Incorporation of Pre‐Treated Straw Improves Soil Aggregate Stability and Increases Crop Productivity
Bibliographic record
Abstract
Core Ideas The ammoniated straw application improved soil structural stability. The ammoniated straw application increased the yield and water use efficiency, regardless of maize or wheat. The ammoniated straw incorporation is better for the short straw than for the long one, and net income with the ammoniated and short straw incorporation was the highest. Crop straws usually have high lignin and cellulose contents and decompose slowly when returned to the soil, which is not always conducive to crop growth. In this study, we investigated the effects of short and ammoniated straw application on soil properties, crop yield, and water use efficiency (WUE) in a summer maize ( Zea mays L.)–winter wheat ( Triticum aestivum L.) rotation system on the Loess Plateau, China. There were four treatments: long straw (ca. 50 mm) mulching (S+M), long straw (ca. 50 mm) plowed into the soil (S+P), ammoniated long straw (ca. 50 mm) plowed into the soil (A+S+P), and ammoniated short straw (ca. <1 mm) plowed into the soil (A+PS+P). The A+S+P and A+PS+P treatments had lower soil organic carbon (SOC) contents and higher total nitrogen (TN) contents, thereby resulting in lower soil C/N ratios than those of the S+M and S+P after 3‐yr study. Furthermore, the A+S+P and A+PS+P treatments improved soil structural stability, as indicated by the large amount of water stable aggregates (>0.25 mm) present. The mean maize and wheat yield in the A+PS+P treatment were statistically significantly higher than those in the S+M (3.5 and 9.5%, respectively) and S+P (4.3 and 7.3%, respectively) treatments. Net income with A+PS+P was the highest and increased by US$257, 212, and 40 ha −1 , respectively, compared with S+M, S+P and A+S+P. We suggest that ammoniated and short straw application is an effective method for improving the soil properties and increasing crop productivity.
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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".