This research study presents the wind resource assessment at two potential onshore wind sites at the Western Cape of South Africa for small and large scale wind energy generation. It is anticipated that by virtue of the enormous wind resources prevalent along the South Africa West Coast, it is economical and cost effective to generate electricity from the wind to offset the increasing cost of energy generation from non-renewable sources (coal-fired, nuclear, gas etc.) which are the major source of power generation. Despite the environmental benefit and economic potentials of the wind energy, its variability and the inability to accurately predict (estimate) the long term energy generation potentials usually lead to difficulties in the selection and development of a suitable wind site for any proposed wind farm project(s) in the country.

No description available.

Atmospheric boundary layer flows around wind turbines distributed in a large wind farm can be examined by the use of large eddy simulation (LES), which is based on the assumption that large eddies in the flow are anisotropic and depend on the mean flow and the configuration geometry, while smaller eddies are isotropic and homogeneous, and can be modeled via subgrid scale models. In this thesis, a pseudo-spectral LES code with inflow conditions imposed through a precursor concurrent simulation is utilized to model the flow around a single wind turbine or a large wind farm operating in thermally-stratified conditions. The effect of the wind turbines on humidity is monitored through an additional scalar convection equation. It is found that on average, the effect of an individual wind turbine on the humidity is less than 1%, while the effect of the wind farm on humidity can reach 1-2% in the cumulative wakes.

Procuring hardwood pulpwood during the winter months for a pulp mill in the Southeast can be difficult. Saturated soils and low soil strength make logging difficult or impossible on many sites, forcing companies to store large volumes of hardwood pulpwood in woodyards for retrieval during wet weather. Hardwood fiber readily available in large volumes on ground that is operable during wet periods at a location near the pulp mill could provide a valuable alternative wood source. Thus, the objectives of this study are to 1) develop a decision model for a manager to use to determine the feasibility of strategically located, intensively-managed, short-rotation hardwood fiber farms as pulp mill furnish, 2) use the model to estimate wood costs for a hypothetical eastern cottonwood plantation, and 3) use the model to determine if a fiber farm grown on drier, upland sites ("green" inventory) could be used to reduce woodyard winter inventories and economically supply a nearby pulp mill during a wood shortage, thus reducing high cost, emergency "spot market" wood purchases. The decision model is incorporated in a spreadsheet and includes all the costs typical for a fiber farm. The model is tested using current establishment and management costs from the literature and yields from an experimental fiber farm in the southeast. Under current yields, delivered costs from the fiber farm averages $71/ton. With potential increased yields that could occur with genetic improvements and operational optimizations, delivered cost for fiber farm wood could be reduced to $56/ton. In comparison, the highest cost wood purchased by the three cooperating pulp mills during the study period was $50.23/ton. The net present values of a fiber farm as "green" inventory were determined using actual wood cost and inventory levels from three cooperating southeastern pulp mills. For the "green" inventory analysis, all three pulp mills would have lowered their overall wood cost using a fiber farm (with higher yield) as "green" inventory, primarily by reducing the amount of wood required as dry inventory on woodyards. Savings accrued during "dry" years offset the higher cost of hardwood plantation deliveries. A sensitivity analysis was performed to determine the optimal size fiber farm for one of the cooperating pulp mills and indicated that 800 acres would be the most beneficial. / Ph. D.

