Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies
Open accessAbstract
The United States Great Lakes Region (USGLR) is a critical geographic area for future bioenergy production. Switchgrass ( Panicum virgatum ) is widely considered a carbon (C)‐neutral or C‐negative bioenergy production system, but projected increases in air temperature and precipitation due to climate change might substantially alter soil organic C (SOC) dynamics and storage in soils. This study examined long‐term SOC changes in switchgrass grown on marginal land in the USGLR under current and projected climate, predicted using a process‐based model (Systems Approach to Land‐Use Sustainability) extensively calibrated with a wealth of plant and soil measurements at nine experimental sites. Simulations indicate that these soils are likely a net C sink under switchgrass (average gain 0.87 Mg C ha −1 year −1 ), although substantial variation in the rate of SOC accumulation was predicted (range: 0.2–1.3 Mg C ha −1 year −1 ). Principal component analysis revealed that the predicted intersite variability in SOC sequestration was related in part to differences in climatic characteristics, and to a lesser extent, to heterogeneous soils. Although climate change impacts on switchgrass plant growth were predicted to be small (4%–6% decrease on average), the increased soil respiration was predicted to partially negate SOC accumulations down to 70% below historical rates in the most extreme scenarios. Increasing N fertilizer rate and decreasing harvest intensity both had modest SOC sequestration benefits under projected climate, whereas introducing genotypes better adapted to the longer growing seasons was a much more effective strategy. Best‐performing adaptation scenarios were able to offset >60% of the climate change impacts, leading to SOC sequestration 0.7 Mg C ha −1 year −1 under projected climate. On average, this was 0.3 Mg C ha −1 year −1 more C sequestered than the no adaptation baseline. These findings provide crucial knowledge needed to guide policy and operational management for maximizing SOC sequestration of future bioenergy production on marginal lands in the USGLR.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The SALUS switchgrass model has been validated under conditions of both loss and gains of SOC.
“The SALUS switchgrass model has been validated under conditions of both loss and gains of SOC (Martinez- Feria & Basso, 2020).”
From Global warming intensity of biofuel derived from switchgrass grown on marginal land in Michigan (2023) · cites this paper as Martinez-Feria & Basso, 2020
The Model evaluation section reports that SALUS reasonably reproduced measured changes in SOC concentrations after five years at two sites. It does not say whether those measurements included both SOC losses and gains. The SOC changes and variability section reports positive simulated long-term gains across the sites, so the claimed validation under SOC-loss conditions is not established. (LLM, conf=0.83)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Use of U.S. Croplands for Biofuels Increases Greenhouse Gases Through Emissions from Land-Use Change
Searchinger, T., Heimlich, R., Houghton, R. A., Dong, F., Elobeid, A., Fabiosa, J., Tokgoz, S., Hayes, D., & Yu, T.-H. (2008) · Science · doi:10.1126/science.1151861 · Cited by 4,888 · cited 1× · see citing works
“Changes in SOC in perennial systems are driven by both the amount of plant-derived C inputs to soils (root and aboveground litter) and organic C decomposition rates (Agostini et al., 2015; Fuss et al., 2014; Searchinger et al., 2008).”
Land Clearing and the Biofuel Carbon Debt
Fargione, J., Hill, J., Tilman, D., Polasky, S., & Hawthorne, P. (2008) · Science · doi:10.1126/science.1152747 · Cited by 3,902 · see citing works
Biophysical and economic limits to negative CO2 emissions
Smith, P., Davis, S. J., Creutzig, F., Fuss, S., Minx, J., Gabrielle, B., Kato, E., Jackson, R. B., Cowie, A., Kriegler, E., et al. (2016) · Nature Climate Change · doi:10.1038/nclimate2870 · Cited by 1,440 · see citing works
LIFE-CYCLE ASSESSMENT OF NET GREENHOUSE-GAS FLUX FOR BIOENERGY CROPPING SYSTEMS
Adler, P. R., Grosso, S. J. D., & Parton, W. J. (2007) · Ecological Applications · doi:10.1890/05-2018 · Cited by 652 · see citing works
Cellulosic biofuel contributions to a sustainable energy future: Choices and outcomes
Robertson, G. P., Hamilton, S. K., Barham, B. L., Dale, B. E., Izaurralde, R. C., Jackson, R. D., Landis, D. A., Swinton, S. M., Thelen, K. D., & Tiedje, J. M. (2017) · Science · doi:10.1126/science.aal2324 · Cited by 440 · see citing works
Popp, A., Dietrich, J. P., Lotze-Campen, H., Klein, D., Bauer, N., Krause, M., Beringer, T., Gerten, D., & Edenhofer, O. (2011) · Environmental Research Letters · doi:10.1088/1748-9326/6/3/034017 · Cited by 203 · see citing works
Soil Carbon Storage by Switchgrass Grown for Bioenergy
Liebig, M. A., Schmer, M. R., Vogel, K. P., & Mitchell, R. B. (2008) · BioEnergy Research · doi:10.1007/s12155-008-9019-5 · Cited by 203 · see citing works
Altered Belowground Carbon Cycling Following Land-Use Change to Perennial Bioenergy Crops
Anderson-Teixeira, K. J., Masters, M. D., Black, C. K., Zeri, M., Hussain, M. Z., Bernacchi, C. J., & DeLucia, E. H. (2013) · Ecosystems · doi:10.1007/s10021-012-9628-x · Cited by 175 · see citing works
Carbon Sequestration by Perennial Energy Crops: Is the Jury Still Out?
