Increasing root trait complementarity in species mixtures may be detrimental for soil carbon storage
Bibliographic record
Abstract
Designing plant mixtures with potential to increase soil organic carbon (SOC) appears to be a powerful nature-based tool to restore some of the carbon lost in agroecosystems. However, we are uncertain about the best way to design such benign plant mixtures. Trait-based approaches are increasingly used to explain the relationship between plant diversity and ecosystem functions, offering a conceptual opportunity to address this knowledge gap. In this study, we combine a global meta-analysis of 407 paired SOC content observations with a root traits database from GRooT, to explore the optimum way for the design of plant mixtures to increase SOC. We found that specific root traits at the community level were important predictors of the response of SOC to plant mixtures. Species mixtures could increase SOC content when the overall plant community had low variation in root mycorrhizal colonization and root tissue density. The positive response of SOC content to species mixtures was linked to increases in soil microbial biomass carbon and root biomass. Additionally, the SOC enhancements by plant mixtures were often found in regions with high precipitation and low sand content. Our meta-analysis presents a framework based on plant traits to enhance SOC sequestration using plant mixtures, which will enable farmers to optimize plant mixtures towards soil carbon sequestration.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".