Measuring and preliminary modeling of drift interception by plant species.

Currently, the concept of plant capture efficiency is not quantitatively considered in the evaluation of off-target drift for the purposes of pesticide risk assessment in the United States. For on-target pesticide applications, canopy capture efficiency is managed by optimizing formulations or tank-mixing with adjuvants to maximize retention of spray droplets. These efforts take into consideration the fact that plant species have diverse morphology and surface characteristics, and as such will retain varying levels of applied pesticides. This work aims to combine plant surface wettability potential, spray droplet characteristics, and plant morphology into describing the plant capture efficiency of drifted spray droplets. In this study, we used wind tunnel experiments and individual plants grown to 10-20 cm to show that at two downwind distances and with two distinct nozzles capture efficiency for sunflower (Helianthus annuus L.), lettuce (Lactuca sativa L.), and tomato (Solanum lycopersicum L.) is consistently higher than rice (Oryza sativa L.), peas (Pisum sativum L). and onions (Allium cepa L.), with carrots (Daucus carota L.) showing high variability and falling between the two groups. We also present a novel method for three-dimensional modeling of plants from photogrammetric scanning and use the results in the first known computational fluid dynamics simulations of drift capture efficiency on plants. The mean simulated drift capture efficiency rates were within the same order of magnitude of the mean observed rates of sunflower and lettuce, and differed by one to two orders for rice and onion. We identify simulating the effects of surface roughness on droplet behavior, and the effects of wind flow on plant movement as potential model improvements requiring further species-specific data collection.

Effectiveness of side-inlet vegetated filter strips at trapping pesticides from agricultural runoff.

Agriculture can be a contributor of pollutants, including pesticides and excess sediment, to aquatic environments. However, side-inlet vegetated filter strips (VFSs), which are planted around the upstream side of culverts draining agricultural fields, may provide reductions in pesticide and sediment losses from agricultural fields, and have the additional benefit of removing less land from production than traditional VFS. In this study, reductions of runoff, the soluble pesticide acetochlor, and total suspended solids were estimated using a paired watershed field study and coupled PRZM/VFSMOD modeling for two treatment watersheds with source to buffer area ratios (SBAR) of 80:1 (SI-A) and 481:1 (SI-B). Based on the paired watershed ANCOVA analysis, runoff and acetochlor load reductions were significant following the implementation of a VFS at SIA but not SI-B, indicating the potential for side-inlet VFS to reduce runoff and acetochlor load from a watershed with an area ratio of 80:1 but not a higher ratio of 481:1. VFSMOD simulations were consistent with the results of the paired watershed monitoring study, where simulated reductions of runoff, acetochlor loads, and TSS loads were substantially lower for SI-B than SI-A. VFSMOD simulations of SI-B with the SBAR ratio observed at SI-A (80:1) also show that VFSMOD can be used to capture variability in effectiveness of VFS based on multiple factors including SBAR. While this study focused on the effectiveness of side-inlet VFSs at the field scale, broader adoption of properly sized side-inlet VFSs could improve surface water quality at the watershed or larger scales. Additionally, modeling at the watershed scale could aid in locating, sizing, and assessing the impacts of side-inlet VFSs at this larger scale.

Evaluation of SEAWAVE-QEX in a high agricultural intensity catchment in Belgium.

Pesticide surface water monitoring data have rarely been used as the only quantitative measure of exposure because the available monitoring data for most pesticides has not been considered robust enough for direct use in pesticide exposure assessments due to infrequent sampling. The cost of daily sample collection and analysis prohibits frequent sampling for most monitoring programs. In this context, a common question raised in assessments is how likely peak concentrations (i.e., annual maxima) may be missed if sampling intervals are longer than daily. The US Geological Survey developed the statistical model "seasonal wave with streamflow adjustment and extended capability" (SEAWAVE-QEX) to address the need to estimate infrequently occurring pesticide concentrations, such as annual maximum daily concentrations, for sites with nondaily monitoring data. This study compares the results of two postprocessing methods and evaluates the capability of SEAWAVE-QEX to estimate annual maximum concentrations of three commonly used herbicides and one metabolite in a catchment in Belgium. The study concludes that the appropriateness of using SEAWAVE-QEX to estimate annual maximum concentrations depends on pesticide characteristics and use and that the model can be particularly sensitive to nonflow correlated exposure events (e.g., point source contributions or drift). Integr Environ Assess Manag 2023;19:513-526. © 2022 Stone Environmental and Bayer AG Crop Science Division. Integrated Environmental Assessment and Management published by Wiley Periodicals LLC on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Probabilistic co-occurrence assessment for suites of listed species.

