Ma Yuping, Wang Peijuan, Wang Da, et al. Reconstruction of crop development model with its simulation test based on sugarcane. J Appl Meteor Sci, 2021, 32(5): 603-617. DOI:  10.11898/1001-7313.20210508.
Citation: Ma Yuping, Wang Peijuan, Wang Da, et al. Reconstruction of crop development model with its simulation test based on sugarcane. J Appl Meteor Sci, 2021, 32(5): 603-617. DOI:  10.11898/1001-7313.20210508.

Reconstruction of Crop Development Model with Its Simulation Test Based on Sugarcane

DOI: 10.11898/1001-7313.20210508
  • Received Date: 2021-03-20
  • Rev Recd Date: 2021-06-09
  • Publish Date: 2021-09-30
  • Crop growth model describes the development and growth process of crop. Development process is the physiological age of crops, which is related to the morphological changes of crops, and is a landmark stage of crop growth to achieve qualitative change. Development process is the time indicator of crop growth model. Reasonable description of the crop development process is the premise of high accuracy of crop growth model. At present, although different crop development models have been developed and widely used in crop growth models, their simulation ability can hardly meet the needs of crop growth simulation. These models only focus on the impact of meteorological conditions in a certain period (day), but do not particularly consider the period of crop development, which may be an important factor for the low accuracy of model simulation.It is assumed that the development rate of a crop on a certain day is not only related to the meteorological conditions of that day, but also related to its development stage. The development stage of crop is represented by the date, so that the temperature-day (TAd) and development unit-day (CHUd) models are constructed. In addition, according to the principle of heat unit corrected by temperature model (THUa), the development unit corrected by temperature model (CHUa) is constructed. Based on the principle of response-adaptation of temperature model (RAM), the response-adaptation of development unit model (CHUr) is established.The adaptability of the development model for sugarcane is analyzed by using the field data of 30 agrometeorological stations in China from 1980 to 2019, and the advantages and disadvantages between the traditional development model and the reconstructed model are compared. The results show that CHUd and TAd model have better adaptability to simulate the development process of sugarcane, especially in the later stage of the development process when the temperature is decreasing. Compared with the original model (CHU), the adaptive ability of CHUa model for sugarcane decreases from seedling emergence to stem elongation but increases from stem elongation to maturity. The theoretical description of CHUr model is not tenable. Sorted by simulation ability, the order of development models is as follows: CHUd, TAd, RAM, CHU, CHUa and the heat unit (THU) model, and their simulation ability values (SCV) calculated by root mean square difference are 4.3, 3.9, 3.7, 3.3, 3.0 and 2.8, respectively.The reconstruction of the development model will further improve the CAMM and promote the development of crop growth simulation theory.
  • Fig. 1  Test of different development models based on the whole sample development data of new planted sugarcane

    Fig. 2  Relationship between parameters of development model and latitude of the station

    Fig. 3  Measurements and simulations of development models based on back training for new planted sugarcane

    Fig. 4  Measurements and simulations of development models based on independent samples for new planted sugarcane

    Table  1  Overview of research data

    作物 站点 所在省份 位置 海拔/m 数据时段
    新植蔗 乌拉特前旗 40.7°N,108.7°E 1022.0 1997—2002年
    新植蔗 临河 40.8°N,107.4°E 1040.8 1992—2002年
    新植蔗 平罗 38.9°N,106.6°E 1099.9 1993—1999年
    新植蔗 赤峰 42.3°N,119.0°E 568.0 1992—2002年
    新植蔗 耿马 23.6°N,99.4°E 1105.4 1993—2020年
    新植蔗 元江 23.4°N,102.0°E 397.6 1994—2020年
    新植蔗 泰和 26.8°N,114.9°E 61.8 1993—2003年
    新植蔗 广丰 28.4°N,118.2°E 96.1 1994—2009年
    新植蔗 仙游 25.4°N,118.7°E 77.0 1993—1997年
    新植蔗 宜山 24.5°N,108.7°E 149.8 2003—2009年
    新植蔗 沙塘 24.5°N,109.4°E 99.1 2003—2010年
    新植蔗 蒙山 24.2°N,110.5°E 147.0 1980—1994年
    新植蔗 平果 23.3°N,107.6°E 112.6 1990—2009年
    新植蔗 来宾 23.8°N,109.2°E 85.2 2003—2009年
    新植蔗 贵县 23.1°N,109.6°E 56.0 1989—2009年
    新植蔗 扶绥 22.6°N,107.9°E 88.9 2003—2012年
    新植蔗 徐闻 20.3°N,110.2°E 69.0 1992—2001年
    宿根蔗 米易 26.9°N,102.1°E 1105.8 1992—1998年
    宿根蔗 德宏州 24.4°N,98.6°E 914.7 1993—2009年
    宿根蔗 耿马 23.6°N,99.4°E 1105.4 2012—2020年
    宿根蔗 蒙自 23.4°N,103.4°E 1301.7 2010—2016年
    宿根蔗 吉安 27.1°N,115.0°E 78.0 2000—2009年
    宿根蔗 泰和 26.8°N,114.9°E 61.8 1992—2009年
    宿根蔗 仙游 25.4°N,118.7°E 77.0 1998—2009年
    宿根蔗 宜山 24.5°N,108.7°E 149.8 2002—2009年
    宿根蔗 沙塘 24.5°N,109.4°E 99.1 2002—2012年
    宿根蔗 蒙山 24.2°N,110.5°E 147.0 2002—2009年
    宿根蔗 漳浦 24.1°N,117.6°E 51.1 2002—2009年
    宿根蔗 平果 23.3°N,107.6°E 112.6 2002—2009年
    宿根蔗 贵县 23.1°N,109.6°E 56.0 1996—2009年
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    Table  2  Calibrated parameter of development models at some sites for new planted sugarcane

