控制土壤呼吸的主要因素研究进展

Zhang Dong-qiu, Shi Peili, Zhang Xianzhou
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引用次数: 17

摘要

土壤呼吸受多种因素的影响,在不同时空生态系统中影响因素各不相同。主要影响因子包括土壤温度、土壤湿度、降雨量、土壤碳氮比等非生物因子,植被类型、生物量、叶面积指数、凋落物等生物因子以及人类活动等。在总结的基础上,讨论了土壤呼吸的Q - 10、关键控制因素以及各因素之间的相互作用。土壤温度、土壤湿度是控制土壤呼吸的关键因素。因此,利用这些因子建立模型有助于估算土壤呼吸量。但是,在低温和高温、湿气条件下,预报分别存在过低估计和过高估计的局限性。为了获得准确的土壤呼吸估算值,进一步研究的主要建议是:将室内土壤呼吸测量与生态系统碳通量测量相结合,同步测量生物因子和非生物因子,尤其关注生物因子的改变对非生物控制器性能的影响,在典型植物物候期和不同天气条件下进行更多的测量,加强实验操作和建模。综上所述,土壤呼吸是一个复杂的生化过程,虽然受多种因素的相互作用调控,但受一定的模型控制。为了得到准确的建模和预测,需要寻找关键因素并估计交互因素的综合影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SOME ADVANCE IN THE MAIN FACTORS CONTROLLING SOIL RESPIRATION
Soil respiration is influenced by many factors, which are different in a variety of spatio-temporal ecosystems. The objective of this paper is to review the main factors including abiotic factors such as the soil temperature, soil moisture, rainfall, soil C/N,biotic factors such as vegetation types, biomass, leaf area index, litter, and human activities as well. Based on the summarization, the Q10 of soil respiration, key controlling factors and interaction among different factors were discussed. Soil temperature, soil moisture are the key factors controlling soil respiration. So it is helpful to estimate the amount of soil respiration on the basis of modeling by these factors. However, the prediction has a limitation of underestimation and overestimation at low and high temperature or moisture conditions respectively. In order to acquire accurate estimation of soil respiration, main suggestions for further study are to: combine measurement of soil respiration by chamber with carbon flux of ecosystem by eddy covariance, measure synchronously the biotic and abiotic factors, especially pay more attention to the role of modification of biotic factors on the performance of abiotic controllers, make more measurement in typical plant phenology phase and different weather conditions, and strengthen experimental manipulation and modeling. In conclusion, soil respiration is a complex biochemical process, which is controlled by certain models although regulated by interactions among multi-factors. In order to get accurate modeling and prediction, key factors need to be sought and estimate synthetic influence of interactive factors.
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