Residual Effect of Bentonite-Humic Acid Amendment on Soil Health and Crop Performance 4–5 Years after Initial Application in a Dryland Ecosystem
Bibliographic record
Abstract
Degraded soils (including salinized, eroded, and low organic matter) resulting from natural and human effects are universal in arid and semi-arid regions all over the world. Bentonite and humic acid (BHA) are increasingly being tested to remediate these degraded lands, with potential benefits on crop production and soil health. A field study was conducted to quantify the effects of BHA application at six rates (0, 6, 12, 18, 24, and 30 Mg ha−1) on (i) dynamic changes in soil properties and (ii) oat crop productivity parameters in a dryland farming ecosystem. The specific objective of this paper was to determine the residual effects four to five years after a one-time BHA application on soil health and crop performance. The findings demonstrated that with the increasing rates of one-time BHA application, soil profile water storage displayed a piecewise linear plus plateau increase, whereas soil electrical conductivity, pH, and bulk density were all reduced significantly (p < 0.05) in the 0–20 cm and 20–60 cm layers. The improved soil environments gave rise to an increased activity of soil enzymes urease, invertase, and catalase that, respectively, reached peak values of 97%, 37%, and 32% of the control at the rates of 18 to 24 Mg BHA ha−1. In turn, this boosted soil nutrient turnover, leading to a 40% higher soil available P. Compared with the control treatment, application of BHA at the estimated optimum rate (roughly 24 Mg ha−1) increased grain yield by 20%, protein yield by 62%, water use efficiency by 41%, and partial factor productivity of N by 20%. The results of this study indicated for the first time that a one-time BHA application would be a new and effective strategy to combat land degradation and drought, and promote a sustainable soil micro-ecological environment in dryland agroecosystems under a varying climate scenario.
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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".