Simulating the Carbon, Nitrogen, and Phosphorus of Plant Above-Ground Parts in Alpine Grasslands of Xizang, China - Mingxue Xiang; Gang Fu; Jianghao Cheng; Tao Ma; Yunqiao Ma; Kai Zheng; Zhaoqi Wang (Agronomy, 2025)
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.