小麦生长模型对拔节期和孕穗期低温胁迫响应能力的比较

doi:10.3864/j.issn.0578-1752.2021.03.005

中国农业科学 ›› 2021, Vol. 54 ›› Issue (3): 504-521.doi: 10.3864/j.issn.0578-1752.2021.03.005

• 耕作栽培·生理生化·农业信息技术 • 上一篇下一篇

小麦生长模型对拔节期和孕穗期低温胁迫响应能力的比较

肖浏骏(),刘蕾蕾,邱小雷,汤亮,曹卫星,朱艳,刘兵()

南京农业大学农学院/国家信息农业工程技术中心/智慧农业教育部工程研究中心/农业农村部农作物系统分析与决策重点实验室/江苏省信息农业重点实验室/江苏省现代作物生产协同创新中心,南京 210095

收稿日期:2020-04-23 接受日期:2020-10-12 出版日期:2021-02-01 发布日期:2021-02-16
联系方式: 肖浏骏,E-mail: liujunxiao@zju.edu.cn。
基金资助:
国家重点研发计划(2019YFA0607404);国家杰出青年科学基金(31725020);国家自然科学基金(32021004);国家自然科学基金(41961124008);国家自然科学基金(31872848);国家自然科学基金(31301234)

Testing the Responses of Low Temperature Stress Routine to Low Temperature Stress at Jointing and Booting in Wheat

XIAO LiuJun(),LIU LeiLei,QIU XiaoLei,TANG Liang,CAO WeiXing,ZHU Yan,LIU Bing()

College of Agriculture, Nanjing Agricultural University/National Engineering and Technology Center for Information Agriculture/ Engineering Research Center of Smart Agriculture, Ministry of Education/ Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs/Jiangsu Key Laboratory for Information Agriculture/Jiangsu Collaborative Innovation Center for Modern Crop Production, Nanjing 210095

Received:2020-04-23 Accepted:2020-10-12 Published:2021-02-01 Online:2021-02-16

摘要/Abstract

摘要：

【目的】作物生长模型是预测和评估气候变化对作物生产力影响的重要量化工具,明确典型作物生长模型对小麦拔节期和孕穗期低温胁迫响应能力的不足,可以为进一步改进低温胁迫对小麦生产力影响的模拟算法提供指导。【方法】本研究将来自4套国际知名小麦生长模型（美国密歇根州立大学的CERES-Wheat、美国华盛顿州立大学的CropSyst、荷兰瓦赫宁根大学的WOFOST和法国国家农业科学研究院的STICS模型）的典型低温胁迫效应算法,与本课题组研发的小麦生长模拟模型WheatGrow相耦合,利用2012—2013年南京和2013—2015如皋不同品种（扬麦16和徐麦30）、不同温度水平（最低至-6℃）和持续时间（2、4、6 d）的人工气候室低温盆栽试验资料,检验和评价了原WheatGrow模型和耦合后低温胁迫效应算法的WheatGrow模型在拔节期和孕穗期低温胁迫下对小麦叶面积指数动态、茎生物量、地上部总生物量、籽粒产量等指标的预测能力。【结果】拔节—孕穗期低温胁迫明显降低了小麦叶面积指数、地上部生物量积累和籽粒产量,且随低温水平的降低和持续时间的增加降低幅度呈明显升高趋势。比较不同处理时期和品种发现,小麦生长发育及产量对孕穗期低温处理较拔节期低温处理更加敏感,扬麦16较徐麦30对低温胁迫更为敏感。耦合了4种低温胁迫效应算法的WheatGrow模型在模拟叶面积指数动态上较原WheatGrow模型有所改善,但模拟误差仍然较大,其中对孕穗期低温处理的模拟误差大于拔节期处理。4种低温胁迫算法均低估了低温胁迫对茎生物量以及成熟期地上部生物量积累的不利影响。综合比较4种低温胁迫算法的预测能力可以看出,对于叶面积指数和地上部生物量的动态模拟,CropSyst模型中的低温胁迫效应算法表现最好;对于茎生物量的动态模拟,WOFOST模型中的低温胁迫效应算法表现最好,特别是孕穗期低温处理;对于籽粒产量的模拟,STICS模型中的低温胁迫效应算法表现最好,其次是CropSyst模型。【结论】耦合低温胁迫效应算法后的WheatGrow模型,在模拟叶面积指数、茎生物量、地上部生物量和籽粒产量上均好于原WheatGrow模型,且在弱低温条件下的模拟效果好于强低温条件,但是4套算法由于没有考虑低温胁迫对茎秆的直接伤害、低温胁迫对干物质分配的影响以及低温胁迫后的恢复和补偿效应,因此在模拟茎生物量积累,以及模拟不同低温持续时间下的地上部生物量积累存在明显不足。此外,4套低温效应算法引入参数较多,为模型的参数化带来一定的困难,有待今后进一步改进和完善。研究结果对改进小麦生长模型对低温胁迫响应,降低气候变化背景下作物生产力的预测预警的不确定性具有重要意义。

