Assessing Soil Quality and Identifying Key Indicators in Agroforestry Systems in Sumberejo Village, Wonogiri Regency, Indonesia
Bibliographic record
Abstract
Assessing soil quality is integral to determining the appropriateness of soil management practices.Agroforestry, a tillage system that strategically integrates tree plantations with annual crops, has a potential impact on soil quality through the augmentation of soil organic matter derived from litter deposited on the soil surface.This study aims to calculate the soil quality index and evaluate the soil quality status across various agroforestry types.The research was conducted in Sumberejo Village, Batuwarno District, Wonogiri Regency, Indonesia, focusing on distinct agroforestry types: teak agroforestry, mahogany agroforestry, mixed agroforestry, and dry land-representing an area where agroforestry systems are not implemented.Fourteen indicators were utilized for the assessment, and subsequent Principal Component Analysis was employed to select the Minimum Data Set.The chosen indicators included soil macrofauna diversity index, cation exchange capacity (CEC), available soil phosphorus (P), total soil nitrogen (N), soil organic carbon (C), soil moisture, base saturation (BS), available soil potassium (K), particle density, pH, and porosity.The findings reveal that the soil quality across all four types of agroforestry is low, with mahogany agroforestry exhibiting the highest soil quality index at 0.35.The soil quality index for teak agroforestry, mixed agroforestry, and dry land was 0.33 each.The study concludes that the primary determinant of soil quality is the cation exchange capacity (CEC).
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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 | 0.001 | 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) | 0.000 | 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".