Ana Paula Dutra Aguiar

Estimating greenhouse gas emissions from cattle raising in Brazil

The study estimated, for the first time, the greenhouse gas emissions associated with cattle raising in Brazil, focusing on the period from 2003 to 2008 and the three principal sources: 1) portion of deforestation resulting in pasture establishment and subsequent burning of felled vegetation; 2) pasture burning; and 3) bovine enteric fermentation. Deforestation for pasture establishment was only considered for the Amazon and Cerrado. Emissions from pasture burning and enteric fermentation were accounted for the entire country. The consolidated emissions estimate lies between approximately 813 Mt CO2eq in 2008 (smallest value) and approximately 1,090 Mt CO2eq in 2003 (greatest value). The total emissions associated with Amazon cattle ranching ranged from 499 to 775 Mt CO2eq, that of the Cerrado from 229 to 231 Mt CO2eq, and that of the rest of the country between 84 and 87 Mt CO2eq. The full set of emissions originating from cattle raising is responsible for approximately half of all Brazilian emissions (estimated to be approximately 1,055 Mt CO2eq in 2005), even without considering cattle related sources not explicitly estimated in this study, such as energy use for transport and refrigeration along the beef and derivatives supply chain. The potential for reduction of greenhouse gas emissions offered by the Brazilian cattle industry is very high and might constitute Brazil’s most important opportunity for emissions mitigation. The study offers a series of policy recommendations for mitigation that can be implemented by public and private administrators at a low cost relative to other greenhouse gas reduction options.

21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions

Tropical carbon emissions are largely derived from direct forest clearing processes. Yet, emissions from drought-induced forest fires are, usually, not included in national-level carbon emission inventories. Here we examine Brazilian Amazon drought impacts on fire incidence and associated forest fire carbon emissions over the period 2003–2015. We show that despite a 76% decline in deforestation rates over the past 13 years, fire incidence increased by 36% during the 2015 drought compared to the preceding 12 years. The 2015 drought had the largest ever ratio of active fire counts to deforestation, with active fires occurring over an area of 799,293 km2. Gross emissions from forest fires (989 ± 504 Tg CO2 year−1) alone are more than half as great as those from old-growth forest deforestation during drought years. We conclude that carbon emission inventories intended for accounting and developing policies need to take account of substantial forest fire emissions not associated to the deforestation process.Deforestation carbon emissions from the Brazilian Amazon have declined steeply, but how much drought-induced forest fire emissions add to this process is still unclear. Here the authors show that gross emissions from forest fires are more than half as great as those from deforestation during drought years.

Dynamical coupling of multiscale land change models

No single model or scale can fully capture the causes of land change. For a given region, land changes may have different impacts at different places. Limits and opportunities imposed by biophysical and socio-economic conditions, such as local policies and accessibility, may induce distinct land change trajectories. These local land change trajectories may, in turn, indirectly affect other places, as local actions interact with higher-level driving forces. Such intraregional interdependencies cannot be captured by studies at a single scale, calling for multiscale and multilocality studies. This paper proposes a software organization for building computational models that support dynamical linking of multiple scales. This structure couples different types of models, such as cell-space models with agent-based models. We show how results in multiscale models can flow both in bottom-up and top-down directions, thus allowing feedback from local actors to regional scales. The proposal is general and independent of specific software, and it is effective to model intraregional, bottom-up and top-down interactions in land change models. To show the model’s potential, we develop a case study that shows how a multiscale model for the Brazilian Amazonia can include feedbacks between local to regional scales.

Processos de ocupação nas novas fronteiras da Amazônia: o interflúvio do Xingu/ Iriri

Este trabalho apresenta os primeiros resultados do esforco conjunto de varias instituicoes, organizadas em torno da rede Geoma (Rede Tematica de Pesquisa em Modelagem Ambiental da Amazonia) para avancar a compreensao dos novos padroes e processos de estruturacao do territorio nas novas frentes no sul do Para, analisando padroes de desmatamento e os processos que dao origem a esses padroes. Busca-se, aqui, produzir os subsidios necessarios para o desenho de politicas publicas responsaveis, que nao privilegiem um unico aspecto do problema, como a abertura de estradas, por exemplo. Aponta-se, entao, a partir desses primeiros resultados, que apenas uma solucao integrada que procure estruturar os principais agentes e processos na cadeia produtiva seria possivel para minorar os efeitos do desmatamento e nortear o desenvolvimento integrado para a regiao, com beneficios para a floresta e para as populacoes que ali vivem.

Amazon deforestation in Brazil: effects, drivers and challenges

The concept of global ecosystem services has become a powerful paradigm for understanding the link between ecosystem processes and related human activities, which is expressed by the economic and ecological quantification of the services in regards to sustainable development. The role of tropical forests in the global climate system and the uncertainty of the exact magnitude of this complex interaction has become a major concern to the scientific community. In this paper we review and synthesize the global effects of Amazon deforestation in Brazil, as well as drivers and challenges related to this process. To this end, we provide data on carbon emissions from combined annual maps of clear cutting of primary forests and spatial information on biomass distribution for different vegetation types and secondary vegetation growth, as well as the temporal dynamic related to the deforestation process and its interregional heterogeneity, the social and institutional drivers. In 2009, during the Conference of Parties, of the United Nation Framework Convention on Climate Change (Copenhagen, Denmark), Brazil announced a voluntary commitment to reduce the national GHG emissions by 2020 and, to this end, such commitment requires reducing Amazon rainforest deforestation by 80% over a decade. To achieve this target, a set of consolidated remote sensing techniques have served to monitor and calculate the extent of deforestation, which became indispensable auditing tools for conservation, forest restoration and implementing climate change mitigation schemes.

