Stephen J. Del Grosso

Nitrogen pools and fluxes in grassland soils sequestering carbon

Carbon sequestration in agricultural, forest, and grassland soils has been promoted as a means by which substantial amounts of CO2 may be removed from the atmosphere, but few studies have evaluated the associated impacts on changes in soil N or net global warming potential (GWP). The purpose of this research was to (1) review the literature to examine how changes in grassland management that affect soil C also impact soil N, (2) assess the impact of different types of grassland management on changes in soil N and rates of change, and (3) evaluate changes in N2O fluxes from differently managed grassland ecosystems to assess net impacts on GWP. Soil C and N stocks either both increased or both decreased for most studies. Soil C and N sequestration were tightly linked, resulting in little change in C:N ratios with changes in management. Within grazing treatments N2O made a minor contribution to GWP (0.1–4%), but increases in N2O fluxes offset significant portions of C sequestration gains due to fertilization (10–125%) and conversion (average = 27%). Results from this work demonstrate that even when improved management practices result in considerable rates of C and N sequestration, changes in N2O fluxes can offset a substantial portion of gains by C sequestration. Even for cases in which C sequestration rates are not entirely offset by increases in N2O fluxes, small increases in N2O fluxes can substantially reduce C sequestration benefits. Conversely, reduction of N2O fluxes in grassland soils brought about by changes in management represents an opportunity to reduce the contribution of grasslands to net greenhouse gas forcing.

Modeling nitrous oxide emissions from irrigated agriculture: testing DayCent with high-frequency measurements

A unique high temporal frequency data set from an irrigated cotton-wheat rotation was used to test the agroecosystem model DayCent to simulate daily N20 emissions from subtropical vertisols under different irrigation intensities. DayCent was able to simulate the effect of different irrigation intensities on N20 fluxes and yield, although it tended to overestimate seasonal fluxes during the cotton season. DayCent accurately predicted soil moisture dynamics and the timing and magnitude of high fluxes associated with fertilizer additions and irrigation events. At the daily scale we found a good correlation of predicted vs. measured N20 fluxes (r2 = 0.52), confirming that DayCent can be used to test agricultural practices for mitigating N20 emission from irrigated cropping systems. A 25-year scenario analysis indicated that N20 losses from irrigated cotton-wheat rotations on black vertisols in Australia can be substantially reduced by an optimized fertilizer and irrigation management system (i.e., frequent irrigation, avoidance of excessive fertilizer application), while sustaining maximum yield potentials.

Ecosystem Responses to Warming and Interacting Global Change Factors

Increases in atmospheric CO 2 concentration in the coming decades will be accompanied by other global changes. Higher air temperatures, altered precipitation patterns, increased tropospheric ozone concentrations, and N depo-sition are among the most prominent of the predicted changes that, along with elevated CO 2 , have a high potential to affect ecosystem structure and function. Although the effect of elevated atmospheric CO 2 on ecosystem function was the primary focus of much of the GCTE effort in ecosystem physiology, each of these additional factors presents the possibility of altering the response of ecosystems to elevated CO 2 – perhaps negating the CO 2 response, enhancing it, or completely changing the nature of the response. Predictions of future ecosystem metabolism based solely on changes in a single factor are likely to be misleading. Hence, in addition to the elevated CO 2 network, GCTE fostered the development of a network to stimulate and coordinate research on ecosystem responses to climatic warming. Through all of its activities, GCTE promoted an agenda that embraced the mandate for understanding multi-factor interactions. In the past, many model simulations of ecosystem response to global change were based on changes in climate alone, in part because the effects of elevated CO 2 were considered insignificant or too uncertain (Solomon 1986). Now, ecosystem and global models include multiple factors, particularly climate and CO 2 , and the predicted responses can differ significantly from predictions based on changes in a single factor (Melillo et al. 2001; Cramer et al. 2001). It is important to the international global change research agenda that progress in experimental approaches keeps pace with model development. While many of the fundamental relations between ecosystem processes and temperature are well known, it is more difficult to have confidence in predictions of the combined responses to temperature and CO 2. Some interactions have a strong theoretical and empirical foundation: the optimum temperature for photosynthesis increases with increasing CO 2 (Long 1991). Temperature affects all biological processes, however, and the net response of an ecosystem to the combined effects of warming and elevated CO 2 are not so simply described (Norby and Luo 2004). Furthermore, ecosystem responses to CO 2 and temperature are likely to be modified by other environmental factors, especially the availabilities of water and nitrogen, which in turn are modified by CO 2 and temperature (Medlyn et al. 2000; McGuire et al. 2001). Here, we …

Understanding the DayCent model

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.

Interaction of Soil Carbon Sequestration and N2O Flux with Different Land Use Practices

The DAYCENT ecosystem model was used to address the feasibility of managing land to sequester carbon in soil after accounting for the greenhouse warming potential of soil N2O emissions and the CO2 emissions associated with N fertilizer production. The model simulated the long term changes in soil C levels, N2O emissions and above ground productivity in agricultural and rangeland systems. The model was run for 100 years of conventional winter wheat/fallow rotations and another 100 years of alternative land use. Simulations showed that corn, corn/alfalfa rotations and silage treatments provide substantial (1.4–1.8 kg C m−2) net C storage for the first 25 year period but net C sequestration fell to less than 0.25 kg C m−2 for the 4th 25 year period. Conventional tillage and no tillage winter wheat/fallow systems both showed a net input of CO2 to the atmosphere during each 25 year period after N2O emissions were considered. Native grassland under ligght to moderate grazing stored small amounts of C but addition of 1 gN m−2 yr−1 greatly increased net C sequestration in rangeland simulations. All of the practices except unfertilized grassland showed significant inverse correlations between soil C storage and N2O emissions. We conclude that the large expanses of land in the U.S. Great Plains that have been historically used for winter wheat/fallow rotations could sequester large amounts of C if water were made available for intensive corn, corn/alfalfa or silage agriculture and that rangeland could store significant amounts of C with light N fertilization.

Modeling the Dynamics of Soil Organic Matter and Nutrient Cycling

This chapter includes a complete description of the mathematical expressions used to simulate the biological, chemical, and physical processes in existing models and a description of the computer models, which are currently being used to simulate soil carbon and nutrient cycling. The models range from analytical, substrate-enzyme-microbe, cohort, multicompartmental, nutrient dynamics, and ecosystem models. A detailed description of three of the most widely used ecosystem models is presented to show the diversity of the approaches used to simulate nutrient cycling and soil carbon dynamics. The chapter also presents a description of the analytical procedures used to classify models, compare different model results, evaluate model performance using observed data, parameterize models, and select the best model for a specific application. The conclusion section presents a critical evaluation of the limitations of the current soil organic matter (SOM) and nutrient cycling model, suggestions on how current knowledge about SOM dynamics should be incorporated into models, and a list of biological and physical processes that need to be incorporated into existing models.

Simulating Impacts of Bioenergy Sorghum Residue Return on Soil Organic Carbon and Greenhouse Gas Emissions Using the DAYCENT Model

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.