RiceGrow水稻模型品种参数敏感性分析

doi:10.19788/j.issn.2096-6369.210303

农业大数据学报 ›› 2021, Vol. 3 ›› Issue (3): 23-32.doi: 10.19788/j.issn.2096-6369.210303

RiceGrow水稻模型品种参数敏感性分析

孟怡君^1,^2,^3,^4,^5,⁶(), 邱小雷^1,^2,^3,^4,^5,⁶, 刘蕾蕾^1,^2,^3,^4,^5,⁶, 刘兵^1,^2,^3,^4,^5,⁶, 朱艳^1,^2,^3,^4,^5,⁶, 曹卫星^1,^2,^3,^4,^5,⁶, 汤亮^1,^2,^3,^4,^5,⁶()

^1.南京农业大学农学院，南京 210095
^2.国家信息农业工程技术中心，南京 210095
^3.智慧农业教育部工程研究中心，南京 210095
^4.农业农村部农作物系统分析与决策重点实验室，南京 210095
^5.江苏省信息农业重点实验室，南京 210095
^6.现代作物生产省部共建协同创新中心，南京 210095

收稿日期:2021-08-25 出版日期:2021-09-26 发布日期:2021-12-22
通讯作者: 汤亮 E-mail:NJAUmyj@126.com;tangl@njau.edu.cn
作者简介:孟怡君，女，硕士研究生，研究方向：作物系统模拟； E-mail： NJAUmyj@126.com
基金资助:
国家自然科学基金面上项目(31571566)

Sensitivity Analysis of Genetic Parameters of RiceGrow Model

Yijun Meng^1,^2,^3,^4,^5,⁶(), Xiaolei Qiu^1,^2,^3,^4,^5,⁶, Leilei Liu^1,^2,^3,^4,^5,⁶, Bing Liu^1,^2,^3,^4,^5,⁶, Yan Zhu^1,^2,^3,^4,^5,⁶, Weixing Cao^1,^2,^3,^4,^5,⁶, Liang Tang^1,^2,^3,^4,^5,⁶()

^1.College of Agriculture, Nanjing Agricultural University, Nanjing 210095, China
^2.National Engineering Technology Center for Information Agriculture, Nanjing 210095, China
^3.Intelligent Agriculture Engineering Research Center, Ministry of Education, Nanjing 210095, China
^4.Key Laboratory for Crop System Analysis and Decision Making, Ministry of Agriculture and Rural Affairs, Nanjing 210095, China
^5.Jiangsu Key Laboratory for Information Agriculture, Nanjing 210095, China
^6.Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing 210095, China

Received:2021-08-25 Online:2021-09-26 Published:2021-12-22
Contact: Liang Tang E-mail:NJAUmyj@126.com;tangl@njau.edu.cn

摘要/Abstract

摘要：

品种参数调试是利用作物生长模型进行模拟前的重要步骤，其调试往往花费大量时间和精力，敏感性分析可以帮助识别敏感参数，提高调试效率。本研究针对水稻生长模型RiceGrow，运用SimLab和MATLAB软件，采用EFAST法对水稻品种参数进行敏感性分析，得出不同地区和不同气候情景下（1981-2015年的历史气象数据和全球未来增温2.0℃气候情景）该模型的参数敏感性，并通过TDCC（Top-Down-Coefficient of Concordance）系数计算敏感性排序一致性。结果表明，影响开花期和总干物质量的最敏感参数为最适温度（OT，Optimum Temperature），其次为温度敏感性（TS，Temperature Sensitivity）、光周期敏感性（PS，Photoperiod Sensitivity）、基本早熟性（IE，Intrinsic Earliness），对成熟期和全生育期的最敏感参数为OT，TS、IE、PS、基本灌浆因子（BFF，Basic Filling Factor）也是敏感参数，影响产量的敏感参数主要为最大光合速率（AMX，Maximum CO₂ assimilation rate）、比叶面积（SLA，Specific Leaf Area）、收获指数（HI，Harvest Index），其次包括IE、TS、BFF、OT、PS；各个地区和不同气候情景下敏感参数较为一致但敏感性排序差异较大，增温气候情景下的多数参数敏感指数略有增加，少数略有减小；不同气候情景下的参数敏感性变化较小，不同地区之间的变化较大。在对生育期和总干物质量输出变量进行调参时，需要重点调试OT；在低温高纬度的地区需重点调试和温度、光周期及光合有关的参数；在对产量进行调参时，需要重点关注AMX、HI、SLA。LAI相对生长速率和消光系数不敏感，可在参数调试中忽略，也可在模型中剔除进行模型简化。研究结果将为作物模型的本地化、提高参数估计效率提供支持。

关键词: RiceGrow, 作物生长模型, 水稻, 敏感性分析, 品种参数, 生育期, 产量

Abstract:

