Vol.28, NO.4, 2017
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Article Navigation >  Journal of Applied Meteorological Science  > 2017  >  28(4): 493-503
Wang Fang, Wu Dingrong, Wang Chunyi. Effects of cultivar shifts on winter wheat phenology under two parameterization methods. J Appl Meteor Sci, 2017, 28(4): 493-503. DOI:  10.11898/1001-7313.20170410.
Citation: Wang Fang, Wu Dingrong, Wang Chunyi. Effects of cultivar shifts on winter wheat phenology under two parameterization methods. J Appl Meteor Sci, 2017, 28(4): 493-503. DOI:  10.11898/1001-7313.20170410.
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Effects of Cultivar Shifts on Winter Wheat Phenology Under Two Parameterization Methods

DOI: 10.11898/1001-7313.20170410

  • Chinese Academy of Meteorological Sciences, Beijing 100081

  • Received Date: 2017-03-16
  • Rev Recd Date: 2016-01-13
  • Publish Date: 2017-07-31
    Abstract
  • Phenology and growth duration of crops have been significantly changed by the combined effects of climate change and cultivar shifts. For the need of accurately evaluating the response of crops phenology to future climate changes, effects of cultivar shift on phenology and its quantitative simulation has become a research hotspot. However, most recent studies are based on the single parameterization method, with less attention paid to effects of different parameterization methods, leading to a certain degree of assessment uncertainty.Winter wheat phenology data and daily meteorological data in 47 agrometeorological observation stations in North China Plain during 1986-2010 are collected. Based on these datasets, a most commonly used phenology model is used to quantize effects of cultivar shifts on phenology, and effects of two parameterization methods on simulated results are also analyzed. The first method uses fixed three cardinal temperatures (Method 1), while in the second method (Method 2) three cardinal temperatures are obtained by minimizing the root mean square error of simulated phenology.Results show that winter wheat critical phenology in North China Plain changes significantly under the frequently change of cultivar during study period. Both two methods perform well in parameterizing the simulation of durations from turning green to heading and from heading to maturity in the winter wheat simulation. The growth duration is prolonged by cultivar shift in the duration from turning green to heading and the duration from heading to maturity, though values given by Method 1 are higher. Both methods indicate effects of cultivar shifts on the duration from heading to maturity is higher than those on the duration from turning green to heading. In addition, the range of simulated trends and their regional distribution are also affected by the different parameterization method used. Among them, the difference of simulation results between two methods in the duration from turning green to heading is higher than the duration from heading to maturity. In the regional distribution, the difference of simulation results between two methods is bigger in the duration from heading to maturity. It verifies that simulation results are potentially affected by parameterization method. Therefore, the selection of parameterization methods and uncertainties introduced by different methods should be carefully considered.
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  • Fig. 1  Location of agricultural meteorological stations in the study area

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    Fig. 2  Calculation method of daily accumulated temperature in Method 1

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    Fig. 3  Calculation method of daily accumulated temperature in Method 2

    (the shaded denotes all possible sets of 3 cardinal temperatures for one station, the optimum set is optimized by minimizing root mean square error)

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    Fig. 4  The percentage of station number(a) and spatial distribution(b) in number of winter wheat cultivars used in North China Plain from 1986 to 2010

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    Fig. 5  Regional mean and trends of observed and simulated heading date(a), maturity date(b), length of turning green to heading(c), heading to maturity(d) of winter wheat in North China Plain under two methods from 1986 to 2010

    (, and indicate observed and simulated value of Method 1 and Method 2; , and indicate trends of observed and simulated value of Method 1 and Method 2; *denotes passing the test of 0.01 level)

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    Fig. 6  Spatial distribution of trends of winter wheat simulated error from 1986 to 2010 under two methods

    (a)Method 1, turning green to heading, (b)Method 1, heading to maturity, (c)Method 2, turning green to heading, (d)Method 2, heading to maturity

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    Fig. 7  Regional statistics of trends of winter wheat simulated error of length of turning green to heading(a) and heading to maturity(b) from 1986 to 2010 under two methods

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    Table  1  Comparisons of simulated results of different development stage under two methods

    发育阶段 参数化方案 相对误差范围/% 平均绝对偏差/d 均方根误差/d
    返青-抽穗期 1 -5.10~7.27 2.11 2.69
    2 -4.24~5.31 1.48 2.04
    抽穗-成熟期 1 -3.12~4.37 1.89 2.41
    2 -2.48~3.12 0.79 1.24
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    Table  2  Trends of major winter wheat phenologies over year in North China Plain from 1986 to 2010

    发育阶段 负趋势占比/% 显著负趋势占比/% 正趋势占比/% 显著正趋势占比/% 平均趋势/(d/(10 a))
    返青期 46.81 6.38* 53.19 8.51* 0.33
    抽穗期 95.74 59.57* 4.26 0.00* -3.13
    成熟期 82.98 34.04* 17.02 2.13* -1.45
    返青-抽穗期 89.36 44.68* 10.64 2.13* -3.46
    抽穗-成熟期 17.02 0.00* 82.98 51.06* 1.68
    注：*表示达到0.05显著性水平。
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    Table  3  Effect of cultivar shifts on the length of different stages in simulation under two methods

    参数化方案 返青-抽穗期 抽穗-成熟期 总影响/(d/(10 a))
    影响值/(d/(10 a)) 影响占比/% 影响值/(d/(10 a)) 影响占比/%
    1 1.02* 34.8 1.91** 65.2 2.93
    2 0.68 28.2 1.73** 71.8 2.41
    注：**，*分别表示达到0.01和0.05显著性水平。
    DownLoad: Download CSV
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    • Received : 2017-03-16
    • Accepted : 2016-01-13
    • Published : 2017-07-31
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