Quantifying soil pH at manifold spatio-temporal scales is critical for examining the impacts of global change on soil quality. It is still unclear whether meteorological data and normalized difference vegetation index (NDVI) can be used to quantify soil pH in grasslands. Here, nine methods (i.e., RF: random-forest, GLR: generalized-linear-regression, GBR: generalized-boosted-regression, MLR: multiple-linear-regression, ANN: artificial-neural-network, CIT: conditional-inference-tree, SVM: support-vector-machine, eXGB: eXtreme-gradient-boosting, RRT: recursive-regression-tree) were applied to quantify soil pH. Three independent variables (i.e., AP: annual precipitation, AT: annual temperature, ARad: annual radiation) were used to quantify potential soil pH (pHp), and four independent variables (i.e., AP, AT, ARad and NDVImax: maximum NDVI during growing season) were applied to quantify actual soil pH (pHa). Overall, the developed eXGB models performed the worst (linear regression slope < 0.60; R2 = 0.99; relative deviation ≤ –43.54%; RMSE ≥ 3.14), but developed RF models performed the best (linear regression slope: 0.99–1.01; R2 = 1.00; relative deviation: from –1.26% to 0.65%; RMSE ≤ 0.28). The linear regression slope, R2, absolute value of relative deviation and RMSE between modelled and measured soil pH were 0.96–1.03, 0.99–1.00, ≤ 3.87% and ≤ 0.88 for the other seven methods, respectively. Accordingly, except the developed eXGB approach, the developed other eight methods can have relative greater accuracies in quantifying soil pH. However, the developed RF had the uppermost quantification accuracy for soil pH. Whether or not meteorological data and normalized difference vegetation index can be used to quantify soil pH was dependent on the chosen models. The RF developed by this study can be used to quantify soil pH from measured meteorological data and NDVImax, and may be conducive to scientific studies related to soil quality and degradation (e.g., soil acidification and salinization) at manifold spatial-temporal under future globe change.

Pennisetum flaccidum can be used as a pioneer species for the restoration of degraded grasslands and as a high-quality forage for local yak and sheep in alpine regions. The geographical distribution pattern of soil fungal community can modify that of P. flaccidum. A field survey along 32 sampling sites was conducted to explore the geo-distribution patterns of soil fungal community of P. flaccidum in Tibet. Soil fungal species, phylogenetic and function diversity generally had a closer correlation with longitude/elevation than latitude. The geo-distribution patterns of soil fungal species, phylogenetic and function diversity varied with soil depth. Soil fungal species, phylogenetic and function diversity had dissimilar geo-distribution patterns. Precipitation had stronger impacts on total abundance, species α-diversity, phylogenetic α-diversity, and function β-diversity than temperature for both topsoil (0–10 cm depth) and subtopsoil (10–20 cm depth). Furthermore, precipitation had stronger impacts on function α-diversity for topsoil, species β-diversity for topsoil, and phylogenetic β-diversity for subtopsoil than temperature. The combination of species, phylogenetic and function diversity can better reflect geo-distribution patterns of soil fungal community. Compared to global warming, the impact of precipitation change on the variation in soil fungal community of P. flaccidum should be given more attention.