Regional Sustainability ›› 2022, Vol. 3 ›› Issue (3): 223-232.doi: 10.1016/j.regsus.2022.10.001

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Spatial variability and driving factors of soil multifunctionality in drylands of China

ZHANG Shihanga,b, CHEN Yusena,c, LU Yongxinga,c, GUO Haoa,b, GUO Xinga,b, LIU Chaohongd, ZHOU Xiaobinga,*(), ZHANG Yuanminga,*()   

  1. aState Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
    bUniversity of Chinese Academy of Sciences, Beijing, 100049, China
    cNational Engineering Technology Research Center for Desert-Oasis Ecological Construction, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830011, China
    dCollege of Grassland and Environment Sciences, Xinjiang Agricultural University, Urumqi, 830052, China
  • Received:2022-06-24 Revised:2022-09-13 Accepted:2022-10-07 Online:2022-09-30 Published:2022-11-29
  • Contact: ZHOU Xiaobing,ZHANG Yuanming;


Drylands are highly vulnerable to climate change and human activities. The drylands of China account for approximately 10.8% of global drylands, and China is the country most severely affected by aridity in Asia. Therefore, studying the spatial variation characteristics in soil multifunctionality (SMF) and investigating the driving factors are critical for elucidating and managing the functions of dryland ecosystems in China. Based on the environmental factors (mean annual precipitation (MAP), mean annual temperature (MAT), solar radiation (Srad), soil acidity (pH), enhanced vegetation index (EVI), and cation exchange capacity (CEC)) and aridity from the Dataset of soil properties for land surface modeling over China, we used non-linear regression, ordinary least square (OLS) regression, structural equation model (SEM), and other analytical methods to investigate the relationships of SMF with environmental factors across different aridity levels in China. SMF in different dryland regions varied significantly and showed a patchy distribution, with SMF index values ranging from -1.21 to 2.42. Regions with SMF index values from -0.20 to 0.51 accounting for 63.0% of dryland area in China. OLS regression results revealed that environmental factors like MAP, MAT, Srad, pH, EVI, and CEC were significantly related to SMF (P<0.05). MAP and MAT were correlated to SMF at the whole aridity level (P<0.05). SEM results showed that the driving factors of SMF differed depending on the aridity level. Soil pH was the strongest driving factor of SMF when the aridity was less than 0.80 (P<0.001). Both soil CEC and EVI had a positive effect on SMF when aridity was greater than 0.80 (P<0.01), with soil CEC being the strongest driving factor. The importance ranking revealed that the relative importance contribution of soil pH to SMF was greatest when aridity was less than 0.80 (66.9%). When aridity was set to greater than 0.80, the relative importance contributions of CEC and EVI to SMF increased (45.1% and 31.9%, respectively). Our findings indicated that SMF had high spatial heterogeneity in drylands of China. The aridity threshold controlled the impact of environmental factors on SMF.

Key words: Drylands, Soil multifunctionality (SMF), Aridity Index (AI), Spatial variability Driving factors, Aridity level