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

doi:10.12302/j.issn.1000-2006.202208069

JOURNAL OF NANJING FORESTRY UNIVERSITY ›› 2024, Vol. 48 ›› Issue (5): 246-254.doi: 10.12302/j.issn.1000-2006.202208069

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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
Contact: ZHU Jingle E-mail:lh382490531@163.com;zhujingle1982@126.com

Abstract

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

CLC Number:

S792

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, 2024, 48(5): 246-254.

Figures/Tables 9

Table 1

Information of origin of 43 Quercus variabilis families"

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

Table 1

Table 2

Demonstration of leaf area prediction model for geometric Q. variabilis"

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× $12$

Table 2

Table 3

Table 4

Fig. 1

Table 5

Establishment of leaf area prediction model for Q. variabilis"

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

Table 5

Table 6

Table 7

Fig. 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

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

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