Effects of Vermicompost, Tithonia Green Manure and Urea on Quality of Swiss Chard (Beta Vulgaris L. Var. Cicla L.) in Kenya
Bibliographic record
Abstract
Swiss chard (Beta vulgaris L. var. cicla) is a popular vegetable because it is nutritious, robust, easy to grow. It requires regular applications of nitrogen, which causes accumulation of oxalates and nitrates within the plant. Oxalates and nitrates are known health hazards but the use of organic fertilizers have been shown to reduce their accumulation and to promote accumulation of beneficial phytochemicals. We therefore determined the effect vermicompost (VC), Tithonia diversifolia green manure (Tithonia) and urea on the quality of Swiss chard. The experiment was laid out in a Randomised Complete Block Design (RCBD) replicated three times with thirteen treatments. Leaf nutrient elements, TSS, polyohenols, Vitamin C and antioxidants were determined using standard procedures. Treatment effects were significant only for aluminum, phosphorus and calcium. The values were highest on VC 50 kg N ha-1 and Urea 50 kg N ha-1, Tithonia 50 kg N ha-1 + Urea 50 kg N ha-1, and VC 50 kg N ha-1, and VC 100 kg N ha-1 for aluminum, phosphorus and calcium, respectively. Lowest response was reported on Tithonia 50 kg N ha-1, control and Tithonia 50 kg N ha-1 + Urea 50 kg N ha-1 for aluminum, phosphorus and calcium, respectively. The treatments significantly influenced the percentage of polyphenols, vitamin C and the antioxidant capacity while no significance was reported for total soluble solutes. The polyphenols and vitamin C content were highest on Tithonia 100 kg N ha-1 and control, respectively. Low polyphenol and vitamin C contents were recorded on VC 50 kg N ha-1 and VC 100 kg N ha-1, respectively. A similar trend was observed for antioxidant activity since a positive correlation was observed between the polyphenols and vitamin C and the antioxidant activity. Tithonia green manure improves Swiss chard quality.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".