dataesr/scientific-paragraphs-categorization huggingface dataset:
Recently, global large datasets of second-generation bioenergy crop yields were compiled  (LeBauer et al. , 2018; Li et al. , 2018a). These datasets provide observation-based crop yields as well as coordinates, climate conditions (e. g. , temperature, precipitation), soil properties (e. g. , clay fraction) and management information, which can potentially be scaled to the globe using machine-learning algorithms. The derived global yield maps not only are valuable in estimating the global bioenergy production potentials but also can be used as input data to IAMs or to evaluate the performances of specific bioenergy crop models and DGVMs. Global yield maps could also help governments or companies in identifying the most promising areas for growing bioenergy crops. The objective of this study is thus to generate spatially explicit bioenergy yields with a machine-learning algorithm (random forest -Breiman, 2001 -trained from a global yield dataset -  Li et al.
Data The global yield dataset used here was compiled from 3963 published field measurements of five main lignocellulosic bioenergy crops: eucalypt, Miscanthus, switchgrass, poplar and willow  (Li et al. , 2018a). All yield records have coordinates (latitude and longitude) and crop types. Other information was also documented if it was reported in the original publications, including the mean annual temperature (MAT), mean annual precipitation (MAP), soil clay fraction (CF), planting information (e. g. , density, rotation length, harvest time, age) and management practices (irrigation and fertilization). Most yield data in this dataset correspond to the mean annual harvested biomass  (Li et al. , 2018a) , and only about one-third of the yield data were reported with age  (Li et al. , 2018a) , so age is not specifically used in this study, since we aimed to produce a spatial yield map for present day without temporal variability.
Only 36 %, 51 % and 14 % of the yield observations were reported together with MAT, MAP and CF, respectively  (Li et al. , 2018a). For the sites without such information, we used climate data from the CRUNCEP gridded dataset  (Viovy, 2011)  and CF data from the Harmonized World Soil Database (HWSD v1. 2;  Nachtergaele et al. , 2012 ; Table  1 ). For the sites with reported MAT and MAP in the yield dataset, we compared the reported values with MAT and MAP from CRUNCEP at the corresponding grid cell, and they are in a good agreement (Fig. S1 ). However the consistency is low between CF from HWSD and those reported in the site-level yield dataset (Fig. S1 ), probably due to the limited number of observations and strong heterogeneity of soil properties. In addition to MAT, MAP and CF, we also used other explanatory variables (Table  1 ): (1) shortwave radiation (SR) derived from the MODIS products  (Ryu et al.
, 2018) , (2) growing-season length (GSL) calculated using daily tem-perature from CRUNCEP  (Viovy, 2011) , (3) a soil water availability index (WAI) calculated from a soil water balance model using ERA-Interim reanalysis data as inputs (see  details in Tramontana et al. , 2016)  and (4) the growing-season summed normalized difference vegetation index (NDVI) from the MODIS NDVI dataset  (Park et al. , 2016). GSL is defined as the number of days between the first 5 successive days with daily average temperatures greater than 5 • C and the first 5 successive days with daily temperatures smaller than 5 • C in a year  (Frich et al. , 2002; Mueller et al. , 2015). For this calculation the years were set to start on 1 January in the Northern Hemisphere and on 1 July in the Southern Hemisphere. Because the spatial resolution of CRUNCEP and the WAI data is 0.
5 • ×0. 5 • (Table  1 ), we performed all analyses at this spatial resolution. Thus, for CF and SR datasets with higher resolutions (Table  1 ), the median values in each 0. 5 • × 0. 5 • grid cell were used as explanatory variables. Although NDVI covers the non-bioenergy vegetation type, we used the maximum value in each 0. 5 • × 0. 5 • grid cell from the original 0. 05 • × 0. 05 • resolution as a spatial proxy of the maximum yield potential that bioenergy crops can reach in the machinelearning upscaling model. The multi-year median values of MAT and MAP from CRUNCEP, SR, WAI, and NDVI between 2001 and 2010 for each grid cell were used to eliminate temporal variability.
