基于积温的文冠果开花物候期预测模型的构建

周祎鸣; 张莹; 田晓华; 唐桂辉; 张东旭; 王俊杰; 王馨蕊; 关文彬

doi:10.13332/j.1000-1522.20180128

基于积温的文冠果开花物候期预测模型的构建

1.
北京林业大学自然保护区学院，北京 100083
2.
辽宁省林业调查规划院，辽宁沈阳 110032
3.
辽宁省阜新蒙古族自治县大巴林场，辽宁阜新 123114
4.
国有北票市塔山林场，辽宁北票 122114
5.
大同大学设施农业技术研发中心，山西大同 037009

基金项目: 同科异属砧木嫁繁育接红花文冠果新品种“金冠霞帔”的技术体系研究（201803D221016-3），文冠果植物新品种测试指南及已知品种数据库项目（2014009），中国特有生物产业树种文冠果良种培育集成技术与示范（2013GA105004）

详细信息

作者简介:
周祎鸣。主要研究方向：野生植物保护生物学。Email：15010819797 @163.com　地址：100083 北京市海淀区清华东路35号北京林业大学自然保护区学院

责任作者:
关文彬，教授。主要研究方向：生物多样性保护与利用。Email：swlab@bjfu.edu.cn　地址：同上

中图分类号: S722.34
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出版历程
- 收稿日期: 2018-04-09
- 修回日期: 2018-05-19
- 网络出版日期: 2019-06-17
- 发布日期: 2019-05-31

Establishment of the flowering phenological model of Xanthoceras sorbifolium based on accumulated temperature

1.
College of Nature Reserve, Beijing Forestry University, Beijing 100083, China
2.
Liaoning Forestry Survey and Planning Institute, Shenyang 110000, Liaoning, China
3.
Fuxin Mongolian Autonomous County, Liaoning Province, Fuxin 123100, Liaoning, China
4.
State-Owned Beipiao Tashan Forest Farm, Beipiao 122114, Liaoning, China
5.
Protected Agricultural Technology Development Center of Shanxi Datong University, Datong 037009, Shanxi, China

摘要

摘要:
目的建立不同地区及不同类型的文冠果物候模型，为文冠果的经营活动和旅游管理提供理论依据。
方法以北京市大东流苗圃文冠果3个遗传类型：白花类型及“金冠霞帔”“匀冠锦霞”两个文冠果新品种为研究对象，于2017年进行了花期表型调查与物候的观测，结合全国文冠果主要分布区的8个省份15个地点白花文冠果初花期、盛花期、末花期的观测数据，应用中国气象数据网上共享气象数据，对花性状与3个开花物候期进行了时间和空间尺度上的分析。
结果（1）3个不同花色遗传类型开花先后顺序为白花类型、“金冠霞帔”“匀冠锦霞”，物候期差异显著或极显著，花序生长随0、3、5、7、10 ℃积温的变化与Logistic生长模型拟合结果较好；花朵数随时间和积温的变化与二次多项式模型拟合较好；（2）各个地区之间同一积温指数各物候期所需积温相差不大，不同积温指数所需积温有显著性差异；不同积温指数和不同物候时期都对物候所需积温影响差异极显著，两个因素交互作用影响差异极显著；（3）5 ℃积温指数（即温暖指数）与物候期日序具有高度相关性，可用于花期预测；（4）白花类型文冠果3个物候期5 ℃积温的日序与经纬度、海拔呈极显著的多元线性回归关系，各观测地点日序的回归模拟值与观测值单因素方差分析证实该回归模型可用于花期预测；（5）用克里金插值法，采用上述预测模型，绘制白花文冠果3个开花物候期的时空分布图。
结论基于5 ℃积温指数（即温暖指数）建立的积温模型可用于文冠果花期预测。
- 文冠果 /
- 品种 /
- 花期预测 /
- 时空尺度 /
- 5 ℃积温
Abstract:
Objective Establishing different regions and different types of phenology model aims to provide theoretical basis for tourism and business activity management.
Method We choose the three genetic types of the Dadongliu Nursery Garden in Beijing, i.e., the white flower; the new varieties as ‘Jinguanxiapei’ and ‘Junguanjinxia’ as the research objects and performed the observation of flowering phenotypes in 2017. Referring to the observed data of the early flowering stage, the full flowering stage and late flowering stage of the white yellowhorn in 15 different locations of 8 provinces in main distribution area of Xanthoceras sorbifolium, we used the sharing meteorological data from the Chinese meteorological data website to analyze floral traits and three flowering phenology periods in different space-time scales.
Result (1) The flowering sequences of three different genetic types of yellowhorn, i.e., the white flower, " Jinguanxiapei” and " Junguanjinxia” as well as the differences in phenological period were significant or extremely significant. In addition, the changes of inflorescence growth with 0, 3, 5, 7, and 10 ℃ accumulative temperature fit the Logistic growth model better and the number of flowers changed with time and accumulative temperature fit the quadratic polynomials better. (2) The required accumulated temperature for each phenology in different locations with same accumulated temperature index showed no difference, the required accumulated temperature in different accumulated temperature index showed significant difference. However, both accumulated temperature index and phenology showed significant influence on required accumulated temperature and there was a significant difference on interaction between them. (3) The 5 ℃ cumulative temperature index (the warmth index) was highly correlated with the phenological date and could be used for flowering prediction. (4) The 5 ℃ accumulated temperature date of three flowering phenologies of white flowers showed an extremely significant multivariable regression relationship with longitude, latitude and altitude. The one-way ANOVO and simulated values at different observation sites confirmed that this regression model could be used for flowering prediction. (5) The Krisking model can be used to draw the space-time distribution maps of the three flowering phenologies of the white flower yellowhorn.
Conclusion The flowering phenological model of Xanthoceras sorbifolium based on 5 ℃ cumulative temperature index (the warmth index) can be used to predict flowering period.
- Xanthoceras sorbifolium /
- variety /
- florescence forecast /
- temporal and spatial scale /
- 5 ℃ accumulated temperature

