基于DNDC模型的红枣生长模拟参数敏感性和产量不确定性分析

doi:10.6048/j.issn.1001-4330.2023.03.016

新疆农业科学 ›› 2023, Vol. 60 ›› Issue (3): 651-663.DOI: 10.6048/j.issn.1001-4330.2023.03.016

• 植物保护·设施农业·农产品加工工程·微生物 • 上一篇下一篇

基于DNDC模型的红枣生长模拟参数敏感性和产量不确定性分析

王德娟¹^,²(), 汪健平³(), 冯建中²(), 井双泉⁴, 许士东⁵, 隋立春⁶, 黄光辉⁴

1.长庆工程设计有限公司,西安 710018
2.中国农业科学院农业信息研究所,北京 100081
3.山东省海洋资源与环境研究院,山东烟台 264000
4.新疆生产建设兵团第十四师农业科学研究所,新疆昆玉 848100
5.新疆农业科学院农业经济与科技信息研究所,乌鲁木齐 830091
6.长安大学地质工程与测绘学院,西安 710054

收稿日期:2022-07-17 出版日期:2023-03-20 发布日期:2023-04-18
通信作者: 汪健平(1987-),男,安徽枞阳人,工程师,硕士,研究方向为空间信息智能处理与应用,(E-mail)631224605@qq.com;
冯建中(1971-),男,四川巴中人,研究员,博士生导师,研究方向为信息技术与数字农业,(E-mail)fengjianzhong@caas.cn
作者简介:王德娟(1996-),女,山东泰安人,硕士研究生,研究方向为作物模型建模与应用,(E-mail)1525998859@qq.com
基金资助:
新疆生产建设兵团重点领域科技攻关计划(2019AB002);中国农业科学院科技创新(CAAS-ASTIP-2016-AII)

Analyses of Parameter Sensitivity and Yield Uncertainty of Jujube Growth Simulation Based on DNDC Model

WANG Dejuan¹^,²(), WANG Jianping³(), FENG Jianzhong²(), JING Shuangquan⁴, XU Shidong⁵, SUI Lichun⁶, HUANG Guanghui⁴

1. Changqing Engineering Design Co., Ltd., Xi'an 710018, China
2. Institute of agricultural information, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. Shandong Marine Resource and Environment Research Institute, Yantai Shandong 264000, China
4. Agricultural Science Research Institute of the 14th division of Xinjiang production and Construction Corps, Kunyu Xinjiang 848100, China
5. Institute of Agricultural Economics and Science and Technology Information, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
6. School of geological engineering and surveying and mapping, Chang'an University, Xi'an 710054, China

Received:2022-07-17 Online:2023-03-20 Published:2023-04-18
Correspondence author: WANG Jianping (1987-), male, native place: Zongyang, Anhui. Master, engineer, mainly engaged in intelligent processing of spatial information and application, (E-mail)631224605@qq.com;
FENG Jianzhong (1971-), male, native place: Bazhong, Sichuan. Professor, doctoral supervisor, mainly engaged in information technology and digital agriculture,(E-mail)fengjianzhong@caas.cn
Supported by:
Tackling Scientific and Technical Problems in Key Ares of XPCC(2019AB002);Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences(CAAS-ASTIP-2016-AII)

摘要/Abstract

摘要：

【目的】研究红枣生长模型模拟输入参数的敏感性和产量预测不确定性,为红枣生长模拟模型的本地化和区域化参数调整优化提供依据,以提高模型模拟预测精度和效率。【方法】以新疆昆玉市现代农业示范区为研究区,应用可扩展傅里叶振幅敏感分析法(EFAST)和蒙特卡罗法分析基于DNDC模型系统新构建的红枣生长模型的输入参数敏感特性和产量预测不确定性。【结果】作物参数中全株生物量中果实比例(Gfra)、最大作物产量(MaxY)、生长积温(TDD)和需水量(WaterR)等指标敏感度最高,土壤参数中田间持水率(FC)和孔隙度(Por)等指标敏感度最高,田间管理参数中灌溉量(IrrAm)、施肥量(FerAm)和有机肥施肥量(ManAm)等指标敏感度最高;随着参数的波动范围由±5%增大到±10%,红枣预测产量正态分布的相关一致性系数增大,模型的平稳性增加。【结论】调整参数优化模型,并对2015~2019年各年份进行产量模拟测试验证,预测产量结果相对误差控制在±8%以内(最小误差为-1.99%),调整红枣产量预测模型参数,提高了模型预测产量的精度,优化趋于合理。

