Assessing Potential Climate Change Impacts and Adaptive Measures on Rice Yields: The Case of Zhejiang Province in China
Bibliographic record
Abstract
Increasing temperatures, greater carbon dioxide concentrations, and changes in related climatic variables will continue to affect the growth and yields of agricultural crops. Rice (Oryza sativa L.) is extremely vulnerable to these climatic changes. Therefore, investigating the degree to which climate changes could influence rice yields and what effective adaptive strategies could be taken to mitigate the potential adverse impacts is of vital importance. In this article, the impacts of climate change on rice yields in Zhejiang province, China, were simulated under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The impacts of climate change, with and without CO2 fertilization effects, were evaluated and the three most effective adaptive measures were examined. Compared with the yield for the baseline time of 1981–2010, the simulated average yields of all cultivars were inevitably projected to decrease under both RCPs when the CO2 fertilization effects were not considered during the three periods of the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2099), respectively. Declines in rice yields were able to be alleviated when the CO2 fertilization effects were accounted for, but the yields were still lower than those of the baseline. Therefore, the three adaptive measures of advancing planting dates, switching to high-temperature-tolerant cultivars, and breeding new cultivars were simulated. The results indicated that adaptive measures could effectively mitigate the adverse effects of climate change. Although the simulation had uncertainties and limitations, the results provide useful insights into the potential impacts of climate change in Zhejiang province while also proposing adaptive measures.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".