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Most of the northeastern region of Brazil (NEB) has a maize production system based on family farming, with no technological advances and totally dependent on the natural rainfall regime, which is concentrated in 4 to 5 months in most parts of the region. This means that the productivity of this crop is low in the NEB. In the northern mesoregions of the NEB, rainfall is concentrated between January and June, in the east of the NEB from April to September, and in the west of the NEB from October to March. The growing season takes place during these semesters. With this in mind, our objective was to develop a model based on canonical correlation analysis (CCA) to predict corn production in the mesoregions of the NEB between 1981 and 2010, using accumulated precipitation per semester as the predictor variable and predicting the observed production in kg/ha. Our results showed that the CCA model presented higher correlations between observed and simulated production than that obtained simply from the direct relationship between accumulated rainfall and production. The other two metrics used, RMSE and NRMSE, showed that, on average, in most mesoregions, the simulation error was around 200 kg/ha, but the accuracy was predominantly moderate, around 29% in most mesoregions, with values below 20% in six mesoregions, indicative of better model accuracy, and above 50% in two mesoregions, indicative of low accuracy. In addition, we investigated how the different combinations between two modes of climate variability with a direct influence on precipitation in the NEB impacted production in these 30 years, with the combination of El Niño and a positive Atlantic dipole being the most damaging to harvests, while years when La Niña and a negative Atlantic dipole acted together were the most favorable. Despite the satisfactory results and the practical applicability of the model developed, it should be noted that the use of only one predictor, rainfall, is a limiting factor for better model simulations since other meteorological variables and non-climatic factors have a significant impact on crops. However, the simplicity of the model and the promising results could help agricultural managers make decisions in all the states that make up the NEB.

Accurate regional seasonal forecasts of the rainy season are essential for the implementation of effective socioeconomic activities and policy. However, current characteristics of the period of occurrence of the rainy season in the Eastern Northeast Brazil (ENEB) region demonstrated that maximum precipitation varies substantially depending on the period analyzed. From 1972 to 2002, the rainy season occurred during the JuneJulyAugust (JJA) quarter, while from 1981 to 2011, it occurred in the AprilMayJune (AMJ) quarter. To access how these differences may be due to different patterns of sea surface temperature (SST), using observed precipitation and SST data from NOAA for the period from 1982 to 2018, this study identified the spatial patterns of inter-annual changes in Pacific and Atlantic SST related to the occurrence of the ENEB rainy seasons. We focus on the statistical method of symmetric mean absolute percentage error (sMAPE) for forecasting these periods based on SST information. Our results revealed five different quarterly periods (FMA, MAM, AMJ, MJJ, JJA) to the rainy season, in which MJJ is more prevalent. The sMAPE values of the SST patterns are inversely proportional to precipitation in the ENEB. Hence, it may be concluded that our climate analysis demonstrates that seasonal SST patterns can be used for forecasting the period of the rainy season.

The cold fronts (CF) operating in south and south-central South America are the main ones responsible for heavy rain, thunderstorms with strong winds, and accentuated temperature drops. This study aimed to perform a seasonal climatology of CFs displacement and intensity in the region above mentioned. A computational algorithm was developed called the Normalized Cold Front Detection Index (NCFI) for detecting and characterizing CFs through the CFSR and CFSv2 reanalysis data, using the meridional component of the wind at 10 meters and the air temperature at 2 meters. Ten areas were delimited: five coastal and five continentals. Seasonal climatology showed a higher frequency in winter, when these systems are more intense, averaging 20 per year. The patterns of displacement and intensity exhibited a decrease in CFs as they advanced toward lower latitudes. The synoptic analysis and the accumulated precipitation composites revealed that the CFs that act in the spring concentrated the accumulated precipitation in Santa Catarinas western portion. In the summer, convection cells are responsible for storms and poorly distributed rain. In the autumn, rainfall is associated with CFs and maritime circulations, impacting Rio Grande do Sul and the west and north of Santa Catarina. In the winter, CFs dominate the precipitation pattern. According to the NCFI, spring, autumn, and winter cases are categorized as intense, while summer cases are moderate.

Sustainability researchers are writing much about levers for transformations towards sustainability but too little about the most powerful means available for obstructing and activating them: mass-reaching media systems. How media systems are structured and governed form a profoundly important meta-level layer of decision-making that ought to be central in the study of environmental politics and in environmental policymaking. A politics- and media-focused account of the rise of Jair Bolsonaro to the presidency of Brazil illustrates the essential role of media systems and the need for new principles, structures, and policies for their governance if the interlinked goals of democracy, equity, and environmental protection are to be achieved. The pervasive inattention to this in environmental research reinforces hegemonic forces and needs to be widely discussed, understood, and overcome to achieve much needed just transformations towards sustainability.

