Articles | Volume 18, issue 10
https://doi.org/10.5194/bg-18-3147-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/bg-18-3147-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 May 2021
Research article |
| 26 May 2021
| 26 May 2021
Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically defined MEMS 2.0 model
Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Jocelyn M. Lavallee
Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Department of Soil and Crop Sciences Colorado State University, Fort Collins, CO 80523, USA
Andy D. Robertson
Shell International Exploration and Production, Shell Technology Center Houston, 3333 Highway 6 South, Houston, TX 77082-3101, USA
Rebecca Even
Department of Soil and Crop Sciences Colorado State University, Fort Collins, CO 80523, USA
Stephen M. Ogle
Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Department of Ecosystem Science and Sustainability, Colorado State University, Fort Collins, CO 80523, USA
Keith Paustian
Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Department of Soil and Crop Sciences Colorado State University, Fort Collins, CO 80523, USA
M. Francesca Cotrufo
Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, CO 80523, USA
Department of Soil and Crop Sciences Colorado State University, Fort Collins, CO 80523, USA
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Cited articles
Abramoff, R., Xu, X., Hartman, M., O'Brien, S., Feng, W., Davidson, E., Finzi, A., Moorhead, D., Schimel, J., Torn, M., and Mayes, M. A.:
The Millennial model: in search of measurable pools and transformations for modeling soil carbon in the new century,
Biogeochemistry,
137, 51–71, https://doi.org/10.1007/s10533-017-0409-7, 2018.
Ahrens, B., Braakhekke, M. C., Guggenberger, G., Schrumpf, M., and Reichstein, M.:
Contribution of sorption, DOC transport and microbial interactions to the 14C age of a soil organic carbon profile: Insights from a calibrated process model,
Soil Biol. Biochem.,
88, 390–402, https://doi.org/10.1016/j.soilbio.2015.06.008, 2015.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.:
Crop evapotranspiration-Guidelines for computing crop water requirements – FAO Irrigation and drainage paper 56, Food and Agriculture Organization, Rome, Italy, 1998.
AmeriFlux: Measuring ecosystem CO2, water, and energy fluxes in North, Central and South America, available at: https://ameriflux.lbl.gov/, last access: 18 May 2020.
Averill, C. and Waring, B.:
Nitrogen limitation of decomposition and decay: How can it occur?,
Glob. Change Biol.,
24, 1417–1427, https://doi.org/10.1111/gcb.13980, 2018.
Batjes, N. H.:
Total carbon and nitrogen in the soils of the world,
Eur. J. Soil. Sci.,
65, 10–21, https://doi.org/10.1111/ejss.12114_2, 2014.
Baudin, M., Boumhaout, K., Delage, T., Iooss, B., and Martinez, J.-M.:
Numerical stability of Sobol'indices estimation formula,
in: Proceedings of the 8th International Conference on Sensitivity Analysis of Model Output (SAMO 2016), 30 November–3 December 2016, Le Tampon, Réunion Island, France, 50–51, 2016.
Beare, M. H., McNeill, S. J., Curtin, D., Parfitt, R. L., Jones, H. S., Dodd, M. B., and Sharp, J.: Estimating the organic carbon stabilisation capacity and saturation deficit of soils: a New Zealand case study, Biogeochemistry, 120, 71–87, https://doi.org/10.1007/s10533-014-9982-1, 2014.
Benbi, D. K., Boparai, A. K., and Brar, K.:
Decomposition of particulate organic matter is more sensitive to temperature than the mineral associated organic matter,
Soil Biol. Biochem.,
70, 183–192, https://doi.org/10.1016/j.soilbio.2013.12.032, 2014.
Bird, M. I., Wynn, J. G., Saiz, G., Wurster, C. M., and McBeath, A.:
The Pyrogenic Carbon Cycle,
Annu. Rev. Earth,
43, 273–298, https://doi.org/10.1146/annurev-earth-060614-105038, 2015.
Bittelli, M., Campbell, G. S., and Tomei, F.:
Soil physics with Python: transport in the soil-plant-atmosphere system,
Oxford University Press, Oxford, 2015.
Braakhekke, M. C., Wutzler, T., Beer, C., Kattge, J., Schrumpf, M., Ahrens, B., Schöning, I., Hoosbeek, M. R., Kruijt, B., Kabat, P., and Reichstein, M.: Modeling the vertical soil organic matter profile using Bayesian parameter estimation, Biogeosciences, 10, 399–420, https://doi.org/10.5194/bg-10-399-2013, 2013.
Brooks, R. H. and Corey, A. T.:
Hydraulic properties of porous media, Hydrology papers, no. 3, Colorado State University,
Fort Collins, Colorado, USA, 1964.
Brunsell, N. A., Nippert, J. B., and Buck, T. L.:
Impacts of seasonality and surface heterogeneity on water-use efficiency in mesic grasslands,
Ecohydrol.,
7, 1223–1233, https://doi.org/10.1002/eco.1455, 2014.
Brzostek, E. R., Fisher, J. B., and Phillips, R. P.:
Modeling the carbon cost of plant nitrogen acquisition: Mycorrhizal trade-offs and multipath resistance uptake improve predictions of retranslocation,
J. Geophys. Res.-Biogeo.,
119, 1684–1697, https://doi.org/10.1002/2014JG002660, 2014.
Buchkowski, R. W., Shaw, A. N., Sihi, D., Smith, G. R., and Keiser, A. D.:
Constraining Carbon and Nutrient Flows in Soil With Ecological Stoichiometry,
Front. Ecol. Evol.,
7, 382, https://doi.org/10.3389/fevo.2019.00382, 2019.
Byrnes, R. C., Eastburn, D. J., Tate, K. W., and Roche, L. M.:
A Global Meta-Analysis of Grazing Impacts on Soil Health Indicators,
J. Environ. Qual.,
47, 758–765, https://doi.org/10.2134/jeq2017.08.0313, 2018.
