Search Results (138)

The Caatinga biome has been severely devastated over the years due to the replacement of native dry forests with grassland areas in the Brazilian semiarid region. Despite this, variations in key soil quality indicators still need to be fully elucidated. We evaluated soil and root respiration dynamics in grassland (GR), agroforestry (AS), and Caatinga forest (CA) areas, during dry and rainy seasons. In situ, monthly ${\mathrm{CO}}_2$ flux (total, root, and heterotrophic respirations), soil moisture ($\theta$v), and temperature (${\mathrm{T}}_{soil}$) were measured. Soil samples were collected every 5 cm layer up to 20 cm depth to analyze total organic carbon (TOC) and microbial activities. The highest parameter values occurred during the rainy season. Total soil respiration was highest in AS, followed by CA and then GR, with 19.3, 13.4, and 8.4 ton C h${\mathrm{a}}^{-1}$ y${\mathrm{r}}^{-1}$, respectively, and root respiration contributed 33.2 and 32.9% to total soil respiration in CA and AS, respectively. However, TOC concentrations and microbial activity were significantly higher in AS than in GR and similar to CA, more than compensating the C losses by respiration. Therefore, agroforestry systems have a high potential for semiarid lands because they preserve soil carbon and microbial activity comparable to Caatinga forests. Full article

The semi-arid Caatinga biome is particularly susceptible to fire dynamics. Periodic droughts amplify fire risks, while anthropogenic activities such as agriculture, pasture expansion, and land-clearing significantly contribute to the prevalence of fires. This research aims to evaluate the effectiveness of a fire detection model and analyze the spatial and temporal patterns of burned areas, providing essential insights for fire management and prevention strategies. Utilizing deep neural network (DNN) models, we mapped burned areas across the Caatinga biome from 1985 to 2023, based on Landsat-derived annual quality mosaics and minimum NBR values. Over the 38-year period, the model classified 10.9 Mha (12.7% of the Caatinga) as burned, with an average annual burned area of approximately 0.5 Mha (0.56%). The peak burned area reached 0.89 Mha in 2021. Fire scars varied significantly, ranging from 0.18 Mha in 1985 to substantial fluctuations in subsequent years. The most affected vegetation type was savanna, with 9.8 Mha burned, while forests experienced only 0.28 Mha of burning. October emerged as the month with the highest fire activity, accounting for 7266 hectares. These findings underscore the complex interplay of climatic and anthropogenic factors, highlighting the urgent need for effective fire management strategies. Full article

This study presents a brief bibliometric investigation of thermogravimetric pyrolysis of carnauba biomass (Copernicia prunifera), a palm tree native to northeastern Brazil belonging to the Arecaceae family. The objective was to analyze the scientific production and methods used to evaluate the kinetic parameters of biomass pyrolysis. An analysis was conducted using the Scopus, ScienceDirect, and Web of Science databases, and VOSviewer and Bibliometrix software. The methodology allows the generation of clusters and tables of scientific production, including authors, co-authors, affiliations, institutions, journals, and keywords. The search yielded 1983 articles, and after the application of exclusion criteria, 919 articles were retained, forming the basis for the bibliometric analysis. It provided an overview of thermogravimetric pyrolysis of carnauba research and identified areas that require further study. It also identified which universities and researchers have devoted the most effort to this area of research, the key findings, and areas that require further investment to complement existing research. Additionally, the study indicated the suitability of the Friedman method for determining kinetic parameters in biomass pyrolysis. Full article

Background: The relationship between human populations and wild animals dates back across centuries of interactions, enabling the construction of belief systems in communities. The socioecological perspective allows us to understand the interactions between the social and ecological dimensions of a population and nature. Methods: In this context, 133 residents of a rural community were interviewed to assess their knowledge and use of animals. We sought to evaluate the following: (i) animal species used by community residents, (ii) types of use and (iii) the effects of socioeconomic variables on different uses of vertebrate animal taxa. Results: The fauna cited were represented by 82 ethnospecies distributed in 48 families; it was not possible to identify nine ethnospecies and two were identified only at the genus level (Columbina sp. and Turdus sp.). Among the identified species, birds presented the greatest taxonomic richness (n = 34/41.0%), followed by mammals (n = 22/27.0%) and reptiles (n = 15/18.0%). Through generalized linear models, we identified which socioeconomic variables influenced the knowledge and use of wildlife resources. Furthermore, the influence of socioeconomic variables varied between taxa and use categories. Conclusions: The influence of socioeconomic variables may vary according to the taxonomic group, as well as the type of cultural domain, favoring the increase or decrease in the use of a resource. Full article

