Transforming plant carbon into soil carbon: Process-level controls on carbon sequestration
Bibliographic record
Abstract
Whalen, J. K., Gul, S., Poirier, V., Yanni, S. F., Simpson, M. J., Clemente, J. S., Feng, X., Grayston, S. J., Barker, J., Gregorich, E. G., Angers, D. A., Rochette, P. and Janzen, H. H. 2014. Transforming plant carbon into soil carbon: Process-level controls on carbon sequestration. Can. J. Plant Sci. 94: 1065–1073. Plants figure prominently in efforts to promote C sequestration in agricultural soils, and to mitigate greenhouse gas (GHG) emissions. The objective of the project was to measure the transformations of plant carbon in soil through controlled laboratory experiments, to further understand (1) root-associated CO2 and N2O production during a plant's life cycle, (2) decomposition of plant residues leading to CO2 production, and (3) stabilization and retention of undecomposed plant residues and microbial by-products in the resistant soil C fraction. Experimental plant materials included transgenic near isolines of Zea mays L. and cell wall mutants of Arabidopsis thaliana, selected for their diverse residue chemistry. Phenology, morphology and above-ground biomass affected soil respiration and N2O production in root-associated soils. Mineralization of C and N from incubated plant–soil mixtures was complemented with stable isotope tracing (13C, 15N) and 13C-phospholipid fatty acid analysis. Advanced chemical techniques such as nuclear magnetic resonance spectroscopy and physical separation (particle size and density separation) were used to track the transformations of plant C into stable soil C compounds. Conceptual models were proposed to explain how the plant residue chemistry×soil physico-chemical interaction affects C sequestration. Incorporating single gene mutations affecting lignin biosynthesis into agricultural and bioenergy crops has the potential to alter short- and long-term C cycling in agroecosystems.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".