Camino-Serrano, M., Guenet, B., Luyssaert, S., Ciais, P., Bastrikov, V., De Vos, B., Gielen, B., Gleixner, G., Jornet-Puig, A., Kaiser, K., Kothawala, D., Lauerwald, R., Peñuelas, J., Schrumpf, M., Vicca, S., Vuichard, N., Walmsley, D., and Janssens, I. A.: ORCHIDEE-SOM: modeling soil organic carbon (SOC) and dissolved organic carbon (DOC) dynamics along vertical soil profiles in Europe, Geosci. Model Dev., 11, 937–957, https://doi.org/10.5194/gmd-11-937-2018, 2018.
Campbell, E. E., Parton, W. J., Soong, J. L., Paustian, K., Hobbs, N. T., and Cotrufo, M. F.:
Using litter chemistry controls on microbial processes to partition litter carbon fluxes with the Litter Decomposition and Leaching (LIDEL) model,
Soil Biol. Biochem.,
100, 160–174, https://doi.org/10.1016/j.soilbio.2016.06.007, 2016.
Castellano, M. J., Mueller, K. E., Olk, D. C., Sawyer, J. E., and Six, J.:
Integrating plant litter quality, soil organic matter stabilization, and the carbon saturation concept,
Glob. Change Biol.,
21, 3200–3209, https://doi.org/10.1111/gcb.12982, 2015.
Chang, J. F., Viovy, N., Vuichard, N., Ciais, P., Wang, T., Cozic, A., Lardy, R., Graux, A.-I., Klumpp, K., Martin, R., and Soussana, J.-F.: Incorporating grassland management in ORCHIDEE: model description and evaluation at 11 eddy-covariance sites in Europe, Geosci. Model Dev., 6, 2165–2181, https://doi.org/10.5194/gmd-6-2165-2013, 2013.
Chen, R., Senbayram, M., Blagodatsky, S., Myachina, O., Dittert, K., Lin, X., Blagodatskaya, E., and Kuzyakov, Y.:
Soil C and N availability determine the priming effect: microbial N mining and stoichiometric decomposition theories,
Glob. Change Biol.,
20, 2356–2367, https://doi.org/10.1111/gcb.12475, 2014.
Christensen, B. T.:
Physical fractionation of soil and structural and functional complexity in organic matter turnover,
Eur. J. Soil Sci.,
52, 345–353, https://doi.org/10.1046/j.1365-2389.2001.00417.x, 2001.
Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R., Galloway, J., and Heimann, M.:
Carbon and other biogeochemical cycles,
in: Climate change 2013: the physical science basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change,
edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K.,
Boschung, J., Nauels, A., Xia, Y., Bex, V., and Midgley, P. M.,
Cambridge University Press, Cambridge, United
Kingdom and New York, NY, USA, 465–570, 2014.
Coleman, K. and Jenkinson, D. S.:
RothC-26.3 – A Model for the turnover of carbon in soil,
in: Evaluation of Soil Organic Matter Models,
edited by: Powlson, D. S., Smith, P., and Smith, J. U., 237–246, Springer, Berlin, Heidelberg, 1996.
Conant, R. T., Ryan, M. G., Ågren, G. I., Birge, H. E., Davidson, E. A., Eliasson, P. E., Evans, S. E., Frey, S. D., Giardina, C. P., Hopkins, F. M., Hyvönen, R., Kirschbaum, M. U. F., Lavallee, J. M., Leifeld, J., Parton, W. J., Steinweg, J. M., Wallenstein, M. D., Wetterstedt, J. Å. M., and Bradford, M. A.:
Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward,
Glob. Change Biol.,
17, 3392–3404, https://doi.org/10.1111/j.1365-2486.2011.02496.x, 2011.
Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K., and Paul, E. A.:
The Microbial Efficiency-Matrix Stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter?, Glob. Change Biol.,
19, 988–995, 2013.
Cotrufo, M. F., Soong, J. L., Horton, A. J., Campbell, E. E., Haddix, M. L., Wall, D. H., and Parton, W. J.:
Formation of soil organic matter via biochemical and physical pathways of litter mass loss,
Nat. Geosci.,
8, 776–779, https://doi.org/10.1038/ngeo2520, 2015.
Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J., and Lugato, E.:
Soil carbon storage informed by particulate and mineral-associated organic matter,
Nat. Geosci.,
12, 989–994, https://doi.org/10.1038/s41561-019-0484-6, 2019.
Craine, J. M., Morrow, C., and Fierer, N.:
Microbial Nitrogen Limitation Increases Decomposition,
Ecology,
88, 2105–2113, https://doi.org/10.1890/06-1847.1, 2007.
Curtin, D.: Possible role of aluminum in stabilizing organic matter in particle size fractions of Chernozemic and solonetizic soils, Can. J. Soil. Sci., 82, 265–268, https://doi.org/10.4141/S01-035, 2002.
Davidson, E. A. and Janssens, I. A.:
Temperature sensitivity of soil carbon decomposition and feedbacks to climate change,
Nature,
440, 165–173, https://doi.org/10.1038/nature04514, 2006.
Davidson, E. A., Trumbore, S. E., and Amundson, R.:
Soil warming and organic carbon content,
Nature,
408, 789–790, https://doi.org/10.1038/35048672, 2000.
de Graaff, M.-A., Classen, A. T., Castro, H. F., and Schadt, C. W.:
Labile soil carbon inputs mediate the soil microbial community composition and plant residue decomposition rates,
New Phytol.,
188, 1055–1064, https://doi.org/10.1111/j.1469-8137.2010.03427.x, 2010.
Del Grosso, S. J., Parton, W. J., Mosier, A. R., Holland, E. A., Pendall, E., Schimel, D. S., and Ojima, D. S.:
Modeling soil CO2 emissions from ecosystems,
Biogeochemistry,
73, 71–91, https://doi.org/10.1007/s10533-004-0898-z, 2005.
Elzein, A. and Balesdent, J.:
Mechanistic Simulation of Vertical Distribution of Carbon Concentrations and Residence Times in Soils,
Soil Sci. Soc. Am. J.,
59, 1328–1335, https://doi.org/10.2136/sssaj1995.03615995005900050019x, 1995.
