幼龄栓皮栎叶面积预测模型建立及应用

doi:10.12302/j.issn.1000-2006.202208069

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南京林业大学学报（自然科学版） ›› 2024, Vol. 48 ›› Issue (5): 246-254.doi: 10.12302/j.issn.1000-2006.202208069

幼龄栓皮栎叶面积预测模型建立及应用

李慧¹^,²(), 张婉¹^,², 常益豪², 杨霞³, 肖祥伟⁴, 朱景乐¹^,^*()

1.中国林业科学研究院经济林研究所,经济林种质创新与利用国家林业和草原局重点实验室,河南郑州 450003
2.河南农业大学林学院,河南郑州 450002
3.国有信阳市南湾林场,河南信阳 464100
4.国有信阳市平桥区天目山林场,河南信阳 464100

收稿日期:2022-08-29 修回日期:2022-10-07 出版日期:2024-09-30 发布日期:2024-10-03
通讯作者: * 朱景乐(zhujingle1982@126.com),副研究员。
作者简介:
李慧(lh382490531@163.com)。
基金资助:
河南省重点研发专项(241111113300);中央级公益性科研院所基本科研业务费专项资金项目(CAFYBB2018ZB001-11)

Construction and application of the leaf area prediction model for young Quercus variabilis

LI Hui¹^,²(), ZHANG Wan¹^,², CHANG Yihao², YANG Xia³, XIAO Xiangwei⁴, ZHU Jingle¹^,^*()

1. Key Laboratory of Non-timber Forest Germplasm Enhancement & Utilization of National Forestry and Grassland Administration, Research Institute of Non-timber Forestry, Chinese Academy of Forestry, Zhengzhou 450003, China
2. College of Forestry, Henan Agriculture University, Zhengzhou 450002, China
3. Nanwan State Forest Farm, Xinyang 464100, China
4. Tianmushan State Forest Farm, Pingqiao District, Xinyang 464100, China

Received:2022-08-29 Revised:2022-10-07 Online:2024-09-30 Published:2024-10-03

摘要/Abstract

摘要：

【目的】建立幼龄栓皮栎叶面积预测模型,实现快速无损测量,并对43个栓皮栎家系的叶片形状变异情况和差异特征进行分析,为栓皮栎遗传多样性研究及良种选育提供理论基础。【方法】从全国不同地区筛选出43个栓皮栎家系作为研究对象,通过对叶长、叶宽、叶长宽比、叶长宽积与实测叶面积的相关性分析及曲线拟合分析,筛选出叶面积预测模型最佳参数;通过几何模型和曲线拟合分析,筛选最优的幼龄栓皮栎叶面积预测模型,并进行验证;运用统计描述、单因素方差分析、相关性分析、聚类分析的方法进行不同家系间叶片的差异分析。【结果】①叶长宽积(X₁×X₂)与幼龄栓皮栎叶面积呈显著相关,可根据这一组合指标进行幼龄栓皮栎叶面积预测模型的建立。②幼龄栓皮栎叶面积预测模型Y=0.595X₁×X₂+257.640最为精确,R²=0.946,标准误差低至32.830 cm²,可以用来预测幼龄栓皮栎叶片的面积。③43个栓皮栎家系间各项叶片指标均有不同程度的变异,不同家系间和家系内叶片差异均较大。④叶片性状与原产地地理信息相关性分析结果表明,除叶干质量与年降水量外,与其他指标均无显著相关性;43个家系不能独立分类,规律性不强。【结论】以叶长宽积为参数,建立了较为精准的幼龄栓皮栎叶面积预测模型Y=0.595X₁×X₂+257.640,为获得栓皮栎叶面积提供了一种高效、无损的方法,为栓皮栎良种培育和家系选择提供理论基础。

关键词: 栓皮栎家系, 叶面积预测模型, 叶片形状, 变异特征

Abstract:

