Search for: All records

Abstract Climate-smart agriculture can be used to build soil carbon stocks, decrease agricultural greenhouse gas (GHG) emissions, and increase agronomic resilience to climate pressures. The US recently declared its commitment to include the agricultural sector as part of an overall climate-mitigation strategy, and with this comes the need for robust, scientifically valid tools for agricultural GHG flux measurements and modeling. If agriculture is to contribute significantly to climate mitigation, practice adoption should be incentivized on as much land area as possible and mitigation benefits should be accurately quantified. Process-based models are parameterized on data from a limited number of long-term agricultural experiments, which may not fully reflect outcomes on working farms. Space-for-time substitution, paired studies, and long-term monitoring of SOC stocks and GHG emissions on commercial farms using a variety of climate-smart management systems can validate findings from long-term agricultural experiments and provide data for process-based model improvements. Here, we describe a project that worked collaboratively with commercial producers in the Midwest to directly measure and model the soil organic carbon (SOC) stocks of their farms at the field scale. We describe this study, and several unexpected challenges encountered, to facilitate further on-farm data collection and the creation of a secure database of on-farm SOC stock measurements. 

Meeting end-of-century global warming targets requires aggressive action on multiple fronts. Recent reports note the futility of addressing mitigation goals without fully engaging the agricultural sector, yet no available assessments combine both nature-based solutions (reforestation, grassland and wetland protection, and agricultural practice change) and cellulosic bioenergy for a single geographic region. Collectively, these solutions might offer a suite of climate, biodiversity, and other benefits greater than either alone. Nature-based solutions are largely constrained by the duration of carbon accrual in soils and forest biomass; each of these carbon pools will eventually saturate. Bioenergy solutions can last indefinitely but carry significant environmental risk if carelessly deployed. We detail a simplified scenario for the U.S. that illustrates the benefits of combining approaches. We assign a portion of non-forested former cropland to bioenergy sufficient to meet projected mid-century transportation needs, with the remainder assigned to nature-based solutions such as reforestation. Bottom-up mitigation potentials for the aggregate contributions of crop, grazing, forest, and bioenergy lands are assessed by including in a Monte Carlo model conservative ranges for cost-effective local mitigation capacities, together with ranges for (a) areal extents that avoid double counting and include realistic adoption rates and (b) the projected duration of different carbon sinks. The projected duration illustrates the net effect of eventually saturating soil carbon pools in the case of most strategies, and additionally saturating biomass carbon pools in the case of reforestation. Results show a conservative end-of-century mitigation capacity of 110 (57 – 178) Gt CO2e for the U.S., ~50% higher than existing estimates that prioritize nature-based or bioenergy solutions separately. Further research is needed to shrink uncertainties but there is sufficient confidence in the general magnitude and direction of a combined approach to plan for deployment now. The dataset is a synthesis of literature values selected based on criteria described in the parent paper’s narrative. The files can be opened in Microsoft Excel or any other spreadsheet that can load Excel-format files. 

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

Abstract. Soil organic matter (SOM) dynamics in ecosystem-scale biogeochemical modelshave traditionally been simulated as immeasurable fluxes between conceptuallydefined pools. This greatly limits how empirical data can be used to improvemodel performance and reduce the uncertainty associated with theirpredictions of carbon (C) cycling. Recent advances in our understanding ofthe biogeochemical processes that govern SOM formation and persistence demanda new mathematical model with a structure built around key mechanisms andbiogeochemically relevant pools. Here, we present one approach that aims toaddress this need. Our new model (MEMS v1.0) is developed from the MicrobialEfficiency-Matrix Stabilization framework, which emphasizes the importance oflinking the chemistry of organic matter inputs with efficiency of microbialprocessing and ultimately with the soil mineral matrix, when studying SOMformation and stabilization. Building on this framework, MEMS v1.0 is alsocapable of simulating the concept of C saturation and representsdecomposition processes and mechanisms of physico-chemical stabilization todefine SOM formation into four primary fractions. After describing the modelin detail, we optimize four key parameters identified through avariance-based sensitivity analysis. Optimization employed soil fractionationdata from 154 sites with diverse environmental conditions, directly equatingmineral-associated organic matter and particulate organic matter fractionswith corresponding model pools. Finally, model performance was evaluatedusing total topsoil (0–20 cm) C data from 8192 forest and grassland sitesacross Europe. Despite the relative simplicity of the model, it was able toaccurately capture general trends in soil C stocks across extensive gradientsof temperature, precipitation, annual C inputs and soil texture. The novelapproach that MEMS v1.0 takes to simulate SOM dynamics has the potential toimprove our forecasts of how soils respond to management and environmentalperturbation. Ensuring these forecasts are accurate is key to effectivelyinforming policy that can address the sustainability of ecosystem servicesand help mitigate climate change.