根系水分胁迫响应函数对土壤水及作物生长动态和产量模拟影响的研究

doi:10.12118/j.issn.1000-6060.2021.292

摘要/Abstract

摘要：

为探究根系水分胁迫响应函数对农田水分动态及产量模拟的影响,基于Richards方程和PS123作物生长模型分别进行了土壤水分动态和小麦产量的模拟,对比分析了VG（S型曲线）、MP（凹凸型曲线）及LS（S型曲线）3种水分胁迫响应函数。采用山西省霍泉站（3 a）及潇河站（2 a）的试验资料对模型中的土壤水力特征参数、水分胁迫响应函数参数进行反演,确定最优的参数值,得到了土壤含水率、蒸散量及籽粒产量的模拟结果。结果表明：（1） VG、MP及LS函数条件下的土壤水分及产量模拟结果均较好,回归估计标准误差（RMSE）值在0.021~0.036之间,模拟值与实测值间的R检验达到了极显著水平;对于霍泉站,VG、MP、LS函数条件下,土壤含水率模拟值与实测值的平均相对误差分别为6.37%、8.26%及7.18%,相关性系数值最小为0.7814,模拟产量的平均相对误差值分别为8.73%、8.40%及8.42%;对于潇河站,土壤含水率模拟值与实测值的平均相对误差结果普遍大于霍泉站,最大为12.47%。（2）土壤水分动态模拟,S型曲线的水分胁迫响应函数较凹凸型曲线的水分胁迫响应函数表现出了更好的效果,其中使用VG函数模拟的平均相对误差较小,模拟的蒸散量更接近于实测值。（3）产量模拟,3种水分胁迫响应函数差异不明显。综上,VG函数是一种精度较高的根系水分胁迫响应函数,且模型简洁、方便;改进的LS函数能够提高模拟精度,但模型的稳定性有待近一步研究。

关键词: 水分胁迫, 根系吸水模式, 农田水分模拟, 产量模拟, 参数反演

Abstract:

To investigate the effect of the root moisture stress response function (RMSRF) on soil moisture dynamic and yield simulation, the soil moisture dynamic, and wheat yield were simulated based on the Richards equation and PS123 crop growth model. The three RMSRF of VG (S-curve), MP (convex curve), and LS (S-curve) were compared. The soil characteristic and RMSRF parameters in the model were inverted to determine the optimal parameter values using the measured data from Huoquan Station (three years) and Xiaohe Station (two years) in Shanxi Province, China to obtain the simulation results of soil moisture content, evapotranspiration, and grain yield. The analysis is as follows. (1) The soil moisture and yield simulations under VG, MP, and LS functions are good. The RMSE value is between 0.021 and 0.036; the r test between the simulated and measured values has reached an extremely significant level. For the Huoquan Station, under the three RMSRF of VG, MP, and LS conditions, the average relative errors of the simulated and measured values for soil moisture content are 6.37%, 8.26%, and 7.18%, respectively. The minimum certainty coefficient value is 0.7814. The average relative error values of the simulated wheat yield are 8.73%, 8.40%, and 8.42%, respectively. For the Xiaohe Station, the average relative error of the simulated and measured values for soil moisture content is greater than that of the Huoquan Station, with a maximum of 12.47%. (2) The simulation of soil moisture dynamic shows that RMSRF by the S-shaped curve shows better results than RMSRF by the concave-convex curve. The average relative error using the VG function is smaller, and the simulated evapotranspiration is closer to the actual measurement. (3) In terms of yield simulation results, there is no significant difference among the three RMSRF. In summary, the VG function is a high-precision RMSRF, and the model is simple and convenient. The suggested LS function can improve the simulation accuracy, but the stability of the model needs further research.

Key words: moisture stress, root moisture uptake model, farmland moisture simulation, yield simulation, parameter inversion

王铁英,王仰仁,柴俊芳,郭文俊. 根系水分胁迫响应函数对土壤水及作物生长动态和产量模拟影响的研究[J]. 干旱区地理, 2022, 45(2): 566-577.

WANG Tieying,WANG Yangren,CHAI Junfang,GUO Wenjun. Effect of root water stress response function on soil water, crop growth dynamics and yield simulation[J]. Arid Land Geography, 2022, 45(2): 566-577.

