Farm size economies size measure the relationship between the size of operation and the average cost of production. Along with increasing farm size, the average cost of production per unit may decline. One reason farms have been growing in size is that larger sized farms tend to have more recent and advanced machines capable of covering more land with less labor. However, it is still questionable how farm size affects on input costs and field operation costs in Saskatchewan. The major objective of this study was to examine the issues related to size economies in larger crop farms in Saskatchewan. The project has taken a different approach than is traditionally done in economies of size research where various forms of statistical data are analyzed. <p>First, the study analyzed several different operating and investment costs to see whether they are decreasing or staying the same as a result of increasing farm size. Next the study determined the probabilities of available field workdays using conditional probability equations derived from the Markov Chain method. The analysis was carried out for the West central and East central Saskatchewan regions to determine spring and fall field workability. Based on the field workdays estimation, the optimal area of combine for larger farms were analysed using a least-cost machinery size approach. The last part of this study analysed farm operational costs per unit for larger crop farms in order to determine how machinery efficiency and farm size have an effect on the farm production costs. <p>The study found that however there were certain combine costs that increase with farm size in Saskatchewan. In addition, soil types, weather conditions and field efficiency can strongly affect combine cost per acre. <p>The results of this research provide a reference for policy makers in designing policy recommendations. In addition, the results may offer useful information for farmers in designing management plans to control farm operation costs.

Farm size economies size measure the relationship between the size of operation and the average cost of production. Along with increasing farm size, the average cost of production per unit may decline. One reason farms have been growing in size is that larger sized farms tend to have more recent and advanced machines capable of covering more land with less labor. However, it is still questionable how farm size affects on input costs and field operation costs in Saskatchewan. The major objective of this study was to examine the issues related to size economies in larger crop farms in Saskatchewan. The project has taken a different approach than is traditionally done in economies of size research where various forms of statistical data are analyzed. <p>First, the study analyzed several different operating and investment costs to see whether they are decreasing or staying the same as a result of increasing farm size. Next the study determined the probabilities of available field workdays using conditional probability equations derived from the Markov Chain method. The analysis was carried out for the West central and East central Saskatchewan regions to determine spring and fall field workability. Based on the field workdays estimation, the optimal area of combine for larger farms were analysed using a least-cost machinery size approach. The last part of this study analysed farm operational costs per unit for larger crop farms in order to determine how machinery efficiency and farm size have an effect on the farm production costs. <p>The study found that however there were certain combine costs that increase with farm size in Saskatchewan. In addition, soil types, weather conditions and field efficiency can strongly affect combine cost per acre. <p>The results of this research provide a reference for policy makers in designing policy recommendations. In addition, the results may offer useful information for farmers in designing management plans to control farm operation costs.

This dissertation contains three essays. The first and second essays examine intergenerational mobility in earnings in Brazil using a data set spanning three generations. I use data from PNAD{a nationally representative household survey in Brazil. I build a three-generations data set consisting of 5,125 grandfather-father- son triplets by restricting the sample to households with adult sons. The first essay estimates some relationships between a child's earnings and family background implied by the Becker-Tomes model. I find that the estimates contradict some of its predictions, like the negative relationship between child's earnings and grandparent's earnings when controlling for parent's earnings. I propose a modified version of the Becker-Tomes model and find that the estimates are consistent with its predictions. I find that family background explains 34.9% of the variation in earnings among young males who live with their parents. If it were possible to eliminate the differences in investment in the children's human capital, the variation in earnings would fall by no more than 21.1%. Additionally, if there were no differences in endowments among children, the variation in earnings would fall by no less than 26%. The second essay examines the evolution of the intergenerational elasticity across generations and im- plications of marriage, education and fertility on mobility. I find that the estimate of the intergenerational elasticity in earnings is 0.847. The elasticity of earnings between son-in-law and father-in-law, 0.89, is approximately the same as the elasticity between son and father, 0.9. Additionally, controlling for fathers' percentile in the earnings distribution, each additional sibling decreases the sons' percentile by 1.77 percentiles. The third essay estimates an indicator of the optimal investment in electricity generation in Texas, and the associated efficiency gains. The essay presents a method to estimate the optimal investment in each technology available to generate electricity. The estimation considers the expected entry and exit of generation plants, future fuel prices, different demand elasticities and a potential carbon allowance mar- kets. Considering a carbon allowance price equal to two times the level in Europe, the optimal investment in electricity generation in Texas is zero.

