Assessment of Factors Influencing the Use of Inorganic Fertilizers by Smallholder Farmers in Rwamagana RW-34 Scheme
Bibliographic record
Abstract
This study used qualitative and quantitative analysis to assess the factors influencing the use of inorganic fertilizers among small holder farmers. This research was conducted in Eastern province, Rwamagana district, in one of eight irrigated scheme called Rwamagana RW-34. The two stage purposive sampling method was performed to select 200 households’ respondents from four sectors as sample size. Descriptive statistics, logistic regression and correlation analysis were used. Regression results revealed that family size has highly significant effect (p=0.010) on use of inorganic fertilizer and has negative relationship with use of inorganic fertilizer. Education level of household and access to extension service were found significant (p=0.1) with positive relationship. Off farm income of household and cooperative membership of household were found significant (p=0.05) with positive relationship with use of inorganic fertilizer. However distance travelled by household to nearest agro-dealer was found significant (p=0.1) with negative relationship with use of inorganic fertilizer. Much emphasize should be put in educating rural population, encouraging farmers to create functional cooperatives, promote training and extension services, creating more off farms employment and decentralize the agro dealers to cell level.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".