孙蕊^{1, 2,},
董心亮¹,
赵长龙^{1, 2},
苏寒^{1, 2},
王金涛¹,
刘小京^{1, 2},
孙宏勇^{1, 2,,}
1.中国科学院遗传与发育生物学研究所农业资源研究中心/中国科学院农业水资源重点实验室/河北省节水农业重点实验室 石家庄 050022
2.中国科学院大学 北京 100049
基金项目: 国家重点研发计划项目2018YFD0300504

详细信息

作者简介:孙蕊, 主要研究方向为农田生态水盐运移过程及调控机制。E-mail:sunrui172@mails.ucas.ac.cn

通讯作者:孙宏勇, 主要研究方向为农田水盐运移过程机理与调控。E-mail:hysun@sjziam.ac.cn

中图分类号:S512.1

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出版历程

收稿日期:2019-09-10
录用日期:2019-11-05
刊出日期:2020-02-01

Effect of climate, genotype, and water management on winter wheat yield and water use efficiency in Hebei Plain

SUN Rui^{1, 2,},
DONG Xinliang¹,
ZHAO Changlong^{1, 2},
SU Han^{1, 2},
WANG Jintao¹,
LIU Xiaojing^{1, 2},
SUN Hongyong^{1, 2,,}
1. Center for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences/Key Laboratory of Agricultural Water Resources, Chinese Academy of Sciences/Hebei Key Laboratory of Agricultural Water-saving, Shijiazhuang 050022, China
2. University of Chinese Academy of Sciences, Beijing 100049, China
Funds: the National Key Research and Development Project of China2018YFD0300504

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Corresponding author:SUN Hongyong, E-mail:hysun@sjziam.ac.cn

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摘要
摘要:冬小麦是华北平原的主要作物，其生长受气候等环境因子和品种、管理措施等因素的共同影响。为了研究气候×基因型×水分管理互作对河北平原冬小麦产量及水分利用效率（WUE）的影响，以‘科农2009’‘藁优2018’和‘师栾02-1’3个该区域主栽冬小麦品种为材料，于2018—2019年沿北纬38°带，选择河北平原冬小麦主产区的4个典型试验站点（衡水、南皮、栾城、南大港）进行了不同水分管理（雨养、灌溉）的大田试验。结果表明：在灌溉条件下，衡水、南皮、栾城和南大港的小麦产量分别为6 316.7kg·hm^-2、5 204.1 kg·hm^-2、4 356.5 kg·hm^-2和2 597.7 kg·hm^-2，WUE分别为1.62 kg·m^-3、1.72 kg·m^-3、1.36 kg·m^-3和1.08 kg·m^-3；在雨养条件下，南皮、栾城、衡水和南大港的小麦产量分别为2 644.4 kg·hm^-2、2 602.8 kg·hm^-2、2 422.3 kg·hm^-2影响1 784.3 kg·hm^-2，WUE分别为1.13 kg·m^-3、1.10 kg·m^-3、1.18 kg·m^-3和1.01 kg·m^-3。统计分析表明，穗数是影响产量的最主要因素，气候×水分管理互作对产量和WUE均有极显著影响（P < 0.01），气候×基因型×水分管理互作对WUE有显著影响（P < 0.05）；水分是影响产量和WUE的最重要因素。综合产量、耗水和WUE分析，在降水量偏少的年份，南皮在4个试验站点中冬小麦耗水量较少、WUE最高、产量较高。分蘖能力强的小麦品种是适宜该区域种植的潜力品种类型。
关键词:小麦品种/
产量/
水分管理/
基因型/
气候/
水分利用效率
Abstract:Winter wheat is the main crop in the North China Plain, and its growth is affected by weather, varieties, and management measures. Many scholars have conducted extensive research on the mechanism of winter wheat growth, but most of these studies focused on a single factor. Moreover, research was mostly conducted at the regional scale, with few studies being conducted in areas at the same latitude. To investigate the effects of weather, genotype, and water management interaction on winter wheat yield and water use efficiency (WUE), field experiments were conducted at four typical experimental sites (Hengshui, Nanpi, Luancheng and Nandagang) in the Hebei Plain in the 38° north latitude zone from 2018 