Ensemble modelling of carbon fluxes in grasslands and croplands

Sándor, Renata; Ehrhardt, Fiona; Grace, Peter R.; Recous, Sylvie; Smith, Pete; Snow, Val; Soussana, Jean‐François; Basso, Bruno; Bhatia, Arti; Brilli, Lorenzo; Doltra, Jordi; Dorich, C.; Doro, Luca; Fitton, Nuala; Grant, Brian; Harrison, Matthew T.; Kirschbaum, Miko U.F.; Klumpp, Katja; Laville, Patricia; Léonard, Joël; Martin, Raphaël; Massad, Raia Silvia; Moore, Andrew D.; Myrgiotis, Vasileios; Pattey, E.; Rolinski, Susanne; Sharp, Joanna; Skiba, U.; Smith, Ward; Wu, Lianhai; Zhang, Qing; Bellocchi, Gianni

doi:10.1016/j.fcr.2020.107791

Cited by 68 publications

(47 citation statements)

References 70 publications

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“…These sizes are lower than that found by Sándor et al. (2020) to provide reliable C‐flux estimates in croplands and grasslands (i.e. ~13 models).…”

Section: Discussioncontrasting

confidence: 61%

“…In terms of the minimum number of models required to obtain reliable results, our study indicates that the suggested minimum ensemble size (~10 models) proposed by Martre et al (2015) for crop growth could be a reference also when model ensembles are implemented to blindly simulate SOC in bare-fallow soils, which can be reduced down to three or four models with a site-specific calibration. These sizes are lower than that found by Sándor et al (2020)…”

Section: Scenarios Of Ensemble Soc Estimatescontrasting

confidence: 64%

“…To explore how MMM varied with the number of models in the ensemble, we performed a calculation for each z ‐score transformed MMM,

z = \frac{MMM ‐ \bar{O}}{s d_{obs}}

, which was obtained by dividing the multi‐model data deviation from the mean of observations (

\overset{O}{true}

) by the standard deviation of the observations ( sd obs ; Sándor et al., 2020). A z ‐score can be placed on the normal distribution curve to indicate how much it deviates from the mean of the distribution.…”

Section: Methodsmentioning

confidence: 99%

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Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

Farina¹,

Sándor

Abdalla

et al. 2020

Global Change Biology

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Simulation models represent soil organic carbon (SOC) dynamics in global carbon (C) cycle scenarios to support climate‐change studies. It is imperative to increase confidence in long‐term predictions of SOC dynamics by reducing the uncertainty in model estimates. We evaluated SOC simulated from an ensemble of 26 process‐based C models by comparing simulations to experimental data from seven long‐term bare‐fallow (vegetation‐free) plots at six sites: Denmark (two sites), France, Russia, Sweden and the United Kingdom. The decay of SOC in these plots has been monitored for decades since the last inputs of plant material, providing the opportunity to test decomposition without the continuous input of new organic material. The models were run independently over multi‐year simulation periods (from 28 to 80 years) in a blind test with no calibration (Bln) and with the following three calibration scenarios, each providing different levels of information and/or allowing different levels of model fitting: (a) calibrating decomposition parameters separately at each experimental site (Spe); (b) using a generic, knowledge‐based, parameterization applicable in the Central European region (Gen); and (c) using a combination of both (a) and (b) strategies (Mix). We addressed uncertainties from different modelling approaches with or without spin‐up initialization of SOC. Changes in the multi‐model median (MMM) of SOC were used as descriptors of the ensemble performance. On average across sites, Gen proved adequate in describing changes in SOC, with MMM equal to average SOC (and standard deviation) of 39.2 (±15.5) Mg C/ha compared to the observed mean of 36.0 (±19.7) Mg C/ha (last observed year), indicating sufficiently reliable SOC estimates. Moving to Mix (37.5 ± 16.7 Mg C/ha) and Spe (36.8 ± 19.8 Mg C/ha) provided only marginal gains in accuracy, but modellers would need to apply more knowledge and a greater calibration effort than in Gen, thereby limiting the wider applicability of models.

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“…These sizes are lower than that found by Sándor et al. (2020) to provide reliable C‐flux estimates in croplands and grasslands (i.e. ~13 models).…”

Section: Discussioncontrasting

confidence: 61%

Section: Scenarios Of Ensemble Soc Estimatescontrasting

confidence: 64%

“…To explore how MMM varied with the number of models in the ensemble, we performed a calculation for each z ‐score transformed MMM,

z = \frac{MMM ‐ \bar{O}}{s d_{obs}}

, which was obtained by dividing the multi‐model data deviation from the mean of observations (

\overset{O}{true}

Section: Methodsmentioning

confidence: 99%

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Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

Farina¹,

Sándor

Abdalla

et al. 2020

Global Change Biology

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“…Forests, agricultural crops, soil and water bodies contribute towards the reduction in CO 2 and act as major sinks [6][7][8][9]. Quantification of exchange of CO 2 between agricultural soils and the atmosphere is important in order to assess the global carbon budgets as CO 2 accounted for 76% of total anthropogenic GHG emissions in 2010 [1][2][3][4][5]10]. Rice (Oriyza sativa) and wheat (Triticum aestivum) are the two important crops that are essential for global food security.…”

