Search Results (79)

This study examines the socio-ecological impacts of soybean cultivation in the Brazilian Amazon, a region of critical importance for global climate regulation and biodiversity conservation. It explores how the expansion of soybean cultivation in this region since the 1990s, driven by international demand and domestic policies, has triggered a series of unsustainable socio-ecological consequences, such as deforestation, overuse of agrochemicals, displacement of indigenous communities, and land tenure conflicts. Inadequate governance, at both national and international levels, has exacerbated these challenges, undermining efforts to balance soybean cultivation with sustainable development in Brazilian Amazon. Through a mixed analysis method, this study proposes pathways for sustainable soybean production in the Amazon, including extending the Soy Moratorium to the Cerrado, strengthening indigenous land rights, enhancing international cooperation, and adopting sustainable agricultural practices such as agroforestry. These findings contributes to reconciling soybean cultivation with sustainable development in the Brazilian Amazon. Full article

Given the growing consumer demand for improved quality of life and health-promoting foods, replacing conventional fats in widely consumed products such as bread with oils derived from native Brazilian fruits represents a promising strategy. This study aimed to evaluate the bioactive and technological potential of buriti (Mauritia flexuosa) and acuri (Attalea phalerata) oils, extracted from palm fruits native to the Cerrado and Amazon biomes. Both oils proved to be rich sources of lipophilic bioactives, particularly carotenoids, tocopherols, and phenolic compounds, and exhibited excellent carotenoid bioaccessibility under in vitro digestion, with recovery rates of 74% for acuri oil and 54% for buriti oil. Notably, buriti oil showed a high β-carotene content (1476.5 µg/g). When incorporated into sandwich bread formulations, these oils enhanced antioxidant activity, improved texture, volume, and color, and maintained high sensory acceptance compared to bread made with soybean oil. Sensory evaluation scores averaged above 7 for all tested attributes. These findings underscore the industrial applicability of buriti and acuri oils as functional lipids aligned with sustainable development and nutritional innovation. Full article

Atmospheric aerosols significantly influence photosynthetically active radiation (PAR), critical for plant photosynthesis and ecosystem functioning. This study systematically reviewed recent research (2021–2025) on aerosol–PAR interactions. Using targeted keywords, 22 open-access articles from Scopus and Google Scholar were analyzed via VOSviewer for thematic, methodological, and geographic trends. Analysis revealed a strong concentration in Earth and Environmental Sciences, showcasing significant advances in radiative transfer modeling, remote sensing, and machine learning for estimating aerosol impacts on PAR. Studies primarily utilized satellite data and models (e.g., DART, SCOPE) to assess diffuse/direct radiation changes. The literature consistently demonstrates how aerosols modulate PAR, influencing canopy light penetration and photosynthetic efficiency. However, critical gaps persist, including limited field validation in tropical biomes (e.g., Amazon, Cerrado, Pantanal) and a lack of studies differentiating aerosol types like black and brown carbon. This synthesis underscores the need for expanded monitoring and integrated modeling efforts to improve understanding of aerosol–PAR interactions, particularly in underrepresented tropical regions. Full article

Throughout history, humans have modified the environment, transforming natural biomes into agricultural areas. In the 1990s, economic policies accelerated the expansion of agricultural frontiers in Brazil, including the Triângulo Mineiro and Alto Paranaíba regions. This study analyzes rainfall variability from 1990 to 2021 and its relationship with land use. For this purpose, satellite data from MapBiomas, ERA5, and NASA POWER were processed using Google Earth Engine and QGIS. Statistical methods included the Spearman correlation and the Mann–Kendall trend test. The results revealed that average annual precipitation decreased from 1663.35 mm in 1991 to 1128.94 mm in 2022—a 32.14% reduction. Simultaneously, agricultural and urban areas increased by 365% and 237.59%, respectively. Spearman analysis showed negative correlations between precipitation and agriculture (ρ = −0.51) and urbanization (ρ = −0.51), and positive correlations with pasture (ρ = +0.52) and water bodies (ρ = +0.46). These trends suggest that land use intensification significantly affects regional rainfall patterns. Unlike studies focusing mainly on Amazon deforestation, this research emphasizes the Cerrado biome’s climatic vulnerability. The use of long-term, high-resolution remote sensing data allows a robust analysis of land use impacts. By highlighting a clear link between land transformation and precipitation decline, this study offers insights for policymaking aimed at balancing agricultural development and water resource preservation. This research underscores the importance of sustainable land management practices, such as agroecology, reforestation, and ecological corridors, for regional climate resilience. Full article

