气孔导度,表面温度的环境响应模型研究

主要综述６０年代以来国内外有关气孔导度和植物表面温度的各类环境响应模型，讨论了模拟模型中存在的问题，并做了初步展望。其中主要包括气孔导度的多因子阶乘模型和组合因子模型，植被表面温度模拟的微气象模型。     

柑橘叶片气孔导度的环境响应模型研究

对柑橘叶片气孔导度的模拟研究结果表明 ,柑橘叶片气孔导度的环境响应模型中环境变量至少需含有光合有效辐射在内的 2个及其以上的环境变量 ;在温度较适宜的生长季除光合有效辐射外 ,环境变量对模型精度影响程度依次为饱和水汽压差 >叶片温度 >空气CO2 浓度 ;在温度和空气CO2 浓度变化较大的季节模型精确度则受单一环境变量和变量间交互作用的影响 ;3月份柑橘叶片气孔导度对环境因子的响应可采用Jarvis模型的G(Qp)g(De)形式 ,而 7月、11月和 3～ 11月份则均可采用G(Qp)g(De)g(Tl)形式描述     

植物气孔导度与表面温度的环境响应模型研究综述

本文主要综述60年代以来国内外有关气孔导度和植物表面温度研究的各类环境响应模型,其中包括气孔导度的多因子阶乘模型和组合因子模型,表面温度的植被表面温度模拟的微气象模型;并讨论了摸拟模型中存在的问题,做了简单展望。     

土壤水分对冬小麦气孔导度及光合速率的影响与模拟

为了更精确掌握水分在作物模型中的贡献,通过田间设置5种不同程度的水分控制试验,分别选择冬小麦抽穗期（2011年4月18日）和开花后期（5月5日）两个典型日,利用Li-6400光合作用仪测定冬小麦叶片气孔导度和光合速率的日变化及其光响应过程,并利用SPSS软件进行分析;考虑土壤湿度因子,对气孔导度模型（Jarvis模型）和光合模型（非直角双曲线模型）进行修订,利用实测资料拟合得到各项参数,并分析其模拟效果。结果表明,气孔导度、光合速率的日变化与土壤水分含量间呈正相关关系,土壤含水量越少,气孔导度、光合速率越小。加入土壤湿度因子的气孔导度和光合作用模型,两个指标均具有更好的模拟效果,实测值与模拟值之间的相关系数由修订前的0.907、0.769分别提高至0.967、0.987,实测值与模型回代值之间的相关系数也由修订前的0.572、0.316分别提高至0.768、0.874,且均方差均显著降低。因此,土壤湿度对调节冬小麦叶片气孔导度和光合作用非常重要,在气孔导度模拟和光合作用模拟中不能忽略土壤湿度的影响。     

土壤水分对冬小麦气孔导度及光合速率的影响与模拟*1

为了更精确掌握水分在作物模型中的贡献,通过田间设置5种不同程度的水分控制试验,分别选择冬小麦抽穗期（2011年4月18日）和开花后期（5月5日）两个典型日,利用Li-6400光合作用仪测定冬小麦叶片气孔导度和光合速率的日变化及其光响应过程,并利用SPSS软件进行分析;考虑土壤湿度因子,对气孔导度模型（Jarvis 模型）和光合模型（非直角双曲线模型）进行修订,利用实测资料拟合得到各项参数,并分析其模拟效果。结果表明,气孔导度、光合速率的日变化与土壤水分含量间呈正相关关系,土壤含水量越少,气孔导度、光合速率越小。加入土壤湿度因子的气孔导度和光合作用模型,两个指标均具有更好的模拟效果,实测值与模拟值之间的相关系数由修订前的0.907、0.769分别提高至0.967、0.987,实测值与模型回代值之间的相关系数也由修订前的0.572、0.316分别提高至0.768、0.874,且均方差均显著降低。因此,土壤湿度对调节冬小麦叶片气孔导度和光合作用非常重要,在气孔导度模拟和光合作用模拟中不能忽略土壤湿度的影响。     

冬小麦气孔导度模型的比较

为评估不同模型模拟冬小麦气孔导度的适用性,从常用的Jarvis模型和Ball-Berry模型中分别选择两种,根据试验资料估算模型参数,并对模型预测效果进行检验和比较。结果表明：运用Jarvis模型1、Jarvis模型2、Ball-Berry模型1和Ball-Berry模型2预测冬小麦气孔导度时预测值与实测值之间的相关系数分别为0.854、0.777、0.751、0.784,均方根误差分别为0.149、0.247、0.183、0.169mol.m^-2.s^-1,据此可确定4种模型的预测精度为Jarvis模型1〉Ball-Berry模型2〉Ball-Berry模型1〉Jarvis模型2。研究结果可为现有的基于Jarvis模型和Ball-Berry模型的农田蒸散、陆面过程和生态系统模型提供参考。     

