JOURNAL ARTICLE
Expulsion by suffocation: Soybean plantations, toxicity, and land grabbing in the Brazilian Amazon.
Published In: Environment & Planning E: Nature & Space, 2025, v. 8, n. 6. P. 2038 1 of 3
Database: Environment Complete 2 of 3
Authored By: Zuker, Fábio 3 of 3
Abstract
This article focuses on the concept of "expulsion by suffocation" to analyze how the glyphosate-driven expansion of soybean plantations in the Lower Tapajós region of the Brazilian Amazon causes slow, chemical, and epistemological violence that displaces Indigenous, Quilombola, and riverine communities. Drawing on eighteen months of ethnographic research, it shows how glyphosate acts both literally—causing respiratory harm and crop failure—and metaphorically—choking knowledge production and traditional ways of life—thereby rendering territories uninhabitable and reinforcing colonial-military imaginaries of an "empty Amazon." The study highlights local narratives of suffocation and dispossession, the masking of these harms through "farces" such as scattered forest strips and roadside houses, and the political resistance of communities like the Tupinambá, who cultivate "living territories" as alternatives to agroindustrial monocultures. By integrating frameworks on slow and chemical violence with counter-plantation dynamics, the article contributes to understanding the socio-environmental impacts of industrial agriculture and the ongoing struggles to defend Amazonian life and land.
Additional Information
- Source:Environment & Planning E: Nature & Space. 2025/12, Vol. 8, Issue 6, p2038
- Document Type:Article
- Subject Area:Law
- Publication Date:2025
- ISSN:2514-8486
- DOI:10.1177/25148486251379616
- Accession Number:188884826
- Copyright Statement:Copyright of Environment & Planning E: Nature & Space is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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