Scientia Agricultura Sinica ›› 2022, Vol. 55 ›› Issue (18): 3530-3542.doi: 10.3864/j.issn.0578-1752.2022.18.005

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY·AGRICULTURE INFORMATION TECHNOLOGY • Previous Articles     Next Articles

Simulation of Canopy Silking Dynamic and Kernel Number of Spring Maize Under Drought Stress

MengQi WANG1(),Na MI2(),Jing WANG1(),YuShu ZHANG2,RuiPeng JI2,NiNa CHEN2,XiaXia LIU1,Ying HAN1,WangYiPu LI1,JiaYing ZHANG1   

  1. 1College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193
    2Institute of Atmospheric Environment, China Meteorological Administration/Key Laboratory of Agrometeorological Disasters, Liaoning Province, Shenyang 110166
  • Received:2021-12-04 Accepted:2022-03-01 Online:2022-09-16 Published:2022-09-22
  • Contact: MI Na,WANG Jing E-mail:wangmengqi@cau.edu.cn;mina@iaesy.cn;wangj@cau.edu.cn

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Abstract:

【Objective】In order to improve simulation accuracy of maize kernel number under drought stress, the study simulated canopy silking dynamic of maize under drought stress and developed the relationships among anthesis-silking interval (ASI), canopy silking percentage and maize kernel number per unit area. 【Method】Firstly, this study measured the average plant growth rate (PGR) of maize around anthesis, daily canopy silking percentage of maize, ASI, the biomass accumulation of ear after anthesis, and the kernel number per plant under different treatments of soil water content based on drought stress controlling experiment at Jinzhou Agrometeorological Experimental Station. Secondly, the parameters of maize canopy silking dynamic model were determined with experimental data. Sensitivity analysis was conducted to investigate the impact of changes in average plan growth rate (PGRAVE) and standard deviation (PGRSD) on simulated canopy silking percentage. Thirdly, based on the ear biomass accumulation dynamic, the canopy silking dynamic was simulated under different drought stresses before and after anthesis by considering the differences in PGR among individual plants in the canopy. The quantitative relationship between ASI and kernel setting rate (the percentage of kernel number per plant to the maximum potential kernel number per plant) was developed based on experimental data. Finally, based on simulated canopy silking percentage after anthesis, maximum potential kernel number per plant, and the kernel setting rate by canopy silking dynamic model, the maize kernel number model was developed and validated under drought stress. 【Result】Sensitivity analysis of change in canopy silking percentage in response to changes in PGRAVE and PGRSD showed that PGRAVE had a greater impact on canopy silking percentage than PGRSD. The larger the PGRAVE and the smaller the PGRSD, the shorter the time for the canopy reaching silking percentage of 50%. The maize canopy silking dynamic model could accurately simulate daily silking percentage after anthesis under drought stress, and the coefficient of determination (R2), the root mean square error (RMSE), and the normalized mean square error (NRMSE) between simulated and observed canopy silking percentage ranged from 0.88 to 0.98, from 4% to 12%, and from 8% to 27%, respectively. Maize kernel number model could accurately simulate the kernel number of maize per unit area under drought stress, and R2, RMSE, and NRMSE between simulated and observed kernel number was 0.85, 185 kernel/m2, and 10%, respectively. 【Conclusion】By introducing the canopy silking dynamic model, the study could simulate the key phenology (silking time, anthesis-silking interval, and silking percentage) and kernel number per unit area under drought stress. The result was an important foundation for the simulation of maize yield based on canopy silking dynamic under drought stress.

Key words: ear biomass, anthesis-silking interval, silking percentage, kernel number, plant growth rate

Fig. 1

Variation of soil relative water content during jointing to physiological maturity of maize at the experimental station in 2020-2021 CK and CK': Soil relative water content of 65%±5% during the whole growth period; T1 treatment: Anthesis period drought with water control starting at the 10th day after jointing (June 22 or 174 d) until 15 days after anthesis (August 3 or 216 d); T2 treatment: Anthesis period drought with water control starting at the 15th day after jointing (June 27 or 179 d) until 15 days after anthesis (August 3 or 216 d); T2' treatment: Anthesis period drought with water control starting at the 15th day after jointing (June 30 or 181 d) until 15 days after anthesis (August 3 or 215 d)"

Fig. 2

Simulation process of maize kernel number"

Fig. 3

Fitting curve of ear biomass (a) and normalized curve of ear accumulation (b) at the 14th day after anthesis"

Table 1

Modelling daily ear biomass accumulation based on plant growth rate"

模型
Model 		参数a
Parameter a		 参数b
Parameter b
EBn=exp(-4.4837+0.2732 dayn) EB14DAAg -4.4837 0.2732

Table 2

Ear biomass threshold of each treatment at three observed dates after silking in 2020-2021"

年份
Year		 处理
Treatment		 日期Date 平均值
Mean (g/ear)
07-21 (g/ear) 07-24 (g/ear) 07-29 (g/ear)
2020 T1 1.1 1.6 2.9 1.9
T2 1.6 2.2 2.3 2.0
CK 2.0 1.5 1.9 1.8
2021 T2' 1.6 1.0 2.3 1.6
CK' 1.8 1.2 2.2 1.7

Fig. 4

Silking of plants based on ear biomass at the individual plant level No silking was recorded as 0, and silking was recorded as 1. The dotted line showed that the cumulative value of ear biomass required for silking was 1.8 g/ear"

Fig. 5

Sensitivity analysis of simulated silking percentage to PGRAVE (a) and PGRSD (b)"

Fig. 6

Comparison between observed and simulated silking percentages after anthesis under drought stress treatments of T1, T2, and T2' for spring maize"

Fig. 7

Comparison between simulated and observed kernel numbers of spring maize per unit area under drought stress treatment of T1, T2, and T2'"

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