【Objective】The leaf area prediction model of young Quercus variabilis was established to apply a rapid and nondestructive measurement. The leaf shape variation and difference characteristics of 43 different Q. variabilis families were analyzed to provide a theoretical basis for studying the genetic diversity and breeding of Q. variabilis. 【Method】A total of 43 Q. variabilis families were selected from different regions of China as the research objects. The optimal parameters of the leaf area prediction model were selected through correlation analysis and curve fitting analysis of leaf length, width, length-width ratio, length-width product, and measured leaf area.Through geometric model and curve fitting analysis, the optimal prediction model of the leaf area of young Q. variabilis was screened, and the results were verified. The differences in leaves among different families were analyzed by statistical description, one-way analysis of variance, correlation analysis, and cluster analysis. 【Result】(1) Leaf length-width product (X₁×X₂) was significantly correlated with the leaf area of young Q. variabilis, and the prediction model of leaf area of young Q. variabilis could be established according to this combination index. (2) The leaf area prediction model of young Q. variabilis Y=0.595X₁ ×X₂+257.640 was the most accurate, R²=0.946, and the standard error was as low as 32.830 cm², which could be used to predict the leaf area of young Q. variabilis.(3) The leaf indexes of the 43 Q. variabilis families had different degrees of variation. The differences in leaves among different families and within families were large. (4) The results of the correlation analysis between leaf traits and geographical information of origin showed no significant correlation between other indexes except leaf dry weight and annual precipitation. 43 families could not be classified independently, and the regularity was not strong. 【Conclusion】A more accurate prediction model of the leaf area of young Q. variabilis Y=0.595X₁×X₂+257.640 was established by using leaf length and width product as parameters, which provided an efficient and nondestructive method for obtaining the leaf area of Q. variabilis and provided a theoretical basis for breeding and family selection of Q. variabilis.

Key words: Quercus variabilis family, leaf area prediction model, leaf shape, variation characteristics

中图分类号:

S792

李慧,张婉,常益豪,等. 幼龄栓皮栎叶面积预测模型建立及应用[J]. 南京林业大学学报（自然科学版）, 2024, 48(5): 246-254.

LI Hui, ZHANG Wan, CHANG Yihao, YANG Xia, XIAO Xiangwei, ZHU Jingle. Construction and application of the leaf area prediction model for young Quercus variabilis[J].Journal of Nanjing Forestry University (Natural Science Edition), 2024, 48(5): 246-254.DOI: 10.12302/j.issn.1000-2006.202208069.

图/表 9

表1

43个栓皮栎家系原产地信息"

