JWSS - Journal of Water and Soil Science

Due to water use increasing, attention to optimal water resources allocation is needed. In recent decades, the use of intelligent evolutionary methods for optimization of water allocation was focused more by researchers. The aim of this study is to development on water resources planning model that determined the proper cultivation, optimal exploitation of groundwater and surface water resources although water allocation among crops is a way to minimize the adverse effects of dehydration and increase its revenue. In this study, for maximizing profits, estimating crop water requirements at different periods to optimize the management of cropping patterns and irrigation management in cultivation in Varamin irrigation network using a new evolutionary algorithm was called the water cycle. Then for validation of this method is that a new approach and ensure the integrity of its performance Its results are compared with a genetic algorithm model and linear programming as our base (R²=0.9963). The results showed that the area cropping pattern was not optimal and the area under cultivation of crops such as wheat, barley, tomatoes, Bamjan, melon, alfalfa reaches zero and the new paradigm of the largest area under cultivation to industrial goods and then was assigned cucumbers. While our revenues have increased about 11 percent. In addition to amount of water in different months remain in the network that can be used for many that such as injection into underground aquifers or other crops based on the amount of water available.

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.

In the present study, a model was developed using a system dynamics approach to simulate and optimize the profitability of crops of the Jofeyr (Isargaran) Irrigation and Drainage Network located in Khuzestan Province. To validate the results, the statistical indicators of root mean square error (RMSE), standard error (SE), mean biased error (MBE), and determination coefficient (R²) were used. To validate the simulation results of the benefit-cost ratio, the values of these indicators were obtained 0.25, 0.19, 0.005, and 0.96, respectively. Then, to determine the optimal cultivated area of the network and increase the profitability, the cropping pattern was determined both non-stepwise and stepwise in 2013 to 2017 cropping years. In the non-stepwise, the cultivated area of each crop changed from zero to 2 times of current situation. In stepwise, due to social and cultural conditions of inhabitants, this change was slow and 10% of the current situation every year. The analysis of the results showed the success of the model in optimizing and achieving the desired goals and the total benefit-cost ratio increased in all years both non-stepwise and stepwise. For example, in 2017 compared to 2016, production costs decreased by 7.1 percent and sales prices increased by 5.8 percent, and increased the benefit-cost in 2017 compared to the previous year. The results showed that the present model has good accuracy in simulating and optimizing the irrigation network, its cropping pattern, and defining other scenarios.

The agricultural sector depends largely upon water and energy resources to fulfill sufficient water for producing adequate food for the rapidly growing world’s population. It requires great effort to improve water and energy productivity for agricultural products to provide consumers’ health as well as environmental protection. In this study, the volume of irrigated water, crop yield, water productivity, and the consumed energy for onion crops irrigated with sprinkler or surface irrigation methods under farmer management were measured and compared. The measurements were recorded from 2020 to 2021, on 17 farms across Esfahan Province where onion was a main crop in the region. The measured data from the foregoing two irrigation methods were statistically analyzed using t-test and Pearson correlation coefficients. The outcomes revealed that the volume of irrigated water as well as crop yield was greater for surface irrigation method compared to sprinkler irrigation, and the differences were statistically significant. Moreover, water productivity for onions irrigated with a sprinkler irrigation system was significantly higher (p<0.01) in comparison with onions irrigated with the surface method. In addition, the results indicated a significantly direct correlation between the volume of irrigated water and onion yield, whereas a significantly indirect correlation was observed between the volume of irrigated water and water productivity. A significantly inverse correlation was found between the productivity of energy for irrigation and energy consumption; so, an increase in the energy for irrigation resulted in a decrease in energy productivity. Based on the results of this study, the sprinkler method is more effective than the surface for irrigation of onion.