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National scale projections of bioenergy crop yields and their environmental impacts are essential to identify appropriate locations to place bioenergy crops and ensure sustainable land use strategies. In this study, we used the process-based Daily Century (DAYCENT) model with site-specific environmental data to simulate sorghum (Sorghum bicolor L. Moench) biomass yield, soil organic carbon (SOC) change, and nitrous oxide emissions across cultivated lands in the continental United States. The simulated rainfed dry biomass productivity ranged from 0.8 to 19.2Mgha−1year−1, with a spatiotemporal average of (Formula presented.) Mgha−1year−1, and a coefficient of variation of 35%. The average SOC sequestration and direct nitrous oxide emission rates were simulated as (Formula presented.) MgCO2eha−1year−1 and (Formula presented.) MgCO2eha−1year−1, respectively. Compared to field-observed biomass yield data at multiple locations, model predictions of biomass productivity showed a root mean square error (RMSE) of 5.6Mgha−1year−1. In comparison to the multi State (n=21) NASS database, our results showed RMSE of 5.5Mgha−1year−1. Model projections of baseline SOC showed RMSE of 1.9kg/m2 in comparison to a recently available continental SOC stock dataset. The model-predicted N2O emissions are close to 1.25% of N input. Our results suggest 10.2 million ha of cultivated lands in the Southern and Lower Midwestern United States will produce >10Mgha−1year−1 with net carbon sequestration under rainfed conditions. Cultivated lands in Upper Midwestern states including Iowa, Minnesota, Montana, Michigan, and North Dakota showed lower sorghum biomass productivity (average: 6.9Mgha−1year−1) with net sequestration (average: 0.13MgCO2eha−1year−1). Our national-scale spatially explicit results are critical inputs for robust life cycle assessment of bioenergy production systems and land use-based climate change mitigation strategies.

Explicit consideration of microbial physiology in soil biogeochemical models that represent coupled carbon–nitrogen dynamics presents opportunities to deepen understanding of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physicochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS with coupled C and nitrogen (N) cycling through litter, microbial, and soil organic matter (SOM) pools. The model was parameterized and validated against C and N data from the Long-Term Inter-site Decomposition Experiment Team (LIDET; six litter types, 10 years of observations, and 13 sites across North America). The model simulates C and N losses from litterbags in the LIDET study with reasonable accuracy (C: R2=0.63; N: R2=0.29), which is comparable with simulations from the DAYCENT model that implicitly represents microbial activity (C: R2=0.67; N: R2=0.30). Subsequently, we evaluated equilibrium values of stocks (total soil C and N, microbial biomass C and N, inorganic N) and microbial process rates (soil heterotrophic respiration, N mineralization) simulated by MIMICS-CN across the 13 simulated LIDET sites against published observations from other continent-wide datasets. We found that MIMICS-CN produces equilibrium values in line with measured values, showing that the model generates plausible estimates of ecosystem soil biogeochemical dynamics across continental-scale gradients. MIMICS-CN provides a platform for coupling C and N projections in a microbially explicit model, but experiments still need to identify the physiological and stoichiometric characteristics of soil microbes, especially under environmental change scenarios.

Nutrients are required for plant growth and development, absence or shortage of this could limit crop productivity. However, the misappropriate application of nutrients could cause environmental challenges like greenhouse gas emission, global warming, and climate change. Nutrient dynamics also seem to be affected by climate change as all the processes in which nutrients are taken up, transferred, and cycled over time in an ecosystem are linked with climatic factors directly or indirectly. Many biogeochemical models including APSIM (Agricultural Production Systems Simulator), CropSyst, CERES-EGC, DayCent, DNDC (DeNitrification DeComposition), DSSAT (Decision Support System for Agrotechnology Transfer), EPIC (Environmental Policy Integrated Climate), PaSim, RothC (Rothamsted Carbon Model), and STICS (SimulateurmulTIdisciplinaire pour les Cultures Standard, or multidisciplinary simulator for standard crops) can be used to study the nutrients dynamics which includes uptake from the soil, assimilation, and remobilization in plants. This chapter presents the application of different biogeochemical models to simulate nutrients dynamics, mainly Carbon (C), Nitrogen (N), and Phosphorus (P). Soil organic carbon (SOC) dynamics and loss of N as nitrous oxide (N2O) emission is also discussed by using models like APSIM and DNDC. Finally, life cycle assessment (LCA) is presented as a valuable tool to study environmental impacts associated with all steps of nutrients distribution among different systems. In conclusion, process-based biogeochemical cycles are valuable tools that can be used to study and manage nutrients in soil-crop system under changing climate.

We demonstrate the use of a surrogate-based optimization framework for large-scale and high-resolution landscape management optimization, using irrigated corn production systems in eastern Colorado, USA as a case study. An artificial neural network was employed to create a surrogate of the DayCent biogeochemical simulation model. Our optimization considered trade-offs among seven different objectives at different scales, including farm profits, irrigation water use, corn grain, corn stover, soil organic carbon (SOC), greenhouse gas (GHG) emissions, and nitrogen leaching. The results show that the surrogate captured greater than 99% of the variations in the DayCent’s simulated outputs and was 6.2 million times faster than the DayCent model for our analysis. Farm-level optimization increased farm profits by 83%–150%, SOC by 16%–53%, grain yield by 10.1–11.3%, and reduced GHG emissions by 19%–55% compared to the ‘business-as-usual’ scenario.

Terrestrial soil organic carbon (SOC) dynamics play an important but uncertain role in the global carbon (C) cycle. Current modeling efforts to quantify SOC dynamics in response to global environme...

Warm season perennial C4 grasses (WSGs), switchgrass (Panicum virgatum L.) and miscanthus species (Miscanthus spp.), have been reported to positively influence short-term (15&ndash, 20 years) soil organic carbon (SOC). In this study, the DayCent model was used to predict changes in long-term SOC stocks under WSGs for moderate (Representative Concentration Pathway (RCP) 4.5) and high (RCP 8.5) warming climate change scenarios in southern Ontario, Canada, and to determine how long the enhanced SOC stock will last when WSGs are converted back to annual crop rotation. The model predicted that a consistent corn&ndash, corn&ndash, soybean&ndash, winter wheat (CCSW) rotation prevented SOC from depletion over the 21st century. Under WSGs, the model predicted high rates of SOC sequestration during the first 20&ndash, 30 years which then tended to stabilize after 50&ndash, 60 years. However, the rate of SOC sequestration over 90 years for RCP 4.5 was 0.26 and 0.94 Mg C ha&minus, 1 yr&minus, 1 for switchgrass and miscanthus, respectively. If 40-year stands of WSGs are converted back to CCSW, the model predicted SOC decline to the previous level in 40&ndash, 50 years. DayCent predicted that under RCP 8.5 scenario in the second half of the 21st century and in the future, there will be a reduction in SOC stocks, especially under miscanthus stands.

