The use of groundwater with high fluoride concentrations poses a health threat to millions of people around the world. This study aims at providing a global overview of potentially fluoriderich groundwaters by modeling fluoride concentration. A large database of worldwide fluoride concentrations as well as available information on related environmental factors such as soil properties, geological settings, and climatic and topographical information on a global scale have all been used in the model. The modeling approach combines geochemical knowledge with statistical methods to devise a rule-based statistical procedure, which divides the world into 8 different "process regions". For each region a separate predictive model was constructed. The end result is a global probability map of fluoride concentration in the groundwater. Comparisons of the modeled and measured data indicate that 60-70% of the fluoride variation could be explained by the models in six process regions, while in two process regions only 30% of the variation in the measured data was explained. Furthermore, the global probability map corresponded well with fluorotic areas described in the international literature. Although the probability map should not replace fluoride testing, it can give a first indication of possible contamination and thus may support the planning process of new drinking water projects.

The Soil and Water Assessment Tool (SWAT) was used to model runoff and sediment in the Beheshtabad (3860 km 2 ) and Vanak (3198 km 2 ) watersheds in the northern Karun catchment in central Iran. Model calibration and uncertainty analysis were performed with sequential uncertainty fitting (SUFI-2), which is one of the programs interfaced with SWAT, in the package SWAT-CUP (SWAT Calibration Uncertainty Programs). Two measures were used to assess the goodness of calibration and uncertainty analysis: (a) the percentage of data bracketed by the 95% prediction uncertainty (95PPU) (P factor), and (b) the ratio of average thickness of the 95PPU band to the standard deviation of the corresponding measured variable (D factor). Ideally, the P factor should tend towards 1 with a D factor close to zero. These measures together indicate the strength of the calibration-uncertainty analysis. Runoff and sediment data from four hydrometric stations in each basin were used for calibration and validation. The P factor for Beheshtabad stations ranged from 0.31 to 0.86, while those for Vanak stations were between 0.71 and 0.80. The D factor for Beheshtabad ranged from 0.3 to 1.1, and for Vanak it was 0.77-1.16. These measures indicate a fair model calibration and accounting of uncertainties. The predicted runoff values were quite similar to those for discharge.

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About 4 billion people will be added onto the present population by 2050. To meet further demand for food, agricultural production should increase on the existing land. Since the Green Revolution, higher crop production per unit area has resulted in greater depletion of soil phytoavailable micronutrients while less attention has been paid to micronutrients fertilization. Now, micronutrient deficiency has become a limiting factor for crop productivity in many agricultural lands worldwide. Furthermore, many food systems in developing countries can not provide sufficient micronutrient content to meet the demands of their citizens, especially low-income families. There are several solutions such as soil and foliar fertilization, crop systems, application of organic amendments to correct micronutrients deficiency and to increase their density in edible parts of plants. This review article presents (1) agronomic approaches to improve crop yield and micronutrient content of food crops, and (2) genotypic variation in uptake and accumulation of micronutrients. Considering ecological concerns, cultivation and breeding of micronutrient-efficient genotypes in combination with proper agronomic management practices appear as the most sustainable and costeffective solution for alleviating food-chain micronutrient deficiency. Micronutrient-efficient genotypes could provide a number of benefits such as reductions in the use of fertilizers, improvements in seedling vigor, and resistance to abiotic and abiotic stresses. Using bioavailable micronutrient-dense staple crop cultivars can also be used to improve the micronutrient nutritional status of human. micronutrients / nutrient efficiency / biofortification / stress-tolerance indicators

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Design and analysis of land-use management scenarios requires detailed soil data. When such data are needed on a large scale, pedotransfer functions (PTFs) could be used to estimate different soil properties. Because existing regression-based PTFs for estimating cation exchange capacity (CEC) do not, in general, apply well to arid areas, this study was conducted (i) to evaluate the existing models and (ii) to develop neural network-based PTFs for predicting CEC in Aridisols of Isfahan in central Iran. As most researches have found a significant correlation between CEC and soil organic matter content (OM) and clay content, we also used these two variables for modelling of CEC. We tested several published PTFs and developed two neural network algorithms using multilayer perceptron and general regression neural networks based on a set of 170 soil samples. The data set was divided into two subsets for calibration and testing of the models. In general, the neural network-based models provided more reliable predictions than the regression-based PTFs.