关键词: DNDC模型; 枣树; 模型本地化; 敏感性与不确定性分析

Abstract:

【Objective】 Analyses of input-parameters sensitivity and yield uncertainty of jujube growth simulation are very significant steps in the hope of providing suggestions for localization and regionalization of jujube growth model to serve as improving the accuracy and efficiency of model simulation prediction.【Methods】 In this paper, the Extended Fourier Amplitude Sensitivity Test (EFAST) and Monte Carlo (MC) method were used to analyze the input-parameter sensitivity and output-parameter uncertainty of a new jujube-yield prediction Denitrification-Decomposition (DNDC) model, which was generated by the crop-type generator of the DNDC model system, in a modern-agriculture demonstration area located in Kunyu city, Xinjiang Uygur Autonomous Region.【Results】 The results showed that the jujube-crop parameters including grain fraction of total biomass (Gfra), maximum grain yield (MaxY), thermal degree days (TDD) and water requirement (WaterR), the soil parameters including field capacity (FC) and porosity (POR), and the field management parameters including Irrigation (IrrAm), Fertilizing amount (FerAm) and Manure amount (ManAm) were the most sensitive to modeling output (i.e., jujube yields), respectively.According to the simulation of jujube yields in a typical year of 2018, when the fluctuation ranges of input-parameters were extended from ±5% to ±10%, the correlation consistency coefficients of jujube-yield prediction with the corresponding normal distribution increased, respectively, which showed the stability of the jujube-yield model increasing.【Conclusion】 Based on the sensitivity and uncertainty analysis of the jujube-yield model parameters, the model parameters were adjusted and optimized, and then, the model was tested and verified to jujube yields from 2015 to 2019.The relative errors of the prediction yields, compared with the in situ data, were controlled within ± 8% (and the minimum error reached -1.99%), which presented a great improvement of the accuracy of the prediction yields and meant that the optimization and adjustment of the model parameters were toward reasonability.

Key words: DNDC model; jujube; model localization; sensitivity and uncertainty analysis

中图分类号:

S665.1

王德娟, 汪健平, 冯建中, 井双泉, 许士东, 隋立春, 黄光辉. 基于DNDC模型的红枣生长模拟参数敏感性和产量不确定性分析[J]. 新疆农业科学, 2023, 60(3): 651-663.

WANG Dejuan, WANG Jianping, FENG Jianzhong, JING Shuangquan, XU Shidong, SUI Lichun, HUANG Guanghui. Analyses of Parameter Sensitivity and Yield Uncertainty of Jujube Growth Simulation Based on DNDC Model[J]. Xinjiang Agricultural Sciences, 2023, 60(3): 651-663.

