Nitrate in groundwater in a recharge area of Guarany aquifer in Brazil.

The region of Ribeirão Preto City located in São Paulo State, southeastern Brazil, is an important sugarcane, soybean and corn producing area. This region is also an important recharge area (Espraiado) for groundwater of the Guarany aquifer, a water supply source for the city and region. It has an intercontinental extension that comprises areas of eight Brazilian states, as well as significant portions of other South American countries like Argentina, Uruguay, and Paraguay, with a total area of approximately 1,200,000 Km2. Due to the high permeability of some soils present in this region, the high mobility of the herbicides and fertilizers applied, and being a recharge area, it is important to investigate the potential transport of applied fertilizers to underlying aquifer. The cultivation sugar cane in this area demands the frequent use of nitrogen as fertilizer. This research was conducted to characterize the potential contamination of groundwater with nitrogen in the recharge area of groundwater. Seven groundwater sample points were selected in the Espraiado stream watershed, during the years of 2005 and 2006. Samples were collected during the months of March, July, and December of each year. Three replications were collected at each site. Groundwater was also collected during the same months from county groundwater wells located throughout the city. The following six wells were studied: Central, Palmares, Portinari, Recreio Internacional, São Sebastião, and São José. Nitrate water samples were analyzed by Cadmium Reduction Method. No significant amount of nitrate was found in the recharge, agricultural, area. However, nitrate levels were detected at concentrations higher than the Maximum Concentration Level (MCL) of 10mg/L in downtown, urban, well located away from agricultural sites with no history of fertilizer or nitrogen application.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area