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There are still great uncertainties about effects of climate warming and no-tillage on soil carbon, nitrogen, phosphorus and potassium contents and pH in alpine farmlands. A warming (control; daytime warming, DW; nighttime warming, NW; all-day warming, DW + NW) and no-tillage (no-tillage vs. tillage) experiment was conducted in an alpine farmland of the Lhasa, Xizang since 2015. Soil organic carbon, total nitrogen, total phosphorus, total potassium (TK), available nitrogen, available phosphorus, available potassium, dissolved organic carbon (DOC), active organic carbon, particulate organic carbon (POC), light fraction organic carbon, and heavy fraction organic carbon contents and pH at four depths (0–5, 5–15, 15–25, and 25–35 cm) were measured. Warming effects on concerned soil variables differed with warming time, soil depth, and no-tillage. No-tillage effects on concerned soil variables differed with warming-time (daytime, nighttime, and all-day warming) and soil depths. Therefore, daytime warming and nighttime warming have different effects on soil variables, although the effects of nighttime warming on soil variables are not always greater than those of daytime warming. Effects of daytime warming and nighttime warming on soil variables are not simple addition or subtraction effects. There are interactions between diurnal asymmetrical warming and no-tillage on soil variables. [ABSTRACT FROM AUTHOR]

Accurately quantifying the relative effects of climate change and human activities on soil carbon, nitrogen, and phosphorus in alpine grasslands and their feedback is an important aspect of global change, and high-precision models are the key to solving this scientific problem with high quality. Therefore, nine models, the random forest model (RFM), generalized boosted regression model (GBRM), multiple linear regression model (MLRM), support vector machine model (SVMM), recursive regression tree model (RRTM), artificial neural network model (ANNM), generalized linear regression model (GLMR), conditional inference tree model (CITM), and eXtreme gradient boosting model (eXGBM), were used for modeling soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), the ratio of SOC to TN (C:N), the ratio of SOC to TP (C:P), and the ratio of TN to TP (N:P) at depths of 0-10, 10-20, and 20-30 cm under non-grazing and free-grazing scenarios in the Xizang grasslands. Annual radiation (ARad), annual precipitation (AP), and annual temperature (AT) were used as independent variables under non-grazing scenarios, whereas ARad, AP, AT, and growing season maximum normalized difference vegetation index (NDVImax) were used as independent variables under free-grazing scenarios. Overall, the RFM and GBRM were more accurate than the other seven models. However, the tree numbers of the GBRM were much larger than those of the RFM, indicating that the GBRM may have a greater model complexity and lower running speed. Therefore, the RFM had the best performance among the nine models in modeling SOC, TN, TP, C:N, C:P, and N:P in the Xizang grasslands. The RFM established in this study can not only help scientists save time and money on massive sampling and analysis, but can also be used to construct a database of SOC, TN, and TP, and their ratios, and further scientific research related to ecological and environmental issues (e.g., examining whether soil systems intensified global warming over the past few decades by exploring whether climate change and human activities altered soil organic carbon) in the grasslands of Xizang Plateau. [ABSTRACT FROM AUTHOR]