ISCE-GCC Grants

Reassessing Drought Indicators Used to Issue Automated Relief Payments in the U.S. Crop Insurance Program  

College of Agriculture and Life Sciences

• Elinor Benami, Agricultural and Applied Economics
  

College of Science

• Manoochehr Shirzaei, Geosciences

Abstract

The U.S. Federal Crop Insurance Program (FCIP) began in the wake of the Dust Bowl of the late 1930s in an effort to guard the US agricultural sector against the catastrophic variability in prices and weather that impoverished communities, yielded acute food insecurity, and fundamentally reconfigured the agrarian landscape of the US for generations that followed (Hansen & Libecap, 2004; Hornbeck, 2012; Rosch [CRS], 2021). Among the several insurance options currently offered by the FCIP, in 2007 a new policy was made available to protect US pasture, forage, and rangelands against the single peril of reduced precipitation. Over the nearly fifteen years since its launch, this program – called the Pasture, Rangeland, and Forage Rainfall Index program, or PRF-RI – has grown to account for 37% of the total acreage insured in the US agricultural insurance portfolio, with the number of policies sold increasing year upon year, per Figure 1 (RMA, 2022).

As the popularity of the PRF-RI program has grown, so have questions arisen about its design and the extent to which it effectively serves as a mechanism to protect producers across the US (Keller & Saitone, 2022; Yu et al., 2019), especially in light of recent severe droughts, gaps in station coverage that serve as important inputs to the determining the correspondence of the program payouts to realized losses, as well as projected changes in the distribution of extreme weather events. In addition to the increasingly-popular rainfall index, the USDA also piloted another index insurance program for a few years in the mid 2010s in ten selected western states that relied upon measures of biomass growth derived from satellite data aggregated to 8km by 8km grids (called PRF-VI, ‘VI’ indicating the vegetation index). Throughout the four years for which we have data on this pilot, enrollment was relatively limited and consistently declined, culminating in the official cancellation of this pilot program in 2016. Notably, the lackluster enrollment in the PRF-VI was generally attributed to lack of familiarity with the program rather than an underlying flaw in the design of the index (Selasco & Hungerford, 2018).

Especially as familiarity with the principles of the index insurance program have increased but as gaps in coverage across the US for the rainfall index have become more apparent, former administrators of the USDA Risk Management Agency (RMA) as well as other researchers have suggested the time may be ripe to resurrect the vegetation index as an option for producers (Van Orden et al., 2020; Willis, 2019).

The emphasis of this proposal is to examine the extent to which novel approaches to assessing environmental and vegetation conditions could shift the distribution of payouts issued to representative participants in both varieties of the US PRF safety-net programs, including the current rainfall as well as now-defunct vegetation index programs. In effect, this research proposal aims to identify opportunities to enhance policyholder welfare in ways that are consistent with the primary PRF goals of protecting against the dominant natural disaster risk for which the US Crop Insurance Program was first established and the leading peril for which it continues to issue payouts: drought.

Using Stable Isotope Analyses to Understand the Relationship between Urbanization and Wildlife Consumption in the Amazon

College of Natural Resources and Environment

• Willandia Chaves, Fish and Wildlife Conservation

College of Science

• Rachel Reid, Geosciences
• Benjamin Gill, Geosciences

Non-Virginia Tech Personnel

• Babriela Nardoto, University of Brasilia, Brazil
• Christy Mancuso, Center for Stable Isotopes, University of New Mexico

Abstract

The world is urbanizing at an unprecedented rate (4-7). Concomitantly, urban demand for natural resources is predicted to increase in the next decades. These patterns have implications for conservation and people, including increased pressure on biodiversity and worsening food insecurity. Understanding natural resource use in the context of rural-urban transitions is key to supporting the United Nations’ Sustainable Development Goals, especially those related to food security (Goal 2), sustainable cities (Goal 11), and sustainable use of terrestrial ecosystems (Goal 15). We will look at human consumption of wildlife (e.g., mammals, birds, reptiles) for food in the Amazon to understand how urbanization affects natural resource use and how this use relates to food insecurity.