图/表 12

参考文献 47

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参数定义 Definition of parameter	取值范围 Value range
最大果实产量(kg/(C·hm²)) GMax(Maximum grain production)	4 000~8 000
全株生物量中果实比例(%) Gfra(Grain fraction of total biomass)	0.45~0.7
全株生物量中叶片比例(%) Lfra(Leaf fraction of total biomass)	0.15~0.45
全株生物量中茎比例(%) 0.Sfra(Stem fraction of total biomass)	0.15~0.45
全株生物量中根比例(%) Rfra(Root fraction of total biomass)	0.15~0.45
果实中碳氮含量比例(%) GCN(C/N ratio for grain)	10~80
茎叶中碳氮含量比例(%) SCN(C/N ratio for stem)	10~80
根中碳氮含量比例(%) RCN(C/N ratio for root)	10~80
固氮指数 Nfix(N fixation index)	1~2
需水量(g/(kg C) WaterR(Water requirement)	150~800
适宜温度(℃) TO(Optimum temperature)	20~30
生长积温(℃) TDD(accumulative degree days for maturity)	2 500~3 500
管束结构指数 VaR(Vascularity index)	0~1
最大作物产量(kg/(C·hm²)) MaxY(Maximum grain yield)	6 000~10 000
最大果实产量(kg/(C·hm²)) GCMax(Optimum grain C)	4 000~8 000
最大叶产量(kg/(C·hm²)) LCMax(Optimum leaf C)	1 500~4 000
最大茎产量(kg/(C·hm²)) LCMax(Optimum stem C)	1 500~4 000
最大根产量(kg/(C·hm²)) RCMax(Optimum root C)	1 500~4 000
整株碳氮比 CN(C/N ratio for entire)	10~80
来自土壤的氮量(kg/(N·hm²)) NFS(N from soil)	90~200
来自大气中的氮量(kg/(N·hm²)) NFA(N from atmospheric N fixation)	90~200
需氮量(kg/(N·hm²)) NDe(Total N demand)	90~200
植株最大高度(m) HMax(Maximum height)	0.1~2

参数定义 Definition of parameter	取值范围 Value range
最大果实产量(kg/(C·hm²)) GMax(Maximum grain production)	4 000~8 000
全株生物量中果实比例(%) Gfra(Grain fraction of total biomass)	0.45~0.7
全株生物量中叶片比例(%) Lfra(Leaf fraction of total biomass)	0.15~0.45
全株生物量中茎比例(%) 0.Sfra(Stem fraction of total biomass)	0.15~0.45
全株生物量中根比例(%) Rfra(Root fraction of total biomass)	0.15~0.45
果实中碳氮含量比例(%) GCN(C/N ratio for grain)	10~80
茎叶中碳氮含量比例(%) SCN(C/N ratio for stem)	10~80
根中碳氮含量比例(%) RCN(C/N ratio for root)	10~80
固氮指数 Nfix(N fixation index)	1~2
需水量(g/(kg C) WaterR(Water requirement)	150~800
适宜温度(℃) TO(Optimum temperature)	20~30
生长积温(℃) TDD(accumulative degree days for maturity)	2 500~3 500
管束结构指数 VaR(Vascularity index)	0~1
最大作物产量(kg/(C·hm²)) MaxY(Maximum grain yield)	6 000~10 000
最大果实产量(kg/(C·hm²)) GCMax(Optimum grain C)	4 000~8 000
最大叶产量(kg/(C·hm²)) LCMax(Optimum leaf C)	1 500~4 000
最大茎产量(kg/(C·hm²)) LCMax(Optimum stem C)	1 500~4 000
最大根产量(kg/(C·hm²)) RCMax(Optimum root C)	1 500~4 000
整株碳氮比 CN(C/N ratio for entire)	10~80
来自土壤的氮量(kg/(N·hm²)) NFS(N from soil)	90~200
来自大气中的氮量(kg/(N·hm²)) NFA(N from atmospheric N fixation)	90~200
需氮量(kg/(N·hm²)) NDe(Total N demand)	90~200
植株最大高度(m) HMax(Maximum height)	0.1~2

参数定义 Definition of parameters	取值范围 Value range
黏土含量(%) SC(Soil clay)	0~0.4
容重(g/cm³) BD(Bulk density)	1.0~1.5
饱和导水率(m/h) HC(Hydro conductivity)	0.5~0.8
田间持水率(%) FC(Field capasity)	0.15~0.4
萎焉点(%) WP(Wilting point)	0.4~0.6
孔隙度(%) Por(Porosity)	0.35~0.8
有机碳量(kg/(C·kg)) SOC(SOC content)	0.015~0.03
起始硝酸根含量(mg/(N·kg)) INO₃(Initial NO₃)	0.5~0.65
起始氨气含量(mg/(N·kg)) INH₄(Initial NH₄)	0.05~0.1
酸碱度 pH(pondus hydrogenii)	8~8.2
顶部均匀土层密度(g/cm³) UtS(Uniform top soil)	0.01~0.1
下层土壤中SOC沉降速率(%) SOCD(SOC decrease in profile)	0.5~5.0