Agostini, F., Gregory, A. S., & Richter, G. M. (2015) · BioEnergy Research · doi:10.1007/s12155-014-9571-0 · Cited by 155 · see citing works
Potential Soil Carbon Sequestration and CO <sub>2</sub> Offset by Dedicated Energy Crops in the USA
Sartori, F., Lal, R., Ebinger, M. H., & Parrish, D. J. (2006) · Critical Reviews in Plant Sciences · doi:10.1080/07352680600961021 · Cited by 131 · see citing works
Nitrogen fertilization challenges the climate benefit of cellulosic biofuels
Ruan, L., Bhardwaj, A. K., Hamilton, S. K., & Robertson, G. P. (2016) · Environmental Research Letters · doi:10.1088/1748-9326/11/6/064007 · Cited by 85 · cited 4× · see citing works
“Additionally, greater nitrous oxide emissions associated with higher N fertilization may offset some or all of the benefits from increased SOC gains from a greenhouse gas mitigation potential perspective (Ruan et al., 2016), as this gas has ~300 times more radiative forcing than CO2 (Davidson & Kanter, 2014).”
BackgroundMethodInfluential
Flagged first, then by citations
Research works printed but not linked: 55
- Albarenque, S. M., Basso, B., Caviglia, O. P., & Melchiori, R. J. M. (2016). Spatio-temporal nitrogen fertilizer response in maize: Field study and modeling approach. Agronomy Journal, 108(5), 2110- 2122. https://doi.org/10.2134/agron j2016.02.0081 · DOI
- Basso, B., Bertocco, M., Sartori, L., & Martin, E. C. (2007). Analyzing the effects of climate variability on spatial pattern of yield in a maize-wheat-soybean rotation. European Journal of Agronomy, 26, 82-91. https://doi.org/10.1016/j.eja.2006.08.008 · DOI
- Basso, B., Dumont, B., Maestrini, B., Shcherbak, I., Robertson, G. P., Porter, J. R., … Rosenzweig, C. (2018). Soil organic carbon and ni- trogen feedbacks on crop yields under climate change. Agricultural and Environmental Letters, 3(1), 1-5. https://doi.org/10.2134/ael20 18.05.0026 · DOI
- Basso, B., Giola, P., Dumont, B., Migliorati, M. D. A., Cammarano, D., Pruneddu, G., & Giunta, F. (2016). Tradeoffs between maize silage yield and nitrate leaching in a Mediterranean ni- trate-vulnerable zone under current and projected climate sce- narios. PLoS One, 11(1), e0146360. https://doi.org/10.1371/journ al.pone.0146360 · DOI
- Basso, B., & Ritchie, J. T. (2012). Assessing the impact of manage- ment strategies on water use efficiency using soil-plant-atmosphere models. Vadose Zone Journal, 11(3), vzj2011.0173. https://doi. org/10.2136/vzj20 11.0173 · DOI
- Basso, B., & Ritchie, J. T. (2015). Simulating crop growth and biogeochem- ical fluxes in response to land management using the SALUS model. In The ecology of agricultural landscapes: Long term research on the path to sustainability (pp. 252-274). New York, NY: Oxford Press.
