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Sara bulukazari; Hossein Babazadeh; Nyazali Ebrahimipak; Seyed Habib Mousavi-Jahromi; Hadi Ramezani_etedali
Abstract
In exploitation of low-quality water in arid and semi-arid regions, irrigation management is essential to increase water use efficiency. Determination of crop-water-salinity production function is an essential tool for proper irrigation management. In this study, the AquaCrop model was first evaluated ...
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In exploitation of low-quality water in arid and semi-arid regions, irrigation management is essential to increase water use efficiency. Determination of crop-water-salinity production function is an essential tool for proper irrigation management. In this study, the AquaCrop model was first evaluated by considering 4 soil and water salinity levels and 4 deficit irrigation levels for the major cereal crops including wheat, barley, and corn in Qazvin Plain. The results showed that the coefficients of determination for wheat, barley, and corn yield were 0.97, 0.86 and 0.91, respectively. Therefore, the model can evaluate the performance in salinity and deficit irrigation conditions with a good approximation. To determine the optimal production functions of each crop, the results of the plant model were compared with three models of linear and nonlinear regression, and artificial neural network. The neural network model was able to estimate the performance compared to the AquaCrop model with lower error and higher correlation (0.99). These values in the linear function for wheat, barley, and corn were 0.98, 0.95, and 0.78 and in the nonlinear function as 0.92, 0.86 and 0.81, respectively. Also, the error calculated in the neural network method for wheat, barley, and maize was 40.16, 62.09, and 57.08 kg, respectively, which were less than the linear model by 75 %, 70 %, and 95 %; and less than the exponential model by 90 %, 85 %, and 93%, respectively. The best trained network for determining the water-salt production function for barley and wheat 5 Nero and for corn 7 Nero was introduced in the single layer structure. Sensitivity analysis on wheat and barley showed that this model had low sensitivity to irrigation and salinity parameters and only corn plant showed a moderate range sensitivity to salinity parameter.
H R; B A
Abstract
Food security, drought, environment protection, and industrial development have made efficient water resources management necessary. The concept of (virtual) water footprint (WF) has a considerable potential to help improve water resources management, especially in agriculture. In this research, WF of ...
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Food security, drought, environment protection, and industrial development have made efficient water resources management necessary. The concept of (virtual) water footprint (WF) has a considerable potential to help improve water resources management, especially in agriculture. In this research, WF of barley production in 15 major barley-producing provinces of Iran was estimated. WF consists of green (effective precipitation), blue (net irrigation requirements), gray (to dilute pollutants to the maximum acceptable concentration level) and white (irrigation losses) components. The results show that the average total WF in Iran’s national barley production for the period 2005-2011 is around 9172 MCM/year, of which the share of green, blue, gray and white WF were 37%, 19%, 17%, and 27 percent, respectively. Nearly 44 percent of total WF was related to the gray and white components, which is a considerable amount. Around 85 percent of the total WF in barley production is consumed in 15 major barley-producing provinces. Khorasan, Isfahan, and Fars provinces have the highest values of total WF in barley production, with 2364, 518 and 489 MCM/year, respectively. Among the 15 selected provinces, the average total WF in irrigated lands was estimated at around 3209 m3/ton with the contribution of green, blue, gray, and white components being 20%, 26%, 18%, and 36 percent, respectively. For rainfed lands, the average total WF was 2594 m3/ton with 89% and 11 percent of green and gray WF, respectively.