几种气孔导度模型在华北地区适应性研究

利用LI-6400便携式光合测定仪,在山东农业大学试验基地对冬小麦品种鲁麦23生理生态多项指标进行系统测定,在此基础上,从Jarvis和Ball-Berry模型中分别选取两种模型,利用最小二乘法对气孔导度参数进行拟合检验,并对模型预测效果进行统计分析和对比。结果表明,Ball-Berry模型2预测精度最高,其预测值与实测值的相关系数和均方根误差分别为0.625(P<0.01)和0.158mol.m-2.s-1,Jarvis模型1优于Jarvis模型2,Ball-Berry模型1预测效果最差,其预测值与实测值的相关系数和均方根误差分别为0.369(P<0.01)和0.235mol.m-2.s-1。4种模型在华北地区的适应性与长江中下游地区相比存在一定差异,因此需要在更多地区对气孔导度模型进行更深入研究,以期为土壤-植被-大气系统研究提供更为可靠的参考依据。     

南方丘陵区油茶气孔导度模型修正

为探求南方丘陵地区油茶叶片气孔导度最优响应模型,该研究于2020年和2021年对油茶叶片的气孔导度、净光合速率、CO2浓度等参数进行观测,运用9种不同组合的Jarvis模型以及2种不同CO2浓度（叶片CO2浓度和胞间CO2浓度）计算的Ball-Woodrow-Berry（BWB）模型和Ball-Berry-Leuning（BBL）模型对油茶叶片气孔导度进行模拟,并引入油茶叶片叶气温差、气孔内外CO2浓度差对Jarvis模型和BBL模型进行修正和比较。结果表明：去除土壤水分函数提高了Jarvis模型的模拟效果,说明在南方丘陵区利用Jarvis模型时可以忽略土壤水分这一因子的影响。Jarvis-8模型以及用叶片CO2浓度计算出的BWB和BBL模型对油茶气孔导度模拟效果较好。在三种经典模型中,BBL模型的相关系数最大（R2=0.76）,绝对值平均误差最小（0.015）,说明其对油茶气孔导度模拟的效果较好。在引入两种参数后,叶气温差对Jarvis-8模型和BBL模型的模拟效果有所提高,但不显著;气孔内外CO2浓度差对Jarvis-8模型无明显改变,但显著提高了BBL模型的精度,引入气孔内外CO2浓度差后的BBL-C模型相关系数从BBL模型的0.76提高到了0.95,模型斜率非常接近于1（1.004）,模拟结果贴近于实测的气孔导度值,很好地模拟了2020年（R2=0.92）和2021年（R2=0.95）油茶生长关键期的叶片气孔导度值以及不同场景下的油茶气孔导度日变化值。因此推荐引入气孔内外CO2浓度差的 BBL模型作为南方丘陵区油茶叶片气孔导度响应模型。研究结果可为南方丘陵区油茶气孔导度模型选取提供参考依据。     

控制灌溉水稻气孔导度变化规律试验研究

根据江西示范区的现场试验资料,分析了晚稻叶片气孔导度的日变化以及全生育期内的变化规律,分析了控制灌溉条件下叶片气孔导度与外界影响因子等的相互关系,并对气孔导度进行了模拟。结果表明：气孔导度在不同的土壤水分条件下表现出不同的日变化规律,较低的土壤水分加大了其在中午的下降幅度;全生育期气孔导度先升后降,并随土壤水分降低而降低,灌水后出现反弹;叶气温差是影响气孔导度的关键因素;在一定的空气温度和CO₂浓度范围内,气孔导度随之增加而增加,超出该范围后,则出现下降趋势。引入叶气温差考虑土壤水分与植物水分亏缺的影响,建立了改进的Leuning-Ball模型,模型对大田试验数据的解释能力有所提高。     

节水灌溉条件下水稻气孔导度模型的改进

为了研究节水灌溉条件下水稻叶片气孔导度与相关因子的响应关系模型,于2006年在河海大学国家重点实验室昆山试验研究基地进行了田间试验。根据实测资料分析了水稻气孔导度与各影响因子的响应关系,引入了水稻叶片叶气温差改进了气孔导度模型,并对两类气孔导度改进模型进行了比较。结果表明：改进的气孔导度模型在节水灌溉条件下具有更高的模拟解释能力;基于Jarvis模型改进的气孔导度模型比Leuning-Ball改进模型具有更好的模拟效果。推荐Jarvis改进气孔导度模型作为节水灌溉条件下的水稻叶片气孔导度响应模型。     