to 2019. Three winter wheat varieties 'KN2009', 'GY2018' and 'SL02-1' and two water management levels-irrigation and rain-fed treatments-were used at all four sites. The soil water content was measured at 20 cm intervals in the 1.6 m soil profile before sowing and after harvest. Grain yield and yield components were also measured. Weather factors were collected from a nearby weather station 200-500 m from the experimental sites. Under irrigation conditions, grain yield was 6 316.7 kg·hm^-2, 5 204.1 kg·hm^-2, 4 356.5 kg·hm^-2, and 2 597.7 kg·hm^-2, respectively; WUE was 1.62 kg·m^-3, 1.72 kg·m^-3, 1.36 kg·m^-3, and 1.08 kg·m^-3, respectively; irrigation water use efficiency (IWUE) was 1.62 kg·m^-3, 3.20 kg·m^-3, 2.19 kg·m^-3, and 1.02 kg·m^-3, respectively, at Hengshui, Nanpi, Luancheng and Nandagang sites. Under rain-fed conditions, grain yield at Nanpi, Luancheng, Hengshui and Nandagang was 2 644.4 kg·hm^-2, 2 602.8 kg·hm^-2, 2 422.3 kg·hm^-2, and 1 784.3 kg·hm^-2, respectively; WUE was 1.13 kg·m^-3, 1.10 kg·m^-3, 1.18 kg·m^-3, and 1.01 kg·m^-3, respectively. Grain yield differed significantly among the four sites, while no significant difference was noted among varieties. With regard to WUE, trends differed between the irrigation and rain-fed treatments. The WUE of different sites under irrigation conditions differed significantly, while there were no significant differences among varieties. The WUE of different sites under rain-fed conditions did not differ significantly, while WUE differed to some extent among varieties. Statistical analysis of results for grain yield and yield components revealed that the number of spikes was the most important factor affecting yield, while the effects of grain number per spike and 1000-grain weight on yield were inconsistent with different sites, varieties, and water conditions. Multivariate analysis of variance revealed that the interaction of weather factors and water management had a highly significant effect on yield and WUE (P < 0.01), while weather factors, genotype, and water management had a significant effect on WUE (P < 0.05). Thus, the effects of climate conditions and management measures on winter wheat yield, water consumption, and WUE were significantly greater than those of variety, and water factor was the influential factor on yield and WUE. Based on comprehensive yield, water consumption, and WUE analysis, Nanpi had relatively high grain yield with low water consumption and relatively high WUE. Wheat varieties with strong tillering ability were potentially suitable for planting in this region.
Key words:Wheat variety/
Yield/
Water management/
Genotype/
Climate/
Water use efficiency

