Crops ›› 2019, Vol. 35 ›› Issue (5): 159-165.doi: 10.16035/j.issn.1001-7283.2019.05.026

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Applicability Assessment of WOFOST Model of Growth and Yield of Summer Maize in Shandong Province

Dong Zhiqiang1,Wang Mengmeng2,Li Hongyi1,Xue Xiaoping1,Pan Zhihua3,Hou Yingyu4,Chen Chen1,Li Nan1,Li Manhua1   

  1. 1 Shandong Provincial Climate Center, Jinan 250031, Shandong, China
    2 Dezhou Meteorological Bureau, Dezhou 253078, Shandong, China
    3 College of Resources and Environmental Science, China Agricultural University, Beijing 100193, China
    4 National Meteorological Center, Beijing 100081, China
  • Received:2019-03-08 Revised:2019-06-06 Online:2019-10-15 Published:2019-11-07
  • Contact: Hongyi Li

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Abstract:

Summer maize is the most important grain crop in Shandong province. The changes of its growth period and yield play an important role in ensuring regional and even national food security. Different combinations of meteorological factors during different growth periods will affect the development process and yield of summer maize significantly. WOFOST crop model has stronger mechanism, higher quantitative level and great efficiency. It can provide technical support for the objective, quantitative and dynamic assessment of the impacts of meteorological factors on summer maize production. To improve the accuracy of WOFOST model, Shandong province was segregated into five regions, including the northwest region, middle region, southwest region, southeast region and peninsula region. Based on the growth and development data of staple varieties from 2012 to 2014 of 10 summer maize observation stations in Shandong province, we completed the parameters adjustment and verification and applicability assessment of WOFOST model. The results showed that the simulation error of the period of emergence of all observation stations is no more than 4d. Its determination coefficient (R 2) is 0.43-0.99. And its normalized root mean square error (nRMSE) is 0.3%-1.9%. The simulation error of the period of flowering and the period of mature of all observation stations in most years are no more than 5d. Their R 2 of most stations are 0.77-0.99 and 0.51-0.99, respectively. And their nRMSE are 0.4%-2.3% and 0.7%-3.2%, respectively. For the simulation of yield, the R 2 of most stations is 0.68-0.99. Its relative error is 0.8%-16.7% with most stations less than 10%. Its nRMSE is 1.2%-19.5%, and all the stations are less than 30% with most stations less than 10%. All assessment indicators are within the acceptable limits. The WOFOST model can accurately simulate the growth period and yield of summer maize in Shandong province.

Key words: WOFOST model, Applicability, Summer maize, Growth period, Yield

Table 1

The parameters of control crop physiological growth"

参数
Parameter		 生物学意义
Biological significance		 最小值
Minimum value		 最大值
Maximum value
TSUMEM 播种到出苗期积温 0 170
TSUM1 出苗期到开花期积温 150 1 800
TUSM2 开花期到成熟期积温 400 1 550
TBASEM 出苗期最低温度阈值 -10 8
DLO 最优日长 6 18
DLC 临界日长 6 18
TEFFMX 出苗期最大有效温度 18 32

Table 2

The parameters of control single and comprehensive factors affecting potential yield"

参数
Parameter		 生物学意义
Biological significance		 最小值
Minimum value		 最大值
Maximum value
AMAXTB 最大CO2同化速率 1 70
SLATB 比叶面积 0.0007 0.0042
SPAN 35℃时叶片寿命 17 50
RGRLAI 叶面积最大相对增长量 0.007 0.500
LAIEM 出苗期叶面积指数 0.0007 0.3000
TDWI 初始总干物重 0.5 300.0
FLTB 叶分配系数 0 1
FSTB 茎分配系数 0 1
FRTB 根分配系数 0 1
FOTB 穗分配系数 0 1
TMPFTB 平均温度最大CO2
同化速率折减系数		 0 1
RDRRTB 根相对死亡速率 0 0.02

Table 3

The parameters of control water-limited affecting yield"

参数
Parameter		 生物学意义
Biological significance		 最小值
Minimum value		 最大值
Maximum value
CFET 作物参考蒸散量校正因子 0.8 1.2
RDMCR 作物成熟最大根深度 50 400
PERDL 水分限制叶片最大相对死亡率 0 0.1
DEPNR 土壤水分耗竭的作物类群数 1 5

