Investigation of Barley Productivity Responses to Different Water Consumption by Using the CERES-Barley Model

Year 2015, Volume: 9 Issue: 27, 119 - 126, 01.04.2016

Zeinab Fatemi Rika Farzad Paknejad

Abstract

Cropping system models have evolved over the last four decades in response to demand for modeling to address more complex questions, including issues on sustainable production, climate change and environmental impact. The present study is about dynamic mechanistic model (CERES (Crop Environment RE source Synthesis)-Barley that was validated by predicting growth and yield of barley (Hordeum vulgare L.) under different water management conditions. My objective was analysis of barley responses to different water consume for optimizing of biomass and yield productivity. Evaluation of analysis showed that performance of the
model was reasonable as indicated by close correspondence of simulated crop phenology, biomass accumulation and grain yield versus measured data. Growth parameters of barley in CERES-Barley were calibrated through field experiments, Karaj (Iran) 2010-2011. Genotypic variables determined for 5 commonly cultivars grown in Karaj. The performance of the models was evaluated using simulated and observed data on anthesis and maturity date, in-season LAI, final yield and its components. Grain yields simulated by model were acceptable when compared with experimental results. The determination coefficient in historical series varied from 0.83 to 0.99 for evaluation of CERES- Barley under normal irrigation. The accuracy of model simulation in dry matter was optimum in based on correlation coefficient 0.91-0.98. Also model acted well for biomass simulation in treatments. As biomass measured data generally have 10-20% error and treatments widely varied in two irrigation system. The objective of this study was determine, whether CERES-Barley model could be forecast yield and biomass in maturity under growing season and ecological management in Karaj.

Keywords

CERES-Barley , Simulation , Yield and Biomass , Drought stress

References

• Banayan, M. 1999. Developing and applying crop simulation models for forecast winter wheat yield. PhD. Thesis, Nott. Univ., UK.
• Bannayan. M., Crout. N. M. J, and Hoogenboom. G. 2003. Application of the CERES- wheat model for within season prediction of winter wheat yields in the United Kingdom. Agron. J. 95: 114-125.
• Chipanshi. A. C., E. A. Ripley., and R. G. Lawford. 1997. Early prediction of spring wheat yields in Saskatchewan from current and historical weather data using the CERES-wheat model. Agric. For. Meteorol. 84: 223-232.
• Egli, D. B., and Bruening, W. 1992. Planting date and Soybean yield: Evaluation of environmental effect with crop simulation model: SoyGRO. Agric. For. Meteorol. 62: 19-92.
• Ghaffari. A., Cook, H.F. and Lee, H.C. 2001. Simulating winter wheat yields under temperate conditions: exploring different management scenarios. Eur. J. Agron. 15:231-240.
• Habekotte, B. 1997. Options for increasing seed yield of winter oil seed rape: A simulation study. Field Crops Res.54:109-126.
• Hanks, R. J. and Rassmussen, V. P. 1982. Wheat development as effected by deficit, high frequency sprinkler irrigation. Agron. 35: 193-215.
• Heng, L. K., Moutonnet, P., Baethgen, W. A., 2001. Optimization of fertilizer application for irrigated wheat systems based on an integration of crop simulation models and unclear techniques. http://www.Icasanet.org/applications/fertilizer.html.
• Hundale. S. S.and P. Kaur.1997. Application of CERES-Wheat model to yield prodictions in the irrigated plains of the Indian-Panjab.J Agric.Scien. Camb.129: 13-18.
• Hunt, L. A. and Pararajasingham. S. 1995. Cropsim-Wheat: A model description the growth and development of wheat. Can. J. Plant Sci. 75: 619-632.
• Hunt, L. A. and Pararajasingham, S. Jonse, J. W. Hoogenboom, G. Imamura, D. T. and Ogoshi, R.M. 1993. GENCALA: Software to facilitate the use of crop models for analyzing field experiment. Agron. J. 85: 1090-1094.
• Jakson, P., Robertson, M., Copper, M. and Hammer, G. L. 1996. The role of physiological understanding in plant breeding: from a breeding perspective. Field Crop Research. 49: 11-37.
• Jamieson, P. D., Porter, J. R., Goudriaan, J., Ritchie, J. T., Van Keulen, H. and Stol, W. 1998. A comparison of the models AFRCWHEAT2, CERES-Wheat, Sirius, SUCROS2 and SHWEAT with measurements from wheat grown under drought. Field Crop Research 55: 23-44.
• 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., and Ritchie, J.T. 2003. The CERES-WHEAT cropping system model. Eur. J. Agron. 18: 235-265.
• Jorgensen, S.E. 1997. Ecological modeling by Ecological modeling. Ecol. Model. 100: 5-10.
• Melkonian, J., Richa, S.J., and Wilks, D.S. 1997. Simulation of elevated CO2 effects on daily net canopy carbon assimilation and crop yield.Agric Syst. 58: 87-106.
• Paknejad, F., Majidifakhr, F., and Mirtaheri, S.M. 2012. Validation of the CERES-Wheat for predication of wheat varieties in irrigation and terminal drought stress. American Journal of agricultural and Biological Sciences 7(2): 180-185.
• Sinclair, T.R, and Seligman, N.G. 2000. Criteria for publishing paperson crop modeling. Field Crops Res. 68: 165–172.
• Singh, A.K., Tripathy, R., and Chopra, U.K. 2008. Evaluation of CERES-Wheat and CropSyst models for water–nitrogen interactions in wheat crop. Agric Water Manag. 95: 776 – 786.
• Soltani, A., Hoogenboom, G., 2007. Assessing crop management with crop simulation models based on generated weather data. Field crop Res. 103: 198-207.
• Soltani, A., Robertson, M.J., Mohammad-Nejad, Y., and Rahemi-Karizaki, A. 2006. Modeling chickpea growth and development: leaf production and senescence. Field Crops Res. 99: 14-23.
• Soltani, A., Torabi, B., Zarei, H. 2005. Modeling crop yield using a modified harvest index-based approach: application in chickpea. Field Crop Res. 91:273-285.