Fatichi, S., Manzoni, S., Or, D., and Paschalis, A.:
A Mechanistic Model of Microbially Mediated Soil Biogeochemical Processes: A Reality Check,
Global Biogeochem. Cy.,
33, 620–648, https://doi.org/10.1029/2018GB006077, 2019.
Feng, W., Plante, A. F., and Six, J.:
Improving estimates of maximal organic carbon stabilization by fine soil particles,
Biogeochemistry,
112, 81–93, https://doi.org/10.1007/s10533-011-9679-7, 2013.
Georgiou, K., Abramoff, R. Z., Harte, J., Riley, W. J., and Torn, M. S.:
Microbial community-level regulation explains soil carbon responses to long-term litter manipulations,
Nat. Commun.,
8, 1223, https://doi.org/10.1038/s41467-017-01116-z, 2017.
Gill, R., Burke, I. C., Milchunas, D. G., and Lauenroth, W. K.: Relationship Between Root Biomass and Soil Organic Matter Pools in the Shortgrass Steppe of Eastern Colorado, Ecosystems, 2, 226–236, https://doi.org/10.1007/s100219900070, 1999.
Griscom, B. W., Adams, J., Ellis, P. W., Houghton, R. A., Lomax, G., Miteva, D. A., Schlesinger, W. H., Shoch, D., Siikamäki, J. V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R. T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M. R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L., Leavitt, S. M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F. E., Sanderman, J., Silvius, M., Wollenberg, E., and Fargione, J.:
Natural climate solutions,
P. Natl. Acad. Sci. USA,
114, 11645–11650, https://doi.org/10.1073/pnas.1710465114, 2017.
Gurung, R. B., Ogle, S. M., Breidt, F. J., Williams, S. A., and Parton, W. J.:
Bayesian calibration of the DayCent ecosystem model to simulate soil organic carbon dynamics and reduce model uncertainty,
Geoderma,
376, 114529, https://doi.org/10.1016/j.geoderma.2020.114529, 2020.
Guyette, R. P., Stambaugh, M. C., Dey, D. C., and Muzika, R.-M.:
Predicting Fire Frequency with Chemistry and Climate,
Ecosystems,
15, 322–335, https://doi.org/10.1007/s10021-011-9512-0, 2012.
Haddix, M. L., Paul, E. A., and Cotrufo, M. F.:
Dual, differential isotope labeling shows the preferential movement of labile plant constituents into mineral-bonded soil organic matter,
Glob. Change Biol.,
22, 2301–2312, https://doi.org/10.1111/gcb.13237, 2016.
Hall, S. J., McNicol, G., Natake, T., and Silver, W. L.: Large fluxes and rapid turnover of mineral-associated carbon across topographic gradients in a humid tropical forest: insights from paired 14C analysis, Biogeosciences, 12, 2471–2487, https://doi.org/10.5194/bg-12-2471-2015, 2015.
Hamilton, E. W., Frank, D. A., Hinchey, P. M., and Murray, T. R.:
Defoliation induces root exudation and triggers positive rhizospheric feedbacks in a temperate grassland,
Soil Biol. Biochem.,
40, 2865–2873, https://doi.org/10.1016/j.soilbio.2008.08.007, 2008.
Hargreaves, G. H. and Allen, R. G.:
History and Evaluation of Hargreaves Evapotranspiration Equation,
J. Irrig. Drain. Eng.,
129, 53–63, https://doi.org/10.1061/(ASCE)0733-9437(2003)129:1(53), 2003.
Harper, R. J. and Tibbett, M.:
The hidden organic carbon in deep mineral soils,
Plant Soil,
368, 641–648, https://doi.org/10.1007/s11104-013-1600-9, 2013.
Hartman, M., Parton, W., Del Grosso, S., Easter, M., Hendryx, J., Hilinski, T., Kelly, R., Keough, C., Killian, K., Lutz, S., Marx, E., McKeown, R., Ogle, S., Ojima, D., Paustian, K., Swan, A., and Williams, S.:
DayCent Ecosystem Model – The Daily Century Ecosystem, Soil Organic Matter, Nutrient Cycling, Nitrogen Trace Gas, and Methane Model: User Manual, Scientific Basis, and Technical Documentation, Colorado Sate University
Fort Collins, Colorado, USA, 2020.
Hassink, J.:
The capacity of soils to preserve organic C and N by their association with clay and silt particles,
Plant Soil,
191, 77–87, https://doi.org/10.1023/A:1004213929699, 1997.
Hinckley, E.-L. S., Bonan, G. B., Bowen, G. J., Colman, B. P., Duffy, P. A., Goodale, C. L., Houlton, B. Z., Marín-Spiotta, E., Ogle, K., Ollinger, S. V., Paul, E. A., Vitousek, P. M., Weathers, K. C., and Williams, D. G.:
The soil and plant biogeochemistry sampling design for The National Ecological Observatory Network,
Ecosphere,
7, e01234, https://doi.org/10.1002/ecs2.1234, 2016.
Hobbs, N. T., Schimel, D. S., Owensby, C. E., and Ojima, D. S.:
Fire and Grazing in the Tallgrass Prairie: Contingent Effects on Nitrogen Budgets,
Ecology,
72, 1374–1382, https://doi.org/10.2307/1941109, 1991.
Hodge, A., Campbell, C. D., and Fitter, A. H.:
An arbuscular mycorrhizal fungus accelerates decomposition and acquires nitrogen directly from organic material,
Nature,
413, 297–299, https://doi.org/10.1038/35095041, 2001.
Huang, W. and Hall, S. J.:
Elevated moisture stimulates carbon loss from mineral soils by releasing protected organic matter,
Nat. Commun.,
8, 1774, https://doi.org/10.1038/s41467-017-01998-z, 2017.
Iooss, B., Da Veiga, S., Janon, A., and Pujol, G.:
Global Sensitivity Analysis of Model Outputs,
available at: https://CRAN.R-project.org/package=sensitivity, last access: 18 May 2020.
Islam, A. K. M. S., Edwards, D. G., and Asher, C. J.:
pH optima for crop growth,
Plant Soil,
54, 339–357, https://doi.org/10.1007/BF02181830, 1980.
