Dennis Ojima

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.

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.

Nutrient Imbalances in Agricultural Development

Nutrient additions to intensive agricultural systems range from inadequate to excessive—and both extremes have substantial human and environmental costs. Nutrient cycles link agricultural systems to their societies and surroundings; inputs of nitrogen and phosphorus in particular are essential for high crop yields, but downstream and downwind losses of these same nutrients diminish environmental quality and human well-being. Agricultural nutrient balances differ substantially with economic development, from inputs that are inadequate to maintain soil fertility in parts of many developing countries, particularly those of sub-Saharan Africa, to excessive and environmentally damaging surpluses in many developed and rapidly growing economies. National and/or regional policies contribute to patterns of nutrient use and their environmental consequences in all of these situations (1). Solutions to the nutrient challenges that face global agriculture can be informed by analyses of trajectories of change within, as well as across, agricultural systems.

Coupled Atmosphere–Biophysics–Hydrology Models for Environmental Modeling

The formulation and implementation of LEAF-2, the Land Ecosystem‐Atmosphere Feedback model, which comprises the representation of land‐surface processes in the Regional Atmospheric Modeling System (RAMS), is described. LEAF-2 is a prognostic model for the temperature and water content of soil, snow cover, vegetation, and canopy air, and includes turbulent and radiative exchanges between these components and with the atmosphere. Subdivision of a RAMS surface grid cell into multiple areas of distinct land-use types is allowed, with each subgrid area, or patch, containing its own LEAF-2 model, and each patch interacts with the overlying atmospheric column with a weight proportional to its fractional area in the grid cell. A description is also given of TOPMODEL, a land hydrology model that represents surface and subsurface downslope lateral transport of groundwater. Details of the incorporation of a modified form of TOPMODEL into LEAF-2 are presented. Sensitivity tests of the coupled system are presented that demonstrate the potential importance of the patch representation and of lateral water transport in idealized model simulations. Independent studies that have applied LEAF-2 and verified its performance against observational data are cited. Linkage of RAMS and TOPMODEL through LEAF-2 creates a modeling system that can be used to explore the coupled atmosphere‐biophysical‐ hydrologic response to altered climate forcing at local watershed and regional basin scales.

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.

Long- and short-term effects of fire on nitrogen cycling in tallgrass prairie

Fires in the tallgrass prairie are frequent and significantly alter nutrient cycling processes. We evaluated the short-term changes in plant production and microbial activity due to fire and the long-term consequences of annual burning on soil organic matter (SOM), plant production, and nutrient cycling using a combination of field, laboratory, and modeling studies. In the short-term, fire in the tallgrass prairie enhances microbial activity, increases both above-and belowground plant production, and increases nitrogen use efficiency (NUE). However, repeated annual burning results in greater inputs of lower quality plant residues causing a significant reduction in soil organic N, lower microbial biomass, lower N availability, and higher C:N ratios in SOM. Changes in amount and quality of below-ground inputs increased N immobilization and resulted in no net increases in N availability with burning. This response occurred rapidly (e.g., within two years) and persisted during 50 years of annual burning. Plant production at a long-term burned site was not adversely affected due to shifts in plant NUE and carbon allocation. Modeling results indicate that the tallgrass ecosystem responds to the combined changes in plant resource allocation and NUE. No single factor dominates the impact of fire on tallgrass plant production.

U.S. agriculture and climate change: New results

We examined the impacts on U.S. agriculture of transient climate change assimulated by 2 global general circulation models focusing on the decades ofthe 2030s and 2090s. We examined historical shifts in the location of cropsand trends in the variability of U.S. average crop yields, finding thatnon-climatic forces have likely dominated the north and westward movement ofcrops and the trends in yield variability. For the simulated future climateswe considered impacts on crops, grazing and pasture, livestock, pesticide use,irrigation water supply and demand, and the sensitivity to international tradeassumptions, finding that the aggregate of these effects were positive for theU.S. consumer but negative, due to declining crop prices, for producers. Weexamined the effects of potential changes in El Niño/SouthernOscillation (ENSO) and impacts on yield variability of changes in mean climateconditions. Increased losses occurred with ENSO intensity and frequencyincreases that could not be completely offset even if the events could beperfectly forecasted. Effects on yield variability of changes in meantemperatures were mixed. We also considered case study interactions ofclimate, agriculture, and the environment focusing on climate effects onnutrient loading to the Chesapeake Bay and groundwater depletion of theEdwards Aquifer that provides water for municipalities and agriculture to theSan Antonio, Texas area. While only case studies, these results suggestenvironmental targets such as pumping limits and changes in farm practices tolimit nutrient run-off would need to be tightened if current environmentalgoals were to be achieved under the climate scenarios we examined

Generalized model for NO x and N2O emissions from soils

We describe a submodel to simulate NOx and N2O emissions from soils and present comparisons of simulated NOx and N2O fluxes from the DAYCENT ecosystem model with observations from different soils. The N gas flux submodel assumes that nitrification and denitrification both contribute to N2O and NOx emissions but that NOx emissions are due mainly to nitrification. N2O emissions from nitrification are calculated as a function of modeled soil NH4+ concentration, water-filled pore space (WFPS), temperature, pH, and texture. N2O emissions from denitrification are a function of soil NO3− concentration, WFPS, heterotrophic respiration, and texture. NOx emissions are calculated by multiplying total N2O emissions by a NOx:N2O equation which is calculated as a function of soil parameters (bulk density, field capacity, and WFPS) that influence gas diffusivity. The NOx submodel also simulates NOx emission pulses initiated by rain events onto dry soils. The DAYCENT model was tested by comparing observed and simulated parameters in grassland soils across a range of soil textures and fertility levels. Simulated values of soil temperature, WFPS (during the non-winter months), and NOx gas flux agreed reasonably well with measured values (r2 = 0.79, 0.64, and 0.43, respectively). Winter season WFPS was poorly simulated (r2 = 0.27). Although the correlation between simulated and observed N2O flux was poor on a daily basis (r2 = 0.02), DAYCENT was able to reproduce soil textural and treatment differences and the observed seasonal patterns of gas flux emissions with r2 values of 0.26 and 0.27, for monthly and NOr flux rates, respectively.