337 research outputs found

    Desenvolvimento agropecuário e florestal de terra firme na Amazônia: sustentabilidade, criticalidade e resiliência.

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    21st Century drought-related fires counteract the decline of Amazon deforestation carbon emissions

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    Tropical carbon emissions are largely derived from direct forest clearing processes. Yet, emissions from drought-induced forest fires are, usually, not included in national-level carbon emission inventories. Here we examine Brazilian Amazon drought impacts on fire incidence and associated forest fire carbon emissions over the period 2003–2015. We show that despite a 76% decline in deforestation rates over the past 13 years, fire incidence increased by 36% during the 2015 drought compared to the preceding 12 years. The 2015 drought had the largest ever ratio of active fire counts to deforestation, with active fires occurring over an area of 799,293 km2. Gross emissions from forest fires (989 ± 504 Tg CO2 year−1) alone are more than half as great as those from old-growth forest deforestation during drought years. We conclude that carbon emission inventories intended for accounting and developing policies need to take account of substantial forest fire emissions not associated to the deforestation process

    Water Availability Is the Main Climate Driver of Neotropical Tree Growth

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    • Climate models for the coming century predict rainfall reduction in the Amazonian region, including change in water availability for tropical rainforests. Here, we test the extent to which climate variables related to water regime, temperature and irradiance shape the growth trajectories of neotropical trees. • We developed a diameter growth model explicitly designed to work with asynchronous climate and growth data. Growth trajectories of 205 individual trees from 54 neotropical species censused every 2 months over a 4-year period were used to rank 9 climate variables and find the best predictive model. • About 9% of the individual variation in tree growth was imputable to the seasonal variation of climate. Relative extractable water was the main predictor and alone explained more than 60% of the climate effect on tree growth, i.e. 5.4% of the individual variation in tree growth. Furthermore, the global annual tree growth was more dependent on the diameter increment at the onset of the rain season than on the duration of dry season. • The best predictive model included 3 climate variables: relative extractable water, minimum temperature and irradiance. The root mean squared error of prediction (0.035 mm.d–1) was slightly above the mean value of the growth (0.026 mm.d–1). • Amongst climate variables, we highlight the predominant role of water availability in determining seasonal variation in tree growth of neotropical forest trees and the need to include these relationships in forest simulators to test, in silico, the impact of different climate scenarios on the future dynamics of the rainforest

    institutional framework of in action against land degradation

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    While econometric and spatial data are increasingly helpful to quantify and locate the extent and costs of land degradation, there is still little understanding of the contextual factors that determine or influence the land users' practices that aggravate or counteract land degradation. In this chapter, we take an institutional economic approach to analyse the persistence of degrading practices, the low adoption of sustainable land management (SLM), or the eventual organisational reaction to land degradation. The chapter reviews four examples of land degradation in different contexts to reveal the multiple driving forces and contextual factors. We then propose a conceptual framework to better understand the incentive structure and factors determining the land users' decision making. A layered analysis of the social phenomena is applied, following Williamson (2000). The chapter shows how actions at different layers can help improve land management. The chapter concludes with practical recommendations for the institutional economic analysis of land degradation
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