Purpose This paper presents an improved methodological approach for studying life cycle impacts (especially global warming) from changes in crop production practices. The paper seeks to improve the quantitative assessment via better tools and it seeks to break down results in categories that are logically separate and thereby easy to explain to farmers and other relevant stakeholder groups. The methodological framework is illustrated by a concrete study of a phosphate inoculant introduced in US corn production. Methods The framework considers a shift from an initial agricultural practice (reference system) to an alternative practice (alternative system) on an area of cropland A. To ensure system equivalence (same functional output), the alternative system is expanded with displaced or induced crop production elsewhere to level out potential changes in crop output from the area A. Upstream effects are analyzed in terms of changes in agricultural inputs to the area A. The yield effect is quantified by assessing the impacts from changes in crop production elsewhere. The field effect from potential changes in direct emissions from the field is quantified via biogeochemical modeling. Downstream effects are assessed as impacts from potential changes in post-harvest treatment, e.g., changes in drying requirements (if crop moisture changes). Results and discussion An inoculant with the soil fungus Penicillium bilaiae has been shown to increase corn yields in Minnesota by 0.44 Mg ha−1 (~ 4%). For global warming, the upstream effect (inoculant production) was 0.4 kg CO2e per hectare treated. The field effect (estimated via the biogeochemical model DayCent) was − 250 kg CO2e ha−1 (increased soil carbon and reduced N2O emissions) and the yield effect (estimated by simple system expansion) was − 140 kg CO2e ha−1 (corn production displaced elsewhere). There were no downstream effects. The total change per Mg dried corn produced was − 36 kg CO2e corresponding to a 14% decrease in global warming impacts. Combining more advanced methods indicates that results may vary from − 27 to − 40 kg CO2e per Mg corn. Conclusion and recommendations The present paper illustrates how environmental impacts from changes in agricultural practices can be logically categorized according to where in the life cycle they occur. The paper also illustrates how changes in emissions directly from the field (the field effect) can be assessed by biogeochemical modeling, thereby improving life cycle inventory modeling and addressing concerns in the literature. It is recommended to use the presented approach in any LCA of changes in agricultural practices.

Increasing the amount of soil organic carbon (SOC) has agronomic benefits and the potential to mitigate climate change. Previous regional predictions of SOC trends under climate change often ignore or do not explicitly consider the effect of crop adaptation (i.e., changing planting dates and varieties). We used the DayCent biogeochemical model to examine the effect of adaptation on SOC for corn and soybean production in the U.S. Corn Belt using climate data from three models. Without adaptation, yields of both corn and soybean tended to decrease and the decomposition of SOC tended to increase leading to a loss of SOC with climate change compared to a baseline scenario with no climate change. With adaptation, the model predicted a substantially higher crop yield. The increase in yields and associated carbon input to the SOC pool counteracted the increased decomposition in the adaptation scenarios, leading to similar SOC stocks under different climate change scenarios. Consequently, we found that crop management adaptation to changing climatic conditions strengthen agroecosystem resistance to SOC loss. However, there are differences spatially in SOC trends. The northern part of the region is likely to gain SOC while the southern part of the region is predicted to lose SOC.

Statistically interpolated weather station data, outputs from climate reanalyses, and results from downscaled general circulation model (GCM) simulations are widely used to drive a variety of agro-ecosystem model applications, including regional- and national-scale crop modeling. In this study, we compared these three types of gridded weather datasets (total of nine datasets) with actual point-level weather station observations and analyzed the biases in predicted ecosystem variables of evapotranspiration (ET), crop grain yield, soil organic carbon (SOC) change, and soil N2O emissions using the process-based DayCent ecosystem model. As a reference system, we defined continuous corn cropping systems for three different regions in the U.S. Our results suggested that the predicted ecosystem variables can be highly sensitive to the sources of weather input data. Interpolated weather data from the PRISM and Daymet data products provided relatively accurate estimations of important ecosystem variables. Compared with the bias-corrected constructed analogs (BCCA) method, GCM results downscaled with the multivariate adaptive constructed analogs (MACA) method performed better for agro-ecosystem simulations under climate change; for datasets using BCCA, the rainfall frequency was positively biased and likely caused models to significantly underestimate solar radiation. For regional climate change studies that use a baseline simulation (historical period) for comparison, we suggest including the uncertainty of the baseline due to biases in the weather data in addition to the uncertainty in the projected weather data. Keywords: Agro-ecosystem model, DayCent model, Prediction bias, Weather data.

An increase in the storage of carbon (C) in soil by changing management practices can help to mitigate climate change and increase soil quality. The objective of this study was to evaluate the best management options for reducing greenhouse gas (GHG) emissions. An ecosystem model DayCent was tested for two rice (Oryza sativa L.) experimental sites in Bangladesh. The sites are under different management practices and we first tested the models ability to simulate SOC turnover, and then estimated the potential for SOC sequestration by comparing change in SOC for each management scenario with baseline management (current farmers’ practices) including conventional tillage, 5% residue incorporation and nitrogen (N) fertilizer. Predicted yield was also compared at both sites to ensure that yield was not compromised by mitigation measures. A control treatment was tested at both sites. At site 1, two other treatments of mineral N fertilizer, and combination of farmyard manure (FYM) and N were tested in a double rice based crop rotation. At site 2, a treatment receiving cowdung (CD) application, and a combination of CD and N were tested in a wheat (Triticum aestivum L.)-rice based crop rotation. The DayCent model was able to simulate SOC increase from the double rice test sites under unfertilized conditions, considering additional N and C sources in the simulations. Assuming N fertilizer (180 kg N ha−1 yr−1) application for site 1, and CD application (25 t ha−1 yr−1) for site 2, respectively, as the baseline, four single, and one integrated, scenarios were implemented in the model to predict SOC and yield at both sites. Two additional scenarios with alternate wet and drying (AWD) as a single treatment, and as part of an integrated approach, were also tested for their mitigation potential at site 1. The highest simulated positive impact on SOC development (60% higher than that of the baseline) was observed at site 1 when FYM was used in place of mineral N fertilizer. As there is a yield penalty associated with the use of only FYM, integrated approaches might show more promise, such as inclusion of 15% residue return, reduced tillage, less mineral N fertilizer, FYM addition, with or without AWD. This approach increases SOC by up to 23% while keeping the yield stable (nearly 3.5 t ha−1). The application of CD only as determined for baseline of site 2, gives a yield of about 1.8 t ha−1 yr−1. In contrast nearly two times more yield was obtained under the scenario associated with integrated management which also increases SOC by 30% relative to the baseline at the second site. Net GHG emissions, including nitrous oxide and methane emissions were estimated using the Intergovernmental Panel on Climate Change (IPCC) tier 1 methods, and country specific emission factors (where available), suggests that the integrated management scenario can reduce the net GHG emissions from 0.58-0.82 t carbon dioxide (CO2)-eq. ha−1 yr−1 (equivalent to 0.16-0.24 t Ceq. ha−1 yr−1) at site 2, while a net reduction in GHGs of nearly 1.00 t CO2-eq. ha−1 yr−1 (equivalent to 0.27 t Ceq. ha−1 yr−1) at site 1 was only achieved if AWD was also implemented with the integrated management scenario. Future studies could attempt to model non-CO2 GHGs with a dynamic model.

Dryland agroecosystems could be a sizable sink for atmospheric carbon (C) due to their spatial extent and level of degradation, providing climate change mitigation. We examined productivity and soil C dynamics under two climate change scenarios (moderate warming, representative concentration pathway [RCP] 4.5; and high warming, RCP 8.5), using long-term experimental data and the DayCent process-based model for three sites with varying climates and soil conditions in the US High Plains. Each site included a no-till cropping intensity gradient introduced in 1985, with treatments ranging from wheat-fallow ( L.) to continuous annual cropping and perennial grass. Simulations were extended to 2100 using data from 16 global circulation models to estimate uncertainty. Simulated yields declined for all crops (up to 50% for wheat), with small changes after 2050 under RCP 4.5 and continued losses to 2100 under RCP 8.5. Of the cropped systems, continuous cropping had the highest average productivity and soil C sequestration rates (78.1 kg C ha yr from 2015 to 2045 under RCP 4.5). Any increase in soil C for cropped rotations was realized by 2050, but grassland treatments increased soil C (up to 69%) through 2100, even under RCP 8.5. Our simulations indicate that reduced frequency of summer fallow can both increase annualized yields and store more soil C. As evapotranspiration is likely to increase, reducing fallow periods without live vegetation from dryland agricultural rotations may enhance the resilience of these systems to climate change while also increasing soil C storage and mitigating carbon dioxide emissions.

