Publicações

This work investigates the diurnal and seasonal behavior of the energy balance residual (E) that results from the observed difference between available energy and the turbulent fluxes of sensible heat (H) and latent heat (LE) at the FLUXNET BR-Ma2 site located in the Brazilian central Amazon rainforest. The behavior of E is analyzed by extending the eddy covariance averaging length from 30 min to 4 h and by applying an Information Flow Dynamical Process Network to diagnose processes and conditions affecting E across different seasons. Results show that the seasonal turbulent flux dynamics and the Bowen ratio are primarily driven by net radiation (Rn), with substantial sub-seasonal variability. The Bowen ratio increased from 0.25 in April to 0.4 at the end of September. Extension of the averaging length from 0.5 (94.6% closure) to 4 h and thus inclusion of longer timescale eddies and mesoscale processes closes the energy balance and lead to an increase in the Bowen ratio, thus highlighting the importance of additional H to E. Information flow analysis reveals that the components of the energy balance explain between 25 and 40% of the total Shannon entropy with higher values during the wet season than the dry season. Dry season information flow from the buoyancy flux to E are 3050% larger than that from H, indicating the potential importance of buoyancy fluxes to closing E. While the low closure highlights additional sources not captured in the flux data and random measurement errors contributing to E, the findings of the information flow and averaging length analysis are consistent with the impact of mesoscale circulations, which tend to transport more H than LE, on the lack of closure.

In tropical areas, pioneer occupation fronts steer the rapid expansion of deforestation, contributing to carbon emissions. Up-to-date carbon emission estimates covering the long-term development of such frontiers depend on the availability of high spatialtemporal resolution data. In this paper, we provide a detailed assessment of carbon losses from deforestation and potential forest degradation from fragmentation for one expanding frontier in the Brazilian Amazon. We focused on one of the Amazonias hot-spots of forest loss, the BR-163 highway that connects the high productivity agricultural landscapes in Mato Grosso with the exporting harbors of the Amazon. We used multidecadal (19842012) Landsat-based time series on forested and non-forested area in combination with a carbon bookkeeping model. We show a 36% reduction in 1984s biomass carbon stocks, which led to the emission of 611.5 TgCO2 between 1985 and 1998 (43.6 TgCO2 year-1 ) and 959.8 TgCO2 over 19992012 (68.5 TgCO2 year-1 ). Overall, fragmentation-related carbon losses represented 1.88% of total emissions by 2012, with an increasing relevance since 2004. We compared the Brazilian Space Agency deforestation assessment (PRODES) with our data and found that small deforestation polygons not captured by PRODES had increasing importance on estimated deforestation carbon losses since 2000. The comparative analysis improved the understanding of data-source-related uncertainties on carbon estimates and indicated disagreement areas between datasets that could be subject of future research. Furthermore, spatially explicit, annual deforestation and emission estimates like the ones derived from this study are important for setting regional baselines for REDD? or similar payment for ecosystem services frameworks.

Water resources are fundamental for the social and economic development of a country and sustainability is the best approach to treat water-related problems. Therefore, sustainability studies of water resources are deemed urgent. Sustainability analysis methods should enable space-temporal monitoring, decision-making, and development of policies necessary for water governance. Furthermore, sustainability analysis methods should also integrate environment and socioeconomic variables into a single system. In this context, this study aimed to assess the water sustainability conditions of the River Grande Basin (BHRG), Brazil, before the implementation of the Integrated Water Resources Plan (IWRP), using the Barometer of Sustainability tool (BS). The River Grande basin was in an almost unsustainable condition and under high environmental stress. A significant imbalance between environmental and human well-being in the system was also observed. To achieve an acceptable sustainability condition, it is thus necessary to improve the environmental quality of the area. Among the priority thematic area, native vegetation recovery was the most urgent. Overall, the sustainability study based on the BS not only facilitates comprehension regarding environment and human interrelationships, but also provide references for policy formulations and water management.