Jackson, R. B., Canadell, J., Ehleringer, J. R., Mooney, H. A., Sala, O. E., and Schulze, E. D.:
A global analysis of root distributions for terrestrial biomes,
Oecologia,
108, 389–411, https://doi.org/10.1007/BF00333714, 1996.
Jenkinson, D. S. and Coleman, K.:
The turnover of organic carbon in subsoils. Part 2. Modelling carbon turnover,
Eur. J. Soil Sci.,
59, 400–413, https://doi.org/10.1111/j.1365-2389.2008.01026.x, 2008.
Johnson, M. O., Mudd, S. M., Pillans, B., Spooner, N. A., Fifield, L. K., Kirkby, M. J., and Gloor, M.:
Quantifying the rate and depth dependence of bioturbation based on optically-stimulated luminescence (OSL) dates and meteoric 10Be,
Earth Surf. Proc. Land.,
39, 1188–1196, https://doi.org/10.1002/esp.3520, 2014.
Kleber, M., Sollins, P., and Sutton, R.:
A conceptual model of organo-mineral interactions in soils: self-assembly of organic molecular fragments into zonal structures on mineral surfaces,
Biogeochemistry,
85, 9–24, https://doi.org/10.1007/s10533-007-9103-5, 2007.
Kleber, M., Eusterhues, K., Keiluweit, M., Mikutta, C., Mikutta, R., and Nico, P. S.: Chapter One – Mineral–Organic Associations: Formation, Properties, and Relevance in Soil Environments, in: Advances in Agronomy, vol. 130, edited by: Sparks, D. L., Academic Press, 1–140, https://doi.org/10.1016/bs.agron.2014.10.005, 2015.
Knicker, H.:
Pyrogenic organic matter in soil: Its origin and occurrence, its chemistry and survival in soil environments,
Quatern. Int.,
243, 251–263, https://doi.org/10.1016/j.quaint.2011.02.037, 2011.
Knorr, M., Frey, S. D., and Curtis, P. S.:
Nitrogen Additions and Litter Decomposition: A Meta-Analysis,
Ecology,
86, 3252–3257, https://doi.org/10.1890/05-0150, 2005.
Kögel-Knabner, I., Guggenberger, G., Kleber, M., Kandeler, E., Kalbitz, K., Scheu, S., Eusterhues, K., and Leinweber, P.:
Organo-mineral associations in temperate soils: Integrating biology, mineralogy, and organic matter chemistry,
J. Plant Nutr. Soil Sci.,
171, 61–82, https://doi.org/10.1002/jpln.200700048, 2008.
Kuzyakov, Y.:
Priming effects: Interactions between living and dead organic matter,
Soil Biol. Biochem.,
42, 1363–1371, https://doi.org/10.1016/j.soilbio.2010.04.003, 2010.
Kuzyakov, Y. and Xu, X.:
Competition between roots and microorganisms for nitrogen: mechanisms and ecological relevance,
New Phytol.,
198, 656–669, https://doi.org/10.1111/nph.12235, 2013.
Kyker-Snowman, E., Wieder, W. R., Frey, S. D., and Grandy, A. S.: Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0), Geosci. Model Dev., 13, 4413–4434, https://doi.org/10.5194/gmd-13-4413-2020, 2020.
Lavallee, J. M., Conant, R. T., Paul, E. A., and Cotrufo, M. F.:
Incorporation of shoot versus root-derived 13C and 15N into mineral-associated organic matter fractions: results of a soil slurry incubation with dual-labelled plant material,
Biogeochemistry,
137, 379–393, 2018.
Lavallee, J. M., Soong, J. L., and Cotrufo, M. F.:
Conceptualizing soil organic matter into particulate and mineral-associated forms to address global change in the 21st century,
Glob. Change Biol.,
26, 261–273, https://doi.org/10.1111/gcb.14859, 2020.
Lehmann, J. and Kleber, M.:
The contentious nature of soil organic matter,
Nature,
528, 60–68, https://doi.org/10.1038/nature16069, 2015.
Li, C., Frolking, S., and Frolking, T. A.:
A model of nitrous oxide evolution from soil driven by rainfall events: 1. Model structure and sensitivity,
J. Geophys. Res.-Atmos.,
97, 9759–9776, https://doi.org/10.1029/92JD00509, 1992.
Li, L.-J., Zhu-Barker, X., Ye, R., Doane, T. A., and Horwath, W. R.:
Soil microbial biomass size and soil carbon influence the priming effect from carbon inputs depending on nitrogen availability,
Soil Biol. Biochem.,
119, 41–49, https://doi.org/10.1016/j.soilbio.2018.01.003, 2018.
Liang, C., Schimel, J. P., and Jastrow, J. D.:
The importance of anabolism in microbial control over soil carbon storage,
Nat. Microbiol.,
2, 17105, https://doi.org/10.1038/nmicrobiol.2017.105, 2017.
Liu, W., Qiao, C., Yang, S., Bai, W., and Liu, L.:
Microbial carbon use efficiency and priming effect regulate soil carbon storage under nitrogen deposition by slowing soil organic matter decomposition,
Geoderma,
332, 37–44, https://doi.org/10.1016/j.geoderma.2018.07.008, 2018.
Liu, X.-J. A., Sun, J., Mau, R. L., Finley, B. K., Compson, Z. G., van Gestel, N., Brown, J. R., Schwartz, E., Dijkstra, P., and Hungate, B. A.:
Labile carbon input determines the direction and magnitude of the priming effect,
Appl. Soil Ecol.,
109, 7–13, https://doi.org/10.1016/j.apsoil.2016.10.002, 2017.
