Impact of tillage and crop establishment methods on rice yields in a rice-ratoon rice cropping system in Southwest China
Bibliographic record
Abstract
Abstract Simplified cultivation methods for rice production offer considerable social, economic, and environmental benefits. However, limited information is available on yield components of rice grown using simplified cultivation methods in a rice-ratoon rice cropping system. A field experiment using two hybrid and two inbred rice cultivars was conducted to compare four cultivation methods (conventional tillage and transplanting, CTTP; conventional tillage and direct seeding, CTDS; no-tillage and transplanting, NTTP; no-tillage and direct seeding, NTDS) in a rice-ratoon rice system from 2017 to 2020. Main season yields for CTDS and NTDS were higher than for CTTP by 6.1% and 2.8%, respectively; whereas ratoon season yields for CTDS and NTDS were equal to or higher than for CTTP. Annual grain yields for CTDS and NTDS were higher than for CTTP by 4.4% and 3.2%, respectively. The higher CTDS and NTDS yields were associated with higher panicle numbers per m2 and biomass production. Rice hybrids had higher yields than inbred cultivars by 15.8–19.3% for main season and by 15.6–19.4% for ratoon season, which was attributed to long growth duration, high grain weight and biomass production. Our results suggest that CTTP can be replaced by CTDS and NTDS to maintain high grain yields and save labor costs. Developing cultivars with high grain weight could be a feasible approach to achieve high rice yields in the rice-ratoon rice cropping system in southwest China.
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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 | 0.001 | 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".