Latha M. Baskaran

Cropland carbon fluxes in the United States: increasing geospatial resolution of inventory-based carbon accounting.

Net annual soil carbon change, fossil fuel emissions from cropland production, and cropland net primary production were estimated and spatially distributed using land cover defined by NASAs moderate resolution imaging spectroradiometer (MODIS) and by the USDA National Agricultural Statistics Service (NASS) cropland data layer (CDL). Spatially resolved estimates of net ecosystem exchange (NEE) and net ecosystem carbon balance (NECB) were developed. The purpose of generating spatial estimates of carbon fluxes, and the primary objective of this research, was to develop a method of carbon accounting that is consistent from field to national scales. NEE represents net on-site vertical fluxes of carbon. NECB represents all on-site and off-site carbon fluxes associated with crop production. Estimates of cropland NEE using moderate resolution (approximately 1 km2) land cover data were generated for the conterminous United States and compared with higher resolution (30-m) estimates of NEE and with direct measurements of CO2 flux from croplands in Illinois and Nebraska, USA. Estimates of NEE using the CDL (30-m resolution) had a higher correlation with eddy covariance flux tower estimates compared with estimates of NEE using MODIS. Estimates of NECB are primarily driven by net soil carbon change, fossil fuel emissions associated with crop production, and CO2 emissions from the application of agricultural lime. NEE and NECB for U.S. croplands were -274 and 7 Tg C/yr for 2004, respectively. Use of moderate- to high-resolution satellite-based land cover data enables improved estimates of cropland carbon dynamics.

Empirical geographic modeling of switchgrass yields in the United States

Switchgrass (Panicum virgatum L.) is a perennial grass native to the United States that has been studied as a sustainable source of biomass fuel. Although many field‐scale studies have examined the potential of this grass as a bioenergy crop, these studies have not been integrated. In this study, we present an empirical model for switchgrass yield and use this model to predict yield for the conterminous United States. We added environmental covariates to assembled yield data from field trials based on geographic location. We developed empirical models based on these data. The resulting empirical models, which account for spatial autocorrelation in the field data, provide the ability to estimate yield from factors associated with climate, soils, and management for both lowland and upland varieties of switchgrass. Yields of both ecotypes showed quadratic responses to temperature, increased with precipitation and minimum winter temperature, and decreased with stand age. Only the upland ecotype showed a positive response to our index of soil wetness and only the lowland ecotype showed a positive response to fertilizer. We view this empirical modeling effort, not as an alternative to mechanistic plant‐growth modeling, but rather as a first step in the process of functional validation that will compare patterns produced by the models with those found in data. For the upland variety, the correlation between measured yields and yields predicted by empirical models was 0.62 for the training subset and 0.58 for the test subset. For the lowland variety, the correlation was 0.46 for the training subset and 0.19 for the test subset. Because considerable variation in yield remains unexplained, it will be important in the future to characterize spatial and local sources of uncertainty associated with empirical yield estimates.

Progress toward Evaluating the Sustainability of Switchgrass as a Bioenergy Crop using the SWAT Model

Adding bioenergy to the U.S. energy portfolio requires long-term profitability for bioenergy producers and long-term protection of affected ecosystems. In this study, we present steps along the path toward evaluating both sides of the sustainability equation (production and environmental) for switchgrass (Panicum virgatum) using the Soil and Water Assessment Tool (SWAT). We modeled production of switchgrass and river flow using SWAT for current landscapes at a regional scale. To quantify feedstock production, we compared lowland switchgrass yields simulated by SWAT with estimates from a model based on empirical data for the eastern U.S. The two produced similar geographic patterns. Average yields reported in field trials tended to be higher than average SWAT-predicted yields, which may nevertheless be more representative of production-scale yields. As a preliminary step toward quantifying bioenergy-related changes in water quality, we evaluated flow predictions by the SWAT model for the Arkansas-White-Red river basin. We compared monthly SWAT flow predictions to USGS measurements from 86 subbasins across the region. Although agreement was good, we conducted an analysis of residuals (functional validation) seeking patterns to guide future model improvements. The analysis indicated that differences between SWAT flow predictions and field data increased in downstream subbasins and in subbasins with higher percentage of water. Together, these analyses have moved us closer to our ultimate goal of identifying areas with high economic and environmental potential for sustainable feedstock production.

Habitat Modeling Within a Regional Context: An Example Using Gopher Tortoise

Abstract Changes in habitat are often a major influence on species distribution and even survival. Yet predicting habitat often requires detailed field data that are difficult to acquire, especially on private lands. Therefore, we have developed a model that builds on extensive data that are available from public lands and extends them to surrounding private lands. This model is applied for a five-county region in Georgia to predict habitats for the gopher tortoise (Gopherus polyphemus), based on analysis of documented locations of gopher tortoise burrows at the Fort Benning military installation in west central Georgia. Burrow associations with land cover, soil, topography and water observed within the military installation were analyzed with binary logistic regression. This analysis helped generate a probability map for the occurrence of gopher tortoise burrows in the five-county region surrounding Fort Benning. Ground visits were made to test the accuracy of the model in predicting gopher tortoise habitat. The results showed that information on land cover, soils, and distances to streams and roads can be used to predict gopher tortoise burrows. This approach can be used to better understand and effectively carry out gopher tortoise habitat restoration and preservation activities.