Luo, Y., Ahlström, A., Allison, S. D., Batjes, N. H., Brovkin, V., Carvalhais, N., Chappell, A., Ciais, P., Davidson, E. A., Finzi, A., Georgiou, K., Guenet, B., Hararuk, O., Harden, J. W., He, Y., Hopkins, F., Jiang, L., Koven, C., Jackson, R. B., Jones, C. D., Lara, M. J., Liang, J., McGuire, A. D., Parton, W., Peng, C., Randerson, J. T., Salazar, A., Sierra, C. A., Smith, M. J., Tian, H., Todd-Brown, K. E. O., Torn, M., van Groenigen, K. J., Wang, Y. P., West, T. O., Wei, Y., Wieder, W. R., Xia, J., Xu, X., Xu, X., and Zhou, T.:
Toward more realistic projections of soil carbon dynamics by Earth system models,
Global Biogeochem. Cy.,
30, 40–56, https://doi.org/10.1002/2015GB005239, 2016.
Matches, A. G.:
Plant Response to Grazing: A Review,
J. Prod. Agric.,
5, 1–7, https://doi.org/10.2134/jpa1992.0001, 1992.
Mayes, M. A., Heal, K. R., Brandt, C. C., Phillips, J. R., and Jardine, P. M.:
Relation between Soil Order and Sorption of Dissolved Organic Carbon in Temperate Subsoils,
Soil Sci. Soc. Am. J.,
76, 1027–1037, https://doi.org/10.2136/sssaj2011.0340, 2012.
McKee, G. A., Soong, J. L., Caldéron, F., Borch, T., and Cotrufo, M. F.:
An integrated spectroscopic and wet chemical approach to investigate grass litter decomposition chemistry, Biogeochemistry, 128, 107–123, https://doi.org/10.1007/s10533-016-0197-5, 2016.
McSherry, M. E. and Ritchie, M. E.:
Effects of grazing on grassland soil carbon: a global review,
Glob. Change Biol.,
19, 1347–1357, https://doi.org/10.1111/gcb.12144, 2013.
Mooshammer, M., Wanek, W., Zechmeister-Boltenstern, S., and Richter, A. A.:
Stoichiometric imbalances between terrestrial decomposer communities and their resources: mechanisms and implications of microbial adaptations to their resources,
Front. Microbiol.,
5, 22, https://doi.org/10.3389/fmicb.2014.00022, 2014.
NASEM (National Academies of Sciences, Engineering, and Medicine):
Negative Emissions Technologies and Reliable Sequestration: A Research Agenda,
The National Academies Press, Washington, DC, 2019.
NEON:
Data Product DP4.00200.001, Bundled data products – eddy covariance,
National Ecological Observatory Network, Battelle, Boulder, CO, USA, 2020a.
NEON: NEON data, available at: https://data.neonscience.org/, last access: 18 May 2020b.
Oak Ridge National Laboratory: MODIS/VIIRS Land Product Subsets, available at: https://modis.ornl.gov/,
last access: 18 May 2020.
Ogée, J. and Brunet, Y.:
A forest floor model for heat and moisture including a litter layer,
J. Hydrol.,
255, 212–233, https://doi.org/10.1016/S0022-1694(01)00515-7, 2002.
Ojima, D. S., Schimel, D. S., Parton, W. J., and Owensby, C. E.:
Long- and short-term effects of fire on nitrogen cycling in tallgrass prairie,
Biogeochemistry,
24, 67–84, https://doi.org/10.1007/BF02390180, 1994.
Ota, M., Nagai, H., and Koarashi, J.:
Root and dissolved organic carbon controls on subsurface soil carbon dynamics: A model approach,
J. Geophys. Res.-Biogeo.,
118, 1646–1659, https://doi.org/10.1002/2013JG002379, 2013.
Parton, W. J., Schimel, D. S., Cole, C. V., and Ojima, D. S.:
Analysis of Factors Controlling Soil Organic Matter Levels in Great Plains Grasslands,
Soil Sci. Soc. Am. J.,
51, 1173–1179, https://doi.org/10.2136/sssaj1987.03615995005100050015x, 1987.
Parton, W. J., Hartman, M., Ojima, D., and Schimel, D.:
DAYCENT and its land surface submodel: description and testing,
Global Planet. Change,
19, 35–48, https://doi.org/10.1016/S0921-8181(98)00040-X, 1998.
Piñeiro, G., Paruelo, J. M., Oesterheld, M., and Jobbágy, E. G.:
Pathways of Grazing Effects on Soil Organic Carbon and Nitrogen,
Rangeland Ecol. Manag.,
63, 109–119, https://doi.org/10.2111/08-255.1, 2010.
Poeplau, C., Don, A., Six, J., Kaiser, M., Benbi, D., Chenu, C., Cotrufo, M. F., Derrien, D., Gioacchini, P., Grand, S., Gregorich, E., Griepentrog, M., Gunina, A., Haddix, M., Kuzyakov, Y., Kühnel, A., Macdonald, L. M., Soong, J., Trigalet, S., Vermeire, M.-L., Rovira, P., van Wesemael, B., Wiesmeier, M., Yeasmin, S., Yevdokimov, I., and Nieder, R.:
Isolating organic carbon fractions with varying turnover rates in temperate agricultural soils – A comprehensive method comparison,
Soil Biol. Biochem.,
125, 10–26, https://doi.org/10.1016/j.soilbio.2018.06.025, 2018.
Raes, D., Steduto, P., Hsiao, T. C., and Fereres, E.:
AquaCrop — The FAO Crop Model to Simulate Yield Response to Water: II. Main Algorithms and Software Description,
Agron. J.,
101, 438–447, https://doi.org/10.2134/agronj2008.0140s, 2009.
Rasmussen, C., Heckman, K., Wieder, W. R., Keiluweit, M., Lawrence, C. R., Berhe, A. A., Blankinship, J. C., Crow, S. E., Druhan, J. L., Hicks Pries, C. E., Marin-Spiotta, E., Plante, A. F., Schädel, C., Schimel, J. P., Sierra, C. A., Thompson, A., and Wagai, R.: Beyond clay: towards an improved set of variables for predicting soil organic matter content, Biogeochemistry, 137, 297–306, https://doi.org/10.1007/s10533-018-0424-3, 2018.
R Core Team: R: A Language and Environment for Statistical Computing,
R Foundation for Statistical Computing, Vienna, Austria, 2017.
