Soil morphostructural characterization and coffee root distribution under agroforestry system with Hevea Brasiliensis
Bibliographic record
Abstract
Land use and tillage practices may change soil structure and undermine sustainable agriculture; however, such changes are hardly identified in the short term. In this sense, agroforestry systems have been used to reduce soil degradation and promote sustainable production in coffee plantations. These areas are expected to have well-structured soils and hence improved root distribution. This study aimed to evaluate soil quality by the morphostructural and root distribution analyses comparing open-grown coffee and coffee in agroforestry systems with rubber trees for 19 years, in an Oxisol in northern Paraná State (Brazil). Treatments consisted of open-grown coffee (OG), coffee partially shaded by rubber trees (PSH), and coffee fully shaded by rubber trees (FSH). The mapping of morphostructural features and soil resistance to penetration in “cultural profile” walls identified changes in soil structure resulting from different tillage systems. Root distribution was better in coffee plants grown in PSH and FSH systems. At greater depths, cultural profiles of FSH and PSH showed a larger numbers of roots compared to OG. Among the three systems, PSH provided a better environment for root growth and distribution. This result could be attributed to the high biological activity and interaction between roots and aggregates in that profile. The FSH agroforestry system provided less compact morphological structures and more roots throughout the soil profile. The agroforestry systems presented fewer soil structural changes by tillage operations and lower values of soil penetration resistance. Coffee root distribution was an effective indicator of soil quality and consistent with the morphostructural characterization of cultural profile.
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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 | 0.000 | 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".