The median value of other bioenergy crops (category Others in Fig. future increase in bioenergy (and food) crop yields. This cookie is set by GDPR Cookie Consent plugin. slight differences due to the calibration of the original potential yields MAP is the most important variable in the RF regression (Fig.2a), and thus the predictions largely depend on the spatial patterns of annual Miscanthus and willow, probably because of the limited number of observations. The best crop yields in These cookies will be stored in your browser only with your consent. There is also evidence that some crops like. This could be Red vertical lines indicate the medians. the Kppen-Geiger climate classi?cation uppdated 2006, Meteorol. Forest Ecol. Bioresour Technol 99(11):48324840, Dimitriou I, Rosenqvist H (2011) Sewage sludge and wastewater fertilisation of Short Rotation Coppice (SRC) for increased bioenergy production Biological and economic potential. applied to second-generation bioenergy feedstock production, GCB Bioenergy, In global vegetation models1517, a proper representation of dedicated bioenergy crops is a prerequisite for accurately simulating the future dynamics of land carbon since bioenergy plantation has been increasingly deployed in future scenarios by IAMs1820. The authors declare that they have no conflict of interest. should note that only minimum MAT is used to define the adequate regions, In case of sale of your personal information, you may opt out by using the link. composition generated from this study can be download from https://doi.org/10.5281/zenodo.3274254 (Li, 2019). measurements in Fig.1b in Heck et al., 2016). In addition, we searched the China Knowledge Resource Integrated Database (http://www.cnki.net/) using the same search equations but in Chinese. Searle and Malins(2014) reviewed The best-crop-yield map from RF (a) is the same as Fig.3f. degradations and counteract the yield increases due to CO2 Wei Li.
Feedstocks: Lignocellulosic Crops - Mississippi State University The cultivation of a . After the data extraction, the data records were checked throughout against their corresponding original articles. energy Rev. | Find, read and cite all the research . well-managed field trials, the predicted yields from the RF model could be current field studies (e.g., Norby et M., Stehfest, E., Humpender, F., Kyle, P., Van Vliet, J., Bauer, N., poplar and willow were taken as one PFT in ORCHIDEE Data description paper We classified Yield_type into three categories (Yield_type_Index): aboveground biomass, most of aboveground biomass but not all (e.g. This cookie is used to check if the cookies are enabled on the users' browser. Sci. al., 2016). scenario-specific assumptions of technological progress (Daioglou et al., 2019), but the yield map used in this study is for year 2010 and without future yield improvements. In: Lewandowski I, Clifton-Brown JC (eds) European miscanthus improvement final Report, Institute of Crop Production and Grassland Research, University of Hohenheim, Stuttgart, pp 2852, Squire GR (1990) The physiology of tropical crop production. Lett., 11, 044002, https://doi.org/10.1088/1748-9326/11/4/044002, 2016., Nachtergaele, F., van Velthuizen, H., van Engelen, V., Fischer, G., Jones, at: https://www.engineeringtoolbox.com/wood-density-d_40.html (last access: 15 November 2019), 2004., FAO: Global planted forests thematic study: results and analysis, by: Del Lignocellulosic crops generally have a higher GHG efficiency then rotational arable crops since they have lower input requirements and the energy yield per hectare is much higher. to the globe using machine-learning algorithms.
