Functional properties of irrigated cotton under urban treated wastewater using an intelligent method

Abstract

The use of urban treated wastewater for agriculture is one of the most important parts of unconventional water use in arid and semi-arid regions, but the proper situation for its application needs to be considered. For this purpose, a study aimed at comparing fve levels of water requirement including well water (control) (T1), urban treated wastewater (T2), 50% well water combination and 50% urban treated wastewater (T3), alternating irrigation between well water and urban treated wastewater each watering (T4), and combination of 33% well water and 66% urban treated wastewater (T5) in a randomized complete block design with three replications on water use efciency and cotton yield. The study was conducted in a selected farm located in Torbat-Heydarieh southeastern Iran during two cropping years (2013 and 2014). Then, a hybrid tree growth optimization algorithm (TGO) and adaptive neuro-fuzzy inference system (ANFIS) were used to predict cotton yield from four independent variables: soil characteristics, well water irrigation, urban treated wastewater irrigation, and meteorological data. Experimental treatments signifcantly altered soil chemistry. Cottonseed weight, cotton yield, and the number of bolls increased during the second year of treatments. A Duncan’s test of the mean showed that T3 signifcantly outperformed the other treatments measured as cottonseed weight, cotton yield, number of bolls, and water use efciency. Overall, treatments utilizing treated wastewater outperformed the control, irrigation with well water. Additionally, based on the modeling results irrigation with an equal ratio of the well and treated wastewater resulted in improving soil and cotton growth conditions and yield during the study.