Post-processing of biochars to enhance plant growth responses: a review and meta-analysis
Bibliographic record
Abstract
A number of processes for post-production treatment of "raw" biochars, including leaching, aeration, grinding or sieving to reduce particle size, and chemical or steam activation, have been suggested as means to enhance biochar effectiveness in agriculture, forestry, and environmental restoration. Here, I review studies on post-production processing methods and their effects on biochar physio-chemical properties and present a meta-analysis of plant growth and yield responses to post-processed vs. "raw" biochars. Data from 23 studies provide a total of 112 comparisons of responses to processed vs. unprocessed biochars, and 103 comparisons allowing assessment of effects relative to biochar particle size; additional 8 published studies involving 32 comparisons provide data on effects of biochar leachates. Overall, post-processed biochars resulted in significantly increased average plant growth responses 14% above those observed with unprocessed biochar. This overall effect was driven by plant growth responses to reduced biochar particle size, and heating/aeration treatments. The assessment of biochar effects by particle size indicates a peak at a particle size of 0.5-1.0 mm. Biochar leachate treatments showed very high heterogeneity among studies and no average growth benefit. I conclude that physiochemical post-processing of biochar offers substantial additional agronomic benefits compared to the use of unprocessed biochar. Further research on post-production treatments effects will be important for biochar utilization to maximize benefits to carbon sequestration and system productivity in agriculture, forestry, and environmental restoration. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42773-021-00115-0.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.017 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".