- Basso, B., & Ritchie, J. T. (2018). Evapotranspiration in high-yielding maize and under increased vapor pressure deficit in the US Midwest. Agricultural & Environmental Letters, 3(1), 1-6. https://doi.org/ 10.2134/ael20 17.11.0039 · DOI
- Basso, B., Ritchie, J. T., Cammarano, D., & Sartori, L. (2011). A stra- tegic and tactical management approach to select optimal N fertil- izer rates for wheat in a spatially variable field. European Journal of Agronomy, 35(4), 215-222. https://doi.org/10.1016/J.EJA.2011. 06.004 · DOI
- Bassu, S., Brisson, N., Durand, J.-L., Boote, K., Lizaso, J., Jones, J. W., … Waha, K. (2014). How do various maize crop models vary in their responses to climate change factors? Global Change Biology, 20(7), 2301-2320. https://doi.org/10.1111/gcb.12520 · DOI
- Becker, D. R., Skog, K., Hellman, A., Halvorsen, K. E., & Mace, T. (2009). An outlook for sustainable forest bioenergy production in the Lake States. Energy Policy Journal, 37, 5687-5693. https://doi. org/10.1016/j.enpol.2009.08.033 · DOI
- Burks, J. L. (2013). Eco-physiology of three perennial bioenergy sys- tems. PhD dissertation, Purdue University.
- Byrd, R. H., Lu, P., Nocedal, J., & Zhu, C. (1995). A limited memory algorithm for bound constrained optimization. SIAM Journal on Scientific Computing, 16(5), 1190-1208. https://doi.org/10.1137/ 0916069 · DOI
- Casler, M. D., & Vogel, K. P. (2014). Selection for biomass yield in up- land, lowland, and hybrid switchgrass. Crop Science, 54, 626-636. https://doi.org/10.2135/crops ci2013.04.0239 · DOI · Paper page
- Crowther, T. W., Todd-Brown, K. E. O., Rowe, C. W., Wieder, W. R., Carey, J. C., Machmuller, M. B., … Bradford, M. A. (2016). Quantifying global soil carbon losses in response to warming. Nature, 540(7631), 104-108. https://doi.org/10.1038/natur e20150 · DOI
- Davidson, E. A., & Kanter, D. (2014). Inventories and scenarios of ni- trous oxide emissions. Environmental Research Letters, 9, 105012. https://doi.org/10.1088/1748-9326/9/10/105012 · DOI
- DeCicco, J. M., & Schlesinger, W. H. (2018). Reconsidering bioenergy given the urgency of climate protection. Proceedings of the National Academy of Sciences of the United States of America, 115(39), 9642-9645. https://doi.org/10.1073/pnas.18141 20115 · DOI
- Durand, J.-L., Delusca, K., Boote, K., Lizaso, J., Manderscheid, R., Weigel, H. J., … Zhao, Z. (2018). How accurately do maize crop models simulate the interactions of atmospheric CO 2 concentration levels with limited water supply on water use and yield? European Journal of Agronomy, 100, 67-75. https://doi.org/10.1016/j.eja.2017. 01.002 · DOI
- Fick, S. E., & Hijmans, R. J. (2017). WorldClim 2: New 1-km spatial resolution climate surfaces for global land areas. International Journal of Climatology, 37(12), 4302-4315. https://doi.org/10.1002/ joc.5086 · DOI
- Field, J. L., Evans, S. G., Marx, E., Easter, M., Adler, P. R., Dinh, T., … Paustian, K. (2018). High-resolution techno-ecological modelling of a bioenergy landscape to identify climate mitigation opportunities in cellulosic ethanol production. Nature Energy, 3(3), 211-219. https:// doi.org/10.1038/s4156 0-018-0088-1 · DOI
- Field, J. L., Marx, E., Easter, M., Adler, P. R., & Paustian, K. (2016). Ecosystem model parameterization and adaptation for sustainable cellulosic biofuel landscape design. GCB Bioenergy, 8(6), 1106- 1123. https://doi.org/10.1111/gcbb.12316 · DOI
- Fletcher, A. L., Brown, H. E., Johnstone, P. R., de Ruiter, J. M., & Zyskowski, R. F. (2011). Making sense of yield trade-offs in a crop sequence: A New Zealand case study. Field Crops Research, 124(2), 149-156. https://doi.org/10.1016/j.fcr.2011.01.007 · DOI
