The Impact of Reducing Greenhouse Gas Emissions in Crop Agriculture: A Spatial- and Production-Level Analysis

With the Waxman-Markey Bill passing the House and the administration’s push to reduce carbon emissions, the likelihood of the implementation of some form of a carbon emissions policy is increasing. This study estimates the greenhouse gas (GHG) emissions of the six largest row crops produced in Arkansas using 57 different production practices predominantly used and documented by the University of Arkansas Cooperative Extension Service. From these GHG emission estimates, a baseline state “carbon footprint†was estimated and a hypothetical GHG emissions reduction of 5, 10, and 20 percent was levied on Arkansas agriculture using a cap-and-trade method. Using current production technology and traditional land use choices, results show that the trading of carbon-emitting permits to reduce statewide GHG emissions by 5 percent from the baseline would enhance GHG emissions efficiency measured as net crop farm income generated per unit of carbon emissions created. The 5 percent reduction in GHG emissions does cause marginal reductions in acres farmed and has marginal income ramifications. Beyond the 5 percent reduction target, gains in GHG emissions efficiency decline but remain positive in most counties through the 10 percent GHG reduction target. However, with a 10 percent GHG reduction, acreage and income reductions more than double compared to the 5 percent level. When GHG emissions are reduced by 20 percent from the baseline, the result is a major cropping pattern shift coupled with significant reductions in traditional row crop acreage, income, and GHG emissions efficiency.greenhouse gas emissions, carbon equivalents, sustainability, cap and trade, Environmental Economics and Policy, Resource /Energy Economics and Policy,

How A Cap-and-Trade Policy of Green House Gases Could Alter the Face of Agriculture in the South: A Spatial and Production Level Analysis.

With the Waxman-Markey Bill passing the House and the Obama administration’s push to reduce carbon emissions, the likelihood of the implementation of some form of a carbon policy is increasing. This study estimates the greenhouse gas (GHG) emissions of the six largest crops produced in Arkansas using 63 different production practices as documented by University of Arkansas Cooperative Extension Service. From these GHG estimates a baseline state “carbon footprint” was estimated and a hypothetical cap-and-trade carbon reduction of 5, 10, and 20% was levied on Arkansas agriculture. Results show that while a modest reduction in GHG emissions (5%) would only affect crop allocations amongst certain crops while marginally reducing state net returns, a 20% reduction would cause major cropping pattern shifts with some traditional row crops nearly disappearing.Cap-and-Trade, carbon, sustainability, Agricultural and Food Policy, Environmental Economics and Policy, Q28, Q52, Q54, Q56,

New TRP Channels in Hearing and Mechanosensation

AbstractDespite extensive biophysical characterization and the superb example of the bacterial MscL channel, molecular identification of eukaryotic mechanosensitive channels has been slow. New members of the TRP superfamily have emerged as candidate channels to mediate touch, hearing, fluid flow, and osmosensation in sensory and nonsensory cells. Distinguishing between direct mechanical activation and indirect second messenger activation is still a challenge

Airgun pellet performance using computational fluid dynamics

Very little formal analysis has been done with respect to flow around small scale projectiles. In late 1991, a local manufacturer of airgun pellets and related equipment approached RIT to investigate the flow characteristics of two particular airgun pellet geometries. Initial work was completed with the RIT wind tunnel through the use of scaled up models of the pellets. The conclusions of this work showed many interesting results, even though only the low speed velocity range of the pellets was studied. In order to study the actual operating velocity range of the pellets, the use of a Computational Fluid Dynamics program was necessary. The results from the CFD analysis of the two airgun pellets are presented in this work

Conversion of MultiCellDS Digital Snapshots to ISA-Tab format.

The MultiCellular Data Standard (MultiCellDS) is an interdisciplinary effort to create a data standard for sharing multicellular experimental, simulation, and clinical data. The ultimate goal of the overall project is to allow for data sharing that will lead to better analyses and simulations for multicellular biology and predictive medicine in association with the PhysiCell, a software that allows for simulations of large numbers of cells in 3D tissues. Digital cell lines are files that contain a standardized representation of a biological cell line and include phenotypic parameters as well as microenvironmental conditions for use in simulations. A Digital Cell Line is a data model rather than a computational model, meaning that it is based on curated measurements of specific cells in certain conditions. A Digital Snapshot is a recording of the current state of an experiment or simulation within the MultiCellDS software. A snapshot contains metadata, which could include user information, software information, experimental setup, and citation information, and a phenotype dataset, which creates mappings of phenotypic measurements with the cellular microenvironment. Snapshots can also reference digital cell being used to create snapshots that come from simulations. This Master’s Thesis project is a subset of the larger MultiCellDS effort. The results of this project allow for MultiCellDS Digital Snapshots to be converted to the ISA-Tab data standard, and ISA-Tab data files to be converted to MultiCellDS digital snapshots. The project uses Python code to convert the digital snapshots, which are produced as XML files, to ISA-Tab tab separated text files. All of the information from each file, both data and metadata, is accounted for and transferred to the proper locations in the other file type. The Python scripts produced this project yield output files that have valid formatting in each data standard and verified contents for all Digital Snapshots currently available in the MultiCellDS Gitlab repository (currently 327 Digital Snapshots) In the open-source nature of the MultiCellDS, all scripts, spreadsheets, and output files created for this Thesis project will be available on GitHub. The conversion between file types will allow for improved collaboration between researchers by allowing for information to be used in a variety of software packages

Seed Yield Prediction Models of Four Common Moist-Soil Plant Species in Texas

Seed production by moist-soil plant species often varies within and among managed wetlands and on larger landscapes. Quantifying seed production of moist-soil plants can be used to evaluate wetland management strategies and estimate wetland energetic carrying capacity, specifically for waterfowl. In the past, direct estimation techniques were used, but due to excessive personnel and time costs, other indirect methods have been developed. Because indirect seed yield models do not exist for moist-soil plant species in east-central or coastal Texas, we developed direct and indirect methods to model seed production on regional managed wetlands. In September 2004 and 2005, we collected Echinochloa crusgalli (barnyard grass), E. walterii (wild millet), E. colona (jungle rice), and Oryza sativa (cultivated rice) for phytomorphological measurements and seed yield modeling. Initial simple linear and point of origin regression analyses demonstrate strong relationships (P \u3c 0.001) among phytomorphological and dot grid methods in predicting seed production for all four species. These models should help regional wetland managers evaluate moist-soil management success and create models for seed production for other moist-soil plants in this region