<p> It is the purpose of this study to determine empirically the contribution of a set of variables to the observed variation in income per hectare on 100 peasant farms in Ecuador. Specific consideration will be given to the relationship between farm size and farm productivity.</p> <p> Twenty-four characteristics were chosen to describe the structure of peasant agriculture. An examination of the distribution of each characteristic over the 100 farms illustrated the heterogeneous nature of the farm sample. Regression analysis applied to the sample at the aggregate level yielded poor results. Large amounts of variability in the dependent variable remained unexplained, and the standard error of the estimate was relatively large in comparison to the observed mean value of the dependent variable. The model was improved when the sample was disaggregated by region, although the standard error remained high which greatly weakened the potential of the model as a predictor equation.</p> <p> To increase the power of the regression model, and to more effectively analyse the significance of the set of variables to variation in farm income, it was decided to type the farms using factor analysis.</p> <p> Principal axes factor analysis was performed on the matrix of correlation coefficients for the twenty-four standardized characteristics. The factors were then rotated using varimax rotation to obtain a more simplified loading matrix. Eight primary factors were produced which together accounted for 62.25 percent of the variance in the original matrix. Ward's hierarchical grouping algorithm was then applied to the matrix of factor scores for the principal eight factors, and a classification containing fourteen types of farming activity was produced.</p> <p> The relationship between income and farm size was then reconsidered by farming type. There was a slight improvement in the power of the model applied to farm type although the amount of explained variability remained small. Simple regression of income per hectare on farm size, then, failed to explain a large proportion of the variance in the dependent variable even when the sample was considered by farming type.</p> <p> In order to reduce the measure of 'non-explained' variability in the dependent variable, and to increase the potential of the regression model as a predictor equation, income per hectare was regressed on the rotated factors. Multiple step-wise regression was performed on (a) the complete sample, (b) the sample disaggregated by region, and (c) two major farming types. The multiple step-wise regression model greatly increased the amount of explained variability in the dependent variable and indicated the significance of the contribution of each independent variable to variation in income per hectare on the farms. The study is presented in five parts: Chapter 1 introduces the problem to be analysed. Chapter II presents the data base, and a simple linear regression analysis examines the relationship between income and farm size on the 100 farms. The results of the regression performed on the aggregate level are poor. The analysis is then repeated on the sample disaggregated by region. The power of the model is greatly increased when the sample is divided into regional subsets, but large amounts of variability are left unexplained and the standard error of the estimate remains high.</p> <p> Chapter III groups the 100 farms according to a typology based on an analysis of the structural and economic organization of the farming unit. A correlation analysis is performed on the twenty-four characteristics and simple correlations between the data are considered. Factor analysis is then performed on the matrix of correlations, and the major dimensions of variation in the data are enumerated. Finally, a grouping algorithm is applied to the matrix of factor scores on the principal factors, and a classification of the farms is produced.</p> <p> Chapter IV reconsiders the relationship between income and farm size by farming type. Multiple step-wise regression then examines the contribution of a set of variables to variation in income per hectare.</p> <p> Chapter V summarizes the merits and weakness in the methodological approach of the study and enumerates the major findings of the analysis.</p> / Thesis / Master of Arts (MA)

Thirty-eight farm sales occurring over a five year period in two adjacent Coastal Plain counties and 84 farm sales in a Piedmont county were studied in an effort to identify and analyze factors affecting sale price. The effect of soil productivity and landscape characteristics were of particular interest in this study. The effect of soil productivity on farm sale values was found to be significant in both study areas but was somewhat Overshadowed by other considerations. Factors affecting sale price in the Coastal Plain study area were: (1) inflation, (2) location, (3) proportion of farm in cropland, (4) soil productivity, and (5) value of improvements. Eighty percent of the variation in sale price per hectare could be accounted for by a multiple regression equation which included variables representing each of these factors. Factors affecting sale price in the Piedmont study area were: (1) value of improvements, (2) proportion of farm in cropland, (3) inflation, (4) soil productivity, and (5) size of flue-cured tobacco allotment. A multiple regression equation including variables representing each of the factors accounted for slightly less than 50% of the variation in sale price per hectare. The effect of tobacco allotment on sale price was found to be considerably diminished from that found by previous studies. Crop yield ratings were more highly correlated with sale price per hectare than were SCS land capability classes among the Piedmont farm sales. The opposite was true for Coastal Plain sales. Classes I, II, and III lands were found to be positively correlated with sale price per hectare while Classes IV, VI, and VII were negatively correlated with sale price per hectare in the case of Coastal Plain farm sales. None of the three land capability classes were significantly correlated with sale price per hectare in the case of the Piedmont farm sales. A weighted average of all land classes occurring on each farm was found to be significantly related to sale price. Crop yield ratings for forages, soybeans, and small grains were found to be significantly related to sale price in the case of the Piedmont farms. Yield ratings for small grains were also found to be significantly related to sale price in the case of the Coastal Plain soils. Indices of soil productivity were found to be more highly correlated with sale price among the Coastal Plain farm sales than was the case among the Piedmont sales. The soils and topography of the Coastal Plain are more conducive to intensive crop production. In both study areas, a closer relationship was found between soil productivity and sale price in those soil associations which were well suited to intensive cropping. Farm sale prices rose rapidly in both study areas during the five year period studied. Farm sale values increased at an average annual rate of 47% in the Coastal Plain sales. In both study areas, about 2% of all farms were involved in bona fide sales within a given year. / Ph. D.

M.S.

No description available.

Thesis (Ph. D.)--University of Maryland, College Park, 1986. / Includes bibliographical references.

Zugl.: Tübingen, Univ., Diss., 2003. - Zsfassung in engl. und portug. Sprache