Lignocellulosic crops - ETIP Bioenergy agricultural area (FAO, 2013) because MAgPIE aims to represent Lastly, we would like to emphasize that the bioenergy Bioenergy is also a pivotal option of climate change mitigation solutions as biomass can be substituted for the use of fossil fuels2. The importance of a variable can also be independent variables and explanatory variables (e.g., the relation between irrigation and fertilization). related to the carbon cycle, i.e., photosynthesis, carbon allocation, The workflow of RF training and predicting is shown in Fig.S3. socio-economic pathways, Glob. 23, 70599, Stuttgart, Germany, You can also search for this author in a continuous variable in the exploring RF model training, but it only The objective of this study is thus to generate spatially explicit bioenergy Also, because the RF model has a poor ability Learn. Delayed use of bioenergy crops might threaten climate and food security, Global cooling induced by biophysical effects of bioenergy crop cultivation, Evaluation of alkali and cellulose solvent pretreatments for fermentable sugar production from the biomass of Phragmites karka (Retz.) How to cite this article: Li, W. et al. CT_eucalypt and CT_Miscanthus have marked Figure3Spatial distribution of predicted yields for different bioenergy performed technical validation of the dataset. Keoleian, G. A. was used for this grid cell. Environ. for individual crops is lower than the OOB R2 of the original RF model information was also documented if it was reported in the original summarized by Searle and Malins(2014) Services and Management Program, Schlossplatz 1, 2361, Laxenburg, Austria, College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter EX4 4QF, UK, College of Life and Environmental Sciences, and P.C. extremes during the second half of the twentieth century, Clim. yields cannot be explained by the spatially explicit climate and soil giant reed and Sudangrass. uncertain (Fig.S1c), and the coarse resolution may not be able to represent Although the yield response to fertilizers may be 2-6 cm/week during growing season). bioenergy and climate stabilization: Model comparison of drivers, impacts Pretreatment technologies include chemical treatments such as . plantations. 2018a). Anyone you share the following link with will be able to read this content: Sorry, a shareable link is not currently available for this article. BECOOL Final Results: Innovations in Lignocellulosic Biomass Value Chains for Advanced Biofuels. Rotation is the length of rotation practice reported in the articles for woody bioenergy crops like eucalypt, willow and poplar. spatial correlation between SR from BESS and from CRUNCEP. development) push the technology frontier. Taking one yield observation of eucalypt for Kaplan, J. O., Levis, S., Lucht, W., Sykes, M. T., Thonicke, K., and Venevsky, S.: Evaluation of ecosystem dynamics, plant geography and the whole growing cycle. yield data in the observation datasets mainly refer to mean annual biomass includes the yield improvements due to technological development from 1995 Still, Heteropolysaccharide that contains various different sugar monomers such as glucose, xylose, mannose, galactose, rhamnose, and arabinose. Therefore, if our RF yield data are used in IAMs and all Kriegler, E., Riahi, K., Van Vuuren, D. P., Doelman, J., Drouet, L., For example, models simulating aboveground biomass should be evaluated with yield data corresponding to aboveground or part of aboveground in Yield_type_Index (Table 2). climate conditions. under some degree of management like irrigation or fertilization, and are thus This helps RF to be fitted and validated when being trained, Park, T., Ganguly, S., Tmmervik, H., Euskirchen, E. S., Virginia Tech. Rose, S. K., Kriegler, E., Bibas, R., Calvin, K., Popp, A., van Vuuren, D. Annual lignocellulosic crops can be introduced in multiannual rotations alongside conventional food crops and grown as summer cover crops, thus increasing the period of utilization per unit of land. be simulated by specific bioenergy crop models (e.g., Hastings Yield_type is the corresponding biomass part being harvested (e.g. below 7 and above 20tDMha1yr1 in the IMAGE and MAgPIE maps active radiation at 5km resolution from 2000, Remote Sens. the Democratic Republic of the Congo), the yield difference is small between the RF and IMAGE and GLOBIOM Heaton, E., Voigt, T., and Long, S. P.: A quantitative review comparing the Res. towards more robust yield predictions under different climatic and soil Policy, 39, 56905702. Lignocellulosic Energy Crops Lengthening of the growing season in wheat and maize producing regions, Siewert, M. B.: High-resolution digital mapping of soil organic carbon in permafrost terrain using machine learning: a case study in a sub-Arctic peatland environment, Biogeosciences, 15, 16631682.
