Evaluation of Reduced Grain Water Absorption Models in Ahwaz Climatic Conditions

Document Type : Original Article

Authors

1 Postgraduate of Irrigation and Drainage, Faculty of Water Sciences Engineering, ShahidChamran university of Ahvaz. Iran

2 Associate Professor of Irrigation and Drainage, Faculty of Water Sciences Engineering, ShahidChamran university of Ahvaz. Iran

3 Professor, Faculty of Water Sciences Engineering, Shahid Chamran University of Ahvaz, Iran

Abstract

Plant water uptake under water stress conditions can be described quantitatively by some mathematical models. Water absorption models are useful tools for irrigation planning and optimal water management if they can provide accurate predictions of plant response to water stress. In general, two broad categories of water absorption modeling approaches, models are microscopic and macroscopic models Microscopic models of water flow to the single root and macroscopic models of water harvesting by the whole root area. The purpose of this study was to evaluate some macroscopic models under water stress conditions. For this purpose, experiment with three irrigation treatments I1, I2, I3, 100, 80, 60% water requirement, respectively, in a completely randomized design with three replications on summer maize cultivar (KSC-704) in Ahvaz climatic conditions in summer 2018. also treatments without cultivation were used to measure evaporation from the soil surface. To evaluate the models used (Fedes et al. 1978, van Genuchten 1987, Dierksen et al. 1993 and Homay 1999) from four statistical indices of coefficient of explanation (R2), root mean square error (RMSE), normalized error (NMSE) and mean relative error percentage (MRE) were used. The results showed that for I2 and I3 treatments, Van Genuchten (1987) model with statistical indices (R2 = 0.58, RMSE = 0.10), for I2 treatments, Van Genuchten& Co (1987) model with Statistical analysis (R2 = 0.35 and RMSE = 0.10) and for the I3 treatment, van Genuchten's (1987) model with R2 = 0.51 and RMSE = 0.10 compared to other models had the best fit with the measured data.

Keywords


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