Cenários futuros para a Amazônia: interações entre o desmatamento, as mudanças climáticas, o ecossistema natural e os sistemas agrícolas

• Ano de defesa: 2012
• Autor(a) principal: Oliveira, Leydimere Janny Cota
• Orientador(a): Costa, Marcos Heil
• Banca de defesa: Hamakawa, Paulo José , Cunha, Dênis Antônio da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de Viçosa
• Programa de Pós-Graduação: Doutorado em Meteorologia Agrícola
• Departamento: Agrometeorologia; Climatologia; Micrometeorologia
• País: BR
• Palavras-chave em Português: Mudanças climáticas; Floresta Amazônica; Sistemas agrícolas
• Palavras-chave em Inglês: Amazon rainforest; Climatic changes; Agricultural systems
• Área do conhecimento CNPq: CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIA
• Link de acesso: http://locus.ufv.br/handle/123456789/1513

The Amazon rainforest is one of the most important ecosystems in the world with the highest biodiversity on the planet. In spite of this reputation however, it has been subjected to anthropogenic pressures in recent decades. The expansion of agriculture and livestock has been considered the main cause of deforestation in the region. Because it is important to understand the biosphere-atmosphere feedbacks processes in the Amazon, this work was done to evaluate the resilience of the natural ecosystem and the productivity of soybean and pasture at different levels of deforestation and increased [CO2]. A simplified model that represents the interactions between climate, forest and land use in Amazonia was used. We implemented five sets of simulations as follows: (1) control, (2) radiative effect (global warming) of CO2, (3) radiative and physiological effects of CO2, (4) effect of changes in land use and (5) radiative and physiological effects of CO2 plus changes in land use. The productivity simulated which considereding all the effects of future scenarios for the years 2020 and 2050 were compared to the control simulation. For the control simulation, we aboveground live biomass (AGB) modeled, including live leaf and wood pools, in Amazonia ranged from 5.1 to 20.7 kgC.m-2, with a mean value of 17.9 kgC.m-2, generally decreasing from north to south. Net primary productivity (NPP) showed the same geographic pattern, with values ranging from 1.04 to 1.29 kgC.m-2.yr-1, with a mean value of 1.21 kgC.m-2.yr-1. While the soybean yield ranged from 1610 to 3665 kg.ha-1, with a mean value of 2704.7 kg ha-1 the dry matter pasture yield ranged from 6,852 to 23,766 kgDM.ha-1.yr-1 with a mean value of 16,245.5 kgDM.ha-1.yr-1. In response to climate change for the year 2020, we found changes in AGB of -16%, in soybean yield of -10% and in pasture yield of -2%. With the introduction of physiological effects in simulations the changes were -7% for AGB, -4% for soybean and +1% for the pasture. In response to the change in land use, AGB declined in relation to control simulation to 37% when the deforested area was replaced by soybeans, and it decreased to 34% when the deforested area was replaced by pasture. The soybean yield and pasture productivity decreased to 18% and 29%, respectively. When all the effects were analyzed jointly, AGB declined to 37%, soybean yield to 21% and pasture productivity to 29%. For the year 2050 changes in AGB, in soybean yield and in pasture productivity were also higher in the simulations in which all effects were considered, with a 63% reduction in the AGB, 31% for soybean yield and 33% for pasture productivity in a pessimistic scenario. The combined effects of climate change (due to changes in atmospheric composition) and change in land use caused a significant reduction in the productivity of the natural ecosystem and in the two crops analyzed. In general, the effect of deforestation was the most important factor for this reduction. The identification of strategic areas for the preservation of natural ecosystems associated with the development of new cultivars adapted to future climatic conditions would strengthen agriculture in the Amazon. This identification would make better use of areas already deforested without the need to move so rapidly in the remaining forests.