Statistical modelling of extreme rainfall in Madeira Island

Extreme rainfall events have triggered a significant number of flash floods in Madeira Island along its past and recent history. Madeira is a volcanic island where the spatial rainfall distribution is strongly affected by its rugged topography. In this thesis, annual maximum of daily rainfall data from 25 rain gauge stations located in Madeira Island were modelled by the generalised extreme value distribution. Also, the hypothesis of a Gumbel distribution was tested by two methods and the existence of a linear trend in both distributions parameters was analysed. Estimates for the 50– and 100–year return levels were also obtained. Still in an univariate context, the assumption that a distribution function belongs to the domain of attraction of an extreme value distribution for monthly maximum rainfall data was tested for the rainy season. The available data was then analysed in order to find the most suitable domain of attraction for the sampled distribution. In a different approach, a search for thresholds was also performed for daily rainfall values through a graphical analysis. In a multivariate context, a study was made on the dependence between extreme rainfall values from the considered stations based on Kendall’s τ measure. This study suggests the influence of factors such as altitude, slope orientation, distance between stations and their proximity of the sea on the spatial distribution of extreme rainfall. Groups of three pairwise associated stations were also obtained and an adjustment was made to a family of extreme value copulas involving the Marshall–Olkin family, whose parameters can be written as a function of Kendall’s τ association measures of the obtained pairs.

Multivariate analysis for the classification and differentiation of Madeira wines according to main grape varieties

In order to differentiate and characterize Madeira wines according to main grape varieties, the volatile composition (higher alcohols, fatty acids, ethyl esters and carbonyl compounds) was determined for 36 monovarietal Madeira wine samples elaborated from Boal, Malvazia, Sercial and Verdelho white grape varieties. The study was carried out by headspace solid-phase microextraction technique (HS-SPME), in dynamic mode, coupled with gas chromatography–mass spectrometry (GC–MS). Corrected peak area data for 42 analytes from the above mentioned chemical groups was used for statistical purposes. Principal component analysis (PCA) was applied in order to determine the main sources of variability present in the data sets and to establish the relation between samples (objects) and volatile compounds (variables). The data obtained by GC–MS shows that the most important contributions to the differentiation of Boal wines are benzyl alcohol and (E)-hex-3-en-1-ol. Ethyl octadecanoate, (Z)-hex-3-en-1-ol and benzoic acid are the major contributions in Malvazia wines and 2-methylpropan-1-ol is associated to Sercial wines. Verdelho wines are most correlated with 5-(ethoxymethyl)-furfural, nonanone and cis-9-ethyldecenoate. A 96.4% of prediction ability was obtained by the application of stepwise linear discriminant analysis (SLDA) using the 19 variables that maximise the variance of the initial data set.

Correlations among behavior, performance and environment in broiler breeders using multivariate analysis

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

Multivariate analysis of fresh-cut carambola slices stored under different temperatures

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

Spontaneous Interblink Time Distributions in Patients with Graves' Orbitopathy and Normal Subjects

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Assessment of agroeconomic indices in polycultures of lettuce, rocket and carrot through uni- and multivariate approaches in semi-arid Brazil

The polyculture among vegetables is an activity that to have good results, needs a proper planning. Although it often requires more labor, has several advantages over monoculture, among them is that polycultures are generally are more productive, provide with productivity of various plant constituents and a more balanced human diet, contribute to economic return, economic and yield stability, social benefits and farmer's direct participation in decision-making. The objective of this study was to evaluate agroeconomic indices of polycultures derived from the combination of two cultivars of lettuce with two cultivars of rocket in two cultures strip-intercropped with carrot cultivar 'Brasilia' through uni-multivariate approaches in semi-arid Brazil. The experimental design used was of randomized complete blocks with five replications and the treatments arranged in a factorial scheme of 2 x 2. The treatments consisted of the combination of two lettuce cultivars (Baba de Verao and Taina) with two rocket cultivars (Cultivada and Folha Larga) in two cultures associated with carrot cv. Brasilia. hi each block were grown plots with two lettuce cultivars and two rocket cultivars, and carrot in sole crop. In each system was determined the lettuce leaf yield, rocket green mass yield and carrot commercial yield. Agrieconomic indices such as operational cost, gross and net income, monetary advantage, rate of return, profit margin, land equivalent ratio and yield efficiency for DEA were used to measure the efficiency of intercropping systems. In the bicropping of lettuce and rocket associated with carrot cv. 'Brasilia', suggests the use of lettuce cultivar 'Taina' combined with rocket cultivars 'Cultivada' or 'Folha Larga'. It was observed significant effect of lettuce cultivars in the evaluation of polycultures of lettuce, carrot and rocket, with strong expression for the lettuce cultivar 'Taina'. Both uni- and multivariate approaches were effective in the discrimination of the best polycultures. (C) 2011 Elsevier Ltd. All rights reserved.

