W271 Improving Switchgrass Yields for Bioenergy Production

Version 3.

Estimating the Impacts of Storage Dry Matter Losses on Switchgrass Production

This poster estimates dry matter losses as a function of harvest method, storage treatment, and time in storage. We then calculate the cost to store switchgrass bales under alternate harvest method and storage treatment scenarios; and determine the breakeven harvest method and storage treatment as a function of biomass price and time in storage.Biomass, bioenergy crops, function form, sustainable systems, Farm Management, Production Economics, Q10, Q42,

Improving Switchgrass Yields for Bioenergy Production

Version 3.

Economic Analysis of the Effects of Winter Cover Crops on No-Tillage Corn Yield Response to Fertilizer Nitrogen

Crop Production/Industries,

VARIABLE RATE NITROGEN APPLICATION ON CORN FIELDS: THE ROLE OF SPATIAL VARIABILITY AND WEATHER

Meta-response functions for corn yields and nitrogen losses were estimated from EPIC-generated data for three soil types and three weather scenarios. These metamodels were used to evaluate variable rate (VRT) versus uniform rate (URT) nitrogen application technologies for alternative weather scenarios and policy option. Except under very dry conditions, returns per acre for VRT were higher than for URT and the economic advantage of VRT increased as realized rainfall decreased from expected average rainfall. Nitrogen losses to the environment from VRT were lower for all situation examined, except on fields with little spatial variability.Corn, environment, meta-response functions, nitrogen restriction, precision farming, site-specific management, spatial variability, weather variability, Crop Production/Industries,

Is Switchgrass Yield Response to Nitrogen Fertilizer Dynamic? Implications for Profitability and Sustainability at the Farm Level

Revised version of the paper submitted 2/11/2010Biomass, Energy Crops, Sequential Inputs, West Tennessee, Crop Production/Industries, Farm Management, Production Economics, Resource /Energy Economics and Policy,

Switchgrass Production in Marginal Environments: A Comparative Economic Analysis across Four West Tennessee Landscapes

Switchgrass (Panicum virgatum L.) has been identified as a model feedstock for the emerging biofuels industry. Its selection was based, in part, upon the observation that switchgrass can produce high yields in marginal production environments. This trait may become particularly valuable in coming years, as renewable fuel mandates begin to take effect and concerns over the food-versus-fuel debate increase. Relatively little research information exists about how management practices and production costs vary across different production environments. The objectives of this research were (a) to compare switchgrass yields as influenced by seeding rate and nitrogen fertilization rates in low-, intermediate-, and high-yielding switchgrass production environments, (b) to determine the economically optimal seeding rate and nitrogen fertilization rate for each environment, and (c) to calculate per-ton production costs. Experimental yield data from four locations were utilized for this study. Plots were seeded in 2004 with treatments of 2.5, 5.0, 7.5, 10.0, and 12.5 lbs/acre. Nitrogen was applied in subsequent intervals at 0, 60, 120 and 180 lbs/acre. For an expected stand lifespan of 10 years, production costs ranged from $45 per ton in a well drained level upland environment ideal for the production of row crops to$70 per ton in a marginal, poorly drained flood plain in which the switchgrass stand was slow to establish and which demonstrated lower overall yields.Crop Production/Industries, International Relations/Trade,

Learning from Power Signals: An Automated Approach to Electrical Disturbance Identification Within a Power Transmission System

As power quality becomes a higher priority in the electric utility industry, the amount of disturbance event data continues to grow. Utilities do not have the required personnel to analyze each event by hand. This work presents an automated approach for analyzing power quality events recorded by digital fault recorders and power quality monitors operating within a power transmission system. The automated approach leverages rule-based analytics to examine the time and frequency domain characteristics of the voltage and current signals. Customizable thresholds are set to categorize each disturbance event. The events analyzed within this work include various faults, motor starting, and incipient instrument transformer failure. Analytics for fourteen different event types have been developed. The analytics were tested on 160 signal files and yielded an accuracy of ninety-nine percent. Continuous, nominal signal data analysis is performed using an approach coined as the cyclic histogram. The cyclic histogram process will be integrated into the digital fault recorders themselves to facilitate the detection of subtle signal variations that are too small to trigger a disturbance event and that can occur over hours or days. In addition to reducing memory requirements by a factor of 320, it is anticipated that cyclic histogram processing will aid in identifying incipient events and identifiers. This project is expected to save engineers time by automating the classification of disturbance events and increase the reliability of the transmission system by providing near real time detection and identification of disturbances as well as prevention of problems before they occur.Comment: 18 page

Inhibition of adenovirus serotype 14 infection by octadecyloxyethyl esters of (S)-[(3-hydroxy-2-phosphonomethoxy)propyl]- nucleosides in vitro.

On September 22, 2008, a physician on Prince of Wales Island, Alaska, notified the Alaska Department of Health and Social Services (ADHSS) of an unusually high number of adult patients with recently diagnosed pneumonia (n = 10), including three persons who required hospitalization and one who died. ADHSS and CDC conducted an investigation to determine the cause and distribution of the outbreak, identify risk factors for hospitalization, and implement control measures. This report summarizes the results of that investigation, which found that the outbreak was caused by adenovirus 14 (Ad14), an emerging adenovirus serotype in the United States that is associated with a higher rate of severe illness compared with other adenoviruses. Among the 46 cases identified in the outbreak from September 1 through October 27, 2008, the most frequently observed characteristics included the following: male (70%), Alaska Native (61%), underlying pulmonary disease (44%), aged > or = 65 years (26%), and current smoker (48%). Patients aged > or = 65 years had a fivefold increased risk for hospitalization. The most commonly reported symptoms were cough (100%), shortness of breath (87%), and fever (74%). Of the 11 hospitalized patients, three required intensive care, and one required mechanical ventilation. One death was reported. Ad14 isolates obtained during the outbreak were identical genetically to those in recent community-acquired outbreaks in the United States which suggests the emergence of a new, and possibly more virulent Ad14 variant. Clinicians should consider Ad14 infection in the differential diagnosis for patients with community-acquired pneumonia, particularly when unexplained clusters of severe respiratory infections are detected

ANALYSIS OF SWITCHGRASS CHARACTERISTICS USING NEAR INFRARED SPECTROSCOPY

Switchgrass varieties grown under various environments were investi-gated by dispersive and Fourier Transform Near-Infrared (NIR) spectro-meters. The collected NIR spectra were analyzed using multivariate approaches. More specifically, principal component analysis (PCA) and projection to latent structures (PLS) regression techniques were employed to classify and predict characteristics of the switchgrass samples. The multivariate results were compared to reflectance indices that are commonly used to study the physiological performance of plants. From near infrared spectra, discrimination between the two growth locations was successfully achieved by PCA. Separation based on the ecotype and the rate of fertilizer applied to the field was also possible by the multivariable analysis of the spectral data. For the classification/ discrimination of the switchgrass samples, the near infrared spectra collected by the dispersive and the Fourier Transform spectrometers provided similar results. From the two near infrared data sets robust models were developed to predict non-structural carbohydrates content and the rate of nitrogen applied to the field. However, the spectra collected by the dispersive spectrometer resulted in more accurate models for these samples