RiceGrow水稻模型品种参数敏感性分析

doi:10.19788/j.issn.2096-6369.210303

Abstract

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

CLC Number:

S117

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.

Figures/Tables 6

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Table 3

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References 21

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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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Sensitivity Analysis of Genetic Parameters of RiceGrow Model

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