25 resultados para funções de produção


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Pós-graduação em Agronomia (Energia na Agricultura) - FCA

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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O século passado foi marcado por grandes avanços tecnológicos na agricultura que culminaram com a chamada “Revolução Verde”, a qual é hoje, comprovadamente, considerada como um modelo insustentável. Tal fato tem motivado instituições científicas e agricultores a buscarem por novos paradigmas para a produção. A agroecologia tem se destacado por abordar o manejo de agroecossistemas dentro de uma visão holística, sistêmica e participativa, subsidiando a recuperação das suas funções e a autonomia durante a transição agroecológica. Nesse contexto, a utilização de ferramentas para avaliação e monitoramento do processo, como a análise de indicadores de sustentabilidade, é fundamental. O presente estudo teve como objetivo monitorar participativamente o grau de sustentabilidade de duas unidades de produção orgânica em Jaguariúna-SP, após cinco anos de uma primeira avaliação. Foram analisados 81 indicadores, abordando quatro dimensões da sustentabilidade: ambiental, social, econômica e política. Os resultados foram obtidos de forma consensual e participativa entre pesquisadores e agricultores, enriquecendo o estudo. Do ponto de vista global, houve um leve avanço no grau de sustentabilidade das duas propriedades. No entanto, ambas continuam no mesmo nível de transição agroecológica apresentado em pesquisa realizada em 2005.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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The objective of this work is to determine the membership functions for the construction of a fuzzy controller to evaluate the energy situation of the company with respect to load and power factors. The energy assessment of a company is performed by technicians and experts based on the indices of load and power factors, and analysis of the machines used in production processes. This assessment is conducted periodically to detect whether the procedures performed by employees in relation to how of use electricity energy are correct. With a fuzzy controller, this performed can be done by machines. The construction of a fuzzy controller is initially characterized by the definition of input and output variables, and their associated membership functions. We also need to define a method of inference and a processor output. Finally, you need the help of technicians and experts to build a rule base, consisting of answers that provide these professionals in function of characteristics of the input variables. The controller proposed in this paper has as input variables load and power factors, and output the company situation. Their membership functions representing fuzzy sets called by linguistic qualities, as “VERY BAD” and “GOOD”. With the method of inference Mandani and the processor to exit from the Center of Area chosen, the structure of a fuzzy controller is established, simply by the choice by technicians and experts of the field energy to determine a set of rules appropriate for the chosen company. Thus, the interpretation of load and power factors by software comes to meeting the need of creating a single index that indicates an overall basis (rational and efficient) as the energy is being used.

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The objective of this study was to dimension the economic risks and returns on adopters of genetically modified (GM) maize in one of the major corn producing regions of São Paulo state. We performed analysis of variation of the quantities and prices of insecticides used, productivity gains, and variation in the price differentials between GM maize and conventional hybrids seeds, according to account to the maize prices oscillation during the period studied. The net benefits methodology was used, in other words, the economic gains minus the costs of GM technology under risk conditions were calculated. The net benefits was calculated as a function of four critical variables: 1) GM maize productivity; 2) costs of pest control; 3) maize price; 4) GM seeds cost. The probability distribution functions of these critical variables were estimated and included in the net benefit equation. Using the Monte Carlo simulation methodology, the following indicator sets were estimated: central tendency measurements, variability in net benefits (total benefits minus total costs), sensitivity analysis of the net benefits in relation to the critical variables, and finally, a map of the risk to GM technology adopters. These indicators allow one to design economic scenarios associated with their probability of occurring. The results showed probability of 85% to positive gains to the farmers who adopted the transgenic maize seed cultivation. The variable with the greatest impact on the farmers' income was the reduction in productivity loss, that means, as higher is the maize productivity, higher will be the net income. The average gain was US$ 137,41 (R$ 2.45/US$)per hectare with the adoption of transgenic maize seed when compared to conventional maize seed.