William J. Parton

Dynamics of C, N, P and S in grassland soils: a model

We have developed a model to simulate the dynamics of C, N, P, and S in cultivated and uncultivated grassland soils. The model uses a monthly time step and can simulate the dynamics of soil organic matter over long time periods (100 to 10,000 years). It was used to simulate the impact of cultivation (100 years) on soil organic matter dynamics, nutrient mineralization, and plant production and to simulate soil formation during a 10,000 year run. The model was validated by comparing the simulated impact of cultivation on soil organic matter C, N, P, and S dynamics with observed data from sites in the northern Great Plains. The model correctly predicted that N and P are the primary limiting nutrients for plant production and simulated the response of the system to inorganic N, P, and S fertilizer. Simulation results indicate that controlling the C:P and C:S ratios of soil organic matter fractions as functions of the labile P and S levels respectively, allows the model to correctly simulate the observed changes in C:P and C:S ratios in the soil and to simulate the impact of varying the labile P and S levels on soil P and S net mineralization rates.

Observations and modeling of biomass and soil organic matter dynamics for the grassland biome worldwide

Century is a model of terrestrial biogeochemistry based on relationships between climate, human management (fire, grazing), soil properties, plant productivity, and decomposition. The grassland version of the Century model was tested using observed data from 11 temperate and tropical grasslands around the world. The results show that soil C and N levels can be simulated to within ±25% of the observed values (100 and 75% of the time, respectively) for a diverse set of soils. Peak live biomass and plant production can be simulated within ± 25% of the observed values (57 and 60% of the time, respectively) for burned, fertilized, and irrigated grassland sites where precipitation ranged from 22 to over 150 cm. Live biomass can be generally predicted to within ±50% of the observed values (57% of the time). The model underestimated the live biomass in extremely high plant production years at two of the Russian sites. A comparison of Century model results with statistical models showed that the Century model had slightly higher r2 values than the statistical models. Data and calibrated model results from this study are useful for analysis and description of grassland carbon dynamics, and as a reference point for testing more physiologically based models predictions of net primary production and biomass. Results indicate that prediction of plant and soil organic matter (C and N) dynamics requires knowledge of climate, soil texture, and N inputs.

Progressive Nitrogen Limitation of Ecosystem Responses to Rising Atmospheric Carbon Dioxide

Abstract A highly controversial issue in global biogeochemistry is the regulation of terrestrial carbon (C) sequestration by soil nitrogen (N) availability. This controversy translates into great uncertainty in predicting future global terrestrial C sequestration. We propose a new framework that centers on the concept of progressive N limitation (PNL) for studying the interactions between C and N in terrestrial ecosystems. In PNL, available soil N becomes increasingly limiting as C and N are sequestered in long-lived plant biomass and soil organic matter. Our analysis focuses on the role of PNL in regulating ecosystem responses to rising atmospheric carbon dioxide concentration, but the concept applies to any perturbation that initially causes C and N to accumulate in organic forms. This article examines conditions under which PNL may or may not constrain net primary production and C sequestration in terrestrial ecosystems. While the PNL-centered framework has the potential to explain diverse experimental results and to help researchers integrate models and data, direct tests of the PNL hypothesis remain a great challenge to the research community.

A comparison of the performance of nine soil organic matter models using datasets from seven long-term experiments

Nine soil organic models were evaluated using twelve datasets from seven long-term experiments. Datasets represented three different land-uses (grassland, arable cropping and woodland) and a range of climatic conditions within the temperate region. Different treatments (inorganic fertilizer, organic manures and different rotations) at the same site allowed the effects of differing land management to be explored. Model simulations were evaluated against the measured data and the performance of the models was compared both qualitatively and quantitatively. Not all models were able to simulate all datasets; only four attempted all. No one model performed better than all others across all datasets. The performance of each model in simulating each dataset is discussed. A comparison of the overall performance of models across all datasets reveals that the model errors of one group of models (RothC, CANDY, DNDC, CENTURY, DAISY and NCSOIL) did not differ significantly from each other. Another group (SOMM, ITE and Verberne) did not differ significantly from each other but showed significantly larger model errors than did models in the first group. Possible reasons for differences in model performance are discussed in detail.

