Climate change, degradation, and land acquisitions: evaluating inequalities among competing interests for suitable cropland in Ethiopia
Bibliographic record
Abstract
Land is the central resource for agriculture. In many parts of Sub-Saharan Africa (SSA), where a large portion of the population relies on agriculture for subsistence and household incomes, future declines in the productive capacity of the land owing to environmental change pose a major threat both to farming and the well-being of smallholders. Smallholders’ access to land is concurrently at risk due to large-scale land acquisitions (LSLA), promoted by governments across SSA as a means to secure capital investments for agricultural growth and economic development. These issues are especially widespread in Ethiopia, which has faced both extensive land degradation and been a primary target country for LSLA investments. This study analyzes the relative quality of land under the control of smallholders vs. large investors in western Ethiopia, with particular attention to how future suitability of land is likely to change for growing three major smallholder crops: Maize (Zea mays), sorghum (Sorghum bicolor), and beans (Phaseolus vulgaris L.). Spatial analyses are applied to compare the suitability in areas allocated to LSLAs and the remaining land available to smallholders in the country’s western farming systems. Crop-specific suitability datasets are used to approximate the change in land quality between baseline conditions and scenarios of future climate change to assess the effects of climate-induced land degradation. Results indicate large areas of decreasing suitability by the late 21st century for all crops across Ethiopia. Furthermore, this study shows that LSLA occupy land with more stable suitability, suggesting more secure agricultural land is being offered to investors.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".