Full-waveform LiDAR (FWF) offers a promising advantage over other technologies to represent the vertical canopy structure of secondary successions in the Amazon region, as the waveform encapsulates the properties of all elements intercepting the emitted beam. In this study, we investigated modifications in the vertical structure of the Amazonian secondary successions across the vegetation gradient from early to advanced stages of vegetation regrowth. The analysis was performed over two distinct climatic regions (Drier and Wetter), designated using the Maximum Cumulative Water Deficit (MCWD). The study area was covered by 309 sample plots distributed along 25 LiDAR transects. The plots were grouped into three successional stages (earlySS1; intermediateSS2; advancedSS3). Mature Forest (MF) was used as a reference of comparison. A total of 14 FWF LiDAR metrics from four categories of analysis (Height, Peaks, Understory and Gaussian Decomposition) were extracted using the Waveform LiDAR for Forestry eXtraction (WoLFeX) software (v1.1.1). In addition to examining the variation in these metrics across different successional stages, we calculated their Relative Recovery (RR) with vegetation regrowth, and evaluated their ability to discriminate successional stages using Random Forest (RF). The results showed significant differences in FWF metrics across the successional stages, and within and between sample plots and regions. The Drier region generally exhibited more pronounced differences between successional stages and lower FWF metric values compared to the Wetter region, mainly in the category of height, peaks, and Gaussian decomposition. Furthermore, the Drier region displayed a lower relative recovery of metrics in the early years of succession, compared to the areas of MF, eventually reaching rates akin to those of the Wetter region as succession progressed. Canopy height metrics such as Waveform distance (WD), and Gaussian Decomposition metrics such as Bottom of canopy (BC), Bottom of canopy distance (BCD) and Canopy distance (CD), related to the height of the lower forest stratum, were the most important attributes in discriminating successional stages in both analyzed regions. However, the Drier region exhibited superior discrimination between successional stages, achieving a weighted F1-score of 0.80 compared to 0.73 in the Wetter region. When comparing the metrics from SS in different stages to MF, our findings underscore that secondary forests achieve substantial relative recovery of FWF metrics within the initial 10 years after land abandonment. Regions with potentially slower relative recovery (e.g., Drier regions) may require longer-term planning to ensure success in providing full potential ecosystem services in the Amazon.

A perda acumulada de 271.735,48 km², ou seja, 24,42% de área de floresta nativa no estado do Pará até 2019 é reflexo da dinâmica do uso da terra na Amazônia, onde o Estado exerce o papel de agente promotor de políticas públicas de desenvolvimento e ordenamento do território, o que dá origem a paisagens antropogênicas diversificadas. É possível diferenciar padrões relacionados ao modo de ocupação e vinculá-los à estrutura econômica dominante em diferentes escalas de espaço e tempo. Esta pesquisa teve por objetivo analisar a dinâmica do desmatamento a partir de diferentes usos da terra, na perspectiva da influência das políticas públicas e interesses econômicos no território paraense. Para tanto, foi analisado o desmatamento em três municípios considerando as atividades da mineração, pecuária e soja.

Amazonia contains the most extensive tropical forests on Earth, but Amazon carbon sinks of atmospheric CO2 are declining, as deforestation and climate-change-associated droughts1-4 threaten to push these forests past a tipping point towards collapse5-8. Forests exhibit complex drought responses, indicating both resilience (photosynthetic greening) and vulnerability (browning and tree mortality), that are difficult to explain by climate variation alone9-17. Here we combine remotely sensed photosynthetic indices with ground-measured tree demography to identify mechanisms underlying drought resilience/vulnerability in different intact forest ecotopes18,19 (defined by water-table depth, soil fertility and texture, and vegetation characteristics). In higher-fertility southern Amazonia, drought response was structured by water-table depth, with resilient greening in shallow-water-table forests (where greater water availability heightened response to excess sunlight), contrasting with vulnerability (browning and excess tree mortality) over deeper water tables. Notably, the resilience of shallow-water-table forest weakened as drought lengthened. By contrast, lower-fertility northern Amazonia, with slower-growing but hardier trees (or, alternatively, tall forests, with deep-rooted water access), supported more-drought-resilient forests independent of water-table depth. This functional biogeography of drought response provides a framework for conservation decisions and improved predictions of heterogeneous forest responses to future climate changes, warning that Amazonia’s most productive forests are also at greatest risk, and that longer/more frequent droughts are undermining multiple ecohydrological strategies and capacities for Amazon forest resilience. Drought response is structured by water-table depth in higher-fertility Southern Amazonia, whereas lower-fertility Northern Amazonia supports more-drought-resilient forests independent of water-table depth.

