Simulating Durum Wheat (Triticum turgidum L.) Response to Root Zone Salinity based on Statistics and Macroscopic Models

Document Type : Scientific - Research


1 Shahid Bahonar University of Kerman

2 University of Guilan


Salinity as an abiotic stress can cause excessive disturbance for seed germination and plant sustainable production. Salinity with three different mechanisms of osmotic potential reduction, ionic toxicity and disturbance of plant nutritional balance, can reduce performance of the final product. Planning for optimal use of available water and saline water with poor quality in agricultural activities is of great importance. Wheat is one of the eight main food sources including rice, corn, sugar beet, cattle, sorghum, millet and cassava which provide 70-90% of all calories and 66-90% of the protein consumed in developing countries. Durum wheat (Triticum turgidum L.) is an important crop grows in some arid and semi-arid areas of the world such as Middle East and North Africa. In these regions, in addition to soil salinity, sharp decline in rainfall and a sharp drop in groundwater levels in recent years has emphasized on the efficient use of limited soil and water resources. Consequently, in order to use brackish water for agricultural productions, it is required to analyze its quantitative response to salinity stress by simulation models in those regions. The objective of this study is to assess the capability of statistics and macro-simulation models of yield in saline conditions.

Materials and methods
In this study, two general approach of simulation includes process-physical models and statistical-experimental models were investigated. For this purpose, in order to quantify the salinity effect on seed relative yield of durum wheat (Behrang Variety) at different levels of soil salinity, process-physical models of Maas & Hoffman, van Genuchten & Hoffman, Dirksen et al. and Homaee et al. models were used. Also, statistical-experimental models of Modified Gompertz Function, Bi-Exponential Function and Modified Weibull Function were used too. In order to get closer to real conditions of growth circumstances in saline soils, a natural saline water was taken from Maharlu Lake, Fars province, Iran. This natural and highly saline water with electrical conductivity of 512 dS/m diluted with fresh water to obtain the designated saline waters required for the experimental treatments. The designed experimental treatments were consisted of a non-saline water and five salinity levels of 2, 4, 6, 8 and 10 dS/m with three replicates. Three statistics of modified coefficient efficiency (E'), modified index of agreement (d') and coefficient of residual mass (CRM) were used to compare the used models and to assess their performances.

Results and discussion
Comparing the relative performance of models based on statistical indices of Modified Coefficient Efficiency (E') and Modified Index of agreement (d') indicated that the nonlinear model of Homaee et al. is most accurate between process-physical models and Modified Gompertz Function is most accurate between statistical-experimental models. Comparison assessment of all models based on statistical index indicated that Homaee et al. model was the most accurate model for simulation of durum wheat yield. This is while the parameters of Homaee et al. equation is well-defined concept and is easily measurable, but in statistical-experimental models, parameters of each model have no biophysical concept and the absolute values of each parameter do not express any information about development status of the plant. So, the nonlinear model of Homaee et al. was chosen as the optimal model in this research.

Most of the plants such as wheat, are sensitive to salinity and by increasing the age, their sensitivity to salinity are reduced. Based on the obtained results of this study, by knowing and quantitative assessment of the dominant cultivars sensitivity of each region, as well as using appropriate simulation models, one can use brackish or saline waters to partly compensate fresh water shortage for scientific and extension Agricultural programs.


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