Soil Agricultural Potential in Four Common Andean Land Use Types in the Highlands of Southern Ecuador as Revealed by a Corn Bioassay
Bibliographic record
Abstract
In the Andes, little is known about the relationships among current land uses and their effect on soil fertility. Corn (Zea mays L.) was used to evaluate soil quality for plant growth on soils of four land uses, along an expected gradient of fertility: native forests (Nf) > pastures (Pa) > Eucalyptus globulus Labill. plantations (Eg) > Pinus patula Schlecht. plantations (Pp). Corn was grown in soils taken from four different areas, for the four land uses in each. In a common garden, a randomized block design was used with four treatments: controls (C), ammonium nitrate (N), triple superphosphate (P), and combined N and P fertilizers (N + P). On soils from Nf, Pa and Eg, fertilization response was N + P > P > N > C; corn biomass (g/pot-1) averaged 4.5 in N + P, 3.3 in P, 1.8 in N, 1.7 in C; P content (mg/pot-1) averaged 12 in N + P, 11.9 in P, 2.3 in N, 2 in C. N + P enhanced growth the most. Mortality was high on Pp soils, growth weak, and fertilization response was P > N + P > C ≥ N; corn biomass (g/pot-1) was 0.9 in P, 0.5 in N + P, 0.8 in C, 0.4 in N; P content (mg/pot-1) was 4.4 in P, 2.3 in N + P, 1.8 in C, 1 in N. All soils had P, K, Ca and Mg deficiencies. Al toxicity possibly occurred only in Pp soils. All control soils had low fertility. Responses to N and P were high except for Pp. Pastures and plantations were once natural forests converted to agriculture, then to pastures as soil fertility declined. Plantations were likely established on poorest pastures; only pine grew on poorest soils. This land use endpoint has the lowest agricultural potential; other land uses have limitations in P, N, and potentially K.
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 | 0.001 |
| 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".