Reichstein, M., Subke, J.-A., Angeli, A. C., and Tenhunen, J. D.:
Does the temperature sensitivity of decomposition of soil organic matter depend upon water content, soil horizon, or incubation time?,
Glob. Change Biol.,
11, 1754–1767, https://doi.org/10.1111/j.1365-2486.2005.001010.x, 2005.
Robertson, A. D., Paustian, K., Ogle, S., Wallenstein, M. D., Lugato, E., and Cotrufo, M. F.: Unifying soil organic matter formation and persistence frameworks: the MEMS model, Biogeosciences, 16, 1225–1248, https://doi.org/10.5194/bg-16-1225-2019, 2019.
Ross, P. J.:
Modeling Soil Water and Solute Transport—Fast, Simplified Numerical Solutions,
Agron. J.,
95, 1352–1361, https://doi.org/10.2134/agronj2003.1352, 2003.
Rowland, A. P. and Roberts, J. D.:
Lignin and cellulose fractionation in decomposition studies using acid-detergent fibre methods,
Commun. Soil Sci. Plan.,
25, 269–277, https://doi.org/10.1080/00103629409369035, 1994.
Rumpel, C. and Kögel-Knabner, I.:
Deep soil organic matter – a key but poorly understood component of terrestrial C cycle,
Plant Soil,
338, 143–158, https://doi.org/10.1007/s11104-010-0391-5, 2011.
Running, S., Mu, Q., and Zhao, M.:
MOD17A3H MODIS/Terra Net Primary Production Yearly L4 Global 500m SIN Grid V006, NASA EOSDIS Land Processes DAAC,
NASA EOSDIS Land Processes DAAC,
Sioux Falls, South Dakota, https://doi.org/10.5067/MODIS/MOD17A3H.006, 2015.
Saxton, K. E. and Rawls, W. J.:
Soil Water Characteristic Estimates by Texture and Organic Matter for Hydrologic Solutions,
Soil Sci. Soc. Am. J.,
70, 1569–1578, https://doi.org/10.2136/sssaj2005.0117, 2006.
Schaefer, G. L., Cosh, M. H., and Jackson, T. J.:
The USDA Natural Resources Conservation Service Soil Climate Analysis Network (SCAN),
J. Atmos. Ocean. Tech.,
24, 2073–2077, https://doi.org/10.1175/2007JTECHA930.1, 2007.
Schaefer, K., Schwalm, C. R., Williams, C., Arain, M. A., Barr, A., Chen, J. M., Davis, K. J., Dimitrov, D., Hilton, T. W., Hollinger, D. Y., Humphreys, E., Poulter, B., Raczka, B. M., Richardson, A. D., Sahoo, A., Thornton, P., Vargas, R., Verbeeck, H., Anderson, R., Baker, I., Black, T. A., Bolstad, P., Chen, J., Curtis, P. S., Desai, A. R., Dietze, M., Dragoni, D., Gough, C., Grant, R. F., Gu, L., Jain, A., Kucharik, C., Law, B., Liu, S., Lokipitiya, E., Margolis, H. A., Matamala, R., McCaughey, J. H., Monson, R., Munger, J. W., Oechel, W., Peng, C., Price, D. T., Ricciuto, D., Riley, W. J., Roulet, N., Tian, H., Tonitto, C., Torn, M., Weng, E., and Zhou, X.:
A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis,
J. Geophys. Res.-Biogeo.,
117, G03010, https://doi.org/10.1029/2012JG001960, 2012.
Schepers, J. S., Francis, D. D., Vigil, M., and Below, F. E.:
Comparison of corn leaf nitrogen concentration and chlorophyll meter readings,
Commun. Soil Sci. Plan.,
23, 2173–2187, https://doi.org/10.1080/00103629209368733, 1992.
Schrumpf, M., Kaiser, K., Mayer, A., Hempel, G., and Trumbore, S.: Age distribution, extractability, and stability of mineral-bound organic carbon in central European soils, Biogeosciences, 18, 1241–1257, https://doi.org/10.5194/bg-18-1241-2021, 2021.
Shelia, V., Šimůnek, J., Boote, K., and Hoogenbooom, G.:
Coupling DSSAT and HYDRUS-1D for simulations of soil water dynamics in the soil-plant-atmosphere system,
J. Hydrol. Hydromech.,
66, 232–245, https://doi.org/10.1515/johh-2017-0055, 2018.
Sierra, C. A., Trumbore, S. E., Davidson, E. A., Vicca, S., and Janssens, I.:
Sensitivity of decomposition rates of soil organic matter with respect to simultaneous changes in temperature and moisture,
J. Adv. Model. Earth Sy.,
7, 335–356, https://doi.org/10.1002/2014MS000358, 2015.
Sihi, D., Davidson, E. A., Chen, M., Savage, K. E., Richardson, A. D., Keenan, T. F., and Hollinger, D. Y.:
Merging a mechanistic enzymatic model of soil heterotrophic respiration into an ecosystem model in two AmeriFlux sites of northeastern USA,
Agr. Forest Meteorol.,
252, 155–166, https://doi.org/10.1016/j.agrformet.2018.01.026, 2018.
Sinsabaugh, R. L., Turner, B. L., Talbot, J. M., Waring, B. G., Powers, J. S., Kuske, C. R., Moorhead, D. L., and Shah, J. J. F.:
Stoichiometry of microbial carbon use efficiency in soils,
Ecol. Monogr.,
86, 172–189, https://doi.org/10.1890/15-2110.1, 2016.
Six, J., Conant, R. T., Paul, E. A., and Paustian, K.:
Stabilization mechanisms of soil organic matter: Implications for C-saturation of soils,
Plant Soil,
241, 155–176, https://doi.org/10.1023/A:1016125726789, 2002.
Smith, P., Smith, J. U., Powlson, D. S., McGill, W. B., Arah, J. R. M., Chertov, O. G., Coleman, K., Franko, U., Frolking, S., Jenkinson, D. S., Jensen, L. S., Kelly, R. H., Klein-Gunnewiek, H., Komarov, A. S., Li, C., Molina, J. A. E., Mueller, T., Parton, W. J., Thornley, J. H. M., and Whitmore, A. P.:
A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments,
Geoderma,
81, 153–225, https://doi.org/10.1016/S0016-7061(97)00087-6, 1997.
