Assessment of scenarios for intensifying pasture management and grazing in fragile micro-watersheds of high Andean mountains
Bibliographic record
Abstract
In the Andean hillsides of Ecuador, indigenous populations use land mainly for grazing sheep and cattle. Animal response depends exclusively on the quality of forage and the soil’s physical and chemical conditions. The objective of this research was to evaluate two scenarios for intensifying pastures based on perennial ryegrass (Lolium perenne) and white clover (Trifolium repens), used at rest periods of 45- and 60 days in sites between 3,000-3,400 m a.s.l. used for grazing sheep and cows. These scenarios were compared with a natural grassland based on a plant community composed of Stipa ichu, Holcus lanatus, Rumex acetocella, and Paspalum sp., used in a traditional system with rest periods of 60-75 days in sites between 3,500-3,700 m a.s.l. in the Chimborazo River micro-watershed. Soil sampling was conducted at both sites to determine the soil fertility profile. Regarding the forage component, chemical composition, animal carrying capacity, milk production, and estimated enteric CH4 emissions were determined. In sheep serum, Ca, P, and Mg profiles, and the activity of AST, ALT, and FA enzymes were analyzed. The data were analyzed using ANOVA and Tukey 5% as a means comparison test. The results showed a better physicochemical property of the soil at the lower altitude. The intensification of pasture management and grazing through the utilization of rest periods of 45 days or less may represent a productive and low-emission option.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 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".