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图 1 观测地的分布

Figure 1. Distribution of observation points

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图 2 不同花色文冠果花朵数与积温和日期的拟合曲线

Figure 2. Change of flower numbers for yellowhorn and its coeerlation and linear fitting with accumulated temperature and date

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图 3 不同花色花序长度随积温和时间变化的Logistic模型

Figure 3. Change of inflorescence length for yellowhorn and its coeerlation and linear fitting with accumulated temperature and day number

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图 4 15个地区5年平均各积温线

Figure 4. 5-year average accumulated temperature for the 15 districts

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图 5 全国文冠果初花期时空分布预测

Figure 5. Prediction of spatial and temporal distribution of early blooming period of yellowhorn

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图 6 全国文冠果盛花期时空分布预测

Figure 6. Prediction of spatial and temporal distribution of full blossom period of yellowhorn

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图 7 全国文冠果末花期时空分布预测

Figure 7. Prediction of spatial and temporal distribution of final flowering period of yellowhorn

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表 1 3种类型文冠果平均开花物候期及多重比较

Table 1 Average flowering time of three types of yellowhorn and multiple comparisons

物候期 Phenological phase	类型 Type	平均日序 Average day number
初花期 Early blooming period	YGJX	19.78 ± 1.30a
	JGXP	18.33 ± 0.50a
	WF	17.78 ± 0.97b
盛花期 Full blossom period	YGJX	20.67 ± 1.41a
	JGXP	21.33 ± 10.00ab
	WF	22.22 ± 1.09b
末花期 Final flowering period	YGJX	29.33 ± 0.71a
	JGXP	30.11 ± 0.78b
	WF	30.67 ± 0.50b
注：YGJX.‘匀冠锦霞’；JGXP.‘金冠霞帔’；WF.白花。不同小写字母表示差异显著（P < 0.05），日序用连续变量表示（从4月1日记起，4月1日记为1，4月2日记为 $\scriptstyle 2, \; \cdots , \;$ 以此类推）。Notes: YGJX, Xanthoceras sorbifolium cv.‘Yunguanjinxia’; JGXP, X. sorbifolium cv. ‘Jinguanxiapei’; WF, white flower. Different lowercases mean significant difference at P < 0.05 level, and the day number is represented by continuous variables (starting from April 1st, April 1st is recorded as 1, April 2nd is recorded as $\scriptstyle 2, \; \cdots , \;$ and so on).

