Abstract :
For assessment of yield changes of wheat (cv. Sardari) and 14 modified genetically cultivars, a simulation experiment was conducted to select optimal plant characteristics based on maximum leaf area index, biomass of seed at filling time, biological yield, seed yield and harvest index, using SSM-Model in rain-fed conditions of urmia for 3 years (2011-2013). Results of simulations showed that the major effective parameter in increasing of grain yield was 20% decreasing in time till grain filling beginning (vegetative phase), 20% increasing in time till grain growth ending and 30 % inversing in radiation use efficiency. The maximum leaf area index, the highest biomass at grain filling time, biological yield, grain yield and harvest index were determined in modified cultivars like: C2, C12, and C8 respectively which was made by increasing in vegetative phase, upgrading of flowering phase (grain filling phase) and improvement of radiation use efficiency (RUE). In cluster analysis which was used by three methods (mean linkage, single linkage and centroid), three groups were obtained: C12 and C8 in group 1, C11 in group 2 and other cultivars in group 3(C1, C2, C3, C4, C5, C6, C7, C9, C10, C13, C14 and Sardari). Recognition analysis of functions indicated that the main effects on clustering of genotypes are LAImax and biomass at the beginning of seed growth. Results, as a whole, showed that it is necessary to take into account climatically changes, methods of increasing of grain filling period, earlier enterance to grain filling period and improvement of RUE in addition to achieving optimum LAI during vegetation growth period in wheat, because it could be the important impacts in increasing of grain yield of wheat.
References:
· Akram-Qaderi, F., and A. Soltani. 2007. Determination of optimum characteristics of plant for Cicer arientinum in irrigate farms of gorgon and Gonbad by computer simulation. Journal of Agricultural Science and Natural Resources. 14(5):1-11. (In Persian).
· Anonymous. 2003. Food and Agricultural Organization of the united. Nations (FAO), Rome, Italy, from http://apps.fao.org.html.
· Anonymous. 2009. Statistical Analysis Software, SAS Institute, V9.2. Carry, NC.
· Boote, K.J., J.W. Jones, W.D. Batchelor, E.D. Nafzige, and O. Myers. 2003. Genetic coefficients in the CROPGRO-Soybean model. Links to field performance and genomics. Agronomy Journal. 95: 32–51.
· Faraji, A., and A. Soltani. 2007. Collection and dispersion of dry matter and nitrogen and threshold of resistance to drought in Cicer on fry-land in Gonbad and Gorgon: A simulation study. Journal of Agricultural Science and Natural Resources. 14(5): 12-23. (In Persian).
· Ghaderi-Far, F., A. Soltani, and A.A. Miri. 2012. Modeling phonological development in Cotton. Journal of Plant Production. 19(1): 107-126.
· Hajjar pour, A., A. Soltani, E. Zeinali, and F. Seidi. 2013. Simulation of climate change effect on Cicer production in dry-land and irrigation conditions in Kermanshah. Journal of Plant Production Research. 20(2): 235-252.
· Hammer, G.L., M.J. Kropff, T.R. Sinclair, and J.R. Porter. 2002. Future contributions of crop modeling – from heuristics and supporting decision making to understanding genetic regulation and aiding crop improvement. European Journal of Agronomy. 18: 15-31.
· Hoogenboom, G., J.W. White, and C.D. Messina. 2004. From genome to crop: integration through simulation modeling. Field Crops Research. 90: 145–163.
· Kantolic, G.A., J.L. Mercau, G.A. Slafer, and V.O. Sadras. 2007. Simulated yield advantages of extending post-flowering development at the expense of a shorter pre-flowering development in soybean. Field Crops Research. 101: 321-330.
· Kiniry, J.R., D.J. Major, R.C. Izaurralde, J.R. Williams, P.W. Gassman, M. Morrison, R. Bergentine, and R.P. Zentner. 1995. EPIC model parameters for cereal, oilseed and forage crop in the northern Great Plains region. Canadian Journal Plant Science. 75: 679-688.
· Ritchz, C., C. Pipper, F. Yndgaard, K. Fredlund, and G. Steinrücken. 2010. Modelling flowering of plants using time-to-event methods. European Journal of Agronomy. 32: 155–161.
· Shiri, A., N. Khaliliaqdam, and T.Mirmahmoodi, 2014. Evaluation of different empirical models for the estimation of leaf area in various cultivars of wheat. Journal of Agronomy and Plant Breeding. 10(3): 73-88. (In Persian).
· Sinclair, T.R. 2000. Model analysis of plant traits leading to prolonged crop survival during severe drought. Field Crops Research. 68: 211-217.
· Sinclair, T.R., and R.C. Muchow. 2001. System analysis of plant traits to increase grain yield on limited water supplies. Agronomy Journal. 93: 263-270.
· Soltani, A. 2009. Mathematical modeling in filed crops, JDM Press, Mashhad, Iran. (In Persian).
· Soltani, A., and M. Gholipoor. 2006. Simulation the impact of climate change on growth, yield and water use of chickpea. Journal of Agricultural Science and Natural Resources. 13(2): 69-79.
· Soltani, A., and T.R. Sincler. 2012. Identifying plant traits to increase chickpea yield in water-limited environments. Field Crops Research. 133: 186–196.
· Soltani, A., and V. Maddah-Yazdi, 2010. Simple, applied programs for education and research in agronomy. Niak Press. (In Persian).
· Soltani, A., F. Akram-Ghaderi, and A. Fraji. 2005. Evaluation the effective traits on increasing yield of chickpea in Gorgan and Gonbad condition. Research Report of Project Gorgan University of Agricultural Sciences and Natural Resources. (In Persian).
· Soltani, A., F.R. Khooie, K. Ghassemi-Golezani, and M. Moghaddam. 2000. A simulation study of chickpea crop response to limited irrigation in a semiarid environment. Agriculture Water Management. 49: 225-237.
van Herwaarden, A.F., J. F. Angus, R.A. Richards, and G.D. Farquhar. 1998. Haying-off, the negative grain yield response of dry land wheat to nitrogen fertilizer. II. Carbohydrate and protein dynamics. Australian Journal of Agricultural Research. 49: 1083-1093.
_||_