昆仑山北坡两种优势荒漠灌木的生物量预测模型

doi:10.13866/j.azr.2024.02.11

Abstract

Abstract:

Mathematical modeling is an important method for estimating shrub biomass. In this study, two desert shrubs, Reaumuria soongarica and Sympegma regelii, commonly found in the Piedmont belt of the northern slopes of the mid-Kunlun Mountains, were observed. The whole-plant harvesting method was employed, and plant height (H), canopy area (S), and plant volume (V) were used as independent variables. Plant above-ground biomass (W₁), below-ground biomass (W₂), and whole-plant biomass (W₃) were used as dependent variables to establish the function model. The selection of optimal biomass estimation models for these two desert shrubs was based on the largest determination coefficient (R²), smallest estimated standard deviation (SEE), and significance level (P < 0.001). The results indicated that quadratic function models were optimal for estimating biomass in both R. soongarica and S. regelii, except for the whole-plant optimal prediction model of S. regelii, which followed a linear function. For R. soongarica, the highest correlation was observed between plant volume (V) and biomass, with R² ranging from 0.820 to 0.920. For S. regelii, the highest correlation was between canopy area (S) and biomass, with R² ranging from 0.935 to 0.973. All optimal models for biomass estimation in R. soongarica and S. regelii passed the significance test (P<0.001), with fit rates ranging from 84.1% to 95.6%. These models were deemed reliable for biomass estimation. The outcomes of this study can offer valuable insights for studying carbon stocks and evaluating carbon sink potential in desert ecosystems.

Key words: desert shrub, biomass, estimation model, Kunlun Mountain

ZHANG Yuanmei, SUN Guili, LU Yan, LI Li, ZHANG Zhihao, ZHANG Dongdong. Biomass estimation models for two dominant desert shrubs on the northern slopes of Kunlun Mountain[J].Arid Zone Research, 2024, 41(2): 284-292.

Figures/Tables 6

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References 41

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名称	H/cm	S/cm²	V/cm³	W₁/g	W₂/g	W₃/g
红砂	21.92±7.37	2264.42±1535.76	18327.49±17394.53	109.97±74.99	59.44±51.13	169.41±122.67
合头草	24.80±7.32	1279.61±1014.76	12571.02±13741.84	29.89±32.44	107.07±100.83	136.72±132.00

名称	因变量	变量	模型	R²	SEE
红砂	W₁	H	W₁=0.0486H²+5.0658H-26.934	0.548	52.653
		S	W₁=-3E-06S²+0.0597S-5.9616	0.831	32.242
		V	W₁=-4E-08V²+0.0062V+17.884	0.909	23.591
合头草	W₁	H	W₁=0.1307H²-3.6823H+34.018	0.684	18.906
		S	W₁=4E-06S²+0.0166S-0.849	0.935	8.55
		V	W₁=5E-09V²+0.002V+2.7985	0.922	9.387

名称	因变量	变量	模型	R²	SEE
红砂	W₂	H	W₂=0.0914H²＋0.017H+10.41	0.468	38.918
		S	W₂=3E-06S²＋0.0116S+13.487	0.694	29.523
		V	W₂=2E-08V²＋0.0012V+23.284	0.820	22.676
合头草	W₂	H	W₂=0.302H²-5.0247H+30.243	0.741	53.216
		S	W₂=-2E-06S²+0.1056S-22.589	0.964	19.739
		V	W₂=-6E-08V²+0.0103V+0.015	0.955	22.143

名称	因变量	变量	模型	R²	SEE
红砂	W₃	H	W₃=0.1399H²+5.0827H-16.524	0.541	86.784
		S	W₃=0.1088S^0.94	0.693	0.505
		V	W₃=-1E-08V²+0.0074V+41.168	0.902	40.211
合头草	W₃	H	W₃=0.4358H²-8.8662H+65.938	0.741	69.607
		S	W₃=0.1283S-27.455	0.973	22.134
		V	W₃=-6E-08V²+0.0123V+2.454	0.962	26.479

Biomass estimation models for two dominant desert shrubs on the northern slopes of Kunlun Mountain

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