Skip to main content

Advertisement

SpringerLink
Log in

Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates

  • Original Paper
  • Published:
Stochastic Environmental Research and Risk Assessment Aims and scope Submit manuscript

Abstract

The biogeochemical complexity of environmental models is increasing continuously and model reliability must be reanalysed when new implementations are brought about. This work aims to identify influential biogeochemical parameters that control the Soil Organic Matter (SOM) dynamics and greenhouse gas emissions in different ecosystems and climates predicted by a physically-based mechanistic model. This explicitly accounts for four pools of organic polymers, seven pools of organic monomers, five microbial functional groups, and inorganic N and C species. We first benchmarked our model against vertical SOM profiles measured in a temperate forest in North-Eastern Bavaria, Germany (Staudt and Foken in Documentation of reference data for the experimental areas of the Bayreuth Centre for Ecology and Environmental Research (BayCEER) at the Waldstein site. Univ, Bayreuth, Department of Micrometeorology, 2007). Next, we conducted a sensitivity analysis to biogeochemical parameters using modified Morris indices for target SOM pools and gas emissions from a tropical, a temperate, and a semi-arid grassland in Australia. We found that greenhouse gas emissions, the SOM stock, and the fungi-to-bacteria ratio in the top soil were more sensitive to the mortality of aerobic bacteria than other biogeochemical parameters. The larger \({\hbox {CO}_2}\) emission rates in forests than in grasslands were explained by a greater dissolved SOM content. Finally, we found that the soil N availability was largely controlled by vegetation inputs in forests and by atmospheric fixation in grasslands.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Abbreviations

AER:

Aerobic bacteria

AmA:

Amino-acids

AmS:

Amino-sugar

AOB:

Ammonia oxidizing bacteria

BAMS2:

Biotic and abiotic model of SOM version 2

C:

Carbon

Cls:

Celulose

DEN:

Denitrifying bacteria

F:

Fungi

GSA:

Global sensitivity analysis

HCls:

Hemi-celulose

Lig:

Lignin

Lip:

Lipids

LSA:

Local sensitivity analysis

Msa:

Monosaccarides

N:

Nitrogen

NOB:

Nitrite oxidizing bacteria

Nti:

Nucleotid

OAT:

One-factor at time method

OraA:

Organic acid

Pgl:

Peptidoglycan

Phe:

Phenols

SAG:

Semi-arid grassland

SOM:

Soil organic matter

TEF:

Temperate forest

TEG:

Temperate grassland

TRG:

Tropical grassland

References

  • Achat DL, Augusto L, Gallet-Budynek A, Loustau D (2016) Future challenges in coupled C–N–P cycle models for terrestrial ecosystems under global change: a review. Biogeochemistry 131(1–2):173–202

    CAS  Google Scholar 

  • Ahrens B, Braakhekke MC, Guggenberger G, Schrumpf M, Reichstein M (2015) 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

    CAS  Google Scholar 

  • Allen RG, Clemmens AJ, Burt CM, Solomon K, O’Halloran T (2005) Prediction accuracy for projectwide evapotranspiration using crop coefficients and reference evapotranspiration. J Irrig Drain Eng 131(1):24–36

    Google Scholar 

  • Allen RG, Pereira LS, Raes D, Smith M et al (1998) Crop evapotranspiration-guidelines for computing crop water requirements-FAO irrigation and drainage paper 56. FAO Rome 300(9):D05109

    Google Scholar 

  • Allison SD, Goulden ML (2017) Consequences of drought tolerance traits for microbial decomposition in the dement model. Soil Biol Biochem 107:104–113

    CAS  Google Scholar 

  • Allison SD, Wallenstein MD, Bradford MA (2010) Soil-carbon response to warming dependent on microbial physiology. Nat Geosci 3(5):336

    CAS  Google Scholar 

  • Alshameri A, He H, Zhu J, Xi Y, Zhu R, Ma L, Tao Q (2018) Adsorption of ammonium by different natural clay minerals: characterization, kinetics and adsorption isotherms. Appl Clay Sci 159:83–93

