Abstract NOTE: The first page of text has been automatically extracted and included below in lieu of an abstract Session 3257 The Oklahoma State University Experience in Teaching Engineering Design and Drafting at the Freshman Level Dr. John W. Nazemetz, Dr. John B. Solie, Dr. David R. Thompson Oklahoma State University Introduction. This paper is intended to convey the process by which a freshman level course in design and drafting was developed at Oklahoma State University and the experiences and lessons learned during the first three years of the course. The course was developed to present the engineering design process by instructing students in the concepts and procedures used in engineering design and then exposing them to the entire design process by requiring student teams to conceptualize, develop, analyze, document (CAD drawings and report), and test a prototype physical device which is to solve a specific problem under time and cost constraints. This course, which provides students with an early, hands-on, thorough design experience, provides a venue for assessing the impact of an early design experience on students. Background The convergence of several independent events in the early 1990’s led to the development of an expanded course in Engineering Design and Drafting. These events included a College decision to move to centralized computer facilities to replace and expand those which had been developed and were being maintained by the individual Schools of Engineering. As Oklahoma State University’s Engineering College operates on a professional school concept, this computer development strategy resulted in a focus on the computing needs of upper-class students. While freshman and sophomore computing labs existed, much of the software was distributed among the various departments; the individual departments had limited budgets and the breadth of software requirements resulted in each department purchasing minimum numbers of copies of software that were used for only a portion of the year (i.e. only for certain classes). Thus, many Schools found that their students were frustrated by the limited number of software copies in the home departments while the same software had been purchased by other departments and was being used at different times of the year but was not available for use by students outside the departments. Several sharing arrangements developed between departments but these were somewhat haphazard and awkward to administer. In order to address the problems with utilization and maintenance, a scheme for centralization of hardware, software, and maintenance was developed. This centralization was administered by the College and its goal was to provide the computer capacity (hardware and software) to support the computing requirements of all students at all levels. The funding source for these resources was a student technology fee which was implemented and collected at the University level. At the same time, the College initiated a study of its freshman drafting course. The course was a one hour manual drafting course. The course focused primarily on sketching (isometric and oblique), lettering, and multiview (2-D) drawing of mechanical parts. The committee reviewing the course represented the various departments of the College and developed a number of objectives for the course after reviewing the literature available. The members of the committee were charged with developing a course that would meet the basic drafting needs of the various Schools of Engineering and, at the same time, introduce the student to the 1996 ASEE Annual Conference Proceedings
Abstract Current methods for making nitrogen (N) recommendations in winter wheat (Triticum aestivum L.) do not adjust for in-season temporal variability of plant available non-fertilizer N sources. The purpose of this study was to compare the use of different N response indices determined in-season (RINDVI and RIPLANTHEIGHT) to the N response index measured at harvest (RIHARVEST). In addition, this study evaluated the use of the in-season response indices for determining topdress N rates for winter wheat. Nine experiments were conducted over two years at eight different locations. A randomized complete block design with nine different treatments and four replications was used at each location. Preplant N source was ammonia nitrate (34-0-0). At Feekes 4–6, RINDVI was measured to determine the topdress N rates. Both RINDVI and RIPLANTHEIGHT were able to predict RIHARVEST (r2 = 0.75 and r2 = 0.74, respectively). Because the sensor-based approach for making N recommendations relies on information obtained from in-season sensor readings, RINDVI should be used to estimate a site's potential for response to additional N. Use of the response index will allow producers to move away from reliance on preplant application of N and start managing N based on the likelihood of achieving an economical response to N fertilizer.
Research is ongoing to develop sensor-based systems to determine crop nitrogen needs. The objective is to determine the expected maximum value of an in season precision nitrogen application system for winter wheat. Farmers could not afford to pay much more than $9 per acre for a precision system.
Physiologically optimal hard red winter wheat (Triticum aestivum L.) plant spacing is achieved by equalizing between-row and average-within-row seed placement distances. Commercial grain drills that are used on most farms in Oklahoma have row spacings that are too wide for physiologically efficient seeding. This study was conducted to determine if an ultranarrow row (UNR) grain drill with 3-in. row spacing would be economical for farmers. A representative farm approach was used to estimate the economic consequences. Estimates were computed for farm sizes of 300 and 1000 acres, for both conventional and UNR production systems. The minimum yield increase required to offset the estimated increase in the cost of the UNR drill, and the maximum UNR drill price given the anticipated yield increase were computed. At the budgeted prices and input levels, the UNR drill would increase returns by $6.79/acre for the 1000-acre farm and $6.03/acre for the 300-acre farm. Yield increases of 1.40 and 1.08 bu/acre would be required for the UNR system to break-even with the conventional drill system for the 300- and 1000-acre farms, respectively. Given the estimated yield increase of 4 bu/acre, the 300-acre farm could pay $28 469 for a 13-ft UNR drill, and the 1000-acre farm could pay $68 997 for a 26-ft UNR drill, for the UNR system to break-even with the conventional drill system. The UNR technology appears to be a promising economical alternative for hard red winter wheat producers in the Southern Plains.
