dataesr/scientific-paragraphs-categorization huggingface dataset:
This wide ranging selection of N-related compound transporters identified as DE very closely matched those previously reported in the ECM fungi Paxillus involutus  [120]  and Laccaria bicolor  [121]. A broad suite of proteases was also DE including four endopeptidases (including subtilisin and bleomycin), two Zn-dependent exopeptidases, three carboxypeptidases (glutamate, serine and zinc), four metallopeptidases, a methionine aminopeptidase, two putative aminopeptidase isoforms and two distinct aspartic peptidase A1 (Table  2 ). Secreted suites of proteases are thought to be similar between Basidiomycota saprotrophs and EMC  [90]. However, the extracellular protein degradation pathways identified here distinctly match those expressed in the EMC P. involutus  [120]. One of the markers identified from Laccaria biocolor but recognised across ECM are Ras/Ras-like proteins  [85, 122, 123] , small GTPases involved in cargo sorting in coated vesicules  [124]  (vesicular turnover is thought to be high in ECM due nutrient and signal exchange with hosts  [125] ) that have been shown to increase in transcript abundance after successful establishment of symbiosis  [126].
a b Fig. 6  Bacterial contigs, total and differentially expressed (DE) transcript origin. Krona graphs  [47]  represent a total annotation of bacterial transcripts (including non-DE) and b annotation of DE bacterial transcripts. The proportion of each taxonomic grouping is defined by the number of unique transcripts, whereas the colour represents the relative abundance (transcripts per million tpm) of transcripts in each taxon. A full contig list including expression data, functional description (if available), gene ontology terms (if available) and secondary annotation (if available) is provided in Additional file 4. A list of bacterial DE transcripts (including protein coding sequences within polycistronic contigs annotated with transdecoder is provided in Additional file 10. Interactive versions of these Krona graphs available at: https://github.
The most convincing in this regard were genes involved in interaction with chitin (fungal cell wall) and the wellknown fungal exudate oxalate, used for habitat manipulation through pH lowering and increasing availability of Fig. 7  A selection of differentially expressed bacterial putative operons in higher abundance in roots of contaminated trees (and discussed in the text). Bacterial contigs were first identified within the assembly as best annotated with a single bacterial protein. To find multiple potential coding regions within bacterial polycistronic sequences, we used TransDecoder software (https://transdecoder. github. io/) [3]  with default parameters. A final hand annotation step was included to remove a minor number of overlapping uncharacterised ORFs. Precedence of transcriptional unit structure (putative operons) was verified in all cases against the database of prokaryotic operons (DOOR  [48] ) unless otherwise stated.
g. , high C : N ratio). Overall, the developed model captures the microbial mechanisms mediating litter stoichiometry and soil mineral N effects on litter decomposition and SOM formation. Due to the simple and generalizable structure of our model, it can be incorporated into existing land surface models for further long-term and large spatial-scale applications. Code and data availability. The CENTURY-based model used here is programmed in MATLAB language. The source code is available online (https://github. com/hchzhang/CENYUTY_CUE/ tree/v1. 0 from 8 July 2018). All the data used in this study can be obtained from published literature. Specific references to these data can be found in Sect. 2. 5. Appendix A    7, 15, 22, 28, 35, 42, 49, 67 and 80, and N min  was measured on days 3, 7, 17, 28 and 80.
Acknowledgements. Haicheng Zhang,  Daniel S. Goll ,  Philippe Ciais  and  Yuanyuan Huang  are funded by the  IMBALANCE-P  project of the  European Research Council  ( ERC-2013-SyG-610028 ). Stefano Manzoni  acknowledges the support of the  Swedish Research Council Vetenskapsrådet  (grants  2016-04146  and  2016-06313 ) and  Formas  (grant  2015-468 ). Edited by: David Lawrence Reviewed by: two anonymous referees.
