Evaluation of CliPest model in simulation of winter wheat (Triticum aestivum L.) and wild oat (Avena ludoviciana L.) competition in Kermanshah

Document Type : Scientific - Research


Department of Agronomy and Plant Breeding, Faculty of Agriculture, Agriculture and Natural Resources Campus, Razi University, Kermanshah, Iran


Crop growth simulation models are powerful tools in determining optimal agriculture management strategies and the sustainability of production in agroecosystems. These models predict plant growth, water use and yield to understand the response of crops to the dynamics of climate–plant–water systems, to evaluate physiological traits for genetic yield improvement and to help make decisions that optimize use of available resources. Since implementing field research required time and cost, thus computer simulation models can save time and money by simulation doing extensive testing. The CliPest model is a generic dynamic simulation model for evaluation of climate change impacts, crop yields and losses due to invasion multiple pests damage. Therefore, the objectives of the present study were: (1) to calibration of the CliPest model (2) to evaluate the performances CliPest model to simulating winter wheat growth, development and grain yield in different wild oat plant densities under Kermanshah weather condition.
Materials and Methods:
A field experiment was done based on randomized complete block design (RCBD) with four replications and a greenhouse experiment was conducted based on completely randomized design (CRD( with three replications to the CliPest model of calibration and validation in the campus of Agriculture and Natural Resources at Razi University during 2014-2015. The treatments were wild oat plant densities (0, 25, 50, 75 and 100 plant m-2) in the field experiment and nitrogen fertilizer application (3.1, 6.2 and 10.1 g urea pot-1) in the greenhouse experiment. The required model inputs were daily solar radiation (MJ.m-2.d-1), and daily minimum and maximum temperature (°C). Model performance was evaluated by comparing simulated and measured values of winter wheat phenological development stages, total dry weight and grain yield for independent wild oat plant densities treatments (fourth replication from the field experiment that did not use in the model calibration process) by root mean square error (RMSE), normalized RMSE (nRMSE) and index of agreement (d).
Results and Discussion:
The results of CliPest calibration showed that nRMSE for total dry weight yield and grain yield of winter wheat and total dry weight of wild oat observed 7.7, 3.1 and 23.4% , respectively. The results of CliPest validation showed that nRMSE for phonological development stages, total dry weight yield, grain yield winter wheat observed 2.4, 24.3, 4.8%, respectively and for phenological development stages and total dry weight of wild oat observed 2.2 and 23.4% of , respectively. The nRMSE for percent of total dry weight yield loss by wild oat damage and percent of grain yield loss due to wild oat damage in winter wheat observed 15.7 and 11.6%, respectively. The results of Clipest showed that with increasing of wild oat plant density, total dry weight and grain yield of winter wheat decreased which was agreement by obtained data in the field experiment. The results of index of agreement (d) and r2 coefficient between observed and simulated data compared to 1:1 line also showed that the CliPest was able to simulate successfully more than 90% and 95% of observed differences in studied traits, respectively.
The results indicated that the CliPest model was able to simulate successfully the observed growth traits of winter wheat and wild oat as well as wheat yield loss by oat damage in different plant densities under Kermanshah climate condition. It seems that careful selection in calibrated parameters in the sensitivity analysis process, measure these parameters in the field and the greenhouse conditions and use of them in the model structure were the main reason to achieve high accuracy for predictions.


Andarzian, B., Bakhshande, A.M., Bannayan, M., Emam, Y., Fathi, G., And Alami-Saeed, K. 2007. CDSS-Model: A simulation model for simulating crop development stages. Pajouhesh and Sazandegi 76: 71-79. (In Persian with English Summary).
Armin, M ., and Asgharipour, M.R. 2011 .Effect of Plant Density on Wild Oat Competition with Competitive and Non-Competitive Wheat Cultivars. Agricultural Sciences in China 10: 1554-1561.
Ayenehband, A. 2012. Production Efficiency of Agroecosystems. Jahade-e-Daneshghahi Mashhad Press. (In Persian).
