The objective of this study was to evaluate the annual standardized precipitation index (SPI) obtained from the DrinC software based on multivariate analysis in the identification of rainfall and drought extremes in the State of Alagoas and its relationship with El Niño-Southern Oscillation. Monthly rainfall data from 1960 to 2016 from National Water Agency were analysed. Annual SPI (SPI-12) has been designed for comparison with ENSO phases via Oceanic Niño Index for 3.4 region and in identifying climate extremes in the State of Alagoas. The principal component analysis and cluster analysis techniques were applied to the rainfall series of SPI-12. Extreme events were identified in both rainy and drought periods according to SPI-12, and were associated with the ENSO phases (El Niño, La Niña, and Neutral). The first four principal components explained 46.68% of the variance. Our findings are crucial for agriculture and civil defence since northeastern Brazil has several areas of risk and social vulnerability.

Brazilian biomes are home to a significant portion of the world's biodiversity, with a total of 14% of existing species and still concentrate 20% of the world's water resources. However, changes in biomes have a direct impact on rainfall patterns and water recycling. Based on this, the objective was to evaluate the variability of rainfall in the four existing biomes in the Northeast Brazil (NEB) and their interaction with the ENSO climate variability mode and regional scale meteorological systems via CHELSA product. For this, monthly rainfall data were used from 1979 to 2013, with a spatial resolution of 1 km × 1 km of the CHELSA product, and seasonal and annual rainfall patterns were extracted via boxplot. It was found that the rainy season in the Amazon, Caatinga and Cerrado biomes occurred between January and April, with varying intensities, except for the Atlantic Forest. Such seasonality patterns are associated with the NEB meteorological systems, with emphasis on ITCZ (all Biomes), UTCV (Amazon, Caatinga and Cerrado), Frontal Systems (extreme south of Caatinga, Cerrado and Atlantic Forest) and EWD/ TWD in the (Atlantic Forest). In the inter-annual scale, the remarkable influence of ENSO was verified,

At present, single-trait best linear unbiased prediction (BLUP) is the standard method for genetic selection in soybean. However, when genetic selection is performed based on two or more genetically correlated traits and these are analyzed individually, selection bias may arise. Under these conditions, considering the correlation structure between the evaluated traits may provide more-accurate genetic estimates for the evaluated parameters, even under environmental influences. The present study was thus developed to examine the efficiency and applicability of multi-trait multi-environment (MTME) models by the residual maximum likelihood (REML/BLUP) and Bayesian approaches in the genetic selection of segregating soybean progeny. The study involved data pertaining to 203 soybean F 2:4 progeny assessed in two environments for the following traits: number of days to maturity (DM), 100-seed weight (SW), and average seed yield per plot (SY). Variance components and genetic and non-genetic parameters were estimated via the REML/BLUP and Bayesian methods. The variance components estimated and the breeding values and genetic gains predicted with selection through the Bayesian procedure were similar to those obtained by REML/BLUP. The frequentist and Bayesian MTME models provided higher estimates of broad-sense heritability per plot (or heritability of total effects of progeny; ) and mean accuracy of progeny than their respective single-trait versions. Bayesian analysis provided the credibility intervals for the estimates of . Therefore, MTME led to greater predicted gains from selection. On this basis, this procedure can be efficiently applied in the genetic selection of segregating soybean progeny.

MELHORAMENTO GENÉTICO VEGETAL -ArtigoUso da metodologia REML/BLUP para seleção de genótipos de algodoeiro com maior adaptabilidade e estabilidade produtiva Use of REML/BLUP methodology for selecting cotton genotypes with higher adaptability and productive stability those tested, since they present high adaptability and productive stability of cotton. There was agreement among the statistics used in discrimination of the most productive genotypes with high adaptability and stability, indicating that they can be part of selective criteria in the routine of cotton breeding programs.

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The search for high yield has identified ammonium toxicity as a problem in arable soils world wide. Ammonia toxicity can be suppressed by the use of silicon, but this fact still needs to be elucidated. Therefore, this review aimed to highlight the harmful effects of ammonium toxicity on model plants, and to determine the effects of Si on the mitigation of abiotic stress. Some plant species are considered as tolerant, and others as sensitive to high N concentrations. In sensitive plants, high ammonium concentrations may hinder the plant's development and even lead to the plant's death due to biochemical, physiological, and nutritional changes. Studies have demonstrated that silicon can mitigate or alleviate the deleterious effects caused by the toxic effect of NH 4 + . These findings were attributed to improvements in the physiological and nutritional parameters of plants. Given the importance of ionic balance between N forms for the plant's development, further studies must be performed to detect mechanisms promoted by Si to decrease or mitigate the harmful effects caused by excess ammonium in plants.Agronomy Journal. 2020;112:635-647. wileyonlinelibrary.com/journal/agj2 635

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ABSTRACT. To date, path analysis has been used with the aim of breeding different cultures. However, for cotton, there have been few studies using this analysis, and all of these have used fiber productivity as the primary dependent variable. Therefore, the aim of the present study was to identify agronomic and technological properties that can be used as criteria for direct and indirect phenotypes in selecting cotton genotypes with better fibers. We evaluated 16 upland cotton genotypes in eight trials conducted during the harvest 2008/2009 in the State of Mato Grosso, using a randomized block design with four replicates. The evaluated traits were: plant height, average boll weight, percentage of fiber, cotton seed yield, fiber length, uniformity of fiber, short fiber index, fiber strength, elongation, maturity of the fibers, micronaire, reflectance, and the degree of yellowing. Phenotypic correlations between the traits and cotton fiber yield (main dependent variable) 2 F.J.C. Farias et al. Genetics and Molecular Research 15 (3): gmr.15038239 were unfolded in direct and indirect effects through path analysis. Fiber strength, uniformity of fiber, and reflectance were found to influence fiber length, and therefore, these traits are recommended for both direct and indirect selection of cotton genotypes.