Discrimination of geomorphic surfaces with multivariate analysis of soil attributes in sandstone - basalt lithosequence

O conceito de superfície geomórfica permite uma interligação entre os diferentes ramos da ciência do solo, tais como geologia, geomorfologia e pedologia. Esta associação favorece a compreensão da distribuição espacial dos solos na paisagem, e torna possível compreender o comportamento dos atributos do solo, que estão principalmente relacionadas com a estratigrafia e formas do relevo. Assim, este estudo visa à aplicação da estatística multivariada para categorizar superfícies geomórficas em uma litossequência arenito-basalto, de modo a fornecer uma base para a avaliação do solo em áreas afins. A área de estudo está localizada no município de Pereira Barreto, São Paulo, Brasil. A área escolhida possui 530 hectares, onde foram localizadas e mapeadas três superfícies geomórficas (I, II e III). Na área, 134 amostras foram coletadas nas profundidades de 0,0-0,2 m e 0,8-1,0 m, foram determinados os conteúdos de areia, silte e argila, pH em CaCl2, conteúdo de MO, P, Ca, Mg, K, Al e H+Al. Com base nos resultados, foram realizadas a análise univariada e multivariada de variância, clusters e principal componente, a fim de comparar as três superfícies geomórficas. A análise estatística univariada dos atributos do solo não foi eficiente na identificação das três superfícies geomórficas. Utilizando-se os atributos físicos e químicos do solo, as técnicas estatísticas multivariada permitiram à separação dos três grupos de corpos naturais do solo que foram equivalentes as três superfícies geomórficas mapeadas. Estes resultados são interessantes, pois demonstram a viabilidade da utilização de classificação numérica das superfícies geomórficas para ajudar no mapeamento de solo.

Spatial variations at different observational scales and the seasonal distributions of stream macroalgae in a Brazilian subtropical region

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Multivariate optimization and validation of an analytical methodology by RP-HPLC for the determination of losartan potassium in capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A control chart based on sample ranges for monitoring the covariance matrix of the multivariate processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A new chart based on sample variances for monitoring the covariance matrix of multivariate processes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Comparison of the univariate and multivariate methods in the optimization of experimental conditions for determining Cu, Pb, Ni and Cd in biodiesel by GFAAS

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

Parturition intervals and distributions of parturitions by months of buffalo in Southern and South-eastern Brazil

Reproductive rate is an important component of economic success in livestock production. Parturition interval (IEP) is a direct measure of the productivity of the animal. Long IEP reduce the number of calves produced per year. The objective this study was to determine the distribution of parturitions across month and to evaluate factors affecting IEP. The data included 7,588 parturitions of Murrah, Mediterranean and Carabobo buffalo from 10 herds in Southern and South-eastern Brazil. The analysis of distribution of parturitions evaluated the effects of month, year and their interaction on birth date of calves by using a Chi-Square test in SAS PROC FREQ (SAS Institute, Cary, NC, USA). Parturition intervals (n = 2,630) were evaluated using analysis of variance in SAS PROC GLM. The model for IEP included the fixed effects of season (December to May = 1, June to November = 2), year, season x year, sex of the preceding parturition, age of weaning of the previous calf, and herd. All sources of variation were significant (P<0.0001) except sex of the preceding parturition (P <0.85). The mean IEP was 446.7 +/- 10.4 days, for seasons 1 and 2 IEP were 419.8 +/- 11.3 and 473.6 +/- 40.7 days, respectively, a difference of 54 days. As weaning age increased there was a lengthening of IEP. Buffalo in Brazil showed seasonal parturition with calving concentrated from January to April, although the frequency by month differed across years (P<0.0001). These months also had the lowest calving interval.

Evaluation of development traits of beef buffalo in Brazil using multivariate analysis

The buffalo population in Brazil increased about 12.9% between 1998 and 2003, to 2.8 million head, evidencing the importance of this species for the country. The objective this work was evaluation of animal growth using multivariate analysis. The data were from 2,944 water buffalo from 10 herds raised in pasture conditions in Brazil. Principal components and genetic distances were estimated using proc PRINCOMP and proc CANDISC in SAS (SAS Inst. Inc. Cary, NC, USA). Variables analyzed were birth weight (BW), age at weaning (AW), weaning weight (WT), weight adjusted to 205 d (W205), total gain between BW and WT (TG), daily gain between BW and WT (DG), weight adjusted to 365 d (W365), total gain between WT and W365 (TG3), daily gain between WT and W365 (TGD3), weight adjusted to 550 d (W550) and weight adjusted to 730 d (W730). Means and standard deviations for each variable were 39.4 +/- 3.2 kg, 225.6 +/- 38.8 d, 209.4 +/- 39.4 kg, 195.4 +/- 30.2 kg, 157.4 +/- 32.0 kg, 0.77 +/- 0.16 kg/d, 282.0 +/- 43.5 kg, 73.9 +/- 33.9 kg, 0.53 +/- 0.21 kg/d, 406.8 +/- 67.9 kg, and 468.2 +/- 70.6 kg, respectively. The eigenvalues to four first principal components were 5.29, 2.54, 1.66, 1.01, and justify 48%, 23%, 15% and 9%, respectively, with a total cumulative 95%. We created an index using the first principal component which is Y. 0.0552 BW + 0.0438 AW + 0.3142 WT + 0.3549 W205 + 0.3426 TG + 0.3426 DG + 0.4070 W365- 0.1531 TG3 - 0.2059 TGD3 - 0.3833 W550 - 0.3966 W730. This index accounted for 48% the variation in the correlation matrix. This principal component emphasizes early growth of the animal. Estimates the pair-wise squared distances between herds, D2(i vertical bar j)= ((x) over bar (i)-(x) over bar (j))' cov(-1)((x) over bar (i)-(x) over bar (j)), using with basis the average of weight of animals, showed the largest distance between herds eight (Murrah: DF) and seven (Murrah: Amazon) and the closest distance between herds one (Mediterranean - RS) and five (Jafarabadi - SP).