Biochar as a Potential Nutrient Carrier for Agricultural Applications
Bibliographic record
Abstract
Abstract Purpose of Review This paper offers a thorough overview of the processes of nutrient enrichment by biochar and how biochar as a nutrient carrier can effectively improve agricultural productivity. The distributions of nutrients in biochar and the dynamics of nutrients in soil are also reviewed in detail. Recent Findings The application of biochar improves soil health by changing the soil’s biological and physico-chemical characteristics of the soil, such as its structure, cation exchange capacity and microbial biomass carbon. Additionally, biochar produced from low pyrolysis temperatures can enhance nutrient retention in soils and be utilized as a carbon-based fertilizer. Summary The maintenance of an adequate amount of organic matter in soil and a dynamic biogeochemical cycle of essential nutrients are key components of sustainable soil management. Biochar is a carbonized biomass derived from various feedstock materials, including wood and crop residues, manures, biosolids and animal carcasses. Biochar has been used for more than two decades as a soil amendment to improve soil physicochemical conditions and mitigate soil contamination. Nutrient-enriched biochar-based fertilizers (NEBBF) can be prepared using various nutrient enrichment procedures and have the potential to increase soil fertility and crop productivity. The application of NEBBF, which is a carbon-based nutrient source, has been shown to enhance microbial activity, thereby increasing the efficiency of nutrient use compared to conventional non-carbon-based synthetic fertilizers. This review identified key research gaps and discussed the importance and necessity of biochar as a nutrient carrier in agriculture.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".