Masoud Mohammadi; K Davary; Bizhan Ghahraman
Abstract
Considering limitations of agricultural productions in arid and semi-arid regions, optimization of irrigation depth and leaching is very important. In this study, calibrated and validated AquaCrop model was used in order to optimize irrigation water depth and leaching for two varieties of winter wheat ...
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Considering limitations of agricultural productions in arid and semi-arid regions, optimization of irrigation depth and leaching is very important. In this study, calibrated and validated AquaCrop model was used in order to optimize irrigation water depth and leaching for two varieties of winter wheat (Ghods and Roshan) in Birjand region and one variety of wheat (spring Roshan) in Mashhad region. For winter wheat, irrigation treatments included 125%, 100%, 75% and 50% of water requirement and water salinities of 1.4, 4.5, and 9.6 dS/m for winter wheat. For spring wheat, irrigation treatments consisted of 100%, 90%, 65%, and 40% of water requirement and water salinities of 0.5, 0.9, 5.25, 8.6, and 10 dS/m. The coding written in Matlab program was linked to the AquaCrop in order to achieve the optimized values of irrigation and leaching in the land constraint conditions. The optimization results showed that net profit for the best irrigation and leaching management at all salinity levels and different wheat varieties, except for salinity levels of 8.6 and 10 dS/m in the spring Roshan variety and level of 9.6 dS/m in the winter Roshan variety, was more than the current management in field conditions. The increases in profits in optimal management compared to the current management for Ghods variety at the salinity levels of 1.4, 4.5, and 9.6 dS/m were 51.4%, 78.9%, and 142.5%, respectively. For the same salinity levels for Roshan variety, the increments were 42.7%, 20.8% and -0.3%, respectively. The increase in profits in optimal management compared to the current management for the spring Roshan variety at the salinity levels of 0.5, 0.9, 5.25, 8.6 and 10 dS/m, were 5%, 13.2%, 34.3%, -27.7%, and -51.4%, respectively. In general, the results show that in the regions where drainage problem due to irrigation water is an important environmental problem and causes dissatisfaction among the downstream farmers, applying less water and accepting negligible decrease in the benefits (minimum 0 and maximum 29%) could resolve the problem.
m m; k d; b gh; h a; a h
Abstract
Crop growth simulation models have been developed for predicting the effects of water and salinity on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for different regions using the data generated from field. This study was carried in Mashhad ...
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Crop growth simulation models have been developed for predicting the effects of water and salinity on grain and biomass yields and water productivity of different crops. These models are calibrated and validated for different regions using the data generated from field. This study was carried in Mashhad for two years (2010 and 2011) in order to evaluate the AquaCrop model under simultaneous salinity and water stress. Calibration was done using the data of 2009-2010 and validation with the data of 2010-2011.Results indicated that AquaCrop successfully simulated yield, biomass, water productivity, harvest index, soil moisture and salinity profiles for spring wheat under salinity and water-limiting treatments with high accuracy, although simulation of harvest index and soil salinity profiles were less accurate. Average value of normalized root mean square error (NRMSE), maximum error (ME), index of agreement (d), coefficient of the residual mass (CRM) and coefficient of determination (R2) in both the calibration and verification were 13.3 %, 36.1 %, 0.95, -0.072, and 0.87, respectively, for grain yield, while these measures were 12.59%, 34.46%, 0.92, 0.057, and 0.77, respectively, for biomass. Also, value of NRMSE, ME, d and CRM were 11.84 %, 25.72 %, 0.93, and 0.032, respectively, for soil moisture, while these measures were 26.25%, 58.5%, 0.91 and -0.12, respectively, for soil salinity. Sensitivity analysis revealed that crop transpiration coefficient (KC-Tr), normalized crop water productivity (WP*), reference harvest index (HIO), volumetric water content at field capacity, soil water content at saturation[S1] , and air temperature were the most sensitive parameters. Although the accuracy of the model simulation decreased with increasing salinity and water stress, AquaCrop can be a valuable model for simulating spring wheat yield and soil water content and salinity in Mashhad region, because the model requires few input data which can be readily available or easily collected. [S1]This is probably”initial conditions” and not saturation.
Masoud Mohammadi; Bijan Ghahreman; Kamran Davari; Majid Vazifehdoost; Hamideh Noori
Abstract
Field studies to determine optimum amount of water required for maximum production are time-consuming and expensive. Therefore, in this study the agro-hydrological model SWAP 3.03 was used to simulate winter wheat yield under different qualities and quantities of irrigation water and to determine water-salinity-yield ...
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Field studies to determine optimum amount of water required for maximum production are time-consuming and expensive. Therefore, in this study the agro-hydrological model SWAP 3.03 was used to simulate winter wheat yield under different qualities and quantities of irrigation water and to determine water-salinity-yield optimum function. Irrigation treatments consisted of four water salinity levels (S1=0.7, S2=2, S3=4 and S4=6 dS/m), three amounts of water (W1=80, W2=100, and W3=120 mm), and six levels of management allowed depletion (MAD1=0.3, MAD2= 0.4, MAD3= 0.5, MAD4= 0.6, MAD5= 0.7 and MAD6= 0.8). Yield and water use efficiency values were determined in different modes and the best MAD value obtained was 0.5. Yield data were fitted to different forms of production functions (simple linear, logarithmic linear, quadratic and transcendental) and the best one was established based on sensitivity analysis. The maximum grain yield (6619 kg/ha) corresponded to W1S1MAD2 treatment and the minimum yield (2048 kg/ha) corresponded to W1S4 MAD3 treatment. The results showed that the quadratic production function was optimal for production and could be recommended. Investigation of the maximum values of error (ME) showed that the logarithmic linear and simple linear functions had the highest error. In the irrigation treatments, W1S1MAD3 and W1S1MAD4 with 0.61 kg /m3 had the highest water use efficiency. However, water use efficiency decreased when water stress and salinity increased. The iso-yield curve showed that by increasing amounts of irrigation, more saline water could be applied without a change in yield.