Land use, land use history and soil type affect greenhouse gas fluxes from intact soil cores sampled across agricultural landscapes of the East African highlands
Bibliographic record
Abstract
Data on greenhouse gas (GHG) fluxes from smallholder based systems in East Africa is limited. Estimation of GHG fluxes in smallholder farming systems is further constrained by the diversity of drivers of GHG fluxes such as land use, soil types, management intensities, climatic variables, and this renders field GHG fluxes measurements costly in terms of time and resources. Use of intact soil core incubation approach can be used as a priori of field measurement to get an understating of the drivers of GHG fluxes in search complex systems. We carried out this work in a 10 x10 km block of Rakai, in southern Uganda (O°40.124’ S 31°26.283’E). Rakai block is one of benchmark sites for CCAFS that represents the Perennial-annual crop farming system, which is widely practiced in the East African highlands. A preliminary study in reference to possible biophysical drivers of GHG fluxes pointed at land use, topographic positions and soil textural classes as likely drivers of GHG fluxes at the landscapes. We also identified a small patch of remaining degraded forests, surrounded by converted land uses to agriculture 3 and 50 years ago. This study therefore aimed at assessing the effects of soil textural class, topography, land use, and land use history on potential soil GHG fluxes (carbon dioxide- CO2 and nitrous oxide- N2O) in the Lake Victoria region. This work aimed at addressing two research questions; 1. To what extent do slope position, soil textural class, and topography and their interaction affect the soil GHG flux potential in the study area? 2. Does time since conversion (from natural forests to agricultural land-use) affect the soil GHG flux potential? Intact soil cores were sampled from randomly selected plots at 0-0.05m soil depth together with soil samples for analysis of soil texture, pH, total nitrogen and carbon and bulk density. Gas samples were first taken from air dried samples after which they were subjected to the different water holding capacities (WHC) .Individual soil cores were subjected to 30, 55 and 80% (WHC) and incubated at 21°C for a period of 48 hours. Gas samples were taken on the first day (three hours after adding deionized water), after 24 hours and at 48 hours. Cumulative fluxes (CO2 and N2O ) for the 48 hours were calculated by integrating the area of all measurement points for the 48-hour period following rewetting. We used cumulative 48hr after re-wetting to study the effect of land use, soil textural classes, slope positions and time of conversion. This dataset consists of cumulative CO2 and N2O at %WHC of 30, 55 and 80, and auxiliary data of soil properties (soil texture, pH, total nitrogen and carbon, C:N ratio and bulk density).
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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 | 0.001 |
| 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".