Impact of Composting on Growth, Vitamin C and Calcium Content of Capsicum chinense
Bibliographic record
Abstract
The nutritional quality of the food has become a serious concern in existing agricultural system as the present world aims to enhance only the food production. A field experiment was carried out to study the effect of different fertilizers on growth, vitamin C and calcium content in yield of Capsicum chinense at Regional Agricultural Research and Development Center, Makandura consisting four treatments as without fertilizer (control/ T1), only compost (T2), compost + inorganic fertilizer (T3) and only inorganic fertilizer (T4) with a randomized complete block design (RCBD) replicating four times. Vitamin C content was measured by Indophenol dye redox titration method and calcium content was analyzed by atomic absorption spectrophotometer. Data was analyzed using analysis of variance. The highest growth was recorded in T3 and no significant differences between treatments in growth parameters at 50% flowering stage.The Vitamin C content was highest in treatment with only compost (T2) and the lowest in treatment compost + inorganic fertilizer (T3). The results indicated that yield from organically managed crops contain significantly higher amount of vitamin C (9.24±2.27 mg/100g, p= 0.0274). The highest calcium content was found in T1 (control) (1.1±0.05 %) and a significant difference (p= 0.0296) was observed between T1 (control) and T3 (calcium=0.75±0.12 %). Compost alone can be used to produce food with high amount of vitamin C. Use of inorganic fertilizer alone or integration of compost with inorganic fertilizer was less effective in producing high quality nutritious foods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".