Earthquakes occur frequently in fragile alpine grassland areas on the Qinghai-Tibet Plateau (QTP), but few studies have evaluated the impacts of seismo-fault of earthquake on alpine grassland vegetation diversity. In this study, we conducted a field survey of plant communities of alpine grassland along the fault zone in the 7.4 Maduo earthquake occurred on 22 May 2021. Surrounding grassland habitat far from the seismo-fault of earthquake was selected as the control. Plant community metrics around and far from seismic rupture were studied. The results showed that plant community metrics were negatively affected by seismo-fault of earthquake. Species composition around seismo-fault was being shifted from sedges-dominant into forbs-dominant. In addition, the diversity and aboveground biomass were significantly decreased around seismo-fault compared with the control. Our findings highlighted that earthquakes can cause species loss and plant community shift and finally lead to productivity reduction of alpine grassland. Additionally, forbs may be more competitive than other functional groups after the earthquake.

Silvopastoral system has been proposed as a sustainable management system with both ecological and economic benefits compared with open pasture. However, little research compared the ecological impact of silvopastoral system compared with pure forest. Therefore, this study focused on the ecological benefits of silvopastoral system on the soil physicochemical properties under different construction modes and construction periods in China based on meta-analysis. From 29 references we extracted a total of 492 paired data that were used for the meta-analysis. Results showed that silvopastoral system could improve soil physical properties by decreasing soil bulk density and soil pH value. However, no consensus conclusion could be found on soil water content except significant increase in surface soil water content in southern China. Silvopastoral system significantly improved the top-soil nutrient contents. Construction of silvopastoral system significantly increased soil available nitrogen contents of all three soil depths and soil available phosphorus content of 0-40cm soil depth. However, no significant effect could be found for soil available potassium content. Silvopastoral system significantly improved soil organic matter content for all three soil depths. Sub-group analysis showed that 2–3 years after construction of silvopastoral system maximally improved soil available nitrogen and phosphorus contents, while soil available potassium content decreased with the construction period. Our results showed that short-term period construction of silvopastoral systems are effective practices to increase soil nutrient content especially in the temperate climate zone. However, we still need long-term monitoring experiments to verify the long-term ecological effect of silvopastoral system.

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To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity–Hue–Saturation and Gram–Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2–L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2–L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models.