    CAS  Google Scholar 

  • Armstrong A, Waldron S, Ostle NJ, Richardson H, Whitaker J (2015) Biotic and abiotic factors interact to regulate northern peatland carbon cycling. Ecosystems 18(8):1395–1409

    CAS  Google Scholar 

  • Bailey VL, Smith JL, Bolton H Jr (2002) Fungal-to-bacterial ratios in soils investigated for enhanced c sequestration. Soil Biol Biochem 34(7):997–1007

    CAS  Google Scholar 

  • Batjes NH (2014) Total carbon and nitrogen in the soils of the world. Eur J Soil Sci 65(1):10–21

    CAS  Google Scholar 

  • Bethke CM (2007) Geochemical and biogeochemical reaction modeling. Cambridge University Press, Cambridge

    Google Scholar 

  • Black A, Waring S (1979) Adsorption of nitrate, chloride and sulfate by some highly weathered soils from south-wast queensland. Soil Res 17(2):271–282

    CAS  Google Scholar 

  • Blanc P, Lassin A, Piantone P, Azaroual M, Jacquemet N, Fabbri A, Gaucher EC (2012) Thermoddem: a geochemical database focused on low temperature water/rock interactions and waste materials. Appl Geochem 27(10):2107–2116

    CAS  Google Scholar 

  • Bressan M, Mougel C, Dequiedt S, Maron P-A, Lemanceau P, Ranjard L (2008) Response of soil bacterial community structure to successive perturbations of different types and intensities. Environ Microbiol 10(8):2184–2187

    Google Scholar 

  • Brooks R, Corey T (1964) Hydrau uc properties of porous media. Hydrol Pap Colorado State Univ 24:37

    Google Scholar 

  • Campolongo F, Cariboni J, Saltelli A (2007) An effective screening design for sensitivity analysis of large models. Environ Modell Softw 22(10):1509–1518

    Google Scholar 

  • Campolongo F, Saltelli A, Cariboni J (2011) From screening to quantitative sensitivity analysis. A unified approach. Comput Phys Commun 182(4):978–988

    CAS  Google Scholar 

  • Cariboni J, Gatelli D, Liska R, Saltelli A (2007) The role of sensitivity analysis in ecological modelling. Ecol Model 203(1–2):167–182

    Google Scholar 

  • Ceriotti G, Guadagnini L, Porta G, Guadagnini A (2018) Local and global sensitivity analysis of Cr (VI) geogenic leakage under uncertain environmental conditions. Water Res Res 54(8):5785–5802

    CAS  Google Scholar 

  • Chen J, Brissette FP, Leconte R (2010) A daily stochastic weather generator for preserving low-frequency of climate variability. J Hydrol 388(3–4):480–490

    Google Scholar 

  • Ciriello V, Di Federico V, Riva M, Cadini F, De Sanctis J, Zio E, Guadagnini A (2013) Polynomial chaos expansion for global sensitivity analysis applied to a model of radionuclide migration in a randomly heterogeneous aquifer. Stoch Environ Res Risk Assess 27(4):945–954

    Google Scholar 

  • Clapp RB, Hornberger GM (1978) Empirical equations for some soil hydraulic properties. Water Res Res 14(4):601–604

    Google Scholar 

  • Colombo I, Nobile F, Porta G, Scotti A, Tamellini L (2018) Uncertainty quantification of geochemical and mechanical compaction in layered sedimentary basins. Comput Methods Appl Mech Eng 328:122–146

    Google Scholar 

  • Colombo I, Porta GM, Ruffo P, Guadagnini A (2017) Uncertainty quantification of overpressure buildup through inverse modeling of compaction processes in sedimentary basins. Hydrogeol J 25(2):385–403

    CAS  Google Scholar 

  • Crawford J (1999) Geochemical modelling–a review of current capabilities and future directions. Department of Chemical Engineering and Technology, Division of Chemical Engineering, Royal Institute of Technology (KTH), Stockholm, Report 262

  • De Vries FT, Hoffland E, van Eekeren N, Brussaard L, Bloem J (2006) Fungal/bacterial ratios in grasslands with contrasting nitrogen management. Soil Biol Biochem 38(8):2092–2103