Soares, M. and Rousk, J.:
Microbial growth and carbon use efficiency in soil: Links to fungal-bacterial dominance, SOC-quality and stoichiometry,
Soil Biol. Biochem.,
131, 195–205, https://doi.org/10.1016/j.soilbio.2019.01.010, 2019.
Soest, P. J. V., Robertson, J. B., and Lewis, B. A.:
Methods for Dietary Fiber, Neutral Detergent Fiber, and Nonstarch Polysaccharides in Relation to Animal Nutrition,
J. Dairy Sci.,
74, 3583–3597, https://doi.org/10.3168/jds.S0022-0302(91)78551-2, 1991.
Sokol, N. W. and Bradford, M. A.:
Microbial formation of stable soil carbon is more efficient from belowground than aboveground input,
Nat. Geosci.,
12, 46–53, https://doi.org/10.1038/s41561-018-0258-6, 2019.
Sokol, N. W., Sanderman, J., and Bradford, M. A.:
Pathways of mineral-associated soil organic matter formation: Integrating the role of plant carbon source, chemistry, and point of entry,
Glob. Change Biol.,
25, 12–24, https://doi.org/10.1111/gcb.14482, 2019.
Soltani, A. and Sinclair, T. R.:
Modeling physiology of crop development, growth and yield,
CABI, Wallingford, xiii + 322 pp., https://doi.org/10.1079/9781845939700.0000, 2012.
Soong, J. L. and Cotrufo, M. F.:
Annual burning of a tallgrass prairie inhibits C and N cycling in soil, increasing recalcitrant pyrogenic organic matter storage while reducing N availability,
Glob. Change Bio.,
21, 2321–2333, https://doi.org/10.1111/gcb.12832, 2015.
Soong, J. L., Parton, W. J., Calderon, F., Campbell, E. E., and Cotrufo, M. F.:
A new conceptual model on the fate and controls of fresh and pyrolized plant litter decomposition,
Biogeochemistry,
124, 27–44, https://doi.org/10.1007/s10533-015-0079-2, 2015.
Stewart, C. E., Moturi, P., Follett, R. F., and Halvorson, A. D.:
Lignin biochemistry and soil N determine crop residue decomposition and soil priming,
Biogeochemistry,
124, 335–351, https://doi.org/10.1007/s10533-015-0101-8, 2015.
Sulman, B. N., Phillips, R. P., Oishi, A. C., Shevliakova, E., and Pacala, S. W.:
Microbe-driven turnover offsets mineral-mediated storage of soil carbon under elevated CO2,
Nat. Clim. Change,
4, 1099–1102, https://doi.org/10.1038/nclimate2436, 2014.
Sulman, B. N., Brzostek, E. R., Medici, C., Shevliakova, E., Menge, D. N. L., and Phillips, R. P.:
Feedbacks between plant N demand and rhizosphere priming depend on type of mycorrhizal association,
Ecol. Lett.,
20, 1043–1053, https://doi.org/10.1111/ele.12802, 2017.
Sulman, B. N., Moore, J. A. M., Abramoff, R., Averill, C., Kivlin, S., Georgiou, K., Sridhar, B., Hartman, M. D., Wang, G., Wieder, W. R., Bradford, M. A., Luo, Y., Mayes, M. A., Morrison, E., Riley, W. J., Salazar, A., Schimel, J. P., Tang, J., and Classen, A. T.:
Multiple models and experiments underscore large uncertainty in soil carbon dynamics,
Biogeochemistry,
141, 109–123, https://doi.org/10.1007/s10533-018-0509-z, 2018.
Sun, G., Zhu-Barker, X., Chen, D., Liu, L., Zhang, N., Shi, C., He, L., and Lei, Y.:
Responses of root exudation and nutrient cycling to grazing intensities and recovery practices in an alpine meadow: An implication for pasture management,
Plant Soil,
416, 515–525, https://doi.org/10.1007/s11104-017-3236-7, 2017.
Tian, K., Kong, X., Yuan, L., Lin, H., He, Z., Yao, B., Ji, Y., Yang, J., Sun, S., and Tian, X.:
Priming effect of litter mineralization: the role of root exudate depends on its interactions with litter quality and soil condition,
Plant Soil,
440, 457–471, https://doi.org/10.1007/s11104-019-04070-5, 2019.
Tiessen, H. and Stewart, J. W. B.:
Particle-size Fractions and their Use in Studies of Soil Organic Matter: II. Cultivation Effects on Organic Matter Composition in Size Fractions,
Soil Sci. Soc. Am. J.,
47, 509–514, https://doi.org/10.2136/sssaj1983.03615995004700030023x, 1983.
Todd-Brown, K. E. O., Randerson, J. T., Post, W. M., Hoffman, F. M., Tarnocai, C., Schuur, E. A. G., and Allison, S. D.: Causes of variation in soil carbon simulations from CMIP5 Earth system models and comparison with observations, Biogeosciences, 10, 1717–1736, https://doi.org/10.5194/bg-10-1717-2013, 2013.
Todd-Brown, K. E. O., Randerson, J. T., Hopkins, F., Arora, V., Hajima, T., Jones, C., Shevliakova, E., Tjiputra, J., Volodin, E., Wu, T., Zhang, Q., and Allison, S. D.: Changes in soil organic carbon storage predicted by Earth system models during the 21st century, Biogeosciences, 11, 2341–2356, https://doi.org/10.5194/bg-11-2341-2014, 2014.
USDA-NRCS: SCAN data, available at: https://www.wcc.nrcs.usda.gov/scan/,
last access: 18 May 2020.
von Lützow, M., Kögel-Knabner, I., Ekschmitt, K., Flessa, H., Guggenberger, G., Matzner, E., and Marschner, B.:
SOM fractionation methods: Relevance to functional pools and to stabilization mechanisms,
Soil Biol. Biochem.,
39, 2183–2207, https://doi.org/10.1016/j.soilbio.2007.03.007, 2007.
