METODOLOGIA COMPUTACIONAL PARA DEFINIÇÃO DE PERÍODOS DE SEMEADURA DE CULTURAS AGRÍCOLAS

• Ano de defesa: 2015
• Autor(a) principal: Szesz Junior, Albino
• Orientador(a): Vaz, Maria Salete Marcon Gomes
• Banca de defesa: Mathias, Ivo Mario , Bittencourt, Juliana Vitoria Messias
• Tipo de documento: Dissertação
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: UNIVERSIDADE ESTADUAL DE PONTA GROSSA
• Programa de Pós-Graduação: Programa de Pós Graduação Computação Aplicada
• Departamento: Computação para Tecnologias em Agricultura
• País: BR
• Palavras-chave em Português: simulação; modelo computacional; dados grometeorológicos; DSSAT; MarkSimGCM
• Palavras-chave em Inglês: simulation; computational model; agrometeorological data; DSSAT; MarkSimGCM
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO
• Link de acesso: http://tede2.uepg.br/jspui/handle/prefix/124

The aim of this work is to present the SEMEARE, a methodology for defining periods of sowing of crops, with four stages, including planning, modeling, experimentation and decision making. At the planning stage the goals and strategies for defining the sowing periods are defined and analyzed the agricultural context of the study area. In the modeling stage, the simulation models are set to be used, sowing dates and other necessary data. After system modeling and simulations performed are obtained yield values, germination time, number of days to harvest, among others, for each specific date. At the trial stage analysis of the information and you can set the best times for sowing culture defined and then make decision making. For validation, the methodology we used the MarkSimGCM a climate data simulator, to generate daily weather data, and the DSSAT, Version 4.5, using CERES-Maize model to simulate growth of corn. The simulation was applied to a hypothetical maize cultivar with average maturity characteristics in a real location (S25 ° 09'18.70 "W 50 ° 05'15.65"),with planting carried out under rainfed conditions, sowing depth 5 cm spacing planting 20 cm between seeds and 50 cm between plant rows and plant population: 10 plants / m2. Thus the DSSAT addresses the data of corn, while the MarkSimGCM simulates weather data for use in DSSAT in creating a weather station with daily data of maximum and minimum temperature, precipitation and solar radiation between 2011 and 2014. We simulated in 29 different sowing dates in Vintage 2011-2012, 2012- 2013, 2013-2014. From this income was generated graphs, maturation cycle,information used in decision making. This research resulted in the development of a generic methodology, which enables the use of different simulation models of crops and climate data, interacting with different systems of decision support as agricultural.