- Follett, R. F., Vogel, K. P., Varvel, G. E., Kimble, J., & Mitchell, R. B. (2012). Soil carbon sequestration by switchgrass and no-till maize grown for bioenergy. BioEnergy Research, 5, 866-875. https://doi. org/10.1007/s1215 5-012-9198-y · DOI · Paper page
- Fuss, S., Canadell, J. G., Peters, G. P., Tavoni, M., Andrew, R. M., Ciais, P., … Yamagata, Y. (2014). Betting on negative emissions. Nature Climate Change, 4(10), 850-853. https://doi.org/10.1038/nclim ate2392 · DOI · Paper page
- Gelfand, I., Hamilton, S. K., Kravchenko, A. N., Jackson, R. D., Thelen, K. D., & Robertson, G. P. (2020). Empirical evidence for the poten- tial climate benefits of decarbonizing light vehicle transport in the U.S. with bioenergy from purpose-grown biomass with and without BECCS. Environmental Science & Technology, 54(5), 2961-2974. https://doi.org/10.1021/acs.est.9b07019 · DOI · Paper page
- Gelfand, I., Sahajpal, R., Zhang, X., Izaurralde, R. C., Gross, K. L., & Robertson, G. P. (2013). Sustainable bioenergy production from marginal lands in the US Midwest. Nature, 493, 14-517. https://doi. org/10.1038/natur e11811 · DOI · Paper page
- Giola, P., Basso, B., Pruneddu, G., Giunta, F., & Jones, J. W. (2012). Impact of manure and slurry applications on soil nitrate in a maize- triticale rotation: Field study and long term simulation analysis. European Journal of Agronomy, 32, 43-53. https://doi.org/10.1016/ j.eja.2011.12.001 · DOI
- IPCC. (2014). Topic 4: Adaptation and mitigation. In Core Writing Team, R. K. Pachauri, & L. A. Meyer (Eds.), Climate change 2014: Synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (pp. 94-112). Geneva, Switzerland: IPCC.
- Jian, J., Steele, M. K., Day, S. D., & Thomas, R. Q. (2018). Future global soil respiration rates will swell despite regional decreases in tem- perature sensitivity caused by rising temperature. Earth's Future, 6(11), 1539-1554. https://doi.org/10.1029/2018E F000937 · DOI
- Jones, C. D., Oates, L. G., Philip Robertson, G., & Cesar Izaurralde, R. (2018). Perennialization and cover cropping mitigate soil carbon loss from residue harvesting. Journal of Environmental Quality, 47(4), 710-717. https://doi.org/10.2134/jeq20 17.04.0177 · DOI
- Kells, B. J., & Swinton, S. M. (2014). Profitability of cellulosic bio- mass production in the northern great lakes region. Agronomy Journal, 106(2), 397-406. https://doi.org/10.2134/agron j2013. 0397 · DOI
- Kim, S., Kim, S., Cho, J., Park, S., Jarrín Perez, F. X., & Kiniry, J. R. (2020). Simulated biomass, climate change impacts, and nitrogen management to achieve switchgrass biofuel production at diverse sites in U.S. Agronomy, 10(4), 503. https://doi.org/10.3390/agron omy10 040503 · DOI
- Liu, L., & Basso, B. (2017). Spatial evaluation of switchgrass productiv- ity under historical and future climate scenarios in Michigan. GCB Bioenergy, 9(8), 1320-1332. https://doi.org/10.1111/gcbb.12417 · DOI
- Liu, L., & Basso, B. (2020). Impacts of climate variability and adaptation strategies on crop yields and soil organic carbon in the US Midwest. PLoS One, 15(1), e0225433. https://doi.org/10.1371/journ al.pone. 0225433 · DOI
- Lowry, D. B., Lovell, J. T., Zhang, L. I., Bonnette, J., Fay, P. A., Mitchell, R. B., … Juenger, T. E. (2019). QTL × environment in- teractions underlie adaptive divergence in switchgrass across a large latitudinal gradient. Proceedings of the National Academy of Sciences of the United States of America, 116(26), 12933-12941. https://doi.org/10.1073/pnas.18215 43116 · DOI
- Mclaughlin, S. B., & Kszos, L. A. (2005). Development of switchgrass (Panicum virgatum) as a bioenergy feedstock in the United States. Biomass and Bioenergy, 28, 515-535. https://doi.org/10.1016/j. biomb ioe.2004.05.006 · DOI · Paper page