In rainfed crop production, root zone plant-available water holding capacity (RZ-PAWHC) of the soil has a large influence on crop growth and the yield response to management inputs such as improved seeds and fertilisers. However, data are lacking for this parameter in sub-Saharan Africa (SSA). This study produced the first spatially explicit, coherent and complete maps of the rootable depth and RZ-PAWHC of soil in SSA. We compiled georeferenced data from 28,000 soil profiles from SSA, which were used as input for digital soil mapping (DSM) techniques to produce soil property maps of SSA. Based on these soil properties, we developed and parameterised (pedotransfer) functions, rules and criteria to evaluate soil water retention at field capacity and wilting point, the soil fine earth fraction from coarse fragments content and, for maize, the soil rootability (relative to threshold values) and rootable depth.
Depth of soil to bedrock reduces the rootable soil volume by 2500 km 3 , aluminium toxicity by 600 km 3 , porosity by 120 km 3 and alkalinity by 20 km 3. The accuracy of the map of rootable depth and thus of RZ-PAWHC could not be validated quantitatively due to absent data on rootability and rootable depth but is limited by the accuracy of the primary soil property maps. The methodological framework is robust and has been operationalised such that the maps can easily be updated as additional data become available.
The SFEF determines the net volume of soil, per depth interval, that can retain soil moisture and that crop roots can effectively exploit. The third component is the total depth interval from which the crop can extract water, which is the rootable soil depth or root zone depth (RZD). This study derives maps of the RZ-PAWHC for maize which has a genetically defined potential root zone depth, attained near anthesis, between 100 and 170 cm (van  Keulen and Wolf, 1986). In this study, a maximum potential root zone depth of 150 cm is used. There are three main ways to map each of the three components defining RZ-PAWHC. The first is to collect sufficient direct observations of the three soil properties, and use these primary soil profile data for producing interpolated maps, either representing individual soil profile layers or the soil profile as a whole.
The third way is to first create interpolated soil property maps, using DSM and primary soil profile data which are available in sufficient quantities and of sufficient coherence, and then use these interpolated coherent maps as input for (pedotransfer) functions, rules and criteria to calculate derived, inferred, maps of the targeted secondary soil properties (first interpolate, then calculate). For each of the three ways, the results for different depth intervals for water retention and the soil fine earth fraction can be aggregated into a single value over the rootable soil depth to produce the RZ-PAWHC map. Because the soil profile data available for this study were not complete for all required variables, and the soil depths sampled were not consistent and often did not include soil layers below 50 cm depth, this third approach was used in this study.
Soil profiles data Soil profiles data used for mapping and validation, and for the development and testing of pedotransfer functions and rules to produce derived data and maps, came from two soil profile datasets generated by the AfSIS project. First, the Africa Soil Profiles database (AfSP) which is a compilation of georeferenced and standardised legacy soil profile data for SSA  (Leenaars et al. , 2014a)  and is available at www. isric. org/projects/africa-soil-profiles-database-afsp. The AfSP version 1. 2 consists of soil data taken at 18500 profile point locations which are described and sampled on average at 4. 1 ( ± 1. 6) depth intervals to an average soil depth of 125 ( ± 65) cm. The second soil dataset was collected more recently from 60 sentinel sites of 10 × 10 km (AfSS) and is available at afsisdb.
qed. ai with data for 9600 point locations sampled at the 0-20 and 20-50 cm depth intervals. Ten percent of the AfSS data is the result of direct measurements and the other 90% is inferred from spectroscopic data  (Sila et al. , 2014). Adding to these two datasets were data on depth of soil to an iron pan (3660 virtual profiles) as interpreted from legacy soil maps  (Boulet and Leprun, 1969)  and georeferenced from polygon centroids. Compiling soil datasets from different sources enhances the data availability but also causes some degree of heterogeneity  (Leenaars et al. , 2014b; Hendriks et al. , 2016). This can cause incompatibilities in producing soil maps or estimating pedotransfer functions but also adds value in other ways. The datasets show overlap in terms of recorded soil properties, such as particle size fractions, pH, electric conductivity, exchangeable cations and the contents of organic carbon, nitrogen and available phosphorus, but the field and laboratory procedures used to assess these properties differ.
