摘要:为快速、准确、无破坏地测定草原地上生物量，在祁连山高寒典型草原植物生长旺季，观测了冬季和春秋季放牧地60个样方内各物种的株高、盖度等生长指标。以冬季牧地紫花针茅（Stipa purpurea）、醉马草（Achnatherum inebrians）、赖草（Leymus secalinus）、扁穗冰草（Agropyron cristatum）、二裂委陵菜（Potentilla bifurca）、银灰旋花（Convolvulus ammannii）6个主要物种的株高、盖度、株高和盖度的乘积为自变量，分别预测同物种、其他物种和群落地上生物量。用春秋季牧场的数据验证模型的精确性和稳定性。结果表明：主要物种的生长指标可预测其自身、其他物种和群落地上生物量。对自身种群，株高和盖度乘积的复合因子预测效果最好；4种禾草对其他物种、二裂委陵菜对菊科植物种群，株高、盖度单因子预测效果优于复合因子；6个主要物种单独或2-6个种结合均可预测群落地上生物量，但是以6个物种株高和盖度的乘积同时预测时决定系数最大，可解释群落地上生物量89.5%的变异，为高寒典型草原群落地上生物量最优预测模型。



Abstract:In order to fastly, accurately and non-destructively to predict the biomass on alpine typical steppe in Qilian Mountains, growth indexes of plants species such as plant height, coverage were observed during the growing season under two different pastures, which are Winter grazing land and spring and autumn grazing land. In total 60 quadrats were measured. Taking the Plant height, coverage, the product of plant height and coverage of 6 major species in winter pasture,such as Stipa purpurea, Achnatherum inebrians, Leymus secalinus, Agropyron cristatum, Potentilla bifurca and Convolvulus ammannii as independent variable, and the main population, other populations and aboveground biomass of the community in the same quadrat were used to develop the predicted model, which was verified and corrected by applying the collected data under Vegetation data of Spring and Autumn Pasture. The results showed that the growth indices of representative species can predict the aboveground biomass of themselves, other species and communities. The composite factor of plant height and coverage product was the best for predicting the population. The single factor of plant height and coverage was better than the compound factor in predicting the other populations of the four grasses and the compositae populations of the Potentilla bifurca. Six main species alone or two to six kinds of combining both predictable community biomass on the ground, but the product of 6 species of plant height and coverage to participate in the prediction of regression curve at the same time, decision coefficient is the largest, can explain community biomass of 89.50% of the variation on the ground, can be used as a typical alpine steppe community biomass optimal prediction model on the ground.





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