Contributions of long‐term tillage systems on crop production and soil properties in the semi‐arid Loess Plateau of China
Bibliographic record
Abstract
BACKGROUND: This study determined the long-term effect of tillage systems on soil properties and crop yields in a semi-arid environment. Field pea (Pisum sativum L.) and spring wheat (Triticum aestivum L.) were alternately grown in six tillage systems at Dingxi (35° 28' N, 104° 44' E), north-west China starting in 2001. RESULTS: After the first 6 years of experiments, conventional tillage with stubble incorporating (TS) and no-till with stubble cover (NTS) increased soil organic matter by 9.9% and 13.0%, respectively, compared to the conventional tillage with stubble removed (T); both TS and NTS also increased soil microbial counts, available K and P, and total N. No-till with stubble removed (NT), NTS and NTP (no-till with plastic mulching) had 20.7%, 62.6% and 43.7% greater alkaline phosphatase activity compared to the T treatment. Soil catalase, urease and invertase activities were all greater in the no-till treatments than in the T treatment. Averaged across 6 years, both wheat and pea achieved highest grain yields under NTS treatment. CONCLUSION: No-till with stubble retention is the most promising system for improving soil physical, biological and chemical properties, and increasing crop yields, and thus, this system can be adopted in areas with conditions similar to the semi-arid north-west China. © 2015 Society of Chemical Industry.
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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 | 0.000 | 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 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".