冬小麦水分利用效率相关驱动因子及其预测模型构建

doi:10.3864/j.issn.0578-1752.2024.07.006

Abstract

Abstract:

【Objective】The water use efficiency can comprehensively reflect the growth suitability and energy conversion efficiency of winter wheat. The driving factors of winter wheat in response to standardized water use efficiency (WP*) at different growth stages were screened and explored, and the WP* prediction model of related driving factors was constructed, which was of great significance for the monitoring of water use efficiency and efficient use of water resources in winter wheat in the Huang-Huai-Hai Plain.【Method】Three water treatments were set up, including water deficit treatments (W1:35 mm, and W2:48 mm) and control treatment (W3: 68 mm), and the canopy temperature parameters, physiological indexes and standardized WP* of winter wheat at the jointing, booting and filling stages were measured. Stepwise regression and pathway analysis were used to screen the main driving factors in response to WP* changes at each growth stage, the relationship between WP* and related drivers was explored, and finally the partial least squares regression (PLSR) and support vector machine (SVM) methods were used to construct a driver-based WP* prediction model in each growth stage. 【Result】 Compared with W3, the canopy temperature parameters, physiological indexes and WP* of winter wheat under the water deficit treatments showed significant differences. Based on the stepwise regression method, the main driving factors in response to WP* at each growth stage were screened, and the sensitivity of each driving factor in response to WP* was ranked by pathway analysis, that is, maximum temperature difference (MTD), stomatal conductance (G_s), leaf water content (LWC) and POD were selected at the jointing stage; canopy relative temperature difference (CRTD), equivalent water thickness (EWT), soluble sugar content (SSC) and crop water stress index (CWSI) were selected at the booting stage; SSC, standard deviation of canopy temperature (CTSD), LWC and G_s were selected at the filling stage. Finally, the driver-based WP* prediction model for each growth stage was construct by using PLSR and SVM. Among them, the prediction model of WP* at booting stage constructed by SVM had the best accuracy, with R²_cal(R²_val), RMSE_cal(RMSE_val) and nRMSE_cal(nRMSE_val) of 0.945 (0.926), 0.533 g·m^-2(0.580 g·m^-2) and 2.844% (3.075%), respectively. 【Conclusion】 By screening the relevant driving factors of WP* at each growth stage of winter wheat and constructing a prediction model of winter wheat water use efficiency, this paper provided a theoretical basis for accurate monitoring and management of winter wheat moisture in the Huang-Huai-Hai Plain.

Key words: winter wheat, standardized water use efficiency (WP*), driving factor, pathway analysis, support vector machine

GAO ChenKai, LIU ShuiMiao, LI YuMing, ZHAO ZhiHeng, SHAO Jing, YU HaoLin, WU PengNian, WANG YanLi, GUAN XiaoKang, WANG TongChao, WEN PengFei. The Related Driving Factors of Water Use Efficiency and Its Prediction Model Construction in Winter Wheat[J].Scientia Agricultura Sinica, 2024, 57(7): 1281-1294.

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Figures/Tables 6

Table 1

Effects of different water treatments on canopy temperature parameters, physiological traits and WP* of two wheat varieties"