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图1不同试验地点小麦试验季(2018—2019年)与多年(1994—2014年)月平均降水量和月平均温度对比
Figure1.Comparison of monthly precipitation and temperature during winter wheat growing season between 2018-2019 and average values from 1994 to 2014


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表1不同试验地点小麦的灌溉时期及灌溉量
Table1.Irrigation time and irrigation amount of winter wheat at different sites?mm

站点 Site	总灌溉量 Total irrigation amount	灌溉时期Irrigation time
		越冬期 Recovering period	拔节期 Jointing stage	抽穗期 Booting stage	灌浆期 Grain filling stage
栾城Luancheng	80	0	80	0	0
衡水Hengshui	240	0	80	80	80
南皮Nanpi	80	0	80	0	0
南大港Nandagang	80	0	80	0	0

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表2不同地点和水分管理下不同小麦品种的产量、构成要素及收获指数(2018—2019年)
Table2.Wheat yields, components and harvest indexes at different sites and irrigation treatments from 2018 to 2019

站点 Site	水分管理 Water management	基因型 Genotype	穗数 Spike number (spikes·m-2)	穗粒数 Grains per spike	千粒重 1000-grain weight (g)	收获指数 Harvest index	产量 Yield (kg·hm-2)
栾城 Luancheng	灌溉 Irrigation	科农2009 KN2009	603b	33a	32.0a	0.45a	4 112.3b
		师栾02-1 SL02-1	789a	28b	27.6b	0.45a	4 601.3a
		藁优2018 GY2018	611b	29b	33.1a	0.41b	4 355.9ab
		平均Mean	668	30	30.9	0.44	4 356.5
	雨养 Rain-fed	科农2009 KN2009	382c	22a	36.3b	0.47a	2 048.1c
		师栾02-1 SL02-1	462a	22a	30.9c	0.47a	3 130.1a
		藁优2018 GY2018	426b	19b	38.9a	0.44b	2 630.1b
		平均Mean	423	21	35.4	0.46	2 602.8
衡水 Hengshui	灌溉 Irrigation	科农2009 KN2009	644b	35a	40.5a	0.49a	6 166.7b
		师栾02-1 SL02-1	765a	32b	34.1b	0.48a	6 383.4a
		藁优2018 GY2018	741a	31b	41.8a	0.45b	6 400.1a
		平均Mean	718	33	38.8	0.47	6 316.7
	雨养 Rain-fed	科农2009 KN2009	402b	22ab	36.8b	0.42b	2 216.7b
		师栾02-1 SL02-1	427a	24a	31.0c	0.47a	2 733.3a
		藁优2018 GY2018	378c	19b	38.3a	0.36c	2 316.8b
		平均Mean	402	22	35.4	0.42	2 422.3
南皮 Nanpi	灌溉 Irrigation	科农2009 KN2009	630b	33a	36.5b	0.45a	5 682.8a
		师栾02-1 SL02-1	862a	30a	30.0c	0.46a	5 530.2a
		藁优2018 GY2018	647b	21b	38.2a	0.41b	4 399.2b
		平均Mean	713	28	34.9	0.44	5 204.1
	雨养 Rain-fed	科农2009 KN2009	415b	25a	34.3b	0.43a	2 478.3b
		师栾02-1 SL02-1	458a	25a	30.3c	0.42ab	2 695.8a
		藁优2018 GY2018	441a	20b	37.8a	0.39b	2 759.0a
		平均Mean	438	23	34.1	0.41	2 644.4
南大港 Nandagang	灌溉 Irrigation	科农2009 KN2009	271c	33ab	36.7a	0.48a	2 111.9b
		师栾02-1 SL02-1	352a	35a	30.8b	0.48a	2 820.9a
		藁优2018 GY2018	317b	31b	37.5a	0.42b	2 860.4a
		平均Mean	314	33	35.0	0.46	2 597.7
	雨养 Rain-fed	科农2009 KN2009	205c	30b	36.6a	0.48a	1 603.7b
		师栾02-1 SL02-1	288a	32a	30.5b	0.49a	2 155.4a
		藁优2018 GY2018	241b	30b	36.5a	0.48a	1 593.8b
		平均Mean	245	31	34.5	0.48	1 784.3
同列数据后不同小写字母表示同一地点不同处理间差异显著(P < 0.05)。Different lowercase letters following the same column data indicate significant differences between treatments at the same location (P < 0.05).

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表3不同地点、品种和水分管理下小麦产量及产量构成要素的相关分析
Table3.Correlation analysis of yield and yield components under different sites, varieties and irrigation treatments

	因素 Factor	千粒重 1000-grain weight	穗粒数 Grains per spike	穗数 Number of spikes
水分 Water	灌溉Irrigation	0.293	0.106	0.770**
	雨养Rain-fed	-0.251	-0.329*	0.711**
品种 Variety	科农2009 KN2009	0.288	0.541**	0.875**
	师栾02-1 SL02-1	0.353	0.206	0.823**
	藁优2018 GY2018	0.302	0.343	0.886**
地点 Site	栾城Luancheng	-0.642**	0.701**	0.678**
	衡水Hengshui	0.415	0.764**	0.961**
	南皮Nanpi	0.139	0.499*	0.837**
	南大港Nandagang	-0.181	-0.008	0.745**
水分处理中n = 36, 地点处理中n = 18, 品种处理中n = 24。所有处理**代表 0.01水平, *代表 0.05水平。n = 36 in water treatment, n = 18 in location treatment, and n = 24 in variety treatment. ** represents 0.01 level and * represents 0.05 level in all treatments.

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表4不同地点和水分管理下不同冬小麦的水分利用效率(WUE) (2018—2019年)
Table4.Water use efficiencies at different sites and under irrigation treatments from 2018 to 2019

