Impact of soil texture on biosurfactant‐mediated soil wetting and water retention
Bibliographic record
Abstract
Abstract Increasing global food demand combined with more frequent and intense periods of drought necessitates new strategies to improve agricultural water use efficiency. Amending soils with biosurfactants provides a method to increase soil wettability and improve soil water retention, thereby reducing freshwater demand. This study evaluates the impacts of soil texture on soil water retention after amendment with the biosurfactant, surfactin. Texture effects were systematically investigated by mixing silty clay loam soil with Ottawa sand, ensuring chemically equivalent soil constituents. Sandy loam texture exhibited the most significant response after 50 mg kg −1 surfactin treatment, indicated by a 25% water contact angle decrease and a twofold increase in soil water retention after a 48‐h dryout period. In contrast, all other soil textures, including silty clay loam, loam, and loamy sand, had no significant improvements. These findings highlight the critical role of soil texture on biosurfactant efficacy for optimized application in agricultural soils. Core Ideas Soil texture plays a critical role in biosurfactant amendment efficiency for improving soil water retention. Texture effects were isolated using mineralogically equivalent soils of varied textures. Sandy loam was the only texture with improvements in wettability and water retention after surfactin amendment. Biosurfactant amendments can increase the economic value of sandy loam soils by improving water retention.
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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 | 0.000 |
| 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".