    Google Scholar 

  • Dell’Oca A, Riva M, Guadagnini A (2017) Moment-based metrics for global sensitivity analysis of hydrological systems. Hydrol Earth Syst Sci 21(12):6219–6234

    Google Scholar 

  • Fabian J, Zlatanovic S, Mutz M, Premke K (2017) Fungal-bacterial dynamics and their contribution to terrigenous carbon turnover in relation to organic matter quality. ISME J 11(2):415

    CAS  Google Scholar 

  • Fisher JB, Whittaker RJ, Malhi Y (2011) Et come home: potential evapotranspiration in geographical ecology. Glob Ecol Biogeogr 20(1):1–18

    Google Scholar 

  • Fontaine S, Barot S (2005) Size and functional diversity of microbe populations control plant persistence and long-term soil carbon accumulation. Ecol Lett 8(10):1075–1087

    Google Scholar 

  • Formaggia L, Guadagnini A, Imperiali I, Lever V, Porta G, Riva M, Scotti A, Tamellini L (2013) Global sensitivity analysis through polynomial chaos expansion of a basin-scale geochemical compaction model. Comput Geosci 17(1):25–42

    Google Scholar 

  • Fornara D, Tilman D (2008) Plant functional composition influences rates of soil carbon and nitrogen accumulation. J Ecol 96(2):314–322

    CAS  Google Scholar 

  • Georgiou K, Abramoff RZ, Harte J, Riley WJ, Torn MS (2017) Microbial community-level regulation explains soil carbon responses to long-term litter manipulations. Nat Commun 8(1):1223

    Google Scholar 

  • Gerber S, Hedin LO, Oppenheimer M, Pacala SW, Shevliakova E (2010) Nitrogen cycling and feedbacks in a global dynamic land model. Glob Biogeochem Cycles 24(1):1–15

    Google Scholar 

  • German DP, Marcelo KR, Stone MM, Allison SD (2012) The Michaelis–Menten kinetics of soil extracellular enzymes in response to temperature: a cross-latitudinal study. Glob Change Biol 18(4):1468–1479

    Google Scholar 

  • Grayston S, Vaughan D, Jones D (1997) Rhizosphere carbon flow in trees, in comparison with annual plants: the importance of root exudation and its impact on microbial activity and nutrient availability. Appl Soil Ecol 5(1):29–56

    Google Scholar 

  • Guggenberger G, Kaiser K (2003) Dissolved organic matter in soil: challenging the paradigm of sorptive preservation. Geoderma 113(3–4):293–310

    CAS  Google Scholar 

  • Hefting MM, Clement J-C, Bienkowski P, Dowrick D, Guenat C, Butturini A, Topa S, Pinay G, Verhoeven JT (2005) The role of vegetation and litter in the nitrogen dynamics of riparian buffer zones in europe. Ecol Eng 24(5):465–482

    Google Scholar 

  • Hengl T, de Jesus JM, Heuvelink GB, Gonzalez MR, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B et al (2017) Soilgrids250m: global gridded soil information based on machine learning. PLoS One 12(2):e0169748

    Google Scholar 

  • Herman J, Kollat J, Reed P, Wagener T (2013) Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models. Hydrol Earth Syst Sci 17(7):2893–2903

    Google Scholar 

  • Howell J, Coyne MS, Cornelius P (1996) Effect of sediment particle size and temperature on fecal bacteria mortality rates and the fecal coliform/fecal streptococci ratio. J Environ Qual 25(6):1216–1220

    CAS  Google Scholar 

  • Iooss B, Lemaître P (2015) A review on global sensitivity analysis methods. In Uncertainty management in simulation-optimization of complexsystems (pp 101–122). Springer, Boston, MA

    Google Scholar 

  • Jenkinson D, Coleman K (2008) The turnover of organic carbon in subsoils. Part 2. Modelling carbon turnover. Eur J Soil Sci 59(2):400–413

    Google Scholar 

  • Kaspar F, Müller-Westermeier G, Penda E, Mächel H, Zimmermann K, Kaiser-Weiss A, Deutschländer T (2013) Monitoring of climate change in Germany-data, products and services of germany’s national climate data centre. Adv Sci Res 10(1):99–106

