Subsoiling Treatment on Soil Permeability and Its Impact on the Growth of Sweet Potato (<i>Ipomoea atatas</i>)
Bibliographic record
Abstract
Subsoiling treatment is a crucial soil management practice that profoundly influences soil physical properties, especially soil permeability.This review aims to explore the impact of subsoiling treatment on soil permeability and sweet potato growth.It provides a comprehensive overview of subsoiling treatment methods, including mechanical, biological, and chemical approaches, emphasizing the pivotal role of soil permeability in field water management and crop growth.Furthermore, it delves into how subsoiling treatment enhances soil infiltration rates and water-holding capacity, creating favorable conditions for root growth and efficient water utilization by plants.The results highlight a significant improvement in soil permeability due to subsoiling treatment, reducing the risks of waterlogging and root water stress, thereby fostering an optimal growth environment for sweet potatoes.Moreover, subsoiling treatment positively impacts sweet potato yield, quality, and stress resilience.This review underscores the critical significance of subsoiling treatment as a soil management tool to enhance agricultural productivity and sustainability.Future research should delve deeper into the mechanisms of subsoiling treatment and explore best practices under varying soil and climatic conditions to support sustainable agriculture.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".