Optimization of water productivity in Bhagwanpur distributary command of India employing TLBO and cuckoo search algorithms

Abstract

Efficient and judicious use of land and water is the need of the hour. In other words, evolving a cropping pattern, which optimizes productivity or net return considering prevailing constraints is quite useful to farmers, because such a cropping pattern is expected to be better than the existing cropping pattern in terms of yielding optimum productivity or net return. A single objective problem consisting of an objective function of optimization of water productivity with prevailing constraints was formulated and three optimization algorithms, namely (i) LINPROG, (ii) teaching learning based optimization (TLBO) and (iii) cuckoo search (CS) were employed to compute optimum water productivity corresponding to various affinity levels. It was observed that all three approaches yielded exactly the same values of water productivity at different affinity levels. TLBO showed better convergence capability as it reached the optimum value of objective function at a lower number of iterations than CS technique. Optimum water productivity at 20% affinity level seems quite practical and reasonable to be recommended in this distributary command for adoption because water productivity value is 2.57 times higher with 98.25 ha less cropped area as compared to the value of water productivity for the existing cropping pattern. Highlights Water productivity is an indicator which can point out hot spots where net return can be improved with less water consuming crops.; A single objective function problem of optimization of water productivity was formulated and solved by three optimization algorithms: (i) LINPROG, (ii) TLBO, (iii) Cuckoo search.; It was observed that all three approaches yielded exactly same values of water productivity at different affinity levels.;

Keywords

• cropping pattern
• cuckoo search algorithm
• optimization
• teaching learning based optimization
• water productivity