Explore the vast range of titles available.

Atmospheric CO2 enrichment may stimulate plant growth directly through (1) enhanced photosynthesis or indirectly, through (2) reduced plant water consumption and hence slower soil moisture depletion, or the combination of both. Herein we describe gas exchange, plant biomass and species responses of five native or semi-native temperate and Mediterranean grasslands and three semi-arid systems to CO2 enrichment, with an emphasis on water relations. Increasing CO2 led to decreased leaf conductance for water vapor, improved plant water status, altered seasonal evapotranspiration dynamics, and in most cases, periodic increases in soil water content. The extent, timing and duration of these responses varied among ecosystems, species and years. Across the grasslands of the Kansas tallgrass prairie, Colorado shortgrass steppe and Swiss calcareous grassland, increases in aboveground biomass from CO2 enrichment were relatively greater in dry years. In contrast, CO2-induced aboveground biomass increases in the Texas C3/C4 grassland and the New Zealand pasture seemed little or only marginally influenced by yearly variation in soil water, while plant growth in the Mojave Desert was stimulated by CO2 in a relatively wet year. Mediterranean grasslands sometimes failed to respond to CO2-related increased late-season water, whereas semiarid Negev grassland assemblages profited. Vegetative and reproductive responses to CO2 were highly varied among species and ecosystems, and did not generally follow any predictable pattern in regard to functional groups. Results suggest that the indirect effects of CO2 on plant and soil water relations may contribute substantially to experimentally induced CO2-effects, and also reflect local humidity conditions. For landscape scale predictions, this analysis calls for a clear distinction between biomass responses due to direct CO2 effects on photosynthesis and those indirect CO2 effects via soil moisture as documented here.

We present a new soil respiration model, describe a formal model testing procedure, and compare our model with five alternative models using an extensive data set of observed soil respiration. Gas flux data from rangeland soils that included a large number of measurements at low temperatures were used to model soil CO2 emissions as a function of soil temperature and water content. Our arctangent temperature function predicts that Q10 values vary inversely with temperature and that CO2 fluxes are significant below 0 °C. Independent data representing a broad range of ecosystems and temperature values were used for model testing. The effects of plant phenology, differences in substrate availability among sites, and water limitation were accounted for so that the temperature equations could be fairly evaluated. Four of the six tested models did equally well at simulating the observed soil CO2 respiration rates. However, the arctangent variable Q10 model agreed closely with observed Q10 values over a wide range of temperatures (r2 = 0.94) and was superior to published variable Q10 equations using the Akaike information criterion (AIC). The arctangent temperature equation explained 16–85% of the observed intra-site variability in CO2 flux rates. Including a water stress factor yielded a stronger correlation than temperature alone only in the dryland soils. The observed change in Q10 with increasing temperature was the same for data sets that included only heterotrophic respiration and data sets that included both heterotrophic and autotrophic respiration.

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. N2O 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·m-2·yr-1 for biomass conversion to ethanol, and >400 g CO2e-C·m-2·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 40%, reed canarygrass by 85%, and switchgrass and hybrid poplar by 115%.

Quantifying where, when, and how much denitrification occurs on the basis of measurements alone remains particularly vexing at virtually all spatial scales. As a result, models have become essential tools for integrating current understanding of the processes that control denitrification with measurements of rate-controlling properties so that the permanent losses of N within landscapes can be quantified at watershed and regional scales. In this paper, we describe commonly used approaches for modeling denitrification and N cycling processes in terrestrial and aquatic ecosystems based on selected examples from the literature. We highlight future needs for developing complementary measurements and models of denitrification. Most of the approaches described here do not explicitly simulate microbial dynamics, but make predictions by representing the environmental conditions where denitrification is expected to occur, based on conceptualizations of the N cycle and empirical data from field and laboratory investigations of the dominant process controls. Models of denitrification in terrestrial ecosystems include generally similar rate-controlling variables, but vary in their complexity of the descriptions of natural and human-related properties of the landscape, reflecting a range of scientific and management perspectives. Models of denitrification in aquatic ecosystems range in complexity from highly detailed mechanistic simulations of the N cycle to simpler source-transport models of aggregate N removal processes estimated with empirical functions, though all estimate aquatic N removal using first-order reaction rate or mass-transfer rate expressions. Both the terrestrial and aquatic modeling approaches considered here generally indicate that denitrification is an important and highly substantial component of the N cycle over large spatial scales. However, the uncertainties of model predictions are large. Future progress will be linked to advances in field measurements, spatial databases, and model structures.

