Showing 5 results for Swap
V. Khaksari, S. A. A. Moosavi, S. A. M. Cheraghi, A. A. Kamgar Haghighi, Sh. Zand Parsa,
Volume 10, Issue 2 (7-2006)
Abstract
Since performing field experiments for determining the optimum amount of water for soil desalinization is costly and time consuming, use of computer models in leaching studies has received more attention. However, the accuracy of the results of these models should be evaluated by comparison with the results of the field experiments. In this study SWAP and LEACHC models were used for the simulation of soil moisture profile and salinity, and the results were compared with those of a field leaching experiment. The SWAP model gave better results in simulating soil moisture movement and profile, compared to LEACHC model, but statistical indexes showed that both models produced satisfactory results in predicting soil moisture profile. LEACHC model gave better results in comparison to SWAP model for the prediction of soil salinity profile at different time, possibly because it takes into account different solute transport mechanisms such as advection, diffusion, dispersion and also chemical interactions such as adsorption, precipitation and dissolution. In spite of the differences between predicted and measured values of salinity in the initial stages of leaching process, both models were able to predict the trend of leaching process with an acceptable accuracy.
M. Navabian, M. Aghajani,
Volume 16, Issue 60 (7-2012)
Abstract
In Guilan province, Sefidrud River, as the main source of irrigating rice in Guilan province, has been subjected to increasing salinity and a decreasing discharge because of decreasing in the volume of sefidrud dam, diverting water upstream and entering different sewages into the river. This research tries to determine optimum irrigation depth and intermittent periods in proportion to salinity resistance at different growth stages using optimization- simulation model. After calibration, Agro-hydrological SWAP model was used to simulate different growth stages of rice. Optimization results were obtained for managing fresh and saline intermittent water, 8-day intermittent period, for salinity of 0.747 dS/m in sensitive maturity stage and salinity of 3.36 dS/m in resistant vegetative, tiller and harvest growth stages. It is suggested that the depth of irrigation water be 1, 3, 3 and 5 cm for vegetative, tiller, maturity and harvest stages, respectively. Comparing managements of irrigation and saline based on the resistance of different growth stages to salinity and exploitation of irrigating water with a constant salinity during growth periods of the plant showed that irrigation management based on resistance of different growth periods of the plant to salinity causes rice yield to be improved by 23percent.
M. Navabian, M. Aghajani, M. Rezaei,
Volume 18, Issue 70 (3-2015)
Abstract
Water Uptake by the root under salinity and water Stress in unsaturated soils was investigated through mathematical equations in three Groups of additive, multiplicative and non-consumptive. This study was an effort to assess six water uptake functions of van Genuchten (additive and multiplicative), Dirksen et al., Van Dam et al, Skaggs et al, and Homaee, for Rasht Hashemi rice under salinity and water stress conditions. Based on field observations of Hashemi Rasht rice in 1386 and 1389, crop growth simulation model of SWAP was calibrated and validated with a correlation coefficient of 0.97 and 0.95, respectively. Water Uptake Reduction Models' parameters were determined by the simulated data using SAS statistical software. Results showed that for the anticipated reduction of Water Uptake in rice water and salinity stress conditions for Rasht Hashemi rice, Homaee model is best.
M. Yazdekhasti, M. Shayannejad, H. Eshghizadeh, M. Feizi,
Volume 22, Issue 3 (11-2018)
Abstract
Due to the dry climate and limitation of fresh water resources, using fresh and salt water is a solution for crop production under salinity conditions. This study was conducted at Isfahan University of Technology as a randomized complete block design with three replications and five irrigation management treatments in 2014. The treatments included irrigation with saline water (with the salinity of 5 dS/m, based on the relative yield of 75%), irrigation with fresh water (municipal water), alternate irrigation (irrigation with saline water and the next irrigation with fresh water), conjunctive irrigation (half of irrigation with saline water and the other one with fresh water) and irrigation with fresh water to reach the raceme stage, and irrigation with saline water. The maximum wet yield, dry yield and grain yield were related to the fresh water treatment with 4.14, 2.45 and 0.588 kg/m2 and the minimum values were obtained for water their water treated with 1.34, 0.765 and 0.0957 kg/m2 respectively. The conjunctive treatment had the highest yield after fresh water treatment. The various statistical indices showed that this model could be used for sorghum in Isfahan. The determination coefficient for yield was 0.65.The priority of model for yield simulation was salt water at the last stage, alternate irrigation, saline water, conjunctive irrigation and fresh water treatments, respectively.
A. Sarkohaki, A. Egdernezhad, S. Minaei,
Volume 25, Issue 1 (5-2021)
Abstract
Crop models evaluationin agriculture has been done by researchers. It helps them to determine the most appropriate crop model for the planning and simulation of crop response in different areas. Using can lead time and cost saving, helping to evaluate the effects of different situations on the crops yield, biomass and water use efficiency (WUE). Given the importance of the subject, this study was conducted for the accuracy and efficiency evaluation of AqauCrop and SWAP under three irrigation types (D: sprinkler irrigation with saline water, F: sprinkler irrigation with saline and fresh water, and S: surface irrigation) and five water qualities (S1: 2.5, S2: 3.2, S3: 3.9, S4: 4.6 and S5: 5.1 dS.m-1). NRMSE results showed that the accuracy of AquaCrop for the simulation of yield, biomass and WUE was 0.07, 0.09 and 0.07, respectively. For SWAP, these were 0.12, 0.04 and 0.13, respectively. According to EF, AquaCrop results for above-mentioned parameters were 0.60, 0.90 and -4.4, and SWAP results were 0.74, 0.73 and -2.0, respectively. So, AquaCrop accuracy and efficiency were better than those of SWAP for the simulation of corn yield and biomass.
