Cited by 38Bioenergy crop production and managementSoil Carbon and Nitrogen DynamicsSoil erosion and sediment transport

Predicting soil carbon changes in switchgrass grown on marginal lands under climate change and adaptation strategies

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Martinez‐Feria, Rafael, Basso, Bruno·2020·GCB Bioenergy

Abstract

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.

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  • 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)

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Research works printed but not linked: 55

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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.
  7. 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
  8. 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
  9. 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
  10. 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
  11. Burks, J. L. (2013). Eco-physiology of three perennial bioenergy sys- tems. PhD dissertation, Purdue University.
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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.
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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
  53. 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
  54. 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
  55. 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

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
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

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