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

干旱区研究 ›› 2024, Vol. 41 ›› Issue (2): 284-292.doi: 10.13866/j.azr.2024.02.11 cstr: 32277.14.j.azr.2024.02.11

昆仑山北坡两种优势荒漠灌木的生物量预测模型

张元梅¹^,²(), 孙桂丽¹^,², 鲁艳³^,⁴(), 李利³^,⁴, 张志浩³^,⁴, 张栋栋⁵

1.新疆农业大学林学与风景园林学院，新疆乌鲁木齐 830052
2.干旱区林业生态与产业技术重点实验室，新疆乌鲁木齐 830052
3.中国科学院新疆生态与地理研究所，新疆荒漠植物根系生态与植被修复重点实验室，新疆乌鲁木齐 830011
4.新疆策勒荒漠草地生态系统国家野外科学观测研究站，新疆策勒 848300
5.石河子大学生命科学学院，新疆石河子 832003

收稿日期:2023-06-11 修回日期:2023-09-08 出版日期:2024-02-15 发布日期:2024-03-11
作者简介:张元梅（1998-），女，硕士研究生，主要从事荒漠化防治研究. E-mail: zhangyuanmeicj@163.com
基金资助:
第三次新疆综合科学考察项目子课题(2021xjkk030401);中国科学院“西部青年学者”项目(2021-XBQNXZ-018)

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

ZHANG Yuanmei¹^,²(), SUN Guili¹^,², LU Yan³^,⁴(), LI Li³^,⁴, ZHANG Zhihao³^,⁴, ZHANG Dongdong⁵

1. College of Forestry and Langscape Architeture, Xinjiang Agricultural University, Urumqi 830052, Xinjiang, China
2. Key Laboratory of Forestry Ecology and Industrial Technology in Arid Areas, Urumqi 830052, Xinjiang, China
3. Xinjiang Desert Plant Roots Ecology and Vegetation Restoration Laboratory, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi 830011, Xinjiang, China
4. Cele National Station of Observation and Research for Desert-Grassland Ecosystem, Cele 848300, Xinjiang, China
5. College of Life Sciences, Shihezi University, Shihezi 832003, Xinjiang, China

Received:2023-06-11 Revised:2023-09-08 Published:2024-02-15 Online:2024-03-11

摘要/Abstract

摘要：

构建数学模型是估算灌木生物量的重要方法之一。本研究以中昆仑山北坡山前荒漠带常见的两种荒漠灌木红砂(Reaumuria soongarica)和合头草(Sympegma regelii)为研究对象。采用全株收获法采集植株，分别以株高(H)、冠幅面积(S)、植株体积(V)为自变量，植株地上生物量(W₁)、地下生物量(W₂)、全株生物量(W₃)为因变量，建立函数模型，选取决定系数(R²)、估计标准差(SEE)、回归检验显著水平(P值)为评价指标，以P<0.001为前提，选取R²尽量大、SEE尽量小的模型为红砂和合头草生物量最优预测模型。结果显示：红砂和合头草的生物量最优预测模型均为二次函数模型，合头草全株最优预测模型为一次函数模型除外。红砂植株体积(V)与生物量的相关性最高，生物量最优预测模型R²为0.820~0.920。合头草冠幅面积(S)与生物量相关性最高，生物量最优预测模型R²为0.935~0.973。红砂和合头草生物量最优预测模型均通过(P<0.001)显著性检验，拟合率在84.1%~95.6%之间，可用于生物量估算，本研究为预测荒漠生态系统碳储量和评价碳汇潜力提供科学依据。

关键词: 荒漠灌木, 生物量, 预测模型, 昆仑山

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

张元梅, 孙桂丽, 鲁艳, 李利, 张志浩, 张栋栋. 昆仑山北坡两种优势荒漠灌木的生物量预测模型[J]. 干旱区研究, 2024, 41(2): 284-292.

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.

