Assessing the Efficiencies and Competitiveness of the Fresh Cassava Storage Root Production Systems in Sierra Leone
Bibliographic record
Abstract
The Policy Analysis Matrix (PAM) was used to assess the efficiencies and competitiveness of fresh cassava storage root production systems in Sierra Leone. Proportional random sampling was used to select study samples. Information was collected using structured questionnaire from a total of 1,880 producer households. Out of the 36 potential storage root production systems identified, only 6 systems are mainly used by producers. The PAM was based on one hectare of land for production and Leone (SSL) as money to evaluate costs and revenues. The analysis indicates that, all the 6 fresh cassava storage root production systems present a Domestic Resource Cost Ratio of less than 1 (DRC < 1) and Cost-Benefit Ratio (RCB) also less than 1 (RCB within 0.14 to 0.42). Discounting potential revenue from stems and cassava leaves in storage root production systems that use improved varieties and fertilizer have higher comparative and competitive advantages. The systems are also profitable, even though producers are not protected from tradable and taxed inputs. Production systems also remain profitable with 25% and 50% yield loss. This was also confirmed by Abiodun and Adefemi (2016). It is therefore better to produce cassava locally in Sierra Leone than import for processing or consumption. This study proposes recommendations to improve cassava productivity in Sierra Leone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".