参数定义 Definition of parameters	取值范围 Value range
黏土含量(%) SC(Soil clay)	0~0.4
容重(g/cm³) BD(Bulk density)	1.0~1.5
饱和导水率(m/h) HC(Hydro conductivity)	0.5~0.8
田间持水率(%) FC(Field capasity)	0.15~0.4
萎焉点(%) WP(Wilting point)	0.4~0.6
孔隙度(%) Por(Porosity)	0.35~0.8
有机碳量(kg/(C·kg)) SOC(SOC content)	0.015~0.03
起始硝酸根含量(mg/(N·kg)) INO₃(Initial NO₃)	0.5~0.65
起始氨气含量(mg/(N·kg)) INH₄(Initial NH₄)	0.05~0.1
酸碱度 pH(pondus hydrogenii)	8~8.2
顶部均匀土层密度(g/cm³) UtS(Uniform top soil)	0.01~0.1
下层土壤中SOC沉降速率(%) SOCD(SOC decrease in profile)	0.5~5.0

参数定义 Definition of parameters	取值范围 Value range
秸秆还田(%)ResI(Residue incorporation)	0~1
犁地时间TillT(Tilling date)	3.25~4.1
犁地深度(cm)TillDe(Tilling depth)	25~30
施肥时间FerT(Fertilizing date)	3.25~10.25
施肥深度(cm)FerDe(Fertilizing depth)	25~30
施肥量(kg/hm²)FerAm(Fertilizing amount)	450~500
施有机肥时间ManT(Manure data)	3.25~10.25
有机肥碳氮比(%)ManCN(Manure C/N ratio)	10~15
有机肥量(kg/hm²)ManAm(Manure amount)	3 000~45 000
灌溉量(kg/hm²)IrrAm(Irrigation amount)	300~400
灌溉时间IrrT(Irrigation date)	6.10~11.20
灌溉深度(cm)IrrDe(Irrigation depth)	15~30
灌溉系数(%)IrrDC(Irrigation index)	0~1

基于DNDC模型的红枣生长模拟参数敏感性和产量不确定性分析

Analyses of Parameter Sensitivity and Yield Uncertainty of Jujube Growth Simulation Based on DNDC Model

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参考文献 47

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Metrics

年份 Year	2015	2016	2017	2018	2019	均值 Average
实测产量Actual production(kg/hm²)	15 132.39	12 543.94	13 555.91	14 520.22	15 106.64	14 171.82
离差Deviation(kg/hm²)	960.57	-1 627.88	-615.91	348.39	934.82	-
相对离差Relative deviation(%)	6.78	-11.49	-4.35	2.46	6.60	-
参数调整前模拟产量 Simulated production before parameter adjustment (kg/hm²)	12 943.38	10 794.99	10 520.92	10 936.45	12 819.61	-
参数调整前模拟产量上调15% The simulated production increased by 15% before parameter adjustment(kg/hm²)	14 884.45	12 414.25	12 099.15	12 576.40	14 743.00	-
参数调整后模拟产量 Simulation production after parameter adjustment (kg/hm²)	14 549.20	13 351.55	13 286.05	14 141.50	13 955.03	-
参数调整前模拟产量相对误差 Relative error of simulated production before parameter adjustment(%)	-14.47	-13.94	-22.39	-24.68	-15.14	-
模拟产量上调15%后相对误差 Relative error of simulated production increased by 15%(%)	-1.64%	-1.03%	-10.75%	-13.39%	-2.41%	-
参数调整后模拟产量相对误差 Relative error of simulated production after parameter adjustment(%)	-3.85	6.44	-1.99	-2.61	-7.62	-

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