Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions
文献类型：期刊论文
| 作者 | Zheng, Jinhui1,2; Zhang, Shuai1,2 |
| 刊名 | AGRICULTURAL SYSTEMS
 |
| 出版日期 | 2025-03-01 |
| 卷号 | 224页码:104215 |
| 关键词 | Model uncertainty Wheat phenology model Climate change Model structure |
| ISSN号 | 0308-521X |
| DOI | 10.1016/j.agsy.2024.104215 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | CONTEXT: The comparison of agricultural models and the conduct of crop improvement research have garnered significant attention in recent times. One of the primary objectives in this field is to pinpoint and mitigate the uncertainties inherent in modeling the effects of climate on crop growth and productivity. Enhancing the precision and reliability of crop models has emerged as a critical concern. OBJECTIVE: In this study, we calibrate and validate four wheat phenology models using wheat phenology data from 1990 to 2009. More importantly, we explain three significant sources of uncertainty in wheat phenology models, namely model structure, model parameters, and weather data inputs. METHODS: This study examines four wheat models-the GLAM-Wheat model, APSIM-Wheat model, SPASSWheat model, and WOFOST model-to simulate phenological changes across 32 agricultural meteorological stations in the North China Plain. Additionally, the three main sources of uncertainty in the model are quantified using the Markov Chain Monte Carlo (MCMC) method. RESULTS AND CONCLUSIONS: The results indicate that all four wheat phenological models effectively simulate the growth of wheat in the study area, with an average RMSE ranging from 4.4 to 5.2 days for the heading stage and from 4.7 to 5.6 days for the maturity stage. The uncertainty analysis encompasses parameters, squared bias, weather data inputs, and model structure. During the heading stage, the overall contributions of these uncertainties are 8.9 %, 40.8 %, 47.4 %, and 2.9%, respectively. During the maturity stage, these contributions are 11.2 %, 51.2 %, 35.0 %, and 2.6 %, respectively. Weather data inputs are identified as the primary sources of uncertainty. SIGNIFICANCE: This study quantifies the uncertainty within wheat phenology models, a critical step towards enhancing the precision and dependability of crop models. Such efforts hold substantial importance in shaping agricultural policies and refining management practices, ultimately aiding in tackling the challenges posed by impending climate change. |
| URL标识 | 查看原文 |
| WOS关键词 | WINTER-WHEAT ; PHENOLOGY PREDICTION ; GLOBAL OPTIMIZATION ; RICE PHENOLOGY ; CLIMATE-CHANGE ; HEAT-STRESS ; CROP MODEL ; LARGE-AREA ; TEMPERATURE ; SIMULATION |
| WOS研究方向 | Agriculture |
| 语种 | 英语 |
| WOS记录号 | WOS:001372550600001 |
| 出版者 | ELSEVIER SCI LTD |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211377]  |
| 专题 | 陆地表层格局与模拟院重点实验室_外文论文 |
| 通讯作者 | Zhang, Shuai |
| 作者单位 | 1.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing 100101, Peoples R China; 2.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zheng, Jinhui,Zhang, Shuai. Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions[J]. AGRICULTURAL SYSTEMS,2025,224:104215. |
| APA | Zheng, Jinhui,&Zhang, Shuai.(2025).Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions.AGRICULTURAL SYSTEMS,224,104215. |
| MLA | Zheng, Jinhui,et al."Decomposing the total uncertainty in wheat modeling: an analysis of model structure, parameters, weather data inputs, and squared bias contributions".AGRICULTURAL SYSTEMS 224(2025):104215. |
入库方式： OAI收割
来源：地理科学与资源研究所
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