Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China
文献类型：期刊论文
| 作者 | Xiang, Mingxue1; Fu, Gang2; Cheng, Jianghao1; Ma, Tao1; Ma, Yunqiao1; Zheng, Kai1; Wang, Zhaoqi1 |
| 刊名 | AGRONOMY-BASEL
 |
| 出版日期 | 2025-06-09 |
| 卷号 | 15期号:6页码:1413 |
| 关键词 | alpine grasslands big data mining global change random forest Qinghai-Xizang Plateau |
| DOI | 10.3390/agronomy15061413 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Carbon (C), nitrogen (N), and phosphorus (P) act as pivotal regulators of biogeochemical cycles, steering organic matter decomposition and carbon sequestration in terrestrial ecosystems through the stoichiometric properties of photosynthetic organs. Deciphering their multi-scale spatiotemporal dynamics is central to unraveling plant nutrient strategies and their coupling mechanisms with global element cycling. In the current study, we modeled biogeochemical parameters (C/N/P contents, stoichiometry, and pools) in plant aboveground parts by using the growing mean temperature, total precipitation, total radiation, and maximum normalized difference vegetation index (NDVImax) across nine models (i.e., random forest model, generalized boosting regression model, multiple linear regression model, artificial neural network model, generalized linear regression model, conditional inference tree model, extreme gradient boosting model, support vector machine model, and recursive regression tree) in Xizang grasslands. The results showed that the random forest model had the highest predictive accuracy for nitrogen content, C:P, and N:P ratios under both grazing and fencing conditions (training R2 >= 0.61, validation R2 >= 0.95). Additionally, the random forest model had the highest predictive accuracy for C:N ratios under fencing conditions (training R2 = 0.84, validation R2 = 1.00), as well as for C pool and P content and pool under grazing conditions (training R2 >= 0.62, validation R2 >= 0.90). Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters. |
| URL标识 | 查看原文 |
| WOS关键词 | NEURAL-NETWORKS ; TIBETAN ; STOICHIOMETRY ; PRODUCTIVITY ; AVAILABILITY ; SHIFTS |
| WOS研究方向 | Agriculture ; Plant Sciences |
| 语种 | 英语 |
| WOS记录号 | WOS:001515229400001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/214621]  |
| 专题 | 拉萨站高原生态系统研究中心_外文论文 |
| 通讯作者 | Fu, Gang |
| 作者单位 | 1.Qinghai Univ, State Key Lab Plateau Ecol & Agr, Xining 810018, Peoples R China; 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Lhasa Plateau Ecosyst Res Stn, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Xiang, Mingxue,Fu, Gang,Cheng, Jianghao,et al. Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China[J]. AGRONOMY-BASEL,2025,15(6):1413. |
| APA | Xiang, Mingxue.,Fu, Gang.,Cheng, Jianghao.,Ma, Tao.,Ma, Yunqiao.,...&Wang, Zhaoqi.(2025).Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China.AGRONOMY-BASEL,15(6),1413. |
| MLA | Xiang, Mingxue,et al."Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China".AGRONOMY-BASEL 15.6(2025):1413. |
入库方式： OAI收割
来源：地理科学与资源研究所
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