植物学报 ›› 1999, Vol. 16 ›› Issue (06): 712-718.

• 技术与方法 • 上一篇    下一篇

一种在野外自然光照条件下快速测定光合作用一光响应曲线的新方法

蒋高明 何维明   

  1. (中国科学院植物研究所 北京 100093)
  • 收稿日期:1999-05-18 修回日期:1999-05-27 出版日期:1999-11-20 发布日期:1999-11-20
  • 通讯作者: 蒋高明

A quick New Method for Determining Light Response Curves of Photosynthesis Under Field Light Conditions

JIANG Gao-Ming and HE Wei-Ming   

  1. (Center for Plant Ecology and Biodiversity Conservation, Institute of Botany, The Chinese Academy of Sciences, Beijing 100093)
  • Received:1999-05-18 Revised:1999-05-27 Online:1999-11-20 Published:1999-11-20
  • Contact: JIANG Gao-Ming

摘要: 本文尝试了一种在野外自然状态下测定植物光合作用-光响应曲线的方法。传统的方法或费时费力,或依赖精密的光发生装置,在野外操作均不方便。本研究使用英国生产的便携式光合作用测定系统LCA4型对毛乌素沙地8种优势植物在全自然条件下进行测定,仅通过改变叶室(含光敏探头)与入射光线之间的角度即可在自然光照条件下快速获得一系列植物的光合作用—光照速率的反应曲线。拟合曲线值与实测值之间显著相关。测定这种曲线过程中环境参数除叶面温度有较明显的变动外,其它参数如大气温度、CO2浓度基本不变,在这种环境下测定的曲线基本反映植物光合作用真实的自然状态,且操作简便、快速、经济、实效。

Abstract: A quick new method for determining light response curves of photosynthesis under field light conditions has been developed. By using only a potable photosynthesis system (LCA4, UK) under full sunlight condition, we could obtain a series of assimilation rate-light (A-light) response curves by varying the angle of incident light rays to the sensing head to get a series of light intensities (0-2 200 µmo·m-2 ·s-1) which were automatically recorded by the machine, with the corresponding assimilation rates of leaf inside the sensing head being measured simultaneously. Eight arid sandy plant species, ( i.e. Salix psammophylla C. Wang et Ch. Y. Yang, Artemisia ordosica Krasch, Hedysarum scoparium Fisch. et Mey., H. laeve Maxim., Caragana intermedia Kuang et H. C. Fu, Sabina vulgaris Ant., Cynanchum komarovii Al-Iljinski, Psaramochloa mongolica Hitche) were tested by this simple method with good results. During each measurement, environmental variables such as air temperature, air humidity and CO2 concentration but not including leaf temperature changed little, and there existed a significant correlation between the measured data and the predicted data.