Spatial relationships of grassland net ecosystem carbon dioxide exchange in a first-order agricultural basin in southern Ontario
Bibliographic record
Abstract
This study quantified the midday (10:00 – 16:00) summer source/sink CO2 relationships of various land-use types, particularly grass-dominated riparian areas, using dynamic chamber techniques, while evaluating the relative contribution of root and microbial components towards the overall soil respiration. The influence of nearby agriculture and was related to elevated N and P, which were 40 and 1000% larger, respectively. A site adjacent to cropped fields showed similar (within 8%) study averaged soil respiration as an open grassland site, but 22% lower soil respiration than a riparian site 250-300 m downstream, which is adjacent to an open grassland fallow. A maple woodlot site exhibited the lowest soil respiration. Patterns were different for vegetative dynamics, such that a grass-dominated riparian site adjacent to agriculture showed 18 to 40% larger ecosystem (soil and vegetation) respiration, 36 and 60% greater net ecosystem CO2 exchange (NEE) and 23 and 45% higher net ecosystem productivity (NEP) than a grass-dominated riparian area adjacent to an open grassland and an open grassland site, respectively. Ambient air (Ta) and soil temperature (Tg) at 5 cm were the best predictors of temporal variability in soil and ecosystem respiration for all grass-dominated sites, whereas Tg was the best predictor of the temporal variability of soil respiration at the maple woodlot site. Volumetric soil moisture content (VSM) also exerted substantial temporal control on soil respiration through a quandratic relationship. Weak temporal relationships between Ta, Tg and VSM, with NEE and NEP, were shown. Spatially, study averaged site ecosystem respiration, NEE and NEP for vegetated plots showed strong positive relationships, close to unity, with the site average total nitrogen, C/N ratio and above-ground biomass. Results showed that grass-dominated riparian and non-riparian areas, with similar vegetation that appear to be homogenous, located approximately 250-300 m from one another, exhibited spatially differing CO2 exchange dynamics based primarily on location within the watershed. In addition, soil and ecosystem dynamics exhibited differing spatial and temporal responses to soil N inputs. This highlights the need to better assess CO2 fluxes from heterogeneous agricultural landscapes. Furthermore, it emphasises that estimates based solely on soil or vegetated surfaces can be rather conservative and may not capture the inherent spatial variability and small scale processes that drive CO2 exchange.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".