Response of the Yam Variety Krengle (Dioscorea rotundata) to Organo-mineral Fertilisation at Bouaké in Central of Côte d’Ivoire
Bibliographic record
Abstract
The decline in yam productivity due to the lack of arable land and impoverished soils forces farmers to use fertiliser. The fertilisers used are mineral, organic or organo-mineral. A study of these fertilisers has been carried out to propose to the farmers a dose or combination that can improve their yield. Thirteen (13) fertilisers obtained from the combination of mineral fertiliser (NPK 15-15-15) and composted cow or poultry manure were tested on the yam variety Krengle in a 3 replicate randomized complete block design. The experiment was conducted over two (2) campaigns at Bouake in Central of Côte d’Ivoire. The results show that high dose mineral fertilisation (NPK 15-15-15, 300 kg/ha) negatively influenced the growth and yield parameters of the yam variety Krengle. High doses of organic fertilisers (Cow manure (CM) 20 t/ha and poultry manure (PM) 20 t/ha) have a positive effect on vegetative growth. While the medium-high doses of organic fertiliser (CM 15 t/ha and PM 15 t/ha) gave better tuber yield (13.5 t/ha) and allowed an increase in tuber yield from 17 to 18%. Organo-mineral fertilisation combining a small dose of organic fertiliser (5 t/ha) with a medium dose of mineral fertiliser (200 kg/ha) gave also fairly good yield (13.3 and 14.42 t/ha) with an increase of 15 to 25%. Thus, 200 kg/ha of NPK 15-15-15 associated with 5 t/ha of poultry or cow manure can be recommended for the cultivation of the yam variety Krengle at Bouake.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".