Evaluation of Cassava (Manihot esculenta Crantz) Productivity in Relation to Termite Attacks in Rural Area in Tivaouane (Senegal)
Bibliographic record
Abstract
In Senegal, the cassava harvest, produced mainly in the department of Tivaouane (Thies), is 7.5 t/ha on average for a potential of 40 t/ha. The main variety produced in Tivaouane is Soya. The objective of this study is to evaluate the productivity of the soya variety farmed in Tivaouane in relation to termite damage. Specifically, it is intended to i) evaluate losses caused by termites and missing plants ii) evaluate the average number of tubers per plant, the average weight of a tuber and iii) calculate the yield of cassava production of the Soya variety farmed in Tivaouane. The methodology is based on sampling in order to evaluate parameters such as losses due to termites and missing plants, evaluation of the number of tubers per plant, the average weight of a tuber and the productivity of this variety of cassava. The average loss due to termite attack on dead feet is 1.2 t/ha and the loss due to missing feet is estimated at 3.4 t/ha. The average number of tubers per stand is 2.8 with an average weight of 1.1 kg per tuber. The theoretical yield obtained is 11 t/ha. The low average number of tubers is related to the variety, the quality of the soil and the crop conditions. However, the productivity of this variety of cassava cropped in Tivaouane is still low considering the potential of Senegal in terms of annual cassava production. An improvement in crop conditions, such as the way cuttings are planted, would be an advantage in increasing the productivity of the Soya variety.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".