Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed from the Microbial Efficiency-Matrix Stabilization framework, which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.

National governments and international organizations perceive bioenergy, from crops such as Miscanthus, to have an important role in mitigating greenhouse gas (GHG) emissions and combating climate change. In this research, we address three objectives aimed at reducing uncertainty regarding the climate change mitigation potential of commercial Miscanthus plantations in the United Kingdom: (i) to examine soil temperature and moisture as potential drivers of soil GHG emissions through four years of parallel measurements, (ii) to quantify carbon (C) dynamics associated with soil sequestration using regular measurements of topsoil (0-30 cm) C and the surface litter layer and (iii) to calculate a life cycle GHG budget using site-specific measurements, enabling the GHG intensity of Miscanthus used for electricity generation to be compared against coal and natural gas. Our results show that methane (CH 4 ) and nitrous oxide (N 2 O) emissions contributed little to the overall GHG budget of Miscanthus, while soil respiration offset 30% of the crop's net aboveground C uptake. Temperature sensitivity of soil respiration was highest during crop growth and lowest during winter months. We observed no significant change in topsoil C or nitrogen stocks following 7 years of Miscanthus cultivation. The depth of litter did, however, increase significantly, stabilizing at approximately 7 tonnes dry biomass per hectare after 6 years. The cradle-to-farm gate GHG budget of this crop indicated a net removal of 24.5 t CO 2 -eq ha À1 yr À1 from the atmosphere despite no detectable C sequestration in soils. When scaled up to consider the full life cycle, Miscanthus fared very well in comparison with coal and natural gas, suggesting considerable CO 2 offsetting per kWh generated. Although the comparison does not account for the land area requirements of the energy generated, Miscanthus used for electricity generation can make a significant contribution to climate change mitigation even when combusted in conventional steam turbine power plants.

<p><strong>Abstract.</strong> Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical models have traditionally been simulated as immeasurable fluxes between conceptually-defined pools. This greatly limits how empirical data can be used to improve model performance and reduce the uncertainty associated with their predictions of carbon (C) cycling. Recent advances in our understanding of the biogeochemical processes that govern SOM formation and persistence demand a new mathematical model with a structure built around key mechanisms and biogeochemically-relevant pools. Here, we present one approach that aims to address this need. Our new model (MEMS v1.0) is developed upon the Microbial Efficiency-Matrix Stabilization framework which emphasizes the importance of linking the chemistry of organic matter inputs with efficiency of microbial processing, and ultimately with the soil mineral matrix, when studying SOM formation and stabilization. Building on this framework, MEMS v1.0 is also capable of simulating the concept of C-saturation and represents decomposition processes and mechanisms of physico-chemical stabilization to define SOM formation into four primary fractions. After describing the model in detail, we optimize four key parameters identified through a variance-based sensitivity analysis. Optimization employed soil fractionation data from 154 sites with diverse environmental conditions, directly equating mineral-associated organic matter and particulate organic matter fractions with corresponding model pools. Finally, model performance was evaluated using total topsoil (0–20 cm) C data from 8192 forest and grassland sites across Europe. Despite the relative simplicity of the model, it was able to accurately capture general trends in soil C stocks across extensive gradients of temperature, precipitation, annual C inputs and soil texture. The novel approach that MEMS v1.0 takes to simulate SOM dynamics has the potential to improve our forecasts of how soils respond to management and environmental perturbation. Ensuring these forecasts are accurate is key to effectively informing policy that can address the sustainability of ecosystem services and help mitigate climate change.</p>

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 (Triticum aestivum 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

Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generally captured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystem respiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) was well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

The lignocellulosic perennial grass Miscanthus has received considerable attention as a potential bioenergy crop over the last 25 years, but few commercial plantations exist globally. This is partly due to the uncertainty associated with claims that land-use change (LUC) to Miscanthus will result in both commercially viable yields and net increases in carbon (C) storage. To simulate what the effects may be after LUC to Miscanthus, six process-based models have been parameterized for Miscanthus and here we review how these models operate. This review provides an overview of the key Miscanthus soil organic matter models and then highlights what measurers can do to accelerate model development. Each model (WIMOVAC, BioCro, Agro-IBIS, DAYCENT, DNDC and ECOSSE) is capable of simulating biomass production and soil C dynamics based on specific site characteristics. Understanding the design of these models is important in model selection as well as being important for field researchers to collect the most relevant data to improve model performance. The rapid increase in models parameterized for Miscanthus is promising, but refinements and improvements are still required to ensure that model predictions are reliable and can be applied to spatial scales relevant for policy. Specific improvements, needed to ensure the models are applicable for a range of environmental conditions, come under two categories: (i) increased data generation and (ii) development of frameworks and databases to allow simulations of ranging scales. Research into nonfood bioenergy crops such as Miscanthus is relatively recent and this review highlights that there are still a number of knowledge gaps regarding Miscanthus specifically. For example, the low input requirements of Miscanthus make it particularly attractive as a bioenergy crop, but it is essential that we increase our understanding of the crop's nutrient remobilization and ability to host N-fixing organisms to derive the most accurate simulations.

Abstract. For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above and below-ground inputs, soil water, and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM), respectively. We present results of calibration and validation of the model with several grassland sites in the U.S. MEMS 2.0 generally captured the soil carbon (C) stocks (R2 of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R2 of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R2 = 0.83), ecosystem respiration (ER; R2 = 0.89), net ecosystem exchange (NEE; R2 = 0.67), and evapotranspiration (ET; R2 = 0.71) were well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

The carbon (C) dynamics of a bioenergy system are key to correctly defining its viability as a sustainable alternative to conventional fossil fuel energy sources. Recent studies have quantified the greenhouse gas mitigation potential of these bioenergy crops, often concluding that C sequestration in soils plays a primary role in offsetting emissions through energy generation. Miscanthus is a particularly promising bioenergy crop and research has shown that soil C stocks can increase by more than 2 t C ha −1 yr −1 . In this study, we use a stable isotope ( 13 C) technique to trace the inputs and outputs from soils below a commercial Miscanthus plantation in Lincolnshire, UK, over the first 7 years of growth after conversion from a conventional arable crop. Results suggest that an unchanging total topsoil (0-30 cm) C stock is caused by Miscanthus additions displacing older soil organic matter. Further, using a comparison between bare soil plots (no new Miscanthus inputs) and undisturbed Miscanthus controls, soil respiration was seen to be unaffected through priming by fresh inputs or rhizosphere. The temperature sensitivity of old soil C was also seen to be very similar with and without the presence of live root biomass. Total soil respiration from control plots was dominated by Miscanthus-derived emissions with autotrophic respiration alone accounting for ∼50 % of CO 2 . Although total soil C stocks did not change significantly over time, the Miscanthus-derived soil C accumulated at a rate of 860 kg C ha −1 yr −1 over the top 30 cm. Ultimately, the results from this study indicate that soil C stocks below Miscanthus plantations do not necessarily increase during the first 7 years.