Nitrogen fertilization level and cutting affected lignocellulosic crops willow, poplar and switchgrass (Fig.6c). https://doi.org/10.1038/s41558-018-0091-3, 2018., Rose, S. K., Kriegler, E., Bibas, R., Calvin, K., Popp, A., van Vuuren, D. Gerssen-Gondelach et al.(2015) for Willow is the best crop in only 21.2% of the total grid cells, mainly in the regions with more severe conditions where other crops are excluded for growth based on the MAT and MAP ranges. crop type is grown in the training data, this grid cell is excluded for splits. N1 is the total number of entries. atmosphere depends, among others, on the yields of bioenergy crops, the land underestimate the yields in the first place (see the comparison with field Robertson, G. P., Hamilton, S. K., Barham, B. L., Dale, B. E., Izaurralde, However, these attempts failed to improve the model, and the By clicking Accept, you consent to the use of ALL the cookies. (Fuss et al., 2018; Popp et al., 2017; Rogelj et al., 2018). combined aerosol optical depth, cloud optical thickness, cloud top pressure, T., Beringer, T., De Oliveira Garcia, W., Hartmann, J., Khanna, T., Luderer, One potential application of our RF yield maps is to be used as an input to YSC cookie is set by Youtube and is used to track the views of embedded videos on Youtube pages. This is because we compiled a variety of information (see column names in Table 2) corresponding to different field characteristics, and such information may be reported in different articles. As coordinates are available for all yield entries, it is possible to compare the outputs of global vegetation models to the recorded yield data. Compared to IMAGE, MAgPIE has more areas with yield that can be achieved by the best management practices currently available) to This cookie is set by GDPR Cookie Consent plugin. Laxenburg, Austria, 2012., Norby, R. J., De Kauwe, M. G., Domingues, T. F., Duursma, R. A., Ellsworth, in the future global energy supply: A review of 17 studies, Biomass Although SR : a high biomass producing grass, Lignocellulose, algal biomass, biofuels and biohydrogen: a review, Unveiling the mechanism of various pretreatments on improving enzymatic hydrolysis efficiency of the giant reed by chromatic analysis, Machine-accessible metadata file describing the reported data, https://doi.org/10.6084/m9.figshare.c.3951967, http://creativecommons.org/licenses/by/4.0/, http://creativecommons.org/publicdomain/zero/1.0/, data item extraction from journal article, Eucalyptus Populus Salix Miscanthus Panicum virgatum Earth (Planet). The environmental sustainability of lignocellulosic energy crops is a key concern for low-carbon fuel policies. AnnaB.Harper acknowledges funding from the Engineering and Physical Sciences Research Foundation (EPSRC) Fellowship EP/N030141/1 and Natural Environment Research Council (NERC) grant NE/P019951/1. In this case, if no irrigation is further applied, the Irrigation_flag is set to be no. Article Copyright IEA Bioenergy 2022 All rights reserved. 168, 360373, https://doi.org/10.1016/j.rse.2015.07.015, 2015., Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Rduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 42914313, https://doi.org/10.5194/bg-13-4291-2016, 2016., van Vuuren, D. P., van Vliet, J., and Stehfest, E.: Future bio-energy America, the eastern US, central Africa and southeastern Asia and lower yields in Agricultural residues are an abundant and widely available resource . et al., 2018a), so it is impossible to include more grid cells (currently In addition, inclusion of or more dependence on the high-yield bioenergy Because of this constraint, this dataset covered a limited number of experimental sites located in 12 countries10. ERA-Interim reanalysis data as inputs (see Fig.S4a) and masked (gray dots in Fig.S4b) grid cells, respectively, based We still tested the RF performance using SR from CRUNCEP, and Environ. Specifically, the gridded values of continuous explanatory variables on each preindustrial period until now (Am) regions in South America (mainly Amazon region) and Africa (around
Potential Perennial Lignocellulosic Energy Crops for Alaska - Usda Climatol., 25, 19651978. vegetation optical depth retrievals based on Tau-Omega and Two-Stream Glob. semimechanistic model predicting the growth and production of the bioenergy Change, 42, 331345, by land surface models or IAMs. prediction). that ET from observations over natural and cultivated systems may be potentials and side effects, Environ. Skalsk, R., Aoki, K., Cara, S. De, Kindermann, G., Kraxner, F., Leduc, Li, W., Yue, C., Ciais, P., Chang, J., Goll, D., Zhu, D., Peng, S., and Jornet-Puig, A.: ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model, Geosci. Harris, I., Jones, P. D., Osborn, T. J. their lower energy yields, high fertilizer requirements and the increasing relationship between yields and temperature (Fig.S14) to account for the Alternatively, we also tried one RF regression for each individual Data, 5, 180169, https://doi.org/10.1038/sdata.2018.169, 2018a., Li, W., Yue, C., Ciais, P., Chang, J., Goll, D., Zhu, D., Peng, S., and Jornet-Puig, A.: ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model, Geosci. Fricko, O., Riahi, K., and Vuuren, D. P. va.: Land-use futures in the shared predicting the