There is a consensus that climate change has affected society. The increase in temperature and reduction in precipitation for some regions of the world have had implications for the intensity and frequency of extreme events. This scenario is worrying for various sectors of water use, such as hydroelectric power generation and agriculture. Reduced flows in river basins, coupled with increased water consumption, can significantly affect energy generation and food production. Within this context, this paper presents an analysis of climate change impacts in a large basin of Brazil between the Amazon and Cerrado biomes, considering the effects of water demands. Inflow projections were generated for seven power plant reservoirs in the Tocantins–Araguaia river basin, using projections from five climate models. The results indicate significant reductions in flows, with decreases of more than 50% in the average flow. For minimum flows, there are indications of reductions of close to 85%. The demand for water, although growing, represents a smaller part of the effects, but should not be disregarded, since it impacts the dry periods of the rivers and can generate conflicts with energy production. Full article

The Cerrado-Amazon Transition (CAT) in Brazil represents one of the most ecologically complex and dynamic tropical ecotones globally; however, it remains insufficiently characterized at high spatial resolution, primarily due to its intricate vegetation mosaics and the limited availability of reliable ground reference data. Accurate land cover maps are urgently needed to support conservation and sustainable land-use planning in this frontier region, especially for distinguishing critical vegetation types such as Amazon rainforest, Cerradão (dense woodland), and Savanna. In this study, we produce the first high-resolution land cover map of the CAT by integrating PlanetScope optical imagery, Sentinel-2 multispectral data, and Sentinel-1 SAR data within a U-net deep learning framework. This data fusion approach enables improved discrimination of ecologically similar vegetation types across heterogeneous landscapes. We systematically compare classification performance across single-sensor and fused datasets, demonstrating that multi-source fusion significantly outperforms single-source inputs. The highest overall accuracy was achieved using the fusion of PlanetScope, Sentinel-2, and Sentinel-1 (F1 = 0.85). Class-wise F1 scores for the best-performing model were 0.91 for Amazon Forest, 0.76 for Cerradão, and 0.76 for Savanna, indicating robust model performance in distinguishing ecologically important vegetation types. According to the best-performing model, 50.3% of the study area remains covered by natural vegetation. Cerradão, although ecologically important, covers only 8.4% of the landscape and appears highly fragmented, underscoring its vulnerability. These findings highlight the power of deep learning and multi-sensor integration for fine-scale land cover mapping in complex tropical ecotones and provide a critical spatial baseline for monitoring ecological changes in the CAT region. Full article

Deep learning models have shown great potential in scientific research, particularly in remote sensing for monitoring natural resources, environmental changes, land cover, and land use. Deep semantic segmentation techniques enable land cover classification, change detection, object identification, and vegetation health assessment, among other applications. However, their effectiveness relies on large labeled datasets, which are costly and time-consuming to obtain. Domain adaptation (DA) techniques address this challenge by transferring knowledge from a labeled source domain to one or more unlabeled target domains. While most DA research focuses on single-target single-source problems, multi-target and multi-source scenarios remain underexplored. This work proposes a deep learning approach that uses Domain Adversarial Neural Networks (DANNs) for deforestation detection in multi-domain settings. Additionally, an uncertainty estimation phase is introduced to guide human review in high-uncertainty areas. Our approach is evaluated on a set of Landsat-8 images from the Amazon and Brazilian Cerrado biomes. In the multi-target experiments, a single source domain contains labeled data, while samples from the target domains are unlabeled. In multi-source scenarios, labeled samples from multiple source domains are used to train the deep learning models, later evaluated on a single target domain. The results show significant accuracy improvements over lower-bound baselines, as indicated by F1-Score values, and the uncertainty-based review showed a further potential to enhance performance, reaching upper-bound baselines in certain domain combinations. As our approach is independent of the semantic segmentation network architecture, we believe it opens new perspectives for improving the generalization capacity of deep learning-based deforestation detection methods. Furthermore, from an operational point of view, it has the potential to enable deforestation detection in areas around the world that lack accurate reference data to adequately train deep learning models for the task. Full article

