Forested lands have lower soil carbon priming effects than croplands in hedgerow agroforestry systems
Bibliographic record
Abstract
The priming effect induced by exogenous organic substrate addition influences soil carbon (C) and nutrient cycling. Agroforestry systems offer a promising land-use approach to increase soil organic C (SOC) sequestration while sustaining agricultural productivity; however, the influence of these systems and their interaction with nitrogen (N) fertilizer application on the soil priming effect remain poorly understood. We conducted a lab incubation experiment with additions of 13 C-labeled glucose and N to assess C loss via the priming effect and the net balance of SOC in top- and subsoils across two common agroforestry systems (hedgerows and shelterbelts) and their component land uses: forested lands and adjacent annual croplands, in central Alberta, Canada. Glucose addition caused a positive priming effect, which was more pronounced in the subsoil than in the topsoil. Nitrogen addition reduced the priming effect in subsoils by 32 %, suggesting that N limitation was a key driver of priming-induced SOC loss. In addition, agroforestry systems and their component land uses interactively affect the priming effect. The priming effect was 34 % lower in the forested land than in the adjacent cropland in the hedgerow system with a more diverse plant community, likely due to greater labile C and nutrient availability in forested lands, reducing the vulnerability of SOC to the priming effect. However, the priming effect was not different between the two land uses in the shelterbelt system, likely due to the smaller differences in SOC and N availability between the two land uses, reducing the contrast in microbial responses to labile C input. Our findings underscore the risk of priming effect-enhanced SOC loss in croplands, and the potential for agroforestry systems to reduce SOC loss through damping the priming effect and mitigate climate change.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 teacher head, 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".