CO2浓度升高下水稻株高、茎蘖与SPAD动态响应及其模拟

doi:10.16258/j.cnki.1674-5906.2023.05.011

生态环境学报 ›› 2023, Vol. 32 ›› Issue (5): 933-942.DOI: 10.16258/j.cnki.1674-5906.2023.05.011

CO₂浓度升高下水稻株高、茎蘖与SPAD动态响应及其模拟

杨凯¹^,²(), 杨靖睿¹^,², 曹培培¹^,², 吕春华¹^,², 孙文娟¹, 于凌飞¹, 邓希³^,^*()

1.中国科学院植物研究所/植被与环境变化国家重点实验室，北京 100093
2.中国科学院大学，北京 100049
3.中山大学大气科学学院，广东珠海 519000

收稿日期:2023-03-01 出版日期:2023-05-18 发布日期:2023-08-09
通讯作者: *邓希（1993年生），女，博士，从事气候变化与粮食安全研究。E-mail: dengxi5@mail.sysu.edu.cn
作者简介:杨凯（1993年生），男，博士，从事气候变化与农业生态研究。E-mail: yangkai@ibcas.ac.cn
基金资助:
国家自然科学基金重点项目(41530533);中国博士后科学基金面上项目(2021M693583)

Dynamic Response of Rice Plant Height, Tillering and SPAD under Elevated CO₂ Concentration and Their Simulation

YANG Kai¹^,²(), YANG Jingrui¹^,², CAO Peipei¹^,², LÜ Chunhua¹^,², SUN Wenjuan¹, YU Lingfei¹, DENG Xi³^,^*()

1. State Key Laboratory of Vegetation and Environmental Change/Institute of Botany, Chinese Academy of Sciences, Beijing 100093, P. R. China
2. University of Chinese Academy of Sciences, Beijing 100049, P. R. China
3. School of Atmospheric Sciences, Sun Yat-sen University, Zhuhai 519000, P. R. China

Received:2023-03-01 Online:2023-05-18 Published:2023-08-09

摘要/Abstract

摘要：

水稻是中国主要粮食作物，CO₂浓度升高直接影响水稻的生产。但在CO₂浓度升高条件下，水稻生长发育的动态特征及其模型模拟研究尚不足。为探究水稻株高、分蘖与SPAD（表征叶绿素相对含量）动态对CO₂浓度升高的响应特征，在2017年与2018年，以常规粳稻“南粳9108”为试验材料，利用开顶式气室（OTC），设置背景大气CO₂浓度与CO₂浓度升高（+200 μmol·mol^-1）两个处理，并采用Logistic方程、Logistic修正方程和多项式回归方程分别对三者的动态曲线进行定量描述。结果表明：CO₂浓度升高对抽穗前的株高无影响，但当抽穗1周后的有效积温（GDD）大于720 ℃·d时，显著增加了最终株高，其增幅为7.1%（P<0.05）。因此，水稻生长的环境温度调控株高对CO₂浓度升高的响应。株高可用大于10 ℃的GDD和CO₂响应比建立的Logistic方程进行有效模拟（r²>0.953）。CO₂浓度升高总体上提高了水稻的分蘖能力，并且以移栽后天数和CO₂响应比为驱动变量，采用Logistic修正方程有效地模拟了茎蘖增长与消亡动态（r²>0.971）。CO₂浓度升高仅增加了2017年抽穗后35 d旗叶与倒二叶的SPAD（P<0.05），但对其他时期或叶位SPAD无显著影响。因此，水稻SPAD对CO₂升高的响应因叶位选择与测定时间而异。多项式回归模型能有效模拟抽穗后不同叶位SPAD动态（r²>0.960），其中抽穗后天数和CO₂响应比是驱动变量。综上所述，CO₂浓度升高对水稻株高、茎蘖与SPAD具有促进作用，其效应与外界因素有关。模型能较好地模拟水稻株高、茎蘖与SPAD对CO₂浓度升高的动态响应。该研究可为未来CO₂浓度升高条件下，水稻生长发育和产量形成的预测提供科学依据。

关键词: 水稻, CO₂浓度升高, 株高动态, 分蘖动态, SPAD动态, 模型模拟

Abstract:

