Soil Organic Carbon and Nitrogen Stocks Following Land Use Changes in a Sub-Humid Climate
Bibliographic record
Abstract
There has been an incessant conversion of natural forests to agricultural land uses such as farmlands, grasslands and parkland agro-forestry in Africa during the last century. This study investigated changes in soil organic carbon (SOC) and total nitrogen (TN) stocks following the conversion of a natural forest to coffee-based agro-forestry, grazing grassland and well-managed maize farm in a sub-humid tropical climate of Ethiopia. Soil samples (up to 1m depth) were taken from each of these four land use types. Taking the natural forest as a baseline and with duration of 35 years since land use conversion, the total SOC and TN stocks were not significantly different (P > 0.05) among the different land use types when the entire 1m soil depth was considered. However, in the upper 0-10cm soil depth, the SOC and TN stocks were significantly higher (P < 0.05) in the natural forest than the agricultural land use types. There were different patterns of SOC and TN distributions along the soil depths for the different land use types. The SOC stocks decreased with depth in natural forest, but did not show any increasing or decreasing trends in maize farm, grazing grassland, and coffee-based agroforestry. The results of this study revealed that the negative effects of converting natural forests to agricultural land use types on SOC and TN can be prevented through appropriate land management practices in cultivated and grazing lands and use of proper agroforestry practices in a sub-humid tropical climate.
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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 | 0.001 | 0.000 |
| 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 | 0.000 | 0.001 |
| 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 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".