Optimizing the sustainable use of water resources across local or regional systems is a complicated management challenge with significant implications. The world population in 2050 will be more than 9.5 billion people that it needs about 60% more food. Furthermore, the water availability for irrigation will decrease because of competing demands from other sectors, i.e., industrial, domestic, and hydroelectric generation. Pre-growing season estimation of agricultural outcomes such as water losses, net benefit and yield production can provide insights to beneficiaries and farmers to make optimum decisions. According literature review, the potential of machine learning algorithms for maize growth simulator was evaluated to develop a decision support system; In that case, three million data such as genotype, environment and management scenarios were incorporated for predicting yields in framework. The results provided a dynamic decision technique for pre-season management. In other case study, maize water use was measured based on the water balance in a 6-year field trial in the west-central Great Plains of the United States. Another research presented an optimization model to allocate irrigation water for maize using crop water production functions under deficit irrigation. The problem structure was determined based on maize and sunflower water productivity data collected during 2008–2011 growing seasons in eastern Colorado. The results show that deficit irrigation does improve farm income at moderate to high leasing prices. Moreover, the proposed model can define the crop and water leasing prices for which deficit irrigation is worthwhile. Therefore, increasing the efficiency of land and water resources can be considered as the main policy to find the sustainable decision. It is necessary to develop methods to improve soil moisture capacity and increase plant biomass in arid and semi-arid areas. This study was conducted to evaluate the impact of sugarcane compost on maize yield under the climate conditions of Gotvand plain in Khuzestan province, southwestern Iran. This agricultural residue can be used for improving the soil porous media structure to increase water availability in the root zone. Economic analysis of the application of sugarcane bagasse has been done at different levels of plant water supply needs, using the development of the growth simulation model and optimization technique.
In this study, simulation of maize yield and soil water content with AquaCrop software under full and deficit irrigation managements and sugarcane bagasse utilization was evaluated. This agricultural residue can be used for improving the soil porous media structure to increase water availability in the root zone. Economic analysis of the application of sugarcane bagasse at different levels of plant water supply needs has been done using the development of the growth simulation model and optimization technique.  A field experiment with three treatments of sugarcane compost application (0, 15, and 30 ton/ha) and four levels of crop water supply (50%, 75,% 100%, and 125% of total water requirement) was performed in three replications for two growing seasons (March to December 2019). Irrigation planning, cultivation costs, biomass, harvest index and canopy cover were measured at different time steps of the growing seasons to provide growth simulation models using AquaCrop software. The error statistics of calibration process were evaluated by root mean square error (RMSE), Nash– Sutcliffe efficiency index (NSE), normalized objective function (NOF), and mean absolute error (MAE) for the simulated and observed yield production values.
Calibration and verification process were carried out based on the two replications and one replication, respectively. The results presented in the scatter diagram and the coefficient of determination values close to one showed that the simulation model can be used to estimate the yield production in different scenarios of irrigation water distribution. The results showed that the use of sugarcane bagasse increases the soil water availability, harvest index and yield production at a confidence level of 1%. Optimal irrigation planning in this study was performed using the gray wolf optimization algorithm with the objective of maximizing crop yield. Optimal irrigation strategies reduce water losses, increase biomass and improve economic efficiency compared to existing conditions. Optimal planning based on daily information can determine the depth and timing of irrigation simultaneously. Increasing the water productivity and the net income for farmers and reducing the costs of bagasse application could be considered as positive implications of the developed simulation-optimization model. Future research could explore water management strategies on the farm by developing new goals for other crops or cultivation patterns.