PROFITABILITY OF SOIL EROSION CONTROL TECHNOLOGIES IN EASTERN UGANDA HIGHLANDS
Bibliographic record
Abstract
The lack of farmer awareness of costs and benefits associated with the use of sustainable land management (SLM) technologies is one of the major constraints to technology adoption in sub-Saharan Africa. The objective of this study was to estimate the profitability of application of SLM in the form of soil erosion control technologies by communities in the highlands of eastern Uganda; a hot spot for this land degradation agent. A survey was conducted using 240 farmers in the highlands of eastern Uganda. The findings from Partial Budget Analysis indicate that the net returns associated with the use of soil erosion control technologies, are sufficiently high to offset the costs involved. For example, for every US$ invested per hectare in terracing and tree planting, there is a return of over US$ 15. However, these returns are likely to be much less if inflation is not regulated. For example, the profits expected from the use of terraces and trees would reduce by about 3 percent if inflation rose to 30 percent. Thus, for the benefits to be sustainable, farmers have to regularly maintain the structures (terraces, contours, and trenches) and the vegetation (trees and grasses). Also, use of soil erosion control technologies would remain profitable only if the Central Bank fulfils its mandate of keeping inflation low and stable.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".