图/表 8

表1

表2

表3

表4

图1

表5

表6

图2

参考文献 48

[1]	李会杰. 黄土高原林地深层土壤根系吸水过程及其对水分胁迫和土壤碳输入的影响[D]. 杨凌: 西北农林科技大学, 2019.
	[ Li Huijie. Root water uptake process in deep soil for forest growing on the Loess Plateau and its effect on water stress and soil carbon input[D]. Yangling: Northwest A & F University, 2019. ]
[2]	康绍忠, 刘晓明, 熊运章. 冬小麦根系吸水模式的研究[J]. 西北农林科技大学学报(自然科学版), 1992, 20(2):5-12.
	[ Kang Shaozhong, Liu Xiaoming, Xiong Yunzhang. Research on the model of mater uptake by winter wheat root system[J]. Journal of Northwest A & F University (Natural Science Edition), 1992, 20(2):5-12. ]
[3]	姜鹏. 东北玉米物质生产及根系吸水对水分胁迫的响应研究[D]. 沈阳: 沈阳农业大学, 2019.
	[ Jiang Peng. Northeast corn material production and root water absorption study on the response of water stress[D]. Shenyang: Shenyang Agricultural University, 2019. ]
[4]	王玉阳, 陈亚鹏. 植物根系吸水模型研究进展[J]. 草业学报, 2017, 26(3):214-225.
	[ Wang Yuyang, Chen Yapeng. Research progress in water uptake models by plant roots[J]. Acta Prataculturae Sinica, 2017, 26(3):214-225. ]
[5]	王春霞, 孙西欢, 马娟娟, 等. 植物根系吸水研究[J]. 山西水利, 2007(1):85-86, 88.
	[ Wang Chunxia, Sun Xihuan, Ma Juanjuan, et al. Research on plant root water absorption[J]. Shanxi Water Resources, 2007(1):85-86, 88. ]
[6]	赵成义, 黄俊梅, 王玉潮, 等. 植物根系吸水特性研究[J]. 干旱区地理, 1999, 22(2):88-96.
	[ Zhao Chengyi, Huang Junmei, Wang Yuchao, et al. Research on water absorption characteristics of plant roots[J]. Arid Land Geography, 1999, 22(2):88-96. ]
[7]	李聪. 农业水文模型中的关键参数对作物蒸腾量影响的数值研究[D]. 杭州: 浙江大学, 2020.
	[ Li Cong. Numerical study on the effects of key parameters of agro-hydrological model on crop transpiration[D]. Hangzhou: Zhejiang University, 2020. ]
[8]	朱永华, 仵彦卿, 吕海深. 荒漠植物根系吸水的数学模型[J]. 干旱区资源与环境, 2001, 15(2):76-80.
	[ Zhu Yonghua, Wu Yanqing, Lü Haishen. Mathematical model of water absorption of eremophyte root system[J]. Journal of Arid Land Resources and Environment, 2001, 15(2):76-80. ]
[9]	Feddes R A, Kowalik P J, Malinka K K, et al. Simulation of field water uptake by plants using a soil water dependent root extraction function[J]. Journal of Hydrology, 1976, 31(1-2):13-26. doi: 10.1016/0022-1694(76)90017-2
[10]	金欣欣, 石建初, 李森, 等. 根系吸水模型模拟覆膜旱作水稻气孔导度[J]. 农业工程学报, 2017, 33(9):107-115.
	[ Jin Xinxin, Shi Jianchu, Li Sen, et al. Modeling stomatal conductance using root-water-uptake in ground cover rice production system[J]. Transactions of the Chinese Society of Agricultural Engineering, 2017, 33(9):107-115. ]
[11]	Bouten W, Heimovaara T J, Tiktak A. Spatial patterns of throughfall and soil water dynamics in a Douglas fir stand[J]. Water Resources Research, 1992, 28(12):3227-3233. doi: 10.1029/92WR01764
[12]	Feddes R A, Kowalik P, Zarandy H. Simulation of field water use and crop yield[M]. Wageningen: Centre for Agricultural Publishing and Documentation, 1978: 189.
[13]	吉喜斌, 康尔泗, 陈仁升, 等. 植物根系吸水模型研究进展[J]. 西北植物学报, 2006, 26(5):1079-1086.
