Master of Science / Department of Agricultural Economics / Timothy J. Dalton / In Kenya, agriculture governs the country’s fiscal economy, and this reliance on agriculture can cause both economic and hunger problems, a result of the country’s dependence upon rainfall for agricultural production. Kenyans must find ways to combat severe drought conditions; this can be accomplished through the adoption of inputs that decrease the probability of crop failure. The objective of this research is to determine whether variability exists in Kenyan maize yields, and whether or not specific inputs, specifically hybrid varieties, are either variance/skewness increasing or decreasing. The data used for this study was collected from a survey, designed by Egerton University’s Tegemeo Institute of Agricultural Policy and Development and Michigan State University, and administered in Kenya in the following years: 1997, 2000, 2004, and 2007. The survey identified factors of crop and field level production, such as inputs, crop mix, marketing data, and demographic information. This research makes use of only the 2007 data, comprising 1,397 households in total. The objectives of this thesis aim to go beyond the scope of typical production function regressions where yield is a function of a set of inputs, by examining further moments of yield, variance, and skewness to determine whether variability exists in Kenyan maize yields. Results indicate that variability does exist within Kenyan maize yields, often a result of differing input levels among households. In terms of overall impact of each variable on mean, variance, and skewness of maize yields, seed quantity, nitrogen use, and hybrid seed contribute the most to influencing these factors. In contrast, years of experience with hybrid maize, land tenure, terraced land and labor have the least influence on mean, variance and skewness within this research. Results also bring to light the popular debate against hybrid varieties versus open pollinated (OPV) or traditional varieties, and identify hybrid varieties as a source of variability in mean, variance and skewness of yields. Hybrid varieties should be paired with the knowledge of how to maximize yield in conjunction with other inputs, to give Kenya the opportunity to see substantial productivity gains throughout the country, especially in arid and semi-arid regions.

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Nanoindentation is an increasingly used tool to investigate the mechanical properties of very small volumes of material. Gold single crystals were chosen as a model system for surface modification studies, because of the electrochemical advantages and the simple structure of the material. Experiments on these samples displayed a spectrum of residual deformation, with measured hardness values on the same surface differing by over a factor of two. The yield point also exhibited considerable variation, but the depth of penetration was independent of this elastic–plastic transition. The onset of plastic deformation in these tests is observed at stress levels on the order of the theoretical yield strength. There are a limited number of defects in a single crystal specimen of gold, especially on the length scale required to influence nearly every indentation experiment. A test matrix was designed to change the concentrations of possible defects in a sample (dislocations, vacancies, and structural features), by altering some of the surface preparation parameters. The results of these experiments were extremely consistent. Observed trends within the matrix, combined with the observations of reduced hardness and earlier plasticity when compared to the preliminary testing, indicate a decline in the structural continuity of the sample. This is surprising considering the extensive material removal and thermal history of some of these surfaces. There is no indication of a cause for the dramatic inconsistencies in mechanical properties observed in preliminary testing, but a consistent surface enables the study of intentional modifications. Changes in contact area that were undetectable in preliminary results now demonstrate predictable shifts in hardness values. The deposition of a single monolayer of gold oxide raised the average load at yield by a factor of three and increased the hardness by over 26%. Attributing this change to the oxide is corroborated by the reduction of hardness when the oxide is stripped. Similar behavior is observed when a lead monolayer is deposited and tested ex-situ. It is surprising that layers <0.5 nm in thickness would have such a dramatic influence on indentation tests at least 35 nm deep. This indicates that no surface layer can be ignored at this scale. These experiments demonstrate that there is still much to be learned about nanoscale deformation mechanisms. / Ph. D.

One important parameter of interest in remote sensing to date is yield variability. Proper understanding of yield variability provides insight on the geo-positional dependences of field yields and insight on zone management strategies. Estimating cotton yield and observing cotton yield variability has proven to be a challenging problem due to the complex fruiting behavior of cotton from reactions to environmental conditions. Current methods require expensive sensory equipment on large manned aircrafts and satellites. Other systems, such as cotton yield monitors, are often subject to error due to the collection of dust/trash on photo sensors. This study was aimed towards the development of a miniature unmanned aerial system that utilized a first-person view (FPV) color camera for measuring cotton yield variability. Outcomes of the study led to the development of a method for estimating cotton yield variability from images of experimental cotton plot field taken at harvest time in 2014. These plots were treated with nitrogen fertilizer at five different rates to insure variations in cotton yield across the field. The cotton yield estimates were based on the cotton unit coverage (CUC) observed as the cotton boll image signal density. The cotton boll signals were extracted via their diffusion potential in the image intensity space. This was robust to gradients in illumination caused by cloud coverage as well as fruiting positions in the field. These estimates were provided at a much higher spatial resolution (9.0 cm2) at comparable correlations (R2=0.74) with current expensive systems. This method could prove useful for the development of low cost automated systems for cotton yield estimation as well as yield estimation systems for other crops. / Master of Science

This dissertation analyzes the impact of climate, and atmospheric carbon dioxide (CO2) on crop yields and grain transportation. The analysis of crop yields endeavors to advance the literature by statistically estimating the effects of atmospheric carbon dioxide (CO2) on observed crop yields. This is done using an econometric model estimated over pooled historical data for 1950-2009 and data from the free air CO2 enrichment experiments. The main findings are: 1) yields of soybeans, cotton, and wheat directly respond to the elevated CO2, while yields of corn and sorghum do not; 2) the effect of crop technological progress on mean yields is non-linear; 3) ignoring atmospheric CO2 in an econometric model of crop yield likely leads to overestimates of the pure effects of climate change and technological progress on crop yields; and 4) average climate conditions and climate variability contribute in a statistically significant way to average crop yields and their variability. To examine climate change impacts on grain transportation flows, this study employs two modeling systems, a U.S. agricultural sector model and an international grain transportation model, with linked inputs/outputs. The main findings are that under climate change: 1) the excess supply of corn and soybeans generally increases in Northern U.S. regions, while it declines in Central and Southern regions; 2) the Corn Belt, the largest producer of corn in the U.S., is anticipated to ship less corn; 3) the importance of lower Mississippi River ports, the largest current destination for U.S. grain exports, diminishes under the climate change cases, whereas the role of Pacific Northwest ports, Great Lakes ports, and Atlantic ports is projected to increase; 4) the demand for grain shipment via rail and truck rises, while demand for barge transport drops.