- Meinshausen, M., Nicholls, Z., Lewis, J., Gidden, M., Vogel, E., Freund, M., … Wang, H. J. (2019). The SSP greenhouse gas concentrations and their extensions to 2500. Geoscientific Model Development Discussions. https://doi.org/10.5194/gmd-2019-222 · DOI
- Ojeda, J. J., Volenec, J. J., Brouder, S. M., Caviglia, O. P., & Agnusdei, M. G. (2017). Evaluation of agricultural production systems simula- tor as yield predictor of Panicum virgatum and Miscanthus × gigan- teus in several US environments. GCB Bioenergy, 9(4), 796-816. https://doi.org/10.1111/gcbb.12384 · DOI
- Parrish, D. J., & Fike, J. H. (2005). The biology and agronomy of switchgrass for biofuels. Critical Reviews in Plant Sciences, 24(5- 6), 423-459. https://doi.org/10.1080/07352 68050 0316433 · DOI · Paper page
- Qin, Z., Dunn, J. B., Kwon, H., Mueller, S., & Wander, M. M. (2016). Soil carbon sequestration and land use change associated with bio- fuel production: Empirical evidence. GCB Bioenergy, 8(1), 66-80. https://doi.org/10.1111/gcbb.12237 · DOI · Paper page
- Ritchie, J. T., & Basso, B. (2008). Water use efficiency is not constant when crop water supply is adequate or fixed: The role of agronomic management. European Journal of Agronomy, 28(3), 273-281. https://doi.org/10.1016/j.eja.2007.08.003 · DOI
- Robertson, G. P., Hamilton, S. K., Del Grosso, S. J., & Parton, W. J. (2011). The biogeochemistry of bioenergy landscapes: Carbon, ni- trogen, and water considerations. Ecological Applications, 21(4), 1055-1067. https://doi.org/10.1890/09-0456. · DOI
- Sainju, U. M., Allen, B. L., Lenssen, A. W., & Mikha, M. (2017). Root and soil total carbon and nitrogen under bioenergy perennial grasses with various nitrogen rates. Biomass and Bioenergy, 107, 326-334. https://doi.org/10.1016/j.biomb ioe.2017.10.021 · DOI
- Sanchez, D. L., Nelson, J. H., Johnston, J., Mileva, A., & Kammen, D. M. (2015). Biomass enables the transition to a carbon-nega- tive power system across western North America. Nature Climate Change, 5(3), 230-234. https://doi.org/10.1038/nclim ate2488 · DOI
- Saxton, K. E., & Rawls, W. J. (2006). Soil water characteristic esti- mates by texture and organic matter for hydrologic solutions. Soil | 755 Science Society of America Journal, 70(5), 1569-1578. https://doi. org/10.2136/sssaj 2005.0117 · DOI
- Senthilkumar, S., Basso, B., Kravchenko, A. N., & Robertson, G. P. (2009). Contemporary evidence of soil carbon loss in the U.S. corn belt. Soil Science Society of America Journal, 73(6), 2078-2086. https://doi.org/10.2136/sssaj 2009.0044 · DOI
- Sprunger, C. D., Oates, L. G., Jackson, R. D., & Robertson, G. P. (2017). Plant community composition influences fine root production and biomass allocation in perennial bioenergy cropping systems of the upper Midwest, USA. Biomass and Bioenergy, 105, 248-258. https://doi.org/10.1016/j.biomb ioe.2017.07.007 · DOI
- Stanfill, B., Mielenz, H., Clifford, D., & Thorburn, P. (2015). Simple approach to emulating complex computer models for global sensi- tivity analysis. Environmental Modelling & Software, 74, 140-155. https://doi.org/10.1016/j.envso ft.2015.09.011 · DOI
- Swinton, S. M., Tanner, S., Barham, B. L., Mooney, D. F., & Skevas, T. (2017). How willing are landowners to supply land for bioenergy crops in the Northern Great Lakes Region? GCB Bioenergy, 9(2), 414-428. https://doi.org/10.1111/gcbb.12336 · DOI
- Syswerda, S. P., Basso, B., Hamilton, S. K., Tausig, J. B., Robertson, G. P., & Kellogg, W. K. (2012). Long-term nitrate loss along an agri- cultural intensity gradient in the Upper Midwest USA. Agriculture, Ecosystems and Environment, 149, 10-19. https://doi.org/10.1016/j. agee.2011.12.007 · DOI
