Water transfer from São Francisco river to semiarid northeast of Brazil: technical data, environmental impacts, survey of opinion about the amount to be transferred

The transposition of the São Francisco River is considered one of the greatest engineering works in Brazil of all time since it will cross an extensive agricultural region of continental dimensions, involving environmental impacts, water, soil, irrigation, water payment and other multidisciplinary themes. Taking into account its importance, this subject was incorporated into a discipline of UFSCar (Federal University of São Carlos - Brazil) named "Pollution and Environmental Impacts". It was noted strong reaction against the project, even before the presentation. To allow a critical analysis, the first objective was to compile the main technical data and environmental impacts. The second objective was to detect the three most important aspects that cause reaction, concluding for the following reasons: assumption that the volume of water to be transferred was much greater than it actually is proposed in the project; lack of knowledge about similar project already done in Brazil; the idea that the artificial canal to be built was much broader than that proposed by the project. The participants' opinion about "volume to be transferred" was raised quantitatively four times: 2-undergraduate students; 1-graduate; 1-outside community. The average resulted 14 times larger than that proposed in the project, significant according to t-test. It was concluded that the reaction to water transfer project is due in part to the ignorance combined with a preconceived idea that tend to overestimate the magnitude of environmental impacts.

Influence of data acquisition geometry on soybean spectral response simulated by the prosail model

View angle and directional effects significantly affect reflectance and vegetation indices, especially when daily images collected by large field-of-view (FOV) sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) are used. In this study, the PROSAIL radiative transfer model was chosen to evaluate the impact of the geometry of data acquisition on soybean reflectance and two vegetation indices (Normalized Difference Vegetation Index - NDVI and Enhanced Vegetation Index -EVI) by varying biochemical and biophysical parameters of the crop. Input values for PROSAIL simulation were based on the literature and were adjusted by the comparison between simulated and real satellite soybean spectra acquired by the MODIS/Terra and hyperspectral Hyperion/Earth Observing-One (EO-1). Results showed that the influence of the view angle and view direction on reflectance was stronger with decreasing leaf area index (LAI) and chlorophyll concentration. Because of the greater dependence on the near-infrared reflectance, the EVI was much more sensitive to viewing geometry than NDVI presenting larger values in the backscattering direction. The contrary was observed for NDVI in the forward scattering direction. In relation to the LAI, NDVI was much more isotropic for closed soybean canopies than for incomplete canopies and a contrary behavior was verified for EVI.

Integration of sunflower (Helianthus annuus) residues with a pre-plant herbicide enhances weed suppression in broad bean (Vicia faba)

Field trial was conducted with the aim of utilizing allelopathic crop residues to reduce the use of synthetic herbicides in broad bean (Vicia faba) fields. Sunflower residue at 600 and 1,400 g m-2 and Treflan (trifluralin) at 50, 75 and 100% of recommended dose were incorporated into the soil alone or in combination with each other. Untreated plots were maintained as a control. Herbicide application in plots amended with sunflower residue had the least total weed count and biomass, which was even better than herbicide used alone. Integration of recommended dose of Treflan with sunflower residue at 1,400 g m-² produced maximum (987.5 g m-2) aboveground biomass of broad bean, which was 74 and 36% higher than control and recommended herbicide dose applied alone, respectively. Combination of herbicide and sunflower residue appeared to better enhance pod number and yield per unit area than herbicide alone. Application of 50% dose of Treflan in plots amended with sunflower residue resulted in similar yield advantage as was noticed with 100% herbicide dose. Chromatographic analysis of residue-infested field soil indicated the presence of several phytotoxic compounds of phenolic nature. Periodic data revealed that maximum suppression in weed density and dry weight synchronized with peak values of phytotoxins observed 4 weeks after incorporation of sunflower residues. Integration of sunflower residues with lower herbicide rates can produce effective weed suppression without compromising yield as a feasible and environmentally sound approach in broad bean fields.

Manual and semi-automatic quantification of in vivo ¹H-MRS data for the classification of human primary brain tumors

In vivo proton magnetic resonance spectroscopy (¹H-MRS) is a technique capable of assessing biochemical content and pathways in normal and pathological tissue. In the brain, ¹H-MRS complements the information given by magnetic resonance images. The main goal of the present study was to assess the accuracy of ¹H-MRS for the classification of brain tumors in a pilot study comparing results obtained by manual and semi-automatic quantification of metabolites. In vivo single-voxel ¹H-MRS was performed in 24 control subjects and 26 patients with brain neoplasms that included meningiomas, high-grade neuroglial tumors and pilocytic astrocytomas. Seven metabolite groups (lactate, lipids, N-acetyl-aspartate, glutamate and glutamine group, total creatine, total choline, myo-inositol) were evaluated in all spectra by two methods: a manual one consisting of integration of manually defined peak areas, and the advanced method for accurate, robust and efficient spectral fitting (AMARES), a semi-automatic quantification method implemented in the jMRUI software. Statistical methods included discriminant analysis and the leave-one-out cross-validation method. Both manual and semi-automatic analyses detected differences in metabolite content between tumor groups and controls (P < 0.005). The classification accuracy obtained with the manual method was 75% for high-grade neuroglial tumors, 55% for meningiomas and 56% for pilocytic astrocytomas, while for the semi-automatic method it was 78, 70, and 98%, respectively. Both methods classified all control subjects correctly. The study demonstrated that ¹H-MRS accurately differentiated normal from tumoral brain tissue and confirmed the superiority of the semi-automatic quantification method.