989 resultados para verão


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With the objective of gathering technical data about soybean cultivars performance in Botucatu, state of São Paulo, Brazil, an experiment was conducted to evaluate seventeen genotypes. The experiment was conducted during the Summer seasons of 2002/03, 2003/04, and 2004/05. The experiment was set in the field according to a complete block design with four repetitions. The soybean cultivars were ‘Embrapa 48’, ‘BRS 132’. ‘BRS 183’, ‘BRS 212’, ‘IAC 22’, and ‘IAC 23’ (early cycled varieties), ‘BRS 133’, ‘BRS 154’, ‘BRS 156’, ‘BRS 184’, ‘BRS 214’, ‘IAC 18’, and ‘IAC 24’ ( semi early varieties), and ‘BRS 134’, ‘BRS 215’, ‘IAC 8.2’, and ‘IAC 19’ (medium cycled varieties ). All the varieties, during the three cropping years, showed adequate plant height and first pod height of insertion for mechanical harvest. Among the production components, mass of 100 grains showed the highest variability. Cultivar ‘BRS 154’ (medium cycle) showed the highest variation in mass of 100 grains and was also the highest yielding variety in the cropping year of 2004/05. The majority of the cultivars yielded above 3,000 kg ha -1 during the cropping years of 2002/03 and 2004/05. The best yielding performance during the three cropping years were displayed by cultivars ‘IAC 22’ (early cycle), ‘BRS 133’ and ‘BRS 156’ ( both semi early cycled varieties).

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The aim of this work is to discriminate vegetation classes throught remote sensing images from the satellite CBERS-2, related to winter and summer seasons in the Campos Gerais region Paraná State, Brazil. The vegetation cover of the region presents different kinds of vegetations: summer and winter cultures, reforestation areas, natural areas and pasture. Supervised classification techniques like Maximum Likelihood Classifier (MLC) and Decision Tree were evaluated, considering a set of attributes from images, composed by bands of the CCD sensor (1, 2, 3, 4), vegetation indices (CTVI, DVI, GEMI, NDVI, SR, SAVI, TVI), mixture models (soil, shadow, vegetation) and the two first main components. The evaluation of the classifications accuracy was made using the classification error matrix and the kappa coefficient. It was defined a high discriminatory level during the classes definition, in order to allow separation of different kinds of winter and summer crops. The classification accuracy by decision tree was 94.5% and the kappa coefficient was 0.9389 for the scene 157/128. For the scene 158/127, the values were 88% and 0.8667, respectively. The classification accuracy by MLC was 84.86% and the kappa coefficient was 0.8099 for the scene 157/128. For the scene 158/127, the values were 77.90% and 0.7476, respectively. The results showed a better performance of the Decision Tree classifier than MLC, especially to the classes related to cultivated crops, indicating the use of the Decision Tree classifier to the vegetation cover mapping including different kinds of crops.

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Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

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Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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Pós-graduação em Ciência e Tecnologia Animal - FEIS

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

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Pós-graduação em Agronomia (Ciência do Solo) - FCAV

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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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Pós-graduação em Agronomia (Produção Vegetal) - FCAV