    站名 纬度 发育模式
    THU CHU CHUa CHUd/105 TAd/105
    乌拉特前旗 40.7°N 1825.6 2149.1 97.4 4.34 6.34
    临河 40.8°N 1741.3 2061.7 94.8 4.04 5.88
    平罗 38.9°N 1737.1 2143.9 108.3 4.38 6.43
    赤峰 42.3°N 1662.9 2043.3 99.7 4.22 6.13
    耿马 23.6°N 3034.5 3723.5 175.2 9.27 13.0
    元江 23.4°N 4569.2 4968.3 178.1 9.93 20.1
    泰和 26.8°N 3415.9 3898.3 157.6 8.29 11.9
    广丰 28.4°N 3190.2 3633.9 145.1 7.30 10.5
    仙游 25.4°N 3804.3 4319.8 175.5 9.04 12.8
    宜山 24.5°N 3584.3 3946.0 150.9 7.82 11.2
    沙塘 24.5°N 3894.0 4366.9 175.4 8.90 12.8
    蒙山 24.2°N 3584.3 4049.9 166.8 8.63 12.4
    平果 23.3°N 4114.0 4560.1 176.2 9.04 12.9
    来宾 23.8°N 3799.4 4149.1 155.4 8.34 11.9
    贵县 23.1°N 3918.3 4315.0 164.3 8.60 12.3
    扶绥 22.6°N 4242.6 4687.0 179.4 10.4 15.0
    徐闻 20.3°N 4888.2 5446.8 207.7 12.9 18.0
    DownLoad: Download CSV

    Table  3  Back training simulation error of development model for new planted sugarcane

    发育阶段 模式 相关系数 平均偏差/d 平均误差/d 平均相对误差/d 均方根误差/d 样本量
    播种-出苗 THU 0.82 1.1 6.2 19.6 8.9 66
    CHU 0.83 0.8 6.0 18.7 8.6 66
    CHUa 0.82 1.3 6.3 19.2 8.9 66
    CHUd 0.83 1.6 6.2 20.5 9.1 66
    TAd 0.83 1.4 6.1 20.0 8.9 66
    RAM 0.84 -1.5 5.4 15.1 8.7 66
    出苗-茎伸长 THU 0.90 0.5 9.5 12.2 14.2 137
    CHU 0.88 0.5 10.3 13.1 14.9 137
    CHUa 0.84 0.6 12.7 16.1 17.3 137
    CHUd 0.91 0.9 8.9 11.2 13.5 137
    TAd 0.90 0.9 8.9 11.2 13.6 137
    RAM 0.86 -0.4 8.3 12.2 16.5 137
    茎伸长-成熟 THU 0.33 8.4 25.2 20.2 43.0 120
    CHU 0.73 1.6 15.5 11.4 23.0 120
    CHUa 0.86 0.1 12.0 8.3 16.5 120
    CHUd 0.86 0.3 11.8 8.5 16.6 120
    TAd 0.76 4.9 14.9 10.3 23.9 120
    RAM 0.55 -1.4 16.2 11.9 36.1 120
    DownLoad: Download CSV

    Table  4  Development model simulation errors based on independent samples for new planted sugarcane

