The goal of the Mississippi State University Extension Service (MSU-ES) is to improve the quality of life for all Mississippians. One specific group that agricultural change agents work with at the county level is beef producers. Grazing lands have received much attention over the last few years regarding environmental concerns and Best Management Practices (BMPs) for beef cattle operations. The adoption of these practices was voluntary during the time this study was conducted, however; adoption was highly encouraged by the MSU-ES and the Natural Resources Conservation Service (NRCS). By knowing the level of adoption of BMPs that Mississippi beef producers have implemented, change agents can more effectively plan educational programming efforts for producers to better understand the importance of BMP adoption. The purpose of this study was to describe the adopter categories of Mississippi beef producers as determined by Rogers (2003) adopter characteristics generalizations based on their (1) socioeconomic status, (2) personality values and communication behavior, and (3) opinions. It also examined the correlations between the adopter categories to predict the level of the three BMPs being studied. The adopter categories were innovator, early adopter, early majority, late majority, and laggard. The three BMPs that were the focus of the study were rotation grazing, riparian buffers, and pasture renovation. The results of the study indicated that Mississippi beef producers could be correctly identified in the adopter categories. By identifying the adopter categories of the Mississippi beef producers and then examining the correlations among the variables, prediction of BMP adoption of rotational grazing and riparian buffers was possible. The relationships between MSU-ES agents and their programming efforts, as well as the relationships between NRCS district conservationist and their programs, were studied. Nonoption, though not an adopter category, was also examined and the reasons for it were cited.

The introduction of glyphosate-resistant (GR) crops in the late 1990s changed the way producers used herbicides to control weeds. Since the introduction of GR crops producers have relied on glyphosate alone for weed control instead of utilizing multiple modes of action for weed control. This over-reliance resulted in several weed species developing resistance to glyphosate. This has resulted in organizations from the public and private sector questioning the sustainability of GR cropping systems. Researchers from Illinois, Indiana, Iowa, Mississippi, Nebraska, and North Carolina established 156 onarm trials to determine the sustainability of GR cropping systems. The objectives of this study were: to determine the economics of a university weed resistance best management practice (BMP) versus a producers’ normal production practice; to evaluate when a producer that is risk neutral (profit maximizing) or risk averse should adopt a weed resistance BMP; and to compare the influences of using a university weed resistance BMP to a producer’s normal production practice on the 27 most common weed species in Mississippi. In all instances, the university weed resistance BMP utilized multiple modes of action in conjunction with glyphosate. A university weed resistance BMP can provide the same level of control on 27 of the most common weeds in Mississippi that a producer has become accustomed to with a glyphosate alone system, while delaying or controlling GR weeds. A university weed resistance BMP resulted in an increase in weed control cost, but similar yields and economic returns when compared to a producer’s normal production practice. Rotating a GR crop with a different GR crop resulted in higher economic returns when compared to a continuous GR cropping system or a GR crop followed by a non-GR crop rotation. Producers are often reluctant to adopt a weed resistance BMP because of the perceived increased cost for weed control. A risk neutral or risk averse producer should adopt a weed resistance BMP and feel confident that their decision will provide weed control equivalent to a glyphosate alone weed control program before resistance developed, delay or control GR weeds and be economically sound.

No description available.

No description available.

Current site scale stormwater management designs typically include multiple distributed stormwater best management practices (BMPs), necessary to meet regulatory objectives for nutrient removal and groundwater recharge. Selection of the appropriate BMPs for a particular site requires consideration of contributing drainage area characteristics, such as soil type, area, and land cover. Other physical constraints such as karst topography, areas of highly concentrated pollutant runoff, etc. as well as economics, such as installation and operation and maintenance cost must be considered. Due to these multiple competing selection criteria and regulatory requirements, selection of optimal configurations of BMPs by manual iteration using conventional design tools is not tenable, and the resulting sub-optimal solutions are often biased. This dissertation addresses the need for an objective BMP selection optimization tool through definition of an objective function, selection of an optimization algorithm based on defined selection criteria, development of cost functions related to installation cost and operation and maintenance cost, and ultimately creation and evaluation of a new software tool that enables multi-objective user weighted selection of optimal BMP configurations. A software tool is developed using the nutrient and pollutant removal logic found in the Virginia Runoff Reduction Method (VRRM) spreadsheets. The resulting tool is tested by a group of stormwater professionals from the Commonwealth of Virginia for two case studies. Responses from case study participants indicate that use of the tool has a significant impact on the current engineering design process for selection of stormwater BMPs. They further indicate that resulting selection of stormwater BMPs through use of the optimization tool is more objective than conventional methods of design, and allows designers to spend more time evaluating solutions, rather than attempting to meet regulatory objectives. / Ph. D.

