Many studies have assessed the potential of agricultural practices to sequester carbon (C). A comprehensive evaluation of impacts of agricultural practices requires not only considering C storage but ...also direct and indirect emissions of greenhouse gases (GHG) and their side effects (e.g., on the water cycle or agricultural production). We used a high‐resolution modeling approach with the Simulateur mulTIdisciplinaire pour les Cultures Standard soil‐crop model to quantify soil organic C (SOC) storage potential, GHG balance, biomass production and nitrogen‐ and water‐related impacts for all arable land in France for current cropping systems (baseline scenario) and three mitigation scenarios: (i) spatial and temporal expansion of cover crops, (ii) spatial insertion and temporal extension of temporary grasslands (two sub‐scenarios) and (iii) improved recycling of organic resources as fertilizer. In the baseline scenario, SOC decreased slightly over 30 years in crop‐only rotations but increased significantly in crop/temporary grassland rotations. Results highlighted a strong trade‐off between the storage rate per unit area (kg C ha−1 year−1) of mitigation scenarios and the areas to which they could be applied. As a result, while the most promising scenario at the field scale was the insertion of temporary grassland (+466 kg C ha−1 year−1 stored to a depth of 0.3 m compared to the baseline, on 0.68 Mha), at the national scale, it was by far the expansion of cover crops (+131 kg C ha−1 year−1, on 17.62 Mha). Side effects on crop production, water irrigation and nitrogen emissions varied greatly depending on the scenario and production situation. At the national scale, combining the three mitigation scenarios could mitigate GHG emissions of current cropping systems by 54% (−11.2 from the current 20.5 Mt CO2e year−1), but the remaining emissions would still lie far from the objective of C‐neutral agriculture.
We used a high‐resolution modeling approach with the Simulateur mulTIdisciplinaire pour les Cultures Standard soil‐crop model to quantify soil organic carbon (C) storage potential, greenhouse gases (GHG) balance, biomass production and nitrogen‐ and water‐related impacts for all arable land in France for current cropping systems and three mitigation scenarios. At the national scale, combining the three mitigation scenarios could mitigate GHG emissions of current cropping systems by 54% (−11.2 from the current 20.5 Mt CO2e year−1), but the remaining emissions would still lie far from the objective of C‐neutral agriculture.
Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation ...tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.
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•We assess simulated C and N cycles in agricultural systems based on published modelling studies.•Biogeochemical models have limits in simulating pedo-climatic conditions and management effects.•We propose explicit modelling of soil microbial biomass to drive SOC turnover.•Improved approaches of gas transport in soil are required for future modelling ork.
Simulation models quantify the impacts on carbon (C) and nitrogen (N) cycling in grassland systems caused by changes in management practices. To support agricultural policies, it is however important ...to contrast the responses of alternative models, which can differ greatly in their treatment of key processes and in their response to management. We applied eight biogeochemical models at five grassland sites (in France, New Zealand, Switzerland, United Kingdom and United States) to compare the sensitivity of modelled C and N fluxes to changes in the density of grazing animals (from 100% to 50% of the original livestock densities), also in combination with decreasing N fertilization levels (reduced to zero from the initial levels). Simulated multi-model median values indicated that input reduction would lead to an increase in the C sink strength (negative net ecosystem C exchange) in intensive grazing systems: −64 ± 74 g C m−2 yr−1 (animal density reduction) and −81 ± 74 g C m−2 yr−1 (N and animal density reduction), against the baseline of −30.5 ± 69.5 g C m−2 yr−1 (LSU livestock units ≥ 0.76 ha−1 yr−1). Simulations also indicated a strong effect of N fertilizer reduction on N fluxes, e.g. N2O-N emissions decreased from 0.34 ± 0.22 (baseline) to 0.1 ± 0.05 g N m−2 yr−1 (no N fertilization). Simulated decline in grazing intensity had only limited impact on the N balance. The simulated pattern of enteric methane emissions was dominated by high model-to-model variability. The reduction in simulated offtake (animal intake + cut biomass) led to a doubling in net primary production per animal (increased by 11.6 ± 8.1 t C LSU−1 yr−1 across sites). The highest N2O-N intensities (N2O-N/offtake) were simulated at mown and extensively grazed arid sites. We show the possibility of using grassland models to determine sound mitigation practices while quantifying the uncertainties associated with the simulated outputs.