In order to identify strategies to support global food security while protecting the environment under future climate in northern high latitude environments such as the Canadian Prairies, the DAYCENT model was calibrated, validated, and subsequently used to project effects of climate change (increased carbon dioxide concentration, precipitation, and temperature), nitrogen (N) application rate, and yield potential (radiation use efficiency of biomass, RUEB) of spring wheat (Triticum aestivum L.) on yield production and environmental outputs. Results indicated that projected grain yield and environmental impacts, i.e. soil organic carbon (SOC), N leached below root zone and nitrous oxide (N2O) emission, are affected by different climate change scenarios, N fertilizer rate and RUEB. From these results, we can assess impacts of fertilizer rates on projected grain yield and environmental impacts (SOC, N leaching and N2O emission) in the near future (2017–2046) and distant future (2047–2076). In the near future, if wheat RUEB is improved from current 38–43 mg C kJ−1, the projected yield over seven climate change scenarios will increase 35% with a fertilizer rate of 100 kg N ha−1 compared to the current rate (50 kg N ha−1). Corresponding increases of N leaching, N2O emission and final SOC in 2046 are 29, 35 and 12%, respectively. Additional increases of yield and SOC will be small if more N is added, while N leaching and N2O emission will be further increased. Assuming the cultivar grown in the distant future is improved to 53 mg C kJ−1 RUEB and the fertilizer rate is raised to 125 kg N ha−1, projected yield, N leaching, N2O emission and final SOC in 2076 will be increased by 69, 26, 56 and 80%, respectively. If the N input is increased to 150 kg N ha−1, corresponding increases will be 83, 30, 103 and 151%. It seems that appropriate N input could be 100–125 kg N ha−1 for the near future and distant future, respectively in order to balance production and environmental impacts. Results of our study indicated that after modification and calibration, DAYCENT model can be used to identify adaptation strategies for food security and environmental protection in high latitude environments under future climate change.

Recent evidences show that terrestrial biogeochemical models have large uncertainty in estimating climate-change effect on grassland net ecosystem productivity (NEP), which is defined as the difference between gross ecosystem photosynthesis (GEP) and respiration (ER). It remains unclear that whether GEP or ER limits the model capability to simulate NEP responses to climate change in semiarid grasslands. Given the surrogate CENTURY-type model is widely used for Earth system modeling, we investigated two of them (i.e., DAYCENT and TECO models) and examined which processes dominate their ability to capture the responses of NEP to experimental climate changes in a temperate steppe of northern China. During the simulation from 2006 to 2008, the two models captured the observed mean annual NEP in the control plots when they were validated by the observations from an adjacent eddy-flux tower. However, they failed to capture the treatment effects of experimental warming and increased precipitation on NEP because of the poor estimations of ER responses. DAYCENT model simulated a higher precipitation effect on ER (37.83%) and TECO model overestimated the warming effect on ER by 8.18%. The simulation of treatment effects on ER and therefore NEP can be improved by an optimized parameterization of the water-related decay functions for soil organic carbon (C). The simulated cumulative loss of total ecosystem C stock during 2010–2100 were decreased when the TECO model used experiment-fitted parameters (0.72 kg C m−2) instead of using the initial validation with eddy-flux data (0.96 kg C m−2). The ecosystem shifted from C sink to source at threshold of 435 mm of annual total precipitation. Our findings indicate that future projection of C cycle in semiarid grasslands could be improved by better understanding of water response of ecosystem respiratory processes.

Considering the importance of soil organic carbon (SOC) and the scarcity of data on how soil management influences its storage in the region, this study assessed the long-term impact of different soil management systems on SOC in southern Ethiopia using the DAYCENT model. The conservation management systems considered were minimum tillage, crop residue (CR) retention, fertilization and their combinations. We parameterized the model with data from studies in the literature. We then modeled conventional cropping system for smallholding farms over a 30-year period (1991–2020) as the business as usual scenario (BAU). Then we assessed the impact of alternative conservation management scenarios compared with the BAU scenario. Our results indicated that the conservation management scenarios increased SOC at 0–20 cm depth in the range 0.34–9.71 Mg C ha−1 over 30 years when compared to BAU practices. The individual effect of fertilization, CR retention or minimum tillage management practices on SOC stock were lower than the response of the combined conservation management practices. The combined 50%–75% CR retention, no-tillage (NT), and 32 kg N ha−1 fertilization provided the highest SOC sequestration. These combinations, increased SOC in the range 8.10–9.71 Mg C ha−1 over 30 years equivalent to rates of 0.27–0.32 Mg C ha−1 yr−1. While long-term empirical data from field experiments are lacking, model results suggest that the combined 50–75% CR retention, NT, and increased N fertilization have a potential to increase SOC sequestration in resource-limited smallholding croplands. The results may be useful for researchers, policy maker and other stakeholders.

Rock fragments (RFs, mineral particles with diameter > 2 mm) can substantially influence soil carbon (C) and nitrogen (N) cycles through different physical mechanisms. These physical mechanisms include changing soil hydraulic parameters (vSH) by reducing fine earth bulk density (vSHBD) and volume (vSHVo), and affecting soil C and N stocks (vCN) by reducing fine earth bulk density (vCNBD) and volume (vCNVo), and increasing fine earth C and N concentrations (vCNCo). In this study, based on soil and climate data in a typical hilly area of China, we construct scenarios by considering these physical mechanisms to investigate the responses of key soil C and N outputs (carbon dioxide or CO2, and nitrous oxide or N2O emissions, and N leaching) to RF content (RFC) in DNDC and DayCent models. Results showed that when considering vSH and vCN, parabolic relationships were observed between these soil C and N outputs and RFC, with maximum in RFC = 0.3–0.6 g g−1. The vCN dominated the responses of soil CO2 emission to RFC, while vSH exerted comparative contributions to the responses of soil N2O emission and N leaching. When only considering vSH, opposite contributions of vSHBD and vSHVo were observed in DNDC model, and contributions of vSHBD overwhelmed those of vSHVo. However, vSHVo dominated these responses in DayCent model. When only considering vCN, the vCNCo dominated these responses, though considerable superimposed influences of vCNBD and vCNVo appeared under high RFC. The discrepancies between these responses to RFC in DNDC and DayCent models might be due to the different model complexities in simulating soil hydrology, biogeochemistry, and the role of bulk density in these models. Relatively, more distinct responses were achieved in DNDC model. These findings can extend our understandings of RFs and soil C and N cycles.

A current national research priority in Brazil is the assessment of potential climate change impacts in agriculture. In this study, the DayCent model was used to predict changes in crop biomass, soil C stocks, and N₂O fluxes of a major agricultural area in Brazil. The model was calibrated and validated using datasets from 30-yr-old experiments. Simulations of current and alternative management practices to 2100 using IPCC climate scenarios A2 and B1 were conducted. Predicted crop biomass increases ranged from 5% (sorghum [B1]) to 65% (soybean [A2]). DayCent simulated higher soil organic carbon stocks in the wheat–soybean system (VAL1) than in the wheat–soybean–vetch–sorghum system (VAL2) in both climate scenarios. Soil organic carbon accumulation by 2100 (±16 Mg C ha⁻¹ above current 46 Mg C ha⁻¹) was forecast in cropping systems with pastures (Cynodon sp.), regardless of the climate scenario. Daily N₂O fluxes were underestimated by ±41% (0.85 g N–N₂O ha⁻¹ d⁻¹) in VAL1 and overestimated by 17.5% (0.28 g N–N₂O ha⁻¹ d⁻¹) in VAL2. Cumulative N₂O fluxes produced mixed results, which were 29% lower than observed in VAL1 and 5% higher in VAL2 but within the range of values reported in other greenhouse gas studies in southern Brazil. Simulations of N₂O fluxes to 2100 with IPCC climate change scenarios B1 and A2 in southern Brazilian indicated higher annual fluxes across the alternative treatments tested in comparison to current fluxes. According to model predictions, climate change would lead to larger relative increases in N₂O emissions from no-tillage (27% in B1 to 41% in A2), but these enhanced fluxes would still be lower than those from tillage by approximately 25%.

