Optimizing Well Water and Wastewater Blending Ratios for Maximizing Forage Maize Yield Using Genetic Algorithm | ||
| پژوهش آب در کشاورزی | ||
| Articles in Press, Accepted Manuscript, Available Online from 05 May 2026 | ||
| Document Type: Research Paper | ||
| DOI: 10.22092/jwra.2026.371395.1101 | ||
| Authors | ||
| Narjes Shakeri-Parkoohi1; Mohammad Mirnaseri* 2; Mojtaba KhoshRavesh1; Reza Delir-Hasan nia3 | ||
| 1Water Engineering Department, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University. | ||
| 2Assistant Professor of Water Engineering Department, Faculty of Agricultural Engineering, Sari Agricultural Sciences and Natural Resources University. | ||
| 3Water Engineering Department, Faculty of Agricultural Engineering, Tabriz University. | ||
| Abstract | ||
| Optimization of irrigation water quality parameters is a key strategy for enhancing forage maize yield and managing limited water resources. This study aimed to determine the optimal combination of water quality parameters for maximizing forage maize (Zea mays L.) yield using Genetic Algorithm. Field data were collected from 45 experimental units over three growing seasons in Mazandaran province, Iran. Water quality parameters including electrical conductivity (EC), sodium (Na⁺), calcium (Ca²⁺), magnesium (Mg²⁺), and sodium adsorption ratio (SAR) were measured. After developing three different regression models, the interactions model was selected as the superior model with an adjusted R² of 0.9993. Parameter optimization was performed using Genetic Algorithm with an initial population of 50 chromosomes. The optimization results revealed that the optimal parameter combination consisted of electrical conductivity 1.18 dSm-1, sodium 1.65 meq/L, sodium adsorption ratio 1.04, calcium 2.86 meq/L, and magnesium 41.60 meq/L. This combination yielded a predicted fresh biomass yield of 26.901 ton/ha, showing 10% improvement over the best existing treatment and 27.30% improvement over the mean yield. The developed model demonstrates high predictive accuracy, and Genetic Algorithm proves to be an efficient tool for multi-parameter optimization of irrigation water quality for forage production. This approach can be implemented in operational management of forage maize fields to achieve optimal biomass yield. | ||
| Keywords | ||
| Multi-objective Optimization; Nonlinear Regression Model; Irrigation Water Quality; Metaheuristic Algorithm; Sustainable Water Management | ||
|
Statistics Article View: 7 |
||
Journal management system. designed by sinaweb