Investigating the effect of irrigation water salinity on wheat irrigation scheduling using CWSI and WDI indices (Case study of Iranshahr)

Document Type : Research Paper


1 PhD student in Irrigation and Drainage/Department of Water Engineering/ Faculty of Water and Soil/ University of Zabol/ Zabol/ Iran

2 Scientific staff in Department of Water Engineering/ Faculty of Water and Soil/ University of Zabol/ Zabol/ Iran

3 Soil and Water Research Institute/ Karaj/ Iran


The presence of salts in irrigation water and high evapotranspiration rate in Sistan and Baluchestan cause the accumulation of salt in the soil and as a result increases the osmotic force in soil. One method for crop irrigation planning is the use of Idso crop water stress index (CWSI) and Moran water deficit index (WDI). In order to investigate the effect of irrigation water salinity on CWSI and WDI and also wheat irrigation planning, an experiment was performed with three different irrigation water quality in Iranshahr, through the 1398-99 crop year. The experiment was performed as a randomized complete block design with 4 replications and 3 treatments including (1) irrigation water with salinity of 0.7, (2) irrigation water with salinity of 2.5 and (3) irrigation water with salinity of 5.2 dSm-1. The results showed that irrigation water with salinity of 0.7 and 2.5 dSm-1 have no significant difference in terms of yield and water use efficiency. However, the use of irrigation water with a salinity of 5.2 dSm-1 caused a significant reduction in yield and water use efficiency at a 1% level. Moreover, the salinity of irrigation water increased the upper baseline in the Idso diagram and the upper side of the proposed trapezoid of Moran, but it had no effect on the baseline (plant transpiration limit under standard conditions). As a result, according to the definition of CWSI and WDI indices, these two indices decreased by 19% and 22%, respectively compared to the control treatment. The average optimal CWSI and WDI in the first (non-saline) treatment were 0.39 and 0.38, respectively, and they were 0.33 and 0.32 in the very saline treatment, respectively. This showed a decreasing trend of irrigation frequency with increasing salinity of irrigation water.


Main Subjects

Extended Abstract


Wheat irrigation planning in Sistan and Baluchistan province is of particular importance, firstly because of its large cultivated area and secondly because of the variety of irrigation water quality. With wheat irrigation planning, it becomes possible to estimate water consumption, manage water allocation and improve its productivity. It is possible to determine the irrigation time using three methods: soil and climate Indices, water balance and plant indices. Using the plant index is more accurate and faster than the other two methods due to the direct use of the plant itself. Using the surface temperature of the vegetation is the most efficient method to know the water stress of the crop. One of the reliable indicators based on the plant stress is the idso's Crop Water Stress Index (CWSI). Moran et al. also presented the Water Deficit Index (WDI) for non-dense vegetation conditions, in which the surface temperature of the soil and plant are measured simultaneously. The ineffectiveness of CWSI in irrigation planning of crops with no full vegetation cover, as well as being time-consuming and espensive compared to WDI on one hand and the salinity of the irrigation water in the region on the other hand caused that in this research, while examining the changes of the upper and lower baselines in the idso and Moran diagrams, the CWSI and WDI indices are investigated under saline irrigation water. It seems that by determining the optimal CWSI and WDI indices in the months of February, March and April, (months with full vegetation), an effective step can be taken in determining the optimal irrigation time for wheat in Iranshahr region.


Materials and Methods

In order to investigate the effect of irrigation water salinity on the upper and lower baselines in the Idso and Moran diagram as well as the index (CWSI) and (WDI), an experiment in a randomized complete block design was caried out with three irrigation treatments including (1) irrigation with water of EC= 0.7 dS/m (control treatment), (2) irrigation with water of EC= 2.5 dS/m and (3) irrigation with water of EC= 5.2 dS/m in 4 replications in the fields of Iranshahr Agricultural and Natural Resources Research Center and nearby lands with an area of at least 2 ha for each treatment. To draw the idso diagram and calculate the CWSI, field observations such as vegetation temperature (Tc) was measurd using an infrared thermometer, air temperature (Ta) using a conventional thermometer, and relative humidity (RH) using a facial wet and dry thermometer. To draw Moran's trapezoid diagram and calculate WDI, Normalized Difference Vegetation Index (NDVI) and Vegetation Temperature (Tc) according to the instructions from different parts of the field (each treatment) were obtained from the NDVI and Tc maps through Landsat 8 satellite images. At the end of the experiment, while examining the effect of irrigation water salinity on the upper and lower baselines in the Idso and Moran diagrams, the optimal values of CWSI and WDI indices in the control treatment were calculated and the basis for evaluating CWSI and WDI for treatments (2) and (3) was placed.


Results and Discussion

The results of the analysis of variance showed that the difference in the salinity of the irrigation water of the second treatment (EC= 2.5 dS/m) compared to the control was not enough to create a significant difference in the performance and efficiency of water consumption. However, the third treatment (EC= 5.2 dS/m) created a significant difference at the level of 1% in the performance and efficiency of water consumption. These results showed that the amount of water absorption by the roots in treatment irrigated with more salty water (5.2 dS/m) is lower than the control treatment and more water must be consumed to achieve the equivalent performance of the control treatment. In other words, the frequency of irrigation in this treatment should be less than the frequency of irrigation in control. The results showed that the values of CWSI and WDI in March were lower than in February and April, and in this month the irrigation treatment with salinity of 2.5 dS/m had the lowest value. With the decrease of these two indicators, it can be concluded that evapotranspiration has increased. Therefore, it can be said that in Iranshahr region, wheat had the most evapotranspiration in March. Comparison of CWSI and WDI in two salinity treatments with control using the coefficient of determination, root mean square error, mean absolute error and mean bias error showed that there is a close relationship between salinity and CWSI and WDI indices.