    Google Scholar 

  • Larocque GR, Bhatti JS, Boutin R, Chertov O (2008) Uncertainty analysis in carbon cycle models of forest ecosystems: research needs and development of a theoretical framework to estimate error propagation. Ecol Model 219(3–4):400–412

    Google Scholar 

  • Lawrence CR, Neff JC, Schimel JP (2009) Does adding microbial mechanisms of decomposition improve soil organic matter models? a comparison of four models using data from a pulsed rewetting experiment. Soil Biol Biochem 41(9):1923–1934

    CAS  Google Scholar 

  • Lehmann J, Kleber M (2015) The contentious nature of soil organic matter. Nature 528(7580):60

    CAS  Google Scholar 

  • Luo Z, Baldock J, Wang E (2017) Modelling the dynamic physical protection of soil organic carbon: insights into carbon predictions and explanation of the priming effect. Glob Change Biol 23(12):5273–5283

    Google Scholar 

  • Maggi F (2019) Brtsim, a general-purpose computational solver for hydrological, biogeochemical, and ecosystem dynamics. arXiv preprint arXiv:1903.07015

  • Maggi F, Gu C, Riley W, Hornberger G, Venterea R, Xu T, Spycher N, Steefel C, Miller N, Oldenburg C (2008) A mechanistic treatment of the dominant soilnitrogen cycling processes: model development, testing, and application. J Geophys Res Biogeosci 113(G2):1–13

    Google Scholar 

  • Maggi F, Tang M, Fiona H, Riley WJ (2018) The thermodynamic links between substrate, enzyme, and microbial dynamics in michaelis-menten-monod kinetics. Int J Chem Kinet 50(5):343–356

    CAS  Google Scholar 

  • Maier CG, Kelley K (1932) An equation for the representation of high-temperature heat content data1. J Am Chem Soc 54(8):3243–3246

    CAS  Google Scholar 

  • Menberg K, Heo Y, Choudhary R (2016) Sensitivity analysis methods for building energy models: comparing computational costs and extractable information. Energy Build 133:433–445

    Google Scholar 

  • Mench M, Martin E (1991) Mobilization of cadmium and other metals from two soils by root exudates of Zea mays l., Nicotiana tabacum l. and Nicotiana rustica l. Plant and Soil 132(2):187–196

    CAS  Google Scholar 

  • Menne MJ, Durre I, Vose RS, Gleason BE, Houston TG (2012) An overview of the global historical climatology network-daily database. J Atmos Ocean Technol 29(7):897–910

    Google Scholar 

  • Moorhead DL, Sinsabaugh RL (2006) A theoretical model of litter decay and microbial interaction. Ecol Monogr 76(2):151–174

    Google Scholar 

  • Moretto AS, Distel RA, Didoné NG (2001) Decomposition and nutrient dynamic of leaf litter and roots from palatable and unpalatable grasses in a semi-arid grassland. Appl Soil Ecol 18(1):31–37

    Google Scholar 

  • Morris MD (1991) Factorial sampling plans for preliminary computational experiments. Technometrics 33(2):161–174

    Google Scholar 

  • Mouginot C, Kawamura R, Matulich KL, Berlemont R, Allison SD, Amend AS, Martiny AC (2014) Elemental stoichiometry of fungi and bacteria strains from grassland leaf litter. Soil Biol Biochem 76:278–285

    CAS  Google Scholar 

  • Parkhurst DL, Appelo C (2013) Description of input and examples for phreeqc version 3: a computer program for speciation, batch-reaction, one-dimensional transport, and inverse geochemical calculations. Technical report, US Geological Survey

  • Parton WJ, Stewart JW, Cole CV (1988) Dynamics of C, N, P and S in grassland soils: a model. Biogeochemistry 5(1):109–131

    CAS  Google Scholar 

  • Pianosi F, Beven K, Freer J, Hall JW, Rougier J, Stephenson DB, Wagener T (2016) Sensitivity analysis of environmental models: a systematic review with practical workflow. Environ Model Softw 79:214–232

