CROP PRODUCTION AND WATER PRODUCTIVITY SIMULTANEOUSLY OPTIMIZATION OF SOYBEAN PLANT USING TWO META-HEURISTIC ALGORITHMS

The maximum crop production achievement in arid and semi-arid regions is the main issue that requires the optimum use of different variables of crop and water. Therefore, this research has been carried out for simultaneous optimization of water productivity (WP) and for high crop productivity under deficit irrigation management conditions. An original data series has been used for this research from an experimental design that was conducted in the form of randomized complete blocks design with three replications and seven irrigation treatments of different growth stages during two conductive crop seasons 2010 and 2011. The genetic algorithm has been applied as a multi-objective (MOGA) and under two scenarios of the priority of objective functions. Also, in order to investigate the application of the simulated annealing algorithm (SA), in a combined optimizing of two objective functions of soybean WP and plant production using weight summation method, it has been converted to a single objective one. The results have shown that under the first scenario conditions, the optimum grain yield and optimum WP are 3,827 and 3,953 kg ha(-1) and 0.53 and 0.58 kg m(-3) ha(-1) in 2010 and 2011, respectively. The results in the combined optimization under the second scenario conditions show the amounts of optimum crop production and WP are 3,838.1 and 3,902.7 kg ha(-1) and 1.12 and 0.75 kg m(-3) ha(-1) in the two seasons, respectively. Comparison of the MOGA and SA results has indicated that MOGA has a better capability in simultaneous optimization of the two objective functions. Maximum crop production was 4446 kg ha(-1) for consuming 664.9 mm irrigation water. Also, the maximum WP was 0.82 kg m(-3) ha(-1) for consuming 375.8 mm irrigation water. Therefore, the dual-objective genetic optimization method can well optimize both objective functions and achieve the desired results in optimal grain yield and WP under constrained water resources.

Automated summary

This research aims to optimize water productivity and crop production simultaneously under deficit irrigation management conditions using genetic algorithm and simulated annealing algorithm. The results show that genetic algorithm performs better in simultaneous optimization of the two objective functions.