Effect of rice cultivars on yield-scaled methane emissions in a double rice field in South China
Bibliographic record
Abstract
Rice cultivar is the most influential factor affecting methane emissions from double rice fields. A two-year field experiment was conducted at Huizhou, Guangdong province, South China, to identify from among nine cultivars those cultivars with high-yield potential and lower yield-scaled methane emissions (YSMEs). Methane emissions were measured using the static chamber – gas chromatograph method. Results indicate that the cultivars Qihuazhan (QH), Yexianzhan 8 (YX8) and Yue’erzhan (YE) provide higher rice grain yield (8.69%) with lower YSME (30.27%) compared to the other six cultivars (Yexianzhan 6, Yuejingsimiao, Hefengzhan, Huangsizhan, Huangruanzhan and Huangxiuzhan) (p < 0.05). In particular, QH has the highest yield potential (6777 kg ha−1) and lowest methane emission intensity (0.36 kg kg−1 yield) capacity. Methane emissions from the double rice field was found to be significantly (p < 0.05) and positively correlated with tiller number, culm biomass and soil organic matter, dissolved soil organic carbon and total carbon content, but negatively correlated (p < 0.05) with rice harvest index (HI), and root and panicle biomass, suggesting that organic source strength provides the substrate of methane production while the oxidation potential in the rhizosphere and the methane transport capacity of rice roots and culm dominate the emissions of methane from soil to the atmosphere. Multivariate decision regression tree (DRT) analysis showed a significant class difference between QH, YX8 and YE with the other six cultivars. These three cultivars are suitable for promotion of low carbon agriculture in South China. DRT analysis also successfully illustrated a potential way to identify rice varieties for low YSME by decisive parameters of tiller number (<15), HI (>0.43) and nitrogen assimilation of leaves (<40). These findings suggest that optimization of rice cultivars may represent an effective way to address both food demand and climate change concerns by improving rice yields while simultaneously minimizing the impact of climate change per unit yield.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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".