Monitoring sugarcane phenology is essential since the globalized market requires reliable information on the quantity of raw materials for the industrial production of sugar and alcohol. In this context, the general objective of this study was to evaluate the phenological seasonality of the sugarcane varieties SP 79-1011 and VAP 90-212 observed from the NDVI time series over 19 years (2001–2020) from global databases. In addition, this research had the following specific objectives: (i) to estimate phenological parameters (Start of Season (SOS), End of Season (EOS), Length of Season (LOS), and Peak of Season (POS)) using TIMESAT software in version 3.3 applied to the NDVI time series over 19 years; (ii) to characterize the land use and land cover obtained from the MapBiomas project; (iii) to analyze rainfall variability; and (iv) to validate the sugarcane harvest date (SP 79-1011). This study was carried out in sugarcane growing areas in Juazeiro, Bahia, Brazil. The results showed that the NDVI time series did not follow the rainfall in the region. The sugarcane areas advanced over the savanna formation (Caatinga), reducing them to remnants along the irrigation channels. The comparison of the observed harvest dates of the SP 79-1011 variety to the values estimated with the TIMESAT software showed an excellent fit of 0.99. The mean absolute error in estimating the sugarcane harvest date was approximately ten days, with a performance index of 0.99 and a correlation coefficient of 0.99, significant at a 5% confidence level. The TIMESAT software was able to estimate the phenological parameters of sugarcane using MODIS sensor images processed on the Google Earth Engine platform during the evaluated period (2001 to 2020). Full article

Background: Croton grewioides Baill., a species native to the Caatinga, popularly known as canela de cunhã, is used in traditional medicine to treat gastrointestinal diseases such as diarrhea, gastritis and stomach ulcers. The combination of essential oils with antibiotics reveals several beneficial effects associated with the increased efficacy of these drugs against pathogenic agents. Through this perspective, this study aimed to identify the chemical composition of the essential oil of C. grewioides (OECG) and evaluate its antibacterial and antibiotic-modifying activities against standard and multiresistant bacteria. Methods: To analyze the compounds present in the oil, the techniques of gas chromatography coupled with mass spectrometry (GC/MS) and gas chromatography with a flame ionization detector (GC/FID) were used. In the bacteriological tests, the Minimum Inhibitory Concentration (MIC) was obtained by the broth microdilution technique. The modulating effect of the essential oil was determined by calculating the subinhibitory concentration, followed by a serial microdilution of the antibiotics. The MIC reduction factor (CRF) was calculated, and its data were expressed as a percentage. Results: The analysis of the chemical composition identified the presence of the major compound estragole with a relative abundance of 50.34%. The MIC values obtained demonstrated efficacy in K. pneumoniae isolated from urine with MIC values of 512 µg/mL. OECG potentiated the effects of all antibiotics tested on the strains S. aureus ATCC 29213, K. pneumoniae Carbapnemase, E. coli ATCC 25922 and S. aureus ATCC 29213 with their CRF of 97.65%, 99.80%, 99.85% and 99.88%, respectively. Conclusions: Thus, the essential oil of C. grewioides presents synergistic effects when combined with the antibiotics tested, in addition to acting in the fight against bacterial resistance to antibiotics. Full article

This study provides a comprehensive overview of Phase I of the deforestation dryland alert system. It focuses on its operation and outcomes from 2020 to 2022 in the Caatinga biome, a unique Brazilian dryland ecosystem. The primary objectives were to analyze deforestation dynamics, identify areas with highest deforestation rates, and determine regions that require prioritization for anti-deforestation efforts and conservation actions. The research methodology involved utilizing remote sensing data, including Landsat imagery, processed through the Google Earth Engine platform. The data were analyzed using spectral unmixing, adjusted Normalized Difference Fraction Index, and harmonic time series models to generate monthly deforestation alerts. The findings reveal a significant increase in deforestation alerts and deforested areas over the study period, with a 148% rise in alerts from 2020 to 2022. The Caatinga biome was identified as the second highest in detected deforestation alerts in Brazil in 2022, accounting for 18.4% of total alerts. Hexagonal assessments illustrate diverse vegetation cover and alert distribution, enabling targeted conservation efforts. The Bivariate Choropleth Map demonstrates the nuanced relationship between alert and vegetation cover, guiding prioritization for deforestation control and native vegetation restoration. The analysis also highlighted the spatial heterogeneity of deforestation, with most deforestation events occurring in small patches, averaging 10.9 ha. The study concludes that while the dryland alert system (SAD-Caatinga—Phase I) has effectively detected deforestation, ongoing challenges such as cloud cover, seasonality, and more frequent and precise monitoring persist. The implementation of DDAS plays a critical role in sustainable forestry by enabling the prompt detection of deforestation, which supports targeted interventions, helps contain the process, and provides decision makers with early insights to distinguish between legal and illegal practices. These capabilities inform decision-making processes and promote sustainable forest management in dryland ecosystems. Future improvements, including using higher-resolution imagery and artificial intelligence for validation, are essential to detect smaller deforestation alerts, reduce manual efforts, and support sustainable dryland management in the Caatinga biome. Full article

