Soil nematode trophic structure and biochar addition in recently converted boreal lands
Bibliographic record
Abstract
Context Climate change facilitated expansion of agriculture into northern regions increases the amount of Podzol dominated farmland. Biochar can improve poor growing conditions in soils. There are no universally accepted soil quality indicators for assessing the sustainability of expanding and intensifying boreal farming. Changes in the soil community structure can inform on soil functional status and the impact of management. Aims We assessed the impacts of biochar added to recently converted agricultural land on soil nematodes. We hypothesised that biochar addition would increase soil pH, correlate with total nematode abundance, and favour bacterivores over fungivores. Methods Biochar was added to soil at 10–80 Mg C ha-1 rates. Physicochemical soil properties, crop yields, nematode community trophic composition, trophic group ratios, and diversity indices were assessed. Key results Soil quality and fertility were improved with biochar, critically through increasing pH from 4.8 to 5.5. The interactions between pH, available metals, and micro-nutrients were related to biochar rate. Biochar was associated with increased bacterivore abundance (CI90 of 328 ± 132 vs 618 ± 50 individuals) indicating accelerated SOM degradation, and increased omnivore abundance (CI90 of 13 ± 17 vs 33 ± 7 individuals) indicating a more resilient community. Changes to Podzol quality may be most reliably indicated by bacterivore abundance and community complexity than by ratios and diversity indices. Conclusions Biochar application improved soil quality as suggested by nematode community structure. Implications Biochar application may be recommended to improve Podzol quality and fertility. Soil nematodes can indicate relative changes to Podzol quality.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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, 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".