Moving forward socio-economically focused models of deforestation.

Whilst high-resolution spatial variables contribute to a good fit of spatially explicit deforestation models, socio-economic processes are often beyond the scope of these models. Such a low level of interest in the socio-economic dimension of deforestation limits the relevancy of these models for decision-making and may be the cause of their failure to accurately predict observed deforestation trends in the medium term. This study aims to propose a flexible methodology for taking into account multiple drivers of deforestation in tropical forested areas, where the intensity of deforestation is explicitly predicted based on socio-economic variables. By coupling a model of deforestation location based on spatial environmental variables with several sub-models of deforestation intensity based on socio-economic variables, we were able to create a map of predicted deforestation over the period 2001-2014 in French Guiana. This map was compared to a reference map for accuracy assessment, not only at the pixel scale but also over cells ranging from 1 to approximately 600 sq. km. Highly significant relationships were explicitly established between deforestation intensity and several socio-economic variables: population growth, the amount of agricultural subsidies, gold and wood production. Such a precise characterization of socio-economic processes allows to avoid overestimation biases in high deforestation areas, suggesting a better integration of socio-economic processes in the models. Whilst considering deforestation as a purely geographical process contributes to the creation of conservative models unable to effectively assess changes in the socio-economic and political contexts influencing deforestation trends, this explicit characterization of the socio-economic dimension of deforestation is critical for the creation of deforestation scenarios in REDD+ projects.

Continuous soil carbon storage of old permanent pastures in Amazonia.

Amazonian forests continuously accumulate carbon (C) in biomass and in soil, representing a carbon sink of 0.42-0.65 GtC yr-1 . In recent decades, more than 15% of Amazonian forests have been converted into pastures, resulting in net C emissions (~200 tC ha-1 ) due to biomass burning and litter mineralization in the first years after deforestation. However, little is known about the capacity of tropical pastures to restore a C sink. Our study shows in French Amazonia that the C storage observed in native forest can be partly restored in old (≥24 year) tropical pastures managed with a low stocking rate (±1 LSU ha-1 ) and without the use of fire since their establishment. A unique combination of a large chronosequence study and eddy covariance measurements showed that pastures stored between -1.27 ± 0.37 and -5.31 ± 2.08 tC ha-1  yr-1 while the nearby native forest stored -3.31 ± 0.44 tC ha-1  yr-1 . This carbon is mainly sequestered in the humus of deep soil layers (20-100 cm), whereas no C storage was observed in the 0- to 20-cm layer. C storage in C4 tropical pasture is associated with the installation and development of C3 species, which increase either the input of N to the ecosystem or the C:N ratio of soil organic matter. Efforts to curb deforestation remain an obvious priority to preserve forest C stocks and biodiversity. However, our results show that if sustainable management is applied in tropical pastures coming from deforestation (avoiding fires and overgrazing, using a grazing rotation plan and a mixture of C3 and C4 species), they can ensure a continuous C storage, thereby adding to the current C sink of Amazonian forests.

Questioning emissions-based approaches for the definition of REDD+ deforestation baselines in high forest cover/low deforestation countries.

BACKGROUND: REDD+ is being questioned by the particular status of High Forest/Low Deforestation countries. Indeed, the formulation of reference levels is made difficult by the confrontation of low historical deforestation records with the forest transition theory on the one hand. On the other hand, those countries might formulate incredibly high deforestation scenarios to ensure large payments even in case of inaction. RESULTS: Using a wide range of scenarios within the Guiana Shield, from methods involving basic assumptions made from past deforestation, to explicit modelling of deforestation using relevant socio-economic variables at the regional scale, we show that the most common methodologies predict huge increases in deforestation, unlikely to happen given the existing socio-economic situation. More importantly, it is unlikely that funds provided under most of these scenarios could compensate for the total cost of avoided deforestation in the region, including social and economic costs. CONCLUSION: This study suggests that a useful and efficient international mechanism should really focus on removing the underlying political and socio-economic forces of deforestation rather than on hypothetical result-based payments estimated from very questionable reference levels.