    Google Scholar 

  • Porta G, la Cecilia D, Guadagnini A, Maggi F (2018) Implications of uncertain bioreactive parameters on a complex reaction network of atrazine biodegradation in soil. Adv Water Res 121:263–276

    CAS  Google Scholar 

  • Postma D, Larsen F, Hue NTM, Duc MT, Viet PH, Nhan PQ, Jessen S (2007) Arsenic in groundwater of the red river floodplain, vietnam: controlling geochemical processes and reactive transport modeling. Geochim Cosmochim Acta 71(21):5054–5071

    CAS  Google Scholar 

  • Rakovec O, Hill MC, Clark M, Weerts A, Teuling A, Uijlenhoet R (2014) Distributed evaluation of local sensitivity analysis (DELSA), with application to hydrologic models. Water Res Res 50(1):409–426

    Google Scholar 

  • Razavi S, Gupta HV (2015) What do we mean by sensitivity analysis? the need for comprehensive characterization of global sensitivity in earth and environmental systems models. Water Res Res 51(5):3070–3092

    Google Scholar 

  • Riley W (2013) Using model reduction to predict the soil-surface c 18 oo flux: an example of representing complex biogeochemical dynamics in a computationally efficient manner. Geosci Model Dev 6(2):345–352

    Google Scholar 

  • Riley W, Maggi F, Kleber M, Torn M, Tang J, Dwivedi D, Guerry N (2014) Long residence times of rapidly decomposable soil organic matter: application of a multi-phase, multi-component, and vertically resolved model (bams1) to soil carbon dynamics. Geosci Model Dev 7(4):1335–1355

    Google Scholar 

  • Saltelli A, Ratto M, Andres T, Campolongo F, Cariboni J, Gatelli D, Saisana M, Tarantola S (2008) Global sensitivity analysis: the primer. John Wiley and Sons, New Jersey

    Google Scholar 

  • Schimel J, Balser TC, Wallenstein M (2007) Microbial stress-response physiology and its implications for ecosystem function. Ecology 88(6):1386–1394

    Google Scholar 

  • Sierra C, Müller M, Trumbore S et al (2012) Models of soil organic matter decomposition: the soilr package, version 1.0. Geosci Model Dev 5:1045–1060

    Google Scholar 

  • Sivakumar B (2008) Dominant processes concept, model simplification and classification framework in catchment hydrology. Stoch Environ Res Risk Assess 22(6):737–748

    Google Scholar 

  • Six J, Conant R, Paul EA, Paustian K (2002) Stabilization mechanisms of soil organic matter: implications for c-saturation of soils. Plant and Soil 241(2):155–176

    CAS  Google Scholar 

  • Song X, Zhang J, Zhan C, Xuan Y, Ye M, Xu C (2015) Global sensitivity analysis in hydrological modeling: review of concepts, methods, theoretical framework, and applications. J Hydrol 523:739–757

    Google Scholar 

  • Staudt K, Foken T (2007) Documentation of reference data for the experimental areas of the Bayreuth Centre for Ecology and Environmental Research (BayCEER) at the Waldstein site. Univ, Bayreuth, Department of Micrometeorology

  • Stern H, Dahni RR (2013) The distribution of climate zones across Australia: identifying and explaining changes during the past century. In 25th Conference on Climate Variability and Change. American Meteorological Society, Austin

    Google Scholar 

  • Strickland MS, Rousk J (2010) Considering fungal: bacterial dominance in soils-methods, controls, and ecosystem implications. Soil Biol Biochem 42(9):1385–1395

    CAS  Google Scholar 

  • Sumner T, Shephard E, Bogle I (2012) A methodology for global-sensitivity analysis of time-dependent outputs in systems biology modelling. J R Soc Interface 9(74):2156–2166

    CAS  Google Scholar 

  • Tang FH, Riley WJ, Maggi F (2019) Hourly and daily rainfall intensification causes opposing effects on C and N emissions, storage, and leaching in dry and wet grasslands. Biogeochemistry 144(2):197–214

