Carbon sequestration potential of tropical pasture compared with afforestation in Panama
Bibliographic record
Abstract
Abstract Tropical forest ecosystems play an important role in regulating the global climate, yet deforestation and land‐use change mean that the tropical carbon sink is increasingly influenced by agroecosystems and pastures. Despite this, it is not yet fully understood how carbon cycling in the tropics responds to land‐use change, particularly for pasture and afforestation. Thus, the objectives of our study were: (1) to elucidate the environmental controls and the impact of management on gross primary production (GPP), total ecosystem respiration (TER) and net ecosystem CO2 exchange (NEE); (2) to estimate the carbon sequestration potential of tropical pasture compared with afforestation; and (3) to compare eddy covariance‐derived carbon budgets with biomass and soil inventory data. We performed comparative measurements of NEE in a tropical C4 pasture and an adjacent afforestation with native tree species in Sardinilla (Panama) from 2007 to 2009. Pronounced seasonal variation in GPP, TER and NEE were closely related to radiation, soil moisture, and C3 vs. C4 plant physiology. The shallow rooting depth of grasses compared with trees resulted in a higher sensitivity of the pasture ecosystem to water limitation and seasonal drought. During 2008, substantial amounts of carbon were sequestered by the afforestation (–442 g C m–2, negative values denote ecosystem carbon uptake), which was in agreement with biometric observations (–450 g C m–2). In contrast, the pasture ecosystem was a strong carbon source in 2008 and 2009 (261 g C m–2), associated with seasonal drought and overgrazing. In addition, soil carbon isotope data indicated rapid carbon turnover after conversion from C4 pasture to C3 afforestation. Our results clearly show the potential for considerable carbon sequestration of tropical afforestation and highlight the risk of carbon losses from pasture ecosystems in a seasonal tropical climate.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".