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

The use of plants in the traditional management of diabetes in Nigeria: Pharmacological and toxicological considerations

Ethnopharmacological relevance: The prevalence of diabetes is on a steady increase worldwide and it is now identified as one of the main threats to human health in the 21st century. In Nigeria, the use of herbal medicine alone or alongside prescription drugs for its management is quite common. We hereby carry out a review of medicinal plants traditionally used for diabetes management in Nigeria. Based on the available evidence on the species׳ pharmacology and safety, we highlight ways in which their therapeutic potential can be properly harnessed for possible integration into the country׳s healthcare system. Materials and methods: Ethnobotanical information was obtained from a literature search of electronic databases such as Google Scholar, Pubmed and Scopus up to 2013 for publications on medicinal plants used in diabetes management, in which the place of use and/or sample collection was identified as Nigeria. ‘Diabetes’ and ‘Nigeria’ were used as keywords for the primary searches; and then ‘Plant name – accepted or synonyms’, ‘Constituents’, ‘Drug interaction’ and/or ‘Toxicity’ for the secondary searches. Results: The hypoglycemic effect of over a hundred out of the 115 plants reviewed in this paper is backed by preclinical experimental evidence, either in vivo or in vitro. One-third of the plants have been studied for their mechanism of action, while isolation of the bioactive constituent(s) has been accomplished for twenty three plants. Some plants showed specific organ toxicity, mostly nephrotoxic or hepatotoxic, with direct effects on the levels of some liver function enzymes. Twenty eight plants have been identified as in vitro modulators of P-glycoprotein and/or one or more of the cytochrome P450 enzymes, while eleven plants altered the levels of phase 2 metabolic enzymes, chiefly glutathione, with the potential to alter the pharmacokinetics of co-administered drugs. Conclusion: This review, therefore, provides a useful resource to enable a thorough assessment of the profile of plants used in diabetes management so as to ensure a more rational use. By anticipating potential toxicities or possible herb–drug interactions, significant risks which would otherwise represent a burden on the country׳s healthcare system can be avoided

Avoiding Conflicts between Future Freshwater Algae Production and Water Scarcity in the United States at the Energy-Water Nexus

Sustainable production of algae will depend on understanding trade-offs at the energy-water nexus. Algal biofuels promise to improve the environmental sustainability profile of renewable energy along most dimensions. In this assessment of potential US freshwater production, we assumed sustainable production along the carbon dimension by simulating placement of open ponds away from high-carbon-stock lands (forest, grassland, and wetland) and near sources of waste CO 2 . Along the water dimension, we quantified trade-offs between water scarcity and production for an ‘upstream’ indicator (measuring minimum water supply) and a ‘downstream’ indicator (measuring impacts on rivers). For the upstream indicator, we developed a visualization tool to evaluate algae production for different thresholds for water surplus. We hypothesized that maintaining a minimum seasonal water surplus would also protect river habitat for aquatic biota. Our study confirmed that ensuring surplus water also reduced the duration of low-flow events, but only above a threshold. We also observed a trade-off between algal production and the duration of low-flow events in streams. These results can help to guide the choice of basin-specific sustainability targets to avoid conflicts with competing water users at this energy-water nexus. Where conflicts emerge, alternative water sources or enclosed photobioreactors may be needed for algae cultivation

Exploring Potential U.S. Switchgrass Production for Lignocellulosic Ethanol

In response to concerns about oil dependency and the contributions of fossil fuel use to climatic change, the U.S. Department of Energy has begun a research initiative to make 20% of motor fuels biofuel based in 10 years, and make 30% of fuels bio-based by 2030. Fundamental to this objective is developing an understanding of feedstock dynamics of crops suitable for cellulosic ethanol production. This report focuses on switchgrass, reviewing the existing literature from field trials across the United States, and compiling it for the first time into a single database. Data available from the literature included cultivar and crop management information, and location of the field trial. For each location we determined latitude and longitude, and used this information to add temperature and precipitation records from the nearest weather station. Within this broad database we were able to identify the major sources of variation in biomass yield, and to characterize dry matter yield as a function of some of the more influential factors, e.g., stand age, ecotype, precipitation and temperature in the year of harvest, site latitude, and fertilization regime. We then used a modeling approach, based chiefly on climatic factors and ecotype, to predict potential dry matter yields for a given temperature and weather pattern (based on 95th percentile response curves), assuming the choice of optimal cultivars and harvest schedules. For upland ecotype varieties, potential yields were as high as 18 to 20 Mg dry mass/ha, given ideal growing conditions, whereas yields in lowland ecotype varieties could reach 23 to 27 Mg/ha. The predictive equations were used to produce maps of potential yield across the continental United States, based on precipitation and temperature in the long term climate record, using the Parameter-elevation Regressions on Independent Slopes Model (PRISM) in a Geographic Information System (GIS). Potential yields calculated via this characterization were subsequently compared to the Oak Ridge Energy Crop County Level data base (ORECCL), which was created at Oak Ridge National Laboratory (Graham et al. 1996) to predict biofuel crop yields at the county level within a limited geographic area. Mapped output using the model was relatively consistent with known switchgrass distribution. It correctly showed higher yields for lowland switchgrass when compared with upland varieties at most locations. Projections for the most northern parts of the range suggest comparable yields for the two ecotypes, but because there were few field trials growing lowland ecotypes at high latitudes it is difficult to fully assess that projection. The final model is a predictor of optimal dry matter yields for a given climate scenario, but does not attempt to identify or account for other limiting or interacting factors. The statistical model is nevertheless an improvement over historical efforts, in that it is based on quantifiable climatic differences, and it can be used to extrapolate beyond the historic range of switchgrass. Additional refinement of the current statistical model, or the use of a different empirical or process-based model, might improve the prediction of switchgrass yields with respect to climate and interactions with cultivar and management practices, assisting growers in choosing high-yielding cultivars within the context of local environmental growing conditions