This dissertation presents a pumping system operation efficiency improvement solution that includes optimal selection and control of the water pump. This solution is formulated based on the performance, operation, equipment and technology (POET) framework. The focus is on the minimization of the operational energy cost. This efficiency improvement solution is divided into three stages in accordance with the operation category of the POET framework. The first stage is to select the optimal pump capacity by considering both energy efficiency and load shifting requirements. The second stage is to develop a flexible pump controlling strategy that combines and balances the contributions from energy efficiency and load shifting. The last stage is to improve the robustness of the control system using the closed-loop model predictive control approach. An optimal pump capacity selection model is formulated. In this model, additional capacity requirements for load shifting are considered along with the traditional energy efficiency requirements. By balancing the contributions from load shifting and energy efficiency, the operational energy cost can be reduced by up to 37%. An optimal pump control is formulated. The objective of this control model is to balance the energy efficiency and load shifting contributions during the operation and minimize the operational energy cost. This control model is tested under different operational conditions and it is compared to other existing control strategies. The simulation and comparison results show that the proposed control strategy achieves the lowest operational energy cost in comparison to other strategies. This optimal pump control model is further modified into the closed-loop model predictive control format to increase the robustness of the control system under operation uncertainties. A mixed integer particle swarm optimization algorithms is employed to solve the optimization problems in this research. AFRIKAANS : Hierdie verhandeling bied ’n verbeterde oplossing vir die operasionele doeltreffendheid van pompstelsels wat die optimale keuse en beheer van die waterpomp insluit. Hierdie oplossing is geformuleer op ’n raamwerk wat werkverrigting, bedryf, toerusting en tegnologie in ag neem. Die oplossing fokus op die vermindering van bedryfsenergie koste. Hierdie oplossing is onderverdeel in drie fases soos bepaal deur die bedryfskategorie gegrond op die bogenoemde raamwerk: Die eerste fase is die keuse van die optimale pompkapasiteit deur beide energiedoeltreffendheid en lasverskuiwing in ag te neem. Die tweede fase is om ’n buigbare pompbeheer strategie te ontwikkel wat ’n goeie balans handhaaf tussen die onderskeie bydraes van energiedoeltreffendheid en lasverskuiwing. Die derde fase is om die stabiliteit van die beheerstelsel te verbeter deur gebruik te maak van ’n geslote-lus beheermodel met voorspellende beheer (Predictive Control). ’n Model vir die keuse van optimale pompkapasiteit is geformuleer. In hierdie model word vereistes vir addisionele pompkapasiteit vir lasverskuiwing sowel as vereistes in terme tradisionele energiedoeltreffendheid in ag geneem. Deur die regte verhouding tussen die onderskeie bydraes van energiedoeltreffendheid en lasverskuiwing te vind kan ’n besparing van tot 37% op die energiekoste verkry word. Optimale pompbeheer is geformuleer. Die doel van die beheermodel is om die bydraes van energiedoeltreffendheid en lasverskuiwing te balanseer en om die bedryfsenergie koste te minimiseer. Hierdie beheermodel is getoets onder verskillende bedryfstoestande en dit is vergelyk met ander bestaande beheerstrategiee. Die simulasie en vergelyking van resultate toon dat die voorgestelde beheerstrategie die laagste bedryfsenergie koste behaal in vergelyking met ander strategiee. Hierdie optimale pomp beheermodel is verder aangepas in ’n geslote beheermodel met voorspellende beheerformaat om die stabiliteit van die beheerstelsel te verbeter onder onsekere bedryfstoestande. ’n Gemende heelgetal partikel swerm optimisasie (Mixed interger particle swarm optimization) algoritme is gebruik om die optimiseringsprobleme op te los tydens hierdie navorsingsoefening. / Dissertation (MEng)--University of Pretoria, 2011. / Electrical, Electronic and Computer Engineering / Unrestricted