Soybean and soil responses to biochar amendment in controlled environments with elevated temperature and carbon dioxide
Bibliographic record
Abstract
Warmer atmospheric temperatures (eT) will increase plant nutrient uptake, and elevated atmospheric CO2 (eCO2) is expected to enhance plant growth, whereas a multicomponent eTeCO2 effect should also be beneficial for agroecosystems. Our goal was to understand if single- (eT, eCO2) or multicomponent (eTeCO2) climate effects, predicted for southern Ontario, Canada, will affect soybean and soil properties differently when soil is amended with manure and biochar (MB) or with manure, nitrogen (N) fertilizer, and biochar (MNB) compared with the addition of manure and N fertilizer (MN). We hypothesized that biochar regulates climate effects and causes soybean and soil properties to be similar to ambient climate conditions than soil without biochar. However, soil amended with biochar functioned independently of single- or multicomponent climate effects. Soybean pod and shoot biomass, shoot height, and shoot:root ratio were greater (p < 0.05) with eT. eCO2 increased (p < 0.05) shoot biomass coinciding with an increase (p < 0.05) in nutrient uptake and uptake efficiency. All climate effects decreased (p < 0.05) soluble carbon (C), available N (NH4+ and NO3−), and the C/N ratio but increased (p < 0.05) orthophosphate. Amendment type MNB decreased (p < 0.05) soil microbial biomass carbon, but climate effects did not affect microbial biomass (p < 0.05). However, climate effects influenced how C and N were accessed by microbes in all amendment types, shifting (p < 0.05) microbial community structure, species richness, and diversity. We rejected our hypothesis and concluded that biochar amended soil does not strongly influence soybean and soil properties, and it does not provide a greater ability for soybeans and soil to cope with climate effects.
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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) | 0.001 | 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 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".