A Simulation of Temporal and Spatial Variations in Carbon at Landscape Level: A Case Study for Lake Abitibi Model Forest in Ontario,Canada

Using a case study of the Lake Abitibi Model Forest (LAMF), this study aims to assess the temporal and spatial variability in carbon storage during 1990–2000, and to present a comprehensive estimation of the carbon budget for LAMF's ecosystems. As well, it provided the information needed by local forest managers to develop ecological and carbon-based indicators and monitor the sustainability of forest ecosystems. Temporal and spatial carbon dynamics were simulated at the landscape level using ecosystem model TRIPLEX1.0 and Geographical Information System (GIS). The simulated net primary productivity (NPP) and carbon storage in forest biomass and soil were compared with field data and results from other studies for Canada's boreal forests. The results show that simulated NPP ranged from 3.26 to 3.34 tC ha⁻¹ yr⁻¹ in the 1990s and was consistent with the range measured during the Boreal Ecosystem-Atmosphere Studies (BOREAS) in central Canada. Modeled NPP was also compared with the estimation from remote sensing data. The density of total above-and belowground biomass was 125.3, 111.8, and 106.5 tC ha⁻¹ for black spruce, trembling aspen, and jack pine in the LAMF ecosystem, respectively. The total carbon density of forested land was estimated at 154.4 tC ha⁻¹ with the proportion of 4:6 for total biomass and soil. The analysis of net carbon balance of ecosystem suggested that the LAMF forest ecosystem was acting as a carbon sink with an allowable harvest in the 1990s.     

Simulated effects of dryland cropping intensification on soil organic matter and greenhouse gas exchanges using the DAYCENT ecosystem model

We present evidence to show that DAYCENT can reliably simulate soil C levels, crop yields, and annual trace gas fluxes for various soils. DAYCENT was applied to compare the net greenhouse gas fluxes for soils under different land uses. To calculate net greenhouse gas flux we accounted for changes in soil organic C, the C equivalents of N2O emissions and CH4 uptake, and the CO2 costs of N fertilizer production. Model results and data show that dryland soils that are depleted of C due to conventional till winter wheat fallow cropping can store C upon conversion to no till, by reducing the fallow period, or by reversion to native vegetation. However, model results suggest that dryland agricultural soils will still be net sources of greenhouse gases although the magnitude of the source can be significantly reduced and yields can be increased upon conversion to no till annual cropping.     

Managing the nitrogen cycle to reduce greenhouse gas emissions from crop production and biofuel expansion

Public policies are promoting biofuels as an alternative to fossil fuel consumption in order to mitigate greenhouse gas (GHG) emissions. However, the mitigation benefit can be at least partially compromised by emissions occurring during feedstock production. One of the key sources of GHG emissions from biofuel feedstock production, as well as conventional crops, is soil nitrous oxide (N₂O), which is largely driven by nitrogen (N) management. Our objective was to determine how much GHG emissions could be reduced by encouraging alternative N management practices through application of nitrification inhibitors and a cap on N fertilization. We used the US Renewable Fuel Standards (RFS2) as the basis for a case study to evaluate technical and economic drivers influencing the N management mitigation strategies. We estimated soil N₂O emissions using the DayCent ecosystem model and applied the US Forest and Agricultural Sector Optimization Model with Greenhouse Gases (FASOMGHG) to project GHG emissions for the agricultural sector, as influenced by biofuel scenarios and N management options. Relative to the current RSF2 policy with no N management interventions, results show decreases in N₂O emissions ranging from 3 to 4 % for the agricultural sector (5.5–6.5 million metric tonnes CO₂?eq.?year^?1; 1 million metric tonnes is equivalent to a Teragram) in response to a cap that reduces N fertilizer application and even larger reductions with application of nitrification inhibitors, ranging from 9 to 10 % (15.5–16.6 million tonnes CO₂?eq.?year^?1). The results demonstrate that climate and energy policies promoting biofuel production could consider options to manage the N cycle with alternative fertilization practices for the agricultural sector and likely enhance the mitigation of GHG emissions associated with biofuels.     

