Simulating Phenology, Growth and Yield of Transplanted Rice at Different Seedling Ages in Northern Iran Using ORYZA2000

Abstract

Abstract: Rice crop growth and yield in the north Iran are affected by crop duration and phenology. The purpose of this study was to calibrate and validate the ORYZA2000 model under potential production based on experimental data for simulating and quantifying the phenological development, crop duration and yield prediction of rice crop influenced by different seedling ages. In order to calibrate and validate the crop parameters of ORYZA2000 model, a two-year field experiment was conducted under potential growth condition for transplanted lowland rice during the 2008–2009 rice growing seasons, using three rice varieties with three seedling ages (17, 24 and 33 days old). The results showed that the seedling age changed crop duration from 7 to 10 d. The ORYZA2000 model could predict well, but consistently underestimated the length of growing period. The range in normalized root mean square error (RMSEn) values for each phenological stage was between 4% and 6%. From our evaluation, we concluded that ORYZA2000 was sufficiently accurate in simulation of yield, leaf area index (LAI) and biomass of crop organs over time. On average, RMSEn values were 13%?15% for total biomass, 18%?21% for green leaf biomass, 17%?20% for stem biomass, 16%?23% for panicle biomass and 24%?26% for LAI. The RMSEn values for final yield and biomass were 12%?16% and 6%?9%, respectively. Generally, the model simulated LAI, an exceeded measured value for younger seedlings, and best-fit was observed for older seedlings of short-duration varieties. The results revealed that the ORYZA2000 model can be applied as a supportive research tool for selecting the most appropriate strategies for rice yield improvement across the north Iran.

Key words: rice, crop model, ORYZA2000, phenology, seedling age

B. Amiri LARIJANI1, Z. T. SARVESTANI1, Gh. NEMATZADEH2, A. M. MANSCHADI3, E. AMIRI4. Simulating Phenology, Growth and Yield of Transplanted Rice at Different Seedling Ages in Northern Iran Using ORYZA2000[J]. RICE SCIENCE, 2011, 18(4): 321-334.