Assessing wildlife consumption accurately through self-reporting remains a major challenge because it is often an illegal activity. Specialized survey techniques were developed to increase accuracy of responses (12-14), but they require large sample sizes and may still present biases (13-15). We propose to test the use of stable isotope analyses as potential unbiased estimates of wildlife consumption. We will combine bulk carbon (δ13C), nitrogen (δ15N), and 1 sulfur (δ34S) isotope analyses with compound-specific C isotope analyses of individual amino acids to maximize accuracy of chemically differentiating the contribution of farmed, wild, aquatic, and terrestrial food sources to human diet.

We will use results of the stable isotope analyses to test the relationship between socioeconomic factors and wildlife consumption. If successful, this technique will represent a major advance for assessing human use of wildlife as food. Bulk analyses have been used to describe human diet (e.g., wild versus farmed). However, this is the first attempt to use bulk (including ẟ34S) and compound-specific analyses to distinguish among wildlife (e.g., fish and other wildlife), and among farmed, wild, terrestrial, and aquatic resources.

Integrated Agro-Economic Modeling to Assess Farmer Uptake of Precision Water and Nutrient Management in a Changing Climate

College of Engineering

• Julie Shortridge, Biological Systems Engineering

College of Agriculture andLife Sciences

• Ryan Stewart, School of Plant and Environmental Sciences
• Wei Zhang, Agricultural and Applied Economics

Abstract

Runoff of agricultural chemicals, including nitrogen (N) and other nutrients, is harmful to water quality as well as farm profitability because growers have to spend more on fertilizer that does not benefit crop growth. Climate change is expected to worsen this problem in the Eastern U.S., as more frequent heavy rainfall (Lall et al. 2018) and increasingly variable rainfall timing (Shortridge 2019) result in greater pollution mobilization and depressed agricultural production. Precision water and nutrient management (PWNM), which optimizes the timing and amount of nutrient and water applications to maximize water and nutrients taken up by the plant, presents a potential “win-win” approach that can reduce agricultural non-point source pollution while also increasing farm profitability (Zurweller et al. 2019; Li et al. 2021). However, PWNM technologies often involve a high upfront cost and annualized benefits that can vary significantly from year to year based on weather conditions, fertilizer costs, and crop prices (USDA 2018). Grower uptake of PWNM has therefore remained stagnant due to perceptions that it is not financially beneficial (Lowenberg-DeBoer and Erickson 2019). There is a critical need for research that can quantify the economic benefits of PWNM across standard technological lifespans (10-15 years) to address barriers to grower adoption.

The objective of this project is to develop an integrated agro-economic simulation model of PWNM adoption using corn production in coastal Virginia as an initial case study. This work will test two hypotheses:  

 1) Relative to conventional water and nutrient management (CWNM), the yield and nitrogen uptake benefits of PWNM are greatest in years with highly variable rainfall (heavy rainfall interspersed with long dry periods).  

2) PWNM is more likely to be adopted when interannual yield variation is larger and when forecasted prices of crop and fertilizers are higher.

Coupling Social Science and Watershed Modeling to Improve Ecological Health of Streams in Agricultural Landscapes

College of Natural Resources and Environment

• Paul Angermeier, Fish and Wildlife Conservation
• Ashley Dayer, Fish and Wildlife Conservation

College of Engineering

• Jon Czuba, Biological Systems Engineering

Abstract

Declining stream health due to agricultural pollution is a major global issue requiring interdisciplinary solutions. Agricultural pollution (e.g., sediment) adversely affects biodiversity and ecosystem services — key components of stream health. Farmers often partner with government agencies through cost-share agreements to apply best management practices (BMPs) to improve stream health while continuing  food production. Although approximately 65,000 BMPs have been applied in the Powell, Clinch and Holston watersheds of southwest VA, water quality goals in many locations remain unmet. These shortfalls can arise from varying BMP efficacy due to interactions among watershed features, BMP types and locations, and ecological responses along hydrological flow paths. Biophysical processes are also influenced by social processes. Most research on BMP efficacy focuses on ecological or hydrological — not social — aspects. However, because farmers participate in BMP programs voluntarily, social science is crucial to understanding efficacy at watershed scales. Social science contributes to conservation through justifying actions, improving management practices, and helping achieve ecological outcomes. Our team will integrate social science, watershed modeling, and ecology to develop and test a novel model to explain BMP efficacy. We will survey landowners to achieve three objectives complementing our current work: 1) assess persistence, 2) determine landowner attributes influencing persistence, and 3) compare effects of watershed features, water quality, and BMP implementation on stream health.