Random forest Random forest (RF) has been used to analyze the relationships between independent variables and explanatory variables (e. g. , the relation between crop yields and climate by  Hoffman et al. , 2018)  and for upscaling local data (e. g. , global soil carbon loss due to human land use by  Sanderman et al. , 2017). RF is a machine-learning algorithm that combines a set of regression trees constructed from a random subset of the observations  (Breiman, 2001). Because each tree fitting in the forest uses a bootstrap sample of the training observations, the part of the dataset not used is called out of bag (OOB) and can be used to test the tree prediction. This helps RF to be fitted and validated when being trained, and thus no extra independent validation dataset is needed. Here we used Python scikit-learn module  (Pedregosa and Varoquaux, 2011)  to perform the RF regressions.
We set the number of trees in forest to be 1000 and the maximum depth of each tree (branch levels) to be 10. We verified that the coefficient of determination (R 2 ) between predictions and observations in the training data and the R 2 of OOB validation remain constant with a number of trees larger than 1000 or maximum depth larger than 10 (Fig. S2 ). The importance of a variable can also be calculated in the scikit-learn module based on how much each variable decreases the weighted impurity, i. e. , the sum of the number of splits across all trees that include this variable, weighted by the number of samples it splits  (Louppe et al. , 2013). Although the RF model is robust to correlated explanatory variables, the importance calculation could be biased if there is a strong collinearity between different variables.
Model training The workflow of RF training and predicting is shown in Fig. S3. The median yield, MAT, MAP and CF of all site observations for each crop type in each 0. 5 • × 0. 5 • grid cell from the global yield dataset were calculated to build the training set. That is, for example, several yield observations were reported in the same 0. 5 • × 0. 5 • grid cell, and the median value of these observations was used for this grid cell. This gives a total of 273 0. 5 • × 0. 5 • grid cells with yield observations. The SR, GSL, WAI and NDVI in these grid cells that are not recorded in the yield observation dataset were derived from each corresponding dataset (Table  1 ) and added in the training set. Crop type (CT; Table 1) was taken as a categorical variable in the RF training and was thus converted to five dummy variables, i.
and relative high demands for water and are thus usually cultivated in tropical and warm temperate regions  (Jacobs, 1981). Also, because the RF model has a poor ability to extrapolate when the values of explanatory variables are outside the ranges of training data, we only limited each crop prediction to the areas that are adequate for growth. Specifically, the minimum MAT and MAP over all grid cells in the training dataset were derived for each crop. The regions adequate for growth of each bioenergy crop were then defined as grid cells with MAT and MAP higher than the minimums in the training data. In another word, if either MAT or MAP in a grid cell is lower than the minimums where a crop type is grown in the training data, this grid cell is excluded for upscaling the yield of this crop.
Bioenergy crop yield maps in IAMs We compared our derived yield maps from RF with the bioenergy yields from three IAMs: IMAGE  (Stehfest et al. , 2014) , MAgPIE  (Popp et al. , 2014)  and GLOBIOM  (Havlík et al. , 2011). The yields used in IMAGE and MAgPIE are simulated by a DGVM -LPJmL  (Beringer et al. , 2011)  and have separate yield data for woody (representing poplar, willow and eucalypt) and herbaceous (representing switchgrass and Miscanthus) bioenergy crops. For comparison, we used the present-day (2010) actual yield maps (derived from RF). In the IMAGE integrated assessment model framework  (Stehfest et al. , 2014) , the LPJmL model is an integral component for crop and grass yields, hydrology, dynamic vegetation, and carbon dynamics  (Müller et al. , 2016). Bioenergy crop yields for sugarcane, maize, and herbaceous and woody crops are represented on the grid level in LPJmL and represent potential yields under current technology.
e. , Eucalyptus, Acacia, Gmelina, Betula, Populus, Salix) and thus only woody bioenergy crops. To generate this map, field-measured yields (i. e. , as mean annual increments of stem wood) for different short-rotation tree species with proper management practices were first collected from various databases (dated between 1984 and 2006 and sourced from different global regions) and then upscaled to a global yield map based on the spatial patterns of potential net pri- mary productivity (NPP) from  Cramer et al. (1999). The estimation of area potentials for tree plantations in the GLO-BIOM maps followed an approach similar to the one proposed by  Zomer et al. (2008) , including thresholds of tree growth based on aridity, temperature, elevation, population density and existing land cover  (Havlík et al.
