The utility of biochar for increasing the fertility of new agricultural lands converted from boreal forests
Bibliographic record
Abstract
The agricultural soils of the Happy Valley-Goose Bay (HV-GB) region of Newfoundland and Labrador, Canada are Podzols that are converted from boreal forest and have limited productivity due to their sandy texture, acidity, low soil organic matter (SOM), cation-exchange capacity (CEC), and water and nutrient retention capacity. Although numerous studies advocate biochar for mitigating soil quality problems and enhancing agronomic productivity, there is limited information on managing biochar for boosting the productivity of Podzols under newly converted agricultural lands. A 5 year experiment evaluated the impact of eight biochar rates (0–80 Mg C·ha−1) on soil properties including SOM, CEC, and availability of plant nutrients and metals in agricultural soils of HV-GB. Both immediate and long-term impacts were thus assessed. Biochar generally improved soil fertility indicators. Although the largest rates (40 and 80 Mg C·ha−1) led to the greatest changes, significant changes were also found with rates as low as 10 Mg C·ha−1. Increasing biochar rates had diminishing returns for quality parameters of soil. The impact of biochar decreased with time after application, but the largest rate led to longer lasting effects. Although biochar was incorporated in topsoil, its effects were also measurable in the subsoil, albeit with a temporal delay, usually of 1 year. Thus, given the diminishing returns of increasing biochar rates, and that beneficial effects diminish over years, it is advisable that biochar be added in smaller amounts immediately after conversion, as little as 10 Mg C·ha−1, with regular supplementation as needed.
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 | 0.001 | 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".