Calibrating RZWQM2 model for maize responses to deficit irrigation

Optimizing Et-Based Irrigation Scheduling for Wheat and Maize with Water Constraints

“…Actual irrigation amount is calculated from a percentage of ET c less the rainfall amount occurring between irrigation events and is also constrained by soil water holding capacity. Based on the results of an ETbased deficit irrigation experiment, Ma et al (2012) found that the RZWQM2-simulated PET was consistent with the PET estimated from the reference ET and crop coefficient, and the simulated irrigation amounts and crop yield for these irrigation schedules were also in good agreement with measured data. The irrigation interval and seasonal amount limit can be set as inputs for the ET-based irrigation management (Fang et al, 2010b(Fang et al, , 2014aand Yu et al, 2006).…”

Optimizing Irrigation Rates for Cotton Production in an Extremely Arid Area Using RZWQM2-Simulated Water Stress

“…More functions have been incorporated into RZWQM2 over the years, and the model can now be applied in simulating evapotranspiration, infiltration and soil water redistribution, subsurface drainage, organic matter and nutrient (N) cycling, as well as the fate and transport of pesticides. Ma et al (2012b) verified the feasibility of applying RZWQM2 in simulating the response of maize growth to irrigation in Colorado; they concluded that the parameterized RZWQM2 was capable of simulating crop growth under different irrigation treatments, and the results could be used to schedule irrigation. Sassendran et al (2010) used RZWQM2 to predict the rotation effect on crop production under semiarid conditions based on 14 years of observations of crop yield and biomass in a wheat-corn-millet rotation system.…”

“…Using this model, Qi et al (2013) estimated growing season ET p for spring wheat (Triticum aestivum L.) grown in Sidney, Montana, to be 558 mm and suggested that an additional 323 mm of irrigation water should be applied to meet the crop's water consumption in this region. Ma et al (2012b) applied a parameterized RZWQM2 model to simulate maize responses to irrigation amounts representing specific percentages of estimated crop ET p and demonstrated the feasibility of using the model to schedule irrigation based on crop water requirements. Fang et al (2010) evaluated RZWQM2 in simulating crop yield and soil water balance responses to different irrigation treatments and investigated irrigation strategies to reach high yield and WUE based on model simulation.…”

Simulation of Wheat Yield Using the RZWQM as Affected by Supplemental Irrigation in the North Western Coast of Egypt

“…Moreover, the RZWQM2 adequately simulated crop yield and biological yield in response to various supplemental irrigation practices, this is clear from the data presented in Table 3 that the highest grain yield and total biomass were recorded when the crop received three irrigation treatments, Many studies addressed the successful use of the RZWQM in simulating the crop growth under water stress conditions and irrigation scheduling (Ma et al, 2003, 2012band Fang et al, 2010, 2014. S yield is the simulated yield; M yield is the measured yield; S T.biomass is the simulated total biomass; M T.biomass is the measured biomass, HI is harvest index; T0 is rainfed only, T1 is one irrigation 50 mm at tillering, T2 is one irrigation 50 mm at booting, T3 is one irrigation 50 mm at filling, T4 is two irrigations (100 mm) at tillering and booting,T5 is two irrigations (100 mm) at tillering and filling, T6 is two irrigations (100 mm) at booting and filling and T 7 is three irrigations (150 mm) at the three stages of tillering, booting and filling, .…”