The results of this research showed that firstly, there is a high correlation between CWSI and WDI, which shows the possibility of using remote sensing technique in crop irrigation planning. Second, in the conditions of using saline water, it is possible to determine the irrigation time of wheat by using CWSI and WDI indices. On the other hand, any type of stress, including water and salinity stresses, has an effect on the upper baseline of the Idso and Moran diagrams, but not on the lower baselines.


Keywords: Crop water stress index, Idso, Irrigation frequency, Satellite imagery, Water deficit index

Agricultural statistics of 2018: horticultural products (third volume). 2019. Publications of the Ministry of Agricultural Jihad, Planning and Economic Deputy, Information and Communication Technology Center, Tehran. (In Persian)
Alghory, A. and Yazar, A. (2019). Evaluation of crop water stress index and leaf water potential for deficit irrigation management of sprinkler-irrigated wheat. Irrigation Science, 37, 61–77
Alizadeh, A. (1998). Principles of designing irrigation systems. (3nd Ed). Publishing of Astan Quds Razavi, Mashhad. pp: 544. (In Persian)
Banzragch, B. M., Damdinpurev, N. M. and Batzorig. E. M. (2018). Monitoring Drought Stress Index in Wheat Field of Mongolia Using Remote Sensing. 14th international conference on precision agriculture June 24th-27th.
Bozkurt Çolaka, Y. Yazarb, A., Çolakc, I., Akçaa, H. and Duraktekina, G. (2015). Evaluation of Crop Water Stress Index (CWSI) for Eggplant under Varying Irrigation Regimes Using Surface and Subsurface Drip Systems. Agriculture and Agricultural Science, Pp: 372 – 382. (
Fashaei, M. (2012) Estimation of Moisture Deficiency Index and Estimation of Soil Moisture Using Remote Sensing (Case Study: Mashhad Plain). Master of Science, Ferdowsi University, Mashhad, Iran. (In Persian)
Girolimetto, D. and Venturini, V. (2013). Water Stress Estimation from NDVI-Ts Plot and the Wet Environment Evapotranspiration. Advances in Remote Sensing, 2, 283-291.
Idso, S. B. (1982). Non-water-stressed baselines: A key to measuring and interpreting plant water stress. Agricultural Meteorology27(1-2), 59-70.
Idso, S. B., Jackson, R. D., Pinter Jr, P. J., Reginato, R. J., & Hatfield, J. L. (1981). Normalizing the stress-degree-day parameter for environmental variability. Agricultural meteorology24, 45-55.
Jolivot A., Labbé S. and Lebou rgeois, V. (2012). Con tribution of remote sensing i n analysis of crop water stress. Casestudy on durum wheat. Options Méditerranéennes, 67, 209- 216.
Khorsand, K. H., Rezaverdinejad, V., Asgarzadeh, H., Majnooni-Heris, A., Rahimi, A., Besharat, S. and Sadraddini, A. (2021). Linking plant and soil indices for water stress management in black gram. Scientific report, 11 (869),
Moran M. S., Clarke T. R., Inoue Y., and Vidal, A. (1994). Estimating crop water deficit using the relation between surface air temperature and spectral vegetation index. Remote Sensing of Environment, 49, 246–263.
O’Toole, J. C. and Hatfield, J. L. (1983). Effect of wind on the crop water stress index derived infrared thermometry. Agronomy Journal, 75(5), 811–817.  https :// j1983 .00021 96200 75000 50019 x (1983).
Saeedinia, M., Hooshmand, A., Boroomand Nasab, S., Soltani Mohamadi, A. and Andarzian, B. (2014). Investigation of the Effect of Water Irrigation Salinity on The Performance of CWSI Index Under Climatic Conditions of Ahvaz. Irrigation and Engeneering Science, 39(4),1-12. (In Persian)
Sezen, S. M., Yazar, A. and Daşgan, Y., Yucel, S., Akyıldız, A., Tekin, S., and Akhoundnejad, Y. (2014). Evaluation of crop water stress index (CWSI) for red pepper with drip and furrow irrigation under varying irrigation regimes. Agricultural Water Management, 143, 59-70.
Tanriverdi, C., Atilgan, A., Degirmenci, H. and Akyuz, A. (2017). Comparasion of crop water stress index (CWSI) and water deficit index (WDI) by using remote sensing (RS). Polish Academy of Sciences, Cracow Branch, pp. 879–894.
Wang, W., Huang, D., Wang, X. G., Liu, Y. R. and Zhou, F. (2011). “Estimation of Soil Moisture Using Trapezoidal Relationship between Remotely Sensed Land Surface Temperature and Vegetation Index,” Hydrology and Earth System Sciences, 15(5), 1699-1712.
Yang, Z., Wen-bin, W.U., Di, L., and Ustundag, B. (2017). Remote sensing for agricultural applications. Journal of Integrative Agriculture, 16(2), 239-241.
Xin, H., Peiling, Y., Shumei, R., Yunkai, L., Guangyu, J., & Lianhao, L. (2016). Quantitative response of oil sunflower yield to evapotranspiration and soil salinity with saline water irrigation. International Journal of Agricultural and Biological Engineering9(2), 63-73.