Soils, Geoenvironments and Ecosystem Services of a Protected Area in Western Brazilian Amazonia.

The Serra do Divisor National Park (SDNP) in the Westernmost Brazilian Amazonia possesses unique Mountain landscapes of sub-andean nature, with high geo-biodiversity and pristine environments, with a potential high contribution in ecosystems services. We studied and mapped the basic geo-environmental units of the main sector of the Park, evaluating soil carbon stocks as a key ecosystem service provided by the Protected Area. For the identification, characterization and mapping of the geoenvironmental units, we integrated pedological, geomorphological and vegetation data obtained by local soil survey and field campaigns, as well as secondary data. Eight geoenvironmental units were identified and mapped, distributed in three main compartments: the Serra do Divisor (SD) the upper Moa River and the medium Moa River. This region presents similar environments to the sub-Andean region, notably the Ceja Forest at the top surface of the SD. Soils at the SD have high organic carbon accumulation, with close association with the nutrient-poor, quartz-rich rocks, and shows organic matter illuviation indicating active podzolization. The SDNP encompasses important ecosystems and services linked with high geo-biodiversity, and high soil carbon stocks, representing a new frontier for scientific research in the only area of transitional sub-andean forested landscape in Brazil.

Neural networks in spatialization of meteorological elements and their application in the climatic agricultural zoning of bamboo.

Bamboo has an important role in international commerce due to its diverse uses, but few studies have been conducted to evaluate its climatic adaptability. Thus, the objective of this study was to construct an agricultural zoning for climate risk (ZARC) for bamboo using meteorological elements spatialized by neural networks. Climate data included air temperature (TAIR, °C) and rainfall (P) from 4947 meteorological stations in Brazil from the years 1950 to 2016. Regions were considered climatically apt for bamboo cultivation when TAIR varied between 18 and 35 °C, and P was between 500 and 2800 mm year-1, or PWINTER was between 90 and 180 mm year-1. The remainder of the areas was considered marginal or inapt for bamboo cultivation. A multilayer perceptron (MLP) neural network with a multilayered "backpropagation" training algorithm was used to spatialize the territorial variability of each climatic element for the whole area of Brazil. Using the overlapping of the TAIR, P, and PWINTER maps prepared by MLP, and the established climatic criteria of bamboo, we established the agricultural zoning for bamboo. Brazil demonstrates high seasonal climatic variability with TAIR varying between 14 and 30 °C, and P varying between < 400 and 4000 mm year-1. The ZARC showed that 87% of Brazil is climatically apt for bamboo cultivation. The states that were classified as apt in 100% of their territories were Mato Grosso do Sul, Goiás, Tocantins, Rio de Janeiro, Espírito Santo, Sergipe, Alagoas, Ceará, Piauí, Maranhão, Rondônia, and Acre. The regions that have restrictions due to low TAIR represent just 11% of Brazilian territory. This agroclimatic zoning allowed for the classification of regions based on aptitude of climate for bamboo cultivation and showed that 71% of the total national territory is considered to be apt for bamboo cultivation. The regions that have restrictions are part of southern Brazil due to low values of TAIR and portions of the northern region that have high levels of P which is favorable for the development of diseases.

Land-use in Amazonia and the cerrado of Brazil

The total area and annual rate of native vegetation clearing is greatest in the Cerrado region followed by the Brazilian states of Par , Mato Grosso, Maranhao and Rond"nia. Amazonian forest clearing proceeds most quickly where abundant natural resources (wood or land) are accessible by roads and close to markets. These regions are concentrated along the eastern and southern flanks of Amazonia, particularly in eastern Par , Cuiab and Rond"nia. There are still large discrepancies in estimates of annual deforestation; Landsat (Thematic Mapper-based) mapping of deforestation in the closed-canopy forests of Amazonia has not include non-Brazilian countries and is incomplete for the cerrado biome. Amazonian deforestation was last mapped in 1994. Current estimates of Amazonian forest clearing do not include most of the forests that are effected by logging each year, which is an area (about 7,000 Km2yr-1) more than half the size of the area of annual deforestation. Logging changes forest structure and increases forest flammability. The intensity of logging ranges from 1- to 100-species harvest, and averages 20 m3 of wood harvested per hectare. Logging may increase dramatically in the coming years. Fire affects large, but difficult to measure, areas of pastureland, logged forests, secondary forests and primary forests. Forest ground fires are particularly difficult to map from satellite data. Fire is more frequent where forest clearing is taking place, and where seasonal drough is most severe. The destiny of Amazonian forest land cleared for crops and cattle pasture is complex, and highly variable regionally. Areal estimates are needed for managed pasture, degraded pasture, cropland and secondary forests, for these ecosystems are functionally distinct. Most forest clearing is for pasture establishment, followed by shifting cultivation. Cattle pasture is the logical land-use for both small-scale and large-scale rural Amazonians because cattle are easily sold or traded, and they maintain their value during inflation. Cattle pastures help secure land claims and increase land value. In the Cerrado, there has been a shift from extesive cattle grazing of natural savannas to pastures planted with African forage grasses; mechanized soy bean production is the second most extensive land-use. Pastures are the most important land-cover for the LBA (Large-Scale Biosphere-Atmosphere experiment in Amazonia) science campaign. Brazilian Amazonia experiences reduced rainfall...