Table 4

The simulation verification of emergence period at observation stations"

站点
Station		 年份
Year		 绝对误差(d)
Absolute error		 绝对误差平均值(d)
Average of
absolute error		 R2 nRMSE
(%)
高密 2012 3 2.0 0.52 1.2
Gaomi 2013 1
2014 -2
菏泽 2012 0 1.7 0.43 1.4
Heze 2013 1
2014 -4
济阳 2012 3 2.7 0.99 1.7
Jiyang 2013 4
2014 1
胶州 2012 1 1.3 0.99 0.8
Jiaozhou 2013 1
2014 2
莒县 2012 1 0.3 0.96 0.3
Juxian 2013 0
2014 0
莱阳 2012 2 2.0 0.99 1.1
Laiyang 2013 2
2014 2
聊城 2012 0 1.3 0.94 1.1
Liaocheng 2013 -1
2014 -3
泰安 2012 -2 2.7 0.99 1.5
Tai′an 2013 -3
2014 -3
潍坊 2012 4 3.0 0.97 1.9
Weifang 2013 2
2014 3
淄博 2012 0 0.7 0.96 0.5
Zibo 2013 1
2014 1

Table 5

The simulation verification of flowering period at observation stations"

站点
Station		 年份
Year		 绝对误差(d)
Absolute error		 绝对误差平均值(d)
Average absolute error		 R2 nRMSE
(%)
高密 2012 5 2.3 0.77 1.4
Gaomi 2013 -1
2014 -1
菏泽 2012 2 1.0 0.98 0.6
Heze 2013 0
2014 -1
济阳 2012 7 4.3 0.13 2.3
Jiyang 2013 5
2014 -1
胶州 2012 2 1.3 0.81 0.7
Jiaozhou 2013 -2
2014 0
莒县 2012 5 4.7 0.99 2.2
Juxian 2013 6
2014 3
莱阳 2012 4 1.3 0.10 1.0
Laiyang 2013 0
2014 0
聊城 2012 1 0.5 - 0.4
Liaocheng 2014 0
2012 -6 2.3 0.92 1.5
泰安 2013 0
Tai′an 2014 -1
2012 3 2.0 0.98 1.0
潍坊 2013 2
Weifang 2014 1
2012 3 3.3 0.17 1.5
淄博 2013 -4
Zibo 2014 3

Table 6

The simulation verification of mature period at observation stations"

站点
Station		 年份
Year		 绝对误差(d)
Absolute error		 绝对误差平均值(d)
Average absolute
error		 R2 nRMSE
(%)
高密 2012 5 2.7 0.27 1.2
Gaomi 2013 -3
2014 0
菏泽 2012 1 1.7 0.96 0.7
Heze 2013 -2
2014 -2
济阳 2012 8 4.0 0.98 1.8
Jiyang 2013 -1
2014 -3
胶州 2012 4 5.3 0.57 2.0
Jiaozhou 2013 -7
2014 -5
莒县 2012 7 7.3 0.99 3.2
Juxian 2013 13
2014 -2
莱阳 2012 8 5.0 0.01 2.0
Laiyang 2013 -2
2014 -5
聊城 2012 -2 2.0 - 0.9
Liaocheng 2014 -2
2012 12 6.3 0.88 2.8
泰安 2013 2
Tai′an 2014 -5
2012 9 5.3 0.86 2.3
潍坊 2013 4
Weifang 2014 3
2012 4 5.3 0.51 2.3
淄博 2013 -3
Zibo 2014 9

Table 7

The simulation verification of yield at observation stations"

站点Station R2 相对误差Relative error (%) nRMSE (%)
高密Gaomi 0.02 16.7 19.5
菏泽Heze 0.99 11.3 11.4
济阳Jiyang 0.84 4.5 5.3
胶州Jiaozhou 0.71 5.8 7.6
莒县Juxian 0.83 9.1 16.6
莱阳Laiyang 0.97 7.2 10.1
聊城Liaocheng - 4.0 5.5
泰安Tai′an 0.69 0.8 1.2
潍坊Weifang 0.68 2.7 3.6
淄博Zibo 0.97 1.3 1.5
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