Section 7 of the Endangered Species Act requires the US Environmental Protection Agency (US EPA) to consult with the Services (US Fish and Wildlife Service and National Marine Fisheries Service) over potential pesticide impacts on federally listed species. Consultation is complicated by the large number of pesticide products and listed species, as well as by lack of consensus on best practices for conducting co-occurrence analyses. Previous work demonstrates that probabilistic estimates of species' ranges and pesticide use patterns improve these analyses. Here we demonstrate that such estimates can be made for suites of sympatric listed species. Focusing on two watersheds, one in Iowa and the other in Mississippi, we obtained distribution records for 13 species of terrestrial and aquatic listed plants and animals occurring therein. We used maximum entropy modeling and bioclimatic, topographic, hydrographic, and land cover variables to predict species' ranges at high spatial resolution. We constructed probabilistic spatial models of use areas for two pesticides based on the US Department of Agriculture Cropland Data Layer and reduced classification errors by incorporating information on the relationships between individual pixels and their neighbors using object-based images analysis. We then combined species distribution and crop footprint models to derive overall probability of co-occurrence of listed species and pesticide use. For aquatic species, we also integrated an estimate of downstream residue transport. We report each separate species-by-use-area co-occurrence estimate and also combine these modeled co-occurrence probabilities across species within watersheds to produce an overall metric of potential pesticide exposure risk for these listed species at the watershed level. We propose that the consultation process between US EPA and the Services be based on such batched estimation of probabilistic co-occurrence for multiple listed species at a regional scale. Integr Environ Assess Manag 2022;18:1088-1100. © 2021 SETAC.

Comparison of pesticide concentrations observed in community water systems to predictions from U.S. regulatory aquatic exposure models.

The Variable Volume Water Model (VVWM), the receiving water body model for the USEPA regulatory assessment of aquatic pesticide exposures, is composed of a set of static and quasistatic receiving water body conceptual models, but research comparing performance of these models to observations is limited. The water body models included are the constant volume (CVol), constant volume with overflow (CVO), and varying volume with overflow (VVO) models. This work quantified the performance of these three VVWM conceptual models compared with atrazine observations in 50 community water systems (CWSs), and the effect of alternative conceptual models on estimated environmental concentrations of pesticides in regulatory screening assessments. The 50 selected CWSs most relevant to the static and quasistatic VVWM concepts were small in size, with estimated time to peak flow of <1.5 d for consistency with the daily runoff assumption in USEPA landscape Pesticide Root Zone Model (PRZM). The CVO and VVO conceptual models resulted in similar distributions of bias across CWSs with the median result being close to no bias, but the CVol model resulted in overestimation in the majority of CWSs with median model bias near three times the observed values. At present, the CVol conceptual model parameterized with conservative input assumptions has been the regulatory standard invoked in VVWM, yet our results showed that a more physically correct conceptual model (CVO or VVO) could be invoked in regulatory exposure modeling for ecological risk assessment, reducing structural model bias while still allowing users to introduce conservative model inputs for screening purposes.

A Probabilistic Co-Occurrence Approach for Estimating Likelihood of Spatial Overlap Between Listed Species Distribution and Pesticide Use Patterns.

Characterizing potential spatial overlap between federally threatened and endangered ("listed") species distributions and registered pesticide use patterns is important for accurate risk assessment of threatened and endangered species. Because accurate range information for such rare species is often limited and agricultural pesticide use patterns are dynamic, simple spatial co-occurrence methods may overestimate or underestimate overlap and result in decisions that benefit neither listed species nor the regulatory process. Here, we demonstrate a new method of co-occurrence analysis that employs probability theory to estimate spatial distribution of rare species populations and areas of pesticide use to determine the likelihood of potential exposure. Specifically, we 1) describe a probabilistic method to estimate pesticide use based on crop production patterns; 2) construct species distribution models for 2 listed insect species whose ranges were previously incompletely described, the rusty-patched bumble bee (Bombus affinis) and the Poweshiek skipperling (Oarisma poweshiek); and 3) develop a probabilistic co-occurrence methodology and assessment framework. Using the principles of the Bayes' theorem, we constructed probabilistic spatial models of pesticide use areas by integrating information from land-cover spatial data, agriculture statistics, and remote-sensing data. We used maximum entropy methods to build species distribution models for 2 listed insects based on species collection and observation records and predictor variables relevant to the species' biogeography and natural history. We further developed novel methods for refinement of these models at spatial scales relevant to US Fish and Wildlife Service (FWS) regulatory priorities (e.g., critical habitat areas). Integrating both probabilistic assessments and focusing on USFWS priority management areas, we demonstrate that spatial overlap (i.e., potential for exposure) is not deterministic but instead a function of both species distribution and land use patterns. Our work serves as a framework to enhance the accuracy and efficiency of threatened and endangered species assessments using a data-driven likelihood analysis of species co-occurrence. Integr Environ Assess Manag 2019;00:1-12. © 2019 SETAC.

