The Effects of Some External Management Factors on the Nitrogen Composition of Cattle Manure on Smallholder Farms

Smallholder farmers in Kenya collect manure from confined cattle housing termed zero-grazing units. Zero-grazing designs may include urine collection, though the effectiveness of these designs in improving manure N content has not been established. The manure-urine mixtures produced in these units were simulated to determine urine effects on manure N composition. Manure and manure-urine mixtures were stored for 120 days during dry and rainy seasons in Kenya. Manure-urine mixtures leached 26% of their mineral N content during the dry season, but only 12% during the rainy season. After storage, manure-urine mixtures had less organic-N and fiber-N than manure alone during the dry season (<0.01), but not during the rainy season. Results suggest that the effect of cattle urine on manure N composition is greater during dry seasons than rainy. Manure should not be stored more than 30 days to minimize N loss to leaching. Farmers may take steps to reduce N loss by controlling leaching and protecting manure from rainfall

Evidence for Holocene Aeolian Activity at the Close of the Middle Bronze Age in the Eastern Carpathian Basin: Geoarchaeological Results from the Mureş River Valley, Romania

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/96325/1/gea21434.pd

Predicting Landscape Effects of Mississippi River Diversions on Soil Organic Carbon Sequestration

Large Mississippi River (MR) diversions (peak water flow \u3e1416 m3/s and sediment loads \u3e165 kg/s) have been proposed as part of a suite of coastal restoration projects and are expected to rehabilitate and rebuild wetlands to alleviate the significant historic wetland loss in coastal Louisiana. These coastal wetlands are undergoing increasing eustatic sea-level rise, land subsidence, climate change, and anthropogenic disturbances. However, the effect of MR diversions on wetland soil organic carbon (SOC) sequestration in receiving basins remains unknown. The rate of SOC sequestration or carbon burial in wetlands is one of the variables used to assess the role of wetland soils in carbon cycling and also to construct wetland carbon budgets. In this study, we examined the effects of MR water and sediment diversions on landscape-scale SOC sequestration rates that were estimated from vertical accretion for the next 50 yr (2010–2060) under two environmental (moderate and less optimistic) scenarios. Our analyses were based on model simulations taken from the Wetland Morphology model developed for Louisiana’s 2012 Coastal Master Plan. The master plan modeled a “future-without-action” scenario as well as eight individual MR diversion projects in two of the hydrologic basins (Barataria and Breton Sound). We examined the effects that discharge rates (peak flow) and locations of these individual diversion projects had on SOC sequestration rates. Modeling results indicate that large river diversions are capable of improving basin-wide SOC sequestration capacity (162–222 g C.m-2.yr-1) by up to 14% (30 g C.m-2.yr-1) in Louisiana deltaic wetlands compared to the future-without-action scenario, especially under the less optimistic scenario. When large river diversions are placed in the upper receiving basin, SOC sequestration rates are 3.7–10.5% higher (6–24 g C.m-2.yr-1) than when these structures are placed in the lower receiving basin. Modeling results also indicate that both diversion discharge and location have large effects on SOC sequestration in low-salinity (freshwater and intermediate marshes) as compared to high-salinity marshes (brackish and saline marshes)

Meta-Analysis or � On the Present State of Musical Discourse

(Statement of Responsibility) by Samuel David Markewich(Thesis) Thesis (B.A.) -- New College of Florida, 1993(Electronic Access) RESTRICTED TO NCF STUDENTS, STAFF, FACULTY, AND ON-CAMPUS USE(Bibliography) Includes bibliographical references.(Source of Description) This bibliographic record is available under the Creative Commons CC0 public domain dedication. The New College of Florida, as creator of this bibliographic record, has waived all rights to it worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law.(Local) Faculty Sponsor: Miles, Stephe

Extracting Structured Information From Academic Documents

Document information extraction is a broad and challenging topic. With a growing number of academic documents published online each year, there is a large amount of knowledge trapped in static files. Naturally, the field of document information extraction can be applied to these information-rich documents for automatic extraction and structuring of knowledge. The process begins with document layout analysis (DLA), where document objects like titles and headings are identified. Then, specific text regions are used as input for further information extraction, reducing the noise and complexity for downstream methods. In this thesis, a new DLA dataset is proposed. This Dense Article Dataset (DAD) provides annotations for 42 document objects, allowing deep learning models to be trained for precise and fine-grained layout analysis. A new bounding box regression method is proposed and used with several popular segmentation networks. The results show that the approach not only increases accuracy when labeling document objects. Furthermore, models trained on DAD can also be used to accelerate the annotation of more data, paving the way for future expansion of DAD and more robust models. With the DLA task complete, the focus moves to textual analysis to better understand document contents. Specifically, a Descriptive Relation Dataset (DReD) is proposed, which trains models to describe the relationship between two noun phrases. Previous relation extraction works rely on classification, but narrow categories limit the amount of relevant information extracted. Several state-of-the-art sequence generation models are trained using DReD and thoroughly prove the ability to model relation descriptions. Existing datasets are also modified to have relation descriptions and compare them to related works. The models trained to predict relation descriptions achieve competitive results with typical classification networks, further proving that describing relations rather than classifying them is a viable approach