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Following an intense occupation process that was initiated in the 1960s, deforestation rates in the Brazilian Amazon have decreased significantly since 2004, stabilizing around 6000 km2 yr−1 in the last 5 years. A convergence of conditions contributed to this, including the creation of protected areas, the use of effective monitoring systems, and credit restriction mechanisms. Nevertheless, other threats remain, including the rapidly expanding global markets for agricultural commodities, large-scale transportation and energy infrastructure projects, and weak institutions. We propose three updated qualitative and quantitative land-use scenarios for the Brazilian Amazon, including a normative ‘Sustainability’ scenario in which we envision major socio-economic, institutional, and environmental achievements in the region. We developed an innovative spatially explicit modelling approach capable of representing alternative pathways of the clear-cut deforestation, secondary vegetation dynamics, and the old-growth forest degradation. We use the computational models to estimate net deforestation-driven carbon emissions for the different scenarios. The region would become a sink of carbon after 2020 in a scenario of residual deforestation (~1000 km2 yr−1) and a change in the current dynamics of the secondary vegetation – in a forest transition scenario. However, our results also show that the continuation of the current situation of relatively low deforestation rates and short life cycle of the secondary vegetation would maintain the region as a source of CO2 – even if a large portion of the deforested area is covered by secondary vegetation. In relation to the old-growth forest degradation process, we estimated average gross emission corresponding to 47% of the clear-cut deforestation from 2007 to 2013 (using the DEGRAD system data), although the aggregate effects of the postdisturbance regeneration can partially offset these emissions. Both processes (secondary vegetation and forest degradation) need to be better understood as they potentially will play a decisive role in the future regional carbon balance.
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 world’s 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.
LuccME is an open-source framework for the development of spatially explicit land use and cover changemodels, built as an extension of the TerraME programming environment. LuccME simplifi es the creation ofdeforestation, agricultural expansion, urban sprawl and other land change processes at different scales bycombining basic components or developing new ones. The goals are to provide a collaborative platform forscientifi c advances in fi eld, and to disseminate the use of dynamic models beyond the academic world. Theframework was released last November during the GLP Workshop on Land Use Transitions in South America,and can be freely downloaded from http://www.terrame.org/luccme.
No single model or scale can fully capturethe causes of land change. For a given region, landchanges may have different impacts at differentplaces. Limits and opportunities imposed by biophys-ical and socio-economic conditions, such as localpolicies and accessibility, may induce distinct landchange trajectories. These local land change trajecto-ries may, in turn, indirectly affect other places, aslocal 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 asoftware organization for building computationalmodels that support dynamical linking of multiplescales. This structure couples different types ofmodels, such as cell-space models with agent-basedmodels. We show how results in multiscale modelscan flow both in bottom-up and top-down directions,thus allowing feedback from local actors to regionalscales. The proposal is general and independent ofspecific software, and it is effective to model intra-regional, bottom-up and top-down interactions in landchange models. To show the model’s potential, wedevelop a case study that shows how a multiscalemodel for the Brazilian Amazonia can include feed-backs between local to regional scales.