Explanatory variables importance The importance of explanatory variables to the RF model is shown in Fig. 2a , indicating their contributions to the overall tree splits in the forest. We verified that spatial R 2 is generally low between any pair of variables (median R 2 = 0. 06; interquartile range -IQR = 0. 14), with a maximum R 2 of 0. 6 between MAT and GSL (Fig. 2b ). MAP is the most important variable in the RF regression, with a contribution of 18. 0 % to the overall tree splits. Another water-related variable, WAI, derived from a simple-bucket model with rainfall and evapotranspiration (ET) datasets, also has a significant contribution (11. 9 %), but we note here that ET from observations over natural and cultivated systems may be different from ET in a world with large areas covered by bioenergy crops.
8 % and 10. 8 %), while the contributions from other crop types (CT_poplar, CT_switchgrass and CT_willow) are low (< 3 %; Fig. 2a ). This reflects the fact that eucalypt and Miscanthus are generally more productive than others  (Li et al. , 2018a). The total importance of all bioenergy crop types indices is 29. 6 %. NDVI, as a proxy of maximum plant productivity in each grid cell (Sect. 2. 1), and SR contribute 8. 4 % and 6. 9 % to the trained RF model. CF, as the only soil property used in the regression, has a minor contribution of 3. 5 % (Fig. 2a ), indicating that soil conditions may have little impact on the bioenergy crop yields. However, this should be interpreted cautiously, considering the mismatch between CF from the HWSD dataset and from the yield observation dataset based on field measurements (Fig.
S6a ) can also be reflected by the best crop type in Fig. 3g. Because the contribution of crop types (poplar, switchgrass and willow) is low in the trained random-forest algorithm (CT_poplar, CT_switchgrass and CT_willow in Fig. 2a ), the predicted yields in the regions where all three crops can grow are controlled by other mutual variables and thus similar. Therefore, the yield differences among these three crops are mainly caused by the different adequate regions for growth (Fig. S5 ) defined by the minimum MAT and MAP in the observation dataset. For example, the regions adequate for willow growth include some areas with lower MAP, like the western US, eastern Europe and central Asia (Fig. S5 ), than for poplar and switchgrass.
4g ). Much lower yields in the IAM maps than the RF map were found in the equatorial winter dry ("Aw" category based on Köppen-Geiger climate classification;  Kottek et al. , 2006)  regions in southeast Brazil, Africa, India and Australia (Fig. 4c, e, g ), especially for IMAGE and MAgPIE. In the equatorial full humid ("Af") and monsoonal ("Am") regions in South America (mainly Amazon region) and Africa (around the Democratic Republic of the Congo), the yield difference is small between the RF and IMAGE and GLOBIOM maps (Fig. 4c, g ). In the "Af" and "Am" regions in southeastern Asia, how- Panels (a, b, d, f) are the best crop yields from each dataset, and the panels (c, e, g) refer to the yield differences between RF and each IAM map (IAM yields minus RF yields where yields are available in both paired maps).
By contrast, a large fraction of areas from the IAM maps are associated with yields lower than 15 t DM ha -1 yr -1 (Fig. 5 ). This is consistent with the generally higher yields in the RF map than the IAM maps in Fig. 4. In fact, the median mean yield in the regions where yields are available in the four datasets (the overlapped regions between Fig. 4a, b, d, f ) from RF is > 50 % higher than the median yields from IAM maps (80 %, 83 % and 59 % for IMAGE, MAgPIE and GLOBIOM, respectively). The shapes of yield distributions among IAMs are also different (Fig. 5 ). There are more areas with yields below 7 and above 20 t DM ha -1 yr -1 in the IM-AGE and MAgPIE maps than the GLOBIOM map. This is also reflected by the higher IQR from IMAGE (IQR = 9. 1) and MAgPIE (8. 7) than GLOBIOM (5. 7 t DM ha -1 yr -1 ).
6a ). In MAgPIE, herbaceous crops' (Miscanthus and switchgrass) yields lie around the 1 : 1 line, but woody crops' (eucalypt, poplar and willow) yields are generally lower than the site observations (Fig. 6b ). Because the bioenergy crops in the GLOBIOM maps refer to short-rotation Earth Syst. Sci. Data, 12, 789-804, 2020  www. earth-syst-sci-data. net/12/789/2020/  trees, the yields are similar to the field measurements of willow and poplar (also switchgrass) but much lower compared to the observed yields of Miscanthus and eucalypt (Fig. 6c ). In addition to the comparison of the best crop yields, we also showed the yields of woody and herbaceous crops in each dataset, respectively (Fig. S7 ). Yields of woody bioenergy crops in the IAM maps are lower than those in the RF map, especially for IMAGE and MAgPIE.
