Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China
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 dynam...
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2025-06-01
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| author | Mingxue Xiang Gang Fu Jianghao Cheng Tao Ma Yunqiao Ma Kai Zheng Zhaoqi Wang |
| author_facet | Mingxue Xiang Gang Fu Jianghao Cheng Tao Ma Yunqiao Ma Kai Zheng Zhaoqi Wang |
| author_sort | Mingxue Xiang |
| collection | DOAJ |
| description | 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 <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.61, validation <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.95). Additionally, the random forest model had the highest predictive accuracy for C:N ratios under fencing conditions (training <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> = 0.84, validation <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> = 1.00), as well as for C pool and P content and pool under grazing conditions (training <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.62, validation <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.90). Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters. |
| format | Article |
| id | doaj-art-0f4882ea58de4824bb7b0ea04d3d3d28 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-06-01 |
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| spelling | doaj-art-0f4882ea58de4824bb7b0ea04d3d3d282025-08-20T02:24:30ZengMDPI AGAgronomy2073-43952025-06-01156141310.3390/agronomy15061413Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, ChinaMingxue Xiang0Gang Fu1Jianghao Cheng2Tao Ma3Yunqiao Ma4Kai Zheng5Zhaoqi Wang6State Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810018, ChinaLhasa Plateau Ecosystem Research Station, Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaState Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810018, ChinaState Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810018, ChinaState Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810018, ChinaState Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810018, ChinaState Key Laboratory of Plateau Ecology and Agriculture, Qinghai University, Xining 810018, ChinaCarbon (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 <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.61, validation <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.95). Additionally, the random forest model had the highest predictive accuracy for C:N ratios under fencing conditions (training <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> = 0.84, validation <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> = 1.00), as well as for C pool and P content and pool under grazing conditions (training <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.62, validation <i data-eusoft-scrollable-element="1">R</i><sup data-eusoft-scrollable-element="1">2</sup> ≥ 0.90). Therefore, the random forest algorithm based on climate data and/or the NDVImax demonstrated superior predictive performance in modeling these biogeochemical parameters.https://www.mdpi.com/2073-4395/15/6/1413alpine grasslandsbig data miningglobal changerandom forestQinghai–Xizang Plateau |
| spellingShingle | Mingxue Xiang Gang Fu Jianghao Cheng Tao Ma Yunqiao Ma Kai Zheng Zhaoqi Wang Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China Agronomy alpine grasslands big data mining global change random forest Qinghai–Xizang Plateau |
| title | Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China |
| title_full | Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China |
| title_fullStr | Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China |
| title_full_unstemmed | Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China |
| title_short | Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China |
| title_sort | simulating the carbon nitrogen and phosphorus of plant above ground parts in alpine grasslands of xizang china |
| topic | alpine grasslands big data mining global change random forest Qinghai–Xizang Plateau |
| url | https://www.mdpi.com/2073-4395/15/6/1413 |
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