Rice is the main cereal crop in China, and elevated CO₂ concentration (e[CO₂]) directly affects the production of rice. However, research on the dynamic characteristics of rice growth and development and their simulation under the condition of e[CO₂] is still insufficient. To investigate the dynamic responses of plant height, tillering and SPAD (characterizing the relative content of chlorophyll) to e[CO₂] in rice (Oryza sativa L.). A conventional Japonica rice variety, “Nangeng 9108”, was cultivated under two CO₂ levels―ambient CO₂ concentration and e[CO₂] (+200 μmol·mol^-1) using open-top chamber (OTC) in 2017 and 2018. Dynamic curves of the height, tillering and SPAD of rice were quantified by using the logistic equation, logistic modified equation and polynomial regression equation, respectively. The results showed that e[CO₂] did not affect plant height before the heading stage. However, when the growing degree day (GDD) was greater than 720 ℃·d, the final plant height increased by 7.1% (P<0.05) one week after heading. Thus, the response of plant height to e[CO₂] was regulated by the environmental temperature of rice growth. The plant height was effectively modeled by the GDD above 10 ℃ and CO₂ response ratio using the logistic equation (r²>0.953). Generally, e[CO₂] enhanced the tillering ability of rice. The tiller growth and extinction dynamics were effectively simulated using the logistic modified equation (r²>0.971), with the days after transplanting and CO₂ response ratio as driving variables. The e[CO₂] increased the SPAD of the flag leaf and second leaf, thirty-five days after heading in 2017 (P<0.05), but had no effect on SPAD of other periods and leaf position. Thus, the response of rice SPAD to e[CO₂] varied with the measured time and leaf position. The dynamic curves of SPAD of different leaf position could be effectively simulated by polynomial regression model (r²>0.960), with days after heading and CO₂ response ratio as driving variables. In summary, e[CO₂] can promote the plant height, tillering and SPAD in rice, and the effect is affected by external factors. The models can effectively simulate their dynamic response to e[CO₂]. Our study can provide a scientific basis for predicting the growth, development and yield formation of rice under of e[CO₂] in future studies.

Key words: rice, elevated CO₂ concentration, plant height dynamics, tillering dynamics, SPAD dynamics, model simulation

中图分类号:

X16
X171.5

杨凯, 杨靖睿, 曹培培, 吕春华, 孙文娟, 于凌飞, 邓希. CO₂浓度升高下水稻株高、茎蘖与SPAD动态响应及其模拟[J]. 生态环境学报, 2023, 32(5): 933-942.

YANG Kai, YANG Jingrui, CAO Peipei, LÜ Chunhua, SUN Wenjuan, YU Lingfei, DENG Xi. Dynamic Response of Rice Plant Height, Tillering and SPAD under Elevated CO₂ Concentration and Their Simulation[J]. Ecology and Environment, 2023, 32(5): 933-942.

图/表 10

参考文献 47

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生育时期	日期
生育时期	2017	2018
播种	2017-05-20	2018-05-20
移栽	2017-06-20	2018-06-20
拔节	2017-07-28	2018-07-26
抽穗	2017-08-23	2018-08-24
成熟	2017-10-30	2018-10-20

生育时期	日期
生育时期	2017	2018
播种	2017-05-20	2018-05-20
移栽	2017-06-20	2018-06-20
拔节	2017-07-28	2018-07-26
抽穗	2017-08-23	2018-08-24
成熟	2017-10-30	2018-10-20

年份	H/cm	a₁	a₂	r²	RMSE
2017	155.6	1.381	-0.0020	0.970	2.96
2018	110.8	0.783	-0.0035	0.953	3.52

年份	H/cm	a₁	a₂	r²	RMSE
2017	155.6	1.381	-0.0020	0.970	2.96
2018	110.8	0.783	-0.0035	0.953	3.52

t	T₁/T₂	a₁/b₁	a₂/b₂	a₃/b₃	r²	RMSE
t≤35 (茎蘖增长)	27.3	-1.660	0.1856	-0.0062	0.971	0.77
t>35 (茎蘖消亡)	10.1	7.0716	-0.1819	0.0009	0.996	0.65

CO₂浓度升高下水稻株高、茎蘖与SPAD动态响应及其模拟

Dynamic Response of Rice Plant Height, Tillering and SPAD under Elevated CO₂ Concentration and Their Simulation

