Optimizing Well Water and Wastewater Blending Ratios for Maximizing Forage Maize Yield Using Genetic Algorithm

Document Type : Research Paper

Authors

1 M.Sc. Student of Irrigation and Drainage, Department of Water Engineering, Faculty of Agricultural Engineering, University of Agricultural Sciences and Natural Resources, Sari, Iran.

2 Assistant Prof., Department of Water Engineering, Faculty of Agricultural Engineering, University of Agricultural Sciences and Natural Resources, Sari, Iran.

3 Associate Prof., Department of Water Engineering, Faculty of Agricultural Engineering, University of Agricultural Sciences and Natural Resources, Sari, Iran.

4 Associate Prof., Department of Water Engineering, Faculty of Agriculture, University of Tabriz, Tabriz, Iran.

10.22092/jwra.2026.371395.1101

Abstract

This study aimed to determine the optimal combination of water quality parameters for maximizing forage maize yield using Genetic Algorithm. This study collected 45 forage maize yield data points from five treatments in three replications over three cropping years 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 0.99. Parameter optimization was performed using Genetic Algorithm with an initial population of 50 chromosomes. The optimization results revealed that optimal parameter combination consisted of 1.18 dSm-1 electrical conductivity, 1.65 meq/L sodium, 1.04 sodium adsorption ratio, 2.86 meq/L calcium, and 41.60 meq/L magnesium. In fact, this study demonstrated that an optimal region exists between 75% and 100% treated wastewater, which can be achieved through more precise adjustment of ionic ratios. This optimal combination resulted in a predicted fresh biomass yield of 26.9 t.ha⁻¹, representing a 10% improvement over the best existing treatment and a 27.3% improvement over the mean forage maize 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

References

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Journal of Water Research in Agriculture
Volume 39, Issue 4
March 2026
Pages 321-340

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