abdollah behmanesh

Safflower is one of the most important oil crops whose yield decreases under water stress. Therefore, determining its response to different irrigation water managements is very important. To do that, in this study, AquaCrop model was used to simulate three irrigation scheduling strategies of safflower for drip, subsurface drip and furrow irrigation methods. At first, AquaCrop was calibrated using data collected from a research farm located at 34˚ 21’ N and 47˚ 9’ E, in Kermanshah, Iran. In the first strategy, apply irrigation up to field capacity at a constant rate (1, 5, 10 and 20 days), in the second strategy, apply a constant amount of irrigation water (values of 10 to 50 mm for drip and subsurface drip irrigation methods and values up to 10 90 mm for furrow method at constant duration 2 and 4 days for drip and subsurface drip irrigation and 10 and 20 days for furrow) and in the third strategy apply water stress levels of 100, 66 and 33% during the growing season for all three Irrigation method was considered. The results showed that the highest yield, biomass and water productivity were obtained in the first and second strategies. Maximum values for yield and biomass were 3.7 and 12.2 fon.ha-1, respectively, and maximum water productivity was 0.54 kg.m-3 in both drip and subsurface drip irrigation and 0.69 kg.m-3 in furrow irrigation. As the second strategy is easier for farmers to implement, it is suggested that this method be used. Therefore, for drip irrigation and subsurface drip scenario, it is recommended to apply 10 mm of irrigation water with a period of two days, and apply 30 mm water irrigation with a period of 10 days.

Safflower is one of the most important oily plants whose yield decreases under water stress. Therefore, determining its response to different amounts of irrigation is very important. Hence,  the AquaCrop model for simulating safflower under different amounts of irrigation water was evaluated in this research. For this purpose, surface drip irrigation at three levels (T1, T2, and T3 represent 100, 66, and 33% of water requirement, respectively), furrow irrigation at three levels (T4: 100% water supply, T5: application of 50 mm of water at one time during flowering and T6: rainfed), and subsurface drip irrigation (T7) were considered. The results showed that the accuracy of this model for simulating yield (NRMSE <0.2), biomass (NRMSE <0.2) and water productivity (NRMSE <0.3) were in the categories of good, good and medium, respectively. In addition, the error of this model for simulating yield, biomass and water productivity was 22, 12, and 23%, respectively. AquaCrop efficiency was optimal for the three parameters so that the EF values ​​for these three parameters were more than 0.5. According to these results, the use of the AquaCrop model to simulate safflower is recommended.

AquaCrop model is one of the water-driven models that has been developed to simulate the growth of crops under different amounts of irrigation water. However, to use this model, it is necessary to perform calibration. For calibration, it is necessary to determine the sensitivity of changing in input parameters. Therefore, the present study performed to evaluate the sensitivity of AquaCrop in simulating safflower biomass to changes in crop growth parameters. So, normalized water productivity (WP*), maximum transpiration coefficient (KcTrx), initial canopy cover (CC0), crop growth coefficient (CGC) and crop reduction coefficient (CDC) was evaluated using Baven method. In this study, data collected from an agricultural research station in Kermanshah, Iran, was used. Data consisted of surface drip irrigation at three levels (T1, T2 and T3 represent the supply of 100, 66 and 33% of water requirement, respectively), furrow irrigation at two levels (T4: 100% water supply and T5: application of 50 mm irrigation water at the same time in flowering period) and rainfed (T6). The results showed that AquaCrop was the most sensitive to change WP*. The lowest sensitivity was assigned to CDC. The sensitivity of this model to CDC parameter changes was negative and for other treatments was positive. Therefore, increasing the amount of CDC decreased safflower biomass while increasing other parameters increased safflower biomass. The sensitivity of each parameter depended on the irrigation treatment. So that increasing the amount of irrigation water for WP* and Kc increased the sensitivity.