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炼山的杉木幼林地水土肥流失动态及预测模型
引用本文:何智英,林开敏.炼山的杉木幼林地水土肥流失动态及预测模型[J].福建林学院学报,1994,14(3):247-252.
作者姓名:何智英  林开敏
作者单位:福建林学院杉木研究所
摘    要:连续四年定位研究结果表明:炼山过程中土壤有机质和氮素以高温挥发和细粒尘埃的飞扬形式而损失,0~10cm土层有机质损失7.1%,全氮损失10%;炼山后1~2年内林地水土肥流失极其严重,年土壤流失量超过本区土壤容许流失量(10t·hm-2.a-1),随后炼山林地水土肥流失呈明显递减趋势,至第4年才与不炼山的基本趋于一致.应用混合数量化模型Ⅰ对影响炼山林地水土肥流失的因素进行分析,建立单次降雨水土肥流失量预测模型.

关 键 词:炼山,杉木,生态系统,预测模型

Study on the Dynamic of Lost Amount of Water, Soiland Accompany Nutrient from Young Chinese Fir Forest Land after Controlled Burning
He Zhiying, Lin Kaimin Yu Xintuo Yang Yusheng He Zongming Ma Xiangqing.Study on the Dynamic of Lost Amount of Water, Soiland Accompany Nutrient from Young Chinese Fir Forest Land after Controlled Burning[J].Journal of Fujian College of Forestry,1994,14(3):247-252.
Authors:He Zhiying  Lin Kaimin Yu Xintuo Yang Yusheng He Zongming Ma Xiangqing
Abstract:The fixd position observation for four years in succession after controlled burning showed that the organic materials and the overall nitrogen of soil were exPOrted by high temperature's violatilizing and during controlled burning; in the top layer (0-10cm), the losses of the organic materials accounted for 7.1% of the soil total organic materials and the losses of the overall nitrogen (N) accounted for 10% of the total soil overall nitrogen(N). the lost amount of water,soil,and accompanying nutrient from the ecosystem was very tremendous within two years after controlled burning; the amount of surface soil erosion per year all surpass the allowable amount of soil erosion in this region. Soon afterward, the losses of water,soil and nutrient from controlled burning sites were obviously decreased with time. In the fourth year, the lost amount from controlled burning sites was similar to that from unburning sites. The factors of effect on losses of water, soil and nutient from controlled burning sited were analysed through applying mixed theory I of quantification, and the prediction equations of the lost amount of water, soil and accompanying nutrient were established.
Keywords:controlled burning  Chinese fir  ecosystem  predicting models  
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