Framework for improved confidence in modeled nitrous oxide estimates for biofuel regulatory standards

Biofuels vary greatly in their carbon intensity, depending on the specifics of how they are produced. Policy frameworks are needed to ensure that biofuels actually achieve intended reductions in greenhouse gas emissions. Current approaches do not account for important variables during cultivation that influence emissions. Estimating emissions based on biogeochemical models would allow accounting of farm-specific conditions, which in turn provides an incentive for producers to adopt low emissions practices. However, there are substantial uncertainties in the application of biogeochemical models. This paper proposes a policy framework that manages this uncertainty while retaining the ability of the models to account for (and hence incentivize) low emissions practices. The proposed framework is demonstrated on nitrous oxide (N₂O) emissions from the cultivation of winter barley. The framework aggregates uncertainties over time, which (1) avoids penalizing producers for uncertainty in weather, (2) allows for a high degree of confidence in the emissions reductions achieved, and (3) attenuates the uncertainty penalties borne by producers within a timescale of several years. Results indicate that with effective management, N₂O emissions from feedstock cultivation may be <?5% of the carbon intensity of gasoline, whereas the existing policy approach estimates emissions >?20% of the carbon intensity of gasoline. If these emissions reductions are monetized, the framework can provide up to $0.002 per liter credits (0.8 cents per gallon) to fuel producers, which could incentivize emissions mitigation practices by biofuel feedstock suppliers, such as avoiding fall N application on silty clay loam soils. The conservatism in the current approach fails to incentivize the adoption of biofuels, while the lack of specificity fails to incentivize site-level mitigation practices. Improved uncertainty accounting and consideration of farm-level practices will incentivize mitigation efforts at landscape to global scales.     

Calibration of the forest vegetation simulator (FVS) model for the main forest species of Ontario,Canada

The forest vegetation simulator (FVS) model was calibrated for use in Ontario, Canada, to predict the growth of forest stands. Using data from permanent sample plots originating from different regions of Ontario, new models were derived for dbh growth rate, survival rate, stem height and species group density index for large trees and height and dbh growth rate for small trees. The dataset included black spruce (Picea mariana (Mill.) B.S.P.) and jack pine (Pinus banksiana Lamb.) for the boreal region, sugar maple (Acer saccharum Marsh.), white pine (Pinus strobus L.), red pine (Pinus resinosa Ait.) and yellow birch (Betula alleghaniensis Britton) for the Great Lakes-St. Lawrence region, and balsam fir (Abies balsamea (L.) Mill.) and trembling aspen (Populus tremuloides Michx.) for both regions. These new models were validated against an independent dataset that consisted of permanent sample plots located in Quebec. The new models predicted biologically consistent growth patterns whereas some of the original models from the Lake States version of FVS occasionally did not. The new models also fitted the calibration (Ontario) data better than the original FVS models. The validation against independent data from Quebec showed that the new models generally had a lower prediction error than the original FVS models.     

A Strategy for the Evaluation of Sensory and Pulmonary Irritation Due to Chemical Emissions from Indoor Sources

Abstract

Sensory and pulmonary irritation are physiological responses to chemical exposure which result in characteristic, measurable changes in respiratory activity in mice. A standard method has been applied to the estimation of sensory irritation associated with a specific chemical exposure. This method has been correlated with human responses to these chemicals. Symptoms associated with chemical irritants are consistent with complaints due to problems with indoor air quality, which may include eye and upper respiratory tract irritation, headaches, and nausea. A stepwise strategy for assessing the contribution of indoor products to sensory and pulmonary irritation is discussed in the current paper. The strategy includes product emissions testing using dynamic environmental chambers, the selection of suspected irritants for respiratory irritation testing, respiratory irritation testing of individual compounds and representative mixtures using synthesized atmospheres, and the evaluation of test data to determine those compounds which may contribute to sensory and pulmonary irritation in humans. The current strategy is being applied to evaluate carpet system materials and their constituent chemicals.     

