Modification of Leaf Water Content for the Photosynthetic and Biochemical Mechanism Model of C4 Plant

Feng Xiaoyu; Zhou Guangsheng

doi:10.11898/1001-7313.20220311

Vol.33, NO.3, 2022

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Article Navigation > Journal of Applied Meteorological Science > 2022 > 33(3): 375-384

Feng Xiaoyu, Zhou Guangsheng. Modification of leaf water content for the photosynthetic and biochemical mechanism model of C4 plant. J Appl Meteor Sci, 2022, 33(3): 375-384. DOI: 10.11898/1001-7313.20220311.

Citation:

Feng Xiaoyu, Zhou Guangsheng. Modification of leaf water content for the photosynthetic and biochemical mechanism model of C4 plant. J Appl Meteor Sci, 2022, 33(3): 375-384. DOI: 10.11898/1001-7313.20220311.

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Modification of Leaf Water Content for the Photosynthetic and Biochemical Mechanism Model of C4 Plant

DOI: 10.11898/1001-7313.20220311

Feng Xiaoyu¹,
Zhou Guangsheng^{2
,
,}

1.
Jinhua Meteorological Service of Zhejiang, Jinhua 321000
2.
Chinese Academy of Meteorological Sciences, Beijing 100081

Received Date: 2021-12-06
Rev Recd Date: 2022-02-28
Publish Date: 2022-05-31

Abstract

Abstract

The accurate simulation of leaf photosynthesis is of great significance to the study of terrestrial ecosystem model and understanding the impact of global change on vegetation. To improve the description of photosynthesis from phenomenon to mechanism, empirical model is gradually replaced by photosynthetic biochemical mechanism model, in which the photosynthetic biochemical mechanism model proposed by Farquhar is widely recognized and used. The effect of CO₂ concentration on plant photosynthesis is considered by the model, but the response of plant photosynthetic parameters to temperature and light intensity is studied without considering water stress. Water is one of the important raw materials of photosynthesis, which directly affects leaf stomatal conductance, transpiration rate and photosynthetic rate, and then affects plant photosynthesis. Therefore, many experiments are carried out and the water response function is gradually established. However, these studies mostly focus on soil water content rather than leaf water content that directly affects photosynthesis, limiting the accurate simulation of photosynthesis.Taking maize from North China as the research object, drought simulation data are studied based on six water gradient tests which are carried out at Gucheng Ecological and Agro-meteorological Experimental Station of Chinese Academy of Meteorological Sciences from June to October in 2014. Different from the previous empirical model of photosynthesis, a C4 plant photosynthetic biochemical mechanism model developed from the biochemical mechanism model proposed by Farquhar and modified by von Caemmerer is applied. The sampling blades and the environmental factors such as temperature, CO₂, relative humidity, and light intensity are kept consistent, and the temperature difference between different observation times are adjusted to quantitatively study the relationship between leaf water content and maximum carboxylation rate accurately. The results show that the relationship between them can be expressed in a quadratic curve significantly (passing the test of 0.01 level), and the determination coefficient of the fitting equation is up to 0.88. With different parameters, the values of the maximum carboxylation rate are different, but the normalized leaf water content correction function is independent of the parameters. Through calculation, when the leaf water content is about 80%, the value of correction function is 1, and when the leaf water content drops to about 70%, the value is 0. This result perfects the photosynthetic biochemical mechanism model of C4 plant from the perspective of leaf water content, which provides a reference for further improving the accuracy of photosynthesis simulation, drought monitoring and early warning of maize.
- leaf water content;
- photosynthesis;
- quantitative simulation;
- maize

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References(35)

Figures and Tables

Fig. 1 Light response under consecutive drought treatments with different water gradients

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Fig. 2 Environmental variables under consecutive drought treatments with different water gradients

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Fig. 3 Relative soil moisture under consecutive drought treatments with different water gradients

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Fig. 4 Relationship between V^* and L

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Fig. 5 Growth stages under consecutive drought treatments with different water gradients

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Fig. 6 Normalized leaf water content influence curve

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Table 1 Leaf water content at different watering treatments in 2014 (unit:%)

水分处理	07-11	07-18	07-31	08-07	08-20
W1	78.9	74.4	72.9	71.6	68.9
W2	81.8	75.8	73.8	72.1	71.6
W3	83.7	75.9	74.1	72.4	71.8
W4	84.3	77.4	76.4	72.9	72.0
W5	84.4	78.3	77.1	74.3	72.1
W6	84.9	78.6	78.1	75.1	72.2

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Table 2 Relative soil water content at different watering treatments in 2014 (unit:%)

水分处理	07-11	07-18	07-31	08-07	08-20
W1	48.8	43.6	41.0	38.9	37.3
W2	58.4	50.3	45.3	44.3	37.7
W3	66.5	55.9	50.6	46.5	45.6
W4	81.8	64.5	51.2	49.2	45.2
W5	88.5	69.7	56.3	49.5	42.1
W6	92.4	71.0	59.4	51.9	49.2

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Table 3 Fitting equations under different values of K_c and K_o

拟合方程	R²	L/%	V₀^*/(μmol·m^-2·s^-1)	K_c和K_o取值参考
y=-0.38x²+61.76x-2450.5	0.8789	80.79	44.22	文献[22]
y=-0.47x²+76.64x-3039.0	0.8764	80.76	55.43	文献[26]
y=-0.57x²+91.92x-3644.8	0.8754	80.75	66.40	文献[4]

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