Comparison of yield traits in rice among three mechanized planting methods in a rice-wheat rotation system
Bibliographic record
Abstract
Understanding the differences in yield traits of rice among pothole seedling of mechanical transplanting (PSMT), carpet seedling of mechanical transplanting (CSMT) and mechanical direct seeding (MDS) is of great importance not only for rice scientists but also for rice farmers to develop a high-yield production system under mechanical conditions in a rice-wheat rotation system. However, such traits are yet to be studied among rice varieties of japonica-indica hybrid rice (JIHR), japonica conventional rice (JCR) and indica hybrid rice (IHR). Field experiments were conducted in 2014 and 2015, where six cultivars of the three rice types JIHR, JCR and IHR were grown individually with PSMT, CSMT and MDS methods, under respective managements for each method to achieve the maximum attainable yield. Results showed that (i) the PSMT significantly increased grain yield of JIHR by 22.0 and 7.1%, of JCR by 15.6 and 3.7% and of IHR by 22.5 and 7.4%, compared to MDS and CSMT on average across the two years, respectively. The highest yield was produced by the combination of JIHR and PSMT; (ii) high yield under PSMT was mainly attributed to large sink capacity and high-efficient dry matter accumulation. With sufficient panicles per hectare, the increase of spikelet number per panicle, especially the increase in spikelet number of the secondary rachis-branches was determined to be the optimal approach for developing a large sink capacity for rice under PSMT. The optimal tillers development, large leaf area index at heading stage, and high leaf area duration, crop growth rate and net assimilation rate during grain-filling phase could be the cause of sufficient dry matter accumulation for rice under PSMT; (iii) moreover, the PSMT favored plant growth as well as enriched the stems plus sheaths during grain-filling phase, as compared with CSMT and MDS. These results suggest that PSMT may be an alternative approach to increasing grain yield in a rice-wheat rotation system in the lower reaches of the Yangtze River in 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 | 0.000 | 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".