Resumo Objetivou-se avaliar o estado nutricional de crianças menores de 5 anos no Brasil no ano de 2009, o associando aos fatores sociais e demográficos. Utilizou-se dados da Pesquisa de Orçamento Familiar (POF 2008/2009), cujo perfil nutricional foi avaliado segundo os índices Peso-para-idade, Estatura-para-idade e Peso-para-estatura (n = 14.569). A associação foi estimada aplicando-se o teste de associação de Pearson, regressões logísticas e análises de correspondência. A análise de correspondência revelou maior associação da magreza com as crianças das regiões Norte e Nordeste, em famílias com menores níveis de renda e de cor/raça preta. O sobrepeso e a obesidade demonstraram maior relação com as crianças residentes nas regiões Sul, Sudeste e Centro-Oeste, do sexo masculino, da zona urbana, de cor/raça branca, com 3 anos de idade e de famílias com faixas de renda intermediárias. O sobrepeso e a obesidade demonstraram distribuição heterogênea quanto a sua espacialização dentre as Unidades da Federação. Aponta-se para uma polarização epidemiológica nutricional, sendo um grande desafio para a saúde coletiva reduzir as carências nutricionais e promover hábitos alimentares saudáveis desde a infância.
The occurrence of extreme climate events (ECEs) in the Amazon basin (AMZ) and northeast Brazil (NEB), such as torrential rains and severe droughts, varies in both spatial and temporal scales. Spatial analysis of trends allows observing changes in behaviour and determining in which regions a particular variable has been experiencing changes over time. Thus, the objective of this study is to analyse trends of 21 climate extremes indices, relative to maximum and minimum precipitation and temperature, as defined by the World Meteorological Organization, for the AMZ and NEB. The 21 indices were selected according to meteorological and climate characteristics of the regions. Through annual analysis it was possible to observe an increase in most of the climate extremes indices for air temperature in all AMZ and NEB subregions. As for extreme precipitation, only a few of the selected indices presented significant increase and/or decrease in their values. Overall, the eastern Amazon subregion in the AMZ presented the highest significant indices for temperature and precipitation. In NEB, both Northern Coast and Southern Coast subregions presented substantial increase or decrease in precipitation and temperature indices.
Brazil’s territory is considerably large and characterized by a variety of climate patterns, which allows the identification of regional climate specificities. The objective of this study was to identify a typology of climatic characteristics for the microregions of Brazil using the grade of membership (GoM) method, which is a multivariate technique based on the fuzzy sets theory. The meteorological variables used were: precipitation (mm), relative humidity (%), maximum and minimum temperature (°C) and wind speed (m/s), obtained from the interpolated database elaborated by Xavier comprising the period from January 1981 to December 2013. Three predominant homoclimatic profiles were found. The GoM method also allowed the identification of five mixed profiles, which is unprecedent in studies in Brazil and corroborates the regional climate diversity in the country. Furthermore, the heterogeneities of Brazilian climates could be better outlined. The extreme profiles—“predominant 1—P1”, “predominant 2—P2” and “predominant 3—P3”—accounted for 42.9% (236) of the total microregions. Additionally, approximately half (53.9%) of the microregions were classified as featuring characteristics of at least two profiles—that is, they presented mixed profiles with hybrid characteristics. These hybrid microregions were located mostly at transition zones between climates.
The objective of this study was to analyze the influence of large-scale atmospheric–oceanic mechanisms (El Niño–Southern Oscillation—ENSO and the inter-hemispheric thermal gradient of the Tropical Atlantic) on the spatial–temporal variability of soy yield in MATOPIBA. The following, available in the literature, were used: (i) daily meteorological data from 1980 to 2013 (Xavier et al., 2016); (ii) (chemical, physical, and hydric) properties of the predominant soil class in the area of interest, available at the World Inventory of Soil Emission Potentials platform; (iii) genetic coefficients of soybean cultivar with Relative Maturity Group adapted to the conditions of the region. The simulations were performed using the CROPGRO-Soybean culture model of the Decision Support System for Agrotechnology Transfer (DSSAT) system, considering sowing dates between the months of October and December of 33 agricultural years, as well as for three meteorological scenarios (climatology, favorable-wet, and unfavorable-dry). Results showed that the different climate scenarios can alter the spatial patterns of agricultural risk. In the favorable-wet scenario, there was a greater probability of an increase in yield and a greater favorable window for sowing soybean, while in the unfavorable-dry scenario these values were lower. However, considering the unfavorable-dry scenario, in some areas the reduction in yield losses will depend on the chosen planting date.
The identification of spatial and temporal rainfall climatology patterns is crucial for hydrometeorological studies over semiarid watersheds, which frequently face water distribution conflicts and socioeconomic issues due to water scarcity. Thus, the objective of this study was to propose a comprehensive approach for the characterization of rainfall climatology over semiarid watersheds. Monthly rainfall time series with up to 30% of gaps measured in 56 rain gauges in the Piranhas-Açu Watershed-Brazilian semiarid region-were used. Data gaps were filled through a combination of simple spatial interpolation techniques. Principal component analysis and cluster analysis identified two homogeneous rainfall subregions in the basin: C1, in the upper portion, and C2, in the middle and lower portions. Rainfall volumes in C2 were up to 23.5% smaller than those in C1, due to orographic structures which contribute to aridity in this region. Rainfall anomalies were calculated in each cluster through the modified Rainfall Anomaly Index (mRAI) and were associated with the phases of the El Niño-Southern Oscillation (ENSO) and the Atlantic Meridional Mode (AMM). In years when the ENSO (AMM) was in its positive (negative) phase, there was a higher probability of occurrence of months with above-average rainfall, while the opposite was also true. Results showed that the effects of the patterns are mutually influenced, which has been previously found at larger scales. Finally, mRAI trends were identified through the Mann-Kendall test, which indicated significant negative trends in C1 and C2, especially during the wet season.
Amazonia and the Northeast region of Brazil exhibit the highest levels of climate vulnerability in the country. While Amazonia is characterized by an extremely hot and humid climate and hosts the world largest rainforest, the Northeast is home to sharp climatic contrasts, ranging from rainy areas along the coast to semiarid regions that are often affected by droughts. Both regions are subject to extremely high temperatures and are susceptible to many tropical diseases. This study develops a multidimensional Extreme Climate Vulnerability Index (ECVI) for Brazilian Amazonia and the Northeast region based on the Alkire-Foster method. Vulnerability is defined by three components, encompassing exposure (proxied by seven climate extreme indicators), susceptibility (proxied by sociodemographic indicators), and adaptive capacity (proxied by sanitation conditions, urbanization rate, and healthcare provision). In addition to the estimated vulnerability levels and intensity, we break down the ECVI by indicators, dimensions, and regions, in order to explore how the incidence levels of climate-sensitive infectious and parasitic diseases correlate with regional vulnerability. We use the Grade of Membership method to reclassify the mesoregions into homoclimatic zones based on extreme climatic events, so climate and population/health data can be analyzed at comparable resolutions. We find two homoclimatic zones: Extreme Rain (ER) and Extreme Drought and High Temperature (ED-HT). Vulnerability is higher in the ED-HT areas than in the ER. The contribution of each dimension to overall vulnerability levels varies by homoclimatic zone. In the ER zone, adaptive capacity (39%) prevails as the main driver of vulnerability among the three dimensions, in contrast with the approximately even dimensional contribution in the ED-HT. When we compare areas by disease incidence levels, exposure emerges as the most influential dimension. Our results suggest that climate can exacerbate existing infrastructure deficiencies and socioeconomic conditions that are correlated with tropical disease incidence in impoverished areas.
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