The main objective of this study is to evaluate different methods to integrate (fusion and combination) Synthetic Aperture Radar (SAR) Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band SAR (PALSAR-1) (Fine Beam Dual mode-FDB) and LANDSAT images in order to identify those which lead to higher accuracy of land-use and land-cover (LULC) mapping in an agricultural frontier region in Amazon. One method used to integrate the multipolarized information in SAR images before the fusion process was also evaluated. In this method, the first principal component (PC1 ) of SAR data was used. Color compositions of fused data that presented better LULC classification were visually analyzed. Considering the proposed objective, the following fusion methods must be highlighted: Ehlers, Wavelet a´ trous, Intensity, Hue and Saturation (IHS), and selective principal component analysis (SPC). These latter three methods presented good results when processed using PC1 from ALOS/PALSAR-1 FBD backscatter filtered image or three SAR extracted and selected features. These results corroborate with the applicability of the proposed method for SAR data information integration. Distinct methods better discriminate different LULC classes. In general, densely forested classes were better characterized by the Ehlers_TM6 fusion method, in which at least the polarization HV was used. Intermediate and initial regeneration classes were better discriminated using SPC-fused data with PC1 of ALOS/PALSAR1 FBD data. Bare soil and pasture classes were better discriminated in optical features and the PC1 of ALOS/PALSAR-1 FBD data fused by the IHS method. Soybean with approximately 40 days from seeding was better discriminated in image classification obtained from ALOS/PALSAR-1 FBD image.

Agricultural expansion and intensification enabled growth of food production but resulted in serious environmental changes. In light of that, debates concerning sustainability in agriculture arises on scientific literature. Land sharing and land sparing are two opposite models for framing agricultural sustainability. The first aims to integrate agricultural activities with biodiversity conservation by means of enhancing the quality of the agricultural matrix in the landscape towards a wildlife friendly matrix. The other model aims to spare natural habitats from agriculture for conservation. This work aimed to explore spatial evidences of land sharing/sparing and its relationship with rural population in the Brazilian Cerrado. A Land Sharing/Sparing Index based on TerraClass Cerrado map was proposed. Spatial analysis based on Global and Local Moran statistics and Geographically Weighted Regression were made in order to explore the influence of local rural population on the probability of spatial land sharing/sparing clusters occurrence. Spatial patterns of land sharing were found in the Cerrado and a positive association with rural population was found in some regions, such as in its northern portion. Land use policies should consider regional infrastructural and participative governance potentialities. The results suggests possible areas where joint agricultural activities and human presence may be favourable for biodiversity conservation.

The Bolivian Amazon wetlands are extensive floodplains distributed over the Mamore, Beni, Madre de Dios and Guapore Rivers. Located within the upper Madeira River Basin, the wetlands play important roles in regulating the biogeochemical processes and hydrological cycle of the region. In addition, they have major ecological and hydrological relevance for the entire Amazon Basin. These wetlands are characterized by the occurrence of episodic floods that result from contrasting hydro-meteorological processes in the Andean Mountain region, the piedmont area and the Amazon lowlands. In this study, we characterized the flood dynamics of the region using multi-temporal flood mapping based on optical altimetry (MODIS Moderate Resolution Imaging Spectroradiometer M*D09A1) and satellite altimetry (ENVISAT RA-2 and SARAL AltiKa altimeters). This study provides new insights regarding the frequency, magnitude and spatial distribution of exogenous floods, which are created by flood waves from the Andes; and endogenous floods, which result from runoff originating in the lowlands. The maximum extent of flooding during 20012014 was 43144 km2 in the Mamore Basin and 34852 km2 in the Guapore Basin, and the total surface water storage in these floodplains reached 94 km3. The regionalization of flood regimes based on water stage time series signatures allowed those regions that are exposed to frequent floods, which are generally located along rivers without a direct connection with the Andes, to be distinguished from floodplains that are more dependent on flood waves originating in the Andes and its piedmonts. This information is of great importance for understanding the roles of these wetlands in the provision of ecosystem services.

Protected areas have been established historically in residual places where the potential for extractive uses is low, implying that places at risk are usually under-protected. Argentina is no exception, with few protected areas established in productive regions that are prone to conversion. Here, using reptiles as a study group and considering the most important human threats in north-western Argentina, we estimated priority conservation areas where we expect species to persist in the face of climate change and land conversion. Protected areas cover no more than 9% of the study region, but represent less than 15% of reptile distributions. There are great opportunities for improving the conservation status in the region by protecting only 8% more of north-western Argentina, with the level of species protection inside the protected area network increasing almost four-fold, reaching 43% of species distributions on average and 59% of the distributions of threatened reptiles. Fortunately, the highest diversity of reptiles in the region does not match the places targeted for agriculture expansion. Our findings suggest that future prioritization schemes should embrace other groups that are especially diverse in the Chaco ecoregion, which overlaps with our study area.

