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Soil fertility of olive orchards along an altitude gradient in Bailong River basin of Longnan, Gansu Province.

JIAO Run-an1,2, LI Chao-zhou1,2*, ZHAO Yang3,4, JIAO Jian3   

  1. (1College of Life Science and Technology, Gansu Agricultural University, Lanzhou 730070,China; 2Gansu Key Laboratory of Crop Genetics & Germplasm Enhancement, Lanzhou 730070, China; 3College of Forestry, Gansu Agricultural University, Lanzhou 730070, China; 4Forestry Science Research Institute of Bailong River, Lanzhou 730070, China).
  • Online:2018-02-10 Published:2018-02-10

Abstract: Soil fertilities of olive orchards  from five different altitudes (960, 990, 1021, 1052 and 1083 m) in Bailong River basin was comprehensively assessed by using the subordinate function method and principal component analysis (PCA) to explore the impacts of altitude on soil fertility. The results showed that at the altitude of 960 m, the activities of soil urease andsucrase were the highest, and soil organic matter content and bulk density were the highest, while the pH value was significantly lower than that at other altitudes. The activities of soil phosphatase and catalase were the highest at 990 m. Soil water content was the highest at 1021 m. Soil available phosphorus and nitrogen contents were the highest at 1052 m. Among the five altitudes, soilbacteria abundance showed no significant variation, but being significantly higher than that at 1083 m. There were no significant difference in the abundance of soil actinomyces and fungiamong all the altitudes. The comprehensive indexes of soil fertility assessed by calculating the weight via correlation coefficients were ranked as 960 m > 990 m> 1052 m > 1021 m > 1083 m. The ranking of the comprehensive indexes using principal component analysis were 960 m > 990 m > 1021 m > 1052 m> 1083 m. The results of two analytical methods differed at 1021 m and 1052 m, but the soil fertility generally showed a decreasing trend with the increases of altitude.

Key words: ecological environment, principal component analysis, remote sensing ecological index (RSEI), Gurbantunggut Desert