Impact of paddy straw and Bio-decomposer with inorganic fertilizers on soil health in wheat (Triticum aestivum L.)
Bibliographic record
Abstract
The study investigated the effect of inorganic fertilizers, paddy straw (rice residue), and Halo-CRD biodecomposer on the soil health of post-harvest wheat fields in the subtropical region of Prayagraj, Uttar Pradesh during the Rabi season of 2019–20 and 2020-21. Results demonstrated the effectiveness of integrated residue management in improving soil physical, chemical and biological properties. There was a significant build-up of soil organic carbon in T7 (Rice residue treated with microbial decomposer Halo-CRD+ RDF@100%) 0.85 and 0.59% and 0.91 and 0.60% content in soil during both seasons of 2019– 20 and 2020-21. Maximum content of available N (257.26, 261.52 kg ha-1 and 249.43, 251.35 kg ha-1), available P (21.62, 22.14 kg ha-1 and 19.67, 19.85 kg ha-1) and K (219.21, 223.20 kg ha-1 and 181.38, 182.68 kg ha-1) was observed in treatment T7 during both seasons. The bacterial and fungal count ranged from 6.40 to 17.70 ×105 and 3.10 to 8.15×105 cfu g-1 dry soil, respectively. Incorporating microbial decomposers enhanced nutrient-use efficiency and soil health while reducing nutrient losses. The findings advocate integrating rice residue management with microbial decomposer Halo-CRD and balanced fertilization to enhance soil chemical, physical and biological properties, improve soil health, and mitigate residue burning’s environmental impacts.
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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) | 0.000 | 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 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".