Forecasting changes in water quality in rivers associated with growing biofuels in the Arkansas-White-Red river drainage, USA

Excess nutrients from agriculture in the Mississippi River drainage, USA have degraded water quality in freshwaters and contributed to anoxic conditions in downstream estuaries. Consequently, water quality is a significant concern associated with conversion of lands to bioenergy production. This study focused on the Arkansas‐White‐Red river basin (AWR), one of five major river basins draining to the Mississippi River. The AWR has a strong precipitation gradient from east to west, and advanced cellulosic feedstocks are projected to become economically feasible within normal‐to‐wet areas of the region. In this study, we used large‐scale watershed modeling to identify areas along this precipitation gradient with potential for improving water quality. We compared simulated water quality in rivers draining projected future landscapes with and without cellulosic bioenergy for two future years, 2022 and 2030 with an assumed farmgate price of

Planning Transboundary Ecological Risk Assessments at Military Installations

50 per dry ton. Changes in simulated water quantity and quality under future bioenergy scenarios varied among subbasins and years. Median water yield, nutrient loadings, and sediment yield decreased by 2030. Median concentrations of nutrients also decreased, but suspended sediment, which is influenced by decreased flow and in‐stream processes, increased. Spatially, decreased loadings prevailed in the transitional ecotone between 97° and 100° longitude, where switchgrass, Panicum virgatum L., is projected to compete against alternative crops and land uses at

Modeling the Effects of Land Use on the Quality of Water, Air, Noise, and Habitat for a Five-County Region in Georgia

50 per dry ton. We conclude that this region contains areas that hold promise for sustainable bioenergy production in terms of both economic feasibility and water quality protection.

Ensuring that Ecological Science Contributes to Natural Resource Management Using a Delphi-Derived Approach

ABSTRACT Ecological risk assessments at military installations that are performed to support natural resources management objectives rely on information from the surrounding region. Stressors such as noise, ozone, and ozone precursors cross installation boundaries, and effects of urbanization and highway development are regional in scale. Ecological populations are not limited to one side of the installation boundary. Therefore, a framework for transboundary ecological risk assessment at military installations is under development. This article summarizes the problem formulation stage. Components include: (1) regional management goals such as installation Integrated Natural Resources Management Plans and land acquisition, (2) involvement of multiple stressors, and (3) large-scale assessment endpoint entities. Challenges of selecting measures of exposure include: quantifying exposure to aggregate stressors, describing land cover consistently in the region, describing rates of land-cover transition, scaling local measurements to a region, and aggregating or isolating exposures from within and outside of the installation. Measures of effect that are important to transboundary or regional ecological risk assessments at military installations are those that represent: effects at a distance from the stressor, large-scale effects, effects of habitat change or fragmentation, spatial extrapolations of localized effects, and integrated effects of multiple stressors. These factors are reflected in conceptual models.

Hydrologic and water quality responses to biomass production in the Tennessee river basin

A computer simulation model, the Regional Simulator (RSim), was constructed to project how landuse changes affect the quality of water, air, noise, and habitat of species of special concern. RSim was designed to simulate these environmental impacts for five counties in Georgia that surround and include Fort Benning. The model combines existing data and modeling approaches to simulate the effects of land-cover changes on: nutrient export by hydrological unit; peak 8-h average ozone concentrations; noise caused by small arms and blasts; and habitat changes for the rare Red-cockaded Woodpecker (Picoides borealis) and gopher tortoise (Gopherus polyphemus). The model also includes submodules for urban growth, new urbanization influenced by existing roads, nonurban land cover transitions, and a new military training area under development at Fort Benning. The model was run under scenarios of business as usual (BAU) and greatly increased urban growth for the region. The projections show that the effects of high urban growth will likely differ from those of BAU for noise and nitrogen and phosphorus loadings to surface water, but not for peak airborne ozone concentrations, at least in the absence of associated increases in industry and transportation use or technology changes. In both scenarios, no effects of urban growth are anticipated for existing populations of the federally endangered Red-cockaded Woodpecker. In contrast, habitat for gopher tortoise in the five-county region is projected to decline by 5 and 40% in the BAU and high urban growth scenarios, respectively. RSim is designed to assess the relative environmental impacts of planned activities both inside and outside military installations and to address concerns related to encroachment and transboundary influences.

Indicators to support environmental sustainability of bioenergy systems

This chapter approaches participatory modeling in environmental decision making from an atypical perspective. It broadly addresses the question of how to assure that science conducted to assist practitioners improves resource management. More specifically, it describes a case involving environmental science and natural resource management at Fort Benning, a United States (US) Army installation in the southeastern US where disparate environmental research projects were funded by a single federal agency to enhance the ability of Fort Benning’s resource managers to achieve their resource management goals. The role of our effort was to integrate the scientific studies in a manner that would be meaningful and useful for resource managers. Hence we assembled a team consisting of an anthropologist, ecologist, microbiologist, statistician, and geographic information systems specialist who developed a common framework that served as the basis for this integration. The team first used a Delphi expert elicitation, which evolved into an approach more akin to facilitated negotiation. This second approach arose organically, particularly when our team took advantage of an opportunity for face-to-face interaction. Although the shift in our approach was unplanned, it proved to be highly productive. We discuss the potential utility of our approach for other situations and suggest that it would be useful to initiate at the beginning of research where the aim is to produce scientific results that meet practitioners’ needs, specifically in the realm of environmental science and resource management.