Crop Stomatal Conductance Model: Research Progress and Application Status

doi:10.11924/j.issn.1000-6850.casb19010031

Abstract

Abstract:

This review aims to provide theoretical references for model selection of crop water consumption and carbon-water exchange. The response of stomatal conductance to single and comprehensive environmental factors is summarized. The existing stomatal conductance models are classified and compared, including the empirical models constructed and improved based on Jarvis model, the semi-empirical models constructed and improved based on BWB model, the models constructed and improved based on ABA control and the models constructed and improved based on the guard cell swell control theory. The application of stomatal conductance model in crop water use research is also reviewed. The multi-scale joint mechanism model establishment with the entry point of the quantitative relationship between leaf stomatal conductance and canopy conductance will be the hotspots of future crop water consumption scale expansion research.

Key words: stomatal conductance model, optimal stomatal theory, environmental factors, Jarvis model, BWB model, water use efficiency, scaling up

CLC Number:

S181

Jiang Hanbing, Zhang Chuanwei, Zhang Yucui, Shen Yanjun. Crop Stomatal Conductance Model: Research Progress and Application Status[J]. Chinese Agricultural Science Bulletin, 2020, 36(12): 1-9.

Figures/Tables 3

环境因子	气孔导度对各环境因子的响应	变化趋势模拟	生理机制
光合有效辐射	光强较低的条件下,g_s随光合有效辐射增强而增大;到达一定阈值后,g_s随其增强而减小	二次曲线^[13]	辐射过强导致温度升高,蒸腾速率加快,水分亏缺诱导气孔关闭,甚至过强的光照会损伤植物组织
CO₂浓度	当CO₂浓度较低时,g_s随CO₂浓度升高而增大;到达一定阈值,g_s保持稳定;CO₂浓度过高时,气孔倾向关闭,g_s减小。	二次曲线^[14]	植物自身调控气孔运动,维持水分散失与CO₂吸收的最优化
空气相对湿度	g_s随空气相对湿度增加而增加;空气湿度过高时会出现波动	正相关^[15]	空气湿度过高多出现在阴雨天气,空气湿度饱和差小,气孔倾向关闭。
土壤含水量	g_s随土壤含水量的增大而增大,土壤含水量过高时g_s可能降低;作物不同生育期响应关系存在差异	二次曲线^[16]	土壤含水量可影响保卫细胞及其周围细胞的膨压,进而影响气孔开度
空气温度	在空气温度较低时,g_s随其升高而增大;到达一定阈值后,随其升高而减小	二次曲线^[17]	高温引起蒸腾加快,保卫细胞失水,气孔关闭

年份	模型函数式	基础假设	特征	应用
1997^[39]	$g bwb = a × ∑ P ∑ ET$	g_s-A_net之间线性关系的斜率与植物和土壤水分状况存在函数关系	引入干旱系数(g_bwb)	在水分胁迫条件下较为精确的模拟和预测气孔导度
2004^[40]	$g bwb = m 1 + (φ pd φ 0) n$		引入参数黎明前的叶片水势(ψ_pd)	在水分胁迫条件下较为精确的模拟和预测气孔导度
1998^[41]	$g bwb, g l = a × [1 - k exp (k s θ cc - θ s θ cc - θ f)]$		引入土壤含水量(θ)	利用土壤特性模拟水分胁迫下的g_s。
2005^[42]	$g s = g 0 + a × A net + R d (C s - Γ) (1 + VPD VP D 0) × f season$ $f season = min (1, k + k s × DOY)$	g_s-A_net之间线性关系的斜率与与季节和时间存在函数关系	引入季节效应(f_season)	模拟不同季节的g_s变化,反应季节效应。
2018^[7]	$g s = 1.6 (1 + g l VPD) A net C a + g 0$ $g l = 3 Γλ / 1.6$	气孔最优化理论	将BWB模型中的斜率参数与最优气孔导度理论的λ结合	提高了模型的机理性和模拟精度

