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With the growing demands for food and biofuel, new technologies and crop management systems are being used to increase productivity and minimize land-use impacts. In this context, estimates of productivity and the impacts of agriculture management practices are becoming increasingly important. Numerical models that describe the soil-surface-atmosphere interactions for natural and agricultural ecosystems are important tools to explore the impacts of these agronomical technologies and their environmental impacts. However, these models need to be validated by considering the different soil and environmental conditions before they can be widely applied. The process-based terrestrial agricultural version of the Integrated Biosphere Simulator (IBIS) model (Agro-IBIS) has only been calibrated and validated for North American sites. Here, the authors validate the Agro-IBIS results for an experimental soybean site in southern Brazil. At this site, soybean was grown under two different management systems: no tillage (NT) and conventional tillage (CT). The model results were evaluated against micrometeorological, soil condition, and biomass observations made during the soybean growing season. The leaf area index (LAI) was underestimated, approaching the values obtained in the CT crop system, with higher error in the leaf senescence period. The model shows higher skill for daily averages and the diurnal cycle of the energy balance components in the period of high LAI. The soil temperature and moisture were robustly simulated, although the latter is best correlated with the observations made at the CT field. The ecosystem respiration is highly underestimated, causing an overestimation of the cumulative net ecosystem exchange (NEE), particularly at the end of the crop cycle. [ABSTRACT FROM AUTHOR]

Climate change scenarios vary considerably over the Amazon region, with an extreme scenario projecting a dangerous (from the human perspective) increase of 3.8°°C in temperature and 30%% reduction in precipitation by 2050. The impacts of such climate change on Amazonian land-use dynamics, agricultural production, and deforestation rates are still to be determined. In this study, the authors make a first attempt to assess these impacts through a systemic approach, using a spatially explicit modeling framework to project crop yield and land-use/land-cover changes in the Brazilian Amazon by 2050. The results show that, without any adaptation, climate change may exert a critical impact on the yields of crops commonly cultivated in the Amazon (e.g., soybean yields are reduced by 44%% in the worst-case scenario). Therefore, following baseline projections on crop and livestock production, a scenario of severe regional climate change would cause additional deforestation of 181 000 km2 (++20%%) in the Amazon and 240 000 km2 (++273%%) in the Cerrado compared to a scenario of moderate climate change. Putting an end to deforestation in the Brazilian Amazon forest by 2020 (and of the Cerrado by 2025) would require either a reduction of 26%%--40%% in livestock production until 2050 or a doubling of average livestock density from 0.74 to 1.46 head per hectare. These results suggest that (i) climate change can affect land use in ways not previously explored, such as the reduction of yields entailing further deforestation, and (ii) there is a need for an integrated/multidisciplinary plan for adaptation to climate change in the Amazon. [ABSTRACT FROM AUTHOR]

While interest in Amazonian deforestation mostly focuses on frontier areas, the amount of forest cover in areas already dominated by human settlement is also changing. Secondary forests play an increasingly important role for maintaining genetic diversity, hydrological functioning, and greenhouse gas emissions of altered landscapes, but secondary forests are also being converted to more intensive agricultural uses. Five dates of Landsat imagery from 1984 to 2002 were analyzed, covering 8000 km2 of the Zona Bragantina of the eastern part of the Brazilian state of Pará, which underwent its most intensive wave of deforestation several decades ago. However, even in this area of relatively long-term human occupation, ongoing decreases of forest cover were found, both in the small remaining areas of mature forest and in the more widespread areas of secondary forests, as human population increased and land use intensified. Although there was an initial increase in the area of secondary forest from 1984 to 1994, there has been a steady decline since then, from 75% secondary forest cover in 1994 to 54% in 2002. The amount of pasture was relatively stable from 1984 to 1994 but more recently has shown a steady increase, reaching 37% cover in 2002. The average rate of carbon loss over the 18-yr study period was 0.9 Mg C ha-1 yr-1 for the 8000 km2 study area. Forests in this long-settled region of eastern Amazonia continue to be degraded, resulting in the loss of ecosystem services and carbon stocks due to continued land-use change. [ABSTRACT FROM AUTHOR]

Although pasture degradation has been a regional concern in Amazonian ecosystems, our ability to characterize and monitor pasture degradation under different environmental and human-related conditions is still limited. Regional analysis of pasture dynamic patterns was conducted using high-frequency temporal satellite data and ancillary data to better understand pasture degradation under varied soil, environmental, and pasture management conditions in the state of Rondônia, Brazil. The 10-day normalized difference vegetation index (NDVI) composite derived from Moderate Resolution Imaging Spectroradiometer (MODIS) 250-m resolution was used to characterize different grass phenological patterns for 32 counties in Rondônia between 2001 and 2003. Six pasture greenness classes showed that high greenness pasture classes dominated in young pastures, while low greenness pasture classes were least common. As pastures aged, the proportion of high greenness pasture classes decreased and the proportion of low greenness pastures increased, indicating a decrease in forage productivity over time in Rondônia. The magnitude of productivity decline depended on environmental constraints and land use systems. To refine this analysis, trajectories of pasture change were determined using spectral mixture analysis applied to Landsat time series data from 1988 to 2001 with the focus on two counties that show contrasting patterns of potential of grass production: Pimenteira, representing the “degraded” pasture category, and Governador Jorge Teixeira, as the “productive” pasture category. The results revealed a clear pasture degradation pattern in Pimenteira, related to low soil fertility and dry climate conditions, while Governador Jorge Teixeira, with better soil fertility and intermediate precipitation, did not show signs of pasture degradation through time. [ABSTRACT FROM AUTHOR]