Yield comparison with other studies Our estimated global median yields (Fig. 3 ) are generally within the ranges summarized by  Searle and Malins (2014)  from field measurements in the literature for five secondgeneration bioenergy crops: 0-51, 5-44, 0-35, 0-21 and 1-35 t DM ha -1 yr -1 , respectively, for eucalypt, Miscanthus giganteus, poplar, willow and switchgrass. The yields from RF also agree with the yield ranges of several bioenergy crop species (e. g. , Miscanthus giganteus, Panicum virgatum, Salix, Populus) based on published yield data  (Laurent et al. , 2015). For Miscanthus and switchgrass, there are only smallscale experimental plots in different regions and no largescale plantation, so no region-or country-scale inventory data are available for comparison. Most yield data at farm levels were already included in our observation yield dataset (see "Field_type" and "Field_size" in Table  2  in  Li et al.
g. , eucalypt and willow in Argentina), although the former is generally lower than the latter (Fig. S8 ). The higher minimum and maximum yields from RF could be caused partly by the exclusion of regions with MAP and MAT below the minimums from the observation dataset (to avoid out-of-range prediction). Especially in some large countries, the inventory data may have plantations in some harsh climates and soils (e. g. , most eucalypt plantations distribute in drier areas in southern Brazil). However, we must note that it is not a fair comparison without knowing the exact plantation locations in each country.
95; see Fig. 2  in  Ryu et al. , 2018). We still tested the RF performance using SR from CRUNCEP, and the OOB R 2 remained unchanged (0. 63), possibly due to the relatively low contribution of SR in the random-forest training (Fig. 2a ) and the high spatial correlation between SR from BESS and from CRUNCEP. We also tried growing-season integrated climate variables instead of annual mean values, but there is no significant improvement in the model training. Therefore, we focused our analyses on 0. 5 • × 0. 5 • grid cells using mostly mean annual values, since the yield dataset only reported MAP and MAT (no growing-season integrated values) from observations. In addition, the soil properties from HWSD are also highly uncertain (Fig. S1c ), and the coarse resolution may not be able to represent the local soil conditions, partly explaining the low importance of CF in the RF model (Fig.
2a ). Furthermore, to avoid possible biases induced by out-of-range prediction, we only limited our predictions in regions with MAT and MAP above the minimums in the training data (Sect. 2. 2. 3). Thus, this gives us 33 216 grid cells in the prediction regions (instead of > 60 000 globally) and avoids biased predictions in regions that are beyond the capacity of our trained random-forest model. Lastly, we would like to emphasize that the bioenergy crop yield observations were found in published articles or reports in several literature databases and systematically collected  (Li et al. , 2018a) , so it is impossible to include more grid cells (currently 273 0. 5 • grid cells -161 after selecting), as there are no more observations available. Using these data, the OOB R 2 that serves as an evaluation of the trained random forest is 0.
63, implying the trained RF algorithm is acceptable for prediction. The temporal resolution and coverage of the training dataset are important for training the machine-learning model given the temporal variations in climate conditions. Therefore, we analyzed the sampling time in the training dataset; ∼ 30 % of the yield observations do not have a reported sampling year in the original dataset as well as ∼ 30 % in the aggregated 0. 5 • × 0. 5 • data used for random-forest training. We thus arbitrarily set 2 years before the publication year as the sampling year for the yield observations without reported sampling years (e. g. , setting 1997 as the sampling year if the reference paper was published in 1999). The frequency of the sampling years in the 0. 5 • data used for random-forest training is shown in Fig.
g. , management drivers) were not included because these explanatory variables are not available on a gridded basis. This may partly be responsible for the moderate OOB R 2 (0. 63) in the model training. One other reason for the difficulty in taking management into account is the incomplete information reported for this variable from field measurements and thus in the yield observation dataset  (Li et al. , 2018a). For example, 75 % of the observations did not report irrigation information  (Li et al. , 2018a). Another reason is that different management practices are difficult to harmonize. For example, fertilization may be applied annually, only one time at a plantation or irregularly  (Li et al. , 2018a) ; there are not enough data samples further classifying crop types (e. g. , species or genotypes).
