Please wait a minute...
Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Front. Earth Sci.    2014, Vol. 8 Issue (4) : 505-511    https://doi.org/10.1007/s11707-014-0468-1
RESEARCH ARTICLE
Modeling the rice phenology and production in China with SIMRIW: sensitivity analysis and parameter estimation
Shuai ZHANG1,Fulu TAO1,*(),Runhe SHI2
1. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
2. Key Laboratory of Geographic Information Science, East China Normal University, Shanghai 200062, China
 Download: PDF(282 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

Crop models are robust tools for simulating the impact of climate change on rice development and production, but are usually designed for specific stations and varieties. This study focuses on a more adaptable model called Simulation Model for Rice-Weather Relations (SIMRIW). The model was calibrated and validated in major rice production regions over China, and the parameters that most affect the model’s output were determined in sensitivity analyses. These sensitive parameters were estimated in different ecological zones. The simulated results of single and double rice cropping systems in different ecological zones were then compared. The accuracy of SIMRIW was found to depend on a few crucial parameters. Using optimized parameters, SIMRIW properly simulated the rice phenology and yield in single and double cropping systems in different ecological zones. Some of the parameters were largely dependent on ecological zone and rice type, and may reflect the different climate conditions and rice varieties among ecological zones.

Keywords rice      phenology      parameter optimization      SIMRIW      simulation     
Corresponding Author(s): Fulu TAO   
Just Accepted Date: 20 October 2014   Online First Date: 17 November 2014    Issue Date: 13 January 2015
 Cite this article:   
Shuai ZHANG,Fulu TAO,Runhe SHI. Modeling the rice phenology and production in China with SIMRIW: sensitivity analysis and parameter estimation[J]. Front. Earth Sci., 2014, 8(4): 505-511.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0468-1
https://academic.hep.com.cn/fesci/EN/Y2014/V8/I4/505
ParameterDescriptionDefault valueRange
Gv/dayMinimum number of days required for heading53.8535.21?72.64
ATSensitivity of the developmental rate to air temperature0.260.10?0.43
Th/°CThe air temperature at which DVR is the half of the maximum rate at the optimum temperature16.659.45?20.43
Lc/hThe critical day length24.3612.60?34.98
KrEmpirical constants0.1180.09?0.14
TcrEmpirical constants12.710.0?13.0
Gr/dayThe minimum number of days for grain-filling period27.425.0?31.0
hmThe maximum harvest index of a giver prefecture obtained under optimum climatic conditions and cultivation practices0.450.38?0.50
Fas/°CAsymptotic value of the leaf area index when temperature is non-limiting75.0?7.5
Tcf /(m2·m?2)The minimum temperature for LAI growth11.510.0?12.0
Tab.1  The estimated parameters in SIMRIW
Fig.1  Sensitivity analysis of SIMRIW parameters for: (a) heading date calculation, (b) maturity date calculation, (c) yield calculation
Rice typesEcological zoneHeading dateMaturity dateYield
GvATLcThKrTcrGrhmFasTcf
Single-ricezone I57.010.2033.4813.960.1111.7526.790.447.1210.21
zone II64.180.3424.9711.480.0911.5825.210.437.2511.28
zone III70.030.2826.1520.310.1110.2525.540.426.9811.89
zone IV68.600.3026.1214.500.0912.7426.310.427.4511.50
zone V38.360.3325.3320.370.1011.9526.870.457.5011.23
Early-ricezone III53.260.1328.9211.210.1012.3525.360.386.8810.96
zone IV44.850.1820.0517.620.1010.6826.220.416.5610.39
zone VI68.520.2015.6517.540.1010.9725.120.446.5310.65
Late-ricezone III57.440.3523.1510.260.1011.9425.430.395.8211.24
zone IV65.140.4121.4719.470.1011.5325.670.385.6711.36
zone VI68.320.3813.3314.330.1010.5525.980.425.8810.26
Tab.2  SIMRIW parameters optimized in each ecological zone
Rice typesEcological zoneHeading dateMaturity dateYield
Mean/doyStd/daysRMSE/daysMean/doyStd/daysRMSE/daysMean/(t·ha-1)Std/(t·ha-1)RMSE/(t·ha-1)
Single-ricezone I218.715.651.56263.126.642.287.911.690.78
zone II231.217.392.43273.377.374.556.482.070.65
zone III224.667.271.25260.514.153.797.012.120.71
zone IV210.605.853.48242.613.504.857.131.980.63
zone V220.277.553.56260.894.964.449.132.420.58
Early-ricezone III176.916.563.82201.725.284.214.931.430.89
zone IV171.817.603.15199.495.913.985.181.660.75
zone VI160.251.152.78187.676.803.996.271.280.74
Late-ricezone III256.006.154.35296.627.694.895.311.720.62
zone IV256.247.704.54295.478.875.115.281.670.71
zone VI274.503.913.79307.735.404.685.851.340.77
Tab.3  SIMRIW validation results of heading date, maturity date and yield for various rice categories in different ecological zones
1 Anderson T R (2010). Progress in marine ecosystem modelling and the “unreasonable effectiveness of mathematics”. J Mar Syst, 81(1–2): 4–11
https://doi.org/10.1016/j.jmarsys.2009.12.015