    发育阶段 模式 相关系数 平均偏差/d 平均误差/d 平均相对误差/d 均方根误差/d 样本量
    播种-出苗 THU 0.83 2.6 6.6 31.5 9.1 24
    CHU 0.82 2.5 6.5 31.2 9.2 24
    CHUa 0.81 3.3 6.6 32.2 9.6 24
    CHUd 0.81 3.0 6.9 31.7 10.1 24
    TAd 0.81 3.0 6.9 32.0 9.9 24
    RAM 0.81 1.4 6.2 29.1 9.1 24
    出苗-茎伸长 THU 0.92 0.9 9.1 11.8 12.4 52
    CHU 0.92 1.0 9.6 12.3 12.7 52
    CHUa 0.87 1.5 11.5 14.6 15.3 52
    CHUd 0.93 3.6 8.9 11.8 12.9 52
    TAd 0.92 3.1 8.9 11.8 12.9 52
    RAM 0.73 2.7 13.0 20.3 23.2 52
    茎伸长-成熟 THU 0.58 5.0 22.9 15.1 35.0 46
    CHU 0.67 5.2 18.2 12.2 27.5 46
    CHUa 0.85 6.3 12.6 8.8 17.3 46
    CHUd 0.86 6.2 12.4 8.5 17.2 46
    TAd 0.71 14.0 19.8 13.7 32.8 46
    RAM 0.21 12.2 29.1 24.0 70.1 46
    DownLoad: Download CSV

