Unleashing the Potential of Biochar Composite as Organic Nutrient Source: Implications as Soil Ameliorant, Seed Yield, and Physiological Attributes of <i>Helianthus annuus</i> L.
Bibliographic record
Abstract
Agriculture needs a reduced dependency on synthetic chemical fertilizers and renewable biomaterials to add nutrients to soil, ameliorating degraded soil and gaining agronomic yield of crops. Biochar mixed with inorganic fertilizer has been effective in improving the crop yield. However, the influence of composite biochar (C‐BC) obtained from poultry feathers (PFs), cow bones (CBs), and rice straw (RS) waste streams as sources of N, P, and K, respectively, is still unclear. This study aimed to unveil a tripartite relationship of biochar composite applications with soil properties and agronomic attributes of sunflower var. orisun. Biochars associated with nutrient acquisition were characterized through Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and energy dispersive x‐ray (EDX) spectroscopy. To assess the bioavailability of nutrients and their impact on agronomic yield, a greenhouse trial was set up with sunflower, using each biochar individually as well as in composite application at three levels of amendments (0%, 2%, 4%, and 6% w/w) along with commercial fertilizer. The surface characterization through FTIR, SEM, and EDX of each biochar illustrated the presence of a wide range of functional groups, porosity, and multiple nutrients. C‐BC showed a significantly positive response to soil properties and exhibited the high availability of nutrients (N, P, and K) in soil as compared to commercial fertilizer. Application of C‐BC at 4% was found effective for dry biomass (47 g) and seed yield (35.16 g), resulted in oil and protein concentrations as high as 44.8% and 23.5%, respectively. Feather‐derived biochar (PF‐BC), bone char (CB‐BC), and RS‐derived biochar (RS‐BC) in the form of biochar composite can become an applicable substitute for chemical fertilizer in sustainable agriculture.
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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".