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Large-scale Amazon deforestation is driven by extensive cattle ranching, particularly in southern Amazonia. This study used Geographically Weighted Regression to analyze deforestation causes from 2010-2019.

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Area of Science:

  • Environmental Science
  • Geography
  • Ecology

Background:

  • Amazon deforestation is a significant global issue, intensified by recent policy shifts in Brazil.
  • Understanding the drivers of deforestation is crucial for effective conservation and policy-making.

Purpose of the Study:

  • To analyze the spatial variability of deforestation causes in the Brazilian Amazon Biome between 2010 and 2019.
  • To identify the primary economic and environmental factors contributing to deforestation.

Main Methods:

  • Selection and regression modeling of 14 relevant variables.
  • Utilized a geographic information system to structure a spatial database.
  • Employed Geographically Weighted Regression (GWR) for spatial analysis.

Main Results:

  • The GWR model achieved an adjusted R-squared of 0.96, explaining 96% of deforestation variations.
  • The number of oxen (cattle) showed the highest correlation with deforestation.
  • Deforestation hotspots were identified in a cluster within the southern Amazon's "arc of deforestation".

Conclusions:

  • Extensive cattle ranching is the main economic driver pressuring large areas for deforestation in southern Amazonia.
  • Geographically Weighted Regression significantly improved the understanding of spatial deforestation patterns.
  • Policy interventions targeting the livestock sector are essential for mitigating Amazon deforestation.