عنوان مقاله [English]
A successful hybrid maize (Zea mays L.) seed production program depends on conformity and synchrony of growth and developmental stages of the parental inbred lines with environmental conditions. Crop simulation models plays a key role in managing such synchrony by providing the simulation of the growth stages occurrence time. To evaluate the power of the DSSAT-CSM-CERES-Maize model to simulate the growth and developmental stages of B73 maize inbred line, an experiment was performed as split plot in randomized complete block design with four replications in Karaj, Iran, in 2013. The experimental factors were including planting date and plant densities in five and four levels, respectively. Time to reach any of the developmental stages of B73 maize inbred line including emergence (VE), tassel initiation (TI), silk appearance as the crop flowering (R1), completion of fertilization or beginning of the seed filling (R2) and physiological maturity (R6) were recorded. Then, the genetic coefficients used in the model including P1, P2, P5 and PHINT were determined based on generalized likelihood uncertainty estimation using GLUE software. These genetic coefficients were 307, 0.33, 970 and 70, respectively. The normalized root of error mean square (nRMSE) values for the recorded five growth stages were calculated as 7.857, 14.0, 7.141, 3,607 2.687, respectively, which show the model can simulate the growth stages of B73 maize inbred line using the new specific genetic coefficients. Overall, the results of current research showed that the CERES-Maize model which already developed to simulate the growth and development of maize hybrid cultivars can be efficient and accurate to simulate the production of maize hybrid seed only if the specific genetic coefficient of each parental inbred line is used.
Angstrom, A. 1924. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society 50: 121-126.##Bannayan, M. and Hoogenboom, G. 2009. Using pattern recognition for estimating cultivar coefficients of a crop simulation model. Field Crops Research 111: 290-302.##Birch, C. J., Vos, J. and Van der Putten, P. E. L. 2003. Plant development and leaf area production in contrasting cultivars of maize grown in a cool temperate environment in the field. European Journal of Agronomy 19(2): 173-188.##Bonhomme, R., Derieux, M. and Edmeades, G. 1994. Flowering of diverse maize cultivars in relation to temperature and photoperiod in multilocation field trials. Crop Science 34: 156-164.##Çakir, R. 2004. Effects of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Research 89(1): 1-16.##Carson, J. S. 2002. Model verification and validation. Simulation conference, 2002. Proceedings of the Winter. Vol. 1. pp: 52-58.##Diepen, C. V., Wolf, J., Keulen, H. V. and Rappoldt, C. 1989. WOFOST: A simulation model of crop production. Soil Use and Management 5: 16-24.##Dobermann, A. R., Arkebauer, T. J., Cassman, K. G., Drijber, R. A., Lindquist, J. L., Specht, J. E., Walters, D. T., Yang, H., Miller, D. N. and Binder, D. L. 2003. Understanding corn yield potential in different environments.University of Nebraska Press.##Evans, M., Passas, H. J. and Poethig, R. S. 1994. Heterochronic effects of glossy15 mutations on epidermal cell identity in maize. Development 120: 1971-1981.##Ganal, M. W., Durstewitz, G., Polley, A., Bérard, A., Buckler, E. S., Charcosset, A., Clarke, J. D., Joets, J., Le Paslier, M. C., McMullen, M. D., Montalent, P., Rose, M., Schön, C. C., Sun, Q., Walter, H., Martin, O. C. and Falque, M., 2011. A large maize (Zea mays L.) SNP genotyping array: Development and germplasm genotyping, and genetic mapping to compare with the B73 reference genome. PloS ONE 6 (12): e28334. doi:10.1371/journal.pone.0028334.