These differences required careful querying of the recorded procedures to compile the data under a common standard. Besides overlap, the datasets also show important differences in terms of recorded soil properties, for details see  Leenaars et al. (2015)  or  Hengl et al. (2015b). Measured and inferred data on the contents of extractable elements, including micro-nutrients, were predominantly available from the AfSS dataset while measured data on bulk density, cation exchange capacity, water retention, coarse fragments content and, though few, root presence as well as data on depth and drainage of the soil profile were available only from the AfSP database. Added value was created by combining the recent AfSS data, explaining short distance variability of some soil properties at shallow depth, with the generally older AfSP data, explaining large distance variability of, both similar and other, soil properties at larger depth.
Soil property maps AfSoilGrids250m  (Hengl et al. , 2015b)  was used for this study. Af-SoilGrids250m is a coherent collection of gridded soil property maps of SSA which were produced in the context of the AfSIS project, available at www. isric. org/projects/soil-property-maps-africa-250-m-resolution. The maps were created in 3D from soil profiles data and maps of explanatory variables ("covariates"), including depth covariates, by using machine learning (random forests) as the DSM technique to model the trends and ordinary kriging to interpolate the residuals. For details of the function, implemented in the GSIF package for R (Global Soil Information Facilities), see  Hengl et al. (2015a). The maps have a spatial resolution of 250 m and report estimated soil property values at six standard depth intervals (i.
Added to the data for the top 50 cm on sodium, inferred from spectroscopic data with the inference being particularly poor for sodium, were data on exchangeable sodium from the AfSP database measured over full profile depths. Added covariates, expected to enhance model performance, were the soil atlas of Africa  (Jones et al. , 2013)  and maps of surficial lithology and land surface forms (USGS Rocky Mountain Geographic Science Center, 2009), groundwater table depth  (Fan et al. , 2013)  and the annual water balance which was calculated from annual precipitation (Africa Soil Information Service (AfSIS), 2013) and annual potential evapotranspiration  (Trabucco and Zomer, 2009). Maps of soil pH-H 2 O, sum of exchangeable bases and clay content were added as covariates to support the predictions of exchangeable sodium, for details see  Leenaars et al.
(2015). The resulting maps were validated according to the procedures described by  Hengl et al. (2015b)  using 5-fold cross-validation where each model was re-fitted five times using 80% of the profiles data and then applied to predict at the remaining 20% of profiles. Predictions were then compared with the put-aside observations (including observations inferred from spectroscopic data which will affect the cross-validation). Calculated were the Root Mean Squared Error (RMSE) and the amount of variation explained by the model, derived as ∑ % = 100 * [1 -(SSE/SST)], where SSE is the sum of squared errors at the cross-validation points (i. e. RMSE 2 • n), and SST is the total sum of squares of the original observations. From these primary soil property maps, resampled from 250 m to 1 km, maps of the three components defining RZ-PAWHC were derived.
Gijsman et al. (2007)  define FC for coarse, medium and fine textured soils at respectively pF 2. 0, 2. 3, 2. 5 (i. e. , 100, 200, 300 cm). For the purpose of producing maps of PAWHC, it was decided not to define FC differently for different textures because the results in a 3D configuration (with textures varying across different positions and depth intervals), would become highly inconsistent as concluded from tests applied to the soil profiles data. Instead, each of the three definitions for FC has been applied, irrespective of texture, to calculate the corresponding PAWHC and the significance of the definition of FC on PAWHC was evaluated. Data on soil water retention, measured at various water potentials including FC and PWP, and also saturation, were available from the AfSP database for approximately 2500 soil profiles (8000 layers).
This amount was considered insufficient to support the production of directly interpolated maps of VMC and of PAWHC for SSA. Data on soil water retention as recorded in the AfSS dataset had been calculated from primary data using a pedotransfer function (PTF) based on  Brooks and Corey (1966). Instead we used a PTF specifically developed for tropical soils  (Hodnett and Tomasella, 2002)  which parameterises the van Genuchten (1980) equations and which was validated by  Wösten et al. (2013)  on the basis of the measured soil profile data from the first version of the AfSP database  (Leenaars, 2012). This PTF requires data on sand, silt and clay contents, organic carbon content, bulk density, cation exchange capacity and pH-H 2 O, with the latter two included as proxies to account for the mineralogy (kaolinite) of highly leached tropical soils.
