Agricultural mechanization, environmental degradation, and gendered livelihood implications in northern Ghana
Bibliographic record
Abstract
Abstract This paper draws theoretical insights from political ecology to examine the environmental and livelihood impacts of smallholder agricultural mechanization in Ghana in the context of the ongoing pursuit of a new Green Revolution for Africa. Our findings highlight the complex linkages between agricultural development, environmental degradation, and rural livelihoods. Despite the associated increased returns‐to‐scale in agricultural productivity and enhanced speed in land preparation with tractor‐based mechanization, the clearing of major trees on farmlands as a precondition for obtaining ploughing services encourages land degradation, including the depletion of vital naturally growing tree species—shea (Vitellaria paradoxa) and dawadawa (Parkia biglobosa)—that have critical food provisioning, cultural, and socioeconomic value. The drive towards extensification has further produced competitive forces that fuel the appropriation of previously inalienable communal lands and weakening of longstanding norms that mediate environmental resource conservation and use. This situation is poised to alter customary land governance and the basis on which women assert their rights to land‐based resources including shea and dawadawa. Marginalized women are progressively shifting their livelihood strategies into environmentally unsustainable subsistence activities. This study demonstrates the adverse ecological, socioeconomic, and political impacts of agricultural mechanization when implemented in agrarian societies marked by widespread poverty and pervasive gender inequities. Given the growing centrality of tractors and trees to rural livelihoods, we recommend conservation agriculture for the simultaneous promotion of sustainable agriculture and environmental conservation. Relevant social policies must also be implemented to ameliorate the adverse livelihood impacts of these agrarian reforms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".