Temperature influences carbon accumulation in moist tropical forests

Evergreen broad-leaved tropical forests can have high rates of productivity and large accumulations of carbon in plant biomass and soils. They can therefore play an important role in the global carbon cycle, influencing atmospheric CO2 concentrations if climate warms. We applied meta-analyses to published data to evaluate the apparent effects of temperature on carbon fluxes and storages in mature, moist tropical evergreen forest ecosystems. Among forests, litter production, tree growth, and belowground carbon allocation all increased significantly with site mean annual temperature (MAT); total net primary productivity (NPP) increased by an estimated 0.2-0.7 Mg C x ha(-1) x yr(-1) x degrees C(-1). Temperature had no discernible effect on the turnover rate of aboveground forest biomass, which averaged 0.014 yr(-1) among sites. Consistent with these findings, forest biomass increased with site MAT at a rate of 5-13 Mg C x ha(-1) x degrees C(-1). Despite greater productivity in warmer forests, soil organic matter accumulations decreased with site MAT, with a slope of -8 Mg C x ha(-1) x degrees C(-1), indicating that decomposition rates of soil organic matter increased with MAT faster than did rates of NPP. Turnover rates of surface litter also increased with temperature among forests. We found no detectable effect of temperature on total carbon storage among moist-tropical evergreen forests, but rather a shift in ecosystem structure, from low-biomass forests with relatively large accumulations of detritus in cooler sites, to large-biomass forests with relatively smaller detrital stocks in warmer locations. These results imply that, in a warmer climate, conservation of forest biomass will be critical to the maintenance of carbon stocks in moist tropical forests.     

Simulated grazing responses on the proposed prairies National Park

The tallgrass prairie version of the ELM Grassland Model was used to evaluate the potential impact of establishing a tallgrass prairie National Park in the Flint Hills region of Kansas. This total ecosystem model simulates (a) the flow of water, heat, nitrogen, and phosphorus through the ecosystem and(b) the biomass dynamics of plants and consumers. It was specifically developed to study the effects of levels and types of herbivory, climatic variation, and fertilization upon grassland ecosystems. The model was used to simulate the impact of building up herds of bison, elk, antelope, and wolves on a tallgrass prairie. The results show that the grazing levels in the park should not be decreased below the prepark grazing levels (moderate grazing with cattle) and that the final grazing levels in the park could be maintained at a slightly higher level than the prepark grazing levels.     

Global potential net primary production predicted from vegetation class, precipitation, and temperature

Net primary production (NPP), the difference between CO2 fixed by photosynthesis and CO2 lost to autotrophic respiration, is one of the most important components of the carbon cycle. Our goal was to develop a simple regression model to estimate global NPP using climate and land cover data. Approximately 5600 global data points with observed mean annual NPP, land cover class, precipitation, and temperature were compiled. Precipitation was better correlated with NPP than temperature, and it explained much more of the variability in mean annual NPP for grass- or shrub-dominated systems (r2 = 0.68) than for tree-dominated systems (r2 = 0.39). For a given precipitation level, tree-dominated systems had significantly higher NPP (approximately 100-150 g C m(-2) yr(-1)) than non-tree-dominated systems. Consequently, previous empirical models developed to predict NPP based on precipitation and temperature (e.g., the Miami model) tended to overestimate NPP for non-tree-dominated systems. Our new model developed at the National Center for Ecological Analysis and Synthesis (the NCEAS model) predicts NPP for tree-dominated systems based on precipitation and temperature; but for non-tree-dominated systems NPP is solely a function of precipitation because including a temperature function increased model error for these systems. Lower NPP in non-tree-dominated systems is likely related to decreased water and nutrient use efficiency and higher nutrient loss rates from more frequent fire disturbances. Late 20th century aboveground and total NPP for global potential native vegetation using the NCEAS model are estimated to be approximately 28 Pg and approximately 46 Pg C/yr, respectively. The NCEAS model estimated an approximately 13% increase in global total NPP for potential vegetation from 1901 to 2000 based on changing precipitation and temperature patterns.