年份	模型函数式	基础假设	特征	应用
1997^[39]	$g bwb = a × ∑ P ∑ ET$	g_s-A_net之间线性关系的斜率与植物和土壤水分状况存在函数关系	引入干旱系数(g_bwb)	在水分胁迫条件下较为精确的模拟和预测气孔导度
2004^[40]	$g bwb = m 1 + (φ pd φ 0) n$		引入参数黎明前的叶片水势(ψ_pd)	在水分胁迫条件下较为精确的模拟和预测气孔导度
1998^[41]	$g bwb, g l = a × [1 - k exp (k s θ cc - θ s θ cc - θ f)]$		引入土壤含水量(θ)	利用土壤特性模拟水分胁迫下的g_s。
2005^[42]	$g s = g 0 + a × A net + R d (C s - Γ) (1 + VPD VP D 0) × f season$ $f season = min (1, k + k s × DOY)$	g_s-A_net之间线性关系的斜率与与季节和时间存在函数关系	引入季节效应(f_season)	模拟不同季节的g_s变化,反应季节效应。
2018^[7]	$g s = 1.6 (1 + g l VPD) A net C a + g 0$ $g l = 3 Γλ / 1.6$	气孔最优化理论	将BWB模型中的斜率参数与最优气孔导度理论的λ结合	提高了模型的机理性和模拟精度

作者	模型函数式	参数个数	应用假设	优点	缺点	应用情况
Jarvis^[28]	$g s = g s max × f (Q) × f (T 1) × f (VPD) × f (C a) × f (φ)$	6	各环境因子对气孔的调控相互独立	形式简单,可就多个环境因子建立多元非线性函数关系	1.忽略了各环境因子间的相互作用关系。2.参数生理意义不明确,调参困难	广泛应用于叶片、冠层, 甚至站点尺度的模拟
Ball^[34]	$g s = g 0 + a × A net × H r C s$	4	气孔导度与光合速率线性相关。假设叶表CO₂浓度和空气湿度保持恒定	参数生理意义明确;样本需求量较低	1.无法反应气孔导度对综合环境因子的响应。2.气孔导度与光合速率不成线性关系的部分仍需耦合其他模型进行模拟。
Leuning^[35]	$g s = g 0 + a × A net (C s - Γ) (1 + VPD D 0)$	7	气孔导度与光合速率线性相关。假设叶表CO₂浓度和空气湿度保持恒定	引入CO₂饱和点和饱和水气压差
Tardieu & Davies^[47]	$g s = g 0 + a × exp ABA × β × exp (δψ)$	5	土壤水分不足时,植物根系通过ABA调控气孔开度	模型机理明确;明确反应气孔导度水分胁迫的反馈	参数获取困难	主要应用于水分亏缺条件下的气孔导度估算
Buckley^[52]	$g s = χ (P g - m P e)$	>10	气孔开度有保卫细胞与外界膨压差调控	形式简单,便于参数化和应用	合理性方面存在争议	主要应用于水分亏缺条件下的气孔导度估算
Wang Qiuling^[7]	$g s = 1.6 (1 + g l VPD) A net C a + g 0$ $g l = 3 Γλ / 1.6$	7	气孔最优化理论	机理性强	参数λ确定困难	主要应用于自然界植被,在农业作物研究中应用较少

作者	模型函数式	参数个数	应用假设	优点	缺点	应用情况
Jarvis^[28]	$g s = g s max × f (Q) × f (T 1) × f (VPD) × f (C a) × f (φ)$	6	各环境因子对气孔的调控相互独立	形式简单,可就多个环境因子建立多元非线性函数关系	1.忽略了各环境因子间的相互作用关系。2.参数生理意义不明确,调参困难	广泛应用于叶片、冠层, 甚至站点尺度的模拟
Ball^[34]	$g s = g 0 + a × A net × H r C s$	4	气孔导度与光合速率线性相关。假设叶表CO₂浓度和空气湿度保持恒定	参数生理意义明确;样本需求量较低	1.无法反应气孔导度对综合环境因子的响应。2.气孔导度与光合速率不成线性关系的部分仍需耦合其他模型进行模拟。
Leuning^[35]	$g s = g 0 + a × A net (C s - Γ) (1 + VPD D 0)$	7	气孔导度与光合速率线性相关。假设叶表CO₂浓度和空气湿度保持恒定	引入CO₂饱和点和饱和水气压差
Tardieu & Davies^[47]	$g s = g 0 + a × exp ABA × β × exp (δψ)$	5	土壤水分不足时,植物根系通过ABA调控气孔开度	模型机理明确;明确反应气孔导度水分胁迫的反馈	参数获取困难	主要应用于水分亏缺条件下的气孔导度估算
Buckley^[52]	$g s = χ (P g - m P e)$	>10	气孔开度有保卫细胞与外界膨压差调控	形式简单,便于参数化和应用	合理性方面存在争议	主要应用于水分亏缺条件下的气孔导度估算
Wang Qiuling^[7]	$g s = 1.6 (1 + g l VPD) A net C a + g 0$ $g l = 3 Γλ / 1.6$	7	气孔最优化理论	机理性强	参数λ确定困难	主要应用于自然界植被,在农业作物研究中应用较少

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