Co-application of wood biochar and paper mill biosolids affects yield and short-term nitrogen and phosphorus availability in temperate loamy soils
Bibliographic record
Abstract
Amending croplands with forest residues may help in restoring soil properties in fields subject to intensive land management. Despite their known benefits when applied separately, co-application of wood biochar with paper mill biosolids (PB) has seen little investigation under field conditions. A study was initiated in Québec, QC, Canada, to determine the effect of a single application of wood biochar with and without PB on the nitrogen (N) and phosphorus (P) availability of two pH-neutral to alkaline loamy soils. Biochar at 0, 10, and 20 Mg dry weight·ha−1 and PB at 30 Mg wet weight·ha−1 were applied before planting of corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] in 2018. Residual effect of this co-application was determined under soybean and corn in the subsequent year. In both years, corn received supplemental N and P from mineral fertilizers according to local agronomic recommendations. Co-applying biochar and PB reduced soil NO3-N availability in the year of application and decreased corn yield by 1.0 Mg·ha−1 compared with biochar or PB applied alone, but these amendments did not affect soybean yields. In the following year, the previous biochar addition increased soybean yield by 0.6 Mg·ha−1 but had little effect on corn. For both years, biochar addition induced a large increase in soil Mehlich-3 P. This study revealed that wood biochar positively impacted P status of these soils but was not a source of N to crops even when co-applied with PB.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Observational | high |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: yes | Bench or experimental | high |
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 | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".