in which samples were collected from experimental plots managed according to organic or conventional farming standards/protocols. Data from all the three types of studies were deemed relevant for the meta-analyses if the authors stated that (1) organic farms included in farm surveys were using organic farming methods, (2) organic products collected in retail surveys were labelled as organic, and (3) organic plots used in EX were managed according to organic farming standards.
Data and information extraction and validation Information and data were extracted from all the selected publications (see above) and compiled in a Microsoft Access database. A list of the information extracted from the publications and recorded in the database is given in Table  S4  (available online). Data reported as numerical values in the text or tables were copied directly into the database. Only data published in graphical form were enlarged, printed, measured (using a ruler) and then entered into the database as described previously  (20). Where data for multiple time points were reported, two approaches were used, depending on whether the analysed crop tissue was likely to be used as food/feed. For crops that are continuously harvested (e. g. tomato and cucumber), analytical data for mature/ripe products (e.
g. fruits) collected at multiple time points during the season were averaged before being used in the standard meta-analyses; if analytical data for immature/unripe products were reported, they were not included in the mean. For crops (e. g. grape and cereals) in which products (e. g. fruits and grain) are harvested/ analysed at different maturity stages, only analytical results for the mature product (that would have been used as food/ feed) were used. In both the standard weighted and standard unweighted analyses, composition data reported for different cultivars/varieties and/or years/growing seasons in the same publication were averaged before being used in the meta-analyses. Publications were assessed for eligibility and data were independently extracted from them by two reviewers. Data extracted by the two reviewers were then compared.
Discrepancies were detected for approximately 2 % of the data extracted, and in these cases, data extraction was repeated to correct mistakes. A list of the publications included in the meta-analyses is given in Table  S2  (available online). Study characteristics, summaries of the methods used for sensitivity analyses and ancillary information are given in Tables S2-S10 (available online). These include information on (1) the number of papers from different countries and publication years used in the meta-analyses (see online supplementary Figs. S1  and S2 ); (2) study type, location and crop/products assessed in different studies (see online supplementary Table  S3 ); (3) the type of material/data extracted from the papers (see online supplementary Table  S4 ); (4) data-handling methods/inclusion criteria and meta-analysis methods used in the sensitivity analyses (see online supplementary Table  S5 ); (5) composition parameters included in the meta-analyses (see online supplementary Table  S6 ); and (6) composition parameters for which metaanalyses were not possible (n , 3; see online supplementary Table  S7 ).
4  and online supplementary Figs. S5 -S41 ). In addition, the results of the standard unweighted analyses are shown in Figs. 3  and 4. Table  S12  (available online) summarises the results of the standard weighted and unweighted meta-analyses for all the composition parameters for which no analyses detected significant differences between organic and conventional products. MPD were calculated for all parameters for which significant effects were detected by the standard unweighted and/or weighted meta-analysis protocols. This was done to facilitate value judgements regarding the biological importance of the relative effect magnitudes. A detailed description of the     3. Results of the standard unweighted and weighted meta-analyses for antioxidant activity, plant secondary metabolites with antioxidant activity, macronutrients, nitrogen compounds and cadmium (data reported for all crops and crop-based foods included in the same analysis).
† Numerical values for MPD and standard errors are given in Table  S9  (available online). ‡ Ln ratio ¼ Ln(ORG/CONV £ 100 %). § Heterogeneity and the I 2 statistic. k Data reported for different compounds within the same chemical group were included in the same meta-analyses. { Outlying data points (where the MPD between ORG and CONV was more than fifty times greater than the mean value including the outliers) were removed. , MPD calculated using data included in the standard unweighted meta-analysis; , MPD calculated using data included in the standard weighted meta-analysis; , SMD. calculations is given in the 'Additional Methods Description' section in the online supplementary material. We also calculated MPD using only data pairs included in the weighted meta-analyses to estimate the impact of excluding data for which no measures of variance were reported on the magnitude of difference.