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参考文献 41

[1]	Tilman D, Reich P B, Knops J M H, et al. Biodiversity and ecosystem stability in a decade-long grassland experiment[J]. Nature, 2006, 441: 629-632. doi: 10.1038/nature04742
[2]	Ives A R, Carpenter S R. Stability and diversity of ecosystems[J]. Science, 2007, 317: 58-62. doi: 10.1126/science.1133258 pmid: 17615333
[3]	郭洁芸, 王雅歆, 李建龙. 氮添加对中国陆地生态系统植物-土壤碳动态的影响[J]. 生态学报, 2022, 42(12): 4823-4833.
	[Guo Jieyun, Wang Yaxin, Li Jianlong. Effects of nitrogen addition on plant-soil carbon dynamics in terrestrial ecosystems of China[J]. Acta Ecologica Sinica, 2022, 42(12): 4823-4833.]
[4]	Mokany K, Raison R J, Prokushkin A S, et al. Critical analysis of root:shoot ratios in terrestrial biomes[J]. Global Change Biology, 2010, 12(1): 84-96. doi: 10.1111/gcb.2006.12.issue-1
[5]	胡会峰, 王志恒, 刘国华, 等. 中国主要灌丛植被碳储量[J]. 植物生态学报, 2006, 30(4): 539-544. doi: 10.17521/cjpe.2006.0071
	[Hu Huifeng, Wang Zhiheng, Liu Guohua, et al. Vegetation carbon storage of major shrub lands in China[J]. Chinese Journal of Plant Ecology, 2006, 30(4): 539-544.] doi: 10.17521/cjpe.2006.0071
[6]	Zandler H, Brenning A, Samimi C, et al. Quantifying dwarf shrub biomass in an arid environment: Comparing empirical methods in a high dimensionalsetting[J]. Remote Sensing of Environment, 2015, 158: 140-155. doi: 10.1016/j.rse.2014.11.007
[7]	沈芳宇, 王永东, 李生宇, 等. 塔里木沙漠公路防护林土壤团聚体特征[J]. 干旱区研究, 2015, 32(5): 910-917.
	[Shen Fangyu, Wang Yongdong, Li Shengyu, et al. Study on soil aggregates of the desert high way shelterbelt in Tarim[J]. Arid Zone Research, 2015, 32(5): 910-917.]
[8]	王永东, 李生宇, 徐新文, 等. 塔里木沙漠公路防护林咸水灌溉土壤盐渍化状况研究[J]. 土壤学报, 2012, 49(5): 886-891.
	[Wang Yongdong, Li Shengyu, Xu Xinwen, et al. Soil salinization of the windbreak forest belts irrigated with saline water alongside the Tarim Desert Highway[J]. Acta Pedologica Sinica, 2012, 49(5): 886-891.]
[9]	杨弦, 郭焱培, 安尼瓦尔·买买提, 等. 中国北方温带灌丛生物量的分布及其与环境的关系[J]. 植物生态学报, 2017, 41 (1): 22-30. doi: 10.17521/cjpe.2016.0199
	[Yang Xian, Guo Yanpei, Mohhamot Anwar, et al. Distribution of biomass in relation to environments in shrublands of temperate China[J]. Chinese Journal of Plant Ecology, 2017, 41(1): 22-30.] doi: 10.17521/cjpe.2016.0199
[10]	马普, 陶梦, 吕世海, 等. 库布齐沙地柠条叶生物量及营养估测模型[J]. 北京林业大学学报, 2018, 40(8): 33-41.
	[Ma Pu, Tao Meng, Lv Shihai, et al. Estimation models of leaf biomass and nutrient content for Caraganakorshinskii in Kubuqi Sandy Land of northern China[J]. Journal of Beijing Forestry University, 2018, 40(8): 33-41.]
[11]	陶冶, 张元明. 荒漠灌木生物量多尺度估测——以梭梭为例[J]. 草业学报, 2013, 22(6): 1-10. doi: 10.11686/cyxb20130601
	[Tao Ye, Zhang Yuanming. Multi-scale biomass estimation of desert shrubs: A case study of Haloxylon ammodendron in the Gurbantunggut Desert, China[J]. Acta Prataculturae Sinica, 2013, 22(6): 1-10.]
[12]	赵梦颖, 孙威, 罗永开, 等. 内蒙古26种常见温带灌木的生物量模型[J]. 干旱区研究, 2019, 36(5): 1219-1228.