Data from a photoelectric diode sensor, equipped with 670 and 780 nm interference filters, were analyzed todetermine the maximum field-of-view and minimum percentage of soil covered by bindweed (Convolvulus arvensis) thatcould be distinguished from bare soil. Images containing one 150-mm bindweed could be distinguished from images ofadjacent bare soil for all fields-of-view implying that adaptive thresholding would enable use of a field-of-view as large as0.71 m2. Detection was 100% for nine of the 11 fields-of-view evaluated and 98% for the other two. When bare soilimages were not paired with adjacent bindweed images, a single NDVI threshold could be used to distinguish between thetwo images with error. Results indicated that the required percentage of bindweed cover within an image was 12% for a5% error in falsely detecting or failing to detect the presence of bindweed. Error for a fixed NDVI threshold decreasedwhen classified by apparent soil moisture when compared with unclassified data.
Visual evaluation of turfgrass quality is a subjective process that requires experienced personnel. Optical sensing of plant reflectance provides objective, quantitative turf quality evaluation and requires no turf experience. This study was conducted to assess the accuracy of optical sensing for evaluating turf quality, to compare the rating consistency among human evaluators and optical sensing, and to develop a model that describes a relationship between optically sensed measurements and visual turf quality. Visual evaluations for turf color, texture, percent live cover (PLC), and optically sensed measurements were collected on the National Turfgrass Evaluation Program (NTEP) tall fescue (Festuca arundinacea Schreb) and creeping bentgrass (Agrostis palustris Huds.) trials at Stillwater, OK. Measurements were made monthly for 12 consecutive months from June 1999 through May 2000. Red (R) and near infrared (NIR) reflectance were collected with sensors and converted to normalized difference vegetative indices (NDVI). The NDVI were closely correlated with visual evaluations for turf color, moderately correlated with percent live cover (PLC), and independent of texture. Measurements of turf color and PLC were evaluated more consistently with optical sensors than by visual ratings. Normalized difference vegetation index (Y) could be reliably predicted by the following generalized model for turf color (X) and PLC (Z): Y = B(0) + B(1)log10X + B(2)Z(3). Optical sensing provided fast, reliable turf assessment and deserves consideration as a supplemental or replacement technique for evaluating turf quality.
A hammer mill removed most of the lemmas, paleas, and pericarps from cheat florets. Typically, the cuticular layer of the testa was the only remaining intact layer, and damage to the embryos and endosperm was severe. A roller mill disrupted tissue organization of lemmas, paleas, and outer layers of the caryopses primarily at the cuts. Large gaps between the aleurone layer and testa, between testa and pericarp, and between the scutellum and endosperm were created. In the field, germination of mill-damaged florets was reduced, and florets exhibited progressive degradation the longer they were buried. Nematodes and fungi penetrated the cuticular layer of mill-damaged seed. Attaching a hammer mill or a roller mill to a grain combine to treat cheat seed before it is returned to the field could provide a novel method of cheat control.
Corn ( Zea mays L.) grain yields are known to vary from plant to plant, but the extent of this variability across a range of environments has not been evaluated. This study was initiated to evaluate by‐plant corn grain yield variability over a range of production environments and to establish the relationships among mean grain yield, standard deviation, coefficient of variation, and yield range. A total of forty‐six 8‐ to 30‐m corn transects were harvested by plant in Argentina, Mexico, Iowa, Nebraska, Ohio, Virginia, and Oklahoma from 2002 to 2004. By‐plant corn grain yields were determined, and the average individual plant yields were calculated. Over all sites in all countries and states, plant‐to‐plant variation in corn grain yield averaged 2765 kg ha −1 (44.1 bu ac −1 ). At the sites with the highest average corn grain yield (11478 and 14383 kg ha −1 , Parana Argentina, and Phillips, NE), average plant‐to‐plant variation in yield was 4211 kg ha −1 (67 bu ac −1 ) and 2926 kg ha −1 (47 bu ac −1 ), respectively. As average grain yields increased, so did the standard deviation of the yields obtained within each row. Furthermore, the yield range (maximum corn grain yield minus the minimum corn grain yield per row) was found to increase with increasing yield level. Regardless of yield level, plant‐to‐plant variability in corn grain yield can be expected and averaged more than 2765 kg ha −1 over sites and years. Averaging yield over distances >0.5 m removed the extreme by‐plant variability, and thus, the scale for treating other factors affecting yield should be less than 0.5 m. Methods that homogenize corn plant stands and emergence may decrease plant‐to‐plant variation and could lead to increased grain yields.
