Effect of trees in the soil nutrient concentration and pasture produtivity in Mediterranean silvo-pastoral systems

Silvo-pastoral are mixed systems of trees and grass, which have been proposed as a means to extend the benefits of forest to farmed land. Agro-forestry systems under semi-arid Mediterranean conditions, called montados in Portugal and dehesas in Spain, cover substantial areas in the world. These silvo-pastoral systems are the most extensive European agro-forestry system, as they cover 3.5–4.0 Mha in Spain and Portugal. Long-term studies are essential to assess the magnitude of the temporal nutrient flow dynamics in terrestrial ecosystems and to understand the response of these systems to fertilizer management. In order to implement the conservation task and recovery of resources through silvo-pastoral systems it is necessary to know and correct potential limiting factors, especially the soil factor, and this requires agronomic knowledge as well as the implmentation of the available new technologies. In this context, this task aims at a better understanding of the contribution of the two components of montado ecosystem (trees and herbaceous vegetation) on the soil nutrient and water dynamics, that allow for the interpretation of the variability of pasture dry matter yield and help the farmer in the management of tree density. Collaterally the task will evaluate and calibrate new technologies that simplify the monitoring of soil, grassland, trees and grazing animals.

Impacts of crop level, soil and irrigation management in grape berries of cv ‘Trincadeira’ (Vitis vinifera L.)

Berry size and crop yield are widely recognized as important factors that contribute to wine quality. The final berry size indirectly affects the phenolic concentration of the wine due to skin surface-to-berry volume ratio. The effects of different irrigation levels, soil management and plant crop level on growth of ‘Trincadeira’ berries were studied. In order to test the influence of different irrigation levels (rainfed, pre-veraison and post-veraison), different soil management (tillage and natural cover crops) and different plant crop levels (8 and 16 clusters per vine), leaf water potential, skin anthocyanin, polyphenols, berry skin and seed fresh weight were measured in fruits. The segregation of berries into three different berry classes: small, medium and large, allowed to identify different levels of contribution of soil management and irrigation level into berry, skin and seeds ratios. As expected, higher water availability due to irrigation and soil tillage management during berry development induced an increase in berry flesh weight and this was more evident in larger berries; however, berry skin and seed fresh weight remained unchanged. Also, anthocyanins did not show significant differences.

Annual crop rotation of tropical pastures with no-till soil as affectedby lime surface application.

Soil acidity and low natural fertility are the main limiting factors for grain production in tropical regionssuch as the Brazilian Cerrado. The application of lime to the surface of no-till soil can improve plant nutrition, dry matter production, crop yields and revenue. The present study, conducted at the Lageado Experimental Farm in Botucatu, State of São Paulo, Brazil, is part of an ongoing research project initi-ated in 2002 to evaluate the long-term effects of the surface application of lime on the soil?s chemical attributes, nutrition and kernel/grain yield of peanut (Arachis hypogaea), white oat (Avena sativa L.) and maize (Zea mays L.) inter cropped with palisade grass (Urochloa brizantha cv. Marandu), as well as the forage dry matter yield of palisade grass in winter/spring, its crude protein concentration, estimated meat production, and revenue in a tropical region with a dry winter during four growing seasons. The experiment was designed in randomized blocks with four replications. The treatments consisted of four rates of lime application (0, 1000, 2000 and 4000 kg ha−1), performed in November 2004. The surface application of limestone to the studied tropical no-till soil was efficient in reducing soil acidity from the surface down to a depth of 0.60 m and resulted in greater availability of P and K at the soil surface. Ca and Mg availability in the soil also increased with the lime application rate, up to a depth of 0.60 m. Nutrient absorption was enhanced with liming, especially regarding the nutrient uptake of K, Ca and Mg by plants.Significant increases in the yield components and kernel/grain yields of peanut, white oat and maize were obtained through the surface application of limestone. The lime rates estimated to achieve the maximum grain yield, especially in white oat and maize, were very close to the rates necessary to increase the base saturation of a soil sample collected at a depth of 0?0.20 m to 70%, indicating that the surface liming of 2000 kg ha−1is effective for the studied tropical no-till soil. This lime rate also increases the forage dry matter yield, crude protein concentration and estimated meat production during winter/spring in the maize-palisade grass inter cropping, provides the highest total and mean net profit during the four growing seasons, and can improve the long-term sustainability of tropical agriculture in the Brazilian Cerrado.

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

Raman spectroscopy of some complex arsenate minerals—implications for soil remediation

The application of spectroscopy to the study of contaminants in soils is important. Among the many contaminants is arsenic, which is highly labile and may leach to non-contaminated areas. Minerals of arsenate may form depending upon the availability of specific cations for example calcium and iron. Such minerals include carminite, pharmacosiderite and talmessite. Each of these arsenate minerals can be identified by its characteristic Raman spectrum enabling identification.