The economics of soil productivity: local, national and global perspectives
Bibliographic record
Abstract
Abstract Soil degradation is a mounting problem on many smallholder lands in developing countries. Economic analysis has been an important tool in addressing this problem, beginning with assessments of the financial attractiveness of investing in soil conservation works. Data compiled from 67 studies of the financial attractiveness of conservation technologies suggest that many can provide positive net returns at the farm level (64·2 per cent). While such studies have made a valuable contribution, economists have been exploring additional applications of economics to the problem, such as the development of new perspectives under the guise of ecological economics. As a result, this paper argues it is an opportune time to assess progress in the field of economic analysis of soil degradation and to consider the policy ramifications of this research. Key issues are grouped into farm‐level considerations, national policy linkages and global issues. A number of policy implications emerge. Clearly, devising effective incentives at the farm or community (collective action) level must be a priority. As part of this effort, even more attention should be paid to the influence of macroeconomic and sectoral policies on soil productivity. Since soil degradation is also a problem with global ramifications, there is a clear rationale for intervention at the international level via mechanisms such as international transfers. Copyright © 2004 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".