Fires drive global ecosystem change, impacting carbon dynamics, atmospheric composition, biodiversity, and human well-being. Biomass burning, a major outcome of fires, significantly contributes to greenhouse gas and aerosol emissions. Among these, fine particulate matter (PM_2.5) is particularly concerning due to its adverse effects on air quality and health, and its substantial yet uncertain role in Earth’s energy balance. Variability in emission factors (EFs) remains a key source of uncertainty in emission estimates. This study evaluates PM_2.5 emission sensitivity to EFs variability in Brazil’s Amazon and Cerrado biomes over 2002–2023 using the 3BEM_FRP model implemented in the PREP-CHEM-SRC tool. We updated the EFs with values and uncertainty ranges from Andreae (2019), which reflect a more comprehensive literature review than earlier datasets. The results reveal that the annual average PM_2.5 emissions varied by up to 162% in the Amazon (1213 Gg yr⁻¹ to 3172 Gg yr⁻¹) and 184% in the Cerrado (601 Gg yr⁻¹ to 1709 Gg yr⁻¹). The Average peak emissions at the grid-cell level reached 5688 Mg yr⁻¹ in the “Arc of Deforestation” region under the High-end EF scenario. Notably, the PM_2.5 emissions from Amazon forest areas increased over time despite shrinking forest cover, indicating that Amazonian forests are becoming more vulnerable to fire. In the Cerrado, savannas are the primary land cover contributing to the total PM_2.5 emissions, accounting for 64% to 80%. These findings underscore the importance of accurate, region-specific EFs for improving emission models and reducing uncertainties. Full article

Understanding genetic diversity is crucial for plant adaptation in a changing world. The neutral genetic variation (NGD) is correlated to adaptation capacity, which is crucial for long-term conservation of threatened species. Brazil, a megadiverse nation with habitats encompassing a great variety of ecosystems, harbors a wealth of plant biodiversity, yet studies on NGD remain scarce. This work analyzed published data on NGD in native Brazilian plant populations, identifying 731 papers through a systematic search on the Scopus database. Results indicated microsatellite markers as the most used for population studies, followed by ISSR. The SNP marker is still underutilized, possibly due to its higher costs and labor-intensiveness. Fabaceae, Bromeliaceae, and Arecaceae were the most studied families. Moreover, the two most studied species were Euterpe edulis and Hancornia speciosa, both economically important species. Notably, trees and herbs dominated the studies with a focus on the Atlantic Forest biome. However, Cerrado and Amazon biomes were also well represented, underscoring the importance of broader investigation across all Brazilian ecosystems. These findings reveal a critical gap in knowledge, where traditional molecular markers are most used and few economically important species are intensively studied. The number of threatened species studied is negligible, and most are not endemic. With looming climate and landscape changes, more comprehensive studies of NGD of threatened flora in Brazil are vital. The lack of genetic diversity information of native species may threaten any conservation efforts in the long term. Full article

Brazil’s Forest Code (FC) is a landmark law that, despite dating back to the 1930s, has low compliance. Illegal deforestation continues, and millions of hectares that were set to be reforested remain degraded. Although sector agreements such as the Amazon Soy Moratorium (ASM) have been important in the fight against deforestation, the implementation of the FC represents the key long-term strategy to halt deforestation in the soy supply chain. Here, we used datasets of the boundaries of rural properties, deforestation permits, environmental licensing, and land cover in Mato Grosso to quantify illegal deforestation and analyzed compliance with the Forest Code (FC) on soy farms to explore how loopholes in the implementation of the FC allow deforestation to continue unabated. Our analyses show that between August 2009 and July 2019, soy farms in Mato Grosso State, the largest Brazilian soy producer, were responsible for 15% (or 468.1 thousand hectares) of all land cleared in registered properties. Half of this deforestation was illegal. The FC implementation within these properties has been slow: only 11% of registered soy farms have made it to the final stage of the registration process, thus being considered fully compliant. This novel analysis reinforces that accelerating the implementation of the FC could significantly reduce deforestation and advance the restoration of illegally cleared land particularly in the Cerrado, where 50% of the original cover has already been lost, as well as in the Amazon. By achieving full compliance in the soy sector, Brazil’s position in the international market would be strengthened as a supplier of sustainably produced, deforestation-free commodities. Full article