As bacias hidrográficas dos rios Piracicaba, Capivari e Jundiaí (PCJ) e Juqueri, que fazem parte do Sistema Cantareira, passaram por grandes mudanças, seja no aspecto social, ambiental e econômico nas últimas décadas. Dessa forma, o entendimento sobre o ambiente em que as pessoas estão inseridas tem grande valor, o que torna importante avaliar informações de uso e cobertura da terra, podendo ajudar na tomada de decisões e no manejo do ambiente. Tais informações são obtidas utilizando técnicas de sensoriamento remoto e geoprocessamento. A área de estudo compreende área de aproximadamente 16 mil Km², abrange 80 municípios, destes, com 64 sedes municipais, e quase seis milhões de habitantes. Com o intuito de compreender a dinâmica do uso e cobertura da terra na região, o objetivo deste trabalho foi quantificar as mudanças de uso e cobertura da terra, ocorridas a partir de 1985, e a cada cinco anos, até 2015. As classes consideradas foram: vegetação nativa, silvicultura, agricultura, pastagem, área urbana e corpos hídricos. Os resultados mostram o predomínio de pastagem em todo período estudado, entretanto, houve recessão, de aproximadamente 12% nesta classe. Destaca-se também, que a área urbana se desenvolveu em toda região de estudo, principalmente na região central.

A Cartografia Social envolve a inclusão das comunidades no processo de desenvolvimento dos mapas e possibilita estabelecer uma metodologia participativa importante para análises quali-quantitativas de percepção socioambiental, contribuindo para a construção do conhecimento coletivo e gestão territorial. O objetivo do artigo é apresentar o uso da Cartografia Social como ferramenta para a análise da percepção socioambiental na bacia do rio Paraíba do Sul, trecho paulista. A análise foi realizada por meio da Cartografia Social, a partir do levantamento de potencialidades e problemas socioambientais e sua comparação com dados de fontes secundárias. Como potencialidades destacaram-se a vegetação nativa, unidades de conservação e atributos turísticos e em relação aos problemas socioambientais, a monocultura do eucalipto, saneamento básico precário, ocupação urbana e extração de areia. Os resultados reforçam o potencial da Cartografia Social em dar visibilidade ao conhecimento local, e em contribuir na construção do conhecimento coletivo e no diagnóstico territorial participativo.

The viewpoint and reaction of a country towards climate change are shaped by its political, cultural, and scientific backgrounds, in addition to the distinct characteristics of its evolving climate and the anticipated and actual consequences of the phenomenon in the times ahead. A regions climate has a significant impact on how water is managed and used, mostly in the primary sector, and both the distribution of ecosystem types and the amount and spreading of species on Earth. As a result, the environment and agricultural practices are affected by climate, so evaluating both distribution and evolution is extremely pertinent. Towards this aim, the climate distribution and evolution in the São Francisco River basin (SFRB) is assessed in three periods (19702000, 19812022) in the past and 20412060 in the future from an ensemble of GCMs under two SSPs (Shared Socioeconomic Pathways), SSP2-4.5 and SSP5-8.5. The Köppen-Geiger (KG) climate classification system is analyzed, and climate change impacts are inferred for this watershed located in central-eastern Brazil, covering an area equivalent to 8% of the country. Results predict the disappearance of the hot summer (Csa) and warm summer (Csb) Mediterranean climates, and a reduction/increase in the tropical savanna with dry winter (Aw)/dry summer (As). A striking increase in the semi-arid hot (BSh-steppe) climate is predicted with a higher percentage (10%) under SSP5-8.5. The source and the mouth of SFRB are projected to endure the major impacts of climate change that are followed by a predicted increase/decrease in temperature/precipitation. Future freshwater resource availability and quality for human use will all be impacted. Consequences on ecosystems, agricultural, and socioeconomic sectors within the SFRB might deepen the current contrasts between regions, urban and rural areas, and even between population groups, thus translating, to a greater extent, the inequality that still characterizes Brazilian society. Maps depicting land use and cover changes in SFRB from 1985 to 2022 highlight tendencies such as urbanization, agricultural expansion, deforestation, and changes in shrubland and water bodies. Urban areas fluctuated slightly, while cropland significantly increased from 33.57% to 45.45% and forest areas decreased from 3.88% to 3.50%. Socioeconomic data reveals disparities among municipalities: 74.46% with medium Human Development Index (HDI), 0.59% with very high HDI, and 9.11% with low HDI. Most municipalities have a Gross Domestic Product (GDP) per capita below US$6000. Population distribution maps show a predominance of small to medium-sized urban and rural communities, reflecting the basins dispersed demographic and economic profile. To achieve sustainable adaptation and mitigation of climate change impacts in SFRB, it is imperative that integrated measures be conducted with the cooperation of stakeholders, the local population, and decision-makers.