Soil Amendment Combining Bentonite and Maize Straw Improves Soil Quality Cropped to Oat in a Semi-Arid Region
Bibliographic record
Abstract
Soil amendments have been proposed as an effective way to enhance soil carbon stocks on degraded soils, particularly in dryland farming areas. Soil organic carbon (SOC) plays an important role in improving soil quality, and soil aggregates are known to be crucial in sequestering and protecting SOC. However, how aggregation and protection of SOC by aggregates respond to a single application of bentonite combined with maize straw remains unknown, especially in the sandy soil of a semi-arid region. A three-year field experiment with four treatments [no amendment (CK), maize straw amendment addition only (T1, 6 Mg ha−1), bentonite amendment addition only (T2, 18 Mg ha−1), and maize straw combined with bentonite amendment (T3, 6 Mg ha−1 maize straw plus 18 Mg ha−1 bentonite)] was conducted in the Loess Plateau of China to assess the effects of bentonite and maize straw on aggregation and SOC. The results indicated that soil bulk density decreased by 2.72–5.42%, and soil porosity increased by 3.38–8.77% with three years of T3 application, especially in the 20–40 cm layer, compared with CK. T3 increased the amount of C input, SOC stock, and SOC stock sequestration rate by 1.04 Mg ha−1 y−1, 0.84–1.08 Mg ha−1, and 0.49 Mg ha−1 y−1, respectively, and it increased the mass proportions and aggregate-associated C stock of >0.25 mm aggregates by 1.15–2.51- and 1.59–2.96-fold compared with CK. Correlation analysis showed a positive correlation of total SOC stock with the C concentration of >2 mm, 0.25–2 mm, and 0.053–0.25 mm aggregates. Aggregates of various sizes in sandy soils have the potential for greater SOC stock. Our findings suggest that the application of maize straw (6 Mg ha−1) combined with bentonite (18 Mg ha−1) would be an effective management strategy to enhance the bulk soil C pools by improving the soil structure and thereby improving soil fertility.
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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".