Dastmalchi, A., Soltani, A., Latifi, N., and Zeinali, E . 2012. Evaluation of CropSyst-Wheat for Simulating of Development, Growth and Yield in Response to Planting date. Iranian Journal of Field Crops Research 10:511-521. (In Persian with English Summary).
Deihimfard, R., Nassiri Mahallati, M., and Koocheki, A. 2015. Simulating the potential yield and yield gaps of sugar beet due to water and nitrogen limitations in Khorasan province using SUCROS model. Agroecology 7: 315-330. (In Persian with English abstract).
De Wit, C.T. 1997. LINTUL1: A simple general crop growth model for optimal growing conditions (example: spring wheat). Graduate School for Production Ecology. Dept of Theoretical Production Ecology of the Wageningen Agricultural University, and dloresearch Centre for Agrobiology and Soil Fertility.
Dezhkam, H., Dejam, M., and Zakerian, A. 2011. Breaking Dormancy and seed germination of (Avena loudviciana L.). National Conference of Agriculture management, 26 May 2011. Jahrom, Islamic Azad University Jahrom. (In Persian with English Summary)
FAOSTAT (Food and Agriculture Organization of the United Nations Statistical Database)., 2014. FAOSTAT Production Statistics of Crops. Available: http://faostat3.fao.org/download/Q/QC/E.
Feyzbakhsh, M.T., Kamkar, B., Mokhtarpour, H., and Asadi, M.E. 2016. Calibration and Evaluation of the CERES-Maize model in Gorgan climatic conditions. Electronic Journal Crop Production 8: 25-49. (In Persian with English Summary).
Fletcher, A.L., Martin, R.J., Ruiter, J.M., Jamieson, P.D., and Zyskowski, R.F. 2008. Simulating Biomass and Grain Yields of Barley and Oat Crops with the Sirius Wheat Model. Crop Modeling and Decision Support. Springer Dordrecht Heidelberg London New York. 192- 203.
Loskutov, G.I. 2001. Influence of vernalization and photoperiod to the vegetation period of wild species of oats (Avena spp.). Euphytica 117: 125-131.
Gimplinger, D.M., and Kaul, K.P. 2009. Calibration and validation of the crop growth model LINTUL for grain amaranth (Amaranthus sp.). Journal of Applied Botany and Food Quality 82: 183-192.
Goudriaan, J. and Van Laar, H.H. 1993. Modeling Potential Crop Growth Processes. Kluwer Academic.
Haghighi-Khah, M., Khajeh-Hosseini, M., and Bannayan-Awal, M. 2013. Effect of Different Treatments on Breaking Dormancy of Various Species of Barnyard Grass (Echinochloa crus galli and Echinochloa awal orizy cola). Journal of plant protection 27: 255-257. (In Persian with English Summary).
Haghjoo, M., and Bahrani, A. 2015. Simulation of Grain Yield and Biomass of Corn at Different Irrigation Regimes and Nitrogen Application. Journal of crop Ecophysiology 9: 167-176. (In Persian with English Summary)
Hassanzadeh-Dlouhy, M., Rahimian-mashhadi, H., Nasiri-mahalati, M., and Nor-mohammdi, G. 2002. The Competitive effects of wild oat (Avena ludoviciana L.) on winter wheat (Triticum aestivum L.) at different densities. Iranian Journal of Crop Sciences 4: 116-127. (In Persian with English Summary).
Khadempir, M., Zeynali, E., Soltani, A., and Torani, M. 2014. Investigation leaf area index, dry matter accumulation and allocation in two cultivars of faba bean (Vicia faba L.) affected by the distance between rows and planting date. Journal of Applied Research of Plant Ecophysiology 1: 15-36. (In Persian with English Summary).
Kiani, M., Gheysari, M., and Mostafazadeh-Fard, B. 2013. Estimation of genetic coefficients and evaluation of OILCROP-SUN model under different levels of nitrogen fertilizer. Journal of Water and Soil Resources Conservation 2: 1-11. (In Persian with English Summary).
