Phytosociology of the remaining xerophytic woodland associated to an environmental gradient at the banks of the São Francisco river - Petrolina, Pernambuco, Brazil1

Floristic and phytosociological surveys were carried out for 12 months in the Embrapa-SPSB, Petrolina, Pernambuco, Brazil. A transect was laid on starting at the river bank extending for 790 m away from the river and divided into 140 10 × 10 m contiguous plots. In each plot, all standing plants, alive or dead, with stem diameter at soil level ? 3 cm and total height ? 1 m were sampled. Along this transect, an elevation range of 9.40 m was registered and five topographical environments were identified: riverside (MR), dike (D), floodable depression (DI), boundary terrace (TL) - all of them belonging to the fluvial terrace with Fluvic Neosol and Haplic Cambisol both silty textured eutrophic soils - and the inlander tableland (TS), with medium sandy-textured Red-Yellow Argisols. Fourty-eight species/morphospecies, distributed into 39 genera and 21 families, were identified. Four phytogeoenvironments (MR, D + TL, DI + TL, and TS) were registered based on environmental variations and floristic similarities among plots using cluster analyses. The MR environment showed the largest total density, total basal area, maximum and medium heights and maximum diameter. Moreover, it had 8.1% of plants with heights above 8 m against 0.6% for D + TL, 0.2% for DI + TL, and 0% for TS. The species with the largest importance value were Inga vera subsp. affinis (DC.) T.D. Pennington in MR, Mimosa bimucronata Kunth in D + TL and DI + TL and M. tenuiflora (Willd.) Poir. in TS.

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

Nodulation, gas exchanges and production of peanut cultivated with Bradyrhizobium in soils with different textures.

Nitrogen fertilization from biological source is an uncommon practice for peanut growers due to the limited results, mainly in environments with water restriction. In this study, the response of a commercial Bradyrhizobium was evaluated on the nodulation and production of peanuts grown in sandy and medium textured soils. Two experiments using different soils were carried out in the field during the dry season, in Campina Grande, Paraíba State, Brazil. Three peanut genotypes were submitted to the following treatments: 1-no nitrogen fertilization (control), 2- chemical fertilization (ammonium sulfate) and 3- inoculation with Bradyrhizobium [commercial strain BR 1405 (SEMIA 6144)]. A completely randomized 3x3 factorial design was adopted with five repetitions for both experiments. The evaluates variables were: height of the main stem, number of nodes/plant, root length, root dry weight, weight of pods/plant and number of pods/plant. In addition, gas exchanges were estimated using IRGA apparatus. Both genotypes (BRS Havana and L7 Bege) were benefited in relation to production due to an inoculation with SEMIA 6144. No physiological response was verified in genotypes or N-treatments to gas exchange, excepting for the Ci/Ca ratio in the medium textured soil experiment. BRS Havana showed low Ci/Ca ratio in Bradyrhizobium treatment, indicating that SEMIA 6144 improved the plants photosynthetic efficiency.