The conversion of caatinga vegetation areas into pastures in the Brazilian semiarid region has depleted soil fertility due to degradation. The natural regeneration of the caatinga has been the alternative adopted to restore soil fertility. However, the real effectiveness of this practice in recovering organic carbon (SOC) and nutrient stocks is unknown. This work evaluated the effect of natural regeneration of the caatinga vegetation on the restoration of levels and stocks of SOC, N, and P as a strategy to maintain the sustainability of the environment. We collected soil samples every 5 cm, up to 20 cm deep, in areas of degraded pasture, caatinga in regeneration (10 years), and regenerated caatinga (35 years) to evaluate the levels and stocks of SOC, N, and P. The depth significantly affects the levels and stocks of SOC, N, and P. Stocks and levels of SOC, N, and P were higher in regenerated caatinga > regenerating caatinga > degraded pasture. The regenerated caatinga increased SOC, N, and P stocks by 115%, 110%, and 117%, respectively, compared to the degraded pasture. Although 35 years of regeneration of the caatinga has improved soil fertility, the levels and stocks of SOC, N, and P remain low. Full article

Background/objectives: Apodanthera glaziovii is an endemic species from the semi-arid Brazilian, which has limited toxicological and pharmacological studies. This species belongs to a well-studied family known for its bioactive compounds used in treating inflammatory. This study aimed to identify secondary metabolites in the stems from A. glaziovii, evaluate toxicity, and investigate the anti-inflammatory potential of the stem hydroalcoholic extract (SHE-Ag). Methods: qualitative and quantitative assays were employed to identify secondary metabolites, along with chromatographic analyses and ¹H and ¹³C NMR. Toxicity was assessed through in vitro hemolytic toxicity, in vivo genotoxicity, and oral acute toxicity tests before the pharmacological assays were conducted. Results: phytochemical screening, HPLC and NMR analyses suggested the presence of saponins of the norcucurbitacin class. The SHE-Ag exhibited no hemolytic activity and no mutagenic potential. However, in vivo toxicity at a dose of 2000 mg/kg revealed hematological and biochemical alterations, while the 500 mg/kg dose was safe. In the anti-inflammatory assays, SHE-Ag at 100 mg/kg reduced paw edema by 55.8%, and leukocyte and neutrophil migration by 62% and 68% in the peritonitis model, respectively; inflammatory cell migration by 70% in the air pouch model, outperforming indomethacin, which showed a 54% reduction. Conclusions: these findings indicate that SHE-Ag is rich in saponins, confirmed through HPLC and ¹H and ¹³C NMR analyses. The SHE-Ag also demonstrated low toxicity. The inflammation models used showed a reduction in inflammation, pro-inflammatory cells, and edema, highlighting the significant anti-inflammatory activity of hydroethanolic extract A. glaziovii stems. Full article

Caatinga is a typical Brazilian biome facing severe threats despite its ecological and socio-economic importance. Conservation strategies are essential in protecting ecosystems and ensuring natural resource sustainability. Mata da Pimenteira State Park (PEMP), launched in 2012, is an example of such a strategy. The current study aims to use orbital remote sensing techniques to assess human impacts on changes in land use and land cover (LULC) after the establishment of PEMP in the semi-arid region known as Caatinga, in Pernambuco State. The effects of this unit on vegetation preservation were specifically analyzed based on using data from the MapBiomas Brasil project to assess trends in LULC, both in and around PEMP, from 2002 to 2020. Man–Kendall and Pettitt statistical tests were applied to identify significant changes, such as converting forest areas into pastures and agricultural plantations. Trends of the loss and gain of LULC were observed over the years, such as forest areas’ conversion into pasture and vice versa, mainly before and after PEMP implementation. These findings highlight the importance of developing conservation measures and planning to help protecting the Caatinga, which is a vital biome in Brazil. Full article

