Characterisation of Rice Production Systems in Northern Agro-Ecological Zone, Uganda
Bibliographic record
Abstract
Rice growing is an important source of food and income to the farming community in the northern agro-ecological zone (NAEZ) of Uganda. NAEZ comprised of 16 districts which form Acholi and Lango sub-regions and it is categorized by tropical dry climate with bimodal rainfall patterns. However, inspite of the importance of rice in the NAEZ, very little information exists that could support prioritization of development in the sector. This paper therefore, bridges information gap by analyzing characteristics of rice production system in the study area based on data obtained from a field survey conducted during 2016. The study used cross-sectional design to collect data which was analysed using the descriptive statistics of the STATA computer package. The results revealed marked difference in households’ characteristics, production output and input utilization, production practices and constraints between lowland and upland systems. The study has concluded that: climate variability, pest and diseases, lack of improved seed variety, labour related constraints and lack of specialization are potential causes of low rice production and productivity in the NAEZ. However, to improve production performance of the systems, the study recommends promotion of climate smart farming in rice and further research into system based effects of climate on productivity as well as farmers’ adaptation to climate variability.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.001 | 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".