| Grant number: | 24/06285-1 |
| Support Opportunities: | Scholarships in Brazil - Post-Doctoral |
| Start date: | October 01, 2024 |
| End date: | September 30, 2026 |
| Field of knowledge: | Agronomical Sciences - Agronomy - Soil Science |
| Principal Investigator: | Tiago Osório Ferreira |
| Grantee: | Danilo César de Mello |
| Host Institution: | Escola Superior de Agricultura Luiz de Queiroz (ESALQ). Universidade de São Paulo (USP). Piracicaba , SP, Brazil |
| Associated research grant: | 21/10573-4 - Center for Carbon Research in Tropical Agriculture (CCARBON), AP.CEPID |
Abstract Mangrove ecosystems play a crucial role in providing essential ecosystem services to the environment and human communities. These ecosystems are characterized by soils with high levels and stocks of soil organic carbon (TOC and SOC stocks, respectively), making them one of the largest carbon reservoirs on the planet. However, the looming threat of climate change poses significant risks to these environments, potentially altering soil attributes and the overall pedoenvironment. This could result in mangrove degradation and the release of previously stabilized organic carbon in soil, into the atmosphere. Despite their critical importance, the effects of various future climate change scenarios on SOC stock dynamics within mangrove ecosystems, as well as on atmospheric carbon feedback, remain uncertain and unquantified. Therefore, our research aims to address this gap by predicting the dynamics of SOC stocks under different climate change scenarios provided by the Intergovernmental Panel on Climate Change (IPCC). We also seek to quantify the potential release of carbon into the atmosphere by assessing carbon-atmosphere feedback and pedogeochemical processes involved. To achieve these objectives, we will utilize a legacy database containing profile samples and mangrove soil samples, along with environmental covariates correlated with SOC stocks. This comprehensive dataset includes physical, chemical, and mineralogical information on soils, as well as details on relief, soil type, and geographic coordinates. We will employ a machine learning (ML) approach for SOC stock and dynamics predictions, testing different algorithms for prediction. Subsequently, we will evaluate prediction performance parameters and conduct data validation. Additionally, we will generate predicted maps of SOC stock dynamics (spatialization of predicted SOC stock dynamic). Among the key expected results of our research are the quantification of carbon released into the atmosphere for each future climate change scenario evaluated, the identification of significant environmental covariates related to SOC stock dynamics, and an understanding of how pedogeochemical processes will influence SOC stock dynamics. By elucidating these mechanisms, our investigation will play a pivotal role in informing robust strategies for the conservation and management of mangroves and refine predictive models of climate change. The insights gleaned from our findings will guide decision-making processes aimed at enhancing the resilience of mangrove ecosystems in response to climate change. Ultimately, this research endeavor holds the promise of safeguarding mangroves and preserving their invaluable ecological services for future generations. | |
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