The sustainability of grazing lands lies in the nexus of human consumption behavior, livestock productivity, and environmental footprint. Due to fast growing global food demands, many grazing lands have suffered from overgrazing, leading to soil degradation, air and water pollution, and biodiversity losses. Multidisciplinary efforts are required to understand how these lands can be better assessed and managed to attain predictable outcomes of optimal benefit to society. This paper synthesizes our understanding based on previous work done on modelling the influences of grazing of soil carbon (SC) and greenhouse gas emissions to identify current knowledge gaps and research priorities. We revisit three widely-used process-based models: DeNitrification DeComposition (DNDC), DayCent, and the Pasture Simulation model (PaSim) and two watershed models: The Soil & Water Assessment Tool (SWAT) and Variable Infiltration Capacity Model (VIC), which are widely used to simulate C, nutrient and water cycles. We review their structures and ability as process-based models in representing key feedbacks among grazing management, SOM decomposition and hydrological processes in grazing lands. Then we review some significant advances in the use of models combining biogeochemical and hydrological processes. Finally, we examine challenges of incorporating spatial heterogeneity and temporal variability into modelling C and nutrient cycling in grazing lands and discuss their weakness and strengths. We also highlight key research direction for improving the knowledge base and code structure in modelling C and nutrient cycling in grazing lands, which are essential to conserve grazing lands and maintain their ecosystem goods and services.

Explicit consideration of microbial physiology in soil biogeochemical models that represent coupled carbon-nitrogen dynamics presents opportunities to deepen understanding of ecosystem responses to environmental change. The MIcrobial-MIneral Carbon Stabilization (MIMICS) model explicitly represents microbial physiology and physicochemical stabilization of soil carbon (C) on regional and global scales. Here we present a new version of MIMICS with coupled C and nitrogen (N) cycling through litter, microbial, and soil organic matter (SOM) pools. The model was parameterized and validated against C and N data from the Long-Term Inter-site Decomposition Experiment Team (LIDET; 6 litter types, 10 years of observations, 13 sites across North America). The model simulates C and N losses from litterbags in the LIDET study with reasonable accuracy (C: R2 = 0.63, N: R2 = 0.29) results that are comparable with simulations from the DAYCENT model that implicitly represents microbial activity (C: R2 = 0.67, N: R2 = 0.30). Subsequently, we evaluated equilibrium values of stocks (total soil C and N, microbial biomass C and N, inorganic N) and microbial process rates (soil heterotrophic respiration, N mineralization) simulated by MIMICS-CN across the 13 simulated LIDET sites against published observations from other continent-wide datasets. We found that MIMICS-CN produces equilibrium values in line with measured values, showing that the model generates plausible estimates of ecosystem soil biogeochemical dynamics across continental-scale gradients. MIMICS-CN provides a platform for coupling C and N projections in a microbial-explicit model but experiments still need to identify the physiological and stoichiometric characteristics of soil microbes, especially under environmental change scenarios.

Cover crops (CC) promote the accumulation of soil organic carbon (SOC), which provides multiple benefits to agro-ecosystems. However, additional nitrogen (N) inputs into the soil could offset the CO2 mitigation potential due to increasing N2 O emissions. Integrated management approaches use organic and synthetic fertilizers to maximize yields while minimizing impacts by crop sequencing adapted to local conditions. The goal of this work was to test whether integrated management, centered on CC adoption, has the potential to maximize SOC stocks without increasing the soil greenhouse gas (GHG) net flux and other agro-environmental impacts such as nitrate leaching. To this purpose, we ran the DayCent bio-geochemistry model on 8,554 soil sampling locations across the European Union. We found that soil N2 O emissions could be limited with simple crop sequencing rules, such as switching from leguminous to grass CC when the GHG flux was positive (source). Additional reductions of synthetic fertilizers applications are possible through better accounting for N available in green manures and from mineralization of soil reservoirs while maintaining cash crop yields. Therefore, our results suggest that a CC integrated management approach can maximize the agro-environmental performance of cropping systems while reducing environmental trade-offs.

Crop management practices that contribute to soil organic carbon (SOC) sequestration can improve productivity and long-term sustainability. A simulation study was conducted using the DAYCENT model over an 80-yr period. The objectives of the study were to assess model performance and forecast SOC changes in conventional tillage and no-tillage management in a dryland winter wheat (Triticum aestivum L.)–summer fallow (WW–SF) system. The treatments studied included fall burning of crop residue (FB0), no burning of crop residue with 0 (NB0), 45 (NB45) and 90 (NB90) kg N ha–¹, pea vines (PV), and cattle manure (MN) addition. The model was accurate with R² values of 0.93 (p < 0.01), 0.95 (p < 0.01), and 0.99 (p < 0.01) for the mean of observed and modeled grain yield, residue yield, and SOC, respectively. The strong positive correlation (r = 0.71–0.91) in different treatments between observed and modeled SOC indicated that model closely simulated the observed values. DAYCENT projected that conventionally tilled WW–SF systems, except MN, lost 866 to 2192 g C m–² SOC from 1931 to 2080. The MN gained 496 g C m–² SOC in the same period. The conversion to no-tillage from 2011 onward, however, minimized SOC loss by 17 to 47% under different WW–SF systems. No-tillage conversion in MN resulted in SOC gain by more than 300%. This study suggested that adoption of a no-tillage system and the addition of organic amendments can improve the long-term sustainability of dryland WW–SF systems.

Warming temperatures and increasing CO2 are likely to have large effects on the amount of carbon stored in soil, but predictions of these effects are poorly constrained. We elevated temperature (canopy: +2.8 °C; soil growing season: +1.8 °C; soil fallow: +2.3 °C) for 3 years within the 9th-11th years of an elevated CO2 (+200 ppm) experiment on a maize-soybean agroecosystem, measured respiration by roots and soil microbes, and then used a process-based ecosystem model (DayCent) to simulate the decadal effects of warming and CO2 enrichment on soil C. Both heating and elevated CO2 increased respiration from soil microbes by ~20%, but heating reduced respiration from roots and rhizosphere by ~25%. The effects were additive, with no heat × CO2 interactions. Particulate organic matter and total soil C declined over time in all treatments and were lower in elevated CO2 plots than in ambient plots, but did not differ between heat treatments. We speculate that these declines indicate a priming effect, with increased C inputs under elevated CO2 fueling a loss of old soil carbon. Model simulations of heated plots agreed with our observations and predicted loss of ~15% of soil organic C after 100 years of heating, but simulations of elevated CO2 failed to predict the observed C losses and instead predicted a ~4% gain in soil organic C under any heating conditions. Despite model uncertainty, our empirical results suggest that combined, elevated CO2 and temperature will lead to long-term declines in the amount of carbon stored in agricultural soils.

Soil organic matter (SOM) is important for sustainable agricultural production. Agricultural management can directly or indirectly affect SOM content and associated soil organic carbon (SOC) stocks. The SOM content and crop productivity are generally low in the farmlands in the arid and semi-arid regions of China, and are highly sensitive to climate changes. An improvement in SOM content may greatly enhance the efficiency of fertilizer and water use, thereby increasing the resilience of agricultural systems to climate change. However, little information is available on the response of SOM to the interactions of cultivation measures and changing climate in the regions. Here we analyzed the effects of climate and long-term management practices on SOM content at three sites in the arid and semi-arid regions of China using the SOM data monitored under a range of management practices. We showed that the manure application, crop residue return and no-tillage had significantly increased SOM content from 12.1% to 127.5% in the long-term experiment at three sites. We also used the DayCent model to simulate the effects of these management practices in combination with future climate changes on the SOM content, and found that climate changes in the near future would not increase SOM content, and even decrease SOM content under conventional agronomic management, but the possible adverse impact of climate change on SOM can be outweighed by the enhancement of agronomic management. Our results provide important information for improving management to maintain SOM content and sustainable production under future climate changes.

