Estimating Root Plus Rhizosphere Contributions to Soil Respiration in Annual Croplands
Bibliographic record
Abstract
Although soil respiration represents an important C transfer from terrestrial ecosystems to the atmosphere, the effects of environmental and biological factors on soil respiration rates are not adequately understood. This is due primarily to the variety of processes that produce CO 2 within the soil. Thus, separating the main CO 2 –producing processes is needed to improve our understanding of soil C cycling and dynamics. Here, we describe and test a model that estimates soil CO 2 emissions derived from anabolic and catabolic processes, representing organic matter decomposition and root + rhizosphere respiration, respectively. Our model is based on the exponential response of organic matter decomposition with respect to temperature, and it requires only measurements of total soil CO 2 emissions and soil temperature as inputs. To test the model, we relied on published measurements of soil respiration rates and soil temperatures in a maize ( Zea mays L.) field in Ottawa, Canada, and on independent estimations of soil and root contributions for this field made on the basis of stable‐C isotope measurements of soil‐derived CO 2 Model‐based and isotope estimations correlated significantly ( r 2 = 0.91, P < 10 −9 ) on a daily basis. Model‐based estimations for root + rhizosphere respiration rates for the entire growing season totaled 145 g C m −2 or 27% of CO 2 emissions, and those based on C isotopes totaled 158 g C m −2 or 30% of the total emissions. The excellent correspondence between model‐based and isotope‐based estimations suggests that this relatively simple model can be used to distinguish root from soil contributions to soil CO 2 emissions in temperate‐zone, annual croplands free of significant water stress.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 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".