ABSTRACT Considering the different levels of efficiency reported in the literature for the use of potassium silicate (PS) on wheat blast, this work investigated whether the foliar spray of PS could reduce blast severity in young and adult wheat cultivars and increase wheat yield indexes. At 28 days after emergence, the ‘BR 18’, ‘BRS 208’, and ‘BRS Louro’ plant cultivars were sprayed with the following PS rates (g/L): 0, 5, 10, 20, and 25. Twenty-four hours after PS application, the plants were inoculated with 105 conidia/mL of P. oryzae. Seven days after the inoculations the blast severity in the control plants of the ‘BRS Louro’, ‘BR 18’, and ‘BRS 208’ cultivars were 85%, 60% and 35%, respectively. For the 5g/L (PS) dose, severity was reduced more than 60% in the three cultivars. Adult plants of the ‘BRS 208’ and ‘BRS Louro’ cultivars were sprayed with water (control) or PS (20g/L) at the tillering, booting, and inflorescence stages. The PS applications were not efficient at reducing blast severity in adult plants and did not affect the wheat yield indexes. PS is only efficient at reducing blast severity in young plants. (PS indexes 2 emergence BR 18, 18 , 18’ BRS 208, 208 Louro g/L gL g L (g/L) 0 5 10 20 25 Twentyfour Twenty four application conidiamL conidia mL P oryzae Louro, 85 85% 60 35 35% respectively 5gL 5g dose (control 20g/L 20gL 20g (20g/L tillering booting stages 1 (g/L 8 6 3

Resumo As cidades de Petrópolis (RJ) e Poços de Caldas (MG) estão situadas em regiões serranas de seus respectivos estados e sofrem frequentemente com estragos provocados por fortes chuvas. Analisar e prever a ocorrência de precipitações máximas nessas localidades são fundamentais para o planejamento de atividades vulneráveis à sua ocorrência. A modelagem dessa variável é feita geralmente com distribuição generalizada de valores extremos (GEV), e a metodologia bayesiana tem apresentado bons resultados na estimação de seus parâmetros. Sendo assim, o presente estudo teve como objetivos ajustar a distribuição GEV às séries históricas de precipitação máxima de Petrópolis e Poços de Caldas e avaliar diferentes estruturas de distribuições a priori, informativas e não informativas, na predição da precipitação máxima esperada para diferentes tempos de retorno. Foram analisados o número de acertos e a precisão a fim de avaliar as previsões obtidas com as informações advindas das precipitações máximas de diferentes localidades para eliciação da distribuição a priori. A obtenção das distribuições marginais a posteriori foi realizada usando-se o método Monte Carlo via cadeias de Markov. A utilização da distribuição a priori informativa fundamentada nos dados de Poços de Caldas foi mais precisa e teve maior número de acertos para predizer as precipitações máximas para Petrópolis, enquanto para Poços de Caldas foi a priori informativa com base nas informações de São João da Boa Vista (SP). Para ambas as localidades, espera-se que, em um tempo médio de cinco anos, ocorra pelo menos um dia com precipitação máxima igual ou superior a 100 mm. RJ (RJ MG (MG chuvas GEV, , (GEV) parâmetros assim retorno usandose usando se Markov SP. SP . (SP) esperase espera que anos 10 mm (GEV (SP 1

ABSTRACT The cities of Petrópolis (RJ) and Poços de Caldas (MG) are located in the mountain range regions of their respective states in Brazil. They frequently suffer from damage caused by heavy rains. Therefore, analyzing and predicting the occurrence of maximum rainfall for these locations is fundamental for planning activities that are vulnerable to its occurrence. The modeling of this variable is done, generally, through the generalized distribution of extreme values (GEV), which the Bayesian methodology has shown good results in estimating its parameters. Therefore, the present study aimed to fit the GEV distribution to the historical series of maximum rainfall in Petrópolis and Poços de Caldas, and to evaluate different structures of prior distributions, informative and non-informative, in predicting the maximum rainfall expected for different return times. The number of successes and precision were analyzed in order to evaluate the predictions obtained with the information from the maximum rainfall of different locations to elicit the prior distribution. To obtain the posterior marginal distributions, the Monte Carlo method via Markov Chains was used. The use of informative prior distribution based on data from Poços de Caldas was more precise and accurate to predict the maximum rainfall for Petrópolis, while for Poços de Caldas, the informative prior based on information from São João da Boa Vista (SP) provided the best results. For both locations, it is expected that, in an average time of 5 years, there will be at least one day with maximum rainfall equal to or greater than 100 mm. RJ (RJ MG (MG Brazil rains Therefore done generally GEV, , (GEV) parameters distributions noninformative, noninformative non informative, non-informative times used SP (SP years 10 mm (GEV 1

RESUMO A análise sensorial do Café supõe que um painel sensorial é formado por provadores treinados, de acordo com as recomendações da American Specialty Coffee Association. No entanto, a escolha determina que a preferência de um café é rotineiramente feita através da experimentação com os consumidores, que em grande parte não tem habilidades especiais em termos de características sensoriais. Por este fato, este estudo objetivou aplicar o método computacional intensivo no estudo de notas sensoriais a partir de um painel sensorial não treinado considerando as distribuições de probabilidade pertencentes à classe dos valores extremos. Assim, foram considerados quatro tipos de cafés especiais produzidos em diferentes processos e alturas na região serrana da Mantiqueira, em Minas Gerais. Concluiu-se que a distribuição generalizada de Pareto pode ser aplicada à análise sensorial para discriminar os diferentes tipos de cafés especiais e que o método de amostragem por importância por simulação de Monte Carlo, considerando o modelo probabilístico ajustado para identificar o café especial, com notas apresentaram a maior variabilidade.

ABSTRACT Coffee sensory analysis is usually made by a sensory panel, which is formed by trained tasters, following the recommendations of the Specialty Coffee Association of America. However, the preference for a coffee is commonly determined by experimentation with consumers, who typically have no special skills in terms of sensory characteristics. Therefore, this study aimed at applying an intensive computational method to study sensory notes given by an untrained sensory panel, considering the probability distributions of the class of extreme values. Four types of specialty coffees produced under different processes and in varied altitudes in the mountainous region of Mantiqueira, Minas Gerais, were considered. We concluded that the generalized Pareto distribution can be applied to sensory analysis to discriminate types of specialty coffees. Furthermore, the method of importance sampling by Monte Carlo simulation showed greater variability considering a probabilistic model adjusted to identify specialty coffees.

ABSTRACT The objective of the present study was to quantify the biomass and carbon stock in a Seasonal Semideciduous Forest remnant in southern Minas Gerais. Forest inventory data taken between 2010 and 2013 in permanent plots, was used to estimate biomass with an allometric equation. Basic wood density (Db) and carbon content were determined in the laboratory and the carbon stock was obtained by multiplying biomass by carbon content. The species with the lowest and highest Db were Nectandra lanceolata (0.38 g cm-3) and Machaerium villosum (0.77 g cm-3). The species that showed the lowest and highest carbon content values were Casearia decandra (41.85%) and Nectandra oppositifolia (46.57%). The biomass stock for the area was 126.92 ± 0.09 t ha-1, which corresponded to 55.91 ± 0.05 t ha -1 of carbon stock and a periodic annual increment of 3.07 t ha-1 year -1.