166 research outputs found
Candidate Perennial Bioenergy Grasses have a Higher Albedo than Annual Row Crops
The production of perennial cellulosic feedstocks for bioenergy presents the potential to diversify regional economies and the national energy supply, while also serving as climate ‘regulators’ due to a number of biogeochemical and biogeophysical differences relative to row crops. Numerous observational and model-based approaches have investigated biogeochemical trade-offs, such as increased carbon sequestration and increased water use, associated with growing cellulosic feedstocks. A less understood aspect is the biogeophysical changes associated with the difference in albedo (a), which could alter the local energy balance and cause local to regional cooling several times larger than that associated with offsetting carbon. Here, we established paired fields of Miscanthus 9 giganteus (miscanthus) and Panicum virgatum (switchgrass), two of the leading perennial cellulosic feedstock candidates, and traditional annual row crops in the highly productive ‘Corn-belt’. Our results show that miscanthus did and switchgrass did not have an overall higher a than current row crops, but a strong seasonal pattern existed. Both perennials had consistently higher growing season a than row crops and winter a did not differ. The lack of observed differences in winter a, however, masked an interaction between snow cover and species differences, with the perennial species, compared with the row crops, having a higher a when snow was absent and a much lower a when snow was present. Overall, these changes resulted in an average net reduction in annual absorbed energy of about 5 W m -2 for switchgrass and about 8 W m -2 for miscanthus relative to annual crops. Therefore, the conversion from annual row to perennial crops alters the radiative balance of the surface via changes in a and could lead to regional cooling
Chlorophyll can be reduced in crop canopies with little penalty to photosynthesis
The hypothesis that reducing chlorophyll content (Chl) can increase canopy photosynthesis in soybeans was tested using an advanced model of canopy photosynthesis. The relationship between leaf Chl, leaf optical properties, and photosynthetic biochemical capacity were measured in 67 soybean accessions showing large variation in leaf Chl. These relationships were integrated into a biophysical model of canopy-scale photosynthesis to simulate the intercanopy light environment and carbon assimilation capacity of canopies with WT, a Chl-deficient mutant (Y11y11), and 67 other mutants spanning the extremes of Chl to quantify the impact of variation in leaf-level Chl on canopy-scale photosynthetic assimilation and identify possible opportunities for improving canopy photosynthesis through Chl reduction. These simulations demonstrate that canopy photosynthesis should not increase with Chl reduction due to increases in leaf reflectance and non-optimal distribution of canopy nitrogen. However, similar rates of canopy photosynthesis can be maintained with a 9% savings in leaf nitrogen resulting from decreased Chl. Additionally, analysis of these simulations indicate that the inability of Chl reductions to increase photosynthesis arises primarily from the connection between Chl and leaf reflectance and secondarily from the mismatch between the vertical distribution of leaf nitrogen and the light absorption profile. These simulations suggest that future work should explore the possibility of using reduced Chl to improve canopy performance by adapting the distribution of the saved nitrogen within the canopy to take greater advantage of the more deeply penetrating light
A new discontinuity adaptive method applied to electromagnetic inverse scattering problem in 3D
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A realistic meteorological assessment of perennial biofuel crop deployment: a Southern Great Plains perspective
Utility of perennial bioenergy crops (e.g., switchgrass and miscanthus) offers unique opportunities to transition
toward a more sustainable energy pathway due to their reduced carbon footprint, averted competition with food
crops, and ability to grow on abandoned and degraded farmlands. Studies that have examined biogeophysical
impacts of these crops noted a positive feedback between near-surface cooling and enhanced evapotranspiration
(ET), but also potential unintended consequences of soil moisture and groundwater depletion. To better understand
hydrometeorological effects of perennial bioenergy crop expansion, this study conducted high-resolution
(2-km grid spacing) simulations with a state-of-the-art atmospheric model (Weather Research and Forecasting
system) dynamically coupled to a land surface model. We applied the modeling system over the Southern Plains
of the United States during a normal precipitation year (2007) and a drought year (2011). By focusing the
