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The complicated nature of beef cattle farming necessitated the development of an effective computerised cattle management system (cattle farm planning system). This system was developed and programmed to be a planning and optimising model for maximum profit for the farmer. Farming systems in South Africa differ markedly, and the emerging farmers as well as small-farming communities depend either entirely or partly on agricultural activities for their survival and income generation. The design was mainly focussed on the new group of emerging farmers as well as small-farming communities. Improving the productivity of agriculture has exercised the ingenuity of politicians, planners, researchers, and extension or development agency staff over the last 30 to 40 years (Norman, 1993). For this study the focus was mainly on medium framed cattle (utilised mainly by the emerging farmers). A mathematical model was developed with the use of data from previous researchers, on growth, fertility, mating and calving percentages and data on the dressing percentages, average producer prices as well as other expenses. Growth curves for animals with three different condition score’s was used in conjunction with the expected meat prices and dressing percentages to calculate expected market prices on the hoof. Sales and marketing costs could be viewed on screen in order to show the farmer his profit margin. Using the information in the tables the system also determined the running costs for all cattle on the specific farm. The output of the program worked as follow. First, the marketing strategy was displayed. For each season the number of cattle recommended for sale was graphically displayed, together with the corresponding gross and net income. The user could also select the marketing strategy for a cattle group for the coming two years in each of eight seasons. The herd composition can also be displayed with financial information. The system was developed in order to give the farmer valuable information to help with the management of the farm. / Dissertation (MSc(Agric))--University of Pretoria, 2009. / Animal and Wildlife Sciences / unrestricted

Diss. (sammanfattning) Alnarp : Sveriges lantbruksuniv. / Härtill 5 uppsatser.