Research related to Cerradão vegetation focuses more on the floristic-structural aspect, with rare studies on the quantification of volume and biomass stocks, and even fewer investigating the increments of these attributes. Using a systematic sampling method with subdivided strips and 400 m² plots, the density found was 1135, 1165, and 1229 trees/ha in 2012, 2020, and 2023, respectively, in Lajeado State Park, Tocantins State, Brazil. Volume was estimated using the equation $\mathrm{v}=0.000085{\mathrm{D}}^{2.122270}{\mathrm{H}}^{0.666217},$ and biomass was estimated using the equation $\mathrm{A}\mathrm{G}\mathrm{B}=0.0673{\left(\rho {\mathrm{D}}^2\mathrm{H}\right)}^{0.976}$. Vegetation dynamics were assessed using growth increment, recruitment, mortality, turnover rate, and time. The results indicated that dynamics have increased since the start of monitoring. Typical Cerrado species, in the strict sense, were replaced by those from forest environments. The total production in volume and biomass was 160.91 m³/ha and 118.10 Mg/ha, respectively, in 2023. The species of Emmotum nitens, Mezilaurus itauba, Ocotea canaliculata, and Sacoglottis guianensis showed the highest increment values in volume and biomass. For the community, the average values were 4.04 m³/ha/year and 3.54 Mg/ha/year. The community has not yet reached its carrying capacity and stores a significant amount of biomass. This is influenced by the transition of the study area from an exploited environment to a conservation unit (park) and by its location in a transitional area with the Amazon biome. Full article

The ecotone zone, located between the Cerrado and Amazon biomes, has been under intensive anthropogenic pressures due to the expansion of commodity agriculture and extensive cattle ranching. This has led to habitat loss, reducing biodiversity, depleting biomass, and increasing CO₂ emissions. In this study, we employed an artificial neural network, field data, and remote sensing techniques to develop a model for estimating biomass in the remaining native vegetation within an 18,864 km² ecotone region between the Amazon and Cerrado biomes in the state of Mato Grosso, Brazil. We utilized field data from a plant ecology laboratory and vegetation indices from Sentinel-2 satellite imagery and trained artificial neural networks to estimate aboveground biomass (AGB) in the study area. The optimal network was chosen based on graphical analysis, mean estimation errors, and correlation coefficients. We validated our chosen network using both a Student’s t-test and the aggregated difference. Our results using an artificial neural network, in combination with vegetation indices such as AFRI (Aerosol Free Vegetation Index), EVI (Enhanced Vegetation Index), and GNDVI (Green Normalized Difference Vegetation Index), which show an accurate estimation of aboveground forest biomass (Root Mean Square Error (RMSE) of 15.92%), can bolster efforts to assess biomass and carbon stocks. Our study results can support the definition of environmental conservation priorities and help set parameters for payment for ecosystem services in environmentally sensitive tropical regions. Full article

We present a dated and calibrated molecular phylogeny for one of the most characteristic genera of Neotropical savannas, Byrsonima (Malpighiaceae), based on the ETS, ITS, and psbA-trnH markers. We sampled 33 species of Byrsonima and four species of the outgroups Blepharandra, Diacidia, and Pterandra to test the monophyly of the infrageneric classification of the genus. Bayesian inference (BI) analysis was performed for the combined molecular dataset. Seven morphological characters were optimized on the obtained tree. Calibration points derived from a published chronogram for Malpighiaceae were used alongside a relaxed, uncorrelated molecular clock on Beast 1.8.4. Ancestral range reconstructions focusing on four main Neotropical biomes (Cerrado, Atlantic rainforest, Amazon rainforest, and Caatinga dry forests) were performed on BioGeoBEARS. Our phylogenetic results corroborated the monophyly of Byrsonima, but all of its subgenera and sections were polyphyletic, with all morphological characters circumscribing these infrageneric ranks being highly homoplastic. The most recent common ancestor of Byrsonima was widespread in South American biomes at 11.41 Ma, posteriorly diversifying in the Amazon rainforests up to 7.72 Ma, when it started massively diversifying in Neotropical savannas. A few re-colonization events from savannas to rain or dry forests occurred from 2.95–0.53 Ma. These results corroborate the mid-Miocene origins of Neotropical savannas, and future studies should aim to sample Mesoamerican species of Byrsonima. Full article

