Constraints to Fertilizer Use in Uganda: Insights from Uganda Census of Agriculture 2008/9
Bibliographic record
Abstract
Uganda’s agriculture faces numerous challenges, including low productivity due to declining soil fertility. Yet, the majority agricultural households in the country do not use organic and inorganic fertilizers due to not well-known constraints. Using data from the Uganda Census of Agriculture 2008/9, this paper provides insights into these constraints. Results show that most of the farm-households that use inorganic fertilizers also apply organic fertilizers. With regard to factors influencing adoption of fertilizer, lack of knowledge on use of and market information on fertilizer due to limited access to fertilizer-specific extension services is found to be perhaps the most limiting factor irrespective of fertilizer type. Low access to credit and constrained access to input and output markets due to distance are also key constraints to fertilizer use. Household characteristics including education level, household size, share of adults in the household, and ownership of livestock/poultry also stand-out as influencing factors on fertilizer adoption decisions. Results suggest that targeted interventions including extensive and intensive extension training and visits, and access to affordable credit and will be pertinent in the promotion fertilizer use in the country.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".