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表 2 不同花色文冠果花朵数变化与积温和时间的二次多项式模型

Table 2 A quadratic polynomial model of variation and accumulation of temperature and time of different flower colors of yellowhorn

自变量 Independent variable	因变量 Dependent variable	参数 Parameter			模型 Model
自变量 Independent variable	因变量 Dependent variable	系数a Coefficient a	系数b Coefficient b	常数A Constant A	方程 Equation	F	Sig.
0 ℃	YGJX	14.883^**	− 0.012^**	− 4 422.569	Y = 14.833t − 0.012t² − 4 422.569	28.877	^**
	JGXP	15.91^**	− 0.013^**	− 4 656.348	Y = 15.91t − 0.013t² − 4 656.348	49	^**
	WF	16.013^**	− 0.013^**	− 4 635.468	Y = 16.013t − 0.013t² − 4 635.468	50	^**
3 ℃	YGJX	15.736^**	− 0.018^**	− 3 244.867	Y = 15.736t − 0.018t² − 3 244.867	31.147	^**
	JGXP	17.581^**	− 0.019^**	− 3 357.824	Y = 17.581t − 0.019t² − 3 357.824	48.844	^**
	WF	17.579^**	− 0.02^**	− 3 312.383	Y = 17.579t − 0.02t² − 3 312.383	47.917	^**
5 ℃	YGJX	17.517	− 0.025	− 2 545.662	Y = 17.517t − 0.025t² − 2 545.662	33.347	^**
	JGXP	17.175	− 0.026	− 2 592.188	Y = 17.175t − 0.026t² − 2 592.188	48.079	^**
	WF	17.066	− 0.027	− 2 534.785	Y = 17.066t − 0.027t² − 2 534.785	45.434	^**
7 ℃	YGJX	22.581	− 0.079	− 1 428.913	Y = 22.581t − 0.079t² − 1 428.913	43.388	^**
	JGXP	22.107	− 0.079	− 1 354.315	Y = 22.107t − 0.079t² − 1 354.315	38.497	^**
	WF	21.334	− 0.078	− 1 272.325	Y = 21.334t − 0.078t² − 1 272.325	31.853	^**
10 ℃	YGJX	17.918	− 0.037	− 1 997.914	Y = 17.918t − 0.037t² − 1 997.914	36.444	^**
	JGXP	18.305	− 0.038	− 1 991.451	Y = 18.305t − 0.038t² − 1 991.451	46.206	^**
	WF	18.036	− 0.038	− 1 924.551	Y = 18.036t − 0.038t² − 1 924.551	41.657	^**
日序 Day number	YGJX	156.854	− 3.142	− 1 782.115	Y = 156.854t − 3.142t² − 1 782.115	19.782	^**
	JGXP	176.402	− 3.622	− 1 955.656	Y = 176.402t − 3.622t² − 1 955.656	42.735	^**
	WF	180.808	− 3.766	− 1 969.161	Y = 180.808t − 3.766t² − 1 969.161	52.779	^**
注：^表示在P < 0.01水平上差异显著。下同。Notes: ^ means significant difference at P < 0.01 level. The same below.

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表 3 不同花色文冠果花序长度变化与积温和时间的Logistic模型

Table 3 Logistic model of inflorescence length for yellowhorn and its coeerlation and linear fitting with accumulated temperature and date