    Table  5  Simulation ability of development models in different tests

    发育阶段 模式 新植蔗 宿根蔗 SCV
    全样本 参数率定 独立样本 全样本 参数率定 独立样本
    播种-出苗 THU 2 4 6 4.0
    CHU 5 6 4 5.0
    CHUa 1 3 3 2.3
    CHUd 3 1 1 1.7
    TAd 4 2 2 2.7
    RAM 6 5 5 5.3
    出苗/发株-茎伸长 THU 3 4 6 3 3 4 3.8
    CHU 2 3 5 2 2 3 2.8
    CHUa 1 1 2 1 1 1 1.2
    CHUd 5 6 4 4 5 6 5.0
    TAd 4 5 3 5 4 5 4.3
    RAM 6 2 1 6 6 2 3.8
    茎伸长-成熟 THU 1 1 2 2 1 1 1.3
    CHU 2 4 4 3 2 2 2.8
    CHUa 5 6 5 6 5 4 5.2
    CHUd 6 5 6 4 3 5 4.8
    TAd 4 3 3 5 4 6 4.2
    RAM 3 2 1 1 6 3 2.7
    DownLoad: Download CSV
  • [1]
    Sinclair T R, Seligman N A G.Crop modeling:From infancy to maturity. Agron J, 1996, 88(5):698-704. doi:  10.2134/agronj1996.00021962008800050004x
    [2]
    Monteith J L. The quest for balance in crop modeling. Agron J, 1996, 88(5): 695-697. doi:  10.2134/agronj1996.00021962008800050003x
    [3]
    Edwards D, Hamson M. Guide to Mathematical Modelling. London: MacMillan Press Ltd, 1989: 1-277.
    [4]
    Hoogenboom G. Contribution of agrometeorology to the simulation of crop production and its applications. Agr Forest Meteorol, 2000, 103(1/2): 137-157.
    [5]
    Ma Y P, Huo Z G, Wang P J, et al. The construction and application of Chinese Agrometeorological Model(CAMM1.0). J Appl Meteor Sci, 2019, 30(5): 528-542. doi:  10.11898/1001-7313.20190502
    [6]
    Van Diepen C A, Wolf J, van Keulen H, et al. WOFOST: A simulation model of crop production. Soil Use Manage, 1989, 5: 16-24. doi:  10.1111/j.1475-2743.1989.tb00755.x
    [7]
    Kropff M J, van Laar H H, Matthews R B. ORYZA1: An Ecophysiological Model for Irrigated Rice Production. SARP Research Proceedings, Wageningen(Netherlands): IRRI/AB-DLO, 1994.
    [8]
    Gao L Z, Jin Z Q, Huang Y, et al. Rice clock model-A computer model to simulate rice development. Agr Forest Meteorol, 1992, 60(1/2): 1-16.
    [9]
    Keating B A, Carberry P S, Hammer G L, et al. An overview of APSIM, a model designed for farming systems simulation. Eur J Agron, 2003, 18(3/4): 267-288.
    [10]
    Jones A J W, Hoogenboom B G, Porter A C H, et al. The DSSAT cropping system model. Eur J Agron, 2003, 18(3/4): 235-265. http://europepmc.org/abstract/AGR/IND44696208
    [11]
    Kapetanaki G, Rosenzweig C. Impact of climate change on maize yield in central and northern Greece: A simulation study with Ceres-Maize. Mitig Adapt Strat Gl, 1997, 1(3): 251-271. doi:  10.1007/BF00517806
    [12]
    Ceglar A, Bogataj L K. Simulation of maize yield in current and changed climatic conditions: Addressing modelling uncertainties and the importance of bias correction in climate model simulations. Eur J Agron, 2012, 37(1): 83-95. doi:  10.1016/j.eja.2011.11.005
    [13]
    Jin Z Q, Ge D K, Zheng X L, et al. Assessing the potential impacts of global climate change on maize production in China. Acta Agronomica Sinica, 1996, 22(5): 513-524. doi:  10.3321/j.issn:0496-3490.1996.05.001
    [14]
    Yin X, Chasalow S D, Dourleijn C J, et al. Coupling estimated effects of QTLs for physiological traits to a crop growth model: Predicting yield variation among recombinant inbred lines in barley. Heredity(Edinb), 2000, 85(6): 539-549.
    [15]
    Wang S L, Ma Y P. The progress in application of crop growth simulation models to agro-meteorological services in China. Meteor Mon, 2008, 34(6): 3-10. https://www.cnki.com.cn/Article/CJFDTOTAL-QXXX200806002.htm
    [16]
    Gao L Z. Foundation of Agricultural Modeling Science. Hong-kong: Tianma Books Ltd, 2004.
    [17]
    Huang J X, Huang H, Ma H Y, et al. Review on data assimilation of remote sensing and crop growth models. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(21): 144-156. doi:  10.11975/j.issn.1002-6819.2018.21.018
    [18]
    Shuai X Q, Wang S L, Ma Y P, et al. Assessment of meteorological condition effects and dynamic yield forecasting based on rice growth model. J Appl Meteor Sci, 2008, 19(1): 71-81. doi:  10.3969/j.issn.1001-7313.2008.01.010
    [19]
    Xiong W. The performance of CERES wheat model in wheat planting areas and its uncertainties. J Appl Meteor Sci, 2009, 20(1): 88-94. doi:  10.3969/j.issn.1001-7313.2009.01.011
    [20]
    Hou Y Y, Zhang L, Wu M X, et al. Advance of modern agrometeorological service and technology in China. J Appl Meteor Sci, 2018, 29(6): 641-656. doi:  10.11898/1001-7313.20180601
    [21]
    Shuai X Q, Lu K D, Huang W H. A comparative study on dynamic forecasting of early rice yield by using different methods in Hunan Province. J Appl Meteor Sci, 2015, 26(1): 103-111. doi:  10.11898/1001-7313.20150111
    [22]
    Liu B C, Wang S L, Zhuang L W, et al. Study of low temperature damage prediction applications in Northeast China based on a scaling-up maize dynamic model. J Appl Meteor Sci, 2003, 14(5): 616-625. doi:  10.3969/j.issn.1001-7313.2003.05.012
    [23]
    McMaster G S, Wilhelm W W. Phenological responses of wheat and barley to water and temperature: Improving simulation models. J Agric Sci, 2003, 141(2): 129-148. doi:  10.1017/S0021859603003460
    [24]
