Assessment of tillage effects on soil quality of pastures in South Africa with indexing methods
Bibliographic record
Abstract
Soil quality of pastures changes through time because of management practices. Excessive soil disturbance usually leads to the decline in soil quality, and this has resulted in concerns about kikuyu (Pennisetum clandestinum)–ryegrass (Lolium spp.) pasture systems in the southern Cape region of South Africa. This study aimed to understand the effects of tillage on soil quality. The soil management assessment framework (SMAF) and the locally developed soil quality index for pastures (SQIP) were used to assess five tillage systems and were evaluated at a scale inclusive of variation in topography, pedogenic characteristics and local anthropogenic influences. Along with assessment of overall soil quality, the quality of the physical, chemical and biological components of soil were considered individually. Soil physical quality was largely a function of inherent pedogenic characteristics but tillage affected physical quality adversely. Elevated levels of certain nutrients may be warning signs to soil chemical degradation; however, tillage practice did not affect soil chemical quality. Soil disturbance and the use of herbicides to establish annual pastures has lowered soil biological quality. The SQIP was a more suitable tool than SMAF for assessing soil quality of high-input, dairy-pasture systems. SQIP could facilitate adaptive management by land managers, environmentalists, extension officers and policy makers to assess soil quality and enhance understanding of processes affecting soil quality.
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 | 0.001 | 0.002 |
| 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) | 0.000 | 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".