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RESUMO.O conhecimento dos níveis de concentração de metais pesados no ambiente e sua disseminação no solo e nas águas são de primordial importância em estudos ambientais, por constituir um dos indicadores de "medição" do equilíbrio da biodiversidade e da estabilidade dos ecossistemas. O presente trabalho teve como objetivo, estudar a distribuição espacial de dois metais pesados (cobre e cádmio) ao longo das margens do rio Meuse, por forma a medir os níveis de contaminação por esses metais. Foram usados dados da concentração de Cd e Cu amostrados em 155 pontos georeferenciados. Estes dados foram obtidos no programa R no pacote "gstat" cuja base de dados denomina-se "Meuse". A metodologia de análise dos dados consistiu em ajustar semivariogramas para análise da dependência espacial, e foram ajustados os modelos esféricos e gaussiano para a concentração de Cobre e cádmio, respectivamente. O grau de dependência espacial foi classificado como forte para a concentração de Cobre e moderada para a concentração de cádmio. A estimação da concentração destes metais pesados em pontos não amostrados foi feita usando o método de krigagem produzindo mapas de distribuição espacial da concentração de cobre e cádmio que apresentaram um padrão similar entre si. Verificou-se que dentre os dois metais pesados, o Cobre apresenta concentrações muito altas que chegam a atingir os 130 mg/Kg de solo. Palavras Chave: Metais pesados, geoestatística, semivariograma, krigagem Study of the spatial variability of copper and cadmium concentration along the Meuse River marginABSTRACT. The knowledge of the concentration levels of heavy metals in the environment and their dissemination in soil and water are of paramount importance in environmental studies, as one of the indicators of "measuring" the balance of biodiversity and the stability of ecosystems. The objective of this study was to study the spatial distribution of two heavy metals (Copper and Cadmium) along the banks of the Meuse River, in order to measure the levels of contamination by these metals. Cd and Cu concentration data were sampled at 155 georeferenced points. These data were obtained in the program R in the package "gstat" whose database is called "Meuse". The data analysis methodology consisted in adjusting semivariograms for spatial dependence analysis, and the spherical and Gaussian models were adjusted for the copper and cadmium concentration, respectively. The degree of spatial dependence was classified as strong for the copper concentration and moderate for the cadmium concentration. The estimation of the concentration of these heavy metals in non-sampled points was done using the kriging

This article has earned an open data badge "Reproducible Research" for making publicly available the code necessary to reproduce the reported results. The results reported in this article were reproduced partially due to their computational complexity.

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Maize is one of the main economic crops and staple food in Mozambique. However, despite the importance of the crop in the country, maize productivity is still low due to several factors including low adoption of improved agricultural technologies. This paper aimed to identify the main factors driving adoption of improved maize varieties applying generalized estimating equations (GEE). The motivation for this class of models is due to the fact that adoption of improved maize varieties is a spatial auto correlated variable and the traditional probit and logit models widely applied in studies of adoption of agricultural technologies do not take into account the structure of correlation existing in the response variable. The study uses data from Integrated Agrarian Survey of 2012 (IAI 2012). The proportion of small farmers who adopted improved maize varieties per district was used as response variable and a set of nine variables were used as covariates classified in social, economic, institutional and technologic factors. The spatial auto correlation of the dependent variable was assessed by global and local Moran indexes. Two classes of models were fitted: The traditional logistic regression (logit model) and the generalized estimating equations approach. The inclusion of spatial auto correlation in GEE was carried out inserting the Moran’s index in the working correlation matrix. The results have shown that the GEE approach for spatial lattice data was the best and all factors analysed in the study including the spatial dependency are the main factors driving adoption of improved maize varieties in Mozambique.