Evaluation of the effect of biochar-based organic fertilizer on the growth performance of fennel and cumin plants for three years
Bibliographic record
Abstract
This three years field study examined the influence of application rates of manure from sheep and goat (S/G) and their mixture with wood-based or farm yard manure-based biochars (FYMB) on growth performance of Foeniculum vulgare (fennel) and Cuminum cyminum (cumin). The fertilizer amendment rates were 1.66, 3.32 and 6.64 t ha−1, which were applied for three consecutive years in field. The nitrogen (N) and phosphorus (P), nitrogen use efficiency (NUE) and phosphorus use efficiency (PUE) in seeds and stover of test crops were analyzed for third year cropping only. Results demonstrated that in general, fertilizers did not influence yield of first and second year crops. The significant (P ≤ 0.05) positive influences of organic fertilizers were observed for third year crops and were of higher magnitude for C. cyminum than F. vulgare (126–306.6% increase for C. cyminum and 24.5–48.4% increase for F. vulgare than control). As compared to S/G applied at 6.64 t ha−1 rate, its co-amendment with wood-derived biochar at all application rates significantly reduced P in seeds; whereas, its co-amendment with both biochar types and at all application rates significantly reduced P in the stover of F. vulgare (Table 3; P ≤ 0.05). For the crop C. cyminum, there was no difference between treatments for the concentration of P in stover. The phosphorus use efficiency (PUE) of stover of F. vulgare was significantly improved by 80–108% and by 60–79% in response to the application of S/G and its co-amendment with FYMB respectively than control. The PUE of seeds of F. vulgare was increased by 100% than control in response to the co-amendment of manure with wood-derived biochar at high application rate (P ≤ 0.05). More profound significant improvement in PUE was observed for third year crop of C. cyminum, as most of the treatments improved PUE of seeds by171 – 561% and stover by 196–294% than control with no significant differences between fertilizer treatments. Results show no relationship between fertilizer application rates and life history trait of crops in space and time, since there was non-consistent and in general non-significant differences between fertilizer treatments for both crops.
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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) | 0.001 | 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".