植株性状 Plant trait	处理 Treatment	拔节期 Jointing stage		孕穗期 Booting stage		灌浆期 Filling stage
植株性状 Plant trait	处理 Treatment	洛麦22 Luomai 22	周麦27 Zhoumai 27	洛麦22 Luomai 22	周麦27 Zhoumai 27	洛麦22 Luomai 22	周麦27 Zhoumai 27
冠层温度极差 MTD (X1)	W1	10.85-14.14	14.29-15.26	9.43-13.40	9.59-13.67	11.37-12.21	10.70-13.35
	W2	8.03-8.63	8.26-9.52	5.99-6.36	7.15-8.06	7.80-8.38	8.41-9.63
	W3	5.52-7.03	6.24-6.68	4.43-5.71	5.38-6.12	5.99-7.40	7.65-8.52
冠层温度标准差 CTSD (X2)	W1	1.21-1.46	1.09-1.64	1.55-1.94	1.66-2.29	1.72-1.95	1.98-2.53
	W2	0.92-1.00	0.96-1.08	0.73-0.87	0.85-0.93	1.35-1.61	1.29-1.63
	W3	0.83-0.88	0.83-0.87	0.55-0.69	0.64-0.79	0.61-0.89	0.73-1.15
冠层相对温差 CRTD (X3)	W1	0.163-0.184	0.174-0.191	0.179-0.191	0.216-0.244	0.167-0.185	0.173-0.208
	W2	0.136-0.144	0.169-0.178	0.117-0.134	0.131-0.143	0.115-0.146	0.126-0.144
	W3	0.113-0.124	0.134-0.162	0.088-0.116	0.101-0.127	0.085-0.106	0.119-0.123
作物水分胁迫指数 CWSI (X4)	W1	0.41-0.45	0.42-0.49	0.42-0.48	0.41-0.49	0.45-0.51	0.50-0.55
	W2	0.34-0.36	0.40-0.43	0.37-0.39	0.42-0.48	0.42-0.45	0.43-0.46
	W3	0.30-0.32	0.34-0.37	0.29-0.33	0.29-0.39	0.31-0.38	0.37-0.41
POD activity (U·g^-1·min^-1) (X5)	W1	244.99±1.93a	234.58±8.90a	393.81±11.03a	374.91±10.89a	495.31±15.98a	460.67±13.66a
	W2	154.22±7.05b	160.52±7.95b	300.15±13.64b	290.72±18.13b	367.40±14.98b	354.13±14.39b
	W3	129.77±5.43c	122.21±5.30c	214.57±13.67c	203.89±10.58c	282.41±8.77c	270.00±11.47c
SOD activity (U·g^-1·min^-1) (X6)	W1	142.87±11.82a	120.10±6.11a	188.94±10.66a	171.51±15.86a	211.25±6.52a	206.24±4.90a
	W2	123.77±7.64a	111.73±10.64a	142.33±8.57b	137.22±5.08b	195.18±3.83b	194.07±3.10b
	W3	95.17±3.48b	77.70±7.58b	116.52±5.07c	102.29±15.42c	183.81±3.31c	180.47±5.64c
可溶性糖含量 Soluble sugar content (mg·g^-1) (X7)	W1	50.98±3.10a	46.70±4.03a	85.70±8.14a	82.33±8.58a	127.44±9.29a	108.13±3.35a
	W2	38.54±2.11b	36.54±2.01b	66.89±6.78b	53.10±1.54b	109.62±5.76a	91.00±7.33b
	W3	33.35±1.17c	27.16±3.11c	41.16±7.30c	39.52±2.12c	83.47±3.75b	63.75±4.13c
脯氨酸含量 Proline content (mg·g^-1) (X8)	W1	94.37±3.70a	89.09±6.03a	149.61±14.80a	136.49±5.74a	226.20±16.21a	202.54±13.54a
	W2	68.34±4.99b	53.45±5.25b	117.94±0.87b	107.05±6.21b	193.19±5.28b	169.05±6.13b
	W3	38.16±3.78c	35.28±3.94c	96.69±8.67c	74.35±6.42c	153.86±5.84c	136.59±8.42c
叶片含水量 Leaf water content (g·g^-1) (X9)	W1	0.772±0.004c	0.739±0.015b	0.640±0.008c	0.623±0.013c	0.529±0.013c	0.514±0.011c
	W2	0.783±0.001b	0.774±0.003a	0.673±0.009b	0.657±0.012b	0.581±0.002b	0.564±0.008b
	W3	0.800±0.002a	0.790±0.002a	0.730±0.008a	0.693±0.013a	0.658±0.005a	0.629±0.015a
等效水厚度 Equivalent water thickness (g·cm^-2) (X10)	W1	0.14±0.002c	0.13±0.007c	0.09±0.001c	0.09±0.001c	0.06±0.005c	0.05±0.003c
	W2	0.15±0.004b	0.15±0.002b	0.11±0.001b	0.11±0.004b	0.07±0.002b	0.06±0.004b
	W3	0.17±0.004a	0.16±0.003a	0.13±0.006a	0.12±0.003a	0.08±0.006a	0.07±0.004a
气孔导度 Stomatal conductance (mol·m^-2·s^-1) (X11)	W1	0.18±0.018b	0.15±0.007c	0.21±0.003c	0.20±0.014c	0.19±0.005c	0.16±0.014c
	W2	0.21±0.017b	0.26±0.012b	0.24±0.008b	0.23±0.009b	0.21±0.015b	0.21±0.011b
	W3	0.26±0.005a	0.28±0.012a	0.30±0.020a	0.29±0.017a	0.25±0.007a	0.23±0.006a
蒸腾速率 Transpirationrate (mol·m^-2·s^-1) (X12)	W1	2.60±0.015c	2.51±0.086c	3.90±0.042c	3.74±0.125c	3.03±0.083c	3.00±0.135c
	W2	2.98±0.152b	2.97±0.035b	4.54±0.054b	4.42±0.124b	4.20±0.021b	4.08±0.059b
	W3	3.43±0.047a	3.41±0.101a	5.74±0.138a	5.80±0.113a	4.50±0.019a	4.35±0.025a
标准化水分利用效率 WP* (g·m^-2) (Y)	W1	16.19±0.32b	15.97±0.64b	18.18±0.34b	18.23±0.39b	18.56±0.35b	18.62±0.06b
	W2	17.65±0.39a	17.69±0.21a	18.83±0.37b	18.53±0.15b	19.21±0.37b	19.19±0.09b
	W3	18.52±0.22a	18.83±0.18a	19.35±0.13a	19.59±0.18a	19.84±0.12a	19.90±0.06a

Table 1

Table 2

Table 3

Fig. 1

Fig. 2

Fig. 3

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响应变量 Response variable	步骤 Step	拔节期 Jointing stage		孕穗期 Booting stage		灌浆期 Filling stage
响应变量 Response variable	步骤 Step	入选变量 Variable entered	R²	入选变量 Variable entered	R²	入选变量 Variable entered	R²
标准化水分利用效率 WP*	S₁	MTD	0.798	CRTD	0.861	SSC	0.694
	S₂	LWC	0.829	CWSI	0.916	CTSD	0.816
	S₃	G_s	0.862	EWT	0.927	LWC	0.888
	S₄	POD	0.886	SSC	0.939	G_s	0.913

生育时期 Growing stage	逐步回归方程 Stepwise regression equation	决定系数 R²
拔节期Jointing stage	Y= 6.546-0.228 X1+0.006 X5+13.259 X9+8.001 X11	0.886
孕穗期Booting stage	Y=19.390-5.983 X3-1.593 X4+11.730 X10-0.007 X7	0.939
灌浆期Filling stage	Y=18.642-0.476 X2-0.010 X7+2.808 X9+1.761 X11	0.913

The Related Driving Factors of Water Use Efficiency and Its Prediction Model Construction in Winter Wheat

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