Evaluation of Watershed-Scale Simulations of In-Stream Pesticide Concentrations from Off-Target Spray Drift.

The estimation of pesticide concentrations in surface water bodies is a critical component of the environmental risk assessment process required by regulatory agencies in North America, the European Union, and elsewhere. Pesticide transport to surface waters via deposition from off-field spray drift can be an important route of potential contamination. The spatial orientation of treated fields relative to receiving water bodies make prediction of off-target pesticide spray drift deposition and resulting aquatic estimated environmental concentrations (EECs) challenging at the watershed scale. The variability in wind conditions further complicates the simulation of the environmental processes leading to pesticide spray drift contributions to surface water. This study investigates the use of the Soil Water Assessment Tool (SWAT) for predicting concentrations of malathion (O,O-deimethyl thiophosphate of diethyl mercaptosuccinate) in a flowing water body when exposure is a result of off-target spray drift, and assesses the model's performance using a parameterization typical of a screening-level regulatory assessment. Six SWAT parameterizations, each including incrementally more site-specific data, are then evaluated to quantify changes in model performance. Results indicate that the SWAT model is an appropriate tool for simulating watershed scale concentrations of pesticides resulting from off-target spray drift deposition. The model predictions are significantly more accurate when the inputs and assumptions accurately reflect application practices and environmental conditions. Inclusion of detailed wind data had the most significant impact on improving model-predicted EECs in comparison to observed concentrations.

Soil and Water Assessment Tool model predictions of annual maximum pesticide concentrations in high vulnerability watersheds.

Recent national regulatory assessments of potential pesticide exposure of threatened and endangered species in aquatic habitats have led to increased need for watershed-scale predictions of pesticide concentrations in flowing water bodies. This study was conducted to assess the ability of the uncalibrated Soil and Water Assessment Tool (SWAT) to predict annual maximum pesticide concentrations in the flowing water bodies of highly vulnerable small- to medium-sized watersheds. The SWAT was applied to 27 watersheds, largely within the midwest corn belt of the United States, ranging from 20 to 386 km2 , and evaluated using consistent input data sets and an uncalibrated parameterization approach. The watersheds were selected from the Atrazine Ecological Exposure Monitoring Program and the Heidelberg Tributary Loading Program, both of which contain high temporal resolution atrazine sampling data from watersheds with exceptionally high vulnerability to atrazine exposure. The model performance was assessed based upon predictions of annual maximum atrazine concentrations in 1-d and 60-d durations, predictions critical in pesticide-threatened and endangered species risk assessments when evaluating potential acute and chronic exposure to aquatic organisms. The simulation results showed that for nearly half of the watersheds simulated, the uncalibrated SWAT model was able to predict annual maximum pesticide concentrations within a narrow range of uncertainty resulting from atrazine application timing patterns. An uncalibrated model's predictive performance is essential for the assessment of pesticide exposure in flowing water bodies, the majority of which have insufficient monitoring data for direct calibration, even in data-rich countries. In situations in which SWAT over- or underpredicted the annual maximum concentrations, the magnitude of the over- or underprediction was commonly less than a factor of 2, indicating that the model and uncalibrated parameterization approach provide a capable method for predicting the aquatic exposure required to support pesticide regulatory decision making. Integr Environ Assess Manag 2018;14:358-368. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

A refined ecological risk assessment for California red-legged frog, Delta smelt, and California tiger salamander exposed to malathion.