As the MPD can be expressed as '% higher' in conventional or organic crops, they provide estimates for the magnitude of composition differences that are easier to correlate with existing information on the potential health impacts of changing dietary intake levels for    S10  (available online). ‡ For parameters for which n # 3 for specific crops/products, results obtained in the weighted meta-analyses are not shown. § Ln ratio ¼ Ln(ORG/CONV £ 100 %). k Data reported for different compounds within the same chemical group were included in the same metaanalyses. { Outlying data points (where the MPD between ORG and CONV was more than fifty times greater than the mean value including the outliers) were removed. , MPD calculated using data included in the standard unweighted meta-analysis; , MPD calculated using data included in the standard weighted meta-analysis; , SMD.
Macronutrients, fibre and DM content Both unweighted and weighted meta-analyses detected significantly higher concentrations of total carbohydrates and significantly lower concentrations of proteins, amino acids and fibre in organic crops/crop-based compound foods (Fig. 3 ). The unweighted meta-analysis also detected significantly higher concentrations of reducing sugars and DM in organic crops (Fig. 4 ). The MPD were 25 (95 % CI 5, 45) % for total carbohydrates, 11 (95 % CI 2, 20) % for carbohydrates (where data reported for all individual phenolic acid compounds were included in the same analysis), 7 (95 % CI 4, 11) % for reducing sugars, 2 15 (95 % CI 2 27, 23) % for proteins, 211 (95 % CI 214, 2 8) % for amino acids, 2 (95 % CI 2 1, 6) % for DM and 28 (95 % CI 214, 2 2) % for fibre. Inclusion of data for which no measures of variance were reported in the calculation of MPD resulted in similar values for carbohydrates, proteins, DM and fibre; higher values for reducing sugars; and lower values for carbohydrates (total) and amino acids (see Fig.
This would have further reduced the number of data sets and sensitivity of meta-analyses and contributed to the lack of significant composition differences being detected. In the meta-analyses carried out in the present study, 'biodynamic-organic' data sets were treated as organic, as biodynamic standards comply with the legal European Union organic farming standards. Data from comparative field experiments were also included, as controlled experimental studies are less affected by confounding factors (e. g. contrasting soil and climatic and agronomic background conditions between farms that supplied organic and conventional samples) than farm and retail surveys. The reason for excluding field experiments carried out in the study of Dangour et al. (19)  is that in the field experiments the organic plots were not certified according to organic farming standards.
Overall publication bias was considered high when indicated by two or more methods, moderate when indicated by one method, and low when indicated by none of the methods. The overall quality of evidence was then assessed across domains as in standard GRADE appraisal. k Outlying data pairs (where the mean percentage difference between the organic and conventional food samples was over fifty times higher than the mean value including outliers) were removed. conventional agronomic practices/protocols and crop composition were included, as the crop management practices rather than the certification process were assumed to affect crop performance and composition. The finding of a four times higher frequency of occurrence of pesticide residues in conventional crops confirms the results of the study of Smith-Spangler et al.
The need for dietary intervention/cohort studies to identify health impacts A recent review by Smith-Spangler et al. (21)  has analysed the results of fourteen studies in which the effects of organic and conventional food (both crop and livestock product) consumption on clinical outcomes (e. g. allergic symptoms and Campylobacter infections) and health markers (e. g. serum lipid and vitamin concentrations) were studied. However, they concluded that the currently available data do not allow clear trends with respect to health markers and outcomes to be identified. Therefore, there is an urgent need for well-controlled human intervention and/or cohort studies to identify/quantify potential human health impacts of organic v. conventional food consumption. Diet composition may have an effect on the relative impact of switching from conventional to organic food consumption, and this should be considered in the design of such studies.
For example, the relative impact of switching from conventional to organic food consumption could be expected to be smaller for diets with high amounts of (poly)phenolic-rich foods. R. S. (an environmental modeller and data analyser) helped to design the literature search and database storage and helped to design and provided guidance for the meta-analyses used; G. B. S. (a research synthesis methodologist specialising in meta-analytical approaches) contributed to and provided advice on the additional analyses carried out in response to referees' recommendations; C. B. (an agronomist specialising on organic production systems) helped with the literature review (especially with respect to studies carried out in North and South America) and the preparation/review of the manuscript; B. B. (an agricultural microbiologist) contributed to the literature search, the critical review of the manuscript and the discussion related to the mechanisms for higher antioxidant concentrations in organic crops; E.