Section: Introductionmentioning

confidence: 99%

Net Ecosystem Exchange of Carbon Dioxide in Rice-Spring Wheat System of Northwestern Indo-Gangetic Plains

Kumar

Bhatia²,

Sehgal³

et al. 2021

Land

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Rice growing under anaerobic conditions followed by spring wheat under an aerobic environment differentially impact the net ecosystem exchange (NEE) of carbon dioxide (CO2) in rice-wheat systems of the north-western Indo-Gangetic Plains (IGP). This is the first estimation of the NEE in a rice-spring wheat sequence via the eddy covariance technique in the north-western Indo-Gangetic Plains, which was partitioned into gross primary productivity (GPP) and ecosystem respiration (RE) and correlated with the environmental variables. Higher CO2 uptake of −10.43 g C m−2 d−1 was observed in wheat during heading as compared to −7.12 g C m−2 d−1 in rice. The net uptake of CO2 was 25% lower in rice. The average daily NEE over the crop season was −3.74 and −5.01 g C m−2 d−1 in rice and wheat, respectively. The RE varied from 0.07–9.00 g C m−2 d−1 in rice and from 0.05–7.09 g C m−2 d−1 in wheat. The RE was positively correlated with soil temperature at 5 cm depth (0.543, p < 0.01) in rice and with air temperature (0.294, p < 0.01) in wheat. The GPP was positively correlated with air temperature (0.129, p < 0.05) and negatively correlated with vapor pressure deficit (VPD) (−0.315, p < 0.01) in rice. In wheat, GPP was positively correlated with air temperature (0.444, p < 0.01) and soil moisture (0.471, p < 0.01). The rate of GPP over the crop duration was nearly the same in both rice and wheat, however, the RE was higher in rice as compared to wheat, thus, the ratio of cumulative RE/GPP was 0.51 in rice and much lower at 0.34 in spring wheat. Rice contributed 46% and 43% to the annual totals of RE and GPP, respectively, while spring wheat contributed 36% and 51%. The NEE of CO2 was higher in spring wheat at −576 g C m−2 d−1 as compared to −368 g C m−2 in rice. Thus, while estimating the carbon sink potential in the intensively cultivated northern IGP, we need to consider that spring wheat may be a moderately stronger sink of CO2 as compared to rice in the rice-wheat system.

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“…This study is aimed to develop a model of priming induced by root exudate input into Nlimited soil, which is a typical case for most natural terrestrial ecosystems in temperate and boreal zones. The developed model can be a module linked with the SOM and terrestrial ecosystem models, where this pattern of plantsoil interactions is still not considered (Bengtson et al 2012;Komarov et al 2017;Sándor et al 2020). In addition to calculating the CO2 flux due to the mineralisation of various sources, the combination of SOM and priming models allowed the estimation of the contribution of microbial growth induced by exudate input to produce SOM and available N in food webs of soil biota.…”

Section: Introductionmentioning

confidence: 99%

An Approach to Incorporate Rhizosphere Priming Effect into Soil Organic Matter Models

Chertov

Kuzyakov²,

Priputina

et al. 2021

Preprint

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Purpose. This study is aimed to develop a model of priming effect (accelerated mineralisation of soil organic matter (SOM)) induced by root exudate input into nitrogen (N) limited rhizosphere soil as a typical case for most terrestrial ecosystems. This ecologically important process in the functioning of the “plant-soil” system was parameterized for temperate and boreal forests.Methods. A model of priming effect has been developed based on the concept of N mining to making up for the N scarcity in exudates by accelerating SOM mineralisation. Lacking N for microbial growth is mined from the SOM mineralisation considering C:N ratio of soil. The model has a built-in food web module, which calculates soil fauna feeding on microorganisms, the release of by-products of faunal metabolism and mineral N used for root uptake.Results. The model verification demonstrated the similar order of the priming effect as in the published experiments. Testing at the pedon level revealed a high sensitivity of the model to N content in root exudates. Testing of the model at the ecosystem level revealed that CO2 emission from the priming can reach 25–30% of CO2 emission from the whole Ah horizon of forest soil. The same intensities were simulated for the fauna-derived N released within the rhizosphere.Conclusion. The new model reflects important ecological consequences of the main target function of priming effects within the “plant – soil – microorganisms – fauna” system – the microbial acceleration of C and N cycling in the rhizosphere and detritusphere to mobilise mineral N for plants.

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Ensemble modelling of carbon fluxes in grasslands and croplands

Cited by 68 publications

References 70 publications

Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

Ensemble modelling, uncertainty and robust predictions of organic carbon in long‐term bare‐fallow soils

Net Ecosystem Exchange of Carbon Dioxide in Rice-Spring Wheat System of Northwestern Indo-Gangetic Plains

An Approach to Incorporate Rhizosphere Priming Effect into Soil Organic Matter Models

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