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

Authors

• Ashkan Jalilian
• Farzad Mondani
• Mahmud Khorami Vafa
• Alireza Bagheri

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

10.22067/jag.v10i1.60082

Abstract

Introduction:
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.
Conclusion:
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.

Keywords

• Calibration
• Crop growth simulation
• Partitioning
• Validation
• Weed competition

20.1001.1.20087713.1397.10.1.18.6

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