JWSS - Journal of Water and Soil Science

The objective of this research was the development of a hydraulic-economic simulation-optimization model for the design of basin irrigation. This model performed hydraulic simulation (design of basin irrigation), using Volume Balance model, economic simulation through calculating sum of four seasonal costs and optimization using NSGAII multi-objective meta-heuristic algorithm. For programming, MATLAB programming software was applied. The optimizations of functional, multi-dimensional, static, constraint, continuous, multi-objective and meta-heuristic were applied for the optimization of the objective functions. Decision variables selected from simulation inputs were calculated in such a way that the  hydraulic objective function (minimizing linear combination of seven performance indicators) and economic objective function (total seasonal cost based on sum of water cost, labor cost, basin preparing cost and channel drilling cost) were minimized. Data of one the experimental field was used for the purpose of simulation. After initial simulation, optimization of the experimental field was done using NSGAII multi-objective meta-heuristic algorithm with tuned parameters. Optimization using the suggested model shoed the decrease (improvement) of objective functions rather than initial simulation performance. As a result, the suggested model could be regarded as is a specialized tool for basin irrigation, showing a good performance, despite its simplicity.

The application of simulation-optimization models is a valuable tool for selecting the appropriate cropping pattern. The main objective of this research is to develop a two-objective simulation-optimization model to determine the pattern of cultivation and water allocation. The model performs the optimization with the multi-objective metamorphic algorithm (MOALO) after simulating different states of the cultivation pattern. The decision variables including land and water allocated to ten-day periods of plant growth were designed in a way that the minimum utilization of water resources and economic maximization were identified as target functions. The developed model was used to simulate and optimize the cultivation pattern with an area of ​​5500 hectares and water allocation of Semnan plain with renewable water at the rate of 60.8 million cubic meters. Harvesting scenarios of 80 (GW80) and 100 (GW100) percent of renewable groundwater and scenarios of change in existing cropping pattern of 30 (AC30) and 60 (AC60) percent were considered and each scenario was simulated with the MOALO algorithm. Optimization using the proposed model in four scenarios improved the water and economic objective functions compared to the initial simulation performance. The results showed that the four proposed scenarios were obtained by minimizing the water objective function and maximizing the economic objective function relative to the current situation (simulation). In general, the proposed model had a good performance despite its simplicity, which is a specialized tool to optimize the crop pattern with water allocation.

In recent years, indirect methods such as remote sensing and data mining have been used to estimate soil salinity. In this research, the electrical conductivity of 94 soil samples from 0 to 100 cm was measured using the Hypercube technique in the Saveh plain. 23 types of input data were used in the form of topographic and spectral categories. Land area parameters such as the Topographic Wetness Index (TWI), Terrain Classification Index (TCI), Stream Power Index (STP), Digital Elevation Model (DEM), and Length of Slope (LS) were considered as topographic inputs using Arc-GIS and SAGA software. Also, salinity spatial and vegetation indices were extracted from Landsat 8 images and were considered spectral inputs. The GMDH neural network was used to model salinity with a ratio of 70% for training and 30% for validation. The results showed that the soil salinity values were between 0.1 and 18 with mean and standard deviation of 5 and 4.7 dS/m, respectively. Also, the results of modeling indicated that the statistical parameters R², MBE, and NRMSE in the training step were 0.80, 0.06, and 42.1%, respectively. The same values in the validation step were 0.79, 0.13, and 48.7%, respectively. Therefore, the application of spectral, topographic, and GMDH neural network indices for modeling soil salinity is effective.

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.