关键词: 小麦, 低温胁迫, 作物生长模型, 算法比较, 模型检验, WheatGrow模型

Abstract:

【Objective】Crop growth model is an essential approach for predicting and evaluating crop productivity under climate change. This study was conducted to clearly demonstrate the shortcomings of the existing models under low temperature stress, and provide instructions to improve the algorithms for simulating effects of low temperature stress on wheat productivity.【Method】The low temperature stress response routines from four famous wheat models, including CERES-Wheat from Michigan State University, CropSyst from Washington State University, WOFOST from Wageningen University in the Netherlands, and STICS from INRA in France, were integrated into the WheatGrow model. And then, the WheatGrow model was used to test and evaluate the responses of low temperatures stress routines in simulating effects of low temperature stress at jointing and booting stages on wheat leaf area index, stem biomass, aboveground biomass and grain yield, with detailed observed datasets from environment-controlled phytotron experiments under different temperature levels (lowest to -6℃) and durations (2 days, 4 days and 6 days) of low temperature stress at Nanjing (2012-2013) and Rugao (2013-2015) with two wheat cultivars (Yangmai16 and Xumai30).【Result】 The results showed that leaf area index, aboveground biomass, and grain yield were decreased significantly under low temperature stress during jointing and booting stages, and the reductions increased with the increasing duration of low temperature stress and the decreasing low temperature level. Wheat growth and grain yield were more sensitive to low temperature stress at booting than at jointing, and Yangmai16 were more sensitive to low temperature stress than Xumai30. The integration of four low-temperature stress algorithms improved the performance of the original WheatGrow model in simulating the dynamics of leaf area index, but the simulation errors were still large, and the simulation errors were larger under low temperature stress at booting stage than at jointing stage. All four low temperature stress routines underestimated the negative effects of low temperature stress on the accumulation of stem and aboveground biomass. Comparing the overall performance of the four low temperature stress routines, the low temperature stress routine from CropSyst model performed best in simulating the dynamics of leaf area index and aboveground biomass. For the simulation of stem biomass dynamic, the low temperature stress algorithm from the WOFOST model performed best among the four routines, especially under low temperature stress at booting. The low temperature stress algorithm from STICS is the best routine in the simulation of grain yield under low temperature stress, followed by CropSyst model. 【Conclusion】The integrated models with four low temperature stress algorithms were better than the original WheatGrow model in predicting aboveground biomass, stem biomass, leaf area index and grain yield, and the simulation error under weak low temperature conditions was smaller than that under strong low temperature conditions. However, there were large uncertainties in simulating the accumulation of stem biomass and simulating above-ground biomass under different durations of low temperature stress from all four low temperature algorithms, because none of the four low temperature stress routines considered the damaging effects of low temperature stress on stem biomass, dry matter partitioning, and the recovery and compensation effects after low temperature stress. As many parameters were introduced in the four low temperature stress algorithms, it was difficult for conducting model parameterization with existing algorithms, and this should be avoided in future model improvement. Our results were critical for improving the simulation of wheat growth and yield for wheat crop models under low temperature stress, and reducing the uncertainty in predicting crop productivity under climate change.

Key words: wheat, low temperature stress, crop growth model, algorithm comparison, model evaluation, WheatGrow model

肖浏骏,刘蕾蕾,邱小雷,汤亮,曹卫星,朱艳,刘兵. 小麦生长模型对拔节期和孕穗期低温胁迫响应能力的比较[J]. 中国农业科学, 2021, 54(3): 504-521.

