Effect of Biochar, Hydrogel and Soil Liming on Nutrient Leaching in a Coarse-Textured Soil
Bibliographic record
Abstract
Nutrient leaching from agricultural fields can degrade soil fertility and groundwater quality, especially in coarse-textured soils. Amending soils with biochar, lime, hydrogel, or their combinations may reduce leaching, but the effects of single versus combined amendments remain unclear. A three-year pot experiment under field conditions was conducted on a loamy sand soil to enhance water and nutrient retention capacity of this soil. Soil samples were mixed with all possible combinations of 1% biochar (B), l% lime (L), and 0.5% hydrogel (H), i.e., BL, BH, HL, and BHL. The amendments were arranged in a randomized complete block design with four blocks. The results showed that compared to control, amendments B, H, BH, HL, and BHL significantly decreased (p ≤ 0.05) nitrate-N leaching per unit biomass by 58%-88%, and L, H, BH, HL, BHL significantly reduced (p ≤ 0.05) orthophosphate-P leaching per unit biomass by 34%-98%. Compared to control, the marketable yield significantly increased (p ≤ 0.05) by 24%-38% under BH, HL, and BHL in 2019, and by 17%-52% under amendments B, L, H, BL, BH, HL, and BHL in 2020. These results were not seen in the first year due to soil conditioning for biochar and lime. Amendments H, BH, HL, and BHL show potential to improve water use efficiency, reduce nutrient leaching, and support sustainable crop production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".