Journal of Agricultural Big Data ›› 2021, Vol. 3 ›› Issue (3): 23-32.doi: 10.19788/j.issn.2096-6369.210303

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Sensitivity Analysis of Genetic Parameters of RiceGrow Model

Yijun Meng1,2,3,4,5,6(), Xiaolei Qiu1,2,3,4,5,6, Leilei Liu1,2,3,4,5,6, Bing Liu1,2,3,4,5,6, Yan Zhu1,2,3,4,5,6, Weixing Cao1,2,3,4,5,6, Liang Tang1,2,3,4,5,6()   

  1. 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;


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