Effect of biochar on soil properties and infiltration in a light salinized soil: Experiments and simulations
Bibliographic record
Abstract
Abstract The sustainable development of agriculture in Xinjiang Province, China has been threatened by soil salinization. Biochar can be an effective amendment to improve salt‐affected soils. An appropriate amount of biochar application and incorporation depth are key factors for amending performance. However, few studies have investigated the effects of differing biochar application amounts on saline soil properties, including soil water infiltration, using a combination of experiments and simulations. In this study, acidulated biochar was applied at rates of 0, 10, 25, 50 and 100 t ha −1 to a farmland topsoil to investigate the impacts of biochar on Xinjiang saline soil's physical and chemical properties and infiltration characteristics. The soil's physical and chemical properties that were investigated included soil pH, soil organic carbon content, soil salt content, saturated hydraulic conductivity ( K s ), soil water retention curves, and infiltration characteristics including cumulative infiltration (CI) and wetting front ( Z f ). HYDRUS‐1D was applied to predict soil water movement under different biochar incorporation depths. Results showed that the rate of change of soil pH, soil organic carbon content, soil salt content and K s were −0.009, 0.102 g kg −1 , 0.045 g kg −1 and 0.035 cm day −1 , respectively, per ton of biochar applied. Soil water retention curves showed that biochar enhanced soil water retention capacity and available soil water content (AWC) in the silt clay loam soil. The Philip model was a good ( R 2 > 0.80) fit to soil water infiltration and indicated that biochar amendment promoted infiltration rates. The van Genuchten model was good for describing soil hydraulic parameters ( R 2 > 0.99) and could be used for HYDRUS‐1D simulations ( R 2 > 0.99, RRMSE <6.8% and NSE >0.98). The optimum biochar application amount for the light salinised soil in Xinjiang was recommended as 25 t ha −1 incorporated to 30 cm depth based on the AWC, soil salt content and incorporation depths. The study provides a reference for future field experiment design. Highlights Effects of biochar on salt‐affected soil studied using a combination of experiments and simulations. Acidulated biochar amendment of saline soils can reduce pH and increase SOC. Biochar incorporation depth was considered to affect soil water infiltration. The biochar application amount at 25 t ha −1 incorporated to 30 cm depth was recommended
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".