Nutrient content in aboveground biomass of Brazilian peanut cultivars in conservation tillage on sugarcane straw and pasture area.

Fields studies were conducted in 2004/2005 in order to evaluate the effects of tillage on nutrient content in aboveground biomass of two peanut cultivars, cultivated in rotation after mechanical harvested sugarcane and pastures. These trials were carried out in two types of soils; Oxisol and Ultisol, respectively in Ribeir?ao Preto and Mirassol, S?ao Paulo State, Brazil. The experimental design was split-plot with four replications. Tillage treatments (conventional, minimum and no-tillage) were main plots while sub-plots were peanut genotypes IAC-Tatu ST (Valencia market-type, erect growth habit, red seed coat, maturity range around 100 days after planting) and IAC-Caiap´o (Runner market-type, prostate growth habit, pink testa, maturity range more than 135 days). From 15 to 90 days after emergence, samples of leaves and stems were harvested, dried, weighted and ground to determine macro and micronutrient concentration. At 75 days after sowing, the cultivar IAC-Caiap´o showed higher contents of N, P, K, Cu, and Zn while IAC-Tatu presented higher concentrations of Ca, Mg, and S. Zn content was higher in conservation tillage than in conventional, mainly in Oxisoil for both of cultivars.

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