Benefits of carbon sequestration in agricultural soils are well recognized, and process-based models have been developed to better understand sequestration potential. However, most studies ignore the uncertainty arising during model prediction—a critical requirement for scientific understanding, policy implementation and carbon emission trading. Furthermore, the dependencies created in process-based models due to many parameters and a relatively small set of empirical data hinder parameterization. We have implemented a Bayesian approach using the sampling importance resampling (SIR) method to calibrate the DayCent ecosystem model for estimating soil organic carbon (SOC) stocks, and to quantify uncertainty in model predictions. A SOC dataset compiled from 19 long-term field experiments, representing 117 combinations of management treatments, with 491 measurements of SOC, was split into independent datasets for model calibration and evaluation. The most important DayCent model parameters were identified through a global sensitivity analysis (GSA) for parameterization and SIR was used to calibrate the model and produce posterior distributions for the most sensitive parameters. On average, the Bayesian calibration reduced the model uncertainty by a factor of 6.6 relative to the uncertainty associated with the prior. The Bayesian model analysis framework will allow for ongoing updates to the model as new datasets and model structural improvements are made in future research, and overall provide a stronger basis for models to support policy and management decisions associated with GHG mitigation through C sequestration in agricultural soils.

Soil tillage for sugarcane production has a significant impact on greenhouse gas (GHG) emissions and soil carbon (C) stocks. However, the short- and long-term effects of different soil tillage methods in sugarcane systems on C footprints are still poorly studied. We conducted a field experiment in a sugarcane system to evaluate the effects of conventional tillage (CT), chiseling (CHI), no tillage (NT), and compacted no-till (NTC) on nitrous oxide (N2O) and methane (CH4) emissions and stalk yields in the cane plant cycle and on soil C changes in the short term (3 years) in a Brazilian subtropical climate. Furthermore, we used the DayCent model to predict soil C dynamics for the next 30 years under green harvest management in scenarios with 0, 50 and 75% straw removal and to estimate the C savings of different soil tillage methods. Soil C stocks were determined before the treatments were applied and 1, 2 and 3 years after planting sugarcane. GHG sampling was performed only 1 year during the sugarcane plant cycle. Soil tillage did not significantly affect sugarcane stalk yield. The CH4 fluxes ranged from − 58.2 to − 3.6 μg CH4 m−2 h−1, and annual emissions did not differ between soil tillage systems. The N2O fluxes after soil tillage for sugarcane ranged from 3.8 to 2279 μg N2O m−2 h−1, and the highest annual emissions were observed in NTC. The conversion from grain production system to sugarcane production resulted in an initial decrease in soil C stocks, with C losses in the 0–20-cm layer over 3 years varying from 0.26 to 2.12 Mg C ha−1. The simulated data for the period 2016–2046 indicated recovery of C stocks in all soil tillage systems, with accumulation rates varying from 0.01 to 0.10 Mg C ha−1 year−1. Regardless of the soil tillage system, straw removal decreased the soil C stocks. The time interval for recovery of the initial C stocks ranged from 10 to 20 years, depending on the intensity of straw removal and soil tillage. Soil C losses and GHG emissions reduced the potential C savings of sugarcane ethanol. The CO2eq emissions observed during soil tillage and the sugarcane growing cycle should be quantified and included in the C savings estimates of biofuel production systems.

Rice (Oryza sativa L.) is cultivated as a major crop in most Asian countries and its production is expected to increase to meet the demands of a growing population. This is expected to increase greenhouse gas (GHG) emissions from paddy rice ecosystems, unless mitigation measures are in place. It is therefore important to assess GHG mitigation potential whilst maintaining yield. Using the process-based ecosystem model DayCent, a spatial analysis was carried out in a rice harvested area in Bangladesh for the period 1996 to 2015, considering the impacts on soil organic carbon (SOC) sequestration, GHG emissions and yield under various mitigation options. An integrated management (IM, a best management practice) considering reduced water, tillage with residue management, reduced mineral nitrogen fertilizer and manure, led to a net offset by, on average, &minus, 2.43 t carbon dioxide equivalent (CO2-eq.) ha&minus, 1 year&minus, 1 (GHG removal) and a reduction in yield-scaled emissions intensity by &minus, 0.55 to &minus, 0.65 t CO2-eq. t&minus, 1 yield. Under integrated management, it is possible to increase SOC stocks on average by 1.7% per year in rice paddies in Bangladesh, which is nearly 4 times the rate of change targeted by the &ldquo, 4 per mille&rdquo, initiative arising from the Paris Climate Agreement.

The DayCent ecosystem model, widely tested in upland agroecosystems, was recently updated to simulate waterlogged soils. We evaluated the new version in a paddy rice experiment in Southern Brazil. DayCent was used to simulate rice yield, soil organic carbon (SOC), and soil CH4 fluxes. Model calibration was conducted with a multiple-year dataset from the conventional tillage treatment, followed by a validation phase with data from the no-tillage treatment. Model performance was assessed with statistics commonly used in modeling studies: root mean square error (RMSE), model efficiency (EF), and mean difference (M). In general, DayCent slightly underestimated rice yields under no-tillage (by 0.07 Mg ha-1, or 9.2 %) and slightly overestimated soil C stocks, especially in the first years of the experiment. A comparison of observed and simulated CH4 daily fluxes showed that DayCent could simulate the general patterns of soil CH4 fluxes with slight discrepancies. Daily soil CH4 fluxes were overestimated by 0.43 kg ha-1 day-1 (12 %). Growth-season CH4 emissions under no-tillage were also somewhat overestimated (11 % or 45.29 kg ha-1). We conclude that DayCent simulated SOC, rice yield, and CH4 with some inaccuracies, but the overall performance was considered adequate. However, the model failed to represent the observed potential of no-tillage to mitigate CH4 emissions, possibly because model algorithms could not capture the actual field conditions derived from no-tillage management, such as soil redox potential, plant senescence, and surface placement of plant residue.

The potential of switchgrass ( Panicum virgatum L.) to offset large-scale greenhouse gas (GHG) emissions depends on optimizing external inputs when the crop is primarily managed as a sustainable source for renewable energy production. Due to the heterogeneity of climate and soil conditions and the complexity of agriculture, an evaluation of the effect of adopting switchgrass as a new biofuel crop into agriculture needs to be done at the regional scale. The objective of the study was to predict long-term (100-yr) GHG emissions under different N fertilization (0, 112, and 224 kg N ha −1 ) and irrigation application (0, 25, 50, 75, and 99 cm H 2 O) levels across the Central Valley of California using the DAYCENT model. Six cultivars (Alamo, Kanlow, Cave-in-Rock, Blackwell, Sunburst, and Trailblazer) were selected. The model results suggest that switchgrass productivity is primarily constrained by N inputs when no or low water stress is expected in a Mediterranean climate. In the short-term (the first decade after establishment), soil organic carbon (SOC) stocks (0–20 cm) increased by 0.42–0.92 Mg C ha −1 yr −1 and N 2 O emissions were 1.37–2.48 kg N 2 O–N ha −1 yr −1 across the cultivars with baseline input rates of 224 kg N ha −1 yr −1 and 99 cm H 2 O. All cultivars were net CO 2 sinks in the near term and the potential decreased by 0.09–0.30 Mg C ha −1 yr −1 (15.5–52.8%) with reduced N input from baseline under varying irrigation rates. There was a reduction in N 2 O emissions by 47.2–61.6% by applying less N fertilizer when irrigated at rates ≥75 cm H 2 O per year over time. In general, higher-yielding cultivars (e.g., Alamo) tended to sequester more CO 2 but also led to higher N 2 O emissions. In the near term, the use of N fertilizer and irrigation is needed for switchgrass systems to be a soil GHG sink, but for longer-term GHG mitigation strategies reducing both N fertilization and irrigation inputs is required.