Do beaver ponds increase methane emissions along Arctic tundra streams?

Beaver engineering in the Arctic tundra induces hydrologic and geomorphic changes that are favorable to methane (CH _4 ) production. Beaver-mediated methane emissions are driven by inundation of existing vegetation, conversion from lotic to lentic systems, accumulation of organic rich sediments, elevated water tables, anaerobic conditions, and thawing permafrost. Ground-based measurements of CH _4 emissions from beaver ponds in permafrost landscapes are scarce, but hyperspectral remote sensing data (AVIRIS-NG) permit mapping of ‘hotspots’ thought to represent locations of high CH _4 emission. We surveyed a 429.5 km ^2 area in Northwestern Alaska using hyperspectral airborne imaging spectroscopy at ∼5 m pixel resolution (14.7 million observations) to examine spatial relationships between CH _4 hotspots and 118 beaver ponds. AVIRIS-NG CH _4 hotspots covered 0.539% (2.3 km ^2 ) of the study area, and were concentrated within 30 m of waterbodies. Comparing beaver ponds to all non-beaver waterbodies (including waterbodies >450 m from beaver-affected water), we found significantly greater CH _4 hotspot occurrences around beaver ponds, extending to a distance of 60 m. We found a 51% greater CH _4 hotspot occurrence ratio around beaver ponds relative to nearby non-beaver waterbodies. Dammed lake outlets showed no significant differences in CH _4 hotspot ratios compared to non-beaver lakes, likely due to little change in inundation extent. The enhancement in AVIRIS-NG CH _4 hotspots adjacent to beaver ponds is an example of a new disturbance regime, wrought by an ecosystem engineer, accelerating the effects of climate change in the Arctic. As beavers continue to expand into the Arctic and reshape lowland ecosystems, we expect continued wetland creation, permafrost thaw and alteration of the Arctic carbon cycle, as well as myriad physical and biological changes

Perceived barriers and advances in integrating earth observations with water resources modeling

Advances in computing, collection, and sharing of Earth Observations (EOs) have significantly improved the potential for integrating EO and water resources models. Inadequate observational data for the systems simulated have been a persistent limitation in developing robust water resources models. Although various EO datasets have been available for decades, they have been under-utilized for water resources modeling. This can be due to sensor and product limitations, including spatial, spectral, and temporal resolutions, and the reluctance of the water resources community to adopt the state-of-art quickly. Motivated by the dual agenda of engaging the water resources community on various aspects of integrating EOs with water resources modeling and understanding the likely factors that limit a deeper integration of EOs in water resources management, we investigated the communities' perception of water resources modeling and EO integration. This paper summarizes the findings of a web-based survey conducted at the annual ASCE-EWRI (American Society of Civil Engineers-Environmental Water Resources Institute) International Water Congress in 2022. The analysis of responses (n = 74) identified limited spatial resolution, atmospheric and cloud interference, and lack of in-situ validation data as the highest perceived barriers to integrating EOs with water resources modeling and management. Perceptions among different groups of participants and even within the groups were different. For example, the perceived barriers often differed between researchers and non-researchers (e.g., policymakers and practitioners). There were differences in perception among the remote-sensing and water resources researchers within the research community. Even among water resources communities, disparities existed between the perceptions of respondents who also identified as knowledgeable about remote sensing and those who didn't. These observations highlighted the need to intentionally develop a convergent group and domain to integrate the disciplines involved and capitalize on the advancements that have improved the EO for water resources management