Vrugt, J. A.:
Markov chain Monte Carlo simulation using the DREAM software package: Theory, concepts, and MATLAB implementation,
Environ. Modell. Softw.,
75, 273–316, https://doi.org/10.1016/j.envsoft.2015.08.013, 2016.
Vrugt, J. A. and Ter Braak, C. J. F.: DREAM(D): an adaptive Markov Chain Monte Carlo simulation algorithm to solve discrete, noncontinuous, and combinatorial posterior parameter estimation problems, Hydrol. Earth Syst. Sci., 15, 3701–3713, https://doi.org/10.5194/hess-15-3701-2011, 2011.
Walse, C., Berg, B., and Sverdrup, H.:
Review and synthesis of experimental data on organic matter decomposition with respect to the effect of temperature, moisture, and acidity,
Environ. Rev.,
6, 25–40, https://doi.org/10.1139/a98-001, 1998.
Wang, G., Post, W. M., and Mayes, M. A.:
Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses,
Ecol. Appl.,
23, 255–272, https://doi.org/10.1890/12-0681.1, 2013.
Watson, R. T., Noble, I. R., Bolin, B., Ravindranath, N. H., Verardo, D. J., and Dokken, D. J.:
Land use, land-use change and forestry: a special report of the Intergovernmental Panel on Climate Change,
Cambridge University Press, Cambridge, United Kingdom, 2000.
Wei, L., HaiZhou, H., ZhiNan, Z., and GaoLin, W.:
Effects of grazing on the soil properties and C and N storage in relation to biomass allocation in an alpine meadow,
J. Soil Sci. Plant Nutr.,
11, 27–39, 2011.
Wieder, W. R., Grandy, A. S., Kallenbach, C. M., and Bonan, G. B.: Integrating microbial physiology and physio-chemical principles in soils with the MIcrobial-MIneral Carbon Stabilization (MIMICS) model, Biogeosciences, 11, 3899–3917, https://doi.org/10.5194/bg-11-3899-2014, 2014.
Wolf, J.:
User guide for LINTUL5: Simple generic model for simulation of crop growth under potential, water limited and nitrogen, phosphorus and potassium limited conditions,
Wageningen University, Wageningen, the Netherlands, 2012.
Yan, H., Wang, S., Billesbach, D., Oechel, W., Bohrer, G., Meyers, T., Martin, T. A., Matamala, R., Phillips, R. P., Rahman, F., Yu, Q., and Shugart, H. H.:
Improved global simulations of gross primary product based on a new definition of water stress factor and a separate treatment of C3 and C4 plants,
Ecol. Model.,
297, 42–59, https://doi.org/10.1016/j.ecolmodel.2014.11.002, 2015.
Yin, X. and van Laar, H. H.:
Crop Systems Dynamics: An Ecophysiological Simulation Model for Genotype-by-environment Interactions,
Wageningen Academic Pub, 169 pp., Wageningen, the Netherlands, 2005.
Yuste, J. C., Baldocchi, D. D., Gershenson, A., Goldstein, A., Misson, L., and Wong, S.:
Microbial soil respiration and its dependency on carbon inputs, soil temperature and moisture,
Glob. Change Biol.,
13, 2018–2035, https://doi.org/10.1111/j.1365-2486.2007.01415.x, 2007.
Zhang, D., Hui, D., Luo, Y., and Zhou, G.:
Rates of litter decomposition in terrestrial ecosystems: global patterns and controlling factors,
J. Plant Ecol.,
1, 85–93, https://doi.org/10.1093/jpe/rtn002, 2008.
Zhang, Y., Lavallee, J., Robertson, A., Even, R., Ogle, S., Paustian, K., and Cotrufo, F.:
Data for manuscript of “Simulating measurable ecosystem carbon and nitrogen dynamics with the mechanistically-defined MEMS 2.0 model”, Zenodo, https://doi.org/10.5281/zenodo.4404685, 2020.
Zhang, Y., Qian, Y., Bremer, D. J., and Kaye, J. P.:
Simulation of Nitrous Oxide Emissions and Estimation of Global Warming Potential in Turfgrass Systems Using the DAYCENT Model,
J. Environ. Qual.,
42, 1100–1108, https://doi.org/10.2134/jeq2012.0486, 2013.
Zhang, Y., Suyker, A., and Paustian, K.:
Improved Crop Canopy and Water Balance Dynamics for Agroecosystem Modeling Using DayCent,
Agron. J.,
110, 511–524, https://doi.org/10.2134/agronj2017.06.0328, 2018.
Zhang, Y., Arabi, M., and Paustian, K.:
Analysis of parameter uncertainty in model simulations of irrigated and rainfed agroecosystems,
Environ. Modell. Softw.,
126, 104642, https://doi.org/10.1016/j.envsoft.2020.104642, 2020a.
Zhang, Y., Gurung, R., Marx, E., Williams, S., Ogle, S. M., and Paustian, K.:
DayCent Model Predictions of NPP and Grain Yields for Agricultural Lands in the Contiguous U. S.,
J. Geophys. Res.-Biogeo.,
125, e2020JG005750, https://doi.org/10.1029/2020JG005750, 2020b.
Zimmermann, M., Leifeld, J., Schmidt, M. W. I., Smith, P., and Fuhrer, J.:
Measured soil organic matter fractions can be related to pools in the RothC model,
Eur. J. Soil. Sci.,
58, 658–667, https://doi.org/10.1111/j.1365-2389.2006.00855.x, 2007.
Short summary
Soil organic matter (SOM) is essential for the health of soils, and the accumulation of SOM helps removal of CO2 from the atmosphere. Here we present the result of the continued development of a mathematical model that simulates SOM and its measurable fractions. In this study, we simulated several grassland sites in the US, and the model generally captured the carbon and nitrogen amounts in SOM and their distribution between the measurable fractions throughout the entire soil profile.
Soil organic matter (SOM) is essential for the health of soils, and the accumulation of SOM...
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