The California red-legged frog (CRLF), Delta smelt (DS), and California tiger salamander (CTS) are 3 species listed under the United States Federal Endangered Species Act (ESA), all of which inhabit aquatic ecosystems in California. The US Environmental Protection Agency (USEPA) has conducted deterministic screening-level risk assessments for these species potentially exposed to malathion, an organophosphorus insecticide and acaricide. Results from our screening-level analyses identified potential risk of direct effects to DS as well as indirect effects to all 3 species via reduction in prey. Accordingly, for those species and scenarios in which risk was identified at the screening level, we conducted a refined probabilistic risk assessment for CRLF, DS, and CTS. The refined ecological risk assessment (ERA) was conducted using best available data and approaches, as recommended by the 2013 National Research Council (NRC) report "Assessing Risks to Endangered and Threatened Species from Pesticides." Refined aquatic exposure models including the Pesticide Root Zone Model (PRZM), the Vegetative Filter Strip Modeling System (VFSMOD), the Variable Volume Water Model (VVWM), the Exposure Analysis Modeling System (EXAMS), and the Soil and Water Assessment Tool (SWAT) were used to generate estimated exposure concentrations (EECs) for malathion based on worst-case scenarios in California. Refined effects analyses involved developing concentration-response curves for fish and species sensitivity distributions (SSDs) for fish and aquatic invertebrates. Quantitative risk curves, field and mesocosm studies, surface-water monitoring data, and incident reports were considered in a weight-of-evidence approach. Currently, labeled uses of malathion are not expected to result in direct effects to CRLF, DS or CTS, or indirect effects due to effects on fish and invertebrate prey. Integr Environ Assess Manag 2018;14:224-239. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Development of groundwater pesticide exposure modeling scenarios for vulnerable spring and winter wheat-growing areas.

Wheat crops and the major wheat-growing regions of the United States are not included in the 6 crop- and region-specific scenarios developed by the US Environmental Protection Agency (USEPA) for exposure modeling with the Pesticide Root Zone Model conceptualized for groundwater (PRZM-GW). The present work augments the current scenarios by defining appropriately vulnerable PRZM-GW scenarios for high-producing spring and winter wheat-growing regions that are appropriate for use in refined pesticide exposure assessments. Initial screening-level modeling was conducted for all wheat areas across the conterminous United States as defined by multiple years of the Cropland Data Layer land-use data set. Soil, weather, groundwater temperature, evaporation depth, and crop growth and management practices were characterized for each wheat area from publicly and nationally available data sets and converted to input parameters for PRZM. Approximately 150 000 unique combinations of weather, soil, and input parameters were simulated with PRZM for an herbicide applied for postemergence weed control in wheat. The resulting postbreakthrough average herbicide concentrations in a theoretical shallow aquifer were ranked to identify states with the largest regions of relatively vulnerable wheat areas. For these states, input parameters resulting in near 90th percentile postbreakthrough average concentrations corresponding to significant wheat areas with shallow depth to groundwater formed the basis for 4 new spring wheat scenarios and 4 new winter wheat scenarios to be used in PRZM-GW simulations. Spring wheat scenarios were identified in North Dakota, Montana, Washington, and Texas. Winter wheat scenarios were identified in Oklahoma, Texas, Kansas, and Colorado. Compared to the USEPA's original 6 scenarios, postbreakthrough average herbicide concentrations in the new scenarios were lower than all but Florida Potato and Georgia Coastal Peanuts of the original scenarios and better represented regions dominated by wheat crops. Integr Environ Assess Manag 2017;13:992-1006. © 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).

Ecological risk assessment for aquatic invertebrate communities exposed to imidacloprid as a result of labeled agricultural and nonagricultural uses in the United States.

A probabilistic ecological risk assessment (ERA) was conducted to determine the potential effects of acute and chronic exposure of aquatic invertebrate communities to imidacloprid arising from labeled agricultural and nonagricultural uses in the United States. Aquatic exposure estimates were derived using a higher-tier refined modeling approach that accounts for realistic variability in environmental and agronomic factors. Toxicity was assessed using refined acute and chronic community-level effect metrics for aquatic invertebrates (i.e., species or taxon sensitivity distributions) developed using the best available data. Acute and chronic probabilistic risk estimates were derived by integrating the exposure distributions for different use patterns with the applicable species or taxon sensitivity distributions to generate risk curves, which plot cumulative probability of exceedance versus the magnitude of effect. Overall, the results of this assessment indicated that the aquatic invertebrate community is unlikely to be adversely affected by acute or chronic exposure to imidacloprid resulting from currently registered uses of imidacloprid in the United States. Environ Toxicol Chem 2017;36:1375-1388. © 2016 SETAC.

