摘要
摘要:水分利用效率（WUE）常被嵌入到多种生态系统模型中，用于评估生态系统对气候变化的响应。然而，自然条件下多种因素不仅直接影响WUE，还通过影响冠层结构等间接影响WUE，其中的影响机制仍不明晰。为了明确多种因素对冬小麦WUE的协同影响，本研究基于2015年（温暖湿润年）和2016年（温暖干旱年）涡度相关系统观测的小麦关键生育期（返青、拔节、抽穗、灌浆）的数据，分析了WUE的变化，并借助结构方程模型（SEM），以叶面积指数（LAI）为中间变量，分析了多种因素[净辐射（R_n）、空气温度（T_a）、饱和水汽压差（VPD）、风速（WS）、土壤含水量（SWC）]对WUE的影响机制。结果表明，2015年平均WUE为1.52 g（C）·kg^-1（H₂O），2016年平均WUE为1.22 g（C）·kg^-1（H₂O）。不管在温暖湿润年还是温暖干旱年，T_a、LAI和VPD均是影响WUE的主要因素。WUE随LAI增加而增加，T_a增加也有助于提高WUE，而当温度相近时，VPD增加会降低WUE。T_a、LAI和VPD对WUE的影响在温暖湿润年和温暖干旱年重要性程度不同，温暖湿润年最重要的影响因素为LAI，温暖干旱年为T_a；VPD在温暖湿润年既直接影响WUE，同时又通过影响LAI的变化间接作用于WUE，但在温暖干旱年仅具有直接影响。R_n在温暖干旱年和温暖湿润年表现也不相同：在温暖湿润年对WUE具有显著的影响，在温暖干旱年影响不显著，这与温暖湿润年降雨量大及降雨频次高有关。显然，模拟WUE时考虑不同年份气象条件会使结果更为准确。WS未对WUE产生显著的影响，潜在原因可能是其对冠层上部接收辐射充足的叶片影响较大，而对冠层内部叶片无显著影响。农田生态系统不同生育阶段对辐射、温度等的耐受性及响应方式不同，SEM可以将LAI设置为中间变量以综合这种阶段性的变化，因此，对于冠层结构季节变幅大的生态系统，SEM是研究其环境控制机制的有力工具。这些研究结果可为今后精确模拟生态系统WUE以及预测WUE对气候变化的响应提供科学依据。
关键词:水分利用效率/
结构方程模型(SEM)/
涡度相关系统/
微气象/
冬小麦
Abstract:Water use efficiency (WUE) is usually embedded in a variety of ecosystem models to assess the ecosystem response to climate change. However, under natural conditions, multiple environmental factors affect WUE directly and indirectly by influencing the canopy structure. Currently, the mechanisms that influence WUE are not clear. In order to clarify the synergistic effect of various factors on the WUE of winter wheat, experiments were conducted in the Luancheng Agro-Ecosystem Experimental Station, Chinese Academy of Sciences. Variables were observed using an eddy covariance system during key growth stages (greening, jointing, heading, filling) of winter wheat in 2015 (warm and wet year) and 2016 (warm and dry year). The variation in winter wheat WUE and the controlling mechanisms of various factors (net radiation, R_n; air temperature, T_a; vapor pressure deficit, VPD; wind speed, WS; soil water content, SWC) were analyzed by means of a structural equation model (SEM). The structural equation model can systematically analyze the impacts of different factors on WUE on the basis of interactions among different factors. Compared to traditional univariate or multiple linear regression, SEM had intermediate variables, which can decompose the effects of micrometeorological factors into direct and indirect effects. In this study, leaf area index (LAI) was the intermediate variable. The results showed that average WUE in 2015 was 1.52 g(C)·kg^-1(H₂O), while it was 1.22 g(C)·kg^-1(H₂O) in 2016. T_a, LAI, and VPD were the main factors that influenced WUE, regardless of whether the year was warm and wet (WW) or warm and dry (WD). Leaf area index and T_a had positive effects on WUE, while VPD inhibited WUE, which means that under similar temperatures, increased water vapor content in the air can enhance WUE. T_a, LAI, and VPD were of different importance in WW and WD years. LAI was the most significant influencing factor in WW years, while T_a played a more important role in WD years. In WW years, VPD not only affected WUE directly but also indirectly through altering LAI, while it only had a direct effect in WD years. R_n also was different between WW and WD years, having a significant effect on WUE in WW year but no significant effect in WD year. This phenomenon was caused by the heavier and more frequent rainfall in WW year. Obviously, taking the climate conditions in different years into consideration will increase accuracy when simulating WUE. WS had no significant effect on WUE, probably because WS only affects the leaves receive sufficient radiation in the upper part of the canopy, and these effects can be ignored for leaves inside the canopy. Farmland ecosystems have different tolerances and responses to radiation and temperature at different growth stages. LAI can be set as an intermediate variable to reveal this stepwise change in SEM. Therefore, for ecosystems with large seasonal changes in canopy structure, SEM is a powerful tool to investigate mechanisms of environmental control. This research can provide a scientific basis for accurately simulating WUE and predicting the response of WUE to climate change.
Key words:Water use efficiency/
Structural equation model (SEM)/
Eddy covariance system/
Micro-meteorology/
Winter wheat



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