References

Aggarwal P K, Kropff M J, Cassman K G, Ten Berge H F M. 1997. Simulating genotypic strategies for increasing rice yield potential in irrigated tropical environments. Field Crops Res, 51: 5–17.
Amiri E. 2008. Evaluation of the rice growth model ORYZA2000 under water management. Asian J Plant Sci, 7(3): 291–297.
Amiri E, Rezaei M. 2009. Testing the modeling capability of ORYZA2000 under water-nitrogen limit condition in Northern Iran. World Appl Sci J, 6: 1113–1122.
Arora V K. 2006. Application of a rice growth and water balance model in an irrigated semi-arid subtropical environment. Agric Water Manage, 88: 51–57.
Ashraf M, Khalid A, Ali K. 1999. Effect of seedling age and density on growth and yield of rice in saline soil. Pak J Biol Sci, 2: 860–862.
Belder P, Bouman B A M, Spiertz J H J, Gouan L. 2007. Exploring option for water saving in lowland rice using a modeling approach. Agric Syst, 92: 91–114.
Boling A, Bouman B A M, Toung T P, Murty M V R, Jatmico S Y. 2007. Increasing rainfed rice productivity in Centreal Java, Indinesia: A modeling approach using ORYZA2000. Agric Syst, 92:115–139.
Bouman B A M, van Keulen H, van Laar H H, Rabbinge R. 1996. The ‘School of de Wit’ crop growth simulation models: A pedigree and historical overview. Agric Syst, 52: 171–198.
Bouman B A M, Kropff M J, Tuong T P, Wopereis M C S, Ten Berge H F M, van Laar H H. 2001. ORYZA2000: Modeling Lowland Rice. Los Ba?os (Philippines): International Rice Research Institute, and Wageningen (Netherlands): Wageningen University and Research Centre: 235.
Bouman B A M, van Laar H H. 2006. Description and evaluation of the rice growth model ORYZA2000 under nitrogen-limited conditions. Agric Syst, 3: 249–273.
Bouman B A M, Feng L, Tuong T P, Lu G, Wang H, Feng Y. 2007. Exploring options to grow rice under water-short condition in northern China using a modeling approach: II. Quantifying yield, water balance components and water productivity. Agric Water Manage, 88: 23–33.
De Datta S K. 1987. Principles and Practices of Rice Production. Malabar, Fla: Krieger.
Dizon M A, Gines H C, Redulia C A, Cassman K G. 1996. Effects of transplanting practices on rice yield components. Phil J Crop Sci, 19: 1–76.
Feng L, Bouman B A M, Tuong T P, Cabangon R J, Li Y, Lu G, Feng Y. 2007. Exploring options to grow rice under water-short condition in northern China using a modeling approach: I. Field experiments and model evaluation. Agric Water Manage, 88: 1–13.
Huang Y, Gao L, Jin Z, Chen H. 1998. Simulating the optimal growing season of rice in the Yangtze River valley and its adjacent area, China. Agric For Meteorol, 91: 251–262.
Hundal S S, Prabhjyot K. 1999. Evaluation of agronomic practices for rice using computer simulation model, CERES-Rice. Oryza, 36: 63–65.
Iran Ministry of Agriculture. 1993. The Feasibility Study on the Irrigation and Drainage Development Project in the Haraz River Basin. Tokyo: Japan International Cooperation Agency.
Jamieson P D, Porter J R, Wilson D R. 1991. A test of the computer simulation model ARCWHEAT1 on wheat crops grown in New Zealand. Field Crops Res, 27(4): 337–350.
Jing Q, Bouman B, Keulen H V, Hengsdijk H, Cao W, Dai T. 2008. Disentangling the effect of environmental factors on yield and nitrogen uptake of irrigated rice in Asia. Agric Syst, 98: 177–188.
Jones J W, Hoogenboom G, Porter C H, Boote K J, Batchelor W D, Hunt L A, Wilkens P W, Singh U, Gijsman A J, Ritchie J T. 2003. The DSSAT cropping system model. Eur J Agron, 18: 235–265.
Kewat M L, Agrawal S B, Agrawal K K, Sharma R S. 2002. Effect of divergent plant spacings and age of seedlings on yield and economics of hybrid rice (Oryza sativa). Ind J Agron, 47: 367–371.
Khakwani A A, Shiraishi M, Zubair M, Baloch M S, Naveed K H, Awan I. 2005. Effect of seedling age and water depth on morphological and physiological aspects of transplanted rice under high temperature. J Zhejiang Univ Sci B, 6(5): 389–395.
Khanal R R. 2005. Phyllochron and leaf development in field grown rice genotypes under varying thermal environments of a high altitude cropping system. Thesis in partial fulfilment of the requirements for the academic degree of Master of Science. Universitat zu Bonn.
Kotera A, Nawata E, Chuong P, Giao N N, Sakuratani T. 2004. A model for phenological development of Vietnamese rice influenced by transplanting shock. Plant Prod Sci, 7: 62–69.
Kropff M J, van Laar H H, Matthews R B. 1994. ORYZA1: An Ecophysiological Model for Irrigated Rice Production. Wageningen: DLO-Research Institute for Agrobiology and Soil Fertility: 110.
Latif M A, Islam M R, Ali M Y, Saleque M A. 2005. Validation of the system of rice intensification (SRI) in Bangladesh. Field Crops Res, 93: 281–292.
Laulanie H. 1993. The intensive rice cultivation system in Madagascar. Tropicultura, 11: 110–114.
