Development of a dairy cattle drinking water quality index (DCWQI) based on fuzzy inference systems

Abstract

The critical role of water quality for all animals, and especially dairy cattle, the dairy products of which are consumed by humans, raises the need for an index which represents well the quality of water consumed by dairy cattle. Noting the high subjectivity and inappropriate classifications that traditional methods apply to development an index, we aimed to develop a better index that measures the quality of drinking water supplied to dairy cattle (DCWQI11dairy cattle water quality index (DCWQI).) based on fuzzy logic. Using fuzzy logic enabled us to capture experts’ knowledge and to simulate the human's way of thinking in the design of the index. Our approach avoided the shortcomings of the previous models. We selected 20 parameters that available literature determined were critical to assessing the quality of water for dairy cattle to drink due mainly to their potential impacts both on dairy cattle and human health. These parameters were: dissolved oxygen (DO), biochemical oxygen demand (BOD), pH, temperature, total dissolved solids (TDS), turbidity, fecal coliform, heterotrophic plate count, hardness, alkalinity, arsenic, lead, mercury, nickel, cadmium, chromium, total phosphorous, H2S, nitrate, and fluoride. We used trapezoidal membership functions and the final ruleset consisted of 550 rules. Mamdani inference system captured experts’ knowledge and experience; center-of-gravity method was used to defuzzify the results. To evaluate the index performance, we conducted a case study of Karun River employing the water quality data from six sampling stations along the river over the period of 2007–2010 and compared the results to those from the National Sanitation Foundation (NSF) water quality index (WQI). Our study found that the water quality of Karun River lies in the low to medium range (annual mean index values of 38–55). In addition, the values from the fuzzy DCWQI were generally lower than the values from the NSF WQI, mainly because the DCWQI included heavy metals in its index, while the NSF WQI did not. Results of the present study suggest that DCWQI can be considered as a comprehensive tool for assessing the quality of water for dairy cattle drinking purposes and can be reliably used for that objective.