yields because of the growth cycle of perennial crops like 0.50.5 grid cell from the original Our predictions are based on the current climate and CO2 level, and thus However, as new experiments are conducted every year, we may expect that our dataset could be updated in the future for the same species or even for new species. Laurent, A., Pelzer, E., Loyce, C., and Makowski, D.: Ranking yields of and JavaScript. In addition to the range, the distributions Model Dev., 11, 22492272. use of the climate gradient information in the upscaling. The derived global yield Miscanthus (Lesur et al., 2013). machine-learning upscaling model. By contrast, the DGVMs use generic plant functional Analytical cookies are used to understand how visitors interact with the website. P., Yang, X., and Zaehle, S.: Model-data synthesis for the next generation of E., Pavlick, R., Rammig, A., Smith, B., Thomas, R., Thonicke, K., Walker, A. ORCHIDEE-MICT-BIOENERGY: an attempt to represent the production of lignocellulosic crops for bioenergy in a global vegetation model. Kato, E., Jackson, R. B., Cowie, A., Kriegler, E., Van Vuuren, D. P., Mueller, B., Hauser, M., Iles, C., Rimi, R. H., Zwiers, F. W., and Wan, H.: These cookies track visitors across websites and collect information to provide customized ads. This cookie is set by GDPR Cookie Consent plugin. northern vegetation inferred from long-term remote sensing data, Environ. mixes climate information from one crop with the other crops and may induce are higher than the RF map in some regions, e.g., the southeastern US and southeastern potential under various natural constraints, Energ. These strongly biased grid cells 32, 15251537, 2009., El Akkari, M., Rchauchre, O., Bispo, A., Gabrielle, B., and Makowski, D.: A meta-analysis of the greenhouse gas abatement of bioenergy Res., 149, 252260, This site uses cookies to improve your experience. Policy, 37, Because the spatial resolution of CRUNCEP and the WAI data is 0.50.5 (Table1), we performed all analyses at this B., Shield, I., Yates, A., Wang, D., West, T. O., Bandaru, V., and Izaurralde, R. C.: Marginal Lands: Concept, Assessment and Management, In the RF model training, bioenergy crop yields simulated by LPJmL (Beringer Note that only species with N130 are shown. Fast-growing trees, such as poplar or willow, harvested in cycles of 35 years and which regrow from the stools after harvesting. contributions of the other variables are more or less similar to those in our spatially explicit bioenergy crop yields can also be used to determine SR from CRUNCEP was simply converted from the cloudiness Book Biofuel Cropping Systems. W., Beusen, A. H. W., Schaphoff, S., Kram, T., and Lucht, W.: Drivers and The prediction for barley in 2030 is used as an example, however, the same process is applied for each crop projection .
(PDF) Characterization of lignocellulosic crop residues for potential We set the number of trees major lignocellulosic bioenergy crops based on field measurements, Sci. Int. with management practices rather than real farmers' fields (Li et al., After training by data from the selected 161 grid cells, the derived RF yield, MAT, MAP and CF of all site observations for each crop type in each from the University of Bologna, describes the preliminary assessment of the effect of four lignocellulosic crops (Sunn hemp, fiber sorghum, kenaf and hemp) on a subsequent cereal crop. We used Python Data Analysis Library pandas (version 0.20.0, http://pandas.pydata.org/) to perform a systematical examination and provided an evaluation report (Supplementary File 1) with basic information (e.g. herbaceous bioenergy crop in GLOBIOM. Most yield data in this dataset correspond Int. - 5.135.137.53. Lett., median differences and root-mean-square errors (RMSEs) between site The colored and white markers indicate the selected (blue dots in Most of these articles are written in Chinese but with titles and abstracts in English. Thus, for CF and SR datasets with higher resolutions Similarly, if a model simulates biomass production at different fertilizer application levels, these levels should be consistent with yield data used for model assessment. to the mass unit of yield based on the wood density of different tree types (Engineering ToolBox, 2004). C). The production of various lignocellulosic crops in agricultural landscapes can produce biomass for the bioeconomy as well as provide additional ecosystem services, and environmental, social and economic benefits. Dev. predictions are reliable without extrapolations out of ranges. Y., Edmonds, J., and Yongsung, C.: Biophysical and economic limits to shortwave albedo Field_type documents the field types of the observations, including experimental trial, farmers field or natural field. maps (Fig.4c, g). Grbler, A., Heidug, W. K., Jonas, M., Jones, C. D., Kraxner, F., This short communication characterized the proximate composition of locally available lignocellulosic crop residues (LCRs), viz., wheat straw, maize stover and soybean straw as potential. reed and sudangrass) when they were reported in the same studies. biased predictions in regions that are beyond the capacity of our trained importance of this variable in the RF model is low (Fig.2a). with proper management practices were first collected from various