Effect of Fertilizer Application on Growth and Yield of Manihot esculenta Crantz.
Bibliographic record
Abstract
To investigate the effect of chemical and organic fertilizer management on growth, yield and nutrient uptake of two cassava cultivars under rainfed conditions, the study was laid out in factorial in randomised complete block design with four replications. The six treatments were TR1) Kasetsart 50 with 312.50 kg ha-1 of 15-15-15, TR2) CMR 33-38-48 with 312.50 kg ha-1 of 15-15-15, TR3) Kasetsart 50 with 312.50 kg ha-1 of 15-7-18, TR4) CMR 33-38-48 with 312.50 kg ha-1 of 15-7-18, TR5) Kasetsart 50 with 6,250 kg ha-1 of organic fertilizer and TR6) CMR 33-38-48 with 6,250 kg ha-1 of organic fertilizer. The results illustrated that Kasetsart 50 responded to chemical fertilizer management especially, 312.50 kg ha-1 of 15-7-18 formula and 6,250 kg ha-1 of organic fertilizer at 1 and 4 months after planting respectively, better than CMR 33-38-48 in terms of storage root fresh and dry weights, percentage of total starch and amylose content. CMR 33-38-48 responds to chemical fertilizer management and organic fertilizers in terms of stem and leaf growth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".