	[ Ji Xibin, Kang Ersi, Chen Rensheng, et al. Research advances about water-uptake models by plant roots[J]. Acta Botanica Boreali-Occidentalia Sinica, 2006, 26(5):1079-1086. ]
[14]	吴训. 土壤水分亏缺对作物蒸腾耗水的胁迫影响及其定量表征[D]. 北京: 中国农业大学, 2019.
	[ Wu Xun. Effects of soil water deficit on crop water consumption by transpiration and their quantitative delineations[D]. Beijing: China Agricultural University, 2019. ]
[15]	Wu X, Zuo Q, Shi J, et al. Introducing water stress hysteresis to the Feddes empirical macroscopic root water uptake model[J]. Agricultural Water Management, 2020, 240:106293, doi: 10.1016/j.agwat.2020.106293. doi: 10.1016/j.agwat.2020.106293
[16]	尚松浩, 毛晓敏, 雷志栋, 等. 土壤水分动态模拟模型及其应用[M]. 北京: 科学出版社, 2009: 65-121.
	[ Shang Songhao, Mao Xiaomin, Lei Zhidong, et al. Dynamic simulation model of soil moisture and its application[M]. Beijing: Science Press, 2009: 65-121. ]
[17]	吴训, 石建初, 左强. 基于作物水分关系改进土壤水分胁迫修正系数的反求方法[J]. 水利学报, 2020, 51(2):212-222.
	[ Wu Xun, Shi Jianchu, Zuo Qiang. Improving the inverse method to estimate the soil water stress reduction function based on crop-water relations[J]. Journal of Hydraulic Engineering, 2020, 51(2):212-222. ]
[18]	孙扬越, 申双和. 作物生长模型的应用研究进展[J]. 中国农业气象, 2019, 40(7):444-459.
	[ Sun Yangyue, Shen Shuanghe. Research progress in application of crop growth models[J]. Chinese Journal of Agrometeorology, 2019, 40(7):444-459. ]
[19]	王锐, 李亚飞, 张丽娟, 等. 土壤湿度驱动WOFOST模型及其适应性[J]. 中国农业气象, 2015, 36(3):263-271.
	[ Wang Rui, Li Yafei, Zhang Lijuan, et al. WOFOST model based on soil moisture driven and its adaptability[J]. Chinese Journal of Agrometeorology, 2015, 36(3):263-271. ]
[20]	郎婷婷, 郝蒙蒙, 吴风华, 等. 基于DSSAT模型的京津冀地区主要农作物用水分析[J]. 干旱地区农业研究, 2019, 37(5):235-242, 248.
	[ Lang Tingting, Hao Mengmeng, Wu Fenghua, et al. Study on water requirements of major crops in Beijing-Tianjin-Hebei region using DSSAT model[J]. Agricultural Research in the Arid Areas, 2019, 37(5):235-242, 248. ]
[21]	刘洪, 郭文利, 宇振荣. 土地生产力模型(PS123)的校正和验证[J]. 中国农业气象, 2005, 26(3):150-154.
	[ Liu Hong, Guo Wenli, Yu Zhenrong. Validation and calibration of land production simulation model (PS123)[J]. Chinese Journal of Agrometeorology, 2005, 26(3):150-154. ]
[22]	杨丽霞, 王仰仁. PS123模型用于山西省冬小麦水管理的适用性评价[J]. 灌溉排水学报, 2014, 33(4/5):409-413.
	[ Yang Lixia, Wang Yangren. Evaluation on the applicability of PS123 model in Shanxi Province[J]. Journal of Irrigation and Drainage, 2014, 33(4/5):409-413. ]
[23]	王国帅, 史海滨, 李仙岳, 等. 基于HYDRUS-1D模型的荒漠绿洲水盐运移模拟与评估[J]. 农业工程学报, 2021, 37(8):87-98.
	[ Wang Guoshuai, Shi Haibin, Li Xianyue, et al. Simulation and evaluation of soil water and salt transport in desert oases of Hetao Irrigation District using HYDRUS-1D model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2021, 37(8):87-98. ]
[24]	王世明, 范敬龙, 赵英, 等. 咸水灌溉条件下塔里木河下游沙漠土壤水盐运移数值模拟[J]. 干旱区地理, 2021, 44(4):1104-1113.
