Mixtures of papermill biosolids and pig slurry improve soil quality and growth of hybrid poplar
Bibliographic record
Abstract
Abstract Hybrid poplar plantations in Quebec, Canada, are generally established on marginal agricultural lands characterized by low pH and low inherent soil fertility. Here, we tested the hypothesis that two potential organic fertilizer (OF) sources, papermill biosolids (PBs) and liquid pig slurry (LPS), would improve soil quality and the growth performance of hybrid poplars ( Populus trichocarpa × Populus deltoides ), especially if applied in mixtures rather than separately. The fertilizer treatments included an unfertilized control, inorganic fertilizer (IF) (calcium ammonium nitrate and triple superphosphate) and OFs (PBs alone, LPS alone and two combinations of PBs and LPS) applied at two rates. Fertilizers were broadcast within 1 m of tree trunks and unincorporated, to prevent damage to tree roots. Hybrid poplar growth was the greatest in plots fertilized with a combination of PBs and LPS, suggesting that the two OFs complemented themselves and/or interacted to improve soil nutritional quality. PBs were the most efficient at raising soil pH, providing plant‐available Ca and increasing nitrification rates over the long term, whereas LPS provided more readily available NO 3 ‐N, P and K. Applied together, PBs and LPS interacted to provide more extractable P and mineralizable NH 4 ‐N than when applied separately. OFs increased soil biological activity, notably basal respiration, microbial biomass, metabolic quotient and mineral N production rates. Community‐level catabolic profiles of the extractable soil microflora in plots with OFs differed significantly from the control and IF treatments. This implies that surface‐applied OFs may induce fundamental changes to the diversity and composition of microbial communities in the underlying rooting zone. Although this study has shown beneficial effects of OF mixtures on soil quality and hybrid poplar growth, further research should focus on their possible environmental impacts.
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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 | 0.001 | 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".