This study had the objective of analyzing the spatial distribution pattern of the precipitation and the spatio-temporal variability of the long-term (drought) rainfall anomalies (SPI-12) in the state of Rondonia, using the Standardized Precipitation Index – SPI, by means of a geostatistical approach. To achieve this goal, we worked with the NOAA historical rainfall data series for the period 1983 to 2014 and with the Standardized Precipitation Index (SPI) on the 12-month scale. Spatial modeling of SPI-12 was conducted by means of the ordinary kriging method and for the precipitation ordinary and indicative kriging was used. These methods made it possible to identify 3 climatologically distinct regions in the state of Rondonia, with a more humid region to the north, a progressively drier area to the southwest and a central transition region. Based on this methodology, it was also possible to identify the years 1984, 1992, 1998, 2000 and 2003 a s years of significant long-term drought (SPI-12). However, the spatial coverage of the drought did not present a preferential region, occurring in a different way through the great territorial extension of the state. RESUMO: O presente estudo teve por objetivo analisar o padrão de distribuição espacial da precipitação e a variabilidade espaço-temporal das anomalias (secas) de chuva de longo prazo (SPI-12) no estado de Rondônia, utilizando o Índice de Precipitação Padronizado (Standarized Precipitation Index -SPI), por meio de uma abordagem geoestatística. Para tanto, trabalhou-se com a série histórica de dados pluviométricos da NOAA para o período de 1983 a 2014 e com o Índice de Precipitação Padronizado (SPI) na escala de 12 meses. A modelagem espacial do SPI-12 foi conduzida pelo método de krigeagem ordinária e para a precipitação utilizou-se a krigeagem ordinária e indicativa. Estes métodos possíbilitaram identificar 3 regiões distintas climatologicamente no estado de Rondônia, tendo-se, ao norte, uma região mais úmida, a sudoeste, uma área progressivamente mais seca e uma região central de transição. Com base nessa metodologia também foi possível identificar os anos de 1984, 1992, 1998, 2000 e 2003 como sendo anos de ocorrência significativa de seca de longa duração (SPI-12). No entanto, a abrangência espacial da seca não apresentou região preferencial, ocorrendo de forma diversa através da grande extensão territorial do estado. Palavras chave: seca; krigeagem; precipitação; Rondônia.

Scenedesmus obliquus (ACOI 204/07) microalgae were cultivated in cattle wastewater in vertical alveolar flat panel photobioreactors, operated in batch and continuous mode, after previous digestion in a hybrid anaerobic reactor. In batch operation, removal efficiencies ranges of 65 to 70% of COD, 98 to 99% of NH4 + and 69 to 77.5% of PO4 −3 after 12 days were recorded. The corresponding figures for continuous flow were from 57 to 61% of COD, 94 to 96% of NH4 + and 65 to 70% of PO4 −3 with mean hidraulic retention time of 12 days. Higher rates of CO2 fixation (327547 mg L−1 d−1 ) and higher biomass volumetric productivity (213358 mg L−1 d−1 ) were obtained in batch mode. This microalgae-mediated process can be considered promising for bioremediation and valorization of effluents produced by cattle breeding yielding a protein-rich microalgal biomass that could be eventually used as cattle feed.

In this study, different nitrogen doses (0, 16, 48, 64, 80, and 96 kg ha−1 ) from two sources, biofertilizer (from anaerobic digestion of cattle wastewater) and urea, were applied to cultivate two sugarcane varieties (RB 867515 and SP 803280). °Brix values higher than 21% were obtained with application of 80 kg ha−1 from biofertilizer. The mean productivity of the cultivar RB 867515 using biofertilizer was 147.5 ton ha−1 , while from urea it was 136.87 ton ha−1 . The cultivar SP 803280 produced an average yield of 152.25 ton ha−1 when applying biofertilizer and 154.37 ton ha−1 with use of urea. Significant differences (P ≤ 0.05) between the use of biofertilizer and urea were detected for cultivar RB 867515 in terms of crude protein concentration. The application of 80 kg of N ha−1 was considered the ideal dose, corresponding to fertirrigation blades of 54 mm of biofertilizer. The experiment showed that the biofertilizer formulation analyzed can replace urea as a nitrogen source for growing sugarcane.

In this paper, we evaluated the performance of the model AquaCrop for crop yield forecasting in the Brazilian semiarid (BSA) using meteorological observation and Eta model seasonal climate forecasts as input data. The study area is characterized by low rainfall that is poorly distributed throughout the rainy season; thus, the region’s agricultural productivity is vulnerable to climate conditions. AquaCrop was first calibrated using field experiments and subsequently applied to simulate an operational crop yield forecast system for maize under rainfed conditions. Simulations were performed with daily data for 37 growing seasons for the period 20012010. The seasonal climate forecast was used in combination with observed meteorological data to anticipate the crop forecast. Soil characteristics were derived from pedotransfer functions (PTFs). We were able to demonstrate the ability of the seasonal crop yield forecast system to provide timely and accurate information about maize yield at least 30 days in advance of the harvest. The development of improved crop yield forecasting system is crucial for implementing drought-preparedness measures in the BSA region.