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应用生态学报 ›› 2017, Vol. 28 ›› Issue (10): 3208-3216.doi: 10.13287/j.1001-9332.201710.020

• 目录 • 上一篇    下一篇

基于离体测量估计长白落叶松原位最大净光合速率的方法

刘强,李凤日*,彭娓   

  1. 东北林业大学, 哈尔滨 150040
  • 收稿日期:2017-03-23 修回日期:2017-07-28 出版日期:2017-10-18 发布日期:2017-10-18
  • 作者简介:刘强,男,1990年生,博士研究生.主要从事林木生长与收获模型研究.E-mail:qiangliu2015@126.com
  • 基金资助:

    本文由国家科技支撑计划项目(2017YFD0600402)和中央高校基本科研业务费专项资金项目(2572016AA29)资助

Estimating in situ maximum net photosynthetic rate of Larix olgensis based on abscised mea-surement.

LIU Qiang, LI Feng-ri*, PENG Wei   

  1. Northeast Forestry University, Harbin 150040, China
  • Received:2017-03-23 Revised:2017-07-28 Online:2017-10-18 Published:2017-10-18
  • Supported by:

    This work was supported by the National Science and Technology Support Program (2017YFD0600402) and the Fundamental Research Funds for the Central Universities of China (2572016AA29).

摘要: 以黑龙江省帽儿山林场15年生长白落叶松人工林为研究对象,通过对针叶进行原位及离体测量获取其原位最大净光合速率(SPn max)及离体最大净光合速率(APn max),分析APn max随离体时间(ta)的变化规律,建立SPn maxAPn maxta的函数关系,并分析了林木大小及环境因子对APn max下降过程的影响,构建了长白落叶松SPn max预估模型.结果表明: 在不恢复水分供应的条件下,针叶APn maxta的增加而降低,且水汽压亏缺(VPD)和叶片温度(Tleaf)越高,APn max的下降速度越快、幅度越大.以VPD和ta为自变量的线性预估模型对SPn max的拟合效果最好(Ra2为0.774,RMSE为20.73),模型的预估精度随着ta的增加而降低,ta超过 20 min后,模型预估精度稳定在97%左右.本文采用离体测量方法通过建立回归模型估计长白落叶松的SPn max,不仅具有较好的预估能力和相对稳定的估计精度,同时大大提高了SPn max的测定效率,具有较高的应用价值.

Abstract: The data of needle in situ maximum net photosynthetic rate (SPn max) and abscised maximum net photosynthetic rate (APn max) were measured for the 15 year-old planted Larix olgensis stand in the Maoershan Forest Farm, Heilongjiang Province, China. The change pattern between APn max and abscised time (ta) was analyzed and the functional relationship between SPn max and APn max with ta was also established. Finally, the prediction model of SPn max for planted L. olgensis trees was developed by analyzing the effect of tree size and environmental factors on the decline of APnmax. The results showed that needle APn max decreased with the increase of ta without restoring water supply. The higher vapor pressure deficit (VPD) and the leaf temperature (Tleaf) would lead to faster and lager reduction of APn max. Taking VPD and ta as the independent variables of the linear regression model had the best goodness-of-fit for SPn max(Ra2 were 0.774 and RMSE was 20.73). The model prediction precision decreased with the increase of ta, but after 20 min it would be stabilized at 97%. Overall, estimating SPn max of L. olgensis trees by developing regression model based on abscised measurement not only had a well predictive ability but also had a stable predictive precision, and greatly improved the efficiency of field measurement. The results of this study could be suitably applied to measure SPn max in practice.