Our understanding of spatiotemporal variability in reference evapotranspiration (ETo) and its long-term trends is of paramount importance for water cycle studies, modeling, and water resource management, especially in the context of climate change. Therefore, the primary aim of this study is to critically evaluate the performance of various CMIP5 global climate models in simulating the Penman–Monteith reference evapotranspiration and its associated climate variables (maximum and minimum air temperature, incident solar radiation, relative humidity, and wind speed). This evaluation is based on data from nine climate models and 33 automatic meteorological stations (AWSs) in the state of Mato Grosso, spanning the period 2007–2020, within the areas of the biomes Amazon and Cerrado and around the Pantanal biome. The statistical metrics used for evaluation include bias, root mean square error, and Pearson and Spearman correlation coefficients. The projections of the most accurate model were then used to analyze the spatial and temporal changes and trends in ETo under the Representative Concentration Pathways (RCPs) of 2.6, 4.5, and 8.5 scenarios from 2007 to 2100. The HadGEM2-ES model projections indicate static averages similar to current conditions until the end of the century in the RCP 2.6 scenario. However, in the RCP 4.5 and 8.5 scenarios, there is a continuous increase in ETo, with the most significant increase occurring during the dry period (May to September). The areas of the Amazon biome in the north of Mato Grosso exhibit the largest increases in ETo when comparing the observed (2007–2020) and projected (2020–2100) averages. The trend analysis reveals significant changes in ETo and its variables across the state of Mato Grosso in the RCP 4.5 and 8.5 scenarios. In the RCP 2.6 scenario, significant trends in ETo are observed only in the northern Amazon areas. Despite not being observed in all AWSs, the trend analysis of the observed data demonstrates more intense changes in ETo and the existence of the evapotranspiration paradox, with an increase in the Cerrado areas and reductions in the Pantanal and southern Amazon areas. Full article

The Tocantins–Araguaia basin is one of South America’s largest river systems, across three Brazilian states (Maranhão, Tocantins, and Pará), within the Legal Amazon region. Despite draining extensive Cerrado savanna and rainforest ecosystems, it has suffered significant degradation, notably in the past 40 years. Human activities, including agricultural expansion, deforestation, and the introduction of non-native species, have worsened the environmental damage, which is alarming since many residents and villages along the middle Tocantins River rely on it for water supply, recreation, and fishing. This study assessed the concentration of potentially toxic and essential elements in water samples from four sampling sites distributed along the middle Tocantins River. The monitoring occurred throughout 2023, involving the measurement of parameters both on-site and in the laboratory. Water quality and its health implications were evaluated using the Weighted Arithmetic Water Quality Index (WAWQI), the Water Quality Index (WQI), and the health risk assessment index. The levels of aluminum, copper, iron, magnesium, and selenium exceeded legal standards. Seasonal fluctuations indicate a complex dynamic influenced by climatic or seasonal factors, with February showing the highest values. Site P1, located in urban areas, exhibited elevated mean concentrations for conductivity, total dissolved solids (TDS), and chlorophyll, indicating the need for continuous monitoring. The nitrogen concentrations at P1 raise concerns regarding drinking water quality, which is a concern for the region’s residents who use untreated river water. Despite seasonal variations in element concentrations, the overall WAWQI categorized all sections as “Excellent,” and the WQI rated as “Good.” Human health risk assessments detected no risks, but continuous monitoring and interventions are crucial for sustained water quality improvement. Full article