Economic Impacts of Technology, Population Growth And Soil Erosion At Watershed Level: The Case Of the Ginchi in Ethiopia
Bibliographic record
Abstract
A dynamic bio‐economic model is used to show that, without technological and policy intervention, soil loss levels, income and nutrition could not be substantially or sustainably improved in a highland area of Ethiopia. Although cash incomes could rise by more than 40% over a twelve‐year planning period, average per ha soil losses could be as high as 31 tonnes per ha. With the adoption of an integrated package of new technologies, however, results show the possibility of an average two‐and‐a‐half‐fold increase in cash incomes and a 28% decline in aggregate erosion levels even with a population growth rate of 2.3%. Moreover, a minimum daily calorie intake of 2000 per adult equivalent could be met from on‐farm production with no significant increases in erosion. However, higher rates of growth in nutritional requirements and population introduce significant strains on the watershed system. From a policy perspective, there is a need for a more secure land tenure policy than currently prevailing to facilitate uptake of the new technology package, and a shift from the current livestock management strategy to one that encourages livestock keeping as a commercial enterprise. It would also imply a shift to a more site‐specific approach to land management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".