Determinants of Households’ Land Allocation for Crop Production in Uganda
Bibliographic record
Abstract
Using UNHS 2005/6 and 2009/10 data, we examined various cropping and land allocations patterns practiced by farming households in Uganda, and their implications on government plan of prioritizing some crops for expansion and zoning. On average, households were observed to cultivate 1.7 ha despite having ownership right to 1.58 ha. A decrease in total cultivated area across all the twelve sub-regions was observed between 2005 and 2009. Over time, only the proportions of land allocation to sweet potato and bean are increasing. Fractional multinomial logit model estimates showed that significant factors that influence share of land allocated to crops include household location within sub-regions, size of cultivated land, distance to output markets and education levels of household head. Efforts to commercialize agriculture through prioritized expansion and zoning of certain crops should also target breaking the current culture of diversified cropping patterns on small sizes of land.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".