Compound effects of biochar application and irrigation on soil water and temperature transport
Bibliographic record
Abstract
The issue of soil salinization poses a significant barrier to sustainable agricultural development, particularly in arid and semi-arid regions. Finding methods to enhance the quality of salinized soils while conserving water resources has become a pressing challenge. In arid and semi-arid environments, conserving water resources while maintaining soil health is a critical challenge. This study, conducted from 2021 to 2023, aimed to explore the combined effects of irrigation and biochar application on soil physicochemical properties, such as bulk density, porosity, and pH, as well as on Weighted Plane Soil Water Storage ( WPSWS ), soil temperature, and soil water evaporation. The experimental design included four irrigation levels, based on actual crop evapotranspiration ( ETc ): I1 (0.6 ETc ), I2 (0.8 ETc ), I3 (1.0 ETc ), and I4 (1.2 ETc ), coupled with four amounts of biochar application ( AOBA ) of 0, 10, 20, and 30 t ha −1 , designated as C0, C10, C20, and C30, respectively. Through binary quadratic regression analysis, we sought to identify the optimal combination of irrigation amount and AOBA for enhancing soil quality. The results revealed that as AOBA increased from 10 to 30 t ha −1 , soil bulk density decreased by 1.31–8.58% and soil pH by 0.23–1.31%. However, higher levels of AOBA adversely affected WPSWS , with the C10 treatment showing the maximum improvement in WPSWS , registering an average increase of 6.77, 7.49, and 11.16% compared to the C0, C20, and C30 treatments, respectively. We observed that an increase in irrigation amount significantly elevated accumulated soil evaporation ( ASE ) and WPSWS but led to a reduction in accumulated soil temperature ( AST ). The most notable soil quality improvements were recorded when irrigation levels were between 340 and 380 mm and AOBA ranged from 10 to 25 t ha −1 . This study provides insights into the effective combination of biochar application and irrigation for optimizing soil resilience, thereby offering a sustainable approach to soil management in water-limited environments.
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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 | 0.000 | 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) | 0.000 | 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".