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•We perform multi-model simulations of C and N fluxes at five grassland sites.•We assess modelled greenhouse gas emissions with alternative management practices.•We use multi-model medians to reduce the uncertainty of the responses.•We identify some shift towards a C sink with decreasing inputs.•We show the considerable effect of N fertilizer reduction on C and N emissions.
Plant functional traits co-vary along strategy spectra, thereby defining trade-offs for resource acquisition and utilization amongst other processes. A main objective of plant ecology is to quantify ...the correlations among traits and ask why some of them are sufficiently closely coordinated to form a single axis of functional specialization. However, due to trait co-variations in nature, it is difficult to propose a mechanistic and causal explanation for the origin of trade-offs among traits observed at both intra- and inter-specific level.
Using the G(EMINI) individual-centered model which coordinates physiological and morphological processes, we investigated with 12 grass species the consequences of deliberately decoupling variation of leaf traits (specific leaf area, leaf lifespan) and plant stature (height and tiller number) on plant growth and phenotypic variability. For all species under both high and low N supplies, simulated trait values maximizing plant growth in monocultures matched observed trait values. Moreover, at the intraspecific level, plastic trait responses to N addition predicted by the model were in close agreement with observed trait responses. In a 4D trait space, our modeling approach highlighted that the unique trait combination maximizing plant growth under a given environmental condition was determined by a coordination of leaf, root and whole plant processes that tended to co-limit the acquisition and use of carbon and of nitrogen.
Our study provides a mechanistic explanation for the origin of trade-offs between plant functional traits and further predicts plasticity in plant traits in response to environmental changes. In a multidimensional trait space, regions occupied by current plant species can therefore be viewed as adaptive corridors where trait combinations minimize allometric and physiological constraints from the organ to the whole plant levels. The regions outside this corridor are empty because of inferior plant performance.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
About 25% of European livestock intake is based on permanent and sown grasslands. To fulfill rising demand for animal products, an intensification of livestock production may lead to an increased ...consumption of crop and compound feeds. In order to preserve an economically and environmentally sustainable agriculture, a more forage based livestock alimentation may be an advantage. However, besides management, grassland productivity is highly vulnerable to climate (i.e., temperature, precipitation, CO2 concentration), and spatial information about European grassland productivity in response to climate change is scarce. The process-based vegetation model ORCHIDEE-GM, containing an explicit representation of grassland management (i.e., herbage mowing and grazing), is used here to estimate changes in potential productivity and potential grass-fed ruminant livestock density across European grasslands over the period 1961-2010. Here "potential grass-fed ruminant livestock density" denotes the maximum density of livestock that can be supported by grassland productivity in each 25 km × 25 km grid cell. In reality, livestock density could be higher than potential (e.g., if additional feed is supplied to animals) or lower (e.g., in response to economic factors, pedo-climatic and biotic conditions ignored by the model, or policy decisions that can for instance reduce livestock numbers). When compared to agricultural statistics (Eurostat and FAOstat), ORCHIDEE-GM gave a good reproduction of the regional gradients of annual grassland productivity and ruminant livestock density. The model however tends to systematically overestimate the absolute values of productivity in most regions, suggesting that most grid cells remain below their potential grassland productivity due to possible nutrient and biotic limitations on plant growth. When ORCHIDEE-GM was run for the period 1961-2010 with variable climate and rising CO2, an increase of potential annual production (over 3%) per decade was found: 97% of this increase was attributed to the rise in CO2, -3% to climate trends and 15% to trends in nitrogen fertilization and deposition. When compared with statistical data, ORCHIDEE-GM captures well the observed phase of climate-driven interannual variability in grassland production well, whereas the magnitude of the interannual variability in modeled productivity is larger than the statistical data. Regional grass-fed livestock numbers can be reproduced by ORCHIDEE-GM based on its simple assumptions and parameterization about productivity being the only limiting factor to define the sustainable number of animals per unit area. Causes for regional model-data misfits are discussed, including uncertainties in farming practices (e.g., nitrogen fertilizer application, and mowing and grazing intensity) and in ruminant diet composition, as well as uncertainties in the statistical data and in model parameter values.