自变量 Independent variable	因变量 Dependent variable	参数 Parameters			模型 Model
自变量 Independent variable	因变量 Dependent variable	常数A Constant A	系数k Coefficient k	系数B Coefficient B	方程 Equation	F	Sig.
0 ℃	YGJX	195.96	0.018^**	1 564.74^*	Y = 195.96/(1 + 1 564.74e^(− 0.018t))	456.664	^**
	JGXP	223.45	0.034^**	861 289.14	Y = 223.45/(1 + 861 289.14e^(− 0.034t))	148.733	^**
	WF	207.075	0.022^**	10 988.85	Y = 207.075/(1 + 10 988.85e^(− 0.022t))	509.331	^**
3 ℃	YGXP	195.96	0.022^**	372.91^*	Y = 195.96/(1 + 372.91e^(− 0.022t))	462.891	^**
	JGXP	223.45	0.042^**	55 810.21^*	Y = 223.45/(1 + 55 810.21e^(− 0.042t))	153.248	^**
	WF	207.075	0.027^**	1 783.95^*	Y = 207.075/(1 + 1 783.95e^(− 0.027t))	512.382	^**
5 ℃	YGJX	195.96	0.026^**	141.29^**	Y = 195.96/(1 + 141.29e^(− 0.026t))	465.164	^**
	JGXP	223.45	0.05^**	8 765.72^*	Y = 223.45/(1 + 8 765.72e^(− 0.05t))	156.395	^**
	WF	207.075	0.033^**	521.62^*	Y = 207.075/(1 + 521.62e^(− 0.033t))	510.585	^**
7 ℃	YGJX	195.96	0.033^**	61.14^**	Y = 195.96/(1 + 61.14e^(− 0.033t))	461.41	^**
	JGXP	223.45	0.062^**	1 776.65^*	Y = 223.45/(1 + 1 776.65e^(− 0.062t))	160.364	^**
	WF	207.075	0.041^**	180.36^**	Y = 207.075/(1 + 180.36e^(− 0.041t))	499.611	^**
10 ℃	YGJX	195.96	0.049^**	24.50^**	Y = 195.96/(1 + 24.50e^(− 0.049t) )	411.135	^**
	JGXP	223.45	0.094^**	311.04^*	Y = 223.45/(1 + 311.04e^(− 0.094t))	166.454	^**
	WF	207.075	0.063^**	56.32^**	Y = 207.075/(1 + 56.32e^(− 0.063t))	427.489	^**
日序 Day number	YGJX	195.96	0.274^**	30.96^**	Y = 195.96/(1 + 30.96e^(− 0.274t))	397.143	^**
	JGXP	223.45	0.523^**	468.13^*	Y = 223.45/(1 + 468.13e^(− 0.523t))	132.709	^**
	WF	207.075	0.348^**	76.20^**	Y = 207.075/(1 + 76.20e^(− 0.348t))	451.133	^**
注：^表示在P < 0.05水平上差异显著。Note: ^ means significant difference at P < 0.05 level.

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表 4 观测地区不同年份不同积温单因素方差分析

Table 4 One-way ANOVA result of diversity of accumulated temperature in different years

积温类别 Accumulated temperature	自由度 df		单因素方差分析 One-way ANOVA	显著度 Significance
积温类别 Accumulated temperature	组间 Between groups	组内 Within group	单因素方差分析 One-way ANOVA	显著度 Significance
0 ℃	4	4 570	29.681	^**
3 ℃	4	4 570	34.593	^**
5 ℃	4	4 570	37.117	^**
7 ℃	4	4 570	34.035	^**
10 ℃	4	4 570	21.727	^**

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表 5 15个地区各开花物候期所需积温

Table 5 Average accumulated temperature in different phenological periods of yellowhorn

积温 Accumulated temperature	物候期 Phenological period	观测数 Number	平均值 ± 标准差 Mean ± SD/℃	最大值 Max./℃	最小值 Min./℃	变异系数 CV/℃	平均值 Mean/℃
0 ℃	初花期 Early blooming period	15	508.13 ± 26.55	540.8	454.4	5.23	590.63 ± 13.33a
	盛花期 Full blossom period	15	557.57 ± 26.98	590.4	500.8	4.84
	末花期 Final flowering period	15	706.19 ± 30.44	745.4	642.1	4.31
3 ℃	初花期 Early blooming period	15	347.71 ± 18.10	372.6	311.9	5.2	415.21 ± 10.79b
	盛花期 Full blossom period	15	388.15 ± 18.86	415	349.3	4.86
	末花期 Final flowering period	15	509.77 ± 23.14	548.2	463.6	4.54
5 ℃	初花期 Early blooming period	15	257.36 ± 13.19	276.2	234.7	5.1	314.86 ± 61.41c
	盛花期 Full blossom period	15	291.80 ± 14.38	312.6	266.1	4.93
	末花期 Final flowering period	15	395.42 ± 20.15	428.3	362.4	5.1
7 ℃	初花期 Early blooming period	15	179.67 ± 10.70	193.8	160.1	6	227.17 ± 51.04d
	盛花期 Full blossom period	15	208.11 ± 11.90	224.2	189	5.72
	末花期 Final flowering period	15	293.73 ± 19.40	322	263.6	6.6
10 ℃	初花期 Early blooming period	15	88.19 ± 10.57	107	67.2	1.2	120.74 ± 36.20e
	盛花期 Full blossom period	15	107.63 ± 10.80	126.5	88.6	1
	末花期 Final flowering period	15	166.39 ± 18.42	196.4	138	1.11

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表 6 白花文冠果不同地区开花所需积温的单因素方差分析

Table 6 One-way ANOVA result of diversity district for the accumulated temperature of flowering phenophase

项目 Item	SS	df	MS	F	P
组间 Between groups	53 516.518	14	3 822.608	0.119	1.000
组内 Within group	6 751 560.268	210	32 150.287
总计 Total	6 805 076.786	224