The sensitivity of surface energy and water fluxes to recent land cover changes is simulated for a small region in northern Mato Grosso, Brazil. The Simple Biosphere Model (SiB2) is used, driven by biophysical parameters derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) at 250-m resolution, to compare the effects of different land conversion types. The mechanisms through which changes in vegetation alter surface fluxes of energy, momentum, water, and carbon are analyzed for both wet and dry seasons. It is found that morphological changes contribute to warming and drying of the atmosphere while physiological changes, particularly those associated with a plant¿s photosynthetic pathway, counterbalance or exacerbate the warming depending on the type of conversion and the season. Furthermore, this study¿s results indicate that initial clearing of evergreen and transition forest to bare ground increases canopy temperature by up to 1.7°C. For subsequent land use such as pasture or cropland, the largest effect is seen for the conversion of evergreen forest to C3 cropland during the wet season,with a 21% decrease of the latent heat flux and 0.4°C increase in canopy temperature. The secondary conversion of pasture to cropland resulted in slight warming and drying during the wet season driven mostly by the change in carbon pathway from C4 to C3. For all conversions types, the daily temperature range is amplified, suggesting that plants replacing forest clearing require more temperature tolerance than the trees they replace. The results illustrate that the effect of deforestation on climate depends not only on the overall extent of clearing but also on the subsequent land use type. [ABSTRACT FROM AUTHOR]

Correctly characterizing the frequency and distribution of fire occurrence is essential for understanding the environmental impacts of biomass burning. Satellite fire detection is analyzed from two sensors—the Advanced Very High Resolution Radiometer (AVHRR) on NOAA-12 and the Moderate Resolution Imaging Spectroradiometer (MODIS) on both the Terra and Aqua platforms, for 2001–03—to characterize fire activity in Brazil, giving special emphasis to the Amazon region. In evaluating the daily fire counts, their dependence on variations in satellite viewing geometry, overpass time, atmospheric conditions, and fire characteristics were considered. Fire counts were assessed for major biomes of Brazil, the nine states of the Legal Amazon, and two important road corridors in the Amazon region. All three datasets provide consistent information on the timing of peak fire activity for a given state. Also, ranking by relative fire counts per unit area highlights the importance of fire in smaller biomes such as Complexo do Pantanal. The local analysis of road corridors shows trends for fire detections with the increasing intensity of land use. Although absolute fire counts differ by as much as 1200%, when summarized over space and time, trends in fire counts among the three datasets show clear patterns of fire dynamics. The fire dynamics that are evident in these trend analyses are important foundations for assessing environmental impacts of biomass burning and policy measures to manage fire in Brazil. [ABSTRACT FROM AUTHOR]

The Brazilian Amazon forest and cerrado savanna encompasses a region of enormous ecological, climatic, and land-use variation. Satellite remote sensing is the only tractable means to measure the biophysical attributes of vegetation throughout this region, but coarse-resolution sensors cannot resolve the details of forest structure and land-cover change deemed critical to many land-use, ecological, and conservation-oriented studies. The Carnegie Landsat Analysis System (CLAS) was developed for studies of forest and savanna structural attributes using widely available Landsat Enhanced Thematic Mapper Plus (ETM+) satellite data and advanced methods in automated spectral mixture analysis. The methodology of the CLAS approach is presented along with a study of its sensitivity to atmospheric correction errors. CLAS is then applied to a mosaic of Landsat images spanning the years 1999–2001 as a proof of concept and capability for large-scale, very high resolution mapping of the Amazon and bordering cerrado savanna. A total of 197 images were analyzed for fractional photosynthetic vegetation (PV), nonphotosynthetic vegetation (NPV), and bare substrate covers using a probabilistic spectral mixture model. Results from areas without significant land use, clouds, cloud shadows, and water bodies were compiled by the Brazilian state and vegetation class to understand the baseline structural typology of forests and savannas using this new system. Conversion of the satellite-derived PV data to woody canopy gap fraction was made to highlight major differences by vegetation and ecosystem classes. The results indicate important differences in fractional photosynthetic cover and canopy gap fraction that can now be accounted for in future studies of land-cover change, ecological variability, and biogeochemical processes across the Amazon and bordering cerrado regions of Brazil. [ABSTRACT FROM AUTHOR]