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年	测定日期	CO₂ 处理	L1	L2	L3	P值
年	测定日期	CO₂ 处理	L1	L2	L3	CO₂	L	CO₂×L
2017	8月29日	a[CO₂]	48.4a	49.3a	48.3a	ns	ns	ns
	8月29日	e[CO₂]	49.9a	49.6a	48.3a	ns	ns	ns
	9月3日	a[CO₂]	47.5a	48.7a	47.4a	ns	ns	ns
	9月3日	e[CO₂]	49.2a	48.7a	47.7a	ns	ns	ns
	9月8日	a[CO₂]	49.2a	49.4a	48.2a	ns	*	ns
	9月8日	e[CO₂]	50.7a	49.2a	48.0a	ns	*	ns
	9月13日	a[CO₂]	48.3a	48.8a	47.7a	ns	*	ns
	9月13日	e[CO₂]	49.8a	49.3a	48.0a	ns	*	ns
	9月18日	a[CO₂]	46.6b	47.3a	45.9a	+	**	ns
	9月18日	e[CO₂]	48.2a	47.9a	45.7a	+	**	ns
	9月23日	a[CO₂]	47.3a	47.7a	44.8a	ns	**	ns
	9月23日	e[CO₂]	47.7a	47.3a	43.8a	ns	**	ns
	9月28日	a[CO₂]	45.1b	43.9b	42.7a	**	**	ns
	9月28日	e[CO₂]	46.8a	46.0a	43.4a	**	**	ns
	10月3日	a[CO₂]	43.8a	43.5a	-	**	ns	ns
	10月3日	e[CO₂]	45.7b	44.5a	-	**	ns	ns
	10月8日	a[CO₂]	41.4a	41.6a	-	**	ns	ns
	10月8日	e[CO₂]	43.9b	43.6a	-	**	ns	ns
	10月13日	a[CO₂]	38.8b	38.2b	-	**	ns	ns
	10月13日	e[CO₂]	43.1a	42.1a	-	**	ns	ns
	10月18日	a[CO₂]	35.4b	33.8b	-	**	ns	ns
	10月18日	e[CO₂]	40.0a	38.9a	-	**	ns	ns
2018	8月25日	a[CO₂]	39.1a	40.9a	41.5a	+	**	ns
	8月25日	e[CO₂]	39.5a	41.1a	42.5a	+	**	ns
	8月30日	a[CO₂]	40.8a	41.3a	42.0a	ns	+	ns
	8月30日	e[CO₂]	40.6a	41.7a	41.9a	ns	+	ns
	9月5日	a[CO₂]	38.9a	39.6a	39.5a	*	+	ns
	9月5日	e[CO₂]	39.5a	40.7a	40.8a	*	+	ns
	9月10日	a[CO₂]	37.0a	37.8a	36.4a	ns	ns	ns
	9月10日	e[CO₂]	37.1a	37.9a	37.4a	ns	ns	ns
	9月15日	a[CO₂]	36.2a	36.1a	34.6a	ns	+	ns
	9月15日	e[CO₂]	36.4a	36.5a	35.2a	ns	+	ns
	9月22日	a[CO₂]	33.3a	32.8a	30.9a	ns	*	ns
	9月22日	e[CO₂]	34.0a	32.9a	31.5a	ns	*	ns
	9月29日	a[CO₂]	23.5b	21.2a	20.2b	**	**	ns
	9月29日	e[CO₂]	28.0a	23.8a	24.7a	**	**	ns
	10月6日	a[CO₂]	16.8a	13.5a	15.3a	ns	**	ns
	10月6日	e[CO₂]	18.8a	12.9a	17.3a	ns	**	ns

年	叶位	a₁	a₂	a₃	r²	RMSE
2017	旗叶	46.95	0.2232	-0.0078	0.983	0.55
	倒二叶	48.01	0.2034	-0.0081	0.980	0.69
	倒三叶	47.31	0.1712	-0.0086	0.960	0.39
2018	旗叶	39.47	0.2062	-0.0179	0.982	1.10
	倒二叶	39.84	0.2756	-0.0217	0.985	1.18
	倒三叶	42.76	-0.1398	-0.0126	0.983	1.20

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