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Simulation models are extensively used to predict agricultural productivity and greenhouse gas emissions. However, the uncertainties of (reduced) model ensemble simulations have not been assessed ...systematically for variables affecting food security and climate change mitigation, within multi‐species agricultural contexts. We report an international model comparison and benchmarking exercise, showing the potential of multi‐model ensembles to predict productivity and nitrous oxide (N2O) emissions for wheat, maize, rice and temperate grasslands. Using a multi‐stage modelling protocol, from blind simulations (stage 1) to partial (stages 2–4) and full calibration (stage 5), 24 process‐based biogeochemical models were assessed individually or as an ensemble against long‐term experimental data from four temperate grassland and five arable crop rotation sites spanning four continents. Comparisons were performed by reference to the experimental uncertainties of observed yields and N2O emissions. Results showed that across sites and crop/grassland types, 23%–40% of the uncalibrated individual models were within two standard deviations (SD) of observed yields, while 42 (rice) to 96% (grasslands) of the models were within 1 SD of observed N2O emissions. At stage 1, ensembles formed by the three lowest prediction model errors predicted both yields and N2O emissions within experimental uncertainties for 44% and 33% of the crop and grassland growth cycles, respectively. Partial model calibration (stages 2–4) markedly reduced prediction errors of the full model ensemble E‐median for crop grain yields (from 36% at stage 1 down to 4% on average) and grassland productivity (from 44% to 27%) and to a lesser and more variable extent for N2O emissions. Yield‐scaled N2O emissions (N2O emissions divided by crop yields) were ranked accurately by three‐model ensembles across crop species and field sites. The potential of using process‐based model ensembles to predict jointly productivity and N2O emissions at field scale is discussed.
The potential of simulation models used to predict variables affecting food security and climate change mitigation has not been systematically assessed. We report an international intercomparison of 24 process‐based models for the estimation of agricultural productivity and N2O emissions (individually or as ensembles) against nine long‐term experimental datasets (rotational crops and grasslands) using a five‐stage modelling protocol. Uncalibrated multi‐model medians were within the range of observed uncertainties for grain yields (wheat, maize and rice) and N2O emissions, while were poor predictor for grasslands ANPP. N2O emissions intensities ranked accurately with reduced ensembles (three models) across stages, crop species and sites.
Reliable diagnosis of vascular parkinsonism (VaP) in the presence of a gait hypokinesia is an issue that is encountered in geriatrics. The EVAMAR-AGEX study was focusing on the phenomenon of ...recurrent falls in older persons (OP) with this parkinsonian gait. The present study is focusing on the diagnosis of VaP-related parkinsonian gait by developing a diagnostic guidance model adapted to OP.
Data from baseline and the 2-year follow-up visit were used to carry out univariate analysis and calculation of odds ratios, allowing to identify relevant variables to include in the diagnostic guidance model. To evaluate the model, confusion matrices were created, evaluating true positive, false negative, false positive and true negative incidences, sensitivity and specificity, and negative and positive predictive values.
79 patients included 58% male; average age 81.24 years. VaP diagnosis according to Zijlmans criteria occurred in 28%; neurodegenerative parkinsonian syndromes in 72%. A 4-criteria model was established to facilitate diagnostic: lack of prior hallucinations, lack of movement disorders tremor excluded, no cognitive fluctuations, and ≥75 years of age at diagnosis. In combination of 4/4 criteria, all of them were required to disclose a specificity of 91% in the diagnosis of VaP. In combination of 3/4, in case of negative test, a negative predictive value for VaP diagnosis of 0.97 was obtained.
The challenge of our tool is both to be able to rule out what is probably not a VaP and to argue what makes a VaP diagnosis probable in OP.
•The most relevant slow gait of neurological origin is the parkinsonian gait.•The two main etiologies are vascular (VaP) and neurodegenerative parkinsonism.•Distinguishing the etiology of gait hypokinesia is clinically relevant.•We have developed a new tool of four items as diagnostic guidance.•It is able to rule out what is probably not a VaP and what makes a VaP probable.