    CAS  Google Scholar 

  • Thiet RK, Frey SD, Six J (2006) Do growth yield efficiencies differ between soil microbial communities differing in fungal: bacterial ratios? reality check and methodological issues. Soil Biol Biochem 38(4):837–844

    CAS  Google Scholar 

  • Thomas RJ, Asakawa N (1993) Decomposition of leaf litter from tropical forage grasses and legumes. Soil Biol Biochem 25(10):1351–1361

    CAS  Google Scholar 

  • Tian W (2013) A review of sensitivity analysis methods in building energy analysis. Renew Sustain Energy Rev 20:411–419

    Google Scholar 

  • Treseder KK, Balser TC, Bradford MA, Brodie EL, Dubinsky EA, Eviner VT, Hofmockel KS, Lennon JT, Levine UY, MacGregor BJ et al (2012) Integrating microbial ecology into ecosystem models: challenges and priorities. Biogeochemistry 109(1–3):7–18

    CAS  Google Scholar 

  • Van Elsas JD, Van Overbeek LS (1993) Bacterial responses to soil stimuli. Starvation in bacteria. Springer, Boston, MA, pp 55–79

    Google Scholar 

  • Wang F, Mladenoff DJ, Forrester JA, Keough C, Parton WJ (2013) Global sensitivity analysis of a modified century model for simulating impacts of harvesting fine woody biomass for bioenergy. Ecol Model 259:16–23

    CAS  Google Scholar 

  • Wang Y, Chen B, Wieder WR, Leite M, Medlyn BE, Rasmussen M, Smith MJ, Agusto FB, Hoffman F, Luo Y (2014) Oscillatory behavior of two nonlinear microbial models of soil carbon decomposition. Biogeosciences 11(7):1817–1831

    CAS  Google Scholar 

  • Xie P, Chen M, Shi W (2010) Cpc unified gauge-based analysis of global daily precipitation. In: Preprints, 24th conference on hydrology, Atlanta, GA, American Meteorological Society, vol 2

  • Yu Y, Finke P, Wu H, Guo Z (2013) Sensitivity analysis and calibration of a soil carbon model (soilgen2) in two contrasting loess forest soils. Geosci Model Dev 6(1):29–44

    CAS  Google Scholar 

Download references

Acknowledgements

The authors greatly acknowledge the support of Alberto Guadagnini and Giovanni Porta in advising and revising the development of this work. This work is supported by the SREI2020 EnviroSphere, the Mid Career Research, and the Sydney Research Accelerator Fellowship (SOAR) of the University of Sydney. The authors acknowledge the Sydney Informatics Hub of The University of Sydney for providing the Artemis high performance computing resources that have contributed to the results reported within this work. The BRTSim solver package can be downloaded at https://sites.google.com/site/thebrtsimproject/home or at the mirror linkhttps://www.dropbox.com/sh/wrfspx9f1dvuspr/AAD5iA9PsteX3ygAJxQDxAy9a?dl=0. G. Ceriotti would like to thank the EU and MIUR for funding in the frame of the collaborative international Consortium (WE-NEED) financed under the ERA-NET WaterWorks2014 Co- funded Call. This ERA-NET is an integral part of the 2015 Joint Activities developed by the Water Challenges for a Changing World Joint Programme Initiative (Water JPI).

Author information

Authors and Affiliations

  1. Institute of Earth Sciences, Building Geopolis, University of Lausanne, Lausanne, Switzerland

    G. Ceriotti

  2. Laboratory for Environmental Engineering, School of Civil Engineering, The University of Sydney, Bld. J05, Sydney, NSW, 2006, Australia

    G. Ceriotti, F. H. M. Tang & F. Maggi

Authors
  1. G. Ceriotti
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. F. H. M. Tang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. F. Maggi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to G. Ceriotti or F. Maggi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 0 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ceriotti, G., Tang, F.H.M. & Maggi, F. Similarities and differences in the sensitivity of soil organic matter (SOM) dynamics to biogeochemical parameters for different vegetation inputs and climates. Stoch Environ Res Risk Assess 34, 2229–2244 (2020). https://doi.org/10.1007/s00477-020-01868-z

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00477-020-01868-z

Keywords

Search

Navigation