高寒草甸牧草产量形成过程及与气象因子的关联分析

The Association Analysis of Herbage Yield and Meteorological Factors in Alpine Meadow

高寒草甸牧草产量的形成过程与生物量的积累过程和自然界各种生物种群消长规律一样，可用逻辑斯谛生长函数来描述。随着季节的进程，依环境条件的周期变化表现为有缓慢积累-快速增加-相对稳定-折损减少等4个阶段。对气象因子影响高寒草甸牧草产量灰色关联分析结果表明，高寒草甸地区降水量可基本满足牧草生长发育的需求，而温度因子则成为主要的限制因子。其气象因子影响程度大小的序次为：1月平均气温＞5～8月平均气温＞5～8月日照时数＞5～8月水热综合配合(降水量与气 温的比值)＞5～8月降水量＞上年度末9～11月的降水量。在分辨系数取0.5的条件下，以上各因子所对应的关联度排列为：0.835＞0.791＞0.771＞0.754＞0.743＞0.638。将上述各气象因子进行主成分处理后，建立评估或预报高寒草甸牧草产量的模拟模型方程：GW=a+aj覺j。其拟合及试报效果很好，可作高寒草甸牧草产量的评估或预测预报工具。

In this paper,accumulation of above-biomass could be described by logistic equation as same as process of growth and decline in other biological populations during the growing season of alpine meadow.The accumulation had four stages,gradually increasing,stupid increasing,stability and decreasing during seasonal process with changes in environmental factors. We employed the method of gray association analysis.The results showed that factors influence herbage production in the alpine meadow suggest that the precipitation in alpine meadow is enough to the water requirement of herbage growth on the whole and the air temperature is the main limiting factor.The ordination of its influence is given below. The average air temperature in January＞the average air temperature from May to August＞the sunshine time from May to August＞the comprehensive cooperation of heat and water(the ratio of the precipitation and average air temperature)from May to August＞the precipitation from May to August＞the precipitation from September to November(when it is)in the late time of last year.If resolving coefficient is 0.5,the ordination of association degree associated with the given factors above is:0.8348＞0.7913＞0.7713＞0.7540＞0.7430＞0.6384. Modeling equation of evaluating and predicting herbage yield alpine meadow was predicted by GW=a+bj覺j after main composition analysis on each meteorological factor discussed above.The equation can be looked as the tool for evaluating and predicting herbage yield in alpine meadow because of its good forecast effect.