Genetic parameter calibration is an important step before applying the crop growth model, which often calls for a lot of time and effort. Sensitivity analysis can help to identify sensitive parameters, improve calibration efficiency, and simplify the model. Using Simlab and Matlab software, this study analyzed the sensitivity of rice genetic parameters of RiceGrow model by EFAST method and obtained the parameter sensitivity of the model in different regions and under different climate scenarios (historical meteorological data from 1981 to 2015 and global future warming 2.0℃ climate scenarios). The TDCC (Top-Downward-Coefficient of Concordance) coefficient was used to calculate the sensitivity ranking consistency. The results showed that Optimum Temperature (OT) was the most sensitive parameter affecting flowering period and total dry matter, followed by Temperature Sensitivity (TS), Photoperiod Sensitivity (PS) and Intrinsic Earliness (IE). OT was the most sensitive parameter affecting maturity period and the whole growth period. TS, IE, PS and Basic Filling Factor (BFF) were also sensitive parameters. The sensitive parameters affecting yield are mainly maximum CO₂ assimilation rate (AMX), Specific Leaf Area (SLA) and Harvest Index (HI), followed by IE, TS, BFF, OT and PS. The sensitivity parameters in all regions and under different climate scenarios are relatively consistent, but the sensitivity ordering varies greatly. The sensitivity indexes of most parameters under warming climate scenarios slightly increase, while a few slightly decrease. The variation of parameter sensitivity under different climate scenarios is small, while which among different regions is large. When calibrating the model for phenology and dry matter, OT is the most sensitivity. In areas with low temperature and high latitude, the parameters related to temperature, photoperiod and photosynthesis should be focused. When calibrating the parameters of the yield, we need to focus on AMX, HI, SLA. Relative growth rate of LAI is not sensitive, so it can be ignored in parameter calibration, and can also be eliminated from the model to simplify the model. The results would be used to localize crop model and provide a way to improve the efficiency of parameter calibration.

Key words: RiceGrow, crop growth model, rice, sensitivity analysis, genetic parameters, growth period, yield

中图分类号:

S117

孟怡君, 邱小雷, 刘蕾蕾, 刘兵, 朱艳, 曹卫星, 汤亮. RiceGrow水稻模型品种参数敏感性分析[J]. 农业大数据学报, 2021, 3(3): 23-32.

Yijun Meng, Xiaolei Qiu, Leilei Liu, Bing Liu, Yan Zhu, Weixing Cao, Liang Tang. Sensitivity Analysis of Genetic Parameters of RiceGrow Model[J]. Journal of Agricultural Big Data, 2021, 3(3): 23-32.

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品种参数 Genetic parameters	定义 Definition	范围 Range
叶面积相对生长速率（d^-1℃） Relative growth rate of LAI（LAIRGR）	叶面积相对生长速率	0.004-0.0075
基本早熟性 Intrinsic earliness (IE)	对不同基因型基本营养阶段所需生理发育时间相对差异的量化	0-1
光周期敏感性 Photoperiod sensitivity（PS）	影响水稻的光周期敏感阶段（从光敏感开始至二次枝梗分化期），值越大感光性越强	0-10
温度敏感性 Temperature sensitivity（TS）	品种特定的参数，TS越大品种对温度反应越敏感	0-20
基本灌浆因子 Basic filling factor(BFF)	反应水稻从抽穗到成熟期的长短的量	0-1
收获指数 Harvest index（HI）	收获时经济产量（籽粒、果实等）与生物产量之比	0.3-0.7
最适温度（℃） Optimum temperature（OT）	作物生长的最适宜温度	15-35
最大光合速率（kg CH₂O ha^-1 h^-1） Maximum CO₂ assimilation rate (AMX)	最大光合速率	20-75
比叶面积（10^-4m²g^-1） Specific leaf area（SLA）	叶的单面面积与其干重之比	100-300
消光系数参数 Parameter of extinction coefficient（EC）	用于量化消光系数的特征参数	0.0001-0.0138

输出变量	历史 Baseline		增温2.0℃ Increase by 2.0℃
Output	历史 Baseline		增温2.0℃ Increase by 2.0℃
variables	TDCC	P value	TDCC	P value
开花 Anthesis	0.7552	0.024	0.6526	0.0315
成熟 Maturity	0.5956	0.0635	0.5465	0.0397
全生育期 Growth duration	0.4319	0.0713	0.4655	0.0862
产量 Yield	0.341	0.1203	0.4613	0.0996
总干物质量 Total dry matter	0.53	0.0648	0.5906	0.0647

输出变量		开花	成熟	全生育期	产量	总干物质量
Output variables		Anthesis	Maturity	Growth duration	Yield	Total dry matter
合肥	TDCC	1	0.5348	0.4491	0.8107	0.4536
Hefei	P value	6E-09	0.126	0.149	0.0796	0.23
高要	TDCC	0.6435	0.5654	0.2497	0.8184	0.8188
Gaoyao	P value	0.0941	0.1178	0.1839	0.0838	0.0758
江口	TDCC	0.3841	0.2855	0.2114	0.8557	0.9348
Jiangkou	P value	0.1913	0.1822	0.1815	0.0531	0.0572
五常	TDCC	0.7101	0.8044	0.5769	0.891	1
Wuchang	P value	0.0978	0.0644	0.1148	0.1286	6E-09
宁都	TDCC	0.942	0.9425	0.9374	0.9673	0.942
Ningdu	P value	0.0468	0.0635	0.0541	0.0606	0.0468
新宾	TDCC	1	0.7687	0.5386	0.9034	0.471
Xinbin	P value	6E-09	0.071	0.1249	0.0471	0.1632
绵阳	TDCC	1	0.9885	0.876	0.6458	0.8623
Mianyang	P value	6E-09	0.0379	0.0527	0.0711	0.0683
西昌	TDCC	0.5435	0.4006	0.4466	0.8392	0.5536
Xichang	P value	0.1411	0.1614	0.1497	0.0654	0.093
耿马	TDCC	0.942	0.5654	0.8377	0.775	1
Gengma	P value	0.0468	0.1178	0.0587	0.0906	6E-09

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RiceGrow水稻模型品种参数敏感性分析

Sensitivity Analysis of Genetic Parameters of RiceGrow Model

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