Tailored Bioactive Compost from Agri-Waste Improves the Growth and Yield of Chili Pepper and Tomato
Bibliographic record
Abstract
An extensive use of chemical fertilizers has posed a serious impact on food and environmental quality and sustainability. As the organic and biofertilizers can satisfactorily fulfill the crop’s nutritional requirement, the plants require less chemical fertilizer application; hence, the food is low in chemical residues and environment is less polluted. The agriculture crop residues, being a rich source of nutrients, can be used to feed the soil and crops after composting and is a practicable approach to sustainable waste management and organic agriculture instead of open-field burning of crop residues. This study demonstrates a feasible strategy to convert the wheat and rice plant residues into composted organic fertilizer and subsequent enrichment with plant-beneficial bacteria. The bioactive compost was then tested in a series of in vitro and in vivo experiments for validating its role in growing organic vegetables. The compost was enriched with a blend of micronutrients, such as zinc, magnesium, and iron, and a multi-trait bacterial consortium AAP (Azospirillum, Arthrobacter, and Pseudomonas spp.). The bacterial consortium AAP showed survival up to 180 days post-inoculation while maintaining their PGP traits. Field emission scanning electron microscopic analysis and fluorescence in situ hybridization (FISH) of bioactive compost further elaborated the morphology and confirmed the PGPR survival and distribution. Plant inoculation of this bioactive compost showed significant improvement in the growth and yield of chilies and tomato without any additional chemical fertilizer yielding a high value to cost ratio. An increase of ≈35% in chlorophyll contents, ≈25% in biomass, and ≈75% in yield was observed in chilies and tomatoes. The increase in N was 18.7 and 25%, while in P contents were 18.5 and 19% in chilies and tomatoes, respectively. The application of bioactive compost significantly stimulated the bacterial population as well as the phosphatase and dehydrogenase activities of soil. These results suggest that bioactive compost can serve as a source of bioorganic fertilizer to get maximum benefits regarding vegetable yield, soil quality, and fertilizer saving with the anticipated application for other food crops. It is a possible win-win situation for environmental sustainability and food security.
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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".