Mapping the soil fine earth volume as fraction of the soil whole earth volume (SFEF) The volume of soil fine earth (particle size < 2 mm) is a fraction of the volume of the soil whole earth excluding the volume of coarse fragments. Maps of the soil fine earth volume were derived for each of the six standard depth intervals from the maps of the volumetric coarse fragments content (v%) deducted from 100%. The maps of coarse fragments content were produced by DSM using data from > 40,000 soil layers of approximately 10,000 soil profiles. Note that the majority of these data were derived from descriptive class values as collected from field observations and consequently these data are not very precise and neither can be the interpolated maps (which are validated as previously described). 2. 5.
This assumption is justified as diffusivity, in soil nearly as dry as wilting point, is generally so high that small gradients in water content suffice to transport water to the -few fineroots at required rate  (de Willigen and van Noordwijk, 1987). Rootability and rootable depth are not directly reflected by any soil property which can be observed during soil field studies if not by obvious properties such as a laterite pan or by actual root density and depth itself. From the AfSP database, observed data on presence or absence of roots in about 2500 soil profiles (8500 layers) are available. Rooted depth, not rootable depth, was recorded for some 4000 profiles. These data represent momentary observations of roots of a wide variety of vegetation types (not of maize at anthesis specifically) and were considered too heterogeneous to produce interpolated maps from using DSM.
Instead, rootability was derived and mapped from a number of relevant soil factors which could be robustly parameterised and inferred from mapped or map-able soil properties and which are soil-intrinsic and thus not easily altered through management or dynamically varying conditions (thus excluding factors such as penetration resistance). Two types of soil factors were defined based on these considerations, including factors to evaluate the individual soil layers separately (adequacy of porosity, volume, textural configuration, cementation, acidity, alkalinity, sodicity, salinity, toxicity and morphology) and factors to evaluate the soil profile as a whole (depth of aerated soil and depth to bedrock). Rules to infer rootability were established and parameterised purely from literature sources due to the absence of data needed to newly develop and calibrate such rules.
Instead we defined an absolute increase from one interval to another, of the content of either clay (f. Clay) or sand (f. Sand), as indicative for the abruptness of textural change and set a mild, root permissive, threshold value which is valid over an assumed minimal distance of 15 cm. 4. Cementation (induration) of the soil is restrictive to root elongation when soil pores are filled by minerals that accumulate, either relatively or absolutely, and then precipitate and harden upon drying. Oxides of iron, aluminium and silica may cause induration in the form of a (petro-) plinthic, gibbsic or duric horizon. Associated data, required for mapping, were not available from the soil profiles datasets and rules were therefore not developed. Excessive contents in the soil of carbonates and sulphates, most commonly associated with calcium or magnesium, also causes soil to cement and the associated data were available.
(2003) ,  Sys et al. (1993)  and were based on the exchangeable sodium content and the exchangeable sodium percentage (ESP) relative to CEC. 9. Toxicity is commonly induced by very high acidity or alkalinity which leads to increased contents (ppm) of aluminium, iron, manganese, zinc, copper, boron, sulphur and other elements (including micro-nutrients). Because we did not have data for all of these elements from a majority of the profiles in the soil datasets, beyond a depth of 50 cm, rules were developed only related to exchangeable aluminium (cmolc/kg), which is assumed equal to exchangeable acidity at pH-H 2 O below 5. 5, and the exchangeable aluminium percentage relative to CEC, based on  Sanchez et al. (2003) ,  Landon (1991) ,  Brenes and Pearson (1973)  and  Hazelton and Murphy (2007).