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表 7 白花文冠果的不同物候期和积温指数的双因素方差分析

Table 7 Two-way ANOVA result of diversity type of accumulated temperature and flowering phenophase district for the accumulated temperature of flowering phenophase

因子 Factor	SS	df	MS	F	P	R²
积温类型 Accumulated temperature	5 837 033.32	4	1 459 258.33	3 933.08	< 0.01	0.988
物候期 Phenological period	774 916.82	2	387 458.41	1 044.30	< 0.01
积温类型 × 物候期 Accumulated temperature × phenological period	67 720.14	8	8 465.02	22.82	< 0.01
误差 ERR	77 914.51	210	371.02
总计 Total	31 815 551.69	225
修正后总计 Total after correction	6 757 584.79	224

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表 8 开花日序与各积温相关性分析

Table 8 Analysis of correlation between flowering day number and accumulated temperature

指标 Factor	日序 Day number	0 ℃积温 0 ℃ accumulated temperature	3 ℃积温 3 ℃ accumulated temperature	5 ℃积温 5 ℃ accumulated temperature	7 ℃积温 7 ℃ accumulated temperature	10 ℃积温 10 ℃ accumulated temperature
日序 Day number	1
0 ℃积温 0 ℃ accumulated temperature	− 0.948^**	1
3 ℃积温 3 ℃ accumulated temperature	− 0.955^**	0.996^**	1
5 ℃积温 5 ℃ accumulated temperature	− 0.956^**	0.985^**	0.995^**	1
7 ℃积温 7 ℃ accumulated temperature	0.969^**	0.982^**	0.995^**	1
10 ℃积温 10 ℃ accumulated temperature	− 0.915^**	0.948^**	0.960^**	0.975^**	0.983^**	1
注：^表示在P < 0.01水平上显著。Notes: ^ means significant difference at P < 0. 01 level.

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表 9 日序与积温逐步回归模型排除的变量

Table 9 Flowering day number and variables excluded from accumulated temperature stepwise regression model

积温 Accumulated temperature	系数β Coefficient β	t检验 t test	显著性 Sig.	偏相关 Partial correlation	共线性统计 Collinearity statistics
积温 Accumulated temperature	系数β Coefficient β	t检验 t test	显著性 Sig.	偏相关 Partial correlation	容差 Tolerance	方差膨胀因子 VIF	最小容差 Min tolerance
0 ℃	− 0.206	− 0.388	0.705	− 0.111	0.03	32.818	0.03
3 ℃	− 0.398	− 0.426	0.678	− 0.122	0.01	102.301	0.01
7 ℃	0.829	0.961	0.356	0.267	0.011	92.487	0.011
10 ℃	0.462	1.159	0.269	0.317	0.049	20.381	0.049

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表 10 日序与积温逐步回归模型检验

Table 10 The test of the day number of flowering and accumulated temperature stepwise regression model

积温 Accumulated temperature	系数 Coefficient				方程 Equation
积温 Accumulated temperature	系数 Coefficient	值 Value	t	P	F	P	R²
5 ℃	k	42.580	22.892	0	111.9	＜0.001	0.896
5 ℃	a	− 0.215	− 10.578	0

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表 11 全国地区文冠果初花期日序与经纬度海拔回归模型

Table 11 The day number and latitude and longitude elevation regression model of early flowering stage of Xanthoceras sorbifolium in China

时期 Phenological period	参数 Parameters				模型 Model
时期 Phenological period	系数a Coefficient a	系数b Coefficient b	系数c Coefficient c	常数A Constant A	方程 Equation	F	Sig.
初花期 Early blooming period	− 0.215^**	0.07^**	0.01^**	113.922^**	y = − 0.215x₁ + 0.07x₂ + 0.01x₃ + 113.922	55.906	^**
盛花期 Full blossom period	− 0.221^**	0.072^**	0.011^**	117.651^**	y = − 0.221x₁ + 0.072x₂ + 0.011x₃ + 117.651	57.777	^**
末花期 Final flowering period	− 0.243^**	0.079^**	0.011^**	127.007^**	y = − 0.243x₁ + 0.079x₂ + 0.011x₃ + 127.007	66.2	^**
注：x₁表示纬度，x₂表示经度，x₃表示海拔。Notes: x₁, latitude; x₂, longitude; x₃, altitude.

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