Climatic, edaphic, and biotic controls over storage and turnover of carbon in soils

Soil carbon, a major component of the global carbon inventory, has significant potential for change with changing climate and human land use. We applied the Century ecosystem model to a series of forest and grassland sites distributed globally to examine large-scale controls over soil carbon. Key site-specific parameters influencing soil carbon dynamics are soil texture and foliar lignin content; accordingly, we perturbed these variables at each site to establish a range of carbon concentrations and turnover times. We examined the simulated soil carbon stores, turnover times, and C:N ratios for correlations with patterns of independent variables. Results showed that soil carbon is related linearly to soil texture, increasing as clay content increases, that soil carbon stores and turnover time are related to mean annual temperature by negative exponential functions, and that heterotrophic respiration originates from recent detritus (∼50%), microbial turnover (∼30%), and soil organic matter (∼20%) with modest variations between forest and grassland ecosystems. The effect of changing temperature on soil organic carbon (SOC) estimated by Century is dSOC/dT= 183e−0.034T. Global extrapolation of this relationship leads to an estimated sensitivity of soil C storage to a temperature of −11.1 Pg° C−1, excluding extreme arid and organic soils. In Century, net primary production (NPP) and soil carbon are closely coupled through the N cycle, so that as temperatures increase, accelerated N release first results in fertilization responses, increasing C inputs. The Century-predicted effect of temperature on carbon storage is modified by as much as 100% by the N cycle feedback. Century-estimated soil C sensitivity (−11.1 Pg° C−1) is similar to losses predicted with a simple data-based calculation (−14.1 Pg° C−1). Inclusion of the N cycle is important for even first-order predictions of terrestrial carbon balance. If the NPP-SOC feedback is disrupted by land use or other disturbances, then SOC sensitivity can greatly exceed that estimated in our simulations. Century results further suggest that if climate change results in drying of organic soils (peats), soil carbon loss rates can be high.

Life-cycle assessment of net greenhouse-gas flux for bioenergy cropping systems.

Bioenergy cropping systems could help offset greenhouse gas emissions, but quantifying that offset is complex. Bioenergy crops offset carbon dioxide emissions by converting atmospheric CO2 to organic C in crop biomass and soil, but they also emit nitrous oxide and vary in their effects on soil oxidation of methane. Growing the crops requires energy (e.g., to operate farm machinery, produce inputs such as fertilizer) and so does converting the harvested product to usable fuels (feedstock conversion efficiency). The objective of this study was to quantify all these factors to determine the net effect of several bioenergy cropping systems on greenhouse-gas (GHG) emissions. We used the DAYCENT biogeochemistry model to assess soil GHG fluxes and biomass yields for corn, soybean, alfalfa, hybrid poplar, reed canarygrass, and switchgrass as bioenergy crops in Pennsylvania, USA. DAYCENT results were combined with estimates of fossil fuels used to provide farm inputs and operate agricultural machinery and fossil-fuel offsets from biomass yields to calculate net GHG fluxes for each cropping system considered. Displaced fossil fuel was the largest GHG sink, followed by soil carbon sequestration. N20 emissions were the largest GHG source. All cropping systems considered provided net GHG sinks, even when soil C was assumed to reach a new steady state and C sequestration in soil was not counted. Hybrid poplar and switchgrass provided the largest net GHG sinks, >200 g CO2e-C x m(-2) x yr(-1) for biomass conversion to ethanol, and >400 g CO2e-C x m(-2) x yr(-1) for biomass gasification for electricity generation. Compared with the life cycle of gasoline and diesel, ethanol and biodiesel from corn rotations reduced GHG emissions by approximately 40%, reed canarygrass by approximately 85%, and switchgrass and hybrid poplar by approximately 115%.

Nitrous oxide emission from Australian agricultural lands and mitigation options: a review