Made available in DSpace on 2016-06-02T19:50:17Z (GMT). No. of bitstreams: 1 4569.pdf: 2774038 bytes, checksum: 78f34cb28f3d27e0d82767631a1e03b1 (MD5) Previous issue date: 2012-08-27 / Farmers have always neglected management procedures in their enterprises in detriment to initiatives regarding production technology. Overall, producers and technicians know the answer to what, how and when to produce, but they don t know the cost or profitability of this technology. The present study shows the importance of the sustainability of agricultural enterprises with reduced governmental intervention and the risk involved in a double crop production system. Therefore, the aim is to propose a model to support the decision-making process, focused on production planning in a representative multi-product farm under conditions of risk. Using quantitative applied research, a theoretical model of agricultural planning was combined with operational research to explain and understand different allocations of resources within the decision-making process. For that purpose, two production regions of Mato Grosso state in Brazil were selected: Sorriso (SRS) and Campo Novo do Parecis (CNP). In SRS, a production system with 76.9% early soybean (SP) and 23.1% soybean (SN) for the first harvest and 76.9% corn (MS) for the second harvest generated a high gross margin and risk. On the other hand, a production system with 90% SN and 10% SP implied less risk. Diversifying the cultivated area with SP and SN in the first crop and MS in the second crop is interesting for farmers; however, land allocation decisions depend on how much risk the producer is willing to take. Since this farm planning strategy reduces SP and MS areas in the production system, gross margin and risk are also decreased. The following distribution of arable land produced the maximum gross margin value in CNP: 62.5% SP, 18.8% SN and 18.7% cotton (ALG) in the first harvest and 62.5% MS for the second harvest. The resulting maximum gross margin of the farm was R$754,260.77, which was R$122,525.78 under high risk conditions. As the area of cotton production is reduced, the representative exposure to risk is protected. In this case, a production system with multiple products does not exactly mean reduced risk for the farm, since the addition one particular product in the production portfolio generates a specific cost, called the sunk cost. Thus, a production system becomes feasible when the use of specific machinery and equipment is maximized, but in the end this procedure penalizes the performance of other products. The decision to allocate land for these products should remunerate the opportunity cost of soybean and corn. The efficient frontier curves correspond to the most efficient investment strategies for the two farms.. The frontier curve for SRS was the allocation of average land used in the last six seasons (2004/05 to 2009/10), showing that the production system adopted (32.5% SP and 67.5% SN for the first crop and 32.5% MS for the second crop) has productive efficiency to minimize risks for a certain income level. This decision corresponds to an aversion rate to risk of 1.05. In the case of CNP, the average combination of crop area used was very close to the efficient frontier curve, indicating that the farm planning production system with 28% SP, 54% SN, 18% ALG for the first harvest, and 28% MS for the second harvest also minimizes risk for a certain income level. On the other hand, the aversion rate to risk corresponds to 3.71. / Agricultores sempre negligenciaram a administração de seus empreendimentos em detrimentos de iniciativas ligadas às tecnologias de produção. Em geral, produtores e técnicos sabem responder o que, como e quando produzir, mas eles tem dificuldade de responder quanto custa ou qual é a rentabilidade dessa tecnologia. Este trabalho mostra a importância do planejamento na sustentabilidade dos empreendimentos agrícolas, com intervenção governamental reduzido e sistema de produção agrícola com duas safras em condição de risco. Para isso, esta tese propõe um modelo de apoio à tomada de decisão, voltado ao planejamento de produção em uma propriedade representativa com multiproduto em condição de risco. A metodologia utilizou pesquisa aplicada quantitativa, que combina modelo teórico de planejamento agrícola com pesquisa operacional para explicar e compreender as diferentes alocações de recursos no processo de decisão. Para tanto, foram escolhidas duas regiões produtoras de Mato Grosso: Sorriso (SRS) e Campo Novo do Parecis (CNP). Em SRS, o modelo mostrou que o sistema de produção com 76,9% de soja precoce (SP) e 23,1% de soja normal (SN) na área de cultivo na primeira safra e 76,9% de milho na segunda safra (MS) obtém maior margem bruta e risco. Por outro lado, a área de produção com predomínio de SN (90%) e SP (10%) assume menor risco. A diversificação da área de cultivo com SP e SN na primeira safra e MS na segunda safra mostrou-se uma alocação interessante para o produtor, mas a decisão da proporção de uso da área agrícola dependerá do quanto de risco o produtor está disposto a assumir. Nesse planejamento agrícola, conforme reduz a área de SP e MS no sistema de produção tem-se uma diminuição da margem bruta e do risco. Em CNP obtém-se o valor máximo de margem bruta média quando a área total de cultivo fica ocupada com 62,5% de SP, 18,8% de SN e 18,7% de algodão (ALG) na primeira safra e com 62,5% de MS, o que motiva a margem bruta máxima da propriedade (R$ 754.260,77) em alta condição de risco (R$ 122.525,78). À medida que o uso da área de cultivo com o algodão na estrutura produtiva é reduzido, protege-se a propriedade representativa da exposição ao risco. Neste caso, o sistema de produção com multiproduto não significou exatamente a redução do risco da propriedade rural, pois a adição de um produto particular na carteira de produção acabou gerando um custo específico sunk cost. Assim, o sistema de produção da propriedade torna viável quando se busca a maximização do uso das máquinas e dos equipamentos específicos, mas isso acaba penalizando o desempenho dos demais produtos. A decisão de alocar o uso da terra com esses produtos deve remunerar o custo de oportunidade da soja e milho. As curvas de fronteira de eficiência revelaram que as duas propriedades representativas maximizam os fatores de produção. Em SRS, a alocação média da área de cultivo nas últimas seis safras (2004/05 a 2009/10) ficou na curva de fronteira, mostrando que o sistema de produção escolhido pelos produtores (32,5% de SP e 67,5% de SN na primeira safra e 32,5% de MS) tem mostrado a eficiência produtiva dos agricultores no sentido de minimizarem o risco para determinado nível de renda. Essa decisão corresponde a uma taxa de aversão ao risco de 1,050. No caso de CNP, a combinação média da área de cultivo ficou muito próxima da curva de fronteira, sinalizando que o planejamento agrícola do sistema de produção (28% de SP, 54% de SN e 18% de ALG na primeira safra, e 28% de MS) também minimiza o risco para determinado nível de renda, mas a uma taxa de aversão ao risco corresponde a 3,71.

A decision support system for preventive integrated pest management (IPM) and nonpoint source (NPS) pollution control was designed, implemented and evaluated. The objective of the system was to generate plans at the farm level to satisfy economic and production goals while limiting risks of insect pest outbreaks, nitrate and pesticide leaching and runoff, and soil erosion. The system is composed of a constraint satisfaction planner (CROPS-LT), a modified version of CROPS (Stone, 1995), a farm-level resource management system (FLAME), an NPS module, which includes a weather generator, CLIGEN (Nicks et al. 1995), and an NPS distributed-parameter model, ANSWERS (Bouraoui, 1994), databases, a database engine and utility programs. The performance of the system was analyzed and performance enhancing features were added to increase the planner's ability to find near-optimal plans within a limited planning time. Using heuristics to sort potential crop rotations based on profit generally improved the planner's performance, as did removal of fields that were not suitable for growing target crops. Not surprisingly, the planner was best able to find plans for crops that can be grown in a variety of rotational systems. Throughout, the ability to apply environmental constraints selectively to individual fields greatly improved the planner's ability to find acceptable plans. Preventive IPM (PIPM) heuristics to control corn rootworms CRW (<I>Diabrotica virgifera virgifera</I> and <I>D. barberi</I>) were added to the planner. The model was represented and solved as a constraint satisfaction problem. Results indicated that plans obtained using PIPM heuristics had less risk of CRW damage, reduced chemical control costs, higher profit and reduced soil erosion as compared to a control plan. Linking the planner to the NPS model in a feedback control loop improved the planner's ability to reduce soil losses while preserving economic and production goals. / Ph. D.