	[ Wang Shiming, Fan Jinglong, Zhao Ying, et al. Numerical simulation of water and salt migration in desert soil in the lower reaches of Tarim River under salt-water irrigation[J]. Arid Land Geography, 2021, 44(4):1104-1113. ]
[25]	Hansen S, Abrahamsen P, Petersen C T, et al. Daisy: Model use, calibration, and validation[J]. Transactions of the ASABE, 2012, 55(4):1315-1333.
[26]	胡克林, 梁浩. 农田土壤-作物系统过程模型及应用[M]. 北京: 科学出版社, 2019: 1-49.
	[ Hu Kelin, Liang Hao. Farmland soil: Crop system process model and application[M]. Beijing: Science Press, 2019: 1-49. ]
[27]	姜志伟, 陈仲新, 周清波, 等. CERES-Wheat作物模型参数全局敏感性分析[J]. 农业工程学报, 2011, 27(1):236-242.
	[ Jiang Zhiwei, Chen Zhongxin, Zhou Qingbo, et al. Global sensitivity analysis of CERES-Wheat model parameters[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(1):236-242. ]
[28]	陈小民, 崔红, 刘志铭, 等. 作物模型在我国玉米生产中的研究与应用[J]. 玉米科学, 2018, 26(3):115-120.
	[ Chen Xiaomin, Cui Hong, Liu Zhiming, et al. Research and application on maize production by crop models in China[J]. Journal of Maize Sciences, 2018, 26(3):115-120. ]
[29]	毛振强, 张银锁, 宇振荣. 基于作物生长模型的夏玉米灌溉需求分析[J]. 作物学报, 2003, 29(3):419-426.
	[ Mao Zhenqiang, Zhang Yinsuo, Yu Zhenrong. Water requirement and irrigation scenarios of summer maize production aided by crop growth simulation model[J]. Acta Agronomica Sinica, 2003, 29(3):419-426. ]
[30]	胡克林, 李保国, 陈研, 等. 作物生长与土壤水氮运移联合模拟的研究Ⅰ——模型[J]. 水利学报, 2007, 38(7):779-785.
	[ Hu Kelin, Li Baoguo, Chen Yan, et al. Coupled simulation of crop growth with soil water-heat-nitrogen transportⅠ: Model[J]. Journal of Hydraulic Engineering, 2007, 38(7):779-785. ]
[31]	王康. 非饱和土壤水流运动及溶质运移[M]. 北京: 科学出版社, 2010: 1-109.
	[ Wang Kang. Unsaturated soil water flow movement and solute transport[M]. Beijing: Science Press, 2010: 1-109. ]
[32]	康绍忠. 土壤-植物-大气连续体水分传播理论及其应用[M]. 北京: 中国水利水电出版社, 1994: 1-121.
	[ Kang Shaozhong. Soil-plant-atmosphere continuum water propagation theory and its application[M]. Beijing: China Water Conservancy and Hydropower Press, 1994: 1-121. ]
[33]	王仰仁. 考虑水分和养分胁迫的SPAC水热动态与作物生长模拟研究[D]. 杨凌: 西北农林科技大学, 2004.
	[ Wang Yangren. Water, heat transfer and crop growth simulation in SPAC with water and nutrient stress[D]. Yangling: Northwest A & F University, 2004. ]
[34]	雷志栋, 杨诗秀, 谢森传. 土壤水动力学[M]. 北京: 清华大学出版社, 1988: 77-130.
	[ Lei Zhidong, Yang Shixiu, Xie Senchuan. Soil water hydrodynamics[M]. Beijing: Tsinghua University Press, 1988: 77-130. ]
[35]	Novák V. Estimation of soil-water extraction patterns by roots[J]. Agricultural Water Management, 1987, 12(4):271-278. doi: 10.1016/0378-3774(87)90002-3
[36]	宇振荣, 王建武, 邱建军. 土地利用系统分析[M]. 北京: 中国农业科技出版社, 1997: 101-162.
	[ Yu Zhenrong, Wang Jianwu, Qiu Jianjun. Land use system analysis[M]. Beijing: China Agricultural Science and Technology Press, 1997: 101-162. ]
[37]	姚丽. 限量供水条件下精准灌溉技术集成效益分析[D]. 天津: 天津农学院, 2020.
	[ Yao Li. Analysis of integrated benefit of precision irrigation technology under limited water supply[D]. Tianjin: Tianjin Agricultural University, 2020. ]