Until recently, Intergovernmental Panel on Climate Change (IPCC) emission factor methodology, based on simple empirical relationships, has been used to estimate carbon (C) and nitrogen (N) fluxes for regional and national inventories. However, the 2005 USEPA greenhouse gas inventory includes estimates of N2O emissions from cultivated soils derived from simulations using DAYCENT, a process-based biogeochemical model. DAYCENT simulated major U.S. crops at county-level resolution and IPCC emission factor methodology was used to estimate emissions for the approximately 14% of cropped land not simulated by DAYCENT. The methodology used to combine DAYCENT simulations and IPCC methodology to estimate direct and indirect N(2)O emissions is described in detail. Nitrous oxide emissions from simulations of presettlement native vegetation were subtracted from cropped soil N(2)O to isolate anthropogenic emissions. Meteorological data required to drive DAYCENT were acquired from DAYMET, an algorithm that uses weather station data and accounts for topography to predict daily temperature and precipitation at 1-km2 resolution. Soils data were acquired from the State Soil Geographic Database (STATSGO). Weather data and dominant soil texture class that lie closest to the geographical center of the largest cluster of cropped land in each county were used to drive DAYCENT. Land management information was implemented at the agricultural-economic region level, as defined by the Agricultural Sector Model. Maps of model-simulated county-level crop yields were compared with yields estimated by the USDA for quality control. Combining results from DAYCENT simulations of major crops and IPCC methodology for remaining cropland yielded estimates of approximately 109 and approximately 70 Tg C(O)2 equivalents for direct and indirect, respectively, mean annual anthropogenic N(2)O emissions for 1990-2003.

Agricultural soils are responsible for the majority of nitrous oxide (N2O) emissions in the USA. Irrigated cropping, particularly in the western USA, is an important source of N2O emissions. However, the impacts of tillage intensity and N fertilizer amount and type have not been extensively studied for irrigated systems. The DAYCENT biogeochemical model was tested using N2O, crop yield, soil N and C, and other data collected from irrigated cropping systems in northeastern Colorado during 2002 to 2006. DAYCENT uses daily weather, soil texture, and land management information to simulate C and N fluxes between the atmosphere, soil, and vegetation. The model properly represented the impacts of tillage intensity and N fertilizer amount on crop yields, soil organic C (SOC), and soil water content. DAYCENT N2O emissions matched the measured data in that simulated emissions increased as N fertilization rates increased and emissions from no-till (NT) tended to be lower on average than conventional-till (CT). However, the model overestimated N2O emissions. Lowering the amount of N2O emitted per unit of N nitrified from 2 to 1% helped improve model fit but the treatments receiving no N fertilizer were still overestimated by more than a factor of 2. Both the model and measurements showed that soil NO3- levels increase with N fertilizer addition and with tillage intensity, but DAYCENT underestimated NO3- levels, particularly for the treatments receiving no N fertilizer. We suggest that DAYCENT could be improved by reducing the background nitrification rate and by accounting for the impact of changes in microbial community structure on denitrification rates.

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.

A Monte Carlo analysis was combined with an empirically based approach to quantify uncertainties in soil nitrous oxide (N2O) emissions from U.S. croplands estimated with the DAYCENT simulation model. Only a subset of croplands was simulated in the Monte Carlo analysis, which was used to infer uncertainties across the larger spatiotemporal domain. Specifically, one simulation representing dominant weather, soil type, and N inputs was performed for each major commodity crop in the 3000 counties occurring within the conterminous United States. We randomly selected 300 counties for the Monte Carlo analysis and randomly drew model inputs from probability distribution functions (100 iterations). A structural uncertainty estimator was developed by deriving a statistical equation from a comparison of DAYCENT-simulated N2O emissions with measured emissions from experiments in North America. We estimated soil N2O emission of 201 Gg N from major commodity crops in 2007, with a 95% confidence interval (CI) of 133–304 Gg N. This implies a relative CI of 34% below and 51% above the estimate at the national scale, but the CIs tended to be larger at the regional level, particularly in regions with low emissions. Spatial variability in emissions was driven primarily by differences in N inputs from fertilizer and manure, while temporal variability was driven more by N mineralization rates, which are correlated with weather patterns in DAYCENT. A higher portion of total uncertainty was due to model structure compared to model inputs, suggesting that improvements in model algorithms and parameterization are needed to produce results with higher precision and accuracy.