Global projections indicate that the demand for fresh water, energy, and food will increase significantly in the coming decades under the pressure of population growth, economic development, climate change, and other factors. Faced with this, technologies that promote sustainable development through the use of clean energy will be imperative. That way, this study aimed at evaluating the productive performance of biomass sorghum and cowpea cultivars in different cropping systems and planting seasons. The experiment was conducted at the Caatinga Experimental Field at Embrapa Semiarid, Petrolina—PE. Four cowpea (BRS Itaim, BRS Gurguéia, BRS Guariba, and BRS Carijó) and two biomass sorghum cultivars (BRS 716 and AGRI-002E) were used in intercropping and monoculture systems. The cultivars were sown during two different seasons: June (season 1—winter) and December (season 2—summer) of 2021. The biometric and productive parameters and land equivalent ratios (LERs) of sorghum and cowpea were evaluated. The data were subjected to multivariate analysis. The productive performance of biomass sorghum cultivars Agri-002E and BRS 716 was higher when planted in December, with an increase of 37% due to the planting season. Cowpea productivity was not influenced by sowing seasons or the cultivation system. Based on the calculation of efficient land use, the intercropping between biomass sorghum cultivar BRS 716 and cowpea cultivars BRS Gurguéia, BRS Guariba, and BRS Carijó was advantageous when compared to monocultures planted in the hottest season. This study showed the importance of cultivar selection, the planting time, and land use efficiency in intercropping systems. Full article

The objective of this study is to evaluate the spatio-temporal dynamics of land vulnerability and pasture areas in the dairy basins of the states of Pernambuco and Alagoas, which are part of the Ipanema River Watershed (IRW) in the Northeast Region of Brazil. Maps of the Land Use and Land Cover (LULC); the Index of Vulnerability to Degradation (IVD); the Land Vulnerability Index (LVI); time series of Effective Herd (EH), Milked Cows (MC), and Milk Production (MP); and Pasture Cover (PC) and Quality (PCQ) were created as parameters. An opposite pattern was observed between the land use classes of Livestock, Agriculture, and Forest. The IRW area has predominantly flat terrain with a very high risk of degradation. The analysis of MC was consistent with the information from the EH analysis as well as with MP. When assessing Pasture Quality, Severe Degradation areas increased from 2010 to 2014, decreased after 2015, and rose again in 2020. Moderate Degradation areas remained high, while Not Degraded pasture areas were consistently the lowest from 2012 to 2020. Over the 10 years analyzed (2010–2020), the area showed a strong degradation process, with the loss of approximately 16% of the native vegetation of the Caatinga Biome and an increase in pasture areas and land vulnerability. 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

Dryland regions around the world are facing intricate challenges due to climate change and human activities. The Caatinga biome in Brazil, an exceptional dryland ecosystem covering approximately 86.3 million hectares, is particularly impacted by human influence. We conducted an extensive study analyzing changes in land use and land cover within the Caatinga region over a span of 35 years, from 1985 to 2019. This study leverages collective knowledge and collaborative effort with the MapBiomas project to provide valuable insights into the biome’s landscape. It maps eight principal land cover classes using Landsat Collection 1 Tier 1 data normalized to top-of-atmosphere reflectance. All data processing was carried out within the Google Earth Engine platform, and the graphics were generate using R version 3.6.2. This study achieved an impressive 80% global accuracy in the time series of Caatinga land use and land cover (LULC) changes, with allocation and area discrepancies of 11.6% and 8.5%, respectively. The extensive 35-year LULC dataset reveals a substantial 11% reduction in natural vegetation in the Caatinga biome, translating to a loss of 6.57 million hectares. This decline is primarily attributed to the expansion of cattle ranching and agriculture; all types of natural vegetation have experienced decreases, with Savanna Formation (SF) areas declining by 11% and Forest Formation (FF) areas declining by 8%. In contrast, pasturelands expanded by 62% and agricultural land expanded by 284% during this period. With their urgent and significant real-world for informing social, economic, and environmental policy decisions within the Caatinga and other dryland regions globally, these findings underscore the importance and immediacy of our research. Full article

Flash droughts (FDs) pose significant challenges for accurate detection due to their short duration. Conventional drought monitoring methods have difficultly capturing this rapidly intensifying phenomenon accurately. Machine learning models are increasingly useful for detecting droughts after training the models with data. Northeastern Brazil (NEB) has been a hot spot for FD events with significant ecological damage in recent years. This research introduces a novel 2D convolutional neural network (CNN) designed to identify spatial FDs in historical simulations based on multiple environmental factors and thresholds as inputs. Our model, trained with hydro-climatic data, provides a probabilistic drought detection map across northeastern Brazil (NEB) in 2012 as its output. Additionally, we examine future changes in FDs using the Coupled Model Intercomparison Project Phase 6 (CMIP6) driven by outputs from Shared Socioeconomic Pathways (SSPs) under the SSP5-8.5 scenario of 2024–2050. Our results demonstrate that the proposed spatial FD-detecting model based on 2D CNN architecture and the methodology for robust learning show promise for regional comprehensive FD monitoring. Finally, considerable spatial variability of FDs across NEB was observed during 2012 and 2024–2050, which was particularly evident in the São Francisco River Basin. This research significantly contributes to advancing our understanding of flash droughts, offering critical insights for informed water resource management and bolstering resilience against the impacts of flash droughts. Full article