[38]	明道绪. 田间试验与统计分析[M]. 第三版. 北京: 科学出版社, 2013: 39-275.
	[ Ming Daoxu. Field experiments and statistical analysis[M]. 3rd ed. Beijing: Science Press, 2013: 39-275. ]
[39]	丁运韬, 程煜, 张体彬, 等. 利用HYDRUS-2D模拟膜下滴灌玉米农田深层土壤水分动态与根系吸水[J]. 干旱地区农业研究, 2021, 39(3):23-32.
	[ Ding Yuntao, Cheng Yu, Zhang Tibin, et al. Modeling of dynamics of deep soil water and root uptake of maize with mulched drip irrigations using HYDRUS-2D[J]. Agricultural Research in the Arid Areas, 2021, 39(3):23-32. ]
[40]	盛钰. 绿洲农田土壤水分运移规律及其对作物生长的影响[D]. 乌鲁木齐: 新疆农业大学, 2004.
	[ Sheng Yu. The law of soil water movement and its influence on growth of crop in oasis farmland[D]. Urumqi: Xinjiang Agricultural University, 2004. ]
[41]	杨诗秀, 刘亶仁, 陆秀文, 等. 应用土壤物理-土壤水和温度应用[M]. 北京: 水利水电出版社, 1984: 123-151.
	[ Yang Shixiu, Liu Danren, Lu Xiuwen, et al. Applied soil physics: Soil water and temperature application[M]. Beijing: China Water Conservancy and Hydropower Press, 1984: 123-151. ]
[42]	蔡福, 米娜, 明惠青, 等. WOFOST模型蒸散过程改进对玉米干旱模拟影响[J]. 应用气象学报, 2021, 32(1):52-64.
	[ Cai Fu, Mi Na, Ming Huiqing, et al. Effects of improving evapotranspiration parameterization scheme on WOFOST model performance in simulating maize drought stress process[J]. Journal of Applied Meteorological Science, 2021, 32(1):52-64. ]
[43]	孙仕军, 张琳琳, 陈志君, 等. AquaCrop作物模型应用研究进展[J]. 中国农业科学, 2017, 50(17):3286-3299.
	[ Sun Shijun, Zhang Linlin, Chen Zhijun, et al. Advances in AquaCrop model research and application[J]. Scientia Agricultura Sinica, 2017, 50(17):3286-3299. ]
[44]	张淑杰, 周广胜, 李荣平. 基于涡度相关的春玉米逐日作物系数及蒸散模拟[J]. 应用气象学报, 2015, 26(6):695-704.
	[ Zhang Shujie, Zhou Guangsheng, Li Rongping. Daliy crop coefficient of spring maize using eddy covariance observation and its actual evapotranspiration simulation[J]. Journal of Applied Meteorological Science, 2015, 26(6):695-704. ]
[45]	康国芳. 河北省冬小麦生长模拟与叶绿素荧光参数分析[D]. 保定: 河北农业大学, 2008.
	[ Kang Guofang. Study on winter growth simulation and chlorophyll fluorescence parameter analysis in Hebei Province[D]. Baoding: Hebei Agricultural University, 2008. ]
[46]	王伟, 黄义德, 黄文江, 等. 作物生长模型的适用性评价及冬小麦产量预测[J]. 农业工程学报, 2010, 26(3):233-237.
	[ Wang Wei, Huang Yide, Huang Wenjiang, et al. Applicability evaluation of CERES-wheat model and yield prediction of winter wheat[J]. Transactions of the CSAE, 2010, 26(3):233-237. ]
[47]	俞明涛, 张科锋. 基于HYDRUS-2D软件的土壤水力特征参数反演及间接地下滴灌的土壤水分运动模拟[J]. 浙江农业学报, 2019, 31(3):458-468.
	[ Yu Mingtao, Zhang Kefeng. Identification of soil hydraulic parameters based on HYDRUS-2D software and simulation of soil water movement under indirect subsurface drip irrigation[J]. Acta Agriculturae Zhejiangensis, 2019, 31(3):458-468. ]
[48]	刘彬彬. 基于微遗传算法的土壤水力特征参数反演及水分运动模拟[D]. 杭州: 浙江大学, 2018.
	[ Liu Binbin. Identification of soil hydraulic property parameters based on the micro-genetic algorithm and simulation[D]. Hangzhou: Zhejiang University, 2018. ]