Measuring, modeling, and forecasting red tide aerosol dispersion along the central Florida gulf coast to facilitate socio-economic adaptation

College of Agriculture and Life Sciences

• Klaus Moeltner, Agriculture and Applied Economics
• David Schmale III, School of Plant and Environmental Sciences

College of Engineering

• Hoesin Foroutan, Civil and Environmental Engineering
• Shane Ross, Aerospace and Ocean Engineering
  

Abstract

Blooms of the marine dinoflagellate Karenia brevis, generally referred to as "Red tides" (RT), have increased in frequency, intensity, and geographic spread along the Florida gulf coast in recent years. Karenia brevis produces a suite of neurotoxins known as brevetoxins that can result in massive mortalities to fish, birds, and marine mammals. Brevetoxins may also become airborne and travel great distances. In humans, these toxins can produce severe and lasting respiratory irritations. In 2017-2018, the central Florida gulf coast experienced the worst and longest RT episode in decades. This episode has brought issues of mitigation and adaptation to the forefront of scientific and political discussion. The primary objective of this project is to develop a fine spatial scale forecasting system for brevetoxins for the central gulf area that covers a broad coastal band. It will use real-time wind, aerosol concentration, and respiratory effects data, as well as weather forecasts to predict ambient brevetoxin concentrations, for up to 24 hours in advance. To develop, validate, and optimize an RT emissions and transport forecast model, we will couple these environmental data, collected via multiple Unmanned Aerial Systems (UASs) with high-fidelity multiscale atmospheric transport models and other data-driven modeling techniques. The second objective is to determine the socio-economic benefits of such an improved forecasting system to the general population. We will hold on-site focus groups to determine how RT irritation affects local households’ daily life, and how improved forecasting would help them better prepare for these impacts. We will also implement a pilot household survey using choice experiments to determine local citizens’ preferences and willingness-to-pay for improved RT forecasts. The third objective for this project is to foster ties between VT and the Mote Marine Lab in Florida, the premier clearinghouse for RT research in the gulf area.

Climate change and the dynamics of mosquito populations in Virginia

College of Agriculture and Life Sciences

• Chloe Lahondere (project lead), Biochemistry
• Clement Vinauger, Biochemistry

College of Natural Resources and Environment

• Luis Escobar, Fish and Wildlife Conservation

College of Liberal Arts and Human Sciences

• Lydia Patton, Philosophy

Abstract

According to the World Health Organization, mosquito bites result in the death of more than one million people per year due to the diseases they transmit. In Virginia, every summer, thousands of people complain to their local and state government officials about the mosquito problems they encounter. Physical parameters such as temperature, precipitation, and time of the day and year directly affect the biology of mosquitoes and it is the complex interplay of these factors that determines the overall effect of climate on local mosquito populations. Considering ongoing climate change, frequent dramatic weather events, and the ability of mosquito species to invade new areas, it is essential to define the link between climatic changes and the response of mosquito populations. However, despite obvious epidemiological consequences, this link remains understudied in Virginia. Furthermore, only 49% of Virginians think climate change is currently harming or will harm people in Virginia within the next 10 years, suggesting that citizens do not link the climate with vector-borne diseases. We propose to determine the yearly spatial and temporal dynamics of mosquito populations, forecast the potential effects of climate change on these dynamics, and leverage citizen participation to monitor the perception of mosquito nuisance. Our hypothesis is that increases in temperature linked with climate change will affect mosquito population dynamics in Virginia, and that these data can be used to sensitize citizens to potential negative consequences of climate change. We will rely on citizen surveys, mosquito trapping, satellite-derived data and advanced ecological niche modeling to accomplish the project objectives.

