Selecting Agricultural Best Management Practices for Water Conservation and Quality Improvements Using Atanassov’s Intuitionistic Fuzzy Sets

Abstract

Improper agricultural practices can affect ground water through leaching, surface water through runoff, algae infestations, deforestation, and air quality through burning operations and ammonia emissions. These effects may be mitigated through the institution of best management practices. The utility of best management practices (BMPs) is recognized and being actively promoted by agricultural agencies; however, identifying a set of mandatory BMPs is inappropriate given variations between climactic, demographic and geographic regions as well as differences in farming practices. In this study, a multi-criteria decision making model based on Attanassov’s Intuitionistic Fuzzy Set (A-IFS) theory is introduced and its utility to rank agricultural best management practices is illustrated using a case-study from South Texas. Implementation of the A-IFS MCDM method to the South Texas region resulted in “irrigation scheduling” being ranked as the most preferred alternative, while “brush control/management” was the least preferred. The A-IFS MCDM approach was particularly suitable for prioritizing and ranking agricultural best management practices because decision makers often tend to have both likes and dislikes with regards to specific BMPs and for a given evaluation attribute. Not only does the A-IFS MCDM method provide a single composite score to rank the BMP alternatives, but the output of the A-IFS MCDM method also includes upper and lower bounds that help identify the uncertainties in the decision making process.