2 Angstr?m A (1924). Solar and terrestrial radiation. Report to the international commision for solar research on actinometric investigations of solar and atmospheric radiation. Q J R Meteorol Soc, 50(210): 121–126
https://doi.org/10.1002/qj.49705021008
3 Bouman B A M, Van Laar H H (2006). Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agric Syst, 87(3): 249–273
https://doi.org/10.1016/j.agsy.2004.09.011
4 Brouwer R, De Wit C T (1969). A simulation model of plant growth with special attention to root growth and its consequences. In: Proceedings of the 15th Easter School in Agri Science. University of Nottingham, 224–242
5 Duan Q, Sorooshian S, Gupta V K (1994). Optimal use of the SCE-UA global optimization method for calibrating watershed models. J Hydrol (Amst), 158(3–4): 265–284
https://doi.org/10.1016/0022-1694(94)90057-4
6 Duncan W G (1971). Simulation of growth and yield in cotton: A computer analysis of the nutritional theory. Proc Beltwide Cotton Prod Res Conf, (78): 45–61
7 Gao L, Jin Z, Huang Y, Zhang L (1992). Rice clock model— A computer model to simulate rice development. Agric Meteorol, 60(1–2): 1–16
https://doi.org/10.1016/0168-1923(92)90071-B
8 Hayashi Y, Jung Y S (2000). Paddy rice production under possible temperature fluctuation in East Asia. Global Environmental Research, 3: 129–137
9 Horie T, Centeno G S, Nakagawa H, Matsui T (1997). Effect of elevated carbon dioxide and climate change on rice production in East and Southeast Asia. In: Proceedings of the International Scientific Symposium on Asian Paddy Fields. Saskatchewan: University of Saskatchewan, 913–967
10 Horie T, Nakagawa H, Ceneno H G S, Kropff M (1995). Rice production in Japan under current and future climates. In: Matthews R B, Kropff M J, Bachelet D, eds. Modeling the Impact of Climate Change on Rice in Asia. Wallingford: CAB International, 143–164
11 Kropff M J, Centeno G S, Bachelet D, Lee M H, Mohan Dass S, Horie T, De Feng S, Singh S, Penning De Vries F W T (1993). Predicting the impact of CO2 and temperature on rice production. In: IRRI Seminar Series on Climate Change and Rice. Los Banos: International Rice Research Institute
12 Lin E, Xiong W, Ju H, Xu Y, Li Y, Bai L, Xie L (2005). Climate change impacts on crop yield and quality with CO2 fertilization in China. Philos Trans R Soc Lond B Biol Sci, 360(1463): 2149–2154
https://doi.org/10.1098/rstb.2005.1743 pmid: 16433100
13 Matthews R B, Kropff M J, Horie T, Bachelet D (1997). Simulating the impact of climate change on rice production in Asia and evaluating options for adaptation. Agric Syst, 54(3): 399–425
https://doi.org/10.1016/S0308-521X(95)00060-I
14 Mei F, Wu X, Yao C, Li L, Wang L, Chen Q (1988). Rice cropping regionalization in China. Chinese Journal Rice Science, 2(3): 97–110 (in Chinese)
15 Peng S, Huang J, Sheehy J E, Laza R C, Visperas R M, Zhong X, Centeno G S, Khush G S, Cassman K G (2004). Rice yields decline with higher night temperature from global warming. Proc Natl Acad Sci USA, 101(27): 9971–9975
https://doi.org/10.1073/pnas.0403720101 pmid: 15226500
16 Peng S, Ingram K T, Neue H U, Ziska L H (1995). Climate Change and Rice. Berlin, Heidelberg: International Rice Research Institute, Springer-verlag
17 Prescott J A (1940). Evaporation from a water surface in relation to solar radiation. Transactions of the Royal Society of South Australia, 64: 114–125
18 Spitters C J T (1986). Separating the diffuse and direct component of global radiation and its implications for modeling canopy photosynthesis Part II. Calculation of canopy photosynthesis. Agric Meteorol, 38(1–3): 231–242
https://doi.org/10.1016/0168-1923(86)90061-4
19 Tao F, Yokozawa M, Hayashi Y, Lin E (2003). Future climate change, the agricultural water cycle, and agricultural production in China. Agric Ecosyst Environ, 97(1–3): 361
https://doi.org/10.1016/S0167-8809(03)00174-9
20 Tao F, Yokozawa M, Xu Y, Hayashi Y, Zhang Z (2006). Climate changes and trends in phenology and yields of field crops in China, 1981?2000. Agric Meteorol, 138(1–4): 82–92
https://doi.org/10.1016/j.agrformet.2006.03.014
21 Wopereis C S, Defoer T, Idinoba P, Diack S, Dugué M J (2009). Effects of temperature on rice. In: Wopereis M C S, Defoer T, Idinoba P, Diack S, Dugue M J, eds. Curriculum for Participatory Learning and Action Research (PLAR) for Integrated Rice Management (IRM) in Inland Valleys of Sub-Saharan Africa: Technical Manual. Cotonou: Africa Rice Center, 39–42
22 Xiong W, Tao F, Xu Y, Lin E (2001). Simulation of rice yields under future climate scenarios in China. Agric Meteorol, 22: 9–14
23 Yin X, Kropff M J (1996). Use of the Beta function to quantify effects of photoperiod on flowering and leaf number in rice. Agric Meteorol, 81(3–4): 217–228
https://doi.org/10.1016/0168-1923(95)02324-0
24 Yoshida S (1981). Fundamentals of Rice Crop Science. Los Banos: International Rice Research Institute, 269
[1] Linan YUAN, Jingjuan LIAO. Exploring the influence of various factors on microwave radiation image simulation for Moon-based Earth observation[J]. Front. Earth Sci., 2020, 14(2): 430-445.
