Comparison of soil properties of native forests, <i>Pinus patula</i> plantations and adjacent pastures in the Andean highlands of southern Ecuador: land use history or recent vegetation effects?
Bibliographic record
Abstract
Abstract In the high Andes of Ecuador scarcity of farmland has led to accelerated deforestation, in particular over the last 40 years. Soil mis‐management has caused the rapid decline of soil fertility and most farmland has been irreversibly transformed into grassland or tree plantations. The present study assessed whether pastures and particularly pine plantations were associated with less soil nutrients. The soils from six sites each of native forests and Pinus patula plantations, and their adjacent pastures were sampled in a geographically large area in the Paute watershed, south Ecuador. Soil analyses showed statistically significant differences for soil cations and effective cation exchange capacity (ECEC) only. ECEC was highest in soils from native forests and their adjacent pastures (6.4 cmol/kg) compared to pine plantations and their pastures (4.2 cmol/kg). Mean soil organic matter and pH were similar in native forests/pastures (39% SOM; pH 5.4) and in plantations/pastures (40% SOM; pH 5). As pasture soils had ECEC concentrations statistically similar to those of their adjacent forest or plantation, they do not form a single homogeneous land use type based on soil nutrients. Therefore, this study cannot conclude that the presence of pines alone has caused soil degradation, but instead that the soil at the site was already degraded before pines were planted. This study proposes the scenario that pine plantations are established in pastures as a last resort, when the soils are already strongly degraded, and more profitable land uses are not available. Farmers are reluctant to use fertile land for tree plantations, and only the planting of well‐known species, such as pines, is officially encouraged.
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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 | 0.000 |
| 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 | 0.001 | 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".