Livestock manure is applied to rangelands as an organic fertilizer to stimulate forage production, but the long-term impacts of this practice on soil carbon (C) and greenhouse gas (GHG) dynamics are poorly known. We collected soil samples from manured and nonmanured fields on commercial dairies and found that manure amendments increased soil C stocks by 19.0 ± 7.3 Mg C ha(-1) and N stocks by 1.94 ± 0.63 Mg N ha(-1) compared to nonmanured fields (0-20 cm depth). Long-term historical (1700-present) and future (present-2100) impacts of management on soil C and N dynamics, net primary productivity (NPP), and GHG emissions were modeled with DayCent. Modeled total soil C and N stocks increased with the onset of dairying. Nitrous oxide (N2 O) emissions also increased by ~2 kg N2 O-N ha(-1) yr(-1) . These emissions were proportional to total N additions and offset 75-100% of soil C sequestration. All fields were small net methane (CH4 ) sinks, averaging -4.7 ± 1.2 kg CH4 -C ha(-1) yr(-1) . Overall, manured fields were net GHG sinks between 1954 and 2011 (-0.74 ± 0.73 Mg CO2 e ha(-1) yr(-1) , CO2 e are carbon dioxide equivalents), whereas nonmanured fields varied around zero. Future soil C pools stabilized 40-60 years faster in manured fields than nonmanured fields, at which point manured fields were significantly larger sources than nonmanured fields (1.45 ± 0.52 Mg CO2 e ha(-1) yr(-1) and 0.51 ± 0.60 Mg CO2 e ha(-1) yr(-1) , respectively). Modeling also revealed a large background loss of soil C from the passive soil pool associated with the shift from perennial to annual grasses, equivalent to 29.4 ± 1.47 Tg CO2 e in California between 1820 and 2011. Manure applications increased NPP and soil C storage, but plant community changes and GHG emissions decreased, and eventually eliminated, the net climate benefit of this practice.

Crop residues are potentially significant sources of feedstock for biofuel production in the United States. However, there are concerns with maintaining the environmental functions of these residues while also serving as a feedstock for biofuel production. Maintaining soil organic carbon (SOC) along with its functional benefits is considered a greater constraint than maintaining soil erosion losses to an acceptable level. We used the biogeochemical model DayCent to evaluate the effect of residue removal, corn stover, and wheat and barley straw in three diverse locations in the USA. We evaluated residue removal with and without N replacement, along with application of a high-lignin fermentation byproduct (HLFB), the residue by-product comprised of lignin and small quantities of nutrients from cellulosic ethanol production. SOC always decreased with residue harvest, but the decrease was greater in colder climates when expressed on a life cycle basis. The effect of residue harvest on soil N2O emissions varied with N addition and climate. With N addition, N2O emissions always increased, but the increase was greater in colder climates. Without N addition, N2O emissions increased in Iowa, but decreased in Maryland and North Carolina with crop residue harvest. Although SOC was lower with residue harvest when HLFB was used for power production instead of being applied to land, the avoidance of fossil fuel emissions to the atmosphere by utilizing the cellulose and hemicellulose fractions of crop residue to produce ethanol (offsets) reduced the overall greenhouse gas (GHG) emissions because most of this residue carbon would normally be lost during microbial respiration. Losses of SOC and reduced N mineralization could both be mitigated with the application of HLFB to the land. Therefore, by returning the high-lignin fraction of crop residue to the land after production of ethanol at the biorefinery, soil carbon levels could be maintained along with the functional benefit of increased mineralized N, and more GHG emissions could be offset compared to leaving the crop residues on the land.

Long-term information on switchgrass ( Panicum virgatum L.) as a biomass energy crop grown on marginally saline soil and the associated impacts on soil carbon (C) and nitrogen (N) dynamics, greenhouse gas (GHG) emissions, and best management practices (BMPs) are limited. In this study, we employed the DAYCENT model, based on a 4-year switchgrass field experiment, to evaluate the long-term biomass yield potential and environmental impacts, and further to develop BMPs for switchgrass in a semi-arid region. The model showed that long-term (14-year) annual mean biomass yields were 9.6 and 5.2 Mg ha −1 for irrigated and rainfed switchgrass systems, respectively. The simulated biomass yields correlated well with field-measured biomass with r 2 values of 0.99 and 0.89 for irrigated and rainfed systems, respectively. Soil organic carbon (SOC) and soil total nitrogen (STN) accumulated rapidly after switchgrass establishment, with mean accrual rates of 0.99–1.13 Mg C ha −1 yr −1 and 0.04–0.08 Mg N ha −1 yr −1 , respectively. Based on the outputs of numerous long-term model simulations with variable irrigation water supplies and N rates, the irrigation regime and N rate with the highest yield to input ratio were chosen as BMPs. The DAYCENT model predicted-BMP was irrigating every 14 days at 70% potential evapotranspiration combined with an N rate of 67 kg ha −1 yr −1 . Switchgrass established and produced biomass reasonably well in this semi-arid region; however, appropriate irrigation and N fertilization were needed for optimal biomass yield. Switchgrass had a great potential to sequester C into soils with low N 2 O emissions while supplying significant quantities of biomass for biofuel synthesis.

The ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO 3 - compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO 3 - and NH 4 + . Post-processing analyses provided insights into parameter-observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent. Several DayCent submodels were calibrated simultaneously using inverse modeling.Parameter estimation reduced DayCent total sum of weighted squared residuals by 56%.Soil temperature and water content are highly informative in DayCent calibration.Parameter estimation is an efficient way to calibrate soil biogeochemical models.Post-estimation analyses provide unique insights into model structure and function.

Soil carbon sequestration can be estimated from field to global scale using numerical soil/ecosystem models. In this chapter, we describe the structure and development of models that have been widely used at international level, from simple models that include carbon only to models that include descriptions of the dynamics of a range of nutrients. We also present examples of the application from field to global scale of different models to answer a range of different questions on the impact of land use and climate changes on soil carbon sequestration.A full discussion of the impact of soil carbon modeling on political and socioeconomical aspects is included to emphasize the need of a close interaction between model developers, researchers, land owners/users and policy makers to ensure the development of robust approaches to climate change, food security and soil protection.Whatever type of models are used to meet future challenges, it is important that they continue to be tested using appropriate data, and that they are used in regions and for land uses where they have been developed and validated.