Ecological risk assessment for Pacific salmon exposed to dimethoate in California.

A probabilistic risk assessment of the potential direct and indirect effects of acute dimethoate exposure to salmon populations of concern was conducted for 3 evolutionarily significant units (ESUs) of Pacific salmon in California. These ESUs were the Sacramento River winter-run chinook, the California Central Valley spring-run chinook, and the California Central Valley steelhead. Refined acute exposures were estimated using the Soil and Water Assessment Tool, a river basin-scale model developed to quantify the impact of land-management practices in large, complex watersheds. Both direct effects (i.e., inhibition of brain acetylcholinesterase activity) and indirect effects (i.e., altered availability of aquatic invertebrate prey) were assessed. Risk to salmon and their aquatic invertebrate prey items was determined to be de minimis. Therefore, dimethoate is not expected to have direct or indirect adverse effects on Pacific salmon in these 3 ESUs. Environ Toxicol Chem 2017;36:532-543. © 2016 SETAC.

Comparison of predicted pesticide concentrations in groundwater from SCI-GROW and PRZM-GW models with historical monitoring data.

BACKGROUND: A key factor in the human health risk assessment process for the registration of pesticides by the US Environmental Protection Agency (EPA) is an estimate of pesticide concentrations in groundwater used for drinking water. From 1997 to 2011, these estimates were obtained from the EPA empirical model SCI-GROW. Since 2012, these estimates have been obtained from the EPA deterministic model PRZM-GW, which has resulted in a significant increase in estimated groundwater concentrations for many pesticides. RESULTS: Historical groundwater monitoring data from the National Ambient Water Quality Assessment (NAWQA) Program (1991-2014) were compared with predicted groundwater concentrations from both SCI-GROW (v.2.3) and PRZM-GW (v.1.07) for 66 different pesticides of varying environmental fate properties. The pesticide environmental fate parameters associated with over- and underprediction of groundwater concentrations by the two models were evaluated. CONCLUSION: In general, SCI-GROW2.3 predicted groundwater concentrations were close to maximum historically observed groundwater concentrations. However, for pesticides with soil organic carbon content values below 1000 L kg(-1) and no simulated hydrolysis, PRZM-GW overpredicted, often by greater than 100 ppb. © 2015 Society of Chemical Industry.

A probabilistic approach for estimating the spatial extent of pesticide agricultural use sites and potential co-occurrence with listed species for use in ecological risk assessments.

A crop footprint refers to the estimated spatial extent of growing areas for a specific crop, and is commonly used to represent the potential "use site" footprint for a pesticide labeled for use on that crop. A methodology for developing probabilistic crop footprints to estimate the likelihood of pesticide use and the potential co-occurrence of pesticide use and listed species locations was tested at the national scale and compared to alternative methods. The probabilistic aspect of the approach accounts for annual crop rotations and the uncertainty in remotely sensed crop and land cover data sets. The crop footprints used historically are derived exclusively from the National Land Cover Database (NLCD) Cultivated Crops and/or Pasture/Hay classes. This approach broadly aggregates agriculture into 2 classes, which grossly overestimates the spatial extent of individual crops that are labeled for pesticide use. The approach also does not use all the available crop data, represents a single point in time, and does not account for the uncertainty in land cover data set classifications. The probabilistic crop footprint approach described herein incorporates best available information at the time of analysis from the National Agricultural Statistics Service (NASS) Cropland Data Layer (CDL) for 5 y (2008-2012 at the time of analysis), the 2006 NLCD, the 2007 NASS Census of Agriculture, and 5 y of NASS Quick Stats (2008-2012). The approach accounts for misclassification of crop classes in the CDL by incorporating accuracy assessment information by state, year, and crop. The NLCD provides additional information to improve the CDL crop probability through an adjustment based on the NLCD accuracy assessment data using the principles of Bayes' Theorem. Finally, crop probabilities are scaled at the state level by comparing against NASS surveys (Census of Agriculture and Quick Stats) of reported planted acres by crop. In an example application of the new method, the probabilistic crop footprint for soybean resulted in national and statewide soybean acreages that are within the error bounds of the average reported NASS yearly soybean acreage over the same time period, whereas the method using only NLCD resulted in an acreage that is over 4 times the survey acreage. When the probabilistic crop footprint for soybean was used in a co-occurrence analysis with listed species locations, the number of potentially proximal species identified was half the number based on the standard NLCD crop footprint method (276 species with the probabilistic crop footprint vs 511 for the conventional method). The probabilistic crop footprint methodology allows for a more comprehensive and representative understanding of the potential pesticide use footprint co-occurrence with endangered species locations for use in effects determinations.

