Effects of Improved Cassava Varieties on Farmers’ Income in Northern Agro-ecological Zone, Uganda
Bibliographic record
Abstract
This paper examines the extent to which improved cassava varieties contribute to improvement in income of smallholder farmers in the Northern agro-ecological zone of Uganda. In order to achieve the objective, data was collected from PRELNOR supported farmers’ fields, other farmers’ fields and baseline cassava fields. Consequently, descriptive statistics, gross margin and stochastic frontier analysis were adopted during analysis. Results from the analysis revealed that higher yields per hectare were registered within PRELNOR supported farmers’ fields and yield from NAROCAS1 surpassed all the varieties (37.3 tons per hectare). Location specific results revealed that Gulu had better yields (34.5 tons per hectare) while Kitgum registered the lowest average (24.1 tons per hectare). Gross margin indicated that every Shilling invested in improved variety earned profit 1.3 to 1.8 times above the local variety and each shillings invested in PRELNOR supported fields generated 5.6 times above the baseline fields. The parameter estimate for profit function revealed that planting material, other production related costs, NAROCAS1, NASE14 and NASE19 were positively correlated with profit but labour and baseline field had negative correlations. Conversely, profit efficiency grew by 40% under improved varieties against local varieties. This study suggests that the difference in yields and profit between locations was caused by biophysical characteristic; disease tolerant varieties can tremendously improve profitability and income; meanwhile the profit gaps were partly attributed to inefficiency. This study recommends fast tracking adoption of pest and disease tolerant varieties and integrated research and development approach throughout the cassava value chain.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".