deployment of bioenergy cropping systems on marginal and abandoned farmland areas (to reduce the potential
conflict with food systems), the research presented here is the first realistic examination of hydrometeorological
impacts associated with perennial bioenergy crop expansion. Our results illustrate that the deployment of perennial
bioenergy crops leads to widespread cooling (1–2 °C) that is largely driven by an enhanced reflection of
shortwave radiation and, secondarily, due to an enhanced ET. Bioenergy crop deployment was shown to reduce
the impacts of drought through simultaneous moistening and cooling of the near-surface environment. However,
simulated impacts on near-surface cooling and ET were reduced during the drought relative to a normal
precipitation year, revealing differential effects based on background environmental conditions. This study
serves as a key step toward the assessment of hydroclimatic sustainability associated with perennial bioenergy
crop expansion under diverse hydrometeorological conditions by highlighting the driving mechanisms and processes
associated with this energy pathway.This work was funded by NSF Grant EAR-1204774S
SMOS Optical Thickness Changes in Response to the Growth and Development of Crops, Crop Management, and Weather
The Soil Moisture and Ocean Salinity (SMOS) remote sensing satellite was launched by the European Space Agency in 2009. The L-band brightness temperature observed by SMOS has been used to produce estimates of both soil moisture and τ, the optical thickness of the land surface. Although τ should theoretically be proportional to the amount of vegetation present within a SMOS pixel, several initial investigations have not been able to confirm this expected behavior. However, when the noise in the SMOS τ product is removed, τ in the U.S. Corn Belt, a region of extensive row-crop agriculture, has a distinct shape that mirrors the growth and development of crops. We find that the peak value of SMOS τ occurs at approximately 1000 °C day (base 10 °C) growing degree days after the mean planting date of maize (corn). We can explain this finding in the following way: τ is directly proportional to the water column density of vegetation; maize contributes the most to growing season changes in τ in the Corn Belt; and maize reaches its maximum water column density at its third reproductive stage of development, at about 1000 °C day growing degree days. Consequently, SMOS τ could be used to monitor the phenology of crops in the Corn Belt at a spatial resolution similar to a U.S. county and a temporal frequency on the order of days. We also examined the magnitude of the change in SMOS τ over the growing season and hypothesized it would be related to the amount of accumulated solar radiation, but found this not to be the case. On the other hand, the change in magnitude was smallest for the year in which the most precipitation fell. These findings are rational since SMOS τ at the satellite scale is in fact a function of both vegetation and soil surface roughness, and soil surface roughness is reduced by precipitation. To fully explain changes in SMOS τ in the Corn Belt it appears that it will be necessary to use in situ and remotely-sensed observations along with agro-ecosystem models to account for land management decisions made by farmers that affect changes in soil surface roughness and all of the relevant biophysical processes that affect the growth and development of crops
Assessing the Potential to Decrease the Gulf of Mexico Hypoxic Zone with Midwest US Perennial Cellulosic Feedstock Production
The goal of this research was to determine the changes in streamflow, dissolved inorganic nitrogen (DIN) leaching and export to the Gulf of Mexico associated with a range of large-scale dedicated perennial cellulosic bioenergy production scenarios within in the Mississippi–Atchafalaya River Basin (MARB). To achieve this goal, we used Agro-IBIS, a vegetation model capable of simulating the biogeochemistry of row crops, miscanthus and switchgrass, coupled with THMB, a hydrology model capable of simulating streamflow and DIN export. Simulations were conducted at varying fertilizer application rates (0–200 kg N ha -1) and fractional replacement (5–25%) of current row crops with miscanthus or switchgrass across the MARB. The analysis also includes two scenarios where miscanthus and switchgrass (MRX and MRS, respectively) each replace the ca. 40% of maize production currently devoted to ethanol. Across the scenarios, there were minor reductions in runoff and streamflow throughout the MARB, with the largest differences (ca. 6%) occurring for miscanthus at the highest fractional replacement scenarios in drier portions of the region. However, differences in total MARB discharge at the basin outlet were less than 1.5% even in the MRX scenario. Reductions in DIN export were much larger on a percentage basis than reductions in runoff, with the highest replacement scenarios decreasing long-term mean DIN export by ca. 15% and 20% for switchgrass and miscanthus, respectively. Fertilization scenarios show that significant reductions in DIN leaching are possible even with application rates of 100 and 150 kg N ha -1 for switchgrass and miscanthus, respectively. These results indicate that, given targeted management strategies, there is potential for miscanthus and switchgrass to provide key ecosystem services by reducing the export of DIN, while avoiding hydrologic impacts of reduced streamflow