The Phaeozem region is an important grain production base in China and soil fertility has declined under long-term cultivation. Studying soil organic carbon (SOC) change in the upland phaeozem of Northeast China under different tillage modes has great theoretical and practical significance for reducing greenhouse gases emissions, increasing soil carbon stocks, and ensuring food security. This study applied the DAYCENT model to simulate six long-term experimental sites in the phaeozem region, and through calibration and validation analysis, it concluded that the DAYCENT model could effectively simulate the dynamic change of SOC in the upland phaeozems; hence, the relevant parameters of each site were determined. Under future climate scenarios (Representative Concentration Pathway 4.5, RCP 4.5), SOC change in agricultural soils (0–20 cm topsoil) of four different management scenarios (fertilizer application, manure amendment, straw return, no-tillage) was simulated. The overall simulated trend was no-tillage > straw return > manure amendment > fertilizer application. Fertilizer had no evident effect on increasing SOC, but the effect would be better if combined with manure; both straw return and no-tillage had the general effect of improving SOC and the simulated values rose rapidly with a large increasing rate within a short period; however, the increasing rate became gentle after 2050, reached a relatively stable level after 2080, then gradually reached carbon saturation. Untill 2100, the SOC content of each site reached a relatively higher level by adopting the no-tillage treatment, where the SOC contents of Harbin, Hailun, Nehe, Dehui, Gongzhuling and Lishu were 2.36 g/100 g, 2.81 g/100 g, 2.22 g/100 g, 2.38 g/100 g, 1.97 g/100 g and 2.01 g/100 g, respectively. The SOC contents increased by 52.47% when compared with the initial value, and the simulated average annual increase of SOC for 84 years was 0.0082 g/100 g.

The Community Land Model (CLM) can simulate planting and harvesting of crops but does not include effects of cultivation on soil carbon decomposition. The biogeochemistry model DayCent does account for cultivation and provides a baseline for evaluating the CLM. With the goal of representing cultivation effects on soil carbon decomposition, we implemented the DayCent cultivation parameterization in the CLM and compared CLM and DayCent simulations at eight Midwestern United States sites with and without the cultivation parameterization. Cultivation decreases soil carbon by about 1350 gC m−2 in the CLM and 1660 gC m−2 in DayCent across the eight sites from the first cultivation (early 1900s) to 2010. CLM crop simulations without cultivation have soil carbon gain, not loss, over this period, in contrast to the expected declining trends in agricultural soil carbon. A global cultivation simulation for 1973–2004 reduces ecosystem carbon by 0.4 Pg yr−1 over temperate corn, soybean, and cereal crop areas, which occupy approximately 1/3 of global crop area. Earth System Models may improve their atmospheric CO2 and soil carbon simulations by accounting for enhanced decomposition from cultivation.

Transformations of plant-derived carbon into soil organic carbon (SOC) influences both local and global carbon cycles. Soil bacteria play a major role in SOC transformations, which are important for maintaining soil fertility and for stabilizing carbon in soil. SOC is consumed by bacteria in soil and transformed into biomass or respired to carbon dioxide. This bacterial-driven partitioning of SOC is defined as Bacterial Growth Efficiency (BGE) and it is an integral component of models that simulate carbon dynamics. We tested the variability of BGE in microbial communities from soil by measuring bacterial production (BP) and respiration (BR), the two components of BGE, in slurries of soils collected from deciduous forests and croplands at the Kellogg Biological Station Long Term Ecological Research site. BP was measured as 3H-leucine incorporation into protein and BR as oxygen consumption. The differences in BP and BR in soil under different land management practices revealed that BGE was not static but varied from 0.23 to 0.63, supporting more recent SOC models. Bacterial communities from soils of soybean monoculture cropland tended to have a higher BGE than those from deciduous forests. BGE of cropland soil microbes exhibited a large seasonal variation not observed in forest soils. Nutrient amendments on rotation cropland soil microcosms showed that BGE is sensitive to substrate availability and nutrient stoichiometry. Using a range of growth efficiency expected of terrestrial ecosystem, simulations of carbon dynamics in a forest using the DAYCENT model revealed the sensitivity of equilibrium soil carbon values to changes in growth efficiency. Decreasing the default growth efficiency of 0.45 to a growth efficiency of 0.35 reduced the active carbon fraction by 22%. This sensitivity emphasizes the importance of site-specific BGE measurements for improving the predictive capacity of SOC models, especially when investigating the effects of changes in land management practices on labile SOC transformation. The weak correlation of BP and BR in most soil tested also showed that BGE is a more valuable measurement than the common interpretation of bacterial activity based on BR.

The prediction of methane (CH4) emissions from rice paddies could play a key role in greenhouse gas mitigation efforts associated with agriculture. We describe a methanogenesis sub-model that has been developed in the DAYCENT ecosystem model for estimating CH4 emissions and assessing mitigation potentials for rice paddies. Methanogenesis is modeled based on the simulation of soil hydrology and thermal regimes, rice plant growth, SOM decomposition, and CH4 transport from the soil to atmosphere. A total of 97 sites from China's rice paddies were used to develop and evaluate the model, in which 25 sites (91 observations) were used for parameterization and 72 sites (204 observations) were used for model evaluation. Comparison of modeled results with measurements demonstrated that CH4 emissions in rice paddies of China can be successfully simulated by the model with an overall R2 of 0.83, and included an evaluation of CH4 emissions for a range of climates and agricultural management practices. The model was most sensitive to parameters influencing the amount of labile C available for methanogenesis.

The DayCENT model was employed to simulate the effects of conventional tillage (CT) and no-till (NT) practices on the dynamics of soil organic carbon (SOC) over 9 yr in a rotational cropping system in Southern Ontario, Canada. Observations of site properties and eddy covariance measurements were used to assess crop productivity, net ecosystem productivity (NEP), and SOC changes. The validated model captured the dynamics of grain yield and net primary production, which indicated that DayCENT can be used to simulate crop productivity for evaluating the effects of tillage on crop residues and heterotrophic respiration (Rₕ) dynamics. The simulation suggested that CT enhanced the annual Rₕ relative to NT by 38.4, 93.7 and 64.2 g C m⁻² yr⁻¹ for corn (Zea mays L.), soybean [Glycine max (L.) Merr], and winter wheat (Triticum aestivum L.), respectively. The combined effect of incorporating crop residues and increased cultivation factors enhanced Rₕ in CT by 35% relative to NT after disk cultivation in the spring. The simulated NEP varied with crop species, tillage practices, and timing/length of the growing season. The seasonal variation of the total SOC pool was greater in CT than NT because of tillage effects on C transfer from the active surface SOC pool to the active soil SOC pool at a rate of 50 to 100 g C m⁻² yr⁻¹. The NT method practiced during the study period accounted for a 10.7 g C m⁻² yr⁻¹ increase in the slow SOC pool. The validated DayCENT model may be applied for longer-term simulations in similar ecosystems for a variety of climate change experiment.

Different residue management practices can affect carbon (C) allocation and thus soil C and nitrogen (N) turnover. A biogeochemical model, DAYCENT, was used to simulate the effects of bioenergy Sorghum [Sorghum bicolor (L.) Moench] residue return on soil temperature and water content, soil organic carbon (SOC), and greenhouse gas (GHG) [carbon dioxide (CO2) and nitrous oxide (N2O)] emissions under bioenergy Sorghum production. Coefficient of determination (r2) was used to test model performance. Coefficients of determination between the observed and simulated soil temperature, soil water content, SOC, and annual CO2 and N2O emissions were 0.94, 0.81, 0.75, 0.97, and 0.0057, respectively, indicating that the DAYCENT model captured the major patterns of soil environmental factors and C turnover but was less accurate in estimating N2O emissions. Compared with the simulated control (0 % residue return), the simulated 50 % residue return treatment had 7.77 %, 15.12 %, and 1.25 % greater SOC, annual CO2, and N2O emissions, respectively, averaged over 2 years’ data (2010 and 2011). Similar patterns in the simulated outputs were also observed in our field trials, with percentages being 4.52 %, 15.98 %, and 12.89 %, respectively. The model also successfully reflected the daily GHG flux variation affected by treatments, management practices, and seasonal changes except for missing some high growing season fluxes. In addition, annual variations in the simulated outputs were comparable with field observations except the N2O emissions in the 50 % residue return treatment. Our study indicated that DAYCENT reasonably simulated the main effects of residue return on soil C turnover but underestimated N2O emissions.

Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle-climate feedbacks are largely untested with respect to litter decomposition. We tested the litter decomposition parameterization of the community land model version 4 (CLM4), the terrestrial component of the community earth system model, with data from the long-term intersite decomposition experiment team (LIDET). The LIDET dataset is a 10-year study of litter decomposition at multiple sites across North America and Central America. We performed 10-year litter decomposition simulations comparable with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes using the LIDET-provided climatic decomposition index to constrain temperature and moisture effects on decomposition. We performed additional simulations with DAYCENT, a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results show large discrepancy between the laboratory microcosm studies used to parameterize the CLM4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, and nitrogen immobilization is biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that soil mineral nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, independent of nitrogen limitation. CLM4 has low soil carbon in global earth system simulations. These results suggest that this bias arises, in part, from too rapid litter decomposition. More broadly, the terrestrial biogeochemistry of earth system models must be critically tested with observations, and the consequences of particular model choices must be documented. Long-term litter decomposition experiments such as LIDET provide a real-world process-oriented benchmark to evaluate models.

The initial distribution of carbon (C) between model pools can strongly influence predictions of soil C models. Models are often initialized by assuming that C stocks are near steady state, but in many cases this is unrealistic. We explored different ways of initializing the DAYCENT model over the range of soils and climate in England and Wales. We ran the model for the main soil types on arable land and managed grass, identified by taking the top five soil-land use combinations in each of 87 50-km grid squares, giving 376 ‘sites’ distributed across the two countries. We compared three initialization methods: (i) using the soil C contents and pool distributions (‘soil microbe C’, ‘slow C’ and ‘passive C’ in DAYCENT) predicted for steady state under the prevailing conditions; (ii) using the steady-state pool distributions but with the true, initial soil C content; and (iii) by fitting the initial pool distribution to the rates of change in soil C observed in the National Soil Inventory (NSI) of England and Wales during the 1980s and 1990s. The calculated mean net primary production (NPP), and hence C inputs to the soil, was realistic for arable land and permanent grass in England and Wales. The calculated rates of change in soil C were sensitive to the initialization method. Method 1 predicted little change with the actual climate over the NSI survey period and simulated management, but Methods 2 and 3 predicted losses of C. Methods 2 and 3 gave similar trends averaged across soil types and locations, but there were large differences for individual soils and locations. The relationships between losses and the mean soil C content were approximately linear with Methods 2 and 3, but the slopes differed. The predicted losses varied with different climate scenarios applied over the NSI survey period. The predicted differences between climate scenarios were less sensitive to initialization method than to C content. We discuss generic implications for modelling.

This study uses DAYCENT model to investigate the sensitivity of soil organic carbon (SOC) at an intensely cultivated site in the U.S. Midwest under an ensemble of scenario climates predicted by IPCC models. The model ensemble includes three IPCC models (Canadian, French, German), three emission scenarios (B1, A1B, A2) and three time periods (late 20th, mid-21st, late 21st century). DAYCENT shows that SOC at the site would decline by 0.3–2.6 kg m−2 (5–35%) depending on the models and scenarios from late 20th to mid-21st century despite a larger increase of future net primary production (NPP) than respiration. The future SOC decrease is mostly attributable to harvest loss. The wide spread in future SOC decline rates are in part because SOC decrease (by respiration) is directly proportional to SOC itself. Any uncertainty in absolute SOC in DAYCENT would translate directly into its trend, unlike other variables such as temperature whose trends are independent of their values themselves, contrasting the reliability of SOC trend with temperature change.

The most widely applied soil carbon models partition the soil organic carbon into two or more kinetically defined conceptual pools. The initial distribution of soil organic matter between these pools influences the simulations. Like many other soil organic carbon models, the DAYCENT model is initialised by assuming equilibrium at the beginning of the simulation. However, as we show here, the initial distribution of soil organic matter between the different pools has an appreciable influence on simulations, and the appropriate distribution is dependent on the climate and management at the site before the onset of a simulated experiment. If the soil is not in equilibrium, the only way to initialise the model is to simulate the pre-experimental period of the site. Most often, the site history, in terms of land use and land management is often poorly defined at site level, and entirely unknown at regional level. Our objective was to identify a method that can be applied to initialise a model when the soil is not in equilibrium and historic data are not available, and which quantifies the uncertainty associated with initial soil carbon distribution. We demonstrate a method that uses Bayesian calibration by means of the Accept–Reject algorithm, and use this method to calibrate the initial distribution of soil organic carbon pools against observed soil respiration measurements. It was shown that, even in short-term simulations, model initialisation can have a major influence on the simulated results. The Bayesian calibration method quantified and reduced the uncertainties in initial carbon distribution.

The goal of this study is to estimate the county-level environmental performance for continuous corn cultivation of corn grain and corn stover grown under the current tillage practices for various corn-growing locations in the US Corn Belt. The environmental performance of corn grain varies with its farming location because of climate, soil properties, cropping management, etc. Corn stover, all of the above ground parts of the corn plant except the grain, would be used as a feedstock for cellulosic ethanol. Two cropping systems are under investigation: corn produced for grain only without collecting corn stover (referred to as CRN) and corn produced for grain and stover harvest (referred to as CSR). The functional unit in this study is defined as dry biomass, and the reference flow is 1 kg of dry biomass. The system boundary includes processes from cradle to farm gate. The default allocation procedure between corn grain and stover in the CSR system is the system expansion approach. County-level soil organic carbon dynamics, nitrate losses due to leaching, and nitrogen oxide and nitrous oxide emissions are simulated by the DAYCENT model. Life cycle environmental impact categories considered in this study are total fossil energy use, climate change (referred to as greenhouse gas emissions), acidification, and eutrophication. Sensitivities on farming practices and allocation are included. Simulations from the DAYCENT model predict that removing corn stover from soil could decrease nitrogen-related emissions from soil (i.e., N2O, NO x , and NO3 − leaching). DAYCENT also predicts a reduction in the annual accumulation rates of soil organic carbon (SOC) with corn stover removal. Corn stover has a better environmental performance than corn grain according to all life cycle environmental impacts considered. This is due to lower consumption of agrochemicals and fuel used in the field operations and lower nitrogen-related emissions from the soil. The primary source of total fossil energy associated with biomass production is nitrogen fertilizer, accounting for over 30% of the total fossil energy. Nitrogen-related emissions from soil (i.e., N2O, NO x , and NO3 − leaching) are the primary contributors to all other life cycle environmental impacts considered in this study. The environmental performance of corn grain and corn stover varies with the farming location due to crop management, soil properties, and climate conditions. Several general trends were identified from this study. Corn stover has a lower impact than corn grain in terms of total fossil energy, greenhouse gas emissions, acidification, and eutrophication. Harvesting corn stover reduces nitrogen-related emissions from the soil (i.e., N2O, NO x , NO3 −). The accumulation rate of soil organic carbon is reduced when corn stover is removed, and in some cases, the soil organic carbon level decreases. Harvesting only the cob portion of the stover would reduce the negative impact of stover removal on soil organic carbon sequestration rate while still bringing the benefit of lower nitrogen-related emissions from the soil. No-tillage practices offer higher accumulation rates of soil organic carbon, lower fuel consumption, and lower nitrogen emissions from the soil than the current or conventional tillage practices. Planting winter cover crops could be a way to reduce nitrogen losses from soil and to increase soil organic carbon levels. County-level modeling is more accurate in estimating the local environmental burdens associated with biomass production than national- or regional-level modeling. When possible, site-specific experimental information on soil carbon and nitrogen dynamics should be obtained to reflect the system more accurately. The allocation approach between corn grain and stover significantly affects the environmental performance of each. The preferred allocation method is the system expansion approach where incremental fuel usage, additional nutrients in the subsequent growing season, and changes in soil carbon and nitrogen dynamics due to removing corn stover are assigned to only the collected corn stover.