The Future of Cotton in Brazil: Agroclimatic Suitability and Climate Change Impacts
Cotton is the most widely consumed natural fiber globally and emits fewer greenhouse gases compared to synthetic alternatives. Brazil is currently the largest cotton exporter, and understanding its potential for sustainable expansion is crucial. This study developed agroclimatic zoning maps for cotton (Gossypium hirsutum L.) across Brazil under current and future climate conditions using data from the World-Clim and MapBiomas platforms. Four climate change scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP5-8.5) were assessed over multiple time periods. Results showed that rising temperatures and reduced rainfall will likely reduce cotton suitability in traditional producing regions such as Bahia. However, areas with potential for cotton cultivation, especially in Mato Grosso, which currently accounts for 90% of national production, remain extensive, with agroclimatic conditions indicating a theoretical expansion potential of up to 40 times the current cultivated area. This projection must be interpreted with caution, as it does not account for economic, logistical, or social constraints. Notably, Brazilian cotton is cultivated with minimal irrigation, low fertilizer input, and high adoption of no-till systems, making it one of the least carbon-intensive globally.
Authors
- Lucas Eduardo de Oliveira Aparecido (ORCID: https://orcid.org/0000-0002-4561-6760)
- Glauco de Souza Rolim (ORCID: https://orcid.org/0000-0003-4683-3203)
- Fernando Giovannetti de Macedo (ORCID: https://orcid.org/0000-0002-6683-9162)
- João Antônio Lorençone (ORCID: https://orcid.org/0000-0002-1950-4853)
- Pedro Antônio Lorençone (ORCID: https://orcid.org/0000-0001-6831-3992)
- Guilherme Botega Torsoni (ORCID: https://orcid.org/0000-0001-7178-2191)
Institutions
- Universidade Federal de Mato Grosso do Sul (BR)
- Instituto Federal do Sul de Minas (BR)
- Universidade Estadual Paulista (Unesp) (BR)
Publication Details
- Journal
- AgriEngineering
- Published
- 2025-06-19
- DOI
- https://doi.org/10.3390/agriengineering7060198
- Citations
- 4
- Primary Topic
- Remote Sensing in Agriculture
- Type
- article
- Field-Weighted Citation Impact
- 6.87