	[Zhao Mengying, Sun Wei, Luo Yongkai, et al. Models for estimating the biomass of 26 temperate shrub species in Inner Mongolia[J]. Arid Zone Research, 2019, 36(5): 1219-1228.]
[13]	张殿岱, 王雪梅. 塔里木盆地北缘绿洲-荒漠过渡带典型植物地上生物量估测[J]. 生态学杂志, 2019, 38(10): 3211-3220.
	[Zhang Diandai, Wang Xuemei. Estimating aboveground biomass of typical plant species in the oasis-desert transition zone of northern Tarim Basin[J]. Chinese Journal of Ecology, 2019, 38(10): 3211-3220.]
[14]	仇瑶, 常顺利, 张毓涛, 等. 天山林区六种灌木生物量的建模及其器官分配的适应性[J]. 生态学报, 2015, 35(23): 7842-7851.
	[Qiu Yao, Chang Shunli, Zhang Yutao, et al. Biomass estimation modeling and adaptability analysis of organ allocation in sixcommon shrub species in Tianshan Mountains forests, China[J]. Acta Ecologica Sinica, 2015, 35(23): 7842-7851.]
[15]	杨昊天, 李新荣, 王增如, 等. 腾格里沙漠东南缘4种灌木的生物量预测模型[J]. 中国沙漠, 2013, 33(6): 1699-1704. doi: 10.7522/j.issn.1000-694X.2013.00254
	[Yang Haotian, Li Xinrong, Wang Zengru, et al. Biomass estimation models of four shrub species at southeastern edge of the Tengger Desert[J]. Journal of Desert Research, 2013, 33(6): 1699-1704.] doi: 10.7522/j.issn.1000-694X.2013.00254
[16]	李香云, 岳平, 程欢, 等. 乌拉特荒漠草原红砂生物量预测模型[J]. 干旱区研究, 2020, 37(2): 462-469.
	[Li Xiangyun, Yue Ping, Cheng Huan, et al. Biomass prediction model for Reaumuria soongorica in the Urat desert steppe in Inner Mongolia[J]. Arid Zone Research, 2021, 37(2): 462-469.]
[17]	党晓宏, 高永, 蒙仲举, 等. 西鄂尔多斯地区5种荒漠优势灌丛生物量分配格局及预测模型[J]. 中国沙漠, 2017, 37(1): 100-108. doi: 10.7522/j.issn.1000-694X.2015.00201
	[Dang Xiaohong, Gao Yong, Meng Zhongju, et al. Biomass allocation patterns and estimation model of five desert shrub species in West Ordos region[J]. Journal of Desert Research, 2017, 37(1): 100-108.] doi: 10.7522/j.issn.1000-694X.2015.00201
[18]	朱军涛, 李向义, 张希明, 等. 昆仑山北坡4种优势灌木的气体交换特征[J]. 生态学报, 2011, 31(12): 3522-3530.
	[Zhu Juntao, Li Xiangyi, Zhang Ximing, et al. The gas exchange characteristics of four shrubs on the northern slope of Kunlun Mountain[J]. Acta Ecologica Sinica, 2011, 31(12): 3522-3530.]
[19]	姚正阳, 刘建军. 西安市4种城市绿化灌木单株生物量估算模型[J]. 应用生态学报, 2014, 25(1): 111-116.
	[Yao Zhengyang, Liu Jianjun. Models for biomass estimation of four shrub species planted in urban area of Xi’an City, Northwest China[J]. Chinese Journal of Applied Ecology, 2014, 25(1): 111-116.] pmid: 24765849
[20]	赵成义, 宋郁东, 王玉潮, 等. 几种荒漠植物地上生物量估算的初步研究[J]. 应用生态学报, 2004, 15(1): 49-52.
	[Zhao Chengyi, Song Yudong, Wang Yuchao, et al. Estimation of aboveground biomass of desert plants[J]. Chinese Journal of Applied Ecology, 2004, 15(1): 49-52.] pmid: 15139186
[21]	马媛, 李钢铁, 潘羿壅, 等. 浑善达克沙地3种灌木生物量的预测模型[J]. 干旱区资源与环境, 2017, 31(6): 198-201.
	[Ma Yuan, Li Gangtie, Pan Yiyong, et al. Prediction model for biomass of 3 shrubs in Hunshandake sandy land[J]. Journal of Arid Land Resources and Environment, 2017, 31(6): 198-201.]
[22]	陈国鹏, 杨克彤, 张金武, 等. 甘肃南部7种高寒杜鹃生物量模拟[J]. 生态学报, 2021, 41(13): 5377-5384.
