Modeling the rice phenology and production in China with SIMRIW: sensitivity analysis and parameter estimation

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

References

[1] Anderson T R (2010). Progress in marine ecosystem modelling and the “unreasonable effectiveness of mathematics”. J Mar Syst, 81(1–2): 4–11

[2] Angström 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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[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

[24] Yoshida S (1981). Fundamentals of Rice Crop Science. Los Banos: International Rice Research Institute, 269

RIGHTS & PERMISSIONS

Higher Education Press and Springer-Verlag Berlin Heidelberg