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• Soil texture effect in temperate grassland region of China is examined. • A strong inverse soil texture effect on vegetation is evidenced. • Loam soil can be used as a benchmark to quantify effect of various textured soils. • Precipitation threshold for inverse texture effect increases with soil coarseness. The inverse texture effect of soil on vegetation has been reported in a few arid regions, but no information is available on the vast Chinese temperate grassland. In addition, no consistent criteria and approaches have been used to classify soil texture groups, determine the precipitation threshold (P θ) below which the inverse texture effect occurs, and quantify the effect size (Z), which has limited the generalization of the rules of the soil texture effect in different ecosystems. We analyzed the soil texture effect on vegetation using the remotely sensed vegetation data (MODIS/NDVI) and the Chinese soil profile texture data at 6851 sites in temperate grasslands of China. We found, first, a strong inverse texture effect, i.e., a higher NDVI on coarse- (>50 % sand) than fine-textured soil in low precipitation areas, and P θ was variable with annual precipitation and averaged 260 mm in the region. Second, we proposed a novel method to use the loam soil as a benchmark against which to quantify the effect of various textured soils, and determine P θ as the point on precipitation gradient where the soil texture effect shift from positive to negative or vice versa. As such, P θ decreased with soil coarseness, and increased with precipitation; and the texture effect decreased from positive to negative for sandy soils, whereas it increased from negative to positive for silt or clay soils, on the gradient of precipitation increase. Our results provide new insights into the relations of vegetation pattern and dynamics with precipitation and soil texture, and a method for quantifying and comparing soil texture effect across ecosystem types. [ABSTRACT FROM AUTHOR]

• Performance of 11 existing soil particle density estimating models were evaluated. • The modified Adams and some regression models gave reasonable estimates. • Particle density of subtropical soils was more affected by texture rather than SOM. • Particle density of subtropical soils was simply a linear function of clay content. Soil particle density (ρ s) is a basic soil physical property which is essential for soil pore volume estimation and sediment source discrimination. The measurement of soil ρ s is labor intensive and time-consuming. Pedotransfer functions (PTFs) have been developed for estimating ρ s from readily available soil properties during the past decades. Existing PTFs are developed mainly based on soil datasets covering wide ranges of soil organic matter (SOM) contents. For soils from subtropical regions with low SOM values the accuracy of previous PTFs remains unknown. In this study we evaluated the performance of existing ρ s PTFs by comparing model estimates to measurements of 175 soils from a subtropical region in China. The investigated soils had relatively low SOM contents (mostly<0.02 g g−1) and covered texture ranges from coarse sand to clayey soils. The Adams-based PTFs performed poorly, while the modified Adams PTFs and several simple linear or multiple regression PTFs produced relatively accurate ρ s estimates for the 175 soils with root mean square errors (RMSEs) around 0.05 g cm−3 and mean biases within ± 0.03 g cm−3. Further multiple regressions indicated that the ρ s of subtropical soils was simply a negative linear function of clay content. Despite conflicting several early reports, an independent dataset showed that the proposed linear model provided accurate ρ s estimates for soils with low SOM contents from subtropical regions with a RMSE of 0.030 g cm−3 and mean bias of −0.013 g cm−3. [ABSTRACT FROM AUTHOR]