Nassiri-Mahallati, M. 2008. Moldling. In A. Koocheki and M. Khajeh-Hosseini (Eds). Modern Agronomy. Jahade-e-Daneshghahi Mashhad Press. p. 420-445. (In Persian).
Mahru-Kashani, A.H., Soltani, A., Galeshi, S., and. Kalate-Arabi, M. 2011. Estimates of genetic coefficients and evaluation of model DSSAT for Golestan province Electronic Journal Crop Production 3: 229-253. (In Persian with English Summary).
Ministry of Jihad-e-Agriculture of Iran. 2014. www.maj.ir.
Mondani, F., Nasiri-Mahallati, M., Koocheki, A., and Hajiyan-Shahri, M. 2015. Simulation of Wild oat (Avena ludoviciana L.) Competition on Winter Wheat (Triticum astivum) Growth and Yield. I: Model Description and Validation. Iranian Journal of Field Crops Research 13: 218-231. (In Persian with English Summary).
Mondani, F. 2012. Simulation effects of climatic change on wild oat and sunn pest damages of winter wheat under Mashhad weather conditions. PhD Dissertation Faculty of Agriculture, Ferdowsi University of Mashhad. (In Persian with English Summary).
Rabie, M., Mirlatifi, S.M., and Gheysari, M. 2012. Calibration and Evaluation of the CSM-CERES-MAIZE Model for Maize Hybrid 704 Single-Cross in Varamin. Journal of Water and Soil 26: 290-299. (In Persian with English Summary).
Rahmani, M., Jami Al-Ahmadi, M., Shahidi, A., and Hadizadeh Azghandi, M. 2015. Effects of climate change on the length of growth stages and water requirement of wheat and barley (Case Study: Birjand Plain). Agroecology 7: 443-460. (In Persian with English abstract).
Rezaei, P., Soltani, A., Ghaderi, A., and Zeinali, E. 2008. Quantifying the occurrence of thermal stesses in wheat in Gorgan. Journal Agriculture Science Natural Resource 15: 1-11. (In Persian with English Summary).
Rezzoug, W., Gabrielle, B, Suleiman, A., and Benabdeli, K. 2008. Application and evaluation of the DSSAT-wheat in the Tiaret region of Algeria. African Journal of Agricultural Research 3: 284-296.
Soltani, A., Robertson, M.J., Mohammad-Nejad, Y., and Rahemi-Karizaki, A. 2006. Modeling chickpea growth and development: leaf production and senescence. Field Crops Research 99: 14-23.
Suriharan, B., Patanothai, A., Pannangpetch, K., Jogloy, S. and Hoogenboom, G. 2007. Peanut Lines for Breeding Applications of the CSM-CROPGRO-Peanut Model. Crop science 47: 607-621.
Willenborg, C.J., Shirtliffe, S.J., and William, E. 2005. Wild Oat (Avena Fatua L.) Time of Emergence and Density Influence Tame Oat (Avena Sativa L.) Yield and Quality. Weed Science 53: 342- 352.
Willocquet, L., Savary, S., Fernandez, L., Elazegui, F., and Teng, P. 2000. Development and evaluation of a multiple-pest, production situation specific model to simulate yield losses of rice in tropical Asia. Ecological Modelling 131: 133-159.
Zarea-Feizabady, A., Sarban, H., Rajabzadeh, M., and Khazaei, H. 2009. Competitive relationship between wheat cultivars at different densities of wild oat. Iranian Journal of Crop Sciences 7: 465-472. (In Persian with English Summary).
Zhang Y., Tang Q., Zou Y., Li D., Qin J., Yang S., Chen L., Xia B., and Peng, S. 2009 Yield potential and radiation use efficiency of ‘‘super’’ hybrid rice grown under subtropical conditions. Field Crops Research 114: 91-98.
Zarakani, F., Kamali, G., and Chizari, A. 2014. The effect of climate change on the economy of rain fed wheat (a case study in Northern Khorasan). Agroecology 6: 301-310. (In Persian with English abstract).