	[Chen Guoping, Yang Ketong, Zhang Jinwu, et al. Biomass simulation of seven alpine Rhododendrons species in the south of Gansu Province[J]. Acta Ecologica Sinica, 2021, 41(13): 5377-5384.]
[23]	王建成, 施翔, 张道远, 等. 克隆植物准噶尔无叶豆的种群生物量结构及回归模型[J]. 中国沙漠, 2012, 32(1): 73-76.
	[Wang Jiancheng, Shi Xiang, Zhang Daoyuan, et al. Biomass allocation and biomass estimation models of the clonal plant population of Eremosparton songoricum[J]. Journal of Desert Research, 2012, 32(1): 73-76.]
[24]	戎荣, 孙斌, 武志涛, 等. 灌丛化草原小叶锦鸡儿灌丛地上生物量测量方法研究[J]. 草业学报, 2023, 32(1): 36-47. doi: 10.11686/cyxb2022201
	[Rong Rong, Sun Bin, Wu Zhitao, et al. Study on above-ground biomass measurement of Caragana microphylla in shrub-encroached grassland[J]. Acta Prataculturae Sinica, 2023, 32(1): 36-47.] doi: 10.11686/cyxb2022201
[25]	崔光帅, 张林, 沈维, 等. 西藏雅鲁藏布江流域中段砂生槐灌丛生物量分配及碳密度[J]. 植物生态学报, 2017, 41(1): 53-61. doi: 10.17521/cjpe.2016.0019
	[Cui Guangshuai, Zhang Lin, Shen Wei, et al. Biomass allocation and carbon density of Sophora moorcroftiana shrublands in the middle reaches of Yarlung Zangbo River, Xizang, China[J]. Chinese Journal of Plant Ecology, 2017, 41(1): 53-61.] doi: 10.17521/cjpe.2016.0019
[26]	汪珍川, 杜虎, 宋同清, 等. 广西主要树种(组)异速生长模型及森林生物量特征[J]. 生态学报, 2015, 35(13): 4462-4472.
	[Wang Zhenchuan, Du Hu, Song Tongqing, et al. Allometric models of major tree species and forest biomass in Guangxi[J]. Acta Ecologica Sinica, 2015, 35(13): 4462-4472.]
[27]	万五星, 王效科, 李东义, 等. 暖温带森林生态系统林下灌木生物量相对生长模型[J]. 生态学报, 2014, 34(23): 6985-6992.
	[Wan Wuxing, Wang Xiaoke, Li Dongyi, et al. Biomass allometric models for understory shrubs of warm temperate forest ecosystem[J]. Acta Ecologica Sinica, 2014, 34(23): 6985-6992.]
[28]	姚雪玲, 姜丽娜, 李龙, 等. 浑善达克沙地6种灌木生物量模拟[J]. 生态学报, 2019, 39(3): 905-912.
	[Yao Xueling, Jiang Lina, Li Long, et al. Biomass simulation of six shrub species in Otindag Sandy Land[J]. Acta Ecologica Sinica, 2019, 39(3): 905-912.]
[29]	秦立厚, 张茂震, 钟世红, 等. 森林生物量估算中模型不确定性分析[J]. 生态学报, 2017, 37(23): 7912-7919.
	[Qin Lihou, Zhang Maozhen, Zhong Shihong, et al. Model uncertainty in forest biomass estimation[J]. Acta Ecologica Sinica, 2017, 37(23): 7912-7919.]
[30]	Huff S, Poudel K P, Ritchie M, et al. Quantifying aboveground biomass for common shrubs in northeastern California using nonlinear mixedeffect models[J]. Forest Ecology and Management, 2018, 424: 154-163. doi: 10.1016/j.foreco.2018.04.043
[31]	叶静芸, 吴波, 刘明虎, 等. 乌兰布和沙漠东北缘荒漠-绿洲过渡带植被地上生物量估算[J]. 生态学报, 2018, 38(4): 1216-1225.
	[Ye Jingyun, Wu Bo, Liu Minghu, et al. Estimation of aboveground biomass of vegetation in the desert-oasis ecotone on the northeastern edge of the Ulan Buh Desert[J]. Acta Ecologica Sinica, 2018, 38(4): 1216-1225.]
[32]	童新风, 杨红玲, 宁志英, 等. 科尔沁沙地优势固沙灌木的生物量预测模型[J]. 中国沙漠, 2018, 38(3): 553-559. doi: 10.7522/j.issn.1000-694X.2018.00033
	[Tong Xinfeng, Yang Hongling, Ning Zhiying, et al. Biomass estimation models for dominant sand-fixing shrubs in Horqin sand land[J]. Journal of Desert Research, 2018, 38(3): 553-559.] doi: 10.7522/j.issn.1000-694X.2018.00033
