Enhancing soil organic carbon, particulate organic carbon and microbial biomass in semi-arid rangeland using pasture enclosures
Bibliographic record
Abstract
Rehabilitation of degraded rangelands through the establishment of enclosures (fencing grazing lands) is believed to improve soil quality and livelihoods, and enhance the sustainability of rangelands. Grazing dominated enclosure (GDE) and contractual grazing enclosure (CGE) are the common enclosure management systems in West Pokot County, Kenya. Under CGE, a farmer owning few animals leases the enclosure to households with relatively more livestock, while GDE is where the livestock utilizing the enclosure are purely owned by the farmer. Livestock management in both systems is via the free-range system. This study evaluated the effect of enclosure management on total soil organic carbon (SOC), particulate organic carbon (POC) and microbial biomass carbon (MBC) and nitrogen (MBN) as key indicators of soil degradation at 0–40 cm depth. The two enclosure systems were selected based on three age classes (3–10, 11–20 and > 20 years since establishment) (n = 3). The adjacent open grazing area (OGR) was used as a reference (n = 9). Relative to OGR, the pasture enclosures significantly decreased soil bulk density and increased the concentrations of total organic C, POC, MBC and MBN compared to the degraded OGR ( P < 0.001). Significantly higher concentrations of POC and MBC was recorded in GDE than CGE ( P = 0.01). The POC accounted for 24.5–29.5% of the total SOC. MBC concentrations ranged from 32.05 ± 7.25 to 96.63 ± 5.31 µg C g −1 of soil in all grazing systems, and was positively correlated with total SOC and POC ( P < 0.001). The proportional increase in POC and MBC was higher in GDE (56.6 and 30.5% respectively) compared to CGE (39.2 and 13.9% for POC and MBC respectively). This study demonstrated that controlling livestock grazing through the establishment of pasture enclosures is the key strategy to enhance total SOC, POC, MBC, and MBN in degraded rangelands; a precondition for improving soil quality. Therefore, the establishment of enclosures is an effective restoration approach to restore degraded soils in semi-arid rangelands.
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 | 0.000 |
| 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 | 0.000 |
| 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 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".