Soil and greenhouse gas responses to biochar additions in a temperate hardwood forest
Bibliographic record
Abstract
Abstract Biochar additions can improve soil fertility and sequester carbon, but biochar effects have been investigated primarily in agricultural systems. Biochar from spruce and maple sawdust feedstocks (with and without inorganic phosphorus in a factorial design) were added to plots in a commercially managed temperate hardwood forest stand in central Ontario, Canada; treatments were applied as a top‐dressing immediately prior to fall leaf abscission in September 2011. Forests in this region have acidic, sandy soils, and due to nitrogen deposition may exhibit phosphorus, calcium, and magnesium limitation. To investigate short‐term impacts of biochar application on soil nutrient supply and greenhouse gas fluxes as compared to phosphorus fertilization, data were collected over the first year after treatment application; linear mixed models were used to analyze data. Two to six weeks after treatment application, there were higher concentrations of potassium in spruce and maple biochar plots, and phosphorus in spruce biochar plots, as compared to the control treatment. There were higher concentrations of calcium, magnesium, and phosphorus in the phosphorus plots. In the following spring and summer (9–12 months after treatment application), there were higher soil calcium concentrations in maple biochar plots, and phosphorus plots still had higher soil phosphorus concentrations than control plots. No treatment effects on fluxes of carbon dioxide, methane, or nitrous oxide were detected in the field; however, laboratory incubations after 12 months showed higher microbial respiration in soils from maple biochar plots as compared to spruce biochar, despite no effect on microbial biomass. The results suggest that the most important short‐term impact of biochar additions in this system is the increased supply of the limiting plant nutrients phosphorus and calcium. We expect that larger changes in mineral soil physical and chemical properties will occur when the surface‐applied biochar becomes incorporated into the soil after a few years.
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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 | 0.001 | 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".