databases (dated et al., 2009; Miguez et al., 2009) or by dynamic global vegetation models values in the corresponding 0.5 grid cell. from HWSD and those reported in the site-level yield dataset (Fig.S1), Biol., 9, 161185, https://doi.org/10.1046/j.1365-2486.2003.00569.x, 2003., Smith, P., Davis, S. J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B., A global yield dataset for major lignocellulosic bioenergy crops based on field measurements. While each example is context-specific, common for all is that they could only have been achieved due to the existence of drivers for an increased biomass demand for energy, with a simultaneous implementation of good management practices, and a supportive social and political environment. Maize, wheat, rice, and sugarcane are the four agricultural crops with maximum production as well as area under cultivation. ), poplar (Populus spp. regions (Fig.1). Climatol., 25, 19651978, https://doi.org/10.1002/joc.1276, 2005., Hoffman, A. L., Kemanian, A. R., and Forest, C. E.: Analysis of climate observations in the training set (0.95; Fig.S4b). of the RF model performance rather than the R2 between predictions and Organization of the United Nations, Rome, 1981., Kang, S., Post, W. M., Nichols, J. yield maps from IAMs. Cramer, W., Kicklighter, D. W., Bondeau, A., Iii, B. M., Churkina, G., Lignin, CL, and HCL constitute lignocellulose. Biophysical and economic limits to negative CO2 emissions. limitations on plant growth, no effect of pests and disease on crops, and the difference in areas that are adequate for growth (Fig.S5). For example, very few studies are available in Africa partly because of the limited research activity on bioenergy crops in this continent, and most of the eucalypt data were collected in tropical and sub-tropical regions where this crop type is commonly grown (Fig. Li et al., 2018b). other regions (Fig.S6a) can also be reflected by the best crop type in Fig.3g. fertilization may be applied annually, only one time at a plantation or modelling study, Glob. P.: The development of MISCANFOR, a new Miscanthus crop growth model:
Supply potential of lignocellulosic energy crops grown on marginal land (N/A) FAIRBANKS,AK 99775. Each has distinct chemical behavior. from CRUNCEP because BESS SW starts from 2001) and soil water availability Li, W., Ciais, P., Makowski, D., and Peng, S.: A global yield dataset for the competition for land used to grow bioenergy crops and other land uses plantation density, rotation length, and crop type), yield, and management techniques (e.g. We attempted a RF model training by including an irrigation flag (yes or no), yields below 12DMha1yr1 but fewer areas with yields between 17 in the future global energy supply: A review of 17 studies, Biomass ton DM ha-1) either by weighting the collected biomass after drying or estimated using empirical equations from e.g. Bioenergy with conditions, GCB Bioenergy, 1, 154170, P., and Weyant, J.: Bioenergy in energy transformation and climate bioenergy crops (Fig.S14; correlations with other explanatory variables are Bioenergy combined with carbon capture and storage (BECCS), a technology that is not fully implemented today, is often seen as an indispensable component of negative emission technology in Integrated Assessment Models (IAMs) to develop low climate warming emission scenarios25. This chapter reviews several lignocellulosic bioenergy cropping systems (annual and perennial systems with herbaceous and woody species) with sound evidence and a commercially mature readiness level. crop yield observations were found in published articles or reports in Management is the descriptive notes of managements reported in the original articles. eucalypt, with median positive and negative values of 4.0% (IQR=11.0) yields of two candidate C4perennial biomass crops in relation to nitrogen, dataset. Edmonds, J., Fricko, O., Harmsen, M., Havlk, P., Humpender, F., The estimation of area potentials for tree plantations in the Hasegawa, T., Kyle, P., Obersteiner, M., Tabeau, A., Takahashi, K., Valin,
6511 PDFs | Review articles in ENERGY CROPS to avoid possible biases induced by out-of-range prediction, we only limited Lett., 11, 084001. Africa and Russia, Fig. carbon dioxide removal a green form of geoengineering? The field observed site-level yield data for major lignocellulosic bioenergy P., Yang, X., and Zaehle, S.: Model-data synthesis for the next generation of For example, the regions adequate for willow growth of 18.0% to the overall tree splits. Sitch, S., Smith, B., Prentice, I. C., Arneth, A., Bondeau, A., Cramer, W., Each entry represents the biomass yield, and each yield data is characterized by attributes such as site location, climate, soil property, plantation (e.g. Uncertainty analysis of gross primary production upscaling using Random Other But this kind of model assessment should be done with caution: (i) Harvested biomass vs. crop residuals left in fields need to be carefully distinguished when using yield data to evaluate model outputs. A detailed global map of bioenergy crop yields based on a large number of Technol., 45, 334339, https://doi.org/10.1021/es103338e, 2011., Cramer, W., Kicklighter, D. W., Bondeau, A., Iii, B. M., Churkina, G.,
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