Bioconversion of Non Edible Vegetables from Market into Biofertilizer for Crop Improvement
Bibliographic record
Abstract
The fruits and vegetables lost due to spoilage in the market can be converted to nutrient rich organic biofertilizer, vermicompost and worm tea. In this study, non-edible vegetables from market [French beans (S1), Lady’s fingers (S2) and Brinjal (S3)] were used for production of vermicompost and worm tea using Eisenia fetida for environmental friendly management and recycling, as value added product for crop production. Vermicomposting was carried out in four bins. Three bins (S1, S2 & S3) consisted of individual pre-digested vegetables and the fourth bin (S4) was set up with a mixture of all three pre-digested vegetables in equal proportions. Physico-chemical properties and essential nutrients like pH, temperature, moisture content, total organic carbon (C), total nitrogen (N), available phosphorus (P) and exchangeable potassium (K) of the pre-digested vegetables and/or compost were analysed at regular time intervals. There was around 6-10 fold increase in earthworm count at the end of vermicomposting. During vermicomposting, the N and P content of the compost showed 2 to 4 fold increase whereas 10 to 15 fold increase in K content was noticed. Low C:N ratio (4 to 9) was obtained for vermicompost VS4. The worm tea (vermi wash) and vermicompost collected were used for plant growth studies on Vigna radiata (Green gram). Growth parameters like germination percentage, vigour index, germination index (GI), shoot length (cm), root length (cm) and leaf length (cm) were studied. There was noticeable improvement in germination % (1.7 fold), vigour index (2.7 fold) and germination index. A 20 fold increase in shoot length was also seen in test plants when compared to control (2 fold). Statistical analysis of various growth parameters like root length and plant height indicated that vermicompost made with waste brinjal has a significant response with p ≤ 0.05. Based on the results obtained, waste brinjal, which is abundantly available locally can be economically converted to organic biofertilizers and used for soil and crop improvement. Through this study, a cost effective and environment friendly method for efficient utilization of market waste vegetables has been proposed for promoting plant growth and development.
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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 | 0.000 | 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".