地点 Site	水分管理 Water management	基因型 Genotype	降雨量 Precipitation (mm)	灌溉量 Irrigation amount (mm)	土壤供水量 Soil water supply (mm)	农田耗水量 Farmland water consumption (mm)	水分利用效率 Water use efficiency (kg·m-3)	灌溉水分利用效率 Irrigation water use efficiency (kg·m-3)
栾城 Luancheng	灌溉 Irrigation	科农2009 KN2009	117.9	80.0	126.4	324.3	1.27a	2.58a
		师栾02-1 SL02-1	117.9	80.0	121.7	319.6	1.44a	1.84b
		藁优2018 GY2018	117.9	80.0	121.3	319.2	1.36a	2.16ab
		平均Mean				321.0	1.36	2.19
	雨养 Rain-fed	科农2009 KN2009	117.9	0	89.3	207.2	0.99ab
		师栾02-1 SL02-1	117.9	0	117.0	234.9	1.33a
		藁优2018 GY2018	117.9	0	149.0	266.9	0.99ab
		平均Mean				236.3	1.10
衡水 Hengshui	灌溉 Irrigation	科农2009 KN2009	80.7	240.0	60.1	380.8	1.62a	1.65ab
		师栾02-1 SL02-1	80.7	240.0	99.8	420.5	1.52a	1.52ab
		藁优2018 GY2018	80.7	240.0	54.4	375.1	1.71a	1.70a
		平均Mean				392.2	1.62	1.62
	雨养 Rain-fed	科农2009 KN2009	80.7	0	126.9	207.6	1.07a
		师栾02-1 SL02-1	80.7	0	129.5	210.2	1.30a
		藁优2018 GY2018	80.7	0	114.8	195.5	1.18a
		平均Mean				204.4	1.18
南皮 Nanpi	灌溉 Irrigation	科农2009 KN2009	72.2	80.0	131.5	283.7	2.00a	4.01a
		师栾02-1 SL02-1	72.2	80.0	160.3	312.5	1.77ab	3.54b
		藁优2018 GY2018	72.2	80.0	161.6	313.8	1.40b	2.05c
		平均Mean				303.3	1.72	3.20
	雨养 Rain-fed	科农2009 KN2009	72.2	0	179.5	251.7	0.98ab
		师栾02-1 SL02-1	72.2	0	152.3	224.5	1.20a
		藁优2018 GY2018	72.2	0	156.6	228.8	1.21a
		平均Mean				235.0	1.13
南大港 Nandagang	灌溉 Irrigation	科农2009 KN2009	72.1	80.0	83.2	235.3	0.90a	0.64b
		师栾02-1 SL02-1	72.1	80.0	97.0	249.1	1.13a	0.83b
		藁优2018 GY2018	72.1	80.0	87.2	239.3	1.20a	1.58a
		平均Mean				241.2	1.08	1.02
	雨养 Rain-fed	科农2009 KN2009	72.1	0	113.4	185.5	0.86b
		师栾02-1 SL02-1	72.1	0	95.6	167.7	1.28a
		藁优2018 GY2018	72.1	0	105.2	177.3	0.90b
		平均Mean				176.8	1.01
同列数据后不同小写字母表示同一地点不同处理间差异显著(P < 0.05)。Different lowercase letters in the same column indicate significant differences between treatments at the same site (P < 0.05).

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表5气候、基因型、水分管理及其互作对冬小麦产量的影响
Table5.Effects of climate, genotype, water management and their interaction on winter wheat yield

变异来源 Source of variation	自由度 d.f.	平方和 SS	平方和占比 SS (%)	均方 MS	F值 F value	P值 P value
气候Climate (C)	3	225 005.253	27.379	75 001.751	55.060	0.000
基因型Genotype (G)	2	11 335.897	1.379	5 667.949	4.161	0.022
水分Water management (W)	1	405 403.601	49.330	405 403.601	297.614	0.000
气候×基因型C × G	6	8 400.445	1.022	1 400.074	1.028	0.419
气候×水分管理C × W	3	92 799.209	11.292	30 933.070	22.709	0.000
基因型×水分管理G × W	2	743.634	0.090	371.817	0.273	0.762
气候×基因型×水分管理C × G × W	6	12 739.775	1.550	2 123.296	1.559	0.180
总和Total	72	821 812.311
P < 0.01代表极显著水平, P < 0.05代表显著水平。P < 0.01 and P < 0.05 represent extremely significant at 0.01 and 0.05 levels, respectively.

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表6气候、基因型、水分及其互作对冬小麦水分利用效率(WUE)的影响
Table6.Effects of climate, genotype, water management and their interaction on water use efficiency (WUE) of winter wheat

变异来源 Source of variation	自由度 d.f.	平方和 SS	平方和占比 SS (%)	均方 MS	F值 F value	P值 P value
气候Climate (C)	3	1.695	21.589	0.565	14.485	0.000
基因型Genotype (G)	2	0.351	4.470	0.175	4.496	0.016
水分Water management (W)	1	2.026	25.805	2.026	51.958	0.000
气候×基因型C × G	6	0.374	4.763	0.062	1.597	0.169
气候×水分管理C × W	3	0.708	9.017	0.236	6.051	0.001
基因型×水分管理G × W	2	0.246	3.133	0.123	3.154	0.052
气候×基因型×水分管理C × G× W	6	0.579	7.374	0.097	2.475	0.036
总和Total	72	7.851
P < 0.01代表极显著水平, P < 0.05代表显著水平。P < 0.01 and P < 0.05 represent extremely significant at 0.01 and 0.05 levels, respectively.

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