Sensitivity analysis of crop growth simulation model performance to crop and weather input data

Sensitivity analysis is a useful tool for understanding the model's mechanism. A sensitivity analysis of model determined the effect of input parameters on output parameter and it’s necessary for model calibration and validation. This study focuse on investigation the permormance of WOFOST (World Food Studies) crop growth simulation model for determination of important Variable for model calibration. The model was run in potential production state for 11 years (2005-2016) with Qazvin station weather parameters. Crop and weather variables was changed in acceptable domain and variation of output was examined. Three of most important output was selected for sensitivity analysis. Total above grand production (TAGP), the total weight of storage organs (TWSO) and potential evapotranspiration (ETP) were investigated versus input parameters variation. The most effective variables on TAGP and TWSO were maximum leaf assimilation rate (AMAXTB), specific leaf area (SLATB), extinction coefficient for diffuse visible light (KDIFTB) in crop parameters and SLATB was most effective variables on ETP. TAGP and TWSO didn’t have any sensitivity against wind speed and vapor pressure, but ETP has been sensitive toward all variables. The maximum sensitivity model in term of crop evapotranspiration is related to radiation. Maximum temperature and radiation change the TAGP up to 42 and 55.8 percent respectively. Based on the result the leaf expansion, light interception, assimilation and phenological parameters play key roles in the WOFOST model. This result aid in future model understanding and accuracy of model calibration. * Corresponding Author: Abdul Rahim Hooshmand  hooshmand_a@scu.ac. ir International Journal of Agronomy and Agricultural Research (IJAAR) ISSN: 2223-7054 (Print) 2225-3610 (Online) http://www.innspub. net Vol. 12, No. 5, p. 72-84, 2018 Int. J. Agron. Agri. R. Ahmadi et al. Page 73 Introduction Calibration is a demanding and critical step for using crop growth simulation models (Gilardelli et al., 2018). It has been shown that application of a crop growth simulation model outside the domain for which it was developed and calibrated for, often leads to disappointing results (Kabat et al., 1995). Before calibration understanding the model behavior versus each input variables is necessary. Sensitivity analysis is a helpful method for increasing the knowledge about model performance. WOFOST model is used in several research domain such as climate change (Gilardelli et al., 2018), yield forecasting (Ma et al., 2013), yield gap (Boogaard et al., 2013) water stress (Kroes and Supit, 2011) and data assimilation (de Wit et al., 2012; Gilardelli et al., 2018; Huang et al., 2015; Ma et al., 2013). Therefore choosing the appropriated variables for calibration and measurement in the field is important for using of WOFOST applying. Sensitivity analysis “SA” is a procedure to determine the effect of different value of input parameters on output parameters. Sensitivity analysis is used to find important and most effective variables on any model outputs. There are basically two general methods for sensitivity analysis, local and global methods. In the local methods, one input is varied and other inputs are kept fixed as default value. This method is used in several types of research because it is a quick and easy to use (Wang et al., 2013). Another method is global sensitively analysis, which in this method the entire range of inputs are considered. There are many algorithms for global SA such as screening methods, regression-based methods, and variance-based methods (Confalonieri et al., 2012). Screening methods is based on calculation of elementary effects of each factor as well as their average, then estimating the total factor importance on the model outputs that are known as the Morris method(Campolongo et al., 2007). Richtera et al (2010) used Morris method to yield formation of Durum wheat and prove that Morris is a reliable and effective method for determining effective and important parameters for model optimizing (Richtera et al., 2010b). Confalonieri et al. 2010 used Morris and Sobol sensitivity analysis methods for the rice model WARM in Europe in deferent climate and locations (Confalonieri et al., 2010). Regression-based methods such as Latin Hypercube Sampling (LHS) Random and Quasi-Random LpTau is based on the computation of standard or partial regression coefficients. this method is assessing the effect of changing variables (Confalonieri et al., 2012). Variance-based methods is used for computation of the variance of the output(s) into terms corresponding to the different inputs and their interactions (Marzban, 2013). Sensitivity is a technique exploring model uncertainty as well as identifying the contribution of each parameter to the model response (Oakley and O'Hagan, 2004; Richtera et al., 2010a). Confalonieri et al. (2006) have assessed the sensitivity analysis of WOFOST for rice biomass simulations. Based on this research final biomass showed high variability toward the inputs especially CO2 assimilation rates and partitioning coefficients was the most relevant. (Confalonieri et al., 2006b). Wang and et al used Fourier Amplitude Sensitivity Test method for WOFOST sensitivity analysis and show that some parameters don’t have a direct effect on biomass but play a key role in certain stage through the plant growth. Farhadi and et al. (2009) survey the sensitivity of WOFOST model to daily solar radiation estimation methods and their result show that sensitivity analysis is necessary for model use and maximum deviation for winter barley and silage maize was 9% variability (Bansouleh et al., 2009) Kanellopoulos et al. (2014) used the WOFOST model for climate change assessment in socio economic scenario for assessment the impact of climate change and socio-economic scenarios on arable farming system (Kanellopoulos et al., 2014). Sensitivity analysis can be helpful for improvement of model calibration and can be used as a guide for use of model. The aim of this research is to investigate the WOFOST model performance toward weather and crop variable variation of Qazvin plean. Int. J. Agron. Agri. R. Ahmadi et al. Page 74 Materials and methods