Improving Salt-Affected Soil Using Biochar and Humic Substances: Insights from incubation, Column Leaching, and Growth Chamber Experiments
Bibliographic record
Abstract
Salt-affected soils, caused by natural processes or human activities like brine spills, limit agricultural productivity. Brine, high in sodium and chloride, has high electrical conductivity (EC) and sodium adsorption ratio (SAR), when exposed to novel environments, this material can negatively impact soil, vegetation, and groundwater. This study evaluated organic and inorganic amendments – biochar, peat, gypsum, and commercially available humic-derived substances (humalite, liquid fulvic (LF), liquid organo hume (LOH), and NanoBind (NB)), as well as combinations (including gypsum as a standard amendment) – for their effectiveness in improving plant growth and soil characteristics in marginally saline topsoil collected from a former battery site (EC = 3.3, SAR = 10.3). The study included 112 days of bench-scale incubation, column leaching, and two growth chamber experiments using beans (Phaseolus vulgaris), a salt-sensitive crop. After 112 days of incubation, biochar and humalite reduced EC from 3.3 to 1.7 and 1.9 dS/m, respectively, while NB increased EC to 6.1 dS/m. Soil EC dropped below the background value of 1.2 dS/m after flushing with five pore volumes during leaching experiments. Peat (4% w/w) did not improve bean growth despite increasing organic carbon (C) by 125% and reducing soil boron (B) concentration. Biochar or humalite, alone or with gypsum, supported the highest biomass and nutrient uptake in beans compared to both salt-impacted and clean topsoil controls. The findings suggest that biochar or humalite amendments can mitigate some of the salinity stress associated with marginally salt-impacted soils and restore soil health.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".