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Campinas, Brazil

Considering the presence of non-stationary components, such as trends, in the extreme minimum air temperature series available from three locations of the State of São Paulo-Brazil, the aim of this research was to describe the probabilistic structure of this variable by using a non stationary model (based on the general extreme value distribution; GEV model) in which the parameters are estimated as a function of time covariate. The Mann-Kendall test has proven the presence of significant increasing trends in all analyzed series. Furthermore, according to the Pettitt (changing-point) test, 1991 is the initial year of these trends (in the three locations). The applied selection criteria indicated that a GEV model in which the location parameter is estimated as a function of time is recommended to describe the probability structure of the variable under evaluation. The others two parameters of this model remained time-independent. According to this non-stationary model, the detected trends in the climate conditions of these locations have shown the same rate of change (0.04°C per year).


Durum wheat (Triticum durum Desf.), also known as "pasta wheat", is characterized to produce yellow flour (semolina) adequate for pasta elaboration. In Brazil, its extensive cultivation is limited by the low crop adaptability to acid soils, with toxic aluminum levels. Aiming to estimate the inheritance of the aluminum toxicity tolerance, crosses were made between tolerant durum wheat genotype P33 and the sensitive IAC-1003 to obtain the hybrid in F 1 generation and subsequent generations F 2, F 3, BC 1F 1, BC 2F 1, BC 1F 2 and BC 2F 2. All genotypes were tested in nutrient solutions and the aluminum toxicity tolerance was measured by the growth capacity of the primary central root after 48 hours of treatment with 2 mg L -1 of aluminum in nutrient solution. The genotype P33 has differed from IAC-1003 by one pair of dominant alleles for the tolerance at the presence of this aluminum concentration in the nutrient solution and it could be used as source of aluminum tolerance in durum wheat breeding programs.


The aim of this study was to describe monthly series of the Standardized Precipitation Index obtained from four weather stations of the State of São Paulo, Brazil. The analyses were carried out by evaluating the normality assumption of the SPI distributions, the spectral features of these series and, the presence of climatic trends in these datasets. It was observed that the Pearson type III distribution was better than the gamma 2-parameter distribution in providing monthly SPI series closer to the normality assumption inherent to the use of this standardized index. The spectral analyses carried out in the timefrequency domain did not allow us to establish a dominant mode in the analyzed series. In general, the Mann-Kendall and the Pettitt tests indicated the presence of no significant trend in the SPI series. However, both trend tests have indicated that the temporal variability of this index, observed at the months of October over the last 60 years, cannot be seen as the result of a purely random process. This last inference is due to the concentration of decreasing trends, with a common beginning (1983/84) in the four locations of the study.


Several studies have applied the Kolmogorov-Smirnov test (KS) to verify if a particular parametric distribution can be used to assess the probability of occurrence of a given agrometeorological variable. However, when this test is applied to the same data sample from which the distribution parameters have been estimated, it leads to a high probability of failure to reject a false null hypothesis. Although the Lilliefors test had been proposed to remedy this drawback, several studies still use the KS test even when the requirement of independence between the data and the estimated parameters is not met. Aiming at stimulating the use of the Lilliefors test, we revisited the critical values of the Lilliefors test for both gamma (gam) and normal distributions, provided easy-to-use procedures capable of calculating the Lilliefors test and evaluated the performance of these two tests in correctly accepting a hypothesized distribution. The Lilliefors test was calculated by using critical values previously presented in the scientific literature (KSLcrit) and those obtained from the procedures proposed in this study (NKSLcrit). Through Monte Carlo simulations we demonstrated that the frequency of occurrence of Type I (II) errors associated with the KSLcrit may be unacceptably low (high). By using the NKSLcrit we were able to meet the significance level in all Monte Carlo experiments. The NKSLcrit also led to the lowest rate of Type II errors. Finally, we also provided polynomial equations that eliminate the need to perform statistical simulations to calculate the Lilliefors test for both gam and normal distributions.


Goncalves-Vidigal M.C.,State University of Maringa | Rubiano L.B.,Instituto Agronomico IAC
Crop Breeding and Applied Biotechnology | Year: 2011

Molecular markers are powerful tools for analyzing genome diversity within a species, and to evaluate genetic relationships between individuals and populations. Among them, microsatellites (SSRs) are one of the most important polymorphic markers that can be used effectively to distinguish germplasm accessions. These markers present high informative content due to their codominant inheritance, multiallelism, mendelian pattern and good genome coverage. The enrichment methodology for microsatellite development has a superior efficiency in plants, especially when performed using biotin-labeled microsatellite oligoprobes and streptavidin-coated magnetic beads. The development of EST-SSR markers has become a fast and relatively inexpensive way but it is limited to species for which this type of database exists. Given the high polymorphism level of microsatellites when compared to other markers, SSRs have been used to study population structure, for genetic diversity analysis, genetic mapping and marker assisted selection.

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