<p>White-tailed deer (<i>Odocoileus virginianus</i>) are aesthetically and economically important to landowners in Texas. Deer herd health, productivity and survivability decline when population size exceeds the available forage. During stressful times, such as dry winter periods, nutrition is limited and forage availability decreases drastically. White-tailed deer winter diets are mainly comprised of browse species because herbaceous production decreases as winter progresses. The objective of this study was to determine the influence of winter progression on nitrogen (N) and fiber concentration as well in-vitro organic matter disappearance (IVOMD) (using white-tailed deer rumen liquid) of six browse species of moderate to high forage importance. Woody plant samples were collected during pre-frost, mid-winter, and late winter from four (replications) properties in the Cross Timbers of Texas, USA over 2 years. There was a difference between years (P &le; 0.05). There was an interaction (P &le; 0.05) between species and season for all forage values. Nitrogen, a desirable nutrient, decreased as winter progressed, IVOMD decreased as fiber increased with winter progression in five of the six browse species. The only exception was evergreen live oak (<i>Quercus virginiana</i> Mill.) which kept its leaves throughout winter and maintained an average 1.33% N with lowest fiber levels and highest IVOMD in late winter. Results confirm that nutritional value of browse, especially N and fiber, decreases after the first freeze when most browse species shed leaves. It also supports the need for plant biodiversity in white-tailed habitat that supports adequate year-round white-tailed deer nutrition. </p>

Multiobjective decision support systems (DSS) are gaining acceptance as tools to evaluate resource management systems. Before applying a DSS, a matrix of decision criteria and alternative management systems is populated using information from measured data, expert opinion or simulation models. As each information source exhibits differences in data availability and accuracy, the extent to which outcomes from the DSS are influenced by the source of information remains an important issue. A conceptual framework links the Prototype Decision Support System (P-DSS) developed by the USDA-ARS Southwest Watershed Research Center in Tucson, Arizona, to a conservation practice physical effects matrix. Four rangeland practices of yearlong (YL) and rotation (ROT) grazing, with mesquite trees retained (+M) and removed (-M), are evaluated against eight decision variables that consider soil, water, plants and wildlife habitat. Each decision variable is quantified using data from four experimental watersheds on the Santa Rita Experimental Range, expert opinions, and two simulation modeling approaches. The simple approach uses the Curve Number method, RUSLE and MUSLE, while the complex approach uses the CREAMS hydrology and erosion models. Outcomes from the P-DSS are sensitive to the source of information. When measured data and complex models quantify the decision variables, the YL-M and ROT-M management systems dominate the current system of YL+M. The simple modeling approach identifies ROT+M in addition to YL-M and ROT-M. However, when a frequency of rank methodology is used, the simple and complex modeling approaches identify ROT-M as the preferred system, while the measured data and expert opinion identify YL-M. Ranking the four management systems quantified by simple models matches the ranking obtained from the expert survey. Rank ordering using the complex models agrees with the opinion of the most knowledgeable expert. Simple and complex modeling estimates of sediment yield are significantly different, as are estimates of peak runoff rate. The results suggest that model complexity improved information accuracy but had limited effect on the outcomes from the P-DSS. The effect of information sources on the outcomes from the P-DSS may become more pronounced if the evaluation changes from a relative assessment to one involving quality standards.

Johnsongrass (Sorshum halepense (L.) Pers.) occurred in nearly pure stands on the Patagonia-Sonoita Creek Preserve, Santa Cruz County, Arizona prior to grazing. This study investigated the use of grazing, mowing and herbicides to decrease the abundance of johnsongrass, and increase the diversity of other plant species. The grazed only treatment allowed remaining overstory and limited grazing access. Mowing lowered the overstory and provided accessibility to grazing attracting animal utilization of the areas. The herbicided and grazed treatment reduced total johnsongrass productivity, tiller numbers and impacted tiller growth form. Lower percent botanical composition and tiller emergence of johnsongrass plants occurred in 1995 than in 1994. The number of other plant species increased between study years.

This item was digitized as part of the Million Books Project led by Carnegie Mellon University and supported by grants from the National Science Foundation (NSF). Cornell University coordinated the participation of land-grant and agricultural libraries in providing historical agricultural information for the digitization project; the University of Arizona Libraries, the College of Agriculture and Life Sciences, and the Office of Arid Lands Studies collaborated in the selection and provision of material for the digitization project.

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In this work we address the problem of object recognition and localization from sparse range data. The method is based upon comparing the 7-D error surfaces of objects in various poses, which result from the registration error function between two convolved surfaces. The objects and their pose values are encoded by a small set of feature vectors extracted from the minima of the error surfaces. The problem of object recognition is thus reduced to comparing these feature vectors to find the corresponding error surfaces between the runtime data and a preprocessed database. Specifically, we present a new approach to the problems of pose determination, object recognition and object class recognition. The algorithm has been implemented and tested on both simulated and real data. The experimental results demonstrate the technique to be both effective and efficient, executing at 122 frames per second on standard hardware and with recognition rates exceeding 97% for a database of 60 objects. The performance of the proposed potential well space embedding (PWSE) approach on large size databases was also evaluated on the Princeton Shape Bench- mark containing 1,814 objects. In experiments of object class recognition with the Princeton Shape Benchmark, PWSE is able to provide better classification rates than the previous methods in terms of nearest neighbour classification. In addition, PWSE is shown to (i) operate with very sparse data, e.g., comprising only hundreds of points per image, and (ii) is robust to measurement error and outliers. / Thesis (Ph.D, Electrical & Computer Engineering) -- Queen's University, 2010-01-24 23:07:30.108

Graduation date: 1966

Graduation date: 1985

Thesis (Ph. D.)--University of Chicago. / "Reprinted form the Eighteenth report of the Bureau of Mines, Ontario, part 1, 1909." Also available on the Internet.

Thesis (Ph. D.)--University of Chicago. / "Reprinted form the Eighteenth report of the Bureau of Mines, Ontario, part 1, 1909."