家系编号 family No.	家系原产地 family origin	经度 longitude (E)	纬度 latitude (N)	年降水量/mm annual precipitation	年平均气温/℃ annual average air temperature
1	安徽滁州南谯区红琊山1号	117°58'20.19″	32°20'45.20″	1 031.0	15.0
2	安徽滁州南谯区红琊山2号	117°58'20.19″	32°20'45.20″	1 031.0	15.0
3	安徽滁州南谯区红琊山3号	117°58'20.19″	32°20'45.20″	1 031.0	15.0
4	安徽滁州南谯区红琊山4号	117°58'20.19″	32°20'45.20″	1 031.0	15.0
5	安徽滁州南谯区红琊山5号	117°58'20.19″	32°20'45.20″	1 031.0	15.0
6	河南洛阳栾川潭头	111°45'42.63″	33°59'58.02″	737.9	12.4
7	河南洛阳栾川杨村	111°27'35.53″	34°04'25.90″	872.6	13.7
8	河南洛阳栾川杨村	111°41'17.25″	33°43'41.21″	872.6	13.7
9	河南洛阳嵩县2号	112°05'8.52″	33°48'56.00″	800.0~1 200.0	14.0~15.0
10	河南洛阳嵩县3号	112°05'8.52″	33°48'56.00″	800.0~1 200.0	14.0~15.0
11	河南洛阳嵩县4号	112°05'8.52″	33°48'56.00″	800.0~1 200.0	14.0~15.0
12	河南洛阳嵩县5号	112°05'8.52″	33°48'56.00″	800.0~1 200.0	14.0~15.0
13	河南洛阳嵩县大章镇1	111°53'23.67″	34°04'16.10″	800.0~1 200.0	14.0~15.0
14	河南洛阳嵩县木直街6	112°05'8.52″	34°08'4.24″	500.0~800.0	16.0
15	河南南阳南召城郊古树	112°25'44.90″	33°29'23.24″	868.8	15.5
16	河南南阳南召南河店	112°16'1.61″	33°18'47.31″	868.8	14.8
17	河南南阳南召四棵树	112°21'29.74″	33°17'17.77″	868.8	15.5
18	河南平顶山鲁山	113°21'40.20″	33°44'0.07″	612.0~1 287.0	22.0
19	河南平顶山鲁山县上河北	112°54'28.87″	33°44'18.74″	612.0~1 287.0	22.0
20	河南信阳鸡公山2号	114°05'4.62″	31°48'13.25″	1 118.7	23.7
21	河南信阳南湾大叶	113°59'42.53″	32°08'8.85″	993.0~1 294.0	15.1
22	湖北巴东1号	110°20'26.70″	31°02'32.39″	1 100.0~1 900.0	16.3
23	湖北巴东2号	110°20'26.70″	31°02'32.39″	1 100.0~1 900.0	16.3
24	湖北巴东3号	110°20'26.70″	31°02'32.39″	1 100.0~1 900.0	16.3
25	湖北大洪山1号	113°07'10.31″	31°01'6.53″	900.0~1 000.0	14.0
26	湖北建始县古树	109°58'49.00″	30°23'48.22″	1 300.0~1 400.0	15.8
27	湖北襄阳1号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
28	湖北襄阳2号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
29	湖北襄阳3号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
30	湖北襄阳4号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
31	湖北襄阳5号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
32	湖北襄阳6号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
33	湖北襄阳7号	112°45'34″	32°02'41.53″	800.0~1 200.0	15.0~16.0
34	湖北襄阳8号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
35	湖北襄阳9号	112°08'7.98″	32°02'41.53″	800.0~1 200.0	15.0~16.0
36	湖北襄阳10号	112°08'7.98″	32°02'41.53″	820.0~1 100.0	15.0~16.0
37	陕西汉中	107°01'54.98″	33°04'4.22″	1 200.0	14.0
38	陕西西安周至楼观台1号	108°19'44.05″	34°03'32.30″	700.0	13.2
39	陕西西安周至楼观台2号	108°19'44.05″	34°03'32.30″	700.0	13.2
40	陕西西安周至楼观台3号	108°19'44.05″	34°03'32.30″	700.0	13.2
41	陕西西安周至楼观台4号	108°19'44.05″	34°03'32.30″	700.0	13.2
42	陕西西安周至楼观台5号	108°19'44.05″	34°03'32.30″	700.0	13.2
43	陕西西安周至楼观台6号	108°19'44.05″	34°03'32.30″	700.0	13.2

表1

表2

几何型栓皮栎叶片面积预测模型演示"

家系编号 family number	几何形状 geometry	预测模型公式 formula of prediction model
1	椭圆形	S₁=π×X₁×X₂× $14$
2	正方形	S₂=S_正-2S_阴影 S₂= $π - 2 4$ × $X 12$
3	双三角形	S₃=X₁×X₂× $12$

表2

表3

表4

图1

表5

栓皮栎叶面积预测模型的建立"