[2] Hongjie WANG, Yi ZHOU, Shixin WANG, Futao WANG. Coupled model constructed to simulate the landslide dam flood discharge: a case study of Baige landslide dam, Jinsha River[J]. Front. Earth Sci., 2020, 14(1): 63-76.
[3] Tong LI, Huadong GUO, Li ZHANG, Chenwei NIE, Jingjuan LIAO, Guang LIU. Simulation of Moon-based Earth observation optical image processing methods for global change study[J]. Front. Earth Sci., 2020, 14(1): 236-250.
[4] Wanben WU, Wei WANG, Michael E. Meadows, Xinfeng YAO, Wei PENG. Cloud-based typhoon-derived paddy rice flooding and lodging detection using multi-temporal Sentinel-1&2[J]. Front. Earth Sci., 2019, 13(4): 682-694.
[5] Pingzhi FANG, Deqian ZHENG, Liang LI, Wenyong MA, Shengming TANG. Numerical and experimental study of the aerodynamic characteristics around two-dimensional terrain with different slope angles[J]. Front. Earth Sci., 2019, 13(4): 705-720.
[6] Jianhong LIU, Le LI, Xin HUANG, Yongmei LIU, Tongsheng LI. Mapping paddy rice in Jiangsu Province, China, based on phenological parameters and a decision tree model[J]. Front. Earth Sci., 2019, 13(1): 111-123.
[7] Yongfeng WANG, Zhaohui XUE, Jun CHEN, Guangzhou CHEN. Spatio-temporal analysis of phenology in Yangtze River Delta based on MODIS NDVI time series from 2001 to 2015[J]. Front. Earth Sci., 2019, 13(1): 92-110.
[8] Xiaoting WANG, Zhan’e YIN, Xuan WANG, Pengfei TIAN, Yonghua HUANG. A study on flooding scenario simulation of future extreme precipitation in Shanghai[J]. Front. Earth Sci., 2018, 12(4): 834-845.
[9] Donal O’Leary III, Dorothy Hall, Michael Medler, Aquila Flower. Quantifying the early snowmelt event of 2015 in the Cascade Mountains, USA by developing and validating MODIS-based snowmelt timing maps[J]. Front. Earth Sci., 2018, 12(4): 693-710.
[10] Kazuyoshi SUZUKI, Milija ZUPANSKI. Uncertainty in solid precipitation and snow depth prediction for Siberia using the Noah and Noah-MP land surface models[J]. Front. Earth Sci., 2018, 12(4): 672-682.
[11] Zhixiong MEI, Hao WU, Shiyun LI. Simulating land-use changes by incorporating spatial autocorrelation and self-organization in CLUE-S modeling: a case study in Zengcheng District, Guangzhou, China[J]. Front. Earth Sci., 2018, 12(2): 299-310.
[12] Dengpan XIAO,Yongqing QI,Zhiqiang LI,Rende WANG,Juana P. MOIWO,Fengshan LIU. Impact of thermal time shift on wheat phenology and yield under warming climate in the Huang-Huai-Hai Plain, China[J]. Front. Earth Sci., 2017, 11(1): 148-155.
[13] Xing SUN,Qin LIU,Jie GU,Xiang CHEN,Keya ZHU. Evaluation of the occluded carbon within husk phytoliths of 35 rice cultivars[J]. Front. Earth Sci., 2016, 10(4): 683-690.
[14] Rui XING,Zhiying DING,Sangjie YOU,Haiming XU. Relationship of tropical-cyclone-induced remote precipitation with tropical cyclones and the subtropical high[J]. Front. Earth Sci., 2016, 10(3): 595-606.
[15] Hongshuo WANG,Hui LIN,Darla K. MUNROE,Xiaodong ZHANG,Pengfei LIU. Reconstructing rice phenology curves with frequency-based analysis and multi-temporal NDVI in double-cropping area in Jiangsu, China[J]. Front. Earth Sci., 2016, 10(2): 292-302.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed