Is There a Positive Synergistic Effect of Biochar and Compost Soil Amendments on Plant Growth and Physiological Performance?
Bibliographic record
Abstract
The combination of biochar (BC) with compost has been suggested to be a promising strategy to promote plant growth and performance, but although “synergistic” effects have been stated to occur, full-factorial experiments are few, and explicit tests for synergism are lacking. We tested the hypothesis that a combination of BC and spent mushroom substrate (SMS) has a positive synergistic effect on plant growth and physiological performance in a nutrient-limited growing media. A greenhouse experiment with a full factorial design was conducted using mixed-wood BC (3.0 kg·m−2) and SMS (1.5 kg·m−2) (the combination was not co-composted) as organic soil amendments for the annual Abutilon theophrasti and the perennial Salix purpurea. Several measurements related to plant growth and physiological performance were taken throughout the experiment. Contrary to the hypothesis, we found that the combination of BC + SMS had neutral or antagonistic interactive effects on many plant growth traits. Antagonistic effects were found on maximum leaf area, above- and belowground biomass, reproductive allocation, maximum plant height, chlorophyll fluorescence, and stomatal conductance of A. theophrasti. The effect on S. purpurea was mostly neutral. We conclude that the generalization that BC and compost have synergistic effects on plant performance is not supported.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".