•Twenty-three models estimated carbon fluxes in croplands and grasslands worldwide.•Vegetation data were needed as a minimum data requirement for model calibration.•C flux simulation improved with a ...multi-model approach (multi-model median).
Croplands and grasslands are agricultural systems that contribute to land–atmosphere exchanges of carbon (C). We evaluated and compared gross primary production (GPP), ecosystem respiration (RECO), net ecosystem exchange (NEE) of CO2, and two derived outputs - C use efficiency (CUE=-NEE/GPP) and C emission intensity (IntC= -NEE/Offtake grazed or harvested biomass). The outputs came from 23 models (11 crop-specific, eight grassland-specific, and four models covering both systems) at three cropping sites over several rotations with spring and winter cereals, soybean and rapeseed in Canada, France and India, and two temperate permanent grasslands in France and the United Kingdom. The models were run independently over multi-year simulation periods in five stages (S), either blind with no calibration and initialization data (S1), using historical management and climate for initialization (S2), calibrated against plant data (S3), plant and soil data together (S4), or with the addition of C and N fluxes (S5). Here, we provide a framework to address methodological uncertainties and contextualize results. Most of the models overestimated or underestimated the C fluxes observed during the growing seasons (or the whole years for grasslands), with substantial differences between models. For each simulated variable, changes in the multi-model median (MMM) from S1 to S5 was used as a descriptor of the ensemble performance. Overall, the greatest improvements (MMM approaching the mean of observations) were achieved at S3 or higher calibration stages. For instance, grassland GPP MMM was equal to 1632 g C m−2 yr-1 (S5) while the observed mean was equal to 1763 m-2 yr-1 (average for two sites). Nash-Sutcliffe modelling efficiency coefficients indicated that MMM outperformed individual models in 92.3 % of cases. Our study suggests a cautious use of large-scale, multi-model ensembles to estimate C fluxes in agricultural sites if some site-specific plant and soil observations are available for model calibration. The further development of crop/grassland ensemble modelling will hinge upon the interpretation of results in light of the way models represent the processes underlying C fluxes in complex agricultural systems (grassland and crop rotations including fallow periods).
► In this study, we develop and evaluate a structure–function–diversity model of grassland ecosystems. ► The model is parameterized from plant functional traits of 13 perennial grass species. ► The ...model is evaluated on species performance in monoculture and mixture across disturbance and fertility gradients. ► We demonstrate the model ability to simulate species performance and plant community structure.
A structure–function–diversity model of grassland ecosystems (Gemini) has been developed. For a potentially unlimited number of clonal plant populations, it explicitly simulates competition for two key resources (light and nitrogen) along vertical canopy and soil profiles. Population turnover, shoot and root morphogenesis, photosynthesis, respiration, transpiration, N acquisition by uptake, allocation of assimilates between structural compartments, and reserve storage and remobilization, are simulated for each plant population. The object-oriented structure of the modeling framework allows to couple, or not, the simulated plant populations to other sub-models describing climate variables, soil functioning, grazing behavior and grassland management. Partitioning of growth between shoot structures, leaf photosynthetic proteins and roots is based on two assumptions: (i) functional balance between root and shoot activity, (ii) coordination of leaf photosynthesis. The model was parameterized from plant functional trait measurements of 13 native perennial pasture grass species grown in monocultures at high N availability and low cutting frequency in a field trial. Predicted and measured annual dry-matter yields were highly correlated without bias across species, N supply and cutting frequency treatments in monocultures and in mixtures of six species. Results show the ability of this mechanistic model to simulate without bias nitrogen and disturbance responses of net primary productivity and of plant community structure.
We assess if capital ratios reduced the occurrence of banking crises in the European Union from 1998 to 2017. We use a Probit model and estimate the effect of two measures: the bank capital to total ...assets ratio and the bank regulatory capital to Risk Weighted Assets (RWA). We found that both measures affect negatively the probability of crisis. This result is robust to the exclusion of outliers, to the inclusion of various control variables for banking, financial and macroeconomic risks. Finally, we show that while the bank regulatory capital to RWA has always a negative effect on the probability of crisis, the bank capital to total assets ratio is only significant above a threshold, estimated between 10 % and 12 %.
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