Sustainability of Agricultural Crop Policies in Rwanda: An Integrated Cost–Benefit Analysis
Bibliographic record
Abstract
Rwanda has aimed to achieve food self-sufficiency but faces binding land and budgetary constraints. A set of government policies have been in force for 20 years that have controlled the major cropping decisions of farmers. A cost–benefit analysis methodology is employed to evaluate the financial and resource flow statements of the key stakeholders. The object of the analysis is to determine the sustainability of the prevailing agricultural policies from the perspectives of the farmers, the economy, and the government budget. A total of seven crops were evaluated. In all provinces, one or more of the crops were either not sustainable from the financial perspective of the farmers or are economically inefficient in the use of Rwanda’s scarce resources. The annual fiscal cost to the government of supporting the sector is substantial but overall viewed to be sustainable. A major refocusing is needed of agricultural policies, away from a monocropping strategy to one that allows the farmers to adapt to local circumstances. A more market-oriented approach is needed if the government wishes to achieve its economic development goal of having a sustainable agricultural sector that supports the policy goal of achieving food self-sufficiency.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".