Abstract The objective of this work was to probabilistically identify susceptible seasons to the highest incidence of stenospermocarpic fruit in 'Palmer' mangoes, using univariate and copula procedures. Additionally, the impact of climatic variables on price fluctuations in the mango market was evaluated in the region of the Vale do Submédio São Francisco, Brazil. The normal, log-normal, gamma, and generalized extreme value distributions, as well as the Frank copula, were fitted to temperature and relative humidity data (2007-2018) obtained from the meteorological station of Universidade do Estado da Bahia. The adequacy of the distributions was verified using the Kolmogorov-Smirnov, Cramér-von Mises, and Anderson-Darling tests. The Frank copula is suitable for the joint modeling of the maximum temperature and minimum relative humidity. The occurrence of high temperatures and low relative humidity affects the price of mangoes. November shows a high probability of the simultaneous occurrences of high temperatures and low relative air humidity, which makes 'Palmer' mango orchards in full bloom (or early fruiting) significantly subject to extreme weather conditions that favor higher rates of stenospermocarpy in the harvests from April to May. Palmer 'Palmer mangoes procedures Additionally Francisco Brazil normal lognormal, lognormal log log-normal gamma 20072018 2007 2018 (2007-2018 Bahia KolmogorovSmirnov, KolmogorovSmirnov Kolmogorov Smirnov, Smirnov Kolmogorov-Smirnov Cramérvon Cramér von Mises AndersonDarling Anderson Darling tests or fruiting May 2007201 200 201 (2007-201 200720 20 (2007-20 20072 2 (2007-2 (2007- (2007 (200 (20 (2 (

Resumo O objetivo deste trabalho foi identificar, de forma probabilística, as épocas suscetíveis à maior incidência de frutos estenoespermocárpicos em mangas 'Palmer', por meio de procedimentos univariados e de cópulas. Além disso, o impacto de variáveis climáticas sobre a oscilação de preços no mercado da manga foi avaliado na região do Vale do Submédio São Francisco, Brasil. As distribuições normal, log-normal, gama e generalizada de valores extremos, bem como a cópula de Frank, foram ajustadas aos dados de temperatura e umidade relativa (2007-2018), obtidos na estação meteorológica da Universidade do Estado da Bahia. A adequação das distribuições foi verificada por meio dos testes de Kolmogorov-Smirnov, Cramér-von Mises e Anderson-Darling. A cópula de Frank é adequada para modelar, conjuntamente, a temperatura máxima e a umidade relativa mínima do ar. As ocorrências de altas temperaturas e baixa umidade relativa afetam os preços de mangas. Novembro apresenta grande probabilidade da ocorrência simultânea de altas temperaturas e baixa umidade relativa do ar, o que torna os pomares de manga 'Palmer' em plena floração (ou frutificação precoce) significativamente sujeitos a condições climáticas extremas, que favorecem taxas mais elevadas de estenoespermocarpia nas safras de abril a maio. identificar probabilística Palmer, Palmer , cópulas disso Francisco Brasil normal lognormal, lognormal log log-normal extremos 20072018, 20072018 2007 2018 (2007-2018) Bahia KolmogorovSmirnov, KolmogorovSmirnov Kolmogorov Smirnov, Smirnov Kolmogorov-Smirnov Cramérvon Cramér von AndersonDarling. AndersonDarling Anderson Darling. Darling Anderson-Darling modelar conjuntamente ar 'Palmer ou precoce extremas maio 2007201 200 201 (2007-2018 200720 20 (2007-201 20072 2 (2007-20 (2007-2 (2007- (2007 (200 (20 (2 (

ABSTRACT. In this work, lacunarity analysis is performed on soil pores segmented by the pure voxel extraction method from soil tomography images. The conversion of forest to sugarcane plantation was found to result in higher sugarcane soil pore lacunarity than that of native forest soil, while the porosity was found to be lower. More precisely, this study shows that native forest has more porous soil with a more uniform spatial distribution of pores, while sugarcane soil has lower porosity and a more heterogeneous pore distribution. Moreover, validation through multivariate statistics demonstrates that lacunarity can be considered a relevant index of clustering and can explain the variability among soils under different land use systems. While porosity by itself represents a fundamental concept for quantification of the impact of land use change, the current findings demonstrate that the spatial distribution of pores also plays an important role and that pore lacunarity can be adopted as a complementary tool in studies directed at quantifying the effect of human intervention on soils.

Resumo Este trabalho objetivou avaliar períodos secos e chuvosos nas sub-regiões da Bacia Hidrográfica do Rio São Francisco (BHSF) utilizando o Índice de Precipitação Padronizado (SPI) e cadeias de Markov. Cada sub-região da BHSF possui características físicas e climáticas específicas, dessa forma foram utilizadas quatro estações pluviométricas contendo dados com 46 anos, de 1970 a 2015, obtidas na Agência Nacional de Águas (ANA), representativas de cada sub-região. O SPI foi calculado para as escalas de tempo de seis e doze meses e as matrizes de probabilidades de transição foram obtidas utilizando as cadeias de Markov. As matrizes de transição mostraram que, em ambas as escalas, caso a condição climática estivesse em classe de seca severa ou chuvosa, a mudança para outra classe seria pouco provável a curto prazo. Correlacionando esta informação com as probabilidades da distribuição estacionária, foi possível encontrar as regiões que têm maiores possibilidades de no momento futuro estar sob clima chuvoso ou de seca. Os tempos de recorrência calculados para as estações inseridas no semiárido foram menores quando comparado com o valor do tempo da estação representativa do Alto São Francisco que possui maiores níveis de precipitação, confirmando a predisposição do semiárido em apresentar maiores chances de períodos futuros de seca.

Abstract This work evaluated dry and rainy conditions in the subregions of the São Francisco River Basin (BHSF) using the Standardized Precipitation Index (SPI) and Markov chains. Each subregion of the BHSF has specific physical and climatic characteristics. The data was obtained from the National Water Agency (ANA), collected by four pluviometric stations (representative of each subregion), covering 46 years of data, from 1970 to 2015. The SPI was calculated for the time scales of six and twelve months and transition probabilities were obtained using the Markov chain. Transition matrices showed that, at both scales, if the climate conditions were severe drought or rainy, switching to another class would be unlikely in the short term. Correlating this information with the probabilities of the stationary distribution, it was possible to find the regions that are most likely to be under rainy or dry weather in the future. The recurrence times calculated for the stations that belong to the semi-arid region were smaller when compared to the value of the return period of the representative station of Upper São Francisco that has higher levels of precipitation, confirming the predisposition of the semi-arid region to present greater chances of future periods of drought.