##Gouesnard, B., Rebourg, C., Welcker, C. and Charcosset, A. 2002. Analysis of photoperiod sensitivity within a collection of tropical maize populations. Genetic Resources and Crop Evolution 49(5): 471-481.##Gungula, D., Kling, J. and Togun, A. 2003. CERES-Maize predictions of maize phenology under nitrogen-stressed conditions in Nigeria. Agronomy Journal95: 892-899.##Hoogenboom, G., Jones, J.,Wilkens, P.,Porter, C.,Boote, K.,Hunt, L.,Singh, U.,Lizaso, J.,White,J. and Uryasev, O. 2015. Decision support system for agrotechnology transfer (DSSAT). Ver. 4.6. DSSAT Foundation, Prosser, Washington.www. DSSAT. net.##Jones, C. and Kiniry, J. 1986. CERES-Maize: A simulation model of maize growth and development. 1986. Texas A & M University Press.##Khalili A. and Sadr H. 1998. Estimation of total solar radiation in the region of Iran based on climatic data. Journal of Geographical Research 13(1):15-35. (In Persian with English Abstract).##Keisling, T. 1982. Calculation of the length of day. Agronomy Journal 74: 758-759.##Kumudini, S., Andrade, F., Boote, K., Brown, G., Dzotsi, K., Edmeades, G., Gocken, T., Goodwin, M., Halter, A. and Hammer, G. 2014. Predicting maize phenology: Intercomparison of functions for developmental response to temperature. Agronomy Journal106: 2087-2097.##Macal, C. M. 2005. Model verification and validation. In Workshop on" Threat Anticipation: Social Science Methods and Models.##Makowski, D., Wallach, D. and Tremblay, M. 2002. Using a bayesian approach to parameter estimation:Comparison of the GLUE and MCMC methods. Agronomy Journal 22 (2): 191-203.##Nielsen, R. L., Thomison, P. R., Brown, G. A., Halter, A. L., Wells, J. and Wuethrich, K. L. 2002. Delayed planting effects on flowering and grain maturation of dent corn. Agronomy Journal
94: 549-558.##Popova, Z., Eneva, S. and Pereira, L. S. 2006. Model validation, crop coefficients and yield response factors for maize irrigation scheduling based on long-term experiments. Biosystems Engineering 95(1): 139-149.##Puppi, G. 2007. Origin and development of phenology as a science. Italian Journal of Agrometeorology 3: 24-29.##Rasse, D. P., Ritchie, J. T., Wilhelm, W. W., Wei, J. and Martin, E. C. 2000. Simulating inbred-maize yields with CERES-IM. Agronomy Journal 92(4): 672-678.##Ritchie, J. T., Singh, U.,Godwin, D. C. and Bowen, W. T. 1998. Cereal growth, development and yield. In: Tsugi, G. Y., Hoogenboom, G. and Thornton, P. K. (Eds.). Underestanding option for agricultural production. Kluwer Academic Press. Boston.##Saseendran, S. A., Ma, L., Nielsen, D., Vigil, M. and Ahuja, L. 2005. Simulating planting date effects on corn production using RZWQM and CERES-Maize models. Agronomy Journal97: 58-71.##Schnable, P. S., Ware, D., Fulton, R. S., Stein, J. C., Wei, F., Pasternak, S., Liang, C., Zhang, J., Fulton, L. and Graves, T. A. 2009. The B73 maize genome: Complexity, diversity and dynamics. Science 326: 1112-1115.##Soler, C. M. T., Sentelhas, P. C. and Hoogenboom, G. 2007. Application of the CSM-CERES-Maize model for planting date evaluation and yield forecasting for maize grown off-season in a subtropical environment. European Journal of Agronomy 27: 165-177.##Soltani A. and Maddah V. 2000. Simple and applied programs for education and research in agronomy. Iranian Scientific Society of Agroecology. (In Persian).##Tsimba, R., Edmeades, G. O., Millner, J. P. and Kemp, P. D. 2013. The effect of planting date on maize grain yields and yield components. Field Crops Research 150: 135-144.##Tsuji, G. Y., Hoogenboom, G. and Thornton, P. K. 1998. Understanding options for agricultural production. Springer Science and Business Media.##van Ittersum, M. K., Cassman, K. G., Grassini, P., Wolf, J., Tittonell, P.and Hochman, Z. 2013. Yield gap analysis with local to global relevance: A review. Field Crops Research 143: 4-17.##Walbot, V. 2009. 10 Reasons to be tantalized by the B73 maize genome. PLoS Genetics 5: e1000723.