Determining Optimal Dose of Chemical Fertilizer on Biofortified Bean in Sud-Kivu Highlands
Bibliographic record
Abstract
Rational application of chemical fertilizer increases crop yield of biofortified bean. This study aimed at determining the optimal dose of chemical fertilizer to apply on two biofortified bean varieties used in the community in order to maximize their yield. Following a split-plot design, a field experiment was carried out on CODMLB001 and HM21-7 varieties, in Kashusha (Kabare territory) in Sud-Kivu Highlands, after a strategic application of increasing doses of chemical fertilizer NPK 17-17-17 (D0: Control; D1: 50 kg ha-1; D2: 75 kg ha-1; D3: 100 kg ha-1; D4: 125 kg ha-1 and D5: 150 kg ha-1). The said doses were applied on the sowing day in a parallel gutter at 5cm from the sowing line. The germination rate, the number of days at both the flowering stage and the stage of physiological maturity, as well as the number of harvested crops and beans per plant, number of beans per pod, weight per 1000 grains and yield were observed. Positive and negative interaction between different increasing doses of chemical fertilizers regarding the two varieties were observed. This strategic application allows increased performance according to considered varieties and doses. For instance, the HM21-7 variety gave the best performance with the D5 dose, while the CODMLB001 variety did better under D2.
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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 | 0.000 | 0.000 |
| 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".