XIAO LiuJun,LIU LeiLei,QIU XiaoLei,TANG Liang,CAO WeiXing,ZHU Yan,LIU Bing. Testing the Responses of Low Temperature Stress Routine to Low Temperature Stress at Jointing and Booting in Wheat[J]. Scientia Agricultura Sinica, 2021, 54(3): 504-521.

图/表 11

表1

图1

表2

图2

表3

原WheatGrow模型和耦合4种低温胁迫效应算法后的WheatGrow模型对叶面积指数和茎生物量动态预测误差的统计检验"

处理 Treatment (Tmax/Tmin)	模型 Model	叶面积指数 LAI										茎生物量 Stem biomass
		拔节期 Jointing (S1)					孕穗期 Booting (S2)					拔节期 Jointing (S1)					孕穗期 Booting (S2)
		MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²	MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²	MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²	MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²
8/-2℃	a	0.5	0.6	23.3	0.97	0.94	0.8	0.9	35.1	0.96	0.96	626.6	791.0	13.0	0.98	0.94	795	934.7	14	0.91	0.85
	b	0.5	0.6	21.6	0.97	0.93	0.7	0.8	30.5	0.96	0.96	536.2	678.1	11.2	0.98	0.95	594.1	749.6	11.2	0.94	0.84
	c	0.5	0.6	21.3	0.97	0.93	0.6	0.7	28.0	0.97	0.95	534.4	675.5	11.1	0.98	0.94	502.9	643.9	9.6	0.95	0.84
	d	0.5	0.6	23.3	0.97	0.94	0.6	0.8	30.1	0.97	0.95	626.6	791.0	13.0	0.98	0.94	582.9	739.3	11	0.94	0.81
	e	0.5	0.6	22.0	0.97	0.93	0.7	0.8	31.4	0.96	0.96	575.7	731.2	12.0	0.98	0.94	638.5	786.9	11.7	0.93	0.85
6/-4℃	a	0.7	0.8	32.3	0.95	0.92	1.1	1.3	58	0.9	0.94	842.7	982.4	17.4	0.96	0.96	2332	2495	49.6	0.74	0.67
	b	0.6	0.7	27.9	0.95	0.86	0.6	0.7	33.4	0.95	0.89	379.2	482.7	8.6	0.99	0.96	1096	1237	24.6	0.85	0.82
	c	0.5	0.6	26.0	0.96	0.89	0.6	0.7	33.1	0.95	0.88	445.9	569.0	10.1	0.99	0.96	997.8	1158	23	0.85	0.8
	d	0.5	0.7	27.2	0.96	0.9	0.6	0.7	33.7	0.95	0.84	565	708.7	12.6	0.98	0.96	617.5	774.1	15.4	0.92	0.84
	e	0.6	0.7	28.4	0.96	0.91	0.7	0.8	35.1	0.95	0.91	676.3	820.7	14.6	0.97	0.96	1271	1407	28	0.82	0.82
4/-6℃	a	0.8	0.9	43.0	0.92	0.91	2.0	2.3	185.0	0.72	0.69	1389	1639.0	32.7	0.9	0.9	5073	5384	249	0.73	0.1
	b	0.7	0.8	37.9	0.91	0.75	0.8	1.1	85.6	0.79	0.5	529.3	641.7	12.8	0.98	0.93	2437	2749	127.2	0.68	0.22
	c	0.5	0.6	29.2	0.95	0.85	1.0	1.2	93.2	0.78	0.5	653.8	881.7	17.6	0.96	0.92	2786	3089	142.9	0.7	0.1
	d	0.5	0.6	29.0	0.95	0.85	0.6	1	78.8	0.75	0.32	632.0	835.4	16.7	0.97	0.93	1244	1484	68.6	0.68	0.37
	e	0.6	0.7	31.7	0.95	0.87	0.9	1.1	87.6	0.79	0.5	871.5	1106.0	22.1	0.95	0.93	2525	2856	132.1	0.68	0.16
低于0℃ T_min<0℃	a	0.7	0.8	32.7	0.95	0.91	1.3	1.6	81.7	0.85	0.78	952.7	1194.0	21.4	0.95	0.91	2733	3468	74.9	0.67	0.15
	b	0.6	0.7	28.9	0.95	0.84	0.7	0.9	44.1	0.94	0.86	481.6	606.8	10.9	0.99	0.94	1376	1794	38.7	0.83	0.67
	c	0.5	0.6	25.3	0.97	0.9	0.7	0.9	45.2	0.93	0.84	544.7	720.5	12.9	0.98	0.94	1429	1941	41.9	0.79	0.58
	d	0.5	0.6	26.3	0.96	0.9	0.6	0.8	42.2	0.94	0.79	607.9	780.1	14.0	0.98	0.95	815	1056	22.8	0.95	0.84
	e	0.5	0.7	27.1	0.96	0.91	0.7	0.9	45.5	0.94	0.86	707.8	900.1	16.2	0.97	0.94	1478	1894	40.9	0.82	0.66

