Increasing seedling number alleviates the adverse effects of warming on grain yield and reduces greenhouse gas emission in late-season rice
Bibliographic record
Abstract
To address the adverse effects of warming on late-season rice, we investigated the impact of increasing the number of seedlings on rice yield, quality, and greenhouse gas (GHG) emissions under canopy warming conditions using the free-air temperature increase (FATI) system. Three treatments were implemented: ambient temperature with two seedlings hill-1 (CKS1), canopy warming with two seedlings hill-1 (WS1), and canopy warming with four seedlings hill-1 (WS2). FATI increased rice canopy temperature and soil temperature by an average of 1.9–2.2°C and 0.6–0.8°C, respectively, over the two years. The yield in WS1 was significantly reduced by 10.1%–12.1% compared with CKS1, which was attributed to a significant decrease in total spikelets m-2 and spikelets panicle-1, despite a notable increase in filled grains in 2023. However, WS2 demonstrated no significant change in yield compared with CKS1. Analysis of yield components revealed that WS2 exhibited significantly higher panicles m-2 than CKS1, while the spikelets panicle-1 were significantly lower than CKS1. No significant changes were observed in grain weight and processing and appearance qualities. Compared with that under CKS1, CH4 was significantly reduced under WS2 treatment in both years, and the global warming potential (GWP) and GHG intensity (GHGI) showed a decrease, with notable differences observed in 2022. Therefore, increasing the number of seedlings hill-1 can alleviate the negative impacts of canopy warming on grain yield and reduce GHG emissions in late-season rice.
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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) | 0.001 | 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".