Land Use, Land Use History, and Soil Type Affect Soil Greenhouse Gas Fluxes From Agricultural Landscapes of the East African Highlands
Bibliographic record
Abstract
Abstract This study aims to explain effects of soil textural class, topography, land use, and land use history on soil greenhouse gas (GHG) fluxes in the Lake Victoria region. We measured GHG fluxes from intact soil cores collected in Rakai, Uganda, an area characterized by low‐input smallholder (<2 ha) farming systems, typical for the East African highlands. The soil cores were air dried and rewetted to water holding capacities (WHCs) of 30, 55, and 80%. Soil CO2, CH4, and N2O fluxes were measured for 48 h following rewetting. Cumulative N2O fluxes were highest from soils under perennial crops and the lowest from soils under annual crops (P < 0.001 for all WHC). At WHC of 55% or 80%, the sandy clay loam soils had lower N2O fluxes than the clay soils (P < 0.001 and P = 0.041, respectively). Cumulative soil CO2 fluxes were highest from eucalyptus plantations and lowest from annual crops across multiple WHC (P = 0.014 at 30% WHC and P < 0.001 at both 55 and 80% WHC). Methane fluxes were below detectable limits, a shortcoming for using soil cores from the top soil. This study reveals that land use and soil type have strong effects on GHG fluxes from agricultural land in the study area. Field monitoring of fluxes is needed to confirm whether these findings are consistent with what happens in situ.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".