Deforestation scenarios for the Bolivian lowlands

Tropical forests in South America play a key role in the provision of ecosystem services such as carbon sinks, biodiversity conservation, and global climate regulation. In previous decades, Bolivian forests have mainly been deforested by the expansion of agricultural frontier development, driven by the growing demands for beef and other productions. In the mid-2000s the Movimiento al Socialismo (MAS) party rose to power in Bolivia with the promise of promoting an alternative development model that would respect the environment. The party passed the worlds first laws granting rights to the environment, which they termed Mother Earth (Law No. 300 of 2012), and proposed an innovative framework that was expected to develop radical new conservation policies. The MAS conservationist discourse, policies, and productive practices, however, have since been in permanent tension. The government continues to guarantee food production through neo-extractivist methods by promoting the notion to expand agriculture from 3 to 13 million ha, risking the tropical forests and their ecosystem services. These actions raise major environmental and social concerns, as the potential impacts of such interventions are still unknown. The objective of this study is to explore an innovative land use modeling approach to simulate how the growing demand for land could affect future deforestation trends in Bolivia. We use the LuccME framework to create a spatially-explicit land cover change model and run it under three different deforestation scenarios, spanning from the present-2050. In the Sustainability scenario, deforestation reaches 17,703,786 ha, notably in previously deforested or degraded areas, while leaving forest extensions intact. In the Middle of the road scenario, deforestation and degradation move toward new or paved roads spreading across 25,698,327 ha in 2050, while intact forests are located in Protected Areas (PAs). In the Fragmentation scenario, deforestation expands to almost all Bolivian lowlands reaching 37,944,434 ha and leaves small forest patches in a few PAs. These deforestation scenarios are not meant to predict the future but to show how current and future decisions carried out by the neo-extractivist practices of MAS government could affect deforestation and carbon emission trends. In this perspective, recognizing land use systems as open and dynamic systems is a central challenge in designing efficient land use policies and managing a transition towards sustainable land use.

Spatial Dynamic Modelling of Deforestation in the Amazon

New GIS technologies have been employed to support public policies and actions towards environmental conservation, aiming to preserve biodiversity and mitigate the undesirable side-effects of human activities. The spatio-temporal simulation of systems dynamics is an example of such new technologies and helps scientists and decision-makers to understand the driving forces lying behind processes of change in environmental systems. In assessing how systems evolve, it is possible to figure out different scenarios, given by diverse socioeconomical, political and environmental conditions (Soares-Filho et al., 2001), and hence, anticipate the occurrence of certain events, like land cover and land use change, including deforestation. According to Openshaw (2000), computer simulation models provide qualitative and quantitative information on complex natural phenomena. In this sense, spatial dynamic models may be defined as mathematical representations of real-world processes or phenomena, in which the state of a given place on the Earth surface changes in response to changes in its driving forces (Burrough, 1998). Spatial dynamic models are commonly founded on the paradigm of cellular automata (CA). Wolfram (1983) defines CA as “[...] mathematical idealisations of physical systems in which space and time are discrete, and physical quantities take on a finite set of discrete values. A cellular automaton consists of a regular uniform lattice (or ‘array’), usually infinite in extent, with a discrete variable at each site (‘cell’). [...] A cellular automaton evolves in discrete time steps, with the value of the variable at the site being affected by the values of variables at sites in its ‘neighbourhood’ on the previous time step. The neighbourhood of a site is typically taken to be the site itself and all immediately adjacent sites. The variables at each site are updated simultaneously (‘synchronously’), based on the values of the variables in their neighbourhood at the preceding time step, and according to a definite set of ‘local rules’.” (Wolfram, 1983, p. 603). This work applies a CA model – Dinamica EGO – to simulate deforestation processes in a region called Sao Felix do Xingu, located in east-central Amazon. EGO consists in an environment that embodies neighbourhood-based transition algorithms and spatial feedback approaches in a stochastic multi-step simulation framework. Biophysical variables

Response of the river discharge in the Tocantins River Basin, Brazil, to environmental changes and the associated effects on the energy potential

Climate change is expected to impact the hydrological regime worldwide, and land use and land cover change may alter the effects of the former in some cases. Secondary growth in deforested and abandoned areas is one of the main consequences of land use and cover changes in Amazonia. Among land uses, the effects of the secondary growth in water availability in large scale basins are not well understood. This work analyzes the potential effects of secondary growth under climate and land use change on water availability and hydropower in the Tocantins basin, in the Legal Amazon region of Brazil, using the MHD-INPE hydrological model driven by different climate scenarios and two future socioeconomic-based potential land use scenarios. The model projects decrease on discharge under climate change scenarios, which further cause the simulated hydropower energy potential to decrease significantly. When only deforestation scenarios are included, the effects of climate change are weakened, but when secondary growth is also considered, the effects of climate change are enhanced. Results suggest that different aspects of environmental change, such as secondary growth, may affect water production and the sectors depending on it.