表3

图3

图4

图5

表4

原WheatGrow模型和耦合4种低温胁迫效应算法后的WheatGrow模型对成熟期地上部生物量和产量预测误差的统计检验"

处理 Treatment (Tmax/Tmin)	模型 Model	地上部生物量 Above ground biomass										籽粒产量 Grain yield
		拔节期 Jointing (S1)					孕穗期 Booting (S2)					拔节期 Jointing (S1)					孕穗期 Booting (S2)
		MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²	MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²	MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²	MAE (m²·m^-2)	RMSE (m²·m^-2)	NRMSE (%)	D-index	R²
8/-2℃	a	2096	2262	17.4	0.71	0.01	2692	2883	23.1	0.72	0.33	1113	1220	24	0.71	0.05	1211	1335	26.7	0.71	0.2
	b	1778	1934	14.8	0.7	0.06	2173	2284	18.3	0.73	0.24	975.3	1077	21.2	0.71	0.05	998.6	1080	21.6	0.72	0.52
	c	974.5	1196	9.2	0.62	0.12	759.6	915.7	7.3	0.81	0.52	334.4	455.6	9	0.74	0.29	412.1	495.2	9.9	0.84	0.7
	d	2096	2262	17.4	0.71	0.01	1988	2094	16.7	0.73	0.31	1113	1220	24	0.71	0.05	922.9	1001	20	0.72	0.45
	e	1192	1328	10.2	0.69	0.41	1239	1343	10.7	0.74	0.45	371.5	453.3	8.9	0.73	0.59	189.5	240.8	4.8	0.94	0.8
6/-4℃	a	3240	3323	28.4	0.74	0.02	6274	6480	73.5	0.74	0.16	1838	1876	43.7	0.74	0.08	3332	3391	118.8	0.74	0.11
	b	1696	1769	15.1	0.73	0.53	3890	3957	44.9	0.74	0.8	1204	1228	28.6	0.74	0.51	2356	2372	83.1	0.75	0.8
	c	622.5	732.3	6.3	0.78	0.41	1014	1148	13	0.84	0.8	475.6	630.9	14.7	0.72	0.66	509.7	566.4	19.9	0.86	0.81
	d	2640	2708	23.2	0.74	0.45	2550	2657	30.1	0.74	0.78	1591	1618	37.7	0.74	0.44	1807	1832	64.2	0.74	0.79
	e	1370	1478	12.6	0.72	0.47	1723	1858	21.1	0.76	0.8	407.5	484.9	11.3	0.75	0.52	466.8	581.5	20.4	0.86	0.81
4/-6℃	a	4903	5023	50.6	0.74	0	11666	11800	366.7	0.75	0.01	2650	2705	79.1	0.74	0.06	5284	5311	650.9	0.75	0.04
	b	1692	1836	18.5	0.72	0.66	6260	6381	198.3	0.74	0.79	1333	1384	40.5	0.74	0.52	3071	3129	383.6	0.74	0.8
	c	609.7	734.9	7.4	0.89	0.77	4247	4314	134.1	0.74	0.82	643.7	776.1	22.7	0.77	0.72	520.7	656.6	80.5	0.81	0.85
	d	3161	3228	32.5	0.74	0.62	2639	3070	95.4	0.74	0.82	1937	1972	57.7	0.74	0.48	1539	1754	214.9	0.71	0.84
	e	1562	1641	16.5	0.73	0.8	3358	3468	107.8	0.74	0.77	407.7	482.4	14.1	0.85	0.7	481.5	559.2	68.5	0.82	0.72
低于0 ℃ T_min<0℃	a	3413	3714	32.2	0.72	0.1	6877	7949	97.2	0.69	0.07	1867	2027	47.5	0.72	0.07	3276	3719	128.6	0.7	0.1
	b	1722	1847	16	0.79	0.79	4108	4531	55.4	0.75	0.85	1171	1236	29	0.74	0.77	2142	2351	81.3	0.73	0.79
	c	735.5	914	7.9	0.92	0.79	2007	2631	32.2	0.85	0.9	484.6	634.5	14.9	0.9	0.83	480.9	576.5	19.9	0.97	0.9
	d	2632	2761	23.9	0.73	0.75	2392	2637	32.2	0.91	0.92	1547	1633	38.3	0.73	0.72	1423	1574	54.4	0.85	0.86
	e	1375	1488	12.9	0.82	0.83	2107	2400	29.3	0.9	0.95	395.6	473.7	11.1	0.93	0.83	379.3	486.1	16.8	0.98	0.96