Increases in the concentrations of greenhouse gases, carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and halocarbons in the atmosphere due to human activities are associated with global climate change. The concentration of N2O has increased by 16% since 1750. Although the atmospheric concentration of N2O is much smaller (314 ppb in 1998) than of CO2 (365 ppm), its global warming potential (cumulative radiative forcing) is 296 times that of the latter in a 100-year time horizon. Currently, it contributes about 6% of the overall global warming effect but its contribution from the agricultural sector is about 16%. Of that, almost 80% of N2O is emitted from Australian agricultural lands, originating from N fertilisers (32%), soil disturbance (38%), and animal waste (30%). Nitrous oxide is primarily produced in soil by the activities of microorganisms during nitrification, and denitrification processes. The ratio of N2O to N2 production depends on oxygen supply or water-filled pore space, decomposable organic carbon, N substrate supply, temperature, and pH and salinity. N2O production from soil is sporadic both in time and space, and therefore, it is a challenge to scale up the measurements of N2O emission from a given location and time to regional and national levels. Estimates of N2O emissions from various agricultural systems vary widely. For example, in flooded rice in the Riverina Plains, N2O emissions ranged from 0.02% to 1.4% of fertiliser N applied, whereas in irrigated sugarcane crops, 15.4% of fertiliser was lost over a 4-day period. Nitrous oxide emissions from fertilised dairy pasture soils in Victoria range from 6 to 11 kg N2O-N/ha, whereas in arable cereal cropping, N2O emissions range from <0.01% to 9.9% of N fertiliser applications. Nitrous oxide emissions from soil nitrite and nitrates resulting from residual fertiliser and legumes are rarely studied but probably exceed those from fertilisers, due to frequent wetting and drying cycles over a longer period and larger area. In ley cropping systems, significant N2O losses could occur, from the accumulation of mainly nitrate-N, following mineralisation of organic N from legume-based pastures. Extensive grazed pastures and rangelands contribute annually about 0.2 kg N/ha as N2O (93 kg/ha per year CO2-equivalent). Tropical savannas probably contribute an order of magnitude more, including that from frequent fires. Unfertilised forestry systems may emit less but the fertilised plantations emit more N2O than the extensive grazed pastures. However, currently there are limited data to quantify N2O losses in systems under ley cropping, tropical savannas, and forestry in Australia. Overall, there is a need to examine the emission factors used in estimating national N2O emissions; for example, 1.25% of fertiliser or animal-excreted N appearing as N2O (IPCC 1996). The primary consideration for mitigating N2O emissions from agricultural lands is to match the supply of mineral N (from fertiliser applications, legume-fixed N, organic matter, or manures) to its spatial and temporal needs by crops/pastures/trees. Thus, when appropriate, mineral N supply should be regulated through slow-release (urease and/or nitrification inhibitors, physical coatings, or high C/N ratio materials) or split fertiliser application. Also, N use could be maximised by balancing other nutrient supplies to plants. Moreover, non-legume cover crops could be used to take up residual mineral N following N-fertilised main crops or mineral N accumulated following legume leys. For manure management, the most effective practice is the early application and immediate incorporation of manure into soil to reduce direct N2O emissions as well as secondary emissions from deposition of ammonia volatilised from manure and urine. Current models such as DNDC and DAYCENT can be used to simulate N2O production from soil after parameterisation with the local data, and appropriate modification and verification against the measured N2O emissions under different management practices.

DAYCENT and its land surface submodel: description and testing

Abstract A land surface submodel was developed for the daily version of the CENTURY ecosystem model (DAYCENT). The goal of DAYCENT to simulate soil N 2 O, NO x , and CH 4 fluxes for terrestrial ecosystems determined the structure and processes represented in the land surface model. The land surface model was set up to simulate daily dynamics of soil water and temperature from a multi-layered soil system (0–1, 1–4, 4–15, 15–30 cm, etc.) and included surface runoff and above field capacity soil water dynamics during intense rainfall events and snowmelt into frozen soils. The comparison of the simulated soil water content (0–10 cm) with observed data from four sites was quite favorable (squared correlation coefficient— γ 2 =0.87, 0.65, 0.86 and 0.58) and the simulated results were comparable for the soil temperature model ( r 2 =0.92 and 0.95 for minimum and maximum 10 cm soil temperatures). Detailed soil water and temperature data during snowmelt time periods and following rainfall events are needed to fully evaluate the performance of the water flow model.

Generalized model for N2 and N2O production from nitrification and denitrification

We describe a model of N2 and N2O gas fluxes from nitrification and denitrification. The model was developed using laboratory denitrification gas flux data and field-observed N2O gas fluxes from different sites. Controls over nitrification N2O gas fluxes are soil texture, soil NH4, soil water-filled pore space, soil N turnover rate, soil pH, and soil temperature. Observed data suggest that nitrification N2O gas fluxes are proportional to soil N turnover and that soil NH4 levels only impact N2O gas fluxes with high levels of soil NH4 (>3 μg N g−1). Total denitrification (N2 plus N2O) gas fluxes are a function of soil heterotrophic respiration rates, soil NO3, soil water content, and soil texture. N2:N2O ratio is a function of soil water content, soil NO3, and soil heterotrophic respiration rates. The denitrification model was developed using laboratory data [Weier et al, 1993] where soil water content, soil NO3, and soil C availability were varied using a full factorial design. The Weiers model simulated observed N2 and N2O gas fluxes for different soils quite well with r2 equal to 0.62 and 0.75, respectively. Comparison of simulated model results with field N2O data for several validation sites shows that the model results compare well with the observed data (r2 = 0.62). Winter denitrification events were poorly simulated by the model. This problem could have been caused by spatial and temporal variations in the observed soil water data and N2O fluxes. The model results and observed data suggest that approximately 14% of the N2O fluxes for a shortgrass steppe are a result of denitrification and that this percentage ranged from 0% to 59% for different sites.

Sustainable Biofuels Redux

Science-based policy is essential for guiding an environmentally sustainable approach to cellulosic biofuels.