番茄叶片气孔导度及渗透调节物质对快速水分胁迫的响应

本研究采用聚乙二醇(PEG-6000)溶液控制番茄的根际水势和利用光合仪控制叶面的空气湿度,监测叶片水势、气孔导度、渗透调节物质含量等指标的变化,揭示快速水分胁迫下番茄的应激性生理响应。结果显示,当Gs小于120 mmol.m 2.s 1时,水分胁迫处理番茄的叶片净光合速率(Pn)小于对照。空气饱和水汽压差(VPD)与番茄叶片Gs呈显著的负相关(R2≥0.892)。在同一空气湿度条件下,水分胁迫处理的番茄叶片Gs仅为对照的43%~51%。快速水分胁迫下番茄叶片的可溶性糖含量呈先增再降的波动,轻度和中度胁迫下可溶性糖含量比对照(0 min)分别增加61.4%~195.8%和56.0%~167.5%。可见,番茄叶片Gs为120 mmol.m 2.s 1是对Pn影响的一个重要阈值。气孔调节是适应快速干旱胁迫的重要应激性生理响应。可溶性糖对维持番茄叶片细胞膨压和适度气孔开度具有一定的渗透调节作用。     

基于夏玉米叶片气孔导度提升的冠层导度估算模型

叶片气孔导度模拟及其向冠层导度的尺度提升是实现蒸散发尺度转换的基础,对农业水资源高效利用与评价意义重大。本文依据夏玉米叶片气孔导度和冠层导度实测值,在建立叶片气孔导度估算模型基础上,构建冠层导度估算模型。结果表明,夏玉米叶片气孔导度每日在10:00－14:00之间达到峰值,其日变化趋势与光合有效辐射的一致性较好,较大的饱和水汽压差对夏玉米叶片气孔导度具有一定的限制作用。根据光合有效辐射和饱和水汽压差建立的叶片气孔导度估算模型能较好反映当地夏玉米叶片气孔导度对主要环境因子的响应过程,以光合有效辐射作为尺度转换因子构建的冠层导度估算模型可较好实现从叶片气孔导度向冠层导度的尺度转换提升。     

考虑植被类型的冠层气孔导度模型

冠层气孔导度指示着植物的光合和蒸腾作用的强弱,冠层气孔导度的精确估算对于研究蒸散、农业生产和生态系统的功能等具有重要意义.以往冠层气孔导度的模型很少考虑植被类型的影响,而不同植被类型间冠层气孔导度存在较大差异,本研究旨在考虑植被类型,引入气孔长度和密度计算最大气孔导度,建立植被冠层气孔导度模型.使用HJ-1卫星数据反演...     

Evapotranspiraton estimation based on scaling up from leaf stomatal conductance to canopy conductance

Evapotranspiraton (ET) estimation based on scaling up from leaf stomatal conductance (g_s) to canopy conductance (g_c) is important in improving effective use and evaluation of agricultural water resources. Taking a summer maize field in north China as an example, after the response of g_s to main environmental factors was analyzed based on the measured value, the Jarvis model for g_s was established and calibrated. Then the weighted integration model (WI model) was established on the basis of weighted model (W model) after considering the difference of intercept diffuse radiation by shaded leaves in different canopy heights and nonlinear relationship between g_s and the photosynthetically active radiation (PAR) to improve g_c estimation for shaded leaves using integration equation. Meanwhile the estimation accuracy of W and WI models for g_c was compared, and then field ET was estimated using the Penman-Monteith equation. Results indicate that the variation of g_s was similar to that of PAR and the Jarvis model could better express the response of g_s to PAR, vapour pressure deficit and air temperature. Compared to the W model, WI model could effectively improve the estimation accuracy of g_c, with the relative error of 4.4%. Penman-Monteith equation overestimated λET by 9.4% using the estimated g_c by the W model, but underestimated λET by 2.3% using the estimated g_c by the WI model. Therefore, Penman-Monteith equation can estimate maize field ET using the estimated g_c by WI model in the region.     

Automated measurement of canopy stomatal conductance based on infrared temperature

Decreased water uptake closes stomates, which reduces transpiration and increases leaf temperature. The leaf or canopy temperature has long been used to make an empirical estimate of plant water stress. However, with a few supplemental measurements and application of biophysical principles, infrared measurement of canopy temperature can be used to calculate canopy stomatal conductance (g_C), a physiological variable derived from the energy balance for a plant canopy. Calculation of g_C requires an accurate measurement of canopy temperature and an estimate of plant height, but all of the other measurements are available on automated weather stations. Canopy stomatal conductance provides a field-scale measurement of daily and seasonal stomatal response to prevailing soil water and atmospheric conditions, and facilitates a comparison of models that scale conductance from single leaves (measured with porometers) to canopies. A sensitivity analysis of the input measurements/estimates showed g_C is highly sensitive to small changes in canopy and air temperature, and less sensitive to the other required measurements (relative humidity, net radiation, wind speed, and plant canopy height). The measurement of g_C becomes increasingly sensitive to all of the component factors as the conditions become cloudier, cooler, and more humid. We determined g_C for alfalfa and turfgrass by making the necessary environmental measurements and coupling them with a two-source (plant canopy layer and soil layer) energy balance model. We then compared these g_C values to maximum single leaf values scaled-up to the canopy level (g_CP, defined as potential canopy stomatal conductance herein) for the two crops. For both crops, g_C matched g_CP within approximately 10% after irrigation. The turfgrass g_C measurements were also compared to mean single leaf values measured with a porometer. At mid-day, g_C values were typically about double the single leaf values. Because this approach for determining g_C allows continuous, non-contact measurement, it has considerable potential for coupling with measurements of soil moisture to better understand plant–soil water relations. It also has potential for use in precision drought stress and irrigation scheduling.     

