Cost Benefit Analysis of Rice (Oryza Sativa, L.) And Maize (Zea Mays, L.) Production. A Comparison Study in Rwangingo Marshland, Nyagatare and Gatsibo Districts
Bibliographic record
Abstract
The marshlands in Rwanda have been developed to increase production by contributing to the reduction of agricultural products imports. To analyze the cost-benefit ratio between rice and maize production in Rwangingo Marshland aimed to see the crop that can give maximum returns through profitability analysis. The research was designed as an analytical study that compared the CBR of rice and maize production. The stochastic production function was used to estimate the impact of drivers’ cost of production on the production and CBR for profitability comparison analysis. Data were collected from 271 respondents and randomly selected using multistage sampling techniques. Stochastic production function results indicated that rice production: capital and labor were statistically significant at 1%. Maize production: labor was statistically significant at 1% and positively affected production, capital had an inverse relationship to the production, and the capital and labor (α+β) indicated a CRTS of 1. Profitability analysis was based on three measures of CBR, and NPV. Rice and maize production gave CBR of 1.9 and 1.5, NPV of 1,103,684Rwf and 1,011,970Rwf, and IRR of 7% for rice and maize respectively. The results recommended that rice production should be cultivated in this marshland because it indicated the maximum return or both crops could be considered under the measures that could maximize the outputs.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".