- Tulbure, M. G., Wimberly, M. C., & Owens, V. N. (2012). Response of switchgrass yield to future climate change. Environmental Research Letters, 7(4), 045903. https://doi.org/10.1088/1748-9326/7/4/045903 · DOI
- Valdez, Z. P., Hockaday, W. C., Masiello, C. A., Gallagher, M. E., & Philip Robertson, G. (2017). Soil carbon and nitrogen responses to nitrogen fertilizer and harvesting rates in switchgrass cropping sys- tems. Bioenergy Research, 10(2), 456-464. https://doi.org/10.1007/ s1215 5-016-9810-7 · DOI
- Volk, T. A., Verwijst, T., Tharakan, P. J., Abrahamson, L. P., & White, E. H. (2004). Growing fuel: A sustainability assessment of willow biomass crops. Frontiers in Ecology and the Environment, 2(8), 411-418. https://doi.org/10.1890/1540-9295(2004)002[0411:GFAS A O]2.0.CO;2 · DOI · Paper page
- Wendt, J. W., & Hauser, S. (2013). An equivalent soil mass procedure for monitoring soil organic carbon in multiple soil layers. European Journal of Soil Science, 64(1), 58-65. https://doi.org/10.1111/ ejss.12002 · DOI
- Wiesmeier, M., Poeplau, C., Sierra, C. A., Maier, H., Frühauf, C., Hübner, R., … Kögel-Knabner, I. (2016). Projected loss of soil organic carbon in temperate agricultural soils in the 21st cen- tury: Effects of climate change and carbon input trends. Scientific Reports, 6(1), 1-17. https://doi.org/10.1038/srep3 2525 · DOI
- Xia, Y., Mitchell, K., Ek, M., Sheffield, J., Cosgrove, B., Wood, E., … Mocko, D. (2012). Continental-scale water and energy flux analysis and validation for the North American Land Data Assimilation System project phase 2 (NLDAS-2): 1. Intercomparison and application of model products. Journal of Geophysical Research: Atmospheres, 117(D3). https://doi.org/10.1029/2011J D0 16048 · DOI
Cited by
Sorted by citations
Redefining marginal land for bioenergy crop production
Khanna, M., Chen, L., Basso, B., Cai, X., Field, J. L., Guan, K., Jiang, C., Lark, T. J., Richard, T. L., Spawn‐Lee, S. A., et al. (2021) · GCB Bioenergy · doi:10.1111/gcbb.12877 · Cited by 105 · see citing works
Self-citationGlobal warming intensity of biofuel derived from switchgrass grown on marginal land in Michigan
Kim, S., Dale, B. E., Martinez‐Feria, R., Basso, B., Thelen, K., Maravelias, C. T., Landis, D., Lark, T. J., & Robertson, G. P. (2023) · GCB Bioenergy · doi:10.1111/gcbb.13024 · Cited by 21 · see citing works
Self-citation
- Authors
- Martinez‐Feria, R., & Basso, B.
- Affiliations
- Michigan State University (US)
- Corresponding author
- Martinez‐Feria, Rafael
- Venue
- GCB Bioenergy
- Publisher
- Wiley
- Published
- 2020-07-21
- Volume
- 12
- Issue
- 9
- Pages
- 742-755
- ISSN
- 1757-1693 (ISSN-L)
- 1757-1707
- OpenAlex
- W3038461245
- Cited by
- 38
- References
- 11
- Citations by year
- 2026: 6 · 2025: 3 · 2024: 7 · 2023: 7 · 2022: 7 · 2021: 7 · 2020: 1
- FWCI
- 6.88
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- GCB Bioenergy (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (submittedVersion)
- Funders
- U.S. Department of Energy (DE‐FC02‐07ER64494, DESC0018409)
- National Institute of Food and Agriculture (2018‐67003‐27406, 2019‐67012‐29595)
- Michigan State University
- Topics
- Life Sciences › Agricultural and Biological Sciences › Agronomy and Crop Science › Bioenergy crop production and management
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil Carbon and Nitrogen Dynamics
- Life Sciences › Agricultural and Biological Sciences › Soil Science › Soil erosion and sediment transport
- Keywords
- switchgrass, soil organic carbon sequestration, climate change impacts, marginal land, energy crops, climate adaptation strategies, soil respiration, nitrogen fertilization, harvest intensity, genotype adaptation, growing season length, Great Lakes region, carbon sink
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review