Position: Home > Articles > Nutrient Cycling and Trend Modeling of Black Locust Plantation Ecosystem in Gullied Loess Plateau Area
Journal of Northwest Forestry University
1998
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黄土残塬沟壑区刺槐人工林生态系统的养分循环与动态模拟
作 者:
刘增文;李玉山;刘秉正;王佑民
单 位:
西北林学院水土保持系;中国科学院水土保持研究所
关键词:
黄土残塬沟壑区;刺槐人工林;养分循环;动态模拟
摘 要:
在生物量、生产力和水量平衡及养分分析的基础上,系统研究了黄土残塬沟壑区刺槐人工林生态系统的养分循环过程。结果表明:生态系统中养分元素总贮量为N10.940t/hm2,P3.755t/hm2,K154.611t/hm2,Ca169.092t/hm2,Mg18.435t/hm2,S1.487t/hm2;生态系统内养分生物循环遵循Ca>N>K>Mg>P>S的顺序;刺槐生产1t干物质需要从土壤吸收N11.67kg,P0.72kg,K3.66kg,Ca15.08kg,Mg2.25kg,S0.34kg;经过循环利用,生态系统年净积累量N1.8073kg/hm2,P0.2106kg/hm2,K1.3756kg/hm2,Ca2.7881kg/hm2,Mg0.440kg/hm2,S0.0621kg/hm2,根层土壤却年净亏损N89.696kg/hm2,P5.492kg/hm2,K34.479kg/hm2,Ca88.991kg/hm2,Mg15.270kg/hm2,S2.511kg/hm2。此外,所建立的该生态系统的养分动态模拟模型,可用于对各分室养分贮量动态的预测。
译 名:
Nutrient Cycling and Trend Modeling of Black Locust Plantation Ecosystem in Gullied Loess Plateau Area
作 者:
Liu Zengwen1), Li Yushan2) Liu Bingzheng1) Wang Youmin1)(1)Dept. of Soil and Water Conser., NWFC, Yangling, Shaanxi 712100; 2)Institute of Soil and Water Conser., Chinese Academy of Sci./Min. of Water Conser
关键词:
gullied loess plateau; black locust; nutrient cycling; trend modeling
摘 要:
Based on measurement of biomass, prodcutivity, water balance and nutrient content, the nutrient cycle proceses of black locust plantation ecosystem in gullied loess plateau has been studied systematically. The results showed that the total nutrients accumulations were N 10.940, P 3.755, K 154.611,Ca 169.092, Mg 18.435, S 1.487 t/hm2; the nutrient biocycling fluxes were in the order of Ca>N>K>Mg>P>S; the uptakes of nutrients from soil to produce l ton dry material of biomass were about to N 11.67, P 0.72, K 3.66, Ca 15.08, Mg 2.25, S 0.34 kg, the annual net accumulations in the total ecosystem were N 1.8073, P 0.2106, K 1.3756, Ca 2.7881, Mg 0.4400, S 0.0621 kg/hm2, but the annual net shortages in the soil layer distributed concentratively with roots were N 89.696, P 5.492, K34.479, Ca 88.991, Mg 15.270, S 2.511 kg/hm2. In addition, the trend models of nutrient cycling could be used to predict the nutrient storages in every compart of the total ecosystem.