表4

图6

图7

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年份 Year	品种 Cultivar	站点 Site	处理时期 Stage	持续时间 Duration	温度设置 (T_max/T_min) (℃)	数据用途 Data utilization
2012-2013	扬麦16 Yangmai16 (V1) 徐麦30 Xumai30 (V2)	南京 Nanjing	拔节期 Jointing (S1) 孕穗期 Booting (S2)	D1 (2 d) D2 (4 d) D3 (6 d)	T1(16/6), T2(13/3) T3(10/0), T4(13/-3)	模型校正 Calibration
2013-2014		如皋Rugao			T1(16/6), T2(12/2) T3(8/-2), T4(4/-6)	模型校正 Calibration
2014-2015		如皋Rugao			T1(16/6), T2(8/-2) T3(6/-4), T4(4/-6)	模型检验 Validation

模型 Model	名称 Name	单位 Unit	描述 Description	参数值 Parameter value
				默认值 Default value	拔节期 Jointing (S1)		孕穗期 Booting (S2)
				默认值 Default value	V1	V2	V1	V2
CERES- Wheat	T_coef	℃	温度常数（叶面积）Temperature coefficient (LAI)	10	0	0	0	0
CERES- Wheat	Th_kill	℃	初始致死温度（植株） Initial killing temperature (plants)	-6	-0.5	-1.5	0	0
CropSyst	LT_{0_HI}	℃	初始致死温度（收获指数） Initial killing temperature (HI)	-0.5	0	0	0	0
	LT_{100_HI}	℃	完全致死温度（收获指数） Maximum stress killing temperature (HI)	-2	-14.3	-16.6	-8.34	-10.2
	Sensitivity	—	温度敏感性（收获指数） Temperature sensitivity (HI)	1	0.43	0.4	0.52	0.5
	LT_{0_LAI}	℃	开始致死温度（叶面积） Initial killing temperature (LAI)	0	0	0	0	0
	LT_{100_LAI}	℃	完全致死温度（叶面积） Maximum stress killing temperature (LAI)	-3	-14.3	-16.6	-8.34	-10.2
WOFOST	LT50	℃	半致死温度（叶面积和植株） Temperature when 50% plants die (y)	—	-7.15	-8.32	-4.17	-5.14
WOFOST	KILL_coef	—	致死速率系数 Steepness coefficient for logistic kill function	1.189	1.74	1.95	1.189	1.189
STICS	T_gelveg10	℃	10%致死温度（叶面积） Temperature when 10% damage (LAI)	-4.5	-4.5	-5	-2.5	-3
	T_gelveg90	℃	90%致死温度（叶面积） Temperature when 90% damage (LAI)	-10	-9.8	-11.7	-5.84	-7.28
	T_gelflo10	℃	10%致死温度（收获指数） Temperature when 10% decreases (HI)	-4.5	-5.8	-6	-3.5	-4
	T_gelflo90	℃	90%致死温度（收获指数） Temperature when 90% decreases (HI)	-6.5	-11.5	-12	-6.8	-7.5

小麦生长模型对拔节期和孕穗期低温胁迫响应能力的比较

Testing the Responses of Low Temperature Stress Routine to Low Temperature Stress at Jointing and Booting in Wheat

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