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COMBINED OPTIMIZATION OF SOYBEAN WATER PRODUCTIVITY AND CROP YIELD BY MULTI‐OBJECTIVE GENETIC ALGORITHM (MOGA)
Author(s) -
Babazadeh Hossein,
Tabrizi Mahdi Sarai
Publication year - 2013
Publication title -
irrigation and drainage
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.421
H-Index - 38
eISSN - 1531-0361
pISSN - 1531-0353
DOI - 10.1002/ird.1743
Subject(s) - yield (engineering) , irrigation , crop , productivity , genetic algorithm , mathematics , completely randomized design , crop yield , agricultural engineering , agriculture , agronomy , microbiology and biotechnology , mathematical optimization , engineering , statistics , biology , economics , materials science , metallurgy , macroeconomics , ecology
One of the meta‐heuristic algorithms for multi‐objective optimization under conditions where there are more than one objective in research is the multi‐objective genetic algorithm (MOGA). The goal of this research is combined optimization of water productivity (WP) and crop yield under deficit irrigation management conditions. The data used in this study are the result of an experimental agricultural design conducted in the form of a randomized complete blocks design in three replications and seven water stress treatments at different growth stages in 2010 in Karaj. The results of this research show that in combined optimization under the first scenario conditions, the amounts of soybean optimum crop yield and optimum WP are 3830 kg ha −1 and 0.529 kg m −3 respectively. Also in combined optimization under the second scenario conditions, the amount of optimum crop yield and WP are 3840 kg ha −1 and 1.12 kg m −3 respectively. Copyright © 2013 John Wiley & Sons, Ltd.