A synthetic population approach to modeling human health and the environment: A tool for adaptation planning

Virginia-Maryland College of Veterinary Medicine

• Julia Gohlke (project lead), Population Health Sciences

College of Architecture and Urban Studies

• Todd Schenk, Urban Affairs and Planning

College of Science

• Shyam Ranganathan
• Eric Smith
  

Biocomplexity Institute

• Samarth Swarup

Abstract

While the nexus of public health and climate change is critically important, it is not yet well understood. The fundamental questions we aim to address are: What are the interacting climate-related factors within the built, social, and natural environments that precipitate adverse health outcomes? How can we increase our understanding of these interactions, and devise policy and planning recommendations that respond to them? Answers to these questions are required to create effective adaptation strategies. We aim to develop a novel analytical method leveraging geocoded birth and death records and spatially-resolved environmental datasets. The proposed method uses simulations from a synthetic population model of movements of individuals through space and time to estimate exposure to built, social, and natural environmental factors. Outputs from the synthetic population are then used within a multi-level statistical model to test hypotheses on the associations between health outcomes and environmental conditions.

Coupled social and ecological dynamics of backyard bird feeding

College of Science

• Dana Hawley, Biological Sciences
  

College of Natural Resources and Environment

• Ashley Dayer, Fish and Wildlife Conservation

External Collaborators: David Bonter, Wesley Hochacka, Tina Phillips, Cornell Laboratory of Ornithology; Richard Hall, School of Ecology, University of Georgia

Abstract

Backyard feeding of birds is arguably one of the most widespread and global forms of direct human-wildlife interaction. However, despite the enormous and growing popularity of bird-feeding, the ecological impacts of this unprecedented spatial scale of wildlife provisioning remain largely unstudied. Moreover, although psychological benefits of bird-feeding have been documented, we lack a clear understanding of how the extent of songbird provisioning by humans is influenced by the perceived or observed ecological impacts of provisioning. Because many of the potential ecological impacts of bird-feeding, whether “positive“ (i.e., increases in bird abundance) or “negative” (i.e., increases in disease or predation), are directly visible to backyard bird-watchers, there is likely to be strong coupling between human decisions about feeding birds and the perceived or observed effects of provisioning on songbird populations. Here we leverage the enormous popularity of bird-feeding to examine both the social and natural dynamics of bird-feeding, and the way in which human and natural systems are coupled. In particular, we ask 1) What factors motivate human food-provisioning behavior, and is provisioning behavior coupled with bird abundance or the prevalence of natural enemies (disease and predators)?; and 2) How do the abundance of birds and their natural enemies change as a function of supplemental food provisioning by humans? To address these questions, we combine online social survey responses from birdwatchers, analysis of large-scale ecological data collected by citizen scientists watching backyard feeders and intensive local field studies that manipulate provisioning and examine ecological impacts.

How does current management of water quality align with ecological health and human well-being? A preliminary study of Virginia

College of Natural Resoures and Environment

• Paul Angermeier (project lead), Fish and Wildlife Conservation
• Marc Stern, Forest Resources and Environmental Conservation

College of Engineering

• Leigh-Anne Krometis, Biological Systems Engineering

Abstract

Water quality (WQ) management directly affects ecosystem health and services, economic growth, and human wellbeing (HWB). However, linkages and conflicts among these concurrent goals are poorly understood, typically not quantified, and not necessarily explicit in discussions of WQ policy. Because WQ management is an integrative, socially negotiated process, the knowledge needed to guide it must be developed through social and ecological lenses.  We hypothesize that reducing discordance among stakeholders and increasing transparency in decision-making will both increase public support for and engagement in surface water management, and more completely realize WQ goals. The overall aim of the proposed effort is to explicitly identify potential sources of discord and synergistic opportunities for cooperation in order to develop strategies to better re-align WQ values of local communities with formal management by public agencies. The proposed work will simultaneously build proof-of-concept analytical strategies that can be broadly applied across the US, identify key regions or socio-cultural groups on which to focus future research, and provide preliminary data necessary for conducting a broader co-orientation study aimed at examining concordance in beliefs between WQ experts and local citizens in terms of WQ goals, conditions, and practices. Ultimately, these analyses can guide WQ managers to address shortcomings in public knowledge, communication, and cooperation in order to more efficiently and equitably meet WQ goals.

How does environmental landscape change shape community and ecological health in the Central Appalachian Coalfields?: A pilot study in Tazewell County, Virginia

College of Engineering

• Leigh-Anne Krometis (project lead), Biological Systems Engineering
• Linsey Marr, Civil and Environmental Engineering

• David Cline, History
• Emily Satterwhite, Religion and Culture

• Korine Kolivras, Geography

• Julia Gohlke, Population Health Sciences
• Susan Marmagas, Population Health Sciences