TY - JOUR
T1 - Selecting Agricultural Best Management Practices for Water Conservation and Quality Improvements Using Atanassov's Intuitionistic Fuzzy Sets
AU - Hernandez, E. Annette
AU - Uddameri, Venkatesh
N1 - Funding Information:
Acknowledgements This material is based upon work supported by the Center for Research Excellence in Science & Technology-Research on Environmental Sustainability of Semi-Arid Coastal Areas (CREST-RESSACA) at Texas A&M University–Kingsville through a cooperative agreement (HRD #0206259) with the National Science Foundation (NSF). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author and do not necessarily reflect the views of the National Science Foundation. The authors would like to thank Garret Engelking (Refugio County Water Conservation District), William Blackwell (United State Department of Agriculture), Carlos Fernandez (Texas Agricultural Experiment Station), Kim Jones (Texas A&M University–Kingsville) and Andy Garza (Texas State Soil and Water Conservation Board) for their insights in best management practice selection and implementation. In addition, the constructive comments provided by anonymous reviewers greatly enhanced this paper.
PY - 2010/12
Y1 - 2010/12
N2 - 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.
AB - 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.
KW - BMPs
KW - Fuzzy sets
KW - Imprecision
KW - Multi-criteria decision making
KW - Vagueness
UR - http://www.scopus.com/inward/record.url?scp=78249237529&partnerID=8YFLogxK
U2 - 10.1007/s11269-010-9681-1
DO - 10.1007/s11269-010-9681-1
M3 - Article
AN - SCOPUS:78249237529
SN - 0920-4741
VL - 24
SP - 4589
EP - 4612
JO - Water Resources Management
JF - Water Resources Management
IS - 15
ER -