Evaluation of AGRO-2014 for Predicting Hydrophobic Organic Chemical Concentrations in Ponds.

Highly hydrophobic organic chemicals (HOCs), like pyrethroids, adsorb strongly to eroded soil and suspended sediment. Therefore, total suspended solids (TSS) concentration in the water column of receiving waters is important for determining the proportion of chemical in the sediment-sorbed vs. the dissolved (bioavailable) state. However, most current regulatory exposure models, such as the Exposure Analysis Modeling System (EXAMS) and Variable Volume Water Model (VVWM), do not include dynamic modeling of TSS. The objective of this study is to compare the performance of those models for simulating observed pesticide concentrations in small water bodies with an updated version of the AGRO model, called AGRO-2014, which includes dynamic sediment processes. The paper also evaluates the importance of explicitly modeling sediment dynamics for HOCs. We calibrated AGRO-2014 for small, static, water bodies using published pyrethroid mesocosm data. To improve the basis for intermodel comparison, AGRO-2014 includes the same algorithm for temperature-dependent degradation found in EXAMS and VVWM, direct acceptance of organic C partition coefficient () inputs, and acceptance of user-defined pesticide loading durations. Differences in sediment processes in AGRO-2014, EXAMS, and VVWM significantly affected predicted concentrations of high- compounds for standardized loading scenarios, whereas differences between the models were less evident for compounds with lower sorption to sediments. AGRO-2014 simulations of drift and slurry pyrethroid applications to ponds closely matched observed concentrations, while EXAMS and VVWM simulations underestimated the observations. The publicly available AGRO-2014 model offers improvements over other models for predicting concentrations of HOC compounds in small water bodies.

Comparison of simulated pesticide concentrations in surface drinking water with monitoring data: explanations for observed differences and proposals for a new regulatory modeling approach.

A primary component to human health risk assessments required by the U.S. Environmental Protection Agency in the registration of pesticides is an estimation of concentrations in surface drinking water predicted by environmental models. The assumptions used in the current regulatory modeling approach are designed to be "conservative", resulting in higher predicted pesticide concentrations than would actually occur in the environment. This paper compiles previously reported modeling and monitoring comparisons and shows that current regulatory modeling methods result in predictions that universally exceed observed concentrations from the upper end of their distributions. In 50% of the modeling/monitoring comparisons, model predictions were more than 229 times greater than the observations, while, in 25% of the comparisons, model predictions were more than 4500 times greater than the observations. The causes for these overpredictions are identified, followed by suggestions for alternative modeling approaches that would result in predictions of pesticide concentrations closer to those observed.

Spatial forecasting of switchgrass productivity under current and future climate change scenarios.

Evaluating the potential of alternative energy crops across large geographic regions, as well as over time, is a necessary component to determining if biofuel production is feasible and sustainable in the face of growing production demands and climatic change. Switchgrass (Panicum virgatum L.), a native perennial herbaceous grass, is a promising candidate for cellulosic feedstock production. In this study, current and future (from 2080 to 2090) productivity is estimated across the central and eastern United States using ALMANAC, a mechanistic model that simulates plant growth over time. The ALMANAC model was parameterized for representative ecotypes of switchgrass. Our results indicate substantial variation in switchgrass productivity both within regions and over time. States along the Gulf Coast, southern Atlantic Coast, and in the East North Central Midwest have the highest current biomass potential. However, these areas also contain critical wetland habitat necessary for the maintenance of biodiversity and agricultural lands necessary for food production. The southern United States is predicted to have the largest decrease in future biomass production. The Great Plains are expected to experience large increases in productivity by 2080-2090 due to climate change. In general, regions where future temperature and precipitation are predicted to increase are also where larger future biomass production is expected. In contrast, regions that show a future decrease in precipitation are associated with smaller future biomass production. Switchgrass appears to be a promising biofuel crop for the central and eastern United States, with local biomass predicted to be high (>10 Mg/ha) for approximately 50% of the area studied for each climate scenario. In order to minimize land conversion and loss of biodiversity, areas that currently have and maintain high productivity under climate change should be targeted for their long-term growth potential.