Global patterns and controls of soil organic carbon dynamics as simulated by multiple terrestrial biosphere models: Current status and future directions.

Soil is the largest organic carbon (C) pool of terrestrial ecosystems, and C loss from soil accounts for a large proportion of land-atmosphere C exchange. Therefore, a small change in soil organic C (SOC) can affect atmospheric carbon dioxide (CO2) concentration and climate change. In the past decades, a wide variety of studies have been conducted to quantify global SOC stocks and soil C exchange with the atmosphere through site measurements, inventories, and empirical/process-based modeling. However, these estimates are highly uncertain, and identifying major driving forces controlling soil C dynamics remains a key research challenge. This study has compiled century-long (1901-2010) estimates of SOC storage and heterotrophic respiration (Rh) from 10 terrestrial biosphere models (TBMs) in the Multi-scale Synthesis and Terrestrial Model Intercomparison Project and two observation-based data sets. The 10 TBM ensemble shows that global SOC estimate ranges from 425 to 2111 Pg C (1 Pg = 1015 g) with a median value of 1158 Pg C in 2010. The models estimate a broad range of Rh from 35 to 69 Pg C yr-1 with a median value of 51 Pg C yr-1 during 2001-2010. The largest uncertainty in SOC stocks exists in the 40-65°N latitude whereas the largest cross-model divergence in Rh are in the tropics. The modeled SOC change during 1901-2010 ranges from -70 Pg C to 86 Pg C, but in some models the SOC change has a different sign from the change of total C stock, implying very different contribution of vegetation and soil pools in determining the terrestrial C budget among models. The model ensemble-estimated mean residence time of SOC shows a reduction of 3.4 years over the past century, which accelerate C cycling through the land biosphere. All the models agreed that climate and land use changes decreased SOC stocks, while elevated atmospheric CO2 and nitrogen deposition over intact ecosystems increased SOC stocks-even though the responses varied significantly among models. Model representations of temperature and moisture sensitivity, nutrient limitation, and land use partially explain the divergent estimates of global SOC stocks and soil C fluxes in this study. In addition, a major source of systematic error in model estimations relates to nonmodeled SOC storage in wetlands and peatlands, as well as to old C storage in deep soil layers.

Development of microbial-enzyme-mediated decomposition model parameters through steady-state and dynamic analyses.

We developed a microbial-enzyme-mediated decomposition (MEND) model, based on the Michaelis-Menten kinetics, that describes the dynamics of physically defined pools of soil organic matter (SOC). These include particulate, mineral-associated, dissolved organic matter (POC, MOC, and DOC, respectively), microbial biomass, and associated exoenzymes. The ranges and/or distributions of parameters were determined by both analytical steady-state and dynamic analyses with SOC data from the literature. We used an improved multi-objective parameter sensitivity analysis (MOPSA) to identify the most important parameters for the full model: maintenance of microbial biomass, turnover and synthesis of enzymes, and carbon use efficiency (CUE). The model predicted that an increase of 2 degrees C (baseline temperature 12 degrees C) caused the pools of POC-cellulose, MOC, and total SOC to increase with dynamic CUE and decrease with constant CUE, as indicated by the 50% confidence intervals. Regardless of dynamic or constant CUE, the changes in pool size of POC, MOC, and total SOC varied from -8% to 8% under +2 degrees C. The scenario analysis using a single parameter set indicates that higher temperature with dynamic CUE might result in greater net increases in both POC-cellulose and MOC pools. Different dynamics of various SOC pools reflected the catalytic functions of specific enzymes targeting specific substrates and the interactions between microbes, enzymes, and SOC. With the feasible parameter values estimated in this study, models incorporating fundamental principles of microbial-enzyme dynamics can lead to simulation results qualitatively different from traditional models with fast/slow/passive pools.

Soil carbon change and net energy associated with biofuel production on marginal lands: a regional modeling perspective.

The use of marginal lands for biofuel production has been proposed as a promising solution for meeting biofuel demands while avoiding food-feed-fuel conflicts. However, uncertainty surrounds whether marginal lands can be reliably located, as well as their inherent biofuel potential and the possible environmental impacts. We developed a quantitative approach that integrates high-resolution land cover and land productivity to classify productive croplands and nonarable marginal lands in a nine-county region in southern Michigan. The classified lands were then examined with the spatially explicit modeling framework using the Environmental Policy Integrated Climate (EPIC) model to estimate net energy (NE) and soil organic carbon (SOC) changes associated with the cultivation of different annual and perennial production systems. Simulation results suggest that biofuel production systems underperform on marginal lands when compared to productive croplands. However, we found perennial grasses could perform better than annual crops. Hence, when growing perennial bioenergy crops on marginal lands instead of productive croplands, less additional land (about 0.09 ha per each hectare planted) would be needed to achieve the same NE than if growing annual bioenergy crops (additional 0.17 ha per hectare planted). Miscanthus ( × ) and switchgrass ( L.) can produce 112.43 and 74.61 GJ ha yr NE, respectively, and have the potential to sequester, on average, 0.59 and 0.23 Mg C ha yr SOC, respectively. Notably, simulation results indicate substantial variability of the NE and SOC storage potential across the study region. Thus, although perennial energy crops are promising options for biofuel production on marginal lands, given the large spatial variability, regional- and site-specific management strategies are required for sustainable biofuel production.

Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting.

Net annual soil carbon change, fossil fuel emissions from cropland production, and cropland net primary production were estimated and spatially distributed using land cover defined by NASA's moderate resolution imaging spectroradiometer (MODIS) and by the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL). Spatially resolved estimates of net ecosystem exchange (NEE) and net ecosystem carbon balance (NECB) were developed. The purpose of generating spatial estimates of carbon fluxes, and the primary objective of this research, was to develop a method of carbon accounting that is consistent from field to national scales. NEE represents net on-site vertical fluxes of carbon. NECB represents all on-site and off-site carbon fluxes associated with crop production. Estimates of cropland NEE using moderate resolution (approximately 1 km2) land cover data were generated for the conterminous United States and compared with higher resolution (30-m) estimates of NEE and with direct measurements of CO2 flux from croplands in Illinois and Nebraska, USA. Estimates of NEE using the CDL (30-m resolution) had a higher correlation with eddy covariance flux tower estimates compared with estimates of NEE using MODIS. Estimates of NECB are primarily driven by net soil carbon change, fossil fuel emissions associated with crop production, and CO2 emissions from the application of agricultural lime. NEE and NECB for U.S. croplands were -274 and 7 Tg C/yr for 2004, respectively. Use of moderate- to high-resolution satellite-based land cover data enables improved estimates of cropland carbon dynamics.

Conversion from agriculture to grassland builds soil organic matter on decadal timescales.

Soil organic matter (SOM) often increases when agricultural fields are converted to perennial vegetation, yet decadal scale rates and the mechanisms that underlie SOM accumulation are not clear. We measured SOM accumulation and changes in soil properties on a replicated chronosequence of former agricultural fields in the midwestern United States that spanned 40 years after perennial-grassland establishment. Over this time period, soil organic carbon (SOC) in the top 10 cm of soil accumulated at a constant rate of 62.0 g x m(-2) x yr(-1), regardless of whether the vegetation type was dominated by C3 or C4 grasses. At this rate, SOC contents will be equivalent to unplowed native prairie sites within 55-75 years after cultivation ceased. Both labile (short turnover time) and recalcitrant (long turnover time) carbon pools increased linearly for 40 years, with recalcitrant pools increasing more rapidly than expected. This result was consistent across several different methods of measuring labile SOC. A model that investigates the mechanisms of SOM formation suggests that rapid formation of stable carbon resulted from biochemically resistant microbial products and plant material. Former agricultural soils of the Great Plains may function as carbon sinks for less than a century, although much of the carbon stored is stable.

Microbial dormancy improves development and experimental validation of ecosystem model.

Climate feedbacks from soils can result from environmental change followed by response of plant and microbial communities, and/or associated changes in nutrient cycling. Explicit consideration of microbial life-history traits and functions may be necessary to predict climate feedbacks owing to changes in the physiology and community composition of microbes and their associated effect on carbon cycling. Here we developed the microbial enzyme-mediated decomposition (MEND) model by incorporating microbial dormancy and the ability to track multiple isotopes of carbon. We tested two versions of MEND, that is, MEND with dormancy (MEND) and MEND without dormancy (MEND_wod), against long-term (270 days) carbon decomposition data from laboratory incubations of four soils with isotopically labeled substrates. MEND_wod adequately fitted multiple observations (total C-CO2 and (14)C-CO2 respiration, and dissolved organic carbon), but at the cost of significantly underestimating the total microbial biomass. MEND improved estimates of microbial biomass by 20-71% over MEND_wod. We also quantified uncertainties in parameters and model simulations using the Critical Objective Function Index method, which is based on a global stochastic optimization algorithm, as well as model complexity and observational data availability. Together our model extrapolations of the incubation study show that long-term soil incubations with experimental data for multiple carbon pools are conducive to estimate both decomposition and microbial parameters. These efforts should provide essential support to future field- and global-scale simulations, and enable more confident predictions of feedbacks between environmental change and carbon cycling.

Multi-scale geospatial agroecosystem modeling: a case study on the influence of soil data resolution on carbon budget estimates.

