Modeling and optimization of yield and physiological indices of fodder maize (Zea mays L.) under the influence of vermicompost and irrigation percentage using response surface methodology (RSM)

Abstract

Today, balanced and optimal use of fertilizer and water are considered to be the most important factors in increasing the production of agricultural products. Drought is one of the most important factors limiting corn production in the world. The use of renewable resources and inputs such as vermicompost is one of the principles of sustainable agriculture. In this experimental study, the effect of vermicompost and irrigation on the yield and physiological indices of fodder maize was investigated. In the present study, RSM was used to model and optimize the yield and physiological indices of fodder maize under different conditions (fertilizer and water consumption). Three different amounts of vermicompost fertilizer (0, 2.5, and 5 tons/ha) and three different levels of irrigation (50, 75, and 100 %) were evaluated as independent variables on yield and physiological indices of fodder maize. The process variable was significant (P ≤ 0.01) in the form of a regression model for the response. By increasing irrigation rate from 50 to 100 % and vermicompost fertilizer from 0 to 5 tons/ha, the yield and physiological indices of fodder maize increased. The maximum fodder maize yield (79.50 tons/ha) was obtained in the treatment of 100 % irrigation and 5 tons of vermicompost fertilizer per hectare. The results showed that RSM was effective as an efficient method in modeling and optimizing fodder maize yield under the influence of vermicompost fertilizer and irrigation percentage.