Soil Phosphorus Fertility Degradation: A Geographic Information System‐Based Assessment
Bibliographic record
Abstract
Abstract Soil phosphorus is a critical macronutrient limiting agricultural productivity in many parts of Nepal. This study evaluates a geographic information system (GIS)‐based approach to assess soil P degradation risk as influenced by site factors and human land use impacts. The status of P fertility in a Nepalese watershed was evaluated by stratifying the soil analysis by soil type, elevation, aspect, and land use (irrigated or rainfed agriculture, rangeland, and forests). Human impacts were shown to be significant, and with GIS overlay techniques it was possible to produce a soil P status map based on land use and soil type. Some 27% of the area was found to be deficient in P. Soil nutrient budgets displaying annual surplus or deficit conditions for the common crops were combined with the soil fertility map to derive a soil P degradation risk map. Low P conditions with high annual deficits posed the greatest degradation risk, while adequate P status with high annual deficits were of more long‐term concern. Forty‐eight percent of the area was considered at low risk because of adequate conditions and minimal deficits for P. In contrast, some 36% of the study area had a high short‐term risk for degradation because of the low status and high annual deficit in P. Given the dynamics of soil fertility, the site factor approach used in combination with soil analysis, nutrient budget calculations based on farm interview data, and GIS overlay techniques provided a unique way of assessing long‐term soil P degradation risks.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".