    Liu J, Yao N, Lin H X, et al. Response mechanism and simulation of winter wheat phonology to soil water stress. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(21): 115-124. doi:  10.11975/j.issn.1002-6819.2016.21.016
    [25]
    McMaster G S, Wilhelm W W. Growing degree-days: One equation, two interpretations. Agr Forest Meteorol, 1997, 87(4): 291-300. doi:  10.1016/S0168-1923(97)00027-0
    [26]
    Wang S L, Ma Y P, Zhuang L W. Improvement study on prediction and assessment model for chilling damage of maize in Northeast China. Journal of Natural Disasters, 2008, 17(4): 12-18. doi:  10.3969/j.issn.1004-4574.2008.04.003
    [27]
    Ma Y P, Wang S L, Li W J. Chilling disaster factors in maize reproductive stage based on crop growth model. Acta Agronomica Sinica, 2011, 37(9): 1642-1649. doi:  10.3969/j.issn.1000-2561.2011.09.010
    [28]
    Brown D M, Chapman L J. Soybean ecology. Ⅱ. Development-temperature-moisture relationships from field studies. Agron J, 1960, 52(9): 496-499. doi:  10.2134/agronj1960.00021962005200090002x
    [29]
    Matthews R B, Hunt L A. GUMCAS: A model describing the growth of cassava(Manihot esculenta L. Crantz). Field Crop Res, 1994, 36(1): 69-84. doi:  10.1016/0378-4290(94)90054-X
    [30]
    Ma Y P, Zhang L, Sun L L, et al. Effects of continuous temperature and soil moisture on development process of maize and its simulation. Chinese Agricultural Science Bulletin, 2015, 3(3): 186-193. https://www.cnki.com.cn/Article/CJFDTOTAL-ZNTB201503032.htm
    [31]
    Wang E, Martre P, Zhao Z, et al. The uncertainty of crop yield projections is reduced by improved temperature response functions. Nat Plants, 2017, 3(8): 17102. doi:  10.1038/nplants.2017.102
    [32]
    Gao L Z, Jin Z Q, Huang Y, et al. Combination of crop simulation and cultivation optimization principle-RCSODS. Crops, 1994(3): 4-7. https://www.cnki.com.cn/Article/CJFDTOTAL-ZWZZ403.002.htm
    [33]
    Yin X Y. A nonlinear model to quantify temperature effect on rice phenology and its application. Acta Agronomica Sinica, 1994, 20(6): 692-700.
    [34]
    Wu D, Wang P, Jiang C, et al. Use of a plastic temperature response function reduces simulation error of crop maturity date by half. Agr Forest Meteorol, 2020, 280: 107770. doi:  10.1016/j.agrformet.2019.107770
    [35]
    Huang Q Y, Qin Z H, QiN Z H, et al. Research progress and prospect on the monitoring of sugarcane disaster. China Agricultural Informatics, 2018, 30(3): 23-40. https://www.cnki.com.cn/Article/CJFDTOTAL-NXTS201803005.htm
    [36]
    Li Y J, Wang C Y, Zhao B, et al. Effects of climate change on agricultural meteorological disaster and crop insects diseases. Transactions of the Chinese Society of Agricultural Engineering, 2010, 26(Suppl I): 263-271. https://www.cnki.com.cn/Article/CJFDTOTAL-NYGU2010S1049.htm
    [37]
    Pan X J, Zhang Z B, Pu C Y. Risk analysis of sugarcane meteorological disaster under global climate anomaly. Insurance Studies, 2014(12): 51-58. https://www.cnki.com.cn/Article/CJFDTOTAL-BXYJ201412007.htm
    [38]
    Li L, Kuang Z M, Mo J F, et al. Assessment of autumn drought risk of sugarcane in Guangxi. J Appl Meteor Sci, 2016, 27(1): 95-101. doi:  10.11898/1001-7313.20160110
    [39]
    Kuang Z M, Li Q, Yao Y M, et al. Application of EOS/MODIS data to monitoring sugarcane cold damage. J Appl Meteor Sci, 2009, 20(3): 360-364. doi:  10.3969/j.issn.1001-7313.2009.03.013
    [40]
    Liao D S, Qin S J. Analysis on production cost accounting and control of sugarcane industry in Guangxi. Academic Forum, 2013(8): 83-87. doi:  10.3969/j.issn.1004-4434.2013.08.018
    [41]
    Inman-bamber N G, Everingham Y L, Muchow R C. Modelling Water Stress Response in Sugarcane: Validation and Application of the APSIM-Sugarcane Model. (2001-02-01)[2020-12-08]. http://www.regional.org.au/au/asa/2001/6/d/inmanbamber.htm.
    [42]
    Keating B A, Robertson M J, Muchow R C, et al. Modelling sugarcane production systems Ⅰ. Development and performance of the sugarcane module. Field Crop Res, 1999, 61(3): 253-271. doi:  10.1016/S0378-4290(98)00167-1
    [43]
    Liu D L, Bull T A. Simulation of biomass and sugar accumulation in sugarcane using a process-based model. Ecol Model, 2001, 144(2/3): 181-211.
    [44]
    Inman-Bamber N G. A growth model for sugar-cane based on a simple carbon balance and the CERES-Maize water balance. South African Journal of Plant and Soil, 1991, 8(2): 93-99. doi:  10.1080/02571862.1991.10634587
    [45]
    Leary G J O. A review of three sugarcane simulation models with respect to their prediction of sucrose yield. Field Crop Res, 2000, 68(2): 97-111. doi:  10.1016/S0378-4290(00)00112-X
    [46]
    Wang P J, Ma Y P, Huo Z G, et al. Construction of the model for soil moisture effects on leaf photosynthesis rate of winter wheat. J Appl Meteor Sci, 2020, 31(3): 267-279. doi:  10.11898/1001-7313.20200302
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    • Received : 2021-03-20
    • Accepted : 2021-06-09
    • Published : 2021-09-30

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