Transforming a Valuable Bioresource to Biochar, Its Environmental Importance, and Potential Applications in Boosting Circular Bioeconomy While Promoting Sustainable Agriculture
Bibliographic record
Abstract
Biochar produced from transforming bioresource waste can benefit sustainable agriculture and support circular bioeconomy. The objective of this study was to evaluate the effect of the application of biochar, produced from wheat straws, and a nitrification inhibitor, sourced from neem (Azadirachta indica), in combinition with the recommended synthetic fertilizer on soil properties, maize (Zea mays L.) plant growth characteristics, and maize grain yield and quality paramters. The nitrification inhibitor was used with the concentrations of 5 and 10 mL pot−1 (N1 and N2, respectively) with four levels of biochar (B0 = 0 g, B1 = 35 g, B2 = 70 g, B3 = 105 g, B4 = 140 g pot−1), one recommended nitrogen, phosphorous, and potassium syntactic fertilizer (250, 125, and 100 kg ha−1, respectively) treatment, and one control treatment. The results showed that the nitrification inhibitor enhanced crop growth while the application of biochar significantly improved soil fertility. The application of biochar significantly enhanced soil organic matter and soil nitrogen as compared with nitrogen–phosphorus–potassium treatment. The highest root length (65.43 cm) and root weight (50.25 g) were observed in the maize plants treated with B4 and N2 combinedly. The grain yield, total biomass production, protein content from biochar’s B4, and nitrogen–phosphorus–potassium treatments were not significantly different from each other. The application of 140 g biochar pot−1 (B4) with nitrification inhibitor (10 mL pot−1) resulted in higher crop yield and the highest protein contents in maize grains as compared to the control treatments. Therefore, the potential of biochar application in combination with nitrification inhibitor may be used as the best nutrient management practice after verifying these findings at a large-scale field study. Based on the experimental findings, the applied potential of the study treatments, and results of economic analysis, it can be said that biochar has an important role to play in the circular bioeconomy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".