Estimates of the annual emissions of carbon from land use, land-use change, and forestry (LULUCF) are important for constructing global, regional, and national carbon budgets, which in turn help predict future rates of climate change and define potential strategies for mitigation. Here, we update a long-term (1850–2020) series of annual national carbon emissions resulting from LULUCF (https://doi.org/10.7910/DVN/U7GHRH, Houghton and Castanho, 2023), based largely, after 1960, on statistics of land use from the Food and Agriculture Organization (FAO) of the United Nations (http://www.fao.org/faostat/en/#data/, FAO, 2021). Those data suggest that rates of deforestation in the tropics (and thus net emissions of carbon) have decreased over the last 10 years (2011–2020). The data also indicate that the net loss of tropical forest area was greater than the net gain in agricultural lands, and we explore four alternative explanations for this apparent forest conversion, one of which is shifting cultivation. We also discuss how opposing trends in recent estimates of tropical deforestation (and emissions) might be reconciled. The calculated emissions of carbon attributable to LULUCF approximate the anthropogenic component of terrestrial carbon emissions, but limiting national carbon accounting to the anthropogenic component may also limit the potential for managing carbon on land.

Dynamic vegetation models forced with spatially homogeneous biophysical parameters are capable of producing average productivity and biomass values for the Amazon basin forest biome that are close to the observed estimates, but these models are unable to reproduce observed spatial variability. Recent observational studies have shown substantial regional spatial variability of above-ground productivity and biomass across the Amazon basin, which is believed to be primarily driven by a combination of soil physical and chemical properties. In this study, spatial heterogeneity of vegetation properties is added to the Integrated Biosphere Simulator (IBIS) land surface model, and the simulated productivity and biomass of the Amazon basin are compared to observations from undisturbed forest. The maximum RuBiCo carboxylation capacity (V_cmax) and the woody biomass residence time (τ_w) were found to be the most important properties determining the modeled spatial variation of above-ground woody net primary productivity and biomass, respectively. Spatial heterogeneity of these properties may lead to simulated spatial variability of 1.8 times in the woody net primary productivity (NPP_w) and 2.8 times in the woody above-ground biomass (AGB_w). The coefficient of correlation between the modeled and observed woody productivity improved from 0.10 with homogeneous parameters to 0.73 with spatially heterogeneous parameters, while the coefficient of correlation between the simulated and observed woody above-ground biomass improved from 0.33 to 0.88. The results from our analyses with the IBIS dynamic vegetation model demonstrated that using single values for key ecological parameters in the tropical forest biome severely limits simulation accuracy. Clearer understanding of the biophysical mechanisms that drive the spatial variability of carbon allocation, τ_w and V_cmax is necessary to achieve further improvements to simulation accuracy.

The Amazon tropical evergreen forest is an important component of the global carbon budget. Its forest floristic composition, structure, and function are sensitive to changes in climate, atmospheric composition, and land use. In this study biomass and productivity simulated by three dynamic global vegetation models (Integrated Biosphere Simulator, Ecosystem Demography Biosphere Model, and Joint UK Land Environment Simulator) for the period 1970–2008 are compared with observations from forest plots (Rede Amazónica de Inventarios Forestales). The spatial variability in biomass and productivity simulated by the DGVMs is low in comparison to the field observations in part because of poor representation of the heterogeneity of vegetation traits within the models. We find that over the last four decades the CO₂ fertilization effect dominates a long-term increase in simulated biomass in undisturbed Amazonian forests, while land use change in the south and southeastern Amazonia dominates a reduction in Amazon aboveground biomass, of similar magnitude to the CO₂ biomass gain. Climate extremes exert a strong effect on the observed biomass on short time scales, but the models are incapable of reproducing the observed impacts of extreme drought on forest biomass. We find that future improvements in the accuracy of DGVM predictions will require improved representation of four key elements: (1) spatially variable plant traits, (2) soil and nutrients mediated processes, (3) extreme event mortality, and (4) sensitivity to climatic variability. Finally, continued long-term observations and ecosystem-scale experiments (e.g. Free-Air CO₂ Enrichment experiments) are essential for a better understanding of the changing dynamics of tropical forests.