水分胁迫下黄瓜叶片光响应过程的模拟

以黄瓜品种"博新525"(Boxin525)为试材,设置4个土壤水分处理,即正常灌溉CK(土壤相对湿度为70%~80%)、轻度胁迫T1(土壤相对湿度为60%~70%)、中度胁迫T2(土壤相对湿度为50%~60%)、重度胁迫T3(土壤相对湿度为35%~45%),利用LI-6400便携式光合仪测定不同水分处理下黄瓜叶片的光响应曲线,采用直角双曲线、非直角双曲线、指数和叶子飘等4种光响应模型对黄瓜叶片光响应过程进行模拟,运用统计方法进行模拟效果评价,以探讨水分胁迫对黄瓜叶片光响应过程的影响。结果表明:(1)水分胁迫导致黄瓜叶片净光合速率(Pn)下降,当光合有效辐射PAR(photosynthetically active radiation)为800μmol·m~(-2)·s~(-1)时,T1、T2和T3处理叶片Pn分别比CK下降17.92%、26.49%和50.00%。实测与模拟的光响应曲线对水分胁迫的响应趋势一致,随着胁迫程度增加,Pn-PAR曲线变化幅度减小。(2)水分胁迫显著影响黄瓜叶片的光响应曲线参数。4种模型模拟的叶片初始量子效率均随胁迫加重呈先升高后降低的变化趋势;叶片暗呼吸速率以T2处理最高;4种模型计算的T1、T2和T3处理叶片光饱和点平均值较CK分别下降24.28%、31.99%和38.33%,叶片最大净光合速率平均值分别降低23.88%、33.19%和55.78%。(3)CK处理的黄瓜叶片光响应曲线模拟值偏离程度及光响应曲线参数的平均相对误差均最低,水分胁迫降低了黄瓜叶片光响应曲线的模拟效果。(4)4种模型模拟效果以叶子飘模型最佳,其后依次为指数模型、非直角双曲线模型、直角双曲线模型。研究认为水分胁迫显著影响黄瓜叶片的光响应过程,降低黄瓜叶片的光合能力。     

Empirical and optimal stomatal controls on leaf and ecosystem level CO2 and H2O exchange rates

Linkage between the leaf-level stomatal conductance (g_s) response to environmental stimuli and canopy-level mass exchange processes remains an important research problem to be confronted. How various formulations of g_s influence canopy-scale mean scalar concentration and flux profiles of CO₂ and H₂O within the canopy and how to derive ‘effective’ properties of a ‘big-leaf’ that represents the eco-system mass exchange rates starting from leaf-level parameters were explored. Four widely used formulations for leaf-level g_s were combined with a leaf-level photosynthetic demand function, a layer-resolving light attenuation model, and a turbulent closure scheme for scalar fluxes within the canopy air space. The four g_s models were the widely used semi-empirical Ball-Berry approach, and its modification, and two solutions to the stomatal optimization theory for autonomous leaves. One of the two solutions to the optimization theory is based on a linearized CO₂-demand function while the other does not invoke such simplification. The four stomatal control models were then parameterized against the same shoot-scale gas exchange data collected in a Scots pine forest located at the SMEAR II-station in Hyytiälä, Southern Finland. The predicted CO₂ (F_c) and H₂O fluxes (F_e) and mean concentration profiles were compared against multi-level eddy-covariance measurements and mean scalar concentration data within and above the canopy. It was shown that F_c comparisons agreed to within 10% and F_e comparisons to within 25%. The optimality approach derived from a linearized photosynthetic demand function predicted the largest CO₂ uptake and transpiration rates when compared to eddy-covariance measurements and the other three models. Moreover, within each g_s model, the CO₂ fluxes were insensitive to g_s model parameter variability whereas the transpiration rate estimates were notably more affected. Vertical integration of the layer-averaged results as derived from each g_s model was carried out. The sensitivities of the up-scaled bulk canopy conductances were compared against the eddy-covariance derived canopy conductance counterpart. It was shown that canopy level g_s appear more sensitive to vapor-pressure deficit than shoot-level g_s.