The economic and environmental costs and benefits of the renewable fuel standard
Mandates, like the renewable fuel standard (RFS), for biofuels from corn and cellulosic feedstocks, impact the environment in multiple ways by affecting land use, nitrogen (N)-leakage, and greenhouse gas (GHG) emissions. We analyze the differing trade-offs these different types of biofuels offer among these multi-dimensional environmental effects and convert them to a monetized value of environmental damages (or benefits) that can be compared with the economic costs of extending these mandates over the 2016–2030 period. The discounted values of cumulative net benefits (or costs) are then compared to those with a counterfactual level of biofuels that would have been produced in the absence of the RFS over this period. We find that maintaining the corn ethanol mandate at 56 billion l till 2030 will lead to a discounted cumulative value of an economic cost of 109 billion of economic costs and 69 billion which will be partly offset by the net discounted monetized value of environmental benefits of 49 billion over the 2016–2030 period. We explore the sensitivity of these net (economic and environmental) costs to alternative values of the social costs of carbon and nitrogen and other technological and market parameters. We find that, unlike corn ethanol, cellulosic biofuels can result in positive net benefits if the monetary benefits of GHG mitigation are valued high and those of N-damages are not very high
On the Long-Term Hydroclimatic Sustainability of Perennial Bioenergy Crop Expansion over the United States
Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switchgrass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. The hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States are quantified using the Weather Research and Forecasting Model dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted using seasonally evolving biophysical representation of perennial bioenergy cropping systems within the LSM based on observational data. Deployment is carried out only over suitable abandoned and degraded farmlands to avoid competition with existing food cropping systems. Results show that near-surface cooling (locally, up to 5°C) is greatest during the growing season over portions of the central United States. For some regions, principal impacts are restricted to a reduction in near-surface temperature (e.g., eastern portions of the United States), whereas for other regions deployment leads to soil moisture reduction in excess of 0.15–0.2 m3 m−3 during the simulated 10-yr period (e.g., western Great Plains). This reduction (~25%–30% of available soil moisture) manifests as a progressively decreasing trend over time. The large-scale focus of this research demonstrates the long-term hydroclimatic sustainability of large-scale deployment of perennial bioenergy crops across the continental United States, revealing potential hot spots of suitable deployment and regions to avoid
Forecasting yields and in-season crop-water nitrogen needs using simulation models
Forecasting crop yields and water-nitrogen dynamics during the growing cycle of the crops can greatly advance in-season decision making processes. To date, forecasting approaches include the use of statistical or mechanistic simulation models, aerial images, or combinations of these to make the predictions. Different approaches and models have different capabilities, strengths, and limitations. System-level mechanistic simulation models (crop and soil models together) usually offer more prediction and explanatory power at the cost of extensive input data. In contrast, statistical approaches or aerial images can be more robust than mechanistic models but their applicability and prediction/explanatory power is limited. The combination of these technologies is viewed as a very promising tool to assist Midwestern agriculture, but in general, all of these technologies are in their initial stages of implementation and more time is needed to prove their potential. Here we present results from a pilot project that aimed to forecast weather, soil water-nitrogen status, crop water-nitrogen demand, and end-of-season crop yields in Iowa using two process-based mechanistic simulation models
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