Bentonite effects on soil physical properties and millet yield components in a semi-arid region in China
Bibliographic record
Abstract
Soil structure plays a critical role in agroecosystems because it controls soil air and water capacity, nutrient availability, and crop root growth. A field experiment was conducted to evaluate the effect of bentonite on soil relative water content, soil cone penetration resistance, soil bulk density, aggregate size distribution, and millet crop yield components in a semi-arid region in northern China from 2011 to 2015. Treatments consisted of six bentonite rates (0, 6, 12, 18, 24, and 30 Mg·ha−1) applied only in the initial year. Addition of bentonite significantly increased soil relative water throughout the profile (0–60 cm) and aggregates in 0.25–2 mm class in 0–40 cm depth; it significantly decreased soil bulk density, soil cone penetration resistance, and aggregates in 0.053–0.25 mm class in the 0–40 cm depth. All of the bentonite treatments significantly increased 1000-kernel weight and panicle number up to 16% and 33%, respectively, but did not affect kernels per spike. Treatment with 24 Mg·ha−1 bentonite achieved the greatest effect on all the soil physical parameters and crop yield components averaged over all years; the highest application rate (30 Mg·ha−1) had an increasing effect over time. This study indicates that application of bentonite improves soil health in sandy-loam soil in a semi-arid region, and thus it would help promote sustainable agriculture development in regions with similar soil and climate.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".