[33]	李宗英, 罗庆辉, 许仲林. 西天山雪岭云杉林分密度对森林生物量分配格局和异速生长的影响[J]. 干旱区研究, 2021, 38(2): 545-552.
	[Li Zongying, Luo Qinghui, Xu Zhonglin. Effects of stand density on the biomass allocation and tree height-diameter allo-metric growth of Picea schrenkiana forest on the northern slope of the western Tianshan Mountains[J]. Arid Zone Research, 2021, 38(2): 545-552.]
[34]	王蕾, 张宏, 哈斯, 等. 基于冠幅直径和植株高度的灌木地上生物量估测方法研究[J]. 北京师范大学学报(自然科学版), 2004, 40(5): 700-704.
	[Wang Lei, Zhang Hong, Ha Si, et al. A study on the estimating method of shrub upper biomass based on the crown diameter and plant height[J]. Journal of Beijing Normal University (Natural Science), 2004, 40(5): 700-704.]
[35]	党晓宏, 高永, 虞毅, 等. 库布其沙漠北缘8种荒漠灌丛生物量预测模型研究[J]. 干旱区资源与环境, 2016, 30(5): 168-174.
	[Dang Xiaohong, Gao Yong, Yu Yi, et al. The biomass estimation models for eight desert shrub species in northern edge of the Hobq Desert[J]. Journal of Arid Land Resources and Environment, 2016, 30(5): 168-174.]
[36]	种培芳, 刘晟彤, 姬江丽, 等. 模拟CO₂浓度升高和降雨量变化对红砂生物量分配及碳氮特征的影响[J]. 生态学报, 2018, 38(6): 2065-2073.
	[Chong Peifang, Liu Shengtong, Ji Jiangli, et al. Influence of elevated CO₂and precipitation regimes on biomass allocation and carbon and nitrogen content characteristics of Reaumuria soongorica[J]. Acta Ecologica Sinica, 2018, 38(6): 2065-2073.]
[37]	张宗芳, 徐将, 师小军. 新疆野苹果幼苗生长及生物量分配对降水量和降水间隔时间的响应[J]. 干旱区研究, 2023, 40(1): 102-110.
	[Zhang Zongfang, Xu Jiang, Shi Xiaojun. Responses of seedling growth and biomass allocation of Malus sieversii to precipitation amount and precipitation interval[J]. Arid Zone Research. 2023, 40(1): 102-110.]
[38]	徐梦琦, 高艳菊, 张志浩, 等. 干旱胁迫对疏叶骆驼刺幼苗生长和生理的影响[J]. 干旱区研究, 2023, 40(2): 257-267.
	[Xu Mengqi, Gao Yanju, Zhang Zhihao, et al. Effects of drought stress on growth and physiology of Alhagi sparsifolia seedlings[J]. Arid Zone Research, 2023, 40(2): 257-267.]
[39]	聂秀青, 杨路存, 李长斌, 等. 三江源地区高寒灌丛生物量空间分布格局[J]. 应用与环境生物学报, 2016, 22(4): 538-545.
	[Nie Xiuqing, Yang Lucun, Li Changbin, et al. Patterns of biomass partitioning across alpine shrubs in the Three-River Source Region Source Region[J]. Chinese Journal of Applied and Environmental Biology, 2016, 22(4): 538-545.]
[40]	杨彪生, 单立山, 马静, 等. 红砂幼苗生长及根系形态特征对干旱-复水的响应[J]. 干旱区研究, 2021, 38(2): 469-478.
	[Yang Biaosheng, Shan Lishan, Ma Jing, et al. Response of growth and root morphological characteristics of Reaumuria soongorica seedlings to drought-rehydration[J]. Arid Zone Research, 2021, 38(2): 469-478.]
[41]	杨洁, 单立山, 白亚梅, 等. 氮添加和降水变化对红砂生理指标的影响[J]. 干旱区研究, 2021, 38(2): 460-468.
	[Yang Jie, Shan Lishan, Bai Yamei, et al. Effects of nitrogen addition and precipitation on Reaumuria soongorica physiological indices[J]. Arid Zone Research, 2021, 38(2): 460-468.]

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

昆仑山北坡两种优势荒漠灌木的生物量预测模型

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

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