Biochar-based organic fertilizers: Influence on yield and concentration of antioxidants in the stigma of saffron and rhizosphere bacterial diversity of slightly saline and non-saline soils
Bibliographic record
Abstract
Being the most expensive spice, saffron has great economic importance. This crop grows well in cold arid deserts. Salinity is one of the important limiting factors for the cultivation of this crop. However, the use of composted manured and co-composted biochar and fertilizers can play a role in attenuating the salinity stress on this crop. In this two-year field study, manures from three sources: sheep and goat (SG), cow and buffalo (FYM), and poultry (PM) farms, and their co-compost with slow-pyrolyzed wood-derived biochar (B) were used for saffron cultivation in slightly saline (electrical conductivity 1.95 dS m-1) and non-saline soils. Yield and concentration of antioxidants of stigma and bacterial diversity in the rhizosphere of this crop, under salinity and non-salinity conditions, were evaluated. Results revealed that in non-saline soil of first-year crops, all fertilizers decreased the yield of stigma than control by 15-49% (P≤0.05) but increased the concentration of carotenoids and total polyphenolics (P≤0.05). In saline soil, no difference in yield was observed between treatments for the first-year crop; however, for the second-year crop, as compared to control, PM and FYM significantly increased yield by 41% and 44% respectively, whereas FYM also increased the concentration of total polyphenolics (P≤0.05). The FYM fertilizer was found suitable for the yield and quality of saffron stigma for second-year crops in both soils (non-saline and saline). The observed OTUs, Chao1, Fischer, and ACE indexes based on 16s rRNA metagenomic analysis revealed 2-4 times greater bacterial diversity in the rhizosphere soil of PM-B and SG-B treatments than in the control. Furthermore, 347 bacterial species were found in PM-B- or SG-B-amended soils absent in control treatments.
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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".