Specific to the resolution of our analyses, it is difficult to derive a median or mean management quantification for a 0. 5 • grid cell from all observations inside. In addition, crop age is an important factor in predicting the yields because of the growth cycle of perennial crops like Miscanthus  (Lesur et al. , 2013). However, the yield data in the observation datasets mainly refer to mean annual biomass yield, which blended the growth cycle, especially for the trees  (Li et al. , 2018a). In the field measurement studies, biomass yield for trees is often calculated by the total biomass divided by age, although some studies may report the biomass increment at a certain age. Also, because only about one-third of observations have age information and we only aimed to produce a spatially explicit map, age is not used as an explanatory variable in the RF model.
We attempted a RF model training by including an irrigation flag (yes or no), fertilization flag (yes or no) and/or fertilization frequency (annual or one time). However, these attempts failed to improve the model, and the importance of these factors was very low (< 1 %). The nitrogen application rate reported in the yield observation dataset was also taken as a continuous variable in the exploring RF model training, but it only contributed < 4 % to the total tree splits. Reasons for the low contribution of fertilization (flag, frequency or application rate) may include unknown basic nutrient availability from soils, the possible existence of nitrogen-fixing bacteria, and dry and wet nitrogen depositions. In addition, the yield response of Miscanthus to fertilizer application may not be significant  (Cadoux et al.
Data availability The field observed site-level yield data for major lignocellulosic bioenergy crops can be downloaded from https://doi. org/10. 6084/m9. figshare. c. 3951967  (Li et al. , 2018a). The 0. 5 • × 0. 5 • gridded global maps for yields of different bioenergy crops and the best crop and for the best crop composition generated from the randomforest model in this study can be download from https://doi. org/10. 5281/zenodo. 3274254 (Li, 2019).
Conclusion We mapped bioenergy crop yields at the global scale using a machine-learning method trained on field yield data and based on several climatic and soil conditions. In addition to evaluating the performances of IAMs and DGVMs, our spatially explicit bioenergy crop yields can also be used to determine the suitable lands with proper bioenergy crop yields, conduct life cycle assessment and estimate the nutrient removal from biomass harvest. Although there are a large number of field measurements in the yield observation dataset used to build the RF model, the geographic coverage is poor in some regions. Therefore, more field measurements in regions with limited observations (e. g. , Africa) and a proper quantification and synthesis of management factors will be useful to improve the predictions of global yields in future.
On the ToF reflectometer FIGARO  39  with horizontal sample geometry, a wavelength band between 2 Å and 20 Å was used with a relative wavelength resolution of 2. 1% (FWHM). The two dimensional multitube detector allows the measurement of specular and off-specular reflectivity simultaneously. The detector has a size of 25 Â 48 cm and a resolution of 2. 2 Â 4. 8 mm 2 (FWHM) at a distance of 2. 8 m from the sample. Two reflection angles were used to cover the full q-range: 0. 622 and 2. 622 degrees. The collimation slits were set to ensure a constant relative angular resolution of Dy/y = 2% and the footprint on the sample was 40 Â 40 mm 2. The raw data was converted to absolute reflectivities as a function of momentum transfer by using the data reduction software COSMOS. N-REX+ 41 at FRM2 is an angle dispersive fixed wavelength (4.
4 Å, resolution 3%) reflectometer with horizontal sample geometry. A 20 Â 20 cm 2 position sensitive detector was used allowing for specular and off-specular reflectivity measurements although only the specular data was analyzed here. LA films on silicon blocks were first measured in air and then in D 2 O. Subsequently, the LA films were measured in solutions of D 2 O containing an increasing BA concentration f BA-W (0%, 0. 05%, 0. 1%, 0. 15%, 0. 2%, 0. 25%, 0. 3%, 0. 35%, 0. 4% and 0. 5%). All NR samples were measured at room temperature (21 1C). Time resolved NR spectra were integrated over 5 min and the measurement repeated several times between 1 h and 12 h in total. 2. 3. 1 Analysis of specular reflectivity. The measured NR data is subsequently compared to simulated NR curves calculated by an optical matrix formalism from a slab model  42.