Incorporating microbial ecology concepts into global soil mineralization models to improve predictions of carbon and nitrogen fluxes

Fujita, Yuki; Witte, J.P.M.; Bodegom, Peter M. van

doi:10.1002/2013gb004595

Cited by 31 publications

(23 citation statements)

References 54 publications

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“…Equally important consequences emerge when implementing nutrient limitation effects on CUE in ecosystem models with different assumptions on how microbes affect decomposition (Fujita et al . ). The optimality hypothesis proposed here offers a minimal theoretical framework to explore how dynamic CUE could affect C pools and fluxes along nutrient availability gradients.…”

Section: Discussionmentioning

confidence: 97%

Optimal metabolic regulation along resource stoichiometry gradients

et al. 2017

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Most heterotrophic organisms feed on substrates that are poor in nutrients compared to their demand, leading to elemental imbalances that may constrain their growth and function. Flexible carbon (C)-use efficiency (CUE, C used for growth over C taken up) can represent a strategy to reduce elemental imbalances. Here, we argue that metabolic regulation has evolved to maximise the organism growth rate along gradients of nutrient availability and translated this assumption into an optimality model that links CUE to substrate and organism stoichiometry. The optimal CUE is predicted to decrease with increasing substrate C-to-nutrient ratio, and increase with nutrient amendment. These predictions are generally confirmed by empirical evidence from a new database of c. 2200 CUE estimates, lending support to the hypothesis that CUE is optimised across levels of organisation (microorganisms and animals), in aquatic and terrestrial systems, and when considering nitrogen or phosphorus as limiting nutrients.

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Section: Discussionmentioning

confidence: 97%

Optimal metabolic regulation along resource stoichiometry gradients

et al. 2017

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“…This response has been observed in the field (Fontaine et al, 2004;Sayer et al, 2011) but cannot predicted by conventional linear soil carbon models without modification (Fujita et al, 2014). Theoretically, decomposition of soil organic carbon is catalysed by extracellular enzymes that are produced by soil microbes.…”

Section: Y-p Wang Et Al: Responses Of Two Nonlinear Microbial Modementioning

confidence: 93%

Responses of two nonlinear microbial models to warming and increased carbon input

et al. 2016

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Abstract.A number of nonlinear microbial models of soil carbon decomposition have been developed. Some of them have been applied globally but have yet to be shown to realistically represent soil carbon dynamics in the field. A thorough analysis of their key differences is needed to inform future model developments. Here we compare two nonlinear microbial models of soil carbon decomposition: one based on reverse Michaelis-Menten kinetics (model A) and the other on regular Michaelis-Menten kinetics (model B). Using analytic approximations and numerical solutions, we find that the oscillatory responses of carbon pools to a small perturbation in their initial pool sizes dampen faster in model A than in model B. Soil warming always decreases carbon storage in model A, but in model B it predominantly decreases carbon storage in cool regions and increases carbon storage in warm regions. For both models, the CO 2 efflux from soil carbon decomposition reaches a maximum value some time after increased carbon input (as in priming experiments). This maximum CO 2 efflux (F max ) decreases with an increase in soil temperature in both models. However, the sensitivity of F max to the increased amount of carbon input increases with soil temperature in model A but decreases monotonically with an increase in soil temperature in model B. These differences in the responses to soil warming and carbon input between the two nonlinear models can be used to discern which model is more realistic when compared to results from field or laboratory experiments. These insights will contribute to an improved understanding of the significance of soil microbial processes in soil carbon responses to future climate change.

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“…Setting the microbial data (microbial biomass and basal respiration) as objectives slightly reduced the uncertainty of predictions of relative basal respiration (Figures and S7, Supporting Information) and predictions of relative microbial biomass (Figures and ) under ‘C Bolinder ,’ but did not improve the predictions with ‘C Hirte .’ Furthermore, the relative microbial biomass data were overestimated under ‘C Bolinder ’ in both the FYM and COM treatments (Figure ) and under ‘C Hirte ’ in the COM treatment. Fujita et al () ran short‐term soil incubations and concluded that the introduction of microbial biomass data in the CENTURY model reduced the error of predictions of respiration rate by 26%. Our analysis suggests that, when running long‐term simulations, the RothC model structure might not take advantage of the microbial biomass data.…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, the relative microbial biomass data were overestimated under 'C Bolinder ' in both the FYM and COM treatments ( Figure 6) and under 'C Hirte ' in the COM treatment. Fujita et al (2014) ran short-term soil incubations and concluded that the introduction of microbial biomass data in the CENTURY model reduced the error of predictions of respiration rate by 26%. Our analysis suggests that, when running long-term simulations, the RothC model structure might not take advantage of the microbial biomass data.…”

Section: Model Uncertainty and Structurementioning

confidence: 99%

“…Integration of this 'grey literature' with the available SOC stock data can potentially reduce the uncertainty of SOC model calibration (Luo et al, 2016) and reveal soil processes embedded in the calibrated parameters. For example, Fujita, Witte, & Van Bodegom (2014) found that including microbial biomass data in the calibration of CENTURY improved the simulation of soil respiration. In the last two decades, several soil physical fractionation techniques have been developed that enable us to relate operationally defined SOC pools to the model pools (Zimmermann, Leifeld, Schmidt, Smith, & Fuhrer, 2007;Skjemstad, Spouncer, Cowie, & Swift, 2004).…”

Section: Introductionmentioning

confidence: 99%

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Multi‐objective calibration of RothC using measured carbon stocks and auxiliary data of a long‐term experiment in Switzerland

Cagnarini

Renella

Mayer

et al. 2019

European J Soil Science

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Interactions between model parameters and low spatiotemporal resolution of available data mean that conventional soil organic carbon (SOC) models are often affected by equifinality, with consequent uncertainty in SOC forecasts. Estimation of belowground C inputs is another major source of uncertainty in SOC modelling. Models are usually calibrated on SOC stocks and fluxes from long‐term experiments (LTEs), whereas other point data are not used for constraining the model parameters. We used data from an agricultural long‐term (>65 years) fertilization experiment to test a multi‐objective parameter estimation approach on the RothC model, combining SOC data from different fertilization treatments with microbial biomass, basal respiration and Zimmermann's fractions data. We also compared two methods to estimate the belowground C inputs: a conventional scaling of belowground biomass from crop harvest yield and an alternative approach based on constant belowground C for cereals measured experimentally in the field. The resulting posterior parameter distributions still suffered from some equifinality; the most stable C pool kinetic constants and composition of exogenous organic matter were the most sensitive parameters. The use of fixed belowground C inputs for cereals improved the model performance, reducing the importance of treatment‐specific parameters and processes. The introduction of microbial biomass and basal respiration data was effective for increasing determination of the calibration, but also suggested a change in the model structure: the microbial biomass pool, which is proportional to the C inputs in the traditional models, could be represented by different microbial physiology functions. Highlights Multi‐objective calibration with SOC and microbial or soil fractionation data can reduce model uncertainty. We compared different methods to estimate belowground C inputs on a long‐term trial in Switzerland. Fixed belowground C inputs measured in the field gave the best model performance. Microbial data can improve model calibration, but a change in model structure is suggested.

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Incorporating microbial ecology concepts into global soil mineralization models to improve predictions of carbon and nitrogen fluxes

Cited by 31 publications

References 54 publications

Optimal metabolic regulation along resource stoichiometry gradients

Optimal metabolic regulation along resource stoichiometry gradients

Responses of two nonlinear microbial models to warming and increased carbon input

Multi‐objective calibration of RothC using measured carbon stocks and auxiliary data of a long‐term experiment in Switzerland

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