Li T, Bouman B A M, Boling A. 2009. The Calibration and Validation of ORYZA2000. Los Banos, Philippines: International Rice Research Institute.
Makarin A K, Balasubramanian V, Syamsiah I, Diratmadja I G P A, Handoko A, Wardana I P, Gani A. 2002. System of rice intensification (SRI), evaluation of seedling age and selected component in Indonesia. In: Bouman B A M, Hengsdijk H, Hardy B, Bindraban P S, Tuong T P, Ladha J K. Water-Wise Rice Production. Phillipines: International Rice Research Institute: 129–139.
Menete M Z L, van Es H M, Brito R M L, DeGloria S D, Famba S. 2008. Evaluation of system of rice intensification (SRI) component practices and their synergies on salt-affected soils. Field Crops Res, 109: 34–44.
Nakagawa H, Horie T. 1995. Modeling and prediction of developmental process in rice: II. A model for simulation panicle development based on daily photoperiod and temperature. Jpn J Crop Sci, 64: 33–42.
Pasuquin E, Lafarge T, Tubana B. 2008. Transplanting young seedlings in irrigated rice fields, early and high tiller production enhanced grain yield. Field Crops Res, 105: 141–155.
Patel J R. 1999. Response of rice (Oryza sativa) to time of transplanting, spacing and age of seedlings. Ind J Agron, 44: 344–346.
Poussin J, Wopereis M C S, Debouzie D, Maeght J. 2003. Determinants of irrigated rice yield in the Senegal River valley. Eur J Agron, 19: 341–356.
Reddy K S, Reddy B B. 1994. Effect of planting time, population density and seedling age on yield and yield parameters of rice. Ind J Agric Res, 28: 171–176.
Ritchie J T, Alocilja E C, Singh U, Uehara G. 1987. IBSNAT and the CERES-Rice model. In: Proceedings of the Workshop on Impact of Weather Parameters on Growth and Yield of Rice. Los Banos, Philippines: International Rice Research Institute: 271–281.
Roetter R, Hoanh C T, Teng P S. 1998. A system approach to analyzing land use options for sustainable rural development in South and Southeast Asia. In: IRRI Discussion Paper Series No. 28. Philippines: International Rice Research Institute: 110.
Salam M U, Street P R, Jones J G W. 1994. Potentioal production of Boro rice in the Haor region of Bangeladesh: Part 1. The simulation model, validation and sensitivity analysis. Agric Syst, 46: 257–278.
Salam M U, Jones J W, Kobayashi K. 2001. Predicting nursery growth and transplanting shock in rice. Exp Agric, 37: 65–81.
Sasaki R. 2004. Characteristics and seedling establishment of rice nursling seedlings. JARQ, 38(1): 7–13.
Sheehy J E, Peng S, Dobermann A, Mitchell P L, Ferrer A, Yang J C, Zou Y B, Zhong X H, Huang J L. 2004. Fantastic yields in the system of rice intensification, fact or fallacy? Field Crops Res, 88: 1–8.
Sie M, Dingkuhn M, Wopereis M C S, Miezan K M. 1998. Rice crop duration and leaf appearance rate in a variable thermal environment: I. Development of an emperically based model. Field Crops Res, 57: 1–13.
Singh N T. 1998. Reclamation and management of alkali soil. In: Tyagi N K, Minhas P S. Agricultural Salinity Management in India. Karnal: Central Soil Salinity Research Institute: 261–278.
Singh R S, Singh B. 1998. Response of rice (Oryza sativa) to age of seedlings, and level and time of application of nitrogen under irrigated condition. Ind J Agron, 43: 632–635.
Slafer G A. 2003. Genetic basis of yield as viewed from a crop physiologist’s perspective. Ann Appl Biol, 142: 117–128.
Takezawa K, Tamura Y. 1991. Use of smoothing splines to estimate rates of development. Agric For Meteorol, 57: 129–145.
Timsina J, Humphreys E. 2003. Performance and application of CERES and SWAGMAN destiny models for rice-wheat cropping systems in Asia and Australia: A review. CSIRO Land and Water Technical Report 16/03. CSIRO Land and Water, Griffith, NSW 2680, Australia: 57.
Uphoff N, Fernandes E C M, Yuan L, Peng J M, Rafaralahy S, Rabenandrasana J. 2002 Assessments of the System for Rice Intensification (SRI): Proceedings of an International Conference Held in Sanya, China, April 1-4. http://ciifad.cornell.edu/sri/ SRI_Assessments.pdf
van Ittersum M K, Leffelaar P A, van Keulen H, Kropff M J, Bastiaans L, Goudriaan J. 2003. On approaches and applications of the Wageningen crop models. Eur J Agron, 18: 201–234.
Wikarmpapraharn C, Kositsakulchai E. 2010. Evaluation of ORYZA2000 and CERESS-Rice models under potentioal growth condition in the central plain of Thailand. Thai J Agric Sci, 43(1): 17–29.
Yin X, Kropff M J, Mclaren G, Visperas R M. 1995. A nonlinear model for crop development as a function of temperature. Agric For Meteorol, 77: 1–16.
Yin X, Struik P C, Tang J, Qi C, Liu T. 2005. Model analysis of flowering phenology in recombinant inbred lines of barley. J Exp Bot, 56(413): 959–965.

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