This thesis is composed of two essays that investigate whole farm planning and crop marketing in western Kentucky. In the first essay, contracting decisions between food corn producers and a mill are analyzed to observe factors affecting the bushel amount farmers contract. Unbalanced panel data containing seven years’ worth of pricing and contract information are used with a fixed-effects model to generate parameter estimates and quantify their effect on bushels contracted. It was found that contract attributes, market condition, and relationship-specific assets had a significant effect on producers’ food corn contracting decisions. The second essay utilizes mixed-integer programming to optimize resource allocation and marketing strategy for a hypothetical farm. Post-optimal analysis is performed to determine non-binding capacities for drying and storage equipment. The model is re-run with these non-binding capacities to observe changes in net returns as well as planting, harvesting, and marketing strategies. New equipment and associated costs are identified, and the change in net returns from the base case is used as net cash flow in a net present value investment analysis. Results of the investment analysis indicate increasing drying and storage capacity is a wise investment given the scenario modeled.

Due to the increase in electrical demand and renewable penetration, electrical utilities need to improve and optimize the grid infrastructure. Fundamental components in this grid infrastructure are transformers, which are designed conservatively on the base of a static rated power. However, load and weather change continuously and hence, transformers are not used in the most efficient way. For this reason a new technology has been developed: Dynamic transformer rating (DTR). By applying DTR, it is possible to load transformers above the nameplate rating without affecting their life time expectancy. This project goes one step further and uses DTR for the short term and long term wind farm planning. The optimal wind farm is designed by applying DTR to the power transformer of the farm. The optimization is carried out using a Mixed-Integer Linear Programming (MILP) model. In respect of the transformer thermal analysis, the linearized top oil model of IEEE Clause 7 is selected. The model is executed for 4 different types of power transformers: 63 MVA, 100 MVA, 200 MVA and 400 MVA. As result, it is obtained that the net present value for the investment and the capacity of the wind farm increase linearly with respect to the size of the transformer. Then, a sensitivity analysis is carried out by modifying the wind speed, the electricity price, the lifetime of the transformer and the selected weather data. From this sensitivity analysis, it is possible to conclude that wind resources and electricity price are key parameters for the feasibility of the wind farm. / På grund av ökningen av efterfrågan av elektricitet och förnybara energin, elförsörgingsföretag måste förbättras och elnätets infrastruktur måste optimeras. Grundläggande komponenter i elnätet är transformatorer, som är designade konservativt efter en statisk märkeffekt. Laster och vädret ändras dock kontinuerligt, detta betyder att transformatorer inte används på de mest effektiva sätten. Av denna anledning har en ny teknik utvecklats: Dynamisk lastbarhet hos transformatorer (DTR). Genom att applicera DTR, gör det möjligt att belasta en transformator högre än märkdata utan att påverka den förväntade livslängden. Detta projekt går ett steg längre och använder DTR för kort och lång sikts vindkraftparkplaneringar. Den optimala vindkraftparken är designad genom att använda DLT på krafttransformatorn för vindkraftsparken. Optimeringen utförst med hjälp av Mixed-Integer Linear programming (MILP) modell. Gällande transformatorns termiska analys, så valdes den linjäriserade toppoljemodellen av IEEE Clause 7. Modellen var utförd för fyra olika krafttransformatorer: 63 MVA, 100 MVA, 200 MVA och 400 MVA. Resultatet blev att nettonuvärdet för investeringen och kapaciteten av vindkraftsparken ökade linjärt med avseende på storleken på transformatorn. En känslighetsanalys var utförd genom att ändra vindhastigheten, elpriset, livstiden av transformatorn och de valda väderdata. Från känslighetsanalysen så var det möjligt att dra slutsatsen att vindresurser och elpriset är nyckelparametrar för vindkraftsparkens genomförbarhet.