The aim of this thesis is to investigate the use of hyperspectral reflectance signals for the discrimination of cogongrass (Imperata cylindrica) from other subtly different vegetation species. Receiver operating characteristics (ROC) curves are used to determine which spectral bands should be considered as candidate features. Multivariate statistical analysis is then applied to the candidate features to determine the optimum subset of spectral bands. Linear discriminant analysis (LDA) is used to compute the optimum linear combination of the selected subset to be used as a feature for classification. Similarly, for comparison purposes, ROC analysis, multivariate statistical analysis, and LDA are utilized to determine the most advantageous discrete wavelet coefficients for classification. The overall system was applied to hyperspectral signatures collected with a handheld spectroradiometer (ASD) and to simulated satellite signatures (Hyperion). A leave-one-out testing of a nearest mean classifier for the ASD data shows that cogongrass can be detected amongst various other grasses with an accuracy as high as 87.86% using just the pure spectral bands and with an accuracy of 92.77% using the Haar wavelet decomposition coefficients. Similarly, the Hyperion signatures resulted in classification accuracies of 92.20% using just the pure spectral bands and with an accuracy of 96.82% using the Haar wavelet decomposition coefficients. These results show that hyperspectral reflectance signals can be used to reliably detect cogongrass from subtly different vegetation.

No description available.

This research developed a process to improve the systematic deployment of best practices in unfamiliar countries in response to rapid globalization in the engineering and construction industry. The engineering and construction industry needs processes, metrics and tools to improve the deployment of best practices in unfamiliar countries to help facilitate project success, as new challenges are encountered. The research identified issues that are commonly encountered when deploying best practices in unfamiliar countries. The issues were identified using content analysis and verified by experts using the Delphi Method. The Analytic Hierarchy Process was used to establish weightings for the importance of each issue. The weightings were then used to create a scoring metric for companies to measure their readiness for projects. In order to overcome the issues identified in the research, a series of processes and mitigation strategies to overcome the issues were developed, through a series of interviews and focus groups. The International Readiness Passport (IRP) is a tool created to support the use of the metric and the mitigation strategies. This tool utilizes a self-scoring section which is applied to the metric. The tool then generates a report with the relevant mitigation strategies related to each issue, based on the score. To ensure that the IRP provides a meaningful benefit to the systematic deployment of best practices in unfamiliar countries, it was validated through a series of retrospective tests. These tests have confirmed the accuracy and relevance of the process, metric, and tool, as well as the tool\'s capabilities. / Master of Science

A rapidly changing global agribusiness environment creates a challenge for commercially oriented agricultural producers to improve business acumen through strategy development and execution. A best management practice is broadly defined as a practice that is considered to be most effective in improving business performance. This study examined the relationship of financial leverage and management practices with financial performance on a group of Minnesota and Northwest farms. Management practices were classified into seven broad categories of management, namely strategic planning, financial management, networking, marketing, technology adoption, family relationship and human resources management. Using multiple regression analysis on 242 observations, the effects of financial leverage and management practices on revenues and profits were determined. While the relationship of best management practices with profitability is less conclusive, this study concludes statistically significant relationships between management practices and financial performance, measured in terms of revenues. There exist positive and statistically significant returns to business planning, transition management, customer management and family relationship management. / Master of Science

ITC/USA 2005 Conference Proceedings / The Forty-First Annual International Telemetering Conference and Technical Exhibition / October 24-27, 2005 / Riviera Hotel & Convention Center, Las Vegas, Nevada / After years of tracing the evolution and solutions to finding the best data, I learned that it isn’t best source selection that we all want. What we need is best data selection.