(2017). The map was produced and cross-validated, using the same procedures as described in  Hengl et al. (2015b) , with a maximum depth on the map fixed at 175 cm. 3. Mapping the depth of aerated soil. Soil rootability is limited by oxygen shortage or poor aeration in the soil profile  (Bengough et al. , 2005; de Willigen and van Noordwijk, 1987). The depth of aeration is determined by the rate of water being drained from the soil and by the associated depth and duration, during rainy periods, of the soil being wet or saturated. We derived this depth of aeration from the drainage class, a soil profile property commonly reported during soil survey and recorded for 13,700 profiles from the AfSP database. The drainage classes range from very poorly drained (1) to excessively drained (7) as defined by Soil Survey Division  Staff (1993).
Methodological framework The workflow to produce the maps of RZ-PAWHC was implemented within the overarching methodological framework as provided by the Global Soil Information Facility which is accessible at cran. r-project. org/web/packages/GSIF/. The procedures used for this study are publicly available online as implemented in the GSIF package for R software  (Hengl et al. , 2015a). All soil data are publicly available according to the data policy of ISRIC as the World Data Centre for Soils, including input data (soil profiles data, except the AfSS dataset, and primary soil property maps), intermediate results (maps derived per depth interval) and final results (maps aggregated over rootable depth), all at 1 km resolution. These data are available at the ISRIC ftp-server (username = public; password = public) which is accessible from www.
The other soil factors evaluated for their adequacy to support rooting are not identified as root restrictive beyond the rootability threshold based on the dataset used in this assessment. RZD is mapped for SSA as shown in Fig. 9  and is on average 96 cm ( ± 49) with a range between 1 and 150 cm. The median is 20 cm deeper with a value of 115 cm. These figures are comparable with observed rooting depths as recorded in the AfSP database (n = 3970) with an average and median of 94 cm ( ± 45) and 100 cm, respectively, in a range between 0 and 400 cm. This comparison is only a casual observation because the AfSP data reported for rooting depth are not specific for maize and not at a given moment in the growing period. The RZD map was not validated quantitatively because RZD data were not available.
Discussion This work resulted in spatially explicit and quantitative maps of the rootable depth and the RZ-PAWHC of the sub-Saharan African soil. While the accuracy of these maps could not be validated due to absent data on rootability or rooting, and is likely limited by the accuracy of the primary soil property maps, the framework used to create the maps is robust and the maps, either for SSA or specific areas of interest, can be updated as additional soil data become available. The maps of RZD and RZ-PAWHC were derived from soil property maps which were produced by interpolation of soil profiles data using high resolution covariates. Value was added by combining legacy soil data (AfSP) with newly collected soil data (AfSS) to produce the soil property maps  (Leenaars et al. , 2014b; Hengl et al.
, 2015b Hengl et al. , , 2017a)). Particularly relevant are the differences in recorded soil properties and covered depths of soil. The AfSS dataset lacks depth to bedrock, drainage class and water retention, coarse fragments content and bulk density while other properties are recorded to a depth of 50 cm only. The AfSP dataset includes these properties and data are recorded for a depth to on average 125 cm, allowing to produce maps beyond 50 cm, which was needed for estimating and mapping RZD and RZ-PAWHC. The accuracy of the primary soil property maps, which was assessed by cross-validation, is for most properties satisfactory but needs to be further improved for drainage class and coarse fragments content and preferably also for electric conductivity and exchangeable sodium. In all cases, high-and low end values measured and recorded in the soil profiles datasets appear to be not sufficiently well captured and represented by the spatial predictions.
That work may benefit from the possibilities offered by the soil inference system as put forward by  McBratney et al. (2002)  and implemented by  Morris et al. (2012). The estimation of water retention, by applying a PTF to the profiles data and the soil property maps, appeared to be reasonably accurate, also when related to different texture classes. A slight overestimate was obtained, especially at higher tensions and PAWHC is therefore slightly underestimated generally. This can be corrected, for instance by adjusting the PTF parameters  (Hodnett and Tomasella, 2002). PAWHC proved very sensitive to the definition of FC and would had been approximately four-tenth higher with FC defined at pF 2. 0 and two-tenth lower with FC defined according to the GlobalSoilMap specifications  (Arrouays et al.