Farmers' Agronomic And Social Evaluation Of The Productivity, Yield And Cooking Quality Of Four Cassava Varieties
Bibliographic record
Abstract
The study evaluated the productivity, yield and cooking quality of four cassava varieties grown on poor soils at Beposo in Wenchi Municipality in the forest/savannah transitional zone of Ghana, between October 2008 and October 2009. The trial included two local varieties selected by the farmers and 2 improved varieties developed by the national agricultural research system, and three fertilizer treatments. The fertilizer treatments were 4 t ha-1 poultry manure, 32-32-32 kg N-P2O5-K2O ha-1 and unfertilized controls. Mean fresh root yield of the four cassava varieties ranged from 8.9 t ha-1 (Afosa) to 30.6 t ha-1 (Bensre). Application of the mineral fertilizer resulted in between 140% and 300% increase in fresh root yield for the improved varieties and between 43% and 63% for the local varieties while application of poultry manure resulted in yield increase of between 86% and 124% for the improved varieties and about 48% for the local varieties. Fertilization significantly improved the mealiness in all the varieties with the local varieties being the mealiest. Farmers’ criteria when selecting a variety for planting included yield, mealiness and maturity. Farmers’ most preferred cassava variety was the local variety Bensre; the least preferred variety was the improved variety, Essam. Although the local varieties were less responsive to fertilization, they appeared to be well-adapted to local conditions and had preferred root quality attributes. These traits can be used for improving root quality and productivity in cassava breeding. Mealiness of cassava roots could also be improved on poor soils through fertilization.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".