Digitized by Kansas State University Libraries

Call number: LD2668 .T4 1961 G79

Call number: LD2668 .T4 1979 P482 / Master of Science

Call number: LD2668 .T4 1985 R62 / Master of Landscape Architecture

Master of Arts / Department of Geography / Kendra K. McLauchlan / The quantitative relationship between pollen assemblages in sediment and vegetation cover is largely unknown because many factors influence this relationship. This lack of quantitative relationship is particularly acute in grassland regions, where both past and future climate change have the potential to determine grassland composition and cover. The tool used to reconstruct past grassland cover is the relative abundance of distinct fossil pollen types preserved in sediment. However, the interpretation of grassland pollen assemblages as grassland vegetation types needs to be refined to improve these reconstructions. Using pollen found in the surface sediments from 24 artificially-constructed ponds in the Flint Hills ecoregion of Kansas, USA, I examined relationships between pollen and vegetation in the tallgrass prairie biome, which includes woody components. By comparing the pollen data to field-surveyed vegetation data and land cover classifications taken from Kansas Gap Analysis Program data, I correlated pollen and vegetation in this ecoregion. Pollen productivity estimates for Artemisia, Ambrosia, Asteraceae, Chenopodiaceae, Cornus, Fabaceae, Juniperus, Maclura, Poaceae, Populus, Quercus, and Salix were calculated via the Extended R-Value Model. Common pollen types identified in sediments are mostly herbaceous grassland plant species such as Poaceae, Artemisia, and Ambrosia, but woody plants such as Populus, Quercus, and Juniperus are also represented. PPEs have been calculated for four of these taxa in Europe, and values from the Flint Hills are higher. These are the first PPEs reported for eight of these taxa. This research will further advance quantitative vegetation reconstructions in the Great Plains of North America and refine interpretations of how climate change affects grasslands.

Master of Science / Department of Agricultural Economics / Glynn Tonsor / The purpose of our analysis was to examine production strategies in the backgrounder and stocker segments of the beef industry within the Flint Hills region of Kansas. The time period analyzed encompassed 1996-2015. September and November placements of steers in the backgrounding sector of the industry were analyzed with an intended March sale date. Placements considered included 425, 500, and 575 pound steers. April and May placements of steers were analyzed for the industry’s stocking sector with an intended July sale date. Placements considered included 450, 600, and 750 pound steers. Within our analysis historical ex-post net incomes were analyzed, prediction errors were calculated (net income, revenue, and cost of gain), and market incentives/signals were analyzed. While for our historical ex-post net income analysis we did not identify one of the four placement strategies as superior in all 20 years of our analysis, we did find scenarios that were typically superior to others. In terms of backgrounding, November placements were typically superior to September placements, in terms of stocking April placements were typically superior to May placements, and when comparing backgrounding and stocking scenarios stocking scenarios were typically superior. In terms of prediction errors, we found that revenue errors are the main drivers of net income error. In general, within the backgrounding scenarios typical producers who are representative of our model assumptions generally overestimate net incomes which is detrimental to them (make lower profits than they anticipate making), while in stocking scenarios producers underestimate net incomes which is generally beneficial to them (make larger profits than they anticipate making). Market signal/incentive and ex-post net income analysis both indicated that steer weight at time of sale was a large factor influencing backgrounder profitability and decision making, and that pasture rents were a large factor influencing stocker profitability and decision making. In all four scenarios it proved economically beneficial to place lighter steer rather than heavy steers. Further research may include, but is not limited to; adding bulls and heifers to our model, analyzing different placement weights within our model, and allowing for animal performance variability within our model.