The Spatial Distribution of Soil Nitrogen Storage and the Factors That Influence It in Central Asia’s Typical Arid and Semiarid Grasslands

Chen Y. Zhang S. Wang Y. Abzhanov T. Dani S. Zhumabekova Z.
June 2022 MDPI

Diversity
2022 #14 Issue 6

Using a structural equation model (SEM), this paper investigates the response of soil nitrogen content of five typical grasslands in the middle line countries of China’s Belt and Road initiative to the changes of climate variables, soil pH value, and normalized vegetation index, and employs the principal component analysis method to determine the spatial variation characteristics and influencing factors of nitrogen reserves in different grasslands. Pontiac grassland (PS), Middle East grassland (MES), Kazakh grassland (KS), Kazakh forest grassland (KFS), and Kazakh semidesert grassland (KFS) are the five grasslands in the research region (KSD). The results indicated that (1) the nitrogen reserves of the five grassland soils (0–100 cm) in the research area were 7.49 Pg, or approximately 5.7 percent of the total world nitrogen reserves. The sum of the five grasslands’ 0–30 cm and 0–50 cm N reserves accounted for 36.3 percent and 63.1 percent, respectively, of the total 0–100 cm N reserves. The density of nitrogen in the soil (0–100 cm) varied significantly between grasslands, ranging from 1.47 to 3.87 kg/m², with an average of 3.10 kg/m² . (2) PCA analysis revealed a substantial positive correlation between soil N and MAP (p < 0.01), a negative correlation between soil N and Srad (p < 0.01), and a high degree of similarity between the three grassland samples, KFS, KS, and KSD. (3) The decision tree algorithm determined that MAP had the most relative importance for changes in soil nitrogen content in PS, MES, and KFS, whereas Srad had the greatest relative importance for changes in soil nitrogen content in KS and KSD. The pH showed the least proportional impact for variations in soil N concentration in all five grasslands. (4) Different factors influence the change in soil N content across diverse grasslands. The principal positive driving factor of soil N content in KS and KSD is Srad, with loads of −0.39 and −0.44, respectively. The principal negative driving factor of soil N content in PS and MES is Map, with loads of 0.38 and 0.2, respectively. In the SEM model of soil nitrogen content in KFS, no environmental variables had a significant effect on N content, and the model’s R² value was 0.08, indicating an average fit.

arid–semiarid grasslands , environmental factors , principal component analysis , spatial distribution of nitrogen stocks , structural equation modeling

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Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences, Urumqi, 830011, China
University of Chinese Academy of Sciences, Beijing, 100049, China
National Engineering Technology Research Center for Desert-Oasis Ecological Construction, Urumqi, 830000, China
State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, 830000, China
Department Forest Resource and Forestry, Saken Seifullin Kazakh Agrotechnical University, Nursultan, 010000, Kazakhstan

Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences
University of Chinese Academy of Sciences
National Engineering Technology Research Center for Desert-Oasis Ecological Construction
State Key Laboratory of Desert and Oasis Ecology
Department Forest Resource and Forestry

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