站名	年份	处理	灌水定额 /mm	灌水时间(月-日)				底肥 /kg·hm^-2	追肥 /kg·hm^-2
站名	年份	处理	灌水定额 /mm	1次	2次	3次	4次	底肥 /kg·hm^-2	追肥 /kg·hm^-2
霍泉站	2017	高水	75	11-22	03-22	05-08	05-22	600	450
		中水	75	11-22	03-22	05-22	-	600	300
		零水	-	-	-	-	-	600	-
	2018	高水	75	03-15	04-18	05-12	05-31	900	563
		中水	75	03-15	04-18	05-12	-	600	375
		零水	-	-	-	-	-	-	-
	2019	高水	75	03-26	04-22	05-22	-	1050	510
		中水	75	03-26	05-08	-	-	750	345
		零水	-	-	-	-	-	-	-
潇河站	2004	高水	75	12-11	04-19	05-07	05-29	750	225
		中水	45	04-19	-	-	-	750	225
		零水	-	-	-	-	-	750	-
	2005	高水	75	12-01	04-02	05-11	05-31	1125	338
		中水	45	04-20	-	-	-	750	225
		零水	-	-	-	-	-	750	-

站名	模型	θ_s/cm³·cm^-3	θ_r/cm³·cm^-3	α/cm^-1	n	K_s/cm·d^-1
霍泉站	VG	0.4468	0.0066	0.0076	1.3769	9.3486
	MP	0.4460	0.0033	0.0245	1.6078	5.5227
	LS	0.4833	0.0095	0.0073	1.8180	2.7249
潇河站	VG	0.4900	0.0036	0.0128	1.4325	7.5811
	MP	0.4502	0.0054	0.0279	1.4296	5.9857
	LS	0.4618	0.0040	0.0303	1.5368	5.1511

站名	VG		MP			LS
站名	P	h₅₀/cm	θ_j/cm³·cm^-3	θ_wp/cm³·cm^-3	A	θ_j/cm³·cm^-3	θ_wp/cm³·cm^-3	B	C
霍泉站	1.98	1438.21	0.3586	0.1495	0.5931	0.3709	0.1680	0.5457	2.9801
潇河站	1.71	1059.34	0.2574	0.1781	0.5954	0.3781	0.1513	0.5427	2.6097

站名	模型	RMSE	RE范围/%	平均RE/%	r	样本数
霍泉站	VG	0.0217	0.05~23.82	6.37	0.8557	191
	MP	0.0273	0.01~22.02	8.26	0.8150	191
	LS	0.0223	0.04~27.27	7.18	0.7814	191
潇河站	VG	0.0363	0.13~38.46	12.47	0.8280	122
	MP	0.0279	0.08~30.98	8.59	0.7820	122
	LS	0.0314	0.09~40.62	10.33	0.7196	122

站名	年份	模型	高水		中水		零水
站名	年份	模型	Ta	ETa	Ta	ETa	Ta	ETa
霍泉站	2017	VG	264.4	401.8	224.2	350.3	160.6	263.5
		MP	201.6	312.9	170.2	275.5	111.6	186.7
		LS	244.4	356.7	194.3	297.7	112.2	193.4
		实测	-	502.0	-	400.2	-	261.3
	2018	VG	248.2	383.3	241.7	359.7	150.1	246.7
		MP	186.2	283.3	184.0	266.8	101.0	163.0
		LS	226.1	331.9	221.5	310.6	104.8	176.6
		实测	-	446.4	-	378.7	-	232.7
	2019	VG	245.9	371.7	216.7	323.5	129.9	224.1
		MP	182.6	271.5	157.4	233.5	85.7	150.7
		LS	220.5	315.1	182.0	262.9	82.8	156.0
		实测	-	404.9	-	374.8	-	235.1
潇河站	2004	VG	303.1	451.5	187.5	283.0	130.9	204.0
		MP	270.2	391.8	146.4	224.2	92.0	157.2
		LS	247.8	372.6	128.2	208.9	78.8	149.0
		实测	-	529.6	-	297.3	-	235.6
	2005	VG	337.1	461.1	187.3	234.7	162.1	206.0
		MP	299.3	397.1	141.3	187.8	114.6	157.9
		LS	276.0	380.1	126.1	180.6	96.0	145.9
		实测	-	515.0	-	237.9	-	213.3