The development of effective measures to stabilize atmospheric CO2 concentration and mitigate negative impacts of climate change requires accurate quantification of the spatial variation and magnitude of the terrestrial carbon (C) flux. However, the spatial pattern and strength of terrestrial C sinks and sources remain uncertain. In this study, we designed a spatially-explicit agroecosystem modeling system by integrating the Environmental Policy Integrated Climate (EPIC) model with multiple sources of geospatial and surveyed datasets (including crop type map, elevation, climate forcing, fertilizer application, tillage type and distribution, and crop planting and harvesting date), and applied it to examine the sensitivity of cropland C flux simulations to two widely used soil databases (i.e. State Soil Geographic-STATSGO of a scale of 1:250,000 and Soil Survey Geographic-SSURGO of a scale of 1:24,000) in Iowa, USA. To efficiently execute numerous EPIC runs resulting from the use of high resolution spatial data (56m), we developed a parallelized version of EPIC. Both STATSGO and SSURGO led to similar simulations of crop yields and Net Ecosystem Production (NEP) estimates at the State level. However, substantial differences were observed at the county and sub-county (grid) levels. In general, the fine resolution SSURGO data outperformed the coarse resolution STATSGO data for county-scale crop-yield simulation, and within STATSGO, the area-weighted approach provided more accurate results. Further analysis showed that spatial distribution and magnitude of simulated NEP were more sensitive to the resolution difference between SSURGO and STATSGO at the county or grid scale. For over 60% of the cropland areas in Iowa, the deviations between STATSGO- and SSURGO-derived NEP were larger than 1MgCha(-1)yr(-1), or about half of the average cropland NEP, highlighting the significant uncertainty in spatial distribution and magnitude of simulated C fluxes resulting from differences in soil data resolution.

A theoretical reassessment of microbial maintenance and implications for microbial ecology modeling.

We attempted to reconcile three microbial maintenance models (Herbert, Pirt, and Compromise) through a theoretical reassessment. We provided a rigorous proof that the true growth yield coefficient (Y(G)) is the ratio of the specific maintenance rate (a in Herbert) to the maintenance coefficient (m in Pirt). Other findings from this study include: (1) the Compromise model is identical to the Herbert for computing microbial growth and substrate consumption, but it expresses the dependence of maintenance on both microbial biomass and substrate; (2) the maximum specific growth rate in the Herbert (µ(max,H)) is higher than those in the other two models (µ(max,P) and µ(max,C)), and the difference is the physiological maintenance factor (m(q) = a); and (3) the overall maintenance coefficient (m(T)) is more sensitive to m(q) than to the specific growth rate (µ(G)) and Y(G). Our critical reassessment of microbial maintenance provides a new approach for quantifying some important components in soil microbial ecology models.

The potential impact of climate change on Australia's soil organic carbon resources.

BACKGROUND: Soil organic carbon (SOC) represents a significant pool of carbon within the biosphere. Climatic shifts in temperature and precipitation have a major influence on the decomposition and amount of SOC stored within an ecosystem and that released into the atmosphere. We have linked net primary production (NPP) algorithms, which include the impact of enhanced atmospheric CO2 on plant growth, to the SOCRATES terrestrial carbon model to estimate changes in SOC for the Australia continent between the years 1990 and 2100 in response to climate changes generated by the CSIRO Mark 2 Global Circulation Model (GCM). RESULTS: We estimate organic carbon storage in the topsoil (0-10 cm) of the Australian continent in 1990 to be 8.1 Gt. This equates to 19 and 34 Gt in the top 30 and 100 cm of soil, respectively. By the year 2100, under a low emissions scenario, topsoil organic carbon stores of the continent will have increased by 0.6% (49 Mt C). Under a high emissions scenario, the Australian continent becomes a source of CO2 with a net reduction of 6.4% (518 Mt) in topsoil carbon, when compared to no climate change. This is partially offset by the predicted increase in NPP of 20.3% CONCLUSION: Climate change impacts must be studied holistically, requiring integration of climate, plant, ecosystem and soil sciences. The SOCRATES terrestrial carbon cycling model provides realistic estimates of changes in SOC storage in response to climate change over the next century, and confirms the need for greater consideration of soils in assessing the full impact of climate change and the development of quantifiable mitigation strategies.

Carbon Management Response curves: estimates of temporal soil carbon dynamics.

Measurement of the change in soil carbon that accompanies a change in land use (e.g., forest to agriculture) or management (e.g., conventional tillage to no-till) can be complex and expensive, may require reference plots, and is subject to the variability of statistical sampling and short-term variability in weather. In this paper, we develop Carbon Management Response (CMR) curves that could be used as an alternative to in situ measurements. The CMR curves developed here are based on quantitative reviews of existing global analyses and field observations of changes in soil carbon. The curves show mean annual rates of soil carbon change, estimated time to maximum rates of change, and estimated time to a new soil carbon steady state following the initial change in management. We illustrate how CMR curves could be used in a carbon accounting framework while effectively addressing a number of potential policy issues commonly associated with carbon accounting. We find that CMR curves provide a transparent means to account for changes in soil carbon accumulation and loss rates over time, and also provide empirical relationships that might be used in the development or validation of ecological or Earth systems models.