Improving rice phenology simulations based on the Bayesian model averaging method
文献类型：期刊论文
| 作者 | Zheng, Jinhui1,2; Zhang, Shuai1,2 |
| 刊名 | EUROPEAN JOURNAL OF AGRONOMY
 |
| 出版日期 | 2023 |
| 卷号 | 142页码:10 |
| 关键词 | Bayesian model averaging Rice phenology model Multi-model ensembles Climate change Phenology change |
| ISSN号 | 1161-0301 |
| DOI | 10.1016/j.eja.2022.126646 |
| 通讯作者 | Zhang, Shuai(zhangshuai@igsnrr.ac.cn) |
| 英文摘要 | Crop simulation models play an increasingly important role in agricultural management. Therefore, improving crop models' accuracy and reliability has become a key issue. Previous studies have shown that multi-model ensembles (MMEs) perform better than individual models and are capable of assessing model uncertainty. Here, we study the use of the Bayesian model averaging (BMA) method to improve the simulation performance of rice phenology models. The main objective of this paper is to apply BMA to five individual rice phenology models (CERES-Rice, the beta model, ORYZA2000, the rice clock model, and the simulation model for the rice-weather relationship) and to compare its simulation performance with an MME based on the mean value (Mean-MME). Furthermore, we quantify and evaluate the uncertainty of the individual and ensemble models. Finally, we analyze the prediction results of two ensemble models and five individual models under future climate scenarios. The result indicates that, compared with the accuracies of individual models, the accuracies of BMA and Mean -MME are 12.75% and 11.48% higher, respectively. BMA performs the best when the individual models had the best parameters, effectively optimizing the phenology simulation results of the individual models. For individual and ensemble models, the squared bias accounts for 48-88%, which is the largest contributor to overall pre-diction uncertainty. Under the future climate scenarios, the five models predict a range of 0.40-9.78 days in the phenological period, whereas the two ensemble models predict a smaller range of 0.54-1.63 days, which shows that the MMEs reduce the uncertainty of the individual models. We conclude that BMA is suitable for improving the accuracy and reliability of crop modeling. |
| WOS关键词 | MULTIMODEL INFERENCE ; CLIMATE-CHANGE ; TEMPERATURE ; PREDICTION ; ENSEMBLE ; EVAPOTRANSPIRATION ; OPTIMIZATION ; UNCERTAINTY ; PHOTOPERIOD ; PARAMETERS |
| 资助项目 | National Key Research and Development Program of China[2016YFD0300201] ; National Science Foundation of China[41801078] |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:000876352700001 |
| 出版者 | ELSEVIER |
| 资助机构 | National Key Research and Development Program of China ; National Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/186369]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Zhang, Shuai |
| 作者单位 | 1.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Jinhui,Zhang, Shuai. Improving rice phenology simulations based on the Bayesian model averaging method[J]. EUROPEAN JOURNAL OF AGRONOMY,2023,142:10. |
| APA | Zheng, Jinhui,&Zhang, Shuai.(2023).Improving rice phenology simulations based on the Bayesian model averaging method.EUROPEAN JOURNAL OF AGRONOMY,142,10. |
| MLA | Zheng, Jinhui,et al."Improving rice phenology simulations based on the Bayesian model averaging method".EUROPEAN JOURNAL OF AGRONOMY 142(2023):10. |
入库方式： OAI收割
来源：地理科学与资源研究所
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