THE ESTIMATE OF FINE ROOT BIOMASS IN UPPER SOIL LAYER OF LARIX OLGENSIS PLANTATION BY GEOSTATISTICS METHOD

doi:10.17521/cjpe.2006.0099

Abstract

Abstract:

Background and Aims Drilling soil core, simply averaging the surveying values and ignoring the information of sampling point locations are in common use in estimating the fine root biomass of forest. Owing to the significant heterogeneity of fine root distribution, using the method above may be not proper. The Larix olgensis stand was chosen for a case study. Answer to the following question was sought: is the fine root biomass estimated by combining the coordinates of sampling points.

Methods Semivariance analysis of Geostatistics was used to quantify the spatial heterogeneity of fine root (<2 mm) biomass in upper layer of soil (0-10 cm) inLarix olgensis stand (14-40 year). Fine root biomass was estimated with kriging interpolation of Geostatistics and definite integral.

Key Results The semivariograms of fine roots in all six Larix olgensis stands were best described by spherical model. The spatial variability of fine root in all six Larix olgensis stands was mainly caused by structural factors with spatial structural ratio >25 %. The scales of spatial heterogeneity of fine roots (1.76-5.58 m) showed a positive linear correlation ( p=0.074 4) with stand age (14-40 year). The sign-test of nonparametric statistics of paired samples showed that the kriging interpolation, based on the results of semivariance analysis, could be used to estimate the fine root biomass in Larix olgensis stands. The relationship between the estimated fine root biomass and the values of its corresponding coordinates was best fitted by bivariate order 10 cosine series polynomial. Based on the result of definite integral to those polynomials (integral range was limited to plot size), total fine root biomass of the 14-year, 19-year, 22-year, 26-year, 32-year, 40-year-old stands was 1.097 3, 1.434 0, 1.185 4, 0.974 3, 1.682 6, 1.255 6 Mg·hm^-2, respectively. No differences (α=0.037 3) were found in the fine root biomass in upper soil layer of Larix olgensis stands with difference stand ages. The estimated of fine root mass of individual stems increased exponentially with stand age (α=0.002).

Conclusions Kriging interpolation method of Geostatistics, combined with multiple regression and definite integral, provide a new optimal alternative for the estimation of fine root biomass in Larix olgensis stands.

Key words: Larix olgensis, Fine root biomass, Geostatistics, Semivariance, Heterogeneity

SUN Zhi-Hu, MU Chang-Cheng. THE ESTIMATE OF FINE ROOT BIOMASS IN UPPER SOIL LAYER OF LARIX OLGENSIS PLANTATION BY GEOSTATISTICS METHOD[J]. Chin J Plant Ecol, 2006, 30(5): 771-779.

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URL: https://www.plant-ecology.com/EN/10.17521/cjpe.2006.0099

https://www.plant-ecology.com/EN/Y2006/V30/I5/771

Figures/Tables 10

References 32

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	林龄 Stand age (a)	平均胸径 DBH (cm)	林木密度 Density (stem·hm^-2)	土层厚度 Thickness of soil (cm)	最小株距 Minimum distance (m)
样地1 Plot 1	14	7.30	3 033	57.7	0.22
样地2 Plot 2	19	10.41	3 122	33.7	0.14
样地3 Plot 3	22	9.50	1 989	57.7	0.36
样地4 Plot 4	26	14.99	1 722	85.7	0.41
样地5 Plot 5	32	13.91	1 100	38.4	1.03
样地6 Plot 6	40	21.24	556	38.9	2.14

	林龄 Stand age (a)	平均胸径 DBH (cm)	林木密度 Density (stem·hm^-2)	土层厚度 Thickness of soil (cm)	最小株距 Minimum distance (m)
样地1 Plot 1	14	7.30	3 033	57.7	0.22
样地2 Plot 2	19	10.41	3 122	33.7	0.14
样地3 Plot 3	22	9.50	1 989	57.7	0.36
样地4 Plot 4	26	14.99	1 722	85.7	0.41
样地5 Plot 5	32	13.91	1 100	38.4	1.03
样地6 Plot 6	40	21.24	556	38.9	2.14

	平均 Mean	最大值 Maximum	观测数 Number	标准差 SD	峰度 Kurtosis	偏度 Skewness	变异系数 CV(%)	K-S值 K-S value
样地1 Plot 1	126.49	448.98	82	86.528	2.079	0.972	68.41	0.086 4
样地2 Plot 2	135.39	671.20	82	121.170	3.342	1.218	89.50	0.148 6
样地3 Plot 3	124.55	494.33	82	99.442	3.216	1.312	79.84	0.109 4
样地4 Plot 4	146.16	648.53	70	142.583	1.552	1.317	97.55	0.160 4
样地5 Plot 5	161.00	412.70	82	101.472	-0.366	0.104	63.03	0.102 2
样地6 Plot 6	176.04	757.37	82	170.204	1.345	1.185	96.68	0.150 5^*
样地1 Plot 1¹⁾	10.22	21.19	82	4.733	0.586	-0.731	46.33	0.148 6
样地6 Plot 6¹⁾	10.97	27.52	82	7.508	-0.829	-0.098	68.44	0.148 2

	平均 Mean	最大值 Maximum	观测数 Number	标准差 SD	峰度 Kurtosis	偏度 Skewness	变异系数 CV(%)	K-S值 K-S value
样地1 Plot 1	126.49	448.98	82	86.528	2.079	0.972	68.41	0.086 4
样地2 Plot 2	135.39	671.20	82	121.170	3.342	1.218	89.50	0.148 6
样地3 Plot 3	124.55	494.33	82	99.442	3.216	1.312	79.84	0.109 4
样地4 Plot 4	146.16	648.53	70	142.583	1.552	1.317	97.55	0.160 4
样地5 Plot 5	161.00	412.70	82	101.472	-0.366	0.104	63.03	0.102 2
样地6 Plot 6	176.04	757.37	82	170.204	1.345	1.185	96.68	0.150 5^*
样地1 Plot 1¹⁾	10.22	21.19	82	4.733	0.586	-0.731	46.33	0.148 6
样地6 Plot 6¹⁾	10.97	27.52	82	7.508	-0.829	-0.098	68.44	0.148 2

	模型 Model	块金值 Nugget (C₀)	基台值 Sill (C₀+C)	范围参数 Range parameter (a₀)	结构比 Proportion (C/(C₀+C))	决定系数 Coefficient of determination (R²)	显著性水平 Significant level (α)
样地1 Plot 1	球状Spherical	11.37	22.75	1.76	0.500	0.401	0.077 1
样地2 Plot 2	球状Spherical	9 390.00	18 790.00	3.40	0.500	0.204	0.254 4
样地3 Plot 3	球状Spherical	30.00	9 830.00	1.02	0.997	0.395	0.172 2
样地4 Plot 4	球状Spherical	15 230.00	30 470.00	4.12	0.500	0.377	0.150 6
样地5 Plot 5	球状Spherical	2 780.00	10 070.00	3.37	0.724	0.561	0.056 1
样地6 Plot 6	球状Spherical	19.10	61.99	5.58	0.692	0.754	0.007 4