Quantifying Spatio-Temporal Patterns of Rice Yield Gaps in Double-Cropping Systems: A Case Study in Pearl River Delta, China
Bibliographic record
Abstract
Quantifying the contributing and limiting factors of yield potential is of vital importance, and the closure of existing yield gaps on currently available agricultural land is regarded as the most effective measure to meet future food demands. In this study, the CERES-Rice model and long-term rice yield records of 12 sites from 1981 to 2010 were combined together to investigate the spatial and temporal distributions of yield potential, yield attainable, yield actual, and yield gaps for double cropping rice in the Pearl River Delta (PRD), China. The evaluated yield potential of all the sites ranged from 7500 to 14,900 kg/ha, while yield attainable was from 6400 to 12,665 kg/ha, and yield actual was from 4000 to 7000 kg/ha. The yield gaps between yield potential and yield actual, yield potential and yield attainable, and yield attainable and yield actual were projected to be 3500 kg/ha, 1400 kg/ha, and 2100 kg/ha, respectively. The decrease of yield potential was due to the increasing temperature for early mature rice and the prolonged sunshine hours for the yield potential of late mature rice, respectively. The social–economic impacts of yield actual were also assessed, and adaptive measures were simulated so that the yield would certainly increase.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".