模型来源 source of the model	原几何模型 original geometric model	初步预测模型 preliminary prediction model	计算值与实测值间的线性回归模型 linear regression model between calculated value and measured value		叶面积预测模型 leaf area prediction model	配对样本t检验结果 t test results of paired samples
						标准误差/cm² standard error	t	P
			回归模型 regression model	R²		标准误差/cm² standard error	t	P
	正方形 S₂= $X 12$ (π-2)/4	Y₁=0.285 4 $X 12$	Y=0.606 Y₁+655.420	R²=0.836	Y=0.173 $X 12$ + 655.420	60.795	-1.987	0.050
几何模型 geometric model	椭圆形 S₁=(X₁×X₂)π/4	Y₁=0.785 X₁×X₂	Y=0.757 Y₁+257.640	R²=0.946	Y=0.595 X₁×X₂+ 257.640	32.830	0.007	0.995
	双三角形 S₃=(X₁×X₂)/2	Y₁=0.5 X₁×X₂	Y=0.190 Y₁+257.640	R²=0.946	Y=0.595 X₁×X₂+ 257.640	34.830	0.003	0.997
参数拟合 parameter fitting	线性回归 S₄=a(X₁×X₂)+b	Y₁=0.595 X₁×X₂+ 257.693	Y=1.000 Y₁+0.080	R²=0.946	Y=0.595 X₁×X₂+ 257.639	34.830	0.036	0.971
	多元线性回归 S₅=aX₁+bX₂+c	Y₁=-3 574.446+ 19.568 X₁+ 110.032 X₂	Y=Y₁-0.072	R²=0.941	Y=-3 574.518+ 19.568 X₁+110.032 X₂	36.536	0.002	0.999
	二次模型 S₆=a $X 12$ +bX₁+c	Y₁=598.287+ 0.841 $X 12$ + 0.170 X₁	Y=0.206 Y₁+530.060	R²=0.836	Y=653.068+0.172 $X 12$ + 0.035 X₁	60.626	-0.009	0.993

表5

表6

表7

图2

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性状指标 character index	叶长/cm leaf length	叶宽/cm leaf breadth	实测叶面积/ cm² the measured value of leaf area	叶长宽比 aspect ratio
叶宽 leaf breadth	0.844^**
实测叶面积 the measured value of leaf area	0.907^**	0.949^**
长宽比 aspect ratio	0.403^**	-0.142	0.054
长宽积 length × width	0.947^**	0.959^**	0.973^**	0.110

曲线类型 curve type	叶长 leaf length	叶宽 leaf breadth	叶长宽积 leaf length × leaf width
线性模型linear	0.823	0.901	0.946
对数模型logarithmic	0.780	0.878	0.893
倒数模型inverse	0.702	0.827	0.730
二次模型quadratic	0.836	0.903	0.946
抛物线模型cubic	0.838	0.903	0.947
复合模型compound	0.844	0.866	0.884
幂函数模型power	0.857	0.896	0.947
S形模型S shape	0.832	0.896	0.888
生长模型growth	0.844	0.866	0.884
指数模型exponential	0.844	0.866	0.884

指标 index	变异来源 variation source	离差平方和 square deviation	自由度 degree of freedom	均方 mean square	F	P
叶长 leaf length	组间	765.219	42	18.219	8.034	<0.001
	组内	1 335.792	589	2.268
叶宽 leaf width	组间	92.426	42	2.201	5.952	<0.001
	组内	217.764	589	0.370
叶干质量 leaf dry weight	组间	1.787	42	0.043	6.771	<0.001
	组内	3.702	589	0.006
叶面积 leaf area	组间	12 588.907	42	299.736	6.999	<0.001
	组内	25 225.887	589	42.828

指标 index	经度 longitude (E)	纬度 latitude (N)	年降水量 annual precipitation	年均气温 annual average air temperature
叶长 leaf length	0.215	-0.203	0.259	0.219
叶宽 leaf width	0.228	-0.253	0.285	0.212
叶干质量 leaf dry weight	0.232	-0.152	0.304^*	0.271
长宽比 aspect ratio	-0.198	0.157	-0.016	0.015
长宽积 length × width	0.225	-0.229	0.260	0.229

幼龄栓皮栎叶面积预测模型建立及应用

Construction and application of the leaf area prediction model for young Quercus variabilis

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